I just posted the discussion guide for Thursday’s lecture on the lecture detail page. You’ll also find a link to easyGWAS, an online tool that allows you to perform GWAS on some publicly available datasets and to upload and analyse your own data.
Remember: 5 points of your grade on Project #6 will be based on your participation in Thursday’s discussion. Please spend some time reading the papers and looking over the discussion guide before you get to class on Thursday.
I’ve posted the notes for association mapping. As usual, you’ll find them on the lecture detail page for Tuesday’s lecture. I will soon have a discussion guide for Thursday’s lecture posted. The papers that are the focus of the discussion are already linked from the lecture detail page for Thursday’s lecture. Nora will lead the discussion on Thursday since I will be in Washington, DC for the spring Board of Directors meeting of BioOne. (I have been Chair of the Board of Directors since 2000.)
Important note: I will mention this again in the discussion guide, on the lecture detail page for Thursday, and in lecture on Tuesday, and I think Nora has mentioned it already, but just to make sure everyone is forewarned, 5 points of your grade on Project #6 will be based on participation in the discussion on Thursday. Please come prepared to discuss the questions that will appear on the discussion guide.
You should already have received an e-mail with a link to the Student Evaluation of Teaching survey. The e-mail would have come from the Office of Institutional Research and Effectiveness. Please take some time to answer the questions and return the survey. I won’t see your responses until well after grades have been posted, but I am very interested in hearing from you so that I can improve the course for future students.
Please keep in mind when you’re filling out the survey that you are evaluating me and the course, not Nora. I will distribute a separate, paper survey to evaluate her teaching in class next Tuesday.
I’ve added a link to notes on the evolution of multivariate phenotypes in the lecture detail page for Thursday’s lecture. We won’t cover Arnold’s extension of the approach in lecture, but you may want to take a look at it. You might find it useful in some of your own work.
I’ve posted notes on using the phenotypic resemblance among relatives to estimate quantitative genetic parameters and on understanding the evolution of quantitative traits. I’m going to post a link to additional notes on multivariate selection gradients in a couple of hours. I want to spend a little time introducing the approach during Thursday’s lecture. That means that I’ll have to cut some of the mathematical details short during lecture and focus on the results and general principles. I know you’re all disappointed in that, but I’m sure you’ll be able to adjust.
We start our survey of quantitative genetics on Tuesday. It’s a field with a long history, and we’ll only scratch the surface, but when we finish out the course, you should understand the basics well enough that you can figure out anything else you need to know on your own. (Of course, I’m alway available for advice if you get involved in something that you can’t figure out. I’m not an expert, but I’ve been around long enough, that I can probably help you figure it out.)
I think you’ll find that this block of material is the most challenging and difficult that we’ve encountered all semester. There is substantially more algebra involved because there’s no other way to understand some of the fundamental concepts (like additive genetic variance). I’ll do my best to keep reminding us why we’re suffering through all of the math, but don’t hesitate to ask for a lifeline if – when – you feel as if you’re drowning.
As you’ll recall, I gave a very crude overview of RAD-seq and GBS on Thursday. I promised that I’d post a link to a recent paper that provides a good review of these and other techniques. You’ll now find the link on the lecture detail page for last Thursday’s lecture and on the consolidated readings page. You’ll also discover that the paper isn’t so recent. It was published in 2011. One of the hazards of getting old is that 6-year-old papers now seem recent. Heck, papers that are 20 years old sometimes seem recent. In any case, it’s still a good overview, and if you use Scopus or Google Scholar to find out who cited it, you’ll find more recent reviews if you’re interested in what’s happening now.
We won’t discuss this very recent paper in class, but in case you think that I’m making things more complicated than they need to be, take a look at this abstract (and then read the whole paper, if you’re really interested):
Population structure can be described by genotypic correlation coefficients between groups of individuals, the most basic of which are the pair-wise relatedness coefficients between any two individuals. There are nine pair-wise relatedness coefficients in the most general model, and we show that these can be reduced to seven coefficients for biallelic loci. Although all nine coefficients can be estimated from pedigrees, six coefficients have been beyond empirical reach. We provide a numerical optimization procedure that estimates all seven reduced coefficients from population-genomic data. Simulations show that the procedure is nearly unbiased, even at 3x coverage, and errors in five of the seven coefficients are statistically uncorrelated. The remaining two coefficients have a negative correlation of errors, but their sum provides an unbiased assessment of the overall correlation of heterozygosity between two individuals. Application of these new methods to four populations of the freshwater crustacean Daphnia pulex reveal the occurrence of half-siblings in our samples, as well as a number of identical individuals that are likely obligately asexual clone mates. Statistically significant negative estimates of these pair-wise relatedness coefficients, including inbreeding coefficents that were typically negative, underscore the difficulties that arise when interpreting genotypic correlations as estimations of the probability that alleles are identical by descent.
This morning I was reviewing the notes I posted yesterday, and I found a couple of small typos. The version that’s posted now fixes the typos, so you have a good excuse for not having downloaded them yet.
I’ve posted the notes for Tuesday’s lecture on Approximate Bayesian Computation and for Thursday’s lecture on population genomics. We won’t have time to do more than scratch the surface of either topic, but we’ll dive into a few examples in enough detail that you should be able to understand papers or seminars where people present this kind of work, and you may be inspired to use some of the techniques in your own research. I’d particularly encourage you to think about ways in which you could use Approximate Bayesian Computation to gain insight into ecological or evolutionary processes that interest you. I’m no expert, but if there were enough interest in the topic, I’d be happy to organize a 1-credit seminar next year in which we dove in and learned more about it together.