Powerful Tests for Multi-Marker Association Analysis Using Ensemble Learning.

ABSTRACT: Multi-marker approaches have received a lot of attention recently in genome wide association studies and can enhance power to detect new associations under certain conditions. Gene-, gene-set- and pathway-based association tests are increasingly being viewed as useful supplements to the more widely used single marker association analysis which have successfully uncovered numerous disease variants. A major drawback of single-marker based methods is that they do not look at the joint effects of multiple genetic variants which individually may have weak or moderate signals. Here, we describe novel tests for multi-marker association analyses that are based on phenotype predictions obtained from machine learning algorithms. Instead of assuming a linear or logistic regression model, we propose t

SUBMITTER: Padhukasahasram B 

PROVIDER: S-EPMC4664402 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature