Beef cattle

Investigating genotype-by-sex interaction in genomic predictions for beef cattle growth traits

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Abstract

Sexual dimorphism is well established in natural populations. Through improvements in management and genetics, the beef industry continues to see significant increases in feedlot growth and carcass weights. Current genetic evaluation models adjust for sex differences when making predictions. However, utilizing these models for genetic improvement programs has resulted in the production of larger mature cows, leading to increased maintenance costs in cow-calf operations. The objective of this study is to investigate the influence of genotype-by-sex interaction effect (Gà—S) on growth traits of beef cattle and to evaluate the performance of sex-based genomic predictions models. Ultimately, the goal would be to intentionally select larger steers and smaller cows by using sex-specific genetic predictions, thus creating increased sexual dimorphism via artificial selection. Data used for this study included birth weight (BW), weaning weight (WW) and yearling weight (YW) records of 46,234, 44,456, and 30,153 animals, linked to 1,536, 1,490 and 1,277 known sires, respectively, born in continental United States between 1986 and 2019. A two-trait model that fits the same growth trait observed in males and females as distinct dependent variables was used to evaluate Gà—S and compared to a single trait model that ignores Gà—S. Each trait was pre-adjusted for fixed effect of contemporary group (breeder zip code, birth year, season and sex). Pedigree and genotype data were available for all animals and was used to create either a numerator relationship or H matrix for subsequent analyses. Three statistical methods including animal BLUP, sire BLUP, and sire single-step GBLUP were analyzed using blupf90 family of programs. Genetic correlations between sexes ranged from 0.71 to 0.97 and were lower for yearling weight across methods. This is evidence for significant Gà—S for mature weight of beef cattle. On average, heritability estimates were larger for animal models compared to sire models. For example, for yearling weight, heritability estimates were 0.536 for animal model, 0.188 for sire model, and 0.191 for sire genomic model. For yearling weight, heritability estimates were lower for sex-specific single trait than for two-trait models, and estimates were higher for sex-specific models than across-sex models. For example, in sire genomic models for yearling weight, heritability estimates were 0.198 for female data, 0.198 for male data, and 0.180 when males and females were analyzed together. For all three traits, sire models were more accurate than animal models, and sex-specific sire models were more accurate than across-sex predictions. This shows that sexed-based genetic prediction may effectively create increased sexual dimorphism for growth traits of beef cattle.

Keywords: 2026

How to Cite:

Akanno, E. & Decker, J., (2026) “Investigating genotype-by-sex interaction in genomic predictions for beef cattle growth traits”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2287347. doi: https://doi.org/10.31274/wcgalp.24272

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