Meta-analysis identifies pleiotropic loci controlling phenotypic trade-offs in sorghum.

Community association populations are composed of phenotypically and genetically diverse accessions. Once these populations are genotyped, the resulting marker data can be reused by different groups investigating the genetic basis of different traits. Because the same genotypes are observed and scored for a wide range of traits in different environments, these populations represent a unique resource to investigate pleiotropy. Here, we assembled a set of 234 separate trait datasets for the Sorghum Association Panel, a group of 406 sorghum genotypes widely employed by the sorghum genetics community. Comparison of genome-wide association studies (GWAS) conducted with two independently generated marker sets for this population demonstrate that existing genetic marker sets do not saturate the genome and likely capture only 35-43% of potentially detectable loci controlling variation for traits scored in this population. While limited evidence for pleiotropy was apparent in cross-GWAS comparisons, a multivariate adaptive shrinkage approach recovered both known pleiotropic effects of existing loci and new pleiotropic effects, particularly significant impacts of known dwarfing genes on root architecture. In addition, we identified new loci with pleiotropic effects consistent with known trade-offs in sorghum development. These results demonstrate the potential for mining existing trait datasets from widely used community association populations to enable new discoveries from existing trait datasets as new, denser genetic marker datasets are generated for existing community association populations.

3D reconstruction identifies loci linked to variation in angle of individual sorghum leaves.

Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.

Reevaluation of the contributions of reactive intermediates to the photochemical transformation of 17ß-estradiol in sewage effluent.

Photodegradation of the natural steroid 17ß-estradiol (E2), an endocrine-disrupting hormone that has been widely detected in aquatic environments, was investigated in wastewater effluents at various pH ranges under simulated solar irradiation. The rate of E2 degradation in the sewage effluents was stable at pH 6.0-7.0 but suddenly increased from pH 8.0-10.0. The second-order reaction rate constants of E2 with 3EfOM* and CO3•- were measured to increase 11.0-fold and 18.0-fold from pH 6.0 to 10.0, respectively. Two main reasons are proposed for this sharp increase. First, the change in the ionization state of E2 made it susceptible to oxidation by triplet-state effluent organic matter (3EfOM*) and carbonate radicals (CO3•-). Second, the steady-state concentration of CO3•- increased with increasing pH. Indirect photolysis was suggested to be the main degradation pathway in the sewage effluents, and 3EfOM* was proposed to play a major role at pH 8.0-9.0, while CO3•- played a significant role at pH 10.0. In this study, EfOM was shown for the first time to inhibit the oxidation of E2 initiated by 3EfOM* and CO3•-. Thus, we suggest that EfOM plays a dual role in the photodegradation of E2: EfOM can not only be activated as 3EfOM* to degrade E2 but also can inhibit the degradation of E2 by reducing the E2 oxidation intermediate back to E2. The estrogenic activity of the photodegradation products was also studied. The in vitro estrogenic activity of E2 solutions decreased approximately as fast as the E2 photodegradation occurred in the effluent water at various pH values, suggesting that solar photodegradation in sewage effluents reduces the risk of endocrine disruption in waters impacted by E2 and subject to continuing inputs. The results of this study are important for predicting the environmental fate of endocrine-disrupting chemicals and developing methods for their removal from aquatic environments.

Semantic Segmentation of Sorghum Using Hyperspectral Data Identifies Genetic Associations.

This study describes the evaluation of a range of approaches to semantic segmentation of hyperspectral images of sorghum plants, classifying each pixel as either nonplant or belonging to one of the three organ types (leaf, stalk, panicle). While many current methods for segmentation focus on separating plant pixels from background, organ-specific segmentation makes it feasible to measure a wider range of plant properties. Manually scored training data for a set of hyperspectral images collected from a sorghum association population was used to train and evaluate a set of supervised classification models. Many algorithms show acceptable accuracy for this classification task. Algorithms trained on sorghum data are able to accurately classify maize leaves and stalks, but fail to accurately classify maize reproductive organs which are not directly equivalent to sorghum panicles. Trait measurements extracted from semantic segmentation of sorghum organs can be used to identify both genes known to be controlling variation in a previously measured phenotypes (e.g., panicle size and plant height) as well as identify signals for genes controlling traits not previously quantified in this population (e.g., stalk/leaf ratio). Organ level semantic segmentation provides opportunities to identify genes controlling variation in a wide range of morphological phenotypes in sorghum, maize, and other related grain crops.

Voxel carving-based 3D reconstruction of sorghum identifies genetic determinants of light interception efficiency.

