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Potential etiologic and functional implications of genome-wide association loci for human diseases and traits
Open accessAbstract
We have developed an online catalog of SNP-trait associations from published genome-wide association studies for use in investigating genomic characteristics of trait/disease-associated SNPs (TASs). Reported TASs were common [median risk allele frequency 36%, interquartile range (IQR) 21%−53%] and were associated with modest effect sizes [median odds ratio (OR) 1.33, IQR 1.20–1.61]. Among 20 genomic annotation sets, reported TASs were significantly overrepresented only in nonsynonymous sites [OR = 3.9 (2.2−7.0), p = 3.5 × 10 −7 ] and 5kb-promoter regions [OR = 2.3 (1.5−3.6), p = 3 × 10 −4 ] compared to SNPs randomly selected from genotyping arrays. Although 88% of TASs were intronic (45%) or intergenic (43%), TASs were not overrepresented in introns and were significantly depleted in intergenic regions [OR = 0.44 (0.34−0.58), p = 2.0 × 10 −9 ]. Only slightly more TASs than expected by chance were predicted to be in regions under positive selection [OR = 1.3 (0.8−2.1), p = 0.2]. This new online resource, together with bioinformatic predictions of the underlying functionality at trait/disease-associated loci, is well-suited to guide future investigations of the role of common variants in complex disease etiology.
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“In these 50 control sets, Ingenuity enrichment p-values for the complement system and for atherosclerosis signaling genes were exceeded 16% and 32% of the time respectively (although these two specific pathways were never implicated in a control dataset).”
From Seven new loci associated with age-related macular degeneration (2013) · cites this paper as [46]
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“To evaluate significance of observed enrichment, we repeated our Ingenuity analysis starting with 50 lists of 19 SNPs randomly drawn from the NHGRI GWAS catalog46 and, again, using the INRICH tool63.”
From Seven new loci associated with age-related macular degeneration (2013) · cites this paper as [46]
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Kent, W. J., Sugnet, C. W., Furey, T. S., Roskin, K. M., Pringle, T. H., Zahler, A. M., & Haussler, A. D. (2002) · Genome Research · doi:10.1101/gr.229102 · Cited by 10,400
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Neel, J. V. (1962) · Europe PMC (PubMed Central) · Cited by 3,172
A Map of Recent Positive Selection in the Human Genome
Voight, B. F., Kudaravalli, S., Wen, X., & Pritchard, J. K. (2006) · PLoS Biology · doi:10.1371/journal.pbio.0040072 · Cited by 2,925
Human non-synonymous SNPs: server and survey
Ramensky, V. E. (2002) · Nucleic Acids Research · doi:10.1093/nar/gkf493 · Cited by 2,299
Diabetes mellitus: a "thrifty" genotype rendered detrimental by "progress"?
(1962) · PubMed Central · Cited by 2,182
Genome-wide detection and characterization of positive selection in human populations
The International HapMap Consortium, Sabeti, P. C., Varilly, P., Fry, B., Lohmueller, J., Hostetter, E., Cotsapas, C., Xie, X., Byrne, E. H., McCarroll, S. A., et al. (2007) · Nature · doi:10.1038/nature06250 · Cited by 2,131
Heritability in the genomics era — concepts and misconceptions
Visscher, P. M., Hill, W. G., & Wray, N. R. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2322 · Cited by 1,921
Hayhurst, G. P., Lee, Y.-H., Lambert, G., Ward, J. M., & Gonzalez, F. J. (2001) · Molecular and Cellular Biology · doi:10.1128/mcb.21.4.1393-1403.2001 · Cited by 1,107
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Cho, J. H. (2008) · Nature Reviews Immunology · doi:10.1038/nri2340 · Cited by 940
Multiple loci identified in a genome-wide association study of prostate cancer
Thomas, G. D., Jacobs, K. B., Yeager, M., Kraft, P. L., Wacholder, S., Orr, N., Yu, K. F., Chatterjee, N., Welch, R., Hutchinson, A. A., et al. (2008) · Nature Genetics · doi:10.1038/ng.91 · Cited by 903
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Klein, R. J., Xu, X., Mukherjee, S., Willis, J., & Hayes, J. (2010) · Cell · doi:10.1016/j.cell.2010.07.026 · Cited by 41
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Zhang, X., Moen, E. L., Liu, C., Mu, W., Gamazon, E. R., Delaney, S. M., Wing, C., Godley, L. A., Dolan, M. E., & Zhang, W. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu313 · Cited by 41
Wojcik, G. L., Kao, W. L., & Duggal, P. (2015) · BMC Genetics · doi:10.1186/s12863-015-0191-2 · Cited by 33
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Chiou, J., Zeng, C., Cheng, Z., Han, J. Y., Schlichting, M., Huang, S., Wang, J., Sui, Y., Deogaygay, A., Okino, M.-L., et al. (2019) · doi:10.1101/693671 · Cited by 189
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Lu, Z., Chen, C., Zhang, H., Li, B., Liu, Y., Guo, J., Xu, R., Shi, K., Ma, Q., Zhang, M., et al. (2025) · Nature Cancer · doi:10.1038/s43018-025-01031-z · Cited by 4
Arai, H., Millstein, J., Loupakis, F., Stintzing, S., Wang, J., Battaglin, F., Kawanishi, N., Jayachandran, P., Soni, S., Zhang, W., et al. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.03.048 · Cited by 2
Metadata
- Authors
- Hindorff, L. A., Sethupathy, P., Junkins, H. A., Ramos, E. M., Mehta, J. P., Collins, F. S., & Manolio, T. A.
- Affiliations
- National Institutes of Health (US)
- National Human Genome Research Institute (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2009-06-09
- Volume
- 106
- Issue
- 23
- Pages
- 9362-9367
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2687147
- OpenAlex
- W2117446594
- Cited by
- 4,013
- References
- 22
- Citations by year
- 2026: 42 · 2025: 91 · 2024: 89 · 2023: 102 · 2022: 147 · 2021: 184 · 2020: 176 · 2019: 166 · 2018: 206 · 2017: 223 · 2016: 268 · 2015: 344 · 2014: 438 · 2013: 480 · 2012: 433
- FWCI
- 205.13
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Human Genome Research Institute
- U.S. National Library of Medicine
- 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 › Genetic and phenotypic traits in livestock
- Keywords
- genome-wide association study, single nucleotide polymorphisms, disease-associated SNPs, SNP-trait association, genome annotation, non-synonymous mutations, promoter, intronic SNPs, intergenic regions, positive selection, common genetic variants, OPAC
- MeSH terms
- Disease, Humans, Polymorphism, Genetic, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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