Coming from the lineage of Jason Moore, I am obliged to occasionally remind everyone that biological systems are inherently complex, and to some degree, we should therefore expect statistical models involving those systems to be complex as well. With the development of GWAS, many approaches to examine epistasis are weighed down by the computational burden of exhaustively conducting billions of statistical tests.

When I started grad school I started using Reference Manager (RefMan), similar to EndNote, to manage my references and bibliographies. It's a real pain, and I often feel like I'm powering my computer with the endless pumping and clicking of the mouse that it takes to import a reference into my library. Recently I've started using Zotero because of how easy it is to import references, store PDFs, and sync between computers.

About a year ago I wrote a post about Dropbox - a free, awesome, cross-platform utility that syncs files across multiple computers and securely backs up your files online. Dropbox is indispensable in my own workflow. I store all my R code, perl scripts, and working manuscripts in my Dropbox. You can also share folders on your computer with other Dropbox users, which makes coauthoring a paper and sharing manuscript files a trivial task.

R evangelist David Smith, marketing VP at Revolution R, will be giving a webinar showing off some of the finer features of Revolution R Enterprise - an integrated development environment (IDE) for R that has an enhanced script editor with syntax highlighting, function completion, suntax checking, mouseover help, R code snippets for common tasks, an object browser, a real debugger, and more. Revolution R Enterprise is free for academics.

I was recently reading a bit about logistic regression in Gelman and Hill's book on hierarchical/multilevel modeling when I first learned about the "divide by 4 rule" for quickly interpreting coefficients in a logistic regression model in terms of the predicted probabilities of the outcome. The idea is pretty simple.

While preparing for my upcoming defense, I found a cool little web app called pubmed2wordle that turns a pubmed query into a word cloud using text from the titles and abstracts returned by the query. Here are the results for a pubmed query for me ("turner sd AND vanderbilt"): And quite different results for where I'm planning to do my postdoc: Looks useful to quickly get a sense of what other people work on.

On Friday, December 3rd, at 8:00 AM, after copious amounts of coffee, my friend, colleague, and perpetual workout buddy Stephen Turner will defend his thesis. Join us in room 206 of the Preston Research Building at Vanderbilt Medical Center for the auspicious occasion!Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

Designer Jesse Dee has an entertaining presentation on Slideshare about how to use Powerpoint effectively (although Edward Tufte may assert that such a thing is impossible). These are all things we probably know, but just don't take into consideration enough when we're giving a presentation. According to Dee, the number one most common mistake is lack of preparation.

I'm working on imputing GWAS data to the 1000 Genomes Project data using MaCH. For the model estimation phase you only need ~200 individuals. Here's a one-line unix command that will pull out 200 samples at random from a binary pedigree .fam file called myfamfile.fam: for i in `cut -d ' ' -f 1-2  myfamfile.fam | sed s/\ /,/g`; do echo "$RANDOM $i"; done | sort |

A few months ago I showed you how to syntax-highlight R code using Github Gists for displaying R code on your blog or other online medium. The idea's really simple if you use blogger - head over to gist.github.com, paste in your R code, create a public "gist", hit "embed", then copy the javascript onto your blog.

Obtaining the probability that zero, one, or two alleles are shared identical by descent (IBD) is useful for many reasons in a GWAS analysis. A while back I showed you how to visualize sample relatedness using R and ggplot2, which requires IBD estimates. Using plink --genome uses IBS and allele frequencies to infer IBD.