ArticlePloS one2019
Assessing the performance of genome-wide association studies for predicting disease risk.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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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
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrative bioinformatics frameworks for abdominal aortic aneurysm using GWAS meta-analysis, biological network construction, and structural modeling.Scientific reports · 2025Pooled it
- Equivalency Between the Generalized Bivariate Bernoulli Model Dependency Test and a Logistic Regression Model With Interaction Effects.Statistics in medicine · 2025Article
- Data simulation to optimize frameworks for genome-wide association studies in diverse populations.Frontiers in genetics · 2025Article
- Genetic Determinants of Chronic Kidney Disease (CKD) in India: A Comprehensive Genomewide Association Study (GWAS) Analysis.International journal of nephrology · 2025Article
- From map to blueprint: the plant pan-genome unraveling genetic mysteries and powering precision breeding.Frontiers in plant science · 2025Review
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Specific Genotypes Associated with Differences in Fasting Insulin Levels and Body Mass Index in Healthy Young Males: Implications for Gene-Nutrient Interactions-an Exploratory Study.Current developments in nutrition · 2023Article
- Association of rs2073618 polymorphism and osteoprotegerin levels with hypertension and cardiovascular risks in patients with type 2 diabetes mellitus.Scientific reports · 2023Article
- Identification of Candidate Genes Associated with Yak Body Size Using a Genome-Wide Association Study and Multiple Populations of Information.Animals : an open access journal from MDPI · 2023Article
- Processing genome-wide association studies within a repository of heterogeneous genomic datasets.BMC genomic data · 2023Article
- Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases.Heliyon · 2023Review
- Genetic architecture and polygenic risk score prediction of degenerative suspensory ligament desmitis (DSLD) in the Peruvian Horse.Frontiers in genetics · 2023Article
- Beyond Genes: Germline Disruption in the Etiology of Autism Spectrum Disorders.Journal of autism and developmental disorders · 2022Article
- Selection signature analyses and genome-wide association reveal genomic hotspot regions that reflect differences between breeds of horse with contrasting risk of degenerative suspensory ligament desmitis.G3 (Bethesda, Md.) · 2022Article
- Association of Genetic Polymorphisms with Complications of Implanted LVAD Devices in Patients with Congestive Heart Failure: A Kazakhstani Study.Journal of personalized medicine · 2022Article
- Deciphering Pleiotropic Signatures of Regulatory SNPs inInternational journal of molecular sciences · 2022Article
- Metabolomics and the Multi-Omics View of Cancer.Metabolites · 2022Review
- Exosomes in Age-Related Cognitive Decline: Mechanistic Insights and Improving Outcomes.Frontiers in aging neuroscience · 2022Review
- Suitability of GWAS as a Tool to Discover SNPs Associated with Tick Resistance in Cattle: A Review.Pathogens (Basel, Switzerland) · 2021Review
- Towards population-scale long-read sequencing.Nature reviews. Genetics · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
To date more than 3700 genome-wide association studies (GWAS) have been published that look at the genetic contributions of single nucleotide polymorphisms (SNPs) to human conditions or human phenotypes. Through these studies many highly significant SNPs have been identified for hundreds of diseases or medical conditions. However, the extent to which GWAS-identified SNPs or combinations of SNP biomarkers can predict disease risk is not well known. One of the most commonly used approaches to assess the performance of predictive biomarkers is to determine the area under the receiver-operator characteristic curve (AUROC). We have developed an R package called G-WIZ to generate ROC curves and calculate the AUROC using summary-level GWAS data. We first tested the performance of G-WIZ by using AUROC values derived from patient-level SNP data, as well as literature-reported AUROC values. We found that G-WIZ predicts the AUROC with <3% error. Next, we used the summary level GWAS data from GWAS Central to determine the ROC curves and AUROC values for 569 different GWA studies spanning 219 different conditions. Using these data we found a small number of GWA studies with SNP-derived risk predictors that have very high AUROCs (>0.75). On the other hand, the average GWA study produces a multi-SNP risk predictor with an AUROC of 0.55. Detailed AUROC comparisons indicate that most SNP-derived risk predictions are not as good as clinically based disease risk predictors. All our calculations (ROC curves, AUROCs, explained heritability) are in a publicly accessible database called GWAS-ROCS (http://gwasrocs.ca). The G-WIZ code is freely available for download at https://github.com/jonaspatronjp/GWIZ-Rscript/.
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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.