ArticleNature genetics2024
Integration of variant annotations using deep set networks boosts rare variant association testing.
Article in Nature genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- AlphaGenome Atlas:medRxiv : the preprint server for health sciences · 2026Article
- Article
- Rare variant effect estimation and polygenic risk prediction.Nature genetics · 2026Article
- An empirical Bayes framework for burden and dispersion association tests helps prioritize rare variants associated with Alzheimer's disease.medRxiv : the preprint server for health sciences · 2026Article
- The Biobank Rare Variant consortium powers the discovery of rare genetic associations through global collaboration.medRxiv : the preprint server for health sciences · 2026Article
- Integrating common and rare variants improves polygenic risk prediction across diverse populations.Nature communications · 2026Article
- HistoGWAS: an AI-enabled framework for automated genetic analysis of tissue phenotypes in histology cohorts.Genome biology · 2026Article
- Archipelago Method for Variant Set Association Test Statistics.Genetic epidemiology · 2026Article
- REECAP: Contrastive learning of retinal aging reveals genetic loci linking morphology to eye disease.medRxiv : the preprint server for health sciences · 2025Article
- A scalable framework for identifying allelic series from summary statistics.American journal of human genetics · 2025Article
- Predicting functional constraints across evolutionary timescales with phylogeny-informed genomic language models.bioRxiv : the preprint server for biology · 2025Article
- Leveraging functional annotations to map rare variants associated with Alzheimer disease with gruyere.American journal of human genetics · 2025Article
- The future of pharmaceuticals: Artificial intelligence in drug discovery and development.Journal of pharmaceutical analysis · 2025Review
- Aberrant gene expression prediction across human tissues.Nature communications · 2025Article
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13 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Rare genetic variants can have strong effects on phenotypes, yet accounting for rare variants in genetic analyses is statistically challenging due to the limited number of allele carriers and the burden of multiple testing. While rich variant annotations promise to enable well-powered rare variant association tests, methods integrating variant annotations in a data-driven manner are lacking. Here we propose deep rare variant association testing (DeepRVAT), a model based on set neural networks that learns a trait-agnostic gene impairment score from rare variant annotations and phenotypes, enabling both gene discovery and trait prediction. On 34 quantitative and 63 binary traits, using whole-exome-sequencing data from UK Biobank, we find that DeepRVAT yields substantial gains in gene discoveries and improved detection of individuals at high genetic risk. Finally, we demonstrate how DeepRVAT enables calibrated and computationally efficient rare variant tests at biobank scale, aiding the discovery of genetic risk factors for human disease traits.
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