Parametric Estimation of the Local False Discovery Rate for Identifying Genetic Associations
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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@vcu.edu Supplementary information:Supplementary data are available at Bioinformatics online.