Genome-Wide Association Study
"Genome-Wide Association Study" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus,
MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure,
which enables searching at various levels of specificity.
An analysis comparing the allele frequencies of all available (or a whole GENOME representative set of) polymorphic markers in unrelated patients with a specific symptom or disease condition, and those of healthy controls to identify markers associated with a specific disease or condition.
| Descriptor ID |
D055106
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| MeSH Number(s) |
E05.318.416.249 E05.318.780.392 E05.393.385.500 E05.393.522.500 E05.393.760.640.500 N06.850.520.445.392 N06.850.520.470.500
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| Concept/Terms |
Genome-Wide Association Study- Genome-Wide Association Study
- Association Studies, Genome-Wide
- Association Study, Genome-Wide
- Genome-Wide Association Studies
- Studies, Genome-Wide Association
- Study, Genome-Wide Association
- Genome Wide Association Scan
- Genome Wide Association Studies
- GWA Study
- GWA Studies
- Studies, GWA
- Study, GWA
- Whole Genome Association Analysis
- Whole Genome Association Study
- Genome Wide Association Analysis
- Genome Wide Association Study
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Below are MeSH descriptors whose meaning is more general than "Genome-Wide Association Study".
Below are MeSH descriptors whose meaning is more specific than "Genome-Wide Association Study".
This graph shows the total number of publications written about "Genome-Wide Association Study" by people in this website by year, and whether "Genome-Wide Association Study" was a major or minor topic of these publications.
To see the data from this visualization as text, click here.
| Year | Major Topic | Minor Topic | Total |
|---|
| 2005 | 1 | 0 | 1 | | 2008 | 1 | 2 | 3 | | 2009 | 14 | 13 | 27 | | 2010 | 16 | 30 | 46 | | 2011 | 13 | 27 | 40 | | 2012 | 16 | 32 | 48 | | 2013 | 18 | 29 | 47 | | 2014 | 32 | 44 | 76 | | 2015 | 14 | 34 | 48 | | 2016 | 22 | 34 | 56 | | 2017 | 23 | 41 | 64 | | 2018 | 14 | 44 | 58 | | 2019 | 18 | 65 | 83 | | 2020 | 15 | 29 | 44 | | 2021 | 30 | 39 | 69 | | 2022 | 42 | 39 | 81 | | 2023 | 20 | 42 | 62 | | 2024 | 27 | 30 | 57 | | 2025 | 14 | 50 | 64 | | 2026 | 7 | 7 | 14 |
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Below are the most recent publications written about "Genome-Wide Association Study" by people in Profiles.
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Purdy AL, Bakhshian Nik A, Arkatkar AA, Hasan P, Flinn MA, Choudhury P, Wood C, Takizawa A, Malloy L, Tutaj M, Drysdale TA, Bridgewater D, Link BA, Plageman TF, Kwitek AE, Dwinell MR, Saba LM, O'Meara CC, Patterson M. Genome-wide association mapping and targeted loss of function studies identify Shroom3 as a driver of hyperpolyploidy and ventricular dilation. Proc Natl Acad Sci U S A. 2026 Jun 02; 123(22):e2522068123.
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Moolhuijsen LME, Zhu J, Mullin BH, Pujol-Gualdo N, Actkins KV, Mack JA, Rao H, Trivedi B, Kentistou KA, Zhao Y, Westergaard D, Tyrmi JS, Thorleifsson G, Zhang Y, Wittemans L, DeVries A, Brewer K, Sisk R, Danning R, Preuss MH, Jones MR, Ruth KS, Andersen M, Azziz R, Banasik K, Boehnke M, Broer L, Brunak S, Chan YM, Chasman DI, Daly M, Ehrmann DA, Fauser BC, Fritsche LG, Hayes MG, He C, Huang H, Kowalska I, Kraft P, Legro RS, Lin N, Loos RJ, Louwers YV, Magi R, McCarthy MI, Morin-Papunen L, Morrison JV, Morton C, Nadkarni GN, Neale BM, Nielsen HS, Nyegaard M, Ostrowski SR, Pedersen OBV, Sørensen E, Mikkelsen C, Erikstrup C, Kaspersen KA, Bruun MT, Aagaard B, Ullum H, Obermayer-Pietsch B, Palotie A, Reeve MP, Salumets A, Saxena R, Spector TD, Stuckey BGA, Thorsteinsdottir U, Uitterlinden AG, Urbanek M, Zöllner S, van Heel DA, Hirschhorn JN, Stefansson K, Perry JRB, Styrkarsdottir U, Wilson SG, Piltonen T, Laisk T, Jarvelin MR, Burns K, Justice AE, Laivuori H, Ong KK, Goodarzi MO, Davis LK, Dunaif A, Lindgren CM, Laven JSE, Franks S, Visser JA, Welt CK, Karaderi T, Day FR. Genomic analyses implicate hormonal and metabolic dysregulation in polycystic ovary syndrome. Nat Genet. 2026 May; 58(5):1040-1050.
