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Comparative Transcriptomic Analysis Provides Insight into the Domestication and Improvement of Pear (P. pyrifolia) Fruit.

Xiaolong LiLun LiuMeiling MingHongju HuMingyue ZhangJing FanBobo SongShaoling ZhangJun Wu
Published in: Plant physiology (2019)
Knowledge of the genetic changes that occurred during the domestication and improvement of perennial trees at the RNA level is limited. Here, we used RNA sequencing analysis to compare representative sets of wild, landrace, and improved accessions of pear (Pyrus pyrifolia) to gain insight into the genetic changes associated with domestication and improvement. A close population relationship and similar nucleotide diversity was observed between the wild and landrace groups, whereas the improved group had substantially reduced nucleotide diversity. A total of 11.13 Mb of genome sequence was identified as bearing the signature of selective sweeps that occurred during pear domestication, whereas a distinct and smaller set of genomic regions (4.04 Mb) was identified as being associated with subsequent improvement efforts. The expression diversity of selected genes exhibited a 20.89% reduction from the wild group to the landrace group, but a 23.13% recovery was observed from the landrace to the improved group, showing a distinctly different pattern with variation of sequence diversity. Module-trait association analysis identified 16 distinct coexpression modules, six of which were highly associated with important fruit traits. The candidate trait-linked differentially expressed genes associated with stone cell formation, fruit size, and sugar content were identified in the selected regions, and many of these could also be mapped to the previously reported quantitative trait loci. Thus, our study reveals the specific pattern of domestication and improvement of perennial trees at the transcriptome level, and provides valuable genetic sources of fruit traits that could contribute to pear breeding and improvement.
Keyphrases
  • genome wide
  • dna methylation
  • copy number
  • single cell
  • healthcare
  • rna seq
  • stem cells
  • quality improvement
  • cell therapy
  • binding protein
  • drinking water
  • genetic diversity
  • network analysis
  • data analysis