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 studiesread more
Citations
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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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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.
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The UK Biobank resource with deep phenotyping and genomic data
Clare Bycroft,Colin Freeman,Desislava Petkova,Desislava Petkova,Gavin Band,Lloyd T. Elliott,Kevin Sharp,Allan Motyer,Damjan Vukcevic,Olivier Delaneau,Olivier Delaneau,Jared O'Connell,Adrian Cortes,Adrian Cortes,Samantha Welsh,Alan Young,Mark Effingham,Gil McVean,Stephen Leslie,Naomi E. Allen,Peter Donnelly,Jonathan Marchini +21 more
TL;DR: Deep phenotype and genome-wide genetic data from 500,000 individuals from the UK Biobank is described, describing population structure and relatedness in the cohort, and imputation to increase the number of testable variants to 96 million.
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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.
Journal ArticleDOI
A unified mixed-model method for association mapping that accounts for multiple levels of relatedness
Jianming Yu,Gaël Pressoir,William H. Briggs,Irie Vroh Bi,Masanori Yamasaki,John Doebley,Michael D. McMullen,Michael D. McMullen,Brandon S. Gaut,Dahlia M. Nielsen,James B. Holland,James B. Holland,Stephen Kresovich,Edward S. Buckler,Edward S. Buckler +14 more
TL;DR: A unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers is developed and provides a powerful complement to currently available methods for association mapping.
References
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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.
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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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