Integrating polygenic and transcriptional risk scores for detecting Alzheimer's disease

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.

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.

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).

Integrating static genetic and dynamic transcriptomic information from blood improves early detection of AD across diverse populations.