Web site for EEB 5348 – Population Genetics at the University of Connecticut
I found the (rather obscure) error in my R Shiny app illustrating resemblance between relatives for a quantitative trait. You can find a link to the app either on the Lecture Detail page or below.
Note: It’s the same link before. The code inside the link has been changed, but not the link itself.
We found an error in the equation describing the covariance in half-sibs as I was lecturing on Thursday. I just verified that the error is also in the accompanying notes – not surprising since the lecture slides are directly extracted from the lecture notes. I’ve now corrected the notes and uploaded new versions. You can find them either from the Lecture Detail page or from the links below. The correction will also appear in the book version of the notes that I’ll compile at the end of the semester.
It took me longer than it should have, but I finally added a link to the app I used in Tuesday’s lecture to illustrate partitioning of the genetic variance into additive and dominance components. You’ll find the link below and in the lecture detail page for Tuesday and for Thursday. In the lecture detail page for Thursday you’ll also find a link to an app illustrating the resemblance between parents and offspring. As noted there, I discovered that there’s a problem in simulations involving non-additive allelic effects. I plan to investigate and fix the problem over Thanksgiving break. I’ll post a note here when I’ve fixed it.
Lab 12 has now been posted to the course website. As usual, you’ll find a link below and from the Lab Schedule page. I spent nearly my whole weekend working out a few details that I realized I didn’t quite understand to make sure that this exercise makes sense. Unfortunately, that means that I still haven’t done any grading. I almost certainly won’t have time this week, so you can guess what I’ll be doing over Thanksgiving break. I apologize for falling so far behind in grading. The good news is that I’ve taken a quick look at a few of the exercises that have been turned in, and if that sample is representative, everyone has a good handle on everything we’re doing.
See you on Tuesday.
As a reminder, the lab exercise that is due on Friday, 11 December is the last exercise for the course and there will not be a final course evaluation. Once you’ve turned in the exercise for Lab 14, you’ve completed everything I expect. I hope when you turn it in you’ll be able to say as one of my former students did, “This was a good thing to have done.”
I mentioned in passing a few weeks ago that Sohini Ramchandaran will be presenting a seminar in EEB this fall. As a matter of fact, she’ll be presenting her seminar this Thursday at 3:30pm via Zoom. The timing of her seminar is perfect. (You might almost think I planned it this way.) She’ll be discussing how to make inferences about the genetic basis of complex traits, and you’ll have a chance to see some very modern approaches to quantitative genetics. It provides a preview of where we’ll be heading from Thursday morning through the end of the semester.
By the way, Sohini and I are academic siblings, although she’s much younger (and more accomplished) than I am. Our major advisor was Marcus Feldman.
I’ve just posted Project 3 on the course website. As with Project 2, you won’t need to analyze any data or run any simulations. Instead, you’ll need to read a paper, think about the techniques the authors used to detect natural selection (which are different from those we discussed in lecture), and answer some questions about the analysis and its implications. You can find a link to the project on the Lab Schedule page or you can follow the link below.
In other course-related news, I’ve included a YouTube video on the lecture detail page for Tuesday’s lecture. It provides a brief overview of sparg, the approach to inferring the spatial location of ancestors and the dispersal history from individual-level data. We’ll discuss sparg on Tuesday, but you may find it helpful to review the video before then.
McVicker, G., D. Gordon, and P. Green. 2009. Widespread genomic signatures of natural selection in hominid evolution. PLoS Genetics https://doi.org/10.1371/journal.pgen.1000471
IMPORTANT NOTE: The link to the paper was working Saturday afternoon, but it seems to be broken now (2:45pm, 7 November). The error message says this is “a likely temporary condition.” I’ll keep an eye on it. If it isn’t fixed soon, we’ll have to regroup. I also tried to get to some other papers on the PLoS Genetics website, and it appears to be affecting the whole site. The error message mentions a server configuration issue.
Update 8:05am, 8 November: I don’t know when the PLoS Genetics site came back up, but it’s up now. If you tried to get to the paper before and couldn’t, you should be able to get to it now. I’ve also downloaded a PDF that I can share if we run into trouble again.
We discussed Tajima’s D in class last week. As you’ll notice when you read this week’s exercise, I realized that I’ve been describing a couple of details concerning Tajima’s D incorrectly for several years. I’ve corrected the online notes, and you’ll find a brief reference to the differences in this week’s exercise. Fortunately, the details I got wrong don’t affect the interpretation of any results, only the way in which Tajima’s D is calculated. As usual, you can also find the exercise linked from the Lab Schedule page.
In grading Lab 7, the one on exploring the coalescent, I realized that the 1-dimensional stepping stone and the finite island model didn’t have quite the properties I expected when the number of populations is low. I verified the pattern many of you found, and the results are displayed here. You can also find a link to the results from the Lab Schedule page. We’ll talk more about what they mean and what they tell us about drift and migration on Tuesday.
Lab 9 is now posted. As usual, you can find it from the Lab Schedule page or from the direct link below. Although there is a little simulation this week, there’s only one simulation, and it runs pretty quickly (4-5 seconds) on my laptop). As with Project #2, the emphasis here is on interpreting the results. If you’d like an overview of sRNases (the proteins produced by the loci used in this exercise), the Igic and Kohn paper below is a bit old (OK, two decades old), but it provides a good overview of the phenomena.
Igic, B., and J.R. Kohn. 2021. Evolutionary relationships among self-incompatibility RNases. Proceedings of the National Academy of Sciences USA 98:13167-13171. doi: 10.1073/pnas.231386798
I’ve just uploaded Project #2. As usual, you can find it either from the Lecture Schedule page or from the direct link below. This project is different from any of the lab exercises you’ve done so far. There isn’t data to analyze, and there aren’t simulations to do. Instead, there’s a paper by Robin Waples and David Teel to read: Conservation Genetics of Pacific Salmon I. Temporal Changes in Allele Frequency. [1]This link takes you to the website of Conservation Biology. I can’t seem to get to the full-text of the paper from off-campus, even though the VPN, but there is a freely available version at: … Continue reading After reading the paper, I have five questions for you to answer. When I grade this project, I will be evaluating how well you use what you’ve learned about genetic drift and natural selection to answer the questions. None of the answers need to be more than a couple of paragraphs. Feel free to submit them in whatever form you find convenient, Word document, R notebook, PDF, or Pages are formats I know I can handle easily. If you send it in a form I don’t recognize, I’ll be in touch.
References
| ↑1 | This link takes you to the website of Conservation Biology. I can’t seem to get to the full-text of the paper from off-campus, even though the VPN, but there is a freely available version at: https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1431&context=usdeptcommercepub. |
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I’ve just posted Lab 7. You’ll find it in the usual places, i.e., from the Lab Schedule page or from the direct link below. As you’ll see, this lab exercise will allow you to explore the coalescent by comparing the time to coalescence of all alleles in a sample as a function of the rate of migration, the number of populations exchanging genes, and the migration model, i.e., either the finite island model you explored in last week’s exercise or the one-dimensional stepping stone. As noted in the exercise itself, the run_simulation() function will run 1000 samples by default. I recommend picking a smaller number first, say 50 or 100, to get a sense of how long a full run will take before you start the simulation. It shouldn’t be surprising that simulations take longer the more populations that you specify. There’s no need to try simulating more than 100 populations. You’ll see any patterns associated with differing numbers of populations by the time you get to that many.