Biology subjects

Schnable, P. S.

Publications and source records attributed to Schnable, P. S..

15 recordsLinked to original sources

bioRxiv2025

Assessing the impact of yield plasticity on hybrid performance in maize

Improving crop resilience in the face of increasingly extreme and unpredictable weather and reduced access to agricultural inputs such as nitrogen fertilizer and water will require an improved understanding of phenotypic plasticity in crops. To understand the roles of different component traits in determining overall plasticity for grain yield, we generated data from a panel of 122 maize (Zea mays) hybrids grown in replicated field trials in 34 environments spanning 700 miles (1126 km) of the U.S. Corn Belt. We observed that the levels of genetic versus environmental control and the relationships between mean parent release year, overall performance, and linear plasticity were trait-dependent across the 18 agronomic and yield components studied. Importantly and unexpectedly, we observed no clear tradeoff between linear plasticity and mean performance and found only rare examples where genotype-by-environment interactions would alter selection decisions based on the environments tested in our dataset. Furthermore, we showed that overall plasticity was repeatable and appears to be under considerable genetic control but that plasticity in response to nitrogen fertilization was not, which may help explain the limited success in breeding for nitrogen use efficiency. Together, these findings improve our understanding of phenotypic plasticity, with implications for maize breeding.

plant biology↗

bioRxiv2024

Maize genetic diversity identifies moisture-dependent root-branch signaling pathways

Plants grow complex root systems to extract unevenly distributed resources from soils. Spatial differences in soil moisture are perceived by root tips leading to the patterning of new root branches towards available water, a process called hydropatterning. Little is known about hydropatterning behavior and its genetic basis in crops plants. Here, we develop an assay to measure hydropatterning in maize and reveal substantial differences between tropical/subtropical and temperate maize breeding germplasm that likely resulted from divergent selection. Genetic dissection of hydropatterning confirmed the regulatory role of auxin and revealed that the gaseous hormone ethylene acts to locally inhibit root branching from air-exposed tissues. These findings demonstrate the crop relevance of hydropatterning and establish its genetic basis.

plant biology↗

bioRxiv2023

Transcriptome brings variations of gene expression, alternative splicing, and structural variations into gene-scale trait dissection in soybean

Genome-wide association study (GWAS) identifies trait-associated loci, but due in part to slow decay of linkage disequilibrium (LD), identifying the causal genes can be a bottleneck. Transcriptome-wide association study (TWAS) addresses this by identifying gene expression-phenotype associations or integrating gene expression quantitative trait loci (eQTLs) with GWAS results. Here, we used self-pollinated soybean as a model to evaluate the application of TWAS in the genetic dissection of traits in plant species that exhibit slow LD decay. The first RNA-Seq analysis of a soybean diversity panel was conducted, which identified the genetic regulation of 29,286 genes. Different TWAS solutions were less affected by LD and robust with source of expression that identified known genes related to traits from different development stages and tissues. A novel gene named pod color L2 was identified via TWAS and functionally validated by genome editing. Our introduction of the new exon proportion feature significantly improves the capture of expression variations resulting from structural variations and alternative splicing. As a result, the genes identified by our TWAS approach exhibited a diverse range of causal variations, including SNP, insertion/deletion, gene fusion, copy number variation, and alternative splicing. Using our TWAS approach, we identified genes associated with flowering time, including both previously known candidates and novel genes that had not been linked to this trait before, providing complementary insights with GWAS. In summary, this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay.

genetics↗

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

Chromosome-level Genome Assembly of a Regenerable Maize Inbred Line A188

The highly embryogenic and transformable maize inbred line A188 is an attractive model for analyzing maize gene function. Here we constructed a chromosome-level genome assembly of A188 using long reads and optical maps. Genome comparison of A188 with the reference line B73 identified pervasive structural variation, including a 1.8 Mb duplication on the Gametophyte factor1 locus for unilateral cross-incompatibility and six inversions of 0.7 Mb or greater. Increased copy number of the gene, carotenoid cleavage dioxygenase 1 (ccd1) in A188 is associated with elevated expression during seed development. High ccd1 expression together with low expression of yellow endosperm 1 (y1) condition reduced carotenoid accumulation, which accounts for the white seed phenotype of A188 that contrasts with the yellow seed of B73 that has high expression of y1 and low expression of the single-copy ccd1. Further, transcriptome and epigenome analyses with the A188 reference genome revealed enhanced expression of defense pathways and altered DNA methylation patterns of embryonic callus.

genomics↗

bioRxiv2020

Automatic Traits Extraction and Fitting for Field High-throughput Phenotyping Systems

High-throughput phenotyping is a modern technology to measure plant traits efficiently and in large scale by imaging systems over the whole growth season. Those images provide rich data for statistical analysis of plant phenotypes. We propose a pipeline to extract and analyze the plant traits for field phenotyping systems. The proposed pipeline include the following main steps: plant segmentation from field images, automatic calculation of plant traits from the segmented images, and functional curve fitting for the extracted traits. To deal with the challenging problem of plant segmentation for field images, we propose a novel approach on image pixel classification by transform domain neural network models, which utilizes plant pixels from greenhouse images to train a segmentation model for field images. Our results show the proposed procedure is able to accurately extract plant heights and is more stable than results from Amazon Turks, who manually measure plant heights from original images.

