Increasing the accuracy of genomic prediction in pure-bred Limousin beef cattle by including cross-bred Limousin data and accounting for an F94L variant in MSTN

Explicitly fitting effects for major genes or QTL that account for a large percentage of variation in a whole genomic prediction model may increase prediction accuracy. This study compared approaches to account for a major effect of an F94L variant in the MSTN gene within the genomic prediction using bovine whole-genomic SNP markers. Among the beef cattle breeds, Limousin have been known to have an F94L variant that is not present in Angus. The reference population in this study consisted of 3060 beef cattle including pure-bred Limousin (PL), cross-bred Limousin with Angus (LF) and pure-bred Angus, genotyped using a BovineSNP50 BeadChip and directly for the MSTN-F94L variant. We compared prediction accuracies in PL animals using the three datasets from only the PL population, admixed PL and LF (AL) or multibreed analysis using all of the PL, LF and Angus (MB) population according to four-fold cross-validation after K-means clustering. The MSTN-F94L variant was the most strongly associated with five traits (birth weight, calving ease direct, milk, weaning weight and yield grade) among the 13 measured traits in PL and AL populations. Fitting the MSTN-F94L variant as a random effect, the genomic prediction accuracies for birth weight increased by 2.7% in PL, by 2.2% in AL and by 3.2% in MB. Prediction accuracies for five traits increased in the MB analysis. Fitting MSTN-F94L as a fixed effect in PL, AL and MB analyses resulted in increased prediction accuracy in PL for eight traits. Prediction accuracies can be improved by including a causal variant in genomic evaluation compared with simply using whole-genome SNP markers. Fitting the causal variant as a fixed effect along with markers fitted as random effects resulted in greater prediction accuracies for most traits. Causal variants should be genotyped along with SNP markers.