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• Hyperspectral flatbed scanner can accurately and quickly characterize maize seeds. • Whole intact kernels are scanned without any sample preparation. • Compositional and morphometric traits are predicted with single kernel resolution. • Hyperspectral imaging allows kernel side-specific prediction models. Large-scale investigations of maize kernel traits important to researchers, breeders, and processors require high throughput methods, which are presently lacking. To address this bottleneck, we developed a novel flatbed platform that automatically acquires and analyzes multiwavelength near-infrared (NIR hyperspectral) images of maize kernels precisely enough to support robust predictions of protein content, density, and endosperm vitreousness. The upward facing-camera design and the automated ability to analyze the embryo or abgerminal sides of each individual kernel in a sample with the appropriate side-specific model helped to produce a superior combination of throughput and prediction accuracy compared to other single-kernel platforms. Protein was predicted to within 0.85% (root mean square error of prediction), density to within 0.038 g/cm3, and endosperm vitreousness percentage to within 6.3%. Kernel length and width were also accurately measured so that each kernel in a rapidly scanned sample was comprehensively characterized. [ABSTRACT FROM AUTHOR]

Seed size is a component of grain yield and an important trait in crop domestication. To understand the mechanisms governing seed size in maize (Zea mays), we examined transcriptional and developmental changes during seed development in populations divergently selected for large and small seed size from Krug, a yellow dent maize cultivar. After 30 cycles of selection, seeds of the large seed population (KLS30) have a 4.7-fold greater weight and a 2.6-fold larger size compared with the small seed population (KSS30). Patterns of seed weight accumulation from the time of pollination through 30 d of grain filling showed an earlier onset, slower rate, and earlier termination of grain filling in KSS30 relative to KLS30. This was further supported by transcriptome patterns in seeds from the populations and derived inbreds. Although the onset of key genes was earlier in small seeds, similar maximum transcription levels were observed in large seeds at later stages, suggesting that functionally weaker alleles, rather than transcript abundance, may be the basis of the slow rate of seed filling in KSS30. Gene coexpression networks identified several known genes controlling cellularization and proliferation as well as novel genes that will be useful candidates for biotechnological approaches aimed at altering seed size in maize and other cereals. [ABSTRACT FROM AUTHOR]

Genomes at the species level are dynamic, with genes present in every individual (core) and genes in a subset of individuals (dispensable) that collectively constitute the pan-genome. Using transcriptome sequencing of seedling RNA from 503 maize (Zea mays) inbred lines to characterize the maize pan-genome, we identified 8681 representative transcript assemblies (RTAs) with 16.4% expressed in all lines and 82.7% expressed in subsets of the lines. Interestingly, with linkage disequilibrium mapping, 76.7% of the RTAs with at least one single nucleotide polymorphism (SNP) could be mapped to a single genetic position, distributed primarily throughout the nonpericentromeric portion of the genome. Stepwise iterative clustering of RTAs suggests, within the context of the genotypes used in this study, that the maize genome is restricted and further sampling of seedling RNA within this germplasm base will result in minimal discovery. Genome-wide association studies based on SNPs and transcript abundance in the pan-genome revealed loci associated with the timing of the juvenile-to-adult vegetative and vegetative-to-reproductive developmental transitions, two traits important for fitness and adaptation. This study revealed the dynamic nature of the maize pan-genome and demonstrated that a substantial portion of variation may lie outside the single reference genome for a species. [ABSTRACT FROM AUTHOR]

