Traditional phenotyping methods, coupled with genetic mapping in segregating populations, have identified loci governing complex traits in many crops. Unoccupied aerial systems (UAS)-based phenotyping has helped to reveal a more novel and dynamic relationship between time-specific associated loci with complex traits previously unable to be evaluated. Over 1,500 maize (Zea mays L.) hybrid row plots containing 280 different replicated maize hybrids from the Genomes to Fields (G2F) project were evaluated agronomically and using UAS in 2017. Weekly UAS flights captured variation in plant heights during the growing season under three different management conditions each year: optimal planting with irrigation (G2FI), optimal dryland planting without irrigation (G2FD), and a stressed late planting (G2LA). Plant height of different flights were ranked based on importance for yield using a random forest (RF) algorithm. Plant heights captured by early flights in G2FI trials had higher importance (based on Gini scores) for predicting maize grain yield (GY) but also higher accuracies in genomic predictions which fluctuated for G2FD (−0.06∼0.73), G2FI (0.33∼0.76), and G2LA (0.26∼0.78) trials. A genome-wide association analysis discovered 52 significant single nucleotide polymorphisms (SNPs), seven were found consistently in more than one flights or trial; 45 were flight or trial specific. Total cumulative marker effects for each chromosome's contributions to plant height also changed depending on flight. Using UAS phenotyping, this study showed that many candidate genes putatively play a role in the regulation of plant architecture even in relatively early stages of maize growth and development.

中文翻译：

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未占用的空中系统发现了被忽视的位点，捕捉了玉米整个生长期的变化

传统的表型分析方法，再加上分离种群中的遗传作图，已经确定了许多作物中控制复杂性状的位点。基于空置航空系统 (UAS) 的表型分析有助于揭示特定时间相关位点与以前无法评估的复杂性状之间更新颖和动态的关系。超过 1,500 种玉米（Zea maysL.) 包含 280 种不同复制玉米杂交种的杂交行地块，在 2017 年进行了农艺评估并使用 UAS 进行了评估。 每周的 UAS 飞行在每年三种不同的管理条件下捕获了生长季节植物高度的变化：有灌溉的最佳种植 (G2FI)、无灌溉的最佳旱地种植 (G2FD) 和压力延迟种植 (G2LA)。使用随机森林 (RF) 算法根据产量的重要性对不同飞行的植物高度进行排序。G2FI 试验中早期飞行捕获的植物高度对于预测玉米籽粒产量 (GY) 具有更高的重要性（基于 Gini 分数），但在基因组预测中也具有更高的准确度，该预测因 G2FD (-0.06∼0.73)、G2FI (0.33∼0.76) 而波动, 和 G2LA (0.26∼0.78) 试验。一项全基因组关联分析发现了 52 个重要的单核苷酸多态性 (SNP)，其中 7 个在多次飞行或试验中一致发现；45 个是飞行或试验特定的。每个染色体对植物高度的贡献的总累积标记效应也随着飞行而变化。使用 UAS 表型分析，这项研究表明，即使在玉米生长和发育的相对早期阶段，许多候选基因也可能在植物结构的调节中发挥作用。