Forward-Time Population Genetics Simulations
Methods, Implementation, and Applications
Published on 25. January 2012
220 pages
978-1-118-18034-1 (ISBN)
Description
The only book available in the area of forward-time populationgenetics simulations--applicable to both biomedical andevolutionary studies
The rapid increase of the power of personal computers has led tothe use of serious forward-time simulation programs in geneticstudies. Forward-Time Population Genetics Simulationspresents both new and commonly used methods, and introducessimuPOP, a powerful and flexible new program that can be used tosimulate arbitrary evolutionary processes with unique features likecustomized chromosome types, arbitrary nonrandom mating schemes,virtual subpopulations, information fields, and Pythonoperators.
The book begins with an overview of important concepts andmodels, then goes on to show how simuPOP can simulate a number ofstandard population genetics models--with the goal ofdemonstrating the impact of genetic factors such as mutation,selection, and recombination on standard Wright-Fisher models. Therest of the book is devoted to applications of forward-timesimulations in various research topics.
Forward-Time Population Genetics Simulationsincludes:
* An overview of currently available forward-time simulationmethods, their advantages, and shortcomings
* An overview and evaluation of currently available software
* A simuPOP tutorial
* Applications in population genetics
* Applications in genetic epidemiology, statistical genetics, andmapping complex human diseases
The only book of its kind in the field today, Forward-TimePopulation Genetics Simulations will appeal to researchers andstudents of population and statistical genetics.
More details
Other editions
Additional editions
Bo Peng | Marek Kimmel | Christopher L. Amos
Forward-Time Population Genetics Simulations
Methods, Implementation, and Applications
Book
03/2012
Wiley
€115.50
Shipment within 10-20 days
Software
01/2012
Wiley
€112.18
Shipment within 15-20 days
Persons
Bo Peng, PHD, is an assistant professor in the Department ofGenetics at The University of Texas MD Anderson Cancer Center. Withhis degrees in applied mathematics and biostatistics, he isapplying advanced computational techniques such as parallelcomputation and large-scale simulations to research topics inpopulation genetics, genetic epidemiology, and bioinformatics.
Marek Kimmel, PHD, is Director of the Doctoral Program inBioinformatics and Statistical Genetics and head of theBioinformatics Group at Rice University. He holds jointappointments as Professor of Statistics at Rice University,Professor of Biostatistics and Applied Mathematics at MD AndersonCancer Center, and Professor of Biometry at The University of TexasSchool of Public Health.
Christopher I. Amos, PHD, is a professor in theDepartment of Genetics at The University of Texas MD AndersonCancer Center. He also holds adjunct appointments at RiceUniversity and in the Department of Epidemiology at The Universityof Texas School of Public Health.
Content
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 diseases89
5.1 Introduction 89
5.2 Controlling disease allele frequencies at the presentgeneration 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
