High-accuracy 3D evaluation of reconstruction quality indicated that dynamic capture of the 3D canopy based on the MVS approach can be potentially used in 3D phenotyping for applications in breeding and field management.
Summary Starch is the most abundant storage carbohydrate in maize kernels and provides calories for humans and other animals as well as raw materials for various industrial applications. Decoding the genetic basis of natural variation in kernel starch content is needed to manipulate starch quantity and quality via molecular breeding to meet future needs. Here, we identified 50 unique single quantitative trait loci (QTLs) for starch content with 18 novel QTLs via single linkage mapping, joint linkage mapping and a genome‐wide association study in a multi‐parent population containing six recombinant inbred line populations. Only five QTLs explained over 10% of phenotypic variation in single populations. In addition to a few large‐effect and many small‐effect additive QTLs, limited pairs of epistatic QTLs also contributed to the genetic basis of the variation in kernel starch content. A regional association study identified five non‐starch‐pathway genes that were the causal candidate genes underlying the identified QTLs for starch content. The pathway‐driven analysis identified ZmTPS9, which encodes a trehalose‐6‐phosphate synthase in the trehalose pathway, as the causal gene for the QTL qSTA4−2, which was detected by all three statistical analyses. Knockout of ZmTPS9 increased kernel starch content and, in turn, kernel weight in maize, suggesting potential applications for ZmTPS9 in maize starch and yield improvement. These findings extend our knowledge about the genetic basis of starch content in maize kernels and provide valuable information for maize genetic improvement of starch quantity and quality.
Summary The nutritional traits of maize kernels are important for human and animal nutrition, and these traits have undergone selection to meet the diverse nutritional needs of humans. However, our knowledge of the genetic basis of selecting for kernel nutritional traits is limited. Here, we identified both single and epistatic quantitative trait loci (QTLs) that contributed to the differences of oil and carotenoid traits between maize and teosinte. Over half of teosinte alleles of single QTLs increased the values of the detected oil and carotenoid traits. Based on the pleiotropism or linkage information of the identified single QTLs, we constructed a trait–locus network to help clarify the genetic basis of correlations among oil and carotenoid traits. Furthermore, the selection features and evolutionary trajectories of the genes or loci underlying variations in oil and carotenoid traits revealed that these nutritional traits produced diverse selection events during maize domestication and improvement. To illustrate more, a mutator distance–relative transposable element (TE) in intron 1 of DXS2, which encoded a rate‐limiting enzyme in the methylerythritol phosphate pathway, was identified to increase carotenoid biosynthesis by enhancing DXS2 expression. This TE occurs in the grass teosinte, and has been found to have undergone selection during maize domestication and improvement, and is almost fixed in yellow maize. Our findings not only provide important insights into evolutionary changes in nutritional traits, but also highlight the feasibility of reintroducing back into commercial agricultural germplasm those nutritionally important genes hidden in wild relatives.
Background and Aims High-throughput phenotyping is a limitation in plant genetics and breeding due to large-scale experiments in the field. Unmanned aerial vehicles (UAVs) can help to extract plant phenotypic traits rapidly and non-destructively with high efficiency. The general aim of this study is to estimate the dynamic plant height and leaf area index (LAI) by nadir and oblique photography with a UAV, and to compare the integrity of the established three-dimensional (3-D) canopy by these two methods. Methods Images were captured by a high-resolution digital RGB camera mounted on a UAV at five stages with nadir and oblique photography, and processed by Agisoft Metashape to generate point clouds, orthomosaic maps and digital surface models. Individual plots were segmented according to their positions in the experimental design layout. The plant height of each inbred line was calculated automatically by a reference ground method. The LAI was calculated by the 3-D voxel method. The reconstructed canopy was sliced into different layers to compare leaf area density obtained from oblique and nadir photography. Key Results Good agreements were found for plant height between nadir photography, oblique photography and manual measurement during the whole growing season. The estimated LAI by oblique photography correlated better with measured LAI (slope = 0.87, R2 = 0.67), compared with that of nadir photography (slope = 0.74, R2 = 0.56). The total number of point clouds obtained by oblique photography was about 2.7–3.1 times than those by nadir photography. Leaf area density calculated by nadir photography was much less than that obtained by oblique photography, especially near the plant base. Conclusions Plant height and LAI can be extracted automatically and efficiently by both photography methods. Oblique photography can provide intensive point clouds and relatively complete canopy information at low cost. The reconstructed 3-D profile of the plant canopy can be easily recognized by oblique photography.
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