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Archive for the ‘genetic association’ Category

Estimates of the local false discovery rate based on prior information: Application to GWAS

1 August 2016 Comments off

MLE of the local FDR

13 February 2013 Comments off

Y. Yang, F. A. Aghababazadeh, and D. R. Bickel, “Parametric estimation of the local false discovery rate for identifying genetic associations,” IEEE/ACM Transactions on Computational Biology and Bioinformatics 10, 98-108 (2013). 2010 version | Slides

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Local FDR estimation software

30 June 2012 1 comment

LFDRenrich is a suite of R functions for the estimation of local false discovery rates by maximum likelihood under a two-component or three-component parametric mixture model of 2X2 tables such as those used in gene enrichment analyses.

LFDRhat is a more general suite of R functions for the estimation of local false discovery rates by maximum likelihood under a two-component or three-component parametric mixture model.

Software for local false discovery rate estimation

15 August 2011 Comments off

LFDR-MLE is a suite of R functions for the estimation of local false discovery rates by maximum likelihood under a two-group parametric mixture model of test statistics.

Quantifying evidence for genetic association

30 November 2010 Comments off

Y. Yang and D. R. Bickel, “Minimum description length and empirical Bayes methods of identifying SNPs associated with disease,” Technical Report, Ottawa Institute of Systems Biology, COBRA Preprint Series, Article 74, available at biostats.bepress.com/cobra/ps/art74 (2010).

This manuscript adapts two new evidential, information-theoretic methods to the problem of detecting SNPs associated with disease on the basis of genome-wide association data. Both an application to coronary artery disease and an extensive set of simulation studies indicate that these parametric methods tend to be more reliable than a popular semi-parametric approach to estimating local false discovery rates. In addition, the paper reports that one of the two novel methods performs better than the other.

The abstract and the discussion section of the preprint provide more detailed summaries.