Population Admixture and Stratification in Genetic Epidemiology

Abstract

Population admixture and stratification generally occur together. Admixture between subpopulations generates allelic associations that decay with map distance, whereas stratification generates allelic associations that are independent of map distance. The autocorrelation of ancestry on gametes inherited from parents of mixed descent can be exploited to localize genes in which the pool of disease risk alleles is differentially distributed between subpopulations. Tests for linkage are based on testing for association of the disease or outcome of interest with locus ancestry, conditioning on parental admixture proportions to eliminate confounding by genetic background. This approach, known as admixture mapping, is an extension of the principles underlying linkage analysis of an experimental cross between inbred strains. Most current approaches to modelling admixture are based on a standard model in which the stochastic variation of ancestry on gametes inherited from admixed parents is generated by independent Poisson arrival processes. For such models, the posterior distribution of locus ancestry and parental admixture proportions can be generated by Markov chain Monte Carlo simulation, given a sample of individuals typed at ancestry-informative marker loci. Tests for linkage are constructed by averaging over this posterior distribution. The same statistical model can be used with unselected marker loci to detect and control for population stratification in ordinary genetic association studies. For studies using arrays of closely spaced tag SNPs, this approach has limitations as it requires that the marker loci be spaced far enough apart for all allelic association to be attributable to admixture and stratification. An alternative approach is to use principal components analysis to infer a few underlying axes of variation that summarize the allelic associations in the dataset. This approach is computationally efficient, and can be extended to datasets with closely spaced markers using a simple adjustment for short-range allelic associations. Tests of the number of underlying latent variables can be constructed; for a given F ST distance between subpopulations, there is a threshold size of dataset at which stratification can be detected. With either modelling approach, confounding by stratification can be controlled by adjusting for 1190Handbook of Statistical Genetics, Third Edition . E dited by D . J. Balding, M . Bishop and C. Cannings.

Population Structure and Cryptic Relatedness in Genetic Association Studies

“…The number of subpopulations can be estimated from the data by optimizing a measure of model goodness of fit, but this increases the computational burden and there is usually no satisfactory estimate because, as we noted above in Section 1.2, the island model is not well suited to most human populations. Indeed, ADMIXMAP was primarily designed for admixture mapping, in which the genomes of admixed individuals are scanned for loci at which cases show an excess of ancestry from one of the founder populations (McKeigue, 2007). Because of the limited number of generations since the admixture event, this approach has features in common with linkage as well as association study designs.…”

African Ancestry is a Risk Factor for Asthma and High Total IgE Levels in African Admixed Populations

“…Differences in African or other ancestry proportions between cases and controls is a confounding factor in genetic association studies of admixed populations because differences in admixture proportions among subgroups can erroneously suggest association with disease instead of ancestry at loci where allele frequencies differ between the ancestral populations (e.g., population stratification) [Cardon and Palmer, ; Spielman et al., ; Ziv and Burchard, ]. This situation is typically addressed by genotyping ancestry informative markers (AIMs) to estimate the individual ancestral proportions for major putative parental populations in admixed groups, and even though multiple approaches can be used, the underlying concept it to incorporate those values represented either as proportions of admixture or eigenvalues derived from principal component analysis (PCA) in the model used to evaluate genetic associations [McKeigue, ; Patterson et al., ; Pritchard and Rosenberg, ].…”

“…Race and ethnicity are complex constructs incorporating social, cultural, and genetic factors. Populations that migrated from continental Africa as part of the African Diaspora were dispersed across the Americas, and their contemporary genotyping ancestry informative markers (AIMs) to estimate the individual ancestral proportions for major putative parental populations in admixed groups, and even though multiple approaches can be used, the underlying concept it to incorporate those values represented either as proportions of admixture or eigenvalues derived from principal component analysis (PCA) in the model used to evaluate genetic associations [McKeigue, 2007;Patterson et al, 2006;Pritchard and Rosenberg, 1999].…”

Genome-wide prediction of breeding values and mapping of quantitative trait loci in stratified and admixed populations

“…Broadly speaking, PS can be defined as any deviation from random mating (MCVEAN 2001;. This agrees with (REDDEN and ALLISON 2006) who found that assortative mating in a homogeneous population can lead to PS.…”

“…PS and admixture are usually occurring together and the same statistical models are used to describe these two phenomena. However, this does not mean that they are necessarily the same (FOULKES 2009;MCKEIGUE 2008). Admixture between genetically divergent populations creates gametes consisting of a mosaic of segments inherited from each of the parental populations.…”