title: Correction for population stratification in random forest analysis.
abstract: BACKGROUND
  Population structure (PS), including population stratification and admixture, is a significant confounder in genome-wide association studies (GWAS), as it may produce spurious associations. Random forest (RF) has been increasingly applied in GWAS data analysis because of its advantage in analysing high dimensional genetic data. RF creates importance measures for single nucleotide polymorphisms (SNPs), which are helpful for feature selections. However, if PS is not appropriately corrected, RF tends to give high importance to disease-unrelated SNPs with different frequencies of allele or genotype among subpopulations, leading to inaccurate results.
  
  
  METHODS
  In this study, the authors propose to correct for the confounding effect of PS by including the information of PS in RF analysis. The correction procedure starts by extracting the information of PS using EIGENSTRAT or multi-dimensional scaling clustering procedure from a large number of structure inference SNPs. Phenotype and genotypes adjusted by the information of PS are then used as the outcome and predictors in RF analysis.
  
  
  RESULTS
  Extensive simulations indicate that the importance measure of the causal SNP is increased following the PS correction. By analysing a real dataset, the proposed correction removes the spurious association between the lactase gene and height.
  
  
  CONCLUSIONS
  The authors propose a simple method to correct for PS in RF analysis on GWAS data. Further studies in real GWAS datasets are required to validate the robustness of the proposed approach.
authors.affiliation: Environmental and Occupational Medicine and Epidemiology Program, Department of Environmental Health, Harvard School of Public Health, Harvard University, Boston, MA, USA.
authors.forename: Yang
authors.initials: Y
authors.lastname: Zhao
authors.forename: Feng
authors.initials: F
authors.lastname: Chen
authors.forename: Rihong
authors.initials: R
authors.lastname: Zhai
authors.forename: Xihong
authors.initials: X
authors.lastname: Lin
authors.forename: Zhaoxi
authors.initials: Z
authors.lastname: Wang
authors.forename: Li
authors.initials: L
authors.lastname: Su
authors.forename: David C
authors.initials: DC
authors.lastname: Christiani
mesh_terms: D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D005828:Genetics, Population / Q000379:methods*; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide