Decoding Neuromuscular Disorders Using Phenotypic Clusters Obtained From Co-Occurrence Networks.

Area of Science:

• Systems biology
• Rare diseases
• Genetics and genomics

Background:

• Neuromuscular disorders (NMDs) are rare diseases with significant morbidity and mortality, often facing diagnostic delays.
• Existing diagnostic approaches can be lengthy, highlighting the need for novel methods to understand NMDs.

Purpose of the Study:

• To develop a systems biology approach for creating functionally coherent phenotype clusters in NMDs.
• To gain insights into cellular functions and phenotypic patterns underlying NMDs.
• To support the diagnosis and understanding of NMDs through novel phenotype clustering.

Main Methods:

• Utilized Human Phenotype Ontology (HPO) as a framework for phenotype analysis.
• Integrated gene and phenotype data from OMIM and Orphanet databases for 424 and 126 NMDs, respectively.
• Constructed tripartite networks (phenotypes, diseases, genes) and validated findings using PubMed abstract co-mentions and KEGG pathways.

Main Results:

• Identified 'Elevated serum creatine kinase' as a highly specific phenotype for NMDs.
• Generated 231 (OMIM) and 150 (Orphanet) clusters of co-occurring NMD phenotypes.
• Derived 40 (OMIM) and 72 (Orphanet) functionally coherent phenotype clusters, revealing novel insights for differential diagnosis and cell dysfunction.

Conclusions:

• The developed systems biology approach effectively clusters functionally related phenotypes in NMDs.
• This method aids in understanding NMDs, supports differential diagnosis, and advances personalized medicine for these rare diseases.
• The study demonstrates reproducible research contributing to better diagnostic tools and deeper knowledge of NMDs.