Deep learning-based polygenic risk analysis for Alzheimer's disease prediction.

ABSTRACT:

Background

The polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk.

Met

SUBMITTER: Zhou X 

PROVIDER: S-EPMC10079691 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature