INHERITANCE OF YELLOW RUST RESISTANCE AND

GLUTENIN CONTENT IN WHEAT

BY

KHILWAT AFRIDI

A dissertation submitted to the University of Agriculture, Peshawar in partial

fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY IN AGRICULTURE

(PLANT BREEDING AND GENETICS)

DEPARTMENT OF PLANT BREEDING AND GENETICS

FACULTY OF CROP PRODUCTION SCIENCES

THE UNIVERSITY OF AGRICULTURE, PESHAWAR

KHYBER PAKHTUNKHWA - PAKISTAN

DECEMBER, 2016

ii

INHERITANCE OF YELLOW RUST RESISTANCE AND

GLUTENIN CONTENT IN WHEAT

Khilwat Afridi and Naqib Ullah Khan

Department of Plant Breeding and Genetics

Faculty of Crop Production Sciences

The University of Agriculture, Peshawar-Pakistan

December, 2016

ABSTRACT

Knowledge of traits inheritance is a prerequisite for any plant breeding program.

Wheat cultivars ‘Pirsabak-85’, ‘Khyber-87’, ‘Saleem-2000’, ‘Pirsabak-04’, ‘Pirsabak-

05’ and ‘Shahkar-13’ were crossed in 6 × 6 diallel fashion during 2010-11 at the Cereal

Crops Research Institute (CCRI), Nowshera - Pakistan to explore genetic basis of early

maturity, some production traits, resistance to yellow rust (Puccinia striiformis

West.f.sp. tritici) and glutenin contents in wheat grains. Six wheat cultivars along with

respective F1 and F2 populations were evaluated during 2011-12 and 2012-13 at the

CCRI, Nowshera. Significant differences were observed among F1 and F2 populations

and their parental cultivars for all traits across both years. In F1 generation, cross

combinations Shahkar-13/Khyber-87 while in F2 populations Pirsabak-04/Khyber-87

and Pirsabak-05/Shahkar-13 showed earliness and had lesser days to heading and

maturity. Cross combination, Pirsabak-85/Pirsabak-04 exhibited maximum spike

length, grains per spike, grain yield, biological yield and yellow rust resistance in F1

generation. In F2 generation, Pirsabak-05/Shakar-13 had lesser days to maturity with

higher flag leaf area, 1000-grain weight, grain yield and yellow rust resistance.

Based on scaling tests, additive dominance model was found partially adequate

for all the traits in F1 and F2 generations. According to Hayman's genetic analysis,

major components of genetic variance i.e. additive (D) and dominance components (H1,

H2) were important in the inheritance of the studied traits. In F1 generation, additive (D)

component was greater than dominance (H1, H2) for earliness, morphological and

yellow rust resistance traits which indicated predominant role of additive gene action in

the inheritance of these traits. Dominance components were larger than additive for

yield and yield related traits, suggesting the involvement of non-additive gene actions

in the expression of these traits in F1 generation. In F2 generation, additive component

was greater than dominance for tillers per plant, 1000-graint weight, grain yield per

plant, harvest index, and yellow rust resistance while for other traits the component D

was smaller than H1 and H2, demonstrating the primary role of non-additive gene

actions. In both generations, the additive and non-additive gene actions for various

traits were validated by the ratios of average degree of dominance and Vr-Wr graphs.

In F1 generation, high estimates of broad-sense (0.80 to 0.99) and narrow-sense

(0.70 to 0.91) heritability values were recorded for days to heading, plant height,

peduncle length, flag leaf area and 1000-grain weight. However, estimates of broad-

sense (0.56 to 0.99) and narrow-sense (0.13 to 0.49) heritability were low to high for

days to maturity, tillers per plant, spike length, spikelets per spike, grains per spike,

grain yield per plant, biological yield, harvest index and yellow rust resistance in F1

generation. In F2 generation, broad-sense heritability ranged from 0.78 to 0.97 and

narrow-sense heritability ranged between 0.59 and 0.65 for tillers per plant, 1000-grain

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weight, harvest index and resistance to yellow rust. However, in F2 generation, the

estimates of broad-sense heritability ranged between 0.75 and 0.95 and narrow-sense

heritability ranged from 0.33 to 0.53 for days to heading, days to maturity, peduncle

length, flag leaf area, spike length, spikelets per spike, grains per spike, grain yield per

plant and biological yield.

In both generations, mean squares due to GCA were significant for days to

heading and maturity, plant height, peduncle length, flag leaf area, tillers per plant,

spike length, spikelets per spike, grain per spike, 1000-grain weight, grain yield,

biological yield, harvest index and yellow rust resistance. The SCA mean squares were

significant for most of traits in both generations. Based on GCA effects, Pirsabak-05

was considered to be the best general combiner for yield traits and rust resistance in F1

generation. However, in F2 generation, cultivar Shahkar-13 appeared as best general

combiner for earliness and yield traits, and rust resistance. The F1 hybrid Pirsabak-

85/Pirsabak-04 and F2 population Pirsabak-05/Shahkar-13 were the promising cross

combinations and had favorable effects for majority of the traits. Greater variances due

to σ2SCA than σ2GCA for most of the traits in F1 and F2 generations, suggested the

predominant role of non-additive gene actions in the expression of these traits.

Parental cultivars, F2 and F3 populations along with check genotypes (Chinese

Spring and Pavon-76) were analyzed for glutenin subunits through SDS-PAGE. Eight

alleles were identified at different loci in both sets of wheat genotypes. Three alleles

(Null, 1 and 2*) were identified at Glu-A1 locus, three allelic pairs (7 + 8, 7 + 9 and 17

+ 18) were observed at Glu-B1 and two allelic pairs (5 + 10 and 2 + 12) were located at

Glu-D1 locus. Pavon-76 had allele '2*' at Glu-A1 locus, '17 + 18' at Glu-B1 and '5 + 10'

at Glu-D1. Similarly, Chinese Spring as a marker was with 'Null' allele at Glu-A1 locus,

'7 + 8' at Glu-B1and '2 + 12' at Glu-D1. The allelic combinations i.e., 2*, 17+18, and

5+10, showing that high quality scores were observed among parental genotypes, F2

and F3 populations indicating their effectiveness in future breeding programs.

Knowledge of gene actions involved in the expression of various traits might be

useful in deciding the breeding procedure to be used for improvement of these traits.

Promising parental cultivars (Pirsabak-05 and Shakar-13), F1 hybrid (Pirsabak-

85/Pirsabak-04) and F2 population (Pirsabak-05/Shakar-13) revealed best performances

in form of earliness, resistance to yellow rust and increased grain yield. These

genotypes could be be used in future for developing early maturing, rust resistant and

high yielding wheat cultivars.

I. INTRODUCTION

Wheat (Triticum aestivum L.) occupies an important position among cereals with

respect to production and utilization. Wheat is dominant crop for a large part of humanity

and is grown over large area of the world with diverse environmental conditions. In

Pakistan, major cultivated area is under wheat and occupies 70% of rabi and 37% of

total cropping area (Irshad et al., 2012). Wheat is the economical source of fiber, protein,

and calories in human diet. It contributes 10.0% to the value added in agriculture and

2.1% to GDP (Pakistan Economy Survey 2014-15). In Pakistan during 2014-15, wheat

was grown on an area of 9.180 million hectares, which produced 25.478 million tons of

grains with average yield of 2775 kg ha-1 (Pakistan Economy Survey 2014-15).

Pakistan has made a significant progress towards increasing the grain yield per unit

area through introduction and hybridization of new high yielding wheat genotypes

accompanied with new packages of production technology for various areas.

To develop high yielding wheat cultivars, it is important to study the genetic

make-up of diverse wheat lines, inheritance pattern of yield contributing traits and

association of various traits with yield under existing environmental conditions.

Characters such as grain yield and its components, number of tillers, plant height, spike

length, grains per spike, seed index, harvest index per plant and protein content are

important and could be used as selection criteria in wheat (Cho et al., 2001; Nawaz et al.,

2013). Traits such as long coleoptiles, semi dwarf stature, water use efficient leaf traits,

reduced unproductive tillers and harvest index are used in trait based wheat breeding

programs (Munns and Richards, 2007). Grain yield is a complex character made up from

interaction between yield components and environmental effects. Grain yield dependency

on yield contributing traits, needs improvement and could be used as selection criteria

(Sener et al., 2009).

Wheat stripe rust (Puccinia striformis f. sp. tritici Westtend) develops mainly

under cool and moist environments (Gocmen et al., 2003). The distinguishing

symptoms of the disease are yellow pustules (urediniospores) appear mostly on the

leaves but in severe conditions also can be seen on the leaf sheaths, spikes, glumes and

awns of the susceptible plants. The urediniospores are elongated and arranged in linear

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rows between veins of the leaf. The fungus produces linear rows of black teleospores

late in the season (Chen et al., 2014).

To control yellow rust of wheat, the only option is to develop disease resistant

cultivars through cost-effective, environment friendly, efficient and sustainable

approach (Pathan and Park, 2007; Ali et al., 2009; De-Vallavieille-Pope et al., 2012;

Paillard et al., 2012). However, a resistant cultivar does not remain resistant for longer

period. Wheat cultivars with uniform genetic background of rust resistance put severe

selection pressure on the pathogen; and threfore, new pathotypes of yellow rust develop

which break the resistance of cultivars (Ahmad et al., 2006). A resistant cultivar is at

“Boom” when it produced more yield and “bust” when the resistance is broken down

after few years of release and severely reduced grain yield (Ahmad et al., 2006).

Severe epidemics have been caused by Puccinia striiformis in Pakistan in past

causing economic losses (Singh et al., 2004; Bahri et al., 2011). In mid of 1990s, the

wheat cultivars Pirsabak-85 and Pak-81 were grown on large area in Khyber

Pakhtunkhwa, Pakistan. The rust resistance of these cultivars was overcome by a new

race and caused rust epidemic in the province of Khyber Pakhtunkhwa, with 40% loss

in grain yield during 1994-95. Wheat cultivars Pirsabak-85 and Pak-81 were replaced

by Inqalab-91 and cultivated on 80% of the area as a new wheat cultivar, posing a high-

risk crop loss due to new races of yellow rust (Anonymous, 2000). Development of

new rust races (stripe rust) and favorable environmental conditions played a key role in

2004-05 rust epidemics and caused yield losses up to 70% especially in Inqalab-91

sown areas. Past studies revealed a wide range of variation in wheat lines response to

yellow rust (Anpilogova and levashova, 1995; Pasquini et al., 1998) which proposes the

development of new wheat cultivars with durable rust resistance and high grain yield.

As resistance is a breakable phenomenon therefore, it is a dire need to identify novel

sources of yellow rust resistant genes against different pathotypes of yellow rust and, to

combine such desirable genes through conventional crossing and genetic engineering in

the prevailing wheat cultivars.

In wheat producing areas, yield losses caused by stripe rust ranged from 10-

70% depending on varietal susceptibility, stage of initial infection, severity of disease,

level of further disease development, favorable environmental conditions and duration

of the disease (Chen, 2005). However, exploitation of resistant wheat cultivars is the best

3

way to decrease the losses due to yellow rust which is an important objective in wheat

breeding programs for crop improvement (Singh et al., 2004). Stripe rust resistance in

bread wheat is controlled by gene designated as Yrs and more than 53 Yrs genes have been

identified until now (McIntosh et al., 2010). Resistance to yellow rust is of two types,

seedling resistance and adult plant resistance. Resistance controlled by a single gene is

called seedling resistance which is highly effective and persists throughout wheat life

cycle. As the plant mature, adult plant resistance expresses, and its expression occurs at

various developmental phases (from boot to early head emergence) depending on genetic

material used (Chen, 2013).

Wheat quality is usually measured by numerous physical and biochemical

properties. Wheat flour derived products also require diverse quality features. The

superiority of wheat flour has been used in different food products and which are

mostly determined by the quantity and quality of gluten proteins (Weegles et al., 1996;

Shehzad et al., 2014). Gluten is made up of proteins that give strength, structure, and

texture to the different forms of the bread.

Wheat grains at maturity contain 8 to 20% protein, while gluten proteins

constitute 80 to 85% of total wheat grains proteins (Shewry et al., 1995; Gautam et al.,

2013). Gliadins and glutenins are structural proteins of gluten, contributing key role in

bread making properties of wheat flour. The distinctive cohesive and elastic properties

of dough are due to glutenin, which regulates the quality of baked products. Gluten

production initiated when dough proteins absorb water and are stretched and pulled in

the kneading process and become long, flexible strands. Gluten strands coagulate as

protein in eggs solidifies when baked.

Glutenins consist of 30-40% of the flour proteins and is a complex of high

molecular weight proteins of polypeptide subunits connected through covalent and non-

covalent bonds. The glutenins demonstrated a wide range of molecular weights from 40

kDa to several millions. Two classes of glutenin subunits have been recognized in

wheat, the high molecular weight (HMW) glutenins (80-130 kDa) and the low

molecular weight (LMW) glutenins (10-70 kDa) (Bietz and Walls, 1973; Khan et al.,

2009). High molecular weight-gluten subunits (HMW-GS) are present in small

quantity; however, play important role to regulate elasticity of the gluten (Payne et al.,

1980). Wheat comprises six different HMW-GS but because of silencing of certain

4

genes, majority of bread wheat cultivars have three to five HMW-GS. The HMW-GS

are encoded at the Glu-1 loci on the long arms of group 1 chromosomes (Glu-A1, Glu-

B1, and Glu-D1) (Payne et al., 1980; Tyler, 2012).

The genetic variability for HMW-GS has been exploited for wheat improvement

due to its correlation with bread making quality, high polymorphism (Gautam et al.,

2013). High molecular weight-gluten subunits are easy to evaluate as compared to other

morphological and molecular markers evaluation (Yasmeen et al., 2015). However, the

variation in bread-making quality among diverse wheat cultivars cannot be described

only by the variation in HMW-GS but also the low molecular weight-gluten subunits

(LMW-GS) and their corelation with the HMW-GS play a vital role in the

measurement of gluten strength and quality (Shehzad et al., 2014).

Low molecular weight-gluten subunits is about 33-34% of the total grain

protein and 60% of total gluten (Bietz and Wall, 1972; Ali et al., 2010). The LMW-GS

are under the control of genes present at loci Glu-A3, Glu-B3 and Glu-D3 on the short

arms of chromosome 1AS, 1BS and 1DS, respectively. In hexaploid and tetraploid

wheat, these proteins have been widely used for varietal identification because of their

widespread polymorphism (Payne et al., 1984; Vu, 2014). Allelic variants vary in the

quantity, flexibility and strength of their components and can be characterized through

sodium dodecyl-sulfate polyacrylamide gel-electrophoresis (SDS-PAGE). The core

objective of present research was to study the glutenin subunits by SDS-PAGE and

compare the banding pattern with Chinese Spring and Pavon-76.

In current era of molecular breeding, conventional breeding has sustainable

base. It is also well known fact that molecular marker application must be certified

through conventional breeding. Transgressive segregation based on the classification of

genotypes having the ability of transmitting genes of interest in specific genotypic

combinations. Biometrical techniques used for genetic analysis of vital traits are helpful to

the plant breeder in picking improved genotypes for different existing environments and

production systems. Diallel analyses are the well-known mechanisms of conventional

breeding to understand allelic and non-allelic gene action, nature and amount of genetic

variance utilized by genotypes in specific combinations (Hayman, 1954b; Mather and

Jinks, 1982; Griffing, 1956). Parental lines and their hybrids can be assessed through

diallel analysis in all possible combinations. Gene action is designated as additive,

5

dominant and epistatic effects and interactions between them as well as with

environmental factors.

Breeders are interested in desirable genes and gene complexes, and selection of

desirable individuals is essential in breeding program. Diallel design is a helpful tool

in identification of potential genotypes and their promising recombinants (F1 hybrids)

confirmed by D, H genetic components and combining ability i.e. general combining

ability (GCA) and specific combining ability (SCA). In diallel mating, parental

cultivars are crossed in all possible combinations and the promising and poorer general

combiners and their specific cross combinations can be separated through GCA and

SCA. The maternal effects can also be assessed through diallel as direct and reciprocal

crosses are involved in this technique.

The present study was conducted to evaluate the six wheat parental cultivars and

their half diallel F1 and F2 populations at Cereal Crops Research Institute Pirsabak

(CCRI), Nowshera, Khyber Pakhtunkhwa, Pakistan with the following objectives:

To study the genetic potential and variability for yield and yield related traits

in F1 and F2 populations in comparison with wheat parental cultivars.

To study the gene action and inheritance patterns (additive vs. dominance)

through Hayman and Griffing approaches in F1 and F2 populations of wheat.

To evaluate yellow rust resistance in parental genotypes and their F1 and F2

wheat populations.

To characterize the high molecular weight glutenin in F2 and F3 populations and

their parental cultivars.

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II. REVIEW OF LITERATURE

Wheat is an important cereal of the world that plays a vital role in meeting the

food requirements of human population. Improvement in grain yield depends upon the

identification of suitable and genetically superior genotypes and their exploitation.

Perception of gene actions controlling quantitative traits are very important in various

breeding methods of the plant populations. Fixation of promising genes in a

homozygous line is desirable to improve the crops. For exploitation of genetic diversity

and variation, different biometrical methods like quantitative genetic analyses need to

be employed to determine the gene action involved in the genetic variation.

Genetic analysis of breeding material also paves way to isolate best ideotypes. It

is evidently admitted that utilization of genetic analyses is the pre-requisite for

germplasm selection and isolation of the best combinations for subsequent study of

genetic architecture and combining ability in different wheat lines / hybrids for

important economic characters (Abedi et al., 2015; Ahmed et al., 2015). Formation of

new rust races (stripe rust) and conducive environmental condition played a key role in

2004-05 wheat rust epidemics which caused 70% grain yield losses especially in cv.

Inqlab-91 sown areas in Pakistan. Past studies revealed that there is a wide range of

variation in wheat lines response to yellow rust which proposed the development of

new wheat cultivars with durable rust resistance and high grain yield (Singh et al., 2004;

Chen, 2005). Therefore, it is a dire need to identify novel sources of yellow rust

resistant genes against different pathotypes of yellow rust and, to combine such

desirable genes through conventional and molecular breeding in existing wheat

cultivars.

Wheat flour quality is an important consideration in breeding and development

of new cultivars (Bian et al., 2015; Yasmeen et al., 2015). A strong correlation between

bread making quality and high-molecular weight glutenin subunits (HMG-GS) has

resulted in the widespread utilization of HMW-GS in wheat breeding. Extensive studies

on genetic analyses in wheat have been carried out through out the world. These

estimates varied with the breeding material used and the climatic conditions where the

crops were raised. Accumulation of relevant literature, concerning the problem under

study is reviewed as under:

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Hayman Genetic Analysis

Fellahi et al. (2015) determined the inheritance pattern of grain yieldand yield

related traits through half diallel-cross study comprising nine bread wheat genotypes. The

analysis of variance specified significant differences among genotypes for biological

yield, spikes per plant, grains per spike, and grain yield. In the partial diallel analysis, the

additive-dominant model adequately described most of the traits. Over-dominant type of

allelic interaction was recorded for biological yield in group 1 and group 2, while partial

dominance and over-dominance gene actions were reported for rest of the traits from both

parental groups. The increases in the magnitude for the studied traits are generally

determined by dominant genetic factors. The genitor Mahon- Demias (group 2) had

greater number of desirable alleles for biological yield, spikesper plants and grains yield.

The parental genotypes Acsad-901 (group 1) and Rmada (group 2) had the most recessive

genes for spikes per plant, grains per spike, biological yield and grain yield.

Kutlu and Olgun (2015) investigated the genetic control of yield and yield

components in 6 × 6 diallel crosses in wheat. According to diallel analysis and estimation

of genetic components of variance (D, H1 and H2), additive and non-additive gene actions

were involved in the inheritance of days to heading and maturity, spike length, spikelets

per spike, grains per spike, peduncle length,plant height, harvest index and grain yield.

The Wr-Vr graphs and average degree of dominance values revealed that most of the traits

i.e. spikelets per spike, grains per spike, peduncle length and plant height were controlled

by genes with partial dominance, while over dominance type of gene action were reported

for harvest index and grain yield. Further more, the regression coefficient of Wr on Vr was

significant from unity for days to heading and maturity,spike length and grain weight per

spike, indicating the presence of epistatic gene action. Selection in early generations was

recommended for spikelets per spike, grains per spike, peduncle length and plant height.

Farooq et al. (2014) studied gene action governing the inheritance of certain

quantitative traits like days to heading and maturity, peduncle length, plant height, flag

leaf area and tillers per plant using 5 × 5 diallel cross. The traits i.e. peduncle length, plant

height, flag leaf area, tillers per plant and days to heading were genetically regulated by

partial dominance type of gene action however, over-dominance gene action was

documented for days to maturity. Selection in later generations was suggested for most of

the studied traits.

8

Kaukab et al. (2014) conducted 5 × 5 diallel crosses involving five wheat cultivars

to determine the gene action controlling some yield related traits like peduncle length,

plant height, spike length, tillers per plant and grain yield. Their results revealed that tillers

per plant were controlled by additive type of gene action which was also confirmed by the

Vr-Wr graph however, peduncle length, plant height, spike length and grain yield were

controlled by over-dominance type of gene action. Transgressive segregates in later

generations could be expected for these parameters whereas, occurrence of partial

dominance type of gene action for tillers per plant revealed that it could be improved by

selection in early generations.

Metwali et al. (2014) carried out 5 × 5 diallel mating to examine the genetic

structure of five cultivars of barley with their F1 and F2 progenies and to assess these

genotypes unders normal and salinity stress conditions. Significant additive and

dominance effects of genes were observed for spikelets per spike, spikes per plant,

spike length and grains per spike, chlorophyll a, b contents, calcium and magnesium

content. In both generations, average degree of dominance was greater than one which

revealed the involvement of non-additive genetic effects in the inheritance of the

chlorophyll a, b, calcium and magnesium content. The average degree of dominance

values for spikelets per spike, spikes per plant, spike length and grains per spike in F1

hybrids was also greater than one. Additive genetic effects was prominent for spikelets

per spike, spikes per plant, spike length and grains per spike for F2 hybrids due to less

values of average degree of dominance than one. The value of H2 was lesser then H1 for

all traits in both generations under normal and stress conditions. The narrow-sense

heritability was high to moderate for all the traits in both generations and conditions.

Nazir et al. (2014) conducted an experiment using 5 ×5 diallel mating in wheat to

assess the gene action for grain yield and yield related traits. Significant additive and

dominant components were reported for all the variables showing the key role of both

these components in the inheritance of the studied traits. The graphical illustration

indicated that 1000-grain weight, spike length, grains per spike, tillers per plant and plant

height were regulated by partial dominance with additive type of gene action. However,

weight of grains per spike, flag leaf area and peduncle length, were govern by over-

dominance type of genes. Regression line passed through the point of origin in Vr-Wr

graph confirmed the role of complete dominance for spikelets per spike and grain yield.

9

Peduncle length, plant height and grains per spike were with high narrow-sense

heritability estimates so chances of improvement were more following early selection

technique in these parameters. The selection for traits with additive genes and partial

dominance should be made in early segregating populations, however, traits with over-

dominance gene action would be improved with delayed selection.

Pervez et al. (2014) studied the economic and grain related traits using 5 × 5 diallel

crosses involving wheat cultivars/lines viz., Millat-11, Punjab-11, 9466, 9469 and 9459-1.

They divulged the inheritance pattern of some quantitative traits like peduncle length,

plant height, 1000-grain weight, tillers per plant and grain yield. Significant genotypic

differences were observed for all the studied traits. The graphical illustration revealed

over-dominance type of gene action for grain yield, proposing that selection in segregating

generation might be productive for improvement of this character. Presence of additive

gene action for peduncle length, plant height, 1000-grain weight and tillers per plant

specified that pedigree selection would be a pre-dominant breeding approach for

manipulating these traits.

Akbari et al. (2013) analyzed 6 × 6 diallel 30 F2 populations along with their

parents to estimate genetic parameters for grain yield, biological yield, days to flowering,

pod formation and maturity in lentil. Partial dominance type of gene action was noted for

grain yield, days to flowering, days to pod formation and biological yield. However, days

to maturity and harvest index were governed by over dominance. The highest narrow

sense heritability was reported for day to maturity and the lowest was witnessed for

harvest index.

Jadoon et al. (2012) studied the inheritance mechanism for plant height, spike

length, grains per spike, biological yield and days to heading in F2 half diallel crosses.

Randomized complete block design with four replication was used to carry out the

experiment. Plot size of the experiment was consist of 4 rows, 5 m long with row to row

distance of 30 cm. Significance of additive and non-additive component showed the

importance of both gene action in controlling all these parameters. However, the value of

non-additive component was greater than additive component, which specified the major

role of non-additive genes. Results of the study were supported by greater value of

average degree of dominance than one for all the parameters in the study. Similarly,

greater values of broad sense heritability than narrow sense heritability revealed the major

10

role of non-additive component, therefore selection in later generation was suggested for

improvent of these traits.

Munis et al. (2012) evaluated 5 × 5 diallel crosses of wheat cultivars and advance

lines viz., Iqbal-2000, Parwaz-94, Shahkar-95, line-6500 and line-8736, and studied the

inheritance pattern of eight yield related parameters. The traits i.e. flag leaf area, tillers per

plant, grains per spike peducncle length and plant height were governed by partial

dominance with additive type of gene action. However, over-dominance type of gene

action was proposed for control of grain yield, spike density and spike length.

Rashid et al. (2012) analyzed awn length, days to heading, plant height, tillers per

plat and grain yield through genetic analysis. Vr-Wr graph suggested additive type of

genes action with partial dominance for majority of the traits. However, the negative

intercept of regression line proposed over-dominance gene action for 1000-grain weight

and days to maturity. Selection in early segregating generations was suggested for traits

controlled by additive genes. However, delayed selection was preferred for the traits

controlled by dominant genes in wheat.

Zare-Kohan and Heidari (2012) evaluated wheat varieties i.e. Chamran, Darab-2,

Marvdasht and their 5 × 5 half diallel crosses in order to get genetic information about

days to heading and maturity, plant height, grain filling duration and grain yield. These

traits were investigated by using two genetic models i.e. Hayman (1953) and Griffing

(1956) and Jinks. Significant GCA and SCA variances indicated that additive and non-

additive genetic component were responsible for genetic expression of days to heading

and maturity, plant height, grain filling duration and grain yield. Parental cultivars and

their F2 populations were sown at Shiraz and Zarghan, Iran, during wheat season 2010-

2011. Significant genotype × location interaction was noted for plant height and days to

grain filling but no such interection was reported for grain yield and days to heading

and murity. Similarly, significant GCA × location interaction was reported for plant

height and days to grain filling indicated the positive role of environment on additive

components. The Baker (1979) ratio for days to heading was 0.90 at Zarghan and 0.91

at Shiraz, for days to maturity (0.81 & 0.82), for grain yield (0.89 & 0.87) at two

experimental locations and for plant height (0.88) at experimental location Shiraz

revealed the primary role of additive variance in genetic expression of these traits. The

GCA estimates showed that Darab-2 was best general combiner for dwarfness and days

11

to heading and maturity, Chamran for days to grain filling, early heading and maturity,

Marvdasht for days to graining filling and grain yield. Average degree of dominance

and graphical analysis illustrated that these traits were controlled by additive with

partial dominance gene action.

Ahmad et al. (2011) conducted 8 × 8 diallel studies in wheat to evaluate gene

action for heading days, 1000-grain weight, productive tillers per plant, grains per spike

and grain yield. Significant additive (D) and dominant (H) components were observed

for productive tillers per plant and grain yield under normal planting however,

significant D and H components were recorded for grains per spike under late planting.

Graphic illustration pointed out partial dominance gene action for heading days and

1000-grain weight in both conditions. Heading days, grains per spike and 1000-grain

weight was governed by over-dominance gene action under late planting, and grain

yield and productive tillers per plant under normal planting.

Farooq et al. (2011a) evaluated spring wheat lines under plastic sheet tunnel for

heat tolerance and selecte seven parental genotypes with diverse heat tolerance.

Analysis of variance indicated significant variation among geotypes for spike density,

spikelets per spike, spike length, grains per spike, spike weight and grain yield under

normal and heat stress. Additive dominance model was partially adequate for spike

length, spikelets per spike and spike density under normal conditions. However, the

said model was fully adequate for grains per spike, spike weight and grain yield. Modle

was partially adequate for all studied traits, except grains per spike under heat stress.

Significant effects were obsereved for additive (D) and dominance (H) for all traits

under normal and heat stress. Both ‘a’ and ‘b’ components were significant for grain

yield under heat stress however, significant additive ‘a’ and non-significant “b” were

reported for yield per plant under normal conditions. Under both conditions, additive

component was significant and greater than dominance for spike density, spike length

and grain yield demonstrating the primary role of additive genetic effects. Dominant

gene action was observed for grains per spike under both conditions. Spike weight and

spikelets per spike exhibited additive effects under heat stress and dominant under

normal conditions. Moderate to high narrow sense heritability estimates were observed

for most of the traits exept spikelets per spike under normal conditions. Under heat

12

stress majority of the traits showed additive genetic effects and proposed early

generation selection for the improvement of these traits.

Farooq et al. (2011b) found high narrow sense heritability for relative cell injury,

flag leaf area, days to heading, days to maturity and grain yield at normal and heat stress

condition while studying 7 × 7 complete diallel of F1 hybrids. Additive component of

genotypic variation (D) was significant and greater than the dominance components (H1

and H2). Unequal values of H1 and H2 and ratio of H2/4H1 indicated the different

distribution of positive and negative genes among parental cultivars for studied traits

however, dominant and recessive genes were equally distributed among parental

genotypes for flag leaf area,grain yield and harvest index under heat stress.

Irshad et al. (2012) analyzed F1 progenies of 7 × 7 diallel cross, and the crosses

were made between four heat tolerant and three susceptible spring wheat genotypes

which were assessed under high terminal heat stress and normal condition.

“Additive”type of gene action with “partial dominance” was observed for days to

heading, spike index at anthesis, plant height, spikes per plant and grain yield per plant

which might be advantageous to improve these traits in terminal heat tolerant genotypes

through modified pedigree selection. “Over-dominance” type of gene action was

recorded for spikelets per spike, signifying that improvement in this trait might be

achieved by bi-parental breeding with few cycles of recurrent selection.

Tammam and El-Rady (2011) crossed bread wheat genotypes in 6 × 6 half diallel

fashions namely Giza164, Giza164, Sids1, Sids7, Sahka 93 and Debeira. The genetic

system of spike length, plant height, days to heading and mutrity was controlled by

additive and non-additive genetic effect. The additive gene effects were predominant

under both normal and late sowing dates. Results specified that late sowing reduced

plant height (13.61 and 7.07%), spike length (6.33 and 5.66 %), days to heading (8.26

and 8.2%) and days to maturity (9.8 and 8.9%) for F1 and F2 generations, respectively.

Positive and negative allels were unevenly distributed among parental cultivars for all

traits under both sowing regimes. High (broad and narrow-sense) heritability estimates

were stated for spike length, plant height, days to heading and maturity.

Kakani and Sharma (2010) evaluated yield and related traits of six rows barley by

using 9 × 9 partial diallel F1 and F2 populations across different environmental conditions.

13

Additive genetic component and dominance were significant for test weight, grains per

spike, spike length, plant height, flag leaf area, days to heading and grain yield. The

dominance components were higher than additive component in both generations, which

specified the primary role of dominant genes in governing these characteristics. Over-

dominance type of gene action was reported for flag leaf area, test weight and grains per

spike in F1 hybrids as the values of average degree of dominance were greater than one. In

F1 generation heritability estimates were high as compared to F2 populations, which

specified that heritability was greatly affected by the generations and environmental

conditions.

Khattab et al. (2010) evaluated six population means of three diverse wheat

cultivars crosses (Golan × Mexipak, Sakha-202 × Wa-4767, Mexipak × Sakha-202) to

estimate genetic parameters. The inheritance of studied traits suggested the involvement of

additive and non-additive genes; however, in most cases, the value of dominance was

greater than the additive gene effects. Average degree of dominace was greater than one,

which proposed over-dominance gene action for genetic control of harvest index,

biological yield, grain yield and grain weight per spike. However, additive gene action

was reported for spikes per plant in cross Mexipak × Sakha-202. High narrow sense

heritability was found for spikes per plant in crosses i.e. Golan × Mexipak, Sakha-202 ×

Wa-4767 and Mexipak × Sakha-202, while grain weight per spike in crosses Golan ×

Mexipak and Mexipak × Sakha-202 which specified the greater chances of improvement

in early generations for these traits.

Nazeer et al. (2010) crossed five wheat cultivars/line i.e. Uqab-2000, SH-2002,

Rohtas-90, Chenab-2000 and 243-1 in 5 × 5 diallel to evaluate gene action for grain yield,

hundred-grain weight, plant height, peduncle length, flag leaf area and tillers per plant.

Significant additive component was recorded for most of the traits, however, the values of

dominant components were high for grain yield, plant height, tillers per plant and flag leaf

area. Therefore, it was suggested that dominance genetic effects performed major role in

controlling these traits, while peduncle length and hundred-grain weight were under

additive genetic effects. High narrow-sense heritability for hundred-grain weight (0.71)

and peduncle length (0.77), demonstrated additive gene action for these parameters. The

analysis of vr-wr graph illustrated over-dominance genetic effects for tillers per plant,

plant height, flag leaf area and grain yield whereas, hundred-grain weight and peduncle

14

length were governed by additive type of gene action with partial dominance as supported

by low narrow-sense heritability. Their results revealed that hundred-grain weight and

peduncle length could be improved by early generation selection, using pedigree method

however, heterosis breeding could be utilized for grain yield, plant height, flag leaf area

and tillers per plant.

Allah et al. (2010) analysed six wheat cultivars i.e. Chakwal-86, Barani-83,

Punjab-96, GA-96, Kohistan-97 and Sehar-06 in 6 × 6 diallel matng. They observed

additive type of gene action with partial dominance for spikelets per spike, plant height,

spike length, tillers per plant and grain yield and over-dominance type of gene action for

peduncle length and suggested early generation selection for these traits.

Rabbani et al. (2009) carried out 8 × 8 diallel experiment to examine the gene

action in wheat cultivars / lines i.e. Inqalab-91, MAW-1, 2KC033, Saleem-2000, No.2495,

3C061, 3C062, and 3C066 in irrigated and rain-fed conditions for grain yield, spike

length, 1000-grain weight, fertile tellers per plant and flag leaf area. The Wr-Vr graph

illustrated that parameters like grain yield, fertile tillers per plant, falg leaf area and 1000-

grain weight were regulated by over-dominance type of gene action under both

environmental conditions whereas spike length demonstrated over-dominance type of

gene action in irrigated environment and additive type of gene action in rain-fed condition.

Rehman et al. (2009) evaluated 8 × 8 diallel crosses to find the inheritance of yield

related traits such as harvest index, grain yield per plant and total dry matter in mungbean.

The significance of ‘a’ and ‘b’, D, H1 and H2 components revealed the importance of both

additive and dominance effects for all parameters in F1 and F2 generations. However, the

greater values of H1 than D for grain yield in F1 and harvest index in F2 generation showed

the primary role of dominant genes in their genetic control and thus delayed selection was

preferred for improvement of these traits. Greater value of D than H1 demonstrated

additive type of gene action for regulating grain yield in F2, harvest index in F1 and total

dry matter in both generations. Grain yield in F1 and harvest index in F2 generation

demonstrated moderate narrow-sense heritabilities, while grain yield in F2, harvest index

in F1 and total dry matter in both generations had higher heritability.

Akram et al. (2008) conducted an experiment to study inheritance mechanism in a

complete 8 × 8 diallel consist of indigenous wheat genotypes, during 2000-2002. Gene

15

action were review in some yield contributing traits like grain filling period, flag leaf area,

number of tillers per m2, plant height, days to heading and days to maturity. The average

degree of dominance for grain filling period (1.081) flag leaf area (1.679), plant height

(1.915) and days to maturity (2.061) indicated that over-dominance type of gene action

regulated these yield contributing traits. However, average degree of dominance for

number of tillers per m2 is 1.00 which suggested the key role of complete dominance in

controlling this trait. Results revealed that selection in early generation for grain filling

period, flag leaf area, plant height, days to maturity and number of tillers per m2 controlled

by over-dominance and complete dominance would be difficult, whereas average degree

of dominance was 0.659 for days to heading which suggested additive type of gene action

and proposing early generation selection.

Hussain et al. (2008) conducted an experiment under artificial leaf rust attack

among 56 F1 diallel crosses in all possible combinations along with eight parental wheat

cultivars to find gene action for grain yield per plant, grains per spike, 100-grain weight,

spikelets per spike, spike length, tillers per plant and peduncle length. Additive and

dominance effects were highly significant with directional dominance effects;

asymmetrical gene distribution and important role of specific genes were found for all

traits. Non-significant maternal effects were recorded for all the parameters except grains

per spike. Analysis for genetic components specified that additive (D) and dominant (H)

components were significant for all traits. Un-equal distribution of positive dominant

alleles were found for grain yield per plant, grains per spike, 100-grain weight, spike

length, tillers per plant and peduncle length whereas positive and negative allels were

equally distributed for spikelets per spike for spikelets per spike.

