October 2008 Archives

Remember the t-shirt article I told you about early in the course? Well, another paper about MHC variation and mate choice in humans just appeared in Proceedings of the Royal Society. Here's the abstract:

Previous studies in animals and humans show that genes in the major histocompatibility complex (MHC) influence individual odours and that females often prefer odour of MHC-dissimilar males, perhaps to increase offspring heterozygosity or reduce inbreeding. Women using oral hormonal contraceptives have been reported to have the opposite preference, raising the possibility that oral contraceptives alter female preference towards MHC similarity, with possible fertility costs. Here we test directly whether contraceptive pill use alters odour preferences using a longitudinal design in which women were tested before and after initiating pill use; a control group of non-users were tested with a comparable interval between test sessions. In contrast to some previous studies, there was no significant difference in ratings between odours of MHC-dissimilar and MHC-similar men among women during the follicular cycle phase. However, single women preferred odours of MHC-similar men, while women in relationships preferred odours of MHC-dissimilar men, a result consistent with studies in other species, suggesting that paired females may seek to improve offspring quality through extra-pair partnerships. Across tests, we found a significant preference shift towards MHC similarity associated with pill use, which was not evident in the control group. If odour plays a role in human mate choice, our results suggest that contraceptive pill use could disrupt disassortative mate preferences. (emphasis added)

You'll want to download a copy of QTL Cartographer for the next problem. More specifically, you'll want to download a copy of WinQTLCart.¹ Although there's a version available for Linux and Mac OS X, it requires you to use a command line interface that's a lot less convenient. If you'd really like to try the analyses with that interface, I'll do what I can to help, but I have to confess to not being very familiar with it. You may be on your own.

For project 2 I'd like you to write out a couple of sentences/paragraphs answering each question.  After that, I'd like you to attach the output from WinBUGS and then your WinBUGS code WITHOUT the data.  This can be emailed to me to save paper.

This is the suggestion I've been giving out, which seems to work well.
1)  Start with the basic code Kent gave and modify it to fit the Mimulus dataset.
2)  Starting with question 1, figure out which variables you need to solve for and then find the equations using terms you (well, technically Kent in this case) have already solved for, then write out the equations in WinBUGS format.
3)  Repeat Step 2 for each problem SEQUENTIALLY.
4)  Send me your code before you run it.  Depending on how you wrote it, there are a couple of different methods to loading the data.

I will be available on Wednesday between 8:30 and 5 (with the exception of 12-12:30 for lunch).  Please stop by my office in Pharm/Bio if you need help.

I've discovered an error in the WinBUGS code for the regression.  Please use this code:

 for (i in 1:n.obs) {
    y[i] ~ dnorm(mu[i], 0.001)
    mu[i] <- beta0 + beta1*x[i]
  }
  beta0 ~ dnorm(0, 0.001)
  beta1 ~ dnorm(0, 0.001)

I've posted a version of the Mimulus data without the last column, so you can try your hand with the code that's already available (before you start worrying about the regression on fitness).

I just ran across a very nice review article on heritability in Nature Reviews Genetics. I've added a link to it in the detail page for today's lecture, and it will also show up in the readings list for the course. Of course, if you click the link on Nature Review Genetics, you'll get there directly.

Hello all,

My available schedule for this week is as follows:
Monday 4-6pm
Tuesday 8am-2pm
Thursday 8-10am
If people can not make ANY of those times and still want help, please email me.

Since the data Dave Carr provided is not yet published, I've set it up in such a way that access is restricted. To retrieve it, you'll have to be inside the UConn.Edu domain. If you're retrieving the data from a computer connected to the campus network, you shouldn't have any problems. If you need to download it when you're off campus, you'll have to log on through the VPN.

I've just posted the data and problem description for Project #2. You'll find the link on the lecture detail page for tomorrow.

As promised (threatened?), you're going to have to fire up WinBUGS again for the solution. That's the bad news. The good news is that you can take the full-sib, half-sib code that I posted when we were talking about variance partitioning on Monday, change the numbers for the number of individual, the number of sires, and the number of dams, and find yourself most of the way to the solution.

Strictly speaking, you wouldn't have to fire up WinBUGS. You could fire up R, SAS, SPPS, or another statistical package and estimate variance components there. But if you do, you'll also need to figure out how to bootstrap over full-sib families to get confidence intervals for your results. While that's possible, it may not be very easy, and I'm afraid you'll be on your own if you try it that way. We won't be able to offer any advice. (Now if you wanted to combine mcmcsamp() with lmer() results in R, I could help you, but (a) you'd need to understand what mcmcsamp() does and you'd need to be familiar enough with R to write your own code to analyze the results it gives you.)

It's taking me longer to put Project #2 together than I had planned. (I'm trying to make sure that the data make sense and aren't too difficult to make sense of.) As a result, instead of assigning it tomorrow, I won't assign it until Friday, and it won't be due until a week from Monday (the 3rd of November). That gives you two weekends to work on it, including Halloween. It also means that we will have covered all of the evolutionary quantitative genetics stuff that's going to be part of the project before it's assigned.

I apologize for taking so long to post these solutions. I have some excuses, but I won't share them.¹ I'll just post the links.

• Problem #1 solution
• Problem #2 solution
  

I've added a link to a paper from Genetics in 1997 with the photos I described in class on Monday. You can find the link on the detail page for Monday's lecture.

You might also be interested to know that the number of loci estimated to influence height variation in humans is 54. See here for a brief discussion and a link to the papers describing the analyses. I'll mention association mapping briefly in another week or two.

Hello everyone!

If you are planning to come meet with me on Wednesday, please be advised that I will be in the fishbowl (across from my office) for help from 9 to 11 and 3 to 5 (ish).  If you know that you are coming to meet with me at a specific time, please email me to let me know so that I can try to avoid backups!

Cheers
Kathryn

I'll give Kathryn a copy of my (WinBUGS) solution to the bonus question later today or tomorrow. So she should be able to give you some more hints after that. In the meantime, I've posted a WinBUGS-friendly version of the data to the course web site (data.txt). Here are the variables you'll find in the data set:

• n.one -- The number of replicate populations produced in the one-generation experiment (100).
  
• n.one.min -- The mininum possible number of alleles in the population (because the sample contains at least that many copies, 34).
• n.time -- The number of generations for the time series (50).
• n.time.min -- The minimum possible number of alleles in any of the populations (because the sample contains at least that many copies, 52).
• one -- The replicate population data for the one-generation experiment.
• time -- The data for the time-series experiment.

Notice that the data has been converted to integers for you. I'll post another hint tomorrow or Wednesday (unless I get questions sooner).

I've posted the description of Problem #3 and two (simulated) data files associated with it. Unlike the problems/projects so far the bonus question associated with this problem is very difficult. I'll be astonished (and very impressed) if anyone is able to solve it without help, but I'll be providing hints over the next week or so for anyone who's interested.