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Polygenic risk scores (PRS) and transcriptional risk scores (TRS) may offer accessible alternatives, but their combined predictive performance remains understudied.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>\n                      We calculated PRS and TRS using genome\u2010wide genotype and blood transcriptome data from two ancestrally distinct cohorts: Alzheimer's Disease Neuroimaging Initiative (ADNI,\n                      <jats:italic>N<\/jats:italic>\n                      \u00a0=\u00a0313) and Seoul National University Bundang Hospital (SNUBH,\n                      <jats:italic>N<\/jats:italic>\n                      \u00a0=\u00a0173). Logistic regression and machine learning models assessed associations of PRS and TRS with AD and AD classification performance.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>Individuals with high PRS and TRS values showed larger odds ratios for AD, 2.5\u2010fold in ADNI and 3.4\u2010fold in SNUBH, compared to those with low PRS and TRS values. The integrated PRS\u2013TRS model achieved better classification performance (area under the curve [AUC] 0.705) than the PRS model (AUC 0.635).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>Integrating static genetic and dynamic transcriptomic information from blood improves early detection of AD across diverse populations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/alz.71408","type":"journal-article","created":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:59:03Z","timestamp":1777892343000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integrating polygenic and transcriptional risk scores for detecting Alzheimer's disease"],"prefix":"10.1002","volume":"22","author":[{"given":"Jiyun","family":"Hwang","sequence":"first","affiliation":[{"name":"Genome and Health Big Data Laboratory, Graduate School of Public Health Seoul National University  Seoul Republic of Korea"},{"name":"Department of Neurology Seoul National University Bundang Hospital and Seoul National University College of Medicine  Seongnam\u2010si Gyeonggi\u2010do Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jung\u2010Min","family":"Pyun","sequence":"additional","affiliation":[{"name":"Department of Neurology Seoul National University Bundang Hospital and Seoul National University College of Medicine  Seongnam\u2010si Gyeonggi\u2010do Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joo\u2010Yeon","family":"Lee","sequence":"additional","affiliation":[{"name":"Genome and Health Big Data Laboratory, Graduate School of Public Health Seoul National University  Seoul Republic of Korea"},{"name":"Institute of Health and Environments Seoul National University  Seoul Republic of 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