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Gene prioritisation for enhancing molecular diagnosis in rare skeletal muscle disease cohort

外显子组测序 基因 表型 疾病 遗传学 候选基因 计算生物学 肌病 肌肉疾病 生物信息学 生物 医学 病理
作者
Victoria Lillback,Gaber Bergant,Maria Francesca Di Feo,Ivana Babič Božović,Annalaura Torella,Mridul Johari,Aleš Maver,Katarina Pelin,Filippo M. Santorelli,Vincenzo Nigro,Peter Hackman,Borut Peterlin,Bjarne Udd,Marco Savarese
出处
期刊:Journal of Medical Genetics [BMJ]
卷期号:62 (5): 350-357 被引量:1
标识
DOI:10.1136/jmg-2024-110212
摘要

Background Inherited rare skeletal muscle diseases cause muscle weakness and wasting of variable severity. Without a molecular diagnosis, patients often endure prolonged diagnostic journeys, leading to delays in appropriate management of the disease. This occurs in approximately 60% of patients with rare diseases. Methods To facilitate reanalysis of 278 unsolved patients, we used a gene prioritisation tool Exomiser, which standardises analysis by ranking causative variants based on phenotype relevance and variant pathogenicity. Before analysis, we benchmarked Exomiser for variant prioritisation with solved cases and for novel disease gene discovery with mock cases with variants in candidate disease genes. Additionally, we studied the significance of the specificity of the phenotype descriptions. Results In our study, Exomiser ranked genes in the top 10 correctly in 97.4% of controls with previously detected causative variants. Moreover, 57.1% of candidate genes in mock cases were similarly prioritised in the top 10. We also showed that three parental muscle disease human phenotype ontologies describing the patient phenotype performed as well as patient-specific ones, with a p value of 0.68 for difference in performance. The provided automation and standardisation of variant interpretation resulted in two novel diagnoses and in findings, either in known muscle disease genes or in novel candidate genes, which need further investigation. Conclusions Exomiser is recommended for initial and periodic reanalyses of exomes in unsolved patients with myopathy, as it benefits from literature updates and minimises effort. This approach could also extend to whole genome sequencing data, aiding the interpretation of variants beyond coding regions.
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