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.

Controlling population structure in human genetic association studies with samples of unrelated individuals

“…It has been discussed in the literature that this is not the correct way to handle confounding, as stated in Kleinbaum et al (Kleinbaum et al , 1998) (Page 195) “One approach sometimes used (incorrectly) to assess confounding is, for example, to conduct a statistical test of H 0 :β 2 = 0 …. Such a test does not address confounding, but rather precision.” Redden et al (2006) mentioned this as well. We will illustrate in a very simple scenario where there is only one candidate marker and no other covariates.…”

“…The genomic control (GC) approach rescales the χ 2 statistics, which may not follow a central χ 2 distribution under the null hypothesis when population structure/admixture exists (Devlin and Roeder, 1999). Another approach is called “structured association test (SAT)”, where a set of unlinked genetic markers are used to estimate the ancestry probabilities for each individual (Falush et al , 2003; Pritchard and Rosenberg, 1999; Pritchard et al , 2000; Redden et al , 2006; Satten et al , 2001; Tang et al , 2005; Wu et al , 2006). These estimates of ancestry probabilities are then used in association analysis to control for population structure/admixture (Pritchard et al , 2000; Redden et al , 2006; Satten et al , 2001; Yu et al , 2006; Zhao et al , 2007).…”

“…Another approach is called “structured association test (SAT)”, where a set of unlinked genetic markers are used to estimate the ancestry probabilities for each individual (Falush et al , 2003; Pritchard and Rosenberg, 1999; Pritchard et al , 2000; Redden et al , 2006; Satten et al , 2001; Tang et al , 2005; Wu et al , 2006). These estimates of ancestry probabilities are then used in association analysis to control for population structure/admixture (Pritchard et al , 2000; Redden et al , 2006; Satten et al , 2001; Yu et al , 2006; Zhao et al , 2007). A third approach uses genetic background derived from a set of independent genetic markers to control for population structure/admixture in association analysis (Bauchet et al , 2007; Chen et al , 2003; Paschou et al , 2008; Price et al , 2006; Zhang et al , 2003; Zhu et al , 2008; Zhu et al , 2002; Zou et al , 2010).…”

Hybrid vigor between native and introduced salamanders raises new challenges for conservation

“…Admixture linkage disequilibrium decreases the likelihood that a particular marker-trait association represents close physical linkage between the marker and a quantitative trait locus (32). By the same token, admixture linkage disequilibrium increases the accuracy of our marker-based estimates of genomic ancestry ( S ) and heterozygosity ( H ) because the sampled loci are correlated with many other regions of the genome (33,34).…”

How to estimate the measurement error variance associated with ancestry proportion estimates

“…Structured association tests (SATs), in which individual admixture proportion estimates (IAPE) [1–4] or individual-level measures of genetic background [5;6] are computed and included in the model as covariates to control for confounding, are widely applied in genetic association tests. We have shown [7] that the SAT approach can be cast in the general linear model framework. If unmodified, the use of this framework implicitly assumes that the predictors are measured without error.…”