Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping.

ABSTRACT:

Background

Complex binary traits are influenced by many factors including the main effects of many quantitative trait loci (QTLs), the epistatic effects involving more than one QTLs, environmental effects and the effects of gene-environment interactions. Although a number of QTL mapping methods for binary traits have been developed, there still lacks an efficient and powerful method that can handle both main and epistatic effects of a relatively large number of possible QTLs.

Results

In this paper, we use a Bayesian logistic regression model as the QTL model for binary traits that includes both main and epistatic effects. Our logistic regression model employs hierarchical priors for regression coefficients similar to the ones used in the Bayesian LASSO linear model for multi

SUBMITTER: Huang A 

PROVIDER: S-EPMC3771412 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature