Preface and acknowledgments

This book was born of two desires, one simple and the other more ambitious, both of which were motivated by my experiences learning and teaching population genetics. My first desire was to create a more up-to-date survey text of the field of population genetics. Several of the widely employed and respected standard texts were originally conceived in the mid-1980s. Although these texts have been revised over time, aspects of their organization and content are inherently dated. At the same time, I set out with the more ambitious goal of offering an alternative body of materials to enrich the manner in which population genetics is taught and learned.

Much of population genetics during the twentieth century was hypothesis-rich but data-poor. The theory developed between about 1920 and 1980 spawned manifold predictions about basic evolutionary processes. However, most of these predictions could not be tested or tested with only very limited power for lack of appropriate or sufficient genetic data. In the last two decades, population genetics has become a field that is no longer data-limited. With the collection and open sharing of massive amounts of genomic data and the technical ability to collect large amounts of genetic information rapidly from almost any organism, population genetics has now become data-rich but relatively hypothesis-poor. Why? Because mainstream population genetics has struggled to develop and employ alternative testable hypotheses in addition to those offered by traditional null models. Innovation in developing context-specific and testable alternative population genetic models is as much a requirement for hypothesis testing as empirical data. Such innovation, of course, first requires a sound understanding of the traditional and well-accepted models and hypotheses.

It is often repeated that the major advance in population genetics over the last decade or two is the availability of huge amounts of genetic data generated by the ability to collect genetic data and to sequence entire genomes. It is certainly true that advances in molecular biology, DNA sequencing technology, and bioinformatics have provided a wealth of genetic data, some of it in the form of divergence or polymorphism data that is grist for the mill of population genetics hypothesis testing.

An equally fundamental advance in population genetics has been the emergence of new models and expectations to match the genetic data that are now readily available. Coalescent or genealogical branching theory is primary among these conceptual advances. During the past two decades, coales-cent theory has moved from an esoteric problem pursued for purely mathematical reasons to an important conceptual tool used to make testable predictions. Nonetheless, teaching of coalescent theory in undergraduate and graduate population genetics courses has not kept pace with the growing influence of coalescent theory in hypothesis testing. A major impediment has been the lack of teaching materials that make coalescent theory truly accessible to students learning population genetics for the first time. One of my goals was to construct a text that met this need with a systematic and thorough introduction to the concepts of coalescent theory and its applications in hypothesis testing. The chapter sections on coa-lescent theory are presented along with traditional theory of identity by descent on the same topics to help students see the commonality of the two approaches. However, the coalescence chapter sections could easily be assigned as a group.

Another of my primary goals for this text was to offer material to engage the various learning styles possessed by individuals. Learning conceptual population genetics in the language of mathematics is often relatively easy for abstract and mathematical learners. However, my aim was to cater to a wide range of learning styles by building a range of features into the text. A key pedagogical feature in the book is formed by boxes set off from the main text that are designed to engage the various learning styles. These include Interact boxes that guide students through structured exercises in computer simulation utilizing software in the public domain. The simulation xii

0 0

Post a comment