By now it’s obvious that I’m not teaching PopGen this spring. The course has been rescheduled to Fall 2021, and it is tentatively scheduled to be held in person – Tuesdays and Thursdays at 8:00am with a 2-hour lab from 9:30am-11:30am on Tuesdays. The lecture schedule you see on the Lecture schedule page is left over from when I last taught PopGen in 2019. I anticipate reorganizing the lectures quite a bit before fall. I also anticipate settling on the schedule by mid-late May and revising lectures over the summer. I also anticipate supplementing the PDF notes with HTML version and R notebooks.
As I mentioned a couple of weeks ago, I’ll be teaching this course as an asynchronous, online offering. I solicited feedback on technology options, both through that posting and through an email to the listserv for graduate students in EEB at UConn. You’ll find a link to the survey results below. Here are the technology choices as a result of those responses, with a brief explanation of each.
- I’ll build a HuskyCT website in addition to the dedicated course website here. There is overwhelming interest in having material available there as well as here. Anyone who’s following along from outside of UConn won’t see what’s in HuskyCT, but all of the notes, exercises, and project assignments will be posted here as well as in HuskyCT. I’ll follow up with another survey next week to see what other things you’d like to see in HuskyCT.
- We’ll use Zoom for videoconferencing. Little explanation needed here. UConn ITS doesn’t support Zoom, but it is strongly preferred, so we’ll use Zoom and take our chances.
- We’ll use Doodle to schedule occasional synchronous meetings. Students from some Universitas 21 partner universities will participate in the course (without receiving UConn credit – Yes. The Provost did approve this.), so finding a time when everyone can meet may be a bit of a challenge, but we’ll do the best we can.
- We’ll use Slack for asynchronous chat. There wasn’t a clear preference here. Slack was slightly preferred to Microsoft Teams, and both were preferred to discussions in HuskyCT. I chose slack because (a) it was marginally more preferred than Teams and (b) it is more likely to be available to non-UConn U21 participants.
If you have strong feelings that I’ve made the wrong choice on any of these points, please leave a comment here.
I know that it’s not even October yet, but my responsibilities as Vice Provost for Graduate Education and Dean of The Graduate School mean that I have to start planning early when I’m planning to teach. I’ll start revising the notes you’ll find on the Notes page before long, and I’ll post notices here when they’ve been revised, but right now I need help on something else.
I’m planning to teach the course this spring as an asynchronous, online offering. There are several technology options, including running this all in HuskyCT. I have my ideas about what I’d prefer, but what I really prefer is whatever it is that most students who will take the course will find most useful.
So, if you are planning to take the course, please follow this link to a Survey Monkey survey (or use the QR code on the right ) and let me know what you think. I’ll keep the survey open until the 11th of October, and I’ll provide more details about my plans then.
On Saturday afternoon I published a consolidated PDF containing the updated notes from EEB 5348 on Figshare: https://figshare.com/articles/Lecture_notes_in_population_genetics/100687 . Since the notes are on Figshare, they also have a DOI (10.6084/m9.figshare.100687.v3) and they are citable, should you choose to do so. You’ll notice a “.v3” at the end of the DOI string. That’s because this is version 3 of the notes. You can find the LaTeX source and accompanying EPS files at Github. The overview page for the notes is at https://kholsinger.github.io/Lecture-Notes-in-Population-Genetics/ and the notes for this release are at https://github.com/kholsinger/Lecture-Notes-in-Population-Genetics/releases/tag/v3.0 . Links to all three releases are available at https://github.com/kholsinger/Lecture-Notes-in-Population-Genetics/releases . I hope you find the notes useful. If you find any errors or find anything confusing, please let me know, and I’ll do my best to correct the problem.
Please note that I’ve released these notes under a CC BY 4.0 license. Feel free to use or modify them consistent with the terms of that license. If you do make changes, though, I’d appreciate hearing about them. There’s a decent chance that I’ll incorporate them into my notes – with proper attribution of course.
One thing I didn’t mention in the notes for Thursday’s lecture is that it is difficult to detect the effect of loci where one variant is rare, both because there’s a good chance you won’t have the variant in your sample (unless your sample is very large) and because it’s difficult to detect an effect when only a small number of individuals show it. The most recent Nature has a news article highlighting the challenge using our favorite polygenic trait, height in humans.
Genetic study homes in on height’s heritability mystery
I just posted notes for Thursday’s lecture on genomic prediction, aka polygenic scores. You’ll find them on the lecture detail page at http://darwin.eeb.uconn.edu/eeb348-resources/lecture.php?rl_id=59. You’ll also find a link to an R script that we’ll use in class to illustrate the idea with a toy example. It’s the same script I used to generate the toy example in the notes. If you bring your laptop to class on Thursday and download the script, you’ll be able to follow along, and we’ll be able to compare results, since each of us will simulate and analyze a different set of data.
We’ve been working through Fisher’s undergraduate honor’s thesis (actually the version that was published in 1918) for the last several lectures, and we’ll have a very brief introduction to genome-wide association mapping (GWAS) and genomic prediction in the final two lectures of the semester. Peter Visscher and Michael E. Goddard just published a relevant paper in Genetics:”From R.A. Fisher’s 1918 Paper to GWAS a Century Later” (https://doi.org/10.1534/genetics.118.301594). Here’s the abstract, but I encourage you to read the whole thing. It’s short and well-written.
The genetics and evolution of complex traits, including quantitative traits and disease, have been hotly debated ever since Darwin. A century ago, a paper from R.A. Fisher reconciled Mendelian and biometrical genetics in a landmark contribution that is now accepted as the main foundation stone of the field of quantitative genetics. Here, we give our perspective on Fisher’s 1918 paper in the context of how and why it is relevant in today’s genome era. We mostly focus on human trait variation, in part because Fisher did so too, but the conclusions are general and extend to other natural populations, and to populations undergoing artificial selection.
I received an e-mail this morning telling me that the online Student Evaluation of Teaching (SET) for this course is now available at https://blueapp.grove.ad.uconn.edu/Blue/. Please take the time to provide your feedback, and please be honest. I won’t see the results from this survey until several weeks after I have turned in final grades, meaning that no matter how poor you found my performance and no matter how strongly you express your opinion, I won’t know that until after your grades have been turned in. Your honest evaluation will help me do a better job the next time I teach this course. I take your evaluations very seriously.
I’ve posted the assignment, data, and R code for Project #5. Feel free to take a look at it. Kristen will show you how to use rstanarm (specifically stan_glmer) for the analysis. I know that you probably don’t want to learn another R package, but this is one that you are likely to find very useful for analysis of all kinds of data, not just genetic data.
It appears that strataG doesn’t work with the latest version of R (v 3.5.3). I did, however, realize that it’s possible to do the analysis using pegas. You use the same command to read a FASTA file in as I showed in the notes. Then you run tajima.test(seqs) on the results (where seqs is the name of the object you read the DNA sequence information into). I’ll update the project assignment to show the new syntax later today.