Transcriptional and metabolic changes were evaluated during senescence induced by preventing pollination in the B73 genotype of maize (Zea mays). Accumulation of free glucose and starch and loss of chlorophyll in leaf was manifested early at 12 d after anthesis (DAA), while global transcriptional and phenotypic changes were evident only at 24 DAA. Internodes exhibited major transcriptomic changes only at 30 DAA. Overlaying expression data onto metabolic pathways revealed involvement of many novel pathways, including those involved in cell wall biosynthesis. To investigate the overlap between induced and natural senescence, transcriptional data from induced senescence in maize was compared with that reported for Arabidopsis (Arabidopsis thaliana) undergoing natural and sugar-induced senescence. Notable similarities with natural senescence in Arabidopsis included up-regulation of senescence-associated genes (SAGs), ethylene and jasmonic acid biosynthetic genes, APETALA2, ethylene-responsive element binding protein, and no apical meristem transcription factors. However, differences from natural senescence were highlighted by unaltered expression of a subset of the SAGs, and cytokinin, abscisic acid, and salicylic acid biosynthesis genes. Key genes up-regulated during sugar-induced senescence in Arabidopsis, including a cysteine protease (SAG12) and three flavonoid biosynthesis genes (PRODUCTION OF ANTHOCYANIN PIGMENT1 (PAP1), PAP2, and LEUCOANTHOCYANIDIN DIOXYGENASE), were also induced, suggesting similarities in senescence induced by pollination prevention and sugar application. Coexpression analysis revealed networks involving known senescence-related genes and novel candidates; 82 of these were shared between leaf and internode networks, highlighting similarities in induced senescence in these tissues. Insights from this study will be valuable in systems biology of senescence in maize and other grasses. [ABSTRACT FROM AUTHOR]

Maize is an important model species and a major constituent of human and animal diets. It has also emerged as a potential feedstock and model system for bioenergy research due to recent worldwide interest in developing plant biomass-based, carbon-neutral liquid fuels. To understand how the underlying genome sequence results in specific plant phenotypes, information on the temporal and spatial transcription patterns of genes is crucial. Here we present a comprehensive atlas of global transcription profiles across developmental stages and plant organs. We used a NimbleGen microarray containing 80 301 probe sets to profile transcription patterns in 60 distinct tissues representing 11 major organ systems of inbred line B73. Of the 30 892 probe sets representing the filtered B73 gene models, 91.4% were expressed in at least one tissue. Interestingly, 44.5% of the probe sets were expressed in all tissues, indicating a substantial overlap of gene expression among plant organs. Clustering of maize tissues based on global gene expression profiles resulted in formation of groups of biologically related tissues. We utilized this dataset to examine the expression of genes that encode enzymes in the lignin biosynthetic pathway, and found that expansion of distinct gene families was accompanied by divergent, tissue-specific transcription patterns of the paralogs. This comprehensive expression atlas represents a valuable resource for gene discovery and functional characterization in maize. [ABSTRACT FROM AUTHOR]

Correction to: Characterizing introgression-by-environment interactions using maize near isogenic lines Graph: Supplementary file1 (XLSX 9869 KB) Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s00122-021-03959-z. [Extracted from the article]

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams' methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition., Competing Interests: Conflict of Interest The authors declare no conflict of interest.

Elucidating gene regulatory networks is a major area of study within plant systems biology. Phenotypic traits are intricately linked to specific gene expression profiles. These expression patterns arise primarily from regulatory connections between sets of transcription factors (TFs) and their target genes. Here, we integrated 46 co-expression networks, 283 protein-DNA interaction (PDI) assays, and 16 million SNPs used to identify expression quantitative trait loci (eQTL) to construct TF-target networks. In total, we analyzed ∼4.6M interactions to generate four distinct types of TF-target networks: co-expression, PDI, trans -eQTL, and cis -eQTL combined with PDIs. To functionally annotate TFs based on their target genes, we implemented three different network integration strategies. We evaluated the effectiveness of each strategy through TF loss-of function mutant inspection and random network analyses. The multi-network integration allowed us to identify transcriptional regulators of several biological processes. Using the topological properties of the fully integrated network, we identified potential functionally redundant TF paralogs. Our findings retrieved functions previously documented for numerous TFs and revealed novel functions that are crucial for informing the design of future experiments. The approach here-described lays the foundation for the integration of multi-omic datasets in maize and other plant systems.

