The DOI matches its Crossref record; 135 of the 136 references it cites were found in a registry or an index.
A HapMap harvest of insights into the genetics of common disease
Open accessAbstract
The International HapMap Project was designed to create a genome-wide database of patterns of human genetic variation, with the expectation that these patterns would be useful for genetic association studies of common diseases. This expectation has been amply fulfilled with just the initial output of genome-wide association studies, identifying nearly 100 loci for nearly 40 common diseases and traits. These associations provided new insights into pathophysiology, suggesting previously unsuspected etiologic pathways for common diseases that will be of use in identifying new therapeutic targets and developing targeted interventions based on genetically defined risk. In addition, HapMap-based discoveries have shed new light on the impact of evolutionary pressures on the human genome, suggesting multiple loci important for adapting to disease-causing pathogens and new environments. In this review we examine the origin, development, and current status of the HapMap; its prospects for continued evolution; and its current and potential future impact on biomedical science.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“A relevant example is evident in genomics research, in which data pooling has, e.g., enabled knowledge of disease-related loci to be expanded substantially [e.g.Lambert et al. (2013); Manolio et al. (2008); Schizophrenia Working Group of the Psychiatric Genomics (2014)], findings which would not have been possible without the sample sizes offered through data-sharing.”
From New directions in hypnosis research: strategies for advancing the cognitive and clinical neuroscience of hypnosis (2017) · cites this paper as Manolio et al. 2008
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Research in other fields has benefited substantially from datasharing initiatives.”
From New directions in hypnosis research: strategies for advancing the cognitive and clinical neuroscience of hypnosis (2017) · cites this paper as Manolio et al. 2008
In the citing paper
“GWAS has successfully identified thousands of common variants99”
From Rare and common variants: twenty arguments (2012) · cites this paper as [99]
In the citing paper
“As of June 2011, the NHGRI GWAS catalog lists 1,449 genome-wide significant associations with 237 traits and diseases, spread across all chromosome except the Y99.”
From Rare and common variants: twenty arguments (2012) · cites this paper as [99]
In the citing paper
“Despite the success of genome wide association (GWA) studies in identifying common single nucleotide variants (SNVs) that contribute to complex diseases1, the vast majority of genetic variants contributing to disease susceptibility are yet to be discovered.”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [1]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Initial sequencing and analysis of the human genome
International Human Genome Sequencing Consortium, Whitehead Institute for Biomedical Research, C. F. G. R., Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., Devon, K., Dewar, K., et al. (2001) · Nature · doi:10.1038/35057062 · Cited by 24,216
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442
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 Structure of Haplotype Blocks in the Human Genome
Gabriel, S. B., Schaffner, S. F., Nguyen, H., Moore, J. M., Roy, J., Blumenstiel, B., Higgins, J., DeFelice, M., Lochner, A., Faggart, M., et al. (2002) · Science · doi:10.1126/science.1069424 · Cited by 5,863
A haplotype map of the human genome
The International HapMap Consortium (2005) · Nature · doi:10.1038/nature04226 · Cited by 5,858
Population Structure and Eigenanalysis
Patterson, N., Price, A. L., & Reich, D. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020190 · Cited by 5,280
Finishing the euchromatic sequence of the human genome
International Human Genome Sequencing Consortium (2004) · Nature · doi:10.1038/nature03001 · Cited by 4,971
Frayling, T. M., Timpson, N. J., Weedon, M. N., Zeggini, E., Freathy, R. M., Lindgren, C. M., Perry, J. R. B., Elliott, K. S., Lango, H., Rayner, N. W., et al. (2007) · Science · doi:10.1126/science.1141634 · Cited by 4,531
Complement factor H polymorphism in age-related macular degeneration
Klein, R. J., Zeiss, C., Chew, E. Y., Tsai, J.-Y., Sackler, R. S., Haynes, C., Henning, A. K., SanGiovanni, J. P., Mane, S. M., Mayne, S. T., et al. (2005) · Science · doi:10.1126/science.1109557 · Cited by 4,492
Flagged first, then by citations
1 of the 136 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- NCBI Reference Assembly. Single nucleotide polymorphism. http://www.ncbi.nlm.nih.gov/projects/SNP/ .
- 10.1126/science.1105436 · DOI
- Colca, 2007, Curr. Opin. Investig. Drugs., 8, 707
Cited by
Sorted by citations
Items 801–888 of 888
Fakra, É., Belzeaux-R, R., Pringuey, D., Cermolacce, M., Correard, N., Micoulaud‐Franchi, J., & Azorin, J. (2013) · L Encéphale · doi:10.1016/s0013-7006(13)70112-x
Genome-Scale Genetics: Lessons from Founder Populations
Kenny, E. E. (2025) · Digital Commons - RU (Rockefeller University) · doi:10.48496/9v1q-rb59
Genomics: Present-day practices and future trends.
