ArticleGenetic epidemiology2013
Adjusting for population stratification in a fine scale with principal components and sequencing data.
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 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Power considerations for λ inflation factor in meta-analyses of genome-wide association studies.Genetics research · 2016Pooled it
- Evaluating confounding in rare variant genome wide association studies.Nature communications · 2026Article
- 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
- Inborn errors of type I IFN immunity in patients with life-threatening COVID-19.Science (New York, N.Y.) · 2020Article
- Article
- Review
- 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 rare and low-frequency genetic variants in common disease.Genome biology · 2017Review
- Quality Control for the Illumina HumanExome BeadChip.Current protocols in human genetics · 2016Article
- Principal component regression and linear mixed model in association analysis of structured samples: competitors or complements?Genetic epidemiology · 2015Article
- Rare-variant association analysis: study designs and statistical tests.American journal of human genetics · 2014Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Population stratification is of primary interest in genetic studies to infer human evolution history and to avoid spurious findings in association testing. Although it is well studied with high-density single nucleotide polymorphisms (SNPs) in genome-wide association studies (GWASs), next-generation sequencing brings both new opportunities and challenges to uncovering population structures in finer scales. Several recent studies have noticed different confounding effects from variants of different minor allele frequencies (MAFs). In this paper, using a low-coverage sequencing dataset from the 1000 Genomes Project, we compared a popular method, principal component analysis (PCA), with a recently proposed spectral clustering technique, called spectral dimensional reduction (SDR), in detecting and adjusting for population stratification at the level of ethnic subgroups. We investigated the varying performance of adjusting for population stratification with different types and sets of variants when testing on different types of variants. One main conclusion is that principal components based on all variants or common variants were generally most effective in controlling inflations caused by population stratification; in particular, contrary to many speculations on the effectiveness of rare variants, we did not find much added value with the use of only rare variants. In addition, SDR was confirmed to be more robust than PCA, especially when applied to rare variants.
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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.