ArticleBioinformatics (Oxford, England)2019
simGWAS: a fast method for simulation of large scale case-control GWAS summary 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 20 papers, 2 of them syntheses 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
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Multi-ancestry genetic architecture of heart failure subtypes.Nature communications · 2026Pooled it
- A gene based combination test using GWAS summary data.BMC bioinformatics · 2023Pooled it
- Locus-specific stratification and prioritization unveil genetic risk mechanism underlying complex diseases.Nature communications · 2026Article
- Multi-ancestry colocalization approaches.PLoS genetics · 2026Article
- Establishing standards for genome wide association studies in eusocial insects through a case study in honey bees.Scientific reports · 2026Article
- Variant-specific priors clarify colocalisation analysis.PLoS genetics · 2025Article
- A New Method for Conditional Gene-Based Analysis Effectively Accounts for the Regional Polygenic Background.Genes · 2024Article
- CoPheScan: phenome-wide association studies accounting for linkage disequilibrium.Nature communications · 2024Article
- Article
- HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes.Bioinformatics (Oxford, England) · 2023Article
- fcfdr: an R package to leverage continuous and binary functional genomic data in GWAS.BMC bioinformatics · 2022Article
- vcferr: Development, validation, and application of a single nucleotide polymorphism genotyping error simulation framework.F1000Research · 2022Article
- Using expression quantitative trait loci data and graph-embedded neural networks to uncover genotype-phenotype interactions.Frontiers in genetics · 2022Article
- Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR.PLoS genetics · 2021Article
- A more accurate method for colocalisation analysis allowing for multiple causal variants.PLoS genetics · 2021Article
- Linking genotype to phenotype in multi-omics data of small sample.BMC genomics · 2021Article
- A catalog of GWAS fine-mapping efforts in autoimmune disease.American journal of human genetics · 2021Review
- Improving the coverage of credible sets in Bayesian genetic fine-mapping.PLoS computational biology · 2020Article
- PGsim: A Comprehensive and Highly Customizable Personal Genome Simulator.Frontiers in bioengineering and biotechnology · 2020Article
- Stochastic search and joint fine-mapping increases accuracy and identifies previously unreported associations in immune-mediated diseases.Nature communications · 2019Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
motivationMethods for analysis of GWAS summary statistics have encouraged data sharing and democratized the analysis of different diseases. Ideal validation for such methods is application to simulated data, where some 'truth' is known. As GWAS increase in size, so does the computational complexity of such evaluations; standard practice repeatedly simulates and analyses genotype data for all individuals in an example study.
resultsWe have developed a novel method based on an alternative approach, directly simulating GWAS summary data, without individual data as an intermediate step. We mathematically derive the expected statistics for any set of causal variants and their effect sizes, conditional upon control haplotype frequencies (available from public reference datasets). Simulation of GWAS summary output can be conducted independently of sample size by simulating random variates about these expected values. Across a range of scenarios, our method, produces very similar output to that from simulating individual genotypes with a substantial gain in speed even for modest sample sizes. Fast simulation of GWAS summary statistics will enable more complete and rapid evaluation of summary statistic methods as well as opening new potential avenues of research in fine mapping and gene set enrichment analysis. AVAILABILITY AND IMPLEMENTATION: Our method is available under a GPL license as an R package from http://github.com/chr1swallace/simGWAS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.