{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T21:56:52Z","timestamp":1781647012613,"version":"3.54.5"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T00:00:00Z","timestamp":1775174400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"vor","delay-in-days":40,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BioData Mining"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Complex disease genetics is a key area of research for reducing disease and improving human health. Genome-wide association studies (GWAS) help in this research by identifying regions of the genome that contribute to complex disease risk. However, GWAS are computationally intensive and require access to individual-level genetic and health information, which presents concerns about privacy and imposes costs on researchers seeking to study complex diseases. Publicly released pan-biobank GWAS summary statistics provide immediate access to results for a subset of phenotypes, but they do not inform about all phenotypes or hand-crafted phenotype definitions, which are often more relevant to study. Here, we present WebGWAS, a new tool that allows researchers to obtain GWAS summary statistics for a phenotype of interest without needing access to individual-level genetic and phenotypic data. Our public web app can be used to study custom phenotype definitions, including inclusion and exclusion criteria, and to produce approximate GWAS summary statistics for that phenotype. WebGWAS computes approximate GWAS summary statistics very quickly (&lt;10\u2009seconds), and it does not store private health information. We also show how the statistical approximation underlying WebGWAS can be used to accelerate the computation of multi-phenotype GWAS among correlated phenotypes. Our tool provides a faster approach to GWAS for researchers interested in complex disease, providing approximate summary statistics in short order, without the need to collect, process, and produce GWAS results. Overall, this method advances complex disease research by facilitating more accessible and cost-effective genetic studies using large observational data.<\/jats:p>","DOI":"10.1186\/s13040-026-00548-y","type":"journal-article","created":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T02:45:27Z","timestamp":1775184327000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["WebGWAS: a web server for instant GWAS on arbitrary phenotypes"],"prefix":"10.1186","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0539-630X","authenticated-orcid":false,"given":"Michael","family":"Zietz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5892-0852","authenticated-orcid":false,"given":"Undina","family":"Gisladottir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6625-4156","authenticated-orcid":false,"given":"Kathleen LaRow","family":"Brown","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2700-2597","authenticated-orcid":false,"given":"Nicholas P.","family":"Tatonetti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,3]]},"reference":[{"issue":"7726","key":"548_CR1","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41586-018-0579-z","volume":"562","author":"C Bycroft","year":"2018","unstructured":"Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The Uk biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203\u201309","journal-title":"Nature"},{"key":"548_CR2","doi-asserted-by":"crossref","unstructured":"All of Us Research Program Investigators. The all of us research program. NEJM Evid. 2019;381(7):668\u201376","DOI":"10.1056\/NEJMsr1809937"},{"issue":"9","key":"548_CR3","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1038\/s41588-018-0184-y","volume":"50","author":"W Zhou","year":"2018","unstructured":"Zhou W, Nielsen JB, Fritsche LG, Dey R, Gabrielsen ME, Wolford BN, et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat Genet. 2018;50(9):1335\u201341","journal-title":"Nat Genet"},{"key":"548_CR4","unstructured":"Pan UKBB Team at Broad Institute. Pan-UK biobank. http:\/\/pan.ukbb.broadinstitute.org. Accessed 1 Aug 2023."},{"key":"548_CR5","doi-asserted-by":"publisher","first-page":"2408","DOI":"10.1038\/s41588-025-02335-7","volume":"57","author":"KJ Karczewski","year":"2025","unstructured":"Karczewski KJ, Gupta R, Kanai M, et al. Pan-uk biobank genome-wide association analyses enhance discovery and resolution of ancestry-enriched effects. Nat Genet. 