development of genetic markers panel to predict …

DEVELOPMENT OF GENETIC MARKERS PANEL TO

PREDICT GROWTH POTENTIAL IN BEETAL GOAT

By

Hafiz Muhammad Waheed

M.Sc. (Hons.) Animal Breeding and Genetics

2000-ag-1526

A thesis submitted in the partial fulfillment of the requirement

for the degree of

DOCTOR OF PHILOSOPHY

IN

ANIMAL BREEDING AND GENETICS

INSTITUTE OF ANIMAL & DAIRY SCIENCES

FACULTY OF ANIMAL HUSBANDRY

UNIVERSITY OF AGRICULTURE

FAISALABAD, PAKISTAN

2020

vi

ACKNOWLEDGMENT

Up and above every this all glory of thee, “O ALLAHA”, the Beneficent, and the Merciful. Who knows

about whatever is there in the universe, hidden or evident. Praises and humble thanks to “ALMIGHTY

ALLAH”, Whose blessing and exaltation flourished my thoughts and ambitious to have the cherish

fruit of my modest efforts in the form of this write-up from the blooming spring of blossoming

knowledge. All blessings and respect are for the last Holy Prophet Muhammad (peace be upon him),

Who is forever a torch of guidance and the city of knowledge for humanity as a whole.

I acknowledge with deep reverence, sincerity and feel utmost pleasure in expressing my heartiest

gratitude to my supervisor Prof. Dr. Muhammad Sajjad Khan, Dean Faculty of Animal Husbandry,

University of Agriculture, Faisalabad for his dynamic and affectionate supervision, unstinting help,

chivalrous behavior and intellectual support to complete this study. I would delectably pay my sincere

gratitude to co-supervisor Dr. Muhammad Moaeen-ud-Din, Associate Professor, Chairman,

Department of Animal Breeding and Genetics, PMAS-Arid Agriculture University Rawalpindi, for his

bountiful support, his gallant personality with gleaming out knowledge and whose inspiring attitude

made it very easy to understand this project as well as steady encouragement. He directly and indirectly

prompted me to continuously seek for perfection. His able guidance and support at critical times saved

the day for me. I feel myself fortunate to have Dr. Muhammad Saif-ur-Rehman and Dr. Muhammad

Shah Nawaz-ul-Rehman on my supervisory committee. These teachers encouraged me constantly and

guided me in technical aspects of my studies.

The study was primarily supported by PAK-USAID funded research project “Collaborative Research

for Genetic Conservation and Improvement of Pakistani Goats” at PMAS-Arid Agriculture University

Rawalpindi, Pakistan and partially by EFS funded research project “Improvement of Beetal Goats and

Indigenous Chicken through Dissemination of Superior Sires” at University of Agriculture Faisalabad,

Pakistan that is highly acknowledged.

I express my gratitude to Dr. Ghulam Bilal for granting his personal attention, constructive criticism

and erudite guidance throughout my research work. I must feel great honor to state my cordial gratitude

to Dr. Faiz-ul-Hassan, Mr. Safdar Imran, Mr. Faisal Ramzan and Mr. M. Asim for their inspiring

guidance, keen interest, consistent encouragement, healthy criticism, unfailing kindness and gentle

assistance during my stay at the campus and throughout the course of my study.

I feel heartfelt gratitude to my esteemed and jovial fellows Mr. M. Ashraf, Mr. Orangzaib, Mr. Khalid

Qadeer, and Mr. Shehzad Ashraf. They spared no efforts to provide every possible research assistance,

ever lasting co-operation, valuable suggestions, sympathetic attitude and best wishes for me.

Hafiz Muhammad Waheed

viii

LIST OF TABLES

Table # Title Page #

2.1 Goat body weight prediction equation 7

2.2 Goat body weight growth potential 11

3.1 Means ± S.D of body weight (Kg) and body measurements (cm) 27

3.2 Effect of body measurements on body weight in Beetal goats 28

3.3 Parameter ± S.E. estimates of different body measurements to predict

body weight 29

3.4 Estimates of Pearson Correlation Coefficients of body weight, age

and different body measurements 31

4.1 Overall basic statistics of live body weight of Beetal goats up to 36

months of age 49

4.2 Results of Tests of Fixed and Random Effects obtained from Proc

GLM/MIXED of SAS 50

4.3 Effect of age on body weight in Beetal Goat obtained from GLM of

SAS 50

4.4 Effect of herd type on live body weight of Beetal goats up to 36

months of age 52

4.5 Effect of sex type on live body weight of Beetal goats up to 36

months of age 53

4.6 Effect of strain type on live body weight of Beetal goats up to 36

months of age 55

5.1 Descriptive statistics of body weight (kg) of Beetal goat breed 69

5.2 Average distances among adjacent SNPs on each chromosome and

distributions of SNPs before and after quality control measures 70

5.3

Genome-wise chromosome-wise significant (p≤0.05) SNPs

associated with age adjusted body weight (growth trait) of Beetal

goat

72

5.4

Blast results for the sequence between 5kb downstream and 5kb

upstream of the SNPs in goat compared with other species (human,

cattle, sheep and mouse) 73

ix

LIST OF FIGURES

Figure # Title Page #

4.1 Overall Monthly growth pattern of Beetal goats up to 36 months of

age 44

4.2 Effect of age on body weight based monthly body weight (1-35

months) of Beetal goats 45

4.3 Body weight of goats for Government vs. Private Herds up to 36

months of age 46

4.4 Body Weight of Goats “Male vs. Female” up to 36 months of age 47

4.5 Body weight of goats for various strains up to 36 months of age 48

5.1 Genome-wide plot of –log10 (p-value) for association of SNP loci

with growth production trait 75

5.2 Quantile-quantile (Q-Q) plot of genome-wide association result for

growth production trait 76

5.3 Principal-component analysis for population stratification in various

strains of Beetal goat breed 77

1

CHAPTER 1

INTRODUCTION

Livestock is major component of the livelihood for people of developing countries. New

interventions in this sector will provide scope for poverty alleviation. Goat in Pakistan is

among the most rapidly increasing livestock species. Current goat population is 74.1 million

(GOP, 2018); third highest in the world goat population Thus, a large proportion of human

population in the countryside has its livelihood associated with goat rearing.

Currently, there are 36 goat breeds in Pakistan; Beetal is the largest in frame size followed by

Kamori. Beetal is considered the most important for producing large sized animals, which are

preferred for slaughtering on Eid-ul-Azha (Khan et al., 2006; Khan et al., 2008). Beetal is the

second most common breed of Punjab after Teddy (GOP, 2006). Preference of Beetal breed to

produce tall, heavier sacrificial males for Eid-ul-Azha is ever increasing over the years.

Although, Beetal is the biggest sized breed, but, still its full growth potential is unexplored.

Growth is time dependent change in weight or size of an organ, composition of tissue/organ,

size and number of cells as well as in live weight of an organism. It is biological phenomenon

that can be interpreted mathematically (An et al., 2011; Eisen, 1976). Growth has important

implications for domestic animal production as it has significant influence on the value of

animal being produced (Waheed, 2011). Therefore, contemporary research emphasizes

primarily on how to make animal growth process quicker and more efficient.

Currently, techniques of molecular genetics have already resulted in the detection of numerous

genes and genetic markers that are linked to different quantitative trait loci having direct effects

on some economically important quantitative traits. Genetic improvement using molecular

genetic applications depend upon the capacity to genotype individuals for specific genetic loci.

Marker-assisted selection may efficiently be used to reach more accurate and efficient selection

by using rapid advancement of biotechnology and molecular biology (Gilles et al., 2006) that

had eventually lead to genomic selection. Accelerated rate of change in economically

important traits is possible using marker assisted selection with combination of traditional

selection methods (Moaeen-ud-Din et al., 2014).

Animal growth and development is a quantitative trait thus controlled by genes and hormones.

2

Growth is constant and vibrant processes and requires combination of various physiological

processes. There are numerous genes, which affect the growth and body mass of the animals,

such as growth hormone gene (GH), growth hormone receptor gene (GHR), insulin-like

growth factor-1 (IGF-1), Leptin, myostatin (MSTN) gene, Pituitary transcription factor-1 gene

(POU1F1) and Bone morphogenetic protein (BMP) gene (Wickramaratne et al., 2011; Alakilli

et al., 2012). Identification of variation in these genes and determination of their association

with growth performance can help establish true worth of animals. Moreover, selection of

animals using genomic and phenotypic records can make animals available having substantial

growth rate which in return will increase the efficiency of whole production system.

Breeders and geneticists have special emphasis on growth and meat production traits of goats

owing to enhanced popularity of meat production in goat industry. Identification of quantitative

trait loci (QTL) to find candidate genes and genome scanning technology were used in

livestock species during the past decades. Moreover, QTLs played a vital role in breeding

animal’s genetic evaluation (Zhang et al., 2013). Many QTL studies have been performed for

different quantitative traits in cattle, chicken and sheep (Machado et al., 2003; Carlborg et al.,

2004; Walling et al., 2004). However, a small number of QTLs have been identified in goat so

far. Nevertheless, it is really an uphill task to identify specific genes affecting relevant

quantitative traits, as confidence interval of QTL is relatively long. Therefore, identification of

some novel gene may remain unobserved. Genome wide association studies have been widely

used in different species to detect and identify candidate genes controlling quantitative traits

with the boom of high throughput SNP genotyping techniques. This in turn brought

revolutionary ideas to enhance the efficacy of animal breeding and selection (Jiang et al.,

2010).

Keeping in view the importance of growth, objectives of the present study were to:

1. Estimate the live body weight in Beetal goat through body measurement in field and

farm conditions

2. Assess potential of growth performance in different strains of Beetal goats through

performance records at field and farm levels

3. Identification of significant SNPs associated with age adjusted body weight of Beetal

goat using GWAS methodology to determine candidate genes for the trait

4

CHAPTER 2

REVIEW OF LITERATURE

Growth can surely be considered as one of the most important traits among all the traits having

economic importance in goat production, Growth of an animal is dynamic and continuous

progression. This is a time dependent variation in the live weight of an animal (Waheed, 2011)

and it affected by combination of many physiological and environmental features occurring

with animal including feeding process of animal, efficiency of metabolism, activity of various

hormones, response to resistance, physiological status, disease condition, parasite infestation

and maintenance of homeostasis.

2.1 Linear body measurements relationship with body weight

The prediction of live-body weight in goat production business is important for different

motives such as the handling and management of the goat herd during the whole raising

process, breeding of animals at proper body weight, maintaining feeding requirements, health

care (dose rate according to the body weight) and marketing of goats on live body weight basis

(Mule et al., 2014; Seifemichael et al., 2014). Different research studies are summarized in

Table 2.1. Estimation of body weight is a serious constituent yet can seldom be recorded in

rural areas because of absence of proper and accurate scales. Eventually, goat keepers need to

depend on inaccurate guesses of the body weights of their goat, causing mistakes to be

happened in decision making and loss in farming business (Mahmud et al., 2014; Younas et

al., 2013). Assessment of live weight by applying body measurements is valuable, quicker,

artless and less lavish in the field areas where goat breeders and ranchers are nor resourceful

(Tsegaye et al., 2013). It is recognized that body measurements and figures have strong

significance with the amount of body weight and linear body measurements prediction of

animals kept for meat purposes (Bello and Adama, 2012). Ultimately, when farmers and buyers

of farm animals are gifted to relay animal body measurement to growth characteristics, best

animal farming and value-based selling systems will be implicated (Tekle, 2014).

Various research attempts are existing concerning the use of linear body measurements of goat

from many areas of the ecosphere and their probable application for estimation of body weights

of farm animals (Mahieu et al., 2011; Moaeen-ud-Din et al., 2006; Khan et al., 2006).

5

Efficient approximation model for live weight of goats includes various linear body

measurements including chest circumference, hip bone stature and neck perimeter. Sex and

age of the animals along with linear body measurements has been proven to be a significant

effect on the accuracy of the prediction model. Body measurements are determined by tailor’s

tape and hanging balance is used to decide live weight of goat. Various numerical exploration

ensued that chest circumference and hip bone height were important body measurements to the

body weight prediction equation (Matsebula et al., 2013).

In male animals, measurement of body length and in female, body length and height at hips

both have optimistic consequence on live weight. Moela, (2014) studied the effect of chest

girth on live body weight in male and female animals and found by path coefficient of 0.62

and 0.58 in female and male indigenous goats of South Africa, respectively.

2.2 Goat growth performance potential

Growth of animal is incessant and dynamic process. The process of growth starts from animal’s

embryonic life and can be defined as the positive change in the mass and size of organ with

relation to time (An et al., 2011; Eisen, 1976). Growth is surely be reflected as one of the most

significant traits among all the traits bearing some profit earning status in goat farming. The

value of animal is significantly affected by its growth rate.

Growth performance of different local goat breeds was studied with limited number of animals

and mostly under research farm conditions (Table 2.2). It was reported that pre-weaning

(131.2±4.20 g) and post-weaning (69.5±1.83 g) growth rates in Dera Din Panah (DDP) goats

(N=350) under different period of fodder availability (Yaqoob et al., 2009). Waheed (2011)

estimated factors of growth curve of Beetal goats applying Brody and Gompertz models based

on limited accounts (N=120) from four government livestock farms of Punjab. In a study with

86 Beetal goats, it was reported that the mean body weights of male animals in four age groups

(04-12, 13-18, 19-24, 24-36 months and above) as 18.60 ± 1.81, 25.25 ± 2.76, 29.86 ± 1.28

and 41.47 ± 1.63kg, respectively. The mean body weight of female animals in the same age

groups were recorded as 14.50 ± 1.19, 21.0 ± 3.47, 24.00 ± 1.25, 33.95 ± 4.97kg, respectively

(Khan et al., 2006). Beetal goat (N=50) growth rate was determined under annual (2 crops)

and accelerated (3 crops) in 2 years and it was observed that there was little difference in

growth rates (Ahmad et al., 2014). Least squares mean for birth weight in Beetal kids was

6

reported as 3.38 ± 0.06 kg with an appreciable twining rate (47.9%) in Beetal goats (Afzal et

al., 2004).

Most of the previous studies conducted so far were undertaken under experimental farm

conditions with limited data, thus little information is available on the growth performance of

Beetal goat under field condition and its comparison with farm born/raised Beetal goat.

7

Table 2.1 Goat body weight Prediction equations

Country Breed No. of Animals Methodology

(CG, BL, WH

etc.)

