# Optimizing Genomic Parental Selection for Categorical and Continuous–Categorical Multi-Trait Mixtures

## TL;DR

This paper introduces a new method for optimizing parental selection in breeding by combining categorical and continuous traits using Bayesian decision theory.

## Contribution

The novel contribution is a unified Bayesian framework for genomic selection that integrates categorical and continuous traits in multi-trait breeding.

## Key findings

- The Bayesian decision theory approach improves precision in selecting parental lines for categorical and continuous traits.
- Simulations validate the effectiveness of the method in enhancing genetic improvements in breeding programs.

## Abstract

This study presents a novel approach for the optimization of genomic parental selection in breeding programs involving categorical and continuous–categorical multi-trait mixtures (CMs and CCMMs). Utilizing the Bayesian decision theory (BDT) and latent trait models within a multivariate normal distribution framework, we address the complexities of selecting new parental lines across ordinal and continuous traits for breeding. Our methodology enhances precision and flexibility in genetic selection, validated through extensive simulations. This unified approach presents significant potential for the advancement of genetic improvements in diverse breeding contexts, underscoring the importance of integrating both categorical and continuous traits in genomic selection frameworks.