Analyzing genome-wide association studies with an FDR controlling modification of the Bayesian Information Criterion.

ABSTRACT: The prevailing method of analyzing GWAS data is still to test each marker individually, although from a statistical point of view it is quite obvious that in case of complex traits such single marker tests are not ideal. Recently several model selection approaches for GWAS have been suggested, most of them based on LASSO-type procedures. Here we will discuss an alternative model selection approach which is based on a modification of the Bayesian Information Criterion (mBIC2) which was previously shown to have certain asymptotic optimality properties in terms of minimizing the misclassification error. Heuristic search strategies are introduced which attempt to find the model which minimizes mBIC2, and which are efficient enough to allow the analysis of GWAS data. Our approach is implemented

SUBMITTER: Dolejsi E 

PROVIDER: S-EPMC4111553 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature