Genetic evaluation of early egg production and maturation traits using
two different approaches in Japanese quail
G. Abou Khadiga,∗,1 B. Y. F. Mahmoud,† and E. A. El-Full†
∗
Faculty of Desert and Environmental Agriculture, Fuka, Alexandria University, Matrouh Branch, 51744
Matrouh, Egypt; and † Faculty of Agriculture, Fayoum University, 63514 Fayoum, Egypt
estimates of maternal heritability were moderate for
AFE (0.19) but low for BWSM (0.04) and DN10 (0.01).
High (0.45 to 0.56) genetic and low (–0.01 to –0.18) phenotypic correlations were observed among the studied
traits. Negative (–0.23 to –0.95) correlations between
additive genetic and maternal genetic effects were observed for all traits. Genetic trends were –0.76 (P =
0.031), 2.54 (P = 0.037), and –0.06 (P = 0.052) with calculated product-moment correlations between breeding
values, estimated by BLUP and phenotypic selection
methods, of 0.78 (P = 0.002), 0.77 (P = 0.004), and 0.61
(P = 0.007) for AFE, BWSM , and DN10 , respectively.
Aggregated breeding value estimation based on animal
model BLUP could be an effective method of constructing a selection program to achieve a favorable selection
response in egg production traits in Japanese quail.
ABSTRACT The objective of the current study was
to evaluate a multi-trait selection program based on
aggregated breeding values using an animal model Best
Linear Unbiased Prediction (BLUP) in Japanese quail.
The estimated genetic gain was compared by both
mixed model and least squares methods. Data of 1,682
female Japanese quails were collected through four consecutive generations to estimate genetic gain, depending on aggregated breeding values, for age at first egg
(AFE), body weight at sexual maturity (BWSM ), and
days needed to produce the first ten eggs (DN10 ). Estimates of cumulative selection response were favorable
for all the studied traits and significant for AFE (–3.03)
and BWSM (10.38), but not significant for DN10 (–0.15).
Estimates of direct heritability were moderate for AFE
(0.21) and BWSM (0.25) but low for DN10 (0.08), while
Key words: Japanese quail, selection, BLUP, genetic parameter, egg production
2016 Poultry Science 95:774–779
http://dx.doi.org/10.3382/ps/pev386
INTRODUCTION
when days needed to produce the first ten eggs (DN10 )
is included as a selection criterion, the total merit of
egg production and growth traits was improved and
showed favorable genetic correlation with production
traits in Japanese quail, increasing the profitability of
such selection programs.
Application of selection methodologies to improve
some traits of quails can adversely affect performance
in other traits, and selection progress for these birds
(Savegnago et al., 2011). Therefore, to maximize genetic
progress simultaneously in all traits, a desirable proposition would be to combine them into an index (Raj
Naryan et al., 2000); or to use a restricted maximum
likelihood (REML) procedure with an animal model
to estimate genetic parameters (Farzin et al., 2013).
Although it is practical, the selection index which is
defined as Best Linear Prediction (BLP) has two major defects in that it does not effectively correct environmental factors or some non-genetic factors; and it
therefore cannot take full advantage of the information
from all relatives (Bao, 2011). However, the Best Linear
Unbiased Prediction (BLUP) method can overcome
the defects of using a selection index. Additionally, it
offers the ability to adjust deviation due to non-random
mating, such as selected mating. BLUP is also capable
Japanese quail are characterized by many favorable
traits such as a fast growth rate, quick sexual maturity,
short generation interval, small body size, and significant egg production ratio compared to other farm birds
(Özsoy and Aktan, 2011; Narinc et al., 2014; Molino et
al., 2015). These advantages suggest that quail could be
a model animal for genetic studies on egg production
(Saatci et al., 2006; Mahmoud et al., 2015). The ultimate goal of a poultry breeder is to improve the overall
genetic economic worth of the bird through multi-trait
selection by considering the maximum number of traits
at a time (Mahmoud et al., 2014; Narinc et al., 2014).
Days needed to produce a certain number of eggs is
an important economic trait in poultry as any decrease
in days needed will consequently decrease production
costs. Biologically, a decrease in days needed means an
increase in clutch length, which would lead to a longer
production life that is desired in selection programs.
Recently, Mahmoud et al. (2014, 2015) indicated that
C 2016 Poultry Science Association Inc.
Received May 30, 2015.
Accepted October 9, 2015.
1
Corresponding author: [email protected]
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SELECTION PROGRAM IN JAPANESE QUAIL
of constant genetic evaluation of individuals from different years, generations, stocks, and age groups (Bao,
2011). Therefore, BLUP has been widely used in genetic evaluation of poultry species (Sezer, 2007; König
et al., 2010; Rozempolska-Rucinska et al., 2013).
