ArticleBioinformatics (Oxford, England)2012
SimRare: a program to generate and analyze sequence-based data for association studies of quantitative and qualitative traits.
Article in Bioinformatics (Oxford, England), 2012. 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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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
14 citing papers in PubMed.
- Pathway analysis of rare variants for the clustered phenotypes by using hierarchical structured components analysis.BMC medical genomics · 2019Article
- PhenotypeSimulator: A comprehensive framework for simulating multi-trait, multi-locus genotype to phenotype relationships.Bioinformatics (Oxford, England) · 2018Article
- Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypes.BMC bioinformatics · 2018Article
- Rare variant association test with multiple phenotypes.Genetic epidemiology · 2017Article
- cophesim: a comprehensive phenotype simulator for testing novel association methods.F1000Research · 2017Article
- Gene-set association tests for next-generation sequencing data.Bioinformatics (Oxford, England) · 2016Article
- Generation of sequence-based data for pedigree-segregating Mendelian or Complex traits.Bioinformatics (Oxford, England) · 2015Article
- Cohen's h for detection of disease association with rare genetic variants.BMC genomics · 2014Article
- Statistical power and significance testing in large-scale genetic studies.Nature reviews. Genetics · 2014Review
- Article
- Identifying rare variants associated with complex traits via sequencing.Current protocols in human genetics · 2013Review
- SeqSIMLA: a sequence and phenotype simulation tool for complex disease studies.BMC bioinformatics · 2013Article
- BioBin: a bioinformatics tool for automating the binning of rare variants using publicly available biological knowledge.BMC medical genomics · 2013Article
- Exome sequencing and complex disease: practical aspects of rare variant association studies.Human molecular genetics · 2012Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
motivationCurrently, there is great interest in detecting complex trait rare variant associations using next-generation sequence data. On a monthly basis, new rare variant association methods are published. It is difficult to evaluate these methods because there is no standard to generate data and often comparisons are biased. In order to fairly compare rare variant association methods, it is necessary to generate data using realistic population demographic and phenotypic models.
resultSimRare is an interactive program that integrates generation of rare variant genotype/phenotype data and evaluation of association methods using a unified platform. Variant data are generated for gene regions using forward-time simulation that incorporates realistic population demographic and evolutionary scenarios. Phenotype data can be obtained for both case-control and quantitative traits. SimRare has a user-friendly interface that allows for easy entry of genetic and phenotypic parameters. Novel rare variant association methods implemented in R can also be imported into SimRare, to evaluate their performance and compare results, e.g. power and Type I error, with other currently available methods both numerically and graphically.
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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.