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Weisburd B, Dolzhenko E, Bennett MF, Danzi MC, Xu IRL, Tanudisastro H, Gu B, English A, Hiatt L, Mokveld T, De Sena Brandine G, Chiu R, Kurtas NE, Jam HZ, Brand H, Rajan-Babu IS, Bahlo M, Chaisson MJP, Züchner S, Gymrek M, Dashnow H, Eberle MA, Rehm HL. Defining a tandem repeat catalog and variation clusters for genome-wide analyses and population databases. Am J Hum Genet. 2026 May 07; 113(5):915-928.
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Hu X, Araujo DS, Khunsriraksakul C, Wang L, Sun Q, Wen J, Zhou L, Ekunwe L, Lange LA, Lange EM, Montgomery SB, Reiner AP, Aguet F, Ardlie KG, Lappalainen T, Gignoux CR, Burchard EG, Taylor KD, Guo X, Rotter JI, Rich SS, Cornell E, Durda P, Tracy RP, Liu Y, Johnson WC, Papanicolaou GP, Perera MA, Cho MH, Liu DJ, Raffield LM, Li Y, Wheeler HE, Im HK, Manichaikul A. Multi-ancestry transcriptome prediction with functionally informed variants in TOPMed MESA improves performance of transcriptome-wide association studies. Am J Hum Genet. 2026 04 02; 113(4):828-841.
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Evans LM, Arehart CH, Gibson RA, Bowman GI, Gignoux CR. A simple approach for multiple observations improves power to detect genetic effects and genomic prediction accuracy. HGG Adv. 2026 Apr 09; 7(2):100586.
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Shemirani R, Belbin GM, Cullina S, Caggiano C, Gignoux CR, Zaitlen N, Kenny EE. A spectral component approach leveraging identity-by-descent graphs to address recent population structure in genomic analysis. Genome Res. 2026 03 02; 36(3):534-546.
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Schneider K, Walker S, Gignoux C, Layer R. STABIX: summary-statistic-based GWAS indexing and compression. Bioinformatics. 2026 Feb 28; 42(3).
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Lawrence JM, Foote IF, Breunig S, Schaffer LS, Lyons S, Abramowitz SA, Levin MG, Damrauer SM, Mallard TT, Grotzinger AD. Shared Genetic Liability across Systems of Psychiatric and Physical Illness. Nat Commun. 2026 Feb 21; 17(1).
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Zhang C, Konigsberg IR, He Y, Zhang J, Chikowore T, Feldman WB, Hu X, Ding Y, Pasaniuc B, Chang D, Chen Q, Lasky-Su JA, Hecker J, Tobin MD, Chen J, Kalra S, Pratte KA, Im HK, Wan ES, Manichaikul A, Silverman EK, Bowler RP, Lange LA, Ortega VE, Martin AR, Cho MH, Moll MR. Multi-trait polygenic scores for COPD and COPD exacerbations implicate druggable proteins. JCI Insight. 2026 Apr 08; 11(7).
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Penaranda C, Brenner EP, Clatworthy AE, Cosimi LA, Ravi J, Hung DT. Genomic comparison and phenotypic characterization of Pseudomonas aeruginosa isolates across environmental and diverse clinical isolation sites. mSystems. 2026 Mar 24; 11(3):e0136225.
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