Equation Accuracy

(R-

Square)

Reference

South

Africa

Kwazulu-

Natal

(indigenous

Nguni-type)

54 (10M+44F) Heart girth Buck

Y = -43.0277+ 0.992924.X

Doe

Y = -47.6799 + 1.07677X

(R2 =

88.1%)

(R2 =

94.3%)

(Slippers et

al., 2000)

Turkey Shami

(Damuascus)

goat

61 Does

62 kids

Heart girth, Hip

width, Withers

height, width of

tuber coxae

Kids BW=0.817 x HG + 1.973

x HW + HH-78.36

Does BW=0.138 x HG + 0.11 x

WH + 0.198 x WTC - 0.654

0.84

0.86

(Gul et al.,

2005)

Nigeria Red-Sokoto,

White-Borno

163 records Chest girth,

Withers Height,

Body length

Pearson correlation coefficients

only

(Adeyinka

and

Mohammed,

2006)

Pakistan Beetal 86(44M+42F) BL, WH, Heart

G


only

(Khan et al.,

2006)

Pakistan Beetal,

Teddy, their

crosses

100 each breed Heart G, BL,

WH

For Beetal BW=39.15 + 0.47

HG

BW=24.39 + 0.45 HG 0.42 BL

0.153

0.210

(Moaeen-ud-

Din et al.,

2006)

8

For cross-bred BW=35.51 –

0.054 WH +0.42 HG

0.124

Nigeria West African

Dwarf

244(84M+160F) HG, BL, WH,

Rump height,

pelvic Width

BW= -13.60 +0.53 x CG

BW= -9.29 + 0.53 x BL

BW= -7.6 + 0.57 x WH

BW= -7.6 + 0.47 x rump height

BW= -8.19 + 2.01 x pelvic

width

0.78

0.57

0.46

0.41

0.66

(Fajemilehin

and Salako,

2008)

Nigeria West African

Dwarf

100 animals Tail length,

distance

between eyes,

ear length and

width


only

(Otoikhian et

al., 2008)

Turkey Saanen 70 female WH, BL, Chest

Depth, Shank

circumference

(SC)

BW = -100.084 + 1.698 x HG

BW = -137.118 + 1.242 x HG +

0.718 x BL

BW = - 146.313 + 1.081 x HG

+ 0.679 x BL + 3.013 x SC

BW = - 151,295 + 1,067 x HG

+ 3,262 x BL + 0,167 x SC +

0,604 x WH + 0,034 x CD

0.89

0.94

0.95

0.95

(Pesmen and

Yardimci,

2008)

9

Guadeloupe

(Caribbean)

Creole of

Guadeloupe

goat (West

Africa)

634

(376M+258F)

Heart girth,

Paunch girth

BW=155*exp(-7.91*exp(-

0.0215*HG)

BW=-

28.1+0.539*HG+0.00221*PG2

0.98

0.95

(Mahieu et al.,

2011)

Pakistan Beetal goat 230 female BL, WH, CG,

Rump,

forehead

BW=-42.3 +1.54BL + 1.00WH 69.1 (Iqbal et al.,

2013)

Swaziland Indigenous

goats

300 Hip bone

height, Heart

G, Neck

circumference

Heart G

Hip bone height

0.92

0.90

(Matsebula et

al., 2013)

Ethiopia Hararghe

highland goat

320 kids

(156M+164F)

610 adults

(175M+435F)

Heart G, BL,

WH, Pelvic

width, Chest

width, rump

height, rump

length, ear

length, horn

length, male

SC and BCS

Heart Girth Male

Heart Girth female

Male BW=CG + BCS +RL

+WH +PW

Female BW= CG + BCS +RL

+WH +PW

0.82

0.73

0.95

0.90

(Tsegaye et

al., 2013)

10

South

Africa

Indigenous

goats

613 (62M+551F) Hip H,

Shoulder H,

BL, Heart G,

Male BW= Heart G +BL

Female BW=Heart G + BL

+Sh. H

--- (Moela, 2014)

Ethiopia Afar goats 800(134M+666F) BL, CG, Ear L,

Horn L, Pelvic

W, Rump H,

WH

BW=-27.945+0.0837HL

+0.1763BL +0.688CG

+0.046WH -0.059RH

0.0014CG2

0.81 (Seifemichael

et al., 2014)

Ethiopia Afar goats 318(105M+213F) CG, WH, BL,

Rump H,

Pelvic W, Ear

L, Neck

Circumference

Male BW=-15.6 +0.3CG

+0.2BL

Female BW=-31.4 +0.57BL

+0.3RH

BW=-21.1 +0.5BL +0.6PW

0.43

0.92

0.70

(Tekle, 2014)

Ethiopia Begait goat 615(78M+537F) Heart G, BL,

WH, Head L,

Ear L, Tail L

Heart G highest value --- (Hagos, 2016)

Philippines Indigenous

goats

300(105M+195F) Heart G, BL,

Rump H, WH

Male BW=-57.066 +0.55HG

+0.368BL +0.335RH

Female BW=-51.413 +0.65HG

+0.355BL +0.168RH

BW=-50.666 +0.587HG

+0.355BL +0.219RH

0.78

0.82

0.80

(Perez O.

Zandro et al.,

2016)

12

Table 2.2 Goat Body weight, growth potential

Country Breed No. of

Animals

Methodology Birth

Weight

Weaning

Weight

6 month

weight

Yearling

Weight

Reference

Kenya East African

Toggenburg

357M

329F

Community

based dairy

goat genetic

improvement

2.98±0.21

3.72±0.19

60 days

6.32±0.15

13.5±0.34

(Ahuya et al.,

2002)

Pakistan Beetal 1850 3 research

station farms

3.65±0.13

3.55±0.08

2.96±0.05

(Afzal et al., 2004)

Pakistan Local breed 86 04-12,13-

18,19-24, 24-

36 months and

above

M

18.60±1.81,

25.25±2.76,

29.86±1.28

and

41.47±1.63

F

14.50±1.19,

21.0±3.47,

24.00±1.25,

33.95±4.97

(Khan et al., 2006)

India Tellicherry 566 20 years

research

station data

2.17±0.03

(566)

3 months

8.71±0.15

(485)

12.05±0.19

(411)

9 month

15.09±0.26

(358)

18.78±0.44

(304)

(Thiruvenkadan et

al., 2009)

13

Pakistan Dera Din

Panah

350 Research

station records

3.9±0.07 19.1±0.51 23.1±0.61

9 months

26.1±0.67

31.4±0.71 (Yaqoob et al.,

2009)

Pakistan Beetal 120

(60M+60F)

4 Research

station records

Mean M 2.7

F 2.6

M 12.3

F 10.8

M 15.9

F 14.0

M 22.1

F 20.7

(Waheed, 2011)

India Rohilkhand Overall

662

Research

station records

2.08±0.32

(57)

1 month

5.11±0.32

2 month

7.25±0.32

3 month

7.55±0.46

(28)

10.28±0.26

(86)

15.01±0.34

(50)

(Fahim et al.,

2013)

India Osmanabadi 799

(270M+

529F)

Survey records 0-3 month

5.99±0.07

(91 M)

6.29±0.06

(179 F)

4-6 month

15.37±0.12

(47 M)

15.20±0.9

(82 F)

7-12

month

19.23±0.19

(40 M)

19.02±0.14

(72 F)

(Mule et al., 2014)

Pakistan Beetal (50) Does Accelerated

kidding &

Annual

kidding

3.08±0.11

&

3.07±0.13

3 month

10.92±0.30

&

12.05±0.32

20.29±0.46

&

21.75±0.23

9 month

27.15±0.29

27.80±0.20

(Ahmad et al.,

2014)

Number of observations in parentheses

14

2.3 Genes involved in growth

Growth is extremely measured mechanism and is multifaceted procedure of harmonization

systems. Various genes are studied and found their significant role in the entire process of

growth. Yet, there are assured genes that can be renowned based on the importance of the role

they play in growth of an individual. Research in the last twenty years in the discipline of

molecular genetics has ensued in the detection of many candidate genes with extensive effects

on growth. Different physiological processes are exaggerated by the alterations in these genes

and eventually the growth process also affected severely. Furthermore, determining of genetic

variations in these genes can work as molecular markers and identification of such markers

that have strong association with growth can upsurge the correctness of traditional selection.

Alakilli et al. (2012) reported the most important genes that can affect growth of an animal i.e.

growth hormone (GH), bone morphogenetic protein (BMP) gene, caprine myostatin (MSTN)

gene, pituitary specific transcription factor-1 (POU1F1) gene and Insulin-like growth factor-

1.

Currently, skills of molecular genetics have previously occasioned in the uncovering of

plentiful genes that have dominant effects on some economically vital quantifiable traits and

of molecular genetic markers that are linked to different quantitative trait loci (QTL). Marker-

assisted selection may competently be applied to reach more precise and effective selection by

using quick development of biotechnology and molecular biology (Gilles et al., 2006) that had

ultimately guide to genomic selection. Enhanced rate of change in economically important

traits is only probable using marker assisted selection with grouping methods of classical

selection (Moaeen-ud-Din et al., 2014).

Detection of deviation in growth related genes and detection of their association with growth

phenotypic measures can help to build true worth of animals. The use of phenotypic records

along with genomic information for the selection of animals can confidently make available

diverse animals having extensive growth rate that in return will enhance the effectiveness of

entire animal production system.

15

2.4 Genome-Wide association studies in goat

Dynamic development is currently undergoing in animal genomics, which is driven by the

boom of high throughput genome analysis methods. In recent times, a large number of animals

have been genotyped using whole genome genotyping assays in the course of genomic

selection programs. Various aspects of livestock genome functioning and diversity can be

properly studied by the use of such genotyping methods including studies on linkage

disequilibrium, selection signatures, runs of homozygosity, genetic differentiation of livestock

populations and copy number variation. These are the achievements of animal genomics,

which can be used to stimulate interest in basic research for better understanding the

complexity and structure of the livestock genomes (Gurgul et al., 2014).

With the development of the 50K ovine SNP chip genomic studies were made possible for

the first time in small ruminants in 2009. Genomic evaluation has now been implemented

in dairy sheep in France, in goats in France and the UK and in sheep in New Zealand and

Australia (Rupp et al., 2016). The status of goat genome has improved since the release of the

reference genome that was made available by the International Goat Genome Consortium

(IGGC) in 2012 (Dong et al., 2013). The GoatSNP50 chip utilizes more than 52,000 SNP

variants to provide uniform genomic coverage and was developed by the IGGC. The

GoatSNP50 chip had power to screen whole genome sequencing data in the following goat

breeds: Boer, Alpine, Creole, Saanen, Katjang and Savanna. For confirmation purpose, 10 goat

breeds were used using 52k SNP content (Tosser-Klopp et al., 2014).

In different breeds of a species, number of polymorphic loci can differ greatly. Exclusion of

fiber-producing breeds, such as the Angora goat, during the development of this genotyping

array necessitates the validation of SNPs included on the chip to allow for genomic

applications that would accelerate genetic progress in mohair production and fiber quality. A

total of 48 unrelated Angora goats were genotyped with the goat SNP50 bead chip. Two traits

of economic important (fiber diameter and fleece weight) were studied showing phenotypic

variation in studied goat. Goat SNP50 bead chip was proved to be informative in the Angora

goats for fiber traits of interest (Lashmar et al., 2015).

2.4.1 Population stratification assessment with GWAS

Population stratification (or population structure) is the presence of a systematic difference in

16

allele frequencies between subpopulations in a population, possibly due to different ancestry,

especially in the context of association studies. So, the confounding due to population

stratification is major problem in GWAS (Pearson and Manolio, 2008). Principal component

analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set

of observations of possibly correlated variables into a set of values of linearly uncorrelated

variables called principal components. In GWAS to adjust stratification in animal populations,

another frequently applied method is the use of Quantile-quantile (Q-Q) plot, which in turn is

quite useful in assessing variations from the SNPs expected distribution related to the trait of

interest (Zhang et al., 2013).

17

REFERENCES

Adeyinka, A., and I. D. Mohammed. 2006. Relationship of liveweight and linear body

measurement in two breeds of goat of Northern Nigeria. J. Anim. Vet. Adv. 5(11): 891-

893.

Afzal, M., K. Javed, and M. Shafiq. 2004. Environmental effects on birth weight in Beetal goat

kids. Pak. Vet. J. 24:104-106.

Ahmad, N., K. Javed, M. Abdullah, A. S. Hashmi, A. Ali, Z. Iqbal, U. Younas, and Z. M. Iqbal.

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22

CHAPTER 3

PREDICTION OF MONTHLY BODY WEIGHT IN BEETAL

GOAT USING BODY MEASUREMENTS UNDER FIELD AND

FARM CONDITIONS

ABSTRACT

The objective of the present study was to estimate the live body weight in Beetal goat at

different ages of the animal based on various linear body measurements. Live body weight and

linear body measurements data of Beetal goat breed were recorded from six private herds as

well as from two government farms of Punjab, Pakistan. Final data file comprised 5011

observations of live body weight (LBW) and different body measurements i.e. whither height

(WH), body length (BL), chest girth (CG), chest width (CW) and pin bone width (PW). The

phenotypic traits were measured by hanging balance (for LBW) and tailor’s tape (for all others)

from January 2016 to May 2017. Thirty-six monthly age classes were defined besides separate

class for newborn kids. Data were analyzed using PROC MEANS, PROC REG, PROC GLM

and PROC CORR of SAS. The overall birth and 36 month means of LBW, WH, BL, CG, CW

and PW were 3.34 & 80.21 (kg), 33.61 & 92.91, 25.11 & 84.86, 30.96 & 93.30, 8.88 & 26.21

and 4.69 & 14.00 (cm), respectively. All body measurements showed highly significant effect

on LBW of animals at 3 and 21 months of age. The CG was highly significantly related to

LBW in all monthly age classes with the exception of 0, 1, 2 and 7 months. The PW had non-

significant effect on LWB for all monthly age classes with the exception of 3, 4, 9, 10, 11, 21,

32 and 35 months. The WH had highly significant effect on LBW of animals in monthly age

classes of 3, 7, 10, 11, 12, 14, 15, 16, 17, 20, 21 and 35. The BL in general showed significant

effect on LBW up to 26 months of age. Correlation was also determined among all traits. In

agreement to regression results, CG had strong positive phenotypic correlation (0.921) with

LBW followed by BL (0.875), WH (0.864), CW (0.777) and PW (0.725). In conclusion, CG,

BL, WH, CW and PW may be used for estimation of live body weight in Beetal goat in the

order of priority. These body measurement traits may be combined in an index with appropriate

weights to more accurately predict live body weight under field conditions. Furthermore, a

measuring tape may be devised preferably based on CG to approximately determine LBW of

goat. The body measurement traits may also be used for indirect genetic selection for improved

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growth rate in Beetal goat while considering precisely estimated genetic parameters for all

these traits.

Key Words: Beetal goat, live body weight, body measurements, Pakistan.

3.1 INTRODUCTION

In Pakistan animal population, goat is one of the most quickly increasing livestock species.

Recent population of goat is 74.1 million (GOP, 2018). From international perspective,

Pakistan is the 3rd largest goat producing country in term of population. A big share of human

population in rural areas earn income to support their families by goat farming business.

Pakistan is blessed with a wide variety of goat breeds as there are 36 reported breeds of goat.

Among these 36 goat breeds, Beetal is the largest in size followed by Kamori (Isani and Baloch,

1996). Beetal goat breed is taken as the most important for producing large size animals, which

are favored on the event of Eid-ul-Azha (Khan et al., 2008; Khan et al., 2006). Teddy goat

breed is common in Punjab followed by Beetal. (GOP., 2006). Furthermore, liking of Beetal

male to produce tall, heavier sacrificial animals is increasing every year. Even though, large

size animals are frequently available, still growth potential at farm and field level

simultaneously is not studied in Beetal.

Growth is a change in the weight or size of organ, composition of tissue/organ, size and number

of cells as well as in live weight of an organism with relation to time. Growth is living

phenomenon that can be understood statistically (Eisen, 1976; Waheed, 2011). Growth has

important implications for domestic animal production as it significantly effects the worth of

the animal being produced (An et al., 2011). Among all the traits having some economic

importance in goat production, growth can surely be considered as one of the most important

traits. Animal growth is continuous and dynamic process.

The awareness of body weight assessment in goats is important for many motives, related to

the husbandry and management of the herd during the whole production process, breeding

(selection), determination of feeding requirements, medicine dose rate estimation and

marketing of goats (Slippers et al., 2000; Pesmen and Yardimci, 2008). Animal live body

weight is a critical element yet can rarely be measured in rural areas because of lack of

judicious accurate scales. Consequently, farmers need to depend on inaccurate estimates of the

body weights of their goat, leading to mistakes in decisions making (Khan et al., 2006).

24

Estimating the live weight utilizing body measurements is useful, faster, simpler and less

expensive in the rural areas where the assets are lacking for the breeders (Tsegaye et al., 2013).

It has been recognized that body weight and linear body measurements of meat animal has

been relevant in estimating body size and shape (Moela, 2014). Eventually, when farmers and

buyers of livestock are able to relate animal body measurement to growth characteristics,

optimum production and value-based trading systems will be realized.

Studies regarding the linear body measurements of goat have been carried in other region of

the world and their possible use for estimating the live-weight of different breeds of goats

(Khan et al., 2006; Pesmen and Yardimci, 2008; Chitra et al., 2012; Tsegaye et al., 2013; Iqbal

et al., 2013). In Pakistan, different studies regarding estimation of live-weight through

regression parameters of few linear body measurements with Beetal and other goat breeds have

been done with less number of records (Waheed, 2011; Iqbal et al., 2013). The estimation of

live-weight in field and farm condition with more number of linear body measurements in

Beetal goat yet not been done. So this study was planned to estimate the live-weight in Beetal

goat at any age of the animal, either in field or farm conditions using regression coefficients

parameters of different linear body measurements.

3.2 MATERIALS AND METHODOLOGY

Beetal goat growth performance data was recorded from field and farm area of Punjab

Province. Different private herds of Beetal goat were visited and observations were recorded.

While, two government farms i.e. Livestock Experiment Station (LES), Allahdad, tehsil

Jahanian, district Khanewal, and Directorate of Farms, University of Agriculture, Faisalabad

(UAF) were also visited and observations were taken. Ear tags and neckbands were used for

identification of animals. The age of government farm animals is determined from birth

register while the herd owners of field animals provided the age record of their animals.

Live-weight and different body measurements were obtained between January 2016 and May

2017. A digital weighing scale (with a capacity of up to 500 kg) was used at UAF to determine

actual live weight, while at LES manual-weighing scale with same capacity was used. In field

conditions (registered farms in Punjab) hanging balance with a capacity of 150 kg was used to

determine actual live-weight. All types of weighing scales were checked and calibrated before

each body-weight record. After determining the live weight, the animal was made to stand

25

upright on a flat ground area. All body measurements were taken with tailor measuring tape

included wither height “WH”, body length “BL”, chest girth “CG”, chest width “CW” and pin

bone width “PW”. The average time to take all these measurements was 15 seconds. All

measurements were taken early in the morning before animals were fed and taken by the same

person to avoid individual variation.

