Bayesian methods for jointly estimating genomic breeding values of one continuous and one threshold trait

Abstract

Genomic selection has become a useful tool for animal and plant breeding. Currently, genomic evaluation is usually carried out using a single-trait model. However, a multi-trait model has the advantage of using information on the correlated traits, leading to more accurate genomic prediction. To date, joint genomic prediction for a continuous and a threshold trait using a multi-trait model is scarce and needs more attention. Based on the previously proposed methods BayesCp for single continuous trait and BayesTCp for single threshold trait, we developed a novel method based on a linear-threshold model, i.e., LT-BayesCp, for joint genomic prediction of a continuous trait and a threshold trait. Computing procedures of LTBayesCp using Markov Chain Monte Carlo algorithm were derived. A simulation study was performed to investigate the advantages of LT-BayesCp over BayesCp and BayesTCp with regard to the accuracy of genomic prediction on both traits. Factors affecting the performance of LT-BayesCp were addressed. The results showed that, in all scenarios, the accuracy of genomic prediction obtained from LT-BayesCp was significantly increased for the threshold trait compared to that from single trait prediction using BayesTCp, while the accuracy for the continuous trait was comparable with that from single trait prediction using BayesCp. The proposed LT-BayesCp could be a method of choice for joint genomic prediction of one continuous and one threshold trait.