22 November 2021

For the past eight weeks, I’ve studied improvisational comedy with the Upright Citizens Brigade. While the UCB is based in Los Angeles, our class sessions and the show occurred over zoom. Please find the ~30-minute video on youtube: Enjoy!


15 February 2021

I’m excited to announce that I’ve updated the R package qtlbim. I forked the code from the CRAN Github repo for qtlbim. Here is the updated qtlbim repository in my Github account. It now works smoothly with gcc10, thanks to patches from Dirk Eddelbuettel. Installation To install qtlbim from my Github repository, type this code: remotes::install_github("fboehm/qtlbim") Getting started with Bayesian QTL mapping Once you’ve installed qtlbim, browse the vignettes to find example analyses.

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9 June 2020

Tomorrow my lab will participate in #ShutDownAcademia #ShutDownSTEM. I’ll stay at home instead of traveling to my office. And instead of doing my usual research, I’ll read, watch, and listen to pieces listed on the organizers’ website:

I hope that you’ll join me in participating.

Mathematician John Urschel recommended this article from the American Mathematical Society:


27 April 2020

I have the opportunity to present a poster at The Allied Genetics Conference (TAGC) 2020. I’ve posted both a pdf of the poster and a mp4 narrated video tour of the poster here.

I’d love to address your questions and hear your suggestions in the Q & A session on Thursday, April 30, at 1:30pm Eastern Time. My poster number is 1333A.

(Last modified: 2020-04-27 15:49:12)


10 January 2020

CRAN now hosts the packages qtl2pleio and gemma2. qtl2pleio performs a d-variate, d-QTL scan over a select genomic region. gemma2 is used by qtl2pleio for the inference of multivariate variance components. They can be installed with: install.packages("qtl2pleio") The statistical model that qtl2pleio fits for each d-tuple of markers (or pseudomarkers) is \[ vec(Y) = Xvec(B) + vec(G) + vec(E) \] where \(Y\) is a n by d matrix of d traits (for each of n subjects), X is a dn by df block-diagonal matrix of founder allele probabilities, B is a f by d matrix of allele effects for each of d traits, G is a n by d matrix of polygenic random effects, and E is a n by d matrix of random errors.

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