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

Characterizing allele-by-environment interactions using maize introgression lines

Relatively small genomic introgressions containing quantitative trait loci can have significant impacts on the phenotype of an individual plant. However, the magnitude of phenotypic effects for the same introgression can vary quite substantially in different environments due to allele-by-environment interactions. To study potential patterns of allele-by-environment interactions, fifteen near-isogenic lines (NILs) with >90% B73 genetic background and multiple Mo17 introgressions were grown in 16 different environments. These environments included five geographical locations with multiple planting dates and multiple planting densities. The phenotypic impact of the introgressions was evaluated for up to 26 traits that span different growth stages in each environment to assess allele-by-environment interactions. Results from this study showed that small portions of the genome can drive significant genotype-by-environment interaction across a wide range of vegetative and reproductive traits, and the magnitude of the allele-by-environment interaction varies across traits. Some introgressed segments were more prone to genotype-by-environment interaction than others when evaluating the interaction on a whole plant basis throughout developmental time, indicating variation in phenotypic plasticity throughout the genome. Understanding the profile of allele-by-environment interaction is useful in considerations of how small introgressions of QTL or transgene containing regions might be expected to impact traits in diverse environments.\n\nKey MessageSignificant allele-by-environment interactions are observed for traits throughout development from small introgressed segments of the genome.

plant biology↗

bioRxiv2018

A novel maize gene, glossy6 involved in epicuticular wax deposition and drought tolerance

Epicuticular waxes, long-chain hydrocarbon compounds, form the outermost layer of plant surfaces in most terrestrial plants. The presence of epicuticular waxes protects plants from water loss and other environmental stresses. Cloning and characterization of genes involved in the regulation, biosynthesis, and extracellular transport of epicuticular waxes on to the surface of epidermal cells have revealed the molecular basis of epicuticular wax accumulation. However, intracellular trafficking of synthesized waxes to the plasma membrane for cellular secretion is poorly understood. Here, we characterized a maize glossy (gl6) mutant that exhibited decreased epicuticular wax load, increased cuticle permeability, and reduced seedling drought tolerance relative to wild type. We combined an RNA-sequencing based mapping approach (BSR-Seq) and chromosome walking to identify the gl6 candidate gene, which was confirmed via the analysis of multiple independent mutant alleles. The gl6 gene represents a novel maize glossy gene containing a conserved, but uncharacterized domain. Functional characterization suggests that the GL6 protein may be involved in the intracellular trafficking of epicuticular waxes, opening a door to elucidating the poorly understood process by which epicuticular wax is transported from its site of biosynthesis to the plasma membrane.\n\nSIGNIFICANCE STATEMENTPlant surface waxes provide an essential protective barrier for terrestrial plants. Understanding the composition and physiological functions of surface waxes, as well as the molecular basis underlying wax accumulation on plant surfaces provides opportunities for the genetic optimization of this protective layer. Genetic studies have identified genes involved in wax biosynthesis, extracellular transport, as well as spatial and temporal regulation of wax accumulation. In this study, a maize mutant, gl6 was characterized that exhibited reduced wax load on plant surfaces, increased water losses, and reduced seedling drought tolerance compared to wild type controls. The gl6 gene is a novel gene harboring a conserved domain with an unknown function. Quantification and microscopic observation of wax accumulation as well as subcellular localization of the GL6 protein provided evidence that gl6 may be involved in the intracellular trafficking of waxes, opening a door for studying this necessary yet poorly understood process for wax loading on plant surfaces.

plant biology↗

bioRxiv2018

Development of reduced gluten wheat enabled by determination of the genetic basis of the lys3a low hordein barley mutant

Celiac disease is the most common food-induced enteropathy in humans with a prevalence of approximately 1% world-wide [1]. It is induced by digestion-resistant, proline- and glutamine-rich seed storage proteins, collectively referred to as \"gluten,\" found in wheat. Related prolamins are present in barley and rye. Both celiac disease and a related condition called non-celiac gluten sensitivity (NCGS) are increasing in incidence [2] [3]. This has prompted efforts to identify methods of lowering gluten in wheat, one of the most important cereal crops. Here we used BSR-seq (Bulked Segregant RNA-seq) and map-based cloning to identify the genetic lesion underlying a recessive, low prolamin mutation (lys3a) in diploid barley. We confirmed the mutant identity by complementing the lys3a mutant with a transgenic copy of the wild type barley gene and then used TILLING (Targeting Induced Local Lesions in Genomes) [4] to identify induced SNPs (Single Nucleotide Polymorphisms) in the three homoeologs of the corresponding wheat gene. Combining inactivating mutations in the three sub-genomes of hexaploid bread wheat in a single wheat line lowered gliadin and low molecular weight glutenin accumulation by 50-60% and increased free and protein-bound lysine by 33%. This is the first report of the combination of mutations in homoeologs of a single gene that reduces gluten in wheat.