The US standard for maize commercially grown for grain specifies that yellow corn can contain at maximum 5% corn of other colors. Inbred parents of commercial hybrids typically have clear pericarp, but transgressive segregants in breeding populations can display variation in pericarp pigmentation. We identified 10 doubled haploid biparental populations segregating for pigmented pericarp and evaluated qualitative genetic models using chi-square tests of observed and expected frequencies. Pigmentation ranged from light to dark brown color, and pigmentation intensity was quantitatively measured across 1,327 inbred lines using hue calculated from RGB pixel values. Genetic mapping was used to identify loci associated with pigmentation intensity. For 9 populations, pigmentation inheritance best fit a hypothesis of a 2- or 3-gene epistatic model. Significant differences in pigment intensity were observed across populations. W606S-derived inbred lines with the darkest pericarp often had clear glumes, suggesting the presence of a novel P1-rw allele, a hypothesis supported by a significant quantitative trait locus peak at P1. A separate quantitative trait locus region on chromosome 2 between 221.64 and 226.66 Mbp was identified in LH82-derived populations, and the peak near p1 was absent. A genome-wide association study using 416 inbred lines from the Wisconsin Diversity panel with full genome resequencing revealed 4 significant associations including the region near P1. This study supports that pericarp pigmentation among dent maize inbreds can arise by transgressive segregation when pigmentation in the parental generation is absent and is partially explained by functional allelic variation at the P1 locus., Competing Interests: Conflicts of interest The authors declare no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.)

Source and sink interactions play a critical but mechanistically poorly understood role in the regulation of senescence. To disentangle the genetic and molecular mechanisms underlying source-sink-regulated senescence (SSRS), we performed a phenotypic, transcriptomic, and systems genetics analysis of senescence induced by the lack of a strong sink in maize (Zea mays). Comparative analysis of genotypes with contrasting SSRS phenotypes revealed that feedback inhibition of photosynthesis, a surge in reactive oxygen species, and the resulting endoplasmic reticulum (ER) stress were the earliest outcomes of weakened sink demand. Multienvironmental evaluation of a biparental population and a diversity panel identified 12 quantitative trait loci and 24 candidate genes, respectively, underlying SSRS. Combining the natural diversity and coexpression networks analyses identified 7 high-confidence candidate genes involved in proteolysis, photosynthesis, stress response, and protein folding. The role of a cathepsin B like protease 4 (ccp4), a candidate gene supported by systems genetic analysis, was validated by analysis of natural alleles in maize and heterologous analyses in Arabidopsis (Arabidopsis thaliana). Analysis of natural alleles suggested that a 700-bp polymorphic promoter region harboring multiple ABA-responsive elements is responsible for differential transcriptional regulation of ccp4 by ABA and the resulting variation in SSRS phenotype. We propose a model for SSRS wherein feedback inhibition of photosynthesis, ABA signaling, and oxidative stress converge to induce ER stress manifested as programed cell death and senescence. These findings provide a deeper understanding of signals emerging from loss of sink strength and offer opportunities to modify these signals to alter senescence program and enhance crop productivity., Competing Interests: Conflict of interest statement. The authors declare no conflict of interest., (© American Society of Plant Biologists 2023. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations and years. The Genomes-to-Fields (G2F) Initiative has tested maize hybrids in more than 130 year-locations in North America since 2014. Here, we curate and expand this data set by generating environmental covariates (using a crop model) for each of the trials. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids. We show how this valuable data set can serve as a benchmark in agricultural modeling and prediction, paving the way for countless G×E investigations in maize. We use multivariate analyses to characterize the data set's genetic and environmental structure, study the association of key environmental factors with traits, and provide benchmarks using genomic prediction models., (© 2023. The Author(s).)

Objectives: This release note describes the Maize GxE project datasets within the Genomes to Fields (G2F) Initiative. The Maize GxE project aims to understand genotype by environment (GxE) interactions and use the information collected to improve resource allocation efficiency and increase genotype predictability and stability, particularly in scenarios of variable environmental patterns. Hybrids and inbreds are evaluated across multiple environments and phenotypic, genotypic, environmental, and metadata information are made publicly available., Data Description: The datasets include phenotypic data of the hybrids and inbreds evaluated in 30 locations across the US and one location in Germany in 2020 and 2021, soil and climatic measurements and metadata information for all environments (combination of year and location), ReadMe, and description files for each data type. A set of common hybrids is present in each environment to connect with previous evaluations. Each environment had a collaborator responsible for collecting and submitting the data, the GxE coordination team combined all the collected information and removed obvious erroneous data. Collaborators received the combined data to use, verify and declare that the data generated in their own environments was accurate. Combined data is released to the public with minimal filtering to maintain fidelity to the original data., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Objectives: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data., Data Description: This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years., (© 2023. The Author(s).)

