The recent settlement between Arizona State University and the Havasupai Indian tribe is calling attention to (and perhaps challenging) the ideas of informed consent. While I'm sure there are arguments to be made supporting both sides of this case, regardless of your position this is an excellent reminder that there are people's lives behind the alleles in our spreadsheets and PED files.

Update 2010-04-21 : I forgot to post the link last time. That would have been helpful. Here you go: Vanderbilt Biostatistics: Statistical Problems to Document and to Avoid ..... At the Regression Modeling Strategies course I attended a few weeks ago, Frank Harrell pointed out the checklist on the biostatistics department's website of statistical problems to document and avoid.

This looks like a must-read for anyone starting out in computational biology without extensive experience at the command line.  The 135-page document linked at the bottom of the Google Group page looks like an excellent primer with lots of examples that could probably be completed in a day or two, and provides a great start for working in a Linux/Unix environment and programming with Perl.

Update Thursday, April 29, 2010: See further commentary at a newer post here. Just finished reading Jon McClellan and Mary-Claire King's Genetic Heterogeneity in Human Disease essay in Cell. It's definitely one of the most forthright and compelling essays I've read on the subject of the inadequacy of GWAS for identifying genes that cause complex human disease. The essay starts with an evolutionary perspective.

Check out this review essay in Cell: Genetic Heterogeneity in Human Disease, by Jon McClellan and Mary-Claire King. (King's lab, incidentally, was the group who discovered via linkage analysis that the gene for early-onset breast and ovarian cancer on chromosome 17q21, nearly 5 years before Myriad Genetics filed for patent protection on the BRCA1/2 genes). Anyhow, looks like a great review on genetic heterogeneity and GWAS. Thanks @JVJAI.

This month's cancer biostatistics workshop on overfitting will be given by Fei Ye and Zhiguo (Alex) Zhao, both in the Department of Biostatistics and the Cancer Biostatistics Center. This looks like a good one, especially after attending Frank Harrell's regression modeling strategies course a few weeks ago. See the link below for the full 2010 series.

Our Program in Computation Genomics Journal Club is starting again, now the 3rd Friday of each month. The next meeting is this Friday, April 16, at 3pm in the CHGR conference room. As usual, please bring in any articles you've found recently and give a brief overview of why you thought it was interesting.

I recently did an analysis for the eMERGE network where I had lots of individuals from a small town in central Wisconsin where many of the subjects were related to one another. The subjects could not be treated as independent, but I could not use a family-based design either. I ended up using a mixed model approach using previously mentioned GenABEL.

I've been using GenABEL for some time now for GWAS analysis using related individuals. It has an excellent set of functions for estimating a kinship matrix from a dense marker panel and then using this in a linear mixed effects model to allow for related individuals in the analysis of a quantitative trait.

A short announcement - my friend, colleague, running partner, and GGD contributor Will Bush is now an assistant professor in the Department of Biomedical Informatics, and investigator in the Center for Human Genetics Research here at Vanderbilt.Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

The previously mentioned Regression Modeling Strategies short course taught by Frank Harrell is nearly over. Here are the handouts (PDF) from the course. Keep an eye out here, I'll be writing a few more posts in the near future on topics Frank covered in this course.Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.