Ahmad et al. (2007) evaluated the inheritance pattern of yield and yield related

traits in 8 × 8 diallel cross in wheat cultivars and their 56 F 1 hybrids. Data were collected

on grain yield, harvest index, spike length, flag leaf area and plant height. Genetic analysis

of traits suggested the involvement of both additive and non-additive gene effects in

controlling the inheritance of these traits. Narrow-sense heritability estimates were

moderate (69.74%) for flag leaf area and high (82.15%) for plant height and they

recommended that improvement for these traits would be possible in early generations.

While low narrow-sense heritability estimates suggested selection at later generations for

grain yield, spike length, and harvest index.

16

Dere and Yildirim (2006) evaluated grain yield, flag leaf width, and flag leaf

length in 8 × 8 wheat F1 hybrid populations. The Vr-Wr graphs specified over-dominance

gene action for flag leaf width and grain yield, while partial dominance was reported for

flag leaf length. The inheritance of grain yield, flag leaf width, and flag leaf length was

studied using diallel analysis in 8 × 8 wheat cross population involving the bread wheat

genotypes i.e. Seri-82, Yureir, Marmara, Kaflifbey, Cumhuriyet, Ziyabey, Basribey and

Malabadi. The analysis of data revealed that the additive variance component (D) was

significant for flag leaf width. The dominance variance component (H1) was significant

for flag leaf width and grain yield. The dominance level variance component (h2) and

corrected dominance variance component (H2) were significant for grain yield, flag leaf

width, and flag leaf length. The Vr-wr graphs indicated overdominance for grain yield and

flag leaf width, while partial dominance was inferred for flag leaf length. Flag leaf length

was significantly and positively correlated with flag leaf width. Yüreir × Malabadi could

be used to increase leaf area ( photosynthetic area).

Saleem et al. (2005) estimated the inheritance pattern of some quantitative

characters in 5 × 5 diallel crosses involving five wheat verities / lines viz., Faisal Abad-83,

Faisal Abad-85. Punjab-96, 9244 and 9247. Plant height, flag leaf area, flag leaf weight

and grain yield were controlled by the over-dominance type of gene action; whereas, flag

leaf area and flag leaf weight seemed to be determined by the additive type of gene action

with partial dominance. Epistasis was found absent for all the characters studied. For the

traits like plant height, flag leaf area, flag leaf weight and grain yield, delayed selection

would be fruitful while, for flag leaf area and flag leaf weight, selection in the early

segregating generations would be most effective.

Griffing's Combining Ability

Ahmed et al. (2015) examined morpho-yield traits in 5 × 5 diallel crosses using

wheat cultivars/advance lines i.e. Millat-11, Shahkar-95, Aas-11, 9272 and 9272. Analysis

of variance displayed highly significant differences among wheat cultivars/ lines for all

parameters. Variety SH-95 was the best general combiner for grains per spike, spike

length, spike density and plant heigh. Among cross combinations, 9272 × Millat-11 was

the best specific combiner for 1000-grain weight and grains per spike. Cross combination,

Shahkar-95 × 9272 was the best specific combiner for grain yield, 1000-grain weight and

flag leaf area. Variance due to GCA was greater than variance due to SCA, which showed

17

additive gene action for grains per spike, spike length, flag leaf area, spike density and

plant height. Therefore early generation selection would be preferred for the improvement

of these parameters. Selection in later generations was suggested for grain yield and 1000-

grain weight due to greater varaince of SCA than GCA.

Al-Layla (2015) used factorial mating (A × B) among seven wheat cultivars i.e.

Aras, Noor, Sham-6, Aiala as male and Maxipak, Tamoz-2, and adnanea as female.

Genetic components (additive, dominance and environment), general and specific

combining ability, average degree of dominance, heritability and correlation among

different traits were estimated. Results showed that there were significant differences

among genotypes for all characters. Ratio of σ2gca/σ2sca were less than one for most

characters, specifying the primary role of non-additive gene action. The additive variance

was greater than one for traits i.e. plant height, grain yield, spike length, tillers per plant,

biological yield and protein percentage, whereas the values of non-additive variance were

greater than additive variance for grains per spike, seed index and harvest index. Broad

sense heritability was high for all characters; whereas narrow sense heritability ranged

from high to medium for some studied traits. Average degree of dominance values were

more than one for all characters except plant height and protein percentage, indicating the

involvement of“over-dominance”in regulating these traits. Spike length, tillers per plant

and grains per spike had positive and significant correlation with grain yield.

Ismail et al. (2015) studied combining ability in half diallel crosses among six

diverse bread wheat cultivars. Results of the study showed that mean square due to GCA

and SCA, were significant for all studied traits reflecting the importance of both additive

and non- additive gene effects in the inheritance of these traits. General combining ability

were higher than specific combining ability, consequently the σ2gca/σ2sca ratios were

more than one revealed the key role of additive gene effect in the inheritance of these

characters. In general, cultivar Sids-4 was a good general combiner for days to heading

and maturity, long spike and grains per spike. Giza-168 was good general combiner for

high grain yield per plant, Gemmiza-10 for 1000-grain weight and Sakha-94 was a good

general combiner for plant height.

Kalhoro et al. (2015) studied general and specific combining ability in four spring

wheat cultivars (Imdad, Tando Jam-1, Sakrand-1, and Moomal). These parental genotypes

were crossed in half diallel mating fashion; thus, six possible cross combinations (F1s)

18

were obtained (Imdad × Tando Jam-1, Imdad × Sakrand-1, Imdad × Moomal, Tando Jam-

1 × Sakrand-1, Tando Jam-1 × Moomal, and Sakrand-1 × Moomal). Mean squares

corresponding to different traits of various wheat varieties stated significant GCA and

SCA effects for the traits i.e. plant height, tillers per plant, spike length, spikelets per

spike, grains per spike, seed index, and grain yield. The mean performance of F1 hybrids

differed significantly for all the traits studied. Among the parental genotypes, Imdad and

Tando Jam-1 proved to be better general combiners for almost all the studied traits. In

regards to SCA effects, the F1 hybrids Imdad × Tando Jam-1 and Imdad × Sakrand-1

expressed higher SCA and heterotic effects for majority of the traits.

Khiabani et al. (2015) studied full diallel crosses in spring wheat with parental

cultivars and F2 populations to evaluate gene action, combining ability and correlations for

grain yield, plant height and their components under irrigated and water deficient stress.

Estimates of the genetic components of variation as well as ratio of σ2gca/σ2sca showed

that all the traits were governed by additive gene action. Partitioning the GCA and SCA

effect to male and female showed that maternal effect case over estimated in value of

general and specific combining ability. The estimates of GCA revealed that dwarf mutant

(As-48) was the good general combiner for plant height and its components were dwarf

mutant (As-48) which appeared to appreciate parent for reduce plant height and also

increase spike length. Their results revealed that early generation selection would be

effective for improvement of plant height and its components. The dwarf mutant (AS-48)

would be helpful for developing semi dwarf and lodging resistant cultivars.

Poodineh and Rad (2015) studied genetic components for physiological

parameters in Bread Wheat. Eight spring wheat cultivars were used as parents and crossed

in a half-diallel fashion. The combining ability analysis of variance revealed that both

GCA and SCA variances were highly significant for plant height, chl(a), chl(b), chl(a+b),

stomatal conductance, relative water content and grain yield except chlorophyll content

for SCA, indicating the importance of both additive and non-additive gene effects.

Genotype chamran for relative water content and grain yield was the best specific

combiner. High broad and low narrow sense heritability demonstrated the main role of

dominant genes in controlling all studied traits. Genotype chamran with large, positive and

significant GCA effects could be used as parent with desirable genes for genetic

improvement.

19

Ammar et al. (2014) performed combining ability studies in a 5 × 5 complete

diallel cross of five wheat genotypes for traits like grain yield per plot, 1000-grain weight,

grains per spike, spikelets per spike, plant height, tillers per m2, peduncle length, spike

length and flag leaf area. Additive type of gene action was involved for tillers per square

meter and grains per spike as GCA variance was greater than SCA. For traits like grain

yield per plot, 1000-grain weight, spikelets per spike, spike length, plant height, peduncle

length and flag leaf area, the SCA variance was greater than GCA, which specified the

key role of non-additive genes in controlling these traits.

Cheruiyot et al. (2014) crossed wheat genotypes in 6 × 6 diallel cross to assess

the gene action regulated the inheritance of adult plant resistance against stem rust and

yield related parameters in wheat. Both GCA and SCA effects were significant with

predominant GCA effects for days to heading, number of tillers, plant height, grain

yield and stem rust infection, suggesting predominance additive genetic effects over

non-additive effects. The Vr-Wr graph displayed partial dominance for stem rust

infection, days to heading and productive tillers whereas over-dominance was reported

for plant height and grain yield. Inclusion of parental genotypes KSL 13 and KSL 42 as

well as crosses KSL 34 × KSL 52, NjBw II × KSL 42, Kwale × KSL 13, KSL 34 ×

KSL 42 in a breeding program would produce desired segregants. However, these

genotypes could then be exploited effectively in improvement of stem rust resistance as

well as yield in areas susceptible to stem rust disease.

Desale et al. (2014) analyzed combining ability in a 10 × 10 half diallel set of

bread wheat. General and specific combining ability mean squares were highly significant

for grain yield per plant, 1000-grain weight, grain weight per spike, grains per spike,

spikelets per spike, spike length, number of effective tillers per plant and peduncle length

and demonstrated the key role of additive and non-additive genes in regulating all these

eight parameters. However, the magnitude of variance due to SCA was greater than GCA,

demonstrating the key role of non-additive gene action. Non-additive gene action was

further confirmed by the ratio of variance of GCA to SCA which was less than unity for

the studied parameters.

Dholariya (2014) studied 8 × 8 diallel mating to assess combining ability and gene

interactions that regulated grain yield and its attributing variables. Highly significant GCA

and SCA were reported for all the traits, which specified the involvement of both additive

20

and non-additive type of gene actions. The ratio of variance of GCA to SCA was less than

one for all parameters except for biological yield per plant, spikelets per main spike, days

to heading and maturity, specified that non-additive components were more influential in

the inheritance of these parameters.

Madić et al. (2014) analyzed combining ability for five two-rows winter barley

cultivars different in spike characters were mated in 5 × 5 half diallel to produce 10 F1 and

F2 populations. Significant GCA and SCA for F1 and F2 hybrids were documented, which

showed the involvement of additive and non-additive genes. The σ2 GCA/σ2SCA ratio in

F1 and F2 suggested the additive gene action for grain weight per spike, spike length and

spike harvest index. The SCA variance was higher than GCA variance for grain weight

per spike, indicating the involvement of non-additive genes.

Salehi et al. (2014) analyzed 8 × 8 diallel mating to find out the effect of

environmental conditions on genetic parameters of biological yield and harvest index in

bread wheat and to measure the mode of inheritance, genes action, general and specific

combining ability. The portion of additive and dominance variances and ratio of GCA to

SCA variance revealed the key role of additive and non-additive genes in controlling

harvest index in both conditions. However in biological yield additive gene action were

more important in controlling this quantitative trait. Low broad and narrow-sense

heritability values were reported for harvest index, while high broad and narrow-sense

heritability were observed for biological yield in both conditions.

Golparvar (2013) compared combining abilities of relative water content, flag

leaf area and grain filling rate of bread wheat in 8 × 8 half diallel crosses under drought

stress. Mean square of GCA and SCA were significant for all the traits but non-

significant SCA was reported for relative water content of leaf. Significant GCA and

SCA for most of the traits indicated the role of both additive and dominant genes under

drought stress.

Hammad et al. (2013) conducted the present study to estimate the combining

ability of five wheat lines i.e. V-03138, V-04022, V-04189, PR-94 and 9247 crossed in

5 × 5 full diallel fashion in 2010-11. The five parental lines and twenty F1 hybrids were

sown in randomized complete block design with three replications. The data were

observed for grain yield, spikelets per spike, spike length, seed index, tillers per plant,

21

plant height, days to heading and days to maturity. Combining ability estimates was

significantly different for all these traits. Most of the traits were with high SCA

variance describing non-additive gene inheritance except plant height. The advance line

V-04022 was with high GCA estimates and considered the best general combiner for

most of the traits in the study. Hybrid (V-03138 × V-04189) demonstrated high SCA

effect for spikelets per plant, days to maturity and tillers per plant. Hybrid (V-04189 ×

PR-94) was with high SCA values for grain yield per plant, seed index and days to

heading. Hybrid (9247 × V-04189) attained high reciprocal effects followed by hybrid

(PR-94 × V-04022) and hybrid (9247 × V-04189) for majority of the characters in the

study.

Zeeshan et al. (2013) crossed five spring wheat cultivars/lines in half diallel

fashion to assess their combining ability for yield and yield attributing components.

Variances of general and specific combining ability were highly significant for yield and

yield associated traits. Non-additive gene effects were predominant for tillers per plant,

spikelets per spike, plant height, grain yield, biological yield and harvest index.

Akram et al. (2011) carried out an experiment to analyze variances and combining

ability effects for yield and quality related traits in 8 × 8 diallel cross of wheat. The GCA

effects were significant for days to heading, plant height, flag leaf area, spike length, grain

filling period, tillers per m2, spikelets per spike, grains per spike, 1000-grain weight and

grain yield per plant. However, SCA effects were significant for the most of the characters

except number of spikelets per spike, flag leaf area and grain yield. The variance due to

SCA was greater than GCA for most of the traits demonstrating the key role of non-

additive gene action.

Kumar et al. (2011) analyzed 7 × 7 diallel set of bread wheat to evaluate

heterosis and combining ability. Significant GCA and SCA mean square were noted for

all the parameters except tillers per plant, plant height and spikelets per spike.

Involvement of additive and non-additive genes in the inheritance of majority traits

were confirmed by combining ability analysis. Superiority over commercial parent and

mid parent was witnessed for all studied parameters. The highest heterosis (21.74%)

was noted for spikelets per spike over commercial parent and moderate heterotic value

(13.73%) was noted for tillers per plant over mid parent.

22

Shabbir et al. (2011) analyzed 5 × 5 diallel crosses to examine the gene action in

wheat cultivars/lines viz., Chakwal-97 (CH-97), Inqalab-91, GA-2002, 6C001 and

6C002 for yield traits i.e. grain yield per plant, thousand grain weight, grains per spike,

spike length and spikelets per spike. Significant SCA effects were noted for thousand

grain weight and grain yield per plant, spike length and spikelets per spike. Non-

additive type of gene action for these parameters proposing that selection would be

productive in F6 to F8 generations. Higher GCA value for grains per spike specified that

this parameter was under the influence of additive type of gene action and selection

would be productive in early generations.

Yao et al. (2011) made 7 × 7 diallel cross comprising of seven wheat genotypes to

determine combining ability, gene action, heterosis, and correlations for plant height and

its components. Heterosis and heterobeltiosis were witnessed in plant height, spike lengt,

peduncle length, second and third internode length, second and first basal internode

length, however, their heterosis and hetrobeltiosis values differ among crosses and

variables. Genetic components and ratio of GCA/SCA variance indicated that all the

parameters were controlled by additive gene action with partial dominance. Narrow sense

heritability values were higher for all parameters. Four groups of dominant genes were

suggested to be responsible for regulation of plant height, while one, two and or three

groups of genes were suggested to be responsible for genetic control of its components.

Internodes length was significantly and positively correlated with plant height, and path

analysis specified that highest effect on plant height were recorded for peduncle length,

followed by the second internode length. Early generation selection for plant height and its

components would be effective as the parameter was under the influence of additive gene

action.

Zahid et al. (2011) studied 8 × 8 diallel cross of spring wheat to assess combining

ability effects and variances for yield and quality related parameters. Significant GCA

effects were noted for all parameters except days to maturity, whereas significant SCA

effects were noted for most of the traits except grain yield, flag leaf area, spikelets per

spike, protein contents and lysine contents. The variance value due to SCA was more than

GCA for the most parameters signifying the primary role of non-additive gene action.

Akinci et al. (2009) studied 6 × 6 half diallel cross to assess the heterosis and

combining ability effects for days to heading, thousand grain weight and yield in durum

23

wheat. Parental genotypes viz., Beyaziye and bagacak (local) and Kunduru 1149,

Cakmak-79, Diyarbakir-81 and Duraking were used. Heterosis for high and mid parent

was -2.16% and -0.74% for days to heading, -1.64% and 3.78% for 1000-grain weight and

-2.24% and 5.24% for grain yield, respectively. Significant GCA and SCA were observed

for days to heading, thousand grains weight and grain yield, which suggested the

involvement of both additive and non-additive genes. The levels of GCA and SCA of

parental lines were sufficient to sustainable production of hybrids and early selection of

lines.

Farooq et al. (2006) crossed five bread wheat cultivars/lines (PBW-222, LU26,

Uqab-2000, 8961 and 8952) in a 5 × 5 diallel fashion. Significant GCA and SCA were

reported for flag leaf area, grains per spike, spikelets per spike, plant height, fertile tillers

per plant and spike length. Reciprocal effects were significant for grains per spike,

spikelets per spike, plant height, flag leaf area and tillers per plant and non-significant for

grain yield per plant, 1000-grain weight and spike length. The greater value of GCA than

SCA variance demonstrating additive gene effects for grains per spike, spikelets per spike,

spike length and flag leaf area. However, high SCA variance were recorded for grain

yield, 1000-grain weight, plant height and tillers per plant which showed non-additive

genetic effect.

Awan et al. (2005) crossed five spring wheat genotypes in all possible

combinations to evaluate their combining ability and gene action involved by using

Griffing’s technique which were selected on the basis of morphological evaluation.

Parental cultivars and F1 hybrids displayed significant differences for all the characters.

Highly significant GCA variance was observed for all the traits except number of

spikelets per spike. The variance due to GCA was greater than SCA which indicated

additive type of gene action for all the parameters whereas variances for SCA and

reciprocals were non-significant. Cultivar Inqalab-91 was good general combiner for

1000-grain weight and grain yield, while the highest value of SCA effects for grain

yield were noted in F1 hybrid Chakwal 86 × Inqlab 91.

Joshi et al. (2004) assessed F1 and F2 descendants of a ten-parent half diallel cross

of wheat for days to heading, days to maturity, plant height, flag leaf area, tillers per plant,

spike length, grain yield per spike, grains per spike, 1000-grain weight, harvest index,

grain yield per plant and protein content. The predominance of additive gene effects for

24

the studied traits was supported by predominant GCA components. They found high grain

yield with high protein combination. Increase grain yield with high protein combinations

were supported by the study. Inclusion of F1 hybrids showing high SCA and having

parents with good GCA, into multiple crosses and/or diallel selective mating could

prove a valuable approach for improvement of grain yield in bread wheat.

Singh et al. (2004) analyzed F1 and F2 progenies of 10-parent diallel cross for

combining ability and other quantitative traits in wheat. The GCA and SCA

components of variance were significant; however, greater magnitude of SCA variance

than GCA indicted predominance of non-additive gene effect for all traits except days

to heading in both generations. The parental cultivars PBW-373 and UP-2425 were

observed to be the best general combiner with high value of GCA and average to high

general combiners for several other economical traits. Parental lines HD-329, WH-542,

UP-2338 and Raj-3077 also exhibited high GCA values for harvest index per plant,

early heading, dwarfism and grain yield per spike, respectively. The hybrids i.e.

Raj 3765 × HD 2285,D 2285 × PBW 343, Raj 3765 × UP 2338 and PBW 343 × Raj 30

77 were observed to be the best crosses for SCA effects. To further envisage increase in

grain yield, the combinations of appropriate yield related attributes were also

highlighted. Their findings revealed that involvement of F1 hybrids with high SCA and

GCA in many crosses, bi-parental mating and diallel selective mating proved a handy

procedure for further enhancement of grain yield in wheat.

Kashif and Khaliq (2003) studied 5 × 5 diallel crosses of wheat cultivars (Inqalab-

91, Uqbab-96, Panjab-96, MH-97 and Faisal abad-85) to assess combining ability effects

of some polygenic parameters. Highly significant SCA mean squares were noted for

grains per spike, spikelets per spike, spike length and fertile tillers per plant, significant

SCA for plant height and flag leaf area and non-significant SCA for grain yield per plant

and 1000-grain weight. Higher GCA variances than SCA for grains per spike, spikelets

per spike, spike length, flag leaf area and fertile tiller per plant, demonstrating additive

type of gene action for regulating these traits. While, grain yield per plant, thousand grain

weight and plant height displayed non-additive genetic effects due to higher SCA

variance.

Rehman et al. (2002) analyzed combining ability of some polygenic characters in

5 × 5 diallel cross. The GCA, SCA and reciprocal effects were found to be significant for

25

grain yield per plant, 1000-grain weight, spike length, spikelets per spike, and plant height

but non-significant reciprocal effect were reported for peduncle length. The value of SCA

variance was more than GCA variance for all the parameters indicating that non-additive

type of gene action was important for the studied parameters. The best general combiner

for grain yield was found to be the genotype 4943 while the best specific cross

combination was Pb.96 × 4943.The best performing lines, 4943 and 4072 with high GCA

effects for most of the traits in this study, could be used in future breeding program.

Yellow Rust Study

Vergara-Diaz et al. (2015) mentioned that a new strain of biotrophic fungus

Puccinia striiformis f. sp. tritici (Warrior/Ambition), against which the existing cultivated

wheat genotypes have no resistance, appeared and spread rapidly. It threatens cereal

production in Europe. The exploration for sources of resistance to this strain was

suggested as the most effective and non-toxic solution to ensure high grain production.

This would be helped by the development of high performance and low cost techniques

for field phenotyping. Under field conditions they analyzed vegetation indices in the Red,

Green, and Blue (RGB) images of crop canopies. They assessed their accuracy in

calculating grain yield and evaluating disease severity in contrast to other field

measurements comprising the Normalized Difference Vegetation Index (NDVI), leaf

chlorophyll content, stomatal conductance, and canopy temperature. Yield components

and agronomic parameters were studied in relation to grain yield and disease severity. The

presence of yellow rust was associated with reduction in, grains per spike, grains per

square meter, grain weight and harvest index. Wheat varieties with delayed heading faced

greater yield losses in the presence of yellow rust. The combination of RGB-based indices

and days to heading together explained 70.9% of the variability in grain yield and 62.7%

of the yield losse.

Ali et al. (2014) assessed the position of virulence and pathotype variability in the

Himalayan region of Pakistan, by using a set of 127 PST (Puccinia striiformis f. sp. tritici)

diseased wheat samples from eight different locations which were collected, increased and

pathotyped and tested by using 36 differential lines from the world set, European and

Chinese sets, and 9 Avocet Yr isolines. After yellow rust resistance (Yr) genes assessment,

virulence (Vr) were reported for 18 out of 24 and from 127 isolated sample 53 pathotypes

were identified. Virulence were identified against resistance genes occasionally, deployed

26

in Pakistan (Vr8) or even world-wide level (Vr5), whereas no virulence was found against

Vilmorin 23 (Yr3+) in Pakistan, a common virulence in Europe. Different pathotypes were

prevailing across different locations, with no clear spatial structuring was witnessed for

the studied sites. Results proposed diversity in virulence and pathotype with suggested

possible role of sexual recombination in the historical conservation of PST in the

Himalayan area of Pakistan. Their findings should be valuable in improvement and

deployment of host resistance genes.

Dehghani et al. (2013) intercrossed five wheat varieties (Tiritea, Pool, Kokart,

Ruapuna and Domino) to obtain F1 half-diallel hybrids for evaluating yellow rust disease

infection type. The diallel data of the five varieties were analyzed through GGE biplot.

The parental cultivars and 10 F1 progenies were assessed in the greenhouse by using three

races i.e. 7E18A-, 38E0A+, and 134E134A+. The first two principal components of biplot

explained 95, 94 and 85% of the variation for the pathotypes 7E18A¯, 38E0A+, and

134E134A+, respectively. Cultivar Ruapuna for the pathotypes 7E18A¯ and 134E134A+,

cultivar Kokart for the pathotype 38E0A+ had negative GCA (more resistance) for

infection type. Parental cultivar Tiritea was the best general combiner for the pathotypes

7E18A¯ and 38E0A+ while this parent was the best general combiner only with testers

Kokart and Pool for the pathotype 134E134A+. Cultivar Kokart was the best general

combiner with testers Domino, Ruapuna, and Tiritea for pathotype 134E134A+. Their

findings revealed that parental cultivar Ruapuna was good against three combinations of

pathotypes (7E18A¯ + 38E0A+ + 134E134A+, 7E18A¯ and 38E0A+ + 134E134A+) and

had ability to show resistance by low infection type. Additive genetic component

indicated the possibility of improving resistance to yellow rust with the lower infection in

breeding programs.

Bux et al. (2012) analyzed both molecular diversity and virulence of forty six

Puccinia striiformis f. sp. tritici isolates from Pakistan Vs nine isolates from US. All the

investigated isolates showed common type of virulence to Yr5, Yr15 and YrSP. Isolates

from Pakistan showed low virulence frequency for differentials cansist of Yr2, (Yr10,

YrMor) and (Yr2, Yr4a, YrYam). Clustering based on virulence data grouped

contemporary isolates together and showed high genetic diversity among pathotypes of

both countries. Molecular analysis using microsatellites markers and sequence tagged site

showed high diversity based on marker index (MI) and polymorphic information contents

27

(PIC) which was higher for single sequence repeats (0.78 and 39.51, respectively) than

sequence tagged site markers (0.04 and 0.29, respectively). Dendrogram based on

molecular marker data grouped together contemporary pathotypes revealed genetic

homogenity. Pathotypes belonging to Pakistan and US clustered together showed common

ancestry. Their findings revealed very low correlation (r = 0.08) between molecular

diversity and virulence indicating independence in both trends of diversity.

Bahri et al. (2011) designed the study to explore the virulence and simple

sequence repeat (SSR) diversity of the Pakistani pucccinia striiformis f. sp. tritici

pathotypes and the ongoing selective pressures of extensively sown wheat varieties.

Analyses of 49 isolates sampled from the Khyber Pakhtunkhwa Province of Pakistan led

to the identification of 12 distinct pathotypes. The virulence frequencies of v2 (virulent

against Yr2), v6, v7, v9, vSU and v27 ranged from 63% to 100%. Virulences v3, v4, v17

and vSD were not common, whilst v5, v10, v15, v24, v32 and vSp were not detected. The

identified pathotypes were classified into 27 diverse genotypes. Three predominant

pathotypes (P1-P3) were 80% of the total studied isolates, which belonged to the same

Pakistani lineage, while other isolate were close to either a North European lineage or a

Mediterranean lineage. Within pathotype (P2) isolates genetic recombination were

detected. Sample from 40 Pakistani wheat varieties showed greater frequency of Yr2, Yr6,

Yr7, Yr9, Yr27 and YrSU resistance genes. Only 11 wheat varieties showed resistance to

P1 to P3. Results revealed that varietal diversity and migration factors might contribute to

maintain current high genetic diversity in puccinica striiformis, and have serious regional

implications for wheat improvement program.

Bux et al. (2011) studied the virulence patterns of yellow rust under field condition

in four diverse environmental locations, Sakrand (Sindh), Fisal Abad (Punjab), Pirsabak

(Khyber Pakhtunkhwa) and Quid-i- Azam University (Islam Abad). Results showed that

yellow rust resistance genes Yr3, Yr5, Yr10, Yr15, Yr26, YrSP and YrCV were resistant,

while Yr18 were moderately susceptible at all locations. Gene combinations Opata

(Yr27+Yr18) and Super kauz (Yr9, Yr27, Y18) and genes YrA-, Yr2, Yr6, Yr7, Yr8, Yr9,

Yr17, Yr27 were found susceptible. Cultivars i.e. Seher-2006, GA-2000, Iqbal-2000,

Marvi-2000 and Barani-70 showed resistance among fifty-one commercial varieties. The

genes found effective against yellow rust under natural conditions might be exploited

singly or in combination to develop high yielding resistant wheat cultivars.

28

Zahravi et al. (2010) evaluated 5 × 5 half diallel crosses made from four yellow

rust resistant advanced breeding lines with a susceptible cultivar (Bolani). Seedlings were

separately grown in greenhouse until the first leaves fully expanded and inoculated with

two races (pathotypes 6E134A+ and 134E148A+). Days to the first pustule outbreak was

recorded as latent period. The best general combiners for longer latent period were

genotypes M-78-1 and M-78-10. Regression analysis and estimates of genetic factors

specified the significance of additive and non-additive gene effects. Pathotype 6E134A+

and pathotype 134E148A+ had broad-sense heritability with 98%. Narrow-sense

heritability was 65% and 80% for pathtypes 6E134A+ and 134E148A+, respectively.

Razavi and Taeb (2009) studied combining ability and genes action for yellow rust

of wheat in a 10 × 10 half diallel cross. Two races of yellow rust (134E134A and 4EOA)

were used to inoculate the wheat genotypes. Latent period and infection types were

measured both in the field and greenhouse. Significant differences were noted between

races and their pathogenecity and among genotypes for their resistance to the pathogen.

The GCA and SCA for all the parameters were significant and additive genes were

responsible to control the gene action. Average degree of dominance ranged from partial

to over dominance for resistance or susceptibility. Except additive component, the non-

additive effect of genes could not be static by self-fertilization.

Ghannadha et al. (2005) studied 5 × 5 half diallel cross and evaluated genetics of

adult plant resistance to yellow rust (Puccinia striiformis West.) by using five wheat

genotypes i.e. Bolani a susceptible cultivar, Brock, Domino, Elit-Lep, and Kotare.

Greenhouse was used to evaluate the parents and ten F1 hybrids by four races viz.,

134E134A+, 140E72A+, 174E174A+ and 230E15A+. Degrees of dominance were

positive and negative for each race that indicated the reversal of dominance. Statistical

analysis displayed the significance of additive and dominance effects in governing the

latent period. Broad-sense heritability ranged from 0.91 to 0.98 while narrow-sense

heritability ranging from 0.59 to 0.92. Additive genetic component significance and

moderate narrow-sense heritability specified the chance of developing for the longer

yellow rust latent period in future breeding programs.

Kaur et al. (2003) intercrossed six partial resistant genotypes to yellow rust i.e.

Apache-81, Noroesta-66, Opata-85, PBW-65, Shailaja and Trap-1 and a rust susceptible

variety WL-711 in 7 × 7 half diallel to investigate the nature of genes controlling

29

resistance to yellow rust measured as terminal yellow rust severity and area under disease

progress curve. Highly significant D, H1 and H2 components for both traits specified

preponderance of both additive and dominant genes governing partial resistance to stripe

rust in the six parents. Highly significant GCA and SCA effects also confirmed the

involvement of additive and non-additive gene action for terminal yellow rust severity and

area under disease progress curve. Parental genotypes PBW-65, Opata-85 and Trap-1

were good general combiners for yellow rust resistance. Of all the possible crosses

studied, the cross Trap-1 × Shailaja was the best specific combiner. The Vr-Wr graphic

analysis specified that susceptible genotype WL-711 has only recessive genes conferring

susceptibility. The genotypes PBW-65, Trap-1 and Opata-85 seemed to contain maximum

number of dominant genes whereas the resistance of Shailaja, Apache-81 and Noroesta-66

were controlled by additive genes.

Hill et al. (2001) analyzed data for the severity of strip rust (Puccinia

striiformis) infection in F1 and F2 half-diallel populations among eight spring wheat

genotypes adapted to the East African highlands. Genetic analysis specified that

additive-dominance model of gene action adequately illuminated the variation

witnessed among the six parents and their individual F1 and F2 progenies, and the

combined F1/F2 diallel. Yellow rust resistance was dominant to susceptible and genes

for resistance were more frequent.

Ghannadha (1995) made 5 × 5 half diallel crosses among five wheat genotypes out

of which one genotype was susceptible and remaining had adult plant resistance (APR)

genes to stripe rust (Puccinia striiformis West.). The five parents and ten F1 progenies

were grown in the glasshouse and were inoculated with three rust pathotypes. Analysis

specified that average effects of alleles (additive) were of greater importance than

dominance in conditioning resistance in response to two races, while for the third race

dominance was valuable.

Glutenin Analysis

Bian et al. (2015) evaluated the genetic pattern with HMW-GS composition

between generations and examined whether agronomic and quality traits were correlated

with each other. A wheat cultivar with high protein content and two cultivars with low

protein content were subjected to a reciprocal cross. A total of 216 seeds from each F2

30

generation were chosen randomly and analyzed for HMW-GS composition using sodium

dodecyl sulfate-polyacrylamide gel electrophoresis. Agronomic and quality characteristics

were not significantly different between reciprocal crosses, indicating no cytoplasmic

effect on the characteristics studied. The separation ratio of 2 HMW-GS loci was 9:3:3:1,

indicating no linkage between any 2 loci. The novel HMW-GS N was detected in cultivar

R145, which did not follow the Mendelian segregation ratio. A Glu-A1a(1) band was not

detected in 1 individual from Tian8901 × R145. Their findings revealed that average grain

weight per spike was significantly correlated with quality characteristics and may be a

suitable criterion for selecting high protein content in wheat breeding programs.

Yasmeen et al. (2015) studied 242 wheat genotypes, including commercial

cultivars of pakistan as well as landraces from provinces of Baluchistan and Punjab, to

evaluate allelic diversity in the Glu-A1, Glu-B1, and Glu-D1 loci encoding HMW-GS.

Land races from Baluchistan had higher genetic diversity for HMG-GS followed by land

races from Punjab and then commercial varieties. Subunits of Glu-A1 were less

polymorphic whereas Glu-B1 subunits were rare and uncommon among these genotypes.

However, Glu-B1 was the highest contributor to overall diversity (78%), with a total of 31

rare alleles, followed by Glu-D1 (20%) with the high quality 5 + 10 allele and other

variants. Commercial cultivars possessed favorable alleles, potentially from indirect

selection for wheat flour quality by the breeders; however, this indirect selection has

decreased the pedigree base of commercial cultivars. Their findings revealed that allelic

combinations, including 2*, 5 + 10, and 17 + 18, were frequent among landraces, which

are responsible for good quality flour, could be used in in future crop improvement and

breeding programs.

Cao et al. (2014) characterized seven HMW-GS of valuable wheat related specie,

Agropyron intermedium by using SDS-PAGE and Western blotting techniques. Two

genes Glu-1Aix1~4 and Glu-1Aiy1~3 were also isolated among the seven genes

responsible for HMW-GS. Based on sequence analysis; two possessed extra residues, four

of them were unusually smaller in size and seven HMW-GS were highly similar to that of

wheat in primary structure. The amino acid sequences revealed that molecular structure of

1Aix1 and 1Aiy1 subunits were in agreement to the hybrid type. The subunit xy-type

consists of x-type N–terminal and y-type C-terminal, whereas yx-type consists of y-type

N-terminal and x-type C-terminal.

31

Doneva et al. (2014) crossed T. turgidum (2n = 28, BBAuAu) with diploid

Aegilops tauschii (2n = 14, DD) to produce seven hexaploid synthetic wheat hybrids (2n =

42, BBAuAuDD), by using colchicine treatment for chromosome doubling and subjected

for seed protein analysis. X-type subunit 1 at the Glu-A1 locus were identified for

amphidiploids 531 and 107, while subunit 1Ax1.1 were identified for synthetics 32, 106,

530 and 532, which was unusual for wheat, that could be an example of increasing allelic

diversity for HMW-GS along with the D-genome derived genes. Five x-type subunits (7,

13, 17, 14 and 22) and four y-type subunits (8, 16, 18 and 15) and their five combinations

were noticed at Glu-B1 locus. Variation at Glu-A1 and Glu-B1 loci were less than

diversity at Glu-D1. The subunit 1Dx1.5 +1Dy10 was mostly detected in synthetics, which

is diverse from the genome of T. aestivum and affect the wheat quality to great level. Two

other identified D-genome subunits were 1Dx2+1Dy11 and 1Dx4+1Dy10.1. Synthetic

hexaploid D-genome appeared to be exceptional sources for choosing diverse glutenin

compositions in wheat breeding.

Ji et al. (2012) analyzed 1942 wheat advanced lines to examine HMW-GS

variations at Glu-1 loci through SDS-PAGE. About 26 alleles and 83 types of HMW-GS

compositions were investigated, counting some eccentric alleles and allelic compositions.

Among the most prevailing HMW-GS allels 26 alleles were null at Glu-A1, 7 + 8 and 7 +

9 at Glu-B1 and 2 + 12 at Glu-D1. Comparatively N, 7 + 8, 2 + 12 and N, 7 + 9 as well as

2 + 12 were the most prevailing HMW-GS compositions providing productive

information for breeding programs.

Chaperzadei et al. (2008) analyzed forty-two landraces of wheat from northwest of

Iran and identified the difference of endosperm protein subunits in these landraces.

