THE IMPORTANCE OF SCIENTIST ́S EXPERTISE IN THE INTERPRETATION OF NGS DATA

CARCIONE, MICAELA; MAZZANTI, CHIARA; BOLLANA, MACARENA; LLAMES MASSINI, CARMEN; VISCONTI, TRIANA; LUCE, LEONELA; GILIBERTO, FLORENCIA

Congreso; LXVII Reunión Anual de la Sociedad Argentina de Investigación Clínica; 2022

Muscular dystrophies (MD) are a group of rare inherited diseases that cause weakness and progressive degeneration of the muscle. Among them, dystrophinopathies are the most prevalent type of MD and are caused by molecular alterations in the DMD gene. Because of the overlap of symptoms between these diseases, genetic or molecular studies are the gold standard for reaching a MD differential diagnosis, for which molecular alterations in MD associated genes can be detected by Whole Exome Sequencing (WES). We aimed to reach a differential diagnosis in patients with MD by a thorough analysis of NGS data. We studied 200 patients with presumptive clinical diagnosis of dystrophinopathy by WES. We applied different predictive programs, conservational and protein modeling tools to establish the pathogenicity of certain variants found. We reached the molecular diagnosis of 151 patients by finding a pathogenic variant in the DMD gene, achieving a detection rate of 76%. We found 1 synonymous variant and 2 missense variants that required further analysis to establish their pathogenicity. Also, we detected 2 variant calling errors among the studied individuals, where the VCF results did not resemble the alteration observed in the raw data analysis. These discordances were due to the presence of deletions in the DMD gene, which caused problems in the alignment process, so manual annotation was necessary. By deepening the screening to all the MD genes, we were able to identify pathogenic variants in other genes in 30 of the remaining patients, reaching a detection rate of 91%. Finally, this work highlights the importance of the expertise of the scientist in charge of the study in order to determine the pathogenicity of variants and to detect the occurrence of variant calling errors by analyzing NGS raw data. Conclu para poster: The implemented algorithm allowed us to reach a differential diagnosis which is crucial to determine the standard of care and genetic counseling. Finally, this work demonstrated different strategies for one of the greatest challenges in the interpretation of NGS data, which is the validation of the pathogenic effect of variants, highlighting the importance of the scientist?s expertise to determine the pathogenicity of variants and to detect the occurrence of variant calling errors by analyzing NGS raw data.