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Identifying relationships among genomic disease regions: Predicting genes at pathogenic SNP associations and rare deletions

Raychaudhuri, S; Plenge, RM; Rossin, EJ; Altshuler, D; Daly, MJ; Purcell, SM; Sklar, P; ... Macedo, A; + view all (2009) Identifying relationships among genomic disease regions: Predicting genes at pathogenic SNP associations and rare deletions. PLoS Genetics , 5 (6) , Article e1000534. 10.1371/journal.pgen.1000534. Green open access

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Abstract

Translating a set of disease regions into insight about pathogenic mechanisms requires not only the ability to identify the key disease genes within them, but also the biological relationships among those key genes. Here we describe a statistical method, Gene Relationships Among Implicated Loci (GRAIL), that takes a list of disease regions and automatically assesses the degree of relatedness of implicated genes using 250,000 PubMed abstracts. We first evaluated GRAIL by assessing its ability to identify subsets of highly related genes in common pathways from validated lipid and height SNP associations from recent genome-wide studies. We then tested GRAIL, by assessing its ability to separate true disease regions from many false positive disease regions in two separate practical applications in human genetics. First, we took 74 nominally associated Crohn's disease SNPs and applied GRAIL to identify a subset of 13 SNPs with highly related genes. Of these, ten convincingly validated in follow-up genotyping; genotyping results for the remaining three were inconclusive. Next, we applied GRAIL to 165 rare deletion events seen in schizophrenia cases (less than one-third of which are contributing to disease risk). We demonstrate that GRAIL is able to identify a subset of 16 deletions containing highly related genes; many of these genes are expressed in the central nervous system and play a role in neuronal synapses. GRAIL offers a statistically robust approach to identifying functionally related genes from across multiple disease regions-that likely represent key disease pathways. An online version of this method is available for public use (http://www.broad.mit. edu/mpg/grail/). © 2009 Raychaudhuri et al.

Type: Article
Title: Identifying relationships among genomic disease regions: Predicting genes at pathogenic SNP associations and rare deletions
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pgen.1000534
Publisher version: http://dx.doi.org/10.1371/journal.pgen.1000534
Language: English
Additional information: © 2009 Raychaudhuri et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Division of Psychiatry
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Genetics, Evolution and Environment
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Genetics and Genomic Medicine Dept
URI: https://discovery.ucl.ac.uk/id/eprint/1433074
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