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This book draws together some mathematical ideas that are useful in population genetics, concentrating on a few aspects which are both biologically relevant and mathematically interesting.
This book draws together some mathematical ideas that are useful in population genetics, concentrating on a few aspects which are both biologically relevant and mathematically interesting.
A definitive account of the origins of modern mathematical population genetics, first published in 2000.
Population genetics has made great strides in applying statistical analysis and mathematical modeling to understand how genes mutate and spread through populations over time. But real populations also live in space. Streams, mountains, and other geographic features often divide populations, limit migration, or otherwise influence gene flow. This book rigorously examines the processes that determine geographic patterns of genetic variation, providing a comprehensive guide to their study and interpretation. Geographical Genetics has a unique focus on the mathematical relationships of spatial statistical measures of patterns to stochastic processes. It also develops the probability and distribution theory of various spatial statistics for analysis of population genetic data, detailing exact methods for using various spatial features to make precise inferences about migration, natural selection, and other dynamic forces. The book also reviews the experimental literature on the types of spatial patterns of genetic variation found within and among populations. And it makes an unprecedented strong connection between observed measures of spatial patterns and those predicted theoretically. Along the way, it introduces readers to the mathematics of spatial statistics, applications to specific population genetic systems, and the relationship between the mathematics of space-time processes and the formal theory of geographical genetics. Written by a leading authority, this is the first comprehensive treatment of geographical genetics. It is a much-needed guide to the theory, techniques, and applications of a field that will play an increasingly important role in population biology and ecology.
"What underlying forces are responsible for the observed patterns of variability, given a collection of DNA sequences?" In approaching this question a number of probability models are introduced and anyalyzed.Throughout the book, the theory is developed in close connection with data from more than 60 experimental studies that illustrate the use of these results.
Various approaches have been developed to evaluate the consequences of spatial structure on evolution in subdivided populations. This book is both a review and new synthesis of several of these approaches, based on the theory of spatial genetic structure. François Rousset examines Sewall Wright's methods of analysis based on F-statistics, effective size, and diffusion approximation; coalescent arguments; William Hamilton's inclusive fitness theory; and approaches rooted in game theory and adaptive dynamics. Setting these in a framework that reveals their common features, he demonstrates how efficient tools developed within one approach can be applied to the others. Rousset not only revisits classical models but also presents new analyses of more recent topics, such as effective size in metapopulations. The book, most of which does not require fluency in advanced mathematics, includes a self-contained exposition of less easily accessible results. It is intended for advanced graduate students and researchers in evolutionary ecology and population genetics, and will also interest applied mathematicians working in probability theory as well as statisticians.
This work reflects sixteen hours of lectures delivered by the author at the 2009 St Flour summer school in probability. It provides a rapid introduction to a range of mathematical models that have their origins in theoretical population genetics. The models fall into two classes: forwards in time models for the evolution of frequencies of different genetic types in a population; and backwards in time (coalescent) models that trace out the genealogical relationships between individuals in a sample from the population. Some, like the classical Wright-Fisher model, date right back to the origins of the subject. Others, like the multiple merger coalescents or the spatial Lambda-Fleming-Viot process are much more recent. All share a rich mathematical structure. Biological terms are explained, the models are carefully motivated and tools for their study are presented systematically.
It is part of the ideology of science that it is an international enterprise, carried out by a community that knows no barriers of nation or culture. But the reality is somewhat different. Despite the best intentions of scientists to form a single community, unseparated by differences of national and political viewpoint, they are, in fact, separated by language. Scientific literature in German is not generally assimilated by French workers, nor that appearing in French by those whose native language is English. The problem appears to have become more severe since the last war, because the ascendance of the United States as the preeminent economic power led, in a time of big and expensive science, to a pre dominance of American scientific production and a growing tendency (at least among English-speakers) to regard English as the international language of science. International congresses and journals of world circulation have come more and more to take English as their standard or official language. As a result, students and scientific workers in the English speaking world have become more linguistically parochial than ever before and have been cut off from a considerable scientific literature. Population genetics has been no exception to the rule. The elegant and extremely innovative theoreticaI work of Malecot, for example, is only now being properly assimilated by population biologists outside France. It was therefore with some sense of frustration that I read Prof.
No leading university department of mathematics or statistics, or library, can afford to be without this unique text. Leading authorities give a unique insight into a wide range of currently topical problems, from the mathematics of road networks to the genomics of cancer.
This book describes the development of genetic resources in amaranths, with a major focus on genomics, reverse, and forward genetics tools and strategies that have been developed for crop improvement. Amaranth is an ancient crop native to the New World. Interest in amaranths is being renewed, due to their adaptability, stress tolerance, and nutritional value. There are about 65 species in the genus, including Amaranthus caudatus L., A. cruentus L., and A. hypochondriacus L., which are primarily grown as protein-rich grains or pseudocereals. The genus also includes major noxious weeds (e.g., A. palmeri). The amaranths are within the Caryophyllales order and thus many species (e.g., A. tricolor) produce red (betacyanin) or yellow (betaxanthin) betalain pigments, which are chemically distinct from the anthocyanins responsible for red pigmentation in other plants. A. hypochondriacus, which shows disomic inheritance (2n = 32; n= 466 Mb), has been sequenced and annotated with 23,059 protein-coding genes. Additional members of the genus are now also been sequenced including weedy amaranths, other grain amaranths, and their putative progenitors.