Polygenic Risk Score Reveals Genetic Heterogeneity of Alzheimer’s Disease between the Chinese and European Populations

Background

The polygenic risk score (PRS) aggregates the effects of numerous genetic variants associated with a condition across the human genome and may help to predict late-onset Alzheimer’s disease (LOAD). Most of the current PRS studies on Alzheimer’s disease (AD) have been conducted in Caucasian ancestry populations, while it is less studied in Chinese.

Objective

To establish and examine the validity of Chinese PRS, and explore its racial heterogeneity.

Design

We constructed a PRS using both discovery (N = 2012) and independent validation samples (N = 1008) from Chinese population. The associations between PRS and age at onset of LOAD or cerebrospinal fluid (CSF) biomarkers were assessed. We also replicated the PRS in an independent replication cohort with CSF data and constructed an alternative PRS using European weights.

Setting

Multi-center genetics study.

Participants

A total of 3020 subjects were included in the study.

Measurements

PRS was calculated using genome-wide association studies data and evaluated the performance alone (PRSnoAPOE) and with other predictors (full model: LOAD ∼ PRSnoAPOE + APOE+ sex + age) by measuring the area under the receiver operating curve (AUC).

Results

PRS of the full model achieved the highest AUC of 84.0% (95% CI = 81.4–86.5) with pT< 0.5, compared with the model containing APOE alone (61.0%). The AUC of PRS with pT< 5e–8 was 77.8% in the PRSnoAPOE model, 81.5% in the full model, and only ranged from 67.5% to 75.1% in the PRS with the European weights model. A higher PRS was significantly associated with an earlier age at onset (P <0.001). The PRS also performed well in the replication cohort of the full model (AUC=83.1%, 95% CI = 74.3–92.0). The CSF biomarkers of Aβ42 and the ratio of Aβ42/Aβ40 were significantly inversely associated with the PRS, while p-Tau181 showed a positive association.

Conclusions

This finding suggests that PRS reveal genetic heterogeneity and higher prediction accuracy of the PRS for AD can be achieved using a base dataset and validation within the same ethnicity. The effective PRS model has the clinical potential to predict individuals at risk of developing LOAD at a given age and with abnormal levels of CSF biomarkers in the Chinese population.