To estimate the live-weight of Beetal goats at any age, the observations were divided in classes

on the basis of age. The ages of animals were divided into thirty-seven classes with birth age

class declared as 0, monthly age class 1 (1-30 days) and so on up to monthly age class 36

(1051-1080d). The animals above three year of age were also grouped in last class.

Microsoft Excel was used for entering and editing the data. General linear model and random

regression model of SAS (SAS., 2017) were used to estimate the significance of different body

measurements on live-weight of animals along with R-Square values for each age class and to

estimate parameters of different body measurement for each age group along with R-Square

values to predict live-weight of animals, respectively. Body weight was considered as response

variable and WH, BL, CG, CW and PW were taken as predictors. The regression model for

each age class was:

Y = β0 + β1X1 + β2X2 + β3 X3 + β4 X4 + β5 X5

Where,

Y = body weight

β0 = Y intercept

β1 ... β5 = regression coefficients

X1 … X5 = predictor variables (WH, BL, CG, CW and PW)

3.3 RESULTS AND DISCUSSION

Overall estimates of means of LWB, WH, BL, CG, CW and PW are provided in Table 3.1 for

each monthly age class. Live body weight and all body measurements displayed an increasing

trend from birth to approximately 36 months of age. At birth, goats had average (± SD) LWB,

WH, BL, CG, CW and PW as 3.34±0.48 kg, 33.61±3.47 cm, 25.11±2.16 cm, 30.96±2.11 cm,

8.88±0.98 cm and 4.69±0.97 cm, respectively. At weaning (4 months of age), goats had

26

average (± SD) LWB, WH, BL, CG, CW and PW as 18.84±3.71 kg, 64.54±4.24 cm,

54.53±3.96 cm, 59.85±3.75 cm, 17.06±2.37 cm and 9.39±1.74 cm, respectively. Means values

(± SD) of LWB, WH, BL, CG, CW and PW at yearling were 33.47±4.83 kg, 76.75±4.43 cm,

66.15±3.66 cm, 73.24±4.07 cm, 20.59±2.78 cm and 11.91±1.67 cm, respectively. The means

values of all traits under study are comparable to previously published data. However, present

study has, for the first time, reported on the monthly means of all traits up to 36 months of age.

Subsequently, the LBW was assumed as dependent/response variable with all body

measurements traits as independent/explanatory variables and were analyzed in PROC GLM

of SAS. The results of this analysis are reported in Table 3.2. With the exception of the first

two and the last age classes, CG had highly significant impact on LWB at all ages proving the

superiority of CG over all other body measurements in explaining the variation in LBW of

goats. The PW has largely non-significant effect (P ≤ 0.05) on LBW in most age classes up to

36 month of age suggesting that PW had little role in explaining variation in LBW of Beetal

goats. Other body measurement traits WH, BL and CW showed intermediate impact on LBW

i.e. between the effects of CG and PW. Based on the results of PROC GLM of SAS, regression

coefficients for all the body measurements are provided in Table 3.3 for age classes 0 to 36.

These equations can be implied to predict live body weight of Beetal goats up to 36 months of

age with modest accuracy.

The values of R-squared are also reported for the regression line in each age class in Table 3.3.

R-squared is a statistical tool, which describes how close the data points are to the fitted

regression line. It is more commonly called as coefficient of determination and may be defined

as the coefficient of multiple determination for multiple regression as was the case in the

present study. The range of R-Squared values for regression model is from 0.19 to 0.72 in

various age groups The value of R2 was lowest (0.19) for birth/0d class probably due to limited

number of observations in this class. Overall value of R2 increased with increasing age and

highest value of R2 was obtained for animals aged as 17 months. With most R2 values above

or around 0.50 (50%) across 36 age classes, the proposed model seems to have adequately

explained the variation in LBW of goats.

The relationship of body measurements and LBW was further studied using PROC CORR of

SAS. Different body measurements showed significant and high positive correlation with

27

LBW as well as with age (Table 3.4). The CG had strong positive/ highest correlation (0.93)

with LBW. The PW also showed strong positive correlation with LBW (0.74) but magnitude

was lowest of all traits pairs under study. These results are in accordance with the results of

other previous studies (Adeyinka and Mohammed, 2006; Otoikhian et al., 2008; Moela, 2014).

Therefore, CG has been noticed the most significant and important measure to estimate the

live-weight of Beetal at any age of the animal. PW is found to be non-significant and with

negative regression coefficients parameters values in many of the age groups.

In a study with 230 female Beetal goats, comparable R2 values (69.1%) were reported for a

model having BL and WH body measurements as explanatory variables (Iqbal et al., in 2013).

In agreement to the results of the present study, CG had been reported as a significant predictor

of LBW in goats (Seifemichael et al., 2014; Pesmen and Yardimci, 2008; Iqbal et al., 2013).

On the lines of present study, (Fahim et al., 2013) developed age classes and also studied the

effects of different body measurements in Shami (Damuascus) goats and developed different

equations for kids and does to predict live-weight, separately.

3.4 CONCLUSIONS

The present study offers the first information on the estimation of live-weight in Beetal goats

from birth to thirty-six months of age under field and farm conditions. All body measurements

have significant correlation coefficients while chest girth has the maximum value. Chest girth

body measurement has also significant and consistent positive regression coefficient value to

predict live-weight in Beetal goats in all age classes. A measuring tape with chest girth values

may be designed to estimate live-weight in Beetal goat in field condition, where body weight

measuring scales are not available.

28

Table 3.1 Means± S.D. of live body weight (kg) and body measurements (cm)

Age groups

(months)

N Live body

weight

Whither

height

Body

length

Chest

girth

Chest

width

Pin bone

width

0 (Birth) 26 3.34±0.48 33.61±3.47 25.11±2.16 30.96±2.11 8.88±0.98 4.69±0.97

1 108 8.31±2.33 48.82±6.99 40.41±5.25 44.97±5.65 12.14±2.78 6.67±1.93

2 153 13.00±2.50 55.95±5.37 46.80±5.46 52.14±6.19 14.21±2.48 7.67±2.00

3 145 17.57±3.82 61.92±5.09 52.08±4.08 57.40±3.98 16.04±2.39 8.59±1.70

4 150 18.84±3.71 64.54±4.24 54.53±3.96 59.85±3.75 17.06±2.37 9.39±1.74

5 109 21.32±3.61 66.62±4.73 56.77±3.68 62.56±4.50 17.78±2.44 9.91±1.57

6 146 21.90±3.96 68.32±4.74 58.13±4.13 63.35±4.26 18.14±2.14 10.00±1.52

7 145 23.59±3.91 69.70±4.21 59.40±3.73 65.41±4.16 18.78±2.20 10.40±1.48

8 171 25.26±3.31 71.26±3.60 60.94±3.29 67.50±3.71 19.01±2.52 10.65±1.55

9 177 27.75±4.54 72.50±4.28 62.59±3.63 69.11±4.22 19.99±2.01 11.14±1.35

10 212 29.85±4.76 74.21±4.06 63.94±3.59 70.52±3.97 19.98±2.29 11.21±1.38

11 261 30.87±4.40 74.67±3.74 64.64±3.31 71.44±3.67 20.46±2.15 11.73±1.44

12 297 33.47±4.83 76.75±4.43 66.15±3.66 73.24±4.07 20.59±2.78 11.91±1.67

13 199 36.13±4.55 77.75±3.71 67.60±3.22 75.26±4.02 21.50±2.12 12.54±1.45

14 223 37.81±5.15 79.29±3.77 68.56±3.30 76.26±3.86 21.72±2.61 12.64±1.84

15 172 40.28±5.43 80.47±4.14 69.58±3.52 78.36±3.81 22.33±2.60 12.78±1.68

16 175 40.56±5.64 80.48±4.24 69.62±3.80 78.75±3.95 22.40±2.21 13.22±1.45

17 152 42.27±5.90 81.26±3.88 70.62±3.46 79.51±4.33 22.52±2.26 13.34±1.72

18 149 42.79±6.27 82.34±3.82 71.57±3.79 79.93±4.11 22.33±2.77 13.23±1.96

19 121 43.32±5.69 82.88±4.01 71.45±3.97 80.68±4.05 23.01±2.35 13.74±1.77

20 157 42.64±6.11 82.91±4.06 71.40±3.64 80.28±3.89 23.02±2.75 13.61±1.86

21 107 42.66±6.16 82.35±4.18 71.85±3.52 80.59±3.80 22.89±2.44 13.50±1.71

22 108 44.99±7.26 82.78±6.02 72.51±3.30 81.27±4.21 22.82±2.61 13.26±1.86

23 132 46.20±6.20 83.17±3.32 72.64±3.04 81.91±3.68 22.91±2.60 13.64±1.74

24 136 47.06±7.36 84.13±3.79 73.85±4.13 82.50±4.71 22.72±3.26 12.98±2.40

25 140 48.96±7.76 85.12±4.91 73.92±4.25 83.71±5.25 24.11±2.82 14.40±1.95

26 109 49.63±7.21 85.05±4.55 74.54±3.74 83.80±4.63 23.58±3.24 14.08±2.19

27 83 49.60±6.08 84.31±4.94 74.59±3.95 84.27±5.14 23.57±3.46 13.75±2.50

28 83 53.13±6.92 85.45±4.07 75.26±3.87 85.54±4.19 24.36±3.11 14.30±2.21

29 68 56.98±7.11 86.25±4.65 75.79±3.73 86.69±4.43 23.27±3.09 13.96±2.55

30 94 56.14±9.14 86.37±5.76 76.85±4.70 86.30±5.38 23.47±3.33 13.27±2.88

31 56 58.48±8.31 87.26±5.80 76.60±5.28 88.35±5.45 24.26±2.89 14.97±2.83

32 65 61.32±9.08 87.89±5.27 77.87±4.82 89.10±4.91 25.13±2.64 15.04±2.70

29

Age groups

(months)

N Live body

weight

Whither

height

Body

length

Chest

girth

Chest

width

Pin bone

width

33 66 63.91±10.61 87.47±4.96 77.66±4.64 90.36±5.15 25.31±2.95 14.93±2.56

34 66 65.86±11.99 88.00±5.61 78.10±5.48 90.89±6.14 26.79±3.40 15.87±2.28

35 227 65.83±10.49 89.03±5.35 79.22±5.66 90.97±5.60 26.72±3.64 15.65±2.40

36 23 80.21±16.79 92.91±8.45 84.86±6.69 93.30±8.24 26.21±5.25 14.00±3.71

Total 5011

Table 3.2 Effect of body measurements on body weight in Beetal goats

Age

groups

(months)

N Probability values for tests of significance (F-test) from PROC GLM of SAS

Whither

height

Body

length

Chest

girth

Chest

width

Pin bone

width

R2 value

0 (Birth) 26 0.4610NS1 0.2524NS 0.8069NS 0.4865NS 0.7851NS 0.187

1 108 0.1511NS <.0001*2 0.0640NS 0.9411NS 0.2328NS 0.477

2 153 0.0589NS 0.1403NS 0.1194NS 0.1935NS 0.2664NS 0.315

3 145 0.0008* 0.0463* 0.0015* 0.0047* <.0001* 0.574

4 150 0.7817NS <.0001* 0.0013* 0.0300* 0.0148* 0.552

5 109 0.0674NS 0.0028* 0.0105* 0.1641NS 0.9233NS 0.579

6 146 0.1730NS <.0001* 0.0054* 0.0025* 0.2686NS 0.633

7 145 0.0390* 0.0174* 0.0737NS 0.0027* 0.9801NS 0.480

8 171 0.5396NS 0.0364* <. 0001* 0.1150NS 0.8087NS 0.328

9 177 0.1436NS 0.1294NS <. 0001* 0.7264NS 0.0041* 0.541

10 212 <.0001* 0.0661NS <.0001* 0.1145NS 0.0020* 0.601

11 261 <.0001* 0.1173NS <.0001* 0.1770NS <.0001* 0.584

12 297 <.0001* <.0001* <.0001* 0.0032* 0.8888NS 0.547

13 199 0.6812NS 0.0036* <.0001* 0.0454* 0.4876NS 0.592

14 223 0.0053* <.0001* <.0001* 0.0021* 0.1353NS 0.519

15 172 <.0001* <.0001* 0.0008* <.0001* 0.5453NS 0.631

16 175 <.0001* 0.1005NS <.0001* 0.0031* 0.6274NS 0.621

17 152 <.0001* 0.0186* <.0001* 0.0427* 0.2657NS 0.708

18 149 0.0669NS <.0001* <.0001* 0.0151* 0.1881NS 0.620

19 121 0.1421NS 0.0205* <.0001* 0.1340NS 0.9694NS 0.640

20 157 0.0022* 0.0664NS <.0001* 0.0052* 0.5643NS 0.646

21 107 0.0294* 0.0010* <.0001* 0.0051* 0.0280* 0.613

22 108 0.0214* 0.2098NS <.0001* 0.4698NS 0.5316NS 0.551

23 132 0.6669NS 0.1329NS <.0001* 0.9879NS 0.7008NS 0.313

30

1NS = Non-significant 2* = Probability values (P ≤ 0.05) show significance

Table 3.3 Parameter ±S.E. estimates of different body measurements to predict body

weight

AAge

groups

Intercept Whither

height

Body

length

Chest girth Chest

width

Pin bone

width

R2

Value

0 -0.23±2.08NS1 0.03±0.04NS 0.06±0.05NS 0.01±0.05NS 0.09±0.13NS -

0.04±0.13NS

0.187

1 -5.46±1.47*2 0.06±0.04NS 0.19±0.05* 0.09±0.05NS -

0.01±0.10NS

-

0.17±0.14NS

0.477

2 -1.11±1.81NS 0.09±0.05NS 0.08±0.06NS 0.08±0.05NS 0.14±0.11NS -

0.14±0.12NS

0.315

3 -19.84±3.23* 0.25±0.07* 0.18±0.09* 0.24±0.07* 0.34±0.12* -

0.77±0.16*

0.574

4 -22.72±3.61* 0.02±0.09NS 0.38±0.09* 0.31±0.09* 0.28±0.13* -

0.41±0.17*

0.551

5 -24.03±3.91* 0.17±0.09NS 0.30±0.10* 0.22±0.09* 0.18±0.13NS 0.02±0.20NS 0.579

6 -28.85±3.42* 0.12±0.09NS 0.36±0.09* 0.24±0.09* 0.46±0.15* -

0.21±0.19NS

0.633

Age

groups

(months)