Key Message: A novel locus was discovered on chromosome 7 associated with a lesion mimic in maize; this lesion mimic had a quantitative and heritable phenotype and was predicted better via subset genomic markers than whole genome markers across diverse environments. Lesion mimics are a phenotype of leaf micro-spotting in maize (Zea mays L.), which can be early signs of biotic or abiotic stresses. Dissecting its inheritance is helpful to understand how these loci behave across different genetic backgrounds. Here, 538 maize recombinant inbred lines (RILs) segregating for a novel lesion mimic were quantitatively phenotyped in Georgia, Texas, and Wisconsin. These RILs were derived from three bi-parental crosses using a tropical pollinator (Tx773) as the common parent crossed with three inbreds (LH195, LH82, and PB80). While this lesion mimic was heritable across three environments based on phenotypic ([Formula: see text] = 0.68) and genomic ([Formula: see text] = 0.91) data, transgressive segregation was observed. A genome-wide association study identified a single novel locus on chromosome 7 (at 70.6 Mb) also covered by a quantitative trait locus interval (69.3-71.0 Mb), explaining 11-15% of the variation, depending on the environment. One candidate gene identified in this region, Zm00001eb308070, is related to the abscisic acid pathway involving in cell death. Genomic predictions were applied to genome-wide markers (39,611 markers) contrasted with a marker subset (51 markers). Population structure explained more variation than environment in genomic prediction, but other substantial genetic background effects were additionally detected. Subset markers explained substantially less genetic variation (24.9%) for the lesion mimic than whole genome markers (55.4%) in the model, yet predicted the lesion mimic better (0.56-0.66 vs. 0.26-0.29). These results indicate this lesion mimic phenotype was less affected by environment than by epistasis and genetic background effects, which explain its transgressive segregation., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objectives: This report provides information about the public release of the 2018-2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions., Data Description: Datasets include phenotypic, climatic, and soil measurements, metadata information, and inbred genotypic information for each combination of location and year. Collaborators in the G2F initiative collected data for each location and year; members of the group responsible for coordination and data processing combined all the collected information and removed obvious erroneous data. The collaborators received the data before the DOI release to verify and declare that the data generated in their own locations was accurate. ReadMe and description files are available for each dataset. Previous years of evaluation are already publicly available, with common hybrids present to connect across all locations and years evaluated since this project's inception., (© 2023. The Author(s).)

Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied toward this goal. Here we predict maize yield using deep neural networks, compare the efficacy of 2 model development methods, and contextualize model performance using conventional linear and machine learning models. We examine the usefulness of incorporating interactions between disparate data types. We find deep learning and best linear unbiased predictor (BLUP) models with interactions had the best overall performance. BLUP models achieved the lowest average error, but deep learning models performed more consistently with similar average error. Optimizing deep neural network submodules for each data type improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best-performing model revealed that including interactions altered the model's sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have a limited physiological basis for influencing yield-those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for the phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors., Competing Interests: Conflicts of interest None declared., (Published by Oxford University Press on behalf of the Genetics Society of America 2023.)

