Methods for Detecting and Correcting for Population Stratification

Abstract

Population stratification (PS) is a primary consideration in studies of the genetic determinants of human traits. Failure to control for it may lead to confounding, causing a study to fail for lack of significant results or resources to be wasted following false-positive signals. Here we review historical and current approaches for addressing PS when performing genetic association studies in human populations. We describe methods for detecting the presence of PS including global and local ancestry methods. We also describe approaches for accounting for PS when calculating association statistics, such that measures of association are not confounded. This is particularly important since many traits are being examined in minority populations, populations that may inherently feature PS.

Quality Control for the Illumina HumanExome BeadChip

“…The standard approach to determining ethnic background is to conduct a principal components analysis (PCA) on normalized genotype data, usually by means of the smartpca program in EIGENSOFT (Patterson et al, 2006). The first two principal components (PCs) roughly describe global ancestry (Guo et al, 2014; also see UNIT 1.22; Edwards and Gao, 2012).…”

Power considerations for λ inflation factor in meta-analyses of genome-wide association studies

“…Accumulating evidence from recently published studies suggests that GC may not be effective in controlling population stratification in association studies (Edwards & Gao, 2012;Wang et al, 2012). This problem may be aggravated under meta-analysis settings where a double GC correction method might lead to more prominent inflation of type I error rates at a marker with significant allele frequency differentiation in subpopulations generated by recent strong selection (Bouaziz et al, 2011;Edwards & Gao, 2012;Wang et al, 2012).…”

“…Accumulating evidence from recently published studies suggests that GC may not be effective in controlling population stratification in association studies (Edwards & Gao, 2012;Wang et al, 2012). This problem may be aggravated under meta-analysis settings where a double GC correction method might lead to more prominent inflation of type I error rates at a marker with significant allele frequency differentiation in subpopulations generated by recent strong selection (Bouaziz et al, 2011;Edwards & Gao, 2012;Wang et al, 2012). Conversely, alternative methods, including principal component analysis (PCA) correction and Bayesian semiparametric algorithm for inferring population structure, could control type I error rates and yield much higher power in meta-analyses compared to the double GC correction method (Bouaziz et al, 2011;Edwards & Gao, 2012;Majumdar et al, 2013).…”

“…This problem may be aggravated under meta-analysis settings where a double GC correction method might lead to more prominent inflation of type I error rates at a marker with significant allele frequency differentiation in subpopulations generated by recent strong selection (Bouaziz et al, 2011;Edwards & Gao, 2012;Wang et al, 2012). Conversely, alternative methods, including principal component analysis (PCA) correction and Bayesian semiparametric algorithm for inferring population structure, could control type I error rates and yield much higher power in meta-analyses compared to the double GC correction method (Bouaziz et al, 2011;Edwards & Gao, 2012;Majumdar et al, 2013). Emerging techniques also incorporate linear mixed models to test for association in GWAS, after taking into account relatedness among samples, population stratification and other confounding factors (Zhou & Stephens, 2012).…”

Phenotyping clinical disorders: lessons learned from pelvic organ prolapse

“…31 Because of population stratification, genetic studies will often focus on one specific race/ethnicity or utilize advanced analytic techniques to adjust for racial genetic differences. 32–34 …”