I’ve updated notes on the interaction of genetic drift with mutation, and to complement the notes, I’ve written a new simulation in R Shiny and Plotly to illustrate both the dynamics of allele frequency changes within populations and the distribution of allele frequencies among populations. I’ve also updated notes on the interaction of genetic drift and natural selection. I’m having some kind of a weird permission problem with my server. A copy of the PDF that I just linked to with the original name (selection-drift.pdf) and the same permissions (at least so far as I can tell) keeps giving me a permission error when I try to download it. I’ll leave temp.pdf on the server, but I hope to fix the permission problem, so it would be best not to link to that file until I get it fixed.
I’ve posted the last set of notes on selection, outlining the principles of estimating viability components of fitness from observations on genotypes before and after selection. I’ve also posted notes on basic principles of genetic drift, including the concept of effective population size. In addition, I’ve written an application in R Shiny that lets you explore the properties of genetic drift. You can pick the initial allele frequency, the number of different populations subject to drift (from one to ten), the (effective) population size of each population, and the number of generations for the simulation to run.
I’ve posted notes on viability selection at one locus with two alleles: http://darwin.eeb.uconn.edu/eeb348-notes/selection.pdf. I also wrote and posted a simply R Shiny application that allows you to see the within-generation effect of viability selection using a fixed set of genotypes and sliders to pick viabilities: https://keholsinger.shinyapps.io/Viability-selection/. Enjoy!
I just posted notes on individual assignment approaches to analyzing genetic structure. The notes on genetic structure of populations are now complete. Next weekend I’ll get started on notes having to do with natural selection and genetic drift.
I posted notes on the Wahlund effect and F-statistics a while ago. I’ve now posted an R Shiny application to illustrate the difference between Nei’s GST and Weir and Cockerham’s FST. The application simulates a sample of 25 diploid genotypes from 10 different populations. The genotypes are a multinomial sample from genotype frequencies calculated from Hardy-Weinberg expectations within each population, given the population allele frequency. That’s statistical sampling. The allele frequencies in each population are sampled from a Beta distribution with a mean of p = 0.5 and a variance of FSTp(1-p). That’s evolutionary sampling (or genetic sampling). Just as the individuals we sampled within each population are a sample of all individuals we could have sampled, the populations we sampled are a sample of all populations we could have sampled.
If you keep the parametric FST the same and just keep hitting “Go”, you’ll see that the genotype counts change every time. That’s the evolutionary sampling. You’ll find a link to the application on the lecture detail page, or you can link directly to the application on shinyapps.io.
As a reminder, if you’re interested in the source code for this or other R Shiny applications I develop for this course, they’ll all be available on Github.
I started learning R Shiny this afternoon. It will take me a while to become fluent with it, but when I do, I’ll be able to construct a variety of web applications to illustrate principles of population genetics. I just finished the first one. It illustrates how the EM algorithm works, following the example in the notes accompanying the lecture for 24 January. You’ll find a link to the web application there, but if you’d like to save yourself a click, here’s the direct link: https://keholsinger.shinyapps.io/EM-algorithm-for-allele-frequencies/ . I expect to add other web applications over the next month or two. You’ll find the code for them in a new Github repository (https://kholsinger.github.io/PopGen-Shiny/). I hope you find the applications useful. If you have suggestions or requests, please drop me a line. I can’t promise that I’ll follow through, but I promise to consider any requests I receive.
I’ve just posted the next sets of notes for the course, notes on inbreeding and self-fertilization and notes on analysis of genetic structure in populations. The notes on inbreeding illustrate the effects of inbreeding by focusing on the simplest and most extreme version of inbreeding, self-fertilization. They introduce several of the senses in which the term “inbreeding coefficient” is used and illustrate how they’re related to one another. The notes on genetic structure illustrate the Wahulund effect and provide an introduction to estimating Wright’s F-statistics.
I mentioned more than a month ago that I’m starting preparations for Population Genetics early this year. It’s taken me this long to post the first updates to notes for the 2019 edition of the course. You’ll find them on the lecture detail pages for 22 January and 24 January. The lecture detail page for 24 January also includes links to a couple of R scripts and accompanying JAGS files that illustrate Bayesian inference both in a simple case where it’s barely needed (estimating the allele frequency at one locus with two alleles) and in a case that’s more useful (estimating allele frequencies in the ABO blood group).
The notes are also available from individual links on the Notes page. As I mention there, the consolidated PDF containing all of the notes is from the 2017 edition of the course. I’ll update that PDF once I’ve updated all of the individual items.
You’ll also see on that page that I’ve linked to Graham Coop’s notes for his course at UC Davis. I encourage you to take a look at his notes, too. You are likely to find that he explains many things better than I do.
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.