Integrating polygenic and transcriptional risk scores for detecting Alzheimer's disease.
| Title: | Integrating polygenic and transcriptional risk scores for detecting Alzheimer's disease. |
|---|---|
| Authors: | Hwang J; Genome and Health Big Data Laboratory, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea.; Department of Neurology, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea.; Pyun JM; Department of Neurology, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea.; Lee JY; Genome and Health Big Data Laboratory, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea.; Institute of Health and Environments, Seoul National University, Seoul, Republic of Korea.; Park JS; Department of Laboratory Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea.; Bice PJ; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.; Saykin AJ; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.; Sung J; Genome and Health Big Data Laboratory, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea.; Institute of Health and Environments, Seoul National University, Seoul, Republic of Korea.; Kim S; Department of Neurology, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea.; Park YH; Department of Neurology, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea.; Nho K; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA. |
| Corporate Authors: | Alzheimer's Disease Neuroimaging Initiative |
| Source: | Alzheimer's & dementia : the journal of the Alzheimer's Association [Alzheimers Dement] 2026 May; Vol. 22 (5), pp. e71408. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: John Wiley & Sons, Ltd Country of Publication: United States NLM ID: 101231978 Publication Model: Print Cited Medium: Internet ISSN: 1552-5279 (Electronic) Linking ISSN: 15525260 NLM ISO Abbreviation: Alzheimers Dement Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2020- : Hoboken, NJ : John Wiley & Sons, Ltd.; Original Publication: Orlando, FL : Elsevier, Inc. |
| MeSH Terms: | Alzheimer Disease*/genetics ; Alzheimer Disease*/diagnosis ; Multifactorial Inheritance*/genetics ; Transcriptome*; Biomarkers/blood ; Genetic Predisposition to Disease/genetics ; Humans ; Male ; Female ; Aged ; Machine Learning ; Genome-Wide Association Study ; Risk Factors ; Aged, 80 and over ; Cohort Studies |
| Abstract: | Introduction: Early detection of Alzheimer's disease (AD) is essential, yet existing biomarkers are invasive or costly. Polygenic risk scores (PRS) and transcriptional risk scores (TRS) may offer accessible alternatives, but their combined predictive performance remains understudied.; Methods: We calculated PRS and TRS using genome-wide genotype and blood transcriptome data from two ancestrally distinct cohorts: Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 313) and Seoul National University Bundang Hospital (SNUBH, N = 173). Logistic regression and machine learning models assessed associations of PRS and TRS with AD and AD classification performance.; Results: Individuals with high PRS and TRS values showed larger odds ratios for AD, 2.5-fold in ADNI and 3.4-fold in SNUBH, compared to those with low PRS and TRS values. The integrated PRS-TRS model achieved better classification performance (area under the curve [AUC] 0.705) than the PRS model (AUC 0.635).; Discussion: Integrating static genetic and dynamic transcriptomic information from blood improves early detection of AD across diverse populations.; (© 2026 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.) |
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| Grant Information: | 2020R1C1C1013718 National Research Foundation of Korea; RS-2024-00461936 National Research Foundation of Korea; AACSFD-24-1310485 United States ALZ Alzheimer's Association; R01AG081951 United States NH NIH HHS; R01LM012535 United States NH NIH HHS; U01AG072177 United States NH NIH HHS; U19AG074879 United States NH NIH HHS; P30AG010133 United States NH NIH HHS; P30AG072976 United States NH NIH HHS; R01AG019771 United States NH NIH HHS; R01AG057739 United States NH NIH HHS; U19AG024904 United States NH NIH HHS; R01LM013463 United States NH NIH HHS; R01AG068193 United States NH NIH HHS; R01AG092591 United States NH NIH HHS; T32AG071444 United States NH NIH HHS; U01AG068057 United States NH NIH HHS; U24AG074855 United States NH NIH HHS |
| Contributed Indexing: | Keywords: Alzheimer's disease; blood biomarkers; early detection; machine learning; multi‐omics; polygenic risk score; risk stratification; transcriptional risk score |
| Substance Nomenclature: | 0 (Biomarkers) |
| Entry Date(s): | Date Created: 20260504 Date Completed: 20260504 Latest Revision: 20260518 |
| Update Code: | 20260519 |
| PubMed Central ID: | PMC13137284 |
| DOI: | 10.1002/alz.71408 |
| PMID: | 42080296 |
| Database: | MEDLINE |
Journal Article