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The effects of human population structure on large genetic association studies.

TLDR
The consequences of population structure on association outcomes increase markedly with sample size, and one method for correcting for population structure (Genomic Control) is examined, which may not correct for structure if too few loci are used and may overcorrect in other settings, leading to substantial loss of power.
Abstract
Large-scale association studies hold substantial promise for unraveling the genetic basis of common human diseases A well-known problem with such studies is the presence of undetected population structure, which can lead to both false positive results and failures to detect genuine associations Here we examine ∼15,000 genome-wide single-nucleotide polymorphisms typed in three population groups to assess the consequences of population structure on the coming generation of association studies The consequences of population structure on association outcomes increase markedly with sample size For the size of study needed to detect typical genetic effects in common diseases, even the modest levels of population structure within population groups cannot safely be ignored We also examine one method for correcting for population structure (Genomic Control) Although it often performs well, it may not correct for structure if too few loci are used and may overcorrect in other settings, leading to substantial loss of power The results of our analysis can guide the design of large-scale association studies

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Citations
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Journal ArticleDOI

Principal components analysis corrects for stratification in genome-wide association studies

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.
Journal ArticleDOI

Fast model-based estimation of ancestry in unrelated individuals

TL;DR: The results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.
Journal ArticleDOI

Population structure and eigenanalysis

TL;DR: An approach to studying population structure (principal components analysis) is discussed that was first applied to genetic data by Cavalli-Sforza and colleagues, and results from modern statistics are used to develop formal significance tests for population differentiation.
References
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Journal ArticleDOI

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.
Journal ArticleDOI

Statistical significance for genomewide studies

TL;DR: This work proposes an approach to measuring statistical significance in genomewide studies based on the concept of the false discovery rate, which offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted.
Journal ArticleDOI

The International HapMap Project

John W. Belmont, +145 more
- 18 Dec 2003 - 
TL;DR: The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance the ability to choose targets for therapeutic intervention.
Journal ArticleDOI

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.
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