{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T17:18:27Z","timestamp":1789233507640,"version":"build-2803163510"},"reference-count":53,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T00:00:00Z","timestamp":1646697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Depression is a common, though heterogenous, comorbidity in late-onset Alzheimer\u2019s Disease (LOAD) patients. In addition, individuals with depression are at greater risk to develop LOAD. In previous work, we demonstrated shared genetic etiology between depression and LOAD. Collectively, these previous studies suggested interactions between depression and LOAD. However, the underpinning genetic heterogeneity of depression co-occurrence with LOAD, and the various genetic etiologies predisposing depression in LOAD, are largely unknown.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Major Depressive Disorder (MDD) genome-wide association study (GWAS) summary statistics were used to create polygenic risk scores (PRS). The Religious Orders Society and Rush Memory and Aging Project (ROSMAP, <jats:italic>n<\/jats:italic> = 1,708) and National Alzheimer\u2019s Coordinating Center (NACC, <jats:italic>n<\/jats:italic> = 10,256) datasets served as discovery and validation cohorts, respectively, to assess the PRS performance in predicting depression onset in LOAD patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The PRS showed marginal results in standalone models for predicting depression onset in both ROSMAP (AUC = 0.540) and NACC (AUC = 0.527). Full models, with baseline age, sex, education, and <jats:italic>APOE\u03b54<\/jats:italic> allele count, showed improved prediction of depression onset (ROSMAP AUC: 0.606, NACC AUC: 0.581). In time-to-event analysis, standalone PRS models showed significant effects in ROSMAP (<jats:italic>P<\/jats:italic> = 0.0051), but not in NACC cohort. Full models showed significant performance in predicting depression in LOAD for both datasets (<jats:italic>P<\/jats:italic> &amp;lt; 0.001 for all).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study provided new insights into the genetic factors contributing to depression onset in LOAD and advanced our knowledge of the genetics underlying the heterogeneity of depression in LOAD. The developed PRS accurately predicted LOAD patients with depressive symptoms, thus, has clinical implications including, diagnosis of LOAD patients at high-risk to develop depression for early anti-depressant treatment.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnins.2022.827447","type":"journal-article","created":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T06:15:18Z","timestamp":1646720118000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Polygenic Risk Score Effectively Predicts Depression Onset in Alzheimer\u2019s Disease Based on Major Depressive Disorder Risk Variants"],"prefix":"10.3389","volume":"16","author":[{"given":"Suraj","family":"Upadhya","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongliang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael W.","family":"Lutz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ornit","family":"Chiba-Falek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,3,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1093\/braincomms\/fcz047","article-title":"A comprehensive analysis of methods for assessing polygenic burden on Alzheimer\u2019s disease pathology and risk beyond APOE.","volume":"2","author":"Altmann","year":"2020","journal-title":"Brain Commun."},{"key":"B2","year":"1987","journal-title":"Diagnostic and Statistical Manual of Mental Disorders"},{"key":"B3","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1002\/gps.5418","article-title":"Alzheimer\u2019s disease biomarkers as predictors of trajectories of depression and apathy in cognitively normal individuals, mild cognitive impairment, and Alzheimer\u2019s disease dementia.","volume":"36","author":"Banning","year":"2021","journal-title":"Int. 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