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A genome-wide association study (GWAS) identifies trait-associated loci, but identifying the causal genes can be a bottleneck, due in part to slow decay of linkage disequilibrium (LD). A transcriptome-wide association study (TWAS) addresses this issue by identifying gene expression-phenotype associations or integrating gene expression quantitative trait loci with GWAS results. Here, we used self-pollinated soybean (Glycine max [L.] Merr.) as a model to evaluate the application of TWAS to the genetic dissection of traits in plant species with slow LD decay. We generated RNA sequencing data for a soybean diversity panel and identified the genetic expression regulation of 29 286 soybean genes. Different TWAS solutions were less affected by LD and were robust to the source of expression, identifing known genes related to traits from different tissues and developmental stages. The novel pod-color gene L2 was identified via TWAS and functionally validated by genome editing. By introducing a new exon proportion feature, we significantly improved the detection of expression variations that resulted from structural variations and alternative splicing. As a result, the genes identified through our TWAS approach exhibited a diverse range of causal variations, including SNPs, insertions or deletions, gene fusion, copy number variations, and alternative splicing. Using this approach, we identified genes associated with flowering time, including both previously known genes and novel genes that had not previously been linked to this trait, providing insights complementary to those from GWAS. In summary, this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Climate change is projected to decrease maize yields due to warmer temperatures and their consequences. Studies using crop growth models (CGMs), however, have predicted that, through a combination of alterations to planting date, flowering time, and maturity, these yield losses can be mitigated or even reversed. Here, we examine three assumptions of such studies: (1) that climate has driven historical phenological trends, (2) that CGM ensembles provide unbiased estimates of yields under high temperatures, and (3) that the effects of temperature on yields are an emergent property of interactions between phenology and environment. We used data on maize phenology from the United States Department of Agriculture, a statistical model of maize hybrid heat tolerance derived from 80 years of public yield trial records across four US states, and outputs of an ensemble of CMIP6 climate models. While planting dates have advanced historically, we found a trend toward later planting dates after 2005 and no trend for silking or maturity, shifting more time into the reproductive period. We then projected maize yields using the historical model and crop calendars devised using three previously proposed adaptation strategies. In contrast to studies using CGMs, our statistical yield model projected severe yield losses under all three strategies. Finally, we projected maize yields accounting for historical genetic variability for heat tolerance, discovering that it was insufficient to overcome the negative effects of projected warming. These projections are driven by greater heat stress exposure under all crop calendars and climate scenarios. Combined with analysis of the internal sensitivities of CGMs to temperature, our results suggest that current projections do not adequately account for the effects of increasing temperatures on maize yields. Climate adaptation in the US Midwest must utilize a richer set of strategies than phenological adaptation, including improvements to heat tolerance and crop diversification., (© 2024 The Author(s). Global Change Biology published by John Wiley & Sons Ltd.)

Phenotypic plasticity describes a genotype's ability to produce different phenotypes in response to different environments. Breeding crops that exhibit appropriate levels of plasticity for future climates will be crucial to meeting global demand, but knowledge of the critical environmental factors is limited to a handful of well-studied major crops. Using 727 maize (Zea mays L.) hybrids phenotyped for grain yield in 45 environments, we investigated the ability of a genetic algorithm and two other methods to identify environmental determinants of grain yield from a large set of candidate environmental variables constructed using minimal assumptions. The genetic algorithm identified pre- and postanthesis maximum temperature, mid-season solar radiation, and whole season net evapotranspiration as the four most important variables from a candidate set of 9150. Importantly, these four variables are supported by previous literature. After calculating reaction norms for each environmental variable, candidate genes were identified and gene annotations investigated to demonstrate how this method can generate insights into phenotypic plasticity. The genetic algorithm successfully identified known environmental determinants of hybrid maize grain yield. This demonstrates that the methodology could be applied to other less well-studied phenotypes and crops to improve understanding of phenotypic plasticity and facilitate breeding crops for future climates., (© 2024 The Author(s). New Phytologist © 2024 New Phytologist Foundation. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Maize plastid terminal oxidase1 (ZmPTOX1) plays a pivotal role in seed development by upholding redox balance within seed plastids. This study focuses on characterizing the white kernel mutant 3735 (wk3735) mutant, which yields pale-yellow seeds characterized by heightened protein but reduced carotenoid levels, along with delayed germination compared to wild-type (WT) seeds. We successfully cloned and identified the target gene ZmPTOX1, responsible for encoding maize PTOX-a versatile plastoquinol oxidase and redox sensor located in plastid membranes. While PTOX's established role involves regulating redox states and participating in carotenoid metabolism in Arabidopsis leaves and tomato fruits, our investigation marks the first exploration of its function in storage organs lacking a photosynthetic system. Through our research, we validated the existence of plastid-localized ZmPTOX1, existing as a homomultimer, and established its interaction with ferredoxin-NADP+ oxidoreductase 1 (ZmFNR1), a crucial component of the electron transport chain (ETC). This interaction contributes to the maintenance of redox equilibrium within plastids. Our findings indicate a propensity for excessive accumulation of reactive oxygen species (ROS) in wk3735 seeds. Beyond its known role in carotenoids' antioxidant properties, ZmPTOX1 also impacts ROS homeostasis owing to its oxidizing function. Altogether, our results underscore the critical involvement of ZmPTOX1 in governing seed development and germination by preserving redox balance within the seed plastids., (© 2024 Society for Experimental Biology and John Wiley & Sons Ltd.)

The efficiency of solar radiation interception contributes to the photosynthetic efficiency of crop plants. Light interception is a function of canopy architecture, including plant density; leaf number, length, width, and angle; and azimuthal canopy orientation. We report on the ability of some maize (Zea mays) genotypes to alter the orientations of their leaves during development in coordination with adjacent plants. Although the upper canopies of these genotypes retain the typical alternate-distichous phyllotaxy of maize, their leaves grow parallel to those of adjacent plants. A genome-wide association study (GWAS) on this parallel canopy trait identified candidate genes, many of which are associated with shade avoidance syndrome, including phytochromeC2. GWAS conducted on the fraction of photosynthetically active radiation (PAR) intercepted by canopies also identified multiple candidate genes, including liguleless1 (lg1), previously defined by its role in ligule development. Under high plant densities, mutants of shade avoidance syndrome and liguleless genes (lg1, lg2, and Lg3) exhibit altered canopy patterns, viz, the numbers of interrow leaves are greatly reduced as compared to those of nonmutant controls, resulting in dramatically decreased PAR interception. In at least the case of lg2, this phenotype is not a consequence of abnormal ligule development. Instead, liguleless gene functions are required for normal light responses, including azimuth canopy re-orientation., Competing Interests: Conflict of interest statement. Y.Z. and A.K., declare no competing interests. P.S.S. is a co-founder and managing partner or CEO of Data2Bio, LLC; Dryland Genetics, Inc; and EnGeniousAg, LLC; he is also a co-founder of LookAhead Breeding LLC. He is a member of the scientific advisory board and a shareholder of Hi-Fidelity Genetics, Inc., and a member of the scientific advisory boards of Kemin Industries and Centro de Tecnologia Canavieira., (© The Author(s) 2024. Published by Oxford University Press on behalf of American Society of Plant Biologists.)

