ArticleGenetic epidemiology2012
Sparse principal component analysis for identifying ancestry-informative markers in genome-wide association studies.
Article in Genetic epidemiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- SPLENDID incorporates continuous genetic ancestry in biobank-scale data to improve polygenic risk prediction across diverse populations.Nature methods · 2026Article
- Optimization of computational ancestry inference for use in cancer cell lines.Biology methods & protocols · 2025Article
- Fast computation of the eigensystem of genomic similarity matrices.BMC bioinformatics · 2024Article
- Gradient-based sparse principal component analysis with extensions to online learning.Biometrika · 2023Article
- Limitations of principal components in quantitative genetic association models for human studies.eLife · 2023Article
- Unsupervised discovery of ancestry-informative markers and genetic admixture proportions in biobank-scale datasets.American journal of human genetics · 2023Article
- A Fast, Provably Accurate Approximation Algorithm for Sparse Principal Component Analysis Reveals Human Genetic Variation Across the World.Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- ) · 2022Article
- Genotypes of informative loci from 1000 Genomes data allude evolution and mixing of human populations.Scientific reports · 2021Article
- Lymphocyte-monocyte-neutrophil index: a predictor of severity of coronavirus disease 2019 patients produced by sparse principal component analysis.Virology journal · 2021Article
- locStra: Fast analysis of regional/global stratification in whole-genome sequencing studies.Genetic epidemiology · 2021Article
- Genotype-Environment Interaction Analysis of NQO1, CYP2E1, and NAT2 Polymorphisms and the Risk of Childhood Acute Lymphoblastic Leukemia: A Report From the Mexican Interinstitutional Group for the Identification of the Causes of Childhood Leukemia.Frontiers in oncology · 2020Article
- LEI: A Novel Allele Frequency-Based Feature Selection Method for Multi-ancestry Admixed Populations.Scientific reports · 2019Article
- Selection of Optimal Ancestry Informative Markers for Classification and Ancestry Proportion Estimation in Pigs.Frontiers in genetics · 2019Article
- Integrative sparse principal component analysis of gene expression data.Genetic epidemiology · 2017Article
- Multiple Group Testing Procedures for Analysis of High-Dimensional Genomic Data.Genomics & informatics · 2016Article
- Dimension reduction techniques for the integrative analysis of multi-omics data.Briefings in bioinformatics · 2016Review
- A Nonlinear Model for Gene-Based Gene-Environment Interaction.International journal of molecular sciences · 2016Review
- 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
- Identifying regions of disease-related variants in admixed populations with the summation partition approach.BMC proceedings · 2016Article
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4 authors.
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Abstract
Genome-wide association studies (GWAS) routinely apply principal component analysis (PCA) to infer population structure within a sample to correct for confounding due to ancestry. GWAS implementation of PCA uses tens of thousands of single-nucleotide polymorphisms (SNPs) to infer structure, despite the fact that only a small fraction of such SNPs provides useful information on ancestry. The identification of this reduced set of ancestry-informative markers (AIMs) from a GWAS has practical value; for example, researchers can genotype the AIM set to correct for potential confounding due to ancestry in follow-up studies that utilize custom SNP or sequencing technology. We propose a novel technique to identify AIMs from genome-wide SNP data using sparse PCA. The procedure uses penalized regression methods to identify those SNPs in a genome-wide panel that significantly contribute to the principal components while encouraging SNPs that provide negligible loadings to vanish from the analysis. We found that sparse PCA leads to negligible loss of ancestry information compared to traditional PCA analysis of genome-wide SNP data. We further demonstrate the value of sparse PCA for AIM selection using real data from the International HapMap Project and a genomewide study of inflammatory bowel disease. We have implemented our approach in open-source R software for public use.
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Registered trials
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.