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Differences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180 angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability of R2 ¼ 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar lant architectures. 

ABSTRACT Plants exhibit extensive environment-dependent intraspecific metabolic variation, which likely plays a role in determining variation in whole plant phenotypes. However, much of the work seeking to use natural variation to link genes and transcript’s impacts on plant metabolism has employed data from controlled environments. Here we generate and employ data on variation in the abundance of twenty-six metabolites across 660 maize inbred lines under field conditions. We employ these data and previously published transcript and whole plant phenotype data reported for the same field experiment to identify both genomic intervals (through genome-wide association studies) and transcripts (through both transcriptome-wide association studies and an explainable AI approach based on the random forest) associated with variation in metabolite abundance. Both genome-wide association and random forest-based methods identified substantial numbers of significant associations including genes with plausible links to the metabolites they are associated with. In contrast, the transcriptome-wide association identified only six significant associations. In three cases, genetic markers associated with metabolic variation in our study colocalized with markers linked to variation in non-metabolic traits scored in the same experiment. We speculate that the poor performance of transcriptome-wide association studies in identifying transcript-metabolite associations may reflect a high prevalence of non-linear interactions between transcripts and metabolites and/or a bias towards rare transcripts playing a large role in determining intraspecific metabolic variation. 

SUMMARY Photosynthetic organisms must cope with rapid fluctuations in light intensity. Nonphotochemical quenching (NPQ) enables the dissipation of excess light energy as heat under high light conditions, whereas its relaxation under low light maximizes photosynthetic productivity. We quantified variation in NPQ kinetics across a large sorghum (Sorghum bicolor) association panel in four environments, uncovering significant genetic control for NPQ. A genome‐wide association study (GWAS) confidently identified three unique regions in the sorghum genome associated with NPQ and suggestive associations in an additional 61 regions. We detected strong signals from the sorghum ortholog ofArabidopsis thaliana Suppressor Of Variegation 3(SVR3) involved in plastid–nucleus signaling. By integrating GWAS results for NPQ across maize (Zea mays) and sorghum‐association panels, we identified a second gene,Non‐yellowing 1(NYE1), originally studied by Gregor Mendel in pea (Pisum sativum) and involved in the degradation of photosynthetic pigments in light‐harvesting complexes. Analysis ofnye1insertion alleles inA. thalianaconfirmed the effect of this gene on NPQ kinetics in eudicots. We extended our comparative genomics GWAS framework across the entire maize and sorghum genomes, identifying four additional loci involved in NPQ kinetics. These results provide a baseline for increasing the accuracy and speed of candidate gene identification for GWAS in species with high linkage disequilibrium.