Sorghum bicolor is a drought tolerant C4 grass used for the production of grain, forage, sugar, and lignocellulosic biomass and a genetic model for C4 grasses due to its relatively small genome (approximately 800 Mbp), diploid genetics, diverse germplasm, and colinearity with other C4 grass genomes. In this study, deep sequencing, genetic linkage analysis, and transcriptome data were used to produce and annotate a high-quality reference genome sequence. Reference genome sequence order was improved, 29.6 Mbp of additional sequence was incorporated, the number of genes annotated increased 24% to 34 211, average gene length and N50 increased, and error frequency was reduced 10-fold to 1 per 100 kbp. Subtelomeric repeats with characteristics of Tandem Repeats in Miniature (TRIM) elements were identified at the termini of most chromosomes. Nucleosome occupancy predictions identified nucleosomes positioned immediately downstream of transcription start sites and at different densities across chromosomes. Alignment of more than 50 resequenced genomes from diverse sorghum genotypes to the reference genome identified approximately 7.4 M single nucleotide polymorphisms (SNPs) and 1.9 M indels. Large-scale variant features in euchromatin were identified with periodicities of approximately 25 kbp. A transcriptome atlas of gene expression was constructed from 47 RNA-seq profiles of growing and developed tissues of the major plant organs (roots, leaves, stems, panicles, and seed) collected during the juvenile, vegetative and reproductive phases. Analysis of the transcriptome data indicated that tissue type and protein kinase expression had large influences on transcriptional profile clustering. The updated assembly, annotation, and transcriptome data represent a resource for C4 grass research and crop improvement.

Sorghum is emerging as an excellent genetic model for the design of C4 grass bioenergy crops. Annual energy Sorghum hybrids also serve as a source of biomass for bioenergy production. Elucidation of Sorghum's flowering time gene regulatory network, and identification of complementary alleles for photoperiod sensitivity, enabled large-scale generation of energy Sorghum hybrids for testing and commercial use. Energy Sorghum hybrids with long vegetative growth phases were found to accumulate more than twice as much biomass as grain Sorghum, owing to extended growing seasons, greater light interception, and higher radiation use efficiency. High biomass yield, efficient nitrogen recycling, and preferential accumulation of stem biomass with low nitrogen content contributed to energy Sorghum's elevated nitrogen use efficiency. Sorghum's integrated genetics-genomics-breeding platform, diverse germplasm, and the opportunity for annual testing of new genetic designs in controlled environments and in multiple field locations is aiding fundamental discovery, and accelerating the improvement of biomass yield and optimization of composition for biofuels production. Recent advances in wide hybridization between Sorghum and other C4 grasses could allow the deployment of improved genetic designs of annual energy Sorghums in the form of wide-hybrid perennial crops. The current trajectory of energy Sorghum genetic improvement indicates that it will be possible to sustainably produce biofuels from C4 grass bioenergy crops that are cost competitive with petroleum-based transportation fuels.

Sorghum is an important C4 grain and grass crop used for food, feed, forage, sugar, and biofuels. In its native Africa, sorghum landraces often grow to approximately 3–4 meters in height. Following introduction into the U.S., shorter, early flowering varieties were identified and used for production of grain. Quinby and Karper identified allelic variation at four loci designated Dw1-Dw4 that regulated plant height by altering the length of stem internodes. The current study used a map-based cloning strategy to identify the gene corresponding to Dw1. Hegari (Dw1dw2Dw3dw4) and 80M (dw1dw2Dw3dw4) were crossed and F2 and HIF derived populations used for QTL mapping. Genetic analysis identified four QTL for internode length in this population, Dw1 on SBI-09, Dw2 on SBI-06, and QTL located on SBI-01 and SBI-07. The QTL on SBI-07 was ~3 Mbp upstream of Dw3 and interacted with Dw1. Dw1 was also found to contribute to the variation in stem weight in the population. Dw1 was fine mapped to an interval of ~33 kbp using HIFs segregating only for Dw1. A polymorphism in an exon of Sobic.009G229800 created a stop codon that truncated the encoded protein in 80M (dw1). This polymorphism was not present in Hegari (Dw1) and no other polymorphisms in the delimited Dw1 locus altered coding regions. The recessive dw1 allele found in 80M was traced to Dwarf Yellow Milo, the progenitor of grain sorghum genotypes identified as dw1. Dw1 encodes a putative membrane protein of unknown function that is highly conserved in plants.

