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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 functional-structural 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.

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.

Sorghum genotypes currently used for grain production in the United States were developed from African landraces that were imported starting in the mid-to-late 19(th) century. Farmers and plant breeders selected genotypes for grain production with reduced plant height, early flowering, increased grain yield, adaptation to drought, and improved resistance to lodging, diseases and pests. DNA polymorphisms that distinguish three historically important grain sorghum genotypes, BTx623, BTx642 and Tx7000, were characterized by genome sequencing, genotyping by sequencing, genetic mapping, and pedigree-based haplotype analysis. The distribution and density of DNA polymorphisms in the sequenced genomes varied widely, in part because the lines were derived through breeding and selection from diverse Kafir, Durra, and Caudatum race accessions. Genomic DNA spanning dw1 (SBI-09) and dw3 (SBI-07) had identical haplotypes due to selection for reduced height. Lower SNP density in genes located in pericentromeric regions compared with genes located in euchromatic regions is consistent with background selection in these regions of low recombination. SNP density was higher in euchromatic DNA and varied >100-fold in contiguous intervals that spanned up to 300 Kbp. The localized variation in DNA polymorphism density occurred throughout euchromatic regions where recombination is elevated, however, polymorphism density was not correlated with gene density or DNA methylation. Overall, sorghum chromosomes contain distal euchromatic regions characterized by extensive, localized variation in DNA polymorphism density, and large pericentromeric regions of low gene density, diversity, and recombination.

Fil: Rotundo, José Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina

Recombinant inbred populations of many plant species exhibit more heterozygosity than expected under the Mendelian model of segregation. This segregation distortion causes the overestimation of recombination frequencies and consequent genetic map expansion. Here we build upon existing genetic models of differential zygotic viability to model a heterozygote fitness term and calculate expected genotypic proportions in recombinant inbred populations propagated by selfing. We implement this model using the existing open-source genetic map construction code base for R/qtl to estimate recombination fractions. Finally, we show that accounting for excess heterozygosity in a sorghum recombinant inbred mapping population shrinks the genetic map by 213 cM (a 13% decrease corresponding to 4.26 fewer recombinations per meiosis). More accurate estimates of linkage benefit linkage-based analyses used in the identification and utilization of causal genetic variation.

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.

Bioenergy sorghum is targeted for production in water-limited annual cropland therefore traits that improve plant water capture, water use efficiency, and resilience to water deficit are necessary to maximize productivity. A crop modeling framework, APSIM, was adapted to predict the growth and biomass yield of energy sorghum and to identify potentially useful traits for crop improvement. APSIM simulations of energy sorghum development and biomass accumulation replicated results from field experiments across multiple years, patterns of rainfall, and irrigation schemes. Modeling showed that energy sorghum's long duration of vegetative growth increased water capture and biomass yield by ~30% compared to short season crops in a water-limited production region. Additionally, APSIM was extended to enable modeling of VPD-limited transpiration traits that reduce crop water use under high vapor pressure deficits (VPDs). The response of transpiration rate to increasing VPD was modeled as a linear response until a VPD threshold was reached, at which the slope of the response decreases, representing a range of responses to VPD observed in sorghum germplasm. Simulation results indicated that the VPD-limited transpiration trait is most beneficial in hot and dry regions of production where crops are exposed to extended periods without rainfall during the season or to a terminal drought. In these environments, slower but more efficient transpiration increases biomass yield and prevents or delays the exhaustion of soil water and onset of leaf senescence. The VPD-limited transpiration responses observed in sorghum germplasm increased biomass accumulation by 20% in years with lower summer rainfall, and the ability to drastically reduce transpiration under high VPD conditions could increase biomass by 6% on average across all years. This work indicates that the productivity and resilience of bioenergy sorghum grown in water-limited environments could be further enhanced by development of genotypes with optimized VPD-limited transpiration traits and deployment of these crops in water limited growing environments. The energy sorghum model and VPD-limited transpiration trait implementation are made available to simulate performance in other target environments.

ABSTRACTThe timing of crop development has significant impacts on management decisions and subsequent yield formation. A large intercontinental dataset recording the timing of soybean developmental stages was used to establish ensembling approaches that leverage both knowledge-based, human-defined models of soybean phenology and data-driven, machine-learned models to achieve accurate and interpretable predictions. We demonstrate that the knowledge-based models can improve machine learning by generating expert-engineered features. The collection of knowledge-based and data-driven models was combined via super learning to both improve prediction and identify the most performant models. Stacking the predictions of the component models resulted in a mean absolute error of 4.41 and 5.27 days to flowering (R1) and physiological maturity (R7), providing an improvement relative to the benchmark knowledge-based model error of 6.94 and 15.53 days, respectively, in cross-validation. The hybrid intercontinental model applies to a much wider range of management and temperature conditions than previous mechanistic models, enabling improved decision support as alternative cropping systems arise, farm sizes increase and changes in the global climate continue to accelerate.

In most cases, cells must increase their size before they can divide. Hence, a small size has been used often as a phenotype for mutants that accelerate initiation of division, such as the celebrated WHI mutants of budding yeast. Recently, we measured the DNA content of all nonessential gene deletion strains in Saccharomyces cerevisiae. Surprisingly, there was little, if any, correlation between mean cell size and cell-cycle progression. Here, we examine this issue further, providing the first systematic analysis of genetic determinants of the cell size at birth. We found that although a large birth size strongly correlates with a large mean size, the converse relationship (i.e., small birth size vs. small mean size) is not as strong. Our data also suggest that mutants that are born large do not have a significant advantage for faster cell-cycle progression. In contrast, mutants that are born small are more likely to progress slower in the cell cycle. The majority of gene deletions that displayed such phenotypes affect protein synthesis or ribosome biogenesis. Overall, our data suggest that birth size may be a more informative parameter for cell-cycle progression than the mean size of a proliferating cell population. In contrast to WHI phenotype expectations, a small size is more likely to be associated with delayed cell-cycle progression.