Population Genetics. Matthew B. Hamilton

Preface and acknowledgements

      This book was originally born of two desires, one relatively 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 an up‐to‐date survey text of the field of population genetics. At the same time, I set out with the more ambitious goal of offering an alternative body of materials to change the manner in which population genetics is taught and learned. The first edition of the book made progress toward these goals, and the second edition provides updates and refinements in that same vein.

      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, many of those predictions could be tested with only very limited power for lack of appropriate or sufficient genetic data. With the advancement of high‐throughput DNA sequencing and its still widening employment, population genetics has become much less data‐limited. Massive amounts of DNA sequence data are being collected for an expanding set of organisms. Polymorphism and divergence data are now available at a scale of many loci to entire genomes per individual. This has led to a new generation in population genetics that is data‐rich. Ironically, this abundance of empirical data has reinforced the central role of models and deductive inference in population genetics. Predictive models have grown to support the genetic data that are now available, fostering innovation at the same time.

      Coalescent or genealogical branching models are primary among the models employed in population genetics to make predictions and test hypotheses. During the past few decades, coalescent theory has moved from an esoteric problem pursued for purely mathematical reasons to a central conceptual tool of population genetics. Despite this, the teaching of coalescent theory in undergraduate and graduate population genetics courses has not kept pace with its role in prediction and 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 will meet 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 the 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. The second edition retains this focus and adds a section on the ancestral recombination graph.

      Another of my goals for this text was to offer a range of explanatory styles. Learning the concepts of 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 of the book is boxes set off from the main text that are designed to engage the various learning styles. 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. These are now augmented in the second edition with additional end‐of‐chapter problems. Math boxes that explain mathematical derivations will not only appeal to mathematical and logical learners but also provide insight for all readers into the mathematical reasoning employed in population genetics. In addition, the large number of illustrations in the text were designed to appeal and help cultivate visual learning.

A novel feature of the text is Interact boxes that guide students through semi‐structured exercises in computer simulations. These Interact boxes utilize web‐based simulations developed specifically for this book or public domain software. The simulation problems are an active learning approach and should appeal to experiential or visual learners. Simulations are one of the best ways to demonstrate the outcome of stochastic processes where replication is required before a pattern or generalization can be seen. Because the comprehension of stochastic processes in genetics is a major hurdle for many students, the Interact boxes should aid understanding of central concepts. Additionally, the simulations, spreadsheet models, and scripts provide applications of algorithmic thinking. Algorithmic and computational approaches to problem‐solving are now central to prediction and data analysis in population genetics and are useful in most fields of biology and in the sciences more broadly.

      The approach to mathematics in the text deserves further explanation. The undergraduate biology curricula employed at most US institutions has students take calculus and applied statistics and usually requires little application of mathematics within biology courses. This leads to students having difficulty in, or avoiding altogether, courses in biological disciplines that require explicit mathematical reasoning. It also leads to courses avoiding explicit mathematical reasoning. Population genetics is built on basic mathematics and probability, and in my experience, students obtain a much deeper understanding of the subject with some comprehension of these mathematical foundations. Therefore, rather than avoid these topics,

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