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

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

Association analyses of host genetics, root-colonizing microbes, and plant phenotypes under different nitrogen conditions in maize

The root-associated microbiome (rhizobiome) affects plant health, stress tolerance, and nutrient use efficiency. However, it remains unclear to what extent the composition of the rhizobiome is governed by intraspecific variation in host plant genetics in the field and the degree to which host plant selection can reshape the composition of the rhizobiome. Here, we quantify the rhizosphere microbial communities associated with a replicated diversity panel of 230 maize (Zea mays L.) genotypes grown in agronomically relevant conditions under high N (+N) and low N (-N) treatments. We analyze the maize rhizobiome in terms of 150 abundant and consistently reproducible microbial groups and we show that the abundance of many root-associated microbes is explainable by natural genetic variation in the host plant, with a greater proportion of microbial variance attributable to plant genetic variation in -N conditions. Population genetic approaches identify signatures of purifying selection in the maize genome associated with the abundance of several groups of microbes in the maize rhizobiome. Genome-wide association study was conducted using the abundance of microbial groups as rhizobiome traits, and n=622 plant loci were identified that are linked to the abundance of n=104 microbial groups in the maize rhizosphere. In 62/104 cases, which is more than expected by chance, the abundance of these same microbial groups was correlated with variation in plant vigor indicators derived from high throughput phenotyping of the same field experiment. We provide comprehensive datasets about the three-way interaction of host genetics, microbe abundance, and plant performance under two N treatments to facilitate targeted experiments toward harnessing the full potential of root-associated microbial symbionts in maize production

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

Association mapping across a multitude of traits collected in diverse environments in maize

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

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 CREAMD (Core Root Excavation using Compressed-air), a high-throughput pipeline for the cleaning of field-grown roots, and COFE (Core Root Feature Extraction), a semi-automated 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 \u3e3,300 maize roots and \u3e1,470 sorghum roots. 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 eRD-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 co-regulated with genes previously shown to affect RSA and 51 (37% of RSA-associated genes) are themselves trans-eQTL 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

ramosa1 in the development and evolution of inflorescence architecture in grasses

Branching architecture of the flower-bearing structures in grasses, known as inflorescences, is a morphological trait that is subject to natural and artificial selection since it affects both reproduction and grain yield. Genes underlying this trait are sought to explain the molecular basis underlying the phenotypic diversity of these structures. The ramosa1 (ra1) gene encodes a transcription factor that controls branching architecture in maize inflorescences (tassel and ear). Reduced ra1 activity in maize produces ears with crooked rows of kernels due to the generation of extra spikelets, a phenotype that may have been selected on during the derivation of modern maize. Patterns of nucleotide diversity coupled with statistical tests and phylogenetics suggest a regulatory element at the ra1 locus was a target of artificial selection during maize's domestication from its wild progenitor teosinte. We also narrowed the timeframe for the probable origin of ra1 during the evolution of grasses and found sequence variations in some species correlate with their respective inflorescence architectures. These results suggest this gene was important in the evolution of inflorescence architecture in other grasses, most notably in sorghum where statistical tests show ra1 may have been a target of artificial selection during its evolution, most likely to increase grain yield. Since this gene may have been important during the domestication and cultivation of two crops, maize and sorghum, this research may lead to future breeding projects to increase grain yield in these and other cereal grasses.</p

Setting the Foundation for Experiential Learning and Academic Success in MBIO 101: Introduction to the Microbiology Major

Introductory courses for majors, typically completed by first-year students, are important to student success and retention as they set the foundation for students in their respective majors. At the University of Nebraska-Lincoln, Microbiology majors complete MBIO 101: Introduction to the Microbiology Major during their first semester as their introductory, foundational course. In this course portfolio, I chose to focus on investigating the impact on student learning of integrating more emphasis on experiential learning knowledge and acquisition within the course through the participation of students in a hands-on workshop and research symposium. Integration of these two events into the MBIO 101 curriculum enhanced student confidence in both knowledge and acquisition of experiential learning while not compromising progression towards achievement of the other learning outcomes in the course. Continued inclusion of these activities into MBIO 101 will help set a sound foundation for academic success for Microbiology majors

ramosa1 in the development and evolution of inflorescence architecture in grasses

Branching architecture of the flower-bearing structures in grasses, known as inflorescences, is a morphological trait that is subject to natural and artificial selection since it affects both reproduction and grain yield. Genes underlying this trait are sought to explain the molecular basis underlying the phenotypic diversity of these structures. The ramosa1 (ra1) gene encodes a transcription factor that controls branching architecture in maize inflorescences (tassel and ear). Reduced ra1 activity in maize produces ears with crooked rows of kernels due to the generation of extra spikelets, a phenotype that may have been selected on during the derivation of modern maize. Patterns of nucleotide diversity coupled with statistical tests and phylogenetics suggest a regulatory element at the ra1 locus was a target of artificial selection during maize\u27s domestication from its wild progenitor teosinte. We also narrowed the timeframe for the probable origin of ra1 during the evolution of grasses and found sequence variations in some species correlate with their respective inflorescence architectures. These results suggest this gene was important in the evolution of inflorescence architecture in other grasses, most notably in sorghum where statistical tests show ra1 may have been a target of artificial selection during its evolution, most likely to increase grain yield. Since this gene may have been important during the domestication and cultivation of two crops, maize and sorghum, this research may lead to future breeding projects to increase grain yield in these and other cereal grasses

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

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

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 CREAMD (Core Root Excavation using Compressed-air), a high-throughput pipeline for the cleaning of field-grown roots, and COFE (Core Root Feature Extraction), a semi-automated 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. 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 eRD-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 co-regulated with genes previously shown to affect RSA and 51 (37% of RSA-associated genes) are themselves trans-eQTL 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.This is a manuscript of an article published as Zheng, Zihao, Stefan Hey, Talukder Jubery, Huyu Liu, Yu Yang, Lisa Coffey, Chenyong Miao et al. "Shared genetic control of root system architecture between Zea mays and Sorghum bicolor." Plant Physiology (2019). DOI: 10.1104/pp.19.00752. Posted with permission.</p