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Long-read sequencing reveals widespread intragenic structural variants in a recent allopolyploid crop plant.

Harmeet Singh ChawlaHueyTyng LeeIulian GaburPaul VollrathSuriya Tamilselvan-Nattar-AmuthaChristian ObermeierSarah V SchießlJia-Ming SongKe-De LiuLiang GuoIsobel A P ParkinRod J Snowdon
Published in: Plant biotechnology journal (2020)
Genome structural variation (SV) contributes strongly to trait variation in eukaryotic species and may have an even higher functional significance than single-nucleotide polymorphism (SNP). In recent years, there have been a number of studies associating large chromosomal scale SV ranging from hundreds of kilobases all the way up to a few megabases to key agronomic traits in plant genomes. However, there have been little or no efforts towards cataloguing small- (30-10 000 bp) to mid-scale (10 000-30 000 bp) SV and their impact on evolution and adaptation-related traits in plants. This might be attributed to complex and highly duplicated nature of plant genomes, which makes them difficult to assess using high-throughput genome screening methods. Here, we describe how long-read sequencing technologies can overcome this problem, revealing a surprisingly high level of widespread, small- to mid-scale SV in a major allopolyploid crop species, Brassica napus. We found that up to 10% of all genes were affected by small- to mid-scale SV events. Nearly half of these SV events ranged between 100 bp and 1000 bp, which makes them challenging to detect using short-read Illumina sequencing. Examples demonstrating the contribution of such SV towards eco-geographical adaptation and disease resistance in oilseed rape suggest that revisiting complex plant genomes using medium-coverage long-read sequencing might reveal unexpected levels of functional gene variation, with major implications for trait regulation and crop improvement.
Keyphrases
  • genome wide
  • copy number
  • single cell
  • dna methylation
  • high throughput
  • climate change
  • single molecule
  • healthcare
  • genetic diversity
  • health insurance
  • bioinformatics analysis