Cited by 4,013Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsGenetic and phenotypic traits in livestock

The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.

Potential etiologic and functional implications of genome-wide association loci for human diseases and traits

Open access

Hindorff, Lucia A., Sethupathy, Praveen, Junkins, Heather A., Ramos, Erin M., Mehta, Jayashri P., Collins, Francis S., Manolio, Teri A.·2009·Proceedings of the National Academy of Sciences

DOI 10.1073/pnas.0903103106PMC PMC2687147

Open this paper in CoChat — free account

Abstract

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.

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

Crossref · 10.1073/pnas.0903103106

Cites

22 of 22 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The Human Genome Browser at UCSC
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
Diabetes mellitus: a "thrifty" genotype rendered detrimental by "progress"?
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
Hepatocyte Nuclear Factor 4α (Nuclear Receptor 2A1) Is Essential for Maintenance of Hepatic Gene Expression and Lipid Homeostasis
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
The genetics and immunopathogenesis of inflammatory bowel disease
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

Show all 22 references

Sorted by citations

Items 201–300 of 1,028

An atlas of dynamic chromatin landscapes in mouse fetal development
Gorkin, D. U., Barozzi, I., Zhao, Y., Zhang, Y., Huang, H., Lee, A. Y., Li, B., Chiou, J., Wildberg, A., Ding, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2093-3 · Cited by 374
The Genetics of Transcription Factor DNA Binding Variation
Deplancke, B., Alpern, D., & Gardeux, V. (2016) · Cell · doi:10.1016/j.cell.2016.07.012 · Cited by 390
Efficient Test and Visualization of Multi-Set Intersections
Wang, M., Zhao, Y., & Zhang, B. (2015) · Scientific Reports · doi:10.1038/srep16923 · Cited by 395
Selecting instruments for Mendelian randomization in the wake of genome-wide association studies.
Swerdlow, D. I., Kuchenbaecker, K. B., Shah, S., Sofat, R., Holmes, M. V., White, J., Mindell, J. S., Kivimaki, M., Brunner, E. J., Whittaker, J. C., et al. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw088 · Cited by 387
GATES: A Rapid and Powerful Gene-Based Association Test Using Extended Simes Procedure
Li, M., Gui, H., Kwan, J. S. H., & Sham, P. C. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.01.019 · Cited by 386
The Three-Dimensional Organization of Mammalian Genomes
Yu, M., & Ren, B. (2017) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-100616-060531 · Cited by 373
Rapid and Pervasive Changes in Genome-wide Enhancer Usage during Mammalian Development
Nord, A. S., Blow, M. J., Attanasio, C., Akiyama, J. A., Holt, A., Hosseini, R., Phouanenavong, S., Plajzer-Frick, I., Shoukry, M., Afzal, V., et al. (2013) · Cell · doi:10.1016/j.cell.2013.11.033 · Cited by 375
Genome-wide identification of SNPs in microRNA genes and the SNP effects on microRNA target binding and biogenesis
Gong, J., Tong, Y., Zhang, H., Wang, K., Hu, T., Shan, G., Sun, J., & Guo, A. (2011) · Human Mutation · doi:10.1002/humu.21641 · Cited by 384
Computational Drug Repositioning: From Data to Therapeutics
Hurle, M. R., Yang, L., Xie, Q. S., Rajpal, D. K., Sanséau, P., & Agarwal, P. (2013) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2013.1 · Cited by 378
Global analysis of DNA methylation variation in adipose tissue from twins reveals links to disease-associated variants in distal regulatory elements
Grundberg, E., Meduri, E., Sandling, J. K., Hedman, Å. K., Keildson, S., Buil, A., Busche, S., Yuan, W., Nisbet, J., Sekowska, M., et al. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.10.004 · Cited by 370
Identifying Recent Adaptations in Large-Scale Genomic Data