Dramatic improvements in measuring genetic variation across agriculturally relevant populations (genomics) must be matched by improvements in identifying and measuring relevant trait variation in such populations across many environments (phenomics). Identifying the most critical opportunities and challenges in genome to phenome (G2P) research is the focus of this paper. Previously (Genome Biol, 23(1):1-11, 2022), we laid out how Agricultural Genome to Phenome Initiative (AG2PI) will coordinate activities with USA federal government agencies expand public-private partnerships, and engage with external stakeholders to achieve a shared vision of future the AG2PI. Acting on this latter step, AG2PI organized the "Thinking Big: Visualizing the Future of AG2PI" two-day workshop held September 9-10, 2022, in Ames, Iowa, co-hosted with the United State Department of Agriculture's National Institute of Food and Agriculture (USDA NIFA). During the meeting, attendees were asked to use their experience and curiosity to review the current status of agricultural genome to phenome (AG2P) work and envision the future of the AG2P field. The topic summaries composing this paper are distilled from two 1.5-h small group discussions. Challenges and solutions identified across multiple topics at the workshop were explored. We end our discussion with a vision for the future of agricultural progress, identifying two areas of innovation needed: (1) innovate in genetic improvement methods development and evaluation and (2) innovate in agricultural research processes to solve societal problems. To address these needs, we then provide six specific goals that we recommend be implemented immediately in support of advancing AG2P research., (© 2023. The Author(s).)

Global climate change is increasing both average temperatures and the frequencies of extreme high temperatures. Past studies have documented a strong negative effect of exposures to temperatures >30°C on hybrid maize yields. However, these studies could not disentangle genetic adaptation via artificial selection from changes in agronomic practices. Because most of the earliest maize hybrids are no longer available, side-by-side comparisons with modern hybrids under current field conditions are generally impossible. Here, we report on the collection and curation of 81 years of public yield trial records covering 4,730 maize hybrids, which enabled us to model genetic variation for temperature responses among maize hybrids. We show that selection may have indirectly and inconsistently contributed to the genetic adaptation of maize to moderate heat stress over this time period while preserving genetic variance for continued adaptation. However, our results reveal the existence of a genetic tradeoff for tolerance to moderate and severe heat stress, leading to a decrease in tolerance to severe heat stress over the same time period. Both trends are particularly conspicuous since the mid-1970s. Such a tradeoff poses challenges to the continued adaptation of maize to warming climates due to a projected increase in the frequency of extreme heat events. Nevertheless, given recent advances in phenomics, enviromics, and physiological modeling, our results offer a degree of optimism for the capacity of plant breeders to adapt maize to warming climates, assuming appropriate levels of R&D investment., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: SS is an advisor and shareholder in Arnetta Technologies Pvt Ltd. PSS is a co-founder and managing partner or CEO of Data2Bio, LLC; Dryland Genetics, Inc; and EnGeniousAg, LLC; he is also a co-founder of LookAhead Breeding LLC. He is a member of the scientific advisory board and a shareholder of Hi-Fidelity Genetics, Inc., and a member of the scientific advisory boards of Kemin Industries and Centro de Tecnologia Canavieira., (Copyright: © 2023 Kusmec et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Roots anchor plants in soil, and the failure of anchorage (i.e. root lodging) is a major cause of crop yield loss. Anchorage is often assumed to be driven by root system architecture (RSA). We made use of a natural experiment to measure the overlap between the genetic regulation of RSA and anchorage. After one of the most devastating derechos ever recorded in August 2020, we phenotyped root lodging in a maize (Zea mays) diversity panel consisting of 369 genotypes grown in 6 environments affected by the derecho. Genome-wide and transcriptome-wide association studies identified 118 candidate genes associated with root lodging. Thirty-four percent (40/118) of these were homologs of genes from Arabidopsis (Arabidopsis thaliana) that affect traits such as root morphology and lignin content, expected to affect root lodging. Finally, gene ontology enrichment analysis of the candidate genes and their predicted interaction partners at the transcriptional and translational levels revealed the complex regulatory networks of physiological and biochemical pathways underlying root lodging in maize. Limited overlap between genes associated with lodging resistance and RSA in this diversity panel suggests that anchorage depends in part on factors other than the gross characteristics of RSA., Competing Interests: Conflict of interest statement. The authors declare no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of American Society of Plant Biologists.)

High-throughput plant phenotyping-the use of imaging and remote sensing to record plant growth dynamics-is becoming more widely used. The first step in this process is typically plant segmentation, which requires a well-labeled training dataset to enable accurate segmentation of overlapping plants. However, preparing such training data is both time and labor intensive. To solve this problem, we propose a plant image processing pipeline using a self-supervised sequential convolutional neural network method for in-field phenotyping systems. This first step uses plant pixels from greenhouse images to segment nonoverlapping in-field plants in an early growth stage and then applies the segmentation results from those early-stage images as training data for the separation of plants at later growth stages. The proposed pipeline is efficient and self-supervising in the sense that no human-labeled data are needed. We then combine this approach with functional principal components analysis to reveal the relationship between the growth dynamics of plants and genotypes. We show that the proposed pipeline can accurately separate the pixels of foreground plants and estimate their heights when foreground and background plants overlap and can thus be used to efficiently assess the impact of treatments and genotypes on plant growth in a field environment by computer vision techniques. This approach should be useful for answering important scientific questions in the area of high-throughput phenotyping., (Copyright © 2023 Xingche Guo et al.)

Trait introgression (TI) can be a time-consuming and costly task that typically requires multiple generations of backcrossing (BC). Usually, the aim is to introduce one or more alleles (e.g. QTLs) from a single donor into an elite recipient, both of which are fully inbred. This article studies the potential advantages of incorporating intercrossing (IC) into TI programs when compared with relying solely on the traditional BC framework. We simulate a TI breeding pipeline using 3 previously proposed selection strategies for the traditional BC scheme and 3 modified strategies that allow IC. Our proposed look-ahead intercrossing method (LAS-IC) combines look-ahead Monte Carlo simulations, intercrossing, and additional selection criteria to improve computational efficiency. We compared the efficiency of the 6 strategies across 5 levels of resource availability considering the generation when the major QTLs have been successfully introduced into the recipient and a desired background recovery rate reached. Simulations demonstrate that the inclusion of intercrossing in a TI program can substantially increase efficiency and the probability of success. The proposed LAS-IC provides the highest probability of success across the different scenarios using fewer resources compared with BC-only strategies., Competing Interests: Conflict of interest GH, LW, and PSS are co-founders of LookAhead Breeding LLC., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Genetics Society of America.)

Anthocyanins are a class of antioxidants that scavenge free radicals in cells and play an important role in promoting human health and preventing many diseases. Here, we characterized a maize Bronze gene ( BZ1 ) from the purple colored W22 introgression line, which encodes an anthocyanin 3-O-glucosyltransferase, a key enzyme in the anthocyanin synthesis pathway. Mutation of ZmBZ1 showed bronze-colored seeds and reduced anthocyanins in seeds aleurone layer, seedlings coleoptile, and stem of mature plants by comparison with purple colored W22 (WT). Furthermore, we proved that maize BZ1 is an aleurone layer-specific expressed protein and sub-located in cell nucleus. Real-time tracing of the anthocyanins in developing seeds demonstrated that the pigment was visible from 16 DAP (day after pollination) in field condition, and first deposited in the crown part then spread all over the seed. Additionally, it was transferred along with the embryo cell activity during seed germination, from aleurone layer to cotyledon and coleoptile, as confirmed by microscopy and real-time qRT-PCR. Finally, we demonstrated that the ZmBZ1 contributes to stress tolerance, especially salinity. Further study proved that ZmBZ1 participates in reactive oxygen scavenging (ROS) by accumulating anthocyanins, thereby enhancing the tolerance to abiotic stress.

Competing Interests: The authors have declared that no competing interests exist.

The relationship between collared leaf number and growing degree days (GDD) is crucial for predicting maize phenology. Biophysical crop models convert GDD accumulation to leaf numbers by using a constant parameter termed phyllochron (°C-day leaf -1 ) or leaf appearance rate (LAR; leaf o C-day -1 ). However, such important parameter values are rarely estimated for modern maize hybrids. To fill this gap, we sourced and analyzed experimental datasets from the United States Corn Belt with the objective to (i) determine phyllochron values for two types of models: linear (1-parameter) and bilinear (3-parameters; phase I and II phyllochron, and transition point) and (ii) explore whether environmental factors such as photoperiod and radiation, and physiological variables such as plant growth rate can explain variability in phyllochron and improve predictability of maize phenology. The datasets included different locations (latitudes between 48° N and 41° N), years (2009-2019), hybrids, and management settings. Results indicated that the bilinear model represented the leaf number vs. GDD relationship more accurately than the linear model ( R 2  = 0.99 vs. 0.95, n  = 4,694). Across datasets, first phase phyllochron, transition leaf number, and second phase phyllochron averaged 57.9 ± 7.5°C-day, 9.8 ± 1.2 leaves, and 30.9 ± 5.7°C-day, respectively. Correlation analysis revealed that radiation from the V3 to the V9 developmental stages had a positive relationship with phyllochron ( r  = 0.69), while photoperiod was positively related to days to flowering or total leaf number ( r  = 0.89). Additionally, a positive nonlinear relationship between maize LAR and plant growth rate was found. Present findings provide important parameter values for calibration and optimization of maize crop models in the United States Corn Belt, as well as new insights to enhance mechanisms in crop models., Competing Interests: PS is a co-lead of the Genomes to Fields Initiative and PI of the USDA-NIFA funded Agricultural Genome to Phenome Initiative. He is co-founder of Data2Bio, LLC; Dryland Genetics, Inc.; EnGeniousAg, LLC; and LookAhead Breeding, LLC. He is a member of the scientific advisory board and a shareholder of Hi-Fidelity Genetics, Inc., and a member of the scientific advisory boards of Kemin Industries and Centro de Tecnologia Canavieira. He is a recipient of research funding from Iowa Corn and Bayer Crop Science. The remaining 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 © 2022 dos Santos, Abendroth, Coulter, Nafziger, Suyker, Yu, Schnable and Archontoulis.)

The accuracy of trait measurements greatly affects the quality of genetic analyses. During automated phenotyping, trait measurement errors, i.e. differences between automatically extracted trait values and ground truth, are often treated as random effects that can be controlled by increasing population sizes and/or replication number. In contrast, there is some evidence that trait measurement errors may be partially under genetic control. Consistent with this hypothesis, we observed substantial nonrandom, genetic contributions to trait measurement errors for five maize (Zea mays) tassel traits collected using an image-based phenotyping platform. The phenotyping accuracy varied according to whether a tassel exhibited "open" versus. "closed" branching architecture, which is itself under genetic control. Trait-associated SNPs (TASs) identified via genome-wide association studies (GWASs) conducted on five tassel traits that had been phenotyped both manually (i.e. ground truth) and via feature extraction from images exhibit little overlap. Furthermore, identification of TASs from GWASs conducted on the differences between the two values indicated that a fraction of measurement error is under genetic control. Similar results were obtained in a sorghum (Sorghum bicolor) plant height dataset, demonstrating that trait measurement error is genetically determined in multiple species and traits. Trait measurement bias cannot be controlled by increasing population size and/or replication number., (© The Author(s) 2021. Published by Oxford University Press on behalf of American Society of Plant Biologists.)

A genome-wide association study (GWAS) is used to identify genetic markers associated with phenotypic variation. In contrast, a transcriptome-wide association study (TWAS) detects associations between gene expression levels and phenotypic variation. It has previously been shown that in the cross-pollinated species, maize (Zea mays), GWAS, and TWAS identify complementary sets of trait-associated genes, many of which exhibit characteristics of true positives. Here, we extend this conclusion to the self-pollinated species, Arabidopsis thaliana and soybean (Glycine max). Linkage disequilibrium (LD) can result in the identification, via GWAS, of false-positive associations. In all three analyzed plant species, most trait-associated genes identified via TWAS are well separated physically from other candidate genes. Hence, TWAS is less affected by LD than is GWAS, demonstrating that TWAS is particularly well suited for association studies in genomes with slow rates of LD decay, such as soybean. TWAS is reasonably robust to the plant organs/tissues used to determine expression levels. In summary, this study confirms that TWAS is a promising approach for accurate gene-level association mapping in plants that is complementary to GWAS, and established that TWAS can exhibit substantial advantages relative to GWAS in species with slow rates of LD decay., (© The Author(s) 2021. Published by Oxford University Press on behalf of American Society of Plant Biologists.)

