Epistasis Blog

From the Artificial Intelligence Innovation Lab at Cedars-Sinai Medical Center (www.epistasis.org)

Saturday, October 14, 2006

ASHG 2006

I just returned from the 2006 meeting of the American Society of Human Genetics (ASHG) in New Orleans. I was pleased to see an increase in the number of abstracts that report studies of epistasis. However, the total number of abstracts that contain the word epistasis (n=22) is very small considering the importance of epistasis in the genetic architecture of common diseases. This is 22 out of a total of 2390 abstracts. You can find these by searching the pdf file with all 2390 abstracts. You can find our abstracts by searching for "j.h. moore".

The most interesting abstract and poster that I saw while I was there is copied below. This work was inspired by the winner's curse that is observed in auctions. I am a big fan of stealing good ideas from other disciplines.

Correcting for the “winner’s curse” in genetic association studies. R. Xiao, M. Boehnke. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI.
Studies of gene-disease association are now commonly used to localize genetic loci that impact disease susceptibility. It is also of interest to estimate the genetic effect of each identified locus. It is known that the initial positive findings of the genetic effect estimate tend to be upwardly biased, a phenomenon known as the “winner’s curse”. In our study, we model the winner’s curse in the context of case-control genetic association studies. We quantify its impact on the naïve estimators of the allele frequency difference between cases and controls as a function of several factors including sample size, minor allele frequency in controls and cases, and the chosen statistical significance level. We also propose a maximum likelihood method to improve the estimate of the allele frequency difference corrected for the ascertainment. Initial analytical and simulation results indicate that our method substantially reduces the observed overestimation, allowing better estimation of locus-specific effect, and more appropriate design
for follow up studies.


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