Modeling X Chromosome Data Using Random Forests: Conquering Sex Bias.

ABSTRACT: Machine learning methods, including Random Forests (RF), are increasingly used for genetic data analysis. However, the standard RF algorithm does not correctly model the effects of X chromosome single nucleotide polymorphisms (SNPs), leading to biased estimates of variable importance. We propose extensions of RF to correctly model X SNPs, including a stratified approach and an approach based on the process of X chromosome inactivation. We applied the new and standard RF approaches to case-control alcohol dependence data from the Study of Addiction: Genes and Environment (SAGE), and compared the performance of the alternative approaches via a simulation study. Standard RF applied to a case-control study of alcohol dependence yielded inflated variable importance estimates for X SNPs, even wh

SUBMITTER: Winham SJ 

PROVIDER: S-EPMC4724236 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature