A Bayesian Genomic Regression Model with Skew Normal Random Errors.

ABSTRACT: Genomic selection (GS) has become a tool for selecting candidates in plant and animal breeding programs. In the case of quantitative traits, it is common to assume that the distribution of the response variable can be approximated by a normal distribution. However, it is known that the selection process leads to skewed distributions. There is vast statistical literature on skewed distributions, but the skew normal distribution is of particular interest in this research. This distribution includes a third parameter that drives the skewness, so that it generalizes the normal distribution. We propose an extension of the Bayesian whole-genome regression to skew normal distribution data in the context of GS applications, where usually the number of predictors vastly exceeds the sample size. How

SUBMITTER: Perez-Rodriguez P 

PROVIDER: S-EPMC5940167 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature