Overview

Instructor:Kent Holsinger
Office:2nd floor Whetten Graduate Center
Phone:486-0983
E-mail:kent.holsinger@uconn.edu
Office hours:by appointment
Teaching assistant:Nora Mitchell
Office:PBB (Pharmacy) 302
Phone:486-5731
E-mail:nora.mitchell@uconn.edu
Office hours:Th 9:30-10:30
Lecture:TuTh 8:00-9:15
TLS 301
Lab:Tu 9:30-11:30
TLS 313

Course description

This course is an introduction to the field of population genetics, the branch of evolutionary biology concerned with the genetic structure of populations and how it changes through time. Some of us see population genetics as the core discipline in evolutionary biology since changes in the genetic composition of a population are the basis for all other evolutionary change within lineages.

There are two aspects of this course that sometimes cause students problems.

  1. Geneticists think differently from most other biologists (and most other human beings, for that matter). They love monohybrid and dihybrid crosses, linkage, penetrance, dominance, and the like. We population geneticists are even worse. To explain things that you can see (like phenotypic differences among individuals) we introduce abstract concepts (like additive genetic variance) that are pure statistical artifacts that no one can see. By the time you finish this course, you’ll not only have had a good review of basic Mendelian genetics (and even a little bit of molecular genetics), you’ll be familiar with a bunch of new and fairly abstract genetic concepts. Just what you were looking for, right?
  2. Population genetics involves a fair amount of mathematics, probability theory, and statistics. That’s because we deal with genetic variation in populations, which is measured in terms of gene and genotype frequencies. The phenomena of Mendelian genetics are themselves inherently statistical. So it shouldn’t be surprising that when we apply these principles to a whole population the problems become even more mathematically involved.

That’s the bad news. The good news is that the math we need is (mostly) quite simple, some basic algebra and probability theory. When we need things that are more advanced, I’ll explain them in class. The other good news is that I expect you to have lost any familiarity you once had with genetics, algebra, and probability, so we’ll be doing almost everything from scratch. The last bit of good news is that I’ll try to emphasize how to apply the basic principles of population genetics, not the math involved in deriving those principles.

I’ll be placing particular emphasis on using different computer packages for analysis and interpretation of data encountered in population genetics, and the problems and projects will involve using those packages. They will evaluate your ability to use the principles and methods of population genetics, not your ability to derive them.

Format

The course consists of two components:

  1. The lecture component.
  2. The lab component.

The two components of the course are tightly integrated. In fact, all of the projects on which grading is based will be assigned and discussed most thoroughly in the lab. The lecture will introduce the concepts and principles you need to understand and apply population genetics. The lab will provide the “hands-on” experience using real (if somewhat simplified) data sets.

Holsinger will present most of the lectures using notes that are available on the Notes page and pages directly linked to individual class periods from the Lecture schedule page. Many of the individual lecture pages will include links to published papers related to the topic being discussed. The links to those papers are there to provide you with additional background material in case you want to delve more deeply into the topic than we have time for in class. In a few cases, I may ask you to read a particular paper ahead of time so that we can discuss it during lecture.

Mitchell will lead the laboratory part of the course. You’ll need to bring a laptop to lab, since all of the assigned work will require you to use the statistical package R in addition to other software. Laboratory periods will focus on analyzing real data sets using the principles introduced in lecture. Analyzing those data will require that you learn a little bit about programming a computer. Since this isn’t a course in computer programming, we’ll keep the required programming as simple as possible, but some of it is inescapable. (Just as learning to use a microscope is inescapable if you’re going to take a course in plant anatomy.) We emphasize the use of R because it is very portable and very powerful. The interface is quite similar on Mac, Windoze, and Linux, and packages are generally available on all of these platforms. It is also a very powerful and very flexible general-purpose statistical package. You are likely to use it a lot, even if you never do any work in population genetics again.

Grading

6 projects16 2/3% each

Grading in the course is based on your performance on 6 projects where you will use real population genetic data to answer questions about evolutionary or ecological processes. Each project will include a small amount of background reading for context. The data set to be analyzed will be extracted from one of the papers assigned for background reading. The assignment will identify a small number of questions, typically two or three, posed by the background reading that can be addressed using the data. Your task will be to identify and perform the appropriate analyses and to interpret the results of those analyses in light of the questions posed in the assignment. We will clean and simplify the data before we provide it to you so that you can focus on using the principles you’ve learned to answer the questions.