Dynamic genetic architecture of plant height in maize

Plant height (PH) is an essential trait in maize (<em>Zea mays L.</em>) 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) reveals 4272 unique trait-associated SNPs and 702 genetic loci. 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. Integrating the plant height-association network with the transcriptome profiles of specific internodes revealed 80 hub genes playing 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, <em>ZmVATE</em>, was validated to regulate plant height-related traits in maize by using genetic mutation. Furthermore, prediction models for final plant height based on i-traits were made using machine learning, and the predictive performance were assessed in validation along developmental stages with a moderate, strong and very strong correlation between prediction and experimental dataset archived from early S4 stage. 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 useful for molecular design breeding and predicting maize varieties with ideal plant architecture.