1. A mixture model approach to multiple testing for the genetic analysis of gene expression
- Author
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Emmanuelle Génin, Cyril Dalmasso, Catherine Bourgain, Philippe Broët, Marianne Tuefferd, and Joseph K. Pickrell
- Subjects
Computer science ,lcsh:R ,lcsh:Medicine ,Context (language use) ,General Medicine ,computer.software_genre ,Mixture model ,Genetic analysis ,General Biochemistry, Genetics and Molecular Biology ,Expression (mathematics) ,Data set ,Proceedings ,Multiple comparisons problem ,Null distribution ,lcsh:Q ,Data mining ,lcsh:Science ,computer ,Parametric statistics - Abstract
With the availability of very dense genome-wide maps of markers, multiple testing has become a major difficulty for genetic studies. In this context, the false-discovery rate (FDR) and related criteria are widely used. Here, we propose a finite mixture model to estimate the local FDR (lFDR), the FDR, and the false non-discovery rate (FNR) in variance-component linkage analysis. Our parametric approach allows empirical estimation of an appropriate null distribution. The contribution of our model to estimation of FDR and related criteria is illustrated on the microarray expression profiles data set provided by the Genetic Analysis Workshop 15 Problem 1.
- Published
- 2007