Population Structure and Cryptic Relatedness in Genetic Association Studies

Abstract

We review the problem of confounding in genetic association studies, which arises principally because of population structure and cryptic relatedness. Many treatments of the problem consider only a simple "island" model of population structure. We take a broader approach, which views population structure and cryptic relatedness as different aspects of a single confounder: the unobserved pedigree defining the (often distant) relationships among the study subjects. Kinship is therefore a central concept, and we review methods of defining and estimating kinship coefficients, both pedigree-based and marker-based. In this unified framework we review solutions to the problem of population structure, including family-based study designs, genomic control, structured association, regression control, principal components adjustment and linear mixed models. The last solution makes the most explicit use of the kinships among the study subjects, and has an established role in the analysis of animal and plant breeding studies. Recent computational developments mean that analyses of human genetic association data are beginning to benefit from its powerful tests for association, which protect against population structure and cryptic kinship, as well as intermediate levels of confounding by the pedigree.

Genome-wide association mapping to candidate polymorphism resolution in the unsequenced barley genome

“…1A). To account for the strong genetic stratification observed, we investigated various methodologies (SI Text, Correction of Genetic Stratification), finding a mixed linear regression model (21) with coefficients of kinship estimated using a matrix of between-individual genetic correlation to perform best (22).…”

Genetic basis of adult migration timing in anadromous steelhead discovered through multivariate association testing

“…In some coastal tributary populations where hatchery stocking has occurred, separation in their spawn timing and habitat preference has generally reinforced some degree of reproductive isolation [15 -17]. This isolation can cause an accumulation of divergence throughout the genome, which would present added challenge to uncovering the genetic basis of the trait of interest because any alleles that vary between subpopulations can lead to spurious associations with the trait [18]. However, our study focused on steelhead from a tributary of the Columbia River, the Klickitat River, which is located in an intermediate geographical location between coastal and inland lineages where neutral genetic divergence is very low for native anadromous steelhead [19] and both migratory life-history types occur in sympatry.…”

Evaluation of multi-locus models for genome-wide association studies: a case study in sugar beet

“…This assumption is, however, usually not met and diversity panels as employed for association mapping are characterized by population stratification and cryptic relatedness (Lander and Schork, 1994;Astle and Balding, 2009;Sillanpää, 2011). Population stratification refers to the origin of individuals from two or more source populations whereas cryptic relatedness describes the different covariances between individuals due to their relatedness.…”