Genetic prediction for growth traits in beef cattle using selected variants from imputed low-pass sequence data

Authors: RUSSELL, CHAD - University Of Nebraska; Kuehn, Larry; Snelling, Warren; SPANGLER, MATTHEW - University Of Nebraska

Submitted to: World Congress of Genetics Applied in Livestock Production
Publication Type: Proceedings
Publication Acceptance Date: 6/2/2023
Publication Date: 7/3/2023
Citation: Russell, C.C., Kuehn, L.A., Snelling, W.M., Spangler, M.L. 2023. Genetic prediction for growth traits in beef cattle using selected variants from imputed low-pass sequence data [abstract]. In: Proceedings of the World Congress of Genetics Applied in Livestock Production, July 3-8, 2022, Rotterdam, The Netherlands. Session 40. p. 47.

Interpretive Summary:

Technical Abstract: A beef cattle population (n=2,343) was used to assess the impact of variants identified from imputed low-pass sequence on the genetic prediction of birth weight (BWT) and post weaning gain (PWG). Variants (n=1,145,892) were selected based on functional impact and were partitioned into low, modifier, moderate, and high based on predicted functional consequences. Each subset was used to construct a genomic relationship matrix (GRM) in univariate animal models. When all variants were included in a single GRM, heritability estimates for BWT and PWG were 0.41±0.05 and 0.37±0.05, respectively. Heritability estimates for BWT when the GRM was comprised of only low, modifier, moderate, or high variants were 0.36±0.05, 0.39±0.05, 0.33±0.05, and 0.10±0.03, respectively. Similar estimates for PWG were 0.33±0.05, 0.34±0.05, 0.32±0.05, and 0.10±0.03, respectively. Results suggest that despite predicted functional consequences, the high variants accounted for only ~24-27% of the genetic variance.