Name
Abstract | ||
|---|---|---|
Association studies based on next-generation sequencing (NGS) technology have become popular, and statistical association tests for NGS data have been developed rapidly. A flexible tool for simulating sequence data in either unrelated case-control or family samples with different disease and quantitative trait models would be useful for evaluating the statistical power for planning a study design and for comparing power among statistical methods based on NGS data.We developed a simulation tool, SeqSIMLA, which can simulate sequence data with user-specified disease and quantitative trait models. We implemented two disease models, in which the user can flexibly specify the number of disease loci, effect sizes or population attributable risk, disease prevalence, and risk or protective loci. We also implemented a quantitative trait model, in which the user can specify the number of quantitative trait loci (QTL), proportions of variance explained by the QTL, and genetic models. We compiled recombination rates from the HapMap project so that genomic structures similar to the real data can be simulated.SeqSIMLA can efficiently simulate sequence data with disease or quantitative trait models specified by the user. SeqSIMLA will be very useful for evaluating statistical properties for new study designs and new statistical methods using NGS. SeqSIMLA can be downloaded for free at http://seqsimla.sourceforge.net. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1186/1471-2105-14-199 | BMC Bioinformatics |
Keywords | Field | DocType |
microarrays,algorithms,phenotype,computer simulation,quantitative trait loci,bioinformatics | Quantitative trait locus,Biology,International HapMap Project,Genetic association,Software,Bioinformatics,Computational biology,Genetics,Locus (genetics),Statistical power,Explained variation,DNA microarray | Journal |
Volume | Issue | ISSN |
14 | 1 | 1471-2105 |
Citations | PageRank | References |
10 | 0.70 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ren-Hua Chung | 1 | 19 | 3.18 |
| Chung-Chin Shih | 2 | 10 | 1.37 |