Genes · 2023
Prognostic Value of Genotype-Phenotype Correlations in X-Linked Myotubular Myopathy and the Use of the Face2Gene Application as an Effective Non-Invasive Diagnostic Tool.
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Kušíková Katarína, Šoltýsová Andrea, Ficek Andrej, Feichtinger René G, Mayr Johannes A, Škopková Martina, Gašperíková Daniela, Kolníková Miriam, Ornig Karoline, Kalev Ognian, Weis Serge, Weis Denisa
Аннотация
BACKGROUND: X-linked myotubular myopathy (XLMTM) is a rare congenital myopathy resulting from dysfunction of the protein myotubularin encoded by the MTM1 gene. XLMTM has a high neonatal and infantile mortality rate due to a severe myopathic phenotype and respiratory failure. However, in a minority of XLMTM cases, patients present with milder phenotypes and achieve ambulation and adulthood. Notable facial dysmorphia is also present.
METHODS: We investigated the genotype-phenotype correlations in newly diagnosed XLMTM patients in a patients' cohort (previously published data plus three novel variants, n = 414). Based on the facial gestalt difference between XLMTM patients and unaffected controls, we investigated the use of the Face2Gene application.
RESULTS: Significant associations between severe phenotype and truncating variants (p < 0.001), frameshift variants (p < 0.001), nonsense variants (p = 0.006), and in/del variants (p = 0.036) were present. Missense variants were significantly associated with the mild and moderate phenotype (p < 0.001). The Face2Gene application showed a significant difference between XLMTM patients and unaffected controls (p = 0.001).
CONCLUSIONS: Using genotype-phenotype correlations could predict the disease course in most XLMTM patients, but still with limitations. The Face2Gene application seems to be a practical, non-invasive diagnostic approach in XLMTM using the correct algorithm.
METHODS: We investigated the genotype-phenotype correlations in newly diagnosed XLMTM patients in a patients' cohort (previously published data plus three novel variants, n = 414). Based on the facial gestalt difference between XLMTM patients and unaffected controls, we investigated the use of the Face2Gene application.
RESULTS: Significant associations between severe phenotype and truncating variants (p < 0.001), frameshift variants (p < 0.001), nonsense variants (p = 0.006), and in/del variants (p = 0.036) were present. Missense variants were significantly associated with the mild and moderate phenotype (p < 0.001). The Face2Gene application showed a significant difference between XLMTM patients and unaffected controls (p = 0.001).
CONCLUSIONS: Using genotype-phenotype correlations could predict the disease course in most XLMTM patients, but still with limitations. The Face2Gene application seems to be a practical, non-invasive diagnostic approach in XLMTM using the correct algorithm.