High-throughput phenotyping enables the efficient collection of plant trait data at scale. One example involves using imaging systems over key phases of a crop growing season. Although the resulting images provide rich data for statistical analyses of plant phenotypes, image processing for trait extraction is required as a prerequisite. Current methods for trait extraction are mainly based on supervised learning with human labeled data or semisupervised learning with a mixture of human labeled data and unsupervised data. Unfortunately, preparing a sufficiently large training data is both time and labor-intensive. We describe a self-supervised pipeline (KAT4IA) that uses K -means clustering on greenhouse images to construct training data for extracting and analyzing plant traits from an image-based field phenotyping system. The KAT4IA pipeline includes these main steps: self-supervised training set construction, plant segmentation from images of field-grown plants, automatic separation of target plants, calculation of plant traits, and functional curve fitting of the extracted traits. To deal with the challenge of separating target plants from noisy backgrounds in field images, we describe a novel approach using row-cuts and column-cuts on images segmented by transform domain neural network learning, which utilizes plant pixels identified from greenhouse images to train a segmentation model for field images. This approach is efficient and does not require human intervention. Our results show that KAT4IA is able to accurately extract plant pixels and estimate plant heights., Competing Interests: The authors declare that they do not have any commercial or associative interest that represents a conflict of interest in connection with the work., (Copyright © 2021 Xingche Guo et al.)

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.)

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 article, 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.

Polyploidization can have a significant ecological and evolutionary impact by providing substantially more genetic material that may result in novel phenotypes upon which selection may act. While the effects of polyploidization are broadly reviewed across the plant tree of life, the reproducibility of these effects within naturally occurring, independently formed polyploids is poorly characterized. The flowering plant genus Tragopogon (Asteraceae) offers a rare glimpse into the intricacies of repeated allopolyploid formation with both nascent (< 90 years old) and more ancient (mesopolyploids) formations. Neo- and mesopolyploids in Tragopogon have formed repeatedly and have extant diploid progenitors that facilitate the comparison of genome evolution after polyploidization across a broad span of evolutionary time. Here, we examine four independently formed lineages of the mesopolyploid Tragopogon castellanus for homoeolog expression changes and fractionation after polyploidization. We show that expression changes are remarkably similar among these independently formed polyploid populations with large convergence among expressed loci, moderate convergence among loci lost, and stochastic silencing. We further compare and contrast these results for T. castellanus with two nascent Tragopogon allopolyploids. While homoeolog expression bias was balanced in both nascent polyploids and T. castellanus , the degree of additive expression was significantly different, with the mesopolyploid populations demonstrating more non-additive expression. We suggest that gene dosage and expression noise minimization may play a prominent role in regulating gene expression patterns immediately after allopolyploidization as well as deeper into time, and these patterns are conserved across independent polyploid lineages., Competing Interests: PSc is a Changjiang Scholar at China Agriculture University, and co-founder and managing partner of Data2Bio, LLC; Dryland Genetics, LLC; and EnGeniousAg, LLC. He is a member of the scientific advisory board and a shareholder of Hi-Fidelity Genetics, Inc. and a member of the scientific advisory boards of Kemin Industries and Centro de Tecnologia Canavieira. H-CH was employed by the company Covance Inc. The remaining 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 Boatwright, Yeh, Hu, Susanna, Soltis, Soltis, Schnable and Barbazuk.)

Background: The maize inbred line A188 is an attractive model for elucidation of gene function and improvement due to its high embryogenic capacity and many contrasting traits to the first maize reference genome, B73, and other elite lines. The lack of a genome assembly of A188 limits its use as a model for functional studies., Results: Here, we present a chromosome-level genome assembly of A188 using long reads and optical maps. Comparison of A188 with B73 using both whole-genome alignments and read depths from sequencing reads identify approximately 1.1 Gb of syntenic sequences as well as extensive structural variation, including a 1.8-Mb duplication containing the Gametophyte factor1 locus for unilateral cross-incompatibility, and six inversions of 0.7 Mb or greater. Increased copy number of carotenoid cleavage dioxygenase 1 (ccd1) in A188 is associated with elevated expression during seed development. High ccd1 expression in seeds together with low expression of yellow endosperm 1 (y1) reduces carotenoid accumulation, accounting for the white seed phenotype of A188. Furthermore, transcriptome and epigenome analyses reveal enhanced expression of defense pathways and altered DNA methylation patterns of the embryonic callus., Conclusions: The A188 genome assembly provides a high-resolution sequence for a complex genome species and a foundational resource for analyses of genome variation and gene function in maize. The genome, in comparison to B73, contains extensive intra-species structural variations and other genetic differences. Expression and network analyses identify discrete profiles for embryonic callus and other tissues.

Identifying mechanisms and pathways involved in gene-environment interplay and phenotypic plasticity is a long-standing challenge. It is highly desirable to establish an integrated framework with an environmental dimension for complex trait dissection and prediction. A critical step is to identify an environmental index that is both biologically relevant and estimable for new environments. With extensive field-observed complex traits, environmental profiles, and genome-wide single nucleotide polymorphisms for three major crops (maize, wheat, and oat), we demonstrated that identifying such an environmental index (i.e., a combination of environmental parameter and growth window) enables genome-wide association studies and genomic selection of complex traits to be conducted with an explicit environmental dimension. Interestingly, genes identified for two reaction-norm parameters (i.e., intercept and slope) derived from flowering time values along the environmental index were less colocalized for a diverse maize panel than for wheat and oat breeding panels, agreeing with the different diversity levels and genetic constitutions of the panels. In addition, we showcased the usefulness of this framework for systematically forecasting the performance of diverse germplasm panels in new environments. This general framework and the companion CERIS-JGRA analytical package should facilitate biologically informed dissection of complex traits, enhanced performance prediction in breeding for future climates, and coordinated efforts to enrich our understanding of mechanisms underlying phenotypic variation., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

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.)