Changes in canopy architecture traits have been shown to contribute to yield increases. Optimizing both light interception and light interception efficiency of agricultural crop canopies will be essential to meeting the growing food needs. Canopy architecture is inherently three-dimensional (3D), but many approaches to measuring canopy architecture component traits treat the canopy as a two-dimensional (2D) structure to make large scale measurement, selective breeding, and gene identification logistically feasible. We develop a high throughput voxel carving strategy to reconstruct 3D representations of sorghum from a small number of RGB photos. Our approach builds on the voxel carving algorithm to allow for fully automatic reconstruction of hundreds of plants. It was employed to generate 3D reconstructions of individual plants within a sorghum association population at the late vegetative stage of development. Light interception parameters estimated from these reconstructions enabled the identification of known and previously unreported loci controlling light interception efficiency in sorghum. The approach is generalizable and scalable, and it enables 3D reconstructions from existing plant high throughput phenotyping datasets. We also propose a set of best practices to increase 3D reconstructions' accuracy.

Carbonate Radical Oxidation of Cylindrospermopsin (Cyanotoxin): Kinetic Studies and Mechanistic Consideration.

Cylindrospermopsin (CYN) is one of the most important cyanobacterial toxins frequently found in surface waters. We reported the detailed kinetics and pathways for the reaction of CYN with carbonate radicals (CO3•-). The rate constants of neutral and deprotonated CYN with CO3•- were found to be (1.2 ± 0.7) × 107 M-1 s-1 and (3.0 ± 0.4) × 108 M-1 s-1, respectively. The transformation products for the oxidation of CYN by CO3•- were identified by high-resolution mass spectrometry, illustrating that the guanidine and bridged hydroxyl portions were the primary moieties attacked by CO3•-. Thus, three transformation pathways, including cleavage of the hydroxymethyluracil moiety, hydroxylation, and oxidation of the bridged hydroxyl group, are proposed for the CO3•- oxidation of CYN. Moreover, this study reported that dissolved organic matter (DOM) reduced the transformation rate of CYN by inhibiting the transformation of oxidation intermediates. Finally, the role of CO3•- in CYN degradation was estimated in both sunlit surface waters and advanced oxidation processes (AOPs), demonstrating that CO3•- played an important role in CYN attenuation under nonacidic environmentally relevant conditions. The kinetic parameters and product information obtained in this study will be of considerable interest for the application of AOPs and predicting the environmental fate of CYN.

Genome-Wide Characterization of DNase I-Hypersensitive Sites and Cold Response Regulatory Landscapes in Grasses.

Deep sequencing of DNase-I treated chromatin (DNase-seq) can be used to identify DNase I-hypersensitive sites (DHSs) and facilitates genome-scale mining of de novo cis-regulatory DNA elements. Here, we adapted DNase-seq to generate genome-wide maps of DHSs using control and cold-treated leaf, stem, and root tissues of three widely studied grass species: Brachypodium distachyon, foxtail millet (Setaria italica), and sorghum (Sorghum bicolor). Functional validation demonstrated that 12 of 15 DHSs drove reporter gene expression in transiently transgenic B. distachyon protoplasts. DHSs under both normal and cold treatment substantially differed among tissues and species. Intriguingly, the putative DHS-derived transcription factors (TFs) are largely colocated among tissues and species and include 17 ubiquitous motifs covering all grass taxa and all tissues examined in this study. This feature allowed us to reconstruct a regulatory network that responds to cold stress. Ethylene-responsive TFs SHINE3, ERF2, and ERF9 occurred frequently in cold feedback loops in the tissues examined, pointing to their possible roles in the regulatory network. Overall, we provide experimental annotation of 322,713 DHSs and 93 derived cold-response TF binding motifs in multiple grasses, which could serve as a valuable resource for elucidating the transcriptional networks that function in the cold-stress response and other physiological processes.

Increased Power and Accuracy of Causal Locus Identification in Time Series Genome-wide Association in Sorghum.

The phenotypes of plants develop over time and change in response to the environment. New engineering and computer vision technologies track these phenotypic changes. Identifying the genetic loci regulating differences in the pattern of phenotypic change remains challenging. This study used functional principal component analysis (FPCA) to achieve this aim. Time series phenotype data were collected from a sorghum (Sorghum bicolor) diversity panel using a number of technologies including conventional color photography and hyperspectral imaging. This imaging lasted for 37 d and centered on reproductive transition. A new higher density marker set was generated for the same population. Several genes known to control trait variation in sorghum have been previously cloned and characterized. These genes were not confidently identified in genome-wide association analyses at single time points. However, FPCA successfully identified the same known and characterized genes. FPCA analyses partitioned the role these genes play in controlling phenotypes. Partitioning was consistent with the known molecular function of the individual cloned genes. These data demonstrate that FPCA-based genome-wide association studies can enable robust time series mapping analyses in a wide range of contexts. Moreover, time series analysis can increase the accuracy and power of quantitative genetic analyses.

