title: 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 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/).
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America.
authors.forename: Stacey J
authors.initials: SJ
authors.lastname: Winham
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America.
authors.forename: Gregory D
authors.initials: GD
authors.lastname: Jenkins
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America.|Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, United States of America.
authors.forename: Joanna M
authors.initials: JM
authors.lastname: Biernacka
mesh_terms: D000437:Alcoholism / Q000235:genetics*; D000465:Algorithms; D015982:Bias; D016022:Case-Control Studies; D041321:Chromosomes, Human, X / Q000235:genetics*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003663:Decision Trees; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012737:Sex Factors
chemical_list: D005819:Genetic Markers
keywords: Random Forest; X chromosome; bias; sex differences; variable importance