Comparison of methods for correcting population stratification in a genome-wide association study of rheumatoid arthritis: principal-component analysis versus multidimensional scaling

Dai Wang,Yu Sun,Marsha A Wilcox,Qingqin Li,Paul Stang,Jesse A Berlin

doi:10.1186/1753-6561-3-s7-s109

Abstract

Population stratification (PS) represents a major challenge in genome-wide association studies. Using the Genetic Analysis Workshop 16 Problem 1 data, which include samples of rheumatoid arthritis patients and healthy controls, we compared two methods that can be used to evaluate population structure and correct PS in genome-wide association studies: the principal-component analysis method and the multidimensional-scaling method. While both methods identified similar population structures in this dataset, principal-component analysis performed slightly better than the multidimensional-scaling method in correcting for PS in genome-wide association analysis of this dataset.

Highlights

In the past few years, the genome-wide association (GWA) approach has become a widely used tool for identifying genetic loci related to disease risk
The objectives of this study were: 1) to compare the population structures identified by principal-component analysis (PCA) and MDS in the rheumatoid arthritis (RA) dataset of Genetic Analysis Workshop 16 (GAW16); and 2) to evaluate the performance of these two approaches for correcting Population stratification (PS) in GWA analyses
(page number not for citation purposes) http://www.biomedcentral.com/1753-6561/3/S7/S109 their expected normal quantiles with a distance of at least one and pruning the SNPs based on LD information, 81,636 autosomal SNPs were included in the second round of PCA

Summary

Introduction

In the past few years, the genome-wide association (GWA) approach has become a widely used tool for identifying genetic loci related to disease risk. Population stratification (PS) is a major challenge in GWA studies (GWAS), because of the risk of generating false positives that represent genetic differences from ancestry rather than genes associated with a disease. Among the methods developed for correcting PS in GWAS, the principal-component analysis (PCA) method [1,2] and the multidimensional-scaling (MDS) method [3,4] are capable of detecting population structure. The PCA method identifies principal components that represent the population structure based on genetic correlations among individuals. The MDS method detects meaningful underlying dimensions that explain observed genetic distance, e.g., pairwise identity-by-state (IBS) distance, among individuals. While other methods for addressing population structure exist, we focused on these two methods in this study

Objectives

Methods

Results

Conclusion