Objective: Inherited myopathies comprise more than 200 different individually rare disease-subtypes, but when combined together they have a high prevalence of 1 in 6,000 individuals across the world. Our goal was to determine for the first time the clinical-and gene-variant spectrum of genetic myopathies in a substantial cohort study of the Indian subcontinent. Methods: In this cohort study, we performed the first large clinical exome sequencing (ES) study with phenotype correlation on 207 clinically well-characterized inherited myopathy-suspected patients from the Indian subcontinent with diverse ethnicities. Results: Clinical-correlation driven definitive molecular diagnosis was established in 49% (101 cases; 95% CI, 42-56%) of patients with the major contributing pathogenicity in either of three genes, GNE (28%; GNE-myopathy), DYSF (25%; Dysferlinopathy), and CAPN3 (19%; Calpainopathy). We identified 65 variant alleles comprising 37 unique variants in these three major genes. Seventy-eight percent of the DYSF patients were homozygous for the detected pathogenic variant, suggesting the need for carrier-testing for autosomal-recessive disorders like Dysferlinopathy that are common in India. We describe the observed clinical spectrum of myopathies including uncommon and rare subtypes in India:
Objective Inherited myopathies comprise more than 200 different individually rare disease-subtypes but when combined together have a high prevalence of 1 in 6000 individuals across the world. Our goal was to determine for the first time the clinical- and gene-variant spectrum of genetic myopathies in a substantial cohort study of the Indian subcontinent. Methods In this cohort-study, we performed the first large clinical exome sequencing (ES) study with phenotype correlation on 207 clinically well-characterized inherited myopathy-suspected patients from the Indian subcontinent with diverse ethnicities. Results Clinical-correlation driven definitive molecular diagnosis was established in 49% (101 cases; 95% CI, 42%-56%) of patients with the major contributing pathogenicity in either of three genes, GNE (28%; GNE-myopathy), DYSF (25%; Dysferlinopathy) and CAPN3 (19%; Calpainopathy). We identified 65 variant alleles comprising 37 unique variants in these three major genes. 78% of the DYSF patients were homozygous for the detected pathogenic variant suggesting the need for carrier-testing for autosomal-recessive disorders like Dysferlinopathy that are common in India. We describe the observed clinical spectrum of myopathies including uncommon and rare subtypes in India: Sarcoglycanopathies (SGCA/B/D/G), Collagenopathy (COL6A1/2/3), Anoctaminopathy (ANO5), telethoninopathy (TCAP), Pompe-disease (GAA), Myoadenylate-deaminase-deficiency-myopathy (AMPD1), myotilinopathy (MYOT), laminopathy (LMNA), HSP40-proteinopathy (DNAJB6), Emery-Dreifuss-muscular-dystrophy (EMD), Filaminopathy (FLNC), TRIM32-proteinopathy (TRIM32), POMT1-proteinopathy (POMT1), and Merosin-deficiency-congenital-muscular-dystrophy-type-1 (LAMA2). 13 Patients harbored pathogenic variants in >1 gene and had unusual clinical features suggesting a possible role of synergistic-heterozygosity / digenic-contribution to disease presentation and progression. Conclusions Application of clinically-correlated ES to myopathy diagnosis has improved our understanding of the clinical and genetic spectrum of different subtypes and their overlaps in Indian patients. This, in turn, will enhance the global gene-variant-disease databases by including data from developing countries/continents for more efficient clinically-driven molecular diagnostics.
Purpose50-60% of neuromuscular-disease patients remain undiagnosed even after extensive genetic testing that hinders precision-medicine/clinical-trial-enrollment. Importantly, those with DNA-based molecular diagnosis often remain without known molecular mechanism driving different degrees of disease severity that hinders patient stratification and trial-readiness. These are due to: a) clinical-genetic-heterogeneity (eg: limb-girdle-muscular-dystrophies(LGMDs)>30-subtypes); b) high-prevalence of variants-of-uncertain-significance (VUSs); (c) unresolved genotype-phenotype-correlations for patient stratification, and (d) lack of minimally-invasive biomarker-driven-assays. We therefore implemented a combinatorial phenotype-driven blood-biomarker functional-genomics approach to enhance diagnostics and trial-readiness by elucidating disease mechanisms of a neuromuscular-disease patient-cohort clinically-suspected of Dysferlinopathy/related-LGMD, the second-most-prevalent LGMD in the US.MethodsWe used CD14+monocyte protein-expression-assay on 364 Dysferlinopathy/related-LGMD-suspected patient-cohort without complete molecular-diagnosis or genotype-phenotype correlation; and then combined with blood-based targeted-transcriptome-sequencing (RNA-Seq) with tiered-analytical-algorithm correlating with clinical-measurements for a subset of patients.ResultsOur combinatorial-approach significantly increased the diagnostic-yield from 25% (N=326; 18%-27%; 95%CI) to 82% (N=38; 69.08% to 84.92%; 95% CI) by combining monocyte-assay with enhanced-RNA-Seq-analysis and clinical-correlation, following ACMG-AMP-guidelines. The tiered-analytical-approach detected aberrant-splicing, allele-expression-imbalance, nonsense-mediated-decay, and compound-heterozygosity without parental/offspring-DNA-testing, leading to VUS-reclassifications, identification of variant-pathomechanisms, and enhanced genotype-phenotype resolution including those with carrier-range Dysferlin-protein-expression and milder-symptoms, allowing patient-stratification for better trial-readiness. We identified uniform-distribution of pathogenic-variants across DYSF-gene-domains without any hotspot suggesting the relevance of upcoming gene-(full-DYSF-cDNA)-therapy trials.ConclusionOur results show the relevance of using a clinically-driven multi-tiered-approach utilizing a minimally-invasive biomarker-functional-genomic platform for precision-medicine-diagnostics, trial-recruitment/monitoring, elucidating pathogenic-mechanisms for patient stratification to enhance better trial outcomes, which in turn, will guide more rational use of current-therapeutics and development of novel-interventions for neuromuscular-disorders, and applicable to other genetic-disorders.
At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.
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