Grossman, S. R., Andersen, K. G., Shlyakhter, I., Tabrizi, S., Winnicki, S., Yen, A., Park, D. J., Griesemer, D., Karlsson, E. K., Wong, S. H., et al. (2013) · Cell · doi:10.1016/j.cell.2013.01.035 · Cited by 368
Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues
Ernst, J., & Kellis, M. (2015) · Nature Biotechnology · doi:10.1038/nbt.3157 · Cited by 357
MultiPhen: Joint Model of Multiple Phenotypes Can Increase Discovery in GWAS
O’Reilly, P. F., Hoggart, C. J., Pomyen, Y., Calboli, F. C. F., Elliott, P., Järvelin, M., & Coin, L. (2012) · PLoS ONE · doi:10.1371/journal.pone.0034861 · Cited by 369
Integrative Annotation of Variants from 1092 Humans: Application to Cancer Genomics
Khurana, E., Fu, Y., Colonna, V., Mu, X. J., Kang, H. M., Lappalainen, T., Sboner, A., Lochovsky, L., Chen, J., Harmanci, A. O., et al. (2013) · Science · doi:10.1126/science.1235587 · Cited by 368
Adult height, nutrition, and population health
Perkins, J. M., Subramanian, S. V., Davey Smith, G., & Özaltin, E. (2016) · Nutrition Reviews · doi:10.1093/nutrit/nuv105 · Cited by 405
Heritability and genomics of gene expression in peripheral blood
Wright, F. A., Sullivan, P. F., Brooks, A. I., Zou, F., Sun, W., Xia, K., Madar, V., Jansen, R., Chung, W., Zhou, Y.-H., et al. (2014) · Nature Genetics · doi:10.1038/ng.2951 · Cited by 362
A compendium of promoter-centered long-range chromatin interactions in the human genome
Jung, I., Schmitt, A., Diao, Y., Lee, A. J., Liu, T., Yang, D., Tan, C., Eom, J., Chan, M., Chee, S., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0494-8 · Cited by 362
Using Extended Genealogy to Estimate Components of Heritability for 23 Quantitative and Dichotomous Traits
Paşaniuc, B., & Zaitlen, N. (2013) · eScholarship (California Digital Library) · Cited by 362
Quality Control Procedures for Genome‐Wide Association Studies
Turner, S., Armstrong, L. L., Bradford, Y., Carlson, C. S., Crawford, D. C., Crenshaw, A., de Andrade, M., Doheny, K. F., Haines, J. L., Hayes, M. G., et al. (2011) · Current Protocols in Human Genetics · doi:10.1002/0471142905.hg0119s68 · Cited by 373
Self-citation
The genetics of P arkinson's disease: Progress and therapeutic implications
Singleton, A., Farrer, M. J., & Bonifati, V. (2013) · Movement Disorders · doi:10.1002/mds.25249 · Cited by 374
The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity
Grimm, D. G., Azencott, C., Aicheler, F., Gieraths, U., MacArthur, D. G., Samocha, K. E., Cooper, D. N., Stenson, P. D., Daly, M. J., Smoller, J. W., et al. (2015) · Human Mutation · doi:10.1002/humu.22768 · Cited by 356
Breast cancer risk–associated SNPs modulate the affinity of chromatin for FOXA1 and alter gene expression
Sallari, R. C., Zhang, X., Wright, J. B., Bailey, S. D., Cole, M. D., Eeckhoute, J., Moore, J. H., & Lupien, M. (2012) · Nature Genetics · doi:10.1038/ng.2416 · Cited by 354
Bringing genome-wide association findings into clinical use
Manolio, T. A. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3523 · Cited by 363
Self-citation
Different differences: The use of ‘genetic ancestry’ versus race in biomedical human genetic research
Fujimura, J. H., & Rajagopalan, R. M. (2010) · Social Studies of Science · doi:10.1177/0306312710379170 · Cited by 360
The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers
Amos, C. I., Dennis, J., Wang, Z., Byun, J., Schumacher, F. R., Gayther, S. A., Casey, G., Hunter, D. J., Sellers, T. A., Gruber, S. B., et al. (2016) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-16-0106 · Cited by 342
The impact of rare and low-frequency genetic variants in common disease
Bomba, L., Walter, K., & Soranzo, N. (2017) · Genome biology · doi:10.1186/s13059-017-1212-4 · Cited by 369
Genome-wide association study reveals two new risk loci for bipolar disorder
