{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T01:19:46Z","timestamp":1789348786307,"version":"build-2803163510"},"reference-count":53,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:00:00Z","timestamp":1688601600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01 AG057195"],"award-info":[{"award-number":["U01 AG057195"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Alzheimer's &amp;amp; Dementia"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>BACKGROUND<\/jats:title>\n                    <jats:p>Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre\u2010symptomatic risk assessment but also for building personalized therapeutic strategies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>We implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD\u2010risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>\n                      Rs561311966 (\n                      <jats:italic>APOC1<\/jats:italic>\n                      ) and rs2229918 (\n                      <jats:italic>ERCC1\/CD3EAP<\/jats:italic>\n                      ) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD\u2010risk SNPs were significant predictors of AD progression.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>The model successfully estimated the contribution of AD\u2010risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/alz.13319","type":"journal-article","created":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T07:03:26Z","timestamp":1688627006000},"page":"5690-5699","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression"],"prefix":"10.1002","volume":"19","author":[{"given":"Jinhyeong","family":"Bae","sequence":"first","affiliation":[{"name":"Department of Neurology, School of Medicine Indiana University School of Medicine  Indianapolis Indiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paige E.","family":"Logan","sequence":"additional","affiliation":[{"name":"Department of Neurology, School of Medicine Indiana University School of Medicine  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