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

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.

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.

Southern Leaf Blight (SLB), Northern Leaf Blight (NLB), and Gray Leaf Spot (GLS) caused by Cochliobolus heterostrophus, Setosphaeria turcica, and Cercospora zeae-maydis respectively, are among the most important diseases of corn worldwide. Previously, moderately high and significantly positive genetic correlations between resistance levels to each of these diseases were identified in a panel of 253 diverse maize inbred lines. The goal of this study was to identify loci underlying disease resistance in some of the most multiple disease resistant (MDR) lines by the creation of chromosome segment substitution line (CSSL) populations in multiple disease susceptible (MDS) backgrounds. Four MDR lines (NC304, NC344, Ki3, NC262) were used as donor parents and two MDS lines (Oh7B, H100) were used as recurrent parents to produce eight BC3F4:5 CSSL populations comprising 1,611 lines in total. Each population was genotyped and assessed for each disease in replicated trials in two environments. Moderate to high heritabilities on an entry mean basis were observed (0.32 to 0.83). Several lines in each population were significantly more resistant than the MDS parental lines for each disease. Multiple quantitative trait loci (QTL) for disease resistance were detected for each disease in most of the populations. Seventeen QTL were associated with variation in resistance to more than one disease (SLB/NLB: 2; SLB/GLS: 7; NLB/GLS: 2 and 6 to all three diseases). For most populations and most disease combinations, significant correlations were observed between disease scores and also between marker effects for each disease. The number of lines that were resistant to more than one disease was significantly higher than would be expected by chance. Using the results from individual QTL analyses, a composite statistic based on Mahalanobis distance (Md) was used to identify joint marker associations with multiple diseases. Across all populations and diseases, 246 markers had significant Md values. However further analysis revealed that most of these associations were due to strong QTL effects on a single disease. Together, these findings reinforce our previous conclusions that loci associated with resistance to different diseases are clustered in the genome more often than would be expected by chance. Nevertheless true MDR loci which have significant effects on more than one disease are still much rarer than loci with single disease effects. © 2019 by the Genetics Society of America. Funding for the work was provided by USDA-ARS, the Corn Growers’ Association of North Carolina and by NSF grant #1127076 to RJW and PBK. LLZ was supported by a grant from Fulbright Colombia and COLCIENCIAS. We thank Cathy Herring and the staff at Central crops for excellent field support, Shannon Sermons, David Rhyne and Greg Marshall for their technical support. We thank Daniel Gorman, David Bubeck and DuPont-Pioneer for their assistance with field trials in Andrews NC. We thank Jim Holland and Marc Cubeta for their advice and for reviewing the manuscript.

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.

The geographical distribution of many crop species spans far beyond their centers of origin and the native range of their wild ancestors. Maize is exemplary of this adaptability, which has contributed to its agricultural..., Understanding the evolutionary capacity of populations to adapt to novel environments is one of the major pursuits in genetics. Moreover, for plant breeding, maladaptation is the foremost barrier to capitalizing on intraspecific variation in order to develop new breeds for future climate scenarios in agriculture. Using a unique study design, we simultaneously dissected the population and quantitative genomic basis of short-term evolution in a tropical landrace of maize that was translocated to a temperate environment and phenotypically selected for adaptation in flowering time phenology. Underlying 10 generations of directional selection, which resulted in a 26-day mean decrease in female-flowering time, 60% of the heritable variation mapped to 14% of the genome, where, overall, alleles shifted in frequency beyond the boundaries of genetic drift in the expected direction given their flowering time effects. However, clustering these non-neutral alleles based on their profiles of frequency change revealed transient shifts underpinning a transition in genotype–phenotype relationships across generations. This was distinguished by initial reductions in the frequencies of few relatively large positive effect alleles and subsequent enrichment of many rare negative effect alleles, some of which appear to represent allelic series. With these genomic shifts, the population reached an adapted state while retaining 99% of the standing molecular marker variation in the founding population. Robust selection and association mapping tests highlighted several key genes driving the phenotypic response to selection. Our results reveal the evolutionary dynamics of a finite polygenic architecture conditioning a capacity for rapid environmental adaptation in maize.