N Probability values for tests of significance (F-test) from PROC GLM of SAS

Whither

height

Body

length

Chest

girth

Chest

width

Pin bone

width

R2 Value

24 136 0.9606NS 0.0085* <.0001* 0.0981NS 0.7442NS 0.556

25 140 0.4415NS 0.0278* <.0001* 0.5618NS 0.7022NS 0.487

26 109 0.4706NS 0.0032* <.0001* 0.7129NS 0.4006NS 0.612

27 83 0.4603NS 0.8040NS 0.0005* 0.1140NS 0.1137NS 0.409

28 83 0.2560NS 0.8501NS <.0001* 0.9549NS 0.2528NS 0.523

29 68 0.8885NS 0.3650NS <.0001* 0.8269NS 0.2659NS 0.452

30 94 0.4704NS 0.6975NS 0.0001* 0.0349* 0.0578NS 0.492

31 56 0.3304NS 0.9057NS 0.0005* 0.7231NS 0.7131NS 0.512

32 65 0.4720NS 0.2847NS 0.0031* 0.0305* 0.0062* 0.497

33 66 0.1351NS 0.7964NS <.0001* 0.0951NS 0.1489NS 0.489

34 66 0.0622NS 0.7433NS <.0001* 0.8635NS 0.7191NS 0.597

35 227 0.0178* 0.9516NS <.0001* <.0001* 0.0042* 0.610

36 23 0.8582NS 0.3892NS 0.0575NS 0.3236NS 0.9099NS 0.715

31

AAge

groups

Intercept Whither

height

Body

length

Chest girth Chest

width

Pin bone

width

R2

Value

7 -26.19±4.46* 0.21±0.10* 0.24±0.10* 0.18±0.10NS 0.47±0.15* -

0.01±0.21NS

0.480

8 -13.76±4.73* 0.05±0.08NS 0.18±0.09* 0.31±0.08* 0.17±0.11NS 0.04±0.16NS 0.328

9 -33.40±4.77* 0.13±0.09NS 0.13±0.09NS 0.52±0.09* 0.05±0.14NS 0.60±0.21* 0.541

10 -48.05±4.59* 0.41±0.08* 0.14±0.08NS 0.40±0.09* 0.19±0.12NS 0.60±0.19* 0.601

11 -44.47±4.27* 0.37±0.07* 0.11±0.07NS 0.41±0.07* 0.15±0.11NS 0.73±0.16* 0.585

12 -46.51±4.33* 0.36±0.06* 0.37±0.06* 0.31±0.07* 0.27±0.09* -

0.02±0.15NS

0.547

13 -37.66±5.80* -0.03±0.07NS 0.23±0.08* 0.71±0.07* 0.25±0.13* 0.12±0.17NS 0.592

14 -49.92±6.19* 0.23±0.08* 0.35±0.09* 0.51±0.08* 0.47±0.15* -

0.30±0.20NS

0.520

15 -63.99±6.69* 0.35±0.08* 0.48±0.08* 0.33±0.10* 0.66±0.17* 0.15±0.25NS 0.631

16 -59.14±6.28* 0.49±0.09* 0.14±0.08NS 0.51±0.11* 0.54±0.18* -

0.13±0.27NS

0.621

17 -64.84±6.23* 0.42±0.10* 0.26±0.11* 0.55±0.09* 0.35±0.17* 0.25±0.23NS 0.708

18 -70.82±7.87* 0.24±0.13NS 0.41±0.10* 0.73±0.12* 0.50±0.20* -

0.37±0.28NS

0.620

19 -53.48±7.17* 0.18±0.12NS 0.29±0.12* 0.67±0.12* 0.30±0.20NS 0.01±0.25NS 0.640

20 -62.50±7.21* 0.33±0.11* 0.22±0.12NS 0.65±0.12* 0.56±0.20* -

0.17±0.30NS

0.646

21 -70.72±9.45* 0.25±0.11* 0.46±0.13* 0.67±0.14* 0.74±0.26* -

0.79±0.35*

0.613

22 -

67.23±12.06*

0.24±0.10* 0.20±0.16NS 0.93±0.16* 0.23±0.32NS -

0.26±0.42NS

0.551

23 -

34.69±13.62*

-0.07±0.16NS 0.31±0.20NS 0.81±0.16* -

0.01±0.27NS

-

0.16±0.40NS

0.313

24 -

54.85±10.50*

-0.01±0.14NS 0.38±0.14* 0.82±0.14* 0.35±0.21NS -

0.10±0.29NS

0.556

25 -45.27±9.52* -0.15±0.19NS 0.47±0.21* 0.88±0.14* -

0.17±0.29NS

0.14±0.35NS 0.487

26 -

68.08±10.39*

0.10±0.14NS 0.46±0.15* 0.87±0.14* -

0.09±0.24NS

0.27±0.32NS 0.612

27 -

10.92±12.32NS

0.11±0.143NS -0.04±0.18NS 0.61±0.17* 0.51±0.32NS -

0.60±0.37NS

0.409

32

AAge

groups

Intercept Whither

height

Body

length

Chest girth Chest

width

Pin bone

width

R2

Value

28 -

55.96±13.68*

0.19±0.17NS 0.03±0.18NS 0.98±0.17* 0.02±0.28NS 0.42±0.37NS 0.523

29 -

43.46±16.56*

-0.03±0.19NS 0.19±0.21NS 0.93±0.20* 0.07±0.32NS 0.40±0.36NS 0.452

30 -

43.80±13.46*

0.13±0.18NS 0.07±0.18NS 0.85±0.21* 0.84±0.39* -

0.75±0.39NS

0.492

31 -

42.40±15.94*

0.21±0.21NS 0.02±0.18NS 0.84±0.22* 0.18±0.50NS 0.19±0.50NS 0.512

32 -

51.91±17.87*

0.18±0.24NS 0.23±0.21NS 0.82±0.27* 0.97±0.43* -

1.16±0.41*

0.497

33 -

76.88±21.06*

0.46±0.31NS -0.07±0.27NS 1.28±0.26* -

0.83±0.49NS

0.78±0.53NS 0.489

34 -

81.54±17.24*

0.52±0.27NS -0.10±0.30NS 1.22±0.28* 0.09±0.53NS -

0.23±0.62NS

0.597

35 -57.32±8.54* 0.27±0.11* 0.01±0.10NS 0.88±0.13* 1.14±0.20* -

0.78±0.27*

0.609

36 -

40.76±30.33NS

-0.10±0.52NS -0.43±0.48NS 1.49±0.73NS 1.09±1.08NS -

0.15±1.35NS

0.715

1NS = Non-significant 2* = Probability values (P ≤ 0.05) show significance

33

Table 3.4. Estimates of Pearson Correlation Coefficients of body weight, age and

different body measurements

Age LBW WH BL CG CW PW

Age 1 0.907*1 0.802* 0.829* 0.863* 0.705* 0.687*

LBW 1 0.864* 0.875* 0.921* 0.777* 0.725*

WH 1 0.924* 0.938* 0.754* 0.709*

BL 1 0.935* 0.771* 0.728*

CG 1 0.811* 0.766*

CW 1 0.870*

PW 1

1* = Probability values (P ≤ 0.05) show significance

LBW=Live body weight, WH= Wither height, BL= Body length, CG=Chest girth

CW=Chest width and PW=Pin bone width

34

REFERENCES

Adeyinka, I.A. and I.D. Mohammed. 2006. Relationship of Liveweight and Linear Body

Measurement in Two Breeds of Goat of Northern Nigeria. J. Anim. Vet. Adv. 5(11):

891-893.



gene and association analysis with growth traits. Czech J. Anim. Sci. 56:529-535.

Chitra, R., S. Rajendran, D. Prasanna, and A. Kirubakaran (2012). Prediction of body weight

using appropriate regression model in adult female Malabari goat, Vet World, 5(7):409-

411.


1023.


body measurements in Rohilkhand local goats. Indian J. Anim. Res. 47 (6): 521-526.



GOP (Government of Pakistan). 2006. Pakistan Livestock Census. Agricultural Cencus


Iqbal, M., K. Javed, and N. Ahmad. 2013. Prediction of body weight through body

measurements in Beetal goats. Pak. J. Sci., 65(4):458-461.

Isani, G. B., and M. N. Baloch. 1996. Sheep and goat breeds of Pakistan. Press Corporation of

Pakistan, Project Division.


Relationship of Body Weight with Linear Body Measurements in Goats. J. Agric. Biol.

Sci. 1(3): 51-54



35

Moela, A.K. 2014. Assessment of the relationship between body weight and body

measurements in indigenous goats using Path analysis. M.Sc. Thesis. Deptt. of Agri.

Econo. and Anim. Prod., Univ. Limpopo (South Africa).

Otoikhian, C., A. Otoikhian, O. Akporhuarho, V. Oyefia, and C. Isidahomen. 2008. Body

measurement parameters as a function of assessing body weight in goats under on-farm

research environment. Afr. J. Gen. Agri. 4(3):135-140.

Pesmen, G., and M. Yardimci. 2008. Estimating the live weight using some body

measurements in Saanen goats. Archiva Zootechnica 11(4):30-40.

SAS, 2017. SAS University Edition. SAS Institute, Inc., Cary, NC.

Seifemichael, M., K. Kefelegn, A. Negassi, and A.K. Banerjee. 2014. Variability in Linear

Body Measurements and their Application in Predicting Body Weight of Afar Goats in

Ethiopia. Int. J. Interdiscip. Multidiscip. Stud. 1(4):17-25.

Slippers, S.C., B.A. Letty, and J.F. De Villiers. 2000. Prediction of the body weight of Nguni

goats. S. Afr. J. Anim. Sci. 30(1):127-128.

Tsegaye, D., B. Belay, and A. Haile. 2013. Linear body measurements as predictor of body

weight in hararghe highland goats under farmers environment: Ethiopia. Global Vet.

11(5):649-656.

Waheed, A., 2011. Characterization of goats for linear type traits in Pakistan. Ph.D thesis. Dept. of

Anim. Breed. and Genet., Univ. Agri., Faisalabad, Pakistan.

36

CHAPTER 4

COMPARING GROWTH PERFORMANCE OF VARIOUS

STRAINS OF BEETAL GOAT BREED UNDER FARM AND

FIELD CONDITIONS OF PUNJAB, PAKISTAN

ABSTRACT

The objective of the present study was to investigate growth potential of various strains of

Beetal goat breed under field and farm conditions in the province of Punjab, Pakistan. Data on

live body weight of Beetal goat (N=5011) were recorded in the field from registered farmers

raising Beetal goats and also from two government farms of Punjab, Pakistan between January

2016 and May 2017. The ages of animals ranged 0-1080d and were divided into thirty-six

monthly classes. The statistical model included fixed effects of age of goat, herd within age,

sex within age and strain within age besides random residual. Data were analyzed using PROC

GLM of SAS. All fixed effects significantly explained variation in live body weight of Beetal

goat (P ≤ 0.0001). Estimate of random residual/error variance remained 29.61. Overall birth,

weaning and yearling weights (±SD, kg) of Beetal goat were 3.35±0.49, 18.84±3.72 and

33.47±4.84, respectively. Overall average daily growth rate from birth to one year of age for

all Beetal strains remained 85.15g under low input system. Beetal goat raised at private herds

had higher (P ≤ 0.05) birth, weaning and yearling weights than the government herds. Males

had significantly higher (P ≤ 0.05) weaning and yearling weights than females with both having

similar birth weights. Nuqri strain had highest weaning and yearling weights followed by

Nagri, Faisalabadi, Makhi-Chini and Gujrati. The similar ranking did hold at around 3 years

of age (35 months) suggesting that Nuqri strain of Beetal goat breed might be considered

superior in term of growth rate whereas Gujrati seems on the bottom line in terms of growth.

The results of current study suggested that Beetal goat breed with various strains based on

color pattern has wide range of growth rate under subsistence farming system. Thus, Beetal

seems to be the most suitable animal for meat production that can compete with international

meat type goat through improved feeding and genomic selection tools.

Key Words: Beetal Goat, strains, body weights, Pakistan

37

4.1 INTRODUCTION

There has been an increasing contribution of livestock sector in national economy of Pakistan

during the last few decades and goat have outnumbered (74.1 million) all other species during

the same period indicating an increasing public interest (GOP, 2018). Moreover, in

international ranking, Pakistan is the 3rd largest goat producing country with a large proportion

of human population especially poor farmers in the rural areas having their livelihood

associated with rearing of goat. That is why goat is commonly considered poor man's cow.

Among the 36 reported breeds of goat in Pakistan, Beetal is the second largest breed in terms

of body size (found in Punjab) after Kamori breed of goat from Sindh province of Pakistan.

Teddy goat breed is maximum in number in Punjab province followed by Beetal goat breed.

(GOP, 2006). Beetal goat breed is recognized as the most efficient for producing heavier

animals with large size body frame, which are liked on Eid-ul-Azha festival of animal sacrifice.

Male animals have preference to produce heavier animals with maximum height (Khan et al.,

2006; Khan et al., 2008).

There is a great variation in body color pattern in Beetal goat. Khan (2016) reported six strains

of Beetal goat breed based on body color pattern variations viz. Nuqri (NQ), Makhi-Cheeni

(MC), Faisalabadi (FS), Nagri (NG), Gujrati (GR) and Raheem-Yar-Khan (RY Khan). NQ

stain of Beetal goat is white in color and a small spot or two at limbs can be ignored but any

other color spot on body is generally not acceptable. Splashing of black and brown is specific

pattern in MC strain and spot of any color is not preferred. Black or brown body color with

white spots and vice versa are characteristic feature of FS strain. NG strain has brown coat

color as compared to GR strain, which has brown background color with light brown spots.

RY khan strain has black coat color with some white spots and has a cylindrical body. Ramzan

(2014) studied preferred traits of Beetal strains and found that Roman nose are less important

for FS, NG and GR breeders but important for RY Khan and NQ breeders. Large body size,

height and length are more preferred traits for all Beetal goat breeders.

Growth of animal is a continuous and vibrant process. The phenomenon of growth begins with

the formation of zygote after fertilization and this is the positive change in the mass and size

of organs with respect to time (Eisen, 1976; An et al., 2011). Among all the traits having some

38

economic importance in goat production, growth can surely be considered as one of the most

important traits. The economic worth of animal is significantly affected by its growth rate.

Previously, growth performances of different local goats were studied with limited number of

animals and mostly under research farm conditions. Moreover, pre-weaning and post-weaning

growth rates were reported in Dera Din Panah (DDP) goats (N=350) under different period of

fodder availability (Yaqoob et al., 2009). Waheed (2011) estimated parameters of growth curve

of Beetal goats using Brody and Gompertz models based on limited records (N=120) from four

government livestock farms of Punjab. Khan et al. (2006), based on 86 Beetal goats, reported

mean body weights of male and female in four age groups (04-12,13-18,19-24, 24-36 months

and above). Ahmad et al. (2014) determined growth rate of Beetal goat (N=50) under annual

(2 crops) and accelerated (3 crops) and observed little difference in growth rates. Afzal et al.

(2004) reported least squares mean for birth weight in Beetal kids with an appreciable twining

rate (47.9%).

Most of the previous studies conducted so far were carried out under experimental farm

conditions with limited data, thus little information is available on the growth performance of

Beetal goat under field condition and its comparison with farm born/raised Beetal goat.

Moreover, due to limited data of the reported studies, it is not known what the monthly

benchmarks of body weight are in Beetal breed. Therefore, the present study was planned with

the aim to investigate growth potential of various strains of Beetal goat under field as well as

under farm conditions in Punjab, Pakistan using large data set. Moreover, the study aimed to

determine the overall suitability of the breed/strain for meat production under field conditions

and providing details related to monthly benchmark weights of Beetal goat.

4.2 MATERIALS AND METHODS

Six private goat herds and two government goat farms (i.e. Livestock Experiment Station

(LES), Allahdad, tehsil Jahanian, district Khanewal, and Directorate of Farms, University of

Agriculture, Faisalabad) raising Beetal goat were randomly identified and registered to obtain

growth related data. In return, the farmers were provided with free of cost technical

consultation on raising goat besides provision of concentrate feed, mineral mixture and

medicines by the investigators from the project funds.

39

Ear tags and neck bands/chains were used for identification of individual animals in a herd.

Data on growth performance (live body weights) of Beetal goat were recorded on monthly

basis between January 2016 and May 2017. Live body weights of animals were determined in

field area by hanging weighing balance. At government farms, digital weighing scale was

available to record precise body weights. At government farms, ages of animals were retrieved

from the birth record register maintained for this purpose while, at private farms, the age of

animal at test day was reported by the farmer. However, ages of animals were further verified

from the teeth of goat by the experienced recorders to improve the accuracy of recording.

Both animal type/strain and production/feeding system were different in the public vs. private

farms. The government farm animals were either of MC or FS strain. The field animals of

Beetal goat included FS, NG, NQ and GR strains. Private breeders provided diversified type

of grazing/browsing whole day-long along with the availability of green fodder when kept

indoor. The pregnant and milking does were fed home-made concentrate feed one month prior

and after kidding. Breeding bucks were provided concentrate ration during breeding days. At

government farms, young animals were kept and grazed separately from mature/breeding

animals up to one year of age. Does and breeding bucks were housed and grazed separately.

The typical grazing period ranged 5-6 hours daily. Animals were collectively provided

formulated concentrate ration mixed with fodder and wheat straw at evening while in the barns.

Weaning age was typically 4 months both at private and government farms.

Primary data were recorded from field and government farms. The data were entered in

spreadsheets of Microsoft Excel in a windows computer. The data were routinely transferred

via email for further editing and preliminary analyses. The ages of animals were divided into

thirty-seven classes with birth age class declared as 0, monthly age class 1 (1-30 days) and so

on up to monthly age class 36 (1051-1080d). All unusual observations were removed to avoid

wrong inferences from the data. There were total of 5011 observations from birth to 3 years of

age. The model included fixed effects of age of goat on test-day, herd, sex and strain within

each age class. Data were analyzed with PROC MIXED/GLM of (SAS, 2017) and differences

between least squares of means were tested by Scheffe test statistics. Following final linear

model of analyses was used:

𝑌ijklm = 𝜇 + 𝐴𝑖 + 𝑆𝑗 (𝑖) + 𝐻𝑘(𝑖) + 𝑇𝑙(𝑖) + 𝐸𝑖𝑗𝑘𝑙𝑚

40

Where,

Yijklm is a measurement of live body weight trait;

µ is overall population mean;

Ai refers to the fixed effect of ith age class (36 levels i.e. 36 monthly classes)

Sj is the fixed effect of jth sex (2 levels i.e. male and female);

Hk is the fixed effect of kth herd (2 levels i.e. government and private herds);

Tl is the fixed effect of lth sub-type/strain of Beetal goat breed (5 levels i.e. MC, FS, NG, NQ

and GR); and

Eijklm is the random residual associated with each record. Residual effect for each trait was

assumed to be distributed as N (0, Iσe2).

