Principal-component-based population structure adjustment in the North American Rheumatoid Arthritis Consortium data: impact of single-nucleotide polymorphism set and analysis method

Abstract

Population structure occurs when a sample is composed of individuals with different ancestries and can result in excess type I error in genome-wide association studies. Genome-wide principal-component analysis (PCA) has become a popular method for identifying and adjusting for subtle population structure in association studies. Using the Genetic Analysis Workshop 16 (GAW16) NARAC data, we explore two unresolved issues concerning the use of genome-wide PCA to account for population structure in genetic associations studies: the choice of single-nucleotide polymorphism (SNP) subset and the choice of adjustment model. We computed PCs for subsets of genome-wide SNPs with varying levels of LD. The first two PCs were similar for all subsets and the first three PCs were associated with case status for all subsets. When the PCs associated with case status were included as covariates in an association model, the reduction in genomic inflation factor was similar for all SNP sets. Several models have been proposed to account for structure using PCs, but it is not yet clear whether the different methods will result in substantively different results for association studies with individuals of European descent. We compared genome-wide association p-values and results for two positive-control SNPs previously associated with rheumatoid arthritis using four PC adjustment methods as well as no adjustment and genomic control. We found that in this sample, adjusting for the continuous PCs or adjusting for discrete clusters identified using the PCs adequately accounts for the case-control population structure, but that a recently proposed randomization test performs poorly.