Differences were identified at HMW-GS, LMW-GS and three different groups of gliadins

(α-, γ- and ω-) play role in allergy in patient with colic disease ω-gliadins region. Subunits

2 + 12 were more frequent than 5 + 10 subunits in these landraces. New protein subunits

of Glu-A1 identified between 1 and 2 bands region and Glu-D1 appeared between 2 and 3

bands region. Differences in structural subunits of glutenin can be utilized by wheat

breeders for the introduction of new genotypes with better bread-making quality.

Northwest of Iran have valuable land races with better biodiversity for glutenin which can

be used in breeding program and in result can increase quality (protein type with respect to

subunits) and quantity (protein content). Some new subunits were also identified, which

32

may cause and favored by unseen natural selection to local environment. To prevent

genetic drift, it is essential to preserve the local wheat germplasm.

Liu et al. (2007) by using SDS-PAGE, 111 bread wheat landraces were

characterized for HMW-GS classifying sixteen alleles for Glu-1 loci with 3 alleles at

Glu-A1 locus, 9 at Glu-B1 locus and 4 alleles at Glu-D1 locus. Two new allels at Glu-

B1 and one at Glu-D1 were also witnessed. By combination of these 16 alleles, fourteen

diverse HMW-GS patterns were identified. The incidence of rare alleles was 62.5% that

were null allele at Glu-A1c, 1Bx7 + 1By8 at Glu-B1b and 1Dx2+1Dy12 at Glu-D1a,

widely detected in six populations. The subset demonstrated relatively greater genetic

diversity, 81.5% within the populations and 18.5% between populations.

Zeller et al. (2007) classified wheat cultivars of German origin for their high

molecular weight glutenins, low molecular weight glutenins and gliadins by using SDS-

PAGE and A-PAGE, separately. For difference in bread loaf volume, the high molecular

weight Glu-A1 allele with Glu-A1a, Glu-A1c, Glu-Bic, Glu-B1d, Glu-D1a and Glu-D1c

were found crucial. Low molecular weight Glu-3 glutenin subunits and Gliadin subunits

viz., Gli-1 was witnessed as well. The occasionally found gliadin subunits viz., Gli-B1c

and Gli-D1g were widely found in quality bread wheat.

Deng et al. (2005) analyzed the gluten strength through SDS-PAGE in three wheat

near-isogenic lines possessing Glu-B1 and Glu-D1 alleles. The line 2 as compared to line

8 and 13 had 14 + 15 at Glu-B1 and 5 + 10 at Glu-D1 subunit which had higher values for

dough rheology, flour and baking qualities. Performance of line 8 with 7 + 9 at Glu-B1

and 5 + 10 at Glu-D1 was good as compared to line 13 comprising 14 + 15 at Glu-B1 and

Glu-D1 in 10 combinations. Several rheological parameters were supposed to be

associated with allelic pair 14 + 15 at Glu-B1 compared to 7 + 9 at Glu-B1. Lines with

subunit 5 + 10 in gluten index were considered to be superior to the lines with 10 at Glu-

D1.

33

III. MATERIALS AND METHODS

Breeding material and procedure

Six diverse wheat cultivars i.e. Pirsabak-85, Pirsabak-04, Pirsabak-05, Shahkar-

13, Khyber-87 and Saleem-2000 with varying parentage, year of release and morph-

yield traits were crossed in a 6 × 6 half diallel fashion to develop 15 F1 hybrids during

2010-2011 (Tables 3.1,3. 2). Parents and their F1 hybrids were sown during 2011-2012

while parents and their F2 populations were grown during 2012-2013 in a randomized

complete block (RCB) design with two and three replications, respectively at Cereal

Crops Research Institute Pirsabak (CCRI), Nowshera, Khyber Pakhtunkhwa - Pakistan.

Table 3.1 Parentage and various characteristics of the parental cultivars used

in half diallel crosses

Parental

cultivars Pedegree

Plant

stature

Resistance

to yr

Yr

genes* Color Maturity

Grains

spike-1

Potential

yield

Pirsabak-85 KVZ/BUSHS/KAL/BB Dwarf Susceptible Yr7,Yr9 Green Late 60 6000

Pirsabak-04 KAUZ/STAR Medium Moderatly Resistant

Yr18 Normal 72 6000

Pirsabak-05 MUNIA/SHTO//AMSEL Tall Resistant - Dark green Normal 50 5500

Shahkar-13 CMH84.339/CMH78.578//MILAN Dwarf Resistant Yr17 Waxy green Normal 56 6000

Saleem-2000 CHAM-6//KITE/PGO Dwarf Moderatly

Resistant Yr18 Waxy green Early 76 6000

Khyber-87 KVZ/TRM//PTM/ANA-CM 43930 Medium Susceptible Yr9+ Green Early 54 4500

Ten plants from the parental cultivars and F1 generation and twenty plants form

parental cultivars and F2 populations were randomly selected for recording the data on

individual plant basis for each trait. Description for each trait is given below.

Days to heading

Days to heading were recorded from date of sowing to the date of spike emergence

in each plot.

Days to maturity

Days to maturity were taken from date of sowing to the date of maturity when

plants physiologically matured.

34

Plant height (cm)

Plant height was measured from the base of plant to the tip of the spike of main

tiller excluding awns.

Peduncle length (cm)

Peduncle length of main tiller was measured from the last inter-node to the base

of spike.

Flag leaf area (cm2)

Flag leaves were selected from the main tillers of the 10 randomly selected plants

in F1 and 20 plants in F2 at post anthesis. Flag leaf area was measured according to

following formula (Francis et al., 1969).

Flag leaf area = Maximum width × length × 0.75.

Tillers per plant

Plants were randomly selected from each plot and tillers per plant from each plant

were recorded.

Spike length (cm)

Spike length was measured in centimeter from the base of the first spikelet to

the tip of the last spikelet excluding awns.

Spikelets per spike

Data on spikelets per spike was recorded by counting the number of productive

spikelets in selected spikes in both generations.

Grains per spike

Ten spikes in F1 and twenty in F2 generation in each plot were randomly

selected and manually threshed for recording data on grains per spike.

35

1000-Grain weight (g)

After manual threshing, a representative sample of 1000 grains was draw out

from each entry in each replication and weighed with the help of an electronic balance

to record data on 1000-grain weight.

Grain yield per plant (g)

Grain yield per plant was taken by weighing the grains of 10 randomly selected

plants in gram in each sub-plot in F1 and 20 in F2 population in each replication after

threshing with single plant thresher.

Biological yield per plant

Biological yield was taken by harvesting the selected plants from each sub-

plot/replication; sun dried and weighed in 10 and 20 plants in F1 and F2 populations,

respectively.

Harvest index per plant

Harvest index per plant was calculated with the help of the following formula.

Disease scoring

Cultivar Morocco (highly susceptible wheat for all rusts) was sown around the

experimental materials in two rows to create inoculum pressure. The yellow rust spores

were collected from cultivar Morocco and then the urediospore suspension was prepared

in sterile distilled water with 2-3 drops of tween-20 (Shah et al., 2010). Parental cultivars,

F1, F2 populations and spreader were inoculated uniformly at booting stage in the evening

by spraying a suspension of 0.1 g spore in 1-1 water by using hand sprayer. The yellow

rust data was recorded following Cobb Scale (Peterson et al., 1948; Stavely, 1985; Ali et

al., 2014). The host reaction (HR) types in order of Immune (I), traces (T), resistance (R),

resistance to moderately resistance (RMR), moderately resistant (MR), moderately

resistant to moderately susceptible (M), moderately susceptible (MS), moderately

susceptible to susceptible (MSS) and susceptible (S) were then converted into HR values

36

through assigning a value of 0.0, 0.1, 0.2, 0.3, 0.4, 0.6, 0.8, 0.9 and 1.0 for each host

reaction, respectively (Roelfs et al., 1992; Cheruiyot et al., 2014).

Whereas; Severity (%): 0-100.

Statistical analyses

Analysis of variance

All the data were subjected to analysis of variance (ANOVA) technique to test the

null hypothesis of no differences among various F1 and F2 populations and their

parental cultivars (Steel et al., 1997). The genotype means for each variable were

further separated and compared by using the least significant difference (LSD) test at

5% level of probability.

Hayman Genetic Analysis

Hayman’s diallel approach (1954a, b) and Mather’s concept of D, H

components of genetic variation for additive and dominance variances, respectively (as

D used for additive variance instead of A, and H1 and H2 for dominance components of

genetic variance instead of D) were used to study the genetic effects for various traits in

both generations. Mather and Jinks (1982) have also made the recent development about

this technique and components of genetic variation were estimated following that

method of diallel analysis (Singh and Chaudhary, 1985). In F2 populations, the

formulae were modified to calculate the components of variance as proposed by

Verhalen and Murray (1969).

Diallel analysis assumptions and tests of adequacy

The validity of information from a group of genotypes obtained from diallel

method is based on following assumptions:

a) Diploid segregation of chromosomes

b) Homozygosity of parents

c) Absence of reciprocal effects

d) Absence of epistasis

e) No multiple allelism

f) Independent distribution of genes among parents

37

Homozygous inbred lines were used in a diallel crossing programme. The

entries in the off diagonal cells of the diallel table were replaced by their means of

direct cross and reciprocal prior to analysis for removing the reciprocal differences. The

remaining three assumptions of non-allelic interaction, multiple allelism and

independent assortment of genes were satisfied through the analysis of variance of Wr-

Vr entities for arrays of each replicated diallel table. Significant "F values" in the

analysis of variance revealed their heterogenity, which invalidates any one of these

assumptions. In order to test the adequacy of the additive-dominance model and

validity of diallel assumptions underlying the genetic model for data sets of various

traits were tested through three scaling tests i.e. t2 test, regression analysis and arrays

analysis of variance (Wr + Vr and Wr – Vr). According to Mather and Jinks (1982), the

regression coefficient is expected to be significantly different from zero (b = 0) but not

from unity (b = 1). Failure of this test indicates presence of epistasis and the data will

be unfit for further genetic analysis. Significant differences between the arrays (Wr

+Vr) and non-significant differences within the arrays (Wr-Vr) show the presence of

dominance and absence of epistasis. Non-significant value of t2 test also confirms

presence of no non-allelic interaction and therefore, the genes will be independent in

their action for random association. If all the tests are found in favor of assumptions,

the genetic model is declared fully adequate, partially adequate if at least one test

fulfills the assumptions. Failure of all the three tests completely invalidates the

additive-dominance model.

Components of genetic variance and their ratio along with standard error were

estimated as follows:

D = Additive genetic variance {D = Volo-E (Volo = Variance of the Parents)}.

H1 = Dominance variance {H1 = Volo-4Wolo1+V1L1-(3n-2) E/n (Wolo = Mean

covariance between the parents and the arrays)}.

H2 = H1 {1-(u-v)2}, where u and v are the proportions of positive and negative genes, in

the parents.

38

F = Mean of Fr values over arrays = 2Volo-4Wolo1-2(n-2) E/n, where Fr is the covariance

of additive and dominance effects in a single array. F is positive where dominant genes are

more frequent than recessive.

h2 = (ML1-MLo)2-4(n-1)E/n2; Dominance effect (as algebraic sum over all loci in

heterozygous phase in all crosses). When frequency of dominant and recessive alleles is

equal, then H1 = H2 = h2. Significance of h2 confirms that dominance is unidirectional.

E = Expected environmental component of variation;

From these estimates, the following genetic ratios were determined.

F1 = H1/D, F2 = √¼H1/D: denotes average degree of dominance, If the value of this ratio

is zero, there is no dominance; If it is greater than zero but less than one, there is partial

dominance; and if it is greater than one, it denotes over-dominance.

H2/4H1: denotes the proportion of genes with positive and negative effects in the parents,

and if the ratio is equal to 0.25, indicates symmetrical distribution of positive and negative

genes.

F1 = 4DH1+F/4DH1-F, F2 = ¼√4DH1+½F/¼√4DH1-½F: denotes the ratio of dominant

and recessive genes in the parents, If the ratio is one, the dominant and recessive genes in

the parents are in equal proportion; if it is less than one, it indicates an excess of recessive

genes; but being greater than one, it indicates excess of dominant genes.

h2/H2: denotes the number of gene groups/genes, which control the character and exhibit

dominance.

Heritability

Broad and narrow sense heritabilities in F1 generation were calculated for each

character according to Mather and Jinks (1982).

nsreplicatio ofNumber / ]d.f.

Reps.S.S. + S.S. Errors[ = E

39

In F2 generation, the narrow sense heritability values were calculated as follows

(Verhalen and Murray, 1969; Singh and Chaudhary, 1985).

Where;

D = Variation due to additive effect.

H1 = Component of variation due to dominance effect of genes.

H2 = H1 [1-(u-v)2] [u = positive and v = negative genes].

F = The mean of "Fr" over the arrays.

E = The expected environmental component of variation.

Combining Ability Analysis

Data were further analyzed through combining ability analysis as outlined by

Griffing (1956) following Method 2, Model-I to assess the genetic variances due to

GCA and SCA effects (Table 2) (Singh and Chaudhary, 1985).

Where,

Yij = mean of i × jth genotype over k and l

m = population mean

gi = general combining ability effect of the ith parent

40

gj = general combining ability effect of the jth parent

Sij = specific combining ability effect of the cross between ith and jth parents

eijkl = the environmental effect associated with ijkth observation

Yi. & Y.j = total of the ith and jth arrays in the mean

Y.. = grand total of the mean

Yij = mean value of the cross of ith parent with jth parent

Table 3.2 ANOVA for combining ability (Model-I, Method2) for half diallel

crosses

Source of variation Degrees of freedom

GCA (n-1)

SCA [n(n-1)/2]

Error (r-1) {{[n(n-1)/2]+ n}-1}

GCA and SCA Variances

Variances due to σ2GCA and σ2SCA and ratio due to σ2GCA/σ2SCA were also

calculated according to the following formuals (Singh and Chaudhary, 1985).

Variance due to σ2GCA = MS GCA - MSE/(n+2)

Variance due σ2SCA = MS SCA - MSE

Ratio of GCA variances to SCA variance = σ2GCA/σ2SCA

Glutenin Analysis (Protein quality)

Protein quality was analyzed through SDS-Page method.

Samples Preparation for Protein Extraction

For high molecular weight (HMW) glutenin extraction, 150 mg fine crushed

seed powdered samples were shifted to eppendorf tubes and 1.5 ml of 1X fresh protein

extraction buffer was added to them. The buffer was prepared from 3X stock solution

composed of SDS 10 g, Tris-HCl 37.5 ml (1.875 M, pH 6.8) and 60 ml glycerol, l50

mg Commassie Brilliant Blue R-250 and 72.3 ml distilled water. Vortexing 4 times for

5 min to homogenize the sample, with a break of 15-20 min each time. Samples were

preserved at room temperature for over night incubation and centrifuged at 10,000 rpm

for 10 min. The supernatant was collected from each sample and shifted to new

eppendorf tubes.

41

Protein Characterization on SDS-PAGE

Protein extract was subjected to characterization on SDS-PAGE of BioRad

through 12% acrylamide gel as described by Laemmli (1970). The glass plates were

washed with 70% ethanol before using in electrophoresis and dried with kimwipe.

Glass plates with 1 mm thick spacers were tightly sealed with rubber gasket and then

whole assembly was tightly fixed with clumps. Running gel prepared was poured in

space between the glass plates up to the point 2 cm beneath the glass top. The

remaining space was filled by stacking gel. Procedure for preparation of running gel

solution was as under (Table 3.3).

Table 3.3 Running / Separating / Resolving Gel (12% Acrylamide Gel).

Reagents Single Gel Double Gel

1.875M Tris-HCl, pH 8.8 1.5 ml 3 ml

Distilled water 3 ml 6 ml

Acrylamide/ Bisacrylamide (29:1) 3 ml 6 ml

Sodium Dodecyl Sulphate (10%) 70 µl 140 ml

After degassing, the following reagents were added

Ammonium Per Sulphate(APS) 5% 45 µl 90 µl

N,N,N,N,-Tetramethylethylenediamine 14 µl 28 µl

Note: As the APS solution (0.025 gm/500 µl) is highly unstable, it is prepared just before addition to gel solution.

The above ingredients were stirred gradually and the solution was emptied

directly into the cell up to the mark (2 cm below the top). Small amount of distilled

water (200-300 µl) was poured above the running gel solution to avoid air entering the

cell and help gel setting. After polymerization (15-20 min) of running gel, stacking gel

was prepared. Procedure for preparation of stacking gel was as under (Table 3.4).

Table 3.4 Stacking / Loading Gel (4% Acrylamide Gel).

Reagents Single Gel Double Gel

0.6 M Tris-HCl, pH 6.8 0.5 ml 1 ml

Distilled water 3.80 ml 7.6 ml

Acrylamide/ Bisacrylamide (29:1) 0.8 ml 1.6 ml

Sodium Dodecyl Sulphate (10%) 50 µl 100 µl

Ammonium Per Sulphate (5%) 40 µl 80 µl

N,N,N,N,-Tetramethylethylenediamine 10 µl 20 µl

42

Distilled water at the top of running gel drained out and wiped with absorbent

tissue. This space was refilled with de-aerated stacking gel solution with comb inserted

into it, preventing bubble development. The 15-20 minutes after polymerization, comb

was taken out carefully forming clear wells in the gel.

Samples loading and electrophoresis

Electrophoresis assembly was combined into the gel tank. To eliminate trapped

gas bubbles below the gel, lower tray was filled with electrode buffer. The upper gel

tray was filled with the same electrode buffer so that the gel immersed completely.

Running buffer was used to wash wells and by using a microsyringe, 15 µl of each

sample was loaded carefully at the bottom of each well. Gel was run at 15 mA constant

current for 2-3 hours by connecting to power supply.

Visualization of proteins (staining and de-staining of resolving gel)

Power supply was switched off after the run had accomplished and spatula was

used to take the gel out from the cell. The separating gel was put into a box having

staining solution after the removal of stacking gel and shaken gently for 1-2 hours. The

staining solution was replaced by de-staining solution and shaken gradually till the blue

background of the gel disappeared. To speed up de-staining a piece of kimwipe was put

into the de-staining solution to absorb access commassie brilliant blue (CBB). With the

help of white light illuminator visualization of the de-stained gel was carried out.

43

IV. RESULTS AND DISCUSSION

The section wise results and their discussion in light of the current review of

each study for various traits are elaborated as follows:

A. Mean performance of F1 and F2 populations along with parental cultivars

Analysis of variance

Analysis of variance displayed significant (p≤0.01, p≤0.05) differences among

the genotypes for all the traits in F1 and F2 generations (Tables 1 & 2). Moreover,

genotype effect was further partitioned into three components i.e. parental cultivars,

generations (F1 and F2), and generations vs. parental cultivars. Significant differences

were observed among the parents for all traits in both generations. The F2 populations

showed significant differences for all the traits. In parents vs. F1 hybrids, significant

differences were observed for most the traits except for days to heading and 1000-grain

weight. In parents vs. F2s, except two traits (grains per spike, harvest index per plant) all

other traits revealed significant differences.

Significant differences were observed among genotypes for spikelets per spike

in genetic study of polygenic characters in bread wheat (Rehman et al., 2002; Joshi et

al., 2004). Chowdhary et al. (2007) reported significant mean square for peduncle

length while analyzing metric traits in wheat. Seboka et al. (2009) recorded significant

mean squares for grain per spike in different varieties of wheat. Significant differences

for plant height, 1000-grain weight and grain yield per plant were observed among

diverse genotypes in wheat under stress and normal conditions (Kulshreshtha and

Singh, 2011; Said, 2014; Abedi et al., 2015). Significant variations were observed

among different wheat genotypes for days to heading and yield traits (Jadoon et al.,

2012; Madić et al., 2014). Singh et al. (2012) reported significant differences among

genotypes for flag leaf area in genetic study of quality traits in wheat. Ali and Sulaiman

(2014) mentioned significant mean values for 1000-grain weight which revealed the

presence of adequate genetic variability among parents and hybrids of wheat.

Significant differences among generations and genotypes for coefficient of infection

were reported in genetic analysis of resistance to yellow rust race (70E0A+) at adult

plant stage (Zandipour et al., 2014). However, non-significant mean squares were

observed for grain yield plant, grains per spike, spikelets per spike, spike length, fertile

44

tillers per plant and days to maturity in F2 wheat populations (Khan, 2013).

Contradiction in the past and present findings might be due to diverse wheat breeding

material and the environmental conditions. The trait-wise results are discussed here in

the light of current review.

Days to heading

In crop production, the days to heading are recognized as a key sign of

earliness. In F1 generation, days to heading ranged from 125 to 134 days among

parental cultivars and 126 to 133 days among F1 hybrids (Table 3). Minimum days to

heading was observed for cultivar Khyber-87 (125 days) which was similar with four

other F1 hybrids i.e. Shahkar-13 × Khyber-87 and Pirsabak-04 × Khyber-87 (126 days),

Saleem-2000 × Khyber-87 (127 days) and Pirsabak-05 × Khyber-87 (128 days).

However, cultivars Pirsabak-85 took maximum days to heading (134 days) in F1

generation. In F2 generation, days to heading among parental cultivar varied from 121

to 129 days and from 118 to 125 days for F2 segregants (Table 3). Minimum and same

days to heading were observed for two F2 segregants i.e. Pirsabak-05 × Shahkar-13 and

Shahkar-13 × Khyber-87 (118 days) which were at par with one other genotype viz.,

Shahkar-13 × Saleem-2000 (120 days). Cultivar Pirsabak-85 was observed with

maximum days to heading (129 days) and was late maturing among all genotypes. In

both generations, cultivar Khyber-87 and some of its F1 hybrids i.e. Pirsabak-04 ×

Khyber-87 and Shahkar-13 × Khyber-87 and F2 population Shahkar-13 × Khyber-87

were found with minimum days to heading.

Parental cultivars had appreciable genetic variability for days to heading, spike

length and grain yield in wheat (Said et al., 2007). Winter wheat cultivar Kharkof was

investigated for days to heading for 70 years at six locations in the Great Plains of

United States. Results showed constant trend of early days to heading at all sites at rate

of 0.8 to 1.8 days per 10 years, and earlier days to heading indicated warmer

temperatures in the spring as days to heading is regulated primarily by temperatures

(Hu et al., 2005).

45

Days to maturity

Days to maturity for parental cultivars ranged from 167 to 173 days and 170 to

173 days among F1 hybrids (Table 3). Cultivar Khyber-87 was observed with lesser

days to maturity (167 days) and was similar with two other genotypes i.e. Saleem-2000

(169 days) and Pirsabak-04 × Khyber-87 (170 days). Maximum days to maturity (173

days) were recorded for four genotypes viz., Pirsabak-05, Pirsabak-85 × Khyber-87,

Pirsabak-05 × Shahkar-13 and Pirsabak-05 × Saleem-2000. Parental cultivars varied

from 167 to 172 days for days to maturity whereas in F2 populations the range was 166

to 170 days (Table 3). Minimum and same days to maturity (166 days) were recorded

for six F2 segregants (Pirsabak-85 × Shahkar-13, Pirsabak-04 × Shahkar-13, Pirsabak-

04 × Saleem-2000, Pirsabak-04 × Khyber-87, Pirsabak-05 × Shahkar-13 and Shahkar-

13 × Khyber-87). Maximum days to maturity were recorded for cultivar Pirsabak-05

(172 days) which was equal with four other genotypes with similar days to maturity

(170 days).

Gardner et al. (1985) have mentioned that maturity is delayed for few days in

cooler environments, where crops get more time to produce assimilates and to transfer

of larger amount of assimilates to sink resulting in higher grain yield. Attarbashi et al.

(2002) observed negative correlation for days to maturity with grain yield in bread

wheat. Wheat genotypes are prone to terminal heat stress and early maturity due to high

temperature reduced the grain yield (Din et al., 2010). Warmer temperatures effect crop

growth and temperature zabove 30 °C during grain filling, not only had negative impact

on grain yield but also days to maturity, days to heading and plant height (Mondal et

al., 2013).

Plant height

Plant height varied from 85 to 107.50 cm among parental cultivars and 92.50 to

115.00 cm among F1 hybrids (Table 4). The lowest and similar plant height was

recorded for two parental cultivars i.e. Shahkar-13 (85.00 cm) and Saleem-2000 (87.50

cm). However, these genotypes were equal in performance with four other genotypes

i.e. parental cultivar Khyber-87 (92.50 cm) and three F1 hybrids i.e. Shahkar-13 ×

Saleem-2000 (92.50 cm), Saleem-2000 × Khyber-87 (95.00 cm) and Shahkar-13 ×

Khyber-87 (95.00 cm). Maximum plant height was noted in F1 hybrid Pirsabak-04 ×

46

Pirsabak-05 (115.00 cm) which was at par with two other parental cultivars and six F1

hybrids ranging from 105.00 to 110.00 cm. In F2 generation, plant height among

parental cultivars varied from 82.93 to 99.77 cm and for F2 populations varied from

89.08 to 106.37 cm (Table 4). The lowest plant height was observed for parental

cultivar Saleem-2000 (82.93 cm) which was similar with cultivars Shahkar-13 and

Pirsabak-85 with values of 83.38 and 87.23 cm, respectively. Maximum plant height of

106.37 cm was observed for F2 population Pirsabak-85 × Khyber-87, and it was found

similar in performance with three F2 segregants i.e. Pirsabak-04 × Pirsabak-05,

Pirsabak-04 × Khyber-87 and Pirsabak-05 × Shahkar-13 with values of 101.67, 102.30

and 102.52 cm, respectively.

After green revolution, dwarf wheat genotypes were found desirable and more

responsive to fertilizer and with more potential to produce more grain yield than former

long stature cultivars (Khush, 2001). However, in present study parental cultivars, F1

and F2 populations with tall staure produced maximum yield. The increased yield of

these genotypes might be due to resistance to yellow rust and having maximum days to

heading. Inamullah et al. (2006) and Çifci (2012) reported that short plant height is

required in wheat because taller plants are likely to lodge and need more energy to

transport photosynthates to the grains. Significant variability among wheat genotypes

was reported for plant height and yield related traits under drought and normal

environment (Ahmad et al., 2007; Khiabani et al., 2015).

Peduncle length

Peduncle length varied from 28.90 to 38.90 cm among parental cultivars

whereas among F1 hybrids it varied from 32.60 to 41.40 cm (Table 4). Minimum

peduncle length (28.90 cm) was observed for cultivar Saleem-2000, which was equal to

cultivar Shahkar-13 (31.20 cm). However, these genotypes were followed by four other

F1 hybrids with at par peduncle length viz., Shahkar-13 × Khyber-87 (32.60 cm),

Shahkar-13 × Saleem-2000 (33.20 cm), Pirsabak-04 × Saleem-2000 (33.30 cm) and

Saleem-2000 × Khyber-87 (33.40 cm). The F1 hybrid i.e. Pirsabak-04 × Pirsabak-05

(41.40 cm) was observed with maximum peduncle length and it was followed by four

other genotypes (Pirsabak-05, Pirsabak-85 × Pirsabak-05, Pirsabak-05 × Shahkar-13

and Pirsabak-05 × Khyber-87) with similar peduncle length ranging from 36.90 to

38.90 cm. In F2 generation, peduncle length varied from 26.49 cm to 36.19 cm among

47

parental cultivars and among F2 segregants, it varied from 31.20 to 38.49 cm (Table 4).

Cultivar Saleem-2000 (26.49 cm) with lowest peduncle length was different from the

rest of the genotypes, followed by cultivar Shahkar-13 (29.00 cm) and Pirsabak-85

(30.44 cm). Maximum peduncle length (38.49 cm) was observed for Shahkar-13 ×

Khyber-87 and it was similar in performance with three other F2 populations i.e.

Pirsabak-05 × Shahkar-13 (38.00 cm), Pirsabak-85 × Khyber-87 (37.42 cm) and

Pirsabak-04 × Pirsabak-05 (37.12 cm).

Peduncle length is an essential feature and major contributor to plant height and

it differs genotype to genotype in wheat. Asseng and Van-Herwaarden (2003) reported

that peduncle length role in stem reserve remobilization was correlated with high grain

yield under stress. The primary role of peduncle length in heat and drought resistance

was proved due to its role in photosynthesis and stem reserve remobilization (Villegas

et al. 2007). Plant height was positively correlated with peduncle length, and

contributing a great deal to plant height in wheat (Zhao and Wang, 2003; Yao et al.,

2011; Amiri et al., 2013).

Flag leaf area

Among parental cultivars, flag leaf area varied from 30.59 to 40.44 cm2 and

32.59 to 41.75 cm2 among F1 hybrids (Table 5). In F1 generation, Pirsabak-05 ×

Khyber-87 (41.75 cm2) and Pirsabak-05 × Saleem-2000 (41.60 cm2) revealed maximum

and alike flag leaf area, and these genotypes were same with six other genotypes

(having one parental line and five F1 hybrids) ranged from 36.51 to 40.57 cm2. The

lowest flag leaf area of 30.59 cm2 was recorded for cultivar Saleem-2000 which was at

par with twelve other genotypes (with four parental cultivars and eight F1 hybrids)

ranging from 32.55 to 35.52 cm2. In F2 generation, flag leaf area varied from 26.14 to

35.70 cm2 and 30.78 to 37.97 cm2 among parental cultivars and F2 populations,

respectively (Table 5). Maximum flag leaf area of 37.97 cm2 was noted for Shahkar-13

× Khyber-87, which was at par with three other genotypes viz., Pirsabak-04 × Pirsabak-

05, Pirsabak-85 × Khyber-87 and Pirsabak-05 × Shahkar-13 ranging from 36.62 to

37.48 cm2. Minimum flag leaf area was recorded for Saleem-2000 (26.14 cm2) in F2

generation.

48

Flag leaf area play a key role in yield of wheat during spike development, as

flag leaf provide photosynthates for grain yield (Ahmad et al., 2013d). Finding of this

study revealed that genotypes with larger flage leaf area produced more grain yield in

both generations. However, crosses among durum wheat genotypes showed that the

size of flag leaf was not associated with grain yield (Grignac, 1974). Zeuli and Qualset

(1990) reported positive correlation between flag leaf area and yield, indicating that

flag leaf area might be a useful parameter for selection of high yielding plants. Non-

significant mean differences were observed for flag leaf area and grain yield among

wheat cultivars (Malik et al., 2005; Rahim et al., 2006).

Tillers per plant

In F1 generation, tillers per plant varied from 10.50 to 14.50 among parental

cultivars and 11.50 to 15.50 among F1 hybrids (Table 5). Maximum and equal tillers

per plant were observed for Pirsabak-04 × Saleem-2000 (15.5) and Pirsabak-85 ×

Saleem-2000 (15.0). However, these genotypes were at par with two other parental

cultivars and nine F1 hybrids ranging from 14.00 to 14.50. Minimum tillers per plant

(10.50) were recorded for Khyber-87, and it was at par with three other genotypes i.e.

Pirsabak-85 × Shahkar-13 (11.50), Pirsabak-05 (11.50) and Shahkar-13 (12.00). In F2

populations, tillers per plant varied from 11.90 to 15.78 among parental cultivars, and

11.58 to 15.65 among F2 populations (Table 5). Maximum and similar tillers per plant

were reported for Saleem-2000 (15.78), Pirsabak-04 × Saleem-2000 (15.65) and

Shahkar-13 (15.45). However, these genotypes were further alike in performance with

one parental cultivar Pirsabak-04 (15.22) and F2 segregant Pirsabak-04× Shahkar-13

(15.02). Minimum tillers per plant were observed for F2 population Shahkar-13 ×

Khyber-87 (11.58) and it was found at par with two other parental cultivars i.e.

Pirsabak-05 (11.90) and Pirsabak-85 (12.70) and two F2 populations i.e. Pirsabak-85 ×

Pirsabak-05 (12.43) and Pirsabak-05 × Khyber-87 (12.63).

Tillers per plant have close positive association with grain yield and playing

greater role in controlling grain yield. Their findings also revealed that not all tillers

will survive and produce ears and this was supposed to be due to competition for light

and nutrients. Significant mean differences were reported among spring wheat

genotypes with diverse genetic back-ground for tillers per plant (Khan and Habib,

2003). Result revealed that genotypes with more tillers were high yielding in F1

49

generation. However, in F2 generation genotypes, more tillers had no impact on grain

yield due to their susceptibility and severity of yellow rust. Zeeshan et al. (2013)

reported that tillers per square meter had positive effect on spike length while negative

effect on spikelets per spike in wheat elite lines. However, Khan et al. (2010) observed

that tillers per m2 had negative effect on 1000-grain weight in recombinant inbred lines

of wheat. Contradiction might be due different wheat populations and the genotype by

environment interaction.

Spike length

In F1 generation, spike length varied from 10.50 to 12.17 cm among parental

cultivars whereas among F1 hybrids, the range was 12.10 to 13.40 cm (Table 6).

Maximum and equal spike length was noted in three F1 populations i.e. Pirsabak-04 ×

Shahkar-13, Pirsabak-85 × Pirsabak-04 and Pirsabak-05 × Shahkar-13 ranging from

13.30 to 13.40 cm. However, these genotypes were also at par with six other F1 hybrids

ranging from 12.65 to 12.90 cm. Minimum spike length (10.50 cm) was recorded for

Saleem-2000 and it was same with two other genotypes viz., Pirsabak-05 (11.20 cm)

and Khyber-87 (11.25 cm) in F1 generation. In F2 generation, spike length ranged from

11.03 to 13.08 cm for parental cultivars, and among F2 populations, the means ranged

from 11.03 cm to 13.61 cm (Table 6). Maximum and equal spike length was observed

for F2 populations i.e. Pirsabak-85 × Pirsabak-04 (13.58 cm) and Pirsabak-85 ×

Saleem-2000 (13.61 cm). However, these F2 segregants were at par with ten other

genotypes ranging from 13.03 to 13.54 cm. Minimum and same spike length was

observed in three genotypes Pirsabak-04 × Pirsabak-05, Pirsabak-05 and Pirsabak-04 ×

Shahkar-13 ranging from 11.03 to 11.10 cm.

Spike length is also an important trait of wheat contributing to grain yield. Long

and dense spike length bear more spikelets that eventually increase grains per spike and

grain yield. In wheat breeding, importance should be given to spike length with dense

spike. The general concept of incorporating dwarfing gene (Rht) in wheat is to improve

assimilate partitioning for development of spikes (Reynolds et al., 2009). Spike length

has an indirect positive effect on grain yield through the number of spikelets and grains

per spike, which suggests that breeders should pay more attention to said trait (Ijaz and

Kashif, 2013).

50

Spikelets per spike

Spikelets per spike varied from 18.00 to 21.00 and 19.50 to 24.00 among

parental cultivars and F1 hybrids, respectively (Table 6). Maximum spikelets per spike

were observed for F1 hybrid Pirsabak-85 × Saleem-2000 (24.00), and it was equal with

four other F1 hybrids viz., Pirsabak-85 × shahkar-13, Pirsabak-85 × Pirsabak-04,

Saleem-2000 × Khyber-87 and Pirsabak-85 × Khyber-87 ranging from 22.50 to 23.00.

In F2 generation, Spikelets per spike varied from 18.42 to 23.08 among parental

cultivars and 19.83 to 23.02 in F2 segregants (Table 6). Maximum spikelets per spike

were observed for cultivar Pirsabak-85 (23.08) and three F2 populations viz., Saleem-

2000 × Khyber-87 (23.02) Pirsabak-85 × Saleem-2000 (22.92) and Pirsabak-04 ×

Saleem-2000 (22.98). However, these genotypes were similar with eight other

genotypes ranging from 21.80 to 22.88 spikelets per spike. Minimum spikelets per

spike were noted in cultivar Pirsabak-05 (18.42) and it was found at par with cultivar

Khyber-87 (19.77).

Spikelets per spike have key role in controlling grain yield, and have significant

positive association with grain yield. Increased number of spikelets per spike might

reduce the grain weight, however, it would contribute to yield (Pinthus and Millet,

1978). Dagusto (2008) and Kalhoro et al. (2015) recorded significant differences

among cultivars and advance lines for spikelets per spike in genetic analysis of some

agronomic traits in wheat. Spikelets per spike significantly affect the number of grains

and grain mass per spike in wheat (Zečević et al., 2009).

Grains per spike

Grains per spike varied from 63.50 to 75.00 and 67.50 to 76.00 among parental

cultivars and F1 hybrids, respectively (Table 7). Maximum and same grains per spike

(76.00) were recorded in two F1 hybrids i.e. Pirsabak-05 × Saleem-2000 and Pirsabak-

85 × Pirsabak-04. These hybrids were also found at par with five other genotypes i.e.

Pirsabak-05 × Khyber-87, Pirsabak-85, Pirsabak-85 × Saleem-2000, Shahkar-13 ×

Khyber-87 and Pirsabak-04 ranging from ranged from 72.50 to 75.00. Minimum grains

per spike were observed for cultivar Pirsabak-05 (63.50) and it was at par with cultivar

Saleem-2000 (64.50). In F2 generation, grains per spike ranged from 59.00 to 67.25

among parental cultivars, and 60.55 to 68.80 among F2 segregants (Table 7). Maximum

51

grains per spike were observed for Pirsabak-85 × Saleem-2000 (68.80), and it was

found at par in performance with ten other genotypes (three parental cultivars and

seven F2 populations) ranging from 65.45 to 67.25. Minimum grains per spike were

noted for Pirsabak-05 (59.00), however, it was alike with three other genotypes i.e.

