Back

Long-read sequencing resolves structural variants in SERPINC1 causing antithrombin deficiency and identifies a complex rearrangement and a retrotransposon insertion not characterized by routine diagnostic methods

de la Morena-Barrio, B.; Stephens, J.; de la Morena-Barrio, M. E.; Stefanucci, L.; Padilla, J.; Miñano, A.; Gleadall, N.; Luis García, J.; López-Fernández, M. F.; Morange, P.-E.; Puurunen, M. K.; Undas, A.; Vidal, F.; NIHR BioResource, ; Raymond, F. L.; Vicente García, V.; Ouwehand, W. H.; Corral, J.; Sanchis-Juan, A.

2020-08-28 genomics
10.1101/2020.08.28.271932 bioRxiv
Show abstract

The identification and characterization of structural variants (SVs) in clinical genetics have remained historically challenging as routine genetic diagnostic techniques have limited ability to evaluate repetitive regions and SVs. Long-read whole-genome sequencing (LR-WGS) has emerged as a powerful approach to resolve SVs. Here, we used LR-WGS to study 19 unrelated cases with type I Antithrombin Deficiency (ATD), the most severe thrombophilia, where routine molecular tests were either negative, ambiguous, or not fully characterized. We developed an analysis workflow to identify disease-associated SVs and resolved 10 cases. For the first time, we identified a germline complex rearrangement involved in ATD previously misclassified as a deletion. Additionally, we provided molecular diagnoses for two unresolved individuals that harbored a novel SINE-VNTR-Alu retroelement insertion that we fully characterized by de novo assembly and confirmed by PCR amplification in all affected relatives. Finally, the nucleotide-level resolution achieved for all the SVs allowed breakpoint analysis, which revealed a replication-based mechanism for most of the cases. Our study underscores the utility of LR-WGS as a complementary diagnostic method to identify, characterize, and unveil the molecular mechanism of formation of disease-causing SVs, and facilitates decision making about long-term thromboprophylaxis in ATD patients.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.