attempted to deconstruct and offer step‐by‐step explanations of the basic mathematics required for a sound understanding. For those readers with more interest or facility in mathematics, the book presents more detailed derivations in boxes that are separated from the main narrative of the text. There are also some chapter sections containing more mathematically rigorous content. These sections can be assigned or skipped depending on the level and scope of a course supported by this text. 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.
For the second edition, I have tried to incorporate the generous and helpful feedback received from readers of the first edition. John Braverman deserves special mention as a dedicated colleague and friend who has provided sustained suggestions and thoughtful comments. Brent Johnson provided helpful suggestions on statistics topics, and Mak Paranjape helped me understand circuit models. Members of my laboratory and the students who have taken my courses provided feedback on chapter drafts, figures, and effective means to explain the concepts herein. This feedback has been invaluable and has helped me shape the text into a more useful and usable resource for students. The web simulations were developed with the help of Marie Kolawole and Steve Moore, aided by an award from the Georgetown University Initiative on Technology Enhanced Learning.
Many people contributed to the first edition, and their suggestions and input still shapes the book. They include Rachel Adams, Genevieve Croft, John Braverman, Paulo Nuin, James Crow, A.W.F. Edwards, Sivan Rottenstreich Leviyang, Judy Miller, John Dudley, Stephen Moose, Michel Veuille, Eric Delwart, John Epifanio, Robert J. Robbins, Peter Armbruster, Ronda Rolfes, and Martha Weiss. I also thank the anonymous reviewers of the first edition from Aberdeen University, Arkansas State University, Cambridge University, Michigan State University, University of North Carolina, and University of Nottingham. Nancy Wilton, Elizabeth Frank, Haze Humbert, Karen Chambers, and Nik Prowse of Wiley‐Blackwell helped bring the first edition to fruition.
Matthew B. Hamilton
October 2020
About the companion websites
This book is accompanied by companion websites for Instructors and Students:
www.wiley.com/go/hamilton/populationgenetics
The Instructor website includes:
Solutions to the end-of-chapter exercises
Powerpoints of all figures from the book for downloading, to aid teaching
The Student website includes:
Chapter resources for Interact Boxes, Problem Boxes, and end-of-chapter exercises
CHAPTER 1 Thinking like a population geneticist
All scientific fields possess a body of concepts as well as a specialized vocabulary used to express these concepts precisely. Population genetics is no different, and the entirety of this book is designed to introduce, explain, and demonstrate these concepts and vocabulary. What may be unique about population genetics among the natural sciences is the way that its practitioners approach questions about the biological world. Population genetics is a dialog between predictions based on the principles of Mendelian inheritance and observations from the empirical measurement of genotype and allele frequencies. Idealized predictions stemming from general principles form the basis of hypotheses that can be tested through observation, experiment, and comparison. At the same time, empirical patterns observed within and among populations are evaluated for evidence of their causes via predictive models. This first chapter will explore some of the ways that population genetics approaches and defines problems that are relevant to the topics in all chapters. The chapter is also intended to give some insight into how to approach the study of population genetics.
1.1 Expectations
What Do We Expect to Happen?
Expectations Are the Basis of Understanding Cause and Effect
In our everyday lives, there are many things that we expect to occur or not to occur based on the knowledge of our surroundings and past experience. For example, you probably do not expect to get hit by a meteorite while walking to your next population genetics class. Why not? Meteorites do impact the surface of the Earth and, on occasion, strike something noticeable to people nearby. A few times in the distant past, in fact, large meteors have hit the Earth and left evidence like the Chicxulub impact crater on the Yucatán Peninsula in Mexico. What influences your lack of concern? It is probably a combination of basic knowledge of the principles of physics that apply to meteors as well as your empirical observations of the frequency and location of meteor strikes. Basic physics tells us that a small meteor on a collision course with the Earth is unlikely to hit the surface since most objects burn up from the friction they experience traveling through the Earth's atmosphere. You might also reason that even if the object is big enough to pass through the atmosphere intact, and there are far fewer of these, then the Earth is a large place and, just by chance, the impact is unlikely to be even remotely near you. Finally, you have most probably never witnessed a large meteorite impact or even heard of one occurring during your lifetime. You have combined your knowledge of the physical world and your experience to arrive (perhaps unconsciously) at a prediction or an expectation: meteorite strikes are possible but are so infrequent that the risk of being struck while on the way to class is miniscule. In this very same way, you have constructed models of many events and processes in your physical and social world and used the resulting predictions to make comparisons and decisions.
Expectation: The expected value of a random variable, especially the average; a prediction or forecast.
The study of population genetics similarly revolves around constructing and testing expectations for genetic variation in populations of individual organisms. Expectations attempt to predict things like how much genetic variation is present in a population, how genetic variation in a population changes over time, and the pattern of genetic variation that might be left behind by a given biological process that acts over time or through space. Building these expectations involves the use of first principles or the set of very basic rules and assumptions that define how natural systems work at their lowest, most basic levels. A first principle in physics is the force of gravity. In population genetics, first principles are the very basic mechanisms of Mendelian particulate inheritance and processes such as mutation, mating patterns, gene flow, and natural selection that increase, decrease, and shape genetic variation. These foundational rules and processes are used and combined in population genetics with the ultimate goal of building a comprehensive set of predictions that can be applied to any species and any genetic system.
Empirical study in population genetics also plays a central role in constructing and evaluating predictions. In population genetics as in all sciences, empirical evidence is drawn from intentional observations, cleverly constructed comparisons, and experiments. Genetic patterns observed in actual populations are compared with expected patterns to test models constructed using general principles and assumptions. For example, we could construct a mathematical or computer simulation model of random genetic drift (change in allele frequency due to sampling from finite populations) based on abstract principles of sampling from a finite population and biological reproduction. We could then compare the predictions of such a model to the observed change in allele frequency through time in a
