GSR: Simulator - ForSim

Basic Package Attributes
Title ForSim
Short Description ForSim: A Forward Evolutionary Computer Simulation
Long Description ForSim is a forward evolutionary simulation system designed to be highly flexible for application to a wide variety of both applied health and life science questions as well as issues in theoretical evolutionary biology. It attempts to simulate in the most natural way the evolutionary process that generates the genetic architecture that underlies present-day traits, and related phenomena such as mate choice, migration bias, population substructure, and interactions with the environment. These phenomena are related to the way natural selection affects underlying genetic variation, molding the trait’s genetic architecture. Variation over the short evolutionary scale, within species or among closely related species, is generally built upon a phylogenetically stable underlying causal genetic architecture upon which mutation, selection, and demographic effects are laid to generate subsequent variation within and among populations.
Version Continually revised
Project Started 2008
Last Release 12 years, 9 months ago
Citations Lambert BW, Terwilliger JD, Weiss KM, ForSim: a tool for exploring the genetic architecture of complex traits with controlled truth., Bioinformatics, 08-15-2008 [ Abstract, cited in PMC ]
GSR Certification This simulator has not yet been evaluated for GSR Certification. Learn more about or request GSR Certification.
Detailed Attributes
Attribute CategoryAttribute
Type of Simulated DataGenotype at Genetic Markers, Diploid DNA Sequence, Haploid DNA Sequence (Through use of multiple parameters),
VariationsBiallelic Marker, Multiallelic Marker, Single Nucleotide Variation, Missing Genotypes (Through post-run data editing),
Simulation MethodForward-time,
Data TypeSaved simulation,
File formatProgram Specific (Program-specific input language.),
Data TypeGenotype or Sequence, Phenotypic Trait, Individual Relationship, Demographic, Mutation, Linkage Disequilibrium (Compute from output data), Diversity Measures (Compute from output data),
Sequencing ReadsOther (Many different statistics are saved (e.g., marker chi-square values, locus entropy, sib relative risks, etc.)),
File FormatProgram Specific, Other (Linkage),
Sample TypeRandom or Independent, Sibpairs, Trios and Nuclear Families, Extended or Complete Pedigrees, Case-control,
Trait TypeBinary or Qualitative, Quantitative, Multiple,
DeterminantsSingle Genetic Marker, Multiple Genetic Markers, Sex-linked, Gene-Gene Interaction, Environmental Factors, Gene-Environment Interaction,
Evolutionary Features
Population Size ChangesLogistic Growth, Bottleneck, Carrying Capacity,
Gene FlowIsland Models (Multiple discrete populations or isolation by distance, serial founder effects), Continent-Island Models (Can be structured this way by input file specifications), Admixed Population, User-defined Matrix, Other (Probabilistic mating between populations),
Life CycleDiscrete Generation Model,
Mating SystemRandom Mating, Monogamous, Polygamous, Assortative or Disassortative,
FecundityRandomly Distributed,
Natural Selection
DeterminantSingle-locus (Fitness can be probabilistic around an expected value based on user-specified criteria, or deterministic by truncation relative to phenotype), Multi-locus, Codon-based, Phenotypic Trait, Environmental Factors,
ModelsDirectional Selection, Balancing Selection, Multi-locus models, Epistasis, Random Fitness Effects (Based on individual or family environmental effects), Phenotype Threshold, Other (Combinations of genotype and environmental effects. mathematical fitness functions can be specified, implementing models such as threshold.),
RecombinationUniform, Varying Recombination Rates,
Mutation ModelsTwo-allele Mutation Model, Codon and Amino Acid Models, Heterogeneity among Sites, Others (Sex-specific),
Events AllowedPopulation Merge and Split, Varying Demographic Features, Varying Genetic Features, Change of Mating Systems, Other (Changes in environmental effects can be imposed. ),
OtherPhenogenetic, Polygenic background,
InterfaceScript-based (Program-specific language),
Tested PlatformsMac OS X, Linux and Unix,
LanguageC or C++,
LicenseGNU Public License,
GSR Certification

The following 8 publications are selected examples of applications that used ForSim.


Weiss KM, Lambert BW, What type of person are you? Old-fashioned thinking even in modern science., Cold Spring Harb Perspect Biol, 01-01-2014 [Abstract]

Chan Y, Lim ET, Sandholm N, Wang SR, McKnight AJ, Ripke S, Daly MJ, Neale BM, Salem RM, Hirschhorn JN, An excess of risk-increasing low-frequency variants can be a signal of polygenic inheritance in complex diseases., Am J Hum Genet, 03-06-2014 [Abstract]

Wang SR, Agarwala V, Flannick J, Chiang CW, Altshuler D, Hirschhorn JN, Simulation of Finnish population history, guided by empirical genetic data, to assess power of rare-variant tests in Finland., Am J Hum Genet, 05-01-2014 [Abstract]


Agarwala V, Flannick J, Sunyaev S, Altshuler D, Evaluating empirical bounds on complex disease genetic architecture., Nat Genet, 12-01-2013 [Abstract]


Zhu Y, Xiong M, Family-based association studies for next-generation sequencing., Am J Hum Genet, 06-08-2012 [Abstract]

Shugart YY, Zhu Y, Guo W, Xiong M, Weighted pedigree-based statistics for testing the association of rare variants., BMC Genomics, 11-24-2012 [Abstract]


Hiekkalinna T, Schäffer AA, Lambert B, Norrgrann P, Göring HH, Terwilliger JD, PSEUDOMARKER: a powerful program for joint linkage and/or linkage disequilibrium analysis on mixtures of singletons and related individuals., Hum Hered, 01-01-2011 [Abstract]

Weiss KM, Buchanan AV, Lambert BW, The Red Queen and her king: cooperation at all levels of life., Am J Phys Anthropol, 01-01-2011 [Abstract]

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