Boissonneault, G. A. (2011) · PubMed
Principles of Human Genome Epidemiology
GWINN, M. L., & Khoury, M. J. (2009) · Elsevier eBooks · doi:10.1016/b978-0-12-369420-1.00040-8
Revealing the genetic roots of obesity and type 2 diabetes
van Vliet‐Ostaptchouk, J. V. (2010)
In silicoanalysis strategies and resources for psychiatric genetics research
Torkamani, A., Bakken, T. E., & Schork, N. J. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9781139025997.005
Transcription and Epigenetic Regulation
Merchant, J. L. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-382026-6.00001-4
Feero, W. G., Zazove, P., & Stevens, N. G. (2011) · Elsevier eBooks · doi:10.1016/b978-1-4377-1160-8.10044-2
QCAT: testing causality of variants using only summary association statisticsPreprint
Lee, D., Bigdeli, T. B., Vladimirov, V. I., Fanous, A. H., & Bacanu, S. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/072355
“Consequently, among the tens of thousands reported GWAS SNPs, only very few are empirically identified as causal variants altering disease risk (3-5) and, despite considerable GWAS results, this limitation hinders the progress towards understanding biological mechanisms or pathways underlying human traits/diseases.”
BackgroundQuantitative trait analysis in a panel of recombinant inbred rat strains
Grieve, I. C. (2010) · doi:10.25560/5617
Statistical analysis for aggregated count data in genetic association studies
Choi, H., Jung, H. Y., & Park, T. (2016) · International Journal of Data Mining and Bioinformatics · doi:10.1504/ijdmb.2016.079802
Naslavsky, M. S. (2016) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.41.2016.tde-14012016-092908
A GENÉTICA NA IMUNOPATOGÉNESE DA NEFROPATIA IgA
de Lemos, T. J. S. (2012) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · doi:10.34626/fvga-4v33
IN TRANSLATION Genome-Wide Association Studies in Nephrology Research
Köttgen, A. (2010)
On the application of estimation of distribution algorithms to multi-marker tagging SNP selection
Hermida, R. S., Mendiburu, A., Zaitlen, N., Eskin, E., & Lozano, J. A. (2009)
Peripheral blood biomarkers in Psychiatric Diseases
Castell, M. S. (2012) · TDX (Tesis Doctorals en Xarxa)
Designing Novel Queries for Analysing NoSQL Data of Gene-Disease Associations
Yaseen, H., Atif, M., & Ferzund, J. (2017) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2017.080546
“2008, in [20] has launched a project with the name of HapMap to enlist human genetic variations and their association studies to common diseases.”
BackgroundYazar, S. (2015) · UWA Profiles and Research Repository (University of Western Australia)
Statistical Analysis for Next-Generation Sequencing data in Family-based designs
최성경 (2016) · Seoul National University Open Repository (Seoul National University)
Genetic and Epigenetic Research
Pollard, S., & Morgan, L. (2017) · Cambridge University Press eBooks · doi:10.1017/9781107585775.013
Kentebe, A. (2014) · TSpace (University of Toronto)
Cecile, A., & Khoury, M. J. (2012)
Schormair, B. (2010) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich)
Identification of X-Linked Genes in Migraine: Fine Mapping and Candidate Gene Studies
Maher, B. H. (2012) · Griffith Research Online · doi:10.25904/1912/2744
Yurkiewich, A. J. (2012) · uO Research (University of Ottawa) · doi:10.20381/ruor-5448
The Genomic Prediction of Disease: Example of type 2 diabetes (T2D)Preprint
Sirovich, L. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/285783
“This feature recalls the frequent observation that complex diseases arise from large collections of weakly contributing loci (7, 9, 11, 16), which leads to the construction of classifiers, which permit the inclusion of many genomic loci.”
BackgroundSridharan, M. (2010) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r37d39
Novel Methods for Family-Based Genetic Studies
Yan, Q. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7868-7_9
Perspectiva genética nas perturbações da ansiadade
Pinheiro, R. P. S. (2012) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · doi:10.34626/f68n-hq65
Genetic Influences on Blood Lipids and Cardiovascular Disease Risk
Ordovás, J. M., & Martha, G.-C. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-391884-0.00028-7
Advances in personalized gastroenterology and hepatology 2016
Hubert, E. B. (2016) · Russian Journal of Gastroenterology Hepatology Coloproctology · doi:10.22416/1382-4376-2016-26-3-4-10
“2: HapMap for chromosomes 13–15 and 17–19 [3].”