Crop genetic diversity for climate adaptations is globally partitioned. We performed experimental evolution in maize to understand the response to selection and how plant germplasm can be moved across geographical zones. Initialized with a common population of tropical origin, artificial selection on flowering time was performed for two generations at eight field sites spanning 25° latitude, a 2800 km transect. We then jointly tested all selection lineages across the original sites of selection, for the target trait and 23 other traits. Modeling intergenerational shifts in a physiological reaction norm revealed separate components for flowering-time plasticity. Generalized and local modes of selection altered the plasticity of each lineage, leading to a latitudinal pattern in the responses to selection that were strongly driven by photoperiod. This transformation led to widespread changes in developmental, architectural, and yield traits, expressed collectively in an environment-dependent manner. Furthermore, selection for flowering time alone alleviated a maladaptive syndrome and improved yields for tropical maize in the temperate zone. Our findings show how phenotypic selection can rapidly shift the flowering phenology and plasticity of maize. They also demonstrate that selecting crops to local conditions can accelerate adaptation to climate change., (© 2023 The Authors. New Phytologist © 2023 New Phytologist Foundation.)

In biological research domains, liquid chromatography-mass spectroscopy (LC-MS) has prevailed as the preferred technique for generating high quality metabolomic data. However, even with advanced instrumentation and established data acquisition protocols, technical errors are still routinely encountered and can pose a significant challenge to unveiling biologically relevant information. In large-scale studies, signal drift and batch effects are how technical errors are most commonly manifested. We developed pseudoDrift, an R package with capabilities for data simulation and outlier detection, and a new training and testing approach that is implemented to capture and to optionally correct for technical errors in LC-MS metabolomic data. Using data simulation, we demonstrate here that our approach performs equally as well as existing methods and offers increased flexibility to the researcher. As part of our study, we generated a targeted LC-MS dataset that profiled 33 phenolic compounds from seedling stem tissue in 602 genetically diverse non-transgenic maize inbred lines. This dataset provides a unique opportunity to investigate the dynamics of specialized metabolism in plants.

Chronic diarrhea is a common reason for consultation in renal transplant patients. Cytomegalovirus infection is the cause of chronic diarrhea of infectious origin in 50% of cases, but coinfection with tuberculosis is rare. We present the case of a renal transplant patient with chronic diarrhea, with a finding of left colon colitis and positive microbiological studies in biopsy for tuberculosis and cytomegalovirus . The patient received valganciclovir and anti-tubercular treatment with adequate evolution. Immunosuppressed patients may have diarrhea secondary to opportunistic infections; therefore, an algorithm for early diagnosis and treatment is recommended.

The stiff-stalk heterotic group in Maize (Zea mays L.) is an important source of inbreds used in U.S. commercial hybrid production. Founder inbreds B14, B37, B73, and, to a lesser extent, B84, are found in the pedigrees of a majority of commercial seed parent inbred lines. We created high-quality genome assemblies of B84 and four expired Plant Variety Protection (ex-PVP) lines LH145 representing B14, NKH8431 of mixed descent, PHB47 representing B37, and PHJ40, which is a Pioneer Hi-Bred International (PHI) early stiff-stalk type. Sequence was generated using long-read sequencing achieving highly contiguous assemblies of 2.13-2.18 Gbp with N50 scaffold lengths >200 Mbp. Inbred-specific gene annotations were generated using a core five-tissue gene expression atlas, whereas transposable element (TE) annotation was conducted using de novo and homology-directed methodologies. Compared with the reference inbred B73, synteny analyses revealed extensive collinearity across the five stiff-stalk genomes, although unique components of the maize pangenome were detected. Comparison of this set of stiff-stalk inbreds with the original Iowa Stiff Stalk Synthetic breeding population revealed that these inbreds represent only a proportion of variation in the original stiff-stalk pool and there are highly conserved haplotypes in released public and ex-Plant Variety Protection inbreds. Despite the reduction in variation from the original stiff-stalk population, substantial genetic and genomic variation was identified supporting the potential for continued breeding success in this pool. The assemblies described here represent stiff-stalk inbreds that have historical and commercial relevance and provide further insight into the emerging maize pangenome., (© 2021 The Authors. The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.)

