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Four new loci associations discovered by pathway-based and network analyses of the genome-wide variability profile of Hirschsprung’s disease

Raquel Ma Fernández12, Marta Bleda23, Rocío Núñez-Torres12, Ignacio Medina34, Berta Luzón-Toro12, Luz García-Alonso3, Ana Torroglosa12, Martina Marbà34, Ma Valle Enguix-Riego12, David Montaner3, Guillermo Antiñolo12, Joaquín Dopazo234* and Salud Borrego12*

Author Affiliations

1 Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS, University Hospital Virgen del Rocío/CSIC/University of Seville, Seville, Spain

2 Centre for Biomedical Network Research on Rare Diseases (CIBERER), Barcelona, Spain

3 Department of Bioinformatics, Research Centre Príncipe Felipe, Valencia, Spain

4 Functional Genomics Node (INB), Research Centre Príncipe Felipe, Valencia, Spain

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Orphanet Journal of Rare Diseases 2012, 7:103  doi:10.1186/1750-1172-7-103

Published: 28 December 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.

HSCR; Pathway-based analysis; Network analysis; GWAS