domingo, 11 de abril de 2010

Genomics in the Scientific Literature [10] - Other



Other
1. Mutation@A Glance: An Integrative Web Application for Analysing Mutations from Human Genetic Diseases
Hijikata A, et al.
DNA Res 2010 Apr

DNA Res. 2010 Apr 1. [Epub ahead of print]
Mutation@A Glance: An Integrative Web Application for Analysing Mutations from Human Genetic Diseases.
Hijikata A, Raju R, Keerthikumar S, Ramabadran S, Balakrishnan L, Ramadoss SK, Pandey A, Mohan S, Ohara O.

1Laboratory for Immunogenomics, RIKEN Research Center for Allergy and Immunology, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
Abstract

Although mutation analysis serves as a key part in making a definitive diagnosis about a genetic disease, it still remains a time-consuming step to interpret their biological implications through integration of various lines of archived information about genes in question. To expedite this evaluation step of disease-causing genetic variations, here we developed Mutation@A Glance (http://rapid.rcai.riken.jp/mutation/), a highly integrated web-based analysis tool for analysing human disease mutations; it implements a user-friendly graphical interface to visualize about 40 000 known disease-associated mutations and genetic polymorphisms from more than 2600 protein-coding human disease-causing genes. Mutation@A Glance locates already known genetic variation data individually on the nucleotide and the amino acid sequences and makes it possible to cross-reference them with tertiary and/or quaternary protein structures and various functional features associated with specific amino acid residues in the proteins. We showed that the disease-associated missense mutations had a stronger tendency to reside in positions relevant to the structure/function of proteins than neutral genetic variations. From a practical viewpoint, Mutation@A Glance could certainly function as a 'one-stop' analysis platform for newly determined DNA sequences, which enables us to readily identify and evaluate new genetic variations by integrating multiple lines of information about the disease-causing candidate genes.

PMID: 20360267 [PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/20360267?dopt=Abstract



2. Robust Replication of Genotype-Phenotype Associations across Multiple Diseases in an Electronic Medical Record
Ritchie MD, et al.
Am J Hum Genet 2010 Mar

Am J Hum Genet. 2010 Mar 31. [Epub ahead of print]
Robust Replication of Genotype-Phenotype Associations across Multiple Diseases in an Electronic Medical Record.
Ritchie MD, Denny JC, Crawford DC, Ramirez AH, Weiner JB, Pulley JM, Basford MA, Brown-Gentry K, Balser JR, Masys DR, Haines JL, Roden DM.

Center for Human Genetics Research, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.
Abstract

Large-scale DNA databanks linked to electronic medical record (EMR) systems have been proposed as an approach for rapidly generating large, diverse cohorts for discovery and replication of genotype-phenotype associations. However, the extent to which such resources are capable of delivering on this promise is unknown. We studied whether an EMR-linked DNA biorepository can be used to detect known genotype-phenotype associations for five diseases. Twenty-one SNPs previously implicated as common variants predisposing to atrial fibrillation, Crohn disease, multiple sclerosis, rheumatoid arthritis, or type 2 diabetes were successfully genotyped in 9483 samples accrued over 4 mo into BioVU, the Vanderbilt University Medical Center DNA biobank. Previously reported odds ratios (OR(PR)) ranged from 1.14 to 2.36. For each phenotype, natural language processing techniques and billing-code queries were used to identify cases (n = 70-698) and controls (n = 808-3818) from deidentified health records. Each of the 21 tests of association yielded point estimates in the expected direction. Previous genotype-phenotype associations were replicated (p < 0.05) in 8/14 cases when the OR(PR) was > 1.25, and in 0/7 with lower OR(PR). Statistically significant associations were detected in all analyses that were adequately powered. In each of the five diseases studied, at least one previously reported association was replicated. These data demonstrate that phenotypes representing clinical diagnoses can be extracted from EMR systems, and they support the use of DNA resources coupled to EMR systems as tools for rapid generation of large data sets required for replication of associations found in research cohorts and for discovery in genome science. Copyright © 2010 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

PMID: 20362271 [PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/20362271?dopt=Abstract

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