Association Mapping in Structured Populations
TL;DR: This article describes a novel, statistically valid, method for case-control association studies in structured populations that uses a set of unlinked genetic markers to infer details of population structure, and to estimate the ancestry of sampled individuals, before using this information to test for associations within subpopulations.
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Abstract: The use, in association studies, of the forthcoming dense genomewide collection of single-nucleotide polymorphisms (SNPs) has been heralded as a potential breakthrough in the study of the genetic basis of common complex disorders. A serious problem with association mapping is that population structure can lead to spurious associations between a candidate marker and a phenotype. One common solution has been to abandon case-control studies in favor of family-based tests of association, such as the transmission/disequilibrium test (TDT), but this comes at a considerable cost in the need to collect DNA from close relatives of affected individuals. In this article we describe a novel, statistically valid, method for case-control association studies in structured populations. Our method uses a set of unlinked genetic markers to infer details of population structure, and to estimate the ancestry of sampled individuals, before using this information to test for associations within subpopulations. It provides power comparable with the TDT in many settings and may substantially outperform it if there are conflicting associations in different subpopulations.
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Citations
Principal components analysis corrects for stratification in genome-wide association studies
Alkes L. Price,Alkes L. Price,Nick Patterson,Robert M. Plenge,Robert M. Plenge,Michael E. Weinblatt,Nancy A. Shadick,David Reich,David Reich +8 more
TL;DR: This work describes a method that enables explicit detection and correction of population stratification on a genome-wide scale and uses principal components analysis to explicitly model ancestry differences between cases and controls.
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References
Inference of population structure using multilocus genotype data
TL;DR: Pritch et al. as discussed by the authors proposed a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations, which can be applied to most of the commonly used genetic markers, provided that they are not closely linked.
The Future of Genetic Studies of Complex Human Diseases
TL;DR: The identification of the genetic basis of complex human diseases such as schizophrenia and diabetes has proven difficult as mentioned in this paper, and Risch and Merikangas proposed that they can best accomplish this goal by combining the power of the human genome project with association studies.
5.6K
•Journal Article
Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM).
TL;DR: The statistical basis for this "transmission test for linkage disequilibrium" (transmission/disequilibrium test] is described and the relationship of this test to tests of cosegregation that are based on the proportion of haplotypes or genes identical by descent in affected sibs is shown.
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Genomic control for association studies.
Bernie Devlin,Kathryn Roeder +1 more
TL;DR: The performance of the genomic control method is quite good for plausible effects of liability genes, which bodes well for future genetic analyses of complex disorders.