Shared Genetic Control of Root System Architecture between Zea mays and Sorghum bicolor.

Determining the genetic control of root system architecture (RSA) in plants via large-scale genome-wide association study (GWAS) requires high-throughput pipelines for root phenotyping. We developed Core Root Excavation using Compressed-air (CREAMD), a high-throughput pipeline for the cleaning of field-grown roots, and Core Root Feature Extraction (COFE), a semiautomated pipeline for the extraction of RSA traits from images. CREAMD-COFE was applied to diversity panels of maize (Zea mays) and sorghum (Sorghum bicolor), which consisted of 369 and 294 genotypes, respectively. Six RSA-traits were extracted from images collected from >3,300 maize roots and >1,470 sorghum roots. Single nucleotide polymorphism (SNP)-based GWAS identified 87 TAS (trait-associated SNPs) in maize, representing 77 genes and 115 TAS in sorghum. An additional 62 RSA-associated maize genes were identified via expression read depth GWAS. Among the 139 maize RSA-associated genes (or their homologs), 22 (16%) are known to affect RSA in maize or other species. In addition, 26 RSA-associated genes are coregulated with genes previously shown to affect RSA and 51 (37% of RSA-associated genes) are themselves transe-quantitative trait locus for another RSA-associated gene. Finally, the finding that RSA-associated genes from maize and sorghum included seven pairs of syntenic genes demonstrates the conservation of regulation of morphology across taxa.

High-throughput analysis of leaf physiological and chemical traits with VIS-NIR-SWIR spectroscopy: a case study with a maize diversity panel.

BACKGROUND: Hyperspectral reflectance data in the visible, near infrared and shortwave infrared range (VIS-NIR-SWIR, 400-2500 nm) are commonly used to nondestructively measure plant leaf properties. We investigated the usefulness of VIS-NIR-SWIR as a high-throughput tool to measure six leaf properties of maize plants including chlorophyll content (CHL), leaf water content (LWC), specific leaf area (SLA), nitrogen (N), phosphorus (P), and potassium (K). This assessment was performed using the lines of the maize diversity panel. Data were collected from plants grown in greenhouse condition, as well as in the field under two nitrogen application regimes. Leaf-level hyperspectral data were collected with a VIS-NIR-SWIR spectroradiometer at tasseling. Two multivariate modeling approaches, partial least squares regression (PLSR) and support vector regression (SVR), were employed to estimate the leaf properties from hyperspectral data. Several common vegetation indices (VIs: GNDVI, RENDVI, and NDWI), which were calculated from hyperspectral data, were also assessed to estimate these leaf properties. RESULTS: Some VIs were able to estimate CHL and N (R2 > 0.68), but failed to estimate the other four leaf properties. Models developed with PLSR and SVR exhibited comparable performance to each other, and provided improved accuracy relative to VI models. CHL were estimated most successfully, with R2 (coefficient of determination) > 0.94 and ratio of performance to deviation (RPD) > 4.0. N was also predicted satisfactorily (R2 > 0.85 and RPD > 2.6). LWC, SLA and K were predicted moderately well, with R2 ranging from 0.54 to 0.70 and RPD from 1.5 to 1.8. The lowest prediction accuracy was for P, with R2 < 0.5 and RPD < 1.4. CONCLUSION: This study showed that VIS-NIR-SWIR reflectance spectroscopy is a promising tool for low-cost, nondestructive, and high-throughput analysis of a number of leaf physiological and biochemical properties. Full-spectrum based modeling approaches (PLSR and SVR) led to more accurate prediction models compared to VI-based methods. We called for the construction of a leaf VIS-NIR-SWIR spectral library that would greatly benefit the plant phenotyping community for the research of plant leaf traits.

Recent polyploidization events in three Saccharum founding species.

The complexity of polyploid Saccharum genomes hindered progress of genome research and crop improvement in sugarcane. To understand their genome structure, transcriptomes of 59 F1 individuals derived from S. officinarumLA Purple and S. robustum Molokai 5829 (2n = 80, x = 10 for both) were sequenced, yielding 11 157 and 8998 SNPs and 83 and 105 linkage groups, respectively. Most markers in each linkage group aligned to single sorghum chromosome. However, 71 interchromosomal rearrangements were detected between sorghum and S. officinarum or S. robustum, and 24 (33.8%) of them were shared between S. officinarum and S. robustum, indicating their occurrence before the speciation event that separated these two species. More than 2000 gene pairs from S. spontaneum, S. officinarum and S. robustum were analysed to estimate their divergence time. Saccharum officinarum and S. robustum diverged about 385 thousand years ago, and the whole-genome duplication events occurred after the speciation event because of shared interchromosomal rearrangements. The ancestor of these two species diverged from S. spontaneum about 769 thousand years ago, and the reduction in basic chromosome number from 10 to 8 in S. spontaneum occurred after the speciation event but before the two rounds of whole-genome duplication. Our results proved that S. officinarum is a legitimate species in its own right and not a selection from S. robustum during the domestication process in the past 10 000 years. Our findings rejected a long-standing hypothesis and clarified the timing of speciation and whole-genome duplication events in Saccharum.

