RNAseq analysis in a cohort of undiagnosed congenital myopathy patients


Pre-Clinical Research

Poster Number: M249


Pamela Barraza, PhD, Boston Children's Hospital / Harvard Medical School, Casie Genetti Genetti, MS, CGC, Boston Children's Hospital, Wanqing Shao, PhD, Boston Children's Hospital, Courtney French, PhD, Boston Children's Hospital, Shira Rockowitz, PhD, Boston Children's Hospital, Alan Beggs, PhD, Boston Children's Hospital / Harvard Medical School

Congenital myopathies (CM) are a group of clinically heterogenous disorders, delineated by muscle histopathology, with overlapping genetic etiologies. With the advent of next generation sequencing, molecular diagnoses are achieved in most cases using gene panels as well as Whole Exome Sequencing (WES) or Whole Genome Sequencing (WGS) analyses. The Beggs Laboratory CM cohort consists of 1,110 CM cases, 68% of whom have an established molecular diagnosis based on DNA testing. However, a subset of patients remains undiagnosed following extensive genomic analysis. We hypothesize that a proportion of these unsolved cases may be due to pathogenic variants in intronic regions that impact splicing and/or mono-allelic expression of heterozygous variants, which are not identifiable with gene panel or WES/WGS analyses alone. To address this, we complemented existing genomic sequence data with RNA sequencing (RNAseq) of patient muscle biopsies. 136 cases (81 with available matching WES/WGS data), underwent RNAseq, of which 89 were undiagnosed. The remaining 47 had mutations in known CM genes, allowing for assessment of sensitivity for detection of known pathogenic variants. The analysis aimed to identify changes in gene expression, splicing patterns, and mono-allelic expression. We used the DROP pipeline bioinformatic tool which includes FRASER, OUTRIDER, and Mono Allelic Expression (MAE) modules for the study of these genetic events. Results were filtered using a comprehensive list of skeletal muscle genes and cases were individually reviewed accounting for their clinicopathological phenotype and suspected inheritance model. Here we present the range of diagnoses facilitated by RNAseq for previously unsolved cases and estimate the utility of this approach in a diagnostic setting. These results highlight the value of RNAseq as a method for diagnosing CM cases that remain unsolved following other genomic approaches.