Tuesday, 25 May 2010

EMMAX — efficient mixed-model association expedited new software for GWAS

New Software Promises to Ramp up GWAS
"While genome-wide association studies have certainly proven their worth when it comes to pinpointing which genes play a role in human disease development, they are far from perfect. Sometimes, the genealogy of the individuals included in these large-scale studies can throw a wrench in the works because rarely are pairs of individuals in a study completely unrelated. This pairwise relatedness has occasionally led researchers to believe they have discovered a gene involved in a particular disease when in fact it is an artifact. While most researchers have statistical approaches for dealing with different levels of relatedness that come in the form of population structure or hidden relatedness, a team of scientists from the University of Michigan and the University of California, Los Angeles, has developed a statistical approach for dealing with both forms of relatedness. The method has the added benefit of dramatically speeding up the analysis process from years to just a few hours. "

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