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Bioinformatics Advance Access originally published online on May 6, 2009
Bioinformatics 2009 25(14):1807-1813; doi:10.1093/bioinformatics/btp305
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Estimating the posterior probability that genome-wide association findings are true or false

József Bukszár *, Joseph L. McClay and Edwin J. C. G. van den Oord

Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Medical College of Virginia, Virginia Commonwealth University, 1112 East Clay Street, PO Box 980533, Richmond, Virginia 23298, USA

* To whom correspondence should be addressed.


  Abstract

Motivation: A limitation of current methods used to declare significance in genome-wide association studies (GWAS) is that they do not provide clear information about the probability that GWAS findings are true of false. This lack of information increases the chance of false discoveries and may result in real effects being missed.

Results: We propose a method to estimate the posterior probability that a marker has (no) effect given its test statistic value, also called the local false discovery rate (FDR), in the GWAS. A critical step involves the estimation the parameters of the distribution of the true alternative tests. For this, we derived and implemented the real maximum likelihood function, which turned out to provide us with significantly more accurate estimates than the widely used mixture model likelihood. Actual GWAS data are used to illustrate properties of the posterior probability estimates empirically. In addition to evaluating individual markers, a variety of applications are conceivable. For instance, posterior probability estimates can be used to control the FDR more precisely than Benjamini–Hochberg procedure.

Availability: The codes are freely downloadable from the web site http://www.people.vcu.edu/~jbukszar.

Contact: jbukszar{at}vcu.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Martin Bishop

Received on January 20, 2009; revised on May 1, 2009; accepted on May 2, 2009

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