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The massive genetic data, such as SNPs from genome\u2010wide association studies (GWAS), together with well\u2010characterized time\u2010to\u2010event phenotypes provide unprecedented opportunities for developing effective survival prediction models. Recent advances in deep learning have made extraordinary achievements in establishing powerful prediction models in the biomedical field. However, the applications of deep learning approaches in survival prediction are limited, especially with utilizing the wealthy GWAS data. Motivated by developing powerful prediction models for the progression of an eye disease, age\u2010related macular degeneration (AMD), we develop and implement a multilayer deep neural network (DNN) survival model to effectively extract features and make accurate and interpretable predictions. Various simulation studies are performed to compare the prediction performance of the DNN survival model with several other machine learning\u2010based survival models. Finally, using the GWAS data from two large\u2010scale randomized clinical trials in AMD with over 7800 observations, we show that the DNN survival model not only outperforms several existing survival prediction models in terms of prediction accuracy (eg, c\u2010index<jats:styled-content>=0.76<\/jats:styled-content>), but also successfully detects clinically meaningful risk subgroups by effectively learning the complex structures among genetic variants. Moreover, we obtain a subject\u2010specific importance measure for each predictor from the DNN survival model, which provides valuable insights into the personalized early prevention and clinical management for this disease.<\/jats:p>","DOI":"10.1002\/sim.8743","type":"journal-article","created":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T07:35:56Z","timestamp":1601019356000},"page":"4605-4620","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Genome\u2010wide association study\u2010based deep learning for survival prediction"],"prefix":"10.1002","volume":"39","author":[{"given":"Tao","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Biostatistics University of Pittsburgh Pittsburgh Pennsylvania USA"},{"name":"Center for Applied Statistics and School of Statistics Renmin University of China Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Biostatistics University of Pittsburgh Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Biostatistics University of Pittsburgh Pittsburgh Pennsylvania USA"},{"name":"Department of Pediatrics University of Pittsburgh Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1352-1000","authenticated-orcid":false,"given":"Ying","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Biostatistics University of Pittsburgh Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,9,24]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nm.2323"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1245\/s10434-017-6024-y"},{"key":"e_1_2_8_4_1","unstructured":"ChiCL StreetWN WolbergWH. 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