Will Bush and I just heard that our paper "Multivariate Analysis of Regulatory SNPs: Empowering Personal Genomics by Considering Cis-Epistasis and Heterogeneity" was accepted for publication and a talk at the Personal Genomics session of the 2011 Pacific Symposium in Biocomputing.
Last year I linked to a series of perspectives in NEJM with contrasting views on the success or failure of GWAS - David Goldstein's paper and Nick Wade's synopsis that soon followed in the New York Times being particularly pessimistic. Earlier this year I was swayed by an essay in Cell by Jon McClellan and Mary-Claire King condemning the common disease common variant hypothesis and chalking up most GWAS hits to population stratification.
Jeff Barret (@jcbarret on Twitter) over at Genomes Unzipped (@GenomesUnzipped) has posted a nice guide for the uninitiated on how to read a GWAS paper. Barret outlines five critical areas that readers should pay attention to: sample size, quality control, confounding (including population substructure), the replication requirement, and biological significance.
Researchers in the ENGAGE consortium used a clever technique to leverage genome-wide expression data to select or prioritize genes for GWAS analysis. The investigators published the novel candidate genes for obesity in this month's PLoS Genetics, but I think the method they used here is more interesting.
NYU PhD student Drew Conway has compiled a very nice list of 10 reasons why grad students should blog. I've been writing GGD for a little over a year now and it's been a great way to extend my own network past the Vanderbilt walls, participate in lively discussions with other scientists oceans away, and to write stuff that people actually read and find useful.
As a graduate student a few years ago, I learned about (and in some cases witnessed) the various phases, fads, and revolutions in the field of human genetics. The mid to late 90's saw a shift from family-based linkage analysis to a plethora of small candidate gene studies. The early 2000's saw the completion of the human genome project, the development of the HapMap project, and the birth of genome-wide association studies.
A few months ago I covered an algorithm called EMMA (Efficient Mixed-Model Association) implemented in R for simultaneously correct for both population stratification and relatedness in an association study.
A few weeks ago I covered an R package for efficient mixed model regression that is capable of simultaneously accounting for both population stratification and relatedness to compute unbiased estimates of standard errors and p-values for genetic association studies.
First, if you haven't taken a look at the comments on my previous post on this paper, go take a look. Thanks to everyone for sharing your thoughts and pointing out some of my own oversight regarding this paper. There was one issue in particular that deserves more attention than just another comment thread.
Check out this paper in PNAS and the corresponding synopsis in the New York Times. The authors take a unique approach to finding genes likely to be associated with human traits using orthologous phenotypes in model organisms, or phenologs. The idea is simple. The authors have a database of ~2000 disease associated genes in humans.