Xavier Farré, N. Spataro, Frédéric Haziza, Jordi Rambla, A. Navarro
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引用次数: 3
Abstract
MOTIVATION
Association studies based on SNP arrays and Next Generation Sequencing (NGS) technologies have enabled the discovery of thousands of genetic loci related to human diseases. Nevertheless, their biological interpretation is still elusive, and their medical applications limited. Recently, various tools have been developed to help bridging the gap between genomes and phenomes. To our knowledge, however none of these tools allows users to retrieve the phenotype-wide list of genetic variants that may be linked to a given disease or to visually explore the joint genetic architecture of different pathologies.
RESULTS
We present the Genome-Phenome Explorer (GePhEx), a web-tool easing the visual exploration of phenotypic relationships supported by genetic evidences. GePhEx is primarily based on the thorough analysis of LD between disease-associated variants and also considers relationships based on genes, pathways or drug-targets, leveraging on publicly available variant-disease associations to detect potential relationships between diseases. We demonstrate that GePhEx does retrieve well-known relationships as well as novel ones, and that, thus, it might help shedding light on the pathophysiological mechanisms underlying complex diseases. To this end, we investigate the potential relationship between schizophrenia and lung cancer, first detected using GePhEx, and provide further evidence supporting a functional link between them.
AVAILABILITY
GePhEx is available at: https://gephex.ega-archive.org/.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
期刊介绍:
The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.