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MOTIVATION The most used criterion in microarray data analysis is nowadays the false discovery rate (FDR). In the framework of estimating procedures based on the marginal distribution of the P-values without any assumption on gene expression changes, estimators of the FDR are necessarily conservatively biased. Indeed, only an upper bound estimate can be(More)
Vascular Endothelial Growth Factor (VEGF) is the main player in angiogenesis. Because of its crucial role in this process, the study of the genetic factors controlling VEGF variability may be of particular interest for many angiogenesis-associated diseases. Although some polymorphisms in the VEGF gene have been associated with a susceptibility to several(More)
Previous studies of the HIV-1 disease have shown that HLA and Chemokine receptor genetic variants influence disease progression and early viral load. We performed a Genome Wide Association study in a cohort of 605 HIV-1-infected seroconverters for detection of novel genetic factors that influence plasma HIV-RNA and cellular HIV-DNA levels. Most of the SNPs(More)
PURPOSE East-Asian (EA) patients with non-small-cell lung cancer (NSCLC) are associated with a high proportion of nonsmoking women, epidermal growth factor receptor (EGFR)-activating somatic mutations, and clinical responses to tyrosine kinase inhibitors. We sought to identify novel molecular differences between NSCLCs from EA and Western European (WE)(More)
MOTIVATION Multiclass response (MCR) experiments are those in which there are more than two classes to be compared. In these experiments, though the null hypothesis is simple, there are typically many patterns of gene expression changes across the different classes that led to complex alternatives. In this paper, we propose a new strategy for selecting(More)
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(More)
Secretory factors that drive cancer progression are attractive immunotherapeutic targets. We used a whole-genome data-mining approach on multiple cohorts of breast tumours annotated for clinical outcomes to discover such factors. We identified Serine protease inhibitor Kazal-type 1 (SPINK1) to be associated with poor survival in estrogen receptor-positive(More)
OBJECTIVE Obesity is a complex trait with a variety of genetic susceptibility variants. Several loci linked to obesity and/or obesity-related traits have been identified, and relatively few regions have been replicated. Studying isolated populations can be a useful approach to identify rare variants that will not be detected with whole-genome association(More)
Recent advances in molecular technologies have resulted in the ability to screen hundreds of thousands of single nucleotide polymorphisms and tens of thousands of gene expression profiles. While these data have the potential to inform investigations into disease etiologies and advance medicine, the question of how to adequately control both type I and type(More)
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(More)