ArticleBMC bioinformatics2018
A simulation study investigating power estimates in phenome-wide association studies.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 3 of them syntheses that pooled it.
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Who cites it
61 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Iron Status and Risk of Heart Disease, Stroke, and Diabetes: A Mendelian Randomization Study in European Adults.Journal of the American Heart Association · 2024Pooled it
- Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.Genome biology · 2022Pooled it
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- A large-scale multi-ancestry mitochondrial variant association analysis for cardiometabolic traits.Nature communications · 2026Article
- Concordance of genetic determinants of acute coronary artery disease between European and Chinese populations.GigaScience · 2026Article
- Blood pressure, plasma proteins, and cardiovascular diseases: a network Mendelian randomization and observational study.European heart journal · 2026Observational
- Social isolation, loneliness, and multi-system medical conditions: phenome-wide association and disease-trajectory analyses.BMC medicine · 2025Article
- G4mer: An RNA language model for transcriptome-wide identification of G-quadruplexes and disease variants from population-scale genetic data.Nature communications · 2025Article
- Clinical implications of bone marrow adiposity identified by phenome-wide association and Mendelian randomization in the UK Biobank.Nature communications · 2025Article
- Causal association analysis between blood metabolomes and osteopenia and therapeutic target prediction for mechanomedicine.Mechanobiology in medicine · 2025Article
- A sex-specific Mendelian randomization-phenome-wide association study of body mass index.eLife · 2025Article
- Integrative multi-omics investigation of sleep apnea: gut microbiome metabolomics, proteomics and phenome-wide association study.Nutrition & metabolism · 2025Article
- The Eating Disorders Genetics Initiative 2 (EDGI2): study protocol.BMC psychiatry · 2025Article
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- Sex-specific Mendelian randomization phenome-wide association study of basal metabolic rate.Scientific reports · 2025Article
- Mendelian randomization provides a multi-omics perspective on the regulation of genes involved in ribosome biogenesis in relation to cardiac structure and function.Clinical epigenetics · 2025Article
- Distinguishing clinical and genetic risk factors for suicidal ideation and behavior in a diverse hospital population.Translational psychiatry · 2025Article
- A comprehensive study of genetic regulation and disease associations of plasma circulatory microRNAs using population-level data.Genome biology · 2024Article
1 more citing papers are in PubMed but not listed here.
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7 authors.
Funding
Abstract
backgroundPhenome-wide association studies (PheWAS) are a high-throughput approach to evaluate comprehensive associations between genetic variants and a wide range of phenotypic measures. PheWAS has varying sample sizes for quantitative traits, and variable numbers of cases and controls for binary traits across the many phenotypes of interest, which can affect the statistical power to detect associations. The motivation of this study is to investigate the various parameters which affect the estimation of statistical power in PheWAS, including sample size, case-control ratio, minor allele frequency, and disease penetrance.
resultsWe performed a PheWAS simulation study, where we investigated variations in statistical power based on different parameters, such as overall sample size, number of cases, case-control ratio, minor allele frequency, and disease penetrance. The simulation was performed on both binary and quantitative phenotypic measures. Our simulation on binary traits suggests that the number of cases has more impact on statistical power than the case to control ratio; also, we found that a sample size of 200 cases or more maintains the statistical power to identify associations for common variants. For quantitative traits, a sample size of 1000 or more individuals performed best in the power calculations. We focused on common genetic variants (MAF > 0.01) in this study; however, in future studies, we will be extending this effort to perform similar simulations on rare variants.
conclusionsThis study provides a series of PheWAS simulation analyses that can be used to estimate statistical power for some potential scenarios. These results can be used to provide guidelines for appropriate study design for future PheWAS analyses.
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