Just posted the first set of notes on molecular evolution. We won't get to them for a while, but I wanted to make them available ahead of time on the off chance that you're reading ahead. More will follow soon, if not tomorrow, then by the end of the week.
A former student in this course (@barbarafenton) just pointed me towards a very interesting site - Count Bayesie (http://www.countbayesie.com).1 If you take a look at the post from February 19, 2015, you'll find a post that is particularly relevant to this course - an explanation of Bayes' theorem using Legos. I may have to buy some Legos so that I can use them to explain Bayes' theorem the next time I teach this course.
I'm back from Shanghai, and I've posted notes on the evolution of quantitative traits and association mapping. We'll focus on evolution of quantitative traits this week. Please come prepared with questions about what we (mostly you and Nora) have covered so far. Before we go any further, I want to take care of any questions about additive and dominance effects, partitioning variance, estimating quantitative genetic parameters from crosses and the like. You need to have those concepts firmly under your belts before we start trying to talk about the evolution of phenotypes.
The following Tuesday I'll give you a whirlwind tour of genome-wide association mapping (GWAS).
I'm in Shanghai wearing my Dean hat until Saturday. Nora will be leading a discussion on two papers by Marc Feldman that discuss the (mis)application of population and quantitative genetics to humans on Tuesday. On Thursday, she'll introduce you to linear regression modeling, including logistic regression. This will be useful for you to know, not only so you have a better grasp of how regression models work in general, but also because it will provide the background you need for the next project.
P.S. As you may be able to guess from the fact that I'm posting this from my hotel room in Shanghai, I do have Internet access, and I will be checking my e-mail in the morning and in the evening. Feel free to drop me an e-mail while I'm gone if you have any questions. It may take me a while to respond, but I promise that I will respond.
Nora pointed out that there were a couple of typos in Project #4. I've replaced the original version with one that includes the corrections, so you may want to download the new version.
By the way, one of the typos arguably isn't a typo. When talking about how to estimate the fraction of populations falling into each allele number category, I referred to chromosome number categories. Now when you read the phrase "chromosome number" you're probably thinking of something like n=23, the haploid number of chromosomes in humans. That is the way that the phrase is normally used. In this context, however, my genetics geekiness took over. Although it's the bw and bw75 alleles that determine body color, the original isogenic lines were constructed using chromosome manipulations, and it's the whole chromosome on which the bw locus is found that is segregating, not just this locus. It's just that the founding population was homozygous at every locus on this chromosome except the bw locus. Make sense? Is that geeky enough for you?
I just realized that I didn't include a link to the notes on selection and drift for Thursday's lecture. I've added links to both the HTML and PDF versions now.
I've posted notes on resemblance among relatives. They are for the lecture on March 12th. Unfortunately, I'll be leaving town that morning for a trip to Arizona, and the notes involve a lot more math than I can ask anyone else to try and cover. So I've done the next best thing.
I wrote a simple simulation in R that generates data from a full-sib, half-sib crossing design and JAGS code that will analyse the data. Nora will walk you through how the code works, and she'll suggest a simple in-class experiment that will illustrate some of the important principles associated with these analyses.
It's not so important that you understand the math for this lecture, but it's really important that you understand the idea that (a) we can partition phenotypic variation into different components based on the experimental design (in this case among sires, among dams within sires, and among offspring within dams) and (b) we can relate those observational components of variance to the causal components of variance that we're trying to learn about (additive, dominance, and environmental). Because of that, we finally have the tools to explore the genetic basis of variation and evolution in quantitative traits.
I've just posted notes for the first two lectures on quantitative genetics. Be forewarned. There is a lot of algebra in these notes. It is unavoidable. There's no way you can possibly understand what additive and dominance variance are without seeing at least some of these details. Even if you use quantitative genetics extensively in your own research, you'll probably never need to refer to it again, but I don't know of a better way for you to develop your intuition about how to interpret quantitative genetic data and to understand the promises and limitations of a quantitative genetic approach to understanding the inheritance and evolution of quantitative traits.
I found a little time to update some more notes. I've now updated notes on the coalescent, which finishes the notes associated with genetic drift. Quantitative genetics is up next.
Just a reminder that your Structure runs for K of 1 to 16 is due to me by next Thursday, 2/12. Please use a burnin of 50,000 reps and then 250,000 post-burnin. This should only take a few hours (depending on your machine), and won't need to be run overnight (though you still can if you want)!