Location: Plant Genetics Research
Title: Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variationsAuthor
ADAK, ALPER - Texas A&M University | |
MURRAY, SETH - Texas A&M University | |
Washburn, Jacob |
Submitted to: New Phytologist
Publication Type: Peer Reviewed Journal Publication Acceptance Date: 1/10/2024 Publication Date: 2/13/2024 Citation: Adak, A., Murray, S.C., Washburn, J.D. 2024. Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations. New Phytologist. 242:121-136. https://doi.org/10.1111/nph.19575. DOI: https://doi.org/10.1111/nph.19575 Interpretive Summary: Understanding the temporal or longitudinal growth dynamics of crops in diverse environmental conditions is crucial for understanding plant development and resilience but requires large datasets and the development and testing of new modeling and analyses approaches. High throughput phenotype data was collected across multiple maize genetic backgrounds and environments. Gaussian peak and Functional Principal Component Analysis were used to model temporal growth. Genetic analysis on these modeled phenotypes indicate the validity and usefulness of the models, demonstrating a path forward to better understanding temporal growth in crops. Technical Abstract: Quantifying the temporal or longitudinal growth dynamics of crops in diverse environmental conditions is crucial for understanding plant development, requiring further modeling techniques. In this study, we analyzed the growth patterns of two different maize (Zea mays L.) populations using high-throughput phenotyping with a maize population consisting of 515 recombinant inbred lines (RILs) grown in Texas and a hybrid population containing 1090 hybrids grown in Missouri. Two models, Gaussian peak and functional principal component analysis (FPCA), were employed to study the Normalized Green–Red Difference Index (NGRDI) scores. The Gaussian peak model showed strong correlations (c. 0.94 for RILs and c. 0.97 for hybrids) between modeled and non-modeled temporal trajectories. Functional principal component analysis differentiated NGRDI trajectories in RILs under different conditions, capturing substantial variability (75%, 20%, and 5% for RILs; 88% and 12% for hybrids). By comparing these models with conventional BLUP values, common quantitative trait loci (QTLs) were identified, containing candidate genes of brd1, pin11, zcn8 and rap2. The harmony between these loci's additive effects and growing degree days, as well as the differentiation of RIL haplotypes across growth stages, underscores the significant interplay of these loci in driving plant development. These findings contribute to advancing understanding of plant–environment interactions and have implications for crop improvement strategies. |