4.3 RESULTS

4.3.1 Basic Statistics

Overall basic statistics of live body weight for Beetal goat are provided in Table 4.1. Overall

means of body weight (kg) in Beetal goats at birth, weaning, 1yr, 1.5yr, 2yr, 2.5yr and 3yr

were 3.35±0.49, 18.84±3.72, 33.47±4.84, 42.80±6.28, 47.06±7.36, 56.15±9.15 and

80.22±16.79. The overall raw means are displayed in Table 4. The overall monthly body

weight is displayed as Figure 4.1. The Figure 4.1 indicated that the growth curve of Beetal goat

polynomial behavior with overall R2 of 83.6%, whereas R2 for female goats is 84.5% and for

male goats is 80.9%. Furthermore, it was interestingly observed that Beetal goat kept on

gaining weight up to 3 years of age instead of showing a static trend after a certain age.

Additionally, rather large standard deviations associated with average monthly/yearly means

indicated a substantial amount of variation in the body weight of goats. This variation is the

raw material that can be exploited in genetic selection program for production/growth

improvement of the breed in question and this model can be expanded to other breeds as well.

4.3.2 Effect of age on live body weight

The effect of age on live body weight of Beetal goat was statistically significant (P ≤ 0.0001)

as displayed in Table 4.2. The results were obtained using PROC MIXED as well as GLM of

41

SAS. The least squares estimates of means (± standard errors) for each monthly age group

starting from birth is reported in Table 4.3. A sketch of LSMeans for live body weight against

age in months is shown in Figure 4.2 that points towards a linear pattern of growth while taking

care of sex, strain and herd differences. The effect of age on live body weight is widely

understood and interestingly Beetal animals kept on gaining weight up to 36 months of age

although growth rate decreased progressively at least up to 24 months of age. Beetal goat

showed an average daily growth rate of 126.50g from birth to weaning (at 4 months), 65.31g

from weaning to yearling age, 35.95g from 1 year to approximately two years of age and from

two years to approximately 3 years as 69.97g based on data given in Table 3 by assuming an

average birth weight of 3.35kg.

4.3.3 Effect of herd type on live body weight

Two type of herds viz. Government vs. Private/field were compared in the present study. The

least squares estimates of means (± standard errors) for both herd types are reported in Table

4.4. Type of herd was unrelated (P > 0.05) to variation in birth weight of Beetal kids although

kids born at private herds were slightly heavier than kids born at Government farms (3.41 vs.

3.22). Kids born at field/private herds also outperformed (P ≤ 0.05) government/farm born kids

in terms of weaning weight (19.62 vs. 17.43). The animals born at private herds had

significantly better growth potential at 12, 18, 23 and 35 months of age (Table 4.4). The

superiority of private herds over government herds up to 24 months of age is clearly evident

in Figure 4.3 suggesting better growth performance of kids born to private farmers.

4.3.4 Effect of sex on live body weight

The least squares estimates of means (± standard errors) for both sex groups are reported in

Table 4.5. The data has been displayed as Figure 4.4 to give an overview of the differences in

growth pattern of both sexes. Birth weight of Beetal kids (overall average around 3.35 kg) did

not vary between the two sexes i.e. male and female Beetal kids has similar birth weights (P =

0.66) as indicated in Table 4.5. However, overall males had slightly higher birth weight than

females. In the present study weaning age was taken as four months and overall average

weaning weight was around 18.84 kg. Weaning weight of kids was significantly affected (P ≤

0.05) by sex as males had higher weaning weight (19.93 kg) than females (17.12kg). Overall

42

average weight of kids at one year of age was approximately 33.47 kg. Male kids (35.94 kg)

were significantly heavier (P ≤ 0.05) than females (32.92 kg) at twelve months of age.

4.3.5 Effect of strain on live body weight

The least squares estimates of means (± standard errors) for both sex groups are reported in

Table 4.6. Birth weight did not differ (P > 0.05) among all five studied strains of Beetal goat

breed. On the contrary, weaning and yearling weights were significantly affected by strain (P

≤ 0.05). NQ strain had highest weaning (22.38 kg) and yearling (37.00 kg) weights followed

by NG, FS, MC and GR. As per weaning weight averages, ranking of strains from highest to

lowest is as follows; NQ (22.38 kg), NG (19.11 kg), FS (18.92 kg), MC (17.81 kg) and GR

(14.41 kg). GR appeared to be on the bottom line in terms of weaning weight although the

strain had low number of records. As per yearling weight averages, ranking of strains from

highest to lowest is as follows; NQ (37.00 kg), GR (34.65 kg), NG (33.75 kg), FS (33.51 kg)

and MC (33.24 kg) and similar ranking did hold at around 3 years of age (35 months)

suggesting apparent superiority of NQ strain of Beetal goat breed over others in terms of meat

production.

4.4 DISCUSSION

The average daily growth rate (overall) from birth to approximately one year of age was

85.15g. The reduced growth rate (up to 1/2) during the second year of age as compared to first

year indicate that raising this breed for more than twelve months of age may not be economical

feasible as one has to maintain similar management conditions (at approximately similar cost)

with reduced gain in live body weight.

Birth weight is considered to be a function of various physiological (related to doe) as well as

environmental (related to farmer/herd) factors. Under typical country conditions, animal

management at private farms/herds is usually believed to be superior to public/government

herds. The sample size was lower (N=25) for kids being born during the experimental period,

which may have affected the results. Nonetheless, data indicated an area of potential

improvement for government herds to improve the management of does during pregnancy for

healthier and heavier kids.

Keeping in view the current findings, it can be estimated that a typical Beetal goat would weigh

approximately 34 kg at one year of age, approximately 48 kg at two years of age and 73kg at

three years of age. These results are comparable to the earlier findings in Beetal goat as well

43

as other breeds as described by (Thiruvenkadan et al., 2009; Yaqoob et al., 2009; Ahmad et

al., 2014). The findings are first of its kind based on large data set from field as well as farm

conditions providing monthly benchmarks for body weight in Beetal goat up to 3 years of age.

The differences in results of the present study with that of other may be attributed to differences

in methods of data analyses, breeds, locality, sample size, utility and level of genetic

improvement programs.

The findings of the present study are comparable to an earlier study on Beetal goat by (Afzal

et al., 2004), who studied birth weight of 1850 Beetal goat raised at 3 different farms over 12

years period. Waheed (2011) described mean birth weight (2.7 kg for male and 2.6 kg for

female) in Beetal goat while studying 120 records from 4 research farms. A more recent study

by (Ahmad et al., 2014) found birth weight (kg) as 3.08±0.11 and 3.07±0.13 in 65 Beetal kids

with two different feeding and breeding schemes. Yaqoob et al. (2009) studied production

performance of 350 Dera Din Panah (DDP) goats and average birth weights (3.6±0.11 to

3.9±0.07 kg) were higher than reported for Beetal goat in the present study. Ahuya et al. (2002)

presented goat breeds birth weight range (2.98±0.21 kg to 4.10±0.38 kg) in pure indigenous

Kenyan goat breeds and their crosses with Toggenburg goat breed (Swiss region goat breed).

The birth weight from this study (3.35 kg) is closer to the highest range of birth weight reported

in the literature. Ahuya et al. (2002) found the range of body weight at 60-days 6.32±0.15 kg

to 13.51±0.34 kg while in the present study, the overall 60-days weight is 13.01 ± 2.50 kg,

which is also comparable to the higher range of weight described. These results also support

the potential of pre-weaning growth rate in Beetal goat. Weaning weight of Beetal goat in

present study is 19.93 kg and 17.12 kg for male and female goat kids, respectively. The overall

weaning weight is 18.84kg. These values are larger than the results described by

(Thiruvenkadan et al., 2009; Waheed, 2011) and are comparable to the results presented by

(Yaqoob et al., 2009) in DDP goat, while (Ahmad et al., 2014) observed 3 months weight

range from 10.92±0.30 to 12.05±0.32 kg in Beetal goat. The overall body weight at 6 months

age in present study is 21.90 kg, while males (22.91 kg) had significantly higher body weight

than female goat (20.69 kg). Ahmad et al. (2014) found lesser amount of 6-months weight in

Beetal goat while, Yaqoob et al. (2009) presented comparable results at same age in DDP goat.

A lower value of 6-months weight has been reported in Tellicherry and Rohilkhand goat breeds

(Thiruvenkadan et al., 2009; Fahim et al., 2013). The overall body weight at 9-months in

44

present study are 27.65±0.46kg while male had significantly higher body weight

(29.73±0.77kg) than female goat (25.77±0.77kg). The results of this study coincide with the

findings of (Yaqoob et al., 2009; Ahmad et al., 2014).

The yearling weight in present study is significantly different for male than female goat. The

overall yearling weight is 33.46±0.38 kg, which is comparable to the results of Yaqoob et al.

(2009) for DDP goat. These findings of the present study are on higher side than the findings

of previous studies (Thiruvenkadan et al., 2009; Fahim et al., 2013; Mule et al., 2014) that

could be attributed to breed differences. The pre-weaning and post-weaning growth potential

in Beetal goat is vital. The body weight at 2 or above years of age is officially considered as

mature body weight in Livestock and Dairy Development Punjab, Pakistan. The overall 2-

years body weight in this study is 47.06 kg and these results are comparable with the findings

by Dhanda et al. (2003) for Beetal goat. Overall various strains of Beetal breed had significant

variation when were compared for growth potential (P ≤ 0.05) at each age class as shown in

Figure 4.5. The most erratic/variable trend has been observed for GR strain owing to limited

data availability in each age class. Overall NQ performed better up to 12 months of age as well

as at 35 months showing variable trend in between 12 and 35. NG, MC and FS strains (showing

little significant difference among three) appear to have comparable/similar growth pattern

after NQ strain.

From international perspective, average daily growth rate (overall) from birth to approximately

one year of age was 85.15g, which is far lower than the famous Boer goat. Boer goat kids

might gain 200 g/d on average in better feeding conditions during the first twelve months of

age (http://www.adga.org/breedinfo.html.). However, the data from the present study were

mostly obtained from resource limited sub-tropical farm and field conditions of Punjab,

Pakistan. Furthermore, growth rate could be higher as 127.81 g/d as indicated in Table 4.1

where maximum yearling weight reported for Beetal kids was 50kg.

4.5 CONCLUSIONS

The study documented the growth potential of Beetal under experimental as well field

conditions. Various strains of Beetal goat breed were found significantly related to the

variation in body weights at various ages. The results from present study based on rather large

data set highlight the potential of Beetal goat to be raised as a preferred meat animal. The

present study provided first report on the effects of gender and production system (field and

45

farm) with large number of observations. The growth potential difference among animals

raised in different production systems in the present study point to the possibility of genetic

improvement in Beetal goat in their respective habitat. The data shows that Beetal bucks and

does could be used for meat production and could be suitable candidates for improvement

through current genetic and genomic selection tools.

46

Figure 4.1 Overall monthly growth pattern of Beetal goats up to 36 months of age

y = -0.0144x2 + 1.9945x + 10.607R² = 0.8355

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

Mo

nth

ly L

ive

Bo

dy

Wei

ght

(kg)

Age of Goat (months)

47

Figure 4.2 Effect of age on body weight based monthly body weight (1-35 month) of Beetal goats

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

LS

Mea

ns

of

Liv

e B

od

y W

eigh

t

Age (months)

48

Figure 4.3 Body Weight of Goats for Government vs. Private Herds up to 36 months of age

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

Liv

e B

od

y W

eigh

t of

Goat

(kg)


Govt_Herds

Private_Herds

49

Figure 4.4 Body Weight of Goats “Male vs. Female” up to 36 months of age

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

Liv

e B

od

y W

eigh

t(k

g)

of

Goat


Male

Female

50

Figure 4.5 Body Weight of Goats for various strains up to 36 months of age

0.00

20.00

40.00

60.00

80.00

100.00

120.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

Liv

e B

od

y W

eigh

t (k

g)

of

Goat


Faisalabadi

Gujrati

Makhi-Cheni

Nagri

Nuqri

51

Table 4.1 Overall basic statistics of live body weight of Beetal goats up to 36 months of

age

Age (Months) N Mean Std Dev Minimum Maximum

0 26 3.35 0.49 3.0 4.0

1 108 8.31 2.33 5.0 14.0

2 153 13.01 2.50 8.0 23.0

3 145 17.58 3.83 11.0 30.0

4 150 18.84 3.72 13.0 32.0

5 109 21.32 3.61 15.0 31.0

6 146 21.90 3.96 16.2 32.0

7 145 23.59 3.91 17.0 34.0

8 171 25.27 3.31 18.9 35.0

9 177 27.75 4.54 20.0 39.1

10 212 29.85 4.76 20.0 41.7

11 261 30.87 4.41 22.4 41.0

12 297 33.47 4.84 25.0 50.0

13 199 36.14 4.55 28.0 50.0

14 223 37.81 5.15 25.0 54.0

15 172 40.28 5.43 30.0 56.0

16 175 40.56 5.64 30.0 60.0

17 152 42.27 5.90 29.8 60.0

18 149 42.80 6.28 31.0 58.1

19 121 43.32 5.69 33.0 57.0

20 157 42.46 6.92 31.0 57.0

21 107 42.67 6.16 31.0 58.0

22 108 44.99 7.27 32.0 68.0

23 132 46.21 6.20 35.0 65.0

24 136 47.06 7.36 36.0 68.0

25 140 48.97 7.76 36.0 72.0

26 109 49.63 7.22 37.0 70.0

27 83 49.61 6.09 40.0 67.0

28 83 53.13 6.92 41.0 68.0

29 68 56.98 7.12 44.0 69.0

30 94 56.15 9.15 43.0 85.0

31 56 58.48 8.31 42.0 72.0

52

Age (Months) N Mean Std Dev Minimum Maximum

32 65 61.32 9.08 45.0 85.0

33 66 63.91 10.61 47.0 101.0

34 66 65.86 11.99 48.0 102.0

35 227 65.84 10.49 51.0 105.0

36 23 80.22 16.79 60.0 115.0

Table 4.2 Results of Tests of Fixed and Random Effects obtained from Proc

GLM/MIXED of SAS

Source DF Sum of Squares Mean Square F Value Pr>F

Model 256 1093233.641 4270.444 144.65 <.0001

Error 4754 140348.662 29.522

Corrected

Total

5010 1233582.303

R-Square Coeff Var Root MSE Mean Body weight

0.886227 14.35075 5.433436 37.86168

Table 4.3 Effect of age on body weight in Beetal Goat obtained from GLM of SAS

Age (months) LS Means of Body Weight ±Stand. Error

0 Not Estimable Not Estimable

1 8.40 1.00

2 13.26 0.91

3 17.98 0.92

Type 3 Tests of Fixed Effects from PROC MIXED OF SAS

Effect Num DF Den DF F Value Pr > F

Age (monthly classes) 36 4754 261.96 <.0001

Herd within Age 35 4754 5.95 <.0001

Sex within Age 37 4754 14.90 <.0001

Strain within Age 146 4754 4.72 <.0001

Residual/Error Variance - - 29.52 -

53


4 18.53 0.90

5 21.55 0.97

6 21.81 0.94

7 24.43 0.97

8 25.22 0.95

9 28.01 1.14

10 29.86 0.89

11 30.99 1.04

12 34.43 0.85

13 36.89 0.93

14 38.70 0.88

15 43.43 0.91

16 42.33 1.02

17 39.42 1.09

18 42.78 0.95

19 40.27 1.05

20 39.35 0.92

21 41.48 1.06

22 46.52 0.95

23 42.04 1.08

24 47.55 0.93

25 49.00 0.98

26 50.60 1.00

27 48.80 1.16

28 53.01 1.14

29 58.72 1.61

30 62.88 1.38

31 57.49 1.89

32 61.87 1.42

54


33 67.99 1.89

34 65.85 1.47

35 73.09 0.84

36 Not Estimable Not Estimable

Table 4.4 Effect of herd type on live body weight of Beetal goats up to 36 months of

age

Age

(Months

)