Pirsabak-04 × Pirsabak-05 (60.55), Pirsabak-04 × Shahkar-13 (60.60) and Khyber-87

(62.10).

Grains per spike and grain size in wheat had provided evidence about the

structure of wheat plant, but slight about the basic causes of variation in grain yield

(Thorne, 1974). Bhuiya and Kamal (1994) specified that the product of four

components i.e. spike per plant, spikelets per spike, grains per spike and individual

grain weight are the key contributors to wheat grain yield. Among genotypes,

significant differences were observed for grains per spike and grain yield in bread

weight (Saad et al., 2010).

1000-grain weight

Among parental cultivars, 1000-grain weight ranged from 37.00 to 43.00 g

while in F1 hybrids the said range was 37.50 to 42.50 g (Table 7). In F1 generation,

highest 1000-grain weight was recorded for parental cultivar Pirsabak-05 (43.00 g) and

it was similar with four F1 hybrids viz., Pirsabak-04 × Shahkar-13, Pirsabak-04 ×

Pirsabak-05, Pirsabak-05 × Shahkar-13 and Pirsabak-85 × Pirsabak-05 ranging from

41.00 to 42.50 g. Minimum 1000-grain weight was recorded for Saleem-2000 (37.00 g)

and it was at par with eight other genotypes (having two parental cultivars and six F1

hybrids) ranging from 37.50 to 39.00 g. In F2 generation, 1000-grain weight varied

from 26.12 to 44.55 g among parental cultivars, and 27.25 to 45.97 g among F2

segregants (Table 7). Maximum 1000-grain weight was noted for F2 population i.e.

Pirsabak-05 × Shahkar-13 (45.97 g), and it was equal in performance with three other

genotypes i.e. Pirsabak-05 (44.55 g), Shahkar-13 (42.87 g) and Pirsabak-04 × Pirsabak-

05 (41.83 g). Minimum and alike 1000-grain weight was noted in two parental

genotypes i.e. Pirsabak-85 (26.12 g) and Saleem-2000 (26.23 g). These genotypes were

found at par with two other F2 populations i.e. Pirsabak-85 × Saleem-2000 (27.25 g)

and Pirsabak-04 × Saleem-2000 (30.60 g).

52

Results revealed that the genotypes with maximum 1000-grain weight were

with high grain yield in both generations. Grains with higher 1000-grain weight have

better milling quality and ensure better emergence (Protic et al., 2007). Akram et al.

(2008) reported that grain yield was positively correlated with 1000-grain weight.

Beche et al. (2013) and Lal et al. (2013) recorded significant differences among

genotypes for 1000-grains weight in spring wheat.

Grain yield per plant

In F1 generation, grain yield varied from 22.50 to 33.50 g and 27.50 to 40.50 g

among parental cultivars and F1 hybrids, respectively (Table 8). Maximum grain yield

was observed for F1 hybrid i.e. Pirsabak-85 × Pirsabak-04 (40.50 g) and it was found

equal in performance with three other F1 hybrids viz., Pirsabak-85 × Pirsabak-05 (38.50

g), Shahkar-13 × Saleem-2000 (35.90 g) and Pirsabak-05 × Shahkar-13 (35.10 g).

Minimum and at par grain yield was noted in two cultivars Khyber-87 (22.50 g) and

Saleem-2000 (23.50 g) and these parental cultivars were at par with four other

genotypes ranging from 25.00 g to 28.50 g. In F2 generation, grain yield varied from

13.80 to 31.00 g among parental cultivars and 16.18 to 31.22 g among F2 populations

(Table 8). Maximum grain yield was observed in Pirsabak-05 × Shahkar-13 (31.2 g)

which was equal to nine other genotypes (with two parental lines and seven F2

populations) ranging from 25.3 to 31.0 g. Minimum grain yield was observed for three

genotypes viz., Pirsabak-85 (13.80 g), Pirsabak-85 × Saleem-2000 (16.18 g) and

Pirsabak-04 (16.45 g). These genotypes were also at par with two other genotypes i.e.

Saleem-2000 (16.92 g) and Pirsabak-04× Saleem-2000 (19.30 g).

Results revealed that F1 hybrid (Pirsabak-85 × Pirsabak-04), F2 population

(Pirsabak-05 × Shahkar-13) and cultivars (Pirsabak-2005, Shahkar-13) with highest

grain yield were due to their better adaptability and resistance to biotic stress i.e. yellow

rust. Amin et al. (2005) reported that a cultivar grown in diverse environmental

conditions have better adaptability if have low degree of fluctuation in grain yield.

Several researchers recorded significant differences among parental cultivars and F1

hybrids for grain yield in bread wheat (Adel and Ali, 2013; Fellahi et al., 2013, 2015).

53

Biological yield per plant

Biological yield varied from 72.00 to 91.50 g among parental cultivars, and

76.50 to 97.50 g among F1 hybrids (Table 8). Maximum biological yield was recorded

for F1 hybrid i.e. Pirsabak-85 × Pirsabak-04 (97.50 g) and it was equal with three F1

hybrids viz., Pirsabak-04 × Pirsabak-05 (95.50 g), Pirsabak-05 × Shahkar-13 (92.00 g),

Pirsabak-85 × Pirsabak-05 (92.00 g) and parental cultivar Pirsabak-05 (91.50 g).

Minimum biological yield per plant was observed for Shahkar-13 (72.00 g) and it was

alike with three parental cultivars and four F1 hybrids ranging from 75.50 to 78.50 g. In

F2 generation, biological yield varied from 49.63 to 72.77 g and 58.12 to 85.07 g

among parental cultivars and F2 populations, respectively (Table 8). Maximum

biological yield was noted for Pirsabak-85 × Khyber-87 (85.07 g) and it was same in

performance with six other F2 populations ranging from 75.00 to 82.58 g. Minimum

and similar biological yield was recorded for two parental cultivars Pirsabak-85 (49.63

g) and Pirsabak-04 (51.33 g). These cultivars were also at par with three other

genotypes i.e. Saleem-2000 (55.13 g), Pirsabak-85 × Saleem-2000 (58.12 g) and

Khyber-87 (60.30 g).

Genotypes with increased plant height were generally observed with higher

biological yield; however, grain yield seems to have a decisive role in determining the

biological yield. In present study, it was observed that genotypes i.e. Pirsabak-05,

Pirsabak-85 × Pirsabak-04 (F1) and Pirsabak-85 × Khyber-87 (F2) with greater plant

stature provided more biological yield. In Pakistan, high biological yield is also

preferred by farmers because they need wheat grains along with good yield of straw

(Bhoosa) for their livestock. Significant difference were observed among genetically

diverse genotypes for biological in bread wheat (Heidari et al., 2006). Genotypes

revealed significant differences for biological yield and grain yield in spring wheat

(Pancholi et al., 2011).

Harvest index per plant

Harvest index varied from 29.24 to 42.31% plant among parental cultivars, and

32.26 to 42.98% among F1 hybrids (Table 9). Maximum harvest index was recorded for

F1 hybrid i.e. Shahkar-13 × Saleem-2000 (42.98%) and it was at par with three parental

cultivars and eleven F1 hybrids ranging from 36.62 to 42.54%. Minimum harvest index

54

was noted for cultivar Khyber-87 (29.24%) and it was equal with two other parental

cultivars and four F1 hybrids ranging from 31.10 to 35.75%. In F2 generation, harvest

index was ranged from 28.15 to 43.86% and 27.95 to 39.52% among parental cultivars

and F2 segregants, respectively (Table 9). Maximum harvest index was recorded for

Shahkar-13 (43.86%) and it was at par with F2 population Shahkar-13 × Khyber-87

(39.52%). Minimum harvest index was observed for Pirsabak-85 × Saleem-2000

(27.95%) and it was alike with two parental cultivars and five F2 segregants ranging

from 28.15 to 33.06%.

Donmenz et al. (2001) mentioned that harvest index in wheat was mostly

associated with increased grain yield in genetic study of yield attributes in winter

wheat. Significant difference were observed for harvest index among spring wheat

genotypes under irrigated and drought conditions (Jatoi et al., 2012).

Yellow rust resistance

The yellow rust resistance was estimated through average coefficient of

infection (ACI). The ACI varied from 0.00 to 20.00 among parental cultivars, and 0.00

to 3.84 among F1 hybrids (Table 9). Minimum ACI (0.00) was observed for nine

genotypes (three parental cultivars and six F1 hybrids) and these genotypes were equal

in resistance to yellow rust with six other genotypes (one parental cultivar and five F1

hybrids) ranging from 0.03 to 0.43. Maximum ACI was recorded for cultivar Pirsabak-

85 (20.00). In F2 generation, the ACI varied from 0.00 to 25.97 among parental

cultivars, and 0.58 to 15.66 among F2 populations (Table 9). Minimum and at par ACI

was recorded for two cultivars Pirsabak-05 (0.00), Shahkar-13 (0.02) and three F2

populations i.e. Pirsabak-05 × Shahkar-13 (0.58), Shahkar-13 × Saleem-2000 (2.58)

and Shahkar-13 × Khyber-87 (2.74). However, maximum severity and ACI was noted

for cultivar Pirsabak-85 (25.97) and it was found highly susceptible as compared to

other genotypes. In both generations, cultivars Pirsabak-05 and Shahkar-13 showed

more resistance to yellow rust with minimum ACI values while Pirsabak-85 with

greater ACI values ranked as the most susceptible genotype among parental cultivars.

Cultivars Saleem-2000 (Yr18) and Khyber-87 (Yr 9+) individually having high

susceptibility. However, their F2 progeny (Saleem-2000 × Khyber-87) showed

resistance to prevailing yellow rust races that may be due to accumulation of some

resistance genes or combined effect of both parents Yr genes.

55

Majority of Pakistani bread wheat cultivars were protected against stripe rust by

incorporating the Yr genes, YrA, Yr2, Yr4 Yr6, Yr7, Yr18, Yr9, Yr22 and Yr27; however,

the genes, Yr6, Yr7 and Yr9 are occurring more frequently either in combination with

other Yr genes or alone (Qamar et al., 2011). Bux et al. (2011, 2012) reported the

virulence for resistant genes YrA, Yr2, Yr6, Yr7, Yr8, Yr9, Yr17, Yr27 and gene

combinations in Mexican wheat cultivars Opata (Yr27 + Yr18) and Super Kauz (Yr9,

Yr27 and Yr18) under natural conditions over four locations with variable

environments. In F2 populations, low ACI was mostly observed in genotypes having

one of the resistant cultivars i.e. Pirsabak-05 and Shahkar-13 in their parentage. Kaur et

al (2003) screened various wheat genotypes for yellow rust resistance and confirmed

the susceptibility in genotype WL-711 and resistance in the genotypes i.e. PBW-65,

Trap-1, Opata-85, Shailaja, Apache-81 and Noroesta-66 against yellow rust.

56

Table 1. Mean square for various traits in 6 × 6 F1 half diallel crosses in wheat.

Variables

Mean squares

CV % Genotypes Parents F1 Parents vs. F1 Error

D.F. 20 5 14 1 20

Days to heading 11.95** 19.88** 9.94** 0.29 0.43 0.51

Days to maturity 4.11** 7.6** 1.59* 21.94** 0.63 0.46

Plant height 117.02** 173.75** 74.4* 430.06** 25.6 4.99

Peduncle length 14.31** 25.04** 10.21** 17.94** 1.29 3.23

Flag leaf area 21.74* 22.67* 19.97* 41.77* 7.86 7.9

Tillers plant-1 3.52** 4.6** 2.25* 16.01* 0.81 6.7

Spike length 1.08** 0.76** 0.33* 13.07** 0.13 2.95

Spikelets spike-1 5.27** 2.8* 3.10* 48.00** 1.18 5.12

Grains spike-1 29.18** 42.28** 22.5** 57.2** 2.6 2.25

1000-grain weight 5.01** 7.88** 4.32** 0.4NS 1.07 2.61

Grain yield plant-1 40.29** 51.95** 27.53** 160.7** 8.32 9.2

Biological yield plant-1 11991.89** 12962.74** 3.104** 115.61** 11.86 9.51

Harvest index plant-1 32.24* 43.02* 23.58* 99.72* 11.76 9.13

Yellow rust resistance 45.09** 140.33** 2.26** 168.52** 0.0804 15.13

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

57

Table 2. Mean square for various traits in 6 × 6 F2 half diallel crosses in wheat.

Variables

Mean squares

CV

% Genotypes Parents F2

Parents

vs. F2 Error

d.f. 20 5 14 1 40

Days to heading 15.79** 24.86** 9.37** 60.36** 3.46 1.52

Days to maturity 10.31** 11.02** 8.99** 25.20** 3.05 1.04

Plant height 117.45** 120.2** 71.83** 742.52** 12.15 3.68

Peduncle length 28.90** 32.90** 14.86** 205.51** 1.48 3.59

Flag leaf area 27.97** 33.59** 14.47** 188.97** 1.46 3.61

Tillers plant-1 4.15** 7.81** 2.85** 3.921* 0.57 5.53

Spike length 2.52** 1.99** 2.52** 5.14** 0.19 3.42

Spikelets spike-1 5.03** 9.72** 3.48** 3.16* 0.72 3.94

Grains spike-1 18.69** 30.15** 15.42** 7.14NS 5.49 3.62

1000-grain weight 98.34** 193.95** 61.24** 139.83** 8.03 7.69

Grain yield plant-1 76.98** 140.50** 46.78** 182.11** 13.62 15.31

Biological yield plant-1 320.64** 290.73** 197.53** 2193.71** 47.07 9.90

Harvest index plant-1 44.77** 99.92** 27.84* 6.05NS 11.09 9.65

Yellow rust resistance 155.77** 379.11** 58.01** 405.10** 2.92 16.96

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

58

Table 3. Mean performance of 6 × 6 F1 and F2 half diallel crosses for days to

heading and maturity.

Parental genotypes / F1 &

F2 populations

Days to heading Days to maturity

F1 F2 F1 F2

Pirsabak-85 134.00 129.00 172.00 170.00

Pirsabak-04 129.00 124.00 171.00 170.00

Pirsabak-05 131.00 123.00 173.00 172.00

Shahkar-13 128.00 121.00 170.00 168.00

Saleem-2000 129.00 124.00 169.00 168.00

Khyber-87 125.00 121.00 167.00 167.00

Pirsabak-85 × Pirsabak-04 131.00 123.00 171.00 169.00

Pirsabak-85 × Pirsabak-05 133.00 122.00 172.00 170.00

Pirsabak-85 × Shahkar-13 133.00 122.00 171.00 166.00

Pirsabak-85 × Saleem-2000 132.00 122.00 172.00 167.00

Pirsabak-85 × Khyber-87 130.00 121.00 173.00 168.00

Pirsabak-04 × Pirsabak-05 129.00 122.00 172.00 169.00

Pirsabak-04 × Shahkar-13 128.00 121.00 172.00 166.00

Pirsabak-04 × Saleem-2000 129.00 121.00 171.00 166.00

Pirsabak-04 × Khyber-87 126.00 121.00 170.00 166.00

Pirsabak-05 × Shahkar-13 129.00 118.00 173.00 165.00

Pirsabak-05 × Saleem-2000 130.00 122.00 173.00 170.00

Pirsabak-05 × Khyber-87 128.00 125.00 172.00 168.00

Shahkar-13 × Saleem-2000 130.00 120.00 172.00 168.00

Shahkar-13 × Khyber-87 126.00 118.00 172.00 166.33

Saleem-2000 × Khyber-87 127.00 122.00 171.00 169.00

LSD0.05 4.02 2.70 3.69 2.07

59

Table 4. Mean performance of 6 × 6 F1 and F2 half diallel crosses for plant height

and peduncle length.

Parental genotypes / F1 &

F2 populations

Plant height (cm) Peduncle length (cm)

F1 F2 F1 F2

Pirsabak-85 100.00 87.23 35.20 30.44

Pirsabak-04 105.00 91.12 35.20 31.51

Pirsabak-05 107.50 99.77 38.90 36.19

Shahkar-13 85.00 83.38 31.20 29.00

Saleem-2000 87.50 82.93 28.90 26.49

Khyber-87 92.50 92.08 35.40 32.75

Pirsabak-85 × Pirsabak-04 110.00 95.68 36.10 35.81

Pirsabak-85 × Pirsabak-05 110.00 98.57 37.50 35.27

Pirsabak-85 × shahkar-13 102.50 98.97 36.00 34.08

Pirsabak-85 × Saleem-2000 102.50 94.18 34.30 32.51

Pirsabak-85 × Khyber-87 107.50 106.37 35.00 37.42

Pirsabak-04 × Pirsabak-05 115.00 101.67 41.40 37.12

Pirsabak-04 × Shahkar-13 102.50 91.90 36.50 33.87

Pirsabak-04 × Saleem-2000 102.50 89.40 33.30 31.20

Pirsabak-04 × Khyber-87 100.00 102.30 35.20 36.44

Pirsabak-05 × Shahkar-13 105.00 102.52 37.50 37.99

Pirsabak-05 × Saleem-2000 105.00 95.58 34.80 33.98

Pirsabak-05 × Khyber-87 105.00 97.18 36.90 35.83

Shahkar-13 × Saleem-2000 92.50 89.08 33.20 31.87

Shahkar-13 × Khyber-87 95.00 96.42 32.60 38.49

Saleem-2000 × Khyber-87 95.00 95.47 33.40 34.05

LSD0.05 3.57 10.55 2.37 2.01

60

Table 5. Mean performance of 6 × 6 F1 and F2 half diallel crosses for flag leaf area

and tillers per plant.

Parental genotypes / F1 & F2

populations

Flag leaf area (cm2) Tillers plant-1

F1 F2 F1 F2

Pirsabak-85 33.61 30.03 14.00 12.70

Pirsabak-04 33.17 31.08 14.50 15.22

Pirsabak-05 40.44 35.70 11.50 11.90

Shahkar-13 33.18 28.61 12.00 15.45

Saleem-2000 30.59 26.14 12.50 15.78

Khyber-87 32.55 32.98 10.50 13.52

Pirsabak-85 × Pirsabak-04 36.51 35.33 14.50 13.42

Pirsabak-85 × Pirsabak-05 37.83 34.79 14.00 12.43

Pirsabak-85 × shahkar-13 36.75 33.63 11.50 13.67

Pirsabak-85 ×Saleem-2000 33.63 32.08 15.00 13.63

Pirsabak-85 × Khyber-87 32.91 36.92 14.00 12.98

Pirsabak-04× Pirsabak-05 37.23 36.62 12.50 13.52

Pirsabak-04× Shahkar-13 32.58 33.42 14.00 15.02

Pirsabak-04×Saleem-2000 33.70 30.78 15.50 15.65

Pirsabak-04× Khyber-87 32.65 35.95 14.50 13.70

Pirsabak-05 × Shahkar-13 40.56 37.48 14.50 13.53

Pirsabak-05 ×Saleem-2000 41.60 33.53 12.50 13.57

Pirsabak-05 × Khyber-87 41.75 35.35 13.00 12.63

Shahkar-13 × Saleem-2000 34.51 31.44 14.00 13.58

Shahkar-13 × Khyber-87 35.52 37.97 14.00 11.58

Saleem-2000 × Khyber-87 34.20 33.59 14.50 14.20

LSD0.05 5.84 1.99 1.88 1.25

61

Table 6. Mean performance of 6 × 6 F1 and F2 half diallel crosses for spike length

and spikelets per spike.

Parental genotypes / F1 & F2

populations

Spike length (cm) Spikelets spike-1

F1 F2 F1 F2

Pirsabak-85 12.17 12.46 18.00 23.08

Pirsabak-04 12.00 12.83 20.50 21.50

Pirsabak-05 11.20 11.03 18.50 18.42

Shahkar-13 11.75 13.08 21.00 22.72

Saleem-2000 10.50 11.95 20.00 21.88

Khyber-87 11.25 11.37 19.00 19.77

Pirsabak-85 × Pirsabak-04 13.40 13.58 22.50 22.53

Pirsabak-85 × Pirsabak-05 12.75 13.16 19.50 21.80

Pirsabak-85 × Shahkar-13 12.45 13.09 22.50 21.45

Pirsabak-85 × Saleem-2000 12.30 13.61 24.00 22.92

Pirsabak-85 × Khyber-87 12.90 13.15 23.00 20.40

Pirsabak-04× Pirsabak-05 12.90 11.03 21.00 19.83

Pirsabak-04× Shahkar-13 13.40 11.10 22.00 20.43

Pirsabak-04× Saleem-2000 12.40 13.40 22.00 22.98

Pirsabak-04× Khyber-87 12.70 13.54 22.00 22.88

Pirsabak-05 × Shahkar-13 13.30 13.10 20.00 21.82

Pirsabak-05 × Saleem-2000 12.25 11.37 22.00 20.70

Pirsabak-05 × Khyber-87 12.70 11.87 21.00 20.73

Shahkar-13 × Saleem-2000 12.50 13.12 22.00 22.00

Shahkar-13 × Khyber-87 12.65 13.03 21.00 22.35

Saleem-2000 × Khyber-87 12.10 13.13 23.00 23.02

LSD0.05 0.76 0.71 2.26 1.40

62

Table 7. Mean performance of 6 × 6 F1 and F2 half diallel crosses for grains per

spike and 1000-grain weigh.

Parental genotypes / F1 & F2

populations

Grains spike-1 1000-grain weight (g)

F1 F2 F1 F2

Pirsabak-85 73.00 67.25 39.50 26.12

Pirsabak-04 75.00 64.50 39.00 31.15

Pirsabak-05 63.50 59.00 43.00 44.55

Shahkar-13 69.50 66.05 39.50 42.87

Saleem-2000 64.50 66.65 37.00 26.23

Khyber-87 71.00 62.10 38.50 36.05

Pirsabak-85 × Pirsabak-04 76.00 66.60 39.50 36.68

Pirsabak-85 × Pirsabak-05 71.00 66.85 42.50 37.82

Pirsabak-85 × Shahkar-13 71.00 64.35 40.00 39.07

Pirsabak-85× Saleem-2000 74.00 68.80 38.50 27.25

Pirsabak-85 × Khyber-87 72.00 64.35 38.50 40.35

Pirsabak-04× Pirsabak-05 70.50 60.55 41.00 41.83

Pirsabak-04× Shahkar-13 71.50 60.60 41.00 36.72

Pirsabak-04× Saleem-2000 69.50 66.70 37.50 30.60

Pirsabak-04× Khyber-87 69.00 64.60 38.00 34.63

Pirsabak-05 × Shahkar-13 67.50 65.45 41.50 45.97

Pirsabak-05× Saleem-2000 76.00 63.10 40.00 40.08

Pirsabak-05 × Khyber-87 72.50 64.50 40.50 36.62

Shahkar-13 × Saleem-2000 71.00 66.00 38.00 38.93

Shahkar-13 × Khyber-87 74.50 66.40 39.50 40.12

Saleem-2000 × Khyber-87 69.00 66.20 38.50 40.22

LSD0.05 3.57 3.86 2.15 4.67

63

Table 8. Mean performance of 6 × 6 F1 and F2 half diallel crosses for grain yield

and biological yield.

Parental genotypes / F1 & F2

populations

Grain yield plant-1 (g) Biological yield plant-1 (g)

F1 F2 F1 F2

Pirsabak-85 32.50 13.80 78.50 49.63

Pirsabak-04 32.50 16.45 88.50 51.33

Pirsabak-05 33.50 27.52 91.50 72.77

Shahkar-13 25.00 31.02 72.00 70.73

Saleem-2000 23.50 16.92 75.50 55.13

Khyber-87 22.50 22.78 77.00 60.30

Pirsabak-85 × Pirsabak-04 40.50 23.27 97.50 69.42

Pirsabak-85 × Pirsabak-05 38.50 24.88 92.00 75.00

Pirsabak-85 × Shahkar-13 30.00 25.30 77.50 68.85

Pirsabak-85× Saleem-2000 28.50 16.18 81.00 58.12

Pirsabak-85 × Khyber-87 27.50 29.50 86.00 85.07

Pirsabak-04× Pirsabak-05 32.10 27.95 95.50 82.53

Pirsabak-04× Shahkar-13 33.40 22.93 78.50 69.25

Pirsabak-04× Saleem-2000 33.00 19.30 79.50 63.58

Pirsabak-04× Khyber-87 28.20 22.73 76.50 66.02

Pirsabak-05 × Shahkar-13 35.10 31.23 92.50 82.58

Pirsabak-05× Saleem-2000 31.50 26.80 88.00 79.12

Pirsabak-05 × Khyber-87 32.00 25.08 84.50 78.58

Shahkar-13 × Saleem-2000 35.90 26.15 83.50 68.88

Shahkar-13 × Khyber-87 32.50 26.85 77.00 67.37

Saleem-2000 × Khyber-87 30.00 29.50 81.00 81.32

LSD0.05 6.01 6.09 7.42 11.32

64

Table 9. Mean performance of 6 × 6 F1 and F2 half diallel crosses for harvest index

and yellow rust resistance.

Parental genotypes / F1 & F2

populations

Harvest index plant-1 Yellow rust resistance

F1 F2 F1 F2

Pirsabak-85 42.31 28.15 20.00 25.97

Pirsabak-04 36.76 32.22 0.00 18.91

Pirsabak-05 36.61 37.40 0.00 0.00

Shahkar-13 34.75 43.86 0.09 0.02

Saleem-2000 31.10 30.58 0.00 21.46

Khyber-87 29.23 37.82 10.17 18.19

Pirsabak-85 × Pirsabak-04 41.53 33.60 0.00 9.96

Pirsabak-85 × Pirsabak-05 41.83 33.06 0.00 11.70

Pirsabak-85 × Shahkar-13 38.99 36.78 0.03 6.65

Pirsabak-85× Saleem-2000 35.43 27.95 0.15 15.49

Pirsabak-85 × Khyber-87 32.25 34.64 3.84 10.17

Pirsabak-04× Pirsabak-05 33.60 34.07 0.00 9.75

Pirsabak-04× Shahkar-13 42.54 33.06 0.17 6.35

Pirsabak-04× Saleem-2000 41.53 30.31 0.43 15.66

Pirsabak-04× Khyber-87 36.88 34.56 1.00 10.81

Pirsabak-05 × Shahkar-13 37.85 37.82 0.00 0.58

Pirsabak-05× Saleem-2000 35.75 33.82 0.27 10.47

Pirsabak-05 × Khyber-87 37.76 31.60 1.87 8.35

Shahkar-13 × Saleem-2000 42.97 37.96 0.00 2.58

Shahkar-13 × Khyber-87 42.15 39.52 0.00 2.74

Saleem-2000 × Khyber-87 37.00 36.04 1.37 5.93

LSD0.05 7.15 5.50 0.59 2.82

65

B. Hayman’s Genetic Analysis

Genetic analysis was carried out according to Hayman (1954) and Mather’s

concept of D and H components of genetic variance for additive and dominance

variances, respectively (Mather and Jinks, 1982). In F2 generation, the components of

genetic variance were studied according to Verhalen and Murray (1969), Verhalen et al.

(1971) and Singh and Chaudhary (1985).

Adequacy of additive-dominance model

Two different scalling tests i.e. t2-test and regression analysis were used to asses

the adequecy of the “additive-dominance” model and validity of diallel assumptions for

various parameters. According to Mather and Jinks (1982), non-allelic interaction of

genes are associated with non-significant value of t2 test and therefore, the genes will

be independently assorted for random association. The regression coefficient is

expected to be significantly different from zero (b = 0) but not from unity (b = 1). If

both tests favored the assumptions, then the genetic model is declared fully adequate,

and model is partially adequate if one of the tests fulfill the assumptions. Failure of

both tests completely invalidates the additive-dominance model.

Additive-dominance model was partially adequate for almost all the traits in

both generations, i.e. days to heading, days to maturity, plant height, peduncle length,

flag leaf area, tillers per plant, spike length, spikelets per spike, grains per spike, 1000-

grain weight, grain yield per plant, biological yield, harvest index and yellow rust

resistance except tillers per plant in F1 generation where the model was found fully

adequate. The trait-wise results for genetic analysis are discussed here in the light of

current review.

Days to heading

Diallel analysis displayed that significance of additive 'a' and non-additive 'b'

components of genetic variance were equally important in genetic control of days to

heading in F1 and F2 populations (Table 12). Additive component accounted for greater

proportion than non-additive component in both generations. Non-significance of 'b1'

component indicated the absence of directional dominance deviation for said trait in F1

generation. However, significance of 'b1' component for F2 displayed dominance

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deviation in one direction. Asymmetrical gene distribution of dominant and recessive

alleles was suggested by the significance of 'b2' values in F1 generation, demonstrating

that some parents had more dominant alleles for days to heading. However,

symmetrical distribution of dominant and recessive alleles was suggested by the non-

significance values of 'b2' in F2 populations. Moreover, residual dominance due to

specific gene complexes was indicated by the significance of 'b3' values in F1s and F2s

along with parents. Past studies revealed additive and non-additive gene actions for

days to heading in bread wheat (Ahmad et al., 2013b; Farshadfar et al., 2013).

In F1 generation, components of genetic variance revealed that additive (D),

dominant components (H1, H2) and E were significant while h2 and F values were non-

significant for days to heading (Table 13). However, the values of H1 and H2 were less

than D, demonstrating additive type of gene action. Average degree of dominance was

also smaller than one (√H1/D = 0.52) which suggested low level of dominance of the

loci effecting this trait and showing additive type of gene action with increasing pattern

of additive genes as justified by non-significant negative value of h2 (-0.04). Unequal

H1 and H2 components and the ratio of H2/4H1 (0.18) exhibited the irregular distribution

of positive and negative genes among the parental genotypes for days to heading in F1

generation. Negative value of F (-0.76) indicated that dominant genes were less

frequent than recessive genes in F1 generation, and the same also confirmed by ratio of

dominant and recessive genes in the parental genotypes i.e. (4DH1)½ + F/(4DH1)

1/2 - F =

0.875. Significant positive value of E (0.07) indicated that environment played an

important role in phenotypic expression of days to heading. Ivanovska et al. (2000)

mentioned non-significant values for additive and dominance components in genetic

study of earliness and yield related traits in wheat. El-Rahman (2013) noted that

average degree of dominance was less than unity for earliness traits in bread wheat.

In F2 generation, components of genetic variance (D, H1, H2, h2 and E) were

significant while F was non-significant for days to heading (Table 13). However, the

values of H1 and H2 were greater than D, demonstrating non-additive type of gene

action as also confirmed by average degree of dominance [(H1/D) 1/2 = 1.247] for days

to heading. The greater value of H1 than H2 component and the ratio of H2/4H1 (0.22)

exhibited the assymetrical distribution of positive and negative genes among the

parental cultivars for days to heading in F2 generation. Positive value of ‘F’ suggested

67

that dominant alleles were more frequent than recessive ones for days to heading,

which was supported by significant positive value of h2 and ratio of dominance and

recessive gene in the parents [1/4(4DH1)1/2 + 1/2F/1/4(4DH1)

1/2 - 1/2F = 1.31]. Mishra

et al. (1994) reported non-significant additive gene action for days to heading in late

sown bread wheat. Additive gene action for days to heading had also been reported by

Chaudhry et al. (1994) and Ahmad et al. (2013b). In past studies, partial dominance

was reported for said trait which suggested that early maturing genotypes were suitable

in late-sown conditions (Patil et al., 1995).

In F1 generation, Vr-Wr graph revealed incomplete dominance for days to

heading as the regression line intercepted the Wr-axis above the point of origin (Fig.

1a). The placement of array points displayed that parental genotypes Khyber-87 and

Pirsabak-05 occupied the intermediary position showing equal proportion of dominant

and recessive genes. Genotype Shahkar-13 had more recessive genes being placed

farthest from the origin, whereas Saleem-2000 and Pirsabak-85 had maximum

dominant genes followed by Pirsabak-04 for the said trait in F1 generation. In F2

generation, Vr-Wr graph displayed over dominance type of gene action as the

regression line intercepted the Wr-axis below the point of origin and was supported by

the higher values of dominant components (H1 and H2) than D (Fig. 1b). Placement of

array points revealed that genotype Saleem-2000 had maximum dominant genes

followed by Pirsabak-04 while maximum recessive genes were noted in Pirsabak-85 for

days to heading. Irshad et al. (2012) reported incomplete dominance for days to

heading in spring wheat under stress.

High values of broad (0.99) and narrow-sense heritabilities (0.91) were recorded

for said trait in F1 generation, while high broad (0.80) and low narrow-sense (0.35)

heritability were observed for days to heading in F2 generation (Table 13). Solomon

and Labuschagne (2004) reported high heritability for days to heading which might be

due to involvement of few major genes in durum wheat.

Days to maturity

Significant 'a' and 'b' components of genetic variance were observed for days to

maturity in F1 and F2 generations (Table 14). Significant 'b1' and 'b2' variance

components were observed for the investigated traits in F1 generation. Significant 'b1'

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component in F1 generation illustrated dominance deviation in one direction.

Significance of 'b2' showed asymmetrical distribution of genes affecting the trait at loci

whereas non-significant 'b3' illustrated the absence of specific genes for days to

maturity. In F2 generation, significance of 'b1' component displayed dominance

deviation in one direction whereas significance of 'b3' exhibited specific gene effects

for days to maturity. Non-significant value of 'b2' suggested symmetrical gene

distribution among parents for days to maturity. Akram et al. (2008) reported highly

significant values of 'b2', 'b3' and ‘b’ while ‘a’ and 'b1' were found non-significant in

genetic determination of yield related attributes in bread wheat.

Components of genetic variance (D, H1, and H2), F, h2 and E were significant in

F1 hybrids. However, in F2 generations, H1, H2 and E were significant while D, h2 and F

were non-significant (Table 15). The dominant components (H1 and H2) were found

greater than D and environmental E components suggested that non-additive gene

action played predominant role in the inheritance of said trait in both generations.

These results were also justified by the high values of the average degrees of

dominance than unity (1.23, 1.679) in F1 and F2 generations, respectively. Unequal H1

and H2 components and the ratios of H2/4H1 (0.19, 0.22) in both generations exhibited

the irregular distribution of positive and negative genes among the parental cultivars.

The frequency of F was positive for both generations and significant in F1 generation,

indicated greater frequency of dominant alleles in the parental genotypes, which also

confirmed by positive value of h2 (6.94, 4.915) and proportion of dominant and

recessive genes in the parental cultivars (2.45, 1.09). Farshadfar et al. (2012) reported

that average degree of dominance was greater than unity for days to maturity in bread

wheat. Zare-Kohan and Heidari (2012) noted higher additive genetic component than

dominant components in estimation of genetic parameters for maturity in diallel crosses

of wheat cultivars using two different models.

According to Vr-Wr graph, the inheritance for days to maturity was regulated

by over-dominance type of genes as the regression line transected the co-variance axis

below the point of origin in F1 and F2 generations (Fig. 2a, b). Over-dominance type of

gene action for said trait was supported by greater value of average degree of

dominance than unity. According array points, different distributed points on regression

line demonstrated that cultivar Pirsabak-05 being nearer to origin had the most

69

dominant genes for days to maturity while cultivar Khyber-87 being far away from

origin had the most recessive genes in F1 generation. In F2 generation, different

distributed points on regression line showed that cultivar Khyber-87 being nearer to the

origin had the most dominant genes while cultivar Pirsabak-05 being far away from

origin had the most recessive genes. Rahman et al. (2003) and Farooq et al. (2011a, b)

reported additive type gene action as the regression line intercepted the co-variance line

above the origin in spring wheat populations.

Broad-sense heritability estimate were high in F1 (0.82) and F2 (0.75)

generations for days to maturity (Table 15). Narrow-sense heritability estimate were

low in both F1 (0.30) and F2 (0.35) generations for said trait. These findings illustrated

that dominant proportion was greater to affect the overall value of heritability for the

studied trait. Ahmad et al. (2013c) observed moderate narrow and high broad-sense

heritability values for days to maturity in genetic analysis for yield and yield

contributing traits in bread wheat.

Plant height

Significance of 'a' and non-significance of 'b' components revealed the primary

role of additive genes in controlling plant height in F1 generation (Table 16). Both

components 'a' and 'b' were significant in F2 generation which showed that additive and

dominance effects were present. Tammam and El-Rady (2011) observed significant

mean squares for 'a' and 'b' items for plant height in F2 generation in bread wheat under

heat stress environments. Results revealed that both additive 'a' and non-additive 'b'

genetic components were equally important in the inheritance of plant height.

Significance of 'b1' component in F1 and F2 generations illustrated dominance deviation

in one direction. In F1 generation, the 'b2' and 'b3' were non-significant whereas in F2

generation significance of 'b2' proposed asymmetrical distribution of dominant and

recessive alleles. This unequal distribution of genes specified that some parental

genotypes have considerably more dominant alleles than others for plant height.