Mühleisen, T. W., Leber, M., Schulze, T. G., Strohmaier, J., Degenhardt, F., Treutlein, J., Mattheisen, M., Forstner, A. J., Schumacher, J., Breuer, R., et al. (2014) · Nature Communications · doi:10.1038/ncomms4339 · Cited by 346
PolymiRTS Database 3.0: linking polymorphisms in microRNAs and their target sites with human diseases and biological pathways
Bhattacharya, A., Ziebarth, J. D., & Cui, Y. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1028 · Cited by 347
Global diversity, population stratification, and selection of human copy-number variation
Sudmant, P. H., Mallick, S., Nelson, B. J., Hormozdiari, F., Krumm, N., Huddleston, J., Coe, B. P., Baker, C., Nordenfelt, S., Bamshad, M., et al. (2015) · Science · doi:10.1126/science.aab3761 · Cited by 345
Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data
Huang, Y.-F., Gulko, B., & Siepel, A. C. (2017) · Nature Genetics · doi:10.1038/ng.3810 · Cited by 337
Conservation of biodiversity in the genomics era
Supple, M. A., & Shapiro, B. (2018) · Genome biology · doi:10.1186/s13059-018-1520-3 · Cited by 367
Extensive Variation in Chromatin States Across Humans
Kasowski, M., Kyriazopoulou-Panagiotopoulou, S., Grubert, F., Zaugg, J. B., Kundaje, A., Liu, Y., Boyle, A. P., Zhang, Q. C., Zakharia, F., Spacek, D. V., et al. (2013) · Science · doi:10.1126/science.1242510 · Cited by 339
The genetics of NAFLD
Anstee, Q. M., & Day, C. P. (2013) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/nrgastro.2013.182 · Cited by 343
TSGene: a web resource for tumor suppressor genes
Zhao, M., Sun, J., & Zhao, Z. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks937 · Cited by 328
Trans-eQTLs Reveal That Independent Genetic Variants Associated with a Complex Phenotype Converge on Intermediate Genes, with a Major Role for the HLA
Fehrmann, R. S. N., Jansen, R. C., Veldink, J. H., Westra, H.-J., Arends, D., Bonder, M. J., Fu, J., Deelen, P., Groen, H. J. M., Smolonska, A., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002197 · Cited by 334
Junk DNA and the long non-coding RNA twist in cancer genetics
Ling, H. Y., Vincent, K., Pichler, M., Fodde, R., Berindan‐Neagoe, I., Slack, F. J., & Călin, G. A. (2015) · Oncogene · doi:10.1038/onc.2014.456 · Cited by 335
APOL1 Risk Variants Are Strongly Associated with HIV-Associated Nephropathy in Black South Africans
Kasembeli, A., Duarte, R. A. B., Ramsay, M., Mosiane, P., Dickens, C., Dix‐Peek, T., Limou, S., Sezgın, E., Nelson, G. W., Fogo, A. B., et al. (2015) · Journal of the American Society of Nephrology · doi:10.1681/asn.2014050469 · Cited by 334
Statistical Power to Detect Genetic (Co)Variance of Complex Traits Using SNP Data in Unrelated Samples
Visscher, P. M., Hemani, G., Vinkhuyzen, A. A. E., Chen, G., Lee, S., Wray, N. R., Goddard, M. E., & Yang, J. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004269 · Cited by 325
Genome-wide analysis identifies 12 loci influencing human reproductive behavior
Barban, N., Study, L. C., Jansen, R., de Vlaming, R., Vaez, A., Mandemakers, J. J., Tropf, F. C., Shen, X., Wilson, J. F., Chasman, D. I., et al. (2016) · Nature Genetics · doi:10.1038/ng.3698 · Cited by 324
The False-positive to False-negative Ratio in Epidemiologic Studies
Ioannidis, J. P. A., Tarone, R., & McLaughlin, J. K. (2011) · Epidemiology · doi:10.1097/ede.0b013e31821b506e · Cited by 321
Genetic Control of Chromatin States in Humans Involves Local and Distal Chromosomal Interactions
Grubert, F., Zaugg, J. B., Kasowski, M., Ursu, O., Spacek, D. V., Martin, A. R., Greenside, P., Srivas, R., Phanstiel, D. H., Pekowska, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.07.048 · Cited by 319
Using genomics to characterize evolutionary potential for conservation of wild populations
Harrisson, K. A., Pavlova, A., Telonis‐Scott, M., & Sunnucks, P. (2014) · Evolutionary Applications · doi:10.1111/eva.12149 · Cited by 321
Epigenetics and Metabolism
