Publications

Authors:

Dosi, Claudia; Rubegni, Anna; Baldacci, Jacopo; Galatolo, Daniele; Doccini, Stefano; Astrea, Guja; Berardinelli, Angela; Bruno, Claudio; Bruno, Giorgia; Comi, Giacomo Pietro; Donati, Maria Alice; Dotti, Maria Teresa; Filosto, Massimiliano; Fiorillo, Chiara; Giannini, Fabio; Gigli, Gian Luigi; Grandis, Marina; Lopergolo, Diego; Magri, Francesca; Maioli, Maria Antonietta; Malandrini, Alessandro; Massa, Roberto; Matà, Sabrina; Melani, Federico; Messina, Sonia; Mignarri, Andrea; Moggio, Maurizio; Pennisi, Elena Maria; Pegoraro, Elena; Ricci, Giulia; Sacchini, Michele; Schenone, Angelo; Sampaolo, Simone; Sciacco, Monica; Siciliano, Gabriele; Tasca, Giorgio; Tonin, Paola; Tupler, Rossella; Valente, Mariarosaria; Volpi, Nila; Cassandrini, Denise; Santorelli, Filippo Maria

Title:

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

Year:

2023

Type of item:

Articolo in Rivista

Tipologia ANVUR:

Articolo su rivista

Language:

Inglese

Format:

Elettronico

Referee:

Sì

Name of journal:

GENES

ISSN of journal:

2073-4425

N° Volume:

14

Number or Folder:

298

Page numbers:

1-14

Keyword:

NGS; RYR1-related myopathies; genotype–phenotype correlation; unsupervised cluster analysis

Short description of contents:

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.

Product ID:

133614

Handle IRIS:

11562/1092928

Last Modified:

May 4, 2023

Bibliographic citation:

Dosi, Claudia; Rubegni, Anna; Baldacci, Jacopo; Galatolo, Daniele; Doccini, Stefano; Astrea, Guja; Berardinelli, Angela; Bruno, Claudio; Bruno, Giorgia; Comi, Giacomo Pietro; Donati, Maria Alice; Dotti, Maria Teresa; Filosto, Massimiliano; Fiorillo, Chiara; Giannini, Fabio; Gigli, Gian Luigi; Grandis, Marina; Lopergolo, Diego; Magri, Francesca; Maioli, Maria Antonietta; Malandrini, Alessandro; Massa, Roberto; Matà, Sabrina; Melani, Federico; Messina, Sonia; Mignarri, Andrea; Moggio, Maurizio; Pennisi, Elena Maria; Pegoraro, Elena; Ricci, Giulia; Sacchini, Michele; Schenone, Angelo; Sampaolo, Simone; Sciacco, Monica; Siciliano, Gabriele; Tasca, Giorgio; Tonin, Paola; Tupler, Rossella; Valente, Mariarosaria; Volpi, Nila; Cassandrini, Denise; Santorelli, Filippo Maria, Using Cluster Analysis to Overcome the Limits of Traditional Phenotype-Genotype Correlations: The Example of RYR1-Related Myopathies «GENES» , vol. 14 , n. 298 , 2023 , pp. 1-14

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