Moreover, significance of 'b3' value in F2 generation endorsed residual dominance, due

to specific genes/genes complexes for the said trait. In past studies, significant values

were recorded for 'a' and 'b' as well as 'b1', 'b2' and 'b3' for plant height in F2 generation

(Jadoon et al., 2012).

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Components of genetic variance i.e. D, H1, H2, h2, F and E were significant in

F1 generation whereas in F2 generation, the H1, H2 and E were significant while D, h2

and F were non-significant (Table 17). The value of (√H1/D = 0.49) was found to be

less than unity which endorsed additive type of gene action in F1 generation. Zare-

Kohan et al. (2012) witnessed additive type gene action for plant height in wheat

cultivars by having average degree of dominance less than unity. In F2 generation, the

value of average degree of dominance (1.51) was also greater than unity, suggesting

over dominant type of gene action. The H1 and H2 components were not similar in both

generations, which specified that positive and negative allele frequencies were not

equal as confirmed by the ratios of H2/4H1 (0.33, 0.23) in F1 and F2 generations,

respectively. The genetic component H2 was less than H1 for plant height in F2

segregants, which specified that favorable positive alleles were not proportional to the

negative alleles at all loci among parents. Negative value of F (-17.678) in F1 indicated

that recessive alleles were greater than dominant alleles as confirmed by ratio of

dominant and recessive genes in the parents (0.609). Positive value of F (8.91) in F2

population showed that dominant alleles were greater than recessive, which was also

supported by ratio of dominant and recessive genes in the parents (1.09). Significant

positive values of E (12.6, 4.22) in F1 and F2, respectively displayed the key role of

environment in the expression of plant stature.

In Vr-Wr graph, the regression line intercepted the co-variance (Wr) axis above

the point of origin in F1 generation, which demonstrated that plant height was

controlled by additive type of gene action with partial dominance (Fig. 3a). The

distribution of varietal array points on regression line revealed that cultivars Pirsabak-

85 and Pirsabak-05 had maximum dominant genes, as these genotypes were closest to

the origin. Whereas, Shahkar-13 had the most recessive genes, being farthest from the

origin for plant height in F1 generation. However, over-dominant type of gene action

was noted for F2 generation (Fig. 3b). These results were supported by greater value of

dominant genetic component (H1) than additive (D). In case of F2 populations,

Pirsabak-05 contained the most dominant genes and Shahkar-13 with most recessive

genes for said trait. Past findings revealed that over-dominance type of gene action was

recorded for plant height in various wheat populations (Mishra et al., 1996). Akhtar and

Chowdhry (2006) and Munis et al. (2012) findings authenticated that partial dominance

type of gene action was responsible for inheritance of plant height in wheat.

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For plant height, high broad-sense heritability values (0.80, 0.90) were recorded

in F1 and F2 generation, respectively. However, narrow-sense heritability values were

high (0.70) and moderate (0.44) in F1 and F2 generation, respectively which illustrated

the major role of environment for plant height in F2 populations (Table 17). Ahmed et

al. (2007) noted low heritability for plant height in wheat hybrid populations. However,

past researchers reported high broad and narrow-sense heritabilities for plant height in

bread wheat (Jatoi et al., 2012; Khiabani et al., 2015).

Peduncle length

Genetic components 'a' and 'b' were significant in both generations, which

demonstrated the role of both additive and non-additive genes in controlling peduncle

length (Table 18). The value of “b1” was also significant in F1 and F2 populations,

presented the existence of directional genes for peduncle length. The value of “b2” was

significant for F1 and F2 populations, which indicated asymmetrical distribution of

genes among the parents. Component “b3” was non-significant for F1, which revealed

the absence of particular gene effects for the said trait. However, “b3” was significant

for F2 populations indicating residual dominance for peduncle length. Significant

values of 'a' and 'b' components were reported in F1 hybrids for peduncle length and it

was suggested that peduncle length was controlled by both additive and non-additive

gene actions (Hussain et al., 2008).

Analysis of genetic components for peduncle length illustrated that D, H1, H2

and E were significant in F1 and F2 populations (Table 19). The F values were non-

significant in both generations while h2 was non-significant in F1s and significant in F2

generation. Additive effects were found to be larger than dominance (H1 and H2) and

environmental (E) components, which specified that additive gene action played major

role in the inheritance of peduncle length in F1 generation. Average degree of

dominance was less than unity (0.75) which recommended additive type of gene action

for peduncle length in F1 generation. Rabbani et al. (2011) observed average degree of

dominance less than one for peduncle length, which revealed the involvement of

additive genes for regulation of said trait in bread wheat. Dominance components (H1

and H2) effects were larger than the additive and environmental components, which

specified that dominant gene action played important role in the inheritance of peduncle

length. Average degree of dominance was more than unity (1.283) which also

72

suggested dominance type of gene action in F2 populations. Unequal H1 and H2

components and the ratios of H2/4H1 (0.20, 0.23) exhibited the asymmetrical

distribution of positive and negative genes among the parental genotypes for peduncle

length in both generations. Positive values of F demonstrated major role of dominant

alleles in the parental genotypes for the peduncle length in both generations, and it also

authenticated by ratios of dominant and recessive genes in the parents (1.385, 1.08).

Ajmal et al. (2011) found that peduncle length exhibited partial dominance with

additive type of gene action in wheat. Nazir et al. (2014) observed significant D, H1,

H2, h2 and E and non-significant negative value of F for peduncle length in F1 wheat

hybrids.

Peduncle length was controlled by additive type of gene action with partial

dominance as the regression line cut the Wr-axis above the origin in F1 generation,

which also supported by the greater value of D than H1 (Fig. 4a). Varietal positions

along the regression line indicated that Pirsabak-85 had the most dominant genes and

Shahkar-13 had the most recessive genes for peduncle length in F1 hybrids. In F2

segregants, regression line intersected the co-variance axis below the point of origin for

peduncle length, demonstrated over-dominance gene action (Fig. 4b). Regression line

exhibited diverse position for parental genotypes, and Pirsabak-05 being nearer to

origin comprised most of the dominant genes for peduncle length while Shahkar-13

being far away from origin owned maximum recessive genes. The current study

proposed that selection in early generations for desired transgressive segregants would

not be effective. Kaukab et al. (2013) recorded over-dominance type of gene action for

peduncle length in graphical analysis as regression line intercepted the Wr-axis below

the origin in spring wheat. However, Pervez et al. (2014) demonstrated additive type

gene action for peduncle length as regression line intercepted the Wr-axis above the

origin.

Broad-sense heritability estimates were high (0.90, 0.93) for F1 and F2

generations, respectively for the character assessed (Table 19). Narrow-sense

heritability estimate were high to moderate i.e. 0.72 and 0.51 in F1 and F2 generations,

respectively. Hussain et al. (2008) reported high-broad and narrow-ense heritability

values for peduncle length in genetic studies of diverse wheat genotypes.

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Flag leaf area

The component 'a' was significant while 'b' was non-significant in F1 generation

whereas both components (a, b) were significant for flag leaf area in F2 generation

(Table 20). Hence, both additive and non-additive genetic components were important

in the inheritance of flag leaf area in segregating generation. Hassan and Khaliq (2008)

observed significant 'a' and 'b' components for flag leaf area and recommended that

additive and non-additive genes regulated the function of flag leaf area in spring wheat.

Significant 'b1' component specified directional dominance in F1 and F2 populations.

However, non-significant 'b2' component showed symmetrical gene distribution among

parents in F1 generation. Asymmetrical gene distribution was observed in F2 generation

due to significant 'b2' component. Significant value of 'b3' demonstrated the residual

dominance effects for flag leaf area in F2 generation, which indicated the involvement

of dominance deviation.

All the components of genetic variation (D, H1, H2 and F) were non-significant

whereas E was significant in F1 generation (Table 21). Ahmad et al. (2013b) recorded

non-significant D, H1 and H2 for flag leaf area, which supported the present results. The

magnitude of D was greater than H1 and H2, which recommended that additive genetic

effects were more prominent than dominance. Average degree of dominance for flag

leaf area was less than unity (0.83), which confirmed that flag leaf area was controlled

by additive type of gene action in F1 generation. The F value was negative for flag leaf

area, which proposed that greater number of recessive alleles were carried by the

parental genotypes in F1 generation, and it was also verified by ratio of dominant and

recessive genes in the parental lines (0.105).

The components of genetic variance displayed that D, H1, H2, h2 and E were

significant in F2 populations (Table 21). Both additive and non-additive components

were important for inheritance of the trait under study however, value of H1 was greater

than D component in F2 population which revealed that flag leaf area was controlled by

non-additive gene action in F2 generation. Average degree of dominance for flag leaf

area was greater than unity, which suggested that the character was regulated by over-

dominance type of gene action in F2 generation. The value of F was non-significant but

positive for flag leaf area, which proposed that greater number of dominant alleles were

carried by the parental genotypes in F2 generation, and it was also supported by ratio of

74

dominant and recessive genes in the parental cultivars (1.09). Unequal H1 and H2

components and the ratios of H2/4H1 (0.29, 0.23) exhibited the asymmetrical

distribution of positive and negative genes among the parental cultivars for flag leaf

area in both generations. Nazeer et al. (2010) reported higher values for H1 and H2 than

D for flag leaf area in F1 hybrids of wheat. Results further revealed that additive and

non-additive gene actions played key role in genetic regulation of this character. Many

researchers studied inheritance pattern of flag leaf area, Joshi et al. (2002) reported the

involvement of additive gene action in the expression of this trait in wheat under

different environmental conditions. Ambreen et al. (2002) observed partial dominance

with additive gene action in genetic determination of flage leaf area in bread wheat.

Hassan and Khaliq (2008) found dominant gene action in quantitative inheritance of

flag leaf area in spring wheat.

In F1 generation, the Vr-Wr graph analysis showed that partial dominance was

responsible for controlling flag leaf area in F1 generation (Fig. 5a). However, the

inheritance of flag leaf area was controlled by over-dominance type of gene action as

regression line touched the y-axis below the point of origin in F2 generation. Nazeer et

al. (2010) and Ajmal et al. (2011) recorded over-dominance type of gene action for flag

leaf area as regression line intercepted y-axis below the point of origin. The relative

distribution of cultivars along the regression line revealed that Pirsabak-05 had

maximum dominant genes and resides closer to the origin in both generations (Fig. 5b).

Cultivar Saleem-2000 and Shakar-13 had maximum number of recessive genes in F1

and F2 generations, respectively as both of these cultivars were farthest from the origin.

Broad-sense heritability values (0.70, 0.95) were high comparatively to narrow-

sense heritability values (0.60, 0.53) in F1 and F2 generations, respectively (Table 21).

Greater broad sense heritabilities than narrow-sense, showed the primary role of genetic

variance as compared to environmental variance. Ahmed et al., (2004) reported high

heritability for flag leaf area in genetic study of wheat cultivars.

Tillers per plant

Analysis of variance exhibited significant values for 'a' and 'b' components in F1

and F2 populations (Table 22). Cheruiyot et al. (2014) observed significant 'a' and 'b'

components, which demonstrated “additive and non-addive gene action” for tillers per

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plant in wheat. The 'b1' and 'b3' components exhibited significant values in both

generations, which suggested the presence of directional dominance and dominance

effects of specific genes, in the expression of tillers per plant. The 'b2' component was

non-significant in F1 and significant in F2 populations, which proposed symmetrical and

asymmetrical distribution of genes, respectively in the expression of said trait.

Analysis of genetic components revealed that D, H1, H2 and E were significant

for tillers per plant in F1 and F2 generations (Table 23). Nazir et al. (2014) observed

significant additive and dominance components for tillers per plant with greater value

of D than H1 in F1 generation. The H1 and H2 were greater than D and E components in

F1 generation, which signified that non-additive gene action was important for the

inheritance of tillers per plant. Results were supported by the greater value of average

degree of dominance than unity (1.64) in F1 generation. The value of D was greater

than dominance components (H1, H2) in F2 segregants, demonstrating additive type of

gene action for the inheritance of tillers per plant. Average degree of dominance

supported additive type of gene action, which was less than unity (0.91) in F2

generation. Similarly, Potla et al. (2013) reported “average degree of dominance” less

than unity for tillers per plant in barley. The value of F was positive for both

generations, demonstrating large number of “dominant” genes in the parental cultivars,

and it was assured by ratios of dominant and recessive genes in the parents (1.86, 1.22).

Significance of h2 indicated the primary role of dominance in F1 generation whereas

non-significant h2 in F2 generation suggested the greater role of additive than

dominance. The values of H1 were greater than H2, which indicated unequal proportion

of positive and negative genes and the ratios of H2/4H1 (0.22, 0.21) also confirmed the

asymmetrical distribution of positive and negative genes among the parental genotypes

for tiller per plant in both generations.

Negative intercept of regression line indicated over-dominant gene action for

tillers per plant in F1 hybrids supported by the greater value of H1 than D (Fig. 6a).

Distribution of parental cultivars on the regression line revealed that Pirsabak-04 was

nearest with maximum dominant while Khyber-87 was located farthest from the origin

confirming maximum recessive genes in F1 generation. Positive intercept of regression

line indicated additive gene action for tillers per plant in F2 generation supported by the

greater value of D than H1 (Fig. 6b). Hafeez (2006) and Kaukab et al. (2014) suggested

76

that additive types of gene regulated tillers per plant with partial dominance as the

regression line cut Wr-axis above the point of origin. In F2 populations, cultivar

Pirsabak-85 was nearest to origin with maximum dominant genes while Shahkar-13

was farthest from origin with maximum recessive genes.

Broad-sense heritability values (0.80 and 0.87) were high than narrow-sense

heritabilities (0.20 0.59) in both generations, which specified higher genetic control for

said trait than environmental effect (Table 23). Eshghi and Akhundova (2010) observed

high broad than narrow sense heritability for tillers per plant and suggested greater role

of non-additive genes in the inheritance of studied trait in hulless barley.

Spike length

For spike length, analysis of variance displayed significant 'a' and 'b'

components in F1 and F2 generations, which demonstrated the involvement of both

additive and non-additive gene actions (Table 24). Significant 'b1' specified the

occurrence of directional genes for spike length in both generations. Symmetrical genes

distribution among the parents was supported by the non-significant value of 'b2' in F1

generation while significant value revealed asymmetrical distribution in F2 generation.

Specific gene effects were present due to significant value of 'b3' in both generations.

Ahmad et al. (2013a) reported significant ‘a’ and 'b' components for spike length in

genetic study of diverse bread wheat cultivars which demonstrated the involvement of

both additive and non-additive gene effects.

Components of genetic variation i.e. D, H1, h2, F were non-significant while H2

and E were significant in F1 generation (Table 25). However, in F2 generation, all the

components of genetic variability (D, H1, H2, F, h2 and E) were significant. Additive

component (D) was less than H1 and H2 suggesting the greater role of dominance in

controlling spike length in both generations. Average degrees of dominance were more

than unity (1.92, 2.196), which specified over-dominance type of gene action in both

generations. Dominance component H1 was greater than H2 for spike length which

specified the asymmetrical distribution of positive and negative alleles, and same also

confirmed by ratios of H2/4H1 (0.26, 0.21) among parental genotypes for spike length in

both generations. Positive value of F showed that dominant genes were more frequent

than recessive genes in both generations, and said results were also confirmed by ratios

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of dominant and recessive genes in the parental genotypes (1.10, 1.19). In both F1s and

F2s, significant positive value of E showing some role of environment in the expression

of said trait. Akram et al. (2009) and Al-Layla (2015) mentioned over-dominance type

of gene action for spike length in spring wheat supporting the current study.

The Vr-Wr graphical analysis showed that spike length was under the control of

over-dominance gene effects as the regression line passed below the origin in both

generations (Fig. 7a, b), which was also supported and verified by greater values of H1

than D and average degree of dominance in both generations. The relative scattering of

array points in graph displayed that Khyber-87 occupied the closer and Pirsabak-05 the

outermost position from the origin, which specified that Khyber-87 and Pirsabak-05

had maximum dominant and recessive alleles, respectively in F1 generation. In F2

generation, the array points in graphical analysis demonstrated that Pirsabak-85

occupied the closest and Pirsabak-04 the outermost location from the origin, which

revealed that cultivar Pirsabak-85 had maximum dominant and Pirsabak-04 had

maximum recessive genes in F2 generation. Gurmani et al. (2007) found additive type

of gene action with partial dominance for spike length as point of intercept was positive

on Wr-axis. Kaukab et al. (2014) and Ljubičić et al. (2014) specified the occurrence of

over-dominance type of gene action as the regression line intercepted Wr-axis below

the origin for spike length in wheat.

Moderate broad (0.56) and low narrow-sense (0.13) heritability values were

recorded in F1 generation (Table 25). However, in F2 generation, the broad-sense was

high (0.95) than narrow-sense heritability (0.33). Badieh et al. (2012) mentioned high

broad and low narrow-sense heritability for spike length in bread wheat, suggesting

non-additive genes in genetic control of spike length.

Spikelets per spike

Genetic analysis displayed significant 'a' and 'b' components in F1 and F2

generations, which indicated the involvement of both additive and non-additive gene

action for spikelets per spike (Table 26). Kutlu and Olgun (2015) observed significant

'a' and 'b' component for spikelets per spike in bread wheat. Significant values of 'b1' in

F1 and F2 populations specified the occurrence of directional genes for spikelets per

spike. Asymmetrical genes distribution among the parents was supported by significant

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value of 'b2' in F1 and F2 generations. Specific gene effects were identified due to

significant value of 'b3' in F2 generation; however, no such gene effect was found in F1

generation due to non-significant value of said component.

According to components of genetic variance, all the components were

significant in both generations except components D and F in F1s and h2 in F2

generation (Table 27). Dominant components (H1, H2) were greater than additive (D)

showing important role of dominance type of gene action for spikelets per spike in both

generations. Average degrees of dominance were greater than unity (2.95, 1.179) which

also suggested over-dominance type of gene action for spikelets per spike in both

generations. Past studies also revealed that average degree of dominance was greater

than unity in normal conditions while less than one under heat stress conditions in

spring wheat (Farooq et al., 2011a). Dominant component H2 value was low than H1 for

spikelets per spike in both generations, which signified that positive and negative genes

were not in proportional at all loci, and it was confirmed by the ratios of H2/4H1 (0.20,

0.20). The F component was positive for spikelets per spike in both generations which

demonstrated unequal distribution of dominant and recessive genes in the parental

genotypes, and said findings also verified by ratios of dominant and recessive genes in

the parental cultivars (1.50, 1.43). Grebennikova et al. (2011) reported key role of

dominance components for spikelets per spike in F1 generation in spring triticale.

However, Hendawy et al. (2009) observed that average degree of dominance was less

than one and suggested that additive type of genes were responsible for genetic control

of spikelets per spike in wheat.

For spikelets per spike, the Vr-Wr graph intercepted the regression line on the

negative side and specified the over-dominance type of gene action in both generations

(Fig. 8a, b). Ahmed et al. (2015) found over-dominance gene action among parents for

spikelets per spike in spring wheat. The scattered parental points along the regression

line revealed that maximum dominant genes were observed for cultivars Pirsabak-04 in

F1 and Shahkar-13 in F2 generation, as these cultivars were closer to the point of origin.

Cultivars Pirsabak-85 and Pirsabak-05 received maximum recessive genes being on

distant positions from the origin in F1 and F2 generations, respectively. Over-dominance

type of gene action indicated that selection in early generations would not be effective

and delayed selection would be recommended in later generations. Dawwam et al.

79

(2012) also observed non-additive type gene action for said trait at different

environmental conditions in characterization and evaluation of wheat genotypes.

Heritability provides the essential information for the transfer of characters from

parents to their progeny, hastens the evaluation of genetic and environmental effects on

phenotype diversity, and helps in selection. High broad-sense (0.82, 0.87) and low

narrow (0.25, 0.38) heritability values were recorded for F1 and F2 generations,

respectively which specified that dominant gene action was responsible for controlling

spikelets per spike in wheat (Table 27).

Grains per spike

Analysis of variance displayed that components 'a' and 'b' were significant for

grains per spike in F1 and F2 generations, which indicated the involvement of both

additive and non-additive gene actions (Table 28). Significant 'b1' in F1 hybrids

specified the occurrence of directional dominance genes while non-significant 'b1' in F2

populations showed absence of directional dominance genes for grains per spike.

Symmetrical genes distribution among the parents was supported by the non-significant

value of 'b2' in both generations. Specific gene effects were observed due to significant

value of 'b3' in F1 and F2 generation.

Components of genetic variation (D, H1 and H2) and E were significant whereas

covariance of additive and dominance effects (F) and h2 were non-significant in both

generations (Table 29). However, dominance components (H1, H2) were greater than

additive variance (D), proposing dominant type of gene action for controlling grains per

spike in F1 and F2 generations. Average degrees of dominance values were greater than

unity (1.19, 1.225) in both generations, indicating over-dominance type of gene action

for grains per spike. Nazir et al. (2014) mentioned over-dominance type of gene action

for grains per spike in spring wheat. Dominant components H1 and H2 were different

from each other, which indicated that positive and negative alleles were different

among parents for grains per spike in both generations, and it was also verified by the

ratios of H2/4H1 (0.24, 0.21). The positive F component for grains per spike in both

generations, demonstrating unequal distribution of dominant and recessive genes in the

parental genotypes, and it was also authenticated by ratios of dominant and recessive

genes in the parental cultivars (1.57, 1.27). Significant positive value of E displayed

80

role of environment in the phenotypic expression of the said trait in both generations.

Asadabadi et al. (2012) reported average degree of dominance greater than one for

grains per spike in genetic study of grain yield in bread wheat.

Over-dominance type of gene action was noted for grains per spike as the

regression line intercepted the Wr-axis below the point of origin in both generations

(Fig. 9a, b). Arrays of parental cultivars were scattered along the regression line and

specified that the parental cultivars were genetically diverse for grains per spike.

Cultivar Khyber-87 had more dominant genes as it was near the origin and Pirsabak-85

was away from the origin with more recessive genes among parental genotypes in F1

generation. Parental cultivars Pirsabak-85 and Khyber-87 were being nearer the origin

had more dominant genes while cultivar Pirsabak-05 was away from the origin and had

more recessive genes for grains per spike in F2 generation. Mirzamasoumzadeh et al.

(2011) reported over-dominance type of gene action for grains per spike in wheat as

regression line intercepted Wr-axis below the point of origin. However, according to

Minhas et al. (2012), the graphical demonstration of Vr-Wr specified the additive gene

action with partial dominance for grains per spike, as the regression line cut off the Wr-

axis above the origin. Contradictory findings might be due to varied genetic make-up of

the wheat breeding material and the environment.

High broad (0.88, 0.77) and low narrow sense (0.38, 0.39) heritability values

were observed for F1 and F2 generations, respectively (Table 29). Heritability

determines the extent of transmissibility of traits from parents to the offspring, thus

traits with high heritability estimates are easier to manipulate (Sabal et al., 2001). Jatoi

et al. (2012) mentioned that heritability ranged from 55.8 to 99.7% in normal

conditions and 86.7 to 99.9% in drought conditions for grains per spike in wheat.

1000-grain weight

Significant mean squares due to 'a' and non-significant 'b' were found for 1000-

grain weight in F1 generation whereas 'a' and 'b' components were significant for 1000-

grain weight in F2 generation (Table 30). Hence, both additive and dominant genetic

components were important in the inheritance of the studied trait in segregating

generation. Minhas et al. (2014) reported significant 'a' and 'b' items in F1 hybrids,

which demonstrated the importance of additive and dominance genetic effects for

81

1000-grain weight. The components b1 was non-significant and significant in F1 and F2

populations, respectively which showed the absence and presence of directional

dominance for 1000-grain. Symmetrical gene distribution was found for 1000-grain

weight, as component 'b2' was non-significant in both generations. Non-significant 'b3'

illustrated the absence of specific genes for 1000-grain weight in F1 generation while

significant 'b3' demonstrated specific gene effects for said trait in F2 segregants.

Additive component of variation (D) was significant whereas other components

i.e. H1, H2, h2, E and F were non-significant for 1000-grain weight in F1 generation

(Table 31). In F2 generation, all the genetic components (D, H1, H2, h2) and E were

significant except 'F' which was non-significant. Additive component was larger than

dominance components for 1000-grain weight in both generations which revealed that

1000-grain weight was regulated by additive type of gene action. Average degrees of

dominance were less than unity (0.305, 0.842) for 1000-grain weight in both

generations. Unequal H1 and H2 components and ratios of H2/4H1 (0.42, 0.24) exhibited

asymmetrical distribution of positive and negative genes among the parental genotypes

for 1000-grain weight in F1 and F2 populations, respectively. In F1 generation, h2 and F

values were negative, showing more recessive genes, and the same was also confirmed

by ratio of dominant and recessive genes in the parental cultivars (0.28). In F2

generation, h2 and F were significant and positive for 1000-grain weight, which

suggested that the parents possessed greater number of dominant alleles which was also

assured by ratio of dominant and recessive genes in the parents (1.26). El-Awady

(2011) observed significant D and H1 components for 1000-grain weight in F2

populations and proposed that selection may be practiced in early generations.

Inheritance pattern for 1000-grain weight seemed to be of partial dominance, as

the regression line cut off the Wr-axis above the point of origin in both generations

(Fig. 10a, 10b). Cultivar Khyber-87 was near the point of origin and possessed

maximum dominant genes in both generations. Parental cultivars i.e. Pirsabak-85 and

Pirsabak-05 reside far away from the point of origin and possessed maximum recessive

genes in F1 and F2 generations, respectively. However, Hussain et al. (2012) specified

over-dominance type of gene action for 1000-grain weight in spring wheat, which was

in contrast with present results. Contrasting views might be due to broad genetic make-

up of the wheat genotypes and the genotype by environment interaction.

82

High broad (0.83, 0.92) and narrow-sense (0.78, 0.60) heritability values were

recorded for 1000-grain weight in F1 and F2 generations, respectively (Table 31). Manal

(2009) reported that high heritability was accompanied by high genetic advance for

1000-grain weight in wheat. However, moderate broad sense heritability for 1000-grain

weight specified that the trait be highly depended on environmental factors (Hassan et

al., 2013).

Grain yield per plant

Significant components i.e. 'a' and 'b' were recorded for grain yield per plant

which showed the involvement of additive and non-additive gene action in both

generations (Table 32). Significant 'b1' was recorded in F1 and F2 generations, which

specified the occurrence of directional genes for grain yield per plant. Non-significant

‘b2’ indicated symmetrical distribution of genes among parents in both generations.

Specific gene effects were observed due to significant values of 'b3' in F1 and F2

populations, respectively.

Components of genetic variation (D, H1 and H2) and E were significant while F

was non-significant for grain yield per plant in both generations (Table 33). The values

of H1 and H2 were greater than D in F1 generation which revealed non-additive gene

action in genetic control of grain yield per plant. However, the value of D was greater

than H1 and H2 in F2 generation which specified the greater role of additive gene action.

Average degree of dominance was greater than unity (1.452) in F1 hybrids, which

indicated over-dominance type of gene action whereas it was less than unity (0.98) in

F2 populations, which specified additive type of gene action. Greater value of H1 than

H2 indicating that positive and negative alleles were different among parents, and it was

confirmed by ratios of H2/4H1 (0.24, 0.23) for grain yield in both generations. Zare-

Kohan and Heidari (2012) reported larger values of H1 and H2 than D for grain yield

per plant in spring wheat. Positive value of F for grain yield demonstrating unequal

distribution of dominant and recessive genes in parental cultivars for both generations.

Significant and non-significant h2 in F1 and F2 generations, respectively supporting the

dominant and additive gene action, however, ratios of dominant and recessive genes

confirmed excess of dominant genes in the parental cultivars (1.39, 1.28). Mohammadi

et al. (2007) and Allah et al. (2010) found that average degree of dominance was less

than unity and proposed additive type of gene action for grain yield in wheat.

83

Significant environmental variance E specified the primary role of environment in

controlling grain yield in wheat.

In Vr-Wr graphical analysis, the regression line cut off the Wr-axis below the

point of origin which revealed over-dominance type of gene action for grain yield per

plant in F1 generation (Fig. 11a). In F2 generation, the regression line intercepted Wr-

axis above the origin, suggesting additive type of gene action for grain yield per plant

(Fig. 11b). Parental cultivars on the regression line revealed that cultivar Pirsabak-05

had the most dominant genes, while cultivar Pirsabak-85 had the most recessive genes

in both generations. Dominance effects were reported for grain yield in genetic analysis

of doubled haploid wheat (Ojaghi and Akhundova, 2010). However, additive type of

gene action was observed for grain yield through Vr-Wr graphical analysis at normal

and heat stress environments in wheat (Farooq et al., 2011a). Contradictions in the past

and present findings about F1 and F2 generations might be due to different genetic

make-up of the wheat genotypes and the environment.

Broad-sense heritability (0.80, 0.83) were greater than narrow-sense (0.30, 0.47)

for grain yield per plant in F1 and F2 generations, respectively (Table 33). Aycicek and

Yildirim (2006) reported low heritability estimates for grain yield in different wheat

populations. Ejaz-ul-Hassan and Khaliq (2008) observed low to moderate heritability

estimates for grain yield in quantitative inheritance of physiological traits for spring

wheat. However, Poodineh and Rad (2015) found greater values for broad than narrow-

sense heritability for grain yield in bread wheat.

Biological yield per plant

The components 'a' and 'b' were significant for biological yield per plant in F1

and F2 generations (Table 34). Occurrence of directional dominance effects due to

significant 'b1', symmetrical distribution of genes due to non-significant 'b2' and vital

role of specific genes due to highly significant 'b3' were reported for biological yield

per plant in both generations. Jadoon et al. (2012) reported significant effects of 'b1',

'b2' and 'b3' for biological yield in wheat segregating populations.

Components of genetic variation i.e. D, H1, H2 and E were significant while F

and h2 were non-significant in F1 generation (Table 35). In F2 generation, all the genetic

components (D, H1, H2, h2) and E were significant except 'F'. Greater values of H1 and

84

H2 than D suggested that dominant gene action was responsible for governing

biological yield in both generations. Average degrees of dominance were greater than

unity (1.316, 1.769) for grain yield in both generations which was also authenticated

over-dominance type of gene action. Asif et al. (2000) found that biological yield were

controlled by over-dominance type of genes in wheat. Pal1 and Kumar (2009) reported

higher value of non-additive genetic component than additive component, which

suggested over-dominance type of gene action for controlling biological yield in barley.

Significant higher value of D than H1 and H2 components indicating additive type of

effect in controlling biological yield under normal and heat stress conditions in wheat

(Farooq et al., 2011a). Unequal H1 and H2 components and the ratios of H2/4H1 (0.24,

0.23) exhibited the asymmetrical distribution of positive and negative genes among the

parental cultivars for biological yield in both generations. Positive value of component

F and h2 showed that dominant genes were in large proportion than recessive among

parental genotypes for biological yield, and the same was also assured by ratios of

dominant and recessive genes (1.01, 1.10). Environmental variance E was significant in

both generations, which indicated the vital role of environment in expression of said

trait. Jadoon et al. (2012) reported greater value of average degree of dominance than

unity for biological yield of wheat in F2 populations. However, Salehi et al. (2014)

found that average degree of dominance less than unity and recommended partial

dominance type of gene action for biological yield in wheat.

Biological yield was controlled by over-dominance type of gene action as the

regression line transected the Wr-axis below the point of origin in both generations

(Fig. 12a, b). Biranvand et al. (2013) found over-dominance type of gene action for

biological yield per plant in chickpea as regression line cut the Wr-axis below the

origin. Varietal positions on regression line demonstrated that cultivar Khyber-87 and

cultivar Pirsabak-05 being nearer to origin had the most dominant genes for biological

yield per plant while cultivar Pirsabak-04 was far away from origin had the most

recessive genes in F1 generation. In F2 generation, the varietal points on regression line

indicated that cultivar Pirsabak-05 being nearer to origin had most dominant genes

while cultivar Pirsabak-85 being away from origin had the most recessive genes for

biological yield.

85

Higher broad (0.88, 0.86) and moderate narrow sense (0.49, 0.33) heritability

values were noted in F1 and F2 populations, which specified the key role of dominant

genes in controlling biological yield (Table 35). However, Aghamiri et al. (2012)

reported high broad and narrow sense heritability that specified the primary role of both

additive and non-additive gene effects in controlling the biological yield in barley.

Harvest index per plant

The components 'a' and 'b' were significant for harvest index per plant in F1

generation, which suggested the involvement of additive and non-additive gene effects

(Table 36). In F2 populations, significant 'a' and non-significant 'b' suggested that

additive type of gene action was involved in genetic control of harvest index per plant.

Akbari et al. (2013) reported significant 'a' and 'b' components for harvest index in

lentil. Significant 'b1' was recorded for F1 generation which specified the occurrence of

directional genes. However, 'b1' was non-significant for F2 generations which indicated

the absence of directional genes. Significant ‘b2’ was observed for both generations,

which indicated asymmetrical distribution of genes among the parents. Non-specific

gene effects were recorded due to non-significant values of 'b3' in F1 and F2

generations.

Non-significant additive and significant dominant genetic components (H1 and

H2) in F1s, indicated the primary role of non-additive genes in genetic control of harvest

index (Table 37). However, Additive component was significant while dominant

genetic components (H1 and H2) were non-significant in F2s, which specified the

greater role of additive gene action in F2 generation. Average degree of dominance was

greater than unity (1.751) in F1 generation which recommended over-dominance type

of gene action. In F2 populations, it was less than one (0.618), which proposed primary

role of partial-dominance in controlling harvest index per plant. Unequal H1 and H2

components and the ratios of H2/4H1 (0.20, 0.24) exhibited the irregular distribution of

positive and negative genes among the parental cultivars for harvest index per plant in

both generations. Ahmad et al. (2007) observed significant additive and non-additive

genetic components, which demonstrated the involvement of both additive and non-

additive gene actions for harvest index in wheat. Positive value of F showed that

dominant genes were more active among parents for harvest index per plant in both

generations, and it was confirmed by ratios of dominant and recessive genes in the

86

parental cultivars (2.07, 1.38). Positive and negative value of h2 was recorded in F1 and

F2 generation, respectively, which recommended the high level of dominant genes in F1

generation and low level in F2 populations. Ahmad et al. (2007) findings revealed that

average degree of dominance was greater than unity for harvest index per plant at early,

normal and late planting conditions, which specified over-dominance type of gene

action for said trait in wheat. However, Ullah (2004) observed additive type of gene

action for harvest index in spring wheat. Vanda and Houshmand (2011) also reported

over-dominance type of gene action for harvest index in estimation of genetic

parameters of grain yield and related traits in durum wheat.

In F1 generation, regression line cut off the covariance line below the origin and

mentioned over-dominance type of gene action (Fig. 13a). In F2 generation, the

regression line cut the covariance line above the origin, which demonstrated partial

dominance for harvest index per plant (Fig. 13b). Inamullah et al. (2006) and Farooq et

al. (2011b) reported partial dominance for harvest index among bread wheat cultivars.

The scattering of genotypes along the regression line illustrated that Pirsabak-05 and

Pirsabak-04 had the maximum dominant genes for harvest index per plant in both

generations whereas Saleem-2000 and Khyber-87 had the maximum recessive genes in

F1 and F2 generations, respectively.

Broad-sense heritability (0.66, 0.78) values were greater than narrow sense

(0.16, 0.60) which specified non-additive gene effects for harvest index per plant in

both generations (Table 37). Farshadfar et al. (2000) found high estimates of narrow-

sense heritability which indicated additive type of gene action for harvest index in

wheat under different environmental conditions. However, Rehman et al. (2005) found

high broad and low narrow-sense heritability values in F1 generation for harvest index

in mungbean. Low narrow-sense heritability estimates under early, normal and late

plantings of spring wheat indicated preponderance of non-additive genetic variations

(Ahmad et al., 2007).

Yellow rust resistance

For yellow rust resistance, significant values of components 'a' and 'b' suggested

the key role of additive and non-additive genes in both generations (Table 38).

Cheruiyot et al. (2014) found significant 'a' and 'b' components for stem rust resistance

which revealed the important role of additive and dominant gene actions in controlling

87

stem rust resistance in spring wheat. Significant value of 'b1' specified the occurrence

of directional genes in F1 and F2 generations. Significant value of 'b2' indicated

asymmetrical gene distribution among the parental cultivars in both generations.

Specific gene effects were found in F2 due to significant value of 'b3' whereas in F1

generation no specific gene effects were observed due its non-significant value.

Genetic components (D, H1, H2, F, h2) and E were significant in both

generations. However, values of H1 and H2 were not equal and less than D in F1 and F2

populations, which demonstrated the vital role of additive gene action with unequal

genes distribution (Table 39). Average degrees of dominance were less than unity

(0.963, 0.807) which also suggested additive type of gene action in both generations.

