The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
The effects of human population structure on large genetic association studies
Abstract
Large-scale association studies hold substantial promise for unraveling the genetic basis of common human diseases. A well-known problem with such studies is the presence of undetected population structure, which can lead to both false positive results and failures to detect genuine associations. Here we examine approximately 15,000 genome-wide single-nucleotide polymorphisms typed in three population groups to assess the consequences of population structure on the coming generation of association studies. The consequences of population structure on association outcomes increase markedly with sample size. For the size of study needed to detect typical genetic effects in common diseases, even the modest levels of population structure within population groups cannot safely be ignored. We also examine one method for correcting for population structure (Genomic Control). Although it often performs well, it may not correct for structure if too few loci are used and may overcorrect in other settings, leading to substantial loss of power. The results of our analysis can guide the design of large-scale association studies.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“To avoid the influence of undesired population structures that can mimic the signal of associations and lead to false positives or missed real effects (Marchini et al., 2004), we estimated the population structure of 239 sweet potato genotypes using the model-based approach as implemented in STRUCTURE.”
From Development and identification of SSR markers associated with starch properties and β-carotene content in the storage root of sweet potato (Ipomoea batatas L.) (2016) · cites this paper as Marchini et al., 2004
In the citing paper
“To avoid the influence of undesired population structures that can mimic the signal of associations and lead to false positives or missed real effects (Marchini et al., 2004), we estimated the population structure of 239 sweet potato genotypes using the model-based approach as implemented in STRUCTURE.”
From Development and identification of SSR markers associated with starch properties and β-carotene content in the storage root of sweet potato (Ipomoea batatas L.) (2016) · cites this paper as Marchini et al., 2004
Citations
Cites
Inference of Population Structure Using Multilocus Genotype Data
Pritchard, J. K., Stephens, M., & Donnelly, P. (2000) · Genetics · doi:10.1093/genetics/155.2.945 · Cited by 33,871
Self-citationStatistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
The International HapMap Project
†The International HapMap Consortium, Genotyping centres: Baylor College of Medicine and ParAllele BioScience, Gibbs, R. A., Belmont, J. W., Hardenbol, P., Willis, T. D., Yu, F., Chinese HapMap Consortium, Yang, H., Ch'ang, L.-Y., et al. (2003) · Nature · doi:10.1038/nature02168 · Cited by 6,057
Self-citationThe Future of Genetic Studies of Complex Human Diseases
Risch, N., & Merikangas, K. (1996) · Science · doi:10.1126/science.273.5281.1516 · Cited by 5,355
The History and Geography of Human Genes
Cavalli-Sforza, L. L., Menozzi, P., & Piazza, A. (2018) · Princeton University Press eBooks · doi:10.2307/j.ctv301gjp · Cited by 3,175
Genomic Control for Association Studies
Devlin, B., & Roeder, K. (1999) · Biometrics · doi:10.1111/j.0006-341x.1999.00997.x · Cited by 3,162
Association mapping in structured populations.
Pritchard, J. K., Stephens, M., Rosenberg, N., & Donnelly, P. (2000) · American Journal of Human Genetics · doi:10.1086/302959 · Cited by 2,066
Self-citationSearching for genetic determinants in the new millennium