Maternal effects influence the progeny phenotype due
to genetic and environmental differences between dams
(Grosso et al., 2010). The precise estimation of additive
genetic effects for a maternally-influenced trait could
be achieved by including maternal effects in the model
(Meyer, 1989; Sezer, 2007). Lee (2002) stated that the
computational burden from the addition of the covariance is not critical. The computation, however, requires
more iterations for estimating genetic parameters by
REML or Bayesian methods. The objectives of the current study were to evaluate a multi-trait selection program that depended on aggregated breeding values in
Japanese quail and involved a unique combination of
traits. Moreover, we compared both mixed model and
least squares approaches in the estimation of genetic
gain.
MATERIALS AND METHODS
Selection Program
Aggregated breeding values of age at first egg (AFE),
body weight at sexual maturity (BWSM ) and DN10
were estimated in 2 female lines of Japanese quail that
were bred simultaneously. The line (S) was selected according to the estimated aggregated breeding values
for four consecutive generations, while a control line
(C) was kept under random mating without selection.
Data of the base population consisted of three successive hatches, then data were collected for the first hatch
only to maintain discrete generations.
Statistical Analysis
The ASReml software package (Gilmour et al., 2008)
was used for all genetic analyses. In preliminary analysis, the fixed effect of generation X hatch combination was significant. Therefore, it is included in the
subsequent analyses. The following multivariate animal
model was used to calculate the aggregated breeding
values:
y = Xb + Za + Wm + e,
Population Structure and Bird Management
This study was conducted at the Poultry Research
Center, Faculty of Agriculture, Fayoum University,
Egypt. Research on live animals met the guidelines approved by the Institutional Animal Care and Use Committee in Egypt. A selection experiment that continued
for four generations and used a total number of 1,682
females (643 base population, 619 for the selected line
and 420 for the control line) was carried out. The selected breeders were housed (2 females were randomly
assigned to each male) in breeding cages with the dimensions 20 × 20 × 25 cm and with a sloping floor for
collecting the eggs. Eggs were collected daily, in a pedigree system for each family depending on the shell color
and patterns of each female, when the females were 11
to 14 weeks of age. The newly hatched chicks were wing
banded by small size plastic bands, which were replaced
by wing metal bands at 14 days of age. Chicks were
brooded on the floor until 10 days of age, at which
time the young birds were transferred to an intermediate battery brooder. From hatch to five weeks of age,
all quail were fed ad libitum on a starter diet containing 24% crude protein (CP), 2,900 K cal/metabolizable
energy (ME), and water. From 6 weeks to the end of
the study, a breeder diet containing 20% CP, 2,900 K
cal/ME, 2.25% calcium, and 0.43% available phosphorous was supplied. Birds were in continuous light for
the first 2 weeks of age and then reduced to 16 hours of
light per day thereafter. All birds were kept under the
same management, hygienic and environmental conditions. Mating of close relatives was avoided to decrease
the rate of inbreeding depression.
where y = vector of observations, b = vector of fixed
effects (i.e., genetic group as a combination of line X
generation = 8 levels), a = vector of random animal
effects, m = vector of random maternal genetic effects,
e = vector of random residual effects for the analyzed
traits, and X, Z, and W are incidence matrices relating
records to fixed, animal, and maternal genetic effects,
respectively.
The covariance structure of the model is:
⎡
⎤
⎡
⎤
Aσ 2 a Aσam
0
a
⎢ ⎥ ⎢
⎥
2
0 ⎦
var ⎣ m ⎦ = ⎣ Aσam Aσ m
e
0 0
Iσ 2 e
G 0
=
0 R
Aσ 2 a Aσam
G=
= G0 ⊗ A R = R0 ⊗ I
Aσam Aσ 2 m
where, σ 2 a is the additive genetic variance, σ 2 m is the
maternal genetic variance, σ am is the additive-maternal
covariance, A is the numerator relationship matrix
(2823 levels), I is the identity matrix, G and R are the
breeding values and residuals (co)variance matrices, G0
and R0 are the additive-maternal genetic and environmental (co)variance matrices of the traits, respectively.
Expectations of the random effects were assumed to be
zero and E(yi ) = Xi bi , cov(a, m) = Aσ am , cov(a, e ) = 0
and ⊗ is the direct product operation. The distribution
of y was assumed normal. The traits were determined
776
ABOU KHADIGA ET AL.
by many additive genes of infinitesimal effects at infinitely many unlinked loci.