N LS Means and standard error (S.E.) of Body Weight for each herd

type

Government Private P-Value

Mean ±SE Mean ±SE

0 26 3.22 0.44 3.41 0.51 0.69

1 10

8

7.12 0.69 9.69 0.47 < 0.01

2 15

3

12.82 0.63 13.71 0.47 0.25

3 14

5

17.99 0.79 17.97 0.60 0.98

4 15

0

17.43 0.75 19.62 0.59 0.01

5 10

9

21.06 0.67 22.03 0.67 0.40

6 14

6

19.65 0.10 24.00 0.76 < 0.01

7 14

5

23.00 0.86 26.00 0.69 < 0.01

8 17

1

24.00 0.76 26.45 0.60 < 0.01

55

Age

(Months

)


type


Mean ±SE Mean ±SE

9 17

7

26.82 1.09 29.18 1.00 0.04

10 21

2

27.68 0.91 32.02 0.75 < 0.01

11 26

1

28.78 0.87 33.19 0.79 < 0.01

12 29

7

33.07 0.84 35.79 0.72 < 0.01

13 19

9

35.72 0.93 38.05 0.80 < 0.01

14 22

3

37.16 0.98 40.23 0.83 < 0.01

15 17

2

42.01 1.18 44.84 0.96 0.05

16 17

5

41.22 1.33 43.44 1.20 0.14

17 15

2

37.31 1.52 41.52 1.26 0.01

18 14

9

38.40 1.46 47.15 1.16 < 0.01

19 12

1

39.68 1.59 40.84 1.28 0.56

20 15

7

31.96 1.37 40.75 1.12 < 0.01

21 10

7

40.72 1.80 42.22 1.46 0.51

56

Age

(Months

)


type


Mean ±SE Mean ±SE

22 10

8

42.45 2.06 50.57 1.56 < 0.01

23 13

2

39.49 1.39 44.58 1.23 < 0.01

24 13

6

45.85 1.66 49.24 1.35 0.08

25 14

0

47.18 1.78 50.81 1.53 0.06

26 10

9

50.88 1.63 50.31 1.32 0.75

27 83 47.41 1.65 50.19 1.31 0.12

28 83 52.88 1.86 53.13 1.54 0.90

29 68 58.97 2.37 58.46 1.84 0.80

30 94 62.72 2.82 63.02 2.33 0.91

31 56 58.79 3.35 56.17 2.40 0.32

32 65 63.46 2.94 60.28 2.32 0.26

33 66 67.31 3.56 68.65 3.48 0.65

34 66 69.17 3.46 62.53 3.24 0.07

35 22

7

74.83 1.64 71.33 1.33 < 0.01

36 23 81.00 5.07 80.00 4.31 0.37

Table 4.5 Effect of sex type on live body weight of Beetal goats up to 36 months of age

57

Age

(Months)

N LS Means and standard error (S.E.) of Body Weight of Beetal

Goat for each sex type

Male Female P-Value

Mean ±SE Mean ±SE

0 26 3.45 0.52 3.27 0.46 0.66

1 108 8.87 0.50 7.94 0.43 0.04

2 153 13.56 0.45 12.97 0.44 0.14

3 145 18.66 0.62 17.30 0.60 0.01

4 150 19.93 0.58 17.12 0.58 < 0.01

5 109 22.85 0.63 20.24 0.69 < 0.01

6 146 22.91 0.75 20.69 0.74 < 0.01

7 145 26.43 0.69 22.42 0.67 < 0.01

8 171 26.52 0.58 23.00 0.60 < 0.01

9 177 30.21 0.97 25.80 0.90 < 0.01

10 212 31.55 0.77 28.15 0.80 < 0.01

11 261 32.65 0.83 29.32 0.76 < 0.01

12 297 35.94 0.78 32.92 0.72 < 0.01

13 199 38.98 0.84 34.79 0.81 < 0.01

14 223 41.28 0.87 36.11 0.85 < 0.01

15 172 45.66 0.90 41.20 0.92 < 0.01

16 175 43.62 1.21 41.04 1.07 0.01

17 152 40.29 1.23 38.54 1.23 0.11

18 149 44.16 1.17 41.38 1.11 0.02

19 121 39.50 1.24 41.02 1.25 0.26

20 157 38.28 1.17 34.44 1.15 < 0.01

21 107 43.13 1.44 39.81 1.34 0.02

22 108 47.45 1.49 45.57 1.40 0.26

23 132 42.13 1.36 41.94 1.21 0.88

24 136 47.62 1.47 47.47 1.29 0.92

25 140 49.96 1.67 48.03 1.47 0.23

58

Age

(Months)

N LS Means and standard error (S.E.) of Body Weight of Beetal

Goat for each sex type

Male Female P-Value

Mean ±SE Mean ±SE

26 109 53.18 1.64 48.01 1.22 < 0.01

27 83 50.28 1.78 47.31 1.11 0.09

28 83 54.35 2.13 51.67 1.32 0.23

29 68 60.11 3.37 57.32 1.28 0.42

30 94 66.51 3.93 59.23 1.65 0.08

31 56 60.54 4.33 54.42 1.68 0.13

32 65 66.31 3.89 57.43 2.01 0.04

33 66 77.80 5.67 58.17 2.00 < 0.01

34 66 70.90 4.28 60.79 2.40 0.17

35 227 81.09 1.78 65.07 1.31 < 0.01

36 23 93.63 5.81 73.07 3.17 0.33

Table 4.6 Effect of strain type on live body weight of Beetal goats up to 36 months of

age

Age

(Mon

ths)

N LS Means and standard deviation (SD) of Body Weight for each strain type of Beetal goat

Faisalabadi Gujrati Makhi-

Cheeni

Nagri Nuqri Overall P-

Value

Mean ±SE Mea

n

±SE Mean ±SE Mea

n

±SE Mean ±SE

0 26 3.22 0.44 3.75 0.35 3.50 0.70 3.50 0.70 3.27 0.47 0.52

1 108 9.30 0.45 7.52 1.36 9.42 0.92 8.79 0.94 7.00 0.62 0.09

2 153 12.54 0.41 13.05 1.72 13.74 0.72 14.05 0.61 12.94 0.53 0.12

3 145 15.96 0.44 12.01 2.31 15.04 0.88 17.94 0.97 19.95 0.62 < 0.01

4 150 18.92a

*

0.44 14.41a 2.21 17.81 a 0.72 19.11a 0.81 22.38b 0.85 < 0.01

5 109 20.10 0.51 19.51 2.27 19.25 0.84 22.15 0.10 25.83 1.11 < 0.01

6 146 22.90 0.56 17.34 2.66 22.38 0.74 22.12 1.06 24.28 1.34 0.16

7 145 24.29 0.51 23.01 2.40 21.74 0.69 26.22 1.00 26.90 1.45 < 0.01

59

Age

(Mon

ths)

N LS Means and standard deviation (SD) of Body Weight for each strain type of Beetal goat

Faisalabadi Gujrati Makhi-

Cheeni

Nagri Nuqri Overall P-

Value

Mean ±SE Mea

n

±SE Mean ±SE Mea

n

±SE Mean ±SE

8 171 26.27 0.45 23.27 2.16 24.62 0.61 27.00 0.80 25.00 1.21 0.01

9 177 28.17 0.55 27.52 2.93 24.72 0.89 30.07 0.86 29.52 2.93 < 0.01

10 212 31.07 0.44 28.32 3.06 28.55 0.78 29.38 0.86 31.94 1.31 < 0.01

11 261 31.62 0.38 30.46 2.78 30.79 0.65 32.25 0.64 29.79 2.28 0.50

12 297 33.51a 0.40 34.65ab 3.17 33.24 ab 0.69 33.75a 0.75 37.00b 0.93 0.01

13 199 36.23 0.47 36.23 3.05 35.93 0.78 37.85 0.83 38.18 1.68 0.54

14 223 38.19 0.47 40.46 3.39 37.81 0.80 39.03 0.99 37.97 1.28 0.86

15 172 40.62 0.73 54.08 3.44 39.28 0.99 43.83 1.29 39.32 1.50 < 0.01

16 175 41.27 0.74 46.68 3.90 39.22 1.02 44.54 1.44 39.93 2.81 0.04

17 152 43.87 0.85 31.39 3.98 43.12 1.09 45.92 1.92 32.77 3.29 < 0.01

18 149 47.07 0.88 43.12 4.03 44.50 1.21 43.16 2.17 36.01 1.69 < 0.01

19 121 44.61 0.91 33.91 3.98 44.01 1.25 43.61 1.88 35.16 2.88 < 0.01

20 157 44.82 0.72 31.00 4.13 45.09 1.11 41.67 1.70 38.60 1.77 < 0.01

21 107 43.45 1.15 32.25 4.43 42.24 1.49 43.27 2.15 45.15 3.24 0.20

22 108 48.61 1.39 54.93 4.93 47.15 1.90 44.11 2.36 37.76 2.22 < 0.01

23 132 48.18 0.75 35.95 4.04 46.60 1.19 44.91 1.41 34.54 3.34 < 0.01

24 136 49.66 1.01 51.30 4.90 45.93 1.60 49.84 2.17 41.00 1.74 < 0.01

25 140 50.11 0.96 53.18 5.41 47.74 1.67 50.47 1.70 43.48 3.22 0.16

26 109 52.28 1.10 53.78 4.62 47.58 1.72 54.84 1.78 44.50 2.25 < 0.01

27 83 52.94 1.10 49.10 4.04 50.01 1.72 48.71 2.35 43.21 2.35 < 0.01

28 83 55.58 1.34 56.07 4.76 52.91 1.96 54.13 2.24 46.35 2.85 0.02

29 68 59.05 1.78 67.75 4.55 59.13 2.93 58.98 2.79 48.65 3.19 < 0.01

30 94 59.53 2.25 76.35 6.20 60.18 3.72 61.68 3.19 56.62 2.92 0.03

31 56 62.68 2.18 47.37 5.91 62.70 3.23 61.96 3.28 52.70 5.05 0.03

32 65 67.85 2.38 54.08 6.20 60.86 3.58 63.82 3.40 62.73 5.18 0.06

33 66 76.14 2.93 63.14 6.07 67.28 4.28 69.21 3.88 64.14 7.14 0.01

34 66 73.17 2.34 58.37 7.65 65.41 4.11 68.92 3.27 63.37 7.65 0.08

35 227 70.65 1.03 68.65 2.67 71.17 1.61 77.19 1.44 77.75 4.97 < 0.01

36 23 85.33 7.88 71.50 5.50 74.50 0.50 98.17 2.48 70.80 5.24 0.08

*Superscript letter within a row show difference at P ≤ 0.05.

60

REFERENCES

Afzal, M., K. Javed, and M. Shafiq. 2004. Environmental effects on birth weight in Beetal goat

kids. Pak. Vet. J. 24:104-106.

Ahmad, N., K. Javed, M. Abdullah, A. S. Hashmi, A. Ali, Z. Iqbal, U. Younas, and Z. M. Iqbal.

2014. Comparative productive performance of Beetal goats in annual and accelerated

system. J. Anim. Plant Sci. 24(4): 979-985.

Ahuya, C. O., A. M. Okeyo, R. O. Mosi, F. M. Murithi, and F. M. Matiri. 2002. Body weight

and pre-weaning growth rate of pure indigenous, Toggenburg goat breeds and their

crosses under smallholder production system in Kenya. In: BSAS International

conference in Merida, Mexico.



gene and association analysis with growth traits. Czech J. Anim. Sci. 56:529-535.

Dhanda, J. S., D. G. Taylor, P. J. Murray, R. B. Pegg, and P. J. Shand. 2003. Goat Meat

Production: Present Status and Future Possibilities. Asian-Aust. J. Anim. Sci.

16(12):1842-1852.


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body measurements in Rohilkhand local goats. Indian J. Anim. Res. 47 (6):521-526.

GOP (Government of Pakistan). 2006. Pakistan Livestock Census. Agricultural Cencus





Relationship of Body Weight with Linear Body Measurements in Goats. J. Agri. Biol.

Sci. 1(3): 51-54

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Khan, M. S., 2016. Documenting Indigenous Genetic Resources-The Beetal Goats. Innovation

Catalogue, University of Agriculture, Faisalabad 101(75):276-278.



Mule, M. R., R. P. Barbind, and R. L. Korake. 2014. Relationship of body weight with linear

body measurement in Osmanabadi goats. Indian J. Anim. Res. 48(2):155-158.

Ramzan, F., 2014. Breeding objectives and selection criteria for four strains of Beetal goats

using participatory approach. M.Sc. (Hons.) thesis. Dept. of Anim. Breed. and Genet.,

Univ. Agri., Faisalabad, Pakistan.


Thiruvenkadan, A. K., M. Murugan, K. Karunanithi, J. Muralidharan, and K. Chinnamani.

2009. Genetic and non-genetic factors affecting body weight in Tellicherry goats. S.

Afr. J. Anim. Sci. 39(supp. 1): 107-111.

Waheed, A., 2011. Characterization of Goats for Linear Type Traits in Pakistan. Ph.D thesis.

Dept. of Anim. Breed. and Genet., Univ. Agri., Faisalabad, Pakistan.

Yaqoob, M., F. Shahzad, M. Aslam, M. Younas, and G. Bilal. 2009. Production performance

of Dera Din Panah goat under desert range conditions in Pakistan. Trop. Anim. Health

Prod. 41(7):1413-1419.

62

CHAPTER 5

GENOME-WIDE ASSOCIATION STUDY FOR GROWTH

TRAIT IN BEETAL GOAT OF PUNJAB, PAKISTAN ABSTRACT

Achievement of reasonably good body weight at younger age is desired economic trait in goat

production. The body weight trait is obviously controlled by underlying genes similar to other

quantitative traits. The objective of the current study was to identify underlying candidate

genes associated with growth trait using single nucleotide polymorphism (SNP) methodology.

Illumina GoatSNP60 BeadChip with 53,347 SNPs was used on 631 purebred Beetal goat breed

animals of different strains to perform genome-wide association study (GWAS) for age

adjusted body weight. Quality control test of genotypic data by PLINK software removed 37

individuals and 7,605 SNPs on the basis of individual’s missing genotype (more than 10%),

low call rate of SNP (less than 90%), minor allele frequency (less than 5%) and Hardy-

Weinberg Equilibrium (0.1%). A total number of 594 animals and 45,744 SNPs were used for

the subsequent GWAS analysis. Age adjusted body weight trait was associated with 18

significant SNPs. Gene annotation was carried out with latest goat genome (ARS1, GenBank

assembly accession GCA_001704415.1) released August 24, 2016. More than one-third SNPs

(7 out of 18) were located within genes while others were located close to goat genes (5538

bp-494349 bp apart). Therefore, seven genes are considered to be the most critical candidate

genes associated with age adjusted body weight (BW) viz. snp20448-scaffold202-3614967

was located within the goat gene VWC2L, snp32850-scaffold38-3940153, snp47231-

scaffold663-207688, snp17052-scaffold1777-307606, snp26374-scaffold276-2938522,

snp11072-scaffold1399-798540 and snp2711-scaffold1079-117622 were located within

PLPPR1, TTC28, INPP5D, NAALADL2, TDP1 and ERAP1 genes respectively. Other eleven

genes (PTTG1, SEMA3C, IPO5, PHOX2B, SUSD1, LOC102184808, PHACTR3, OLFM3,

ERG, MDFIC2 and ABCC4) were also important candidate genes affecting BW trait. Principal

component analysis (PCA) showed that all studied strains of Beetal goat breed were genetically

similar to each other. The findings of the study will contribute to the idea of genomic selection

of goat for better growth and meat production in future.

Key Words: GWAS, Beetal goat, body weight, SNP, Punjab.

5.1 INTRODUCTION

63

Economy of Pakistan is mainly based on agricultural and livestock (GOP, 2018). Rural families

of Pakistan are involved in keeping and raising livestock. Goat farming is a low input business

compared to other livestock species because animal is easy to handle and grazing is the main

source of its feeding (GOP, 2018). Goat farming has huge share in the livelihood of rural

people, especially women, who practice goat farming as a supplementary business to fulfill

their routine household needs (Muhammad et al., 2015). The availability of mutton is a major

source of good quality animal protein (Ahlawat et al., 2015). The goat meat is preferred over

beef and lamb and it is consumed as delicacy and luxury (Fatah Ullah Khan and Ashfaq, 2010).

Beetal is one of the renowned indigenous goat breeds of Pakistan. Farmers, who raise this

animal for sacrificial purpose, feed them extensively and male adult individuals of Beetal may

weight up-to 200 kg body weight (Khan et al., 2008).

Breeders and geneticists have special emphasis on growth and meat production traits of goats

owing to enhanced popularity of meat production in goat industry. Identification of candidate

gene and genome scanning technology was used to find quantitative trait loci (QTL) in

livestock during the past decades. QTL play vital role in the breeding animal’s genetic

evaluation (Zhang et al., 2013). Many QTL studies have been performed for different

quantitative traits in cattle, chicken and sheep (Machado et al., 2003; Carlborg et al., 2004;

McRae et al., 2005).