Keating, S. T., & El‐Osta, A. (2015) · Circulation Research · doi:10.1161/circresaha.116.303936 · Cited by 324
Evaluating historical candidate genes for schizophrenia
Farrell, M. S., Werge, T., Sklar, P., Owen, M. J., Ophoff, R. A., O'Donovan, M. C., Corvin, A., Cichon, S., & Sullivan, P. F. (2015) · Molecular Psychiatry · doi:10.1038/mp.2015.16 · Cited by 315
Systematic dissection of regulatory motifs in 2000 predicted human enhancers using a massively parallel reporter assay
Kheradpour, P., Ernst, J., Melnikov, A., Rogov, P., Wang, L., Zhang, X., Alston, J., Mikkelsen, T. S., & Kellis, M. (2013) · Genome Research · doi:10.1101/gr.144899.112 · Cited by 315
C-reactive protein levels and body mass index: elucidating direction of causation through reciprocal Mendelian randomization
Timpson, N. J., Nordestgaard, B. G., Harbord, R. M., Zacho, J., Frayling, T. M., Tybjærg-Hansen, A., & Davey Smith, G. (2011) · International Journal of Obesity · doi:10.1038/ijo.2010.137 · Cited by 310
SNiPA: an interactive, genetic variant-centered annotation browser
Arnold, M., Raffler, J., Pfeufer, A., Suhre, K., & Kastenmüller, G. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu779 · Cited by 309
Integrative eQTL-Based Analyses Reveal the Biology of Breast Cancer Risk Loci
Li, Q., Seo, J.-H., Stranger, B. E., McKenna, A., Pe’er, I., LaFramboise, T., Brown, M. A., Tyekucheva, S., & Freedman, M. L. (2013) · Cell · doi:10.1016/j.cell.2012.12.034 · Cited by 313
Genome-Wide Association Study of Coronary Heart Disease and Its Risk Factors in 8,090 African Americans: The NHLBI CARe Project
Lettre, G., Palmer, C. D., Young, T., Ejebe, K. G., Allayee, H., Benjamin, E. J., Bennett, F., Bowden, D. W., Chakravarti, A., Dreisbach, A., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1001300 · Cited by 272
A Human Genome Structural Variation Sequencing Resource Reveals Insights into Mutational Mechanisms
Kidd, J. M., Graves, T., Newman, T. L., Fulton, R. S., Hayden, H. S., Malig, M., Kallicki, J., Kaul, R., Wilson, R. K., & Eichler, E. E. (2010) · Cell · doi:10.1016/j.cell.2010.10.027 · Cited by 303
Genotyping‐by‐sequencing approaches to characterize crop genomes: choosing the right tool for the right application
Scheben, A., Batley, J., & Edwards, D. (2016) · Plant Biotechnology Journal · doi:10.1111/pbi.12645 · Cited by 323
Low-coverage sequencing: Implications for design of complex trait association studiesPreprint
Li, Y., Sidore, C., Kang, H. M., Boehnke, M., & Abecasis, G. R. (2011) · Genome Research · doi:10.1101/gr.117259.110 · Cited by 313
Extremely low-coverage sequencing and imputation increases power for genome-wide association studies
Pasaniuc, B., Rohland, N., McLaren, P. J., Garimella, K., Zaitlen, N., Li, H., Gupta, N., Neale, B. M., Daly, M. J., Sklar, P., et al. (2012) · Nature Genetics · doi:10.1038/ng.2283 · Cited by 299
Effect of natural genetic variation on enhancer selection and function
Heinz, S., Romanoski, C. E., Benner, C., Allison, K. A., Kaikkonen, M. U., Orozco, L. D., & Glass, C. K. (2013) · Nature · doi:10.1038/nature12615 · Cited by 298
The Role of Genetics in the Etiology of Schizophrenia
Gejman, P. V., Sanders, A. R., & Duan, J. (2010) · Psychiatric Clinics of North America · doi:10.1016/j.psc.2009.12.003 · Cited by 312
Genetics of Nonsyndromic Orofacial Clefts
Rahimov, F., Jugessur, A., & Murray, J. C. (2011) · The Cleft Palate-Craniofacial Journal · doi:10.1597/10-178 · Cited by 305
Genome-wide allele-specific analysis: insights into regulatory variation
Pastinen, T. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2815 · Cited by 291
Ensembl's 10th year
Flicek, P., Aken, B. L., Ballester, B., Beal, K. M., Bragin, E., Brent, S., Chen, Y., Clapham, P., Coates, G. M. P., Fairley, S., et al. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp972 · Cited by 292
