Models to estimate maternal effects for juvenile body weight in broiler chickens

The estimation of genetic and environmental maternal effects by restricted maximum likelihood was considered for juvenile body weight (JBWT) data on 139 534 and 174 668 broiler chickens from two populations. Of the biometrical models usually assumed in the estimation of maternal effects ('reduced Willham' models), a genetic model allowing for direct and maternal genetic effects with a covariance between them and a permanent environmental maternal effect provided the best fit. The maternal heritabilities (0.04 and 0.02) were low compared to the direct heritabilities (0.32 and 0.27), the direct-maternal genetic correlations (r(AM)) were negative and identical for both strains (- 0.54) and environmental maternal effects of full sibs (0.06 and 0.05) were approximately a factor of two greater than maternal half sibs (0.03 and 0.02). A possible environmental dam-offspring covariance was accounted for in the mixed model by (1) estimation of the covariance between the environmental maternal and the environmental residual effects (c(EC)) and (2) a maternal phenotypic effect through regression on the mother's phenotype (F-m, 'Falconer' model). Whilst increasing the likelihoods considerably, these extended models resulted in somewhat more negative r(AM) values owing to positive estimates of c(EC) (0.04-0.08 and 0.03-0.09) and F-m (0.01-0.14 and 0.01-0.11). A more detailed fixed effects model, accounting for environmental effects due to individual parental flocks, reduced estimates of r(AM) (- 0.18 to - 0.33). Results suggested a limited importance of maternal genetic effects exerting a non-Mendelian influence on JBWT. The present integrated 'Falconer-Willham' models allowing for both maternal genetic (co)variances and maternal action through regression on the mother's phenotype in a mixed model setting might offer attractive alternatives to the commonly used 'Willham' models for mammalian species (eg, beef cattle) as was illustrated by their superior goodness-of-fit to simulated data.