Motivation Ancestral haplotype maps provide useful information about genomic variation and insights into biological processes. Reconstructing the descendent haplotype structure of homologous chromosomes, particularly for large numbers of individuals, can help with characterizing the recombination landscape, elucidating genotype-to-phenotype relationships, improving genomic predictions and more. Inferring haplotype maps from sparse genotype data is an efficient approach to whole-genome haplotyping, but this is a non-trivial problem. A standardized approach is needed to validate whether haplotype reconstruction software, conceived population designs and existing data for a given population provides accurate haplotype information for further inference. Results We introduce SPEARS, a pipeline for the simulation-based appraisal of genome-wide haplotype maps constructed from sparse genotype data. Using a specified pedigree, the pipeline generates virtual genotypes (known data) with genotyping errors and missing data structure. It then proceeds to mimic analysis in practice, capturing sources of error due to genotyping, imputation and haplotype inference. Standard metrics allow researchers to assess different population designs and which features of haplotype structure or regions of the genome are sufficiently accurate for analysis. Haplotype maps for 1000 outcross progeny from a multi-parent population of maize are used to demonstrate SPEARS. Availabilityand implementation SPEARS, the protocol and suite of scripts, are publicly available under an MIT license at GitHub (https://github.com/maizeatlas/spears). Supplementary information Supplementary data are available at Bioinformatics online.

Southern Leaf Blight, Northern Leaf Blight, and Gray Leaf Spot, caused by ascomycete fungi, are among the most important foliar diseases of maize worldwide. Previously, disease resistance quantitative trait loci (QTL) for all three diseases were identified in a connected set of chromosome segment substitution line (CSSL) populations designed for the identification of disease resistance QTL. Some QTL for different diseases co-localized, indicating the presence of multiple disease resistance (MDR) QTL. The goal of this study was to perform an independent test of several of the MDR QTL identified to confirm their existence and derive a more precise estimate of allele additive and dominance effects. Twelve F2:3 family populations were produced, in which selected QTL were segregating in an otherwise uniform genetic background. The populations were assessed for each of the three diseases in replicated trials and genotyped with markers previously associated with disease resistance. Pairwise phenotypic correlations across all the populations for resistance to the three diseases ranged from 0.2 to 0.3 and were all significant at the alpha level of 0.01. Of the 44 QTL tested, 16 were validated (identified at the same genomic location for the same disease or diseases) and several novel QTL/disease associations were found. Two MDR QTL were associated with resistance to all three diseases. This study identifies several potentially important MDR QTL and demonstrates the importance of independently evaluating QTL effects following their initial identification.

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.

Crop species exhibit an astounding capacity for environmental adaptation, but genetic bottlenecks resulting from intense selection for adaptation and productivity can lead to a genetically vulnerable crop. Improving the genetic resiliency of temperate maize depends upon the use of tropical germplasm, which harbors a rich source of natural allelic diversity. Here, the adaptation process was studied in a tropical maize population subjected to 10 recurrent generations of directional selection for early flowering in a single temperate environment in Iowa, USA. We evaluated the response to this selection across a geographical range spanning from 43.05° (WI) to 18.00° (PR) latitude. The capacity for an all-tropical maize population to become adapted to a temperate environment was revealed in a marked fashion: on average, families from generation 10 flowered 20 days earlier than families in generation 0, with a nine-day separation between the latest generation 10 family and the earliest generation 0 family. Results suggest that adaptation was primarily due to selection on genetic main effects tailored to temperature-dependent plasticity in flowering time. Genotype-by-environment interactions represented a relatively small component of the phenotypic variation in flowering time, but were sufficient to produce a signature of localized adaptation that radiated latitudinally, in partial association with daylength and temperature, from the original location of selection. Furthermore, the original population exhibited a maladaptive syndrome including excessive ear and plant heights along with later flowering; this was reduced in frequency by selection for flowering time.

