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ICAR-CIRC, Meerut
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ICAR-NBAGR, Karnal
Editor
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ICAR-NBAGR, Karnal
Advisory Board
Dr MR JayshankarHead, AG & B, Veterinary College, Hebbal, Bangalore
Dr Sosamma IypeVechur Conservation Trust, Mannuthy, Thrissur
Dr GS BrahDirector, School of Animal Biotechnology, GADVASU, Ludhiana
Dr BP Mishra Joint Director Research, ICAR-IVRI, Izatnagar
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Dr SM Deb Director, ICAR- NRC on Yak, Dirang
Dr BK Joshi Ex-Director, ICAR-NBAGR, Karnal
An of�icial publication of the Society for Conservation of Domestic Animal Biodiversity
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JOURNAL OF LIVESTOCK BIODIVERSITY VOLUME 5, NUMBER 1-2, 2015
Sikkim Black goats – characters, management and microsatellite based
genetic pro�ile
NK Verma, Pushp Raj Shivahre, Rak Aggarwal, Rekha Sharma, PS Dangi and
NT Bhutia
Genetic and phenotypic response to selection in various traits of IWH and
IWI strains of White Leghorn under long term selection
MC Kataria, A K Mishra, R K S Bais, S Kumar, Raj Narayan, S Johari and R Gopal
Low variability at Hinf I locus of major histocompatibility complex (MHC) –
DQB gene in Indian Mithun (Bos frontalis)
DS Gonge, S K Niranjan, S K Mishra, R K Singh, S Kumar and R S Kataria
Effect of different diets of full fat soybean (�lake) on the meat composition of
broilers
DS Rasane and SS Kamble
Tissue related insilico mining of single nucleotide polymorphisms (SNPs)
from expressed sequence tags (ESTs) in livestock species
Neeraj Kumar Dhaliwal, Aruna Pandey, Birham Prakash, Avnish Kumar Bhatia
Production and reproduction performance of Red Sindhi cow
RP Jadhav and SS Kamble
Genetic evaluation of White Leghorn layers under reciprocal recurrent
selection
Ramesh Kumar, S Kalra, Satbir Singh and B Parkash
01
07
11
14
18
22
25
Sikkim Black goats – characters, management and microsatellitebased genetic pro�ile
1 2N K Verma, Pushp Raj Shivahre , RAK Aggarwal, Rekha Sharma, PS Dangi, NT Bhutia , ICAR - National Bureau of Animal Genetic Resources, Karnal – 132001 (Haryana) India
ABSTRACT
Keywords : microsatellite, genetic diversity, goat 1 2
Present Address: Ph.D scholar dairy cattle breeding division ICAR-NDRI Karnal, Additional
Director, Department of Animal Husbandry, Dairying and Fisheries, Govt. of Sikkim, Gangtok
Corresponding author: [email protected]
INTRODUCTION
The goat population of Sikkim state of India is th
1,13,364 (19 livestock census, 2012) spread in all
the four districts. The male and females are almost in
equal proportion (53103 and 60261 respectively).
The study was planned to know the phenotype and
biometry of Sikkim goats. The visits were made to
North, East and West districts of Sikkim and
information on phenotypic and biometric traits of its
native goats were collected and analyzed. The �locks
seen during the survey consisted of black, white,
brown and mixture of these colours. The goats,
Singharey, with stripes on face extending from base
of horn to the muzzle mainly constituted the �locks
for which the phenotypic and biometric pro�ile has
also been studied (Verma et al. 2015a). Apart from
this, goats with jet black uniform colour were also
seen. These goats are distinct from Singharey goats
in respect of coat colour, type of horns and their size.
They look like Black Bengal but are bigger in their
body size and have longer horn. The phenotypic
traits of these goats were recorded by visual
Sikkim Black goats are distinct from Singharey goats, the main population of Sikkim state, in respect of coat colour, type of
horns and their size. Data on phenotypic and biometric traits was generated on 93 animals belonging to different �locks in
their native tract. Blood samples were collected from genetically unrelated animals. Sikkim Black goats are of medium
body size. Head is proportionate to body. Nose is straight. The horns are strong, broader at base, pointed tip, grey in
colour, curving backward. Muzzle is black, hooves are grey. Underbelly is also black. Beard is seen in few animals of both
sexes. These goats are slightly shorter than Singharey goats but their face, horn and tail are comparatively longer. The
mean estimates for height at withers, body length, Chest/heart girth, paunch girth, face length, horn length, ear length and
tail length in adult ( >18 months) female goats were 50.06±0.85, 58.75±0.73, 70.65±0.73, 79.57±1.17, 17.11±0.20,
10.17±0.53, 12.70±0.23, 10.35±0.24 cm, respectively whereas for males the average measurements were 54.70±1.19,
61.80±1.39, 75.97±1.48, 82.30±1.76, 17.90±0.29, 15.070±.77, 13.50±0.36 cm. The average body weight for adult females
was 28.48±0.72 kg and males 34.87±1.56 kg. Observed number of alleles varied from 2 to 9 with mean value 5.391±0.411,
effective number varied from 1.145 to 6.400 with mean 2.771±0.261. Observed heterozygosity (Ho) was less than the
expected (He) at most of the loci leaving ILSTS008, ILSTS005, ILSTS0087, ILSTS0029 and ILSTS034. The values ranged
from 0.125 to 0.882 with mean 0.420±0.038 for observed and 0.127 to 0.844 with mean 0.575±0.035 for expected
heterozygosity. Shannon's information index value varied from 0.291 (ILSTS029) to 1.950 (ILSTS030) with mean value
1.168±0.085. F estimates varied from -0.175 ( ILSTS087) to 0.835 (ILSTS058) with mean value 0.239±0.058. The
population also exhibited HW equilibrium w.r.t. ten loci where chi square values were non-signi�icant. A normal 'L' shaped
distribution of mode–shift test, non-signi�icant heterozygote excess suggested absence of bottleneck in the existing
Sikkim Black goat population. The study concluded that there was a reduction in genetic variability in Sikkim Black goat
population. Looking at the distinct phenotype but sharing the breeding tract with Singharey goats, there is a need to
conserve and propagate the population through appropriate scienti�ic management.
Volume 5, Number 1-2, 2015
01
observation. Blood samples were collected from
possibly genetically different animals for estimating
the genetic diversity.
MATERIAL AND METHODS
The measurements orf different body traits viz.
height at withers, body length, Chest/heart girth,
paunch girth, face length, horn length, ear length and
tail length and body weights were recorded on 93 (
63 females and 30 males) animals. The body weights
were taken with the help of spring balance. Means
with standard errors were estimated to know the
average pro�ile of studied traits.
Genomic DNA was extracted from blood samples
using a standard phenol: chloroform extraction
method (Sambrook et al., 1989). A battery of 23
microsatellite markers based on the guidelines of
ISAG & FAO's DAD-IS program and used by us
(Aggarwal et al., 2006, Verma et al., 2007, Dixit et al
2010, Verma et al., 2015) for other goat populations
was utilized to generate allelic data. Microsatellites
ampli�ication was carried out using �luorescent-
labeled primers. The ampli�ied products were
analyzed with a DNA capillary sequencer ABI
Prism® 310 Genetic Analyzer (Applied Biosystems).
Gene AlEX software package (Peakall and Smouse,
2012) was used to calculate allele frequencies,
observed number of alleles, effective number of
alleles (Kimura and Crow, 1964), observed (Ho) and
expected (He) heterozygosity at each locus.
Polymorphism information content (PIC) value for
each locus was calculated by using the method
described by Bostein et al. (1980). Finally the
bottleneck hypothesis was investigated using
BOTTLENECK 1.2.01 (Conuet and Luikart, 1996).
RESULTS AND DISCUSSION
Morphological features of Sikkim Black goats are
shown in �ig. 1. They are of medium size. Head is
proportionate to body. Nose is straight. The horns
are strong, broader at base, pointed tip, grey in
colour, curving backward. Muzzle is black, hooves are
grey. Underbelly is also black. Beard is seen in few
animals of both sexes. These goats are slightly
shorter than Singharey goats but their face, horn and
tail are comparatively longer.
The average measurements of different body
traits are presented in table 1. The mean
estimates for height at withers, body length,
Chest/heart girth, paunch girth, face length,
horn length, ear length and tail length in adult
( >18 months) female goats were 50.06±0.85,
58.75±0.73, 70.65±0.73, 79.57±1.17, 17.11±0.20,
1 0 . 1 7 ± 0 . 5 3 , 1 2 . 7 0 ± 0 . 2 3 , 1 0 . 3 5 ± 0 . 2 4 c m ,
respectively whereas for males the average
measurements were 54.70±1.19, 61.80±1.39,
75.97±1.48, 82.30±1.76, 17.90±0.29, 15.070±.77,
13.50±0.36 cm. The average body weight for adult
females was 28.48±0.72 kg and males 34.87±1.56 kg.
The averages for the same biometric traits of adult
Singharey goats were 52.52±0.71, 60.29±0.66,
67.98±0.59, 74.52±1.25, 16.16±0.19, 8.84±0.33,
13.48±0.18 & 10.88±0.24 cm respectively in
females and 55.67±0.93, 61.48±0.86, 71.66±0.85,
76.47±1.16, 17.20±0.22, 14.58±0.57, 12.92±0.24 &
11.89±0.25 cm respectively in males. The body
weights were 27.33±0.65 kg in adult females and
31.03±0.92 in males ( Verma et al. 2015b). On
comparing the biometry of two populations Sikkim
Black goats were found to be heavier due to more
Figure 1. Sikkim Black goats exhibiting the different morphological features
Volume 5, Number 1-2, 2015
02
TRAITS Females Male
(n=63) (n=30)
Body Height 50.06±0.85 54.70±1.19
Body Length 58.75±0.73 61.80±1.39
Chest Girth 70.65±0.73 75.97±1.48
Paunch Girth 79.57±1.17 82.30±1.76
Face Length 17.11±0.20 17.90±0.29
Horn Length 10.17±0.53 15.070±.77
Ear Length 12.70±0.23 13.50±0.36
Tail Length 10.35±0.24 12.20±0.40
Body Weight 28.48±0.72 34.87±1.56
Table 1. Mean body measurement (cm) and body weights (kg) of adult Sikkim Black goats
average values of Chest girth, paunch girth, face
length and horn length.
Management: The �locks varying from 2 to more than
20 animals were observed in the �ield (�ig.2). These
goats like other goats of Sikkim are also maintained
under semi extensive production system where the
animals are left for pasture grazing in the morning
and brought back in the evening. Animal when stay
at home are kept on stall feeding. They are fed with
available local grass, leaves and sometimes
supplemented with crushed maize. At night, animals
are sheltered in temporary houses made of wooden
logs, bamboos and planks. The �loor of such houses
is made 3-4 ft above the ground. This type of housing
helps in maintaining cleanliness and proper
ventilation. Some houses have inbuilt mangers. The
metallic utensils are also used to serve the feed. A
special type of feeding manger is also made out of
wooden logs (�ig.3). Breeding is through natural
mating. Kidding season is March-April and October
–November. Since these goats are reared for meat
purpose, milk is not drawn but left for suckling of
kids. The male goats mature and becomes available
to serve at the age of 9-10 months but the males
reared for meat purpose are castrated at the age of 3
months. The age of maturity in female goats also
varies between 9-12 months. The average gestation
period is about �ive months. Twinning is very
common in these goats.
G e n e t i c D i v e r s i t y : T h e a l l e l i c n u m b e r,
heterozygosities, Shannon's information index, f
values were estimated for Sikkim Black goats and are
given in table 2. Observed number of alleles varied
from 2 to 9 with mean value 5.391±0.411 whereas
effective number varied from 1.145 to 6.400 with
mean 2.771±0.261. Observed number of alleles is
more than the expected number across the loci.
Observed heterozygosity (Ho) was less than the
Figure 2. A ock of Sikkim Black goats Figure 3. Housing and stall feeding
Volume 5, Number 1-2, 2015
03
Locus
Allelic Number Hetrozygosity Shannon’s
Information Index
F estimate HWE
Na Ne Ho He
ChiSq Sign.
ETH225 3.000 1.686 0.333 0.407 0.699 0.181 21.000 ***
ILSTS044 6.000 2.374 0.200 0.579 1.248 0.654 59.710 ***
ILSTS008 5.000 1.896 0.500 0.473 0.933 -0.058 18.503 *
OarHH64 6.000 3.703 0.579 0.730 1.507 0.207 31.711 **
ILSTS059 4.000 2.014 0.250 0.503 0.937 0.503 17.108 **
ILSTS065 7.000 2.616 0.316 0.618 1.240 0.489 87.801 ***
OarJMP29 4.000 1.656 0.304 0.396 0.769 0.232 19.454 **
ILSTS033 7.000 2.160 0.438 0.537 1.173 0.185 27.878 ns
OarFCB48 4.000 3.522 0.556 0.716 1.320 0.224 6.407 ns
OMHC1 7.000 5.120 0.625 0.805 1.771 0.223 20.836 ns
ILSTS005 3.000 1.489 0.385 0.328 0.619 -0.171 0.737 ns
ILSTS019 3.000 2.502 0.222 0.600 0.998 0.630 13.835 **
ILSTS058 6.000 4.129 0.125 0.758 1.581 0.835 40.000 ***
ILSTS087 8.000 4.014 0.882 0.751 1.630 -0.175 50.711 **
ILSTS029 3.000 1.145 0.133 0.127 0.291 -0.053 0.077 ns
ILSTS049 6.000 2.916 0.364 0.657 1.397 0.447 35.750 **
ILSTS030 8.000 6.400 0.583 0.844 1.950 0.309 32.333 ns
ILSTS034 5.000 2.113 0.615 0.527 1.043 -0.169 6.181 ns
ILSTS022 4.000 1.866 0.421 0.464 0.902 0.093 8.252 ns
RM088 8.000 3.130 0.611 0.681 1.461 0.102 47.739 *
RM4 2.000 1.902 0.455 0.474 0.667 0.041 0.414 ns
ILSTS082 6.000 2.674 0.421 0.626 1.215 0.327 23.697 ns
OARE129 9.000 2.714 0.353 0.631 1.520 0.441 85.170 ***
Mean 5.391 2.771 0.420 0.575 1.168 0.239
SE 0.411 0.261 0.038 0.035 0.085 0.058
Table 2. Allele frequency and Genetic diversity of microsatellite loci in Sikkim Black goats
expected (He) at most of
the loci leaving ILSTS008,
ILSTS005, ILSTS0087,
ILSTS0029 and ILSTS034.