Meta-analysis of Genome-wide Association Studies Identifies 1q22 as a Susceptibility Locus for Intracerebral Hemorrhage
Woo, D., Falcone, G. J., Devan, W. J., Brown, W. M., Biffi, A., Howard, T. D., Anderson, C. D., Brouwers, H. B., Valant, V., Battey, T. W. K., et al. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.02.012 · Cited by 290
All SNPs Are Not Created Equal: Genome-Wide Association Studies Reveal a Consistent Pattern of Enrichment among Functionally Annotated SNPs
Schork, A., Thompson, W., Pham, P. H., Torkamani, A., Roddey, J., Sullivan, P., Kelsoe, J., O'Donovan, M., Furberg, H., Schork, N., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003449 · Cited by 320
Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases
So, H.-C., Gui, A. H. S., Cherny, S. S., & Sham, P. C. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20579 · Cited by 289
What should the genome-wide significance threshold be? Empirical replication of borderline genetic associations
Panagiotou, O. A., Ioannidis, J. P. A., & for the Genome-Wide Significance Project (2012) · International Journal of Epidemiology · doi:10.1093/ije/dyr178 · Cited by 286
Genetics of Sudden Cardiac Death
Bezzina, C. R., Lahrouchi, N., & Priori, S. G. (2015) · Circulation Research · doi:10.1161/circresaha.116.304030 · Cited by 292
The Psychiatric GWAS Consortium: Big Science Comes to Psychiatry
Sullivan, P. F. (2010) · Neuron · doi:10.1016/j.neuron.2010.10.003 · Cited by 285
High-resolution interrogation of functional elements in the noncoding genome
Sanjana, N. E., Wright, J. B., Zheng, K., Shalem, O., Fontanillas, P., Joung, J., Cheng, C. S., Regev, A., & Zhang, F. (2016) · Science · doi:10.1126/science.aaf7613 · Cited by 290
dmGWAS: dense module searching for genome-wide association studies in protein–protein interaction networks
Jia, P., Zheng, S., Long, J., Zheng, W., & Zhao, Z. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq615 · Cited by 286
A Liver-Enriched Long Non-Coding RNA, lncLSTR, Regulates Systemic Lipid Metabolism in Mice
Li, P., Ruan, X., Yang, L., Kiesewetter, K., Zhao, Y. C., Luo, H., Chen, Y., Guček, M., Zhu, J., & Cao, H. (2015) · Cell Metabolism · doi:10.1016/j.cmet.2015.02.004 · Cited by 285
Human Disease-Associated Genetic Variation Impacts Large Intergenic Non-Coding RNA Expression
Kumar, V., Westra, H.-J., Karjalainen, J. M., Zhernakova, D. V., Esko, T., Hrdličková, B., de Almeida, R., Zhernakova, A., Reinmaa, E., Võsa, U., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003201 · Cited by 279
Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets
Leung, M. K. K., Delong, A., Alipanahi, B., & Frey, B. J. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2494198 · Cited by 284
Unravelling the human genome–phenome relationship using phenome-wide association studies
Bush, W. S., Oetjens, M. T., & Crawford, D. C. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2015.36 · Cited by 269
De novo mutations in regulatory elements in neurodevelopmental disorders
Short, P. J., McRae, J. F., Gallone, G., Sifrim, A., Won, H., Geschwind, D. H., Wright, C. F., Firth, H. V., FitzPatrick, D. R., Barrett, J. C., et al. (2018) · Nature · doi:10.1038/nature25983 · Cited by 274
Decoding enhancers using massively parallel reporter assays
Inoue, F., & Ahituv, N. (2015) · Genomics · doi:10.1016/j.ygeno.2015.06.005 · Cited by 272
Genetically Predicted Body Mass Index and Breast Cancer Risk: Mendelian Randomization Analyses of Data from 145,000 Women of European Descent
Guo, Y. P., Andersen, S. W., Shu, X., Michailidou, K., Bolla, M. K., Wang, Q., García‐Closas, M., Milne, R. L., Schmidt, M. K., Chang-Claude, J. C., et al. (2016) · PLoS Medicine · doi:10.1371/journal.pmed.1002105 · Cited by 276
Genetic susceptibility to periodontitis
Laine, M. L., Wim Crielaard, A., & Loos, B. G. (2011) · Periodontology 2000 · doi:10.1111/j.1600-0757.2011.00415.x · Cited by 272
