HGVA: the Human Genome Variation Archive. - Lopez, Javier; Coll, Jacobo; Haimel, Matthias; Kandasamy, Swaathi; Tárraga, Joaquín; Furio-Tari, Pedro; Bari, Wasim; Bleda, Marta; Rueda, Antonio; Gräf, Stefan; Rendon, Augusto; Dopazo, Joaquin; et al. (Nucleic Acids Res, 2017)
High-profile genomic variation projects like the 1000 Genomes project or the Exome Aggregation Consortium, are generating a wealth of human genomic variation knowledge which can be used as an essential reference for identifying disease-causing genotypes. However, accessing these data, contrasting the various studies and integrating those data in downstream analyses remains cumbersome. The Human Genome Variation Archive (HGVA) tackles these challenges and facilitates access to genomic data for key reference projects in a clean, fast and integrated fashion. HGVA provides an efficient and intuitive web-interface for easy data mining, a comprehensive RESTful API and client libraries in Python, Java and JavaScript for fast programmatic access to its knowledge base. HGVA calculates population frequencies for these projects and enriches their data with variant annotation provided by CellBase, a rich and fast annotation solution. HGVA serves as a proof-of-concept of the genome analysis developments being carried out by the University of Cambridge together with UK's 100 000 genomes project and the National Institute for Health Research BioResource Rare-Diseases, in particular, deploying open-source for Computational Biology (OpenCB) software platform for storing and analyzing massive genomic datasets.
Keywords: Genetic Variation; Genome, Human; Humans; Internet; Software; User-Computer Interface

Pathways systematically associated to Hirschsprung's disease. - Fernández, Raquel M; Bleda, Marta; Luzón-Toro, Berta; García-Alonso, Luz; Arnold, Stacey; Sribudiani, Yunia; Besmond, Claude; Lantieri, Francesca; Doan, Betty; Ceccherini, Isabella; Lyonnet, Stanislas; Hofstra, Robert Mw; et al. (Orphanet J Rare Dis, 2013)
Despite it has been reported that several loci are involved in Hirschsprung's disease, the molecular basis of the disease remains yet essentially unknown. The study of collective properties of modules of functionally-related genes provides an efficient and sensitive statistical framework that can overcome sample size limitations in the study of rare diseases. Here, we present the extension of a previous study of a Spanish series of HSCR trios to an international cohort of 162 HSCR trios to validate the generality of the underlying functional basis of the Hirschsprung's disease mechanisms previously found. The Pathway-Based Analysis (PBA) confirms a strong association of gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other processes related to the disease. In addition, network analysis recovers sub-networks significantly associated to the disease, which contain genes related to the same functionalities, thus providing an independent validation of these findings. The functional profiles of association obtained for patients populations from different countries were compared to each other. While gene associations were different at each series, the main functional associations were identical in all the five populations. These observations would also explain the reported low reproducibility of associations of individual disease genes across populations.
Keywords: Female; Genetic Predisposition to Disease; Genotype; Hirschsprung Disease; Humans; Male; Polymorphism, Single Nucleotide

Four new loci associations discovered by pathway-based and network analyses of the genome-wide variability profile of Hirschsprung's disease. - Fernández, Raquel Ma; Bleda, Marta; Núñez-Torres, Rocío; Medina, Ignacio; Luzón-Toro, Berta; García-Alonso, Luz; Torroglosa, Ana; Marbà, Martina; Enguix-Riego, Ma Valle; Montaner, David; Antiňolo, Guillermo; Dopazo, Joaquin; et al. (Orphanet J Rare Dis, 2012)
Finding gene associations in rare diseases is frequently hampered by the reduced numbers of patients accessible. Conventional gene-based association tests rely on the availability of large cohorts, which constitutes a serious limitation for its application in this scenario. To overcome this problem we have used here a combined strategy in which a pathway-based analysis (PBA) has been initially conducted to prioritize candidate genes in a Spanish cohort of 53 trios of short-segment Hirschsprung's disease. Candidate genes have been further validated in an independent population of 106 trios. The study revealed a strong association of 11 gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other HSCR-related processes. Among the preselected candidates, a total of 4 loci, RASGEF1A, IQGAP2, DLC1 and CHRNA7, related to signal transduction and migration processes, were found to be significantly associated to HSCR. Network analysis also confirms their involvement in the network of already known disease genes. This approach, based on the study of functionally-related gene sets, requires of lower sample sizes and opens new opportunities for the study of rare diseases.
Keywords: Female; Genetic Predisposition to Disease; Genome-Wide Association Study; Genotype; Hirschsprung Disease; Humans; Male