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Long-read sequencing and structural variant characterization in 1,019 samples from the 1000 Genomes Project.

Siegfried SchloissnigSamarendra PaniBernardo Rodriguez-MartinJana EblerCarsten HainVasiliki TsapalouArda SöylevPatrick HütherHufsah AshrafTimofey ProdanovMila AsparuhovaSarah E HuntTobias RauschTobias MarschallJan O Korbel
Published in: bioRxiv : the preprint server for biology (2024)
Structural variants (SVs) contribute significantly to human genetic diversity and disease 1-4 . Previously, SVs have remained incompletely resolved by population genomics, with short-read sequencing facing limitations in capturing the whole spectrum of SVs at nucleotide resolution 5-7 . Here we leveraged nanopore sequencing 8 to construct an intermediate coverage resource of 1,019 long-read genomes sampled within 26 human populations from the 1000 Genomes Project. By integrating linear and graph-based approaches for SV analysis via pangenome graph-augmentation, we uncover 167,291 sequence-resolved SVs in these samples, considerably advancing SV characterization compared to population-wide short-read sequencing studies 3,4 . Our analysis details diverse SV classes-deletions, duplications, insertions, and inversions-at population-scale. LINE-1 and SVA retrotransposition activities frequently mediate transductions 9,10 of unique sequences, with both mobile element classes transducing sequences at either the 3'- or 5'-end, depending on the source element locus. Furthermore, analyses of SV breakpoint junctions suggest a continuum of homology-mediated rearrangement processes are integral to SV formation, and highlight evidence for SV recurrence involving repeat sequences. Our open-access dataset underscores the transformative impact of long-read sequencing in advancing the characterisation of polymorphic genomic architectures, and provides a resource for guiding variant prioritisation in future long-read sequencing-based disease studies.
Keyphrases
  • single molecule
  • single cell
  • genetic diversity
  • endothelial cells
  • quality improvement
  • copy number
  • healthcare
  • neural network
  • deep learning
  • health insurance
  • amino acid
  • data analysis