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Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive, and models show poor transferability among different datasets. This study had three specific objectives: first, to assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum; second, to evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur); and third, to investigate the usefulness of this spectral library for predicting external datasets (n=445) including soybean and camelina using extra-weighted spiking. Internal cross-validation showed satisfactory performance of the spectral library to estimate all nine traits (mean R2=0.688), with partial least-squares regression outperforming deep neural network models. Models calibrated solely using the spectral library showed degraded performance on external datasets (mean R2=0.159 for camelina, 0.337 for soybean). Models improved significantly when a small portion of external samples (n=20) was added to the library via extra-weighted spiking (mean R2=0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping, whilst extra-weight spiking improves model transferability and extends its utility.

Motivation Over the last decade, RNA-Seq whole-genome sequencing has become a widely used method for measuring and understanding transcriptome-level changes in gene expression. Since RNA-Seq is relatively inexpensive, it can be used on multiple genomes to evaluate gene expression across many different conditions, tissues and cell types. Although many tools exist to map and compare RNA-Seq at the genomics level, few web-based tools are dedicated to making data generated for individual genomic analysis accessible and reusable at a gene-level scale for comparative analysis between genes, across different genomes and meta-analyses. Results To address this challenge, we revamped the comparative gene expression tool qTeller to take advantage of the growing number of public RNA-Seq datasets. qTeller allows users to evaluate gene expression data in a defined genomic interval and also perform two-gene comparisons across multiple user-chosen tissues. Though previously unpublished, qTeller has been cited extensively in the scientific literature, demonstrating its importance to researchers. Our new version of qTeller now supports multiple genomes for intergenomic comparisons, and includes capabilities for both mRNA and protein abundance datasets. Other new features include support for additional data formats, modernized interface and back-end database and an optimized framework for adoption by other organisms’ databases. Availability and implementation The source code for qTeller is open-source and available through GitHub (https://github.com/Maize-Genetics-and-Genomics-Database/qTeller). A maize instance of qTeller is available at the Maize Genetics and Genomics database (MaizeGDB) (https://qteller.maizegdb.org/), where we have mapped over 200 unique datasets from GenBank across 27 maize genomes. Supplementary information Supplementary data are available at Bioinformatics online.

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.

Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data—18M markers—from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction.

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.

Malate transport shuttles atmospheric carbon into the Calvin–Benson cycle during NADP-ME C4 photosynthesis. Previous characterizations of several plant dicarboxylate transporters (DCT) showed that they efficiently exchange malate across membranes. Here, we identify and characterize a previously unknown member of the DCT family, DCT4, in Sorghum bicolor. We show that SbDCT4 exchanges malate across membranes and its expression pattern is consistent with a role in malate transport during C4 photosynthesis. SbDCT4 is not syntenic to the characterized photosynthetic gene ZmDCT2, and an ortholog is not detectable in the maize reference genome. We found that the expression patterns of DCT family genes in the leaves of Zea mays, and S. bicolor varied by cell type. Our results suggest that subfunctionalization, of members of the DCT family, for the transport of malate into the bundle sheath plastids, occurred during the process of independent recurrent evolution of C4 photosynthesis in grasses of the PACMAD clade. We also show that this subfunctionalization is lineage independent. Our results challenge the dogma that key C4 genes must be orthologues of one another among C4 species, and shed new light on the evolution of C4 photosynthesis.

High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

Pearl millet is a non-model grain and fodder crop adapted to extremely hot and dry environments globally. In India, a great deal of public and private sectors’ investment has focused on developing pearl millet single cross hybrids based on the cytoplasmic-genetic male sterility (CMS) system, while in Africa most pearl millet production relies on open pollinated varieties. Pearl millet lines were phenotyped for both the inbred parents and hybrids stage. Many breeding efforts focus on phenotypic selection of inbred parents to generate improved parental lines and hybrids. This study evaluated two genotyping techniques and four genomic selection schemes in pearl millet. Despite the fact that 6× more sequencing data were generated per sample for RAD-seq than for tGBS, tGBS yielded more than 2× as many informative SNPs (defined as those having MAF > 0.05) than RAD-seq. A genomic prediction scheme utilizing only data from hybrids generated prediction accuracies (median) ranging from 0.73-0.74 (1000-grain weight), 0.87-0.89 (days to flowering time), 0.48-0.51 (grain yield) and 0.72-0.73 (plant height). For traits with little to no heterosis, hybrid only and hybrid/inbred prediction schemes performed almost equivalently. For traits with significant mid-parent heterosis, the direct inclusion of phenotypic data from inbred lines significantly (P < 0.05) reduced prediction accuracy when all lines were analyzed together. However, when inbreds and hybrid trait values were both scored relative to the mean trait values for the respective populations, the inclusion of inbred phenotypic datasets moderately improved genomic predictions of the hybrid genomic estimated breeding values. Here we show that modern approaches to genotyping by sequencing can enable genomic selection in pearl millet. While historical pearl millet breeding records include a wealth of phenotypic data from inbred lines, we demonstrate that the naive incorporation of this data into a hybrid breeding program can reduce prediction accuracy, while controlling for the effects of heterosis per se allowed inbred genotype and trait data to improve the accuracy of genomic estimated breeding values for pearl millet hybrids.

