ArticleBMC genomics2016
Systematic identification of phenotypically enriched loci using a patient network of genomic disorders.
Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Assigning protein function from domain-function associations using DomFun.BMC bioinformatics · 2022Article
- Systematic identification of genetic systems associated with phenotypes in patients with rare genomic copy number variations.Human genetics · 2021Article
- Polyamines in mammalian pathophysiology.Cellular and molecular life sciences : CMLS · 2019Review
- Applications of molecular networks in biomedicine.Biology methods & protocols · 2019Review
- Phenotype-loci associations in networks of patients with rare disorders: application to assist in the diagnosis of novel clinical cases.European journal of human genetics : EJHG · 2018Article
- What We Know and What We Need to Know about Aromatic and Cationic Biogenic Amines in the Gastrointestinal Tract.Foods (Basel, Switzerland) · 2018Review
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Authors and funding
7 authors.
Funding
Abstract
backgroundNetwork 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.
methodsHere 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.
resultsMany 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.
conclusionsWe 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.
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