A multi-trait Bayesian method for mapping QTL and genomic prediction.

ABSTRACT:

Background

Genomic prediction and quantitative trait loci (QTL) mapping typically analyze one trait at a time but this may ignore the possibility that one polymorphism affects multiple traits. The aim of this study was to develop a multivariate Bayesian approach that could be used for simultaneously elucidating genetic architecture, QTL mapping, and genomic prediction. Our approach uses information from multiple traits to divide markers into 'unassociated' (no association with any trait) and 'associated' (associated with one or more traits). The effect of associated markers is estimated independently for each trait to avoid the assumption that QTL effects follow a multi-variate normal distribution.

Results

Using simulated data, our multivariate method (BayesMV) detected a la

SUBMITTER: Kemper KE 

PROVIDER: S-EPMC5866527 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature