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

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

Haasl, Ryan J.

ウェブストア価格 ¥18,878（本体¥17,162）
Springer Nature Switzerland AG（2023/09発売）
外貨定価 US$ 89.99
ポイント 171pt

オンデマンド(OD/POD)版です。キャンセルは承れません。

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製本 Paperback:紙装版/ペーパーバック版／ページ数 313 p.
商品コード 9783030973834

Full Description

Dramatic advances in computing power enable simulation of DNA sequences generated by complex microevolutionary scenarios that include mutation, population structure, natural selection, meiotic recombination, demographic change, and explicit spatial geographies. Although retrospective, coalescent simulation is computationally efficient—and covered here—the primary focus of this book is forward-in-time simulation, which frees us to simulate a wider variety of realistic microevolutionary models. The book walks the reader through the development of a forward-in-time evolutionary simulator dubbed FORward Time simUlatioN Application (FORTUNA). The capacity of FORTUNA grows with each chapter through the addition of a new evolutionary factor to its code. Each chapter also reviews the relevant theory and links simulation results to key evolutionary insights. The book addresses visualization of results through development of R code and reference to more than 100 figures. All code discussedin the book is freely available, which the reader may use directly or modify to better suit his or her own research needs. Advanced undergraduate students, graduate students, and professional researchers will all benefit from this introduction to the increasingly important skill of population genetic simulation.

Introduction and relevance.- Retrospective and prospective simulation.- Data structures and computational efficiency.- Mutation.- Population size and genetic drift.- Migration and population structure.- Meiotic recombination.- Natural selection.- Implementing all five factors simultaneously.- Modeling different life histories.- Spatially-explicit simulation.- Calculating summary statistics and visualization.- Approximate Bayesian computation: preliminaries.- Approximate Bayesian computation: implementation.- Comparing simulated genetic data to 1000 Genomes data.- The spread of the invasive species Japanese hops in the Upper Midwest, USA.