The effects of human population structure on large genetic association studies

By Jonathan Marchini, Lon R. Cardon, Michael S. Phillips, Peter Donnelly. 2004

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In Genetics

Citations in the corpus

Brain age prediction using deep learning uncovers associated sequence variants (2019, background): “It is well-known that the presence of undetected population structure can lead to both false positive results and failure to detect genuine associations in genetic association studies 55 , in an effort to combat this our analysis was constrained to 12378 individuals of white British ancestry.”
Combining population genomics and quantitative genetics: finding the genes underlying ecologically important traits (2008, background): “However, one of the major hurdles facing LD mapping is the need to control for cryptic population structure or stratification, which can lead to false positives (see Pritchard et al., 2000a, b; Cardon and Palmer, 2003; Marchini et al., 2004; Yu et al., 2005).”
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.”
Integrating Case‐control and TDT Studies (2005, background): “Recent studies have shown that methods that attempt to control for population stratification in association studies can be suboptimal in the presence of some levels of population structure (Marchini et al. 2004; Freedman et al. 2004).”
Fast and Efficient Estimation of Individual Ancestry Coefficients (2014, background): “Estimation of ancestry proportions is important in many respects, for example in delineating genetic clusters, drawing inference about the history of a species, screening genomes for signatures of natural selection, and performing statistical corrections in genome-wide association studies (Pritchard”
Population Structure and Cryptic Relatedness in Genetic Association Studies (2009, background): “Marchini et al. (2004a) concluded from a simulation study that, even in populations with relatively modest levels of structure (such as Europe or East Asia), when the sample is large enough to provide the required power, the most significant SNPs can have their p-values reduced by a factor of three…”
Genome-wide association studies: theoretical and practical concerns (2005, background): “Although cases and controls are generally more powerful and logistically easier to collect, they can suffer from hidden POPULATION STRATIFICATION and ADMIXTURE”
Enabling Privacy-Preserving GWAS in Heterogeneous Human Populations (2016, neutral): “Such confounding effects are a major problem that can render the results of a GWAS (particularly one with a large sample size) nearly nonsensical (Marchini et al., 2004).”
Estimating Individual Admixture Proportions from Next Generation Sequencing Data (2013, background): “that the individuals are sampled from the same homogeneous population and a violation of this assumption will lead to an uncontrolled false positive rate (Marchini et al., 2004; Clayton et al., 2005).”
An efficient multi-locus mixed model approach for genome-wide association studies in structured populations (2012, neutral): “However, both designs are subject to confounding by population structure, leading to an inflation of test statistics and a high false positive rate 1,2 .”
PCA-Correlated SNPs for Structure Identification in Worldwide Human Populations (2007, background): “With data from large-scale association studies becoming increasingly available, it has become apparent that population substructure resulting from recent admixture or biased sampling can increase the number of false-positive results or mask true correlations [2–5].”
STrengthening the REporting of Genetic Association Studies (STREGA)— An Extension of the STROBE Statement (2009, neutral): “However, as studies of association and interaction typically address moderate or small effects and hence require large sample sizes, a small bias arising from population stratification may be important [81].”