Genomic analysis of the inbreeding load for body weight, carcass and reproductive traits in the Rubia Gallega beef cattle population.

backgroundInbreeding, resulting from mating between relatives, leads to inbreeding depression, which can be traced back to hidden ancestral inbreeding loads. These loads exhibit variability and act as additive genetic effects that are only expressed in the inbred offspring. The objective of this study was to quantify the variance of the inbreeding loads and its correlation with additive genetic effects for seven traits in the Rubia Gallega population: birth weight, weaning weight, cold carcass weight, carcass conformation, carcass fatness, calving interval, and age at first parity. A single-step GBLUP Bayesian analysis was used by a Gibbs sampler. Additionally, the equivalence between GBLUP and SNP-BLUP was used for locating the genomic regions associated with the highest variances.

resultsThe pedigree included 522,885 animals, of which 4984 were genotyped with the Axiom_BovMDv3 chip. A total of 246,393 individuals were inbred, with an average inbreeding coefficient of 0.044 ± 0.059, attributed to 4712 ancestors through 9.8 million partial inbreeding contributions. The estimated proportion of phenotypic variance explained by inbreeding loads for an inbreeding coefficient of 0.10 ranged from 0.012 (Birth weight) to 0.101 (Weaning weight), consistently below the heritabilities of the traits. Genetic correlations between inbreeding load and additive effects were always negative. The average prediction accuracy for inbreeding-load effects in young selection candidates was low and exceeded 0.7 only in older animals. The genomic distribution of additive and inbreeding load variances was uneven, with some regions overlapping and others being specific to inbreeding load.

conclusionsThis study demonstrates that the inbreeding load variance is low compared to the additive genetic variance across a range of growth, carcass, and reproductive traits. The results were also consistent with a previous study that theoretically demonstrated a negative correlation between additive effects and inbreeding load. The potential to purge deleterious alleles appears limited, largely due to the low prediction accuracy observed in young individuals. Nevertheless, the higher accuracy of inbreeding-load estimates in ancestral animals could still be exploited to guide the unavoidable inbreeding in small populations through informed mating strategies, thereby minimizing undesirable inbreeding-depression effects. The heterogeneous genomic distribution of the inbreeding load suggests new opportunities for identifying genes in which deleterious or semideleterious alleles may be located.