Dairy cattle

Genetics of udder conformation traits derived from robotic milking systems data using genomic random regression models in American Holstein cattle

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Abstract

Udder conformation traits are crucial in dairy production systems, influencing milking efficiency, susceptibility to mastitis, and cow welfare and longevity. The increasing use of automatic milking systems (AMS) has enabled continuous and objective recording of udder conformation traits, creating new opportunities to improve their genetic evaluation in dairy cattle breeding programs. Therefore, the main objective of this study was to estimate genetic parameters for AMS-derived udder conformation traits and daily milk yield (DMY) in American Holstein cows using genomic random regression models (RRM). A total of 10.4 million individual milking records from 7,546 cows were collected by 36 AMS on a commercial farm in Indiana, USA. Udder conformation traits were calculated based on three-dimensional Cartesian coordinates (X, Y, Z) automatically recorded by the AMS. For the genetic analyses, 3.8 million daily records were considered for udder depth (UD), front teat distance (FTD), rear teat distance (RTD), distance front-rear (DFR), udder balance (UB), and DMY. Genomic data from 6,378 cows (60,499 SNPs after quality control) were used to build the genomic relationship matrix. Genetic parameters were estimated using RRMs with Legendre orthogonal polynomials (up to fifth order) across days in milk (DIM) under a GBLUP framework implemented in the blupf90+ software. The model accounted for fixed regression and contemporary groups (calving year à— season) as fixed effects, and additive genetic, permanent environmental, and residual as random effects. Two residual variance structures (homogeneous vs. heterogeneous across 10 DIM classes) were compared, and model selection was based on the model with the lowest BIC value. The best-fitting RRM for each trait ranged from third- to fifth-order, and models assuming heterogeneous residual variances provided a better fit compared to those with homogeneous residual variances. Across lactations, RRMs revealed changes in the magnitude of additive genetic variances (σ²ₐ), with higher σ²ₐ in later parities. Heritability (h²) estimates across DIM ranged from 0.10 for RTD (second lactation) to 0.71 for UD (third lactation). Udder depth showed the highest h² (0.33 to 0.71), followed by DFR (0.40 to 0.64), FTD (0.36 to 0.63), RTD (0.10 to 0.47), UB (0.15 to 0.44), and DMY (0.13 to 0.33). Genetic correlations (rg) across DIM were high ( > 0.75) for all udder conformation traits. Udder depth showed a moderate negative rg with FTD, RTD, and DFR (lowest rg value = -0.39), whereas FTD and RTD were highly positive genetic correlated (rg up to 0.85). Associations with DMY were weak to moderate, suggesting that different sets of genes regulate DMY and udder conformation traits. These results indicate that AMS-derived udder conformation traits are moderately to highly heritable and consistent across parities, underscoring their importance as valuable phenotypes for enhancing genomic selection and improving udder conformation in dairy cattle populations.

Keywords: 2026

How to Cite:

Rodrigues, G., Maskal, J., Oliveira, H., Silva Neto, J., Chen, S., Mercadante, M. & Brito, L., (2026) “Genetics of udder conformation traits derived from robotic milking systems data using genomic random regression models in American Holstein cattle”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2286607. doi: https://doi.org/10.31274/wcgalp.24063

Rights: 1