Nature in Silico: Population Genetic Simulation and its Evolutionary Interpretation Using C++ and R

Preface
Acknowledgments
Contents
Acronyms and Symbols
1 Simulation as a Form of Scientific Investigation
	1.1 Simulations as Enlivened Models
	1.2 Borges: How Detailed Should a Model Be?
	1.3 A Short, Selective History of Computer Simulation
		1.3.1 Origination of the Monte Carlo Method
		1.3.2 Early Computer-Based Simulation
		1.3.3 Early Simulations of Biological Evolution
			1.3.3.1 The Molecular Population Geneticists
			1.3.3.2 The Paleobiologists
	1.4 Philosophy and Simulation
		1.4.1 Plato and Representational Fiction
		1.4.2 Fortuna and Chance Ontology
		1.4.3 Epistemological Concerns
	1.5 Whom This Book Will Benefit
	1.6 Required Background Knowledge and Online Resources
	References
2 Retrospective and Prospective Simulation
	2.1 Background: Retrospective Versus Prospective Simulation
	2.2 Background: Coalescent Theory
	2.3 Coalescent Simulations in MS and R
		2.3.1 Package SCRM
			2.3.1.1 Scenario A
			2.3.1.2 Scenario B
			2.3.1.3 Scenario C
			2.3.1.4 Scenario D
			2.3.1.5 Interpreting Genealogical Results When Recombination Is Simulated
		2.3.2 Package COALA
	2.4 The Utility of Retrospective, Coalescent Simulation
	References
3 Mutation and Genetic Drift
	3.1 Background
	3.2 A Textbook Simulation
	3.3 Some Practicalities
		3.3.1 Efficient Representation of a Genetic Sequence
		3.3.2 Simulating Point Mutation on a Sequence
	3.4 Forward Simulation of Mutation and Genetic Drift
		3.4.1 Parameters
		3.4.2 main() Function
		3.4.3 Class Population
			3.4.3.1 Basic Class Structure: Constructor and Private Variables
			3.4.3.2 Member Functions
		3.4.4 Class Individual
		3.4.5 Class Allele
		3.4.6 Summarystats Header File
			3.4.6.1 Nucleotide Diversity, π
			3.4.6.2 Watterson's θ, θW
			3.4.6.3 The Inferential Merit of π and θW
	3.5 Aspects of Genetics, Population Biology, and Environment Not Yet Modeled
	3.6 Validation: Comparing Simulation Output with Theoretical Expectations
	3.7 Avoiding the Burn: Coalescent Simulation Followed by Forward Simulation
	References
4 Demographic Change
	4.1 Background
		4.1.1 Models of Demographic Change
			4.1.1.1 Exponential Population Growth or Decline
			4.1.1.2 Logistic Population Growth
		4.1.2 Using Coalescent Simulation to Build Intuition Regarding the Genetic Consequences of Demographic Change
	4.2 Forward Simulation of Demographic Change
		4.2.1 Requisite New Parameters
		4.2.2 Calculating Tajima's D
		4.2.3 Final Changes to Program Files
	4.3 Simulating a Bottleneck Followed by Logistic Growth
	4.4 The Varying Utility of Summary Statistics for Inference
	References
5 Meiotic Recombination
	5.1 Background
		5.1.1 Crossing-Over and Independent Assortment
		5.1.2 Linkage Disequilibrium
		5.1.3 Variation in Recombination Rate
	5.2 Forward Simulation of Meiotic Recombination Among Multiple Linked and Unlinked Loci
		5.2.1 Code
		5.2.2 Results
	5.3 Forward Simulation of Crossing-Over Along a Sequence
		5.3.1 Additional Parameters Required to Model Recombination Along a Sequence
		5.3.2 Modifying population.h
		5.3.3 Modifying individual.h
		5.3.4 Window-Based Summary Statistics and Calculating the Number of Haplotypes K in summarystats.h
		5.3.5 Results
		5.3.6 Effect of Recombination on the Number of Unique Haplotypes, K
		5.3.7 Visualizing the Distribution of a Summary Statistic Across Simulations and by Window
		5.3.8 Effect of Recombination on Tajima's D and Simulation as Exploration
	References
6 Population Structure and Migration
	6.1 Background and Theory
	6.2 Forward Simulation of Two Demes
		6.2.1 Two Formerly Independent Demes Begin Exchanging Migrants
			6.2.1.1 Reading Parameters for More Than One Population
			6.2.1.2 Class Metapopulation
			6.2.1.3 Modifications to Class Population
			6.2.1.4 Results of Simulating Two Demes Connected by Different Rates of Migration
	6.3 Forward Simulation of n Demes
		6.3.1 Deme Splitting and Merger
		6.3.2 Distributional and Longitudinal Visualization of Summary Statistics
		6.3.3 Diploid Sampling and Outputting Full Haplotypes at Specified Time Points
		6.3.4 Calculating Multilocus FST
	6.4 Printing Allele History File
		6.4.1 Results and Validation
	References
7 Natural Selection
	7.1 Background and Theory
		7.1.1 Natural Selection as Optimization?
		7.1.2 Fitness
			7.1.2.1 Marginal Fitness and Mean Population Fitness
			7.1.2.2 Deterministic Changes to Allele Frequencies
	7.2 Stochastic Simulation of the Selected Variant Only
		7.2.1 Frequency-Independent Selection
		7.2.2 Negative Frequency-Dependent Selection
		7.2.3 Overdominance
	7.3 Selection at Two Linked Sites
		7.3.1 Simulation of Two-Locus Selection withRecombination
			7.3.1.1 Deterministic Simulation
			7.3.1.2 Stochastic Simulation
	References
8 Effects of Selection on Linked Variants
	8.1 Modeling Natural Selection and Linked Polymorphism
	8.2 Positive Selection
		8.2.1 Selection on a New Variant
		8.2.2 Selection on Standing Variation
		8.2.3 No Recombination
		8.2.4 Overdominance
		8.2.5 Negative Frequency-Dependent Selection
	8.3 Purifying Natural Selection and Background Selection
	References
9 Quantitative Traits
	9.1 Background and Theory
	9.2 Neutral Quantitative Trait Evolution
		9.2.1 Some Preliminaries
		9.2.2 Modifying FORTUNA to Model Quantitative Trait Evolution
	9.3 Multiple Runs of Sequence or Quantitative Trait Evolution
	9.4 Random Genotypes for Initiating Quantitative Trait Simulation
	9.5 Quantitative Traits Under Selection and Evolutionary Constraints
		9.5.1 Natural Selection Using a Gaussian Fitness Function
		9.5.2 Evolutionary Constraints on Quantitative Phenotype
		9.5.3 Artificial Selection
FORTUNA Parameter Documentation
	A.1 Global Parameters
	A.2 Deme-Specific Parameters