The values ranged from
0.125 to 0.882 with mean
0 . 4 2 0 ± 0 . 0 3 8 f o r
observed and from 0.127
t o 0 . 8 4 4 w i t h m e a n
0.575±0.035 for expected
h e t e r o z y g o s i t y .
Shannon's information
index value varied from
0.291 (ILSTS029) to 1.950
(ILSTS030) with mean
value 1.168±0.085. F
estimates varied from -
0.175 ( ILSTS087) to
0.835 (ILSTS058) with
mean value 0.239±0.058.
Some loci (ILSTS008,
ILSTS005, ILSTS0087,
Volume 5, Number 1-2, 2015
04
ILSTS0029 and ILSTS034) having higher observed
heterozygosities than the expected showed negative
F values. These loci indicated inbreeding depression
as a result of which there was increase in
heterozygosity. The population also exhibited HW
equilibrium w.r.t. ten loci where chi square values
were non-signi�icant, however, the reason for HW
disequilibrium at other loci is dif�icult to mark in the
small population of Sikkim Black which is also likely
to be subjected to in and out movement of animals
from the population.
Possibility of differentiation between Black Sikkim
and Singharey goats of Sikkim was also explored.
The multi-locus F values of breed differentiation for ST
different populations of Sikkim goats indicated that
only 3.6% of the total genetic variation was due to
unique allelic differences between the populations,
with the remaining 96.4% corresponding to
d ifferences among individuals within the
populations across the 23 markers (Verma et al.,
2015b). Dixit et al., (2012) observed moderate level
(16.5%) of genetic differentiation among Indian goat
breeds. Pairwise population Nei's genetic distance
and genetic identity values between Black Sikkim
and Singharey goat populations were 0.122 and
0.885 respectively. The assignment test based on
likelihood method with the leave one out procedure
assigned 57% of the Black Sikkim individuals to
Singharey population indicating the intermixing of
two populations. Principle Component Analysis
performed also did not indicate the distinctiveness of
these populations.
Bottleneck in�luences the distribution of genetic
variation within and among populations. In recently
bottlenecked populations, the majority of loci
exhibited an excess of heterozygotes, exceeding the
heterozygosity expected in a population at mutation
drift equilibrium. Signi�icant deviations from the
mutation-drift equilibrium are simply assessed by the
probability given by each test (Standardized differences
test or Wilcoxon test). A probability less than 0.05 would
allow accepting the hypothesis of genetic bottleneck
within the analyzed data. To estimate the excess of
such heterozygosity Standardized differences and
Wilcoxon sign rank tests were utilized. The actual
mutation model of evolution followed by our
microsatellites is not known, thus all the three
models; In�inite allele model (IAM), stepwise
mutation model (SMM) and two-phase model of
mutation (TPM) were applied. A normal 'L' shaped
distribution of mode–shift test (�ig. 4), non-
signi�icant heterozygote excess on the basis of
d i ff e re n t m o d e l s , a s re ve a l e d f ro m S i g n ,
Standardized differences and Wilcoxon sign rank
tests (table 3) suggested that there was no recent
bottleneck in the existing Sikkim Black goat
population.
The study concluded that there was genetic diversity
among the studied population but because of small
population size, and its intermixing with Singharey
goats, there is a reduction in genetic variability in
Sikkim Black goat population. Principal Component
analysis also showed the mixing of Sikkim Black and
Singharey goats in the same cluster. About 40% of
the loci were not in HW equilibrium and were
heterozygotic de�icit. Looking at the distinct
phenotype but sharing the breeding tract with
Singharey goats, there is a need to conserve and
propagate the population through appropriate
scienti�ic management.
Model used I.A.M. T.P.M. S.M.M.
Sign test (No. of loci with hetrozygosity excess) Exp 13.27 13.43 13.80
Obs 9 7* 5*
P- value 0.05588 0.00622 0.00021
Standardized differences test T2 value -0.838 -3.830* -7.909*
P- value 0.20104 0.00006 0.0000
Wilcoxon test (one tail for H excess) P- value 0.77740 0.99106 0.99985
Table 3. Population bottle neck analysis in Sikkim Black goat
Volume 5, Number 1-2, 2015
05
ACKNOWLEDGMENTS
The authors are highly thankful to the Director
NBAGR for providing the facilities to carry out this
work. We sincerely thank the Secretary and the
Director, Department of Animal Husbandry,
Livestock, Fisheries and Veterinary Sciences, Govt. of
Sikkim. The Farmers / Goat keepers deserve our
special thanks for permitting us to take blood
samples from their animals.
REFERENCES
19th Livestock Census. 2012. Ministry of Agriculture,
Department of Animal Husbandry, Dairying
and Fisheries, Krishi Bhawan, N. Delhi.
Aggarwal RAK, Dixit SP, Verma NK, Mathew S, Kumar
D, Ahlawat SPS, Kumar S and Kumar Y. 2006.
Genetic diversity in Attapaddy breed of
Indian goat as analyzed with microsatellite
markers. Korean Journal of Genetics 28(3):
237-242.
Bostein, D, White R L, Skolnick M and Davis R W.
1980. Construction of genetic linkage maps
in man using restriction fragment length
polymorphism. American Journal of Human
Genetics 32: 314–331.
Cornuet J M and Luikart G. 1996. Description and
power analysis of two tests for detecting
recent population bottlenecks from allele
frequency data. Genetics 144: 2001–14.
Dixit S P, Verma N K, Aggarwal R A K, Vyas M K, Rana J
and Sharma A. 2012. Genetic diversity and
relationship among Indian goat breeds based
on microsatellite markers. Small Ruminant
Research 105: 38– 45.
Kimura M and Crow J W. 1964. The number of alleles
that can be maintained in a �inite population.
Genetics 49: 725–38.
Sambrook J, Fritsch E F, Maniatis T. 1989. Molecular
Cloning: A Laboratory Manual. Cold Spring
Harbor Laboratory Press, Cold Spring
Harbor, New York, USA
Verma N K, Dixit S P, Aggarwal R A K, Chander R,
Kumar S and Ahlawat S P S. 2007. Genetic
analysis of the Sirohi breed of Indian goat
(Capra hircus). Korean Journal of Genetics 29:
129–36.
Verma NK, Mishra P, Aggarwal RAK, Dixit SP, Dangi PS
and Dash SK 2015. Characterization,
performance, and genetic diversity among
goats of Odisha. Indian Journal of Animal
Sciences 85(2) : 168-171.
Verma N K, Aggarwal RAK, Sharma R, Dangi P S and
B h u t i a N T. 2 0 1 5 a . P h e n o t y p i c
characterization, biometry and management
of Singharey goat of Sikkim. Indian Journal of
Animal Sciences 85 (7): 810–812.
Verma N K, Aggarwal, Sharma R, Dangi P S Shivhare, P
and Bhutia N T. 2015b. Goat germplasm of
Sikkim state. A Monograph # 94/2015, ICAR
NBAGR.
Volume 5, Number 1-2, 2015
06
thA study was undertaken on male (IWH) and female (IWI) line of White leghorn completing 29 generation of selection.
The average age at sexual maturity declined signi�icantly in both the strains with the estimates of –1.404 and –1.28 days in
IWH and IWI strains respectively on phenotypic scales. Corresponding estimates on genetic scales were –0.47 and –0.35 thdays per generation in IWH and IWI strains. The change in the average response per generation for egg weight at 28 and
th40 wk of age in both the selected strains were mostly non-signi�icant except for signi�icant negative genetic association of th th40 weeks egg production with egg weight at 40 week of age in IWI strain. Egg production (40 wk) improved signi�icantly
in the both the selected strains with the estimates of 0.880.15 and 0.690.15 eggs per generation in IWH and IWI strains on
genetic scale. Average responses per generation for various economic traits observed at 64th wks of age revealed that
body weight (BW-64) declined non-signi�icantly in both the selected strains except for positive but non-signi�icant thresponse for 64 wk body weight on phenotypic scale.
Genetic and phenotypic response to selection in various traits of IWH andIWI strains of White Leghorn under long term selection
M C Kataria, A K Mishra*, R K S Bais, S Kumar, Raj Narayan, S Johari and R Gopal AG & B Division, ICAR- Central Avian Research Institute, Izatnagar, Bareilly – 243122
ABSTRACT
Key words: Selection, genetic gains, White Leghorn
Present address: ICAR - National Bureau of Animal Genetic Resources, Karnal 132001
*Corresponding author: [email protected]
INTRODUCTION
The commercial layer breeders are using specialized
male and female lines for production of commercial
layers. At present most of the high yielding pure line
strains of layer stocks appear to be on the verge of
approaching plateau, due to exhaustion of additive
genetic variance and/ or other reasons (Bais et al.
2008). The IWH and IWI strains of White Leghorn are
undergone family index selection for increased egg
production to 64 weeks of age over more than 35
years. In any selection programme, the selection is
practiced either for a single or a combination of traits
and the individuals above a certain value for the
criteria of selection are selected as parents, thus
selection causes the variance among the parents to
be reduced. Therefore, the present study was carried
to evaluate phenotypic and genetic gains realized in
various economic traits in two strains of White
Leghorn selected for improved egg production.
MATERIALS AND METHODS
Two White Leghorn pure line populations viz. IWH &
IWI and along with control (IWC) maintained at
Experimental Layer Farm ICAR- Central Avian
Research Institute, Izatnagar, Bareilly (U.P.) since th
early seventies and completing 29 generation as
closed �lock constituted the genetic stock for the
present study. Having undergone family index
selection over 28 generation, the IWH (male) and IWI
(female) lines were hatched simultaneously along
with the control line (IWC).
All the selected and control lines were maintained
under standard and uniform management condition.
In general chicks were obtained in 3-6 hatches in
different generations. The chicks were identi�ied
individually and brooded on deep litter up to 8 weeks
of age. After separation of sexes at 8 weeks of age,
only 2 male chicks per dam in the selected lines and 1
male chick per dam in the control line and all the
female chicks were retained for further study. The
pullets were housed in 3 tier individual laying cages
at about 18 weeks of age.
The hatch corrected data were utilized for estimating
genetic parameters by full sib correlation method
using mixed model Least squares and maximum
likelihood (LSMLMW) computer programme
(Harvey 1990). The phenotypic responses per
Volume 5, Number 1-2, 2015
07
Traits Strain
IWH IWI
BW at 16thwk -24.45±23.012 -10.79±18.88
BW at 40thwk -6.11±1.826** 4.06±6.15
BW at 64thwk -5.81±19.58 8.26±20.48
ASM -1.404±0.16** -1.28±0.13**
EW at 28th wk -0.324±0.29 -0.16±0.28
EW at 40th wk 0.019±0.047 -0.03±0.04
EW at 64thwk -0.45±0.32 -0.15±0.48
EP up to 40th wk 1.452±0.14** 1.253±0.15**
EP up to 64thwk 1.55±0.37** 1.65±0.36**
Table 1. Phenotypic response in various production traits in
generation within line for economic traits were
estimated as the regression of generation means (Y)
on generation numbers (X). The realized genetic
gains per generation in both selected and correlated
traits were estimated by the regression of control
deviated generation means of selected lines on
generation numbers.
RESULTS AND DISCUSSION
Average response per generation for various
economic traits up to 64 wks of age were estimated
on genetic and phenotypic scales and presented in
table 1 and 2. The average response per generation thfor initial body weight (16 wk ) was found to be
negative and non signi�icant for both the selected
strains, both on genetic and phenotypic scales; the
estimates were –24.4523.01 g, -10.7918.88 g and
–4.2920.13 g in IWH, IWI and IWC strains th
respectively. For 40 wk body weight, the average
response per generation was negative and
signi�icant with the estimates of –4.051.61** g and
–6.111.82** g on genetic and phenotypic scales
respectively in IWH strain. The results agreed well
with the report of Bais et al. (2008). The negative
response may be attributing to negative genetic
association of 40 weeks body weight with egg
production for this line. In IWI strain the estimates
were positive but non-signi�icant with the estimates
of 6.126.15 g and 4.066.15 g on genetic and
phenotypic scales respectively.
The average age at sexual maturity declined
signi�icantly in both the strains with the estimates of
–1.4040.16** days and –1.280.13** days in IWH and
IWI strains respectively on phenotypic scales.
Corresponding estimates on genetic scales were
–0.470.16** days and –0.350.11 days per generation
in IWH and IWI strains. The egg production was the
criterian of selection hence, reduction in age of
sexual maturity might have contributed to the
principal trait i.e. egg production upto 64 weeks of
age. Similar �indings/arguments have also been
reported by Bais et al. (2008). Kumar and Singh
(2009) also reported reduction in age at sexual
maturity for broiler dam and sire lines. The reduction
in ASM is expected when the criterion of selection is
egg production at �ixed age. In such cases the
selection for increased number of eggs favors the
early maturity on account of their negative genetic
association (Bais et al. 2008).
The change in the average response generation for th th
egg weight at 28 and 40 wk of age in both the
selected strains (H & I) were mostly non-signi�icant
except for signi�icant negative genetic association of th th
EP (40 wk) with egg weight at 40 week of age in IWI
strain. At 64 weeks of age body weight (BW-64) and
egg weight (EW-64) declined non-signi�icantly in
both the selected strains except for positive but non-th
signi�icant response for 64 wk body weight on thphenotypic scale and for 64 wk egg weight on
** Signi�icant at P<0.01; BW= Body weight, ASM = age at sexual maturity,
EW= egg weight, EP= egg production.
Volume 5, Number 1-2, 2015
08
Traits Strain
IWH IWI IWC
BW at 16thwk -20.15±10.59 -6.49±11.60 -4.29±20.13
BW at 40thwk -4.05±1.61** 6.12±6.15 -2.06±1.48
BW at 64thwk -32.87±17.36 -18.60±16.90 26.87±14.38
ASM -0.47±0.16** -0.35±0.11** -0.93±0.15**
EW at 28th wk 0.14±0.22 0.297±0.17 -0.46±0.31
EW at 40th wk 0.06±0.04 -0.11±0.03** 0.076±0.04
EW at 64thwk -0.08±0.32 0.22±0.25 -0.37±0.36
EP up to 40th wk 0.88±0.15** 0.69±0.15** 0.56±0.17**
EP up to 64thwk 1.22±0.29** 1.32±0.68 0.33±0.38
Table 2. Genetic response in selected strains and time trend in control population for various production traits.
** Signi�icant at P<0.01; BW= Body weight, ASM = age at sexual maturity,
EW= egg weight, EP= egg production.
genetic scale in IWI strain. The negative genetic gains
in egg weight indicated a declining trend for egg
weight across the generation and agreed full with the
�indings of Pattanayak and Patro (1995) and Bais et
al. (2008). t h
Egg production (40 and 64 wk) improved
signi�icantly in the both the selected strains with the
estimates of 1.22 0.29** and 1.320.68** at 64 weeks
of age in IWH and IWI strains on genetic scale.
Corresponding estimates on phenotypic scales were
observed to be 1.55 0.37** and 1.650.36** in IWH
and IWI strains. In contrast to present study
Shrivastava et al. (1989) could not �ind any
signi�icant response in spite of improvement in the
selected trait. However, John et al. (2000), Chatterjee
and Mishra (2002) and Devi and Reddy (2004)
reported positive and signi�icant phenotypic
response in egg production up to 280 days of age.
Average response per generation for various th
economic traits up to 64 wk of age in the control
population revealed non-signi�icant changes in body th th th
weight at 16 and 40 wk of age and egg weight at 28 th th
and 40 weeks of age. However, for ASM and 40 wk
egg number, the changes were signi�icant which
might be due signi�icant changes due to overall
improved managemental conditions provided to the
control population along with the selected strains in
the preceding few generations and also natural
selection of birds of 64 wks of age. It is quite natural
that the birds reproduce next generation after 64
wks of age will good egg producers. It will be t himportant to mention here that for 64 wk
production traits, IWC showed non-signi�icant
changes in the preceding generations.
The genetic gains realized in both the strains were
indicative of successful application of selection
programme for improving egg production.
ACKNOWLEDGEMENT
The authors are highly grateful to the Director, ICAR-
Central Avian Research Institute Izatnagar for
providing necessary facilities and Dr R D Sharma for
their help for statistical analysis of data. The authors
are also thankful to Indian Council of Agricultural
Research New Delhi for providing funds.
REFERENCES
Bais R K S, Kataria M C, Johari D C, Sharma D, Hazary R
C and Nischal. 2008. Genetic gains from
reciprocal recurrent selection for part period
egg production in White Leghorn layers,
Indian Journal of Poultry Science, 43(2): 143 –
149.
Chatterjee R N and Mishra B S .2002. Realized
phenotypic response in the performance and
time trends of genetic parameters in a White
Leghorn population, Indian Journal of Poultry
Science, 37: 35-39.
Devi K S and Reddy P M. 2004. Phenotypic and
Volume 5, Number 1-2, 2015
09
genetic response in primary and various
correlated traits in White Leghorn layers,
Indian Journal of Poultry Science, 39: 54-56.
Harvey W R. 1990. User's guide. LSMLMW and
MIXMDL PC -2 version. Mixed model least-
squares and maximum likelihood computer
programme, Ohio, USA.
John C L, Jalaluddin A and Anitha P. 2000. Impact of
selection for part period egg production in
two strains of White Leghorn, Indian Journal
of Poultry Science, 35: 156 – 160.
Kumar S and Singh R P. 2009. Expected and realized
response in various traits of broiler dam and
sire lines, Indian Journal of Animal Sciences,
79: 1066 -1068.
Pattanayak G R and Patro B N .1995. Evaluation of a
selection experiment for egg number and egg
weight in White Leghorn chicken, Indian
Journal of Animal Sciences, 65: 1131 – 1138.
Srivastava P N, Khan A G, Poulose M V and O P Dutta .
1989. Selection for egg production.1. Short
term response to selection in four strains of
while leghorn, Indian J of Animal Sciences, 59:
840-845.
Volume 5, Number 1-2, 2015
10
Genomic region corresponding to exon 2 of DQB gene was ampli�ied from 79 Indian mithuns. Three primers were used
(two forward and one reverse) to carry out Polymorphism studies. Ampli�ied products of DQB genes were digested with
HinfI restriction enzymes, which revealed a total of 2 restriction patterns in DQB. Nearly all the mithun population was
found to be �ixed for AA restriction pattern. PCR-RFLP results revealed very low variability at Hinf I site in Bofr-DQB,
contrary to PCR-RFLP studies of DQB gene in other bovines. Very low genetic diversity was observed at Hinf I loci at exon 2
region of mithun DQB, indicating conserved recognition site of Hinf I. Our study indicated that Hinf I enzyme may not be
good enough for the typing of DQB alleles in mithun population, on other side tetracutters may a good choice for the
genotyping of Bofr-DQB.
Low variability at Hinf I locus of Major Histocompatibility Complex (MHC) –DQB gene in Indian Mithun (Bos frontalis)
1 2 3D S Gonge , S K Niranjan*, S K Mishra, R K Singh , S Kumar and R S Kataria
ICAR - National Bureau of Animal Genetic Resources, Karnal – 132001 (Haryana) India
ABSTRACT
Key words: Mithun; MHC; DQ genes; duplicated haplotype; PCR-RFLP
INTRODUCTION
Mithun (Bos frontalis), is a massive, semi-
domesticated and rare bovine species and
considered descendent from wild Indian gaur
(Simoons 1984). Through contributing in terms of
meat, leather and drafting power, it plays an
important role in the day-to-day socioeconomic,
cultural and religious life of the local tribal
population of North East region of the India (Arora,
1998). Presently, India possesses 0.29 million
Mithun distributed mainly in Arunachal Pradesh, t h
Nagaland, Mizoram and Manipur states (19
Livestock Census, 2012). In view of small and
scattered populations of mithun in the region, major
efforts are being done to increase the population by
the various government and non-government
agencies in this region. Census shows the recent
increase of the mithun population in region during th threcent time (18 Livestock Census 19 Livestock
Census, 2012). However, it is essential to assess the
genetic diversity of functional markers in mithun, as
it re�lects the �itness as well adaptation of the
population to the local environment.
Functional markers like Major Histocomaptability
Complex (MHC) are preferred over neutral markers
like microsatellite for assessing the genetic diversity
in rare wild species as these, being directly
associated with host's immune response. Evidence
s u g g e s t s t h a t p o p u l a t i o n s w i t h e x t r e m e
monomorphism shown by neutral markers may
persists suf�icient genetic variation at the MHC
(Aguilar et al, 2004). Apart from critical role in
immune response, the genetic variations at MHC loci
are thought to have a signi�icant impact on
population �itness and regarded as being exploited
for increasing the diversity under conservation
programme for some of the rare wild animal species
(Yasukochi et al. 2012). Generally, MHC region is an
organized cluster of highly polymorphic genes
related with immunity. MHC class II genes,
speci�ically DRB3, DQA and DQB are extremely
polymorphic in ruminants Andersson and Rask,
1988, Sigurdardottir et al 1992, Marello et al 1995,
Ballingall et al., 1997). However, no such study has
been conducted to assess the variability at MHC loci
in mithun species. In view of small and scattered
populations of Mithun in the region, the genetic
diversity of these populations has been prime
1 2Present Address : Research Scholar, DCB, NDRI, Karnal, India; NRC on Mithun, Jharnapani, 3Medziphema India; Division of Animal Genetics, IVRI, Izatnagar, India
*Corresponding author: [email protected]
Volume 5, Number 1-2, 2015
11
S.N. Patterns Animals Frequency (%)
1 AA 76 96.20
2 AB 3 3.80
Table 1. Hinf I restriction patterns of Bofr-DQB
importance to assess the �itness for their better
survival and adaptation to the local environment.
Therefore, we conducted a study to identify genetic
variability at MHC-DQB loci in Mithun using PCR-
RFLP.
MATERIAL AND METHODS
Seventy nine mithun blood samples were collected
from different �ield areas of Nagaland and Arunachal
Pradesh of India. Genomic DNA was isolated by by
phenol-chloroform extraction method by standard
protocol (Sambrook and Russel, 2001) The purity of
t h e g e n o m i c D N A w a s a s s e s s e d b y
spectrophotometry the ratio of optical densities
value at 260 and 280 nm was used as a criteria for
purity. To amplify genomic sequence of DQB
encompassing exon 2, a set of three primers were
used referred by Traul et al . , (2005). For
ampli�ication of Bofr-DQB, two forward primers
(B1G-F-5'-TCCCCCGCAGAGGATTTCGTG-3' and
B2G-F -5'-CTCCCCGCAGAGGATTTCGTG-3') with
r e v e r s e p r i m e r B G - R - 5 ' -
CGCACTCACCTCGCCGCTGC-3). were multiplexed .
The optimized concentrations for 20 µl volume of
reaction mixture were 1.5 mM of MgCl , 2mM of 2
dNTPs and 10 pmol of primers each, 100 ng of
genomic DNA as template, 10x PCR assay buffer and
3 units of Taq DNA polymerase. Ampli�ication
conditions were: initial denaturation at 95 °C for 3
minutes, 32 cycles of denaturation for DQA and 34 cycles for DQB at 94 °C for 30 seconds, annealing 65
°C f for 30 seconds and extension at 72 °C for 1
minute for DQA and 40 seconds for DQB and �inal
extension at 72 °C for 10 minutes. Ampli�ied PCR
products were digested with HinfI restriction
enzyme for RFLP analysis. Digested products were
analyzed by gel electrophoresis by using 2.5% high
resolution agarose gel in gel electrophoresis at 80V
for 2 hour. The frequency of different patterns was
estimated by using standard method.
RESULTS AND DISCUSSION
289 long nucleotide region encompassing respective
highly variable regions (exon 2) nucleotide
sequences of DQB genes, respectively were ampli�ied
in 79 Mithun. Ampli�ied products of DQB genes were
digested with HinfI restriction enzymes, which
revealed a total of 2 restriction patterns in DQB
(Figure 1). The frequencies of different patterns with
fragment size are given in table 1. Nearly all the
mithun population was found to be �ixed (96.2%) for
AA restriction pattern. PCR-RFLP results revealed
very low variability at Hinf I site in Bofr-DQB,
contrary to PCR-RFLP studies of DQB gene in other
bovines (Marello et al, 1995, Niranjan et al, 2010a &
b). Although, more number of restriction patterns
has been identi�ied in cattle and buffalo DQB,
however by using other enzymes. Niranjan and
coworkers (2010b) also adopted the Hae III enzyme
for typing of DQA alleles in buffaloes and revealed six
patterns.
Our results indicated that the recognition site
(G/ANTC) of Hinf I in DQB alleles may be conserved.
Further, Hinf I enzyme was tried in this study,
however, never been reported for typing of DQB
alleles in any species, to the best of our knowledge, by
any worker. Our study indicated that Hinf I enzyme
may not be good enough for the typing of DQB alleles
in mithun population, on other side tetracutters like
Hae III, Rsa I may a good choice for the genotyping of
Bofr-DQB.
In conclusion, very low genetic diversity was
observed at Hinf I loci at exon 2 region of mithun
DQB, indicating conserved recognition site of Hinf I.
Figure 1: Electrophoretic mobility of RE fragments obtained by digestion ofBofr-DQB exon-2 region with HinfI ( in 2.5% agarose gel)Lane 1-15: Different restriction patternsLane M: 100 bp DNA ladder
Volume 5, Number 1-2, 2015
12
Our study indicated that Hinf I enzyme may not be
good enough for the typing of DQB alleles in mithun
population, on other side tetracutters may a good
choice for the genotyping of Bofr-DQB.