A recent study on Beetal goat using PCR-RFLP technique to identify candidate genes

association with growth trait by applying genetic markers like; growth hormone (GH), insulin-

like growth factor-1 (IGF-1) and bone morphogenetic protein-15 (BMP-15) revealed

involvement of only 1 SNP in GH gene associated with low or high body weight (Shareef,

2017).

The genome-wide association studies (GWAS) are now extensively in use to identify and

localize the candidate genes for quantitative traits in many species by using individual’s whole

genome genotyping by high throughput SNP technologies. The efficiency and accuracy in

animal breeding and selection decisions could be improved by using this technology

(Matukumalli et al., 2009; Fan et al., 2011; Jiang et al., 2010). The research focus is now

changed from classical QTL mapping to GWAS in the last fifteen years or so. Several

important candidate genes in different species have been identified and research findings were

64

published (Lasky‐ Su et al., 2008; Zhang et al., 2013). Research work were also done on goat

GWAS related to selection signatures, horns size and coat color or hair texture (Nazari-

Ghadikolaei et al., 2018; Guo et al., 2018; Mucha et al., 2018; Onzima et al., 2018).

Economically important traits like meat production, growth rate and milk production traits are

not so extensively studied in goat as in other livestock species.

The main objective of this study was to genotype Beetal goat animals with the Illumina

GoatSNP60 SNPchip at GeneSeek (Lincoln, NE USA) to identify the genes related to growth.

The genotyping was used to employ GWAS methodology for identification of significant SNPs

associated with age adjusted body weight for exploring and forecasting of major candidate

genes in Beetal goat strains. The observed SNP loci with significant effect provided

comprehensive information for the initiation of additional studies of same kind. This will

eventually lead to the identification of causal genetic variants influencing enhanced growth

and meat production traits in Beetal goat.

5.2 MATERIALS AND METHODS

5.2.1 Animal resources

This study was carried out in Punjab province of Pakistan. The Beetal goat population is found

in all areas of the province. The animal population used in this study consisted of different

Beetal goat strains. The strains are phenotypically different by body color, color pattern and

size of roman nose. There is a great variation in body color pattern in Beetal goat. Khan (2016)

reported six strains of Beetal goat breed based on body color pattern variations viz. Nuqri (NQ),

Makhi-Cheeni (MC), Faisalabadi (FS), Nagri (NG), Gujrati (GR) and Raheem-Yar-Khan (RY

Khan). NQ stain of Beetal goat is white in color and a small spot or two at limbs can be ignored

but any other color spot on body is generally not acceptable. Splashing of black and brown is

specific pattern in MC strain and spot of any color is not preferred. Black or brown body color

with white spots and vice versa are characteristic feature of FS strain. NG strain has brown

coat color as compared to GR strain, which has brown background color with light brown

spots. RY Khan strain of Beetal goat has black coat color with some white spots and has a

cylindrical body. Ramzan (2014) studied preferred traits of Beetal strains and found that

Roman nose are less important for FS, NG and GR breeders but important for RY Khan and

NQ breeders. Large body size, height and length are more preferred traits for all Beetal goat

65

breeders. A total of 631 purebred Beetal goat (151 males and 480 females), were randomly

selected from Faisalabad field area farmers, Research Farm, University of Agriculture,

Faisalabad (UAF), Livestock Experiment Station, Allah Dad district Khanewal (LES) and

PAK-USAID research project registered animals, respectively. The details are given in Table

5.1.

5.2.2 Measurement of age adjusted body weight

This study mainly focused on association of age adjusted body weight (BW) with SNP

variations. Electronic weighing scale was used to measure BW of goat at farm levels while

hanging balance was used at field level to weigh the animals. At farms, ages of animals were

retrieved from the birth record register maintained for this purpose while, at private farms, the

age of animal at test day was reported by the farmer. However, ages of animals were further

verified from the teeth of goat by the experienced recorders to improve the accuracy of

recording.

5.2.3 Blood Sampling and genotyping

Traditional method of blood samples collection was used. Blood samples were loaded on cards

and shipped to GeneSeek (Lincoln, NE USA) for genotyping. After DNA extraction and

quantification, Illumina GoatSNP60 SNPchip having 53,347 SNPs was used to genotype the

every sampled individual. The UltraEdit software was used to prepare genotype files. The files

used for PLINK (ped- and map-files) were prepared by using software accessed from website

(http://git-scm.com/book/en/Getting-Started-Installing-Git). The individual information

related to phenotype, sex and FIDs (family identification for each individual) were updated

using PLINK (v1.9, http:// cog-genomics.org/˜Shaun Purcell, Christopher Chang) software.

5.2.4 Genotype quality control

PLINK software was used to exclude individuals and remove SNPs from the 631 individuals

and 53,347 SNPs. An individual was rejected if (1) missing genotypes were more than 10%,

(2) sex not correctly detected, or (3) duplicated sample genotyped. A SNP was removed if (1)

call rate was less than 90%, (2) minor allele frequency (MAF) was less than 5% and (3) Hardy-

Weinberg Equilibrium (HWE) at 0.1%. A total of 594 individuals and 45,744 SNPs were used

after adopting these quality control measures, in order to carry out the GWAS analysis.

http://git-scm.com/book/en/Getting-Started-Installing-Git

66

5.2.5 Genome-wide association analysis

GWAS was performed using PLINK software (v1.9, http:// cog-genomics.org/˜Shaun Purcell,

Christopher Chang) based on a linear model

Y = b0 + b1 + b2 (age as covariate) + b3 (sex) + e

Where Y is the predicted body weight with b0 is alpha level and b1 is regression coefficient, b2

and b3 are coefficients of age and sex, respectively.

5.2.6 Statistical interpretation

Bonferroni method of statistical analysis was used for the adjustment of multiple SNP loci

being detected. Genome-wise significance level for each adjusted SNP was determined with a

raw P-value of ≤0.05/N, where N is the number of SNP loci while chromosome-wise

significance level was calculated with raw P-value ≤0.05/n, and n is the number of SNP loci

on each chromosome used in this study.

5.2.7 Population stratification analysis

In this study animals were genotyped from different strains of Beetal goat breed. The sources

of all strains were very clear and different. Principal component analysis (PCA) was carried

out to minimize the effect of confounding variable in sampled population. PCA was

determined for all strains to assess the genotypic differences among strains because

confounding by population stratification was major concern in genome-wide association

studies (Pearson and Manolio, 2008). Twelve main principal components (PCs) were accessed

using pruned SNPs to correct for population structure. R project for statistical computing

software was used with SNPs pruned by PLINK. Quantile-quantile (Q-Q) plot was also used

to determine variation from expected distribution of no SNPs being correlated with the age

adjusted body weight.

5.2.8 Gene annotation

National Center for Biotechnology Information (NCBI) and University of California Santa

Cruz (UCSC) Genome Bioinformatics were used to know the relationship between significant

Capra hircus SNPs of this study with latest goat genome (ARS1, GenBank assembly accession

GCA_001704415.1) released August 24, 2016 (Zhang et al., 2013). There is limited genomic

information and research on goat genome (before January, 2019), the genomic information of

67

other species such as human, cattle, sheep and mouse were also used to predict relationship

between significant goat SNPs and genome of other species.

5.3 RESULTS

5.3.1 Phenotype statistics and SNP distribution before and after quality control

The descriptive statistics of body weight in kg of Beetal goat breed is presented in Table 5.1.

The overall body weight mean was 42.19 kg. However, female animal had slightly less mean

body weight than male. Region-wise means of body weight were coincide with overall mean

except a low body weight in UAF animals. MC strain had maximum mean body weight while,

NG and GR strains had least mean body weight. After quality control tests by PLINK software,

37 animals were removed leaving 594 goat for association analysis while 7603 SNPs were

removed leaving 45744 SNPs.

The detail of SNPs on each chromosome before and after quality control cleaning and average

distances between adjacent SNPs on each chromosome are provided in Table 5.2. The position

of SNPs on each chromosome was derived from latest goat genome assembly (ARS1). These

positions were helpful to run the gene annotation with greater consistency. Goat chromosome

8 had 3 significant SNPs while; chromosomes 1, 3, 7 and 12 have two significant SNPs each.

5.3.2 Genome-wide association analysis

The details of significant SNPs for age adjusted body weight (BW) trait is presented in Table

5.3. This table depicted a statistical signal overview of the associated SNPs through the whole

goat genome. Table 5.3 has the details of the genome-wise adjusted P-values, chromosome-

wise adjusted P-values, significant SNPs and their positions in goat genome assembly (ARS1),

the nearest genes and the raw P-values corresponding to trait of interest (BW). The profile of

P-values (in terms of –log (P)) of all studied SNPs are presented in Figure 5.1. The total number

of different SNPs that were significant and associated with BW (indicated in bold in Table 5.3)

at chromosome-wise and also genome-wise were 18 in number. Out of these 18 significant

SNPs, seven SNPs are within the genes. The range of distance of genes from remaining eleven

significant SNPs is from 5538 to 494349 base pairs.

5.3.3 Q-Q plot

The Q-Q plot fot the test statistics are shown in Figure 5.2. On X-axis are the expected P-

68

values with null hypothesis (no variations) and on the Y-axis are given the observed P-values.

The observed SNPs were greater than the expected SNPs in Q-Q plot for BW indicated that

SNPs were associated with the trait of interest (BW) at the adjusted genome-wise significant

level.

5.3.4 Population stratification

Principal component analysis (PCA) shows that 5 strains of Beetal goat breed are genetically

closely related to each other and their genomic closeness is presented in Figure 5.3.

5.4 DISCUSSION

This study was planned to associate genome-wide different SNPs with body weight trait

(growth) in Beetal goat population. This is the first GWA study in Pakistani goat breed to the

best of our knowledge. The Illumina GeneSeek (Lincoln, NE USA) GoatSNP60 SNPchip was

used to identify association between SNPs with BW in order to precisely predict and identify

important candidate genes in Beetal goat strains. Therefore, the results of this study might be

used to identify and discover unique candidate genes, even for the implementation of practical

exploration of promising genes later. In this study 18 significant SNPs were detected for body

weight adjusted with age and all are genome-wise and chromosome-wise significant. Among

these 18 significant SNPs, 7 SNPs are within genes viz. von Willebrand factor C domain

containing 2 like gene (VWC2L), phospholipid phosphatase related 1 gene (PLPPR1),

tetratricopeptide repeat domain 28 gene (TTC28), Inositol Polyphosphate-5-Phosphatase D

gene (INPP5D), N-Acetylated Alpha-Linked Acidic Dipeptidase Like 2 gene (NAALADL2),

tyrosyl-DNA phosphodiesterase-1 gene (TDP1) and Endoplasmic Reticulum Aminopeptidase

1 gene (ERAP1).

VWC2L (von Willebrand factor C domain containing 2 like) gene is responsible to play a role

in neurogenesis, in bone differentiation and matrix mineralization (Zimin et al., 2009).

VWC2L secrete a novel protein that promotes matrix mineralization by modulating Osterix

expression likely through transforming growth factor beta (TGF-β) superfamily growth factor

signaling pathway (Ohyama et al., 2012).

PLPPR1 (phospholipid phosphatase related 1) gene encodes a member of the plasticity-related

gene (PRG) family. The protein encoded by this gene does not perform its function through

69

enzymatic phospholipid degradation. This gene is strongly expressed in brain. It shows

dynamic expression regulation during brain development and neuronal excitation (Huttlin et

al., 2015); (Leal-Gutiérrez et al., 2018). TTC28 (tetratricopeptide repeat domain 28) is a

protein-coding gene. Diseases associated with TTC28 include Cleft Soft Palate. This gene is

involved in the condensation of spindle midzone microtubules, which further leads to the

creation of midbody during mitosis (Chen et al., 2018).

INPP5D (Inositol Polyphosphate-5-Phosphatase D) gene is responsible for the coding of a

protein with an N-terminal SH2 domain, an inositol phosphatase domain, and two C-terminal

protein interaction domains. This gene is reported to be associated with Alzheimer's disease

(AD) through modulating the inflammatory process and immune response (Jing et al., 2016;

Hunter, 2010).

NAALADL2 (N-Acetylated Alpha-Linked Acidic Dipeptidase Like 2) is characterized

properly by prostate-specific membrane antigen and it is a member of glutamate

carboxypeptidase II family. NAALADL2 promotes adhesion to extracellular matrix protein

and it was located at the basal cell surface (Whitaker et al., 2014). TDP1 (tyrosyl-DNA

phosphodiesterase-1) gene hydrolyzes 3'-phosphotyrosyl bonds to generate 3'-phosphate DNA

and free tyrosine in vitro and is acting as a member of the phospholipase D (PLD) (Raymond

et al., 2004). It is reported that Tdp1 catalytic cycle involves a covalent reaction intermediate

in which a histidine residue is connected to a DNA 3'-phosphate through a phosphoamide

linkage. Most surprisingly, Tdp1 can hydrolyze this linkage unlike a topoisomerase I-DNA

complex, which requires modification to be an efficient substrate for Tdp1, the native form of

Tdp1 can be removed from the DNA (Interthal et al., 2005).

ERAP1 (Endoplasmic Reticulum Aminopeptidase 1) gene delivers instructions for making a

protein called endoplasmic reticulum aminopeptidase 1 (ERAP1). ERAP1 strongly prefers

substrates 9-16 residues long, the lengths of peptides transported efficiently into the ER by the

transporter associated with antigen processing (TAP) transporter (Chang et al., 2005). Cai et

al. (2015) described that ERAP1 polymorphisms were associated with ankylosing spondylitis

(AS) in Caucasians, but their association with AS in Asians needs further exploration.

PTTG1 (pituitary tumor-transforming 1) gene is located 5538 bp downstream of the goat SNP

(snp2506-scaffold1070-287828). This gene is homologous to a mammalian securing, plays a

http://www.malacards.org/card/cleft_soft_palate

70

pivotal role in cell transformation and is overexpressed in numerous cancer cell lines and

tissues. PTTG functions in the control of mitosis, cell transformation, DNA repair and gene

regulation (Genkai et al., 2006). SEMA3C (semaphorin 3C) gene is located 235425 bp away

in upstream of the goat SNP. The gene is expressed by endothelial cells and controls vascular

morphogenesis through integrin inhibition. Sema 3C is also required for normal cardiovascular

patterning, (Banu et al., 2006). IPO5 (Importin-5) gene is also located on the upstream of the

goat SNP (16583 bp distance). The protein encoded by this gene is a member of

the importin beta family (Deng et al., 2006).

PHOX2B (paired like homeobox 2B) gene is found 13465 bp downstream to the goat SNP

(snp40068-scaffold511-1694741) on chromosome 6. The gene is necessary for autonomic

nervous-system development (Trochet et al., 2005). SUSD1 (sushi domain containing 1) is

another gene located downstream to goat SNP (snp31862-scaffold356-2790706). Tic Disorder

(mental disorder in humans) is the disease associated with SUSD1. Gene Ontology (GO)

annotations of this gene includes calcium ion binding (Clark et al., 2003).

LOC102184808 is located 52078 bp away downstream to the goat SNP (snp44561-

scaffold606-1561353). The closet gene is nucleolar protein 56 pseudogene in Capra hircus.

PHACTR3 (phosphatase and actin regulator 3) gene is found downstream to the SNP. The

encoded protein is associated with the nuclear scaffold in proliferating cells, and binds to actin

and the catalytic subunit of protein phosphatase-1 and the gene is likely to be a key regulator

of endothelial cell function properties (Jarray et al., 2011). OLFM3 (olfactomedin 3) gene is

located 226076 bp upstream to the SNP (snp48352-scaffold687-394326). In the human eye,

optimedin is expressed in the retina and the trabecular meshwork. Both optimedin and myocilin

are localized in Golgi and are secreted proteins. The C-terminal olfactomedin domains are

essential for interaction between optimedin and myocilin, while the N-terminal domains of

both proteins are involved in the formation of protein homodimers (Torrado et al., 2002).

ERG gene is family of ETS (erythroblast transformation-specific). All members of this family

are key regulators of embryonic development, cell proliferation, differentiation, angiogenesis,

inflammation, and apoptosis (Lee et al., 2018; Loughran et al., 2008). MDFIC2 (MyoD family

inhibitor domain containing 2) gene found on downstream to goat SNP (snp13860-

scaffold154-2852556). The MyoD family inhibitor domain-containing protein (MDFIC) is

https://en.wikipedia.org/wiki/Importin

http://www.malacards.org/card/tic_disorder

71

identified as a binding partner for glucocorticoids (GR). MDFIC is associated with GR in the

cytoplasm of cells, and treatment with glucocorticoids results in the dissociation of the GR-

MDFIC complex (Oakley et al., 2017).

