{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T05:18:55Z","timestamp":1785388735466,"version":"3.55.0"},"reference-count":34,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2020,8,2]],"date-time":"2020-08-02T00:00:00Z","timestamp":1596326400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["DFG HE 3690\/5\u20101"],"award-info":[{"award-number":["DFG HE 3690\/5\u20101"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["NIH R01 EY RES 511967"],"award-info":[{"award-number":["NIH R01 EY RES 511967"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Genetic Epidemiology"],"published-print":{"date-parts":[[2020,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Imaging technology and machine learning algorithms for disease classification set the stage for high\u2010throughput phenotyping and promising new avenues for genome\u2010wide association studies (GWAS). Despite emerging algorithms, there has been no successful application in GWAS so far. We establish machine learning\u2010based phenotyping in genetic association analysis as misclassification problem. To evaluate chances and challenges, we performed a GWAS based on automatically classified age\u2010related macular degeneration (AMD) in UK Biobank (images from 135,500 eyes; 68,400 persons). We quantified misclassification of automatically derived AMD in internal validation data (4,001 eyes; 2,013 persons) and developed a maximum likelihood approach (MLA) to account for it when estimating genetic association. We demonstrate that our MLA guards against bias and artifacts in simulation studies. By combining a GWAS on automatically derived AMD and our MLA in UK Biobank data, we were able to dissect true association (\n                    <jats:italic>ARMS2<\/jats:italic>\n                    \/\n                    <jats:italic>HTRA1<\/jats:italic>\n                    ,\n                    <jats:italic>CFH<\/jats:italic>\n                    ) from artifacts (near\n                    <jats:italic>HERC2<\/jats:italic>\n                    ) and identified eye color as associated with the misclassification. On this example, we provide a proof\u2010of\u2010concept that a GWAS using machine learning\u2010derived disease classification yields relevant results and that misclassification needs to be considered in analysis. These findings generalize to other phenotypes and emphasize the utility of genetic data for understanding misclassification structure of machine learning algorithms.\n                  <\/jats:p>","DOI":"10.1002\/gepi.22336","type":"journal-article","created":{"date-parts":[[2020,8,2]],"date-time":"2020-08-02T08:24:18Z","timestamp":1596356658000},"page":"759-777","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Chances and challenges of machine learning\u2010based disease classification in genetic association studies illustrated on age\u2010related macular degeneration"],"prefix":"10.1002","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6582-1174","authenticated-orcid":false,"given":"Felix","family":"Guenther","sequence":"first","affiliation":[{"name":"Department of Genetic Epidemiology University of Regensburg Regensburg Germany"},{"name":"Statistical Consulting Unit StaBLab, Department of Statistics Ludwig Maximilian University of Munich Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8223-6137","authenticated-orcid":false,"given":"Caroline","family":"Brandl","sequence":"additional","affiliation":[{"name":"Department of Genetic Epidemiology University of Regensburg Regensburg Germany"},{"name":"Department of Ophthalmology University Hospital Regensburg Regensburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0292-5421","authenticated-orcid":false,"given":"Thomas W.","family":"Winkler","sequence":"additional","affiliation":[{"name":"Department of Genetic Epidemiology University of Regensburg Regensburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veronika","family":"Wanner","sequence":"additional","affiliation":[{"name":"Department of Genetic Epidemiology University of Regensburg Regensburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7832-1942","authenticated-orcid":false,"given":"Klaus","family":"Stark","sequence":"additional","affiliation":[{"name":"Department of Genetic Epidemiology University of Regensburg Regensburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6372-2487","authenticated-orcid":false,"given":"Helmut","family":"Kuechenhoff","sequence":"additional","affiliation":[{"name":"Statistical Consulting Unit StaBLab, Department of Statistics Ludwig Maximilian University of Munich Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iris M.","family":"Heid","sequence":"additional","affiliation":[{"name":"Department of Genetic Epidemiology University of Regensburg Regensburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,8,2]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-26629-5"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gky1120"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaophthalmol.2017.3782"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0579-z"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781420010138"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2415-10-31"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.4324\/9780203771587"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-58347-1_1"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1001\/archopht.123.11.1484"},{"key":"e_1_2_13_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006-341X.1999.00997.x"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3448"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2018.02.037"},{"key":"e_1_2_13_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201900039"},{"key":"e_1_2_13_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-4076(98)00015-3"},{"key":"e_1_2_13_16_1","unstructured":"Heinze\u2010Deml C. &Meinshausen N.(2017). 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