A trio-based long-read sequencing workflow identifies a pathogenic transposable element insertion in a previously...

Area of Science:

• Genomics
• Human Genetics
• Bioinformatics

Background:

• Structural variants (SVs) are key drivers of genetic diseases.
• Long-read sequencing (LRS) offers advantages for SV detection.
• Trio-based analysis of LRS data for SVs presents computational challenges.

Purpose of the Study:

• To establish an efficient workflow for prioritizing pathogenic SVs from trio-based LRS whole-genome sequencing data.
• To demonstrate the clinical utility of LRS for diagnosing rare genetic diseases caused by complex SVs.

Main Methods:

• Integrated high-accuracy basecalling, trio-based phasing, and multiple SV callers (CuteSV, Sniffles2).
• Utilized the PanPop Realign and Thin (PART) process for SV merging.
• Applied a Japanese population-specific SV frequency dataset (JSV1) for filtering common variants.
• Evaluated the workflow on 12 family trios from the IRUD project.

Main Results:

• Successfully narrowed down tens of thousands of SV loci to fewer than ~20 candidates for manual inspection using X-linked homozygous and de novo models.
• Identified a pathogenic AluY element insertion in the GPC3 gene in a Simpson-Golabi-Behmel syndrome patient.
• The identified pathogenic variant was missed by previous short-read whole-exome sequencing.

Conclusions:

• The developed workflow effectively prioritizes pathogenic SVs from LRS data.
• LRS-based trio analysis is clinically valuable for diagnosing rare diseases involving complex SVs.
• The workflow enables SV detection without prior knowledge of candidate genes or loci.