Regional Admixture Mapping and Structured Association Testing: Conceptual Unification and an Extensible General Linear Model

Individual genetic admixture estimates, determined both across the genome and at specific genomic regions, have been proposed for use in identifying specific genomic regions harboring loci influencing phenotypes in regional admixture mapping (RAM). Estimates of individual ancestry can be used in structured association tests (SAT) to reduce confounding induced by various forms of population substructure. Although presented as two distinct approaches, we provide a conceptual framework in which both RAM and SAT are special cases of a more general linear model. We clarify which variables are sufficient to condition upon in order to prevent spurious associations and also provide a simple closed form “semiparametric” method of evaluating the reliability of individual admixture estimates. An estimate of the reliability of individual admixture estimates is required to make an inherent errors-in-variables problem tractable. Casting RAM and SAT methods as a general linear model offers enormous flexibility enabling application to a rich set of phenotypes, populations, covariates, and situations, including interaction terms and multilocus models. This approach should allow far wider use of RAM and SAT, often using standard software, in addressing admixture as either a confounder of association studies or a tool for finding loci influencing complex phenotypes in species as diverse as plants, humans, and nonhuman animals.Synopsis: In recent years, scientific efforts to find genes influencing disease and health-related traits have sought to capitalize on the unique genetic characteristics of admixed populations. Admixture can refer to the event of two or more genetically diverse populations intermating and producing an admixed population. Admixture creates the potential for efficient identification of trait-influencing genes. However, genetic association studies using admixed populations are also prone to incorrectly concluding that a gene is linked and associated with a trait even when it is not. Several researchers have produced promising statistical methodologies for genetic association studies within admixed populations. In this paper, the authors show how these statistical methods can be unified in a broadly applicable regression framework and discuss which variables should be included in the regression models for valid testing. Because the variables required in this regression framework can only be measured with error, the authors show the consequences of these measurement errors and present measurement error correction methods applicable to this problem. By recasting the statistical methods for genetic association studies within admixed populations as regression models, a broader range of modeling and hypothesis testing becomes available.