Risch, N. J. (2000) · Nature · doi:10.1038/35015718 · Cited by 1,839
Use of Unlinked Genetic Markers to Detect Population Stratification in Association Studies
Pritchard, J. K., & Rosenberg, N. A. (1999) · The American Journal of Human Genetics · doi:10.1086/302449 · Cited by 1,198
From Genotypes to Genes: Doubling the Sample Size
Sasieni, P. D. (1997) · Biometrics · doi:10.2307/2533494 · Cited by 897
Flagged first, then by citations
Cited by
Sorted by citations
Items 701–800 of 876
A Powerful Nonparametric Statistical Framework for Family-Based Association Analyses
Li, M., He, Z., Schaid, D. J., Cleves, M. A., Nick, T. G., & Lu, Q. L. (2015) · Genetics · doi:10.1534/genetics.115.175174 · Cited by 2
Combined detection and introgression of QTL in outbred populations
Yazdi, M. H., Sonesson, A. K., Woolliams, J. A., & Meuwissen, T. H. E. (2010) · Genetics Selection Evolution · doi:10.1186/1297-9686-42-16 · Cited by 2
Statistical methods for examining genetic influences of resistance to anti-epileptic drugs
Baksh, M. F., & Kelly, P. J. (2008) · Expert Review of Clinical Pharmacology · doi:10.1586/17512433.1.1.137 · Cited by 2
Orthogonal projection correction for confounders in biological data classification
Li, M., & Zhang, S. (2015) · International Journal of Data Mining and Bioinformatics · doi:10.1504/ijdmb.2015.071553 · Cited by 2
Measuring the spatial correlations of protein binding sites
Wei, Y., & Wu, H. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw058 · Cited by 2
Association Mapping of Agronomic QTLs in U.S. Spring Barley Breeding Germplasm
(2014) · CSA News · doi:10.2134/csa2014-59-10-4 · Cited by 2
Kemppainen, P., Rønning, B., Kvalnes, T., Hagen, I. J., Ringsby, T. H., Billing, A. M., Pärn, H., Lien, S., Husby, A., Sæther, B., et al. (2016) · Molecular Ecology Resources · doi:10.1111/1755-0998.12631 · Cited by 2
Genetics in the Prevention and Treatment of Sepsis
Reilly, J. P., Meyer, N. J., & Christie, J. D. (2017) · Respiratory medicine · doi:10.1007/978-3-319-48470-9_15 · Cited by 2
Zhang, W., Cheng, L., & Huang, G. (2021) · Genes & Genomics · doi:10.1007/s13258-021-01057-4 · Cited by 2
Molecular Mechanisms of Host Resistance to Hemibiotrophs and Necrotrophs
Saharan, G. S., Mehta, N. K., & Meena, P. D. (2021) · Springer eBooks · doi:10.1007/978-981-16-1974-8_3 · Cited by 2
Hybrid autoencoder with orthogonal latent space for robust population structure inference
Yuan, M., Hoskens, H., Goovaerts, S., Herrick, N., Shriver, M. D., Walsh, S., & Claes, P. D. (2023) · Scientific Reports · doi:10.1038/s41598-023-28759-x · Cited by 4
Dimensionality reduction approach for genotypic data
Al-Husain, L., & Hafez, A. M. (2015) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2015.7300305 · Cited by 2
Lappalainen, T. (2009) · Työväentutkimus Vuosikirja · Cited by 2
Strategies for Studying Complex Genetic Traits
Nathan, B. M., Hirschhorn, J. N., & Palmert, M. R. (2004) · The Endocrinologist · doi:10.1097/01.ten.0000146242.75018.a9 · Cited by 2
Pharmacogenomics in Drug Development
Judson, R., & Moss, A. J. (2007) · Humana Press eBooks · doi:10.1007/978-1-59259-884-7_5 · Cited by 2
Gerber, S., Fournier, D., Hewel, C., & Horenko, I. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/255315 · Cited by 2
Epistasis Detection using Model Based Multifactor Dimensionality Reduction in Structured PopulationsPreprint
Abegaz, F., Lishout, F. V., John, J. M., Chiachoompu, K., Bhardwaj, A., Gusareva, E. S., Wei, Z., Hakonarson, H. H., & Steen, K. V. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/541946 · Cited by 2
A parsimonious model for mass-univariate vertex-wise analysisPreprint
Couvy‐Duchesne, B., Zhang, F., Kemper, K. E., Sidorenko, J., Wray, N. R., Visscher, P. M., Colliot, O., & Yang, J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.22.427735 · Cited by 2