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.

Plants are attacked by pathogens representing diverse taxonomic groups, such that genes providing multiple disease resistance (MDR) are expected to be under positive selection pressure. To address the hypothesis that naturally occurring allelic variation conditions MDR, we extended the framework of structured association mapping to allow for the analysis of correlated complex traits and the identification of pleiotropic genes. The multivariate analytical approach used here is directly applicable to any species and set of traits exhibiting correlation. From our analysis of a diverse panel of maize inbred lines, we discovered high positive genetic correlations between resistances to three globally threatening fungal diseases. The maize panel studied exhibits rapidly decaying linkage disequilibrium that generally occurs within 1 or 2 kb, which is less than the average length of a maize gene. The positive correlations therefore suggested that functional allelic variation at specific genes for MDR exists in maize. Using a multivariate test statistic, a glutathione S -transferase ( GST ) gene was found to be associated with modest levels of resistance to all three diseases. Resequencing analysis pinpointed the association to a histidine (basic amino acid) for aspartic acid (acidic amino acid) substitution in the encoded protein domain that defines GST substrate specificity and biochemical activity. The known functions of GSTs suggested that variability in detoxification pathways underlie natural variation in maize MDR.

Nested association mapping (NAM) offers power to resolve complex, quantitative traits to their causal loci. The maize NAM population, consisting of 5,000 recombinant inbred lines (RILs) from 25 families representing the global diversity of maize, was evaluated for resistance to southern leaf blight (SLB) disease. Joint-linkage analysis identified 32 quantitative trait loci (QTLs) with predominantly small, additive effects on SLB resistance. Genome-wide association tests of maize HapMap SNPs were conducted by imputing founder SNP genotypes onto the NAM RILs. SNPs both within and outside of QTL intervals were associated with variation for SLB resistance. Many of these SNPs were within or near sequences homologous to genes previously shown to be involved in plant disease resistance. Limited linkage disequilibrium was observed around some SNPs associated with SLB resistance, indicating that the maize NAM population enables high-resolution mapping of some genome regions.

A thorough understanding of quantitative disease resistance (QDR) would contribute to the design and deployment of durably resistant crop cultivars. However, the molecular mechanisms that control QDR remain poorly understood, largely due to the incomplete and inconsistent nature of the resistance phenotype, which is usually conditioned by many loci of small effect. Here, we discuss recent advances in research on QDR. Based on inferences from analyses of the defense response and from the few isolated QDR genes, we suggest several plausible hypotheses for a range of mechanisms underlying QDR. We propose that a new generation of genetic resources, complemented by careful phenotypic analysis, will produce a deeper understanding of plant defense and more effective utilization of natural resistance alleles.

The selection response of a complex maize population improved primarily for quantitative disease resistance to northern leaf blight (NLB) and secondarily for common rust resistance and agronomic phenotypes was investigated at the molecular genetic level. A tiered marker analysis with 151 simple sequence repeat (SSR) markers in 90 individuals of the population indicated that on average six alleles per locus were available for selection. An improved test statistic for selection mapping was developed, in which quantitative trait loci (QTL) are identified through the analysis of allele-frequency shifts at mapped multiallelic loci over generations of selection. After correcting for the multiple tests performed, 25 SSR loci showed evidence of selection. Many of the putatively selected loci were unlinked and dispersed across the genome, which was consistent with the diffuse distribution of previously published QTL for NLB resistance. Compelling evidence for selection was found on maize chromosome 8, where several putatively selected loci colocalized with published NLB QTL and a race-specific resistance gene. Analysis of F2 populations derived from the selection mapping population suggested that multiple linked loci in this chromosomal segment were, in part, responsible for the selection response for quantitative resistance to NLB.

