Literature DB >> 19963217

Lost in the space of bioinformatic tools: a constantly updated survival guide for genetic epidemiology. The GenEpi Toolbox.

Stefan Coassin1, Anita Brandstätter, Florian Kronenberg.   

Abstract

Genome-wide association studies (GWASs) led to impressive advances in the elucidation of genetic factors underlying complex phenotypes and diseases. However, the ability of GWAS to identify new susceptibility loci in a hypothesis-free approach requires tools to quickly retrieve comprehensive information about a genomic region and analyze the potential effects of coding and non-coding SNPs in a candidate gene region. Furthermore, once a candidate region is chosen for resequencing and fine-mapping studies, the identification of several rare mutations is likely and requires strong bioinformatic support to properly evaluate and prioritize the found mutations for further analysis. Due to the variety of regulatory layers that can be affected by a mutation, a comprehensive in-silico evaluation of candidate SNPs can be a demanding and very time-consuming task. Although many bioinformatic tools that significantly simplify this task were made available in the last years, their utility is often still unknown to researches not intensively involved in bioinformatics. We present a comprehensive guide of 64 tools and databases to bioinformatically analyze gene regions of interest to predict SNP effects. In addition, we discuss tools to perform data mining of large genetic regions, predict the presence of regulatory elements, make in-silico evaluations of SNPs effects and address issues ranging from interactome analysis to graphically annotated proteins sequences. Finally, we exemplify the use of these tools by applying them to hits of a recently performed GWAS. Taken together a combination of the discussed tools are summarized and constantly updated in the web-based "GenEpi Toolbox" (http://genepi_toolbox.i-med.ac.at) and can help to get a glimpse at the potential functional relevance of both large genetic regions and single nucleotide mutations which might help to prioritize the next steps. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

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Year:  2009        PMID: 19963217     DOI: 10.1016/j.atherosclerosis.2009.10.026

Source DB:  PubMed          Journal:  Atherosclerosis        ISSN: 0021-9150            Impact factor:   5.162


  17 in total

1.  Statistical Optimization of Pharmacogenomics Association Studies: Key Considerations from Study Design to Analysis.

Authors:  Benjamin J Grady; Marylyn D Ritchie
Journal:  Curr Pharmacogenomics Person Med       Date:  2011-03-01

2.  IMHOTEP-a composite score integrating popular tools for predicting the functional consequences of non-synonymous sequence variants.

Authors:  Carolin Knecht; Matthew Mort; Olaf Junge; David N Cooper; Michael Krawczak; Amke Caliebe
Journal:  Nucleic Acids Res       Date:  2017-02-17       Impact factor: 16.971

3.  Somatic mutations throughout the entire mitochondrial genome are associated with elevated PSA levels in prostate cancer patients.

Authors:  Anita Kloss-Brandstätter; Georg Schäfer; Gertraud Erhart; Alexander Hüttenhofer; Stefan Coassin; Christof Seifarth; Monika Summerer; Jasmin Bektic; Helmut Klocker; Florian Kronenberg
Journal:  Am J Hum Genet       Date:  2010-12-10       Impact factor: 11.025

4.  Cyclin D1 rare variants in UK multiple adenoma and early-onset colorectal cancer patients.

Authors:  Carolina Bonilla; Jérémie H Lefèvre; Bruce Winney; Elaine Johnstone; Susan Tonks; Chrystelle Colas; Tammy Day; Katarzyna Hutnik; Abdelhamid Boumertit; Rachel Midgley; David Kerr; Yann Parc; Walter F Bodmer
Journal:  J Hum Genet       Date:  2010-11-25       Impact factor: 3.172

5.  Clear detection of ADIPOQ locus as the major gene for plasma adiponectin: results of genome-wide association analyses including 4659 European individuals.

Authors:  Iris M Heid; Peter Henneman; Andrew Hicks; Stefan Coassin; Thomas Winkler; Yurii S Aulchenko; Christian Fuchsberger; Kijoung Song; Marie-France Hivert; Dawn M Waterworth; Nicholas J Timpson; J Brent Richards; John R B Perry; Toshiko Tanaka; Najaf Amin; Barbara Kollerits; Irene Pichler; Ben A Oostra; Barbara Thorand; Rune R Frants; Thomas Illig; Josée Dupuis; Beate Glaser; Tim Spector; Jack Guralnik; Josephine M Egan; Jose C Florez; David M Evans; Nicole Soranzo; Stefania Bandinelli; Olga D Carlson; Timothy M Frayling; Keith Burling; George Davey Smith; Vincent Mooser; Luigi Ferrucci; James B Meigs; Peter Vollenweider; Ko Willems van Dijk; Peter Pramstaller; Florian Kronenberg; Cornelia M van Duijn
Journal:  Atherosclerosis       Date:  2009-12-02       Impact factor: 5.162

6.  Sex and age interaction with genetic association of atherogenic uric acid concentrations.

Authors:  Anita Brandstätter; Claudia Lamina; Stefan Kiechl; Steven C Hunt; Stefan Coassin; Bernhard Paulweber; Felix Kramer; Monika Summerer; Johann Willeit; Lyudmyla Kedenko; Ted D Adams; Florian Kronenberg
Journal:  Atherosclerosis       Date:  2009-12-16       Impact factor: 5.162

7.  SPOCK3, a risk gene for adult ADHD and personality disorders.

Authors:  Heike Weber; Claus-Jürgen Scholz; Christian P Jacob; Julia Heupel; Sarah Kittel-Schneider; Angelika Erhardt; Susanne Hempel; Brigitte Schmidt; Tilman Kiel; Alexandra Gessner; Klaus-Peter Lesch; Andreas Reif
Journal:  Eur Arch Psychiatry Clin Neurosci       Date:  2013-11-29       Impact factor: 5.270

8.  Anorectal atresia and variants at predicted regulatory sites in candidate genes.

Authors:  Tonia C Carter; Denise M Kay; Marilyn L Browne; Aiyi Liu; Paul A Romitti; Devon Kuehn; Mary R Conley; Michele Caggana; Charlotte M Druschel; Lawrence C Brody; James L Mills
Journal:  Ann Hum Genet       Date:  2012-11-06       Impact factor: 1.670

Review 9.  Candidate gene association studies: a comprehensive guide to useful in silico tools.

Authors:  Radhika Patnala; Judith Clements; Jyotsna Batra
Journal:  BMC Genet       Date:  2013-05-09       Impact factor: 2.797

Review 10.  The success of pharmacogenomics in moving genetic association studies from bench to bedside: study design and implementation of precision medicine in the post-GWAS era.

Authors:  Marylyn D Ritchie
Journal:  Hum Genet       Date:  2012-08-25       Impact factor: 4.132

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