ACKNOWLEDGMENT
The authors wish to acknowledge the Director
of NBAGR and NDRI, Karnal, for use of
facilities. The �inancial assistance provided by
Department of Animal Genetics and Breeding
are also duly acknowledged.
REFERENCES
Aguilar A, Roemer G, Debenham S, Binns M,
Garcelon D, and Wayne RK. 2004. High MHC
diversity maintained by balancing selection
in an otherwise genetically monomorphic
mammal Evolution. Proc Natl Acad Sci U S A.
101(10): 3490–3494.
Andersson, L. and Rask, L. 1988. Characterization
of the MHC class II region in cattle: The
number of DQ genes varies between
haplotypes. Immunogenetics 27, 110-120.
Arora CL. 1998 Less used animal: Yak and
Mithun- an over view. Indian Journal of
Animal Science. 68: 735-742.
Ballingall, K.T., Luyai, A. and McKeever, D.J. 1997.
Analysis of genetic diversity at the DQA loci
in African cattle: evidence for a BoLA DQA3
locus? Immunogenetics 46, 237-247
Marello, K.L., Gallagher, A., McKeever, D.J., Spooner,
R.L. and Russell, G.C. 1995. Expression of
multiple DQB genes in Bos indicus cattle.
Animal Genetics. 26: 345-49.
Niranjan, S. K., Deb S. M., Sharma A., Kumar S., Mitra
A., Sakaram D. Naskar S. and Sharma S. R.
2010a. Allelic diversity at MHC class II DQ
loci in buffalo (Bubalus bubalis): Evidence
for duplication. Veterinary Immunology and
Immunopathology 138: 206-212
Niranjan, S.K., Sharma, A. Deb, S.M., Naskar, S., and
Sakaram, D. 2010b. PCR-RFLP based
identi�ication of duplicated DQA loci in
riverine buffalo. Journal of Livestock
Biodiversity, 2: 64-66.
Sambrook J. and Russel, D.W. 2001. Molecular
coloning : A laboratory manual 3rd edition.
Cold Spring Harbor Laboratory Press, New
York.
Sigurdardottir, S., Borsch, C., Gustafsson, K.,
Andersson, L., 1992. Gene duplications and
sequence polymorphism of bovine class II
DQB Genes. Immunogenetics 35 (3),
205–213.
Traul DL, Bhushan B, Eldridge JA, Crawford TB, Li
H, Davies CJ. 2005. Characterization of Bison
bison major histocompatibility complex
class IIa haplotypes. Immunogenetics,
57(11):845-54.
Volume 5, Number 1-2, 2015
13
Effect of different diets of full fat soybean (�lake) on the meat compositionof broilers
DS Rasane and SS Kamble*
Mahatma Phule Krishi Vidyapeeth, Rahuri-413 722 (India).
In the three feeding trials of broilers the meat composition at 6 weeks and 8 weeks age was studied. The meat
composition at 6 weeks age indicated that the water content in Trial-I, II and III irrespective feed treatment
ranged from 73.23 to 74.70 per cent. The corresponding values at 8 weeks age ranged from 71.70 to 72.75 per
cent.There was no signi�icant difference in all the treatments as well as all the trials. However, water content in
the meat of birds at advanced age (8 weeks) was comparatively less than 6 weeks age.The protein content of
meat at 6 and 8 weeks age ranged from 18.06 to 18.25 per cent. The protein percentage of meat was neigher
affected by treatment diets nor by different trials. The fat content in meat at 6 weeks age ranged from 6.03 to
7.33 per cent in all treatments and all the three trials. At 8 weeks age fat content in meat was ranged from 7.83
to 8.90 per cent. The fat content of meat was higher in T followed by T , T and T in all the trials. The fat 3 2 1 0
content of meat at 8 weeks was signi�icantly higher than 6 weeks meat in all the treatment diets of all the trials.
The ash content in the meat at 6 weeks age amongst the three trials did not show much differences and
remained almost similar. Similar trend was observed 8 weeks age however the results indicated that ash
content in the meat higher at increasing age. Amongst the different treatment diets �ish meal containing diet
(T ) showed higher ash percentage. The ash content in the meat was in decreasing order with increase in FFSB 0
level in the diet. In T containing diet meat ash content was signi�icantly lower than T , T and T .3 0 1 2
INTRODUCTION
For getting tender meat broilers are marketed at �ive
to six weeks age. However in broiler market, rates are
�luctuating very much and many times broiler
owners have to bear the losses. To avoid such losses
the birds are retained even up to eight weeks of age.
At eight weeks of age the tenderness is not affected
but what sort of chemical changes takes place at
different age of marketing of birds is to be studied.
Therefore in the present experiment chemical
composition of broiler meat at six and eight weeks
age under different diets have been studied.
MATERIALS AND METHODS
In this investigation three experimental trials were
conducted i.e. in the month of September-October,
November-December (2000) and March-April, 2001.
During these trials different diets were given to the
birds, viz., T (Control) received 5 per cent �ish meal, 0
T , T and T received the diet containing 5, 10 and 15 1 2 3
per cent full fat soybean �lake (FFSB), respectively.
After attaining 6 weeks age and 8 weeks age,
5 birds from each treatment were slaughtered for
studying the meat composition. The birds were
fasted for 12 hours for complete emptying of crop
and intestine. The birds were weighed and
sacri�iced. The birds were slaughtered by modi�ied
Koshers' method (Panda and Mohapatra, 1998). In
this method the jugalar vein was severed just below
the ear taking care not to cut the wind pipe and
oesophagus. This method is widely used since the
birds could be better bled. The meat analysed by
AOAC (1990) method for determination of water,
protein, fat and ash content.
RESULTS AND DISCUSSIONS
Water Content in meat : Raw meat composition
(Table 1) indicated that the moisture percentage at 6
weeks age was in the range of 73.34 to 74.70, 74.15 to
74.66 and 73.23 to 73.77 in Trial-I, Trial-II and Trial-
III, respectively. Water content of meat in T was 0
signi�icantly higher than T , T and T indicated that 1 2 3
ABSTRACT
Key words : Meat composition, broiler, soybean
Corresponding author : [email protected]
Volume 5, Number 1-2, 2015
14
there was signi�icant difference in all the
treatments as well as in the three trials. However,
in the Trial-III water content was less as compared
to the �irst two trials, which might be due to hot
climatic conditions prevailing during that trial.
Water content in meat at 8 weeks age (Table 2)
ranged from 72.43 to 72.86, 72.50 to 72.95 and
71.70 to 71.95 in Trial-I, Trial-II and Trial-III,
respectively. The water content in meat of T was 0
signi�icantly higher than T , T and T . There was 1 2 3
signi�icant different in all the treatments as well as
in the three trials. At eight weeks also the water
content in meat of third trial was comparatively
less than the �irst two trials. Sahoo and Shingari
(1992) reported the moisture percentage of
broiler meat ranging from 70.5 to 71.9. They
further stated that in summer and spring season
m o i s t u r e p e r c e n t a g e i n t h e m e a t w a s
comparatively less than winter season.
Protein Content in meat : The protein content in the
meat of birds in Trial-I, Trial-II and Trial-III at 6
weeks age was almost similar which ranged
between 18.06 and 18.25 per cent. The protein
percentage of meat was thus not affected by
various treatment diets.
Similarly at the age of 8th week (Table 2) the
protein content of meat in all three trials was in the
range of 18.12 to 18.25 per cent. The protein
percentage of meat at 6 weeks and 8 weeks age
remained almost constant. There was no
signi�icant difference in all the treatments at 6
weeks and 8 weeks age. Bonami et al. (1970)
revealed that the different levels of �ish meal in the
diet did not give any difference in the meat
composition. Similarly Sahoo and Shingari (1992)
reported that the protein per cent of meat did not
change at 6 and 8 weeks age.
Fat Content in meat : The fat content in the meat
from 6 weeks broiler ranged from 6.33 to 7.30, 6.03
to 6.52 and 6.90 to 7.33 per cent in Trial-I, Trial=II
Volume 5, Number 1-2, 2015
Table 1. Average chemical composition of meat of broilers under different treatment diets and trials at 6 weeks age
Treatment Parameters (per cent)
Water Protein Fat Ash
Trial - I
T0
T1
T2
T3
Cal. 't' value
74.70
73.83
73.67
73.34
5.293
18.06
18.06
18.12
18.12
1.845
6.33
6.67
6.95
7.30
3.141
0.84
0.82
0.82
0.77
2.443
Trial - II
T0
T1
T2
T3
Cal. 't' value
74.66
74.30
74.27
74.15
5.435
18.00
18.12
18.12
18.15
2.153
6.03
6.37
6.45
6.52
2.572
0.85
0.83
0.83
0.80
2.315
Trial - III
T0
T1
T2
T3
Cal. 't' value
73.77
73.70
73.58
73.23
6.145
18.12
18.18
18.18
18.25
2.233
6.90
6.97
6.97
7.33
3.015
0.83
0.83
0.82
0.76
2.714
Each value is an average of 5 observations
15
and Trial-III, respectively. The fat per cent was
signi�icantly higher in T (7.34%) followed by T 3 2
(6.95%), T (6.67%) and the lowest was in T (6.33%) 1 0
in the �irst trial.
In the Trial-II, similar trend was observed i.e. T 3
(6.52%) containing signi�icantly higher fat followed
by T (6.45%), T (6.37%) and the lowest was in the T 2 1 0
(6.03%).
The fat content of meat from Trial-III also indicated
the similar pattern. The T group contained 3
signi�icantly higher fat (7.33%) in the meat followed
by T (6.97%), T (6.97%) and T (6.90%) groups 2 1 0
respectively. The higher fat content in T and T may 3 2
be due to more synergic effect of poly-saturated acid
resulting in higher level of full fat soybean diet.
Barua et al. (1991) reported that the increase in
ambient temperature increased carcass fat content.
They observed non-signi�icant differences in the
carcass
fat content of broilers when reared at 27°C and 29°C.
However, when temperature was higher than 30°C
fat content increased in the carcass.
In trial-III fat per cent of meat was comparatively
higher because during hot part of the year water
losses from the body of bird might be more leading to
comparatively less water content of meat and higher
fat content. The fat content in meat of 8 weeks
broilers in Trial-I, II and III ranged from 8.00 to 8.23,
7.83 to 8.17 and 8.65 to 8.90, respectively. The fat
content in meat was higher in T followed by T , T 3 2 1
and T in all the trial. It was observed that the fat 0
content in 8 weeks meat was higher than 6 weeks
meat. Barua et al. (1991) revealed that the fat
content in carcass increased with body weight and
age, the percentage of water, protein and ash
decreased with increased fat content. According to
Singh and Panda (1992) fat content in carcass was
the most variable component and any increase in the
Volume 5, Number 1-2, 2015
Table 2 : Average chemical composition of meat of broilers under different treatment diets and trials at 8 weeks age
Treatment Parameters (per cent)
Water Protein Fat Ash
Trial - I
T0
T1
T2
T3
Cal. 't' value
72.86
72.75
72.55
72.43
5.447
18.12
18.12
18.18
18.18
1.935
8.07
8.00
8.15
8.23
2.524
0.88
0.87
0.82
0.80
3.013
Trial - II
T0
T1
T2
T3
Cal. 't' value
72.95
72.77
72.63
72.50
5.177
18.12
18.15
18.15
18.18
1.623
7.83
7.95
8.03
8.17
2.995
0.89
0.89
0.87
0.85
2.146
Trial - III
T0
T1
T2
T3
Cal. 't' value
71.95
71.91
71.82
71.70
4.166
18.18
18.15
18.25
18.25
1.983
8.65
8.73
8.87
8.90
3.115
0.87
0.86
0.86
0.84
2.238
Each value is an average of 5 observations
16
fat was accompanied by a parallel decrease in water
content and vice-versa.
Ash content in meat : The chemical analysis of meat at
6 weeks age showed that the total ash content in the
meat was the highest in T (0.84%) followed by T 0 1
and T (0.82%) and the lowest was in T (0.77%) in 2 3
Trial-I. Similar trend was observed in Trial-II and
Trial-III. Student 't' test indicated that in all the trials
T , T and T were at par, only T was signi�icantly 0 1 2 3
lower in ash content than the rest of treatments.
The results of 8 weeks in Trial-I indicated that ash
content in meat was signi�icantly higher in T 0
(0.88%) and T (0.87%) than T (0.82%) and T 1 2 3
(0.80%) . Similar trend was observed in Trial-II and
Trial-III.
The ash content in the meat at 6 weeks age amongst
the three trials did not show much differences and
remained almost similar. Although the similar trend
was observed at 8 weeks, the results indicated that
ash content in the meat was higher at this age.
Further, it was also observed that amongst the
different treatment diets studied the �ish meal
containing diet (T ) showed higher ash percentage. 0
The ash content in the meat was in decreasing order
with increase in FFSB level in the diet. There was not
much difference in the ash content of meat in T , T 0 1
and T . Whereas in T diet ash content in 2 3
the meat was signi�icantly lower than the T , T and 0 1
T . It may be due to higher fat content in the diet 2
containing 15 per cent full fat soybean.
The higher level of un-extracted soybean diet intake
reduced the calcium and phosphorus retention
(Leeson et al. 1988). Similarly Mahapatra (1992)
observed that the high fat intake in the diet interfered
with the absorption of calcium and vitamin-D3.
REFERENCES
AOAC. 1990. Of�icial methods of analysis of the
Association of of�icial Analytical chemists. th15 edition, Association of of�icial Analytical
chemist INC Suite, Arlington, Verginia,
U.S.A.1 : 69-84, 949
Barua A, Howlider MAR and Rahman MA. 1991.
