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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.

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.

Similar progressive leaf lesion phenotypes, named conring for "concentric ring", were identified in 10 independently-derived maize lines. Complementation and mapping experiments indicated that the phenotype had the same genetic basis in each line - a single recessive gene located in a 1.1 Mb region on chromosome 2. Among the 15 predicted genes in this interval, Zm00001d003866 (subsequently renamed Conring or Cnr) had insertions of four related 138 bp transposable element (TE) sequences at precisely the same site in exon 4 in nine of the 10 cnr alleles. The tenth cnr allele had a distinct insertion of 226 bp of in exon 3. Genetic evidence suggested that the 10 cnr alleles were independently-derived, and arose during the derivation of each line. The four TEs, named COINa (for COnring INsertion) through COINd, have not been previously characterized and consist entirely of imperfect 69 bp terminal inverted repeats (TIRs) characteristic of the Foldback class of TEs. They belong to three clades of a family of maize TEs comprising hundreds of sequences in the genome of the B73 maize line. COIN elements preferentially insert at TNA sequences with a preference for C and G nucleotides in the immediately flanking 5' and 3' regions, respectively. They produce a three base target site duplication and do not have homology to other characterized TEs. We propose that Cnr is an unstable gene that is insertionally mutated at high frequency, most commonly due to COIN element insertions at a specific site in the gene.

Remarkable productivity has been achieved in crop species through artificial selection and adaptation to modern agronomic practices. Whether intensive selection has changed the ability of improved cultivars to maintain high productivity across variable environments is unknown. Understanding the genetic control of phenotypic plasticity and genotype by environment (G × E) interaction will enhance crop performance predictions across diverse environments. Here we use data generated from the Genomes to Fields (G2F) Maize G × E project to assess the effect of selection on G × E variation and characterize polymorphisms associated with plasticity. Genomic regions putatively selected during modern temperate maize breeding explain less variability for yield G × E than unselected regions, indicating that improvement by breeding may have reduced G × E of modern temperate cultivars. Trends in genomic position of variants associated with stability reveal fewer genic associations and enrichment of variants 0–5000 base pairs upstream of genes, hypothetically due to control of plasticity by short-range regulatory elements., Breeding has increased crop productivity, but whether it has also changed phenotypic plasticity is unclear. Here, the authors find maize genomic regions selected for high productivity show reduced contribution to genotype by environment variation and provide evidence for regulatory control of phenotypic stability.

High-throughput sequencing (HTS) of reduced representation genomic libraries has ushered in an era of genotyping-by-sequencing (GBS), where genome-wide genotype data can be obtained for nearly any species. However, there remains a need for imputation-free GBS methods for genotyping large samples taken from heterogeneous populations of heterozygous individuals. This requires that a number of issues encountered with GBS be considered, including the sequencing of nonoverlapping sets of loci across multiple GBS libraries, a common missing data problem that results in low call rates for markers per individual, and a tendency for applicability only in inbred line samples with sufficient linkage disequilibrium for accurate imputation. We addressed these issues while developing and validating a new, comprehensive platform for GBS. This study supports the notion that GBS can be tailored to particular aims, and using Zea mays our results indicate that large samples of unknown pedigree can be genotyped to obtain complete and accurate GBS data. Optimizing size selection to sequence a high proportion of shared loci among individuals in different libraries and using simple in silico filters, a GBS procedure was established that produces high call rates per marker (>85%) with accuracy exceeding 99.4%. Furthermore, by capitalizing on the sequence-read structure of GBS data (stacks of reads), a new tool for resolving local haplotypes and scoring phased genotypes was developed, a feature that is not available in many GBS pipelines. Using local haplotypes reduces the marker dimensionality of the genotype matrix while increasing the informativeness of the data. Phased GBS in maize also revealed the existence of reproducibly inaccurate (apparent accuracy) genotypes that were due to divergent copy number variants (CNVs) unobservable in the underlying single nucleotide polymorphism (SNP) data.

