Biology subjects

Schnable, J. C.

Publications and source records attributed to Schnable, J. C..

54 records · Page 3Linked to original sources

bioRxiv2021

The genome of stress tolerant crop wild relativePaspalum vaginatum leads to increased biomassproductivity in the crop Zea mays

A number of crop wild relatives can tolerate extreme stressed to a degree outside the range observed in their domesticated relatives. However, it is unclear whether or how the molecular mechanisms employed by these species can be translated to domesticated crops. Paspalum Paspalum vaginatum is a self-incompatible and multiply stress-tolerant wild relative of maize and sorghum. Here we describe the sequencing and pseudomolecule level assembly of a vegetatively propagated accession of P. vaginatum. Phylogenetic analysis based on 6,151 single-copy syntenic orthologous conserved in 6 related grass species placed paspalum as an outgroup of the maize-sorghum clade demonstrating paspalum as their closest sequenced wild relative. In parallel metabolic experiments, paspalum, but neither maize nor sorghum, exhibited significant increases in trehalose when grown under nutrient-deficit conditions. Inducing trehalose accumulation in maize, imitating the metabolic phenotype of paspalum, resulting in autophagy dependent increases in biomass accumulation.

plant biology↗

bioRxiv2021

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.

plant biology↗

bioRxiv2021

A UAV-based high-throughput phenotyping approach to assess time-series nitrogen responses and identify traits associated genetic components in maize

Advancements in the use of genome-wide markers have provided new opportunities for dissecting the genetic components that control phenotypic trait variation. However, cost-effectively characterizing agronomically important phenotypic traits on a large scale remains a bottleneck. Unmanned aerial vehicle (UAV)-based high-throughput phenotyping has recently become a prominent method, as it allows large numbers of plants to be analyzed in a time-series manner. In this experiment, 233 inbred lines from the maize diversity panel were grown in a replicated incomplete block under both nitrogen-limited conditions and following conventional agronomic practices. UAV images were collected during different plant developmental stages throughout the growing season. A pipeline for extracting plot-level images, filtering images to remove non-foliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed. After applying the pipeline, about half a million plot-level image clips were obtained for 12 different time points. High correlations were detected between VIs and ground truth physiological and yield-related traits collected from the same plots, i.e., Vegetative Index (VEG) vs. leaf nitrogen levels (Pearson correlation coefficient, R = 0.73), Woebbecke index vs. leaf area (R = -0.52), and Visible Atmospherically Resistant Index (VARI) vs. 20 kernel weight - a yield component trait (R = 0.40). The genome-wide association study was performed using canopy coverage and each of the VIs at each date, resulting in N = 29 unique genomic regions associated with image extracted traits from three or more of the 12 total time points. A candidate gene Zm00001d031997, a maize homolog of the Arabidopsis HCF244 (high chlorophyll fluorescence 244), located underneath the leading SNPs of the canopy coverage associated signals that were repeatedly detected under both nitrogen conditions. The plot-level time-series phenotypic data and the trait-associated genes provide great opportunities to advance plant science and to facilitate plant breeding.

plant biology↗

bioRxiv2021

Quantitative resistance loci to southern rust mapped in a temperate maize diversity panel

Southern rust is a severe foliar disease of maize resulting from infection with the obligate biotrophic fungus, Puccinia polysora. The disease reduces photosynthetic productivity which reduces yields with the greatest yield losses (up to 50%) associated with earlier onset infections. Puccinia polysora urediniospores overwinter only in tropical and subtropical regions but cause outbreaks when environmental conditions favor initial infection. Increased temperatures and humidity during the growing season, combined with an increased frequency of moderate winters are likely to increase the frequency of severe southern rust outbreaks in the US corn belt. In summer 2020, a severe outbreak of Southern Rust was observed in eastern Nebraska (NE), USA. Disease incidence severity showed significant variation among maize genotypes. A replicated maize association panel planted in Lincoln, NE was scored for disease severity. Genome wide association studies identified four loci associated with significant quantitative variation in disease severity which were associated with candidate genes with plausible links to quantitative disease resistance and a transcriptome wide association study conducted identified additional associated genes. Together these results indicate substantial diversity in resistance to southern rust exists among current temperate adapted maize germplasm, including several candidate loci which may explain observed variation in resistance to southern rust.

