I've posted some very sketchy notes concerning a recent analysis of population structure in the UK for tomorrow's lecture. You can find them on the detail page for our last lecture.
I've added a link to the paper describing different techniques for generation of SNP data using next-generation sequencing techniques to the lecture detail page for April 28. Here's a direct link to that page:
I've also added a link to a paper describing analysis of fine-scale genetic structure of the human population in Great Britain. With a little luck, I'll have some notes about the paper posted later tonight. Right now the lecture detail page for tomorrow (http://darwin.eeb.uconn.edu/eeb348/lecture.php?rl_id=236) has a link to the same set of notes I posted last weekend for Tuesday's lecture.
By now you should have received an e-mail inviting you to participate in an online evaluation of my performance this spring. If you haven't already done so, please take the time to visit the SET site and respond to the survey. Every time I teach this course I learn something new from the evaluations that students provide. I've already learned a lot from you this semester, and here's one more chance to give me more direct, more personal, and completely anonymous feedback.
In case you're wondering, I won't receive the results of your evaluations until well after I've submitted grades for the semester. Even then I will only receive a summary of results. There's no way I'll be able to identify any individual responses. So feel free to be as brutal as you want. All I ask is that at the same time as being brutal, please be constructive and give me some advice I can use.
If you want more information about SETs, you'll find an FAQ at http://www.oir.uconn.edu/onlineset/SET_Spring14_Student_FAQs.pdf
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.