I've posted notes (finally) on coalescent approaches to phylogeny and approximate Bayesian computation. There's a lot of scary mathematics hiding behind the simple summary in these notes. Don't worry. We won't spend time on the scary math in lecture either. But I do want to introduce you to some of the sophisticated and powerful methods now available for analysis of population genetic data. You'll see more examples on Thursday when Nora talks about applications to human population genetics and next Tuesday when Sohini Ramchandaran comes for a vist. In the final lecture of the course a week from Thursday^{1} I'll use a recent example or two of work in human population genomics to illustrate the kinds of questions that can be addressed using data derived from next-generation sequencing.

Materials for Project #6 have been posted so that you have a chance to look at them *before* lab on Thursday. You'll need to install another R package, but you won't have to write any JAGS code. (Stop cheering. Your excitement at being done with JAGS - for this course at least - is unseemly.)

I just posted notes on patterns of nucleotide substition, an example of selection on the *Adh* locus in *Drosophila melanogaster*, Tajima's *D*, AMOVA, and statistical phylogeography. You'll find much more in the notes than we're going to have time to cover, but at least you now have an easy to find reference to some ideas that you're liable to encounter in the future.

Here are links to the articles we discussed in class today:

The Guardian - Risk of sex offending linked to genetic factors, study finds

The International Journal of Epidemiology - Sexual offending runs in families: a 37-year nationwide study

If today's discussion on GLMMs has sparked your interest, you may wish to check out this Highland Statistics book "Mixed Effects Models and Extensions in Ecology with R" by Zuur et al. (2009). Eldridge Adams taught a biostat seminar using this book a few semesters ago and it explains different types of models and has examples to use in R. Unfortunately, everything is in a likelihood framework (not Bayesian) but it's still good for understanding concepts and actually running the models.

Nora pointed out that the text of Project #5 as originally posted says that the samples are from wild boar. If you read the paper, you'll realize they're actually from African buffalo. The name of the data file, `syncerus.csv`, should have reminded me when I was writing this up, but my brain was clearly still in Shanghai. Sorry about that.

The data set and the questions are fine, and there's a new version of the Project #5 assignment on the website, in case you want the one with the name of the right organism in the text.

I've had requests to post Project #5 a little early so that you have a chance to look at it before lab on Thursday. You'll find it on the detail page for tomorrow's lab. Enjoy!

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).