ABCC4 (ATP binding cassette subfamily C member 4) gene encodes a transmembrane protein

involved in the export of pro-inflammatory molecules, including leukotriene, prostaglandin,

and sphingosine-1-phosphate across the plasma membrane (Palikhe et al., 2017).

5.5 CONCLUSIONS

This study highlighted the technique of GWAS and finding significance of SNPs with the

related genes. Candidate genes were analyzed at preliminary level. The present study results

might be beneficial as it provided basic framework to facilitate further genomic studies on

Beetal goat. Further research might include exploring analysis of network of genes related to

growth traits and functional validation of candidate genes, which could definitely disclose the

mutations in goat genome associated with growth production in Beetal goat.

72

Table 5.1 Descriptive statistics of body weight (kg) of Beetal goat breed

Number Mean Stand.

Deviation

Minimum Maximum Stand. Error

Overall

631 42.19 16.07 3.0 115.0 0.6398

Sex-wise*

1 (151) 42.47 19.56 7.0 115.0 1.5916

2 (480) 42.11 14.83 3.0 82.0 0.6769

Regions-wise#

1 (157) 42.24 20.23 6.0 115.0 1.6151

2 (52) 39.67 7.073 17.0 50.0 0.9808

3 (294) 42.43 14.86 15.0 103.0 0.8668

4 (128) 42.60 15.67 3.0 105.0 1.3853

Strain-wise$

1 (112) 44.26 12.88 15.0 94.0 1.2174

2 (414) 42.00 15.98 3.0 115.0 0.7855

3 (65) 42.18 20.89 6.0 85.0 2.5911

4 (34) 38.56 15.81 3.0 105.0 2.7113

5 (6) 37.50 17.60 16.0 65.0 7.1868

*Sex: 1=Male, 2=Female

#Region 1=Faisalabad field, 2=UAF, 3=LES Allah Dad, 4=USAID project

$Strain: 1=Makhi-Cheeni, 2=Faisalabadi, 3=Nagri, 4=Nuqri, 5=Gujrti

73

Table 5.2 Average distances among adjacent SNPs on each chromosome and

distributions of SNPs before and after quality control measures

Chromosome No. of SNPs Chromosomal Length

(bp)a

Average Distance (kb)

Before

QC

After

QC

Before QC After QC

1 3256 2823 157403528 48.34 55.76

2 2829 2513 136510947 48.25 54.32

3 2380 2045 120038259 50.44 58.70

4 2415 2136 120734966 49.99 56.52

5 2243 1953 119020588 53.06 60.94

6 2437 2047 117642375 48.27 57.47

7 2191 1927 108433636 49.49 56.27

8 2351 2064 112672867 47.93 54.59

9 1894 1660 91568626 48.35 55.16

10 2098 1849 101087560 48.18 54.67

11 2138 1859 106225002 49.68 57.14

12 1749 1547 87277232 49.90 56.42

13 1649 1438 83034183 50.35 57.74

14 1911 1672 94672733 49.54 56.62

15 1639 1410 81904557 49.97 58.09

16 1592 1404 79370172 49.86 56.53

17 1469 1289 71137785 48.43 55.19

18 1291 1037 67275902 52.11 64.88

19 1227 1071 62516450 50.95 58.37

20 1495 1322 71784255 48.02 54.30

21 1430 1271 69425955 48.55 54.62

22 1169 1010 60283066 51.57 59.69

23 1047 900 48866549 46.67 54.30

24 1323 1151 62310066 47.10 54.14

25 855 716 42858509 50.13 59.86

74

Chromosome No. of SNPs Chromosomal Length

(bp)a

Average Distance (kb)

Before

QC

After

QC

Before QC After QC

26 1044 922 51421553 49.25 55.77

27 928 812 44709034 48.18 55.06

28 914 807 44672302 48.88 55.36

29 977 842 51332696 52.54 60.97

X 1986 1486 115943529 58.38 78.02

0b 1420 761

a: Derived from latest goat genome sequence assembly (ARS1)

(https://www.ncbi.nlm.nih.gov/assembly/?term=Capra+hircus)

b: These SNPs are not assigned to any chromosomes.

https://www.ncbi.nlm.nih.gov/assembly/?term=Capra+hircus

75

Table 5.3 Genome-wise and chromosome-wise significant (P ≤ 0.05) SNPs associated with age adjusted body weight (growth

trait) of Beetal goat

Genome-wise

adjusted p-value

Chr. Chromosome-

wise adjusted p

values

SNP ARS1

Position (bp)

Nearest Gene Raw p

value Name Distance#

(bp)

1.810E-10 7 4.30E-09 snp2506-scaffold1070-287828 38573151 PTTG1 5538 8.278E-06

2.072E-10 2 3.77E-09 snp20448-scaffold202-3614967 33391710 VWC2L Within 9.479E-06

3.189E-10 8 7.07E-09 snp32850-scaffold38-3940153 90590028 PLPPR1 Within 1.459E-05

5.719E-10 4 1.22E-08 snp13658-scaffold1527-251233 80614741 SEMA3C -235425 2.616E-05

7.680E-10 17 2.73E-08 snp47231-scaffold663-207688 3479687 TTC28 Within 3.513E-05

8.598E-10 12 2.54E-08 snp39100-scaffold498-780230 11291833 IPO5 -16583 3.933E-05

9.013E-10 3 2.02E-08 snp17052-scaffold1777-307606 7891413 INPP5D Within 4.123E-05

9.499E-10 6 2.12E-08 snp40068-scaffold511-1694741 61203225 PHOX2B 13465 4.345E-05

9.855E-10 1 1.60E-08 snp26374-scaffold276-2938522 91496816 NAALADL2 Within 4.508E-05

1.323E-09 8 2.93E-08 snp31862-scaffold356-2790706 101128944 SUSD1 51850 6.053E-05

1.518E-09 10 3.76E-08 snp11072-scaffold1399-798540 1544408 TDP1 Within 6.945E-05

1.619E-09 8 3.59E-08 snp44561-scaffold606-1561353 49658842 LOC102184808 52078 7.408E-05

1.704E-09 13 5.42E-08 snp51614-scaffold758-921766 55525198 PHACTR3 494349 7.797E-05

1.837E-09 3 4.11E-08 snp48352-scaffold687-394326 79466927 OLFM3 -226076 8.405E-05

1.865E-09 1 3.02E-08 snp37564-scaffold46-2767684 150552092 ERG 7915 8.53E-05

1.942E-09 22 8.80E-08 snp13860-scaffold154-2852556 31160055 MDFIC2 49365 8.885E-05

2.139E-09 12 6.32E-08 snp24119-scaffold246-617122 16377554 ABCC4 19699 9.783E-05

2.225E-09 7 5.28E-08 snp2711-scaffold1079-117622 14422892 ERAP1 Within 0.0001018

Genome-wise significant SNPs are labeled in bold.

SNPs located within caprine genes are labeled in italics. #Positive value denotes the genome located downstream of SNP, negative value denotes the gene located upstream of SNP.

76

Table 5.4 Blast results for the sequence between 5kb downstream and 5kb upstream of the SNPs in goat compared with

other species (human, cattle, sheep and mouse)

SNP name Nearest gene (human) Nearest gene (cattle) Nearest gene (sheep) Nearest gene (mouse)

Name Distance

(bp)

Name Distance

(bp)

Name Distance

(bp)

Name Distance

(bp)

snp2506-scaffold1070-287828 PTTG1 Within SZRD1 2148 ACACA Within Pttg1 Within

snp20448-scaffold202-3614967 VWC2L Within EDA Within ACACA Within Vwc2l Within

snp32850-scaffold38-3940153 CELF2 Within CGGBP1 19595 ESR2 678869 Kdf1 105413

snp13658-scaffold1527-251233 TECTB Within PIH1D3 110769 PRNP 31344 Washc5 Within

snp47231-scaffold663-207688 TTC28 Within AK7 Within ESR2 178869 Ttc28 Within

snp39100-scaffold498-780230 TMEFF1 Within ESF1,

ISM1,

TASP1

Within RAP2A 340999 Gpr176 Within

snp17052-scaffold1777-307606 INPP5D Within INPP5D,

KCNB2,

TERF1

Within MEF2C 540235 Inpp5d Within

snp40068-scaffold511-1694741 PHOX2B 12970 TMEM33 176240 AGTR1 238871 RP23-176M22.7 Within

snp26374-scaffold276-2938522 AL445426.1,

LINC01933

Within SLC38A2 68003 PPIA 152071 4930429D17Rik 16471

snp31862-scaffold356-2790706 ZBTB7C Within UGCG 1976056 LPAR1 717252 Pld5 Within

snp11072-scaffold1399-798540 TDP1 Within TDP1 Within B4GALNT2 Within Tdp1 Within

snp44561-scaffold606-1561353 LINC02588 19811 CD164 88011 RAB21 1092305 Smad1 Within

snp51614-scaffold758-921766 SHISA7 Within SULF2 Within SLC25A15 40694 Mob4, Rftn2,

Plcl1

Within

snp48352-scaffold687-394326 ZEB1 9848 OLFM3 214909 SLC30A7 1260495 Ncoa1 Within

77

SNP name Nearest gene (human) Nearest gene (cattle) Nearest gene (sheep) Nearest gene (mouse)

Name Distance

(bp)

Name Distance

(bp)

Name Distance

(bp)

Name Distance

(bp)

snp37564-scaffold46-2767684 ERG,

LINC01203,

CHRND

Within ERG 3187 ATP1A1 33906 5530601H04Rik,

Ankrd36

Within

snp13860-scaffold154-2852556 SAMMSON ,

AL160411.1

Within TATDN1 3321 SQLE 323264 Phf20l1 Within

snp24119-scaffold246-617122 AL157712.1,

AC008050.1

Within OPA3,

PACSIN2

Within RPS17 1248250 Faxc, Znrf3 Within

snp2711-scaffold1079-117622 ERAP1,

DHRS9

Within ERAP1,

F13A1

Within CAST 8053 Erap1,

3110009E18Rik

Within

Genome-wise significant SNPs are labeled as bold.

BLAST results for the sequence between more than 5 kb downstream and more than 5 kb upstream of the SNPs in goat compared with other species (human,

cattle, sheep and mouse) are labeled in bold and italics.

The BLAST work was done before January 2019.

78

Manhattan Plot

Figure 5.1 Genome-wide plot of –log10 (p-values) for association of SNP loci with age

adjusted body weight. Chromosomes 1-29 are presented by separate colors. The horizontal

line shows the genome-wise significance levels (-log10(1 x 10-4).

79

Body weight

Figure 5.2 Quantile-quantile (Q-Q) plot of genome-wide association result for age

adjusted body weight trait. Under the null hypothesis of no association at any SNP locus, the

points would be expected to follow the slope line. Deviation from the slope line corresponds

to loci that deviate from null hypothesis.

80

Figure 5.3 Principal-component analysis for population stratification in five strains of

Beetal goat breed.

81

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CHAPTER 6

SUMMARY

Livestock is major component of livelihood in developing countries and new interventions in

this sector can provide scope for poverty alleviation. Goat population is among the most rapidly

increasing livestock species in Pakistan.

Different linear body measurements were used to estimate the live body weight in Beetal goat

at various ages. Beetal goat live body weight and linear body measurements data were recorded

from six private herds and from two government farms of Punjab, Pakistan. Final data file

contained 5011 observations of live body weight (LBW) and different body measurements i.e.

whither height (WH), body length (BL), chest girth (CG), chest width (CW) and pin bone width

(PW). The traits were recorded by hanging balance (for LBW) and tailor’s tape (for all others)

from January 2016 to May 2017. Thirty six monthly age classes were defined besides separate

class for new born kids. Data were analyzed using PROC MEANS, PROC REG, PROC GLM,

PROC CORR of SAS. The overall birth and 36 month means of LBW, WH, BL, CG, CW and

PW were 3.34 and 80.21 kg, 33.61 and 92.91, 25.11 and 84.86, 30.96 and 93.30, 8.88 and

26.21 and 4.69 and 14.00 cm, respectively. All body measurements showed highly significant

effect on LBW of animals at 3 and 21 months of age. The CG was highly significantly related

to LBW in all monthly age classes with the exception of 0, 1, 2 and 7 months. The PW had

non-significant effect on LWB for all monthly age classes with the exception of 3, 4, 9, 10, 11,

21, 32 and 35 months. The WH had highly significant effect on LBW of animals in monthly

age classes of 3, 7, 10, 11, 12, 14, 15, 16, 17, 20, 21 and 35. The BL showed significant effect

on LBW up to 26 months of age. Correlation coefficients were also determined among all

traits. In agreement to regression results, CG had strong positive phenotypic correlation (0.921)

with LBW followed by BL (0.875), WH (0.864), CW (0.777) and PW (0.725). In conclusion,

CG, BL, WH, CW and PW may be used for estimation of live body weight in Beetal goat in

the order of priority. These body measurement traits may be combined in an index with

appropriate weights to more accurately predict live body weight under field conditions.

Furthermore, a measuring-tape may be devised preferably based on CG to approximately

determine LBW of goat. The body measurement traits may also be used for indirect genetic

88

selection for improved growth rate in Beetal goat while considering precisely estimated genetic

parameters for all these traits.

Growth potential of various strains of Beetal goat breed was investigated under field and farm

conditions. Data on live body weight of Beetal goat (N=5011) were recorded in the field from

registered farmers raising Beetal goats and also from two government farms of Punjab,

Pakistan between January 2016 and May 2017. The ages of animals ranged from 0-1080 d and

were divided into thirty six monthly classes. The statistical model included fixed effects of age

of goat, herd within age, sex within age, strain within age besides random residual. Data were

analyzed using PROC GLM of SAS. All fixed effects significantly explained variation in live

body weight of Beetal goat (P ≤ 0.0001). Estimate of random residual/error variance remained

29.61. Overall birth, weaning and yearling weights (±SD, kg) of Beetal goat were 3.35±0.49,

18.84±3.72 and 33.47±4.84, respectively. Overall average daily growth rate from birth to one

year of age for all Beetal strains was 85.0 g under low input system. Beetal goat raised at

private herds had higher (P ≤ 0.05) birth, weaning and yearling weights than the government

herds. Males had significantly higher (P ≤ 0.05) weaning and yearling weights than females

with both having similar birth weights. Nuqri strain had highest weaning and yearling weights

followed by Nagri, Faisalabadi, Makhi-Chini and Gujrati. Similar ranking did hold at around

3 years of age (35 months) suggesting that Nuqri strain of Beetal goat breed might be

considered superior in term of growth rate whereas Gujrati on the bottom. The results of current

study indicate that Beetal goat breed with various strains based on color pattern has wide range

of growth rate under subsistence farming system. Thus, Beetal seems to be the most suitable

animal for meat production that can compete with international meat type goat through

improved feeding and genomic selection tools.

To attain a good body weight at low age is desired economic trait in goat production. Candidate

genes associated with growth trait were identified with the use of single nucleotide

polymorphism (SNP) methodology. Illumina GoatSNP60 BeadChip with 53,347 SNPs was

used on 631 purebred Beetal goat breed individuals of different strains to perform genome-

wide association study (GWAS) for age adjusted body weight. Quality control test of genotypic

data by PLINK software removed 37 individuals and 7,605 SNPs on the basis of individual’s

missing genotype, low call rate of SNP, minor allele frequency and Hardy-Weinberg

Equilibrium (HWE). Five hundred ninety-four Beetal goat and 45,744 SNPs were used for the

89

subsequent GWAS analysis. Age adjusted body weight trait was associated with 18 significant

SNPs. Gene annotation was executed with latest goat genome (ARS1) (released August 2016).

More than one-third SNPs (7 out of 18) were located within goat genes, others were located

close to goat genes (5538 bp-494349 bp apart). Seven genes are considered to be the most

critical candidate genes associated with age adjusted body weight (BW): snp20448-

scaffold202-3614967 was located within the goat gene VWC2L, snp32850-scaffold38-

3940153, snp47231-scaffold663-207688, snp17052-scaffold1777-307606, snp26374-

scaffold276-2938522, snp11072-scaffold1399-798540 and snp2711-scaffold1079-117622

were located within PLPPR1, TTC28, INPP5D, NAALADL2, TDP1 and ERAP1 genes,

respectively. Other eleven genes (PTTG1, SEMA3C, IPO5, PHOX2B, SUSD1,

LOC102184808, PHACTR3, OLFM3, ERG, MDFIC2 and ABCC4) were also important

candidate genes affecting BW trait. Principal component analysis (PCA) showed that all

studied strains of Beetal goat breed are genetically very close to each other. Findings of this

study will contribute to the idea of genomic selection of goat for better growth and meat

production in future.

development of genetic markers panel to predict …

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