Physiological leaf spotting, or flecking, is a mild-lesion phenotype observed on the leaves of several commonly used maize (Zea mays) inbred lines and has been anecdotally linked to enhanced broad-spectrum disease resistance. Flecking was assessed in the maize nested association mapping (NAM) population, comprising 4,998 recombinant inbred lines from 25 biparental families, and in an association population, comprising 279 diverse maize inbreds. Joint family linkage analysis was conducted with 7,386 markers in the NAM population. Genome-wide association tests were performed with 26.5 million single-nucleotide polymorphisms (SNPs) in the NAM population and with 246,497 SNPs in the association population, resulting in the identification of 18 and three loci associated with variation in flecking, respectively. Many of the candidate genes colocalizing with associated SNPs are similar to genes that function in plant defense response via cell wall modification, salicylic acid- and jasmonic acid-dependent pathways, redox homeostasis, stress response, and vesicle trafficking/remodeling. Significant positive correlations were found between increased flecking, stronger defense response, increased disease resistance, and increased pest resistance. A nonlinear relationship with total kernel weight also was observed whereby lines with relatively high levels of flecking had, on average, lower total kernel weight. We present evidence suggesting that mild flecking could be used as a selection criterion for breeding programs trying to incorporate broad-spectrum disease resistance.

Quantitative resistance to maize common rust (causal agent Puccinia sorghi) was assessed in an association mapping population of 274 diverse inbred lines. Resistance to common rust was found to be moderately correlated with resistance to three other diseases and with the severity of the hypersensitive defense response previously assessed in the same population. Using a mixed linear model accounting for the confounding effects of population structure and flowering time, genome-wide association tests were performed based at 246,497 single-nucleotide polymorphism loci. Three loci associated with maize common rust resistance were identified. Candidate genes at each locus had predicted roles, mainly in cell wall modification. Other candidate genes included a resistance gene and a gene with a predicted role in regulating accumulation of reactive oxygen species.

The study of phenotypic variation in plant pathogenesis provides fundamental information about the nature of disease resistance. Cellular mechanisms that alter pathogenesis can be elucidated with confocal microscopy; however, systematic phenotyping platforms-from sample processing to image analysis-to investigate this do not exist. We have developed a platform for 3D phenotyping of cellular features underlying variation in disease development by fluorescence-specific resolution of host and pathogen interactions across time (4D). A confocal microscopy phenotyping platform compatible with different maize-fungal pathosystems (fungi: Setosphaeria turcica, Cochliobolus heterostrophus, and Cercospora zeae-maydis) was developed. Protocols and techniques were standardized for sample fixation, optical clearing, species-specific combinatorial fluorescence staining, multisample imaging, and image processing for investigation at the macroscale. The sample preparation methods presented here overcome challenges to fluorescence imaging such as specimen thickness and topography as well as physiological characteristics of the samples such as tissue autofluorescence and presence of cuticle. The resulting imaging techniques provide interesting qualitative and quantitative information not possible with conventional light or electron 2D imaging. Microsc. Res. Tech., 81:141-152, 2018. © 2016 Wiley Periodicals, Inc.

Improved capacity of genomics and biotechnology has greatly enhanced genetic studies in different areas. Genomic selection exploits the genotype-to-phenotype relationship at the whole-genome level and is being implemented in many crops. Here we show that design-thinking and data-mining techniques can be leveraged to optimize genomic prediction of hybrid performance. We phenotyped a set of 276 maize hybrids generated by crossing founder inbreds of nested association mapping populations for flowering time, ear height, and grain yield. With 10 296 310 SNPs available from the parental inbreds, we explored the patterns of genomic relationships and phenotypic variation to establish training samples based on clustering, graphic network analysis, and genetic mating scheme. Our analysis showed that training set designs outperformed random sampling and earlier methods that either minimize the mean of prediction error variance or maximize the mean of generalized coefficient of determination. Additional analyses of 2556 wheat hybrids from an early-stage hybrid breeding system and 1439 rice hybrids from an established hybrid breeding system validated the approaches. Together, we demonstrated that effective genomic prediction models can be established with a training set 2%–13% of the size of the whole set, enabling an efficient exploration of enormous inference space of genetic combinations.

Background Targeted resequencing with high-throughput sequencing (HTS) platforms can be used to efficiently interrogate the genomes of large numbers of individuals. A critical issue for research and applications using HTS data, especially from long-read platforms, is error in base calling arising from technological limits and bioinformatic algorithms. We found that the community standard long amplicon analysis (LAA) module from Pacific Biosciences is prone to substantial bioinformatic errors that raise concerns about findings based on this pipeline, prompting the need for a new method. Results A single molecule real-time (SMRT) sequencing-error correction and assembly pipeline, C3S-LAA, was developed for libraries of pooled amplicons. By uniquely leveraging the structure of SMRT sequence data (comprised of multiple low quality subreads from which higher quality circular consensus sequences are formed) to cluster raw reads, C3S-LAA produced accurate consensus sequences and assemblies of overlapping amplicons from single sample and multiplexed libraries. In contrast, despite read depths in excess of 100X per amplicon, the standard long amplicon analysis module from Pacific Biosciences generated unexpected numbers of amplicon sequences with substantial inaccuracies in the consensus sequences. A bootstrap analysis showed that the C3S-LAA pipeline per se was effective at removing bioinformatic sources of error, but in rare cases a read depth of nearly 400X was not sufficient to overcome minor but systematic errors inherent to amplification or sequencing. Conclusions C3S-LAA uses a divide and conquer processing algorithm for SMRT amplicon-sequence data that generates accurate consensus sequences and local sequence assemblies. Solving the confounding bioinformatic source of error in LAA allowed for the identification of limited instances of errors due to DNA amplification or sequencing of homopolymeric nucleotide tracts. For research and development in genomics, C3S-LAA allows meaningful conclusions and biological inferences to be made from accurately polished sequence output. Electronic supplementary material The online version of this article (10.1186/s12859-018-2293-0) contains supplementary material, which is available to authorized users.

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.

Plant diseases are responsible for substantial crop losses each year and pose a threat to global food security and agricultural sustainability. Improving crop resistance to pathogens through breeding is an environmentally sound method for managing disease and minimizing these losses. However, it is challenging to breed varieties with resistance that is effective, stable and broad-spectrum. Recent advances in genetic and genomic technologies have contributed to a better understanding of the complexity of host-pathogen interactions and have identified some of the genes and mechanisms that underlie resistance. This new knowledge is benefiting crop improvement through better-informed breeding strategies that utilize diverse forms of resistance at different scales, from the genome of a single plant to the plant varieties deployed across a region.

Automating the extraction and quantification of features from three-dimensional (3-D) image stacks is a critical task for advancing computer vision research. The union of 3-D image acquisition and analysis enables the quantification of biological resistance of a plant tissue to fungal infection through the analysis of attributes such as fungal penetration depth, fungal mass, and branching of the fungal network of connected cells. From an image processing perspective, these tasks reduce to segmentation of vessel-like structures and the extraction of features from their skeletonization. In order to sample multiple infection events for analysis, we have developed an approach we refer to as macroscopic microscopy. However, macroscopic microscopy produces high-resolution image stacks that pose challenges to routine approaches and are difficult for a human to annotate to obtain ground truth data. We present a synthetic hyphal network generator, a comparison of several vessel segmentation methods, and a minimum spanning tree method for connecting small gaps resulting from imperfections in imaging or incomplete skeletonization of hyphal networks. Qualitative results are shown for real microscopic data. We believe the comparison of vessel detectors on macroscopic microscopy data, the synthetic vessel generator, and the gap closing technique are beneficial to the image processing community., Comment: This is submitted to ICIP 2017

High-speed confocal microscopy has shown great promise to yield insights into plant-fungal interactions by allowing for large volumes of leaf tissue to be imaged at high magnification. Currently, segmentation is performed either manually, which is infeasible for large amounts of data, or by developing separate algorithms to extract individual features within the image data. In this work, we propose the use of a single deep convolutional neural network architecture dubbed DeepXScope for automatically segmenting hyphal networks of the fungal pathogen and cell boundaries and stomata of the host plant. DeepXScope is trained on manually annotated images created for each of these structures. We describe experiments that show each individual structure can be accurately extracted automatically using DeepXScope. We anticipate that plant scientists will be able to use this network to automatically extract multiple structures of interest, and we plan to release our tool to the community1.