Factors affecting abdominal fat and
procedures for its reduction in broilers.
Poultry Guide 28 (8) : 49-51.
Bonami A, Bianchi M, and Benatti, G. 1970. In�luence
of rations with different contents of animal
protein on the health and productivity of
meat chicken. Nutr. Abstr. Rev. 41 (4) : 1391.
Leeson S, Atteh JO and Summers JD. 1988. Effects of
increasing dietary levels of commercially
heated soybeans on performance, nutrient
retention and carcass quality of broiler
chicken. Nutr. Abstr. Rev. 58 (5) : 314.
Mahapatra N. 1992. Importance of fats and fatty
acids in poultry with special emphasis on
health problems. Indian Poult. Rev. 23 (1) :
33-37.
Panda B and Mohapatra SC. 1998. Poultry
production. Publication and information
division, ICAR, Krishi Anusandhan Bhavan,
N.Delhi : 119-122.
Sahoo G and Shingari BK. 1992. Effect of �loor space
on meat quality in commercial broilers.
Poultry Guide 29 (11) : 39-43.ndSingh KS and Panda B. 1992. Poultry nutrition. 2
ed. Kalyani Publ. Co. Pvt. Ltd., Ludhiana : 33.
Volume 5, Number 1-2, 2015
17
Tissue related in-silico mining of single nucleotide polymorphisms (SNPS) fromexpressed sequence tags (ESTS) in livestock species
Neeraj Kumar Dhaliwal, Aruna Pandey, Birham Prakash and Avnish Kumar Bhatia*ICAR - National Bureau of Animal Genetic Resources, Karnal – 132001 (Haryana) India
INTRODUCTION
Expressed Sequence Tags (ESTs) are partial
sequences of complementary DNA (cDNA) clones
measuring several hundred nucleotides [Baxevanis
and Ouellette, 2001]. There have been voluminous
increases in EST data generation and submission,
especially for livestock species, to the primary
databases such as NCBI, DDBJ and EMBL. Single
nucleotide polymorphisms (SNPs) are the simplest
type of genomic variation. Over the past decade,
SNPs have been the genetic markers of choice due to
their high density, stability and the highly automated
techniques for their detection [Kerstens et al., 2009].
Thousands of potentially informative SNP markers
can be identi�ied for development of high density
SNP maps [Zimdahl et al., 2004], which are an
essential resource to identify genes responsible for
variation of complex traits or Quantitative Traits Loci
(QTL) [Andersson, 2001; Andersson and Georges,
2004]. SNP analysis provides an important tool in
applications such as genetic linkage mapping, �ine
mapping of candidate regions and to determine
haplotypes associated with traits of interest [Panitz
et al., 2007]. With availability of genome sequence
assembly of a number of livestock species like cow,
sheep, chicken, pig and horse, mining of sequence
data for identi�ication of SNPs is a major task for
researchers. Huge amount of EST data for livestock
species- pig and cattle on different tissues like skin,
mammary gland, spleen, liver, intestine etc. are
available in public databases. ESTs data allow
discovery of SNPs in the transcribed regions [Marth,
2003].
There are few studies showing importance of tissue-
Related SNPs, particularly in species of economic
importance such as livestock species. In a study of
Tissue Speci�ic Temporal (TST) exome capture,
presence of tissue (muscle) speci�ic genes and SNPs
in Bubalus bubalis has been revealed [Jakhesara et al.,
2012]. Recent Genome Wide Association Studies
(GWAS) in humans have revealed that the genetic
variants may be operating in tissue dependent
manner. Subsets of genetic polymorphisms show a
statistical association with transcript expression
levels, and have therefore been called as expression
quantitative trait loci (eQTLs) [Nicolae et al., 2010].
In this study we have used a bioinformatics pipeline
for mining of insilico tissue-related SNPc from EST
data in pig and cattle. Discovered SNPs have been
validated by revealing their availability in dbSNP
database. A database of tissue wise SNPs for livestock
ABSTRACT
Tissue speci�ic Single nucleotide polymorphisms (SNPs) hold signi�icance as potential expressed
Quantitative Trait Loci (eQTL). Although there have been numerous studies for mining SNPs in livestock
species, there is little focus on discovery of tissue-speci�ic SNPs. We performed tissue related insilico SNP
mining from Expressed Sequence Tags (ESTs) in two livestock species - pig and cattle. EST data for tissues
such as skin, liver, spleen, intestine and mammary gland for the two species were downloaded from NCBI
website. ESTs were pre-processed using the online tool EGassembler and assembled into contigs using CAP3
program. SNPs were predicted from contigs using QualitySNP tool. Contigs were searched in the genome
assembly of a species using Blat tool in UCSC genome browser. Perl scripts were written to �ind genomic
position of SNPs from the alignment of contigs with the genomic segments, and to �ind availability of
predicted SNPs in the dbSNP database. A database of tissue related SNPs was developed.
Keywords: EST, SNP, tissue-speci�ic, livestock, eQTL
*Corresponding author: [email protected]
Volume 5, Number 1-2, 2015
18
species have also been developed with additional
information on genes.
METHODS
EST data processing : EST data for different tissues of
pig and cattle were downloaded from dbEST
d a t a b a s e a v a i l a b l e a t N C B I
(www.ncbi.nlm.nih.gov/nucest). ESTs needs
processing to remove low quality DNA sequences,
contaminating sequences such as vector sequences
and repetitive sequences [Chou and Holmes, 2001].
The preprocessing was performed using online tool
EGassembler [Masoudi-Nejad et al., 2006], which
performs sequence cleaning, repeats masking and
vector cleaning as a single operation. Repbase
repeats library for the respective species was
selected as repeats library to remove repetitive
sequences and NCBI core vector library was chosen
for vector masking process. Processed ESTs were
free from low quality sequences, poly A/ poly T tail,
repetitive sequences and the vector sequences.
ESTs were assembled in contigs using the cluster
assembly program CAP3 [Huang and Madan, 1999]
using default parameters, which performs clustering
or assembly of the sequences into contigs by
pairwise sequence similarity searches between
sequences.
SNP identi�ication : Contigs were analyzed for SNPs
using SNP prediction tool QualitySNP [Tang et al.,
2006] with default parameters. This tool takes the
contigs.ace �ile generated by CAP3 tool as input and
predicts SNPs on contigs. SNPs predicted by the tool
are differentiated into three categories, viz., potential
SNPs, High Quality SNPs and Reliable SNPs. It
provides position of SNPs on contigs, number of SNPs
on each contig, major and minor alleles and d-value
denoting the standard deviation of normalized
number of SNPs per haplotype, which identify
clusters that probably contains paralogs.
SNPs position on chromosome and availability in
dbSNP Database : Contigs with potential SNPs were
searched in the genome assembly of a species in
UCSC Genome Browser using BLAT tool (http://
genome.ucsc .edu/cgi-bin/hgBlat? command=start).
This tool provides alignment of the contig with
particular chromosome segment of a species.
SNPs data of both the species were downloaded from
E n s e m b l G e n o m e B r o w s e r ( h t t p : / / a s i a .
ensembl.org/biomart/martview/), which included
S N P n a m e , S N P t y p e , c h ro m o s o m e n a m e ,
chromosome position, chromosome strand, SNP
alleles, Ensembl gene id, gene name, and gene start
and gene end positions. SNP data were retrieved
separately for each chromosome for these two
species for ease of data handling on a computer.
Perl script was written to �ind position of SNPs in the
best alignment of contigs and genomic sequences as
obtained using the BLAT tool. These SNPs were
searched for their presence in the dbSNP database
a v a i l a b l e i n t h e E n s e m b l B i o m a r t
(http://asia.ensembl.org/biomart/martview/). The
perl script is available for download within online
database described in the section 3.
Database development : A database on Tissue-wise
SNPs mined from ESTs was developed using MySQL
and PHP to provide user-friendly interface on tissue
wise SNPs and their availability in dbSNP database.
SNPs data in text �iles was inserted into the database
using a perl script. Information on chromosomes and
genes were also inserted in the database.
RESULTS
Table-1 displays information of ESTs and SNPs
discovered in pig and cattle. There were 50410 ESTs
in dbEST database for skin tissue of pig. Assembly of
these ESTs provided 6476 contigs and a total of 3444
SNPs were identi�ied out of which 1550 were high
quality SNPs and 834 were reliable SNPs. Only 421
out of 3444 detected SNPs were found in dbSNP
database.
The database developed on tissue-wise SNPs
provides a user-friendly web interface for data query
and visualization on search terms such as species
name, tissue name, chromosome name and gene
name. Output �ield settings include dbSNP name,
gene name, chromosome name, contig name and
sequence. SNPs are retrived with information on
high quality SNPs, reliable SNPs, Blat strand, dbSNP
strand, dbSNP id, dbSNP allele, contig allele, contig
name and sequence. The database also provides
genomic information such as Ensembl gene id,
Ensembl gene name, gene start position, gene end
Volume 5, Number 1-2, 2015
19
position, chromosome name and position of SNP on
chromosome. Information on tissue-wise SNPs is
linked to other databases such as Ensembl genome
browser and dbSNP database. The database is
accessible through a link 'Database' available at the
website http://www.nabgr.res.in/.
DISCUSSION
Signi�icance of tissue speci�ic SNPs, genes and eQTLs
has been highlighted recently in humans [Dimas et
al., 2009; Nica et al., 2011; Hernandez et al., 2012].
Tissue speci�ic SNPs have also been studied in
Bubalis bubalus [Jakhesara et al, 2012]. In these
studies, effects of genetic variants have been
reported in which 69 to 80% of the regulatory
variants are operating in cell-speci�ic manner. Also
eQTLs have been identi�ied, which may be unique or
shared among cell types or tissues. Many tissues and
cell types have speci�ic gene expression patterns and
so it is not clear how frequently eQTLs found in one
tissue type will be replicated in others. Therefore,
tissue speci�ic studies using SNPs to detect eQTLs
have been taken up. A unique set of tissue Related
eQTLs have been identi�ied in blood and brain tissues
in humans [Nica et al., 2011]. Nicolae et al., 2010
showed that SNPs associated with complex traits are
more likely to be eQTLs comparted to minor-allele-
frequency matched SNPs from GWAS. In view of
these studies, tissue speci�ic SNP mining should be of
considerable value in the livestock species.
We have performed tissue-related insilico SNP
mining from EST data in two agriculturally
important livestock species - pig and cattle. These
SNPs were mined from EST data retrieved for a tissue
and therefore the SNPs discovered should be related
to the tissue. These SNPs were then located on the
genome assembly of the respective species and
validated by searching in the dbSNP database.
The present study reports a bioinformatics pipeline
for insilico tissue-related SNP mining from EST data
in pig and cattle, locating these SNPs on the genome
assembly of the respective species, and searching
their availability in dbSNP database using perl script.
A number of potential SNPs were discovered in the
two investigated livestock species-pig and cattle
using the approach. A database of tissue-Related
SNPs has also been developed for use by researchers.
ACKNOWLEDGMENTS
Authors thankfully acknowledge funding by Indian
Council of Agricultural Research through National
Agricultural Innovation Project.
REFERENCES
Andersson L. 2001. Genetic dissection of phenotypic
diversity in farm animals.
Volume 5, Number 1-2, 2015
Table 1. Tissue related SNPs mined from ESTs in pig and cattle species
Livestock
species
Tissue ESTs Contigs Contigs
with
SNPs
SNPs High
Quality
SNP
ReliableSNP
Matches
in dbSNP database
Pig Skin 6476 698 3444 1550 834 421
Spleen 7575 761 5032 2389 1463 595
Mammary
Gland
1545 146 1687 1027 311 74
Liver 17941 2761 17082 8466 4499 2246
Cattle
Mammary
gland
8189 1158 10150 6031 2468 43
Intestine 394 32 192 75 55 0
Spleen 4890 808 3690 979 754 15
Liver
50410
73312
23061
129323
106150
3010
25781
178249 19664 4310 31023 16361 7141 157
20
Nature Reviews Genetics 2:130-38.
Andersson L and Georges M. 2004. Domestic animal
genomics: Deciphering the genetics of complex
traits. Nature Reviews Genetics 5: 202-12.
B a x e v a n i s A D a n d O u e l l e t t e B F F. 2 0 0 1 .
Bioinformatics: A Practical Guide to Analysis of
Genes and Proteins. Second edition. John Wiley &
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Chou HH and Holmes MH. 2001. DNA sequence
q u a l i t y t r i m m i n g a n d ve c to r re m ova l .
Bioinformatics 17(12): 1093-1104.
Dimas AS, Deutsch S, Stranger BE, Montgomery SB,
Borel C, Cohen HA, Ingle C, Beazley C, Arcelus MG,
Sekowska M, Gagnebin M, Nisbett J, Deloukas P,
Dermitzakis ET and Antonarakis SE. 2009.
Common regulatory variation impacts gene
expression in a cell type dependent manner.
Science 325 (5945): 1246-50.
Hernandez DG, Nalls MA, Moore M, Chong S, Dillman
A, Trabzuni D, Gibbs JR, Ryten M, Arepalli S, Weale
ME, Zonderman AB, Troncoso J, O'Brien R, Walker R, Smith C, Bandinelli S, Traynor BJ,Hardy
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Huang X, Madan A. 1999. CAP3: A DNA sequence
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Jakhesara SJ, Ahir VB, Padiya KB, Koringa PG, Rank
DN, and Joshi CG. 2012. Tissue-speci�ic temporal
exome capture revealed muscle-speci�ic genes
and SNPs in Indian buffalo Bubalus bubalis.
