title: A mixture model approach to multiple testing for the genetic analysis of gene expression.
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.
authors.affiliation: JE 2492 Universite Paris-Sud, Hôpital Paul Brousse - Batiment 15/16, 16 Avenue Paul Vaillant Couturier, Villejuif CEDEX 94807, France. dalmasso@vjf.inserm.fr
authors.forename: Cyril
authors.initials: C
authors.lastname: Dalmasso
authors.forename: Joseph
authors.initials: J
authors.lastname: Pickrell
authors.forename: Marianne
authors.initials: M
authors.lastname: Tuefferd
authors.forename: Emmanuelle
authors.initials: E
authors.lastname: Génin
authors.forename: Catherine
authors.initials: C
authors.lastname: Bourgain
authors.forename: Philippe
authors.initials: P
authors.lastname: Broët