ArticlePLoS genetics2010
The genetic interpretation of area under the ROC curve in genomic profiling.
Article in PLoS genetics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 195 papers, 10 of them syntheses that pooled it.
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Who cites it
195 citing papers in PubMed, 10 syntheses or guidelines pooled it.
- Polygenic Risk Scores for Myopia: A Systematic Review of Predictive Performance and Clinical Potential.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Pooled it
- Combining schizophrenia and depression polygenic risk scores improves the genetic prediction of lithium response in bipolar disorder patients.Translational psychiatry · 2021Pooled it
- Machine learning for genetic prediction of psychiatric disorders: a systematic review.Molecular psychiatry · 2021Pooled it
- Systematic Review of Polygenic Risk Scores for Type 1 and Type 2 Diabetes.International journal of molecular sciences · 2020Pooled it
- Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies.The Lancet. Neurology · 2019Pooled it
- Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes.PLoS genetics · 2014Pooled it
- Parity and pancreatic cancer risk: evidence from a meta-analysis of twenty epidemiologic studies.Scientific reports · 2014Pooled it
- Variation at 3p24.1 and 6q23.3 influences the risk of Hodgkin's lymphoma.Nature communications · 2013Pooled it
- Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes.Nature genetics · 2012Pooled it
- Genetic variants underlying risk of endometriosis: insights from meta-analysis of eight genome-wide association and replication datasets.Human reproduction updatePooled it
- Improving genomic prediction accuracy of complex traits by integrating massive types of functional annotation information.Nature communications · 2026Article
- GWKBR: a novel method integrating machine learning and Bayesian inference framework to improve genomic prediction accuracy.Briefings in bioinformatics · 2026Article
- Evaluating the impact of family history and polygenic risk scores on cardiometabolic disease risk.BMC medicine · 2026Article
- Evaluating genetic-based disease prediction approaches through simulation.Human genetics · 2026Article
- Molecular effects of digital psychological intervention for perinatal stress: cell culture, animal model validation, and machine learning-based biomarker identification.Scientific reports · 2026Article
- Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning.Molecular psychiatry · 2025Review
- Genomics of schizophrenia, bipolar disorder and major depressive disorder.Nature reviews. Genetics · 2025Review
- Augmenting small tabular health data for training prognostic ensemble machine learning models using generative models.BMC medical informatics and decision making · 2025Article
- Using genomic context informed genotype data and within-model ancestry adjustment to classify type 2 diabetes.Communications medicine · 2025Article
- Global epidemiology of spondyloarthritis.Nature reviews. Rheumatology · 2025Review
135 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide association studies in human populations have facilitated the creation of genomic profiles which combine the effects of many associated genetic variants to predict risk of disease. The area under the receiver operator characteristic (ROC) curve is a well established measure for determining the efficacy of tests in correctly classifying diseased and non-diseased individuals. We use quantitative genetics theory to provide insight into the genetic interpretation of the area under the ROC curve (AUC) when the test classifier is a predictor of genetic risk. Even when the proportion of genetic variance explained by the test is 100%, there is a maximum value for AUC that depends on the genetic epidemiology of the disease, i.e. either the sibling recurrence risk or heritability and disease prevalence. We derive an equation relating maximum AUC to heritability and disease prevalence. The expression can be reversed to calculate the proportion of genetic variance explained given AUC, disease prevalence, and heritability. We use published estimates of disease prevalence and sibling recurrence risk for 17 complex genetic diseases to calculate the proportion of genetic variance that a test must explain to achieve AUC = 0.75; this varied from 0.10 to 0.74. We provide a genetic interpretation of AUC for use with predictors of genetic risk based on genomic profiles. We provide a strategy to estimate proportion of genetic variance explained on the liability scale from estimates of AUC, disease prevalence, and heritability (or sibling recurrence risk) available as an online calculator.
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