1. Using Cluster Analysis to Overcome the Limits of Traditional Phenotype–Genotype Correlations: The Example of RYR1-Related Myopathies
- Author
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Claudia Dosi, Anna Rubegni, Jacopo Baldacci, Daniele Galatolo, Stefano Doccini, Guja Astrea, Angela Berardinelli, Claudio Bruno, Giorgia Bruno, Giacomo Pietro Comi, Maria Alice Donati, Maria Teresa Dotti, Massimiliano Filosto, Chiara Fiorillo, Fabio Giannini, Gian Luigi Gigli, Marina Grandis, Diego Lopergolo, Francesca Magri, Maria Antonietta Maioli, Alessandro Malandrini, Roberto Massa, Sabrina Matà, Federico Melani, Sonia Messina, Andrea Mignarri, Maurizio Moggio, Elena Maria Pennisi, Elena Pegoraro, Giulia Ricci, Michele Sacchini, Angelo Schenone, Simone Sampaolo, Monica Sciacco, Gabriele Siciliano, Giorgio Tasca, Paola Tonin, Rossella Tupler, Mariarosaria Valente, Nila Volpi, Denise Cassandrini, and Filippo Maria Santorelli
- Subjects
unsupervised cluster analysis ,genotype–phenotype correlation ,NGS ,RYR1-related myopathies ,Genetics ,Genetics (clinical) - 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.
- Published
- 2023