Genomics , Proteomics & Bioinformatics
10(2):107-13.
Kerstens HD, Kollers S, Kommadath A, Rosario MD,
Dibbits B, Kinders SM, Crooijmans RP and
Groenen M. 2009. Mining for single nucleotide
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BMC Genomics 10: 4.
Masoudi-Nejad A, Tonomura K, Kawashima S, Moriya
Y, Suzuki M, Itoh M, Kanehisa M, Endo T and Goto
S. 2006. EGassembler: online bioinformatics
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Nucleic Acids Res 34: W459-62.
Marth GT. 2003. Computational SNP discovery in
DNA sequence data. Methods in Molecular
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Nica AC, Parts L, Glass D, Nisbet J, Barrett A, Sekowska
M, Travers M, Potter S, Grundberg E, Small K,
Hedman AK, Bataille V, Bell JT, Surdulescu G,
Dimas AS, Ingle C, Nestle FO, Meglio PD, Min JL,
Wilk J, Hammond CJ, Hassanali N, Yang TP,
Montgomery SB, O'Rahilly S, Lindgren CM,
Zondervan KT,Soranzo N, Barroso I, Durbin R,
Ahmadi K, Deloukas P, McCarthy MI. Dermitzakis
ET and Spector TD. 2011. The Architecture of
Gene Regulatory Variation across Multiple
Human Tissues: The MuTHER Study. PLoS
Genetics 7(2): e1002003.
Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME,
et al. 2010. Trait-associated SNPs are more likely
to be eQTLs: annotation to enhance discovery
from GWAS. PLoS Genetics 6(4): e1000888.
Panitz F, Stengaard H, Hornshoj H, Gorodkin J,
Hedegaard J, Cirera S, Thomsen B, Madsen LB,
Hoj A, Vingborg RK, Zahn B, Wang X, Wang X,
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Havgaard JH, Brunak S, Fredholm M and
Bendixen C. 2007. SNP mining porcine EST with
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Leunissen JA. 2006. QualitySNP: a pipeline for
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Volume 5, Number 1-2, 2015
21
Production and reproduction performance of Red Sindhi cow
R. P. Jadhav and S. S. Kamble*Department of Animal Husbandry and Dairy Science,
Mahatma Phule Krishi Vidyapeeth, Rahuri- 413 722 (India)
INTRODUCTION
Red Sindhi cow, also known as Sindhi Cow, Red
Karachi Cow is one of the most popular of all Zebu
dairy breeds. The breed originated in the Sindh
province of Pakistan and is widely kept for milk
production across India, Pakistan, Bangladesh, Sri
Lanka and other countries. They have been used for
crossbreeding with temperate (European) origin
dairy breeds in many countries to combine their
tropical adaptations (heat tolerance, tick resistance,
disease resistance, fertility at higher temperatures,
etc.) with the higher milk production found in
temperate regions.
MATERIAL AND METHODS
The data pertaining to milk production of purebred
Red Sindhi cows maintained at College of
Agriculture, Dhule spread over a period of 20 years
(1991 - 2010) was used for the present investigation.
Effect of age at �irst calving, season of calving, period
of calving and lactation order over production and
reproduction traits were studied by adopting the
least square technique (Harvey 1990). Duncan's
multiple range test (DMRT) as modi�ied by Kramer
(1957) was used for comparison between pairs of
means. Interrelationships between economically
important traits were studied.
RESULT AND DISCUSSION
The overall least square means for LL, DP, CI,
LMY and 300 DMY were 310.876 ± 6.07 days, 81.99 ±
4.78 days, 392.86 ± 7.92 days, 1816.66 ± 38.95 kg and
1820.56 ± 34.18 kg respectively.
The value of LL was higher than that reported by
Pundir et al (2007) in Red Sindhi cow. Lactation
order had highly signi�icant effect over LL.
Supportive result was reported by Bhoite and Kale
(1996) in BFG groups. Effect of age at �irst calving,
period of calving and season of calving was found to
be non signi�icant. In DMRT it was found that
lactation length gradually increased with advances in
lactation order. Cows in fourth lactation order had
signi�icantly higher lactation length of 341.34 ± 9.94
days than cows in �irst and second lactation order
having 287.69 ± 12.53 and 293.35 ± 11.09 days of
lactation length respectively. Further the lactation
length of cows in third and fourth lactation was at
par.
The overall least square mean of dry period and
calving interval was 81.99 ± 4.78 and 392.86 ± 7.92
days. Higher duration of dry period and calving
interval has been reported by Pundir et al. (2007).
Effect of age at �irst calving, season of calving, period
of calving and lactation order on both the traits was
found to be non signi�icant.
The overall least square means for LMY and 300 DMY
were 1816.66 ± 38.95 kg and 1820.56 ± 34.18 kg
respectively. Lower LMY and 300 DMY than the
current investigation have been reported by Rehman
and Khan (2012) in Sahiwal cattle, however higher
ABSTRACT
The overall least square means for lactation length (LL), dry period (DP), calving interval (CI), lactation milk
yield (LMY) and 300 days milk yield (300 DMY) in Red Sindhi cow were 310.876 ± 6.07 days, 81.99 ± 4.78
days, 392.86 ± 7.92 days, 1816.66 ± 38.95 kg and 1820.56 ± 34.18 kg respectively. The lactation length had
signi�icant effect over lactation milk yield, whereas, period of calving had signi�icant effect over lactation milk
yield and 300 days milk yield. The effect of age at �irst calving, season of calving, period of calving and lactation
order over all the other traits under study was non signi�icant.
Key words : Red Sindhi, Productive traits, Reproductive traits
*Corresponding author: [email protected]
Volume 5, Number 1-2, 2015
22
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calv
ing
P1
1
4 3
24
.31
17
.57
84
.56
13
.85
40
8.8
7 2
2.9
0 1
57
4.5
9b 1
12
.66
15
62
.65b
98
.86
P2
5
1 3
05
.46
9.2
5
71
.20
7.2
9
37
6.6
6 1
2.0
5 1
93
5.8
1a 5
9.3
0 1
93
9.3
0a 5
2.0
4
P3
4
3 3
14
.02
9.8
3
87
.02
7.7
5
40
1.0
5 1
2.8
2 2
08
3.0
9a 6
3.0
5 2
07
9.9
2a 5
5.3
3
P4
3
0 2
99
.70
12
.41
85
.17
9.7
8
38
4.8
7 1
6.1
8 1
67
3.1
6b 7
9.5
9 1
70
0.3
8b 6
9.8
4
Lac
tati
on
ord
er
L1
4
3 2
87
.69b
12
.53
71
.25
7.8
3
36
6.9
8 1
6.3
3 1
70
9.5
0 8
0.3
5 1
73
9.8
8 6
2.4
0
L2
3
5 2
93
.35b
11
.09
79
.29
8.7
4
37
7.9
3 1
5.0
6 1
79
3.7
5 7
4.1
0 1
83
1.1
8 5
5.9
2
L3
3
1 3
21
.10ab
1
1.5
6 8
4.5
8 9
.11
4
13
.94
14
.45
18
06
.80
71
.11
18
29
.11
70
.50
L4
2
9 3
41
.34a
9.9
4
85
.26
9.8
7
41
2.6
0 1
2.9
5 1
95
6.5
9 6
3.7
3 1
88
2.0
9 6
5.0
2
Mea
ns
in t
he
sam
e co
lum
n w
ith
diff
eren
t su
per
scri
pt
diff
ered
sig
ni�
ican
tly
Volume 5, Number 1-2, 2015
23
values for LMY and 300 DMY have been reported by
Kale (1984) in HF X G crosses. LMY and 300 DMY
were signi�icantly affected by period of calving. The
DMRT table revealed that there was gradual increase
in LMY and 300 DMY with advance in period of
calving. Signi�icantly higher LMY and 300 DMY were
recorded against P3 (2083.09 ± 63.05 and 2079.92 ±
55.33 kg resp.) than P1 (1574.59 ± 112.66 and
1562.65 ± 98.86 resp.) and P4 (1673.16 ± 79.59 and
1700.38 ± 69.84 kg resp.). Subsequently, values of
LMY and 300 DMY for group of cows that calved in
period P2 and P3 were at par, whereas cows calving
in period P1 and P4 were at par.
The correlation of lactation length with calving
interval and lactation milk yield was positive and
highly signi�icant. The correlation between dry
period and calving interval was found to be highly
signi�icant and positive. The correlation of calving
interval with LMY was positive and highly signi�icant.
REFERENCES
Bhoite, U.Y., Kale, K.M.1996. Production performance
of three breed Gir crosses. Indian Vet. J.
73(4):473- 474.
Harvey, W.R. 1990. Least squares analysis of data
with unequal subclasses number. USD. ARS.
20: 8.
Kale, K.M. 1984. Growth, reproduction and
production Performance of Gir cows and its
exotic crosses. Ph.D.Thesis, MPKV, Rahuri.
Krammer C. Y. (1957) Extension of multiple range
test to group correlated adjusted mean.
Biomatrics, 13:13-20
Pundir,R.K., Singh,P.K., Upadhaya, S.N. and Ahlawat S
P S (2007). Status, characteristics and
performance of Red Sindhi cattle. Indian J.
Anim. Sci., 77(8): 755-758.
Rehman Z. and Khan M.S., 2012. Environmental
factors affecting performance traits of
Sahiwal cattle in Pakistan. Pak Vet J, 32(2):
229-233.
Volume 5, Number 1-2, 2015
24
Genetic evaluation of White Leghorn layers under reciprocalrecurrent selection
1Ramesh Kumar*, S Kalra, Satbir Singh and B ParkashKrishi Vigyan Kendra, Jind -126102 (Haryana) India
INTRODUCTION
The primary objective of a poultry breeder is to alter
gene frequencies and distribution by employing
various mating systems/selection methods to
improve different traits of economic importance that
will maximize the ef�iciency of production and
increase pro�itability. There are different methods of
selection, each with several variants. RRS Reciprocal
Recurrent Selection method of selection increases
the frequency of both additive and non-additive
genes, hence improve pure-line as well as cross-line
performance. The main reason for this is that
crossbreds often exhibit heterosis that indicates the
existence of non- additive effects and the two
populations under reciprocal recurrent selection do
not have identical gene frequency which causes the
covariance between them to be small or negative.
Reciprocal recurrent selection leads to a high
performance for lowly heritable and heterotic traits.
Crossbreeding is a standard practice in poultry
breeding programs as a way of exploiting heterosis.
Furthermore, the goal of breeding is not to maximize
heterosis, but to maximize overall pro�itability in the
commercial cross, the parents and the pure lines.
Considering cross-bred and pure-bred performance
as two separate, but correlated traits offers an
elegant way to take environmental effects into
account.
Instead of describing theory of RRS kindly give
objective of your study and also give some reference
in introduction part which are related with earlier
studies).
ABSTRACT
Present study revealed that crossbreds were marginally superior in most of the performance traits than the
purebreds. AFE of purebreds was lower than observed for crossbreds in all generations except G , G and G 4 5 6
generations.The EN was more during the initial three generations of study whereas it declined sharply in the
fourth and sixth generations. No de�inite trend was observed for the difference in EW of two reciprocal
crosses in all the generations under study. EN was found to be more important than EW in determining the
EM. Differences in the mean over the generations may be accounted for by the effect of selection and
environmental factors.Heritability estimates for BW , BW and AFE were moderate to high, but EN was low 20 40
heritable. Heritabilities of EM, EM/BW and EM/AFE were moderate indicating that some form of family 20
selection may be effective for their improvement. Standard errors of heritability estimates were small
suggesting that a reasonably high degree of reliance can be placed on these estimates. Large standard errors
of heritabilities of traits of crossbreds than the purebreds might be due to smaller population size in
comparison to purebred strains. Body weight, age at �irst egg and egg number were negatively correlated with
each other both at genetic and phenotypic levels. Egg weight was positively correlated with body weights at
phenotypic level both in purebreds and crossbreds but negatively genetically correlated in purebreds and
reverse was observed in crossbreds. Most of the phenotypic correlations were highly signi�icant both in
purebreds and crossbreds. It might be concluded that heavier birds lay large sized eggs but higher weight
during laying period is not a desirable proposition. Results suggest the use of RRS selection schemes for
further improvement of egg production and its component traits of hybrid layer.
Key words : Genetic evaluation, egg production, hybrid layer, RSS1Present Address: KVK, Sangrur (Punjab)
* Corresponding author : [email protected]
Volume 5, Number 1-2, 2015
25
MATERIALS AND METHODS
Data pertaining to the present study were collected
from records spreading over nine generations i.e.
1994-95 to 2002-03 for the performance traits of
both strains (which both strains is not clear, pl give
name and in one line details of strains)and their
crosses. The chicks of all the four genetic groups
(H×H, C×C, H×C and C×H) were brooded and reared
hatch-wise. The progenies were produced in
different hatches at weekly intervals during the
month of April and May each year. All the chicks were
pedigreed, wing-banded at the time of hatching and
reared hatch-wise using standard managemental
practices. Cockerels were separated from the pullets
at eight weeks of age. At 20 weeks of age, the body
weights were recorded and pullets were housed in
layer houses. Trap-nest records of each pullet were
maintained to record the age at �irst egg and egg thproduction upto 40 weeks of age. During 40 week,
three eggs from each pullet were weighed and
averages of these were considered as egg weight of
the pullet. At 40 weeks of age, body weights of hens
were also recorded. Standard managemental
practices were followed during the course of present
study. The performance traits viz. body weight at 20
(BW ) and 40 weeks age (BW ), age at �irst egg 20 40
(AFE), egg number upto 40 weeks age (EN) and egg
weight during 40 weeks of age (EW) were recorded
on individual purebred and crossbred pullets.
