Application of full-genome analysis to diagnose rare monogenic disorders

Current genetic tests for rare diseases provide a diagnosis in only a modest proportion of cases. The Full-Genome Analysis method, FGA, combines long-range assembly and whole-genome sequencing to detect small variants, structural variants with breakpoint resolution, and phasing. We built a variant prioritization pipeline and tested FGA’s utility for diagnosis of rare diseases in a clinical setting. FGA identified structural variants and small variants with an overall diagnostic yield of 40% (20 of 50 cases) and 35% in exome-negative cases (8 of 23 cases), 4 of these were structural variants. FGA detected and mapped structural variants that are missed by short reads, including non-coding duplication, and phased variants across long distances of more than 180 kb. With the prioritization algorithm, longer DNA technologies could replace multiple tests for monogenic disorders and expand the range of variants detected. Our study suggests that genomes produced from technologies like FGA can improve variant detection and provide higher resolution genome maps for future application.

Application of Full Genome Analysis to Diagnose Rare Monogenic Disorders

Current genetic tests for rare diseases provide a diagnosis in only a modest proportion of cases. The Full Genome Analysis method, FGA, combines long-range assembly and whole-genome sequencing to detect small variants, structural variants with breakpoint resolution, and phasing. We built a variant prioritization pipeline and tested FGA’s utility for diagnosis of rare diseases in a clinical setting. FGA identified structural variants and small variants with an overall diagnostic yield of 40% (20 of 50 cases) and 35% in exome-negative cases (8 of 23 cases), 4 of these were structural variants. FGA detected and mapped structural variants that are missed by short reads, including non-coding duplication, and phased variants across long distances of more than 180kb. With the prioritization algorithm, longer DNA technologies could replace multiple tests for monogenic disorders and expand the range of variants detected. Our study suggests that genomes produced from technologies like FGA can improve variant detection and provide higher resolution genome maps for future application.

Dominant and rare SARS-Cov2 variants responsible for the COVID-19 pandemic in Athens, Greece.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel Coronavirus responsible for the Coronavirus Disease-2019 (COVID-19) pandemic. Since the beginning of the pandemic, the virus has spread in almost the entire world. Tracing and tracking virus international and local transmission has been an enormous challenge. Chains of infections starting from various countries worldwide seeded the outbreak of COVID-19 in Athens, capital city of Greece. Full-genome analysis of isolates from Athens' Hospitals and other healthcare providers revealed the variety of SARS-CoV-2 that initiated the pandemic before lock-down and passenger flight restrictions. The present work may serve as reference for resolving future lines of infection in the area and Europe especially after resumption of passenger flight connections to Athens and Greece during summer of 2020.