plant biology↗

bioRxiv2020

Automation of Leaf Counting in Maize and Sorghum Using Deep Learning

Leaf number and leaf emergence rate are phenotypes of interest to plant breeders, plant geneticists, and crop modelers. Counting the extant leaves of an individual plant is straightforward even for an untrained individual, but manually tracking changes in leaf numbers for hundreds of individuals across multiple time points is logistically challenging. This study generated a dataset including over 150,000 maize and sorghum images for leaf counting projects. A subset of 17,783 images also includes annotations of the positions of individual leaf tips. With these annotated images, we evaluate two deep learning-based approaches for automated leaf counting: the first based on counting-by-regression from whole image analysis and a second based on counting-by-detection. Both approaches can achieve RMSE (root of mean square error) smaller than one leaf, only moderately inferior to the RMSE between human annotators of between 0.57 and 0.73 leaves. The counting-by-regression approach based on CNNs (convolutional neural networks) exhibited lower accuracy and increased bias for plants with extreme leaf numbers which are underrepresented in this dataset. The counting-by-detection approach based on Faster R-CNN object detection models achieve near human performance for plants where all leaf tips are visible. The annotated image data and model performance metrics generated as part of this study provide large scale resources for the comparison and improvement of algorithms for leaf counting from image data in grain crops.

plant biology↗

bioRxiv2020

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 both pleiotropy and genotype by environment interactions. 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 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.

plant biology↗

bioRxiv2020

Rhizosphere Microbiomes in a Historical Maize/Soybean Rotation System respond to Host Species and Nitrogen Fertilization at Genus and Sub-genus Levels

Root associated microbes are key players in plant health, disease resistance, and nitrogen (N) use efficiency. It remains largely unclear how the interplay of biological and environmental factors affects rhizobiome dynamics in agricultural systems. Here, we quantified the composition of rhizosphere and bulk soil microbial communities associated with maize (Zea mays L.) and soybean (Glycine max L.) in a long-term crop rotation study under conventional fertilization and low N regimes. Over two growing seasons, we evaluated the effects of environmental conditions and several treatment factors on the abundance of rhizosphere and soil colonizing microbial taxa. Time of sampling, host plant species and N fertilization had major effects on microbiomes, while no effect of crop rotation was observed. Using variance partitioning as well as 16S sequence information, we further defined a set of 82 microbial genera and sub-genus groups that show distinct responses to treatment factors. We identified taxa that are highly specific to either maize or soybean rhizospheres, as well as taxa that are sensitive to N fertilization in plant rhizospheres and bulk soil. This study provides insights to harness the full potential of soil microbes in maize and soybean agricultural systems through plant breeding and field management.

plant biology↗

bioRxiv2020

Functional principal component based 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 phenotypic change over time. Identifying genetic loci regulating differences in the pattern of phenotypic change remains challenging. In this study we used functional principal component analysis (FPCA) to achieve this aim. Time-series phenotype data was collected from a sorghum diversity panel using a number of technologies including RGB and hyperspectral imaging. Imaging lasted for thirty-seven days centered on reproductive transition. A new higher density SNP set was generated for the same population. Several genes known to controlling trait variation in sorghum have been 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 phenotype. Partitioning was consistent with the known molecular function of the individual cloned genes. FPCA-based genome-wide association studies can enable robust time-series mapping analyses in a wide range of contexts. Time-series analysis can increase the accuracy and power of quantitative genetic analyses.

plant biology↗

bioRxiv2019

Using Machine Learning and Gene Nonhomology Features to Predict Gene Ontology

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random forest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations.

bioinformatics↗

bioRxiv2019

Simulated Plant Images Improve Maize Leaf Counting Accuracy

Automatically scoring plant traits using a combination of imaging and deep learning holds promise to accelerate data collection, scientific inquiry, and breeding progress. However, applications of this approach are currently held back by the availability of large and suitably annotated training datasets. Early training datasets targeted arabidopsis or tobacco. The morphology of these plants quite different from that of grass species like maize. Two sets of maize training data, one real-world and one synthetic were generated and annotated for late vegetative stage maize plants using leaf count as a model trait. Convolutional neural networks (CNNs) trained on entirely synthetic data provided predictive power for scoring leaf number in real-world images. This power was less than CNNs trained with equal numbers of real-world images, however, in some cases CNNs trained with larger numbers of synthetic images outperformed CNNs trained with smaller numbers of real-world images. When real-world training images were scarce, augmenting real-world training data with synthetic data provided improved prediction accuracy. Quantifying leaf number over time can provide insight into plant growth rates and stress responses, and can help to parameterize crop growth models. The approaches and annotated training data described here may help future efforts to develop accurate leaf counting algorithms for maize.