Family-based designs for genome-wide association studies
Ott, J., Kamatani, Y., & Lathrop, M. G. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2989 · Cited by 278
Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins
Postmus, I., Trompet, S., Deshmukh, H., Barnes, M. R., Li, X., Warren, H. R., Chasman, D. I., Zhou, K., Arsenault, B. J., Donnelly, L. A., et al. (2014) · Nature Communications · doi:10.1038/ncomms6068 · Cited by 268
Genetic rescue: A critique of the evidence supports maximizing genetic diversity rather than minimizing the introduction of putatively harmful genetic variation
Ralls, K., Sunnucks, P., Lacy, R. C., & Frankham, R. (2020) · Biological Conservation · doi:10.1016/j.biocon.2020.108784 · Cited by 267
Pancreatic cancer risk variant in LINC00673 creates a miR-1231 binding site and interferes with PTPN11 degradation
Zheng, J., Huang, X., Tan, W., Yu, D., Du, Z., Chang, J., Wei, L., Han, Y., Wang, C., Che, X., et al. (2016) · Nature Genetics · doi:10.1038/ng.3568 · Cited by 264
Unbiased analysis of potential targets of breast cancer susceptibility loci by Capture Hi-CPreprint
Dryden, N. H., Broome, L. R., Dudbridge, F., Johnson, N., Orr, N., Schoenfelder, S., Nagano, T., Andrews, S., Wingett, S., Kozarewa, I., et al. (2014) · Genome Research · doi:10.1101/gr.175034.114 · Cited by 261
The Genotype-Tissue Expression (GTEx) project
Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., et al. (2013) · Nature Genetics · doi:10.1038/ng.2653 · Cited by 263
Generalization and Dilution of Association Results from European GWAS in Populations of Non-European Ancestry: The PAGE Study
Carlson, C. S., Matise, T. C., North, K. E., Haiman, C. A., Fesinmeyer, M. D., Buyske, S. G., Schumacher, F. R., Peters, U., Franceschini, N., Ritchie, M. D., et al. (2013) · PLoS Biology · doi:10.1371/journal.pbio.1001661 · Cited by 267
Self-citation
Isotope-targeted glycoproteomics (IsoTaG): a mass-independent platform for intact N- and O-glycopeptide discovery and analysis
Woo, C. M., Iavarone, A. T., Spiciarich, D. R., Palaniappan, K. K., & Bertozzi, C. R. (2015) · Nature Methods · doi:10.1038/nmeth.3366 · Cited by 259
Genome-wide association study indicates two novel resistance loci for severe malaria
Timmann, C., Thye, T., Vens, M., Evans, J., May, J., Ehmen, C., Sievertsen, J., Muntau, B., Ruge, G., Loag, W., et al. (2012) · Nature · doi:10.1038/nature11334 · Cited by 260
Bioinformatics challenges for personalized medicine
Fernald, G. H., Capriotti, E., Daneshjou, R., Karczewski, K. J., & Altman, R. B. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr295 · Cited by 268
Childhood intelligence is heritable, highly polygenic and associated with FNBP1L
Benyamin, B., Pourcain, B., Davis, O. S. P., Davies, G., Hansell, N. K., Brion, M., Kirkpatrick, R. M., Cents, R. A. M., Franić, S., Miller, M. B., et al. (2013) · Molecular Psychiatry · doi:10.1038/mp.2012.184 · Cited by 256
A High-Resolution Enhancer Atlas of the Developing Telencephalon
Visel, A., Taher, L., Girgis, H., May, D., Golonzhka, O., Hoch, R. V., McKinsey, G. L., Pattabiraman, K., Silberberg, S. N., Blow, M. J., et al. (2013) · Cell · doi:10.1016/j.cell.2012.12.041 · Cited by 257
Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets
Vinayagam, A., Gibson, T. E., Lee, H., Yilmazel, B., Roesel, C. L., Hu, Y., Kwon, Y. V., Sharma, A., Liu, Y., Perrimon, N., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1603992113 · Cited by 255
Sequencing studies in human genetics: design and interpretation
Goldstein, D. B., Allen, A. S., Keebler, J., Margulies, E. H., Petrou, S., Petrovski, S., & Sunyaev, S. R. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3455 · Cited by 262
GRASP: analysis of genotype–phenotype results from 1390 genome-wide association studies and corresponding open access database
