Association mapping in structured populations.

By J. K. Pritchard, Matthew Stephens, Noah A. Rosenberg, Peter Donnelly. American Journal of Human Genetics, 2000

2,032 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fbbdc2e73d4d49f42686b414473a9c876a33d875

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Citations in the corpus

Association Analysis as a Strategy for Improvement of Quantitative Traits in Plants (2006, background): “In contrast, population structure is expected to affectthepatternofLDoverthewholegenomeandmust be controlled a priori for correct association analysis ( Pritchard et al., 2000b ).”
The future of association studies: gene-based analysis and replication. (2004, background): “The situation may be further complicated by the presence of hidden stratification in some populations, producing spurious association or altering the pattern of a true association (Morton and Collins 1998; Pritchard et al. 2000; Thomas and Witte 2002; Stumpf and Goldstein 2003; Freedman et al. 2004)”
Convergent adaptation of human lactase persistence in Africa and Europe (2006, background): “Because genetic substructure can result in false genotype-phenotype association”
An Arabidopsis Example of Association Mapping in Structured Samples (2006, methodology): “used the population assignments produced by the STRUCTURE algorithm (the Q matrix) as a fixed effect in the model, as in structured association [12,14,16].”
Multi-population GWA mapping via multi-task regularized regression (2010, methodology): “For example, Strat (Pritchard et al., 2000) first learns the population structure using structure, performs an association test within each population and then combines the results across populations.”
Mixed Membership Stochastic Blockmodels (2007, background): “They have been successfully applied in many domains, such as document analysis [1], image processing [7], and population genetics [9].”
A method for meta‐analysis of molecular association studies (2005, methodology): “Methods using markers at multiple loci have recently been developed to detect strati cation [36–38], and it may be possible to appropriate these methods to meta-analysis.”
Association Mapping: Critical Considerations Shift from Genotyping to Experimental Design (2009, methodology): “Structured association (Pritchard et al., 2000b) involves using the program STRUCTURE (Pritchard et al., 2000a) to identify populations and then estimate the proportion of each individual’s (A) The power of an association test is a function of the allele frequency and the effect size.”
Characterizing Safflower Germplasm with AFLP Molecular Markers (2007, methodology): “The effi ciency of association mapping can be improved through the recognition of population structure ( Pritchard et al., 2000b ).”
Association Mapping for Grain Quality in a Diverse Sorghum Collection (2012, methodology): “h e PCA was colorcoded based on the structure results and it generally agrees with STRUCTURE (Pritchard et al., 2000) classii cation of i ve subpopulations.”
BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions (2018, neutral): “Initially, population structure was derived as the proportions of individuals belonging to sub-populations [12, 13].”
An overview of STRUCTURE: applications, parameter settings, and supporting software (2013, methodology): “STRUCTURE is a freely available program for population analysis developed by Pritchard et al. (2000a).”
Common variants in MS4A4/MS4A6E, CD2uAP, CD33, and EPHA1 are associated with late-onset Alzheimer’s disease (2011, neutral): “For the first step, either the STRUCTURE software packag”
Application of biotechnology in breeding lentil for resistance to biotic and abiotic stress (2006, background): “…issues to consider in designing and implementing any association mapping studies in plants are: (1) determination of the population structure (Pritchard et al., 2000); (2) estimation of nucleotide diversity (Zhu et al., 2003); (3) estimates of haplotype frequencies and LD (nonrandom…”
Molecular Markers in a Commercial Breeding Program (2007, methodology): “A publicly available program called structure (Falush et al., 2003; Pritchard et al., 2000b) was developed to analyze association studies (Th ornsberry et al., 2001).”
Improvement of drought resistance in rice. (2009, methodology): “Fingerprinting by anonymous markers across the genome can be applied to estimate the genome-wide effects of structure and used in statistical methods to remove the effects of population structure and kinship (Pritchard et al., 2000).”
False discovery rates: a new deal (2016, methodology): “Approaches to deal with unmeasured confounders can be largely divided into two types: those that simply attempt to correct for the resulting inflation of test statistics [39, 40], and those that attempt to infer confounders using clustering, principal components analysis, or factor models [21, 41–43”