plant biology↗

bioRxiv2019

Genome wide analysis of Ga1-s modifiers in maize

A one way reproductive barrier exists between most popcorn varieties and dent corn varieties grown in the United States. This barrier is predominantly controlled by the ga1 locus. Using data from a diverse population of popcorn accessions pollinated by a dent corn tester, we found that the non-reciprocal pollination barrier conferred by ga1 is more complex than previously described. Individual accessions ranged from 0% to 100% compatible with dent corn pollen. Using conventional genotyping-by-sequencing data from 371 popcorn accessions carrying Ga1-s, seven significant modifiers of dent pollen compatibility were identified on five chromosomes. One locus may either be a nonfunctional ga1 allele present within popcorn, or second necessary gene for the reproductive barrier in genetic linkage with ga1, while the other modifiers are clearly genetically unlinked. The existence of ga1 modifiers segregating in a popcorn genetic background may indicate selective pressure to allow gene flow between populations, which should be incorporated into future models of the impact of genetic incompatibility loci on gene flow in natural and agricultural plant populations.

plant biology↗

bioRxiv2018

Optimizing the identification of causal variants across varying genetic architectures in crops

BackgroundAssociation studies use statistical links between genetic markers and variation in a phenotypes value 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.\n\nResultsHere we employ simulation studies utilizing real-world genotype datasets from association populations in four species with distinct minor allele frequency distributions, population structures, and patterns linkage disequilibrium to evaluate the impact of variation in both heritability and trait complexity on both conventional mixed linear model based GWAS and two new approaches specifically developed for complex traits. Mixed linear model based GWAS rapidly losses power for more complex traits. FarmCPU, a method based on multi-locus mixed linear models, provides the greatest statistical power for moderately complex traits. A Bayesian approach adopted from genomic prediction provides the greatest statistical power to identify causal genetic loci for extremely complex traits.\n\nConclusionsUsing estimates of the complexity of the genetic architecture of target traits can guide the selection of appropriate statistical methods and improve the overall accuracy and power of GWAS.

genomics↗

bioRxiv2017

Integrating phylogenetic and network approaches to study gene family evolution: the case of the AGAMOUS family of floral genes

The study of gene family evolution has benefited from the use of phylogenetic tools, which can greatly inform studies of both relationships within gene families and functional divergence. Here, we propose the use of a network-based approach that in combination with phylogenetic methods can provide additional support for models of gene family evolution. We dissect the contributions of each method to the improved understanding of relationships and functions within the well-characterized family of AGAMOUS floral development genes. The results obtained with the two methods largely agreed with one another. In particular, we show how network approaches can provide improved interpretations of branches with low support in a conventional gene tree. The network approach used here may also better reflect known and suspected patterns of functional divergence relative to phylogenetic methods. Overall, we believe that the combined use of phylogenetic and network tools provide more robust assessments of gene family evolution.

evolutionary biology↗

bioRxiv2017

Parallels between artificial selection in temperate maize and natural selection in the cold-adapted crop-wild relative Tripsacum

Artificial selection has produced varieties of domesticated maize which thrive in temperate climates around the world. However, the direct progenitor of maize, teosinte, is indigenous only to a relatively small range of tropical and sub-tropical latitudes and grows poorly or not at all outside of this region. Tripsacum, a sister genus to maize and teosinte, is naturally endemic to the majority of areas in the western hemisphere where maize is cultivated. A full-length reference transcriptome for Tripsacum dactyloides generated using long-read isoseq data was used to characterize independent adaptation to temperate climates in this clade. Genes related to phospholipid biosynthesis, a critical component of cold acclimation on other cold adapted plant lineages, were enriched among those genes experiencing more rapid rates of protein sequence evolution in T. dactyloides. In contrast with previous studies of parallel selection, we find that there is a significant overlap between the genes which were targets of artificial selection during the adaptation of maize to temperate climates and those which were targets of natural selection in temperate adapted T. dactyloides. This overlap between the targets of natural and artificial selection suggests genetic changes in crop-wild relatives associated with adaptation to new environments may be useful guides for identifying genetic targets for breeding efforts aimed at adapting crops to a changing climate.

plant biology↗

bioRxiv2017

Conventional and hyperspectral time-series imagingof maize lines widely used in field trials

Maize (Zea mays ssp. mays) is one of three crops, along with rice and wheat, responsible for more than 1/2 of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenotyping is currently the largest constraint on plant breeding efforts. Datasets linking new types of high throughput phenotyping data collected from plants to the performance of the same genotypes under agronomic conditions across a wide range of environments are essential for developing new statistical approaches and computer vision based tools. A set of maize inbreds - primarily recently off patent lines - were phenotyped using a high throughput platform at University of Nebraska-Lincoln. These lines have been previously subjected to high density genotyping, and scored for a core set of 13 phenotypes in field trials across 13 North American states in two years by the Genomes to Fields consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence and thermal infrared photos has been released. Correlations between image-based measurements and manual measurements demonstrated the feasibility of quantifying variation in plant architecture using image data. However, naive approaches to measuring traits such as biomass can introduce nonrandom measurement errors confounded with genotype variation. Analysis of hyperspectral image data demonstrated unique signatures from stem tissue. Integrating heritable phenotypes from high-throughput phenotyping data with field data from different environments can reveal previously unknown factors influencing yield plasticity.

plant biology↗

bioRxiv2017

Differentially regulated ortholog analysis demonstrations that early transcriptional responses to cold have diverged slowly than downstream responses in panicoid grasses

Cross-species comparisons of transcriptional regulation have the potential to identify functionally constrained transcriptional regulation and genes for which a change in transcriptional regulation correlates with a change in phenotype. Conventional differential gene expression analysis and a different approach based on identifying differentially regulated orthologs (DROs) are compared using paired time course gene expression data from two species which respond similarly to cold - maize (Zea mays) and sorghum (Sorghum bicolor). Both approaches suggest that, for genes conserved at syntenic positions for millions of years, the majority of cold responsive transcriptional regulation is species specific, although initial transcriptional responses to cold appear to be more conserved between the two species than later responses. In maize, the promoters of genes with both species specific and conserved transcriptional responses to cold tend to contain more micrococcal nuclease hypersensitive sites in their promoters, a proxy for open chromatin. However, genes with conserved patterns of transcriptional regulation between the two species show lower ratios of nonsynonymous to synonymous substitutions consistent with this population of genes experiencing stronger purifying selection. We hypothesize that cold responsive transcriptional regulation is a fast evolving and largely neutral molecular phenotype for the majority of genes in Andropogoneae, while a smaller core set of genes involved in perceiving and responding to cold stress are subject to functionally constrained cold responsive regulation.

genetics↗

bioRxiv2017

Evolutionarily Conserved Alternative Splicing Across Monocots

One difficulty when identifying and analyzing alternative splicing (AS) events in plants is distinguishing functional AS from splicing noise. One way to add confidence to the validity of a splice isoform is to observe that it is conserved across evolutionarily related species. We use a high throughput method to identify junction based conserved AS events from RNA-Seq data across nine plant species including: five grass monocots (maize, sorghum, rice, Brachpodium and foxtail millet), plus two non-grass monocots (bananan and African oil palm), the eudicot Arabidopsis and the basal angiosperm Amborella. In total, 9,804 conserved AS events within 19,235 genes were identified conserved between 2 or more species studied. In grasses containing large regions of conserved synteny, the frequency of conserved AS events is twice that observed for genes outside of conserved synteny blocks. In plant-specific RS and RS2Z subfamilies, we observe both conservation and divergence of AS events after the whole genome duplication in maize. In addition, plant-specific RS and RS2Z subfamilies are highly connected with R2R3-MYB in splicing networks. Furthermore, we discovered that the network based on genes harboring conserved AS events is enriched for phosphatases, kinases and ubiquitylation genes, which suggests that AS may participate in regulating signaling pathways. These data lay the foundation for identifying and studying conserved AS events in the monocots, particularly across grass species, and this conserved AS resource identifies an additional layer between genotype to phenotype that may impact future crop improvement efforts.

bioinformatics↗

bioRxiv2017

Tunable Genotyping-By-Sequencing (tGBS(R)) Enables Reliable Genotyping of Heterozygous Loci

Most Genotyping-by-Sequencing (GBS) strategies suffer from high rates of missing data and high error rates, particularly at heterozygous sites. Tunable genotyping-by-sequencing (tGBS(R)), a novel genome reduction method, consists of the ligation of single-strand oligos to restriction enzyme fragments. DNA barcodes are added during PCR amplification; additional (selective) nucleotides included at the 3-end of the PCR primers result in more genome reduction as compared to conventional GBS methods. By adjusting the number of selective bases different numbers of genomic sites can be targeted for sequencing. Because this genome reduction strategy concentrates sequencing reads on fewer sites, SNP calls are based on more reads than conventional GBS, resulting in higher SNP calling accuracy (>97-99%) even for heterozygous sites and less missing data per marker. tGBS genotyping is expected to be particularly useful for genomic selection, which requires the ability to genotype populations of individuals that are heterozygous at many loci.

genomics↗