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Updated: May 16, 2025

Optimization and Comparative Analysis of Plant Organellar DNA Enrichment Methods Suitable for Next-generation Sequencing
Published on: July 28, 2017
Structural variation-based and gene-based pangenome construction reveals untapped diversity of hexaploid wheat
Hong Cheng1, Lingpeng Kong2, Kun Zhu3
1Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518100, China; College of Grassland Agriculture, Northwest A&F University, Yangling, Shaanxi 712100, China.
None:
Increasing number of structural variations (SVs) have been identified as causative mutations for diverse agronomic traits. However, the systematic exploration of SVs quantity, distribution, and contribution in wheat was lacking. Here, we report high-quality gene-based and SV-based pangenomes comprising 22 hexaploid wheat assemblies showing a wide range of chromosome size, gene number, and TE component, which indicates their representativeness of wheat genetic diversity. Pan-gene analyses uncover 140,261 distinct gene families, of which only 23.2 % are shared in all accessions. Moreover, we build a ∼16.15 Gb graph pangenome containing 695,897 bubbles, intersecting 5132 genes and 230,307 cis-regulatory regions. Pairwise genome comparisons identify ∼1,978,221 non-redundant SVs and 497 SV hotspots. Notably, the density of bubbles as well as SVs show remarkable aggregation in centromeres, which probably play an important role in chromosome plasticity and stability. As for functional SVs exploration, we identify 2769 SVs with absolute relative frequency differences exceeding 0.7 between spring and winter growth habit groups. Additionally, several reported functional genes in wheat display complex structural graphs, for example, PPD-A1, VRT-A2, and TaNAAT2-A. These findings deepen our understanding of wheat genetic diversity, providing valuable graphical pangenome and variation resources to improve the efficiency of genome-wide association mapping in wheat.
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