Forward–Time Population Genetics Simulations: Meth ods, Implementation, and Applications

Bo Peng, PHD, is an assistant professor in the Department of Genetics at The University of Texas MD Anderson Cancer Center. With his degrees in applied mathematics and biostatistics, he is applying advanced computational techniques such as parallel computation and large-scale simulations to research topics in population genetics, genetic epidemiology, and bioinformatics. Marek Kimmel, PHD , is Director of the Doctoral Program in Bioinformatics and Statistical Genetics and head of the Bioinformatics Group at Rice University. He holds joint appointments as Professor of Statistics at Rice University, Professor of Biostatistics and Applied Mathematics at MD Anderson Cancer Center, and Professor of Biometry at The University of Texas School of Public Health. Christopher I. Amos, PHD , is a professor in the Department of Genetics at The University of Texas MD Anderson Cancer Center. He also holds adjunct appointments at Rice University and in the Department of Epidemiology at The University of Texas School of Public Health.

Preface ix Acknowledgments xiii List of examples xxiii 1. Basic concepts and models 1 1.1 Biological and genetic concepts 2 1.2 Population and evolutionary genetics 6 1.3 Statistical genetics and genetic epidemiology 17 2. Simulation of population genetics models 25 2.1 Random genetic drift 25 2.2 Demographic models 29 2.3 Mutation 31 2.4 Migration 34 2.5 Recombination and linkage disequilibrium 36 2.6 Natural selection 37 2.7 Genealogy of forward-time simulations 41 3. Ascertainment bias in population genetics 47 3.1 Introduction 47 3.2 Methods 49 3.3 Results 54 3.4 Discussion and Conclusions 58 4. Observing properties of evolving populations 63 4.1 Introduction 64 4.2 Simulation of the evolution of allele spectra 66 4.3 Extensions to the basic model 78 5. Simulating populations with complex human diseases 89 5.1 Introduction 89 5.2 Controlling disease allele frequencies at the present generation 91 5.3 Forward-time simulation of realistic samples 102 5.4 Discussion 119 6. Nonrandom mating and its applications 125 6.1 Assortative mating 126 6.2 More complex non-random mating schemes 132 6.3 Hetergeneous mating schemes 140 6.4 Simulation of age structured populations 145 Appendix: Forward-time simulations using stimulPOP 157 A.1 Introduction 157 A.2 Population 160 A.3 Operators 172 A.4 Evolve on or more populations 181 A.5 A complete stimuPOP script 185