Variance components, direct-maternal genetic covariance, and genetic parameters were estimated using
REML procedures. Starting values of population parameters used in calculating breeding values were obtained from series of univariate analyses using the
REML method. These estimates served to build up the
genetic and residual (co)variance matrices used as initial values for estimation of variance components in
a multi-trait analysis. All analyses included pedigrees
back to the base population. In the REML analyses,
the convergence criterion for all runs was 10−9 .
Genetic Parameters
Phenotypic variance was calculated as (σ 2 P = σ 2 a +
σ m + σam + σ 2 e ) allowing us to estimate direct heri2
2
tability h2 = σσ2 Pa , maternal heritability as m2 = σσ 2mP ,
2
and total heritability as h2 t = (σ a +0. 5σσ 2mP +1. 5σ a m ) according to Willham (1972). The direct-maternal genetic
correlation (ram ) was computed as the ratio of the estimates of direct maternal covariance (σ am ) to the product of the square roots of estimates of σ 2 a and σ 2 m as
ram = σσa a.σmm .
All available information was used to estimate breeding values. So, the aggregated breeding value of an animal (j) can be calculated as v ûj , where v is the vector
with relative weights for the q traits and ûj is the vector
with the q breeding values for animal j (Schneeberger
et al., 1992). Mrode (2014) described the calculation of
the aggregated breeding value (H) in a multivariate selection program. He defined H as a linear function of the
additive genetic values of the traits of interest of an individual. Thus, H can be calculated as H = w1 a1 + w2 a2
. . . + wn an , where ai is the breeding value of the ith trait
and wi is the weighting factor which expresses the relative economic importance associated with the ith trait.
The relative weights used in the current study were –
1.00, 1.00, and –0.53 for AFE, BWSM , and DN10 , respectively. After estimation of breeding values, the animals
were given ranks according to their genetic merit. Pearson product-moment rank correlation (PPC) among
animals’ estimated breeding values of different traits
was calculated according to Steel and Torrie (1980).
2
2
Genetic Gain
Both least squares and mixed model methodologies
were used in determining the amount of genetic gain.
Least squares methodology was used to estimate the
selection response in S line by the deviation from the C
line, taking into account the initial difference at the first
generation. The cumulated selection response (CSR) at
generation n was calculated by the following equation
(Chen and Tixier-Boichard, 2003):
CSR = (Sn −Cn ) − (S1 −C1 )
where Sn and Cn were least square means for selection
criteria at generation n in the S line and C line, respectively.
In the framework of mixed model methodology (Henderson, 1973), BLUP was used to obtain estimated
breeding values (EBV) to evaluate genetic gain. For
this evaluation, variance components obtained from the
REML analysis on the entire data set was used. The
averages of the predicted additive values in each generation were regressed on generation number to estimate
the genetic trend.
RESULTS AND DISCUSSION
To our knowledge, no existing research addresses an
animal model selection experiment depending on aggregated breeding values for improving this combination
of female traits (AFE, DN10 , and BWSM ) in Japanese
quail. Least squares means of the studied traits along
with the number of individuals in both S and C lines
through four generations are presented in Table 1. Our
findings are in line with the reviewed reports for AFE
(Narendra Nath et al., 2011; Özsoy and Aktan, 2011;
Valente et al., 2011) and BWSM (Karabağ et al., 2010;
Özsoy and Aktan, 2011). However, lower values for
BWSM were reported by Okenyi et al. (2013) than those
observed in this study.
Estimated direct, maternal and total heritability of
AFE, BWSM , and DN10 are presented in Table 2. Estimates of direct heritability were moderate for AFE
(0.21) and BWSM (0.25) but low for DN10 (0.08). Similar results were reported by Sezer, 2007 (0.24) for AFE
and Okenyi et al., 2013 (0.30) for BWSM . Higher estimates of direct heritability for AFE were revealed by
Sezer et al., 2006 (0.33), Valente et al., 2011 (0.275) and
Özsoy and Aktan, 2011 (0.37). The latter authors reported higher estimates of direct heritability for BWSM
(0.58) than those found in the current study, which
could be due to population structure, environmental
factors, ignored maternal effect, and the Bayesian procedures that they used in their study. One main reason
to estimate non-additive and maternal genetic variances
is to avoid biased estimation of heritability in a narrow
sense. Omission of such effects would lead to overestimation of error variance and would bias the direct
heritability estimate upwards. Furthermore, estimation
of these variances would lead to more accurate prediction of breeding values. The highest total heritability
was found for AFE (0.43) disclosing the magnitude of
maternal genetic effects in heredity of this trait. The
estimate of maternal genetic effect was quite close in
amount to the additive genetic effect for that trait. In
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SELECTION PROGRAM IN JAPANESE QUAIL
Table 1. Least squares means (±standard errors) of the studied traits through generations in selected and control
lines.