The tassel of the maize plant is responsible for the production and dispersal of pollen for subsequent capture by the silk (stigma) and fertilization of the ovules. Both the amount and timing of pollen shed are physiological traits that impact the production of a hybrid seed. This study describes an automated end-to-end pipeline that combines deep learning and image processing approaches to extract tassel flowering patterns from time-lapse camera images of plants grown under field conditions. Inbred lines from the SAM and NAM diversity panels were grown at the Curtiss farm at Iowa State University, Ames, IA, during the summer of 2016. Using a set of around 500 pole-mounted cameras installed in the field, images of plants were captured every 10 minutes of daylight hours over a three-week period. Extracting data from imaging performed under field conditions is challenging due to variabilities in weather, illumination, and the morphological diversity of tassels. To address these issues, deep learning algorithms were used for tassel detection, classification, and segmentation. Image processing approaches were then used to crop the main spike of the tassel to track reproductive development. The results demonstrated that deep learning with well-labeled data is a powerful tool for detecting, classifying, and segmenting tassels. Our sequential workflow exhibited the following metrics: mAP for tassel detection was 0.91, F1 score obtained for tassel classification was 0.93, and accuracy of semantic segmentation in creating a binary image from the RGB tassel images was 0.95. This workflow was used to determine spatiotemporal variations in the thickness of the main spike-which serves as a proxy for anthesis progression., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Seyed Vahid Mirnezami et al.)

Popcorn (Zea mays L. var. Everta) is the most ancient type of cultivated maize. However, there is little known about the genetics of popping-related traits based on genotyping-by-sequencing (GBS) technology. Here, we characterized the phenotypic variation for seven popping-related traits in maize kernels among 526 CIMMYT inbred lines (CMLs). In total, 155 083 high-quality single nucleotide polymorphism (SNP) markers were identified by a GBS approach. Several trait-associated loci were detected by genome-wide association study for color, popping expansion volume, shape, pericarp, flotation index, floury/vitreous, and protein content, explaining a majority of the observed phenotypic variance, and these were validated by a diverse panel comprising 764 tropical landrace accessions. Sixty two of the identified loci were recognized to have undergone selection. On average, there was a 55.27% frequency for alleles that promote popping in CMLs. Our work not only pinpoints previously unknown loci for popping-related traits, but also reveals that many of these loci have undergone selection. Beyond establishing a new benchmark for the genetics of popcorn, our study provides a foundation for gene discovery and breeding. It also presents evidence to investigate the role of a gradual loss of popping ability as a by-product of diversification of culinary uses throughout the evolution of teosinte-to-modern maize., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

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.)

A "smart canopy" ideotype has been proposed with leaves being upright at the top and more horizontal toward the bottom of the plant to maximize light interception and conversion efficiencies, and thus increasing yield. The genetic control of leaf angle has, to date, been studied on one or two leaves, or data have been merged from multiple leaves to generate average values. This approach has limited our understanding of the diversity of leaf angles across layers and their genetic control. Genome-wide association studies and quantitative trait loci mapping studies in sorghum ( Sorghum bicolor ) were performed using layer-specific angle data collected manually and via high-throughput phenotyping strategies. The observed distribution of angles in indoor and field settings is opposite to the ideotype. Several genomic regions were associated with leaf angle within layers or across the canopy. The expression of the brassinosteroid-related transcription factor BZR1 / BES1 and the auxin-transporter Dwarf3 were found to be highly correlated with the distribution of angles at different layers. The application of a brassinosteroid biosynthesis inhibitor could not revert the undesirable overall angle distribution. These discoveries demonstrate that the exploitation of layer-specific quantitative trait loci/genes will be instrumental to reversing the natural angle distribution in sorghum according to the "smart canopy" ideotype., (© 2020 American Society of Plant Biologists. All Rights Reserved.)

Effective evaluation of millions of crop genetic stocks is an essential component of exploiting genetic diversity to achieve global food security. By leveraging genomics and data analytics, genomic prediction is a promising strategy to efficiently explore the potential of these gene banks by starting with phenotyping a small designed subset. Reliable genomic predictions have enhanced selection of many macroscopic phenotypes in plants and animals. However, the use of genomicprediction strategies for analysis of microscopic phenotypes is limited. Here, we exploited the power of genomic prediction for eight maize traits related to the shoot apical meristem (SAM), the microscopic stem cell niche that generates all the above-ground organs of the plant. With 435 713 genomewide single-nucleotide polymorphisms (SNPs), we predicted SAM morphology traits for 2687 diverse maize inbreds based on a model trained from 369 inbreds. An empirical validation experiment with 488 inbreds obtained a prediction accuracy of 0.37-0.57 across eight traits. In addition, we show that a significantly higher prediction accuracy was achieved by leveraging the U value (upper bound for reliability) that quantifies the genomic relationships of the validation set with the training set. Our findings suggest that double selection considering both prediction and reliability can be implemented in choosing selection candidates for phenotyping when exploring new diversity is desired. In this case, individuals with less extreme predicted values and moderate reliability values can be considered. Our study expands the turbocharging gene banks via genomic prediction from the macrophenotypes into the microphenotypic space., (© 2020 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.)

Although, the Pacific crabapple, Malus fusca, is a hardy and disease resistant species, studies relating to the genetics of its unique traits are very limited partly due to the lack of a genetic map of this interesting wild apple. An accession of M. fusca (MAL0045) of Julius Kühn-Institut collection in Germany is highly resistant to fire blight disease, incited by different strains of the causative pathogen-Erwinia amylovora. This is the most destructive bacterial disease of Malus of which most of the domesticated apples (Malus domestica) are susceptible. Using a scarcely dense genetic map derived from a population of 134 individuals of MAL0045 × 'Idared', the locus (Mfu10) controlling fire blight resistance mapped on linkage group 10 (LG10) and explained up to 66% of the phenotypic variance with different strains. Although the development of robust and tightly linked molecular markers on LG10 through chromosome walking approach led to the identification of a major candidate gene, any minor effect locus remained elusive possibly due to the lack of marker density of the entire genetic map. Therefore, we have developed a dense genetic map of M. fusca using tunable genotyping-by-sequencing (tGBS) approach. Of thousands of de novo SNPs identified, 2677 were informative in M. fusca and 90.5% of these successfully mapped. In addition, integration of SNP data and microsatellite (SSR) data resulted in a final map comprising 17 LGs with 613 loci spanning 1081.35 centi Morgan (cM). This map will serve as a template for mapping using different strains of the pathogen.

Key Message: Significant introgression-by-environment interactions are observed for traits throughout development from small introgressed segments of the genome. 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 introgression-by-environment interactions. To study potential patterns of introgression-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 introgression-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 introgression-by-environment interaction varies across traits. Some introgressed segments were more prone to introgression-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 introgression-by-environment interaction in NILs is useful in consideration of how small introgressions of QTL or transgene containing regions might be expected to impact traits in diverse environments.

