Bayesian discrete lognormal regression model for genomic prediction

Theor Appl Genet. 2024 Jan 14;137(1):21. doi: 10.1007/s00122-023-04526-4.ABSTRACTGenomic prediction models for quantitative traits assume continuous and normally distributed phenotypes. In this research, we proposed a novel Bayesian discrete lognormal regression model. Genomic selection is a powerful tool in modern breeding programs that uses genomic information to predict the performance of individuals and select those with desirable traits. It has revolutionized animal and plant breeding, as it allows breeders to identify the best candidates without labor-intensive and time-consuming phenotypic evaluations. While several statistical models have been developed, most of them have been for quantitative continuous traits and only a few for count responses. In this paper, we propose a discrete lognormal regression model in the Bayesian context, that with a Gibbs sampler to explore the corresponding posterior distribution and make the predictions. Two datasets of resistance disease is used in the wheat crop and are then evaluated against the traditional Gaussian model and a lognormal model. The results indicate the proposed model is a competitive and natural model for predicting count genomic traits.PMID:38221602 | DOI:10.1007/s00122-023-04526-4

Source: TAG. Theoretical and Applied Genetics - January 14, 2024 Category: Genetics & Stem Cells Authors: Abelardo Montesinos-L ópez Humberto Guti érrez-Pulido Sof ía Ramos-Pulido Jos é Cricelio Montesinos-López Osval A Montesinos-L ópez Jos é Crossa Source Type: research