BackgroundThe Microbiome in Liver Diseases
Blum, H. E. (2020) · Springer eBooks · doi:10.1007/978-3-030-24432-3_19
Abedian, S., Abbasi, A., de Boer, A., Stricker, B. H. C., Bakker, S. J. L., van der Harst, P., Sedaghat, S., Darvishian, M., Ikram, M. A., Navis, G. J., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-82276-3
Application of Synthetic External RNA Controls in a Targeted Hybrid Capture Assay
Roth, E. (2021) · Digital Access to Scholarship at Harvard (DASH) (Harvard University)
Complex Genetic Interactions/Data Mining/Dimensionality Reduction
Bush, W. S., & Turner, S. (2021) · doi:10.1002/9781119104100.ch11
Cancer Patient Healthcare Analysis by Genomic Prediction
Kakulapati, V., Shaik, S., & Reddy, S. M. (2021) · Springer eBooks · doi:10.1007/978-981-16-6542-4_20
(2019) · Wiley eBooks · doi:10.1002/9781118768853.refs
Scale effects on genomic modelling and prediction
Berger, S. (2015) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-5230
Stanley, L. A., & Greim, H. (2017) · doi:10.1002/9783527695447.ch4
Perspectives on the human genome
Upadhyaya, K. C., & KUMAR, A. T. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-811710-1.00029-x
Ramsden, J. J. (2015) · Computational biology · doi:10.1007/978-1-4471-6702-0_20
Science and education in XXI century. Vol. 13
(2014) · Consulting company Ucom eBooks · doi:10.17117/2014.10.31.13
Weiss, K. M. (2010) · Evolutionary Anthropology Issues News and Reviews · doi:10.1002/evan.20249
Genetics of Type 2 Diabetes: From Candidate Genes to Genome-Wide Association Analysis
Kleinberger, J. W., Brown, K. M., Silver, K. D., & Shuldiner, A. R. (2017) · Springer eBooks · doi:10.1007/978-3-319-18741-9_10
Transcription and Epigenetic Regulation
Merchant, J. L. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809954-4.00001-3
GENOMICS: Present-day practices and future trends
&NA; (2011) · JAAPA · doi:10.1097/01720610-201101000-00004
Rolla, E. D. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.727087425.793557775
Genetics of Type 2 Diabetes: From Candidate Genes to Genome-Wide Association Analysis
Kleinberger, J. W., Brown, K. M., Silver, K. D., & Shuldiner, A. R. (2017) · Springer eBooks · doi:10.1007/978-3-319-20797-1_10-2
Genetics of Type 2 Diabetes: From Candidate Genes to Genome-Wide Association Analysis
Kleinberger, J. W., Shuldiner, A. R., & Silver, K. D. (2016) · Springer eBooks · doi:10.1007/978-3-319-20797-1_10-1
Human Genome‐Wide Association (GWA) Studies
Kiel, D. P. (2013) · Wiley eBooks · doi:10.1002/9781118453926.ch13
Molecular Biological Analyses and Molecular Pathology in Clinical Chemistry
Lo, Y. M. D., Wong, I. H. N., Tsui, N. B. Y., & Lam, C. (2009) · Wiley eBooks · doi:10.1002/9780470027318.a0534.pub2
Nagy, N., Bata‐Csörgõ, Z., Kemény, L. V., & Széll, M. (2022) · Bőrgyógyászati és Venerológiai Szemle · doi:10.7188/bvsz.2022.98.2.4
Nagy, N., & Széll, M. (2022) · Bőrgyógyászati és Venerológiai Szemle · doi:10.7188/bvsz.2022.98.2.5
Genetics in Cardiovascular Behavioral Medicine
McCaffery, J. M. (2022) · Springer eBooks · doi:10.1007/978-0-387-85960-6_31
Research in genodermatoses at the Szeged Department of Dermatology and Allergology
Nagy, N., Danis, J., Ádám, É., & Széll, M. (2023) · Bőrgyógyászati és Venerológiai Szemle · doi:10.7188/bvsz.2023.99.2.2
Ramsden, J. J. (2023) · Computational biology · doi:10.1007/978-3-030-45607-8_26
Genetic Factors in the Pathogenesis of Drug-Induced Liver Injury
Daly, A. K., & Day, C. P. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-387817-5.00013-3
Ulcerative colitis and estrogen receptor-negative breast cancer risk: a Mendelian randomized studyPreprint