Traditional phenotyping methods, coupled with genetic mapping in segregating populations, have identified loci governing complex traits in many crops. Unoccupied aerial systems (UAS)-based phenotyping has helped to reveal a more novel and dynamic relationship between time-specific associated loci with complex traits previously unable to be evaluated. Over 1,500 maize (Zea mays L.) hybrid row plots containing 280 different replicated maize hybrids from the Genomes to Fields (G2F) project were evaluated agronomically and using UAS in 2017. Weekly UAS flights captured variation in plant heights during the growing season under three different management conditions each year: optimal planting with irrigation (G2FI), optimal dryland planting without irrigation (G2FD), and a stressed late planting (G2LA). Plant height of different flights were ranked based on importance for yield using a random forest (RF) algorithm. Plant heights captured by early flights in G2FI trials had higher importance (based on Gini scores) for predicting maize grain yield (GY) but also higher accuracies in genomic predictions which fluctuated for G2FD (-0.06∼0.73), G2FI (0.33∼0.76), and G2LA (0.26∼0.78) trials. A genome-wide association analysis discovered 52 significant single nucleotide polymorphisms (SNPs), seven were found consistently in more than one flights or trial; 45 were flight or trial specific. Total cumulative marker effects for each chromosome's contributions to plant height also changed depending on flight. Using UAS phenotyping, this study showed that many candidate genes putatively play a role in the regulation of plant architecture even in relatively early stages of maize growth and development., (© 2021 The Authors. The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.)

Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Jarquin, de Leon, Romay, Bohn, Buckler, Ciampitti, Edwards, Ertl, Flint-Garcia, Gore, Graham, Hirsch, Holland, Hooker, Kaeppler, Knoll, Lee, Lawrence-Dill, Lynch, Moose, Murray, Nelson, Rocheford, Schnable, Schnable, Smith, Springer, Thomison, Tuinstra, Wisser, Xu, Yu and Lorenz.)

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., (Published by Oxford University Press on behalf of Genetics Society of America 2021. This work is written by US Government employees and is in the public domain in the US.)

There is an increasing evidence supporting the existence of coagulopathy in coronavirus disease 2019 (COVID-19) patients. Most of reports are mainly focused on d-dimer. Our objective is to describe coagulation parameters in these patients that could be involved in a hypercoagulate state and to test platelet function to see if there are short closure times. We analyzed coagulation samples from 80 patients admitted with COVID-19 in our hospital. We also tested platelet function by closure times in a small subgroup of patients. Most of samples had increased d-dimer (96.2%) (median of d-dimer: 1158 ng/ml FEU), increased fibrinogen (75.2%) (median: 5.23 g/l), increased factor VIII (86%) (median: 264.8 U/dl), decreased protein S (22.5% of women, 62.5% of men) (median: 62.8 and 68.5 U/dl, respectively), decreased protein C (7.6%) (median: 100 U/dl), decreased factor XII (25.3%) (median: 90.3 U/dl) and decreased antithrombin activity (21%) (median: 86 U/dl). International normalized ratio was higher than normal in 24 patients (30%) (median: 1.13). The activated partial thromboplastin time ratio was below the normal range in nine patients (11.2%) and above normal in three (3.75%) (median: 0.93). The closure times were short in the 20% and 40% of samples of collagen and ADP and collagen and epinephrine, respectively. Twelve of the 80 patients (15%) had a thrombotic event and all had several abnormal coagulation parameters related with increased thrombotic risk. The results of this study support a hypercoagulability state in COVID-19 patients and it may help to explain the microvascular thrombosis caused by the inflammatory response., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Invited for this month's cover is the research team from the D.O.E. Great Lake Bioenergy Research Center (GLBRC) at the University of Wisconsin-Madison. The cover image shows how a diverse team with expertise in many different fields works together in an integrated fashion to address complex problems. Only when the whole system, from field to the liquid fuels and co-products, is assessed, can we identify the key parameters needed to design an economically viable biorefinery-based economy. Cover art by Chelsea Mamott. The Full Paper itself is available at 10.1002/cssc.201903345., (© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Objectives: Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F's genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014-2017., Data Description: Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.

