simuPOP Operators

simuPOP is large, consisting of more than 70 operators and various functions that covers all important aspects of genetic studies. These includes mutation (k-allele, stepwise, generalized stepwise), migration (arbitrary, can create new subpopulation), recombination (uniform or nonuniform), gene conversion, quantitative trait, selection, penetrance (single or multi-locus, hybrid), ascertainment (case-control, affected sibpairs, random), statistics calculation (allele, genotype, haplotype, heterozygote number and frequency; expected heterozygosity; bi-allelic and multi-allelic \(D\), \(D'\) and \(r^{2}\) linkage disequilibrium measures; \(F_{st}\), \(F_{it}\) and \(F_{is}\)); pedigree tracing, visualization (using R or other Python modules). This chapter covers the basic and some not-so-basic usages of these operators, organized roughly by genetic factors.

• Introduction to operators
  • Apply operators to selected replicates and (virtual) subpopulations at selected generations
  • Applicable populations and (virtual) subpopulations
  • Dynamically determined loci (parameter loci) *
  • Write output of operators to one or more files
  • During-mating operators
  • Function form of an operator
• Initialization
  • Initialize individual sex (operator InitSex)
  • Initialize genotype (operator InitGenotype)
  • Initialize information fields (operator InitInfo)
• Expressions and statements
  • Output a Python string (operator PyOutput)
  • Execute Python statements (operator PyExec)
  • Evaluate and output Python expressions (operator PyEval)
  • Expression and statement involving individual information fields (operator InfoEval and InfoExec) *
  • Using functions in external modules in simuPOP expressions and statements
• Demographic changes
  • Migration (operator Migrator)
    • Migration by probability
    • Migration by proportion and counts
    • Theoretical migration models
    • migrate from virtual subpopulations *
    • Arbitrary migration models **
  • Migration using backward migration matrix (operator BackwardMigrator)
  • Split subpopulations (operators SplitSubPops)
  • Merge subpopulations (operator MergeSubPops)
  • Resize subpopulations (operator ResizeSubPops)
  • Time-dependent migration rate
• Genotype transmitters
  • Generic genotype transmitters (operators GenoTransmitter, CloneGenoTransmitter, MendelianGenoTransmitter, SelfingGenoTransmitter, HaplodiploidGenoTransmitter, and MitochondrialGenoTransmitter) *
  • Recombination (Operator Recombinator)
  • Gene conversion (Operator Recombinator) *
  • Tracking all recombination events **
• Mutation
  • Mutation models specified by rate matrixes (MatrixMutator)
  • k-allele mutation model (KAlleleMutator)
  • Diallelic mutation models (SNPMutator)
  • Nucleotide mutation models (AcgtMutator)
  • Mutation model for microsatellite markers (StepwiseMutator)
  • Simulating arbitrary mutation models using a hybrid mutator (PyMutator)*
  • Mixed mutation models (MixedMutator) **
  • Context-dependent mutation models (ContextMutator)**
  • Manually-introduced mutations (PointMutator)
  • Apply mutation to (virtual) subpopulations *
  • Allele mapping **
  • Mutation rate and transition matrix of a MatrixMutator**
  • Infinite-sites model and other simulation techniques **
  • Recording and tracing individual mutants **
• Penetrance
  • Map penetrance model (operator MapPenetrance)
  • Multi-allele penetrance model (operator MaPenetrance)
  • Multi-loci penetrance model (operator MlPenetrance)
  • Hybrid penetrance model (operator PyPenetrance)
• Quantitative trait
  • A hybrid quantitative trait operator (operator PyQuanTrait)
• Natural Selection
  • Natural selection through the selection of parents
  • Natural selection through the selection of offspring *
  • Are two selection scenarios equivalent? **
  • Map selector (operator MapSelector)
  • Multi-allele selector (operator MaSelector)
  • Multi-locus selection models (operator MlSelector)
  • A hybrid selector (operator PySelector)
  • Multi-locus random fitness effects (operator PyMlSelector)
  • Alternative implementations of natural selection
  • Frequency dependent or dynamic selection pressure *
  • Support for virtual subpopulations *
  • Natural selection in heterogeneous mating schemes **
• Tagging operators
  • Inheritance tagger (operator InheritTagger)
  • Summarize parental informatin fields (operator SummaryTagger)
  • Tracking parents (operator ParentsTagger)
  • Tracking index of offspring within families (operator OffspringTagger)
  • Assign unique IDs to individuals (operator IdTagger)
  • Tracking Pedigrees (operator PedigreeTagger)
  • A hybrid tagger (operator PyTagger)
  • Tagging that involves other parental information
• Statistics calculation (operator Stat)
  • How statistics calculation works
  • defdict datatype
  • Support for virtual subpopulations
  • Counting individuals by sex and affection status
  • Number of segregating and fixed sites
  • Allele count and frequency
  • Genotype count and frequency
  • Homozygote and heterozygote count and frequency
  • Haplotype count and frequency
  • Summary statistics of information fields
  • Linkage disequilibrium
  • Genetic association
  • population structure
  • Hardy-Weinberg equilibrium test
  • Measure of Inbreeding
  • Effective population size
  • Other statistics
  • Support for sex and customized chromosome types
• Conditional operators
  • Conditional operator (operator IfElse) *
  • Conditionally terminate an evolutionary process (operator TerminateIf)
  • Conditional removal of individuals (operator DiscardIf)
• Miscellaneous operators
  • An operator that does nothing (operator NoneOp)
  • dump the content of a population (operator Dumper)
  • Save a population during evolution (operator SavePopulation)
  • Pause and resume an evolutionary process (operator Pause) *
  • Measuring execution time of operators (operator TicToc) *
• Hybrid and Python operators
  • Hybrid operators
  • Python operator PyOperator *
  • During-mating Python operator *
  • Define your own operators *