中国农业科学院Xianmin Diao，Guanqing Jia和美国纽约大学Michael Purugganan共同合作，近期取得重要工作进展，他们研究发现了模型植物谷子基于图谱的基因组和泛基因组变异。相关研究成果2023年6月8日在线发表于《自然—遗传学》杂志上。

据介绍，Setaria italica（谷子）是东亚农业的创始作物，是C4光合作用和开发跨多种气候的适应性育种方法的模式植物。

研究人员通过从世界范围内收集110个具有代表性的基因组，建立了谷子的泛基因组。泛基因组由73528个基因家族组成，其中核心基因、软核心基因、可有可无基因和私有基因分别占23.8%、42.9%、29.4%和3.9%；还检测到202884个非冗余结构变体。泛基因组变异的表征表明了它们在谷子驯化和改良过程中的重要性，例如产量基因SiGW3的鉴定，其中366bp存在/不存在启动子变异伴随着基因表达变异。

总之，研究人员开发了一个基于图谱的基因组，并对13个环境中的68个性状进行了大规模遗传研究，在不同的地理位置确定了小米改良的潜在基因。这些可以用于标记辅助育种、基因组选择和基因组编辑，以加速不同气候条件下的作物改良。

附：英文原文

Title: A graph-based genome and pan-genome variation of the model plant Setaria

Author: He, Qiang, Tang, Sha, Zhi, Hui, Chen, Jinfeng, Zhang, Jun, Liang, Hongkai, Alam, Ornob, Li, Hongbo, Zhang, Hui, Xing, Lihe, Li, Xukai, Zhang, Wei, Wang, Hailong, Shi, Junpeng, Du, Huilong, Wu, Hongpo, Wang, Liwei, Yang, Ping, Xing, Lu, Yan, Hongshan, Song, Zhongqiang, Liu, Jinrong, Wang, Haigang, Tian, Xiang, Qiao, Zhijun, Feng, Guojun, Guo, Ruifeng, Zhu, Wenjuan, Ren, Yuemei, Hao, Hongbo, Li, Mingzhe, Zhang, Aiying, Guo, Erhu, Yan, Feng, Li, Qingquan, Liu, Yanli, Tian, Bohong, Zhao, Xiaoqin, Jia, Ruiling, Feng, Baili, Zhang, Jiewei, Wei, Jianhua, Lai, Jinsheng, Jia, Guanqing, Purugganan, Michael, Diao, Xianmin

Issue&Volume: 2023-06-08

Abstract: Setaria italica (foxtail millet), a founder crop of East Asian agriculture, is a model plant for C4 photosynthesis and developing approaches to adaptive breeding across multiple climates. Here we established the Setaria pan-genome by assembling 110 representative genomes from a worldwide collection. The pan-genome is composed of 73,528 gene families, of which 23.8%, 42.9%, 29.4% and 3.9% are core, soft core, dispensable and private genes, respectively; 202,884 nonredundant structural variants were also detected. The characterization of pan-genomic variants suggests their importance during foxtail millet domestication and improvement, as exemplified by the identification of the yield gene SiGW3, where a 366-bp presence/absence promoter variant accompanies gene expression variation. We developed a graph-based genome and performed large-scale genetic studies for 68 traits across 13 environments, identifying potential genes for millet improvement at different geographic sites. These can be used in marker-assisted breeding, genomic selection and genome editing to accelerate crop improvement under different climatic conditions.

DOI: 10.1038/s41588-023-01423-w

Source: https://www.nature.com/articles/s41588-023-01423-w