In Molecular plant
Plant height (PH) is an essential trait in maize (Zea mays L.) which is tightly associated with planting density, biomass, lodging resistance and grain yield in the field. Dissecting the dynamics of maize plant architecture will be beneficial for ideotype-based maize breeding and prediction, as the genetic basis controlling PH in maize remains largely unknown. Here, we developed an automated high-throughput phenotyping platform (HTP) to systematically and noninvasively quantify 77 image-based traits (i-traits) and 20 field traits (f-traits) for 228 maize inbred lines across all developmental stages. Time-resolved i-traits with novel digital phenotypes and complex correlations with agronomic traits were characterized to reveal the dynamics of maize growth. An i-trait-based genome-wide association study (GWAS) identified 4945 trait-associated SNPs, 2603 genetic loci, and 1974 corresponding candidate genes. Interestingly, we found that rapid growth of maize plants mainly occurs at two developmental stages, Stage 2 (S2) to S3 and S5 to S6, accounting for the final PH indicators. By integrating the plant height-association network with the transcriptome profiles of specific internodes, we revealed 13 hub genes that might play vital roles during the rapid growth. The candidate genes and novel i-traits identified at multiple growth stages might be used as potential indicators for final PH in maize. The function of one candidate gene, ZmVATE, was validated to regulate plant height-related traits in maize by using genetic mutation. Furthermore, we used machine learning to build prediction models for final plant height based on i-traits, and predictive performance was assessed in validation across developmental stages. Moderate, strong, and very strong correlations between prediction and experimental datasets were achieved from early S4 (tenth-leaf) stage. Overall, our study provided a valuable tool for dissecting the spatiotemporal formation of specific internodes and the genetic architecture of PH, as well as resources and prediction models that are useful for molecular design breeding and predicting maize varieties with ideal plant architectures.
Wang Weixuan, Guo Weijun, Le Liang, Yu Jia, Wu Yue, Li Dongwei, Wang Yifan, Wang Huan, Lu Xiaoduo, Qiao Hong, Gu Xiaofeng, Tian Jian, Zhang Chunyi, Pu Li
2022-Nov-28
GWAS, machine learning, maize, phenomics, plant height, prediction
