ArticleNature communications2025
Performance of deep-learning-based approaches to improve polygenic scores.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- Robust cis-by-trans epistasis in the human plasma proteome highlights an ABO-centered interaction network.American journal of human genetics · 2026Article
- Which RJournal of human genetics · 2026Article
- Evidence standards for multi-ancestry polygenic prediction.Genome biology · 2026Review
- An introduction to polygenic scores - methodological basics and recent advances.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2026Article
- Deep-Neural-Network-Aided Genetic Association Testing in Samples with Related Individuals.Current issues in molecular biology · 2026Article
- Choice of phenotype scale is critical in biobank-based G×E tests.bioRxiv : the preprint server for biology · 2026Article
- Toward personalized medicine in AD/ADRD through genetic-exposome dementia risk assessments.NPJ dementia · 2026Review
- Integrating extensive functional annotations and multiomics of cattle enhances climate resilience prediction and mapping.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Recommendations for responsible use of population descriptors in polygenic risk score development.Nature genetics · 2025Review
- Performance of deep-learning-based approaches to improve polygenic scores.Nature communications · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.