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oai:repisalud.isciii.es:20.500.12105/171312024-09-21T20:27:35Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16927

00925njm 22002777a 4500 dc Reyes-Palomares, Armando author Bueno, Aníbal author Rodríguez-López, Rocío author Medina, Miguel Ángel author Sánchez-Jiménez, Francisca author Corpas, Manuel author Ranea, Juan A G author 2016-03-15 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. 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. Many of these clusters of patients represent new genotype-phenotype associations, suggesting the identification of newly discovered phenotypically enriched loci (indicative of potential novel syndromes) that are currently absent from reference genomic disorder databases such as ClinVar, OMIM or DECIPHER itself. 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. 10.1186/s12864-016-2569-6 1471-2164 BMC genomics http://hdl.handle.net/10668/9920 26980139 http://hdl.handle.net/20.500.12105/17131 Systematic identification of phenotypically enriched loci using a patient network of genomic disorders.