Genomic prediction of service sire effect on female reproductive performance in Holstein cattle: A comparison between different methods, validation population and marker densities.

Abstract

Reproductive traits of dairy cattle are bound to the actual efficiency of farm operation, which therefore show great economic importance. Among them, some traits were deemed to be simultaneously affected by service sire and mating cow. Service sires are proved to play an important role in reproduction process of cows. However, limited study explored the genetic effect of service sire (GESS), let alone the genomic prediction of this effect. In the present study, 2244 genotyped bulls together with phenotypic records were used to predict the GESS on conception rate, 56-day non-return rate, calving ease, stillbirth and gestation length. The feasibilities of multi-step genomic best linear unbiased predictor (msGBLUP) and single-step genomic best linear unbiased predictor (ssGBLUP) were investigated under different scenarios, that is, different marker densities and validation population. The predictive accuracies and unbiasedness for GESS ranged from 0.159 to 0.647 and from 0.202 to 2.018, respectively, when validated on young bulls, while the accuracies and unbiasedness ranged from 0.409 to 0.802 and 0.333 to 1.146 when validated on random split data sets. It is feasible to predict GESS on reproductive traits by using a linear mixed model and genomic data, and high-density marker panel had limited contribution to the prediction. This research investigated the potential factors that influence the genomic prediction of GESS on reproductive traits and indicated the possibility of genomic selection on GESS, both in ideal and practical circumstances.