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

Abstract

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.

Potential negative effects of genomic selection

“…Examples are accumulated inbreeding ( Guinan et al, 2023 ) and fast changes in genetic parameters ( Hidalgo et al, 2020 ). It is well known that selection changes genetic parameters ( Walsh and Lynch, 2018 ), but the speedup in changes due to genomics were only recently reported ( Hidalgo et al, 2020 ; Sosa-Madrid et al, 2023 ). In a few years of GS in pigs, the heritability for a growth trait dropped by one-half, and the genetic antagonism between growth and fitness traits became 50% stronger ( Hidalgo et al, 2020 ).…”

“…In a few years of GS in pigs, the heritability for a growth trait dropped by one-half, and the genetic antagonism between growth and fitness traits became 50% stronger ( Hidalgo et al, 2020 ). In broiler chicken, large changes were reported since 2017 ( Sosa-Madrid et al, 2023 ), when heritabilities declined from about 0.40 to 0.35 for body weight and from about 0.30 to 0.15 for egg production, and genetic correlations between these two traits became more negative, dropping from −0.25 to −0.40. Estimates in this study may be affected by preselection as genotypes were not used.…”

Temporal dynamics of genetic parameters and SNP effects for performance and disorder traits in poultry undergoing genomic selection

“…Due to strong selection, genetic parameters may change, leading to inaccurate predictions of genetic gain when using outdated values ( McMillan et al, 1995 ). Fluctuations in genetic parameters over time have been presented in populations under selection in chicken ( Sosa-Madrid et al, 2023 ), in swine ( Hidalgo et al, 2020 ), in dairy ( Lawlor et al, 2002 ; Tsuruta et al, 2004b ), and in beef ( Meyer, 2004 ). These changes were theorized to be caused by several potential factors such as the Bulmer effect, changes in trait definition, genetic drift, accumulation of inbreeding, or changes in selection index ( Van Grevenhof et al, 2012 ).…”