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

Abstract

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.

Admixture as the basis for genetic mapping

“…Thus, in a general linear model (Box 3), phenotypic variation among individuals can be partitioned into variation owing to genotypes at a focal locus, estimates of parental admixture and any other appropriate predictors (e.g. coancestry, sex, environmental covariates [12,15,28]). The use of a general linear model makes these tests possible in standard statistical software (e.g.…”

“…Recombination in admixed individuals will reduce the extent of linkage disequilibrium [64][65][66]. A variety of different approaches have been developed to control statistically for genome-wide admixture and ancestry in modeling phenotypes [12,13,15,67]. At this relatively early stage in the development of methods, we need to know more about the utility of different approaches with different population and ancestry structures [12][13][14]16,66,67].…”

“…' Association mapping uses recombination events accumulated in natural populations over many generations to detect statistical associations between genetic loci of known genomic location and phenotypic traits of interest [11]. Association mapping generally requires the absence of population structure in the sample or statistical methods to account for structure [12][13][14][15]. Similarly, admixture Excess admixture quantifies the extent to which alleles and genotypes are introgressed into a foreign genetic background or are found in their native background.…”

“…The ability to account for population structure, in spite of the absence of controlled crosses or pedigrees, is a common feature of association and admixture mapping (Box 2). The absence of confounding population structure allows true, developmental genetic associations to be recognized, rather than the large number of spurious associations between allelic and phenotypic variation that are expected in the presence of population structure, where the effects of individual loci are not separated from their genetic background [12][13][14]16,17].…”

“…Finally, many of the models and methods developed for association mapping are relevant to admixture mapping. However, it is not clear that the population histories that have been modeled and investigated for association mapping encompass the considerable population structure and linkage disequilibrium that result from crosses between substantially diverged species and populations [12][13][14][15][16]66]. For example, more work remains to be done to compare the performance of different methods of analysis when population structure is great (e.g.…”