Case-Control Association Tests Correcting for Population Stratification
Köhler, K. (2006) · doi:10.53846/goediss-3678 · Cited by 2
A guidance of model selection for genomic prediction based on linear mixed models for complex traits
Duan, J., Zhang, J., Liu, L., & Wen, Y. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.1017380 · Cited by 1
Serpico, D., & Maziarz, M. (2023) · European Journal for Philosophy of Science · doi:10.1007/s13194-023-00559-0 · Cited by 1
Influence of Admixture on Phenotypes
Brugger, S. W., & Davis, M. F. (2023) · Current Protocols · doi:10.1002/cpz1.953 · Cited by 1
Estimation of genetic admixture proportions via haplotypes
Ko, S., Sobel, E. M., Zhou, H., & Lange, K. L. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.11.043 · Cited by 1
Liu, R., Huang, Y., Jiang, M., Xu, F., Pei, Q., Ma, J., Li, Y., Shen, S., Zhang, B., Guo, X., et al. (2025) · Mechanobiology in Medicine · doi:10.1016/j.mbm.2025.100137 · Cited by 1
Bone Tumors: Epidemiology, Classification, Pathology
Han, C., Howard, K., & Kindblom, L. (2025) · Medical radiology · doi:10.1007/174_2025_589 · Cited by 1
Accurate and scalable genome-wide ancestry estimation using haplotype clusters
Meisner, J. (2026) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2026.100561 · Cited by 1
Genetic Data in Forensic Science: Use, Misuse and Abuse
Barbujani, G., & Tassi, F. (2012) · Springer eBooks · doi:10.1007/978-88-470-2032-0_19 · Cited by 1
The Current Knowledge of Genetic Susceptibility Influencing Dental Implant Outcomes
Alvim‐Pereira, F., Cristina, C., & Cristina, P. (2011) · InTech eBooks · doi:10.5772/18065 · Cited by 1
Statistical models for analyzing human genetic variation
Jordan, M. I., & Sankararaman, S. (2010) · Cited by 1
Kumada, M., Munkhtulga, L., Utsumi, N., Omi, T., Gotoh, T., Kamesaki, T., Okuda, H., Kajii, E., & Iwamoto, S. (2008) · Public Health Genomics · doi:10.1159/000113877 · Cited by 1
Assessment of Population Structure and Its Effects on Genome-Wide Association Studies
Xu, H., & George, V. T. (2009) · Communication in Statistics- Theory and Methods · doi:10.1080/03610920902947188 · Cited by 1
Genome-Wide Association Study Heterogeneous Cohort Homogenization via Subject Weight Knock-Down
Valente, A. X. C. N., Zischkau, J., Shin, J. H., Gao, Y., & Sarkar, A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0048653 · Cited by 1
Family-based genome-wide association study for simulated data of Framingham Heart Study
Xu, H., Mathew, G., & George, V. T. (2009) · BMC Proceedings · doi:10.1186/1753-6561-3-s7-s124 · Cited by 1
Pharmacogenomics in Drug Development and Clinical Research
Judson, R. (2008) · Springer eBooks · doi:10.1007/978-1-84628-854-8_48 · Cited by 1
Statistical mo dels for inferring the structure and history of populations from genetic data
Sir, J. (2012) · Cited by 2
Modelling genes: mathematical and statistical challenges in genomics
Donnelly, P. (2007) · Proceedings of the International Congress of Mathematicians Madrid, August 22–30, 2006 · doi:10.4171/022-3/29 · Cited by 1
Self-citationPCA-based Informative SNP Selection for Analyzing Population Structure
Limpiti, T., Intarapanich, A., & Tongsima, S. (2016) · International Conference Computational Systems-Biology and Bioinformatics · doi:10.1145/3029375.3029379 · Cited by 1
Shen, J., Li, Z., & Shi, Y. (2015) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2015.06.007 · Cited by 1
Genome-wide association studies of cancer predisposition
Stadler, Z. K., Shah, S., & Offit, K. (2013) · Cambridge University Press eBooks · doi:10.1017/cbo9781139046947.003 · Cited by 1
Polymorphismes génétiques : comment interpréter les études ?
