I'm afraid that Professor Ramachandaran had to cancel her planned visit because of a family emergency. That means you'll be stuck with me on both Tuesday and Thursday this week.1 I can't cover the fancy coalescent stuff she was going to talk about, so I'll spend more time talking about the use of next-generation sequencing data in population and evolutionary genetics. If we have time on Thursday, I'll illustrate by discussing one last example from human population geneticcs.
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 Thursday1 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.