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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
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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
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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
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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
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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.
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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
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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).
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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
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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.
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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
Pearson correlation coefficients
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)
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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
Pearson correlation coefficients
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)
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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)
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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
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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
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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
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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.
An, X. P., J. G. Wang, J. X. Hou, H. B. Zhao, L. Bai, G. Li, L. X. Wang, X. Q. Liu, W. P.
Xiao, Y. X. Song, and B. Y. Cao. 2011. Polymorphism identification in the goat MSTN
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.
Eisen, E. J. 1976. Result of growth curve analysis in mice and rats. J. Anim. Sci. 42:1008-
1023.
Fahim, A., B. H. M. Patel and V. V. Rijasnaz. 2013. Relationship of body weight with linear
body measurements in Rohilkhand local goats. Indian J. Anim. Res. 47 (6): 521-526.
GOP (Government of Pakistan). 2018. Economic Survey of Pakistan. 2017-2018.
[http://www.finance.gov.pk/survey/chapters_16/02_Agriculture.pdf].
GOP (Government of Pakistan). 2006. Pakistan Livestock Census. Agricultural Cencus
Organization, Statistics Division, Government of Pakistan, Lahore.
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.
Khan, H., F. Muhammad, R. Ahmad, G. Nawaz, Rahimullah, and M. Zubair. 2006.
Relationship of Body Weight with Linear Body Measurements in Goats. J. Agric. Biol.
Sci. 1(3): 51-54
Khan, M. S., M. A. Khan, and S. Mahmood. 2008. Genetic resources and diversity in Pakistani
goats. Int. J. Agri. Biol. 10:227–231.
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).
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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
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Tsegaye, D., B. Belay, and A. Haile. 2013. Linear body measurements as predictor of body
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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)
Age of Goat (months)
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
Age of Goat (months)
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
Age of Goat (months)
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
Age (months) LS Means of Body Weight ±Stand. Error
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
Age (months) LS Means of Body Weight ±Stand. Error
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
)
N LS Means and standard error (S.E.) of Body Weight for each herd
type
Government Private P-Value
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
)
N LS Means and standard error (S.E.) of Body Weight for each herd
type
Government Private P-Value
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
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Ahmad, N., K. Javed, M. Abdullah, A. S. Hashmi, A. Ali, Z. Iqbal, U. Younas, and Z. M. Iqbal.
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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
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Xiao, Y. X. Song, and B. Y. Cao. 2011. Polymorphism identification in the goat MSTN
gene and association analysis with growth traits. Czech J. Anim. Sci. 56:529-535.
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Fahim, A., B. H. M. Patel and V. V. Rijasnaz. 2013. Relationship of body weight with linear
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[http://www.finance.gov.pk/survey/chapters_16/02_Agriculture.pdf].
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Khan, M. S., 2016. Documenting Indigenous Genetic Resources-The Beetal Goats. Innovation
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Khan, M. S., M. A. Khan, and S. Mahmood. 2008. Genetic resources and diversity in Pakistani
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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.
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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.
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
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
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.
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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87
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
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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.
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