Systematic identification of phenotypically enriched loci using a patient network of genomic disorders. (December 2016)

• Record Type: Journal Article
• Authors: Reyes-Palomares, Armando; Bueno, Aníbal; Rodríguez-López, Rocío; Medina, Miguel; Sánchez-Jiménez, Francisca; Corpas, Manuel; Ranea, Juan
• Is Part Of: BMC genomics. Volume 17:Number 1(2016)
• Page Start: 1
• Page End: 14
• Publication Date: 2016-12
• Subjects: Genomes -- Periodicals; Gene mapping -- Periodicals; Genomics -- Periodicals; Base Sequence -- Periodicals; Chromosome Mapping -- Periodicals; Genetic Techniques -- Periodicals; Sequence Analysis, DNA -- Periodicals; 572.8605
• DOI: 10.1186/s12864-016-2569-6
• Languages: English
• ISSNs: 1471-2164

Abstract

Abstract Background Network medicine is a promising new discipline that combines systems biology approaches and network science to understand the complexity of pathological phenotypes. Given the growing availability of personalized genomic and phenotypic profiles, network models offer a robust integrative framework for the analysis of "omics" data, allowing the characterization of the molecular aetiology of pathological processes underpinning genetic diseases. Methods Here we make use of patient genomic data to exploit different network-based analyses to study genetic and phenotypic relationships between individuals. For this method, we analyzed a dataset of structural variants and phenotypes for 6, 564 patients from the DECIPHER database, which encompasses one of the most comprehensive collections of pathogenic Copy Number Variations (CNVs) and their associated ontology-controlled phenotypes. We developed a computational strategy that identifies clusters of patients in a synthetic patient network according to their genetic overlap and phenotype enrichments. Results Many of these clusters of patients represent new genotype-phenotype associations, suggesting the identification of newly discovered phenotypically enrichedloci (indicative of potential novel syndromes) that are currently absent from reference genomic disorder databases such as ClinVar, OMIM or DECIPHER itself. Conclusions We provide a high-resolution map of pathogenic phenotypes associated with their respective significant genomic regions and a new powerful tool for diagnosis of currently uncharacterized mutations leading to deleterious phenotypes and syndromes.