Increasing popularity of high-throughput phenotyping technologies, such as image-based phenotyping, offer novel ways for quantifying plant growth and morphology. These new methods can be more or less accurate and precise than traditional, manual measurements. Many large-scale phenotyping efforts are conducted to enable genome-wide association studies (GWAS), but it is unclear exactly how alternative methods of phenotyping will affect GWAS results. In this study we simulate phenotypes that are controlled by the same set of causal loci but have differing heritability, similar to two different measurements of the same morphological character. We then perform GWAS with the simulated traits and create receiver operating characteristic (ROC) curves from the results. The areas under the ROC curves (AUCs) provide a metric that allows direct comparisons of GWAS results from different simulated traits. We use this framework to evaluate the effects of heritability and the number of causative loci on the AUCs of simulated traits; we also test the differences between AUCs of traits with differing heritability. We find that both increasing the number of causative loci and decreasing the heritability reduce a trait's AUC. We also find that when two traits are controlled by a greater number of causative loci, they are more likely to have significantly different AUCs as the difference between their heritabilities increases. When simulation results are applied to measures of tassel morphology, we find no significant difference between AUCs from GWAS using manual and image-based measurements of typical maize tassel characters. This finding indicates that both measurement methods have similar ability to identify genetic associations. These results provide a framework for deciding between competing phenotyping strategies when the ultimate goal is to generate and use phenotype-genotype associations from GWAS., (Copyright © 2018 Gage et al.)

Objectives: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F's genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available., Data Description: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.

Seed size is an important component of grain yield and a key determinant trait for crop domestication. The Krug Yellow Dent long-term selection experiment for large and small seed provides a valuable resource to dissect genetic and phenotypic changes affecting seed size within a common genetic background. In this study, inbred lines derived from Krug Large Seed (KLS) and Krug Small Seed (KSS) populations and reciprocal F1 crosses were used to investigate developmental and molecular mechanisms governing seed size. Seed morphological characteristics showed striking differences between KLS and KSS inbred lines, and the reciprocal cross experiment revealed a strong maternal influence on both seed weight and seed size. Quantification of endosperm area, starchy endosperm cell size, and kernel dry mass accumulation indicated a positive correlation between seed size, endosperm cell number, and grain filling rate, and patterns of grain filling in reciprocal crosses mirrored that of the maternal parent. Consistent with the maternal contribution to seed weight, transcriptome profiling of reciprocal F1 hybrids showed substantial similarities to the maternal parent. A set of differentially expressed genes between KLS and KSS inbreds were found, which fell into a broad number of functional categories including DNA methylation, nucleosome assembly, and heat stress response. In addition, gene co-expression network analysis of parental inbreds and reciprocal F1 hybrids identified co-expression modules enriched in ovule development and DNA methylation, implicating these two processes in seed size determination. These results expand our understanding of seed size regulation and help to uncover the developmental and molecular basis underlying maternal control of seed size in maize., (© The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

A maize (Zea mays L. subsp. mays) diversity panel consisting of 26 maize lines exhibiting a wide range of cell-wall properties and responses to hydrolysis by cellulolytic enzymes was employed to investigate the relationship between cell-wall properties, cell-wall responses to mild NaOH pre-treatment, and enzymatic hydrolysis yields. Enzymatic hydrolysis of the cellulose in the untreated maize was found to be positively correlated with the water retention value, which is a measure of cell-wall susceptibility to swelling. It was also positively correlated with the lignin syringyl/guaiacyl ratio and negatively correlated with the initial cell-wall lignin, xylan, acetate, and p-coumaric acid (pCA) content, as well as pCA released from the cell wall by pre-treatment. The hydrolysis yield following pre-treatment exhibited statistically significant negative correlations to the lignin content after pre-treatment and positive correlations to the solubilized ferulic acid and pCA. Several unanticipated results were observed, including a positive correlation between initial lignin and acetate content, lack of correlation between acetate content and initial xylan content, and negative correlation between each of these three variables to the hydrolysis yields for untreated maize. Another surprising result was that pCA release was negatively correlated with hydrolysis yields for untreated maize and, along with ferulic acid release, was positively correlated with the pre-treated maize hydrolysis yields. This indicates that these properties that may negatively contribute to the recalcitrance in untreated cell walls may positively contribute to their deconstruction by alkaline pre-treatment., (© The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

