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Background  In the context of genomic association studies, for which a large number of statistical tests are performed simultaneously, the local False Discovery Rate ( lFDR ), which quantifies the evidence of a specific gene association with a clinical or biological variable of interest, is a relevant criterion for taking into account the multiple testing problem. The  lFDR  not only allows an inference to be made for each gene through its specific value, but also an estimate of Benjamini-Hochberg's False Discovery Rate ( FDR ) for subsets of genes.    Results  In the framework of estimating procedures without any distributional assumption under the alternative hypothesis, a new and efficient procedure for estimating the  lFDR  is described. The results of a simulation study indicated good performances for the proposed estimator in comparison to four published ones. The five different procedures were applied to real datasets.    Conclusion  A novel and efficient procedure for estimating  lFDR  was developed and evaluated.

A constrained polynomial regression procedure for estimating the local False Discovery Rate