Adaptation and Natural Selection

Here non-random shifts in allele frequencies over time are introduced, as well as how to incorporate varying levels of selection into a model of a single population through time. This chapter highlights the difference between weak and strong selection, the dynamics of single allele versus genotype-level selection, and how selection strength and population size affect allele frequency distributions over time. Finally the inference of the selection coefficient from allele frequency data is discussed, alongside the concepts of overdominance and underdominance.

Allele and Genotype Frequencies

An introduction to basic terminology in population genetics such as alleles, genotypes, and loci. This chapter introduces the basics of Hardy-Weinberg assumptions and predictions and explains how these assumptions can be used to provide insights and make basic inferences for real populations. It provides examples from real data showing how these inferences are consistent, even though the theory underlying them seems to make too many unrealistic assumptions.

Population Differences

This chapter addresses different approaches to quantifying genetic divergence between populations. It discusses the concept of “missing heterozygosity” and uses this to quantify the level of divergence between two populations with a measure called FST. The chapter works through how to determine a population of origin from blue whale data and touches on the concept of DNA fingerprinting to identify individuals.

Pointing the Way to Additional Topics

This concluding chapter highlights many of the concepts that are important to understanding modern-day population genetics research and explains that while they may not have been covered in this book, they are built on the foundations laid out in the preceding chapters. A series of small sections are provided which briefly introduce important concepts for continued learning. These focus especially on the coalescent theory but also touch on tests of neutrality, linkage disequilibrium, deleterious alleles, fixation probability, selfish genetic elements, future directions, and R packages.

Statistical Tests and Algorithms

In this chapter statistical tests are performed in R, including the χ‎2 tests; testing can be started if allele and genotype frequency data match the reader’s expectations. Writing and running algorithms are introduced as functions, specifically working through building an Expectation Maximization function. The function is then used to estimate and update the reader’s inferences of allele frequencies from observed and expected genotypes.

Basic Commands in R

This chapter is an introduction to the basics of object-oriented programming in R. It covers how to create and view objects in R, as well as how to perform basic calculations, manipulate objects, and identify the “class” of different objects. Finally, it addresses how to create and subset data frames, change row and column names, and cover matrix multiplication.

Learning through Programming

A brief summary of the way the authors hope this book can be used. It explains how the book will attempt to tie each concept to a set of practical exercises and justify the idea that implementing concepts via programming can help in understanding even seemingly insurmountably complex concepts. This chapter briefly introduces population genetics as a discipline and the R statistical language, as well as describing the basic formatting and organization of the book.

Genetic Variation

An introduction to randomness and the neutral theory of molecular evolution and how it contributes to levels of genetic diversity in populations. The estimation of random shifts in allele frequencies over time is discussed using the binomial probability distribution. This chapter visualizes how different initial allele frequencies relate to their probability of being fixed in a population when only drift is acting. The outputs of multiple simulations of a population undergoing random increase and decrease in allele frequencies per generation are built, run, and visualized. The fixation index (F) is introduced to quantify the loss of genetic variance over time.

Downloading and Installing R

This chapter is a primer on getting started with the R language. It provides a brief background and history of R and a guide to downloading and installing R on three different operating systems (Windows, Mac, and Linux). It also gives an introduction to the Comprehensive R Archive Network (CRAN), the installation and use of the RStudio Integrated Development Environment (IDE), the basic commands concerning saving code that’s been written, and changing working directories.