Leslie, R., O’Donnell, C. J., & Johnson, A. D. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu273 · Cited by 254
Molecular genetics of human obesity: A comprehensive review
Singh, R. K., Kumar, P., & Mahalingam, K. (2017) · Comptes Rendus Biologies · doi:10.1016/j.crvi.2016.11.007 · Cited by 272
Functional annotations of three domestic animal genomes provide vital resources for comparative and agricultural research
Kern, C., Wang, Y., Xu, X., Pan, Z., Halstead, M. M., Chanthavixay, G., Saelao, P., Waters, S., Xiang, R., Chamberlain, A. J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22100-8 · Cited by 252
SNVer: a statistical tool for variant calling in analysis of pooled or individual next-generation sequencing data
Wei, Z., Wang, W., Hu, P., Lyon, G. J., & Hakonarson, H. H. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr599 · Cited by 258
Metabonomic, transcriptomic, and genomic variation of a population cohort
Inouye, M., Kettunen, J., Soininen, P., Silander, K., Ripatti, S., Kumpula, L. S., Hämäläinen, E., Jousilahti, P., Kangas, A. J., Männistö, S., et al. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.93 · Cited by 250
Large-Scale Gene-Centric Meta-analysis across 32 Studies Identifies Multiple Lipid Loci
Asselbergs, F. W., Guo, Y., van Iperen, E. P. A., Sivapalaratnam, S., Tragante, V., Lanktree, M. B., Lange, L. A., Almoguera, B., Appelman, Y., Barnard, J., et al. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.08.032 · Cited by 253
Variants Near FOXE1 Are Associated with Hypothyroidism and Other Thyroid Conditions: Using Electronic Medical Records for Genome- and Phenome-wide Studies
Denny, J. C., Crawford, D. C., Ritchie, M. D., Bielinski, S. J., Basford, M., Bradford, Y., Chai, H. S., Bastarache, L. A., Zuvich, R. L., Peissig, P. L., et al. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.09.008 · Cited by 252
Self-citation
Sex differences in the genome-wide DNA methylation pattern and impact on gene expression, microRNA levels and insulin secretion in human pancreatic islets
Hall, E., Volkov, P., Dayeh, T., Esguerra, J. L. S., Salö, S., Eliasson, L., Rönn, T., Bacos, K., & Ling, C. (2014) · Genome Biology · doi:10.1186/s13059-014-0522-z · Cited by 249
Genome-Wide Association Study Identifies Loci for Body Composition and Structural Soundness Traits in Pigs
Fan, B., Onteru, S. K., Du, Z., Garrick, D. J., Stalder, K. J., & Rothschild, M. F. (2011) · PLoS ONE · doi:10.1371/journal.pone.0014726 · Cited by 248
A long noncoding RNA associated with susceptibility to celiac disease
Castellanos–Rubio, A., Fernández‐Jiménez, N., Kratchmarov, R., Luo, X., Bhagat, G., Green, P. H. R., Schneider, R. J., Kiledjian, M., Bilbao, J. R., & Ghosh, S. (2016) · Science · doi:10.1126/science.aad0467 · Cited by 245
Genetic determinants of common obesity and their value in prediction
Loos, R. J. F. (2012) · Best Practice & Research Clinical Endocrinology & Metabolism · doi:10.1016/j.beem.2011.11.003 · Cited by 256
The polymorphism rs944289 predisposes to papillary thyroid carcinoma through a large intergenic noncoding RNA gene of tumor suppressor type
Jendrzejewski, J., He, H., Radomska, H. S., Li, W., Tomšič, J., Liyanarachchi, S., Davuluri, R. V., Nagy, R. S., & de la Chapelle, A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1205654109 · Cited by 253

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)
ORCID: Hindorff, Lucia A.
Sethupathy, Praveen
Ramos, Erin M.
Collins, Francis S.
Manolio, Teri A.
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
DOI: 10.1073/pnas.0903103106
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

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

Potential etiologic and functional implications of genome-wide association loci for human diseases and traits

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks