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<p>Detecting intermediate-sized structural variants (SVs) remains challenging in diagnostics, as tools for single-nucleotide and copy-number variants, particularly read-depth-based methods, are often insufficient. GRIDSS addresses this gap by integrating paired-end mapping, split-read analysis, and assembly-based approaches. However, its use in targeted sequencing and diagnostic workflows remains complex. NGS panel data from 9726 patients with suspected hereditary cancer were analyzed using GRIDSS. A filtering strategy was developed to prioritize clinically relevant germline SVs. Multiple parameter settings were tested to optimize performance. The initial dataset of 1,307,592 variants was reduced to 89 candidates after applying the selected filtering strategy. Of these, 24 had been previously detected by routine callers and were not further analyzed. Among the remaining 65, 13 were considered likely true positives after visual inspection using IGV. Experimental validation was performed by Sanger/Nanopore long-read sequencing for these variants, all of which were confirmed. Eight were classified as (likely) pathogenic, including two frameshift duplications in <i>MSH6</i>, one splicing variant in <i>BARD1</i>, and five mobile element insertions in <i>APC</i>, <i>BRCA2</i>, and <i>PALB2</i>. Altogether, GRIDSS implementation increased diagnostic yield while maintaining feasibility for diagnostic workflows.</p><p></p>

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Optimizing GRIDSS for clinical use: A targeted NGS filtering strategy for germline structural variant detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月16日

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  • Elisabet Munté,
  • Paula Rofes,
  • Miriam Millán-Castillo,
  • Ares Solanes,
  • Xavier Muñoz,
  • Olga Campos,
  • Ania Alay,
  • Maria Ajenjo-Bauza,
  • Esther Navarro,
  • Belén de la Morena-Barrio,
  • Monica Salinas,
  • Gardenia Vargas-Parra,
  • Raquel Cuesta,
  • José Marcos Moreno-Cabrera,
  • David Cordero,
  • Marta Pineda,
  • Jesús del Valle,
  • Conxi Lázaro,
  • Lidia Feliubadaló

摘要

Detecting intermediate-sized structural variants (SVs) remains challenging in diagnostics, as tools for single-nucleotide and copy-number variants, particularly read-depth-based methods, are often insufficient. GRIDSS addresses this gap by integrating paired-end mapping, split-read analysis, and assembly-based approaches. However, its use in targeted sequencing and diagnostic workflows remains complex. NGS panel data from 9726 patients with suspected hereditary cancer were analyzed using GRIDSS. A filtering strategy was developed to prioritize clinically relevant germline SVs. Multiple parameter settings were tested to optimize performance. The initial dataset of 1,307,592 variants was reduced to 89 candidates after applying the selected filtering strategy. Of these, 24 had been previously detected by routine callers and were not further analyzed. Among the remaining 65, 13 were considered likely true positives after visual inspection using IGV. Experimental validation was performed by Sanger/Nanopore long-read sequencing for these variants, all of which were confirmed. Eight were classified as (likely) pathogenic, including two frameshift duplications in MSH6, one splicing variant in BARD1, and five mobile element insertions in APC, BRCA2, and PALB2. Altogether, GRIDSS implementation increased diagnostic yield while maintaining feasibility for diagnostic workflows.