Generations
Line
selected
Control
N
AFE
BWSM
DN10
N
AFE
BWSM
DN10
1
2
3
4
173
48.90 ± 0.38
254.45 ± 3.39
11.14 ± 0.20
90
55.22 ± 0.64
246.66 ± 2.46
13.63 ± 0.54
140
46.86 ± 0.45
246.10 ± 2.83
11.26 ± 0.19
103
52.51 ± 0.70
236.88 ± 3.19
15.20 ± 0.71
188
46.85 ± 0.30
239.64 ± 2.45
11.06 ± 0.12
116
62.65 ± 0.61
234.83 ± 2.38
14.16 ± 0.38
118
47.45 ± 0.58
266.13 ± 3.69
10.32 ± 0.13
111
56.80 ± 0.62
255.30 ± 4.36
12.96 ± 0.30
N, number of records; AFE, age at first egg; BWSM , body weight at sexual maturity and DN10, days needed to produce the first
ten eggs.
Table 2. Estimates (±standard errors) of genetic parameters for the studied
traits.
AFE
BWSM
DN10
h2
m2
h2 T
ram
0.21 ± 0.01
0.25 ± 0.01
0.08 ± 0.02
0.19 ± 0.01
0.04 ± 0.02
0.01 ± 0.02
0.43 ± 0.01
0.25 ± 0.01
0.09 ± 0.02
−0.95 ± 0.01
−0.56 ± 0.01
−0.23 ± 0.02
h2 , direct heritability; m2 , maternal heritability; h2 T , total heritability; ram , correlation
between additive and maternal genetic effects; AFE, age at first egg; BWSM , body weight
at sexual maturity and DN10, days needed to produce the first ten eggs.
other words, the maternal heritability estimate of AFE
in this study (0.19) indicates a potential maternal influence on that trait, which should be accounted for
when making breeding value estimations and selection
decisions. On the other hand, maternal heritability’s for
DN10 and BWSM were low in magnitude (0.01 and 0.04,
respectively). Moreover, Sezer et al. (2006) estimated
lower maternal heritability for AFE (0.01) than that
found in this study. They reported that variance due to
maternal effects on body weight disappeared gradually
for males but rapidly for females as the chicks grew
older, definitely after the second week of age. These
findings could explain the low amount of maternal effects for BWSM in the current study. DN10 seems to have
a similar trend as well. Contrarily, AFE tends to have
a different pattern. This might be due to the nature of
the trait and type of employed measurement.
Theoretically, correlation between additive and maternal genetic effects could be existed in nature. In the
current study, correlations between additive genetic and
maternal genetic effects (ram ) were negative for all traits
(Table 2). Estimates varied in magnitude as moderate
for DN10 (–0.23), high for BWSM (–0.56), and high for
AFE (–0.95). A biological explanation of genetic antagonism between direct and maternal genetic effects is
currently unavailable, but these results may indicate a
negative genetic association between additive and maternal genetic effects that looks stronger at maturation
age, or failure to remove all pretest environmental effects (Sezer et al., 2006). Robinson (1996) reported that
the correlations between direct and maternal EBV substantially reflect the structure of the data records and
estimates of direct-maternal correlations may be nega-
Table 3. Genetic (above the diagonal); phenotypic (below the diagonal) correlations (±standard errors) for the
studied traits.
AFE
AFE
BWSM
DN10
0.18 ± 0.05
0.08 ± 0.04
BWSM
0.53 ± 0.08
− 0.01 ± 0.05
DN10
0.45 ± 0.06
0.56 ± 0.10
AFE, age at first egg; BWSM , body weight at sexual maturity
and DN10, days needed to produce the first ten eggs.
tive not only because of genetic antagonism, but also
because of additional sire X year variation or negative
dam-offspring covariance’s. Potential heterogeneity of
the genetic direct-maternal correlation could be avoided
by considering different sources of variance, and interactions among these sources. Similar trends of negative correlations between additive and maternal genetic
effects for egg production and maturity-related traits
were observed in chickens (Yousefi Zonuz et al., 2013).