Unequal H1 and H2 components exhibited asymmetrical distribution of positive and

negative genes among the parental genotypes for yellow rust resistance in F1 and F2

generations, and it was confirmed by ratios of H2/4H1 (0.15, 0.18). Zahravi et al. (2010)

mentioned greater value of additive genetic component than dominant for strip rust

resistance in advanced lines of wheat. Positive F-value indicated the important role of

dominant genes in both generations, and the same was also authenticated by ratios of

dominant and recessive genes in the parental cultivars for yellow rust resistance in both

generations (4.17, 1.58). Farahani et al. (2014) reported complete dominance for yellow

rust resistance as the value of average degree of dominance was one in genetic study of

yellow rust resistance in wheat cultivars. Past studies revealed that degrees of

dominance for resistance in cross Coker-9835 × VA96W-270V were 0.38 and 0.59

during 2004 and 2005, respectively, indicating that resistance was partially dominant

(Markell et al., 2009). Average degree of dominance was less than unity for Ascochyta

blight resistance in F1 and F2 generations, which suggested additive type of gene action

for resistance to said blight in chickpea (Labdi et al., 2015). The values of h2 were

positive in both generations, which showed that dominant genes were acting mostly

towards the susceptibility. Significant positive values of environmental component in

both generations illustrated the primary role of environment in inheritance of said trait.

The Vr-Wr graphs revealed that regression line intercepted the covariance line

above the origin, which revealed partial dominance type of gene action in both generations

(Fig. 14a, b). Cheruiyot et al. (2014) illustrated partial dominance for stem rust resistance

as regression line intercepted covariance line above the origin. Significant D, H1 and H2

88

components were observed for yellow rust resistance and area under disease progress

curve, which specified preponderance of both additive and dominant genes governing

partial resistance to stripe rust in the six parental cultivars (Kaur et al., 2003). Estimates of

genetic parameters indicated the role of additive and non-additive gene effects in latent

period of stripe rust in advanced lines of wheat (Zahravi et al., 2010). However, Alam et

al. (2013) reported over-dominance type of gene action for disease infection in groundnut.

The scattered positions of cultivars on regression line illustrated that cultivars Pirsabak-04,

Pirsabak-05, Shahkar-13 and Saleem-2000 had maximum dominant genes, whereas

Pirsabak-85 had maximum recessive genes in F1 generation. In F2 generation, parental

genotype Shahkar-13 had maximum dominant while Saleem-2000 had maximum

recessive genes to govern the inheritance of yellow rust resistance. Kaur et al. (2003)

findings revealed that susceptible cultivar WL-711 had maximum recessive genes

conferring susceptibility, and the cultivars PBW-65, Trap-1 and Opata-85 seemed to

contain maximum number of dominant genes.

Broad-sense (0.99, 0.98) heritability values were greater than narrow sense

(0.38, 0.65) in both generations (Table 39). High broad-sense heritability estimates

demonstrating less effect of environment in the expression of yellow rust resistance.

However, narrow-sense heritability of yellow rust resistance was moderately high

indicating that additive effects of genes were essential in inheritance of this trait in F2

generation. Zahravi et al. (2010) reported high broad (0.98) and moderate narrow-sense

(0.65) heritability values for yellow rust race and suggested the important role of

additive genes.

89

Table 10. Adequacy of additive-dominance model for various traits in 6 × 6 F1

half diallel crosses of wheat.

Variables t2 test Regression analysis

Conclusion

b0 b1

Days to heading -0.0035NS 0.1500NS -0.1722NS Partially adequate

Days to maturity 1.4270NS -0.2735NS 1.2853NS Partially adequate

Plant height -0.0015NS 0.0280NS -0.0350NS Partially adequate

Peduncle length -0.0225NS 0.2008NS -0.2511NS Partially adequate

Flag leaf area -0.0095NS 0.4164NS -0.6661NS Partially adequate

Tillers plant-1 -1.2292NS 2.2465S -3.0605NS Fully adequate

Spike length -0.1493NS 0.3147NS -0.5551NS Partially adequate

Spikelets spike-1 2.0397 NS -0.0831NS 2.9959 NS Partially adequate

Grains spike-1 -0.1493NS 0.3147NS -0.5551NS Partially adequate

1000-grain weight -0.0022NS 0.3901NS -0.4383NS Partially adequate

Grain yield plant-1 -0.0159NS 0.1626NS -0.2420NS Partially adequate

Biological yield plant-1 -0.5076NS 0.0698NS -0.1271NS Partially adequate

Harvest index plant-1 2.5162NS -0.4991NS 0.9176NS Partially adequate

Yellow rust resistance -0.1120NS -0.0087NS 0.0792NS Partially adequate

90

Table 11. Adequacy of additive-dominance model for various traits in 6 × 6 F2

half diallel crosses of wheat.

Variables t2 test Regression analysis

Conclusion

b0 b1

Days to heading -0.0781NS 4.5486NS -8.980NS Partially adequate

Days to maturity 0.3913NS -1.1187NS 2.4668NS Partially adequate

Plant height -0.0008NS 0.0840NS -0.1267NS Partially adequate

Peduncle length -0.0034NS 0.3256NS -0.4895NS Partially adequate

Flag leaf area -0.0146NS 0.3427NS -0.5172NS Partially adequate

Tillers plant-1 -0.0615NS 1.2913NS -1.7890NS Partially adequate

Spike length 4.5546NS -111.16NS 249.1114NS Partially adequate

Spikelets spike-1 -0.0249NS 1.0822 NS -1.4096NS Partially adequate

Grains spike-1 -0.0068NS 0.7065NS -1.1497NS Partially adequate

1000-grain weight -0.1115NS 0.1012NS -0.1546NS Partially adequate

Grain yield plant-1 -0.0283NS 0.2220NS -0.3788NS Partially adequate

Biological yield plant-1 -1.2565NS 0.1559NS -0.3339NS Partially adequate

Harvest index plant-1 -0.1642NS 0.1168NS -0.1481NS Partially adequate

Yellow rust resistance -0.0014NS 0.0252NS -0.0307NS Partially adequate

Table 12. Genetic analysis for days to heading in 6 × 6 F1 and F2 half diallel

crosses of wheat.

Source of

variation

Days to heading

F1 F2

d.f. Components values d.f. Components values

Replications 1 0.08 2 5.82

a 5 47.77** 5 28.91**

b 15 1.37** 15 11.42**

b1 1 0.00 1 60.36**

b2 5 1.90** 5 7.6

b3 9 1.22** 9 8.1*

Error 20 0.12 40 3.46

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

Table 13. Components of genetic variance for days to heading in 6 × 6 F1 and F2

half diallel crosses of wheat.

Components of genetic variance Days to heading

F1 F2

D 10.9* ±0.77 7.1807* ±2.69

H1 2.97* ±0.4 11.1621* ±3.55

H2 2.17* ±0.29 9.7079* ±2.81

F -0.76 ±0.65 4.8385 ±3.35

h2 -0.036 ±0.05 12.4778* ±5.82

E 0.07* ±0.02 1.1045* ±0.18

F1: (H1/D)1/2, F2: (1/4H1/D)1/2 0.52 1.247

H2/4H1 0.18 0.22

F1: (4DH1)1/2 + F / (4DH1)

1/2 – F 0.875 -

F2: 1/4(4DH1)1/2 + 1/2F / 1/4(4DH1)

1/2 - 1/2F - 1.31

h2/H2 -0.0199 1.5424

Heritability (bs) 0.99 0.80

Heritability (ns) 0.91 0.35

93

Fig. 1a. Vr-Wr graph for days to heading in 6 × 6 F1 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

Fig. 1b. Vr-Wr graph for days to heading in 6 × 6 F2 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

94

Table 14. Genetic analysis for days to maturity in 6 × 6 F1 and F2 half diallel

crosses of wheat.

Source of

variation

Days to maturity

F1 F2

d.f. Components values d.f. Components values

Replications 1 0.41 2 0.9

a 5 7.6** 5 18.08**

b 15 2.94** 15 7.74**

b1 1 21.94** 1 25.2**

b2 5 3.43** 5 5.63

b3 9 0.56 9 5.63*

Error 20 0.63 40 5.63

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

Table 15. Components of genetic variance for days to maturity in 6 × 6 F1 and

F2 half diallel crosses of wheat.

Components of genetic variance Days to maturity

F1 F2

D 3.46* ±1.02 2.6347 ±1.89

H1 5.25* ±1.32 7.4257* ±3.04

H2 3.97* ±0.95 6.5073* ±2.39

F 3.59* ±1.35 0.7475 ±2.39

h2 6.9401* ±2.24 4.915 ±3.60

E 0.34* ±0.08 1.0394* ±0.17

F1: (H1/D)1/2, F2: (1/4H1/D)1/2 1.23 1.679

H2/4H1 0.19 0.22

F1: (4DH1)1/2 + F / (4DH1)

1/2 – F 2.45 -

F2: 1/4(4DH1)1/2 + 1/2F / 1/4(4DH1)

1/2 - 1/2F - 1.09

h2/H2 2.0981 0.9064

Heritability (bs) 0.82 0.75

Heritability (ns) 0.30 0.35

95

Fig. 2a. Vr-Wr graph for days to maturity in 6 × 6 F1 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

Fig. 2b. Vr-Wr graph for days to maturity in 6 × 6 F2 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

96

Table 16. Genetic analysis for plant height in 6 × 6 F1 and F2 half diallel crosses

of wheat.

Source of

variation

Plant height

F1 F2

d.f. Components values d.f. Components values

Replications 1 0.62 2 43.31*

a 5 357.29** 5 211.82**

b 15 36.94 15 85.99**

b1 1 430.06** 1 742.53**

b2 5 7.29 5 36.82*

b3 9 9.72 9 40.36**

Error 20 25.6 40 12.15

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

Table 17. Components of genetic variance for plant height in 6 × 6 F1 and F2

half diallel crosses of wheat.

Components of genetic variance Plant height

F1 F2

D 74.24* ±30.91 35.8484* ±11.98

H1 17.91 ±25.13 81.3739* ±16.166

H2 23.79 ±21.54 73.5089* ±13.54

F -17.68 ±26.69 8.909 ±13.10

h2 132.94* ±63.58 158.2735* ±37.22

E 12.63* ±3.03 4.2167* ±0.72

F1: (H1/D)1/2, F2: (1/4H1/D)1/2 0.49 1.507

H2/4H1 0.33 0.23

F1: (4DH1)1/2 + F / (4DH1)

1/2 – F 0.61 -

F2: 1/4(4DH1)1/2 + 1/2F / 1/4(4DH1)

1/2 - 1/2F - 1.09

h2/H2 6.7054 2.5837

Heritability (bs) 0.80 0.90

Heritability (ns) 0.70 0.45

97

Fig. 3a. Vr-Wr graph for plant height in 6 × 6 F1 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

Fig. 3b. Vr-Wr graph for plant height in 6 × 6 F2 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

98

Table 18. Genetic analysis for peduncle length in 6 × 6 F1 and F2 half diallel

crosses of wheat.

Source of

variation

Peduncle length

F1 F2

d.f. Components values d.f. Components values

Replications 1 36.21** 2 8.67**

a 5 43.12** 5 58.38**

b 15 4.29* 15 18.99**

b1 1 18.02** 1 204.69**

b2 5 4.05* 5 5.83**

b3 9 2.89 9 5.67**

Error 20 1.41 40 1.49

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

Table 19. Components of genetic variance for peduncle length in 6 × 6 F1 and F2

half diallel crosses of wheat.

Components of genetic variance Peduncle length

F1 F2

D 11.9* ±2.85 10.2644* ±2.26

H1 6.49* ±2.43 16.9027* ±2.91

H2 5.23* ±1.76 15.6352* ±2.51

F 2.84 ±2.93 2.0086 ±2.35

h2 5.4609 ±3.13 43.8981* ±8.13

E 0.75 0.6371* ±0.11

F1: (H1/D)1/2, F2: (1/4H1/D)1/2 0.74 1.283

H2/4H1 0.20 0.23

F1: (4DH1)1/2 + F / (4DH1)

1/2 – F 1.39 -

F2: 1/4(4DH1)1/2 + 1/2F / 1/4(4DH1)

1/2 - 1/2F - 1.08

h2/H2 1.2539 3.3692

Heritability (bs) 0.90 0.93

Heritability (ns) 0.72 0.51

99

Fig. 4a. Vr-Wr graph for peduncle length in 6 × 6 F1 half diallel crosses of wheat. [1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87]

Fig. 4b. Vr-Wr graph for peduncle length in 6 × 6 F2 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

100

Table 20. Genetic analysis for flag leaf area in 6 × 6 F1 and F2 half diallel

crosses of wheat.

Source of

variation

Flag leaf area

F1 F2

d.f. Components values d.f. Components values

Replications 1 4.66 2 8.73**

a 5 61.34** 5 58.99**

b 15 8.25 15 17.58**

b1 1 39.01* 1 188.93**

b2 5 4.74 5 5.09*

b3 9 6.78 9 5.48**

Error 20 7.97 40 1.46

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

Table 21. Components of genetic variance for flag leaf area in 6 × 6 F1 and F2

half diallel crosses of wheat.

Components of genetic variance Flag leaf area

F1 F2

D 7.05 ±6.60 10.6879* ±2.10

H1 4.85 ±8.09 15.887* ±2.37

H2 5.6 ±6.32 14.7333* ±2.07

F -9.47 ±6.18 2.2733 ±2.12

h2 10.63 ±11.41 40.5679* ±6.77

E 3.95* ±0.95 0.4911* ±0.08

F1: (H1/D)1/2, F2: (1/4H1/D)1/2 0.83 1.219

H2/4H1 0.29 0.23

F1: (4DH1)1/2 + F / (4DH1)

1/2 – F 0.11 -

F2: 1/4(4DH1)1/2 + 1/2F / 1/4(4DH1)

1/2 - 1/2F - 1.09

h2/H2 2.2787 3.3042

Heritability (bs) 0.70 0.95

Heritability (ns) 0.60 0.54

101

Fig. 5a. Vr-Wr graph for flag leaf area in 6 × 6 F1 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

Fig. 5b. Vr-Wr graph for flag leaf area in 6 × 6 F2 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

102

Table 22. Genetic analysis for tillers per plant in 6 × 6 F1 and F2 half diallel

crosses of wheat.

Source of

variation

Tillers per plant

F1 F2

d.f. Components values d.f. Components values

Replications 1 7.71** 2 1.45

a 5 4.6** 5 11.48**

b 15 3.17** 15 1.84**

b1 1 16.01** 1 4.71**

b2 5 2.19 5 1.67*

b3 9 2.28* 9 1.61*

Error 20 0.81 40 0.62

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

Table 23. Components of genetic variance for tiller per plant in 6 × 6 F1 and F2

half diallel crosses of wheat.

Components of genetic variance Tiller per plant

F1 F2

D 1.93* ±0.85 2.4114* ±2.41

H1 5.21* ±1.31 1.9987* ±0.64

H2 4.5* ±1.05 1.6461* ±0.47

F 1.9 ±1.11 1.0337 ±0.74

h2 5.00* ±2.12 0.9191 ±0.67

E 0.37* ±0.09 0.1954* ±0.033

F1: (H1/D)1/2, F2: (1/4H1/D)1/2 1.64 0.91

H2/4H1 0.22 0.21

F1: (4DH1)1/2 + F / (4DH1)

1/2 – F 1.86 -

F2: 1/4(4DH1)1/2 + 1/2F / 1/4(4DH1)

1/2 - 1/2F - 1.27

h2/H2 1.332 0.67

Heritability (bs) 0.80 0.87

Heritability (ns) 0.20 0.59

103

Fig. 6a. Vr-Wr graph for tiller per plant in 6 × 6 F1 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

Fig. 6b. Vr-Wr graph for tiller per plant in 6 × 6 F2 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

104

Table 24. Genetic analysis for spike length in 6 × 6 F1 and F2 half diallel crosses

of wheat.

Source of

variation

Spike length

F1 F2

d.f. Components values d.f. Components values

Replications 1 0.13 2 2.27**

a 5 1.57** 5 3.39**

b 15 1.29** 15 2.24**

b1 1 8.89** 1 5.16**

b2 5 0.38 5 1.84**

b3 9 0.94** 9 2.13**

Error 20 0.17 40 0.18

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

Table 25. Components of genetic variance for spike length in 6 × 6 F1 and F2

half diallel crosses of wheat.

Components of genetic variance Spike length

F1 F2

D 0.34 ±0.49 0.605* ±0.18

H1 1.26 ±0.76 2.9183* ±0.36

H2 1.33* ±0.65 2.4124* ±0.28

F 0.06 ±0.6 0.463* ±0.25

h2 2.71 ±1.56 1.0865* ±0.36

E 0.34* ±0.08 0.0545* ±6.26

F1: (H1/D)1/2, F2: (1/4H1/D)1/2 1.92 2.196

H2/4H1 0.26 0.21

F1: (4DH1)1/2 + F / (4DH1)

1/2 – F 1.10 -

F2: 1/4(4DH1)1/2 + 1/2F / 1/4(4DH1)

1/2 - 1/2F - 1.19

h2/H2 2.4357 0.5404

Heritability (bs) 0.56 0.95

Heritability (ns) 0.13 0.33

105

Fig. 7a. Vr-Wr graph for spike length in 6 × 6 F1 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

Fig. 7b. Vr-Wr graph for spike length in 6 × 6 F2 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

106

Table 26. Genetic analysis for spikelets per spike in 6 × 6 F1 and F2 half diallel

crosses of wheat.

Source of

variation

Spikelets per spike

F1 F2

d.f. Components values d.f. Components values

Replications 1 3.43 2 3.9**

a 5 4.72* 5 10.2**

b 15 5.46** 15 3.37**

b1 1 48.01** 1 2.99*

b2 5 4.32* 5 3.27**

b3 9 1.37 9 3.47**

Error 20 1.18 40 0.72

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

Table 27. Components of genetic variance for spikelets per spike in 6 × 6 F1 and

F2 half diallel crosses of wheat.

Components of genetic variance Spikelets per spike

F1 F2

D 0.907 ±0.80 3.0482* ±0.79

H1 7.8968* ±1.95 4.2372* ±0.97

H2 6.3343* ±1.46 3.4389* ±0.73

F 1.0762 ±1.33 2.5257* ±1.01

h2 15.3077* ±4.14 0.525 ±0.60

E 0.4929* ±0.12 0.2375* ±0.04

F1: (H1/D)1/2, F2: (1/4H1/D)1/2 2.95 1.179

H2/4H1 0.20 0.20

F1: (4DH1)1/2 + F / (4DH1)

1/2 – F 1.50 -

F2: 1/4(4DH1)1/2 + 1/2F / 1/4(4DH1)

1/2 - 1/2F - 1.43

h2/H2 2.9 0.1832

Heritability (bs) 0.82 0.87

Heritability (ns) 0.25 0.38

107

Fig. 8a. Vr-Wr graph for spikelets per spike in 6 × 6 F1 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

Fig. 8b. Vr-Wr graph for spikelets per spike in 6 × 6 F2 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

108

Table 28. Genetic analysis for grains per spike in 6 × 6 F1 and F2 half diallel

crosses of wheat.

Source of

variation

Grains per spike

F1 F2

d.f. Components values d.f. Components values

Replications 1 22.87* 2 8.27

a 5 59.47** 5 39.3**

b 15 18.69** 15 11.82*

b1 1 25.75* 1 6.75

b2 5 4.97 5 10.52

b3 9 25.52** 9 13.11*

Error 20 3.68 40 5.42

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

Table 29. Components of genetic variance for grains per spike in 6 × 6 F1 and

F2 half diallel crosses of wheat.

Components of genetic variance Grains per spike

F1 F2

D 22.69* ±5.88 8.4602* ±3.69

H1 32.3* ±6.56 12.6963* ±4.74

H2 31.38* ±5.97 10.7654* ±3.61

F 12.09 ±6.28 4.8837 ±4.64

h2 7.44 ±5.88 0.6214 ±2.76

E 1.78* ±0.43 1.6423* ±0.27

F1: (H1/D)1/2, F2: (1/4H1/D)1/2 1.19 1.225

H2/4H1 0.24 0.21

F1: (4DH1)1/2 + F / (4DH1)

1/2 – F 1.57 -

F2: 1/4(4DH1)1/2 + 1/2F / 1/4(4DH1)

1/2 - 1/2F - 1.27

h2/H2 0.2845 0.0693

Heritability (bs) 0.88 0.77

Heritability (ns) 0.38 0.39

109

Fig. 9a. Vr-Wr graph for grains per spike in 6 × 6 F2 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

Fig. 9b. Vr-Wr graph for grains per spike in 6 × 6 F2 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

110

Table 30. Genetic analysis for 1000-grain weight in 6 × 6 F1 and F2 half diallel

crosses of wheat.

Source of

variation

1000-grain weight

F1 F2

d.f. Components values d.f. Components values

Replications 1 18.67** 2 28.24*

a 5 17.73** 5 263.79**

b 15 0.78 15 38.96**

b1 1 0.4 1 126.05**

b2 5 0.29 5 11.29

b3 9 1.09 9 44.65**

Error 20 1.07 40 8.15

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

Table 31. Components of genetic variance for 1000-grain weight in 6 × 6 F1 and

F2 half diallel crosses of wheat.

Components of genetic variance 1000-grain weight

F1 F2

D 3.46331* ±1.30 59.1223* ±11.67

H1 0.3216 ±0.87 41.886* ±9.51

H2 0.5374 ±0.71 40.4528* ±8.23

F -1.1934 ±1.10 22.7171 ±12.09

h2 -0.1132 ±0.55 25.8854* ±13.00

E 0.0005 ±0.12 2.646* ±0.44

F1: (H1/D)1/2, F2: (1/4H1/D)1/2 0.305 0.842

H2/4H1 0.42 0.24

F1: (4DH1)1/2 + F / (4DH1)

1/2 – F 0.28 -

F2: 1/4(4DH1)1/2 + 1/2F / 1/4(4DH1)

1/2 - 1/2F - 1.26

h2/H2 -0.2528 0.7679

Heritability (bs) 0.83 0.92

Heritability (ns) 0.78 0.60

111

Fig. 10a. Vr-Wr graph for 1000-grain weight in 6 × 6 F1 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

Fig. 10b. Vr-Wr graph for 1000-grain weight in 6 × 6 F2 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

112

Table 32. Genetic analysis for grain yield per plant in 6 × 6 F1 and F2 half diallel

crosses of wheat.

Source of

variation

Grain yield per plant

F1 F2

d.f. Components values d.f. Components values

Replications 1 13.71 2 26.79

a 5 66.37** 5 182.28**

b 15 31.83** 15 41.97**

b1 1 159.72** 1 182.32**

b2 5 11.27 5 19.6

b3 9 29.05** 9 38.81*

Error 20 8.31 40 13.62

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

Table 33. Components of genetic variance for grain yield per plant in 6 × 6 F1

and F2 half diallel crosses of wheat.

Components of genetic variance Grain yield per plant

F1 F2

D 21.4871* ±10.16 42.41* ±12.97

H1 45.3295* ±13.77 40.33* ±12.78

H2 43.5633* ±11.83 37.71* ±10.70

F 10.2828 ±11.68 20.40 ±14.44

h2 49.4745* ±22.54 37.14 ±19.43

E 4.4879* ±1.04 4.42* ±0.708

F1: (H1/D)1/2, F2: (1/4H1/D)1/2 1.452 0.98

H2/4H1 0.24 0.23

F1: (4DH1)1/2 + F / (4DH1)

1/2 – F 1.39 -

F2: 1/4(4DH1)1/2 + 1/2F / 1/4(4DH1)

1/2 - 1/2F - 1.28

h2/H2 1.3628 1.1817

Heritability (bs) 0.80 0.83

Heritability (ns) 0.30 0.47

113

Fig. 11a. Vr-Wr graph for grain yield per plant in 6 × 6 F1 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

Fig. 11b. Vr-Wr graph for grain yield per plant in 6 × 6 F2 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

114

Table 34. Genetic analysis for biological yield per plant in 6 × 6 F1 and F2 half

diallel crosses of wheat.

Source of

variation

Biological yield per plant

F1 F2

d.f. Components values d.f. Components values

Replications 1 1.17 2 29.76

a 5 206.34** 5 464.48**

b 15 61.95** 15 272.69**

b1 1 105** 1 2193.71**

b2 5 24.34 5 94.78

b3 9 78.06** 9 158.08**

Error 20 12.67 40 47.07

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

Table 35. Components of genetic variance for biological yield per plant in 6 × 6

F1 and F2 half diallel crosses of wheat.

Components of genetic variance Biological yield per plant

F1 F2

D 52.7693*±19.15 81.84* ±35.64

H1 91.3485* ±23.94 256.20* ±55.96

H2 88.5097* ±21.03 239.00* ±48.22

F 0.5923 ±18.83 26.96 ±42.75

h2 37.6441 ±25.62 466.28* ±126.79

E 6.9308* ±1.59 15.07* ±2.55

F1: (H1/D)1/2, F2: (1/4H1/D)1/2 1.316 1.769

H2/4H1 0.24 0.23

F1: (4DH1)1/2 + F / (4DH1)

1/2 – F 1.01 -

F2: 1/4(4DH1)1/2 + 1/2F / 1/4(4DH1)

1/2 - 1/2F - 1.10

h2/H2 0.5104 2.3411

Heritability (bs) 0.88 0.86

Heritability (ns) 0.49 0.33

115

Fig. 12a. Vr-Wr graph for biological yield per plant in 6 × 6 F1 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

Fig. 12b. Vr-Wr graph for biological yield per plant in 6 × 6 F2 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

116

Table 36. Genetic analysis for harvest index per plant in 6 × 6 F1 and F2 half

diallel crosses of wheat.

Source of

variation

Harvest index per plant

F1 F2

d.f. Components values d.f. Components values

Replications 1 29.13 2 21.75

a 5 35.85* 5 135.93**

b 15 31.04* 15 14.41

b1 1 99.65** 1 6.04

b2 5 30.86 5 9.87

b3 9 23.51 9 17.86

Error 20 11.75 40 11.12

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

Table 37. Components of genetic variance for harvest index in 6 × 6 F1 and F2

half diallel crosses of wheat.

Components of genetic variance Harvest index per plant

F1 F2

D 15.1697 ±11.38 29.92* ±10.01

H1 46.4872* ±19.31 11.43 ±8.16

H2 37.3496* ±14.37 10.95 ±6.47

F 18.5652 ±16.70 11.87 ±10.55

h2 29.065 ±24.16 -.42 ±4.50

E 6.3404* ±1.49 3.38* ±0.56

F1: (H1/D)1/2, F2: (1/4H1/D)1/2 1.751 0.618

H2/4H1 0.20 0.24

F1: (4DH1)1/2 + F / (4DH1)

1/2 – F 2.07 -

F2: 1/4(4DH1)1/2 + 1/2F / 1/4(4DH1)

1/2 - 1/2F - 1.38

h2/H2 0.9338 -0.0459

Heritability (bs) 0.66 0.78

Heritability (ns) 0.16 0.60

117

Fig. 13a. Vr-Wr graph for harvest index per plant in 6 × 6 F1 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

Fig. 13b. Vr-Wr graph for harvest index per plant in 6 × 6 F2 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

118

Table 38. Genetic analysis for yellow rust resistance in 6 × 6 F1 and F2 half

diallel crosses of wheat.

Source of

variation

Yellow rust resistance

F1 F2

d.f. Components values d.f. Components values

Replications 1 1.14 2 4.56

a 5 91.04** 5 424.04**

b 15 37.4** 15 65.12**

b1 1 210.04** 1 401.94**

b2 5 68.38** 5 83.12**

b3 9 1.01 9 17.7**

Error 20 1.98 40 2.94

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

Table 39. Components of genetic variance for yellow rust resistance in 6 × 6 F1

and F2 half diallel crosses of wheat.

Components of genetic variance Yellow rust resistance

F1 F2

D 69.9357* ±1.90 125.43* ±9.72

H1 64.7952* ±1.99 81.72* ±83

H2 39.7898* ±1.19 57.78* ±5.53

F 82.581* ±2.54 91.48* ±11.29

h2 54.5629* ±2.82 86.35* ±13.47

E 0.0643* ±0.01 0.95* ±0.16

F1: (H1/D)1/2, F2: (1/4H1/D)1/2 0.963 0.807

H2/4H1 0.15 0.18

F1: (4DH1)1/2 + F / (4DH1)

1/2 – F 4.17 -

F2: 1/4(4DH1)1/2 + 1/2F / 1/4(4DH1)

1/2 - 1/2F - 1.58

h2/H2 1.6455 1.7934

Heritability (bs) 0.99 0.97

Heritability (ns) 0.38 0.65

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Fig. 14a. Vr-Wr graph for yellow rust resistance in 6 × 6 F1 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

Fig. 14b. Vr-Wr graph for yellow rust resistance in 6 × 6 F2 half diallel crosses of wheat. 1-Pirsabak-85, 2-Pirsabak- 2004, 3-Pirsabak-2005, 4-Shahkar-2013, 5-Saleem-2000, 6-Khyber-87

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C. Griffing's Combining Ability Analysis

Combining ability analysis is used to estimate the general combining ability

(GCA) effects of the parental genotypes, and specific combining ability (SCA) of the

specific cross combinations F1 and F2 populations, which guided the breeder in selecting

the desirable parental genotypes and their F1 hybrids and F2 populations. Variance due to

GCA (σ2GCA) is a measure of additive gene action, while variance due to SCA (σ2SCA)

is measure of non-additive gene action. Malik et al. (2004) reported that GCA is due to

genes which are additive in nature while SCA is due to the genes with dominance or

epistatic effects. The GCA effect is considered important in wheat, because it regulates

additive gene action, while the variance due to SCA is related to non-additive gene

actions (Rashid et al., 2007).

Present results revealed significant (p≤0.01, p≤0.05) mean squares due to GCA

for all traits in both generations (Tables 40 and 41). In case of SCA, significant mean

square were recorded for majority of traits, however, non-significant SCA mean square

were observed for days to heading, plant height, tillers per plant and 1000-grain weight

in F1 generation. In F2 populations, SCA mean squares were significant (p≤0.01, p≤0.05)

for all the traits except harvest index per plant (Tables 40, 41). Sheikh and Singh (2000)

reported significant mean squares for SCA and GCA in wheat genotypes under different

environmental conditions. Rehman et al. (2002) reported significant GCA and SCA

mean squares with greater magnitude of SCA than GCA variances for grain yield and

yield components in bread wheat, which suggested non-additive gene action for

controlling these traits. Golparvar et al. (2011) found significantly different GCA and

SCA for flag leaf area and grain yield under stress and normal environmental conditions.

Saeed et al. (2005) observed significant mean squares due to SCA and non-significant

GCA for 1000-grain weight, grains per spike and grain yield per plant in different wheat

populations. Desale et al. (2014) reported significant mean square due to GCA and SCA

for peduncle length which suggested positive role of both additive and non-additive

genes in controlling said trait in wheat F1 hybrids. Significant mean squares due to GCA

and SCA were reported for days to heading in F1 and F2 populations in spring wheat

(Joshi et al., 2004; Iqbal et al., 2007).

In present study, the variances due to σ2SCA were greater than σ2GCA for traits

i.e. days to maturity, tillers per plant, spike length, spikelets per spike, grains per spike,

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grain yield per plant, biological yield per plant, harvest index per plant and yellow rust

resistance in F1 generation which suggested that these traits were controlled by non-

additive gene action (Tables 40, 41). In F2 generation, σ2SCA were also greater than

σ2GCA for traits viz., days to heading and maturity, plant height, peduncle length, flag

leaf area, spike length, spikelets per spike, grains per spike, grain yield per plant,

biological yield per plant, and yellow rust resistance which indicated the primary role of

non-additive gene action in inheritance of these traits (Tables 40, 41). Significant mean

squares due to SCA while non-significant GCA revealed that yield components and grain

yield were controlled by nonadditive gene action in wheat (Chowdhry et al., 2005).

Greater SCA variance than GCA for tillers per plant and yield traits demonstrating the

key role of non-additive gene effects for the said trait in bread wheat (Esmail, 2007;

Dagusto, 2008; Desale et al., 2014).

Present results further revealed that variances due to GCA were greater than

σ2SCA for days to heading, plant height, peduncle length, flag leaf area and 1000-grain

weight in F1 generation. The variances due to GCA were also greater than σ2SCA for

tillers per plant, 1000-grain weight and harvest index in F2 generation which revealed

that these traits were governed by additive gene action (Tables 40, 41). Significant GCA

and non-significant SCA variances for earliness traits demonstrating that these variables

were regulated by additive type of gene action (Kumar et al., 2011; Farshadfar et al.,

2013). Akram et al. (2011) also reported additive gene action for majority traits by

getting significant GCA and non-significant SCA effects for spikelets per spike, flag leaf

area and grain yield which specified additive gene action for these traits in different

wheat populations. However, Adel and Ali (2013), Akbar et al. (2009) and Ammar et al.

(2014) reported significant GCA and SCA for days to heading, tillers per plant, spikes

per plant and grain yield which indicated the involvement of both additive and non-

additive gene effects for these traits in wheat F1 hybrids. Cheruiyot et al. (2014) observed

significant GCA and SCA for stem rust resistance in genetic study of adult plant

resistance in wheat. Contradictions in past and present findings might be due to diverse

wheat populations and the environment in which studied. The trait-wise results about

combining ability are presented herein.

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Days to heading

Parental cultivars with negative value for days to heading were considered

desirable by having least days to heading. Overall, the GCA effects ranged from -2.40 to

2.73 and -1.72 to 1.61 in F1 and F2 generations, respectively (Table 42). For days to

heading, three cultivars in F1 and two in F2 generation revealed negative while three

cultivars in F1 and four in F2 populations showed positive GCA effects. Among parental

cultivars, maximum negative and significant GCA effects was recorded for Khyber-87 (-

2.40) for days to heading in F1 generation. In F2 generation, highest negative and

significant GCA effects were recorded for Shahkar-13 (-1.72). These genotypes were

considered as best general combiners for earliness.

For days to heading, the SCA effects ranged from -0.66 to 1.34 and -2.54 to 3.63

in F1 and F2 generations, respectively (Table 43). For days to heading nine F1 hybrids

and twelve F2 populations revealed negative SCA effects. However, six F1 and three F2

segregants showed positive SCA effects. Among F1 hybrids, maximum negative and

significant SCA effects (-0.66) were observed in F1 hybrids Pirsabak-85 × Pirsabak-04

for days to heading. In F2 populations, Pirsabak-05 × Shahkar-13 showed highest

negative and significant SCA effects (-2.54). Overall, maximum negative and significant

SCA effects were recorded for F1 hybrid Pirsabak-85 × Pirsabak-04 (-0.66) in F1

generation. In F2 generation, Pirsabak-05 × Shahkar-13 (-2.54) revealed significant and

maximum negative SCA for days to heading, and these cross combination were

considered as best specific combiners. Parental genotypes of cross combination Pirsabak-

85 × Pirsabak-04 were having positive × negative GCA effect to develop F1 hybrids with

desirable negative SCA effects. Parental cultivars of cross combination Pirsabak-05 ×

Shahkar-13 were with high positive × high negative GCA effects that produced F2

population with negative SCA effects for days to heading. Variances due to σ2GCA were

greater than σ2SCA and the ratio due to σ2GCA/σ2SCA was greater than unity indicating

additive gene effect for days to heading in F1 generation (Table 40). However, in F2

generation the values of σ2GCA and σ2SCA and ratio due to σ2GCA/σ2SCA specified

non-additive gene effects for days to heading in F2 generation (Table 41).

Generally, negative GCA and SCA effects are desired in the selection for

maturity traits, whereas positive GCA and SCA values are desired for yield and its

components (Beche et al., 2013). Past studies revealed that additive genetic effects were

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more prevalent than non-additive genetic effects for days to heading in spring wheat

(Subhani and Chowdhry, 2000; Singh et al., 2006; Vanpariya et al., 2006; Kumar et al.,

2011). Significant GCA and non-significant SCA effects were observed for days to

heading that demonstrated that earliness traits were regulated by additive type of gene

action (Farshadfar et al., 2013). Contrasting views in past and present findings might be

due to diverse wheat populations and the environment.

Days to maturity

General combining ability effects for parental cultivars varied from -0.88 to 1.00

and -1.19 to 1.35 in F1 and F2 generations, respectively (Table 42). Three parental

genotypes in F1 and four in F2 generation were observed with negative GCA effects.

However, three genotypes in F1 and two in F2 populations showed positive GCA effects

for days to maturity. Maximum negative and significant GCA effects were recorded for

parental cultivars Khyber-87 (-0.88) and Shahkar-13 (-1.19) in F1 and F2 generations,

respectively and ranked as best general combiners for days to maturity.

Specific combining ability effect for days to maturity ranged from -0.71 to 1.86

among F1 hybrids and -3.04 to 1.75 in F2 populations (Table 43). In F1 generation, five

F1 hybrids were with negative and ten were with positive SCA effects. However, eight F2

segregants with negative and seven with positive SCA effects were observed in F2

generation. The highest negative and significant SCA effects were found in the cross

combination Pirsabak-85 × Pirsabak-04 (-0.71) in F1 whereas in F2 generation, Pirsabak-

05 × Shahkar-13 was observed with highest negative and significant SCA effects (-3.04).