Premise: Maize yields have significantly increased over the past half-century owing to advances in breeding and agronomic practices. Plants have been grown in increasingly higher densities due to changes in plant architecture resulting in plants with more upright leaves, which allows more efficient light interception for photosynthesis. Natural variation for leaf angle has been identified in maize and sorghum using multiple mapping populations. However, conventional phenotyping techniques for leaf angle are low throughput and labor intensive, and therefore hinder a mechanistic understanding of how the leaf angle of individual leaves changes over time in response to the environment., Methods: High-throughput time series image data from water-deprived maize ( Zea mays subsp. mays ) and sorghum ( Sorghum bicolor ) were obtained using battery-powered time-lapse cameras. A MATLAB-based image processing framework, Leaf Angle eXtractor (LAX), was developed to extract and quantify leaf angles from images of maize and sorghum plants under drought conditions., Results: Leaf angle measurements showed differences in leaf responses to drought in maize and sorghum. Tracking leaf angle changes at intervals as short as one minute enabled distinguishing leaves that showed signs of wilting under water deprivation from other leaves on the same plant that did not show wilting during the same time period., Discussion: Automating leaf angle measurements using LAX makes it feasible to perform large-scale experiments to evaluate, understand, and exploit the spatial and temporal variations in plant response to water limitations., (© 2020 Kenchanmane Raju et al. Applications in Plant Sciences published by Wiley Periodicals LLC on behalf of Botanical Society of America.)

Plant breeders make selection decisions based on multiple traits, such as yield, plant height, flowering time, and disease resistance. A commonly used approach in multi-trait genomic selection is index selection, which assigns weights to different traits relative to their economic importance. However, classical index selection only optimizes genetic gain in the next generation, requires some experimentation to find weights that lead to desired outcomes, and has difficulty optimizing nonlinear breeding objectives. Multi-objective optimization has also been used to identify the Pareto frontier of selection decisions, which represents different trade-offs across multiple traits. We propose a new approach, which maximizes certain traits while keeping others within desirable ranges. Optimal selection decisions are made using a new version of the look-ahead selection (LAS) algorithm, which was recently proposed for single-trait genomic selection, and achieved superior performance with respect to other state-of-the-art selection methods. To demonstrate the effectiveness of the new method, a case study is developed using a realistic data set where our method is compared with conventional index selection. Results suggest that the multi-trait LAS is more effective at balancing multiple traits compared with index selection., (Copyright © 2020 by the Genetics Society of America.)

The phenotypes of plants develop over time and change in response to the environment. New engineering and computer vision technologies track these phenotypic changes. Identifying the genetic loci regulating differences in the pattern of phenotypic change remains challenging. This study used functional principal component analysis (FPCA) to achieve this aim. Time series phenotype data were collected from a sorghum ( Sorghum bicolor ) diversity panel using a number of technologies including conventional color photography and hyperspectral imaging. This imaging lasted for 37 d and centered on reproductive transition. A new higher density marker set was generated for the same population. Several genes known to control trait variation in sorghum have been previously 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 phenotypes. Partitioning was consistent with the known molecular function of the individual cloned genes. These data demonstrate that FPCA-based genome-wide association studies can enable robust time series mapping analyses in a wide range of contexts. Moreover, time series analysis can increase the accuracy and power of quantitative genetic analyses., (© 2020 American Society of Plant Biologists. All Rights Reserved.)

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.

Landraces often contain genetic diversity that has been lost in modern cultivars, including alleles that confer enhanced local adaptation. To comprehensively identify loci associated with adaptive traits in soya bean landraces, for example flowering time, a population of 1938 diverse landraces and 97 accessions of the wild progenitor of cultivated soya bean, Glycine soja was genotyped using tGBS ® . Based on 99 085 high-quality SNPs, landraces were classified into three sub-populations which exhibit geographical genetic differentiation. Clustering was inferred from STRUCTURE, principal component analyses and neighbour-joining tree analyses. Using phenotypic data collected at two locations separated by 10 degrees of latitude, 17 trait-associated SNPs (TASs) for flowering time were identified, including a stable locus Chr12:5914898 and previously undetected candidate QTL/genes for flowering time in the vicinity of the previously cloned flowering genes, E1 and E2. Using passport data associated with the collection sites of the landraces, 27 SNPs associated with adaptation to three bioclimatic variables (temperature, daylength, and precipitation) were identified. A series of candidate flowering genes were detected within linkage disequilibrium (LD) blocks surrounding 12 bioclimatic TASs. Nine of these TASs exhibit significant differences in flowering time between alleles within one or more of the three individual sub-populations. Signals of selection during domestication and/or subsequent landrace diversification and adaptation were detected at 38 of the 44 flowering and bioclimatic TASs. Hence, this study lays the groundwork to begin breeding for novel environments predicted to arise following global climate change., (© 2019 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.)

Determining the genetic control of root system architecture (RSA) in plants via large-scale genome-wide association study (GWAS) requires high-throughput pipelines for root phenotyping. We developed Core Root Excavation using Compressed-air (CREAMD), a high-throughput pipeline for the cleaning of field-grown roots, and Core Root Feature Extraction (COFE), a semiautomated pipeline for the extraction of RSA traits from images. CREAMD-COFE was applied to diversity panels of maize ( Zea mays ) and sorghum ( Sorghum bicolor ), which consisted of 369 and 294 genotypes, respectively. Six RSA-traits were extracted from images collected from >3,300 maize roots and >1,470 sorghum roots. Single nucleotide polymorphism (SNP)-based GWAS identified 87 TAS (trait-associated SNPs) in maize, representing 77 genes and 115 TAS in sorghum. An additional 62 RSA-associated maize genes were identified via expression read depth GWAS. Among the 139 maize RSA-associated genes (or their homologs), 22 (16%) are known to affect RSA in maize or other species. In addition, 26 RSA-associated genes are coregulated with genes previously shown to affect RSA and 51 (37% of RSA-associated genes) are themselves transe-quantitative trait locus for another RSA-associated gene. Finally, the finding that RSA-associated genes from maize and sorghum included seven pairs of syntenic genes demonstrates the conservation of regulation of morphology across taxa., (© 2020 American Society of Plant Biologists. All Rights Reserved.)

The shoot apical meristem (SAM) orchestrates the balance between stem cell proliferation and organ initiation essential for postembryonic shoot growth. Meristems show a striking diversity in shape and size. How this morphological diversity relates to variation in plant architecture and the molecular circuitries driving it are unclear. By generating a high-resolution gene expression atlas of the vegetative maize shoot apex, we show here that distinct sets of genes govern the regulation and identity of stem cells in maize versus Arabidopsis. Cell identities in the maize SAM reflect the combinatorial activity of transcription factors (TFs) that drive the preferential, differential expression of individual members within gene families functioning in a plethora of cellular processes. Subfunctionalization thus emerges as a fundamental feature underlying cell identity. Moreover, we show that adult plant characters are, to a significant degree, regulated by gene circuitries acting in the SAM, with natural variation modulating agronomically important architectural traits enriched specifically near dynamically expressed SAM genes and the TFs that regulate them. Besides unique mechanisms of maize stem cell regulation, our atlas thus identifies key new targets for crop improvement., (© 2019 Knauer et al.; Published by Cold Spring Harbor Laboratory Press.)