Courivaud, C., Saas, P., & Ducloux, D. (2011) · Néphrologie & Thérapeutique · doi:10.1016/j.nephro.2011.07.411 · Cited by 1
Dubé, M., & Rioux, J. D. (2009) · Springer eBooks · doi:10.1007/978-1-84882-927-5_8 · Cited by 1
Prom-Wormley, E., Wells, J., Landes, L., Edmondson, A. N., Sankoh, M., Jamieson, B., Delk, K. J., Surya, S., Bhati, S., & Clifford, J. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1103966 · Cited by 1
Yu, G. (2009) · Open Scholarship Institutional Repository (Washington University in St. Louis) · doi:10.7936/k7k35rn2 · Cited by 1
Chapter 42. Genetics of Osteoporosis
Eisman, J. A. (2008) · Wiley eBooks · doi:10.1002/9780470623992.ch42 · Cited by 1
Genetics of cardiovascular disease: a candidate gene study of USF1
Kristiansson, K. (2008) · Työväentutkimus Vuosikirja · Cited by 1
Khor, C. C., Chapman, S. J., Vannberg, F. O., Hill, A. V., & O'Neill, L. A. J. (2008) · Nature Genetics · doi:10.1038/ng0308-262 · Cited by 1
Reliability and utility of single nucleotide polymorphisms for genetic association studies
Pearce, C., & Hirschhorn, J. (2005) · Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics · doi:10.1002/047001153x.g206206 · Cited by 1
Population Stratification, Adjustment for
Gao, X., & Edwards, T. L. (2010) · Wiley eBooks · doi:10.1002/9780470015902.a0020384 · Cited by 1
Principles of High-Quality Genotyping
Chanock, S. J. (2008) · CRC Press eBooks · doi:10.3109/9781420052923-7 · Cited by 1
Genetics of Rheumatic Diseases
Eyre, S., Gregersen, P. K., & Barton, A. C. (2016) · Elsevier eBooks · doi:10.1016/b978-0-323-31696-5.00021-8 · Cited by 1
Alligood, K. (2017) · Scholars' Bank (University of Oregon) · Cited by 1
BRASS: permutation methods for binary traits in genetic association studies with structured samplesPreprint
Mbatchou, J. S., Abney, M., & McPeek, M. S. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/451377 · Cited by 1
Genetics of Rheumatic Diseases
Gregersen, P. K. (2009) · Elsevier eBooks · doi:10.1016/b978-1-4160-3285-4.10018-x · Cited by 1
Genetics of Large Populations and Association Studies
Garnier‐Géré, P. H., & Chikhi, L. (2008) · Encyclopedia of Life Sciences · doi:10.1002/9780470015902.a0005446.pub2 · Cited by 1
Faculty Opinions recommendation of The UK Biobank resource with deep phenotyping and genomic data.
Gao, F. (2018) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.734198301.793551571 · Cited by 1
Deep learning identified genetic variants associated with COVID-19 related mortalityPreprint
Li, Z., Dai, W., Wang, S., Yao, Y., & Heping Zhang, D. (2022) · medRxiv · doi:10.1101/2022.05.05.22274731 · Cited by 1
Hybrid Autoencoder with Orthogonal Latent Space for Robust Population Structure InferencePreprint
Yuan, M., Hoskens, H., Goovaerts, S., Herrick, N., Shriver, M. D., Walsh, S., & Claes, P. D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.16.496401 · Cited by 1
Genetic and ecological drivers of molt in a migratory birdPreprint
Contina, A. J., Bossu, C. M., Allen, D. C., Wunder, M. B., & Ruegg, K. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.17.504285 · Cited by 1
The co-occurrence of genetic variants in the TYR and OCA2 genes confers susceptibility to albinismPreprint
Green, D. J., Michaud, V., Lasseaux, E., Plaisant, C., Fitzgerald, T. W., Birney, E., Black, G. C. M., Arveiler, B., & Sergouniotis, P. I. (2023) · medRxiv · doi:10.1101/2023.01.19.23284597 · Cited by 1
Tournebize, R. (2017) · theses.fr (ABES) · doi:10.70675/81813a3dz2c65z4dd3za3c5zbba963081c73 · Cited by 1
Matuszczak, M. (2013) · Rośliny Oleiste - Oilseed Crops
Ellis, J. (2013) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/gcaq-x895
Genetic studies of elite athlete status
Wang, G. (2013) · Enlighten: Theses (The University of Glasgow) · doi:10.5525/gla.thesis.4597
Bayesian analysis of models of population divergence for SNP variation data
Macintyre, S. (2010) · Enlighten: Theses (The University of Glasgow)
Li, M., & Hanson, T. (2010) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/j.1467-9876.2010.00741.x · Cited by 1
Pin1: A New Enzyme Pivotal for Protecting Against Alzheimer’s Disease
Nakamura, K., Ma, S. L., & Lu, K. P. (2011) · InTech eBooks · doi:10.5772/20852
Hägg, D. A. (2008) · Gothenburg University Publications Electronic Archive (Gothenburg University)
Epidemiology and Polymorphisms Related to Bladder Cancer in Ecuadorian Individuals
Paz-y-Mi�o, C., & Jos, M. (2012) · InTech eBooks · doi:10.5772/26829
Avoiding stratification in association studies
Devlin, B., & Roeder, K. (2005) · Wiley eBooks · doi:10.1002/047001153x.g206304
Estudios de asociación mediante rastreo genómico y su contribución en la genética del asma
Chávez, Y. A., & Martínez, B. (2010) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)
Olsson, M. M. (2010) · Gothenburg University Publications Electronic Archive (Gothenburg University)
Population Genetics in the Genomic Era
Xu, S., & Ji, W. (2012) · InTech eBooks · doi:10.5772/34056
Population Genetic Principles and Human Populations
Dermitzakis, E. T. (2009) · Springer eBooks · doi:10.1007/978-3-540-37654-5_18
Validity of Reported Genetic Risk Factors for Acute Coronary Syndrome
Wang, W. Y. S. (2007) · JAMA · doi:10.1001/jama.298.15.1758-b
Forensic DNA Typing: Quo Vadis?
