scholarly journals Population genetic simulation study of power in association testing across genetic architectures and study designs

2019 ◽  
Vol 44 (1) ◽  
pp. 90-103 ◽  
Author(s):  
Dominic M. H. Tong ◽  
Ryan D. Hernandez
Keyword(s):  
Simulation Study ◽  
Population Genetic ◽  
Association Testing ◽  
Genetic Simulation ◽  
Study Designs

scholarly journals Clotho: addressing the scalability of forward time population genetic simulation

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Patrick P. Putnam ◽  
Philip A. Wilsey ◽  
Ge Zhang
Keyword(s):  
Population Genetic ◽  
Genetic Simulation

scholarly journals A community-maintained standard library of population genetic models

2019 ◽  
Author(s):  
Jeffrey R. Adrion ◽  
Christopher B. Cole ◽  
Noah Dukler ◽  
Jared G. Galloway ◽  
Ariella L. Gladstein ◽  
...  
Keyword(s):  
Population Genetic ◽  
Genomic Data ◽  
Simulation Models ◽  
Easy Access ◽  
Major Barrier ◽  
Simulation Engine ◽  
Complex Models ◽  
Demographic Inference ◽  
Genetic Simulation ◽  
Inference Methods

AbstractThe explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

scholarly journals A community-maintained standard library of population genetic models

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Jeffrey R Adrion ◽  
Christopher B Cole ◽  
Noah Dukler ◽  
Jared G Galloway ◽  
Ariella L Gladstein ◽  
...  
Keyword(s):  
Population Genetic ◽  
Genomic Data ◽  
Simulation Models ◽  
Easy Access ◽  
Major Barrier ◽  
Simulation Engine ◽  
Complex Models ◽  
Demographic Inference ◽  
Genetic Simulation ◽  
Inference Methods

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly, these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here, we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

scholarly journals skelesim: an extensible, general framework for population genetic simulation in R

2016 ◽  
Vol 17 (1) ◽  
pp. 101-109 ◽  
Author(s):  
Christian M. Parobek ◽  
Frederick I. Archer ◽  
Michelle E. DePrenger-Levin ◽  
Sean M. Hoban ◽  
Libby Liggins ◽  
...  
Keyword(s):  
Population Genetic ◽  
General Framework ◽  
Genetic Simulation

Population genetic simulation and data analysis with Plabsoft

Euphytica ◽  
2007 ◽  
Vol 161 (1-2) ◽  
pp. 133-139 ◽  
Author(s):  
Hans Peter Maurer ◽  
Albrecht E. Melchinger ◽  
Matthias Frisch
Keyword(s):  
Data Analysis ◽  
Population Genetic ◽  
Genetic Simulation

scholarly journals Population genetic simulation study of power in association testing across genetic architectures and study designs

2019 ◽  
Author(s):  
Dominic Ming Hay Tong ◽  
Ryan D. Hernandez
Keyword(s):  
Complex Traits ◽  
Statistical Power ◽  
Rare Variants ◽  
Association Studies ◽  
Case Control ◽  
Population Variation ◽  
Control Panel ◽  
Evolutionary Forces ◽  
Association Testing ◽  
Genetic Simulation

AbstractWhile it is well established that genetics can be a major contributor to population variation of complex traits, the relative contributions of rare and common variants to phenotypic variation remains a matter of considerable debate. Here, we simulate rare variant association studies across different case/control panel sampling strategies, sequencing methods, and genetic architecture models based on evolutionary forces to determine the statistical performance of RVATs widely in use. We find that the highest statistical power of RVATs is achieved by sampling case/control individuals from the extremes of an underlying quantitative trait distribution. We also demonstrate that the use of genotyping arrays, in conjunction with imputation from a whole genome sequenced (WGS) reference panel, recovers the vast majority (90%) of the power that could be achieved by sequencing the case/control panel using current tools. Finally, we show that for dichotomous traits, the statistical performance of RVATs decreases as rare variants become more important in the trait architecture. Our results extend previous work to show that RVATs are insufficiently powered to make generalizable conclusions about the role of rare variants in dichotomous complex traits.

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