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Integrating common and rare genetic variation in diverse human populations
Open accessAbstract
Despite great progress in identifying genetic variants that influence human disease, most inherited risk remains unexplained. A more complete understanding requires genome-wide studies that fully examine less common alleles in populations with a wide range of ancestry. To inform the design and interpretation of such studies, we genotyped 1.6 million common single nucleotide polymorphisms (SNPs) in 1,184 reference individuals from 11 global populations, and sequenced ten 100-kilobase regions in 692 of these individuals. This integrated data set of common and rare alleles, called 'HapMap 3', includes both SNPs and copy number polymorphisms (CNPs). We characterized population-specific differences among low-frequency variants, measured the improvement in imputation accuracy afforded by the larger reference panel, especially in imputing SNPs with a minor allele frequency of <or=5%, and demonstrated the feasibility of imputing newly discovered CNPs and SNPs. This expanded public resource of genome variants in global populations supports deeper interrogation of genomic variation and its role in human disease, and serves as a step towards a high-resolution map of the landscape of human genetic variation.
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“These validated the HLA association but showed no significant linkage to loci outside the MHC.”
From Genome‐wide association studies of multiple sclerosis (2018) · cites this paper as [26]
Experimental — accuracy not yet measuredIn the citing paper
“High levels of structural variation, including copy-number variation, were previously described for both processes15,22,39.”
From Mapping copy number variation by population-scale genome sequencing (2011) · cites this paper as [39]
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Metadata
- Authors
- The International HapMap 3 Consortium
- Venue
- Nature
- Published
- 2010-09
- Volume
- 467
- Issue
- 7311
- Pages
- 52-58
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3173859
- OpenAlex
- W2107916366
- Cited by
- 3,026
- References
- 26
- Citations by year
- 2026: 101 · 2025: 183 · 2024: 130 · 2023: 129 · 2022: 137 · 2021: 155 · 2020: 156 · 2019: 132 · 2018: 143 · 2017: 172 · 2016: 250 · 2015: 274 · 2014: 309 · 2013: 257 · 2012: 285
- FWCI
- 133.56
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Archive ouverte UNIGE (University of Geneva) (acceptedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- ANU Open Research (Australian National University) (submittedVersion)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Funders
- Wellcome Trust (068545/Z/02), 077011, 089062, 082371, 077014, 068545, 091746, 089061, 076113)
- National Human Genome Research Institute (U54HG003273)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK043351)
- 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
- single nucleotide polymorphisms, copy number variation, imputation accuracy, reference panel, low-frequency variants, human genetic variation
- MeSH terms
- Humans, Genome, Human, Human Genome Project, Polymorphism, Single Nucleotide, Population Groups, DNA Copy Number Variations
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