Mertens, G. (2009) · The Open Forensic Science Journal · doi:10.2174/1874402800902010021
Edwards, T. L., Edwards, D. R. V., & Ritchie, M. D. (2011) · Springer eBooks · doi:10.1007/978-1-61779-530-5_5
Genetics of Large Populations and Association Studies
Chikhi, L., & Garnier‐Géré, P. H. (2006) · Encyclopedia of Life Sciences · doi:10.1038/npg.els.0005446
Using Network Methodology to Infer Population Substructure
Prokopenko, D., Hecker, J., Silverman, E. K., Nöthen, M. M., Schmid, M. C., Lange, C., & Fier, H. L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130708
Role of the OX40 ligand/receptor pair in coronary artery disease
Ria, M. (2006) · Figshare
Maretty, L., Sibbesen, J. A., & Krogh, A. (2014)
GENETICS OF CARDIOVASCULAR DISEASE
Kristiansson, K. (2008)
Bhaskar, A. (2013) · eScholarship (California Digital Library)
Iterative principal component analysis for population structure correction in large-scale datasets
Klotzbuecher, K., Bedő, J., Leckie, C. A., & Kowalczyk, A. (2013) · F1000Research · doi:10.7490/f1000research.1094089.1
The Genetics of General Cognitive Ability
Kirkpatrick, R. M. (2013) · University of Minnesota Digital Conservancy (University of Minnesota)
Hulsman, L. L. (2013) · OakTrust (Texas A&M University Libraries)
Genetic diversity in Brassica napus and association studies with seed glucosinolate content
Justus-Liebig-University Giessen (2008)
Principles of Human Genome Epidemiology
GWINN, M. L., & Khoury, M. J. (2009) · Elsevier eBooks · doi:10.1016/b978-0-12-369420-1.00040-8
Human genetic variation and its implication in understanding 'race'/ethnicity and admixture
Barnholtz-Sloan, J., Halder, I., & Shriver, M. (2016) · CRC Press eBooks · doi:10.3109/9781420052923-11
Introduction to statistical methods in genome-wide association studies
Yang, C., Li, C., Chung, D., Chen, M., Gelernter, J. E., & Zhao, H. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781107337459.005
Motsinger‐Reif, A. A., & Reif, D. M. (2015) · Apple Academic Press eBooks · doi:10.1201/b18597-3
Reporting and Interpreting Results
Little, J. (2008) · CRC Press eBooks · doi:10.3109/9781420052923-19
Metadata
- Authors
- Marchini, J., Cardon, L. R., Phillips, M. S., & Donnelly, P.
- Affiliations
- University of Oxford (GB)
- Centre for Human Genetics (GB)
- McGill Genome Centre (CA)
- Corresponding author
- Donnelly, Peter
- Venue
- Nature Genetics
- Published
- 2004-03-28
- Volume
- 36
- Issue
- 5
- Pages
- 512-517
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- OpenAlex
- W1986265706
- Cited by
- 869
- References
- 31
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 14 · 2023: 19 · 2022: 29 · 2021: 34 · 2020: 16 · 2019: 30 · 2018: 38 · 2017: 35 · 2016: 33 · 2015: 39 · 2014: 28 · 2013: 46 · 2012: 48
- FWCI
- 31.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Keywords
- population structure, genetic association studies, single nucleotide polymorphisms, genomic control, false positives, statistical power, common diseases
- MeSH terms
- Genetic Markers, Genetics, Population, Humans, Models, Genetic, Genetic Variation, Linkage Disequilibrium, Quantitative Trait, Heritable, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review