Genomic prediction of bull fertility in US Jersey dairy cattle

摘要

Service sire has a major effect on reproductive successin dairy cattle. Recent studies have reported accuratepredictions for Holstein bull fertility using genomicdata. The objective of this study was to assess thefeasibility of genomic prediction of sire conception rate(SCR) in US Jersey cattle using alternative predictivemodels. Data set consisted of 1.5k Jersey bulls withSCR records and 95k SNP covering the entire genome.The analyses included the use of linear and Gaussiankernel-based models fitting either all the SNP or subsetsof markers with presumed functional roles, such asSNP significantly associated with SCR or SNP locatedwithin or close to annotated genes. Model predictiveability was evaluated using 5-fold cross-validation with10 replicates. The entire SNP set exhibited predictivecorrelations around 0.30. Interestingly, either SNPmarginally associated with SCR or genic SNP achievedhigher predictive abilities than their counterparts usingrandom sets of SNP. Among alternative SNP subsets,Gaussian kernel models fitting significant SNP achievedthe best performance with increases in predictive correlationup to 7% compared with the standard wholegenomeapproach. Notably, the use of a multi-breedreference population including the entire US HolsteinSCR data set (11.5k bulls) allowed us to achieve predictivecorrelations up to 0.315, gaining 8% in accuracycompared with the standard model fitting a pure Jerseyreference set. Overall, our findings indicate that genomicprediction of Jersey bull fertility is feasible. The useof Gaussian kernels fitting markers with relevant rolesand the inclusion of Holstein records in the trainingset seem to be promising alternatives to the standardwhole-genome approach. These results have the potentialto help the dairy industry improve US Jersey sirefertility through accurate genome-guided decisions.