Optimising the identification of causal variants across varying genetic architectures in crops.

Association studies use statistical links between genetic markers and the phenotype variation across many individuals to identify genes controlling variation in the target phenotype. However, this approach, particularly conducted on a genome-wide scale (GWAS), has limited power to identify the genes responsible for variation in traits controlled by complex genetic architectures. In this study, we employ real-world genotype datasets from four crop species with distinct minor allele frequency distributions, population structures and linkage disequilibrium patterns. We demonstrate that different GWAS statistical approaches provide favourable trade-offs between power and accuracy for traits controlled by different types of genetic architectures. FarmCPU provides the most favourable outcomes for moderately complex traits while a Bayesian approach adopted from genomic prediction provides the most favourable outcomes for extremely complex traits. We assert that by estimating the complexity of genetic architectures for target traits and selecting an appropriate statistical approach for the degree of complexity detected, researchers can substantially improve the ability to dissect the genetic factors controlling complex traits such as flowering time, plant height and yield component.

Genotype-Corrector: improved genotype calls for genetic mapping in F2 and RIL populations.

F2 and recombinant inbred lines (RILs) populations are very commonly used in plant genetic mapping studies. Although genome-wide genetic markers like single nucleotide polymorphisms (SNPs) can be readily identified by a wide array of methods, accurate genotype calling remains challenging, especially for heterozygous loci and missing data due to low sequencing coverage per individual. Therefore, we developed Genotype-Corrector, a program that corrects genotype calls and imputes missing data to improve the accuracy of genetic mapping. Genotype-Corrector can be applied in a wide variety of genetic mapping studies that are based on low coverage whole genome sequencing (WGS) or Genotyping-by-Sequencing (GBS) related techniques. Our results show that Genotype-Corrector achieves high accuracy when applied to both synthetic and real genotype data. Compared with using raw or only imputed genotype calls, the linkage groups built by corrected genotype data show much less noise and significant distortions can be corrected. Additionally, Genotype-Corrector compares favorably to the popular imputation software LinkImpute and Beagle in both F2 and RIL populations. Genotype-Corrector is publicly available on GitHub at https://github.com/freemao/Genotype-Corrector .

The pineapple genome and the evolution of CAM photosynthesis.

Pineapple (Ananas comosus (L.) Merr.) is the most economically valuable crop possessing crassulacean acid metabolism (CAM), a photosynthetic carbon assimilation pathway with high water-use efficiency, and the second most important tropical fruit. We sequenced the genomes of pineapple varieties F153 and MD2 and a wild pineapple relative, Ananas bracteatus accession CB5. The pineapple genome has one fewer ancient whole-genome duplication event than sequenced grass genomes and a conserved karyotype with seven chromosomes from before the ρ duplication event. The pineapple lineage has transitioned from C3 photosynthesis to CAM, with CAM-related genes exhibiting a diel expression pattern in photosynthetic tissues. CAM pathway genes were enriched with cis-regulatory elements associated with the regulation of circadian clock genes, providing the first cis-regulatory link between CAM and circadian clock regulation. Pineapple CAM photosynthesis evolved by the reconfiguration of pathways in C3 plants, through the regulatory neofunctionalization of preexisting genes and not through the acquisition of neofunctionalized genes via whole-genome or tandem gene duplication.

ALLMAPS: robust scaffold ordering based on multiple maps.

The ordering and orientation of genomic scaffolds to reconstruct chromosomes is an essential step during de novo genome assembly. Because this process utilizes various mapping techniques that each provides an independent line of evidence, a combination of multiple maps can improve the accuracy of the resulting chromosomal assemblies. We present ALLMAPS, a method capable of computing a scaffold ordering that maximizes colinearity across a collection of maps. ALLMAPS is robust against common mapping errors, and generates sequences that are maximally concordant with the input maps. ALLMAPS is a useful tool in building high-quality genome assemblies. ALLMAPS is available at: https://github.com/tanghaibao/jcvi/wiki/ALLMAPS .