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ARS Home » Southeast Area » Mississippi State, Mississippi » Crop Science Research Laboratory » Corn Host Plant Resistance Research » Research » Publications at this Location » Publication #369629
Research Project: Enhanced Resistance of Maize to Aspergillus flavus Infection, Aflatoxin Accumulation, and Insect Damage

Location: Corn Host Plant Resistance Research

Title: CUBIC: an atlas of genetic architecture promises directed maize improvement

Author
item LIU, HAI-JUN - Huazhong Agricultural University
item WANG, XIAQING - Huazhong Agricultural University
item XIAO, YINGJIE - Huazhong Agricultural University
item LUO, JINGYUN - Huazhong Agricultural University
item QIAO, FENG - Huazhong Agricultural University
item YANG, WENYU - Huazhong Agricultural University
item ZHANG, RUYANG - Beijing Research Center For Information Technology In Agriculture, Beijing Academy Of Agriculture A
item MENG, YIJIANG - Agricultural University Of Hebei
item SUN, JIAMIN - Huazhong Agricultural University
item YAN, SHIJUAN - Guangdong Academy Of Agricultural Sciences
item PENG, YONG - Huazhong Agricultural University
item NIU, LUYAO - Huazhong Agricultural University
item JIAN, LIUMEI - Huazhong Agricultural University
item SONG, WEI - Beijing Research Center For Information Technology In Agriculture, Beijing Academy Of Agriculture A
item YAN, JIALI - Huazhong Agricultural University
item LI, CHUNHUI - Beijing Research Center For Information Technology In Agriculture, Beijing Academy Of Agriculture A
item ZHAO, YANXIN - Beijing Research Center For Information Technology In Agriculture, Beijing Academy Of Agriculture A
item LIU, YA - Beijing Research Center For Information Technology In Agriculture, Beijing Academy Of Agriculture A
item Warburton, Marilyn
item ZHAO, JIURAN - Beijing Research Center For Information Technology In Agriculture, Beijing Academy Of Agriculture A
item YAN, JIANBING - Huazhong Agricultural University

Submitted to: Genome Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/8/2020
Publication Date: 1/24/2020
Citation: Liu, H., Wang, X., Xiao, Y., Luo, J., Qiao, F., Yang, W., Zhang, R., Meng, Y., Sun, J., Yan, S., Peng, Y., Niu, L., Jian, L., Song, W., Yan, J., Li, C., Zhao, Y., Liu, Y., Warburton, M.L., Zhao, J., Yan, J. 2020. CUBIC: an atlas of genetic architecture promises directed maize improvement. Genome Biology. 21(20):1-17. https://doi.org/10.1186/s13059-020-1930-x.
DOI: https://doi.org/10.1186/s13059-020-1930-x

Interpretive Summary: Plant breeding has always tried to select the plants with the best traits. Traditionally, this has been done by selecting the plants that looked the best, but with some traits, plants could look good because of factors in the environment in which they were grown; this good performance was not passed on to their offspring. Recently, geneticists have been able to identify some of the genes that cause good expression of the trait and select them in the lab; while these are passed on to the offspring, not all the genetic information needed for best expression of the trait have ever been identified. This is partly because sometimes, the combination of two genes gives a synergistic boost to the trait, and having only one of the two genes gives less than half as good performance. This extra boost is often hidden by traditional genetics experiments, and this paper shows an analysis method to uncover these synergies and exploit them. This should improve plant breeding for any trait of interest, and will therefore be a very important new plant breeding tool.

Technical Abstract: 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 1404individuals created by extensively intercrossing 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 contribute to phenotypic variance widely. Integrative cross-population analysis and cross-omics mapping allows 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.