Comparing the Utility of Mitochondrial and Nuclear DNA to Adjust for Genetic Ancestry in Association Studies

Abstract

Mitochondrial genome-wide association studies identify mitochondrial single nucleotide polymorphisms (mtSNPs) that associate with disease or disease-related phenotypes. Most mitochondrial and nuclear genome-wide association studies adjust for genetic ancestry by including principal components derived from nuclear DNA, but not from mitochondrial DNA, as covariates in statistical regression analyses. Furthermore, there is no standard when controlling for genetic ancestry during mitochondrial and nuclear genetic interaction association scans, especially across ethnicities with substantial mitochondrial genetic heterogeneity. The purpose of this study is to (1) compare the degree of ethnic variation captured by principal components calculated from microarray-defined nuclear and mitochondrial DNA and (2) assess the utility of mitochondrial principal components for association studies. Analytic techniques used in this study include a principal component analysis for genetic ancestry, decision-tree classification for self-reported ethnicity, and linear regression for association tests. Data from the Health and Retirement Study, which includes self-reported White, Black, and Hispanic Americans, was used for all analyses. We report that (1) mitochondrial principal component analysis (PCA) captures ethnic variation to a similar or slightly greater degree than nuclear PCA in Blacks and Hispanics, (2) nuclear and mitochondrial DNA classify self-reported ethnicity to a high degree but with a similar level of error, and 3) mitochondrial principal components can be used as covariates to adjust for population stratification in association studies with complex traits, as demonstrated by our analysis of height—a phenotype with a high heritability. Overall, genetic association studies might reveal true and robust mtSNP associations when including mitochondrial principal components as regression covariates.