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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. [ABSTRACT FROM AUTHOR]

Background: A better understanding of the genetic architecture underlying complex traits (e.g., the distribution of causal variants and their effects) may aid in the genomic prediction. Here, we hypothesized that the genomic variants of complex traits might be enriched in a subset of genomic regions defined by genes grouped on the basis of "Gene Ontology" (GO), and that incorporating this independent biological information into genomic prediction models might improve their predictive ability. Results: Four complex traits (i.e., milk, fat and protein yields, and mastitis) together with imputed sequence variants in Holstein (HOL) and Jersey (JER) cattle were analysed. We first carried out a post-GWAS analysis in a HOL training population to assess the degree of enrichment of the association signals in the gene regions defined by each GO term. We then extended the genomic best linear unbiased prediction model (GBLUP) to a genomic feature BLUP (GFBLUP) model, including an additional genomic effect quantifying the joint effect of a group of variants located in a genomic feature. The GBLUP model using a single random effect assumes that all genomic variants contribute to the genomic relationship equally, whereas GFBLUP attributes different weights to the individual genomic relationships in the prediction equation based on the estimated genomic parameters. Our results demonstrate that the immune-relevant GO terms were more associated with mastitis than milk production, and several biologically meaningful GO terms improved the prediction accuracy with GFBLUP for the four traits, as compared with GBLUP. The improvement of the genomic prediction between breeds (the average increase across the four traits was 0.161) was more apparent than that it was within the HOL (the average increase across the four traits was 0.020). Conclusions: Our genomic feature modelling approaches provide a framework to simultaneously explore the genetic architecture and genomic prediction of complex traits by taking advantage of independent biological knowledge. [ABSTRACT FROM AUTHOR]