ArticleScientific reports2019
Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 32 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using a Polygenic Score to Predict the Risk of Developing Primary Osteoporosis.International journal of molecular sciences · 2022Pooled it
- Disease activity and treatment response in early rheumatoid arthritis: an exploratory metabolomic profiling in the NORD-STAR cohort.Arthritis research & therapy · 2025Trial
- Article
- The Progress of Gout Prediction Models Based on Multi-source Data.Current rheumatology reviews · 2026Review
- Augmenting small tabular health data for training prognostic ensemble machine learning models using generative models.BMC medical informatics and decision making · 2025Article
- Polygenic scores and their applications in kidney disease.Nature reviews. Nephrology · 2025Review
- Article
- A deep learning model for prediction of autism status using whole-exome sequencing data.PLoS computational biology · 2024Article
- The effect of family structure on the still-missing heritability and genomic prediction accuracy of type 2 diabetes.Human genomics · 2024Article
- DeepRisk: A deep learning approach for genome-wide assessment of common disease risk.Fundamental research · 2024Article
- Efficacy of federated learning on genomic data: a study on the UK Biobank and the 1000 Genomes Project.Frontiers in big data · 2024Article
- Biobank-scale methods and projections for sparse polygenic prediction from machine learning.Scientific reports · 2023Article
- Evaluation of a genetic risk score computed using human chromosomal-scale length variation to predict breast cancer.Human genomics · 2023Article
- Molecular Evaluation of the Impact of Polymorphic Variants in Apoptotic (Current issues in molecular biology · 2023Article
- Breaking bad news in the era of artificial intelligence and algorithmic medicine: an exploration of disclosure and its ethical justification using the hedonic calculus.AI and ethics · 2022Article
- Polygenic Health Index, General Health, and Pleiotropy: Sibling Analysis and Disease Risk Reduction.Scientific reports · 2022Article
- Risk-stratified strategies in population screening for colorectal cancer.International journal of cancer · 2022Review
- Article
- Associations of Polygenic Risk Score for Late-Onset Alzheimer's Disease With Biomarkers.Frontiers in aging neuroscience · 2022Review
- Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches.Methods in molecular biology (Clifton, N.J.) · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
We construct risk predictors using polygenic scores (PGS) computed from common Single Nucleotide Polymorphisms (SNPs) for a number of complex disease conditions, using L1-penalized regression (also known as LASSO) on case-control data from UK Biobank. Among the disease conditions studied are Hypothyroidism, (Resistant) Hypertension, Type 1 and 2 Diabetes, Breast Cancer, Prostate Cancer, Testicular Cancer, Gallstones, Glaucoma, Gout, Atrial Fibrillation, High Cholesterol, Asthma, Basal Cell Carcinoma, Malignant Melanoma, and Heart Attack. We obtain values for the area under the receiver operating characteristic curves (AUC) in the range ~0.58-0.71 using SNP data alone. Substantially higher predictor AUCs are obtained when incorporating additional variables such as age and sex. Some SNP predictors alone are sufficient to identify outliers (e.g., in the 99th percentile of polygenic score, or PGS) with 3-8 times higher risk than typical individuals. We validate predictors out-of-sample using the eMERGE dataset, and also with different ancestry subgroups within the UK Biobank population. Our results indicate that substantial improvements in predictive power are attainable using training sets with larger case populations. We anticipate rapid improvement in genomic prediction as more case-control data become available for analysis.
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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.