CUBIC: an atlas of genetic architecture promises directed maize improvement

文献类型： 外文期刊

第一作者： Liu, Hai-Jun

作者： Liu, Hai-Jun;Wang, Xiaqing;Xiao, Yingjie;Luo, Jingyun;Qiao, Feng;Yang, Wenyu;Sun, Jiamin;Peng, Yong;Niu, Luyao;Jian, Liumei;Yan, Jiali;Yan, Jianbing;Wang, Xiaqing;Zhang, Ruyang;Song, Wei;Li, Chunhui;Zhao, Yanxin;Liu, Ya;Zhao, Jiuran;Qiao, Feng;Yang, Wenyu;Meng, Yijiang;Yan, Shijuan;Warburton, Marilyn L.

作者机构：

关键词： Population development; Genome-wide association mapping; Cross-omics; Functional genomics; Zea mays

期刊名称：GENOME BIOLOGY （ 影响因子：13.583； 五年影响因子：17.433 ）

ISSN： 1474-760X

年卷期： 2020 年 21 卷 1 期

页码：

收录情况： SCI

摘要： Background Identifying genotype-phenotype links and causative genes from quantitative trait loci (QTL) is challenging for complex agronomically important traits. To accelerate maize gene discovery and breeding, we present the Complete-diallel design plus Unbalanced Breeding-like Inter-Cross (CUBIC) population, consisting of 1404 individuals created by extensively inter-crossing 24 widely used Chinese maize founders. Results Hundreds of QTL for 23 agronomic traits are uncovered with 14 million high-quality SNPs and a high-resolution identity-by-descent map, which account for an average of 75% of the heritability for each trait. We find epistasis contributes to phenotypic variance widely. Integrative cross-population analysis and cross-omics mapping allow effective and rapid discovery of underlying genes, validated here with a case study on leaf width. Conclusions Through the integration of experimental genetics and genomics, our study provides useful resources and gene mining strategies to explore complex quantitative traits.

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