2020
DOI: 10.1002/gepi.22362
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On the application, reporting, and sharing of in silico simulations for genetic studies

Abstract: In silico simulations play an indispensable role in the development and application of statistical models and methods for genetic studies. Simulation tools allow for the evaluation of methods and investigation of models in a controlled manner. With the growing popularity of evolutionary models and simulation-based statistical methods, genetic simulations have been applied to a wide variety of research disciplines such as population genetics, evolutionary genetics, genetic epidemiology, ecology, and conservatio… Show more

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Cited by 4 publications
(3 citation statements)
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“…Our review also revealed that the evaluation stage of NRSs is incomplete, with methods of internal testing, external verification, and application assessment being applied inconsistently and often confusedly. Some methods, such as using simulation data, may overlap and serve as both internal testing and external verification data [53] . Additionally, the evaluation metrics in the included NRSs focused primarily on accuracy, operability, feasibility, and authenticity.…”
Section: Discussionmentioning
confidence: 99%
“…Our review also revealed that the evaluation stage of NRSs is incomplete, with methods of internal testing, external verification, and application assessment being applied inconsistently and often confusedly. Some methods, such as using simulation data, may overlap and serve as both internal testing and external verification data [53] . Additionally, the evaluation metrics in the included NRSs focused primarily on accuracy, operability, feasibility, and authenticity.…”
Section: Discussionmentioning
confidence: 99%
“…These data should be useful for evaluating genomic patterns and disease inheritance in ascertained families, and for validation and bench-marking of statistical analysis methods in family-based sequencing. Our data and simulation scripts answer important calls in genetic epidemiology [2] for the reuse of existing datasets to compare statistical methods and maximize benefit from research investment and for the sharing of source code and simulated data sets in public repositories to facilitate reuse and reproducibility. …”
Section: Value Of the Datamentioning
confidence: 99%
“…Our data and simulation scripts answer important calls in genetic epidemiology [2] for the reuse of existing datasets to compare statistical methods and maximize benefit from research investment and for the sharing of source code and simulated data sets in public repositories to facilitate reuse and reproducibility.…”
Section: Value Of the Datamentioning
confidence: 99%