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Aging Neurosci."],"abstract":"<jats:p>To better capture the polygenic architecture of Alzheimer\u2019s disease (AD), we developed a joint genetic score, MetaGRS. We incorporated genetic variants for AD and 24 other traits from two independent cohorts, NACC (<jats:italic>n<\/jats:italic> = 3,174, training set) and UPitt (<jats:italic>n<\/jats:italic> = 2,053, validation set). One standard deviation increase in the MetaGRS is associated with about 57% increase in the AD risk [hazard ratio (HR) = 1.577, <jats:italic>p<\/jats:italic> = 7.17 E-56], showing little difference from the HR for AD GRS alone (HR = 1.579, <jats:italic>p<\/jats:italic> = 1.20E-56), suggesting similar utility of both models. We also conducted APOE-stratified analyses to assess the role of the e4 allele on risk prediction. Similar to that of the combined model, our stratified results did not show a considerable improvement of the MetaGRS. Our study showed that the prediction power of the MetaGRS significantly outperformed that of the reference model without any genetic information, but was effectively equivalent to the prediction power of the AD GRS.<\/jats:p>","DOI":"10.3389\/fnagi.2023.1168638","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T15:09:12Z","timestamp":1690470552000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["The prediction of Alzheimer\u2019s disease through multi-trait genetic modeling"],"prefix":"10.3389","volume":"15","author":[{"given":"Kaylyn","family":"Clark","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chia-Lun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pei-Chuan","family":"Ho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wan-Ping","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shin-Yi","family":"Chou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li-San","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jung-Ying","family":"Tzeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,7,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-13848-1","article-title":"Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke.","volume":"10","author":"Abraham","year":"2019","journal-title":"Nat. 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