Using Cluster Analysis to Overcome the Limits of Traditional Phenotype–Genotype Correlations: The Example of RYR1-Related Myopathies

Abstract

Thanks to advances in gene sequencing, RYR1-related myopathy (RYR1-RM) is now known to manifest itself in vastly heterogeneous forms, whose clinical interpretation is, therefore, highly challenging. We set out to develop a novel unsupervised cluster analysis method in a large patient population. The objective was to analyze the main RYR1-related characteristics to identify distinctive features of RYR1-RM and, thus, offer more precise genotype–phenotype correlations in a group of potentially life-threatening disorders. We studied 600 patients presenting with a suspicion of inherited myopathy, who were investigated using next-generation sequencing. Among them, 73 index cases harbored variants in RYR1. In an attempt to group genetic variants and fully exploit information derived from genetic, morphological, and clinical datasets, we performed unsupervised cluster analysis in 64 probands carrying monoallelic variants. Most of the 73 patients with positive molecular diagnoses were clinically asymptomatic or pauci-symptomatic. Multimodal integration of clinical and histological data, performed using a non-metric multi-dimensional scaling analysis with k-means clustering, grouped the 64 patients into 4 clusters with distinctive patterns of clinical and morphological findings. In addressing the need for more specific genotype–phenotype correlations, we found clustering to overcome the limits of the “single-dimension” paradigm traditionally used to describe genotype–phenotype relationships.

NGS-Based Genetic Analysis in a Cohort of Italian Patients with Suspected Inherited Myopathies and/or HyperCKemia

“…In addition, RYR1 is expressed only in muscle, and transcript analysis cannot be performed on other easily accessible biological samples. The difficulties in identifying a consistent genotype-phenotype correlation in RYR1 patients have been recently demonstrated by Dosi et al [18], who tried to cluster with a high-level data integration system a large cohort (75 patients) of individuals with RYR1 positive molecular diagnosis. Hence, further investigation, particularly segregation in family members, will be required for a diagnostic conclusion.…”

Clustering Functional Magnetic Resonance Imaging Time Series in Glioblastoma Characterization: A Review of the Evolution, Applications, and Potentials

“…To address some of these limitations, various strategies can be employed, such as using more advanced clustering algorithms, more accurate data preprocessing, and cross-validating results. Additionally, the integration of statistical approaches and machine learning techniques could help overcome some of the challenges associated with fMRI data analysis [ 89 , 90 ].…”

KLHL40-Related Myopathy: A Systematic Review and Insight into a Follow-up Biomarker via a New Case Report

“…The mutations in our patient were detected in our genetic laboratory using a multiexon amplicon panel containing a total of 241 genes known to be associated with muscular dystrophies and myopathies. The data were analyzed and prioritized using bioinformatic tools and modalities already reported elsewhere [20]. No other variants, except the KLHL40 reported, were pathogenic/likely pathogenic and segregated in family members.…”