341 Impact of genomic selection for growth on feet and leg structure in Angus cattle

Abstract

Abstract Historically fitness and production traits have shown an antagonistic relationship at the genetic level. Genetic selection mainly focused on growth traits (GT) might have influenced feet and leg structure traits (FT; recently added trait), causing changes over time. We aimed to examine genetic parameter changes in FT and GT over time in Angus cattle. Currently, genetic parameters are estimated using Variance Components Estimation (VCE), which has limitations in handling large datasets and is time demanding. To address these limitations, we explored the use of predictivity-based formulas to estimate heritability and genetic correlations in GT. The analysis was conducted using the blupf90+ family of programs. To estimate variance components (VCE), sampled data (2011-2022) from the American Angus population included 311K GT records [birth (BW), weaning weight (WW), and post-weaning gain (PWG)], ~70K FT records [foot claw set (CS) and foot angle (FA)], and 142K genotypes. FT are taken on the worst foot of the animal and can be scored multiple times throughout the life of the animal, but in this study, we did not include repeated records. The dataset was split into 5-yr intervals. Since FT ranged from 5 to 9, and 5 is ideal, a negative correlation is desired between FT and GT. The correlation FA-PWG, changed in an undesirable way over time, as it changed from -0.10 to 0.02 in the last interval. The correlation FA-WW is becoming more favorable over time, shifting from 0.05 to -0.15. The GT selection process shifted the correlations between CS and GT in the desired direction, changing from positive (~0.1) to almost zero in the more recent two intervals. To test the predictivity-based formulas, Genomic Estimated Breeding Values (GEBV) were estimated using GT data from 6.3M animals born from 2000 to 2022, and 900K genotyped animals. The data were divided into four slices, with a 2-yr validation period. Over time, heritabilities remained constant: ~0.3 for BW and WW, and ~0.2 for PWG. Correlations between GT also remained constant, approximately at 0.45 for BW-WW, 0.30 for BW-PWG, and 0.75 for WW-PWG. Although the formulas show promising results for heritabilities, interpreting the outcomes of genetic correlation estimation remains unclear, needing further tests. While certain correlations between FT and GT suggest a positive impact of genetic selection, some undesirable outcomes are realized. Probably due to the early FT genetic selection implementation (2019). This emphasizes the need for a multi-trait genomic selection approach to mitigate genetic antagonism. Adopting predictivity-based formulas reduces the time for genetic parameter estimation and allows the use of complete data instead of sampled data, as currently done in VCE.