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Alzheimers. Dement., May 2026, 22 (5) | https://doi.org/10.1002/alz.71408 Integrating polygenic and transcriptional risk scores for detecting Alzheimer's disease

Jiyun Hwang ^{1 2 †}, Jung-Min Pyun^{2 †}, Joo-Yeon Lee ^{1 3}, Jeong Su Park ⁴, Paula J Bice ⁵, Andrew J Saykin⁵, Joohon Sung ^{1 3}, SangYun Kim ², Young Ho Park ^{2 *}, Kwangsik Nho^{5 *}

¹Genome and Health Big Data Laboratory, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea.
²Department of Neurology, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea.
³Institute of Health and Environments, Seoul National University, Seoul, Republic of Korea.
⁴Department of Laboratory Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea.
⁵Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.

^†Jiyun Hwang and Jung-Min Pyun have contributed equally to this study.

^*Corresponding authors: correspondence to Young Ho Park or Kwangsik Nho

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.

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