Course overview for EEB 5348 (Spring 2018) updated and finalized

I’ve updated the final pieces of the course website: the Course Overview and the Lab Schedule. When you look at the Lab Schedule page, you’ll notice that it has only the dates for the labs. That’s because from now on Kristen will be updating the Lab Schedule page. She and I will be determining the specifics of each lab assignment as we go along based on the interests you express and the parts of the course where more hands on experience will be of the greatest benefit. Kristen will do her best to have materials posted by noon on the Monday before each lab, if not sooner. If she doesn’t manage to pull that off, it will be my fault for not letting her know where we’re going sooner, not hers.

Notes on resemblance among relatives, evolution of quantitative traits, and association mapping

I just added three more sets of notes to the website:

  1. Resemblance among relatives – The mathematical underpinnings of how we use resemblance among relatives to estimate components of the genetic variance when we don’t know the underlying genes.
  2. Evolution of quantitative traits – The mathematics of how selection on phenotypes results in changes in allele frequency from one generation to the next that then result in a new set of phenotypes in the following generation. That’s R = h^2 S. There’s also a derivation of Fisher’s Fundamental Theorem of Natural Selection at one locus with two alleles.
  3. Association mapping – A very cursory introduction to the principles of association mapping, including some notes on 2-locus population genetics.

I expect to get the notes on genomic prediction written next weekend. Once I do, I’ll also be posting an updated one-volume version of the notes.

An R Shiny application illustrating resemblance between parents and offspring

In my continuing effort to develop R Shiny applications that illustrate principles of population genetics, I’ve just added one that illustrates the resemblance between parents and offspring. It’s based on a really simple model (one locus, two alleles, and the same environmental variance for all genotypes). You can see phenotype distributions, components of genetic variance (calculated from the underlying genotypic values and allele frequency) and simulate a parent-offspring regression with different sample sizes.

The additive effect of alleles and partitioning genetic variance

I’ve written an R Shiny app to illustrate the mathematical relationships among genotypic values, allele frequencies, the additive effect of alleles, and components of the genetic variance. I haven’t yet revised the accompanying notes, but when I do you’ll be able to find them at

As a reminder, you can find a list of all of the R Shiny apps I’ve written on Github, where you can also download all of the source if you want to run the apps locally.

Statistical phylogeography – notes are updated an R Shiny application will follow

I just updated notes on AMOVA, statistical phylogeography, and population genomics. There will be an R Shiny application on (I hope). It’s the one I referred to on the 27th. It’s written and it runs on my computer (it will run on yours if you download it and follow the instructions). I’m working with R Studio support to sort out the problem, and I hope it will be fixed before long.

As usual, you can find the notes from the Lecture notes page or from the lecture detail page on the Lecture schedule.

The genealogy of the coalescent at one locus in two populations with migration and mutation

I just posted another R Shiny application illustrating properties of the coalescent in two populations with mutation and migration. Unfortunately, to see this one, you’ll have to download the source from Github (app.R) and run it in your local version of R. It’s not difficult, but it’s less convenient than running it on The problem arises because I use ggtree() to plot and color the tree. ggtree() is a BioConductor package, and I’m running into an error installing it in the application package. If I can’t figure it out, I may install a version of R Studio Server here and host it locally.

Here’s how to run the application in your local version of R:

    • Follow the link to Github and download app.R (click on the button labeled “Raw” at the right side of the screen and use “File->Save” to save it somewhere convenient on your hard drive.
    • Make sure your version of R has the libraries mentioned at the top of app.R installed. They are: ggplot2, shiny, cowplot, plotly, coala, ggtree, and ape. (Actually, I think you can delete cowplot and plotly from the list of libraries that are loaded. They’re leftover from some earlier experiments. To install ggtree(), you’ll first have to make sure that you have BiocManager installed. Then you can simply BiocManager::install("ggtree").
    • Launch R and make sure your working directory contains the source for app.R.
    • Then runApp() and enjoy the ride!