Estimation & Prediction

Methods for joint genetic prediction of multiple ordinal categorical and continuous traits

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Abstract

The landscape of animal breeding is rapidly evolving. Data on health, disease, welfare, and fertility, which are key fitness traits for sustainable production, have been extensively collected. The categorical nature of these traits violates the normality assumptions of traditional linear mixed models, requiring threshold or threshold-linear models. These models can be fitted either via Gibbs sampling or by maximizing the posterior density (maximum a posteriori, MAP). While Gibbs sampling is flexible and can handle multiple-trait models with several categorical and continuous traits, its computational and memory demands, and the need to evaluate whether the Markov chain converged limit its applicability in genetic evaluations. MAP methods are computationally efficient and suitable for large-scale datasets. However, existing MAP methods are restricted to a single categorical trait and many continuous ones, making them insufficient for the new generation of data. The lack of proper methods to fit threshold-linear models to real-life applications has persisted for two to three decades. Failing to model categorical traits accurately can lead to reduced genetic gain and deterioration of the traits over time. This study aimed to develop the first theoretically sound and computationally efficient framework for jointly analyzing multiple categorical and linear traits, specifically multi-trait threshold-linear models, under BLUP and single-step genomic BLUP (ssGBLUP). The proposed methods are based on Newton-Raphson and Expectation-Maximization schemes. Using a simulated dataset and treating Gibbs sampling as the benchmark, we show that the breeding values estimated by our methods agree nearly perfectly with those obtained from Gibbs sampling, while greatly reducing computational cost and allowing scalability to very large datasets. As expected, the Newton-Raphson iteration outperformed the Expectation-Maximization algorithm in terms of computational time. Our results demonstrate that routine genetic evaluations incorporating multiple categorical traits are now feasible using the presented methodology. Solving this decades-old enigma enables accurate, large-scale genetic and genomic predictions of key categorical fitness traits under multi-trait models.

Keywords: 2026

How to Cite:

Bermann, M., Legarra, A., Lourenco, D. & Misztal, I., (2026) “Methods for joint genetic prediction of multiple ordinal categorical and continuous traits”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2284609. doi: https://doi.org/10.31274/wcgalp.23603

Rights: 1