Genetic and phenotypic parameters of the traits of
purebred and crossbred were estimated from sire
component of variances and covariances. The
performance traits viz BW and BW AFE, EN and 20 40,
EW were recorded on individual purebred and
crossbred pullet. Egg mass upto 40 weeks of age
(EM), ratio of egg mass to body weight at 20 weeks
age (EM/ BW and ratio of egg mass to age at �irst 20)
egg (EM/AFE) were calculated for individual pullet.
Generation wise, genetic and phenotypic variances
and covariances among the traits, least square
means, heritabilities and correlations among traits of
purebreds and crossbreds were estimated using
Mixed Model Least Squares Maximum Likelihood
Computer Programme of Harvey (1987).
RESULTS AND DISCUSSION
Generation wise least squares means alongwith
standard error for body weight at 20 and 40 weeks
(g) , age at �irst egg (days) and egg number of
different genetic groups are given in Table 1. The
results depicted that crossbred pullets were
signi�icantly heavier at 20 weeks of age than the
purebred pullets in G and G but statistically non 1 8
signi�icant in G generations only. At 40 weeks of age, 4
crossbred pullets were heavier than purebred
pullets in all generations except G , G and G 2 3 9
generations. BW for purebreds ranged from 20
1202.53±2.89 to 1316.81±3.94g and the crossbreds
ranged from 1202.21±12.11 to 1311.33±7.19g. The
corresponding �igures for BW were 1406.36±10.25 40
t o 1 6 6 7 . 0 4 ± 5 . 5 9 g a n d 1 4 6 0 . 3 7 ± 7 . 7 4 t o
1713.25±7.41g, respectively. Sakunthala (2001) and
Singh (2001) reported similar body weights at both
ages in purebred strains con�irming the present
results. The results on crossbreds, body weights at
both ages are in agreement with those of Singh
(2001). On the contrary, Brah et al. (2002) reported
lower BW and BW weeks age in purebred and 20 40
crossbred groups while Yahaya et al. (2009) reported
higher values for these traits (BW andBW ) than 20 40
observed in this study.
Average AFE of purebred pullets was signi�icantly
lower than observed for the crossbred pullets in all
generations except G , G and G generations. AFE of 4 5 6
purebred and crossbred pullets ranged from
135.04±0.24 to 162.11±0.68 days and 143.78±0.85
to 163.44±0.79 days, respectively (Table 1). Lower
AFE in G generation compared to other generations 6
might be due to indirect selection as the criterion of
selection was EN to �ixed age (280 days).Similar
�indings to the present results have also been
reported earlier in literature (Sakunthala , 2001 and
Singh, 2001). Higher values for this trait in purebreds
and crossbreds were reported by Yahaya et al (2009).
The average EN ranged from 55.28±0.63 to
81.48±0.75 and 57.02±1.04 to 81.25±0.71 in
purebreds and crossbreds, respectively (Table 1).
Difference in the performance of two reciprocal
crosses suggests that a particular strain should be
used as male line and the other as female line for
Volume 5, Number 1-2, 2015
26
producing a commercial hybrid layer. Brah et al.
(2002) reported higher EN upto 40 weeks of age for
purebreds than the crossbreds which is contrary to
the present results. However, Yahaya et al (2009) and
Momoh et al (2010) reported higher egg production
in crossbreds than purebreds and are in agreement
to the present study.
The egg production declined during fourth and sixth
generation. The main reason of this decline may be
due to Gumboro outbreak during……. Year? in this
population causing death of high producing birds.
Another possible reason might be that while
selecting the pullets, emphasis on egg weight was
also given in later generations thus causing decline in
egg production because of negative genetic
correlation between these two traits.
Average EW was signi�icantly higher for the
crossbreds than the purebreds in generations G and 1
G only and signi�icantly lower in G , G and G . In the 2 3 6 7
remaining generations the EW of purebreds and
crossbreds were similar. The averages for EW in
purebreds ranged from 49.15±0.18 g to 53.70±0.18 g
and 48.06±0.32 g to 53.21±0.22 g in crossbreds over
the generations (Table 2) which is in close
conformity with the �indings of Singh (2001),Brah et
al. (2002) and Momoh et al (2010). Yahaya et al.
(2009) reported superiority of crossbreds over
purebreds in two generations of RRS.
Average EM was higher in crossbreds than the
purebreds in the generation G and signi�icant in G , 1 3
G , G and G generations. The averages for EM ranged 5 8 9
from 2936.65±33.06 g to 4213.35±31.26 g and
2887.89±60.74 g to 4080.74±35.93 g in purebreds
and crossbreds, respectively (Table 2). Momoh et al
(2010) reported lower values than the present study
for this trait in purebreds but higher values in
crossbreds. Highest EN (Table 1) and EM (Table 2) in
generation G indicated that EN is more important 2
than EW in determining the EM.
Ratio of EM/BW ranged from 2.23±0.03 to 2 0
3.44±0.03 in purebreds and 2.21±0.05 to 3.37±0.03
in crossbreds, respectively (Table 2). The ratio was
higher for the crossbreds in generations G and 1
signi�icant in G G , and G only and lower in rest of the 3, 5 9
generations. Ratio of EM/AFE ranged from
20.01±0.31 to 31.30±0.25 in purebreds and
20.27±0.52 to 26.77±0.26 in crossbreds (Table 2).
Crossbreds were superior to purebreds in
generations G , G and G for this trait. From the above 4 5 7
results, it may be concluded that in general
crossbreds were slightly superior to the purebreds
for most of the performance traits. Differences in
generation means for different traits may be
accounted for by the effect of both selection and
environmental factors.
Heritability : Heritabilities estimated from sire
component of variance pooled over generations
along with their standard errors for performance
traits of purebred and crossbreds are presented in
Table 3.
The results showed that heritability estimates for
BW and BW age was medium for the purebreds 20 40
(0.391±0.043, 0.352±0.054) and high for crossbreds
(0.418±0.034, 0.444±0.089). The present results are
in con�irmation with the �indings of Shakunthala
(2001) for purebreds at 20 weeks age. The reported
estimates of heritability of crossbred in present
study are lower for both traits than those of Singh
(2001) but higher than reported by Yahaya et al
(2009).
Medium heritability of BW and BW has also been 20 40
reported earlier (Chatterjee et al, 2008). The results
of present study indicated that BW for crossbreds is 40
highly heritable. The higher heritability of crosses
over pures for these traits is caused by higher genetic
variation and lower environmental variation. Higher
heritability estimates in crosses than in pures were
also reported by Yahaya et al (2009) whereas
Pirchner (1976) observed only slight differences in
heritabilities between crosses and pures. The excess
of sire component heritability in crosses over the
pures for body weight (as reported in literature)
showed that body weights are in�luenced by non-
additive genetic effects. Thus, the magnitude of
heritability of a particular cross was more dependent
on the male-parent than that of female-parent
strain.
The pooled estimates of heritability for AFE were
found to be 0.427±0.099 and 0.565±0.106 in
purebreds and crossbreds respectively. Higher
Volume 5, Number 1-2, 2015
27
Volume 5, Number 1-2, 2015
28
Table1. Generation wise least squares means along with standard error for body weight at 20 and 40 weeks (g),
age at �irst egg (days) and egg number of different genetic groups
Purebreds Crossbreds
Gener- 20wk (gm) 40wk (gm) AFE (days) EN 20wk (gm) 40wk (gm) AFE (days) EN
ations
1. 1202.53a±2.89 1593.21a±3.86 147.20a±0.27 75.04a±0.45 1217.06b±3.71 1599.08a±4.21 154.08b±0.34 76.59a±0.98
(478) (328)
2. 1223.93a±3.14 1561.37a±3.58 135.04b±0.24 81.48a±0.75 1212.67a±5.58 1550.77a±4.75 153.20a±0.46 78.76b±0.68
(628) (300)
3. 1275.25a±4.15 1646.34a±4.51 138.88b±0.40 65.60b±0.74 1202.21b±12.11 1597.06b±11.55 146.86a±1.12 72.35a±1.84
(358) (68)
4. 1230.61a±4.28 1553.13b±4.97 151.16a±0.47 58.96a±0.66 1239.63a±5.73 1582.94a±8.33 143.82b±0.48 58.32a±0.93
(294) (136)
5. 1295.25a±2.75 1617.33a±4.47 159.70a±0.33 76.74b±0.51 1289.33a±3.72 1625.00a±5.85 154.35b±0.51 81.25a±0.71
(472) (240)
6. 1316.81a±3.94 1667.04b±5.59 145.87a±0.54 55.28a±0.63 1311.33a±7.19 1713.25a±7.41 143.78a±0.85 57.02a±1.07
(285) (83)
7. 1311.02a±3.67 1541.57a±4.99 145.17b±0.36 65.68a±0.55 1242.70b±3.37 1555.75a±8.22 149.14a±0.56 62.86b±0.91
(362) (174)
8. 1225.91b±10.34 1406.36b±10.25 142.96b±0.66 61.06b±1.06 1290.75a±11.56 1479.79a±10.19 159.00a±0.62 64.84a±0.90
(110) (146)
9. 1222.28a±4.51 1479.70a±6.31 162.11a±0.68 66.33b±0.88 1212.48a±5.64 1460.37a±7.74 163.44a±0.79 72.51a±0.92
(267) (246)
Overall 1255.74a±1.51 1575.93a±1.98 145.22b±0.22 72.03b±0.30 1237.36b±2.36 1561.49b±2.91 149.82a±0.30 73.86a±0.36
(3254) (1721)
Figures in parentheses are the number of observations. Means bearing different superscripts (among genetic groups and crosses separately) differ signi�icantly (P<0.05)
Table 2. Generation wise least squares means along with standard error for egg weight (gm), egg mass (gm), ratio of egg
mass to body weight at 20 weeks age and ratio of egg mass to age at �irst egg of different genetic groups
Purebreds Crossbreds
Gener- 20wk (gm) 40wk (gm) AFE (days) EN 20wk (gm) 40wk (gm) AFE (days) EN
ations
1. 51.32a±0.12 3898.07a±31.28 3.27a±0.03 26.50a±0.25 52.68b±0.14 4056.62a±38.22 3.33a±0.03 26.34a±0.30
(478) (328)
2. 50.92b±0.10 4213.35a±31.26 3.44a±0.03 31.30a±0.25 51.86a±0.08 4072.85b±31.02 3.37a±0.03 26.77b±0.26
(628) (300)
3. 50.42a±0.17 3274.87b±30.14 2.57b±0.02 23.76a±0.26 48.06b±0.32 3465.28a±83.88 2.89a±0.07 23.75a±0.64
(358) (68)
4. 53.70a±0.18 3165.74a±37.15 2.58a±0.03 21.04a±0.27 53.21a±0.22 3106.04a±52.36 2.51a±0.04 21.65a±0.39
(294) (136)
5. 50.83a±0.14 3897.16b±26.96 3.01b±0.02 24.49b±0.19 50.32b±0.21 4080.74a±35.93 3.17a±0.03 26.55a±0.27
(472) (240)
6. 52.88a±0.14 2936.65a±33.06 2.23a±0.03 20.37a±0.29 50.54b±0.23 2887.89a±60.74 2.21a±0.05 20.27a±0.52
(285) (83)
7. 52.79a±0.13 3465.04a±29.04 2.65a±0.02 24.05a±0.25 51.12b±0.19 3209.02b±46.24 2.59a±0.04 21.60b±0.34
(362) (174)
8. 50.68a±0.30 3101.93b±57.47 2.54a±0.05 21.81a±0.44 50.01a±0.27 3249.63a±45.05 2.53a±0.04 20.53b±0.32
(110) (146)
9. 49.15a±0.18 3271.54b±43.55 2.68b±0.04 20.01b±0.31 49.07a±0.20 3554.11a±45.73 2.94a±0.04 22.02a±0.35
(267) (246)
Overall 51.43a±0.06 3688.27b±14.49 2.95b±0.01 25.78a±0.12 51.00b±0.08 3764.77a±19.41 3.06a±0.02 25.47a±0.16
(3254) (1721)
Figures in parentheses are the number of observations. Means bearing different superscripts (among genetic groups and crosses separately) differ signi�icantly (P<0.05)
Volume 5, Number 1-2, 2015
29
estimates of heritability in crossbreds than in
purebreds were reported by Singh (2001). The
�indings of this study resembles with the reports of
Chatterjee et al. (2000, 2008) for purebreds. The
pooled heritability estimates over generations were
lower for purebreds (0.255±0.054) than the
crossbreds (0.324±0.090). Singh (2001) and
Ravikumar (2003) reported heritability of part year
egg production ranging from 0.02±0.17 to 0.35±0.23
which are in agreement with the present results.
The higher pooled heritability estimates in
crossbreds than in the purebreds were reported by
Singh (2001). Contrary to the present results, higher
heritability estimates in purebreds than in
crossbreds have been reported by various workers
(Chaudhary et al., 1997). The heritability obtained in
the present study as well as reported by earlier
workers indicates that egg number is low to
moderate heritable and can be improved by
following some form of combined selection. The
lower heritability estimates are indicative of
increased role of various environmental in�luences.
