Assessing the impact of population stratification on genetic association studies

By M. Freedman, David Reich, K. Penney, G. McDonald, André A. Mignault, Nick Patterson, Stacey Gabriel, Eric J. Topol, Jordan W. Smoller, C. Pato and 8 more. Nature Genetics, 2004

830 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Genetics, Pediatrics, Perinatology and Child Health

View Paper: https://www.semanticscholar.org/paper/44c406f43660c6d96ef65365ddecab3b8ef982ad

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In Experimental and Cognitive Psychology

L. Duncan

In Genetics

In Pediatrics, Perinatology and Child Health

Citations in the corpus

Genotyping a large collection of pepper (Capsicum spp.) with SSR loci brings new evidence for the wild origin of cultivated C. annuum and the structuring of genetic diversity by human selection of cultivar types (2013, background): “Testing for statistical associations between the genotypes at the marker loci and the phenotypes in a sample of accessions is directly affected by the presence of groups of related accessions with different allele frequencies and may lead to false associations (Freedman et al. 2004).”
Replication of putative candidate-gene associations with rheumatoid arthritis in >4,000 samples from North America and Sweden: association of susceptibility with PTPN22, CTLA4, and PADI4. (2005, background): “However, it is becoming increasingly clear that subtle stratification exists even in well-matched cohorts (Freedman et al. 2004; Marchini et al. 2004).”
Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology (2008, neutral): “These include comparing observed with expected 2 association statistics for a number of random markers that have been used previously to examine stratification [101, 102] tests of stratification using AIM, which will have more power at detecting population stratification than random markers [103–105”
PLINK: a tool set for whole-genome association and population-based linkage analyses. (2007, background): “One well-acknowledged source of confounding in population-based association studies is population stratification.(6) However, in the context of WGAS, this perhaps presents less of a problem, given the availability of hundreds of thousands of markers across the genome, which allows for a very accurat”
Population Structure and Eigenanalysis (2006, background): “We believe they will prove useful both in medical genetics, where population structure may cause spurious disease associations [1,40–43]; and in population genetics, where our statistical methods provide a strong indication of how many axes of variation are meaningful.”
Conditions Under Which Genome-Wide Association Studies Will be Positively Misleading (2010, background): “Larger, population-based cohort studies, however, may soon find themselves running into the kinds of large-scale population structure inherent in the human species (Freedman et al. 2004; Novembre et al. 2008).”
A resource-efficient tool for mixed model association analysis of large-scale data (2019, background): “Population stratification 21,22 and relatedness 23,24 are the two major confounders in GWAS, which could potentially lead to spurious associations if not well-controlled for.”