This book Population Genetics – was born of two desires, one simple and the other more ambitious, both of which were moti- vated 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.
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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 contextspecific 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, coalescent 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 coalescent 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 stylespossessed by individuals. Learning conceptual popu- lation 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 problems are active rather than reflective and should appeal to trial-and-error or visual learners. Additionally, simulations uniquely demonstrate the outcome of stochastic processes where the evaluation of numerous replicates is required before a pattern or generalization can be seen. Because understanding the biological impact of stochastic
processes is a major hurdle for many students, the Interact boxes should improve learning and retention. Problem boxes placed in the text rather than at the end of chapters are designed to provide practice and to reinforce concepts as they are encountered, appealing to experiential learners. Math boxes that fully explain mathematical derivations appeal to mathematical and logical learners and also provide a great deal of insight for all readers into the many
mathematical approximations employed in population genetics. Finally, the large number of two-color illustrations in the text were designed to appeal to and help cultivate visual learning. The teaching strategy employed in this text to cope with mthematics proficiency deserves further explanation. The undergraduate biology curricula employed at most US institutions has students take calculus in their first year and usually does not require the application of much if any mathematics within biology courses. This leads to students who
have difficulty in or who avoid courses in biological disciplines that require explicit mathematical reasoning. Population genetics is built on basic mathematics and, in my experience, students obtain a much richer and nuanced understanding of the subject with some comprehension of these math ematical foundations.
Therefore, I have attempted to deconstruct and offer step-by-step explanations the basic mathematics (mostly probability) required for a sound understanding of population genetics. Forthose readers with more interest or facility in math- ematics, such as graduate students, the book also presents more difficult and detailed mathematical derivations in boxes that are separated from the main narrative of the text as well as chapter sections containing more mathematically rigorous content.
These sections can be assigned or skipped depending on the level and scope of a course using this text. The Appendix further provides some very basic background in statistical concepts that are useful throughout the book and especially in Chapter 3 on genetic drift and Chapters 9 and 10 on quantitative genetics. This approach will hopefully provide students with the tools to develop their abilities in basic mathematics through application, and at the same time learn population genetics more fully.
Members of my laboratory and the students who have taken my population genetics course provided a range of feedback on chapter drafts, figures, and effective means to explain the concepts herein. This feedback was absolutely invaluable and helped meshape the text into a more useful and usable resource for students. James
Crow graciously reviewed each chapter and offered many insightful comments on points both nuanced and technical. Rachel Adams, Genevieve Croft, and Paulo Nuin provided many
useful comments on each of the chapters as I wrote them. A.W.F. Edwards reviewed the material on the fundamental theorem in Chapter 6 and also provided the photograph of R.A. Fisher. Sivan Rottenstreich and Judy Miller patiently helped me with numerous mathematical points and derivations, including material included in the Math boxes. John Braverman supplied me with insights and thought-provoking discussions that contributed to this book.
Ronda Rolfes and Martha Weiss also provided com- ments and suggestions. I also thank Paulo Nuin forhis collaboration and hard work on the creation of PopGene.S2. I also thank the anonymous reviewers from Aberdeen University, Arkansas State Univer sity, Cambridge University, Michigan State University, University of North Carolina, and University of
Nottingham who provided feedback on some or all of the draft chapters.
John Epifanio provided the allozyme gel picture in Chapter 2. Eric Delwart provided the original data used to draw a figure in Chapter 6. Michel Veuille shared information on Drosophila simulans DNA sequences used in an Interact box in Chapter 8. Peter Armbruster shared unpublished mosquito pupal mass data used in Chapter 9. John Dudley and Stephen Moose generously shared the Illinois Long- Term Selection experiment data used in Chapter 9.
Robert J. Robbins kindly provided high-resolution scans from Sewall Wright’s Chapter in an original copy of the Proceedings of the Sixth International Congress of Genetics(see www.esp.org). I am grateful to Nancy Wilton for pushing me at the right times and for getting this project off the ground initially. Elizabeth Frank, Haze Humbert, and Karen Chambers of Wiley-Blackwell helped bring this book to fruition. I thank Nik Prowse for his expertise as a copy editor. I owe everyone at the Mathworks an enormous debt of gratitude since all of the simulations and many of the figures for this text were produced using Matlab.
Free Books Online PDF: Population Genetics – 2009