liu, X., Wang, S., Lv, H., Chen, E., & Yu, J. (2024) · Research Square · doi:10.21203/rs.3.rs-4157388/v1
Cardiovascular disease and breast cancer risk: a Multivariate Mendelian randomizationPreprint
liu, X., Wang, S., Lv, H., chen, E., & Yu, J. (2024) · Research Square · doi:10.21203/rs.3.rs-4390484/v1
Exhaustive Variant Interaction Analysis Using Multifactor Dimensionality Reduction
Gómez-Sánchez, G., Alonso, L., Pérez, M. Á., Morán, I., Torrents, D., & Berral, J. L. (2024) · Applied Sciences · doi:10.3390/app14125136
Liu, X., Wang, S., Lv, H., Chen, E., & Yu, J. (2024) · Research Square · doi:10.21203/rs.3.rs-4616971/v1
Venous thromboembolism and ovarian cancer risk: a Mendelian randomized study
Liu, X., Wang, S., Lv, H., Chen, E., & Yu, J. (2024) · Discover Oncology · doi:10.1007/s12672-024-01446-7
Taheri, F., Goharifar, N., Safari, M., Goharifar, A., Salmaninejad, A., Sarhangi, N., Hasanzad, M., & Amoli, M. M. (2024) · Springer eBooks · doi:10.1007/978-981-97-4974-4_2
Shewaikh, H. S. E., Attia, G. F., Ibrahim, W. S., Sharshar, R. S., & Ali, D. A. (2025) · Journal of Investigative Medicine · doi:10.1177/10815589251348921
Dhakal, U., & Toomajian, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.25.678624
Quast, C. (2014) · Electronic Theses of LMU Munich (Ludwig-Maximilians-Universität München) · doi:10.5282/edoc.16546
Bioinformatics Approaches in Noncoding RNAs Research
Khan, F., Kumar, A., & Akhtar, S. (2026) · Experientia supplementum · doi:10.1007/978-3-032-06948-1_13
Guinot, F. (2018) · theses.fr (ABES) · doi:10.70675/d82eec53ze21bz4fcfzab1az14855da3ddab
Guo, M., Miao, J., Li, Y., Yin, J., Wang, P., Luo, F., Li, S., Hu, J., Liu, W., Zhang, T., et al. (2026) · BMC Plant Biology · doi:10.1186/s12870-026-08473-2
“Although Tag SNPs widely applied in human genetics and medicine [30, 42], this approach remains highly valuable for crop improvement.”
Ikhane, P. A., Adebamowo, S. N., Yusuf, T., & Adebamowo, C. A. (2026) · Research Square · doi:10.21203/rs.3.rs-10412231/v1
Donohue, C. (2026) · Studies in history and philosophy of science · doi:10.1007/978-3-032-25925-7_12
Adebamowo, C. A., Adebamowo, S. N., & Ikhane, P. A. (2026) · Research Square · doi:10.21203/rs.3.rs-11324546/v1
FAIRsharing record for: Genome-Wide Association Studies Catalog
FAIRsharing Team, Lister, A., & Parkinson, H. (2018) · FAIRsharing.org · doi:10.25504/fairsharing.blumrx
Dębiec, R. (2013) · Figshare
Bhattacharya, N., Basu, N., Banerjee, S. K., & Malakar, D. (2014) · Springer eBooks · doi:10.1007/978-1-4471-6542-2_28
Metadata
- Authors
- Manolio, T. A., Brooks, L. D., & Collins, F. S.
- Affiliations
- National Human Genome Research Institute (US)
- Venue
- Journal of Clinical Investigation
- Published
- 2008-05-01
- Volume
- 118
- Issue
- 5
- Pages
- 1590-1605
- ISSN
- 0021-9738 (ISSN-L)
- PMC
- PMC2336881
- OpenAlex
- W2101983393
- Cited by
- 889
- References
- 136
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 10 · 2023: 10 · 2022: 14 · 2021: 16 · 2020: 14 · 2019: 22 · 2018: 23 · 2017: 30 · 2016: 30 · 2015: 34 · 2014: 55 · 2013: 53 · 2012: 88
- FWCI
- 14.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- Locations
- Journal of Clinical Investigation (publishedVersion, public-domain)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Keywords
- HapMap, human genetic variation, genome-wide association study, disease pathophysiology, therapeutic target, genetic risk
- MeSH terms
- Chromosome Mapping, Genetics, Population, Haplotypes, Humans, Genetic Variation, Linkage Disequilibrium, Genome, Human, Evolution, Molecular, Genetic Predisposition to Disease, Databases, Genetic
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
