ArticleOrphanet journal of rare diseases2012
Four new loci associations discovered by pathway-based and network analyses of the genome-wide variability profile of Hirschsprung's disease.
Article in Orphanet journal of rare diseases, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Network-based multiple sclerosis pathway analysis with GWAS data from 15,000 cases and 30,000 controls.American journal of human genetics · 2013Pooled it
- Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease.Biomolecules · 2024Article
- JEPEG: a summary statistics based tool for gene-level joint testing of functional variants.Bioinformatics (Oxford, England) · 2015Article
- Pathways systematically associated to Hirschsprung's disease.Orphanet journal of rare diseases · 2013Article
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Authors and funding
13 authors at 4 institutions in 1 country.
Funding
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Abstract
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.
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