Role of inbreeding depression, non-inbred dominance deviations and random year-season effect in genetic trends for prolificacy in closed rabbit lines.

In closed rabbit lines selected for prolificacy at the Polytechnic University of Valencia, genetic responses are predicted using BLUP. With a standard additive BLUP model and year-season (YS) effects fitted as fixed, genetic trends were overestimated compared to responses estimated using control populations obtained from frozen embryos. In these lines, there is a confounding between genetic trend, YS effects and inbreeding, and the role of dominance is uncertain. This is a common situation in data from reproductively closed selection lines. This paper fits different genetic evaluation models to data of these lines, aiming to identify the source of these biases: dominance, inbreeding depression and/or an ill-conditioned model due to the strong collinearity between YS, inbreeding and genetic trend. The study involved three maternal lines (A, V and H) and analysed two traits, total born (TB) and the number of kits at weaning (NW). Models fitting YS effect as fixed or random were implemented, in addition to additive genetic, permanent environment effects and non-inbred dominance deviations effects. When YS was fitted as a fixed effect, the genetic trends were overestimated compared to control populations, inbreeding had an apparent positive effect on litter size and the environmental trends were negative. When YS was fitted as random, the genetic trends were compatible with control populations results, inbreeding had a negative effect (lower prolificacy) and environmental trends were flat. The model fitting random YS, inbreeding and non-inbred dominance deviations yielded the following ratios of additive and dominance variances to total variance for NW: 0.06 and 0.01 for line A, 0.06 and 0.00 for line V and 0.01 and 0.08 for line H. Except for line H, dominance deviations seem to be of low relevance. When it is confounded with inbreeding as in these lines, fitting YS effect as random allows correct estimation of genetic trends.