Improving genetic risk modeling of dementia from real-world data in underrepresented populations.

ABSTRACT:

Background

Genetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited.

Methods

We employed an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compared this model with APOE and polygenic risk score models across genetic ancestry groups, using electronic health records from UCLA Health for discovery and All of Us cohort for validation.

Results

Our model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 21-61% and the area-under-the-receiver-operat

SUBMITTER: Fu M 

PROVIDER: S-EPMC10871463 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature