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

TL;DR

This study improves dementia risk prediction in underrepresented populations by using machine learning and functional genomic data to create a more accurate genetic risk model.

Contribution

The novel contribution is the development of an Elastic Net model that integrates functional genomic data and multiple neurodegenerative diseases for improved dementia risk prediction in diverse populations.

Findings

The Elastic Net model outperformed APOE and polygenic risk score models by 21-61% in precision-recall and 10-21% in ROC-AUC across multiple ancestries.

Shared and ancestry-specific risk genes and pathways were identified, expanding current understanding of dementia genetics.

The study demonstrates the value of combining functional mapping, multi-disease data, and machine learning for precision medicine in dementia.

Abstract

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

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.

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-operating characteristic by 10-21% compared to the APOEand the polygenic risk score models. We identified shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge.

Our study highlights benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.

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