Parental cultivars with positive × negative GCA effects were involved to produce best

specific combiner Pirsabak-85 × Pirsabak-04 for days to maturity with negative SCA

effects in F1 generation. However, in F2 generation, high positive × high negative GCA

parents were involved to produce best specific combiner Pirsabak-05 × Shahkar-13 with

negative SCA effects for days to maturity. Estimates of variance due to σ2GCA and

σ2SCA and ratio due to σ2GCA/σ2SCA revealed that σ2SCA were greater than σ2GCA

which suggested non-additive gene effect for days to maturity in F1 and F2 populations

(Tables 40, 41).

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Previous studies revealed that negative GCA parents resulted in F1 hybrids with

negative SCA effects for days to maturity and revealed earliness in spring wheat

populations (Ahmad et al., 2013d). Significant GCA and SCA for days to maturity,

suggesting the key role of “additive and non-additive genes” in tomato (Saleem et al.,

2013). However, Pawar et al. (2014) reported that positive × positive GCA parents

provided negative SCA effects in F1 hybrids for days to maturity in bread wheat and

found that best combinations mostly involved positive × negative and negative ×

negative general combiners for maturity and yield traits in wheat. Contradictory reviews

might be due to diverse genetic background of wheat genotypes and the G x E

environment interactions.

Plant height

For plant height, the GCA effects among parental cultivars ranged from -5.21 to

5.73 and -4.29 to 3.89 in F1 and F2 generations, respectively (Table 42). For plant height,

three each cultivars revealed negative and positive GCA effects in F1 and F2 generations.

Among parental cultivars, maximum negative and significant GCA effects were recorded

for cultivar Shahkar-13 (-5.21) for plant height in F1 generation. In F2 generation,

maximum negative and significant GCA effects were recorded for cultivar Saleem-2000

(-4.29) for plant height and suggested to be the best general combiners for desirable plant

stature.

The SCA effects for plant height ranged from -2.46 to 5.98 and -3.80 to 8.74 in

F1 and F2 generations, respectively (Table 43). For plant height, one F1 hybrid and four

F2 populations revealed negative while fourteen F1 hybrids and eleven F2 segregants

showed positive SCA effects. Among F1 hybrids, maximum negative and significant

SCA effects (-2.46) were recorded for Pirsabak-04 × Khyber-87 and designated as best

specific combinations in F1 generation. In F2 populations, Pirsabak-05 × Khyber-87 was

the best specific combination by having maximum negative and significant SCA effects

(-3.80) for plant height. Parental genotypes of the cross combination Pirsabak-04 ×

Khyber-87 involved positive × negative GCA cultivars. Parental cultivars with positive ×

positive GCA effects were involved to produce F2 population i.e. Pirsabak-05 × Khyber-

87 with negative SCA effects for plant height. Crosses with best SCA effects and GCA

effects of their parents indicated that best specific cross combinations were the result of

high × high, high × low and low × low combinations, and thus, a good cross combination

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is not necessarily the result of high × high general combiners (Desai et al., 2005).

However, parental genotypes with high × low GCA effects were involved to produce

promising F1 hybrid with negative SCA effects for plant height in bread wheat

(Kulshreshtha and Singh, 2011). Variance due to σ2GCA were greater than σ2SCA and

ratio due to σ2GCA/σ2SCA were greater than unity indicating additive gene effects for

plant height in F1 generation (Table 40). The variances of σ2GCA and σ2SCA and ratio

due to σ2GCA/σ2SCA indicated non-additive gene effect for plant height in F2 generation

(Table 41).

Highly significant GCA and SCA variances were reported for plant height in both

generations however, magnitude of GCA was greater than SCA which indicated the

primary role of additive genes in regulating plant height in wheat (Singh et al., 2004;

Vanpariya et al., 2006). However, SCA variances were predominant and played

important role in genetic control of plant height and peduncle length in various spring

wheat populations (Hasnain et al., 2006; Bogale et al., 2011).

Peduncle length

General combining ability effects for peduncle length among parental genotypes

varied from -2.42 to 2.47 and -2.60 to 1.89 in F1 and F2 generations, respectively (Table

42). Three each parental genotypes were having negative GCA effects in F1 and F2

generations, while with same pattern three each parental cultivars showed positive GCA

effects in both generations. Significant and maximum negative GCA effects were

recorded for Saleem-2000 in F1 (-2.42) and F2 generations (-2.60) and ranked as best

general combiner for peduncle length.

For peduncle length, specific combining ability effects ranged from -1.29 to 2.93

among F1 hybrids and -1.27 to 3.67 among F2 populations (Table 43). In F1 generation,

eight F1 hybrids were observed with negative and seven with positive SCA effects.

Among F2 segregants, three were having negative while twelve were with positive SCA

effects. Significant and highest negative SCA effects were found in the cross

combination i.e. Shahkar-13 × Khyber-87 (-1.29) in F1 hybrids. In F2 generation, cross

combination Pirsabak-05 × Khyber-87 (-1.27) was observed with significant and highest

desirable negative SCA effects. These F1 and F2 populations were considered as best

specific combiners. Parental cultivars with negative × negative GCA effects were

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involved to produce best specific combiner i.e. Shahkar-13 × Khyber-87 (-1.29) for

peduncle length with negative SCA effect in F1 generation. However, in F2 generation,

positive × positive GCA parents were involved to produce best specific combiner

Pirsabak-05 × Khyber-87 (-1.27) with negative SCA effect for peduncle length. Variance

due to σ2GCA was larger than σ2SCA and ratio due to σ2GCA/σ2SCA was more than

unity demonstrating additive gene effect for peduncle length in F1 generation (Table 40).

Variances due to GCA and SCA and ratio due to σ2GCA/σ2SCA suggested non-additive

gene effects for peduncle length in F2 generation (Table 41).

Superiority of low × low and high × low GCA combinations might be due to

greater genetic diversity among parents and transgressive segregation which indicate the

importance of non-additive effects in wheat (Kulshreshtha and Singh, 2011). Non-

significant GCA and significant SCA effects with non-additive gene action was reported

for peduncle length and plant height in wheat (Chowdhry et al., 2005). Predominance of

non-additive gene effects was recorded for peduncle length, plant height, grain yield and

days to maturity in bread wheat (Seboka et al., 2009).

Flag leaf area

The GCA effects for flag leaf area among parental cultivars ranged from -1.21 to

3.92 and -2.60 to 1.84 in F1 and F2 generations, respectively (Table 42). For flag leaf

area, one parent cultivar in F1 and two in F2 segregants showed positive GCA effects

while five parent cultivars in F1 and four in F2 generation were observed with negative

GCA effects. Among parental cultivars, significant and maximum positive GCA effects

were recorded for Pirsabak-05 (3.92) and (1.84) for flag leaf area in F1 and F2

generations, respectively and recorded as best general combiner for flag leaf area in both

generations.

The SCA effects for flag leaf area, ranged from -1.46 to 3.39 and -1.40 to 3.48 in

F1 and F2 generations, respectively (Table 43). For flag leaf area, six F1 hybrids and three

F2 populations revealed negative while nine F1s and twelve F2s showed positive SCA

effects. Significant and maximum positive SCA effects were observed for cross

combinations i.e. Pirsabak-05 × Saleem-2000 (3.39) and Shahkar-13 × Khyber-87 (3.48)

in F1 and F2 populations, respectively and ranked the best specific combinations for flag

leaf area. In cross combination Pirsabak-05 × Saleem-2000 (3.39), parental genotypes

with high × low GCA effects were involved to produce F1 hybrids with maximum SCA

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effects. However, low × high GCA parents were involved to produce F2 segregant i.e.

Shahkar-13 × Khyber-87 (3.48) with highest positive SCA effects for flag leaf area.

Estimates of σ2GCA were greater than σ2SCA and ratio due to σ2GCA/σ2SCA was more

than unity indicating additive gene effect for flag leaf area in F1 generation (Table 40).

Variances due to GCA and SCA and ratio of σ2GCA/σ2SCA suggested non-additive gene

effect for flag leaf area in F2 generation (Table 41).

Desirable transgressive segregants were observed in crosses involving high × low

and low × low general combiners with high SCA effects in spring wheat (Singh and

Singh, 2003). Dere (2006) found F1 hybrid with maximum SCA effects for flag leaf area

from high × low GCA parents in bread wheat. Significant GCA and non-significant SCA

effects were recorded for the flag leaf area in F1 wheat populations (Akram et al., 2011).

The crosses involving parents with high × medium, medium × medium and medium ×

low general combiners, indicated non-additive type of gene actions in specific cross

combinations in wheat (Singh et al., 2012). Greater role of additive genes in genetic

regulation of flag leaf area illustrated that genetic efficiency of selection was greater for

increasing flag leaf area particularly in early generations in wheat (Golparvar, 2013).

Parental cultivars with high GCA effects produced hybrids with low SCA effects which

might be due to absence of complementary parent genes in wheat (Kumari et al., 2015).

Tillers per plant

General combining ability effects for tillers per plant among parental cultivars

varied from -0.60 to 0.71 and -0.80 to 0.79 in F1 and F2 generations, respectively (Table

42). Three each parental cultivars were having positive GCA effects in F1 and F2

generations, while with same pattern, three each parental genotypes showed negative

GCA effects in both generations. Maximum positive and significant GCA effects were

recorded for parental cultivars Pirsabak-04 (0.71) in F1 and Saleem-2000 (0.79) in F2

generations, and ranked as best general combiners for tillers per plant.

Specific combining ability effect for tillers per plant ranged from -2.02 to 1.92

among F1 hybrids and -1.94 to 0.43 in F2 populations (Table 43). In F1 generation, eleven

F1 hybrids were with positive while four revealed negative SCA effects. Eight F2

segregants with positive while seven with negative SCA effects were observed in F2

generation. Significant and maximum positive SCA effects for tiller per plant were found

in the cross combination Pirsabak-05 × Shahkar-13 (1.92) in F1 generation. However, in

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F2 generation, the cross combination Pirsabak-04 × Saleem-2000 showed highest

positive SCA effects (0.43) for tillers per plant. Parental cultivars with low × low GCA

effects were involved to produce best specific combination i.e. Pirsabak-05 × Shahkar-13

for tillers per plant with maximum SCA effects in F1 generation. However, in F2

generation high × high general combiners were involved to produce best specific

combination i.e. Pirsabak-04 × Saleem-2000 with maximum SCA effects for tillers per

plant. Variance due to σ2GCA was lesser than variance due to σ2SCA and ratio due to

σ2GCA/σ2SCA was smaller than unity which indicated non-additive gene effect for

tillers per plant in F1 generation (Table 40). In F2 generation, the values of σ2GCA and

σ2SCA and ratio due to σ2GCA/σ2SCA indicated additive type of gene action for tillers

per plant (Table 41).

Parental cultivars with medium × high and medium × low GCA effects produced

promising specific combinations for tillers per plant in bread wheat (Singh and Singh,

2003). High SCA variances were observed for tillers per plant that demonstrated the key

role of non-additive gene effects for the said trait in bread wheat (Esmail, 2007; Farooq

et al., 2011a). Khan et al. (2007) found greater magnitude of GCA effects for tillers per

plant which indicated the involvement of additive genes in controlling this trait. Higher

GCA and SCA effects for tillers per plant, spike length, spikelets per spike, 1000-grain

weight and grain yield, specified the role of both additive and non-additive genes in

regulating these traits in bread wheat (Akbar et al., 2009). Significant GCA and SCA

effects for tillers per plant authenticated the occurrence of both additive and non-additive

gene actions for controlling tillers per plant in various wheat populations (Zeeshan et al.,

2013).

Spike length

For parental cultivars, the GCA effects ranged from -0.50 to 0.28 and -0.68 to

0.44 in F1 and F2 generations, respectively for spike length (Table 42). Three parental

cultivars in F1 and four in F2 generation showed positive GCA effects, while three

parental cultivars in F1 and two in F2 generations showed negative GCA effects for spike

length. Among parental cultivars, maximum positive and significant GCA effects (0.28)

were recorded for Pirsabak-04 for spike length in F1 generation. In F2 generation,

significant and maximum positive GCA effects (0.44) were recorded in Pirsabak-85, and

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both genotypes were considered as best general combiners for spike length in their

specified generation.

The SCA effects ranged from -0.27 to 0.81 and -1.71to 1.01 for spike length in F1

and F2 generations, respectively (Table 43). For spike length, fourteen F1 and eleven F2

populations revealed positive while one F1 and four F2 cross combinations showed

negative SCA effects. Significant and maximum positive SCA effects were recorded for

Pirsabak-05 × Shahkar-13 (0.81) and (1.01) for spike length in F1 and F2 generations,

respectively. Parental cultivars of cross combination Pirsabak-05 × Shahkar-13 (0.81)

and (1.01), low × high general combiners were involved to produce F1 and F2 population

with desirable and maximum SCA effects for spike length. Estimates of variance due to

σ2GCA and σ2SCA and ratio due to σ2GCA/σ2SCA specified that σ2SCA variances were

greater than σ2GCA that recommended non-additive gene effects for spike length in F1

and F2 generations (Tables 40, 41).

Significant GCA and SCA effects were reported for spike length in F1 hybrids in

wheat (Sener, 2009; Hammad et al., 2013). Superiority of moderate × moderate and

moderate × low GCA combinations might be due to genetic diversity among the parental

genotypes, which indicated the importance of non-additive effects for spike length in

wheat (Srivastava and Singh, 2012). Crosses which were demonstrating high SCA

effects for spike length were obtained from parental genotypes with various types of

GCA effects (high × high, high × low and low × low) in wheat (Ljubičić et al., 2014).

However, Ismail (2015) reported involvement of both additive and non-additive gene

action due to significance of GCA and SCA effects for spike length in various wheat

populations.

Spikelets per spike

For spikelets per spike, the GCA effects among parental cultivars varied from -

0.98 to 0.65 and -1.17 to 0.54 in F1 and F2 generations, respectively (Table 42). Three

parental cultivars in F1 and four in F2 generation showed positive GCA effects, while

three parental cultivars in F1 and two in F2 generation indicated negative GCA effects.

Significant and maximum positive GCA effects were recorded for Saleem-2000 (0.65

and 0.54) in both generations and classified as best general combiner for spikelets per

spike.

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Specific combining ability effect for spikelets per spike ranged from -0.67 to 2.71

among F1 hybrids and -1.52 to 1.50 among F2 populations (Table 44). Eleven F1 hybrids

and nine F2 populations were with positive SCA effects while four F1 hybrids and six F2

segregants revealed negative SCA effects for spikelets per spike. The highest positive

and significant SCA effects (2.71) were observed in the cross combination Pirsabak-85 ×

Saleem-2000 in F1 generation. However, in F2 generation, cross combination Pirsabak-04

× Khyber-87 revealed significant and maximum positive SCA effects (1.50) for said

trait. Parental cultivars with low × high GCA effects were involved to produce best

specific combiner i.e. Pirsabak-85 × Saleem-2000 for spikelets per spike in F1

generation. However, in F2 generation, medium × low GCA combiners played important

role in production of best specific combiner (Pirsabak-04 × Khyber-87) with maximum

SCA effect. Estimates of variance due to σ2GCA and σ2SCA and ratio due to

σ2GCA/σ2SCA specified that variances due to SCA were greater than GCA which

confirmed non-additive gene effects for spikelets per spike in both generations (Tables

40, 41).

High positive SCA values for spikelets per spike were reported in F1 hybrids

obtained from parental genotypes with high × medium, medium × medium and medium

× low GCA effects, which indicated the predominance of non-additive gene effects in

barley (Bhatnagar and Sharma, 1995; Kakani et al., 2007). Significant GCA and non-

significant SCA variances suggested the involvement of additive gene action for

controlling spikelets per spike in wheat (Gorjanovic et al., 2007; Mahpara et al., 2008).

Kulshreshtha and Singh (2011) reported higher GCA for spikelets per spike among

wheat genotypes under saline conditions. High SCA effects in some of the crosses

having high × high GCA combining parents reflected additive × additive type gene

action and superiority of favorable genes contributed by wheat parental genotypes (Raj

and Kandalkar, 2013). Highly significant GCA and SCA variances for spikelets per spike

specified the occurrence of epistatic and dominant genes among wheat genotypes for

controlling the said trait (Zeeshan et al., 2013).

Grains per spike

The GCA effects for grains per spike ranged from -1.58 to 1.60 and -1.89 to 1.49

in parental genotypes in F1 and F2 generations, respectively (Table 42). Three each

parental genotypes were having positive GCA effects in F1 and F2 generations, while

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with same pattern three each parental cultivars showed negative GCA effects in both

generations. Among parental cultivars, maximum positive and significant GCA effects

were exhibited by genotypes Pirsabak-85 (1.60) and (1.49) for grains per spike in F1 and

F2 generations, respectively and suggested to be the best general combiner for grains per

spike in both generations.

The SCA effects ranged from -3.42 to 7.64 and -3.68 to 2.46 in F1 and F2

generations, respectively for grains per spike (Table 44). For grains per spike, six F1 and

nine F2 segregants revealed positive while nine F1 and six F2 populations showed

negative SCA effects. Significant and maximum positive SCA effects were observed for

Pirsabak-05 × Saleem-2000 (7.64) and Pirsabak-85 × Pirsabak-05 (2.46) for grains per

spike in F1 and F2 generations, respectively. Parental genotypes with low × low GCA

effects were involved to produce promising F1 hybrid (Pirsabak-05 × Saleem-2000) with

maximum SCA effects. However, cross combination Pirsabak-85 × Pirsabak-05 was

having parental cultivars with high × low GCA effects to produce F2 population with

maximum positive SCA effects for grains per spike. Variance due to σ2GCA were lesser

than σ2SCA and ratio due to σ2GCA/σ2SCA were smaller than unity demonstrating that

non-additive gene action controlled the inheritance of grains per spike in F1 and F2

generations (Tables 40 and 41).

Greater SCA effects were found in F1 hybrids involving genotypes with high

GCA effects that showed possibility of genetic improvement of wheat through pedigree

selection, however, low × low general combiners were involved for SCA determination

in some hybrids which indicated epistasis and non-allelic interaction (Sheikh, 2004).

Some past studies revealed high magnitude of GCA than SCA effects, which suggested

additive type of gene action for grains per spike in F2 populations (Javaid et al., 2001;

Joshi et al., 2004). Additive genetic effects were of prime importance because of high

GCA variance for grains per spike. While, grain yield, tillers per plant, and 1000-grain

weight were managed by non-additive gene action due to higher SCA variances in wheat

(Hassan et al., 2007; Ammar et al., 2014). However, crosses between parental genotypes

with high × low GCA effects often resulted in promising SCA values for grains per spike

and grain components in barley in both generations (Singh et al., 2007; Madić et al.,

2014).

132

1000-grain weight

For 1000-grain weight, the GCA effects in parental cultivars varied from -1.31 to

1.81 and -3.55 to 4.18 in F1 and F2 generations, respectively (Table 42). Three each

parental genotypes showed negative and positive GCA effects in both generations.

Maximum positive and significant GCA effects (1.81, 4.18) were recorded for parental

cultivar Pirsabak-05 in F1 and F2 generations, respectively and ranked as best general

combiner for 1000-grain weight in both generations.

Specific combining ability effect for 1000-grain weight ranged from -0.70 to 1.43

among F1 hybrids and -5.18 to 6.16 among F2 populations (Table 44). Seven F1 hybrids

were noted with positive and eight with negative SCA effects. Eight F2 segregants were

observed with positive and seven with negative SCA effects. The highest positive and

significant SCA effects (1.43) were found in cross combination Pirsabak-04 × Shahkar-

13 in F1 generation. In F2 generation, cross combination Saleem-2000 × Khyber-87

revealed significant and maximum positive SCA effects (6.16). Parental cultivars with

low × high GCA effects were involved to produce best specific combination i.e.

Pirsabak-04 × Shahkar-13 for 1000-grain weight with maximum SCA effects in F1

generation. Similarly, in F2 generation, low × high GCA genotypes played important role

in production of best specific combination i.e. Saleem-2000 × Khyber-87 for 1000-grain

weight. Variances due to σ2GCA and σ2SCA and ratio due to σ2GCA/σ2SCA specified

that variances due to GCA were greater than SCA which suggested that additive gene

action controlled 1000-grain weight in both generations (Tables 40 and 41).

Significant GCA and SCA were recorded for 1000-grain weight in F1 generation

for yield traits in wheat, which suggested the involvement of both additive and non-

additive genes for controlling 1000-grain weight (Hassan et al., 2007). However,

Chowdhry et al. (2005) recorded significant GCA and non-significant SCA for 1000-

grains weight in bread wheat. Significant GCA and SCA effects were observed for 1000-

grain weight and grain yield in wheat, and were seen to be initiated from genotypes

having high × high, high × low, medium × low and low × low GCA effects (Kamaluddin

et al., 2007). Predominance of non-additive gene effects were observed for 1000-grain

weight and other yield traits in wheat (Seboka et al., 2009; Majeed et al., 2011),

however, Chandrashekhar and Kerketta (2004) reported additive gene action for yield

traits in wheat.

133

Grain yield per plant

For grain yield per plant, the GCA ranged from -3.03 to 2.10 and -2.75 to 3.22 in

F1 and F2 generations, respectively (Table 42). Three each parental genotypes were

having positive GCA effects in F1 and F2 generations, while with same pattern three each

parental varieties showed negative GCA effects in both generations. Among parental

cultivars, maximum positive and significant GCA effects (1.81, 4.18) were observed for

cultivars Pirsabak-05 and Shahkar-13 for grain yield in F1 and F2 generations,

respectively which suggested to be the best general combiners for grain yield in both

generations.

For grain yield, the SCA effects ranged from -2.94 to 6.56 and -3.12 to 6.83 in F1

and F2 generations, respectively (Table 44). Eight F1 and F2 cross combinations revealed

positive while six each F1 and F2 populations showed negative SCA effects for grain

yield. Significant and highest positive SCA effects (6.56, 6.83) were recorded for

Shahkar-13 × Saleem-2000 and Pirsabak-85 × Khyber-87 for grain yield per plant in F1

and F2 generations, respectively. The cross combination Shahkar-13 × Saleem-2000 was

having parental genotypes with low × low GCA effects to produce F1 hybrid with

maximum SCA effect. However, parental cultivars of the cross combination Pirsabak-85

× Khyber-87 were low × high general combiners to produce F2 segregants with

maximum positive SCA effects for grain yield per plant. Variances due to σ2GCA were

lesser than σ2SCA and ratios due to σ2GCA/σ2SCA were also less than unity indicating

non-additive gene effect for grain yield per plant in both generations (Tables 40 and 41).

Additive type of gene action was observed for grain yield in bread wheat

genotypes (Arshad and Chowdry, 2002). Significant variances due to GCA and SCA

were observed for grain yield per plant in F1 and F2 generations among wheat genotypes

(Joshi et al., 2004). Non-additive gene effects were exhibited for grain yield, suggesting

possibility for improvement of this trait through transgressive segregates and heterosis

breeding for developing genotypes with greater yield potential (Sanjeev et al., 2005). The

F1 hybrids demonstrating high SCA effects for grain yield, grain filling duration and seed

weight were observed to be derived from wheat genotypes having high × high, high ×

low, low × low and medium × low general combiners (Kamaluddin et al., 2007).

Additive gene effects were observed for grain weight per spike due higher GCA,

however, grain yield, tillers per plant, and 1000-grain weight displayed non-additive

134

gene effects due to highest SCA variances (Hassan et al., 2007; Majeed et al., 2011).

Significant GCA for grain yield and its components played an important role in selecting

parental cultivars to develop high yielding genotypes in wheat (Masood et al., 2014).

Biological yield per plant

The GCA effects ranged from -3.94 to 6.38 and -3.97 to 7.27 in F1 and F2

generations, respectively for biological yield per plant (Table 42). For biological yield,

three each parental genotypes were observed with negative and positive GCA effects in

both generations. Among parental cultivars, maximum positive and significant positive

GCA effects (6.38, 7.27) were recorded for Pirsabak-05 in F1 and F2 generations,

respectively for biological yield and considered as best general combiner for said trait in

both generations.

The SCA effects ranged from -6.31 to 10.69 and -5.32 to 17.72 in F1 and F2

generations, respectively for biological yield per plant (Table 44). For biological yield,

nine F1 and twelve F2 populations revealed positive SCA effects while six F1 and three F2

cross combinations showed negative SCA effects. Among F1 hybrids, Pirsabak-85 ×

Pirsabak-04 (10.69) was the best specific combination whereas among F2 populations,

Pirsabak-85 × Khyber-87 (17.72) was the best specific cross by having maximum

positive and significant SCA effects for biological yield. Parental genotypes of hybrid

Pirsabak-85 × Pirsabak-04 with high × high GCA effects were involved to produce F1

hybrid with maximum SCA effects. In F2 generation, Pirsabak-85 × Khyber-87 was

having low × high general combiners to produce F2 population with maximum SCA

effects for biological yield. Estimates of σ2GCA were lesser than σ2SCA and ratios due

to σ2GCA/σ2SCA were also smaller than unity showing non-additive gene effects for

inheritance of biological yield in F1 and F2 generations (Tables 40 and 41).

Significant GCA and SCA effects for biological yield suggested the positive role

of both additive and non-additive genes in genetic control of biological yield (Golparvar,

2014). However, non-additive gene effects were more prominent than additive in

inheritance of biological yield under stress environment in bread and durum wheat

populations (Altintas et al., 2008).

135

Harvest index per plant

The GCA effects ranged from -2.30 to 1.46 and -2.41 to 3.91 in F1 and F2

generations, respectively for harvest index per plant (Table 42). For harvest index, three

each parental genotypes were with positive and negative GCA effects in both

generations. Among parental cultivars, highest positive and significant GCA effects

(1.46, 3.91) were recorded for cultivars Pirsabak-85 and Shahkar-13 for harvest index in

F1 and F2 generations, respectively and classified to be the best general combiners in

both generations.

The SCA effects ranged from -4.47 to 5.04 and -4.66 to 2.94 in F1 and F2

generations, respectively for harvest index per plant (Table 44). Eight each F1 and F2

populations revealed positive SCA while six each F1 and F2 crosses showed negative

SCA effects for harvest index. Maximum positive and significant SCA effects (5.04,

2.94) were recorded for cross combinations i.e. Shahkar-13 × Saleem-2000 and Pirsabak-

85 × Pirsabak-04 for harvest index in F1 and F2 generations, respectively. The F1 hybrid

Shahkar-13 × Saleem-2000 and F2 segregant Pirsabak-85 × Pirsabak-04 with maximum

positive SCA effects involved high × low and low × low GCA parents, respectively for

harvest index. Variance due to σ2GCA was lesser than σ2SCA and ratio due to

σ2GCA/σ2SCA was also smaller than unity showing non-additive gene effects for harvest

index in F1 generation (Table 40). In F2 generation, values of σ2GCA and σ2SCA and

ratio due to σ2GCA/σ2SCA proposed additive gene effects for harvest index (Table 41).

Significant GCA and SCA effects indicated important role of both additive and

non-additive genes in regulating harvest index in wheat cultivars under drought stress

and non-stress conditions (Salehi et al., 2014). Non-additive gene effects were more

important for harvest index and grain weight per spike in macaroni wheat (Gorjanović

and Kraljević-Balalić, 2004). Non-additive gene effects were also reported for harvest

index per plant in bread wheat (Dagusto, 2008). Hannachi et al. (2013) findings also

revealed greater role of non-additive gene effects in inheritance of harvest index under

irrigated conditions in durum wheat.

Yellow rust resistance

For yellow rust resistance, the GCA effects ranged from -1.59 to 3.86 and -6.45

to 4.42 in F1 and F2 generations, respectively (Table 42). For yellow rust resistance, four

136

parental genotypes observed with negative GCA while two were recorded with positive

GCA effects in F1 generation. In F2 generation, two parental cultivars were observed

with negative while four with positive GCA effects. Among parental cultivars, maximum

negative and significant GCA effects were recorded for cultivar Shahkar-13 (-1.59)

followed by Pirsabak-04 (-1.44) and Pirsabak-05 (-1.37) for yellow rust resistance in F1

generation. In F2 generation, cultivar Shahkar-13 (-6.45) was again the leading genotype

by having maximum negative and significant GCA effects followed by Pirsabak-05 (-

3.72) for yellow rust resistance. Therefore, cultivar Shakar-13 was suggested to be the

best general combiner by having maximum resistance to yellow rust in both generations.

The SCA effects for yellow rust resistance ranged from -4.36 to 1.36 and -7.44 to

1.51 in F1 and F2 generations, respectively (Table 44). For yellow rust resistance, nine F1

and eight F2 populations revealed negative SCA effects while six F1s and seven F2s

showed positive SCA effects. Among F1 hybrids, Pirsabak-85 × Pirsabak-05 (-4.36) was

the best specific combination whereas F2 segregant, Saleem-2000 × Khyber-87 (-7.44)

was the best specific population for yellow rust resistance by having maximum negative

and significant SCA effects. Parental genotypes with positive × negative GCA effects

were involved to produce promising F1 hybrid i.e. Pirsabak-85 × Pirsabak-05 with

maximum desirable negative SCA effects. Parental cultivars of cross combination

Saleem-2000 (Yr-18) × Khyber-87 (Yr-9+) were with positive × positive GCA effects to

produce F2 segregants with maximum negative and desirable SCA effects for yellow rust

resistance. Variances due to σ2GCA were less than σ2SCA and ratios due to

σ2GCA/σ2SCA were also less than unity, presenting non-additive gene effect for yellow

rust resistance in F1 and F2 populations (Tables 40 and 41).

Past studies revealed that parental genotype (MV-17) was with low GCA effects

for latent period, infection type, pustule size and number of pustules, and identified as

suitable parent to be used in breeding programs for development of yellow rust resistance

lines (Khodarahmi et al., 2013). Significant GCA and SCA effects were observed for

stem rust resistance in wheat (Cheruiyot et al., 2014). Significant GCA and SCA effects

were reported for four yellow rust resistance components (latent period, infection type,

pustule density and size) in spring wheat and suggested additive and non-additive effects

in genetic control of yellow rust resistance (Khodarahmi et al., 2014). Significant GCA

and SCA effects suggested the involvement of additive and non-additive gene action for

137

terminal yellow rust severity and area under disease progress curve (Kaur et al., 2003). In

their studies, parental genotypes PBW-65, Opata-85 and Trap-1 were considered good

general combiners for yellow rust resistance.

In F1 generation, parental cultivar Pirsabak-05 was found to be the best general

combiner by having appropriate GCA effects for majority traits i.e. flag leaf area, 1000-

grain weight, grain yield and biological yield per plant, followed by 2nd top general

combiners i.e. Shahkar-13 and Pirsabak-85. In F2 generation, genotype Shahkar-13 was

considered to be the best general combiner for majority of the traits by having desirable

GCA effects for days to heading and maturity, grain yield per plant, harvest index per

plant, and yellow rust resistance. Cultivar Pirsabak-05 ranked as second best general

combiner with desirable GCA effects for flag leaf area, 1000-grain weight, and

biological yield per plant. In both generations, cultivar pirsabak-05 showed sustainability

in improvement for 1000-grain weight, grain yield and biological yield.

In case of SCA effects, F1 hybrid Pirsabak-85 × Pirsabak-04 was the most

prominent cross combination for most of the traits by having desirable SCA effects for

days to heading and maturity and biological yield per plant in F1 generation. The cross

combination Pirsabak-05 × Shahkar-13 (for tillers per plant and spike length) and

Shahkar-13 × Saleem-2000 (for grain yield per plant and harvest index) ranked as second

best F1 hybrids with appropriate SCA effects in F1 generation. The F1 hybrids, Pirsabak-

85 × Pirsabak-05 was observed with the desirable SCA effects for yellow rust resistance

in F1 generation.

In F2 generation, Pirsabak-05 × Shahkar-13 was the promising F2 population for

majority of the traits by having desirable SCA effects for days to heading and maturity

and spike length. The F2 segregant, Saleem-2000 × Khyber-87 was observed with the

desirable SCA effects for yellow rust resistance against yellow rust pathotypes. In said

cross, the parental cultivars Saleem-2000 (Yr-18) and Khyber-87 (Yr-9+) were not good

general combiners regarding disease resistance and positive × positive GCA parents were

involved in the expression of yellow rust resistance in F2 generation. Parental genotypes

with high GCA effects produced hybrid with low SCA effects which might be due to the

absence of complementation of the parent’s genes (Kumari et al., 2015).

138

Table 40. Mean squares of general and specific combing ability for various

traits in 6 × 6 F1 half diallel crosses in wheat.

Variables

F1 generation

Mean squares Variance components

GCA SCA Error σ2GCA σ2SCA σ2GCA/σ2SCA

Days to heading 22.57** 0.44NS 0.22 2.79 0.22 12.45

Days to maturity 3.26** 1.65** 0.22 0.37 1.34 0.28

Plant height 178.65** 18.47NS 12.80 20.73 5.67 3.66

Peduncle length 14.92** 14.31* 0.65 2.69 1.49 1.81

Flag leaf area 30.65** 4.27NS 3.93 3.34 0.34 9.75

Tillers plant-1 2.096** 1.65** 0.41 0.21 1.24 0.17

Spike length 0.66** 0.50** 0.07 0.07 0.43 0.17

Spikelets spike-1 2.36** 2.73** 0.59 0.22 2.14 0.10

Grains spike-1 12.45** 10.40** 1.46 1.37 8.94 0.15

1000-grain weight 8.86** 0.39NS 0.53 1.04 -0.14 -7.20

Grain yield plant-1 33.31** 15.76** 4.16 3.64 11.60 0.31

Biological yield plant-1 127.65** 27.72** 6.33 15.16 21.38 0.71

Harvest index plant-1 17.92* 15.52* 5.88 1.51 9.65 0.16

Yellow rust resistance 43.08** 15.70** 0.04 5.38 15.66 0.34

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

139

Table 41. Mean squares of general and specific combing ability for various

traits in 6 × 6 F2 half diallel crosses in wheat.

Variables F2 generation

Mean squares Variance components

GCA SCA Error σ2GCA σ2SCA σ2GCA/σ2SCA

Days to heading 9.64** 3.80** 1.15 1.06 2.65 0.40

Days to maturity 6.03** 2.57* 1.02 0.63 1.56 0.40

Plant height 70.61** 28.67** 4.05 8.32 24.62 0.34

Peduncle length 19.52** 6.34** 0.49 2.38 5.84 0.41

Flag leaf area 19.72** 5.86** 0.49 2.40 5.37 0.45

Tillers plant-1 3.84** 0.56** 0.19 0.46 0.37 1.23

Spike length 1.12** 0.75** 0.06 0.13 0.68 0.19

Spikelets spike-1 3.37** 1.11** 0.24 0.39 0.87 0.45

Grains spike-1 13.1** 3.94* 1.83 1.41 2.11 0.67

1000-grain weight 90.35** 13.59** 2.68 10.96 10.92 1.00

Grain yield plant-1 60.68** 13.99** 4.54 7.02 9.45 0.74

Biological yield plant-1 154.83** 90.1** 15.69 17.39 75.21 0.23

Harvest index plant-1 45.28** 4.80NS 3.70 5.20 1.11 4.69

Yellow rust resistance 142.67** 21.67** 0.97 17.72 20.69 0.86

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant

Table 42. General combing ability effects of parental genotypes for various traits in 6 × 6 F1 and F2 half diallel crosses in wheat.

Variables Pirsabak-85 Pirsabak-04 Pirsabak-05 Shahkar-13 Saleem-2000 Khsyber-87 S.E. (gj)

F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2

Days to heading 2.73** 1.61** -0.52** 0.28 0.6** 0.19 -0.52** -1.72** 0.10 0.19 -2.4** -0.56 0.15 0.35

Days to maturity 0.38* 0.51 -0.31 -0.028 1.00** 1.35** 0.00 -1.19** -0.19 -0.15 -0.88** -0.49 0.18 0.33

Plant height 2.92* 0.54 3.85** -0.094 5.73** 3.89** -5.21** -2.29** -4.58** -4.29** -2.71* 2.25** 1.15 0.65

Peduncle length 0.39 -0.18 0.84** 0.002 2.47** 1.89** -1.00** -0.39 -2.42** -2.60** -0.28 1.29** 0.26 0.23

Flag leaf area -0.45 -0.21 -1.19 -0.03 3.92** 1.84** -0.28 -0.42 -1.21 -2.6** -0.80 1.41** 0.64 0.23

Tillers plant-1 0.33 -0.55** 0.71** 0.73** -0.6** -0.8** -0.29 0.30* 0.27 0.79** -0.42 -0.47** 0.21 0.14

Spike length 0.20* 0.44** 0.28** 0.04 -0.03 -0.68** 0.16** 0.20* -0.50** 0.07 -0.12 -0.07* 0.08 0.08

Spikelets spike-1 -0.04 0.52** 0.27 0.07 -0.98** -1.17** 0.15 0.30 0.65* 0.54** -0.04 -0.27* 0.25 0.16

Grains spike-1 1.60** 1.49** 1.17** -0.69 -1.58** -1.89** -0.33 0.17 -1.08* 1.32** 0.23 -0.41 0.39 0.44

1000-grain weight 0.13 -3.07** -0.25 -1.9** 1.81** 4.18** 0.25 3.57** -1.31** -3.55** -0.63* 0.76* 0.24 0.53

Grain yield plant-1 1.32 -2.75** 1.60** -2.45** 2.10** 2.78** -0.31 3.22** -1.69* -2.12** -3.03** 1.32 0.66 0.69

Biological yield plant-1 0.81 -3.69** 2.50** -3.97** 6.38** 7.27** -3.94** 1.66 -2.56** -3.00* -3.19** 1.72 0.81 1.28

Harvest index plant-1 1.46 -2.41** 0.83 -1.45** -0.37 0.45 1.38 3.91** -1.01 -1.80** -2.30** 1.30* 0.78 0.62

Yellow rust resistance 3.86** 4.42** -1.44** 2.48** -1.37** -3.72** -1.59** -6.45** -1.36** 2.81** 1.91** 0.48 0.06 0.32

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant, S.E. (Gj) = Standard error

141

Table 43. Specific combing ability effects in 6 × 6 F1 and F2 half diallel crosses for various traits in wheat.

F1 and F2 populations Day to headings Day to maturity Plants height Peduncle length Flage leaf area Tillers plant-1 Spike length

F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2

Pirsabak-85 × Pirsabak-04 -0.66** -0.62 -0.71** 0.96* 1.92 0.39 -0.23 2.07** 2.65** 2.07** -0.02 -0.47* 0.55** 0.53**

Pirsabak-85 × Pirsabak-05 0.21 -1.87** -0.52* 0.25 0.045 -0.71 -0.53 -0.36 -1.13 -0.33 0.80** 0.08 0.22 0.82**

Pirsabak-85 × Shahkar-13 1.34** -0.29 -0.52* -1.54** 3.48* 5.87** 1.44** 0.74* 1.98* 0.75* -2.02** 0.21 -0.27* -0.12

Pirsabak-85 × Saleem-2000 -0.29 -1.87** 0.67* -1.58** 2.86 3.09** 1.17** 1.38** -0.2 1.39** 0.92** -0.31 0.23* 0.53**

Pirsabak-85 × Khyber-87 0.21 -2.12** 1.86** 0.42 5.98** 8.74** -0.28 2.40** -1.34 2.22** 0.61 0.30 0.46** 0.21

Pirsabak-04 × Pirsabak-05 -0.54* -0.87 -0.33 -0.21 4.11* 3.02** 2.93** 1.31** -1.00 1.31** -1.08** -0.11 0.28* -0.9

Pirsabak-04 × Shahkar-13 -0.41* 0.38 1.12** -1.00* 2.55 -0.57 1.49** 0.34 -1.46 0.36 0.11 0.29 0.60** -1.71**

Pirsabak-04 × Saleem-2000 -0.036 -1.20* 0.36 -1.71** 1.92 -1.06 -0.29 -0.12 0.59 -0.09 1.04** 0.43* 0.25* 0.72**

Pirsabak-04 × Khyber-87 -0.54* -0.45 -0.46 -1.71** -2.46 5.30** -0.52 1.23** -0.86 1.07** 0.73* -0.26 0.18 1.00**

Pirsabak-05 × Shahkar-13 -0.54* -2.54** 0.36 -3.04** 3.17* 6.06** 0.86* 2.57** 1.42 2.56** 1.92** 0.34 0.81* 1.01**

Pirsabak-05 × Saleem-2000 -0.16 -0.12 1.05** 0.92* 2.55 1.14 -0.41 0.78* 3.39** 0.79* -0.64* -0.12 0.41** -0.59**

Pirsabak-05 × Khyber-87 0.34 3.63** 0.73** -0.75 0.67 -3.80** -0.45 -1.27** 3.12** -1.40** 0.54 0.21 0.49** 0.04

Shahkar-13 × Saleem-2000 0.96** -0.20 0.55* 1.13* 0.98 0.81 1.45** 0.94** 0.50 0.95** 0.54 -1.20** 0.48** 0.28*

Shahkar-13 × Khyber-87 -0.54* -1.45** 1.73** 0.13 1.61 1.61 -1.29** 3.67** 1.09 3.48** 1.23** -1.94** 0.25* 0.32**

Saleem-2000 × Khyber-87 -0.16 0.30 0.92** 1.75** 0.98 2.67** 0.94* 1.45** 0.70 1.28** 1.17** 0.18 0.36** 0.56**

S.E. (ij) 0.20 0.45 0.24 0.43 1.51 0.85 0.34 0.30 0.84 0.29 0.27 0.18 0.18 0.11 0.10

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant, S.E.(ij) = Standard error

142

Table 44. Specific combing ability effects in 6 × 6 F1 and F2 half diallel crosses for various traits in wheat.

F1 and F2 populations Spikelets spike-1 Grain spike-1

1000-grain

Weight

Grain yield

plant-1

Biological yield

plant-1

Harvest Index

plant-1

Yellow rust

resistance

F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2

Pirsabak-85 × Pirsabak-04 1.08** 0.35 2.21** 1.00 0.05 4.80** 6.23** 4.37** 10.69** 7.75** 1.67 2.94** -4.30** -7.01**

Pirsabak-85 × Pirsabak-05 -0.67 0.86** -0.05 2.46** 1.00** -0.15 3.73** 0.74 1.32 2.10 3.17** 0.51 -4.36** 0.92*

Pirsabak-85 × Shahkar-13 1.21** -0.96** -1.30* -2.10** 0.05 1.71* -2.36* 0.72 -2.88 1.57 -1.42 0.77 -4.11** -1.39**

Pirsabak-85 × Saleem-2000 2.71** 0.27 2.46** 1.20* 0.12 -2.98** -2.48** -3.05** -0.75 -4.52* -2.58* -2.36* -4.22** -1.82**

Pirsabak-85 × Khyber-87 1.90** -1.44** -0.86 -1.52* -0.57 5.81** -2.14* 6.83** 4.88** 17.72** -4.47** 1.24 -3.81** -4.8**

Pirsabak-04 × Pirsabak-05 0.52 -0.65** -0.11 -1.67** -0.13 2.70** -2.94** 3.52** 3.13** 9.92** -4.43** 0.55 0.94** 0.91*

Pirsabak-04 × Shahkar-13 0.40 -1.52** -0.36 -3.68** 1.43** -1.81** 0.77 -1.94* -3.56** 2.25 2.76* -3.92** 1.33** 0.25

Pirsabak-04 × Saleem-2000 -0.11 0.79** -1.61** 1.27* -0.51 -0.80 1.74 -0.23 -3.94** 1.23 4.14** -0.96 1.36** 0.29

Pirsabak-04 × Khyber-87 0.58 1.50** -3.42** 0.91 -0.70* -1.08 -1.72 -0.23 -6.31** -1.05 0.79 0.19 -1.34** -2.22**

Pirsabak-05 × Shahkar-13 -0.36 1.10** -1.61** 2.37** -0.13 1.36 1.97* 1.13 6.56** 4.35* -0.73 -1.05 1.09** 0.67

Pirsabak-05 × Saleem-2000 1.14** -0.25 7.64** -1.13 -0.07 2.60** -0.26 2.03* 0.69 5.52** -0.44 0.66 1.13** 1.29**

Pirsabak-05 × Khyber-87 0.83* 0.59** 2.83** 2.01** -0.26 -5.18** 1.58 -3.12** -2.19 0.28 2.87* -4.66** -0.54** 1.51**

Shahkar-13 × Saleem-2000 0.02 -0.42* 1.39* -0.28 -0.51* 2.06** 6.56** 0.94 6.50** 0.91 5.04** 1.33 1.08** -3.86**

Shahkar-13 × Khyber-87 -0.29 0.74** 3.58** 1.85** 0.30 -1.07 4.49** -1.79 0.63 -5.32** 5.51** -0.20 -2.19** -1.37**

Saleem-2000 × Khyber-87 1.21** 1.17** -1.17* 0.50 0.87* 6.16** 3.37** 6.20** 3.25** 13.27** 2.75* 2.02* -1.05** -7.44**

S.E. (ij) 0.32 0.21 0.51 0.57 0.31 0.69 0.86 0.90 1.06 1.67 1.02 0.81 0.08** 0.42

*, ** = Significant at P≤0.05 and P≤0.01, NS = Non-significant, S.E. (ij) = Standard error

D. High Molecular Weight Glutenin Subunits

The protein diversity in wheat cultivars has proved to be beneficial not only for

diversity but also to boost the variation in germplasm collection and in breeding

genotypes with better bread making quality. Improvement in grain protein

concentration is a major objective in bread wheat making program world-wide.

Achieving this goal without a concurrent loss in grain yield has been difficult due to the

well documented negative correlation between these two economically essential traits

(Costa and Kronstad, 1994; Dencic et al., 2000). Although reports of negative

correlation between grain protein and grain yield dominated in past literature, however,

some studies on winter wheat suggested that genetic improvement in grain yield and

grain protein can occur simultaneously (Huebner et al., 1997; Mikhaylenko et al.,

2000). Payne and Lawrence (1983) published the catalogue of Glu-1 alleles and

reported 03 alleles (Null, 1 and 2*) at Glu-A1 locus, 11 alleles (7, 20, 21, 22, 7 + 8, 7 +

9, 6 + 8, 13 + 16, 13 + 19, 14 + 15, and 17 + 18) at Glu-B1 locus, and 6 alleles (2 + 12,

3 + 12, 4 + 12, 5 + 10, 2 + 10, and 2.2 + 12) at Glu-D1 locus. McIntosh et al. (2003)

reported 22 alleles at Glu-A1 locus, while 56 alleles at Glu-B1 locus, and 37 alleles at

Glu-D1 locus. In present studies, eight wheat cultivars, fifteen each F2 and F3

populations were analyzed for the composition of high molecular weight glutenin

subunits as follows.

High molecular weight glutenin subunits characterization

Thirty-eight genotypes (including six parental cultivars, 15 each F2 and F3

populations and two check genotypes) were grouped in two sets each having 20

genotypes i.e. i) comprising six parents, six each F2 and F3 populations and two checks,

ii) nine each F2 and F3 populations and two checks (Tables 45 and 46). Each set was

run on gel and parental genotypes, F2 and F3 populations were compared with two

wheat genotypes/markers i.e. Pavon-76 and Chinese Spring for HMW-GS

identification in SDS-PAGE. Parental cultivars, F2 and F3 populations were scored for

allelic pairs at Glu-A1, Glu-B1 and Glu-D1 loci and their classification was done based

on banding pattern and quality status. Quality scores were calculated by adding

together the score of individual sub-units according to Payne (1987).

Genetic composition of all the wheat populations (parental and check cultivars

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+ F2 + F3) at Glu-A1, Glu-B1 and Glu-D1 are presented in Tables 45 and 46. Eight

alleles were identified in the first set of wheat parental cultivars, F2 and F3 populations

at different loci (Table 45, Fig. 15). Three alleles were identified at Glu-A1 locus (Null,

1 and 2*), three allelic pairs were detected at Glu-B1 (7 + 8, 7 + 9 and 17 + 18) and two

were located at Glu-D1 locus (5 + 10 and 2 + 12). Pavon-76 was used as a marker and

had '2*' allele at Glu-A1 locus, '17 + 18' at Glu-B1 and '5 + 10' at Glu-D1. Similarly,

Chinese Spring was also used as a marker with “Null” allele at Glu-A1 locus, '7 + 8' at

Glu-B1 and '2 + 12' at Glu-D1.

Among the parental cultivars, genotype Pirsabak-05 had allele '1' at Glu-A1,

while allele '2*' at same locus shown by parental cultivars (Pirsabak-85, Pirsabak-04,

Shahkar-13, Saleem-2000 and Khyber-87), F2 populations (Pirsabak-85 × Pirsabak-04,

Pirsabak-85 × Pirsabak-05, Pirsabak-85 × Saleem-2000, Pirsabak-85 × Shahkar-13,

Pirsabak-85 × Khyber-87 and Pirsabak-04 × Pirsabak-05) (Table 45, Fig. 15). The F3

populations (Pirsabak-85 × Pirsabak-04, Pirsabak-85 × Pirsabak-05, Pirsabak-85 ×

Shahkar-13, Pirsabak-85 × Saleem-2000, and Pirsabak-85 × Khyber-87 and Pirsabak-

04 × Pirsabak-05) also owned allele '2*' at Glu-A1.

Glutenin subunit pair i.e. '17 + 18' was identified at Glu-B1 locus in fifteen

genotypes i.e. parental cultivars (Pavon-76, Pirsabak 85, Pirsabak-05 and Shahkar-13),

F2 populations (Pirsabak-85 × Pirsabak-04, Pirsabak-85 × Pirsabak-05, Pirsabak-85 ×

Shahkar-13, Pirsabak-85 × Saleem-2000, Pirsabak-85 × Khyber-87 and Pirsabak-04 ×

Pirsabak-05) (Table 45, Fig.15). In F3 populations (Pirsabak-85 × Pirsabak-04,

Pirsabak-85 × Pirsabak-05, Pirsabak-85 × Shahkar-13, Pirsabak-85 × Saleem-2000,

Pirsabak-85 × Khyber-8 and Pirsabak-04 × Pirsabak-05) the same glutenin subunit pair

i.e. '17 + 18' was identified at Glu-B1. However, glutenin subunit pair i.e. '7 + 9' was

found in parental cultivars i.e. Pirsabak-04, Saleem-2000 Khyber-87. However, the

check genotype Chinese Spring showed gene pair '7 + 8' at Glu-B1.

All parental genotypes, F2, F3 populations and cutivar pavon-76 had allelic pair

'5 + 10' whereas Chinese Spring had '2 + 12' at Glu-D1 locus (Table 45, Fig. 15). Tahir

et al. (1996) assessed 50 spring wheat cultivars of Pakistan for HMW glutenin subunits

and mentioned that non of the spring wheat genotype possessed null allele at the Glu-

A1 locus. Quijano et al. (2001) reported that genes coding for D genome subunits play

vital role in determining the bread-making quality. Shah (2009) reported the same

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banding pattern for Pirsabak-85 in grain flour quality characters of wheat cultivars

under different agro-ecological conditions. Tabasum et al. (2011) found '2*' allele at

Glu-A1 locus, '7 + 9' at Glu-B1 and '5 + 10' at Glu-D1 for Saleem-2000 and present

results got support from these findings. Nosrati et al (2013) proposed that good bread

quality in wheat is mostly related with the existence of subunit '5 + 10' at locus Glu-D1.

Highly scored Pakistani cultivars and CIMMYT lines were due to the occurrence of

subunits 5 + 10, 2*, 1, 17 + 18, 7 + 8 and 13 + 16 having the quality points of 4, 3, 3, 3,

3 and 3 respectively (Sajjad et al., 2012), which supported the currents results as all the

genotypes were with score points 4, 3, 3 and 3, 3, 3. Cross and Guo (1993) reported that

the 'Null' allele mostly in land races in glutenin variation in a diverse pre 1935 world

wheat germplasm. Yasmeen et al. (2015) and Shuaib et al. (2010) reported '2*' allele at

Glu-A1 locus, '7 + 9' at Glu-B1 and '5 + 10' at Glu-D1 for Pirsabak-04, Saleem-2000

and Khyber-87.

In second set, the protein samples of F2 and F3 populations with check

genotypes (Pavon-76 and Chinese spring) were run on gel (Table 46, Fig. 16). The

allele ''2*' was shown at Glu-A1 locus by F2 populations (Pirsabak-04 × Shahkar-13,

Pirsabak-04 × Saleem-2000, Pirsabak-04 × Khyber-87, Pirsabak-05 × Khyber-87,

Shahkar-13 × Salem-2000, Shahkar-13 × Khyber-87, Salem-2000 × Khyber-87) and F3

populations (Pirsabak-04 × Shahkar-13, Pirsabak-04 × Saleem-2000, Pirsabak-04 ×

Khyber-87, Pirsabak-05 × Khyber-87, Shahkar-13 × Salem-2000, Shahkar-13 ×

Khyber-87, Salem-2000 × Khyber-87) while rest of the F2 and F3 populations possessed

allele “1' at Glu-A1 locus. Most of genotypes contained '17 + 18' at Glu-B1 except F2

population (Pirsabak-04 × Khyber-87, Pirsabak-04 × Saleem-2000 and Salem-2000 ×

Khyber-87) and F3 populations (Pirsabak-04 × Saleem-2000, Pirsabak-04 × Khyber-87

and Salem-2000 × Khyber-87) which had allele '7 + 9' at the same locus.

There were no variation among genotypes for allele pair '5 + 10' at Glu-D1

(Table 46, Fig. 16). Bian et al. (2015) examined HMW-GS composition of different

wheat F1 and F2 populations and proposed that separations followed Mendelian law of

independent assortment, suggesting no linkage between any two loci. Anwar et al.

(2003) observed that '7 + 8' at Glu B1 and '2 + 12' allele at Glu D1 were the most

frequent allele pairs in wheat land races of Pakistan. Payne et al (1981) demonstrated

that some allelic sub-units contributed diverse effects on gluten quality (the allelic

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differences at the Glu-D1 locus of bread wheat), where the alternative pairs of subunits

'5 + 10' (associated with good quality) and subunits '2 + 12' (related with weaker bread

making quality) were identified. Ji et al. (2012) found the allels 'Null' and '1' in greater

frequency than the allelic subunit '2*' at the Glu-A1 by studying variations in high-

molecular-weight glutenin subunits in the main wheat growing zones of Chinas.

However, Masood et al. (2004) reported that '7 + 8' was the most frequent allelic pair at

Glu-B1 in genetic diversity study in wheat landraces from Pakistan.

Present results revealed that development of commercial cultivars had narrow

down the genetic diversity of bread wheat in terms of HMW-GS, as lower genetic

diversity was observed in commercial cultivars. Use of limited germplasm and races for

breeding economically important traits has narrowed the genetic constitution of the

prevailing wheat cultivars in Pakistan (Sajjad et al., 2011). Therefore, to prevent

genetic drift, it is essential to preserve the local wheat germplasm and land races

(Chaperzadei et al., 2008). Doneva et al. (2014) reported that synthetic hexaploid D-

genome appeared to be exceptional sources for choosing diverse glutenin compositions

in wheat breeding. Allelic combinations '2*', '5 + 10', and '17 + 18' demonstrating high

quality and frequent scores among commercial cultivars and landraces specifying their

effectiveness in future breeding programs (Deng et al., 2005; Zeller et al., 2007;

Yasmeen et al., 2015).

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Table 45. High molecular weight glutenin subunits (HMW-GS) and genome

score in first set of 20 wheat genotypes using SDS-PAGE.

Parental cultivars, F2, F3

populations & check genotypes

Genomes Genome

score Total

D A B

Pavon-76 5 + 10 2* 17 + 18 4 + 3 + 3 10

Chinese spring 2 + 12 Null 7 + 8 2 + 2 + 2 6

Pirsabak-85 5 + 10 2* 17 + 18 4 + 3 + 3 10

Pirsabak-04 5 + 10 2* 7 + 9 4 + 3 + 2 9

Pirsabak-05 5 + 10 1 17 + 18 4 + 3 + 3 10

Shahkar-13 5 + 10 2* 17 + 18 4 + 3 + 3 10

Saleem-2000 5 + 10 2* 7 + 9 4 + 3 + 2 9

Khyber-87 5 + 10 2* 7 + 9 4 + 3 + 2 9

Pirsabak-85 × Pirsabak-04 (F2) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Pirsabak-85 × Pirsabak-04 (F3) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Pirsabak-85 × Pirsabak-05 (F2) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Pirsabak-85 × Pirsabak-05 (F3) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Pirsabak-85 × Shahkar-13 (F2) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Pirsabak-85 × Shahkar-13 (F3) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Pirsabak-85 × Saleem-2000 (F2) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Pirsabak-85 × Saleem-2000 (F3) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Pirsabak-85 × Khyber-87 (F2) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Pirsabak-85 × Khyber-87 (F3) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Pirsabak-04 × Pirsabak-05 (F2) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Pirsabak-04 × Pirsabak-05 (F3) 5 + 10 2* 17 + 18 4 + 3 + 3 10

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Table 46. High molecular weight glutenin subunits (HMW-GS) and genome

score in the second set of 20 wheat genotypes using SDS-PAGE.

F2, F3 populations & check

genotypes

Genomes Genome

score Total

D A B

Pavon-76 5 + 10 2* 17 + 18 4 + 3 + 3 10

Chinese spring 2 + 12 Null 7 + 8 2 + 2 + 2 6

Pirsabak-04 × Shahkar-13 (F2) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Pirsabak-04 × Shahkar-13 (F3) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Pirsabak-04 × Saleem-2000 (F2) 5 + 10 2* 7 + 9 4 + 3 + 2 9

Pirsabak-04 × Saleem-2000 (F3) 5 + 10 2* 7 + 9 4 + 3 + 2 9

Pirsabak-04 × Khyber-87 (F2) 5 + 10 2* 7 + 9 4 + 3 + 2 9

Pirsabak-04 × Khyber-87 (F3) 5 + 10 2* 7 + 9 4 + 3 + 2 9

Pirsabak-05 × Shahkar-13 (F2) 5 + 10 1 17 + 18 4 + 3 + 3 10

Pirsabak-05 × Shahkar-13 (F3) 5 + 10 1 17 + 18 4 + 3 + 3 10

Pirsabak-05 × Saleem-2000 (F2) 5 + 10 1 17 + 18 4 + 3 + 3 10

Pirsabak-05 × Saleem-2000 (F3) 5 + 10 1 17 + 18 4 + 3 + 3 10

Pirsabak-05 × Khyber-87 (F2) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Pirsabak-05 × Khyber-87 (F3) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Shahkar-13 × Saleem-2000 (F2) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Shahkar-13 × Saleem-2000 (F3) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Shahkar-13 × Khyber-87 (F2) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Shahkar-13 × Khyber-87 (F3) 5 + 10 2* 17 + 18 4 + 3 + 3 10

Saleem-2000 × Khyber-87 (F2) 5 + 10 2* 7 + 9 4 + 3 + 3 9

Saleem-2000 × Khyber-87 (F3) 5 + 10 2* 7 + 9 4 + 3 + 3 9

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Fig. 15. High molecular weight glutenin subunits (HMW-GS) in the first set of 20 wheat

genotypes (including parental cultivars, F2 and F3 populations) analyzed by SDS-PAGE.

From R to L 1 = Chinese spring, 2 = Pavon-76, 3 = Ps85, 4 = Ps04, 5= Ps05, 6 = Sh13, 7 = Sm, 8 = Kh87, 9 =

Ps85 × Ps04 F2, 10 = Ps85 × Ps04 F3, 11 = Ps85 × Ps05F2, 12 = Ps85 × Ps05F3, 13 = Ps85 × Sh13F2, 14 = Ps85

× Sh13F3, 15 = Ps85 × SmF2, 16 = Ps85 × SmF3, 17 = Ps85 × Kh87F2, 18 = Ps85 × Kh87F3, 19 = Ps04 ×

Ps05F2, 20 = Ps04 × Ps05F3, 21 = Chinese spring, 22 = Pavon-76

Fig. 16. High molecular weight glutenin subunits (HMW-GS) in the second set of 20 wheat

genotypes (including F2 and F3 populations and check genotypes) analyzed by SDS-PAGE.

From R to L 1 = Chinese spring, 2 = Pavon-76, 3 = Ps04 × Sh13F2, 4 = Ps04 × Sh13F3, 5 = Ps04 × SmF2, 6 = Ps04

× SmF3, 7 = Ps04 × Kh87F2, 8 = Ps04 × Kh87F3, 9 = Ps05 × ShF2, 10 = Ps05 × Sh13F3, 11 = Ps05 × SmF2, 12 =

Ps05 × SmF3, 13 = Ps05 × Kh87F2, 14 = Ps-05 × Kh87F3, 15 = Sh13 × SmF2, 16 = Sh13 × SmF3, 17 = Sh13 ×

Kh87F2, 18 = Sh13 × Kh87F3, 19 = Sm × Kh87F2, 20 = Sm × Kh87F3, 21 = Chinese spring, 22 = Pavon-76

150

V. SUMMARY

The study pertaining to inheritance of earliness, yield traits, yellow rust

resistance and glutenin contents was undertaken using 6 × 6 half diallel crosses in

wheat at Cereal Crops Research Institute (CCRI), Nowshera - Pakistan. Six diverse

wheat cultivars including i.e. Pirsabak-85, Khyber-87, Saleem-2000, Pirsabak-04,

Pirsabak-05 and Shahkar-13 were crossed in a half diallel fashion during 2010-2011.

Parental cultivars and their F1 and F2 progenies were evaluated during 2011-12 and

2012-13, respectively at CCRI, Nowshera. Recommended cultural practices and inputs

were applied to all the genotypes in both generations. Present research was carried out

with objectives to study the i) mean performance of F1 and F2 populations with parental

genotypes, ii) inheritance of various traits through Hayman’s approach, iii) combining

ability analysis, and iv) and molecular studies of the glutenin contents in F2 and F3

populations and their parental cultivars. Data were recorded on earliness,

morphological and yield traits i.e. days to heading and maturity, plant height, peduncle

length, flag leaf area, tillers per plant, spike length, spikelets per spike, grains per spike,

1000-grain weight, grain yield per plant, biological yield, harvest index, yellow rust

resistance in in F1 and F2 progenies, and glutenin subunits in F2 and F3 progenies along

with parental cultivars in both generations.

Highly significant differences were observed among genotypes for all the traits

in F1 and F2 generations. Cultivar Khyber-87 was classified as best parental genotype

for earliness and by taking lesser days to heading and maturity in both generations. For

medium plant stature, Shahkar-13 and Saleem-2000 were identified as promising

cultivars by giving minimum values for plant height and peduncle length in both

generations. Pirsabak-04 and Saleem-2000 were the best parental cultivars for tillers

per plant in F1 and F2 generations, respectively. For spike length, spikelets per spike,

and harvest index, cultivars Pirsabak-85 and Shahkar-13 showed best performance in

both generations. Highest grains per spike were found for Pirsabak-04 in F1 and

Pirsabak-85 in F2 generation. Pirsabak-05 and Shahkar-13 were the best cultivars for

flag leaf area, 1000-grain weight, biological yield, and grain yield per plant in F1 and F2

generations.

Among F1 hybrids, cross combinations Shahkar-13 × Khyber-87 and Pirsabak-

04 × Khyber-87 in F1 progenies and Pirsabak-05 × Shahkar-13 among F2 populations

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showed earliness by taking least days to heading and maturity. Desirable minimum

plant height and peduncle length were observed in Shahkar-13 × Saleem-2000,

Shahkar-13 × Khyber-87 and Pirsabak-04 × Saleem-2000 in both generations. The

highest tillers per plant and flag leaf area were observed in Pirsabak-04 × Saleem-2000

and Shahkar-13 × Khyber-87 in both generations. Genotypes Pirsabak-85 × Saleem-

2000 and Saleem-2000 × Khyber-87 produced maximum spikelets per spike in F1 and

F2 generations, respectively. Maximum grains per spike were observed for Pirsabak-05

× Saleem-2000 and Pirsabak-85 × Pirsabak-04 in F1 and Pirsabak-85 × Pirsabak-04 in

F2 generation. Genotype Pirsabak-85 × Pirsabak-04 produced maximum spike length,

grains per spike, grain yield, biological yield and yellow rust resistance in F1

populations. In F2 generation, Pirsabak-05 × Shakar-13 was the promising cross

combination for days to maturity, flag leaf area, 1000-grain weight, grain yield per

plant and yellow rust resistance.

For adequacy of the additive-dominance model, two scaling tests i.e. t2 test and

regression analysis were used to test the validity of the diallel assumptions underlying

the genetic model for recorded data sets of various traits in F1 and F2 generations.

Based on these scaling tests, additive dominance model was found partially adequate

for all the traits in F1 and F2 generations except tillers per plant in F1s where the model

was found fully adequate.

According to Hayman's genetic analysis, major components of genetic variance

i.e. additive (D) and dominance components (H1, H2) were significant for majority of

the traits. Results revealed that both additive and dominant gene effects played

important role in the inheritance of the studied traits. However, additive (D) was greater

than dominance (H1, H2) components for days to heading, plant height, peduncle

length, flag leaf area, 1000-grain weight, and yellow rust resistance which indicated the

predominant role of additive gene action in inheritance of these traits in F1 generation.

The H1 and H2 components were larger than D for days to maturity, tillers per plant,

spike length, spikelets per spike, grains per spike, grain yield, biological yield and

harvest index per plant, suggesting the involvement of non-additive gene action for

these traits in F1 generation.

In F2 generation, additive component was greater than dominance for tillers per

plant, 1000-graint weight, grain yield per plant, harvest index, and yellow rust

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resistance which showed that these traits were controlled by additive gene action.

However, magnitude of D was smaller than H1 and H2 for days to heading and

maturity, plant height, peduncle length, spike length, spikelets per spike, grains per

spike and biological yield, demonstrating the primary role of non-additive gene action

in F2 generation. In both generations, these additive and non-additive gene actions for

various traits were also confirmed by the ratios of average degree of dominance and Vr-

Wr graphs.

High broad and narrow sense heritability values were recorded for days to

heading (0.99, 0.91), plant height (0.80, 0.70), peduncle length (0.90, 0.72), flag leaf

area (0.80, 0.70) and 1000-grain weight (0.83, 0.78), respectively which demonstrated

the involvement of both additive and non-additive genes in governing these traits in F1

generation. High broad and medium/low narrow sense heritability estimates were

observed for days to maturity (0.82, 0.30), tillers per plant (0.80, 0.20), spike length

(0.56, 0.13), spikelets per spike (0.82, 0.25), grains per spike (0.88, 0.38), grain yield

per plant (0.80, 0.30), biological yield (0.88, 0.49), harvest index (0.66, 0.16) and

yellow rust resistance (0.99, 0.38), respectively which indicated the predominance of

non-additive gene action for these traits in F1 generation.

In F2 generation, high broad and narrow sense heritability were found for tillers

per plant (0.87, 0.59), 1000-grain weight (0.92, 0.60), harvest index (0.78, 0.60) and

yellow rust resistance (0.97, 0.65) respectively which revealed the involvement of both

additive and non-additive gene action for controlling these traits. However, high broad

and medium/low narrow sense heritability were recorded for traits i.e. days to heading

(0.80, 0.35), days to maturity (0.75, 0.35), peduncle length (0.93, 0.51), flag leaf area

(0.95, 0.53), spike length (0.95, 0.33), spikelets per spike (0.87, 0.38), grains per spike

(0.77, 0.39), grain yield per plant (0.83, 0.47) and biological yield per plant (0.86,

0.33), respectively which may authenticated the primary role of non-additive gene

effects in inheritance of these traits in F2 generation.

In combining ability analysis based on Griffing’s approach, mean squares due to

GCA were significant for all traits i.e. days to heading and maturity, plant height,

peduncle length, flag leaf area, tillers per plant, spike length, spikelets per spike, grain

per spike, 1000-grain weight, grain yield, biological yield, harvest index and yellow

rust resistance in both generations. The SCA mean squares were significant for most of

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traits in both generations, except for plant height, flag leaf area and 1000-grains weight

in F1 and harvest index in F2 generation.

Based on GCA effects, parental cultivar Pirsabak-05 was considered to be the

best general combiner for yield traits and rust resistance in F1 generation. In F2

generation, cultivar Shahkar-13 was identified as best general combiner for earliness,

yield traits, and rust resistance. In case of SCA effects, F1 hybrid Pirsabak-85 ×

Pirsabak-04, and F2 population Pirsabak-05 × Shahkar-13 were the superior cross

combinations for majority of the traits.

In F1 generation, variances due to σ2SCA were greater than σ2GCA for traits i.e.

days to maturity, tillers per plant, spike length, spikelets per spike, grains per spike,

grain yield per plant, biologyical yield per plant, harvest index and yellow rust

resistance which suggested that these traits were under influence of non-additive gene

action. Variances due to σ2SCA were also greater than σ2GCA for most of traits viz.,

days to heading and maturity, plant height, peduncle length, flag leaf area, spike length,

spikelets per spike, grains per spike, grain yield per plant, biological yield, and yellow

rust resistance in F2 generations, showing the primary role of non-additive gene action

in inheritance of these traits. Present results further revealed that variances due to GCA

were more pronounced than σ2SCA for days to heading, plant height, peduncle length,

flag leaf area and 1000-grain weight in F1s while for tillers per plant, 1000-grain weight

and harvest index in F2 generation which revealed that these traits were governed by

additive gene action.

In biochemical characterization for high molecular weight glutenin subunits, the

parental wheat cultivars with diverse genetic makeup, and their F2 and F3 populations

were compared with check genotypes i.e. Pavan-76 and Chinese Spring. At locus Glu-

A1, three different types of alleles were recorded i.e. 'null', '1' and '2*'. At Glu-B1 locus,

allelic subunits '7 + 8', '7 + 9' and '17 + 18' were observed, while at Glu-D1 locus '5 +

10' and '2 + 12' were identified. In the present study, Chinese Spring possessed 'null'

allele, five genotypes possessed '1' allele and other 22 genotypes (parental cultivars, F2

and F3 populations) possessed '2*' allele at Glu-A1 locus. At Glu-B1 locus, Chinese

Spring had allelic subunits '7 + 8', nine genotypes possessed allelic subunits '7 + 9' and

28 possessed allelic subunits '17 + 18'.

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Similarly, at Glu-D1, 37 genotypes possessed allelic subunits '5 + 10', while

Chinese Spring possessed allelic pair '2 + 12'. Overall, the parental cultivars (Pirsabak-

85, Pirsabak-04, Pirsabak-05), and F2 and F3 populations of Pirsabak-85 × Pirsabak-04,

Pirsabak-85 × Pirsabak-05, Pirsabak-85 × Khyber-87, Pirsabak-04 × Pirsabak-05,

Pirsabak-04 × Shahkar-13, Pirsabak-04 × Saleem-2000, Pirsabak-04 × Khyber-87 and

Pirsabak-05 × Shahkar-13, Pirsabak-85 × Saleem-2000 (F2) and Pirsabak-85 ×

Shahkar-13 (F3) owned alleles '1' and '2*' at Glu-A1, '17 + 18' at Glu-B1 and '5 + 10' at

Glu-D1 locus, which showed superior bread making qualities.

In glutenin analysis, the HMW-GS combinations (2*, 17 + 18, 5 + 10) revealed

high frequency (63.16%) of the total wheat genotypes, which indicated that majority of

the genotypes have good bread-making quality. However, HMW-GS combination null,

7 + 8, 2 + 12 (0.26%) followed by 1, 17 + 18, 5 + 10 (13.16%) showed lesser

frequencies than other banding patterns. Three alleles (Null, 1 and 2*) were identified

at Glu-A1 locus, three allelic pairs (7 + 8, 7 + 9 and 17 + 18) were detected at Glu-B1.

However, greater homogeneity for the Glu-D1 locus was recorded i.e. 97.37% of wheat

genotypes had the Glu-D1d allele (5 + 10), and allelic combination 2+12 (Glu-D1a)

related with bad quality was only found in Chinese spring (check). The allelic

combinations i.e., 2*, 17+18, and 5+10, showing that high quality scores were observed

among parental genotypes, F2 and F3 populations indicating their effectiveness in future

breeding programs.

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VI. CONCLUSIONS AND RECOMMENDATIONS

Cultivars Pirsabak-05 and Shahkar-13 being best general combiners, F1 hybrid

Pirsabak-85 × Pirsabak-04 and F2 population Pirsabak-05 × Shahkar-13 as

specific combiners, showed maximum rust resistance and grain yield.

In case of yellow rust resistance, the F1 hybrids i.e. Pirsabak-85 × Pirsabak-04,

Pirsabak-85 × Pirsabak-05, Pirsabak-04 × Pirsabak-05, Pirsabak-05 × Shahkar-13,

Shahkar-13 × Saleem-2000 and Shahkar-13 × Khyber-87 while F2 population

Pirsabak-05 × Shahkar-13 showed minimum average coefficeint of infection

(ACI) which might be due to inclusion of resistant cultivars (Pirsabak-05 and

Shahkar-13) in the crosses.

According to components of genetic variance and combining ability, majority of

the traits were controlled non-additively in F1 and F2 generations.

For glutenin analysis, eight alleles were identified at different loci in two sets of

wheat genotypes (parental cultivars, F2 and F3 populations and check genotypes).

Three alleles (Null, 1 and 2*) were identified at Glu-A1 locus, three allelic pairs

(7 + 8, 7 + 9 and 17 + 18) were detected at Glu-B1, while two allelic pairs (5 + 10

and 2 + 12) were identified at Glu-D1 locus.