Advancing molecular diagnostics of myotonic dystrophy type 1 using short-read whole genome sequencing - Ingrid Lojova; Marcel Kucharik; Zuzana Pös; Andrej Balaz; Andrea Zatkova; Eva Tothova Tarova; Jaroslav Budis; Ludevit Kadasi; Tomas Szemes; Jan Radvanszky (Molecular and Cellular Probes)
Myotonic dystrophy type 1 (DM1) is a serious multisystem disorder caused by GCA repeat expansions in the DMPK gene. Early and accurate diagnosis, often requiring reliable DNA-diagnostic techniques, is critical for preventing life-threatening cardiac complications. Clinically, two main diagnostic challenges exist. Firstly, because of overlapping symptomatology with other conditions, conventional DNA-testing methods focusing on DM1 expansion detection ensure diagnostic results only in a small subset of patients, and frequently, further DNA-testing in remaining cases is necessary. Secondly, because of variable symptomatology and age of onset, not all DM1 patients are referred for DM1 genetic testing, leading to unrecognized but at-risk cases. When using conventional methods, the main technical problems are expanded-allele sizing and sensitivity to the presence of sequence interruptions. On a set of 50 individual genomes, including ten DM1 patients, we tested the performance of short-read whole-genome sequencing (WGS), one of the most up-to-date molecular testing methods. We identified all expansion-range DM1 alleles and characterized sequence interruptions in seven expansion-range/premutation-range alleles. Although neither the tested conventional methods, nor WGS allowed expanded-allele sizing, conventional methods provided higher sizing limits for normal-range alleles. Genotyping concordance rate was found to be 95–99 %. WGS was found to be superior in elucidating the sequence structure of the motifs, even if they fall outside the sizing limit (from partial reads). In addition, WGS enables the identification of genetic modifiers in other genes and the detection of alternative diagnoses in DM1-negative patients by extension of the bioinformatic evaluation of the generated data.