This paper marks the development and introduction of our phylogenetically corrected logistic regression algorithm. This allows us to do all the standard logistic regression analyses--test for differential effects or measure effect size--as logistic regression, but do it while correcting for phylogenetic structure. You can use the tool yourself here, though we have to limit to single analyses. If you'd like an executable version of the code, email me. We're working on a better, scalable solution, so stay tuned.
We used this approach to look at an interesting phenomenon: although we like to group HLA alleles by the their tendency to bind similar epitopes, we find that, in vivo, the escapes that evolution selects for differ by HLA. For example, when B*57:03 and B*57:02 (two very similar HLA alleles) present the same epitope, the observed escape mutations are usually different. Very surprising indeed, as it forces us to think more carefully about how (and if) we group alleles, as well as what the role of differential escape is. This work was in collaboration with Philip Goulder, John Frater, Roger Shapiro and Thumbi Ndung'u and thier labs.