ArticleGenetic epidemiology2013
Adjustment for population stratification via principal components in association analysis of rare variants.
Article in Genetic epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed.
- Adjusting for principal components can induce spurious associations in genome-wide association studies in admixed populations.bioRxiv : the preprint server for biology · 2024Article
- Integrative Analysis of Germline Rare Variants in Clear and Non-clear Cell Renal Cell Carcinoma.European urology open science · 2024Article
- A comparison between similarity matrices for principal component analysis to assess population stratification in sequenced genetic data sets.Briefings in bioinformatics · 2023Article
- Taking population stratification into account by local permutations in rare-variant association studies on small samples.Genetic epidemiology · 2021Article
- Controlling for human population stratification in rare variant association studies.Scientific reports · 2021Article
- Genetic Analysis of Functional Rare Germline Variants across Nine Cancer Types from an Electronic Health Record Linked Biobank.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2021Article
- GABAergic polygenic risk for cocaine use disorder is negatively correlated with precuneus activity during cognitive control in African American individuals.Addictive behaviors · 2021Article
- Article
- Gene association detection via local linear regression method.Journal of human genetics · 2020Article
- A practical approach to adjusting for population stratification in genome-wide association studies: principal components and propensity scores (PCAPS).Statistical applications in genetics and molecular biology · 2018Article
- On the substructure controls in rare variant analysis: Principal components or variance components?Genetic epidemiology · 2018Article
- The impact of a fine-scale population stratification on rare variant association test results.PloS one · 2018Article
- A Nonparametric Regression Approach to Control for Population Stratification in Rare Variant Association Studies.Scientific reports · 2016Article
- Utilizing the Jaccard index to reveal population stratification in sequencing data: a simulation study and an application to the 1000 Genomes Project.Bioinformatics (Oxford, England) · 2016Article
- Whole-exome sequencing of over 4100 men of African ancestry and prostate cancer risk.Human molecular genetics · 2016Article
- A pathway-centric approach to rare variant association analysis.European journal of human genetics : EJHG · 2016Article
- A powerful and adaptive association test for rare variants.Genetics · 2014Article
- Sex- and age-interacting eQTLs in human complex diseases.Human molecular genetics · 2014Article
- Adaptive combination of P-values for family-based association testing with sequence data.PloS one · 2014Article
- Adjusting for population stratification and relatedness with sequencing data.BMC proceedings · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
For unrelated samples, principal component (PC) analysis has been established as a simple and effective approach to adjusting for population stratification in association analysis of common variants (CVs, with minor allele frequencies MAF > 5%). However, it is less clear how it would perform in analysis of low-frequency variants (LFVs, MAF between 1% and 5%), or of rare variants (RVs, MAF < 5%). Furthermore, with next-generation sequencing data, it is unknown whether PCs should be constructed based on CVs, LFVs, or RVs. In this study, we used the 1000 Genomes Project sequence data to explore the construction of PCs and their use in association analysis of LFVs or RVs for unrelated samples. It is shown that a few top PCs based on either CVs or LFVs could separate two continental groups, European and African samples, but those based on only RVs performed less well. When applied to several association tests in simulated data with population stratification, using PCs based on either CVs or LFVs was effective in controlling Type I error rates, while nonadjustment led to inflated Type I error rates. Perhaps the most interesting observation is that, although the PCs based on LFVs could better separate the two continental groups than those based on CVs, the use of the former could lead to overadjustment in the sense of substantial power loss in the absence of population stratification; in contrast, we did not see any problem with the use of the PCs based on CVs in all our examples.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.