Predicting male fertility in dairy cattle using markers with large effect and functional annotation data

• Autores: Nani, Juan Pablo; Rezende, Fernanda M.; Peñagaricano, Francisco
• Año de publicación: 2019
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: BMC Genomics 20 : 258 (April 2019)
• Materia: Ganado de Leche; Fertilidad; Marcadores Genéticos; Toro; Genómica; Dairy Cattle; Fertility; Genetic Markers; Bulls; Genomics; Marcadores Moleculares
• Institución: Instituto Nacional de Tecnología Agropecuaria

Background: Fertility is among the most important economic traits in dairy cattle. Genomic prediction for cow fertility has received much attention in the last decade, while bull fertility has been largely overlooked. The goal of this study was to assess genomic prediction of dairy bull fertility using markers with large effect and functional annotation data. Sire conception rate (SCR) was used as a measure of service sire fertility. Dataset consisted of 11.5 k U.S. Holstein bulls with SCR records and about 300 k single nucleotide polymorphism (SNP) markers. The analyses included the use of both single-kernel and multi-kernel predictive models fitting either all SNPs, markers with large effect, or markers with presumed functional roles, such as non-synonymous, synonymous, or non-coding regulatory variants. Results: The entire set of SNPs yielded predictive correlations of 0.340. Five markers located on chromosomes BTA8, BTA9, BTA13, BTA17, and BTA27 showed marked dominance effects. Interestingly, the inclusion of these five major markers as fixed effects in the predictive models increased predictive correlations to 0.403, representing an increase in accuracy of about 19% compared with the standard model. Single-kernel models fitting functional SNP classes outperformed their counterparts using random sets of SNPs, suggesting that the predictive power of these functional variants is driven in part by their biological roles. Multi-kernel models fitting all the functional SNP classes together with the five major markers exhibited predictive correlations around 0.405. Conclusions: The inclusion of markers with large effect markedly improved the prediction of dairy sire fertility. Functional variants exhibited higher predictive ability than random variants, but did not outperform the standard whole-genome approach. This research is the foundation for the development of novel strategies that could help the dairy industry make accurate genome-guided selection decisions on service sire fertility.
EEA Rafaela
Fil: Nani, Juan Pablo. University of Florida. Department of Animal Sciences; Estados Unidos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; Argentina
Fil: Rezende, Fernanda M. University of Florida. Department of Animal Sciences; Estados Unidos. Universidade Federal de Uberlândia. Faculdade de Medicina Veterinária; Brasil
Fil: Peñagaricano, Francisco. University of Florida. Department of Animal Sciences; Estados Unidos. University of Florida. University of Florida Genetics Institute; Estados Unidos