ArticleBioinformatics (Oxford, England)2019
SummaryAUC: a tool for evaluating the performance of polygenic risk prediction models in validation datasets with only summary level statistics.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Evaluating Individual Level Performance of Polygenic Risk Scores Using Early Onset High Genetic Risk Coronary Artery Disease as a Benchmark.medRxiv : the preprint server for health sciences · 2026Article
- Data sharing in the PRIMED Consortium: Design, implementation, and recommendations for future policymaking.American journal of human genetics · 2025Review
- Polygenic Risk Prediction for Normal-Tension Glaucoma.Investigative ophthalmology & visual science · 2025Article
- Optimizing and benchmarking polygenic risk scores with GWAS summary statistics.Genome biology · 2024Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Tuning parameters for polygenic risk score methods using GWAS summary statistics from training data.Nature communications · 2024Article
- A new method for multiancestry polygenic prediction improves performance across diverse populations.Nature genetics · 2023Article
- Genetic prediction of complex traits with polygenic scores: a statistical review.Trends in genetics : TIG · 2021Review
- PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics.Genome biology · 2021Article
- A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information.Journal of the American Statistical Association · 2021Article
- Genetic risk scores and hallucinations in patients with Parkinson disease.Neurology. Genetics · 2020Article
- Penalized regression and model selection methods for polygenic scores on summary statistics.PLoS computational biology · 2020Article
- Multiomics Evaluation of Gastrointestinal and Other Clinical Characteristics of COVID-19.Gastroenterology · 2020Article
- Assessing the performance of genome-wide association studies for predicting disease risk.PloS one · 2019Article
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Authors and funding
4 authors.
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Abstract
motivationPolygenic risk score (PRS) methods based on genome-wide association studies (GWAS) have a potential for predicting the risk of developing complex diseases and are expected to become more accurate with larger training datasets and innovative statistical methods. The area under the ROC curve (AUC) is often used to evaluate the performance of PRSs, which requires individual genotypic and phenotypic data in an independent GWAS validation dataset. We are motivated to develop methods for approximating AUC of PRSs based on the summary level data of the validation dataset, which will greatly facilitate the development of PRS models for complex diseases.
resultsWe develop statistical methods and an R package SummaryAUC for approximating the AUC and its variance of a PRS when only the summary level data of the validation dataset are available. SummaryAUC can be applied to PRSs with SNPs either genotyped or imputed in the validation dataset. We examined the performance of SummaryAUC using a large-scale GWAS of schizophrenia. SummaryAUC provides accurate approximations to AUCs and their variances. The bias of AUC is typically <0.5% in most analyses. SummaryAUC cannot be applied to PRSs that use all SNPs in the genome because it is computationally prohibitive. AVAILABILITY AND IMPLEMENTATION: https://github.com/lsncibb/SummaryAUC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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