The plant circadian clock evolved to increase fitness by synchronizing physiological processes with environmental oscillations. Crop fitness was artificially selected through domestication and breeding, and the circadian clock was identified by both natural and artificial selections as a key to improved fitness. Despite progress in Arabidopsis , our understanding of the crop circadian clock is still limited, impeding its rational improvement for enhanced fitness. To unveil the interactions between the crop circadian clock and various environmental cues, we comprehensively mapped abiotic stress inputs to the soybean circadian clock using a 2-module discovery pipeline. Using the "molecular timetable" method, we computationally surveyed publicly available abiotic stress-related soybean transcriptomes to identify stresses that have strong impacts on the global rhythm. These findings were then experimentally confirmed using a multiplexed RNA sequencing technology. Specific clock components modulated by each stress were further identified. This comprehensive mapping uncovered inputs to the plant circadian clock such as alkaline stress. Moreover, short-term iron deficiency targeted different clock components in soybean and Arabidopsis and thus had opposite effects on the clocks of these 2 species. Comparing soybean varieties with different iron uptake efficiencies suggests that phase modulation might be a mechanism to alleviate iron deficiency symptoms in soybean. These unique responses in soybean demonstrate the need to directly study crop circadian clocks. Our discovery pipeline may serve as a broadly applicable tool to facilitate these explorations., Competing Interests: The authors declare no competing interest., (Copyright © 2019 the Author(s). Published by PNAS.)

There is an unmet need for improved fertilizer management in agriculture. Continuous monitoring of soil nitrate would address this need. This paper reports an all-solid-state miniature potentiometric soil sensor that works in direct contact with soils to monitor nitrate-nitrogen (NO 3 - -N) in soil solution with parts-per-million (ppm) resolution. A working electrode is formed from a novel nanocomposite of poly(3-octyl-thiophene) and molybdenum disulfide (POT-MoS 2 ) coated on a patterned Au electrode and covered with a nitrate-selective membrane using a robotic dispenser. The POT-MoS 2 layer acts as an ion-to-electron transducing layer with high hydrophobicity and redox properties. The modification of the POT chain with MoS 2 increases both conductivity and anion exchange, while minimizing the formation of a thin water layer at the interface between the Au electrode and the ion-selective membrane, which is notorious for solid-state potentiometric ion sensors. Therefore, the use of POT-MoS 2 results in an improved sensitivity and selectivity of the working electrode. The reference electrode comprises a screen-printed silver/silver chloride (Ag/AgCl) electrode covered by a protonated Nafion layer to prevent chloride (Cl - ) leaching in long-term measurements. This sensor was calibrated using both standard and extracted soil solutions, exhibiting a dynamic range that includes all concentrations relevant for agricultural applications (1-1500 ppm NO 3 - -N). With the POT-MoS 2 nanocomposite, the sensor offers a sensitivity of 64 mV/decade for nitrate detection, compared to 48 mV/decade for POT and 38 mV/decade for MoS 2 . The sensor was embedded into soil slurries where it accurately monitored nitrate for a duration of 27 days.

Understanding the genomic basis of adaptation in maize is important for gene discovery and the improvement of breeding germplasm, but much remains a mystery in spite of significant population genetics and archaeological research. Identifying the signals underpinning adaptation are challenging as adaptation often coincided with genetic drift, and the base genomic diversity of the species in massive. In this study, tGBS technology was used to genotype 1,143 diverse maize accessions including landraces collected from 20 countries and elite breeding lines of tropical lowland, highland, subtropical/midaltitude and temperate ecological zones. Based on 355,442 high-quality single nucleotide polymorphisms, 13 genomic regions were detected as being under selection using the bottom-up searching strategy, EigenGWAS. Of the 13 selection regions, 10 were first reported, two were associated with environmental parameters via EnvGWAS, and 146 genes were enriched. Combining large-scale genomic and ecological data in this diverse maize panel, our study supports a polygenic adaptation model of maize and offers a framework to enhance our understanding of both the mechanistic basis and the evolutionary consequences of maize domestication and adaptation. The regions identified here are promising candidates for further, targeted exploration to identify beneficial alleles and haplotypes for deployment in maize breeding., (© 2019 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd.)

New genotyping technologies have made large amounts of genotypic data available for plant breeders to use in their efforts to accelerate the rate of genetic gain. Genomic selection (GS) techniques allow breeders to use genotypic data to identify and select, for example, plants predicted to exhibit drought tolerance, thereby saving expensive and limited field-testing resources relative to phenotyping all plants within a population. A major limitation of existing GS approaches is the trade-off between short-term genetic gain and long-term potential. Some approaches focus on achieving short-term genetic gain at the cost of reduced genetic diversity necessary for long-term gains. In contrast, others compromise short-term progress to preserve long-term potential without consideration of the time and resources required to achieve it. Our contribution is to define a new "look-ahead" metric for assessing selection decisions, which evaluates the probability of achieving high genetic gains by a specific time with limited resources. Moreover, we propose a heuristic algorithm to identify optimal selection decisions that maximize the look-ahead metric. Simulation results demonstrate that look-ahead selection outperforms other published selection methods., (Copyright © 2019 Moeinizade et al.)

Cuticular waxes, long-chain hydrocarbon compounds, form the outermost layer of plant surfaces in most terrestrial plants. The presence of cuticular waxes protects plants from water loss and other environmental stresses. Cloning and characterization of genes involved in the regulation, biosynthesis, and extracellular transport of cuticular waxes onto the surface of epidermal cells have revealed the molecular basis of cuticular 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, DUF538 domain. This study suggests that the GL6 protein may be involved in the intracellular trafficking of cuticular waxes, opening the door to elucidating the poorly understood process by which cuticular wax is transported from its site of biosynthesis to the plasma membrane., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

Celiac disease is the most common food-induced enteropathy in humans, with a prevalence of approximately 1% worldwide. It is induced by digestion-resistant, proline- and glutamine-rich seed storage proteins, collectively referred to as gluten, found in wheat ( Triticum aestivum ). Related prolamins are present in barley ( Hordeum vulgare ) and rye ( Secale cereale ). The incidence of both celiac disease and a related condition called nonceliac gluten sensitivity is increasing. This has prompted efforts to identify methods of lowering gluten in wheat, one of the most important cereal crops. Here, we used bulked segregant RNA sequencing 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 targeting-induced local lesions in genomes to identify induced single-nucleotide polymorphisms in the three homeologs of the corresponding wheat gene. Combining inactivating mutations in the three subgenomes of hexaploid bread wheat in a single wheat line lowered gliadin and low-molecular-weight glutenin accumulation by 50% to 60% and increased free and protein-bound lysine by 33%., (© 2019 American Society of Plant Biologists. All Rights Reserved.)