Sorghum is an important C4 grass crop grown for grain, forage, sugar, and bioenergy production. While tall, late flowering landraces are commonly grown in Africa, short early flowering varieties were selected in US grain sorghum breeding programs to reduce lodging and to facilitate machine harvesting. Four loci have been identified that affect stem length (Dw1-Dw4). Subsequent research showed that Dw3 encodes an ABCB1 auxin transporter and Dw1 encodes a highly conserved protein involved in the regulation of cell proliferation. In this study, Dw2 was identified by fine-mapping and further confirmed by sequencing the Dw2 alleles in Dwarf Yellow Milo and Double Dwarf Yellow Milo, the progenitor genotypes where the recessive allele of dw2 originated. The Dw2 locus was determined to correspond to Sobic.006G067700, a gene that encodes a protein kinase that is homologous to KIPK, a member of the AGCVIII subgroup of the AGC protein kinase family in Arabidopsis.

Dissecting the genetic basis of complex traits is aided by frequent and nondestructive measurements. Advances in range imaging technologies enable the rapid acquisition of three-dimensional (3D) data from an imaged scene. A depth camera was used to acquire images of sorghum (Sorghum bicolor), an important grain, forage, and bioenergy crop, at multiple developmental time points from a greenhouse-grown recombinant inbred line population. A semiautomated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images. Automated measurements made from 3D plant reconstructions identified quantitative trait loci for standard measures of shoot architecture, such as shoot height, leaf angle, and leaf length, and for novel composite traits, such as shoot compactness. The phenotypic variability associated with some of the quantitative trait loci displayed differences in temporal prevalence; for example, alleles closely linked with the sorghum Dwarf3 gene, an auxin transporter and pleiotropic regulator of both leaf inclination angle and shoot height, influence leaf angle prior to an effect on shoot height. Furthermore, variability in composite phenotypes that measure overall shoot architecture, such as shoot compactness, is regulated by loci underlying component phenotypes like leaf angle. As such, depth imaging is an economical and rapid method to acquire shoot architecture phenotypes in agriculturally important plants like sorghum to study the genetic basis of complex traits.

The efficiency with which a plant intercepts solar radiation is determined primarily by its architecture. Understanding the genetic regulation of plant architecture and how changes in architecture affect performance can be used to improve plant productivity. Leaf inclination angle, the angle at which a leaf emerges with respect to the stem, is a feature of plant architecture that influences how a plant canopy intercepts solar radiation. Here we identify extensive genetic variation for leaf inclination angle in the crop plant Sorghum bicolor, a C4 grass species used for the production of grain, forage, and bioenergy. Multiple genetic loci that regulate leaf inclination angle were identified in recombinant inbred line populations of grain and bioenergy sorghum. Alleles of sorghum dwarf-3, a gene encoding a P-glycoprotein involved in polar auxin transport, are shown to change leaf inclination angle by up to 34°(0.59 rad). The impact of heritable variation in leaf inclination angle on light interception in sorghum canopies was assessed using functionalstructural plant models and field experiments. Smaller leaf inclination angles caused solar radiation to penetrate deeper into the canopy, and the resulting redistribution of light is predicted to increase the biomass yield potential of bioenergy sorghum by at least 3%. These results show that sorghum leaf angle is a heritable trait regulated by multiple loci and that genetic variation in leaf angle can be used to modify plant architecture to improve sorghum crop performance.KEYWORDS leaf angle; crop modeling; sorghum canopy; bioenergy sorghum; dwarf-3; P-glycoprotein; auxin transport S USTAINABLY increasing the productivity of crops on land currently used for agriculture without depleting natural resources is a global priority (Foley et al. 2011;Drewry et al. 2014). Improving the efficiency with which plants intercept solar radiation is one means to sustainably improve crop productivity. Leaf angle, or leaf erectness, is a plant canopy parameter that has drawn considerable attention because of the predicted improvement in photosynthetic efficiency and reduction in plant stress afforded by the redistribution of solar radiation from upper to lower levels of canopies (Tollenaar and Wu 1999;Duvick 2005;Murchie et al. 2009;Zhu et al. 2010;Murchie and Reynolds 2012;Drewry et al. 2014;Mansfield and Mumm 2014). Performance improvements predicted by theoretical models are corroborated by positive correlations between small leaf angles and cereal crop yields; post-green revolution rice cultivars have smaller leaf inclination angles and higher yields relative to their pre-green revolution predecessors (Yoshida 1972;Sinclair and Sheehy 1999;Sakamoto et al. 2006), and modern maize is also characterized by small inclination angles as a consequence of selection for increased grain yield in breeding programs (Duvick 2005;Lee and Tollenaar 2007;Hammer et al. 2009;Tian et al. 2011;Mansfield and Mumm 2014).Despite the association of leaf angle with increased productivity, its g...

