0000000000537889
AUTHOR
Dilafruz Juraeva
Genetic contribution to alcohol dependence: Investigation of a heterogeneous german sample of individuals with alcohol dependence, chronic alcoholic pancreatitis, and alcohol-related cirrhosis
The present study investigated the genetic contribution to alcohol dependence (AD) using genome-wide association data from three German samples. These comprised patients with: (i) AD; (ii) chronic alcoholic pancreatitis (ACP); and (iii) alcohol-related liver cirrhosis (ALC). Single marker, gene-based, and pathway analyses were conducted. A significant association was detected for the ADH1B locus in a gene-based approach (puncorrected = 1.2 × 10-6; pcorrected = 0.020). This was driven by the AD subsample. No association with ADH1B was found in the combined ACP + ALC sample. On first inspection, this seems surprising, since ADH1B is a robustly replicated risk gene for AD and may therefore be …
XRCC5 as a Risk Gene for Alcohol Dependence : Evidence from a Genome-Wide Gene-Set-Based Analysis and Follow-up Studies in Drosophila and Humans
Genetic factors play as large a role as environmental factors in the etiology of alcohol dependence. Although genome-wide association studies (GWAS) enable systematic searches for loci not hitherto implicated in the etiology of alcohol dependence, many true findings may be missed due to correction for multiple testing. The aim of the present study was to circumvent this limitation by searching for biological system-level differences, and then following up these findings in humans and animals. Gene-set based analysis of GWAS data from 1333 cases and 2168 controls identified 19 significantly associated gene-sets of which five could be replicated in an independent sample. Clustered in these ge…
Revised risk estimation and treatment stratification of low- and intermediate-risk neuroblastoma patients by integrating clinical and molecular prognostic markers.
Abstract Purpose: To optimize neuroblastoma treatment stratification, we aimed at developing a novel risk estimation system by integrating gene expression–based classification and established prognostic markers. Experimental Design: Gene expression profiles were generated from 709 neuroblastoma specimens using customized 4 × 44 K microarrays. Classification models were built using 75 tumors with contrasting courses of disease. Validation was performed in an independent test set (n = 634) by Kaplan–Meier estimates and Cox regression analyses. Results: The best-performing classifier predicted patient outcome with an accuracy of 0.95 (sensitivity, 0.93; specificity, 0.97) in the validation coh…