Modeling X Chromosome Data Using Random Forests: Conquering Sex Bias

Genet Epidemiol. 2016 Feb;40(2):123-32. doi: 10.1002/gepi.21946. Epub 2015 Dec 7.


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" (

Keywords: Random Forest; X chromosome; bias; sex differences; variable importance.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Alcoholism / genetics*
  • Algorithms
  • Bias*
  • Case-Control Studies
  • Chromosomes, Human, X / genetics*
  • Computer Simulation
  • Data Interpretation, Statistical
  • Decision Trees*
  • Genetic Markers / genetics
  • Genetic Predisposition to Disease*
  • Humans
  • Models, Genetic
  • Phenotype
  • Polymorphism, Single Nucleotide / genetics*
  • Sex Factors


  • Genetic Markers