AI Article Synopsis

  • The study developed a high-fidelity long-read sequencing (LRS) method to detect gene variations in the SMN1 and SMN2 genes, as conventional methods struggle to identify all variations simultaneously.
  • LRS demonstrated exceptional accuracy, identifying copy numbers and specific genetic variants, including previously undetected mutations, while correcting errors seen in older methods.
  • The findings suggest that LRS provides a more thorough and precise diagnostic tool for spinal muscular atrophy (SMA), which could improve early treatment and management strategies.

Article Abstract

Background: We aimed to develop a high-fidelity long-read sequencing (LRS)-based approach to detect SMN gene variants in one step. It is challenging for conventional step-wise methods to simultaneously detect all kinds of variations between homologous SMN1 and SMN2.

Methods: In this study, LRS was developed to analyze copy numbers (CNs), full sequences, and structure of SMN1 and SMN2. The results were compared with those from the step-wise methods in 202 samples from 67 families.

Results: LRS achieved 100% (202/202) and 99.5% (201/202) accuracy for SMN1 and SMN2 CNs, respectively. It corrected SMN1 CNs from MLPA, which was caused by SNVs/indels that located in probe-binding region. LRS identified 23 SNVs/indels distributing throughout SMN1, including c.22dup and c.884A > T in trans-configuration, and a de novo variant c.41_42delinsC for the first time. LRS also identified a SMN2 variant c.346A > G. Moreover, it successfully determined Alu-mediated 8978-bp deletion encompassing exon 2a-5 and 1415-bp deletion disrupting exon 1, and the exact breakpoints of large deletions. Through haplotype-based pedigree trio analysis, LRS identified SMN1 2 + 0 carriers, and determined the distribution of SMN1 and SMN2 on two chromosomes.

Conclusions: LRS represents a more comprehensive and accurate diagnosis approach that is beneficial to early treatment and effective management of SMA.

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Source
http://dx.doi.org/10.1016/j.cca.2023.117743DOI Listing

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