GWAS & Selection signatures

Disentangling Mature Cow Weight and Body Condition Score: Comparative GWAS of Different Modeling Strategies

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Abstract

Mature cow weight (MWT; heritability = 0.37 ± 0.004) is a trait genetically correlated with body condition score (BCS; heritability = 0.10 ± 0.003). Our previous study showed that sire rankings can shift depending on how this BCS is accounted for, indicating that different modeling strategies can influence selection outcomes. The recursive modeling approach has been established as a method for obtaining MWT that is genetically independent of BCS, providing an alternative to phenotypic pre-adjustment. The objective of this study was to determine whether different modeling approaches genuinely capture different genetic architectures or merely produce statistical artifacts. The final dataset provided by the American Angus Association comprised 381,496 MWT and BCS records from 209,491 cows. Of these, 20,000 phenotyped cows were genotyped and imputed to a common marker density of 54,609 markers. The pre-adjusted MWT dataset consisted of a single record per animal, as provided by the Association. We performed genome-wide association analyses (GWAS) and functional genomic analyses to compare the genomic architecture of phenotypically pre-adjusted MWT with MWT that is genetically independent of BCS derived from the recursive approach. We identified 42 significant SNPs across 8 chromosomes for the pre-adjusted MWT and 44 significant SNPs across 9 chromosomes for the recursive MWT, with 28 SNPs shared between the two definitions. These variants corresponded to 107 annotated genes in the pre-adjusted analysis and 137 annotated genes in the recursive analysis, including 62 genes shared across both traits. In both modeling approaches, the major association signals were concentrated on BTA20, BTA7, and BTA14. Across the genome, all significant SNPs jointly explained 3.93% of the total additive genetic variance for the pre-adjusted MWT and 4.29% for the recursive MWT. The top three genomic regions accounted for 2.89%, 0.79%, and 0.20% of the variance in the pre-adjusted analysis and 3.00%, 0.86%, and 0.36% in the recursive analysis. Across all SNPs evaluated, the correlation between estimated SNP effects for the pre-adjusted and recursive definitions of MWT was 0.76, although the overall GEBV correlation was 0.87. Despite differences in genome-wide SNP effects, both definitions converged on nearly identical core biological signals, suggesting that both methods effectively identify the primary genetic drivers of mature cow weight independent of body condition.

Keywords: 2026

How to Cite:

Garcia, A., Mulim, H., Ojo, A., Oliveria, H. & Retallick, K., (2026) “Disentangling Mature Cow Weight and Body Condition Score: Comparative GWAS of Different Modeling Strategies”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2286610. doi: https://doi.org/10.31274/wcgalp.24064

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