plant biology↗

bioRxiv2017

FarmCPUpp: Efficient Large-Scale GWAS

Genome-wide association studies (GWAS) are computationally demanding analyses that use large sample sizes and dense marker sets to discover associations between quantitative trait variation and genetic variants. FarmCPU is a powerful new method for performing GWAS. However, its performance is hampered by details of its implementation and its reliance on the R programming language. In this paper we present an efficient implementation of FarmCPU, called FarmCPUpp, that retains the R user interface but improves memory management and speed through the use of C++ code and parallel computing.

bioinformatics↗

bioRxiv2017

Toward A Scalable Exploratory Framework for Complex High-Dimensional Phenomics Data

Phenomics is an emerging branch of modern biology that uses high throughput phenotyping tools to capture multiple environmental and phenotypic traits, often at massive spatial and temporal scales. The resulting high dimensional data represent a treasure trove of information for providing an in-depth understanding of how multiple factors interact and contribute to the overall growth and behavior of different genotypes. However, computational tools that can parse through such complex data and aid in extracting plausible hypotheses are currently lacking. In this paper, we present Hyppo-X, a new algorithmic approach to visually explore complex phenomics data and in the process characterize the role of environment on phenotypic traits. We model the problem as one of unsupervised structure discovery, and use emerging principles from algebraic topology and graph theory for discovering higher-order structures of complex phenomics data. We present an open source software which has interactive visualization capabilities to facilitate data navigation and hypothesis formulation. We test and evaluate Hyppo-X on two real-world plant (maize) data sets. Our results demonstrate the ability of our approach to delineate divergent subpopulation-level behavior. Notably, our approach shows how environmental factors could influence phenotypic behavior, and how that effect varies across different genotypes and different time scales. To the best of our knowledge, this effort provides one of the first approaches to systematically formalize the problem of hypothesis extraction for phenomics data. Considering the infancy of the phenomics field, tools that help users explore complex data and extract plausible hypotheses in a data-guided manner will be critical to future advancements in the use of such data.

bioinformatics↗

bioRxiv2017

Genotype-by-Environment Interactions Affecting Heterosis in Maize

The environment can influence heterosis, the phenomena in which the offspring of two inbred parents exhibits phenotypic performance beyond the inbred parents for specific traits. In this study we measured 25 traits in a set of 47 maize hybrids and their inbred parents grown in 16 different environments, and each had varying levels of average productivity. By quantifying 25 vegetative and reproductive traits across the life cycle we were able to analyze interactions between the environment and multiple distinct instances of heterosis. The magnitude and rank among hybrids of better-parent heterosis (BPH) varied for the different traits and environments. Across the traits, a higher within plot variance was observed for inbred lines compared to hybrids. However, for most traits, variance across environments was not significantly different for inbred lines compared to hybrids. Further, for many traits the correlations of BPH to hybrid performance and BPH to better parent performance were of comparable magnitude. These results indicate that inbreds and hybrids are showing similar trends in environmental response and are both contribute to genotype-by-environment interactions for heterosis. This study highlights that degree of heterosis is not an inherent trait of a specific hybrid, but varies depending on the trait measured and the environment where that trait is measured. Studies that attempt to correlate molecular processes with heterosis are hindered by the fact that heterosis is not a consistent attribute of a specific hybrid.

genetics↗

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↗

bioRxiv2017

Transposons modulate transcriptomic and phenotypic variation via the formation of circular RNAs in maize

Circular RNAs (circRNAs) are covalently closed, single-stranded RNA molecules. Recent studies in human showed that circRNAs can arise via transcription of reverse complementary pairs of transposons. Given the prevalence of transposons in the maize genome and dramatic genomic variation driven by transposons, we hypothesize that transposons in maize may be involved in the formation of circRNAs and further modulate phenotypic variation. To test our hypothesis, we performed circRNA-Seq on B73 seedling leaves and integrate these data with 977 publicly available mRNA-Seq datasets. We uncovered 1,551 high-confidence maize circRNAs, which show distinct genomic features as compared to linear transcripts. Comprehensive analyses demonstrated that LINE1-like elements (LLE) and their Reverse Complementary Pairs (LLERCPs) are significantly enriched in the flanking regions of circRNAs. Interestingly, the accumulation of circRNA transcripts increases, while the accumulation of linear transcripts decreases as the number of LLERCPs increases. Furthermore, genes with LLERCP-mediated circRNAs are enriched among loci that are associated with phenotypic variation. These results suggest that LLERCPs can modulate phenotypic variation by the formation of circRNAs. As a proof of concept, we showed that the presence/absence variation of LLERCPs could result in expression variation of one cicrRNA, circ352, and further related to plant height through the interaction between circRNA and functional linear transcript. Our first glimpse of circRNAs uncovers a new role for transposons in the modulation of transcriptomic and phenotypic variation via the formation of circRNAs.

genomics↗