Overview of techniques to account for confounding due to population stratification and cryptic relatedness in genomic data association analyses

Abstract

Population-based genomic association analyses are more powerful than within-family analyses. However, population stratification (unknown or ignored origin of individuals from multiple source populations) and cryptic relatedness (unknown or ignored covariance between individuals because of their relatedness) are confounding factors in population-based genomic association analyses, which inflate the false-positive rate. As a consequence, false association signals may arise in genomic data association analyses for reasons other than true association between the tested genomic factor (marker genotype, gene or protein expression) and the study phenotype. It is therefore important to correct or account for these confounders in population-based genomic data association analyses. The common correction techniques for population stratification and cryptic relatedness problems are presented here in the phenotype–marker association analysis context, and comments on their suitability for other types of genomic association analyses (for example, phenotype–expression association) are also provided. Even though many of these techniques have originally been developed in the context of human genetics, most of them are also applicable to model organisms and breeding populations.