Estimates of genetic parameters for monthly egg production in a commercial female broiler line using random regression models

Farzin, N.; Torshizi, R Vaez; Gerami, A.; Seraj, A.

doi:10.1016/j.livsci.2013.01.015

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…The heritability estimates for HHP tended to be lower than those for growth traits. Our estimate, using all data available, was similar to those of other studies, where egg production was recorded at 40 weeks (0.11) [28], until 48 weeks of age (0.13) [29], between 1 and 17 weeks of laying (0.15) [33], and from the 3rd to the 8th month (0.125-0.184) [34,35]. In our findings, the HHP estimates were lower for the Bayesian methods when using a bivariate model than the estimates when using REML.…”

Section: Genetic Inbreeding Coancestry and Drift Parameterssupporting

confidence: 87%

Genetic Variance Estimation over Time in Broiler Breeding Programmes for Growth and Reproductive Traits

Sosa-Madrid,

Maniatis,

Ibáñez-Escriche

et al. 2023

Animals

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Monitoring the genetic variance of traits is a key priority to ensure the sustainability of breeding programmes in populations under directional selection, since directional selection can decrease genetic variation over time. Studies monitoring changes in genetic variation have typically used long-term data from small experimental populations selected for a handful of traits. Here, we used a large dataset from a commercial breeding line spread over a period of twenty-three years. A total of 2,059,869 records and 2,062,112 animals in the pedigree were used for the estimations of variance components for the traits: body weight (BWT; 2,059,869 records) and hen-housed egg production (HHP; 45,939 records). Data were analysed with three estimation approaches: sliding overlapping windows, under frequentist (restricted maximum likelihood (REML)) and Bayesian (Gibbs sampling) methods; expected variances using coefficients of the full relationship matrix; and a “double trait covariances” analysis by computing correlations and covariances between the same trait in two distinct consecutive windows. The genetic variance showed marginal fluctuations in its estimation over time. Whereas genetic, maternal permanent environmental, and residual variances were similar for BWT in both the REML and Gibbs methods, variance components when using the Gibbs method for HHP were smaller than the variances estimated when using REML. Large data amounts were needed to estimate variance components and detect their changes. For Gibbs (REML), the changes in genetic variance from 1999–2001 to 2020–2022 were 82.29 to 93.75 (82.84 to 93.68) for BWT and 76.68 to 95.67 (98.42 to 109.04) for HHP. Heritability presented a similar pattern as the genetic variance estimation, changing from 0.32 to 0.36 (0.32 to 0.36) for BWT and 0.16 to 0.15 (0.21 to 0.18) for HHP. On the whole, genetic parameters tended slightly to increase over time. The expected variance estimates were lower than the estimates when using overlapping windows. That indicates the low effect of the drift-selection process on the genetic variance, or likely, the presence of genetic variation sources compensating for the loss. Double trait covariance analysis confirmed the maintenance of variances over time, presenting genetic correlations >0.86 for BWT and >0.82 for HHP. Monitoring genetic variance in broiler breeding programmes is important to sustain genetic progress. Although the genetic variances of both traits fluctuated over time, in some windows, particularly between 2003 and 2020, increasing trends were observed, which warrants further research on the impact of other factors, such as novel mutations, operating on the dynamics of genetic variance.

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Section: Genetic Inbreeding Coancestry and Drift Parameterssupporting

confidence: 87%

Genetic Variance Estimation over Time in Broiler Breeding Programmes for Growth and Reproductive Traits

Sosa-Madrid,

Maniatis,

Ibáñez-Escriche

et al. 2023

Animals

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“…Persistence of egg laying, however, might be a new subject on breeding of laying quails, just as it is currently observed in laying hens (Wolc and Szwaczkowski, 2009) and female broiler lines (Farzin et al, 2013).…”

Section: Canonical-correlation Analysismentioning

confidence: 92%

Canonical-correlation analysis applied to selection-index methodology in quails

et al. 2014

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a b s t r a c tGenetic evaluations in dual-purpose quails (Coturnix coturnix) have demonstrated that overall genetic gains in a breeding program are achieved not only based on a specific trait, but on several. The most common technique to use all this information is the selection index. Another alternative may be the canonical-correlation analysis applied to selection index. There is, however, a lack of studies using canonical correlation in quails. Hence, the objectives of this study were to apply canonical-correlation analysis to estimate the relationship of nine traits and to compare genetic gains obtained by this methodology to desired-gain selection index in three lines of quails. Data for three lines of layer quails consisted of body weight at 28 days (W28), egg weight (EW), age at first egg (AFE) and egg production at 30, 60, 90, 120, 150 and 180 days after onset of lay. Two sets of traits were established: the first one contained predictor variables (W28, EW and AFE) and the second one contained variables related to egg production. A selection index was constructed using the standardized coefficients of canonical covariates as weighting factors when a given canonical correlation was significant. We constructed two desired-gain selection indices: DG-SI1 and DG-SI2. The difference between them is that DG-SI2 had a desired gain for body weight set to 0. The estimated canonical correlations were as follows: 0.811, 0.058 and 0.003 for the yellow, 0.821, 0.181 and 0.076 for the red, and 0.825, 0.117 and 0.038 for the blue line. Only the first pair of canonical variates was significant (P o 0.05). AFE and early stages of egg production were very influent and showed great importance in defining the canonical variates and, consequently, the estimated canonical correlations. All lines had, in general, similar results for the canonical analysis indicating that traits that drive management decisions in these lines would be the same. The indices under study showed differences in response to selection; however, they generally resulted in consistent favorable genetic gains. For all lines, the canonical selection index resulted in the lowest AFE and highest egg production at 30 days. The DG-SI1 showed the highest genetic gains for W28 in all lines. There was a general lower genetic gain of other traits for DG-SI1 .se (A. Marubayashi Hidalgo).Livestock Science 169 (2014) [35][36][37][38][39][40][41] at the expense of the desired genetic gain for W28. Selection for AFE, according to the canonical-correlation analysis, would have a great impact on the number of eggs produced. Canonical selection index is a good alternative for a desired-gain selection index.

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