| Title: | Performance of deep-learning-based approaches to improve polygenic scores |
| Journal: | Nature Communications |
| Published: | 2 Jun 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40456720/ |
| DOI: | https://doi.org/10.1038/s41467-025-60056-1 |
| URL: | https://www.nature.com/articles/s41467-025-60056-1.pdf |
Publication 15095
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Abstract
Polygenic scores, which estimate an individual's genetic propensity for a disease or trait, have the potential to become part of genomic healthcare. Neural-network based deep-learning has emerged as a method of intense interest to model complex, nonlinear phenomena, which may be adapted to exploit gene-gene and gene-environment interactions to potentially improve polygenic scores. We fit neural-network models to both simulated and 28 real traits in the UK Biobank. To infer the amount of nonlinearity present in a phenotype, we also present a framework using neural-networks, which controls for the potential confounding effect of linkage disequilibrium. Although we found evidence for small amounts of nonlinear effects, neural-network models were outperformed by linear regression models for both genetic-only and genetic+environmental input scenarios. In this work, we find that the usefulness of neural-networks for generating polygenic scores may currently be limited and confounded by joint tagging effects due to linkage disequilibrium.</p>
13 Keywords
- Deep Learning
- Gene-Environment Interaction
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Humans
- Linear Models
- Linkage Disequilibrium
- Models, Genetic
- Multifactorial Inheritance
- Neural Networks, Computer
- Phenotype
- Polymorphism, Single Nucleotide
- United Kingdom
8 Authors
- Martin Kelemen
- Yu Xu
- Tao Jiang
- Jing Hua Zhao
- Carl A. Anderson
- Chris Wallace
- Adam Butterworth
- Michael Inouye
1 Application
| Application ID | Title |
|---|---|
| 7439 | Comparissons of measures of adiposity including body-mass index, waist-circumference, waist-to-hip ratio, and percentage body fat mass |
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