Application of RNA-seq for single nucleotide variation identification in a cohort of patients with hypertrophic cardiomyopathy.

A variety of techniques for DNA sequencing, such as specific gene sequencing, whole genome sequencing, or exome sequencing, are currently used to detect single nucleotide variations (SNVs). Although RNA-seq can be used to identify SNVs, studies that employ this approach are uncommon, and those that do often rely on outdated mapping methods or methods that are more suitable for genomic and exomic alignment. In this work, our aim is to apply modern RNA-seq specific alignment method in order to identify SNV in a cohort of HCMP patients, and characterize those SNV to gain insight into possible mechanisms of HCMP pathogenesis. The algorithm of identification of SNV based on transcriptomic sequencing data has been developed and evaluated. The algorithm was evaluated and the optimal quality threshold was determined based on allelic discrimination for the rs397516037 mutation (MYBPC3 c.3697 C > T) among patients. A total of 42,809 SNVs with a quality of 75 or higher were identified in 48 transcriptomes of hypertrophic cardiomyopathy (HCMP) myocardial tissue. Verification of missense and nonsense variants in key HCMP genes using Sanger sequencing confirmed the accuracy of the pipeline results. To identify variants potentially associated with HCMP pathogenesis, a filtration process was conducted based on minor allele frequency, substitution prediction score and ClinVar outcome. 214 missense mutations and 6 nonsense mutations were selected. Together with nonsense mutations, 19 mutations meeting the strictest SIFT and PolypPhen criteria were identified as potential factors influencing HCMP pathogenesis. We have developed and validated a method for identifying SNVs based on transcriptomic data, which can be used to identify putative pathogenic variants. We identified mutations in key HCMP genes MYBPC3 and MYH7 in a cohort of patients. We also found potentially pathologic mutations in genes ANXA6 and FEM1 A and obtained data supporting the role of NEBL in myocardial diseases. This method would be useful in analyzing transcriptomic data available in the Gene Expression Omnibus, but should be used with caution as we have tested it on a specific disease.