Population Stratification In Genome-wide Association Studies Research Articles

A mixed model reduces spurious genetic associations produced by population stratification in genome-wide association studies

Population stratification can produce spurious genetic associations in genome-wide association studies (GWASs). Mixed model methodology has been regarded useful for correcting population stratification. This study explored statistical power and false discovery rate (FDR) with the data simulated for dichotomous traits. Empirical FDRs and powers were estimated using fixed models with and without genomic control and using mixed models with and without reflecting loci linked to the candidate marker in genetic relationships. Population stratification with admixture degree ranged from 1% to 10% resulted in inflated FDRs from the fixed model analysis without genomic control and decreased power from the fixed model analysis with genomic control (P<0.05). Meanwhile, population stratification could not change FDR and power estimates from the mixed model analyses (P>0.05). We suggest that the mixed model methodology was useful to reduce spurious genetic associations produced by population stratification in GWAS, even with a high degree of admixture (10%).

Establishment of a standardized system to perform population structure analyses with limited sample size or with different sets of SNP genotypes

Recent studies have demonstrated that principal component analysis (PCA) can detect the presence of population mixture and admixture in a sample and thus can be used to correct population stratification in genome-wide association studies (GWAS). We propose a complementary approach to PCA that compensates for potential weaknesses associated with PCA, so that one can perform population structure analyses using limited numbers of subjects and single-nucleotide polymorphisms (SNPs). Our method first requires a PCA of the largest reference sample from a population to standardize the system. Once the system is established, it can perform PCA for each individual with a much smaller number of SNPs drawn from the same population. This is because of the introduction of the probabilistic PCA, so that the prediction of the principal components (PCs) is performed under a rigorous probabilistic framework. The subsequent linear discriminant analysis also helps to understand from which ancestries or subpopulations a given individual is more likely to derive, in terms of posterior probabilities given the predicted PCs. A real-world prototype of the system for the Japanese population is developed based on 19 260 subjects, which illustrates the potential usefulness of the system as an aid in the detection of population structures in validation samples, or to help with the correction of population stratification in GWAS.

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

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.