Flowering time is a major determinant of the local adaptation of plants. Although numerous loci affecting flowering time have been mapped in maize, their underlying molecular mechanisms and roles in adaptation remain largely unknown. Here, we report the identification and characterization of MADS-box transcription factor ZmMADS69 that functions as a flowering activator through the ZmRap2.7-ZCN8 regulatory module and contributes to adaptation. We show that ZmMADS69 underlies a quantitative trait locus controlling the difference in flowering time between maize and its wild ancestor, teosinte. Maize ZmMADS69 allele is expressed at a higher level at floral transition and confers earlier flowering than the teosinte allele under long days and short days. Overexpression of ZmMADS69 causes early flowering, while a transposon insertion mutant of ZmMADS69 exhibits delayed flowering. ZmMADS69 shows pleiotropic effects for multiple traits of agronomic importance. ZmMADS69 functions upstream of the flowering repressor ZmRap2.7 to downregulate its expression, thereby relieving the repression of the florigen gene ZCN8 and causing early flowering. Population genetic analyses showed that ZmMADS69 was a target of selection and may have played an important role as maize spread from the tropics to temperate zones. Our findings provide important insights into the regulation and adaptation of flowering time., (© 2018 The Authors. New Phytologist © 2018 New Phytologist Trust.)

Epicuticular waxes provide a hydrophobic barrier that protects land plants from environmental stresses. To elucidate the molecular functions of maize glossy mutants that reduce the accumulation of epicuticular waxes, eight non-allelic glossy mutants were subjected to transcriptomic comparisons with their respective wild-type siblings. Transcriptomic comparisons identified 2279 differentially expressed (DE) genes. Other glossy genes tended to be down-regulated in glossy mutants; by contrast stress-responsive pathways were induced in mutants. Gene co-expression network (GCN) analysis found that glossy genes were clustered, suggestive of co-regulation. Genes that potentially regulate the accumulation of glossy gene transcripts were identified via a pathway level co-expression analysis. Expression data from diverse organs showed that maize glossy genes are generally active in young leaves, silks, and tassels, while largely inactive in seeds and roots. Through reverse genetics, a DE gene homologous to Arabidopsis CER8 and co-expressed with known glossy genes was confirmed to participate in epicuticular wax accumulation. GCN data-informed forward genetics approach enabled cloning of the gl14 gene, which encodes a putative membrane-associated protein. Our results deepen understanding of the transcriptional regulation of the genes involved in the accumulation of epicuticular wax, and provide two maize glossy genes and a number of candidate genes for further characterization., (© 2018 The Authors The Plant Journal © 2018 John Wiley & Sons Ltd.)

Broomcorn millet (Panicum miliaceum L.) is the most water-efficient cereal and one of the earliest domesticated plants. Here we report its high-quality, chromosome-scale genome assembly using a combination of short-read sequencing, single-molecule real-time sequencing, Hi-C, and a high-density genetic map. Phylogenetic analyses reveal two sets of homologous chromosomes that may have merged ~5.6 million years ago, both of which exhibit strong synteny with other grass species. Broomcorn millet contains 55,930 protein-coding genes and 339 microRNA genes. We find Paniceae-specific expansion in several subfamilies of the BTB (broad complex/tramtrack/bric-a-brac) subunit of ubiquitin E3 ligases, suggesting enhanced regulation of protein dynamics may have contributed to the evolution of broomcorn millet. In addition, we identify the coexistence of all three C 4 subtypes of carbon fixation candidate genes. The genome sequence is a valuable resource for breeders and will provide the foundation for studying the exceptional stress tolerance as well as C 4 biology.

Because structural variation in the inflorescence architecture of cereal crops can influence yield, it is of interest to identify the genes responsible for this variation. However, the manual collection of inflorescence phenotypes can be time consuming for the large populations needed to conduct genome-wide association studies (GWAS) and is difficult for multidimensional traits such as volume. A semiautomated phenotyping pipeline, TIM (Toolkit for Inflorescence Measurement), was developed and used to extract unidimensional and multidimensional features from images of 1,064 sorghum ( Sorghum bicolor ) panicles from 272 genotypes comprising a subset of the Sorghum Association Panel. GWAS detected 35 unique single-nucleotide polymorphisms associated with variation in inflorescence architecture. The accuracy of the TIM pipeline is supported by the fact that several of these trait-associated single-nucleotide polymorphisms (TASs) are located within chromosomal regions associated with similar traits in previously published quantitative trait locus and GWAS analyses of sorghum. Additionally, sorghum homologs of maize ( Zea mays ) and rice ( Oryza sativa ) genes known to affect inflorescence architecture are enriched in the vicinities of TASs. Finally, our TASs are enriched within genomic regions that exhibit high levels of divergence between converted tropical lines and cultivars, consistent with the hypothesis that these chromosomal intervals were targets of selection during modern breeding., (© 2019 American Society of Plant Biologists. All Rights Reserved.)

Advances in next generation sequencing technologies and statistical approaches enable genome-wide dissection of phenotypic traits via genome-wide association studies (GWAS). Although multiple statistical approaches for conducting GWAS are available, the power and cross-validation rates of many approaches have been mostly tested using simulated data. Empirical comparisons of single variant (SV) and multi-variant (MV) GWAS approaches have not been conducted to test if a single approach or a combination of SV and MV is effective, through identification and cross-validation of trait-associated loci. In this study, kernel row number (KRN) data were collected from a set of 6,230 entries derived from the Nested Association Mapping (NAM) population and related populations. Three different types of GWAS analyses were performed: 1) single-variant (SV), 2) stepwise regression (STR) and 3) a Bayesian-based multi-variant (BMV) model. Using SV, STR, and BMV models, 257, 300, and 442 KRN-associated variants (KAVs) were identified in the initial GWAS analyses. Of these, 231 KAVs were subjected to genetic validation using three unrelated populations that were not included in the initial GWAS. Genetic validation results suggest that the three GWAS approaches are complementary. Interestingly, KAVs in low recombination regions were more likely to exhibit associations in independent populations than KAVs in recombinationally active regions, probably as a consequence of linkage disequilibrium. The KAVs identified in this study have the potential to enhance our understanding of the genetic basis of ear development., (Copyright © 2018 Yang et al.)