The genomic prediction of unobserved genetic values or future phenotypes for complex traits has revolutionized agriculture and human medicine. Fertility traits are complex traits of great economic importance to the dairy industry. While genomic prediction for improved cow fertility has received much attention over the last years, bull fertility has been largely ignored. The first aim of this study was to investigate the feasibility of genomic prediction of sire conception rate (SCR) in US Holstein dairy cattle. Standard genomic prediction often ignores any available information about functional features of the genome, although is believed that such information can yield more accurate and more persistent predictions. Hence, the second objective was to incorporate prior biological information into predictive models and evaluate their performance. The analyses included the use of kernel-based models fitting either all SNPs (55K) or only markers with presumed functional roles, such as SNPs linked to Gene Ontology (GO) or Medical Subject Headings (MeSH) terms related to male fertility, or SNPs significantly associated with SCR. Both single- and multi-kernel models were evaluated using linear and Gaussian kernels. Predictive ability was evaluated in 5-fold cross-validation. The entire set of SNPs exhibited predictive correlations around 0.35. Neither GO or MeSH gene sets achieved predictive abilities higher than their counterparts using random sets of SNPs. Notably, kernel models fitting significant SNPs achieved the best performance with increases in accuracy up to 5% compared with the standard whole-genome approach. Models fitting Gaussian kernels outperformed their counterparts fitting linear kernels irrespective of the set of SNPs. Overall, our findings suggest that genomic prediction of bull fertility is feasible in dairy cattle. This provide potential for accurate genome-guided decisions, such as culling bull calves with low SCR predictions. In addition, exploiting nonlinear effects using Gaussian kernels together with the incorporation of relevant variants seems a promising alternative to the standard approach. The inclusion of gene set results into prediction models deserves further research. Keywords: complex trait prediction, gene set, kernel model, sire conception rate
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Electronic Poster Session - Methods and Tools - Prediction 3, , 683, 2018
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