Yes, I know that it’s early September and that my first lecture in Population Genetics isn’t scheduled until 22 January 2019 (140 days from now), but I’m starting preparations early this year for several reasons.
- Because of my responsibilities as Vice Provost for Graduate Education and Dean of The Graduate School, I don’t have a lot of time during the week to spend on revising lectures or on finding datasets for projects and making sure the projects can be finished in a reasonable amount of time. By starting now, I hope to be far enough ahead of the game by the time Spring Semester arrives that I don’t have to kill myself keeping up.
- I’m significantly expanding my treatment of population genomics. In Spring 2017 I devoted only one lecture to it. It deserved more than one lecture then, and it certainly deserves more than one lecture now. If you look at the lecture schedule as it stands now, you’ll see three placeholder lectures: Population genomics I, II, and III. Not only will it take me a long time to decide what among the host of things I could spend my time on is most important for purposes of this course, it will also take me a long time to decide how to remove two lectures worth of material out of other lectures in the course.
- I hope to have all of my lecture notes revised before the semester begins. That way anyone who’s taking the course can choose either to download PDFs of individual lectures as we get to them or they can download a PDF with all of them (and some old lectures I no longer maintain) as a single PDF. If all goes well, that single PDF will be available on Figshare as version 3 of Lecture Notes in Population Genetics. Versions 1 and 2 (from 2012 and 2017 respectively) are already there.
If you’re thinking of enrolling in Population Genetics in Spring 2019, please take a look at the lecture schedule and let me know if there are things you’d like to know more about that either aren’t on the schedule or don’t seem to be given as much time on the schedule as you’d like. And whether you’re thinking of enrolling or not, if you’re reading this and have thoughts about what “greatest hits of population genomics” I should squeeze into the three days I’ve allotted for it, please drop me a line. Better yet, leave a comment so that others can see your suggestion.
I’ll be making short posts as I get each chapter of notes revised. If you’d like to see when they’re posted you can either follow me on Twitter (@keholsinger) or you can follow the course hashtag (#EEB5348).
I just posted grades in PeopleSoft. I presume you’ll get an automated notification from the system letting you know.
Thanks for a great semester. I really enjoyed working with you. I hope you found the course useful, if not enjoyable.
I expect to produce a single PDF containing all of the lecture notes for the semester soon. There’s a good chance that I’ll have a link to it from the Notes page by late next Sunday. In any case, I’ll post a note here to let you know when the consolidated notes are available, just in case you happen to be interested.
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.