Modeling X Chromosome Data Using Random Forests: Conquering Sex Bias. Issue 2 (7th December 2015)

• Record Type: Journal Article
• Authors: Winham, Stacey J.; Jenkins, Gregory D.; Biernacka, Joanna M.
• Is Part Of: Genetic epidemiology. Volume 40:Issue 2(2016)
• Page Start: 123
• Page End: 132
• Publication Date: 2015-12-07
• Subjects: Random Forest -- X chromosome -- sex differences -- variable importance -- bias; Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.21946
• Languages: English
• ISSNs: 0741-0395

Abstract

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 when sex was included as a variable, but the results of the new RF methods were consistent with univariate regression‐based approaches that correctly model X chromosome data. Simulations showed that the new RF methods eliminate the bias in standard RF variable importance for X SNPs when sex is associated with the trait, and are able to detect causal autosomal and X SNPs. Even in the absence of sex effects, the new extensions perform similarly to standard RF. Thus, we provide a powerful multimarker approach for genetic analysis that accommodates X chromosome data in an unbiased way. This method is implemented in the freely available R package "snpRF" (http://www.cran.r‐project.org/web/packages/snpRF/ ).