Genetic Data Simulators and their Applications: An Overview

Peng, Bo; Chen, Huann-Sheng; Mechanic, Leah E.; Racine, Ben; Clarke, John; Gillanders, Elizabeth M.; Feuer, Eric J.

doi:10.1002/gepi.21876

Cited by 31 publications

(28 citation statements)

References 56 publications

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“…Forward simulations allow investigators to model the effects of demography and selection on sampled DNA sequences [Peng et al., ]. Because recombination and natural selection jointly impact the number of segregating sites and the proportion of sites at low frequency, it may be important to accurately account for these features when performing power calculations.…”

Section: Resultsmentioning

confidence: 99%

Population Genetic Simulations of Complex Phenotypes with Implications for Rare Variant Association Tests

Uricchio

Torres

Witte

et al. 2014

Genetic Epidemiology

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Demographic events and natural selection alter patterns of genetic variation within populations, and may play a substantial role in shaping the genetic architecture of complex phenotypes and disease. However, the joint impact of these basic evolutionary forces is often ignored in the assessment of statistical tests of association. Here, we provide a simulation-based framework for generating DNA sequences that incorporates selection and demography with flexible models for simulating phenotypic variation (sfs_coder). This tool also allows the user to perform locus-specific simulations by automatically querying annotated genomic functional elements and genetic maps. We demonstrate the effects of evolutionary forces on patterns of genetic variation by simulating recently inferred models of human selection and demography. We use these simulations to show that the demographic model and locus-specific features, such as the proportion of sites under selection, may have practical implications for estimating the statistical power of sequencing-based rare variant association tests. In particular, for some phenotype models, there may be higher power to detect rare variant associations in African populations compared to non-Africans, but power is considerably reduced in regions of the genome with rampant negative selection. Furthermore, we show that existing methods for simulating large samples based on resampling from a small set of observed haplotypes fail to recapitulate the distribution of rare variants in the presence of rapid population growth (as has been observed in several human populations).

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Section: Resultsmentioning

confidence: 99%

Population Genetic Simulations of Complex Phenotypes with Implications for Rare Variant Association Tests

Uricchio

Torres

Witte

et al. 2014

Genetic Epidemiology

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“…Despite such a large number of genetic simulation programs available, many seem to be rarely utilized. An evaluation of five recent issues of the journal Genetic Epidemiology noted that only eight of 36 articles that included simulated data, used existing genetic simulators, or simulators already catalogued by GSR [Peng et al., ]. These results may suggest that only a small fraction of the scope and diversity of genetic simulations are covered by existing simulators.…”

Section: Software and Methods Developmentmentioning

confidence: 99%

“…Development and support of a curriculum in genetic simulation to train researchers in genetic simulation, programming, and best practices, including accurate documentation and evaluation of programs dropping methods excel in certain application areas and were also frequently used for simple homemade simulations. Phylogenetic simulation was utilized less frequently for genetic epidemiologic studies [Peng et al, 2014]. Each simulation method has pros and cons, therefore it is important to consider which method is most appropriate for the scientific question being asked and the type of data structures being simulated [Hoban et al, 2011;Liu et al, 2008;Ritchie and Bush, 2010].…”

Section: State Of the Sciencementioning

confidence: 99%

Genetic Simulation Tools for Post‐Genome Wide Association Studies of Complex Diseases

Chen

Hutter

Mechanic

et al. 2014

Genetic Epidemiology

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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: (i) identify opportunities, challenges and resource needs for the development and application of genetic simulation models; (ii) improve the integration of tools for modeling and analysis of simulated data; and (iii) 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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“…A large collection of software tools for genetic data simulation has been developed in the past decades. Extensive descriptions of available forward simulators show that a wide but incomplete range of genetic and population processes are considered by these tools [6, 7, 8, 9].…”

Section: Introductionmentioning

confidence: 99%

SANTA-SIM: Simulating Viral Sequence Evolution Dynamics Under Selection and Recombination

Jariani

Warth

Deforche

et al. 2018

Preprint

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Simulations are widely used to provide expectations and predictive distributions under known conditions against which to compare empirical data. Such simulations are also invaluable for testing and comparing the behavior and power of inference methods. We describe SANTA-SIM, a software package to simulate the evolution of a population of gene sequences forwards through time. It models the underlying biological processes as discrete components: replication, recombination, point mutations, insertion-deletions, and selection under various fitness models and population size dynamics. The software is designed to be intuitive to work with for a wide range of users and executable in a cross-platform manner.

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Genetic Data Simulators and their Applications: An Overview

Cited by 31 publications

References 56 publications

Population Genetic Simulations of Complex Phenotypes with Implications for Rare Variant Association Tests

Population Genetic Simulations of Complex Phenotypes with Implications for Rare Variant Association Tests

Genetic Simulation Tools for Post‐Genome Wide Association Studies of Complex Diseases

SANTA-SIM: Simulating Viral Sequence Evolution Dynamics Under Selection and Recombination

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