Highlight A new genome wide scan method using signals of positive selection identifies C4 candidate genes in the grasses independent of a priori knowledge of C4 biochemistry., C4 photosynthesis is perhaps one of the best examples of convergent adaptive evolution with over 25 independent origins in the grasses (Poaceae) alone. The availability of high quality grass genome sequences presents new opportunities to explore the mechanisms underlying this complex trait using evolutionary biology-based approaches. In this study, we performed genome-wide cross-species selection scans in C4 lineages to facilitate discovery of C4 genes. The study was enabled by the well conserved collinearity of grass genomes and the recently sequenced genome of a C3 panicoid grass, Dichanthelium oligosanthes. This method, in contrast to previous studies, does not rely on any a priori knowledge of the genes that contribute to biochemical or anatomical innovations associated with C4 photosynthesis. We identified a list of 88 candidate genes that include both known and potentially novel components of the C4 pathway. This set includes the carbon shuttle enzymes pyruvate, phosphate dikinase, phosphoenolpyruvate carboxylase and NADP malic enzyme as well as several predicted transporter proteins that likely play an essential role in promoting the flux of metabolites between the bundle sheath and mesophyll cells. Importantly, this approach demonstrates the application of fundamental molecular evolution principles to dissect the genetic basis of a complex photosynthetic adaptation in plants. Furthermore, we demonstrate how the output of the selection scans can be combined with expression data to provide additional power to prioritize candidate gene lists and suggest novel opportunities for pathway engineering.

Highlight The C-terminus of SiAGO1b is an essential motif for the interaction between SiAGO1b and SiHYL1, and plays a key role in regulating growth, development and stress responses in Setaria italic., Foxtail millet (Setaria italica (L.) P. Beauv), which belongs to the Panicoideae tribe of the Poaceae, is an important grain crop widely grown in Northern China and India. It is currently developing into a novel model species for functional genomics of the Panicoideae as a result of its fully available reference genome sequence, small diploid genome (2n=18, ~510Mb), short life cycle, small stature and prolific seed production. Argonaute 1 (AGO1), belonging to the argonaute (AGO) protein family, recruits small RNAs and regulates plant growth and development. Here, we characterized an AGO1 mutant (siago1b) in foxtail millet, which was induced by ethyl methanesulfonate treatment. The mutant exhibited pleiotropic developmental defects, including dwarfing stem, narrow and rolled leaves, smaller panicles and lower rates of seed setting. Map-based cloning analysis demonstrated that these phenotypic variations were attributed to a C–A transversion, and a 7-bp deletion in the C-terminus of the SiAGO1b gene in siago1b. Yeast two-hybrid assays and BiFC experiments revealed that the mutated region was an essential functional motif for the interaction between SiAGO1b and SiHYL1. Furthermore, 1598 differentially expressed genes were detected via RNA-seq-based comparison of SiAGO1b and wild-type plants, which revealed that SiAGO1b mutation influenced multiple biological processes, including energy metabolism, cell growth, programmed death and abiotic stress responses in foxtail millet. This study may provide a better understanding of the mechanisms by which SiAGO1b regulates the growth and development of crops.