2025;57:2408\u201317","journal-title":"Nat Genet"},{"key":"548_CR6","unstructured":"The UK Biobank Whole-Genome Sequencing Consortium. Whole-genome sequencing of 490,640 UK Biobank participants. Accessed: 2026-01-08: https:\/\/www.nature.com\/articles\/s41586-025-09272-9,"},{"issue":"3","key":"548_CR7","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1038\/s41588-018-0047-6","volume":"50","author":"M Kanai","year":"2018","unstructured":"Kanai M, Akiyama M, Takahashi A, Matoba N, Momozawa Y, Ikeda M, et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat Genet. 2018;50(3):390\u2013400","journal-title":"Nat Genet"},{"issue":"7","key":"548_CR8","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1038\/s41588-020-0640-3","volume":"52","author":"K Ishigaki","year":"2020","unstructured":"Ishigaki K, Akiyama M, Kanai M, Takahashi A, Kawakami E, Sugishita H, et al. Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases. Nat Genet. 2020;52(7):669\u201379","journal-title":"Nat Genet"},{"issue":"2","key":"548_CR9","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1038\/s41588-017-0009-4","volume":"50","author":"P Turley","year":"2018","unstructured":"Turley P, Walters RK, Maghzian O, Okbay A, Lee JJ, Fontana MA, et al. Multi-trait analysis of genome-wide association summary statistics using mtag. Nat Genet. 2018;50(2):229\u201337","journal-title":"Nat Genet"},{"issue":"5","key":"548_CR10","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1038\/s41562-019-0566-x","volume":"3","author":"AD Grotzinger","year":"2019","unstructured":"Grotzinger AD, Rhemtulla M, de Vlaming R, Ritchie SJ, Mallard TT, Hill WD, et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat Hum Behaviour. 2019;3(5):513\u201325","journal-title":"Nat Hum Behaviour"},{"issue":"1","key":"548_CR11","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1038\/s41588-020-00754-2","volume":"53","author":"PA Demange","year":"2021","unstructured":"Demange PA, Malanchini M, Mallard TT, Biroli P, Cox SR, Grotzinger AD, et al. Investigating the genetic architecture of noncognitive skills using gwas-by-subtraction. Nat Genet. 2021;53(1):35\u201344","journal-title":"Nat Genet"},{"issue":"17","key":"548_CR12","doi-asserted-by":"publisher","first-page":"2951","DOI":"10.1093\/bioinformatics\/bty197","volume":"34","author":"HV Meyer","year":"2018","unstructured":"Meyer HV, Birney E. Phenotypesimulator: a comprehensive framework for simulating multi-trait, multi-locus genotype to phenotype relationships. Bioinformatics. 2018;34(17):2951\u201356","journal-title":"Bioinformatics"},{"issue":"9","key":"548_CR13","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1093\/bioinformatics\/btq126","volume":"26","author":"JC Denny","year":"2010","unstructured":"Denny JC, Ritchie MD, Basford MA, Pulley JM, Bastarache L, Brown-Gentry K, et al. Phewas: demonstrating the feasibility of a phenome-wide scan to discover gene\u2013disease associations. 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WebGWAS Backend v0.8.0. 2024. https:\/\/github.com\/tatonetti-lab\/webgwas-backend, 2024"},{"issue":"1","key":"548_CR20","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1093\/bioinformatics\/btr599","volume":"28","author":"C Bellenguez","year":"2012","unstructured":"Bellenguez C, Strange A, Freeman C. Wellcome Trust case control consortium, Peter Donnelly, and Chris CA Spencer. A robust clustering algorithm for identifying problematic samples in genome-wide association studies. Bioinformatics. 2012;28(1):134\u201335","journal-title":"Bioinformatics"}],"container-title":["BioData Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13040-026-00548-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-026-00548-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-026-00548-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T21:25:57Z","timestamp":1781645157000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13040-026-00548-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,3]]},"references-count":20,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["548"],"URL":"https:\/\/doi.org\/10.1186\/s13040-026-00548-y","relation":{},"ISSN":["1756-0381"],"issn-type":[{"value":"1756-0381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,3]]},"assertion":[{"value":"4 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Access to the UK Biobank was granted under Approved Research ID 41,039. All participants provided written informed consent to the UK Biobank for use of their de-identified data in health-related research. The study was conducted in accordance with relevant ethical guidelines and regulations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"39"}}