Evaluation of imputed sequence variant subsets across biological types of beef cattle

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

Submitted to: Midwestern Section of the American Society of Animal Science
Publication Type: Abstract Only
Publication Acceptance Date: 10/31/2024
Publication Date: 5/20/2025
Citation: Russell, C.A., Kuehn, L.A., Snelling, W.M., Spangler, M.L. 2025. Evaluation of imputed sequence variant subsets across biological types of beef cattle. Journal of Animal Science. 103(Suppl. 1):19-20. https://doi.org/10.1093/jas/skaf102.021.
DOI: https://doi.org/10.1093/jas/skaf102.021

Interpretive Summary:

Technical Abstract: A crossbred beef cattle population (n=2,212) was used to assess the impact of variants identified from imputed low-pass sequence (LPS) on the robustness of predictions across breed types for the traits of birth weight (BWT) and post-weaning gain (PWG). Variants were selected based on functional impact and were partitioned into four groups (Low, Modifier, Moderate, High) and later re-partitioned based on consequence of mutation (i.e., missense and untranslated region variants, etc.) into six groups (G1-G6). Each subset was used to construct a genomic relationship matrix (GRM) for univariate animal models. In addition, the top 30% of variants based on squared marker effect estimates were evaluated. Cross-validation was conducted for each variant subset using biological type (British, Continental, and Indicus). Animals were partitioned into biological types of > 25% Indicus, 0-24% British, 25-49% British, 50-74% British, and > 75% British (Indi25, Brit0, Brit25, Brit50, and Brit75, respectively). Cross-validation statistics included the Pearson correlation between EBV from the testing set and adjusted phenotype, and the ratio of that correlation to the square root of heritability (Ratio). These estimates were benchmarked against all LPS variants in a single GRM and a commercial array (e.g., GeneSeek Genomic Profiler 100K; Chip) genotypes. Results showed that variants in the subsets Modifier and G1 (untranslated region) yielded the highest heritability estimates and were similar to the inclusion of all variants, while estimates from GRM containing only variants in the categories High, G4 (non-coding transcript exon), and G6 (start and stop loss/gain) were the lowest. All variants combined provided similar heritability estimates to Chip and provided minimal to no additional information when combined with Chip. Ratio estimates for BWT ranged from 0.11-0.42 for Chip, 0.21-0.38 for Full, 0.20-0.38 for Low, 0.26-0.37 for Modifier, 0.21-0.41 for Moderate, and 0.14-0.49 for High. Ratio estimates for PWG ranged from 0.11-0.42 for Chip, 0.13-0.35 for Full, 0.04-0.38 for Low, 0.17-0.33 for Modifier, 0.08-0.37 for Moderate, and -0.11-0.72 for High. The top 30% variants generally yielded higher Ratios than those from all variants, particularly for BWT. Ratio estimates for BWT ranged from 0.44-0.68 for Chip, 0.45-0.49 for Full, 0.35-0.49 for Low, 0.43-0.49 for Modifier, 0.42-0.55 for Moderate, and 0.20-0.53 for High. Ratio estimates for PWG ranged from 0.18-0.35 for Chip, 0.19-0.37 for Full, 0.12-0.43 for Low, 0.12-0.38 for Modifier, 0.14-0.35 for Moderate, and -0.37-0.60 for High. Cross-validation results showed LPS subsets performed similarly to Chip for most biological types. However, when predicting BWT in Indicus, the LPS subsets provided more favorable results than the array; the same was not true for PWG.The USDA is an equal opportunity employer and provider.