This study investigates the temporal plant height (TPHT) of maize using data collected from unmanned aerial systems (UAS, UAV, drones) in three recombinant inbred line (516 RILs) populations (Ki3/NC356, LH82/LAMA, and Tx740/NC356) under irrigated and non-irrigated growth conditions across 10 time points (43–133 days after planting). Genetic variance exhibited significant fluctuations across flight times leading to varying heritabilities between 0.42 and 0.84 depending on specific growth stages. The temporal trajectory of plant height, revealed by the Weibull fit, used inflection point to calculate the Plant Height Growth Ratio (PHGR), a promising quantitative trait related to source strength in maize. PHGR showed significant differences across distinct growth conditions consistently for three RIL populations, whereas TPHT showed limited phenotypic differences between these conditions, despite significant loci emerging at specific time points. For PHGR, a significant SNP (chr1_274716256) was discovered exclusively under non-irrigated conditions in the LH82/LAMA population. This SNP is located within a strong linkage disequilibrium (LD) block spanning approximately 260–280 kb on chromosome 1. Candidate genes within this region include d8, kn1, knox3, and phy1. Several significant loci were mapped for TPHT at various time points under both growth conditions. Key growth regulator candidate genes such as toc1, elm1, an1, cct1, and vp8 were identified among these loci. Notably, mads69, a flowering activator gene, was discovered using growth rate data (calculated between consecutive TPHT time points) before flowering under non-irrigated conditions, emphasizing its potential role in drought resistance. Genomic prediction analyses showed varying prediction abilities for TPHT in diffirent management conditions, with higher accuracy at later growth stages and under irrigated conditions. PHGR exhibited a more stable prediction ability across time points compared with TPHT, which showed a broader range (0.11–0.55) between growth conditions, with occasional peaks exceeding PHGR values. The temporal effects of genomic markers varied across growth conditions, showing stable trends in irrigated environments and more variable changes in non-irrigated conditions. These insights into temporal marker effects and genomic prediction, aided by UAS data, can enhance breeding efficiency and the development of crops better adapted to specific growing conditions.

Genomic insights into temporal growth patterns in maize using phenotyping technologies

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发表日期：

2025年9月23日

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Alper Adak,
Aaron J. DeSalvio,
Steven L. Anderson,
Seth C. Murray

摘要

This study investigates the temporal plant height (TPHT) of maize using data collected from unmanned aerial systems (UAS, UAV, drones) in three recombinant inbred line (516 RILs) populations (Ki3/NC356, LH82/LAMA, and Tx740/NC356) under irrigated and non-irrigated growth conditions across 10 time points (43–133 days after planting). Genetic variance exhibited significant fluctuations across flight times leading to varying heritabilities between 0.42 and 0.84 depending on specific growth stages. The temporal trajectory of plant height, revealed by the Weibull fit, used inflection point to calculate the Plant Height Growth Ratio (PHGR), a promising quantitative trait related to source strength in maize. PHGR showed significant differences across distinct growth conditions consistently for three RIL populations, whereas TPHT showed limited phenotypic differences between these conditions, despite significant loci emerging at specific time points. For PHGR, a significant SNP (chr1_274716256) was discovered exclusively under non-irrigated conditions in the LH82/LAMA population. This SNP is located within a strong linkage disequilibrium (LD) block spanning approximately 260–280 kb on chromosome 1. Candidate genes within this region include d8, kn1, knox3, and phy1. Several significant loci were mapped for TPHT at various time points under both growth conditions. Key growth regulator candidate genes such as toc1, elm1, an1, cct1, and vp8 were identified among these loci. Notably, mads69, a flowering activator gene, was discovered using growth rate data (calculated between consecutive TPHT time points) before flowering under non-irrigated conditions, emphasizing its potential role in drought resistance. Genomic prediction analyses showed varying prediction abilities for TPHT in diffirent management conditions, with higher accuracy at later growth stages and under irrigated conditions. PHGR exhibited a more stable prediction ability across time points compared with TPHT, which showed a broader range (0.11–0.55) between growth conditions, with occasional peaks exceeding PHGR values. The temporal effects of genomic markers varied across growth conditions, showing stable trends in irrigated environments and more variable changes in non-irrigated conditions. These insights into temporal marker effects and genomic prediction, aided by UAS data, can enhance breeding efficiency and the development of crops better adapted to specific growing conditions.