High genetic and low phenotypic correlations were
observed among the studied traits (Table 3). It is evident that correlations between body weight and egg
production traits were not high and could have negative estimates (Narendra Nath et al., 2011). A similar trend was found for phenotypic correlation between AFE and BWSM (Karabağ et al., 2010; Özsoy
and Aktan, 2011). Sezer et al. (2006) reported positive
(0.18) genetic correlation and negative (–0.24) phenotypic correlation between body weight at 6 weeks of
age and age at sexual maturity in females of Japanese
quail. Moreover, Özsoy and Aktan (2011) revealed lower
positive genetic correlations between AFE and BWSM
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ABOU KHADIGA ET AL.
Table 4. Estimates (±standard errors) of cumulative selection response (CSR), genetic trend (T) and their Pearson product-moment correlation coefficient (PPC) of the
studied traits.
AFE
BWSM
DN10
CSR
T
PPC
− 3.03 ± 0.02∗
10.38 ± 0.07∗
− 0.15 ± 0.05
− 0.76 ± 0.02∗
2.54 ± 0.02∗
− 0.06 ± 0.03
0.78 ± 0.04∗∗
0.77 ± 0.04∗∗
0.61 ± 0.04∗∗
AFE, age at first egg; BWSM , body weight at sexual maturity
and DN10, days needed to produce the first ten eggs.
∗
and ∗∗ , P < 0.05 and P < 0.01, respectively.
(0.17) than that found in this study. Selection for the
aggregated breeding value of the animals helps to overcome the antagonistic relationships and to optimize different characters. Furthermore, if the correlation between the additional trait and egg production traits is
positive, we can improve both traits simultaneously.
Genetic Gain
CSR as a comparison between S and C lines was favorable for all traits and showed the superiority of the
selected line (Table 4). Responses were significant (P
< 0.05) for AFE (−3.03) and BWSM (10.38), but not
for DN10 (–0.15) between S and C lines. However, the S
line had better performance for the latter trait. Similar
results were reported for AFE (Narendra Nath et al.,
2011). On the other hand, Okenyi et al. (2013) reported
lower BWSM in a selected line for short-term 30 day egg
production for two generations. Additionally, the mean
of the predicted additive values by generation is plotted
in Figure 1 for AFE, BWSM , and DN10 .
Influential genetic responses over generations, without linearity, were observed. Mostly, there were fluctuations in response to selection affecting the means of
breeding values, which could be due to the data struc-
ture as every generation consisted of a single hatch.
Nevertheless, under mixed model methodology, desired
trends could be detected for all selected traits. Estimates of genetic trends were –0.76 (P = 0.031), 2.54
(P = 0.037) and –0.06 (P = 0.052) for AFE, BWSM ,
and DN10 , respectively (Table 4). In practice, environmental effects such as fluctuations in feed composition
and room temperature could have effects on the performance of the birds for a given trait and could lead to
differences between genetic and phenotypic levels. The
expected response is influenced by restrictions on the
number of animals that can be selected every generation, changes in genetic parameters as a consequence
of selection, and ignorance of the impact of correlations between breeding values on the selection intensity (Meuwissen, 1991). When comparing the mixed
model and least squares methods to estimate genetic
gain in this study, the obtained PPC between breeding values estimated by BLUP and phenotypic selection
methods were 0.78 (P = 0.002), 0.77 (P = 0.004) and
0.61 (P = 0.007) for AFE, BWSM , and DN10 , respectively (Table 4). These high and significant estimates
of PPC emphasized the similarity of animal rankings
when estimates of breeding values resulted from both
methodologies in the current study. It also proved the
robustness of the model included non-additive genetic
effects under mixed model methodology. Actually, the
estimates of genetic trends of AFE and BWSM agreed
with CSR estimated by the least squares method. The
genetic trend of DN10 was slightly higher than the estimated response of the control population in the current
study. However, the case of non-conformity between two
approaches in estimation of genetic gain was observed in
chickens (Morris and Pollott, 1997) and rabbits (Garcı́a
and Baselga, 2002). One explanation considers that if
the dominance variance was not included in the model,
the heritability could be overestimated (Misztal and
Besbes, 2000).
Figure 1. Means of the predicted additive values by generation of age at first egg (AFE), days needed to produce the first ten eggs (DN10 )
and body weight at sexual maturity (BWSM ).
SELECTION PROGRAM IN JAPANESE QUAIL
CONCLUSIONS
Construction of a selection program depending on
the aggregated breeding value based on animal model
BLUP could result in a great improvement in genetic
gain. This is particularly true for poorly heritable traits
or those that are measurable in only one sex. Low rates
of inbreeding were achieved by controlling mating system and selection intensity. The additional use of a trait
that is measurable in individuals and genetically correlates with egg production traits is an advantage for
improving egg production when animal model BLUP is
used for selection. Furthermore, if the correlation between the additional trait and egg production is positive, we can improve both traits simultaneously.
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