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.
In case you want to get an early start on Project #4, I’ve posted it to the lecture detail page for April 2nd. After you’ve installed strataG in R, you may encounter an error that prevents the library from loading. I had that problem with v3.5.3 on an iMac, but v3.5.1 works fine on my MacBook. I’ll try to sort out the problem before tomorrow, but if I can’t, Kristen and I will develop a workaround.
I updated the R Shiny app that compare’s Nei’s Gst and Weir and Cockerham’s θ. This version allows you to set Fis, although it is set to the same value in every population. I’ll use it for a demonstration in class tomorrow, and Kristen may have you play with it a bit more during lab.
Speaking of lab, come prepared to install at least one new R package. It allows you to make estimates of Fst using the Weir and Cockerham approach. If you ever collect your own data where you need Fst estimates, it’s the most convenient approach to use.
I’ve just posted Project #1 for anyone who wants to get an early look. If you do take an early look, don’t be frightened. You’ll see some things that we haven’t covered yet. We will cover them in lecture tomorrow. I will also take time towards the end of lecture tomorrow to describe the project and what we’ll be looking for in the answers, so there’s no need to rush out and look at it now. And if you do, remember I warned you not to be frightened.
I also updated the R code associated with binomial.jags. The new version of binomial.R will display a histogram showing the posterior density corresponding to your sample. Kristen will walk you through some exercises with in tomorrow in lab.
I’ve just posted two new apps illustrating topics we’re covering right now: