Abstract 5503: A simple correction for population stratification in random forest analysis
Authors: Yang Zhao, Rihong Zhai, Xihong Lin, Mike Zhaoxi Wang, Li Su, David C. Christiani
Journal: Cancer Research (2012)

Abstract Introduction Random forest (RF) has been increasingly applied in Genome-wide Association (GWA) studies because of its advantage in handling high dimensional genetic data. RF creates importance measures for SNPs which are helpful for feature selections. However, if the confounding effect of population structure, including population stratification and admixture, is not appropriately corrected, RF tends to give greater importance to disease-unrelated SNPs with different frequencies of allele or genotype among subpopulations, leading to inaccurate results. Methods We propose to correct for the confounding effect of population structure by including the EIGENSTRAT approach in the RF analysis. The axes of variation are firstly extracted from a large number of structure inference SNPs. Instead of including the axes of variation in the RF model directly, the phenotype and genotypes are regressed on these axes, respectively. The residuals for phenotype and genotyoes are then used as the outcome and predictors in RF analysis. We conduct simulation studies to investigate whether the proposed method works well in RF analysis. We simulate 4 scenarios. Scenario 1 to 3 are designed to have 2, 3 and 4 underlying subpopulations, respectively. In each of the 3 scenarios, two levels (moderate and extreme) of population stratification are simulated by varying the proportions of individuals in cases and controls from the different subpopulations. In scenario 4, we consider an admixed population formed by two ancestral populations. For each setting in the different scenarios, we generate 1,000 datasets. Each dataset consists of 10,000 SNPs for PS inference, 90 disease-unrelated random SNPs, 9 disease-unrelated differentiated SNPs and 1 causal SNP with RR=1.5. Results Our extensive simulations show that the correction for PS improves the ability of identifying the causal SNP and decreases the ranks of differentiated SNPs when compared with RF analysis without PS correction. We apply the proposed method to analyze a subset of SNPs from a GWA dataset on NSCLC. The dataset is analyzed by RF either without or with PS correction. Lists of the top 1000 SNPs from the 2 analyses are mapped to genes using the “20kb rule”. GeneGO disease (by biomarkers) analysis is performed for each of the 2 gene lists. In both analysis, “Tobacco Use Disorder” ranks the 1st. Two lung-related diseases, “Adenocarcinoma, Bronchiolo-Alveolar” and “Lung Diseases, Obstructive,” rank the 12th and 18th in the RF with PS correction analysis, whereas they rank the 69th and 104th when being analyzed by RF without PS correction. Conclusion The confounding effect of population structure can be easily corrected by including the EIGENSTRAT procedure in RF analysis. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5503. doi:1538-7445.AM2012-5503