Search

The objective of the present experiment work was to evaluate the effect of the inclusion of genomic information on the additive genetic variance of birth weight (BW) of Charolais cattle in Mexico. Variance components and heritability were estimated using four linear models. The first model was the base model (BM) from which single and composite effects of selected single-nucleotide polymorphism (SNP) markers were evaluated (BM1, BM2, and a composite BM3). Genetic markers were included in a regression model and analyzed by stepwise regression against adjusted BW from a panel of growth-related traits candidate gene markers. After two regression rounds, two SNPs (R (2) > 0.02) were chosen to include into the animal models as fixed effects. Growth hormone receptor gene GHR 4.2 and GHR 6.1 SNPs were selected from a panel of 39 SNPs. GHR 4.2 had a negligible effect on BW, whilst GHR6.1, interestingly, explained ∼9 % of genetic variance (p = 0.0877) with an αG>A = 0.509. The inclusion of markers in M2 and M3 reduced 19 and 15 % of the additive genetic variance, respectively. Both adjusted significantly better the linear model (LRT = p < 0.01). Results obtained suggest that the previous selection of markers in a candidate gene approach and subsequent inclusion of selected SNPs into animal model might provide a better fit, avoiding the overestimation of genetic variance components and breeding values for BW.