%0 Journal Article
%T Tagging large CNV blocks in wheat boosts digitalization of germplasm resources by ultra-low-coverage sequencing.
%A Niu J
%A Wang W
%A Wang Z
%A Chen Z
%A Zhang X
%A Qin Z
%A Miao L
%A Yang Z
%A Xie C
%A Xin M
%A Peng H
%A Yao Y
%A Liu J
%A Ni Z
%A Sun Q
%A Guo W
%J Genome Biol
%V 25
%N 1
%D 2024 07 1
%M 38951917
暂无%R 10.1186/s13059-024-03315-6
%X The massive structural variations and frequent introgression highly contribute to the genetic diversity of wheat, while the huge and complex genome of polyploid wheat hinders efficient genotyping of abundant varieties towards accurate identification, management, and exploitation of germplasm resources.<BR>We develop a novel workflow that identifies 1240 high-quality large copy number variation blocks (CNVb) in wheat at the pan-genome level, demonstrating that CNVb can serve as an ideal DNA fingerprinting marker for discriminating massive varieties, with the accuracy validated by PCR assay. We then construct a digitalized genotyping CNVb map across 1599 global wheat accessions. Key CNVb markers are linked with trait-associated introgressions, such as the 1RS·1BL translocation and 2NvS translocation, and the beneficial alleles, such as the end-use quality allele Glu-D1d (Dx5 + Dy10) and the semi-dwarf r-e-z allele. Furthermore, we demonstrate that these tagged CNVb markers promote a stable and cost-effective strategy for evaluating wheat germplasm resources with ultra-low-coverage sequencing data, competing with SNP array for applications such as evaluating new varieties, efficient management of collections in gene banks, and describing wheat germplasm resources in a digitalized manner. We also develop a user-friendly interactive platform, WheatCNVb ( http://wheat.cau.edu.cn/WheatCNVb/ ), for exploring the CNVb profiles over ever-increasing wheat accessions, and also propose a QR-code-like representation of individual digital CNVb fingerprint. This platform also allows uploading new CNVb profiles for comparison with stored varieties.<BR>The CNVb-based approach provides a low-cost and high-throughput genotyping strategy for enabling digitalized wheat germplasm management and modern breeding with precise and practical decision-making.