On rare variants in principal component analysis of population stratification.

ABSTRACT:

Background

Population stratification is a known confounder of genome-wide association studies, as it can lead to false positive results. Principal component analysis (PCA) method is widely applied in the analysis of population structure with common variants. However, it is still unclear about the analysis performance when rare variants are used.

Results

We derive a mathematical expectation of the genetic relationship matrix. Variance and covariance elements of the expected matrix depend explicitly on allele frequencies of the genetic markers used in the PCA analysis. We show that inter-population variance is solely contained in K principal components (PCs) and mostly in the largest K-1 PCs, where K is the number of populations in the samples. We propose F_PC, ratio

SUBMITTER: Ma S 

PROVIDER: S-EPMC7077175 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature