ArticleGenetic epidemiology2015
Genetic simulation tools for post-genome wide association studies of complex diseases.
Article in Genetic epidemiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
17 citing papers in PubMed.
- Establishing standards for genome wide association studies in eusocial insects through a case study in honey bees.Scientific reports · 2026Article
- An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences.NAR genomics and bioinformatics · 2026Article
- GENLIB: new function to simulate haplotype transmission in large complex genealogies.Bioinformatics (Oxford, England) · 2023Article
- On the application, reporting, and sharing of in silico simulations for genetic studies.Genetic epidemiology · 2021Review
- A roadmap for the generation of benchmarking resources for antimicrobial resistance detection using next generation sequencing.F1000Research · 2021Article
- Presidential address: Six open questions to genetic epidemiologists.Genetic epidemiology · 2019Article
- Genetic Simulation Resources and the GSR Certification Program.Bioinformatics (Oxford, England) · 2019Article
- sim1000G: a user-friendly genetic variant simulator in R for unrelated individuals and family-based designs.BMC bioinformatics · 2019Article
- Analysis validation has been neglected in the Age of Reproducibility.PLoS biology · 2018Article
- Host and Microbiome Genome-Wide Association Studies: Current State and Challenges.Frontiers in genetics · 2018Review
- Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases.American journal of epidemiology · 2017Article
- A Model of Compound Heterozygous, Loss-of-Function Alleles Is Broadly Consistent with Observations from Complex-Disease GWAS Datasets.PLoS genetics · 2017Article
- Machine learning and data mining in complex genomic data--a review on the lessons learned in Genetic Analysis Workshop 19.BMC genetics · 2016Article
- GESDB: a platform of simulation resources for genetic epidemiology studies.Database : the journal of biological databases and curation · 2016Article
- Genetic data simulators and their applications: an overview.Genetic epidemiology · 2015Article
- Population genetic simulations of complex phenotypes with implications for rare variant association tests.Genetic epidemiology · 2015Article
- Reproducible simulations of realistic samples for next-generation sequencing studies using Variant Simulation Tools.Genetic epidemiology · 2015Article
Corrections and comments
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Authors and funding
19 authors.
Funding
Abstract
Genetic simulation programs are used to model data under specified assumptions to facilitate the understanding and study of complex genetic systems. Standardized data sets generated using genetic simulation are essential for the development and application of novel analytical tools in genetic epidemiology studies. With continuing advances in high-throughput genomic technologies and generation and analysis of larger, more complex data sets, there is a need for updating current approaches in genetic simulation modeling. To provide a forum to address current and emerging challenges in this area, the National Cancer Institute (NCI) sponsored a workshop, entitled "Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases" at the National Institutes of Health (NIH) in Bethesda, Maryland on March 11-12, 2014. The goals of the workshop were to (1) identify opportunities, challenges, and resource needs for the development and application of genetic simulation models; (2) improve the integration of tools for modeling and analysis of simulated data; and (3) foster collaborations to facilitate development and applications of genetic simulation. During the course of the meeting, the group identified challenges and opportunities for the science of simulation, software and methods development, and collaboration. This paper summarizes key discussions at the meeting, and highlights important challenges and opportunities to advance the field of genetic simulation.
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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.