I’ve updated the notes on the genetics of natural selection and on estimating viability (as an example of how the components of selection may be measured). As noted in the lecture detail page for Tuesday, we’ll start by reviewing inbreeding, F-statistics, and Structure. Please come with prepared with questions, whether they concern things you’d like to know more about or things you’re confused about.
I recorded a new version of the lecture for 12 September. It seems to be working now (and you won’t have to suffer through as many bad jokes as if I were there).
I don’t know what the problem is, but I just checked the video I uploaded yesterday, and it isn’t working. The video stream is there, but there isn’t any sound. I’ll try to record it later this afternoon (Zurich time). I hope there will be a functioning version on the website by early afternoon. Keep your fingers crossed.!
Project #1 is now available. There is a bit of a hitch right now, though. For some reason, I can’t get the ZIP files to download from the links in the Project notes, even though the ZIP files are there. There’s a good chance I won’t have that fixed before Tuesday, but Nick has copies of the ZIP files he can share, and I can find another way to share them if we need to.
There is also a short recording with a bit of information about Strucutre linked from the lecture detail page for Tuesday. Please email me if you have any questions. It may take me a while to respond, but I’ll do my best to get back to you while I’m away. We will also take some time a week from Tuesday to talk more about Structure. So come with. questions then. too.
I’ve posted the lecture notes for analysis of genetic structure with individual assignment. As noted in the lecture detail page for 12 September, I will be attending a conference in Zurich, Switzerland that week. If all goes well, I will record a brief video and post it to the Kaltura channel going over the basic principles underlying <tt>Structure</tt>. The project should help you become familiar with how the program works and how to interpret the results. If you have questions about how it all works, be sure to remember them. I’ll take as much time as we need to. take on Tuesday the 19th to answer questions you have.
On a different note, I am adding links to recordings of the individual lectures to the lecture detail page. That means you can find them either through. the Kaltura channel linked on the Overview page or through the lecture detail page.
I’ve established a Kaltura channel that will include recordings of all lectures. I have to edit them slightly after the lecture, and it takes a while for them to post, so don’t expect to see lectures in the channel until the afternoon of the lecture or even the next day. I’ll post a note here to let you know when the lecture is available, but if you subscribe to the channel, I think you’ll get a notice whenever something new is added.
That’s the good news (if you want to refer to lectures outside of class).
The bad news is that it turns out that although McHugh 206 allows Kaltura recording, it doesn’t allow Kaltura live streaming. If you can’t be in person for a lecture, you’ll have to wait for the recording. (Of course, that also means that you don’t have to get up by 8:00am to see the live stream.) I had hoped that we’d have both live streaming and recording, but I guess recording only will have to do. Sorry about that.
By the way if you lose the link to the Kaltura channel that’s in this message, you’ll see that on the Overview page I now have a link to the channel instead of a link to the live stream (which didn’t work).
One last thing, I don’t know why but the video of today’s lecture didn’t capture my screen. You’ll see a green screen instead, but you will hear my voice. That combined with the lecture notes should give you a pretty good idea of what’s going on.
I just uploaded the final set of notes on genomic prediction. As you’ll see, there are some things you’ll need to be careful of if you want to use genomic prediction in your own work. Although I don’t mention this explicitly in the notes, the implication of the last caveat about genomic prediction is that GWAS may not be very useful in understanding the genetic basis of variation in a complicated trait beyond the population in which the analysis was done. A further leap is that even a traditional quantitative genetic analysis may not extrapolate well to other populations, even if the environmental conditions are similar.
In short, quantitative genetics provides some very powerful tools, but powerful tool can also cause a lot of damage. Be careful when you wield this one.
Congratulations! You still have a couple of lectures to survive, but you’ve almost joined the survivors of EEB 5348. I’ve now posted the last lab exercise for the semester. It builds on the locus-by-locus GWAS in last week’s lab and extends it to genomic prediction. We could easily spend several weeks, probably even an entire semester, exploring all of the ins and outs of GWAS and genomic prediction, but this is an overview course. My goal is to give you enough of an introduction to approaches like these that you not only know they exist but that you also can figure out how to use them on your own if it seems appropriate for your research.
If you downloaded the notes on genomic prediction before noon on Sunday, I suggest that you download them again. I’ve modified them a bit to reflect the new R notebook I put together on Saturday to illustrate the simple example that is discussed in the notes. I also suggest that you download the R notebook so that you can explore different simulated data sets and see how they perform.
I am working on the final lab exercise, and I’ve finished running nearly all of the analyses I want to run before typing it up and making it available. Since I have some non-teaching related things to attend to this afternoon, there’s a decent chance that the lab won’t be posted until some time on Monday, but I’ll do my best to have it posted before lecture on Tuesday.
It took me a bit longer to make sure the data and associated scripts were performing the way I wanted them to, but the lab exercise for week 13 is now posted. You’ll be running a genome-wide association analysis on a subset of data collected in an analysis of gypsy moths (Lymantria dispar). This is only an example. You can look at the Stan code if you want. If you do, you’ll see that it’s just the linear regression that we’ll discuss in class on Thursday with some magic added to take care of the relatedness among individuals. If you were doing a real GWAS, I wouldn’t recommend using this script. I’d recommend using GEMMA or something like it that was built by experts in GWAS specifically for the purpose of GWAS. There are a lot of things to be careful about that aren’t built into these simple scripts, but they illustrate the principle so that you can get a sense of how GWAS might be used in your own research.
As usual, you can find a link to the lab from the Lecture Schedule page (or you can in a few minutes when I get it posted) or from the link here.