36 Matching cow’s genetics to the environment using genomics Academic Article uri icon

abstract

  • Abstract Cattle poorly adapted to their environment result in lost revenue and jeopardize the stability of the food supply. Genomic data now allows us to rigorously analyze adaptations and avoid the generation of animals that will not thrive. We used selection scans for local adaptation, genotype-by-environment genome-wide association analyses, creation of hair shedding genomic predictions and environmental region-specific genomic predictions of growth traits to characterize and predict local adaptation in beef cattle. Analyzing ~40,000 cattle from three breed associations with ~850,000 high-accuracy imputed SNPs, we used novel selection mapping methods to identify genomic loci responsible for adaptation. We identify 19 different loci (harboring 24 annotated genes) as responding to selection to local adaptation. In cooperation with 74 producers across the United States, over 12,000 cattle were scored on a scale of 1–5 for the early-summer hair shedding phenotype in 2016, 2017, and 2018. Participating cattle were genotyped using the GGP-F250 SNP panel developed by the University of Missouri, which contains ~170,000 candidate functional variants and ~30,000 variants in common with beef cattle industry standard genotyping assays. Genomic breeding values were generated with a repeated records model using these phenotypes. Further, we identified loci with large allele substitution effects for hair shedding. When local adaptations exist, ranking animals using a regional genetic evaluation will be different from national cattle evaluations. We developed region-specific genomic predictions using a multivariate model in which phenotypes from different regions were fit as separate dependent variables. Genetic correlations between regions were moderate, indicating substantial re-ranking between environmental regions. These genomic predictions will allow rapid identification of cattle best suited to an environment.

author list (cited authors)

  • Decker, J. E., Rowan, T. N., Nilson, S., Durbin, H. J., Braz, C. U., Schnabel, R. D., & Seabury, C.

citation count

  • 0

publication date

  • December 2019