ReviewNature genetics2025
Using large-scale population-based data to improve disease risk assessment of clinical variants.
Review in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- Genetic influences on haematopoiesis.Nature reviews. Genetics · 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
- NGS identifies novel HLA-DQA1 and DPB1 associations with aplastic anemia in the Kazakhstani population.Frontiers in immunology · 2026Article
- Toward personalized medicine in AD/ADRD through genetic-exposome dementia risk assessments.NPJ dementia · 2026Review
- From Normal Variation in Sleep to Clinical Sleep Disorders: Genetic Insights from Over One Million Individuals.medRxiv : the preprint server for health sciences · 2025Article
- Prediction of human missense variant effects from functional evidence.Research square · 2025Article
- Complement-mediated HUS revisited: evolving insights into pathophysiology, diagnosis, and treatment.Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Understanding the disease risk of genetic variants is fundamental to precision medicine. Estimates of penetrance-the probability of disease for individuals with a variant allele-rely on disease-specific cohorts, clinical testing and emerging electronic health record (EHR)-linked biobanks. These data sources, while valuable, each have limitations in quality, representativeness and analyzability. Here, we provide a historical account of the currently accepted pathogenicity classification system and data available in ClinVar, a public archive that aggregates variant interpretations but lacks detailed data for accurate penetrance assessment, highlighting its oversimplification of disease risk. We propose an integrative Bayesian framework that unifies pathogenicity and penetrance, leveraging both functional and real-world evidence to refine risk predictions. In addition, we advocate for enhancing ClinVar with the inclusion of high-priority phenotypes, age-stratified data and population-based cohorts linked to EHRs. We suggest developing a community repository of population-based penetrance estimates to support the clinical application of genetic data.
Indexed as
Identifiers
What Socratic 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 Socratic graph.