ArticleBMC genomics2017
Ancestry inference using principal component analysis and spatial analysis: a distance-based analysis to account for population substructure.
Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Genetic Architecture of Idiopathic Inflammatory Myopathies From Meta-Analyses.Arthritis & rheumatology (Hoboken, N.J.) · 2025Pooled it
- The Shared Genetic Architectures Between Lung Cancer and Multiple Polygenic Phenotypes in Genome-Wide Association Studies.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2021Pooled it
- Population genetic considerations for using biobanks as international resources in the pandemic era and beyond.BMC genomics · 2021Pooled it
- Trial
- Identifying the strongest novel gene-respiratory disease interactions for rheumatoid arthritis risk.Seminars in arthritis and rheumatism · 2026Article
- Post-Traumatic Headache in Children and Genetic Risk of Migraine: An Observational Cohort Study.Neurology. Genetics · 2026Article
- Unravelling the genetics of heat-responsive fruit traits in pepper [Capsicum annuum L.] via GWAS under subtropical climates.BMC plant biology · 2026Article
- Linkage disequilibrium score regression identifies genetic correlations between hepatocellular carcinoma and clinically relevant traits.International journal of cancer · 2026Article
- Polygenic risk scores improve CAD risk prediction in individuals at borderline and intermediate clinical risk.NPJ cardiovascular health · 2025Article
- Uncovering specific genetic-respiratory disease endotypes for rheumatoid arthritis risk.Annals of the rheumatic diseases · 2025Article
- Multi-ancestry genome-wide association analyses: a comparison of meta- and mega-analyses in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study.BMC genomics · 2025Article
- Optimization of computational ancestry inference for use in cancer cell lines.Biology methods & protocols · 2025Article
- Article
- Shared genetic architecture of non-viral cirrhosis with several pleiotropic traits: A nested case-control study in the UK Biobank.Liver international communications · 2024Article
- Genetics of glucose homeostasis in pregnancy and postpartum.Diabetologia · 2024Article
- Gene-respiratory disease interactions for rheumatoid arthritis risk.Seminars in arthritis and rheumatism · 2023Article
- Admixture Mapping in African Americans Identifies New Risk Loci for HCV-Related Cirrhosis.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2023Article
- Proteomics Analysis of Genetic Liability of Abdominal Aortic Aneurysm Identifies Plasma Neogenin and Kit Ligand: The ARIC Study.Arteriosclerosis, thrombosis, and vascular biology · 2023Article
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6 authors.
Funding
Abstract
backgroundAccurate inference of genetic ancestry is of fundamental interest to many biomedical, forensic, and anthropological research areas. Genetic ancestry memberships may relate to genetic disease risks. In a genome association study, failing to account for differences in genetic ancestry between cases and controls may also lead to false-positive results. Although a number of strategies for inferring and taking into account the confounding effects of genetic ancestry are available, applying them to large studies (tens thousands samples) is challenging. The goal of this study is to develop an approach for inferring genetic ancestry of samples with unknown ancestry among closely related populations and to provide accurate estimates of ancestry for application to large-scale studies.
methodsIn this study we developed a novel distance-based approach, Ancestry Inference using Principal component analysis and Spatial analysis (AIPS) that incorporates an Inverse Distance Weighted (IDW) interpolation method from spatial analysis to assign individuals to population memberships.
resultsWe demonstrate the benefits of AIPS in analyzing population substructure, specifically related to the four most commonly used tools EIGENSTRAT, STRUCTURE, fastSTRUCTURE, and ADMIXTURE using genotype data from various intra-European panels and European-Americans. While the aforementioned commonly used tools performed poorly in inferring ancestry from a large number of subpopulations, AIPS accurately distinguished variations between and within subpopulations.
conclusionsOur results show that AIPS can be applied to large-scale data sets to discriminate the modest variability among intra-continental populations as well as for characterizing inter-continental variation. The method we developed will protect against spurious associations when mapping the genetic basis of a disease. Our approach is more accurate and computationally efficient method for inferring genetic ancestry in the large-scale genetic studies.
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