HighlightsWe describe and demonstrate a multidimensional framework to integrate environmental and genomic predictors to enable crop improvement for a circular bioeconomy.A model training procedure based on multiple phenotypes is shown to improve predictive skill.The decision set comprised of model outputs can inform selection for both productivity and circularity metrics.Abstract. Contemporary agricultural systems are poised to transition from linear to circular, adopting concepts of recycling, repurposing, and regeneration. This transition will require changing crop improvement objectives to consider the entire system, and thus provide solutions to improve complex systems for higher productivity, resource use efficiency, and environmental quality. The methods and approaches that underpinned the doubling of yields during the last century may no longer be fully adequate to target crop improvement for circular agricultural systems. Here we propose a multidimensional framework for prediction with outcomes useful to assess both crop performance traits and environmental sustainability of the designed agricultural systems. The study focuses on maize harvestable grain yield and total carbon production, water use, and use efficiency for yield and carbon. The framework builds on the crop growth model whole genome prediction system, which is enabled by advanced phenomics and the integration of symbolic and sub-symbolic artificial intelligence. We demonstrate the approach and prediction accuracy advantages over a standard statistical genomic prediction approach used to breed maize hybrids for yield, flowering time, and kernel set using a dataset comprised of 7004 hybrids, 103 breeding populations, and 62 environments resulting from six years of experimentation in maize drought breeding in the U.S. We propose this framework to motivate a dialogue for how to enable circularity in agriculture through prediction-based systems design. Keywords: Circular bioeconomy, Circular economy, Crop improvement, Crop models, Drought, Gene editing, Genomic prediction, Maize, Plant breeding.

Recent advances in variant calling made available in the Genome Analysis Toolkit (GATK) enable the use of validated single-nucleotide polymorphisms and indels to improve variant calling. However, large collections of variants for this purpose often are unavailable to research communities. We introduce a workflow to generate reliable collections of single-nucleotide polymorphisms and indels by leveraging available genomic resources to inform variant calling using the GATK. The workflow is demonstrated for the crop plant Sorghum bicolor by (i) generating an initial set of variants using reduced representation sequence data from an experimental cross and association panels, (ii) using the initial variants to inform variant calling from whole-genome sequence data of resequenced individuals, and (iii) using variants identified from whole-genome sequence data for recalibration of the reduced representation sequence data. The reliability of variants called with the workflow is verified by comparison with genetically mappable variants from an independent sorghum experimental cross. Comparison with a recent sorghum resequencing study shows that the workflow identifies an additional 1.62 million high-confidence variants from the same sequence data. Finally, the workflow’s performance is validated using Arabidopsis sequence data, yielding variant call sets with 95% sensitivity and 99% positive predictive value. The Recalibration and Interrelation of genomic sequence data with the GATK (RIG) workflow enables the GATK to accurately identify genetic variation in organisms lacking validated variant resources.