Annals of Indian Academy of Neurology
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ORIGINAL ARTICLE
Year : 2017  |  Volume : 20  |  Issue : 3  |  Page : 302-308

Detection of dysferlin gene pathogenic variants in the Indian Population in patients predicted to have a dysferlinopathy using a blood-based monocyte assay and clinical algorithm: A model for accurate and cost-effective diagnosis


1 Centre for Advanced Molecular Diagnostics in Neuromuscular Disorders, Atlanta, Georgia, USA
2 Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
3 Department of Neurology, Sir J J Group of Hospitals, Grant Medical College, Mumbai, Maharashtra, India
4 Department of Neurology, NIMHANS, Bengaluru, Karnataka, India
5 Department of Neurology, Nizam's Institute of Medical Sciences, Hyderabad, Telangana, India
6 Jain Foundation Inc., Seattle, WA, USA

Correspondence Address:
Rashna Sam Dastur
Centre for Advanced Molecular Diagnostics in Neuromuscular Disorders, Mumbai, Maharashtra
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/aian.AIAN_129_17

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Background: Limb-girdle muscular dystrophy (LGMD) is the most common adult-onset class of muscular dystrophies in India, but a majority of suspected LGMDs in India remain unclassified to the genetic subtype level. The next-generation sequencing (NGS)-based approaches have allowed molecular characterization and subtype diagnosis in a majority of these patients in India. Materials and Methods: (I) To select probable dysferlinopathy (LGMD2B) cases from other LGMD subtypes using two screening methods (i) to determine the status of dysferlin protein expression in blood (peripheral blood mononuclear cell) by monocyte assay (ii) using a predictive algorithm called automated LGMD diagnostic assistant (ALDA) to obtain possible LGMD subtypes based on clinical symptoms. (II) Identification of gene pathogenic variants by NGS for 34 genes associated with LGMD or LGMD like muscular dystrophies, in cases showing: absence of dysferlin protein by the monocyte assay and/or a typical dysferlinopathy phenotype, with medium to high predictive scores using the ALDA tool. Results: Out of the 125 patients screened by NGS, 96 were confirmed with two dysferlin variants, of which 84 were homozygous. Single dysferlin pathogenic variants were seen in 4 patients, whereas 25 showed no variants in the dysferlin gene. Conclusion: In this study, 98.2% of patients with absence of the dysferlin protein showed one or more variants in the dysferlin gene and hence has a high predictive significance in diagnosing dysferlinopathies. However, collection of blood samples from all over India for protein analysis is expensive. Our analysis shows that the use of the “ALDA tool” could be a cost-effective alternative method. Identification of dysferlin pathogenic variants by NGS is the ultimate method for diagnosing dysferlinopathies though follow-up with the monocyte assay can be useful to understand the phenotype in relation to the dysferlin protein expression and also be a useful biomarker for future clinical trials.


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