Maize silage is forage of high quality and yield, and represents the second most important use of maize in the United States. The Wisconsin Quality Synthetic (WQS) maize population has undergone five cycles of recurrent selection for silage yield and composition, resulting in a genetically improved population. The application of high-density molecular markers allows breeders and geneticists to identify important loci through association analysis and selection mapping, as well as to monitor changes in the distribution of genetic diversity across the genome. The objectives of this study were to identify loci controlling variation for maize silage traits through association analysis and the assessment of selection signatures and to describe changes in the genomic distribution of gene diversity through selection and genetic drift in the WQS recurrent selection program. We failed to find any significant marker-trait associations using the historical phenotypic data from WQS breeding trials combined with 17,719 high-quality, informative single nucleotide polymorphisms. Likewise, no strong genomic signatures were left by selection on silage yield and quality in the WQS despite genetic gain for these traits. These results could be due to the genetic complexity underlying these traits, or the role of selection on standing genetic variation. Variation in loss of diversity through drift was observed across the genome. Some large regions experienced much greater loss in diversity than what is expected, suggesting limited recombination combined with small populations in recurrent selection programs could easily lead to fixation of large swaths of the genome., (Copyright © 2015 Lorenz et al.)

Transcriptional and metabolic changes were evaluated during senescence induced by preventing pollination in the B73 genotype of maize (Zea mays). Accumulation of free glucose and starch and loss of chlorophyll in leaf was manifested early at 12 d after anthesis (DAA), while global transcriptional and phenotypic changes were evident only at 24 DAA. Internodes exhibited major transcriptomic changes only at 30 DAA. Overlaying expression data onto metabolic pathways revealed involvement of many novel pathways, including those involved in cell wall biosynthesis. To investigate the overlap between induced and natural senescence, transcriptional data from induced senescence in maize was compared with that reported for Arabidopsis (Arabidopsis thaliana) undergoing natural and sugar-induced senescence. Notable similarities with natural senescence in Arabidopsis included up-regulation of senescence-associated genes (SAGs), ethylene and jasmonic acid biosynthetic genes, APETALA2, ethylene-responsive element binding protein, and no apical meristem transcription factors. However, differences from natural senescence were highlighted by unaltered expression of a subset of the SAGs, and cytokinin, abscisic acid, and salicylic acid biosynthesis genes. Key genes up-regulated during sugar-induced senescence in Arabidopsis, including a cysteine protease (SAG12) and three flavonoid biosynthesis genes (PRODUCTION OF ANTHOCYANIN PIGMENT1 (PAP1), PAP2, and LEUCOANTHOCYANIDIN DIOXYGENASE), were also induced, suggesting similarities in senescence induced by pollination prevention and sugar application. Coexpression analysis revealed networks involving known senescence-related genes and novel candidates; 82 of these were shared between leaf and internode networks, highlighting similarities in induced senescence in these tissues. Insights from this study will be valuable in systems biology of senescence in maize and other grasses.

Recombinant inbred lines developed from the maize (Zea mays ssp. mays) inbreds B73 and Mo17 have been widely used to discover quantitative trait loci controlling a wide variety of phenotypic traits and as a resource to produce high-resolution genetic maps. These two parents were used to produce a set of near-isogenic lines (NILs) with small regions of introgression into both backgrounds. A novel array-based genotyping platform was used to score genotypes of over 7,000 loci in 100 NILs with B73 as the recurrent parent and 50 NILs with Mo17 as the recurrent parent. This population contains introgressions that cover the majority of the maize genome. The set of NILs displayed an excess of residual heterozygosity relative to the amount expected based on their pedigrees, and this excess residual heterozygosity is enriched in the low-recombination regions near the centromeres. The genotyping platform provided the ability to survey copy number variants that exist in more copies in Mo17 than in B73. The majority of these Mo17-specific duplications are located in unlinked positions throughout the genome. The utility of this population for the discovery and validation of quantitative trait loci was assessed through analysis of plant height variation.