Isolating and sequencing specific regions in a genome is a cornerstone of molecular biology. This has been facilitated by computationally encoding the thermodynamics of DNA hybridization for automated design of hybridization and priming oligonucleotides. However, the repetitive composition of genomes challenges the identification of target-specific oligonucleotides, which limits genetics and genomics research on many species. Here, a tool called ThermoAlign was developed that ensures the design of target-specific primer pairs for DNA amplification. This is achieved by evaluating the thermodynamics of hybridization for full-length oligonucleotide-template alignments — thermoalignments — across the genome to identify primers predicted to bind specifically to the target site. For amplification-based resequencing of regions that cannot be amplified by a single primer pair, a directed graph analysis method is used to identify minimum amplicon tiling paths. Laboratory validation by standard and long-range polymerase chain reaction and amplicon resequencing with maize, one of the most repetitive genomes sequenced to date (≈85% repeat content), demonstrated the specificity-by-design functionality of ThermoAlign. ThermoAlign is released under an open source license and bundled in a dependency-free container for wide distribution. It is anticipated that this tool will facilitate multiple applications in genetics and genomics and be useful in the workflow of high-throughput targeted resequencing studies.

Alleles that confer multiple disease resistance (MDR) are valuable in crop improvement, although the molecular mechanisms underlying their functions remain largely unknown. A quantitative trait locus, qMdr9.02, associated with resistance to three important foliar maize diseases-southern leaf blight, gray leaf spot and northern leaf blight-has been identified on maize chromosome 9. Through fine-mapping, association analysis, expression analysis, insertional mutagenesis and transgenic validation, we demonstrate that ZmCCoAOMT2, which encodes a caffeoyl-CoA O-methyltransferase associated with the phenylpropanoid pathway and lignin production, is the gene within qMdr9.02 conferring quantitative resistance to both southern leaf blight and gray leaf spot. We suggest that resistance might be caused by allelic variation at the level of both gene expression and amino acid sequence, thus resulting in differences in levels of lignin and other metabolites of the phenylpropanoid pathway and regulation of programmed cell death.

Despite the reduction in the price of sequencing, it remains expensive to sequence and assemble whole, complex genomes of multiple samples for population studies, particularly for large genomes like those of many crop species. Enrichment of target genome regions coupled with next generation sequencing is a cost-effective strategy to obtain sequence information for loci of interest across many individuals, providing a less expensive approach to evaluating sequence variation at the population scale. Here we evaluate amplicon-based enrichment coupled with semiconductor sequencing on a validation set consisting of three maize inbred lines, two hybrids and 19 landrace accessions. We report the use of a multiplexed panel of 319 PCR assays that target 20 candidate loci associated with photoperiod sensitivity in maize while requiring 25 ng or less of starting DNA per sample. Enriched regions had an average on-target sequence read depth of 105 with 98% of the sequence data mapping to the maize 'B73' reference and 80% of the reads mapping to the target interval. Sequence reads were aligned to B73 and 1,486 and 1,244 variants were called using SAMtools and GATK, respectively. Of the variants called by both SAMtools and GATK, 30% were not previously reported in maize. Due to the high sequence read depth, heterozygote genotypes could be called with at least 92.5% accuracy in hybrid materials using GATK. The genetic data are congruent with previous reports of high total genetic diversity and substantial population differentiation among maize landraces. In conclusion, semiconductor sequencing of highly multiplexed PCR reactions is a cost-effective strategy for resequencing targeted genomic loci in diverse maize materials.