The identification of conserved syntenic regions enables discovery of predicted locations for orthologous and homeologous genes, even when no such gene is present. This capability means that synteny-based methods are far more effective than sequence similarity-based methods in identifying true-negatives, a necessity for studying gene loss and gene transposition. However, the identification of syntenic regions requires complex analyses which must be repeated for pairwise comparisons between any two species. Therefore, as the number of published genomes increases, there is a growing demand for scalable, simple-to-use applications to perform comparative genomic analyses that cater to both gene family studies and genome-scale studies. We implemented SynFind, a web-based tool that addresses this need. Given one query genome, SynFind is capable of identifying conserved syntenic regions in any set of target genomes. SynFind is capable of reporting per-gene information, useful for researchers studying specific gene families, as well as genome-wide data sets of syntenic gene and predicted gene locations, critical for researchers focused on large-scale genomic analyses. Inference of syntenic homologs provides the basis for correlation of functional changes around genes of interests between related organisms. Deployed on the CoGe online platform, SynFind is connected to the genomic data from over 15,000 organisms from all domains of life as well as supporting multiple releases of the same organism. SynFind makes use of a powerful job execution framework that promises scalability and reproducibility. SynFind can be accessed at http://genomevolution.org/CoGe/SynFind.pl. A video tutorial of SynFind using Phytophthrora as an example is available at http://www.youtube.com/watch?v=2Agczny9Nyc.

Summary Following polyploidy events, genomes undergo massive reduction in gene content through a process known as fractionation. Importantly, the fractionation process is not always random, and a bias as to which homeologous chromosome retains or loses more genes can be observed in some species. The process of characterizing whole genome fractionation requires identifying syntenic regions across genomes followed by post-processing of those syntenic datasets to identify and plot gene retention patterns. We have developed a tool, FractBias, to calculate and visualize gene retention and fractionation patterns across whole genomes. Through integration with SynMap and its parent platform CoGe, assembled genomes are pre-loaded and available for analysis, as well as letting researchers integrate their own data with security options to keep them private or make them publicly available. Availability and Implementation FractBias is freely available as a web application at https://genomevolution.org/CoGe/SynMap.pl. The software is open source (MIT license) and executable with Python 2.7 or iPython notebook, and available on GitHub (https://goo.gl/PaAtqy). Documentation for FractBias is available on CoGepedia (https://goo.gl/ou9dt6) Supplementary information Supplementary data are available at Bioinformatics online.

Maize (Zea mays) displays an exceptional level of structural genomic diversity, which is likely unique among higher eukaryotes. In this study, we surveyed how the genetic divergence of two maize inbred lines affects the transcriptomic landscape in four different primary root tissues of their F1-hybrid progeny. An extreme instance of complementation was frequently observed: genes that were expressed in only one parent but in both reciprocal hybrids. This single-parent expression (SPE) pattern was detected for 2341 genes with up to 1287 SPE patterns per tissue. As a consequence, the number of active genes in hybrids exceeded that of their parents in each tissue by >400. SPE patterns are highly dynamic, as illustrated by their excessive degree of tissue specificity (80%). The biological significance of this type of complementation is underpinned by the observation that a disproportionally high number of SPE genes (75 to 82%) is nonsyntenic, as opposed to all expressed genes (36%). These genes likely evolved after the last whole-genome duplication and are therefore younger than the syntenic genes. In summary, SPE genes shape the remarkable gene expression plasticity between root tissues and complementation in maize hybrids, resulting in a tissue-specific increase of active genes in F1-hybrids compared with their inbred parents.

The genome sequence of the paleohexaploid Brassica rapa shows that fractionation is biased among the three subgenomes and that the least fractionated subgenome has approximately twice as many orthologs as its close (and relatively unduplicated) relative Arabidopsis than had either of the other two subgenomes. One evolutionary scenario is that the two subgenomes with heavy gene losses (I and II) were in the same nucleus for a longer period of time than the third subgenome (III) with the fewest gene losses. This “two-step” hypothesis is essentially the same as that proposed previously for the eudicot paleohexaploidy; however, the more recent nature of the B. rapa paleohexaploidy makes this model more testable. We found that subgenome II suffered recent small deletions within exons more frequently than subgenome I, as would be expected if the genes in subgenome I had already been near maximally fractionated before subgenome III was introduced. We observed that some sequences, before these deletions, were flanked by short direct repeats, a unique signature of intrachromosomal illegitimate recombination. We also found, through simulations, that short—single or two-gene—deletions appear to dominate the fractionation patterns in B. rapa. We conclude that the observed patterns of the triplicated regions in the Brassica genome are best explained by a two-step fractionation model. The triplication and subsequent mode of fractionation could influence the potential to generate morphological diversity—a hallmark of the Brassica genus.