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BMC Genomics. 2016 Mar 15;17:232. doi: 10.1186/s12864-016-2569-6.

Systematic identification of phenotypically enriched loci using a patient network of genomic disorders.

Author information

1
Universidad de Málaga, Andalucía Tech, Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, and IBIMA (Biomedical Research Institute of Málaga), E-29071, Málaga, Spain. armando.reyes@embl.de.
2
CIBER de Enfermedades Raras (CIBERER), E-29071, Málaga, Spain. armando.reyes@embl.de.
3
Present address: The European Molecular Biology Laboratory Heidelberg, 69117, Heidelberg, Germany. armando.reyes@embl.de.
4
Universidad de Málaga, Andalucía Tech, Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, and IBIMA (Biomedical Research Institute of Málaga), E-29071, Málaga, Spain.
5
CIBER de Enfermedades Raras (CIBERER), E-29071, Málaga, Spain.
6
The Genome Analysis Centre, Norwich Research Park, Norwich, NR4 7UH, UK.
7
Universidad de Málaga, Andalucía Tech, Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, and IBIMA (Biomedical Research Institute of Málaga), E-29071, Málaga, Spain. ranea@uma.es.
8
CIBER de Enfermedades Raras (CIBERER), E-29071, Málaga, Spain. ranea@uma.es.

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 enriched loci (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.

PMID:
26980139
PMCID:
PMC4792099
DOI:
10.1186/s12864-016-2569-6
[Indexed for MEDLINE]
Free PMC Article

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