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.

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.

Previously mentioned LocusZoom has undergone some major updates over the last few months. Many of the bugs mentioned in my previous post are now fixed, and now there's a good bit of documentation available. There are also a few new features, including the ability to add an extra column to your results file to change the plotting symbol to reflect your own custom annotation (i.e. whether the SNP was imputed or genotyped, or the SNP's function).

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.

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.

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.