ArticlePLoS genetics2020
Phenotype-genotype comorbidity analysis of patients with rare disorders provides insight into their pathological and molecular bases.
Article in PLoS genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 29 citations in OpenAlex.
- A text mining and ontology-based approach using phenotypes to obtain relevant literature for rare diseases.iScience · 2026Article
- Shared and distinct phenotypic profiles among neurodevelopmental disorder genes.medRxiv : the preprint server for health sciences · 2026Article
- Detecting comorbidity patterns in rare disease patients with machine learning.Frontiers in epidemiology · 2026Article
- Exploring Recent Developments in the Manifestation, Diagnosis, and Treatment of Patients with Smith-Lemli-Opitz Syndrome: From Molecular Pathways to Clinical Innovations.International journal of molecular sciences · 2025Review
- Advancing edge-based clustering and graph embedding for biological network analysis: a case study in RASopathies.Briefings in bioinformatics · 2025Article
- Better understanding the phenotypic effects of drugs through shared targets in genetic disease networks.Frontiers in pharmacology · 2024Article
- PheNominal: an EHR-integrated web application for structured deep phenotyping at the point of care.BMC medical informatics and decision making · 2022Article
- New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches.International journal of molecular sciences · 2022Review
- Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View.Genes · 2022Review
- A Visual Phenotype-Based Differential Diagnosis Process for Rare Diseases.Interdisciplinary sciences, computational life sciences · 2022Article
- Assigning protein function from domain-function associations using DomFun.BMC bioinformatics · 2022Article
- Editorial: The genetics of human Mendelian skin disorders.Frontiers in genetics · 2022Article
- Evaluating, Filtering and Clustering Genetic Disease Cohorts Based on Human Phenotype Ontology Data with Cohort Analyzer.Journal of personalized medicine · 2021Article
- Leveraging network analysis to evaluate biomedical named entity recognition tools.Scientific reports · 2021Article
- Decoding Neuromuscular Disorders Using Phenotypic Clusters Obtained From Co-Occurrence Networks.Frontiers in molecular biosciences · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Genetic and molecular analysis of rare disease is made difficult by the small numbers of affected patients. Phenotypic comorbidity analysis can help rectify this by combining information from individuals with similar phenotypes and looking for overlap in terms of shared genes and underlying functional systems. However, few studies have combined comorbidity analysis with genomic data. We present a computational approach that connects patient phenotypes based on phenotypic co-occurence and uses genomic information related to the patient mutations to assign genes to the phenotypes, which are used to detect enriched functional systems. These phenotypes are clustered using network analysis to obtain functionally coherent phenotype clusters. We applied the approach to the DECIPHER database, containing phenotypic and genomic information for thousands of patients with heterogeneous rare disorders and copy number variants. Validity was demonstrated through overlap with known diseases, co-mention within the biomedical literature, semantic similarity measures, and patient cluster membership. These connected pairs formed multiple phenotype clusters, showing functional coherence, and mapped to genes and systems involved in similar pathological processes. Examples include claudin genes from the 22q11 genomic region associated with a cluster of phenotypes related to DiGeorge syndrome and genes related to the GO term anterior/posterior pattern specification associated with abnormal development. The clusters generated can help with the diagnosis of rare diseases, by suggesting additional phenotypes for a given patient and potential underlying functional systems. Other tools to find causal genes based on phenotype were also investigated. The approach has been implemented as a workflow, named PhenCo, which can be adapted to any set of patients for which phenomic and genomic data is available. Full details of the analysis, including the clusters formed, their constituent functional systems and underlying genes are given. Code to implement the workflow is available from GitHub.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.