ArticleThe plant genome2021
Unoccupied aerial systems discovered overlooked loci capturing the variation of entire growing period in maize.
Article in The plant genome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 30 citations in OpenAlex.
- Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits.The Plant journal : for cell and molecular biology · 2024Article
- Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize.G3 (Bethesda, Md.) · 2024Article
- Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions.G3 (Bethesda, Md.) · 2023Article
- Functional Allele Validation by Gene Editing to Leverage the Wealth of Genetic Resources for Crop Improvement.International journal of molecular sciences · 2022Review
- Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms.Scientific reports · 2022Article
- Unoccupied aerial systems discovered overlooked loci capturing the variation of entire growing period in maize.The plant genome · 2021Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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