ReviewNature reviews. Genetics2020
Electronic health records and polygenic risk scores for predicting disease risk.
Review in Nature reviews. Genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05515653 (Impact of the Genetic Background as a Risk Factor for Atherosclerotic Cardiovascular Disease in the Brazilian Population), which is not on this map. Cited by 62 papers, 3 of them syntheses 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.
Impact of the Genetic Background as a Risk Factor for Atherosclerotic Cardiovascular Disease in the Brazilian Population
Who cites it
62 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Genetic and peripheral biomarkers of comorbid posttraumatic stress disorder and traumatic brain injury: a systematic review.Frontiers in neurology · 2025Pooled it
- Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Glaucoma Genetic Risk Scores in the Million Veteran Program.Ophthalmology · 2022Pooled it
- A pre-train and fine-tune framework for adaptive boosting of pre-trained polygenic risk scores.Nature communications · 2026Article
- ProLM: a plasma proteomics pretrained model for the general population.Nature communications · 2026Article
- Unlocking multi-institutional insights into disease progression with PEAL as a lossless, one-shot federated learning solution.NPJ digital medicine · 2026Article
- Prediction of the short-term prognosis of acute ischaemic stroke in patients with high treatment platelet reactivity using explainable machine learning.Scientific reports · 2025Article
- Machine learning-based penetrance of genetic variants.Science (New York, N.Y.) · 2025Article
- Article
- Towards a new taxonomy of preterm birth.Journal of perinatology : official journal of the California Perinatal Association · 2025Review
- Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births.BMC pregnancy and childbirth · 2025Article
- Association of dietary carbohydrate ratio, caloric restriction, and genetic factors with breast cancer risk in a cohort study.Scientific reports · 2025Article
- Tau protein mediates the association between frailty and postoperative delirium: a machine learning model incorporating cerebrospinal fluid biomarkers.Frontiers in neurology · 2025Article
- Type 2 diabetes and susceptibility to COVID-19: a machine learning analysis.BMC endocrine disorders · 2024Article
- Enhancing Genetic Risk Prediction through Federated Semi-Supervised Transfer Learning with Inaccurate Electronic Health Record Data.Statistics in biosciences · 2024Article
- Blood protein assessment of leading incident diseases and mortality in the UK Biobank.Nature aging · 2024Article
- Calibrated prediction intervals for polygenic scores across diverse contexts.Nature genetics · 2024Article
- Using routinely collected clinical data for circadian medicine: A review of opportunities and challenges.PLOS digital health · 2024Review
- An ensemble penalized regression method for multi-ancestry polygenic risk prediction.Nature communications · 2024Article
- An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction.bioRxiv : the preprint server for biology · 2024Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Accurate prediction of disease risk based on the genetic make-up of an individual is essential for effective prevention and personalized treatment. Nevertheless, to date, individual genetic variants from genome-wide association studies have achieved only moderate prediction of disease risk. The aggregation of genetic variants under a polygenic model shows promising improvements in prediction accuracies. Increasingly, electronic health records (EHRs) are being linked to patient genetic data in biobanks, which provides new opportunities for developing and applying polygenic risk scores in the clinic, to systematically examine and evaluate patient susceptibilities to disease. However, the heterogeneous nature of EHR data brings forth many practical challenges along every step of designing and implementing risk prediction strategies. In this Review, we present the unique considerations for using genotype and phenotype data from biobank-linked EHRs for polygenic risk prediction.
Indexed as
Identifiers
32235907What 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.