ArticleScience (New York, N.Y.)2025
Machine learning-based penetrance of genetic variants.
Article in Science (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Consideration of inherited cancer risk on a continuum: An international and multidisciplinary perspective: A points to consider statement of the American College of Medical Genetics and Genomics (ACMG).Genetics in medicine : official journal of the American College of Medical Genetics · 2026Guideline
- The Genetic Architecture of Chronic Cough: From Sensory Hypersensitivity to Treatable Trait.Pulmonary therapy · 2026Review
- Characterization of Adult Patients With Neurometabolic Disorders: A Cross-Sectional Study at a Tertiary Neurology Center in Sweden.JIMD reports · 2026Article
- Longitudinal alignments and syntheses of multimodal clinical data for personalized medicine with the PULSE framework.Nature computational science · 2026Article
- Magnetic Composite Formed by Exsolution for Enhanced Exchange Coupling and Breaking the Permeability-Frequency Limitation.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Review
- Deep-learning-enabled multi-omics analyses for prediction of future metastasis in cancer.Nature communications · 2026Article
- Genetic influences on haematopoiesis.Nature reviews. Genetics · 2026Review
- Advancing risk gene discovery across the allele frequency spectrum.HGG advances · 2026Review
- When splicing is not all or none: GT>GC 5' splice-site variants as a model for intermediate effects and challenges in variant classification.HGG advances · 2026Article
- AI in variant analysis: fast track to genetic diagnoses.Human genetics · 2026Review
- Circular RNA as emerging precision therapeutics: from RNA regulation to peptide translation.Precision clinical medicine · 2026Review
- Five tenets for advancing evidence-based precision medicine.Nature medicine · 2026Review
- Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026Review
- Editorial: Genetic horizons: exploring genetic biomarkers in therapy and evolution with the aid of artificial intelligence.Frontiers in genetics · 2026Article
- A New Paradigm for Precision Medicine in Heatstroke: From Molecular Subtyping to Targeted Prevention and Treatment.Journal of inflammation research · 2026Review
- A machine learning framework for predictive interpretation of variants of uncertain significance in hereditary cancer.Frontiers in systems biology · 2026Article
- What Should a Clinical Cardiologist Know About Cardiogenetics?Journal of the American Heart Association · 2025Review
- Article
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Authors and funding
11 authors.
Funding
Abstract
Accurate variant penetrance estimation is crucial for precision medicine. We constructed machine learning (ML) models for 10 diseases using 1,347,298 participants with electronic health records, then applied them to an independent cohort with linked exome data. Resulting probabilities were used to evaluate ML penetrance of 1648 rare variants in 31 autosomal dominant disease-predisposition genes. ML penetrance was variable across variant classes, but highest for pathogenic and loss-of-function variants, and was associated with clinical outcomes and functional data. Compared with conventional case-versus-control approaches, ML penetrance provided refined quantitative estimates and aided the interpretation of variants of uncertain significance and loss-of-function variants by delineating clinical trajectories over time. By leveraging ML and deep phenotyping, we present a scalable approach to accurately quantify disease risk of variants.
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What OpenQuestion holds
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.