The low magnitude of heritability estimates of EN in
purebreds as compared to other traits indicates two
possibilities. Firstly, it could be because of the fact
that egg production, being a �itness trait, its
heritability estimates were low. Secondly, continued
selection for egg production practiced during the last
two decades could have been responsible for a
gradual reduction in the genetic variation in this
trait.
Heritability estimates for EW were higher in
crossbreds than purebreds suggesting that this stock
had more additive genetic variance for further
utilization through selection. The heritability
estimates were 0.286±0.060 and 0.369±0.061 in
purebreds and crossbreds respectively. The
estimates obtained by Besbes and Gibson (1999)
were higher than those observed in the present
study. On the contrary, lower heritability estimates
were reported by Chaudhry et al. (1997) both in
purebreds and crossbreds. The higher estimates of
heritability in crosses than in purebreds have also
been reported earlier (Singh, 2001).
The heritability estimates for EM were 0.276±0.058
and 0.408±0.083 in purebreds and crossbreds
respectively. These estimates are lower than
reported by Thangaraju and Ulaganathan (1990)
(0.809±0.183 in Forsgate strain and 0.683±0.166 in
Meyer Strain). Medium to high heritability of this
trait indicates the possibility of improvement
through some form of intra-population selection.
The estimates of heritability for ratio trait
(EM/BW ) were also higher in crossbreds than 20
purebreds like other performance traits. The
estimates were 0.284±0.060 and 0.365±0.077 in
purebreds and crossbreds, respectively. On the
contrary, Thangaraju and Ulaganathan (1990)
reported higher estimates of heritability for this trait
(0.637±0.155 and 0.953±0.209 in Forsgate and
Meyer strains, respectively). The present results
suggest that this trait can be improved effectively by
mass selection.
Heritability estimates of ratio trait (EM/AFE) were
estimated as 0.276±0.075 and 0.363±0.076 in
purebreds and crossbreds, respectively. These
estimates were also higher in crossbreds than
purebreds. These results are of low magnitude than
those obtained by Thangaraju and Ulaganathan
(1990) for purebreds (0.872±0.192 in Forsgate and
0.418±0.122 in Meyer strain).
Based on the present �indings, it may be concluded
that heritability estimates for BW , BW and AFE 20 40
were moderate to high. The standard errors were
small, suggesting that a reasonably high degree of
reliance can be placed on these estimates. However,
the standard errors were relatively larger in the
crossbreds which may be because of small
population size as compared to the purebreds. EN is
low heritable and to some extent may be in�luenced
by non-additive gene action. Heritability estimates of
EM and its ratio traits were moderate indicating that
some form of family selection may be effective for
their improvement.
Heritability estimates of all the traits under study
were higher for the crosses than the purebreds
s u g g e s t i n g t h e b u ff e r i n g q u a l i t i e s o f t h e
heterozygous genotypes in relation to a changing
environment. Also the additive genetic variation
observed among crossbred progenies may contain
Volume 5, Number 1-2, 2015
30
Table 3. Heritabilities (±SE) of performance traits of purebreds and crossbreds
estimated from sire component of variances
Traits Purebreds Crossbreds
BW20 0.391±0.043 0.418±0.034
BW40 0.352±0.054 0.444±0.089
AFE 0.427±0.099 0.565±0.106
EN 0.255±0.054 0.324±0.090
EW 0.286±0.060 0.369±0.061
EM 0.276±0.058 0.408±0.083
EM/BW20 0.284±0.060 0.365±0.077
EM/AFE 0.276±0.075 0.363±0.076
Table 4. Genetic correlations along with standard error among performance traits of purebreds estimated fromsire component of variances and covariances
Traits BW40 AFE EN EW EM EM/BW20 EM/AFE
BW20 0.522±0.206 0.048±0.248 0.020±0.295 –0.158±0.245 –0.161±0.284 –0.354±0.294 –0.140±0.261
BW40 –0.550±0.194 0.346±0.243 0.004±0.248 0.320±0.236 0.197±0.247 0.408±0.205
AFE –0.866±0.173 0.039±0.206 –0.883±0.161 –0.855±0.158 –0.945±0.135
EN –0.237±0.160 0.969±0.015 0.917±0.038 0.963±0.022
EW 0.008±0.241 0.132±0.240 0.015±0.221
EM 0.980±0.011 0.988±0.010
EM/BW20 0.967±0.019
Table 5. Genetic correlations along with standard error among performance traits of crossbreds estimated from
sire component of variances and covariances
Traits BW40 AFE EN EW EM EM/BW20 EM/AFE
0.506±0.134 0.105±0.180 0.213±0.176 0.012±0.190 0.236±0.175 0.023±0.186 0.179±0.180
BW40 –0.487±0.126 0.095±0.157 0.466±0.131 0.199±0.153 0.068±0.160 0.375±0.139
AFE –0.205±0.151 0.440±0.137 –0.128±0.155 –0.128±0.156 –0.255±0.152
EN –0.439±0.141 0.975±0.008 0.960±0.014 0.871±0.038
EW 0.233±0.157 0.266±0.156 0.064±0.166
EM 0.975±0.009 0.925±0.023
EM/BW20 0.903±0.031
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31
Table 6. Phenotypic correlation along with standard error among performance traits of purebreds
Traits BW40 AFE EN EW EM EM/BW20 EM/AFE
0.648**±0.009 –0.119**±0.011 0.115**±0.008 0.028±0.012 0.093*±0.07 0.254**±0.00 8 0.027±0.009
BW40 –0.233***±0.012 0.024±0.011 0.162**±0.011 0.057±0.008 –0.118**±0.00 7 0.106±0.009
AFE –0.434**±0.010 0.055±0.009 –0.450**±0.008 –0.419**±0.008 –0.656**±0.009
EN –0.443**±0.011 0.973**±0.012 0.935**±0.008 0.943**±0.007
EW 0.232**±0.011 0.224**±0.011 0.231**±0.012
EM 0.962**±0.018 0.965**±0.019
EM/BW20 0.925**±0.015
* = P±0.05, ** = P±0.01
Table 7. Phenotypic correlation along with standard error among performance traits of crossbreds
Traits BW40 AFE EN EW EM EM/BW20 EM/AFE
0.685**±0.008 –0.212**±0.011 0.188**±0.009 0.050±0.011 0.111*±0.008 0.244**±0.007 0.129**±0.008
BW40 –0.264**±0.012 0.068±0.009 0.252**±0.011 0.148**±0.009 –0.109*±0.008 0.209**±0.007
AFE –0.101*±0.008 0.142**±0.009 –0.136**±0.011 –0.091*±0.008 –0.417**±0.009
EN –0.224**±0.009 0.949**±0.017 0.894**±0.015 0.898**±0.011
EW 0.086±0.007 0.060±0.008 0.116**±0.007
EM 0.933**±0.018 0.954**±0.017
EM/BW20 0.881**±0.016
* = P±0.05 ** = P±0.01
both the additive genetic variation found in the
purebred lines plus the purebred's non-additive
genetic variation which is seen as additive genetic
variation in the crossbred. From the results it may
also be inferred that non-additive genetic variation
existed for various components of egg laying
productive traits. This type of genetic variation may
be exploited through some sort of crossbred
selection schemes.
Genetic and phenotypic correlations : Genetic and
phenotypic correlations among various traits in the
present investigation were estimated by sire
component of variance and covariances and are
presented in Tables 4 to 7 respectively.
Correlation of body weight with other traits :
Genetic correlation of BW was found to be negative 20
with EW, EM, EM/ BW and EM/AFE in purebreds 20
while positive with all other traits in both purebreds
and crossbreds. The phenotypic correlation between
BW and AFE were highly signi�icant and negative 20
but low in magnitude both in purebreds and
crossbreds. Kumar (2001) also reported negative
genetic correlation between BW and EW while 20
several authors reported in reverse direction.
Negative genetic association of BW with AFE and 20
EN and phenotypic correlations between BW and 20
AFE were also reported to be negative and low both
in purebreds and crossbreds (Singh, 2001). Genetic
and phenotypic correlations between BW and AFE 40
were observed to be negative both in purebreds and
crossbreds. On an average, magnitudes of genetic
correlation between these traits were low in
Volume 5, Number 1-2, 2015
32
crossbreds compared to purebreds. The results
indicated that pullets with higher BW attained sexual
maturity earlier, con�irming the fact that optimum
BW is also important in layer �locks too.
Genetic and phenotypic correlations of BW and 20
BW with EN were found to be positive both in 40
purebreds and crossbreds. Yahaya et al. (2009) also
reported positive genetic association between BW 20
and EN. On the basis of the present �indings and
reports available in the literature, it may be
concluded that pullets which attain more weight
before sexual maturity produce more eggs but more
body weight during laying is not desirable as heavy
birds are likely to produce less number of eggs.
The genetic correlation between BW and 20
EW was negative in purebreds but positive in
crossbreds but of low magnitude. However,
phenotypic correlations of BW and BW with EW 20 40
were positive in purebreds as well as in crossbreds.
From these results, it may be concluded that heavier
birds are likely to lay large sized eggs.
Correlation of age at �irst egg with other traits : T h e
genetic and phenotypic correlations between AFE
and EN were found to be negative for both purebreds
(-0.866±0.173 and -0.434±0.010) and crossbreds (-
0.205±0.151 and -0.101±0.008). Most of the
phenotypic correlations of AFE with other traits
were highly signi�icant (P±0.01) both in purebreds
and crossbreds. Similar estimates between these
traits have also reported by Singh (2001), Khalil et al
(2004) and Anees et al (2010). Contrary to the
present �indings, Singh (1994) reported positive
genetic correlation between these traits. From
present results it may be inferred that direct
selection for high EN may lower down the age at
sexual maturity concomitantly. The negative
genotypic and phenotypic correlation among these
traits indicated that early maturing pullets laid more
eggs upto 40 weeks of age. Hence, strong negative
association between these traits will be bene�icial to
the breeder as long as there is no adverse impact on
egg weight.
The genetic and phenotypic correlation between
AFE and EW were positive in both purebreds
(0.039±0.206 and 0.055±0.009) and crossbreds
(0.440±0.137 and 0.142±0.009). Chatterjee et al.
(2000) reported low correlation between AFE and
EW. However, negative associations between these
traits were reported by Singh (2001) and Shad et al
(2007).
The genetic as well as phenotypic correlations of AFE
with EM, EM/ BW and EM/AFE were found to be 20
negative in both purebreds and crossbreds. The
estimates of correlation of AFE with other traits were
higher in purebreds than in crossbreds.
Correlation of egg number with other traits :
Negative genetic and phenotypic correlations
between EN and EW in both purebreds (-
0.237±0.160 and -0.443±0.111) and crossbreds (-
0.439±0.141 and -0.224±0.009) were observed.
Present results are in agreement with the �indings of
earlier workers (Singh, 2001; Chatterjee et al., 2008
and Yahaya et al, 2009). On the contrary, Kumar et al.
(2001) reported positive genetic correlation
between these traits. Magnitude of genetic
correlation between these traits was higher in
crossbreds than in purebreds. From the present
results, it may be concluded that high laying pullets
will produce comparatively small sized eggs and
therefore, to improve simultaneously both the traits,
some specialized selection programs should be
practiced.
EN was highly positively correlated with EM, EM/
BW and EM/AFE, both at genetic and phenotypic 20
levels in purebreds as well as in crossbreds.
Correlation of egg weight with other traits :
Positive but low genetic and phenotypic correlation
between EW and EM were observed both in
purebreds and crossbreds. Similar trend was
observed for the correlation between EW and EM/
BW and EM/AFE in both purebreds and crossbreds. 20
The magnitude of genetic correlation was higher in
crossbreds than the purebreds. Standard errors of
genetic correlations were large indicating that
estimates are extremely variable, possibly due to
inadequate population sizes. Thangaraju and
Ulaganathan (1990) reported negative genetic
correlations of EW with EM and EM/AFE in Forsgate
strain but their results of Meyer strain were similar
to the present �indings.
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33
Correlation among egg mass, ratio of egg mass to
body weight at 20 weeks and ratio of egg mass to age
at �irst egg : Positive high genetic and phenotypic
correlations were observed among these three
traits both in purebreds and crossbreds. These
results are in conformity with those reported by
Thangaraju and Ulaganathan (1990).
Genetic correlations of body weights with
AFE and EW were negative in purebreds but
positive between BWs and EW in crossbreds.
Genetic correlation between AFE and EN was
negative both in purebreds and crossbreds.
Genetically AFE was positively associated with EW,
but negatively with EM and both ratio traits in
purebreds and crossbreds. Positive but low genetic
correlation of EW with EM and both ratio traits was
observed both in purebreds and crossbreds.
Phenotypic correlations of BWs with AFE were
negative and highly signi�icant both in purebreds
and crossbreds. Positive and highly signi�icant
correlation of BW with EW was also observed. 40
Phenotypic correlations of AFE with EN, EM and
both ratio traits were negative and highly
signi�icant both in purebreds and crossbreds.
Negative and highly signi�icant phenotypic
correlation was found between EN and EW both in
purebreds and crossbreds. High positive
correlation between EN and EM suggested that
selection for EM may bring about concomitant
increase both in EN and EW unlike selection for EN
alone causing a decrease in EW as correlated
response. Most of the correlations among all the
traits under study were highly signi�icant (P±0.01)
both in purebreds and crossbreds.
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Volume 5, Number 1-2, 2015
