Description
Amended draft Table of Contents 1. Introduction and relevance a. Who this book benefits b. Required background c. Review of population genetic/genomic simulation resources d. When to write your own simulations 2. Retrospective and prospective simulation a. Retrospective, coalescent simulation b. Prospective simulation c. Individual-based models 3. Data structures and computational efficiency a. Data structures b. Computer clusters c. Graphical processor units (GPU) programming Part I. Simulating the five factors that affect population dynamics and genetic diversity 4. Mutation a. Background and theory b. The bitset as a data structure for storing genetic sequence data c. Writing individual and population classes d. Common types of mutation i. Point mutations ii. Indels iii. Microsatellites iv. AFLPs e. Haplotypes 5. Population size and genetic drift a. Background and theory b. Fixation of alleles c. Demographic change: expansions and bottlenecks 6. Migration and population structure a. Background and theory b. Panmixia c. Isolation by barrier d. Isolation by distance e. Admixture f. Metapopulations 7. Meiotic recombination a. Background and theory b. Unlinked loci and independent assortment c. Linked loci and crossing-over d. Linked loci and gene conversion 8. Natural selection a. Background and theory b. Fitness c. Viability and fecundity selection d. Positive natural selection e. Purifying natural selection and background selection f. Frequency-dependent selection g. Assortative mating h. Selection on a protein-coding gene Part II. Adding biological and ecological realism 9. Implementing all five factors simultaneously a. A generation function: the order of things b. Birth and death c. Overlapping vs. non-overlapping generations d. Using coalescent simulation to obtain random starting populations 10. Modeling different life histories a. Ploidy b. Monoecious, dioecious, and hermaphroditic species 11. Spatially-explicit simulation a. Neutral evolution b. The impact of landscape on dispersal c. The impact of environment on fitness Part III. Statistical inference in population genetics 12. Calculating summary statistics and visualization a. Sequence-based summary statistics b. Locus-specific summary statistics c. Null distributions d. Visualizing simulated and empirical data 13. Approximate Bayesian computation: preliminaries a. Statistical and historical background b. Parameters c. Prior distributions d. Sufficient summary statistics e. Tolerance level f. The relevance of data type: SNPs, microsatellites, AFLPs 14. Approximate Bayesian computation: implementation a. Rejection algorithms b. Regression-based algorithms c. Markov Chain Monte Carlo algorithms d. Sequential Monte Carlo algorithms e. Hierarchical models f. Model selection g. Parameter estimation h. Marginal, joint, and conditional distributions i. Posterior predictive distributions and model validation j. When simulation is costly: Approximate approximate Bayesian computation Part IV. In-depth examples 15. Comparing simulated genetic data to 1000 Genomes data 16. The spread of the invasive species Japanese hops in the Upper Midwest, USA Appendices C++: Review and reference R: Review and reference