The intermated B73 × Mo17 (IBM) population, an advanced intercross recombinant inbred line population derived from a cross between the maize lines B73 (susceptible) and Mo17 (resistant), was evaluated in four environments for resistance to southern leaf blight (SLB) disease caused by Cochliobolus heterostrophus race O. Two environments were artificially inoculated, while two were not inoculated and consequently had substantially lower disease pressure. Four common SLB resistance quantitative trait loci (QTL) were identified in all environments, two in bin 3.04 and one each in bins 1.10 and 8.02/3. There was no significant correlation between disease resistance and days to anthesis. A direct comparison was made between SLB QTL detected in two populations, independently derived from the same parental cross: the IBM advanced intercross population and a conventional recombinant inbred line population. Several QTL for SLB resistance were detected in both populations, with the IBM providing between 5 and, in one case, 50 times greater mapping resolution.

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.

Fifty publications on the mapping of maize disease resistance loci were synthesized. These papers reported the locations of 437 quantitative trait loci (QTL) for disease (dQTL), 17 resistance genes (R-genes), and 25 R-gene analogs. A set of rules was devised to enable the placement of these loci on a single consensus map, permitting analysis of the distribution of resistance loci identified across a variety of maize germplasm for a number of different diseases. The confidence intervals of the dQTL were distributed over all 10 chromosomes and covered 89% of the genetic map to which the data were anchored. Visual inspection indicated the presence of clusters of dQTL for multiple diseases. Clustering of dQTL was supported by statistical tests that took into account genome-wide variations in gene density. Several novel clusters of resistance loci were identified. Evidence was also found for the association of dQTL with maturity-related QTL. It was evident from the distinct dQTL distributions for the different diseases that certain breeding schemes may be more suitable for certain diseases. This review provides an up-to-date synthesis of reports on the locations of resistance loci in maize.

Much research has been devoted to understanding the biology of plant-pathogen interactions. The extensive genetic analysis of disease resistance in rice, coupled with the sequenced genome and genomic resources, provides the opportunity to seek convergent evidence implicating specific chromosomal segments and genes in the control of resistance. Published data on quantitative and qualitative disease resistance in rice were synthesized to evaluate the distributions of and associations among resistance loci. Quantitative trait loci (QTL) for resistance to multiple diseases and qualitative resistance loci (R genes) were clustered in the rice genome. R genes and their analogs of the nucleotide binding site–leucine-rich repeat class and genes identified on the basis of differential representation in disease-related EST libraries were significantly associated with QTL. Chromosomal segments associated with broad-spectrum quantitative disease resistance (BS-QDR) were identified. These segments contained numerous positional candidate genes identified on the basis of a range of criteria, and groups of genes belonging to two defense-associated biochemical pathways were found to underlie one BS-QDR region. Genetic dissection of disease QTL confidence intervals is needed to reduce the number of positional candidate genes for further functional analysis. This study provides a framework for future investigations of disease resistance in rice and related crop species.

cDNA microarray spot variability arises from many sources, and different systems have varying requirements for achieving the desired level of precision. We determined relative contributions to variance and investigated sequence discrimination using a multiple-array experimental design, with arrays subdivided to determine position and pin effect. Related fragments of 67 resistance gene homologs (RGHs) isolated from Theobroma cacao L. and grouped by sequence similarity were spotted onto arrays, using two of the same RGHs in the fluorescent dye channels (Cy™3, Cy5) of the hybridization solution in a “dye-flip” design. A comprehensive statistical model accounted for variability well, giving a coefficient of variation (CV) based on experimental error of 2.12%. Although we were able to separate 85% of RGH group means clearly, some groups more similar to the target were indistinguishable due to nonspecific hybridization. Genetic factors together contributed 72.2% of the total variation, while position and pin effects and their interactions contributed 9.8%. Replication effect was statistically significant. Otherwise, no tests for position effects were significant. The results of the analysis indicate that our Genetic Microsystems 417™ arrayer and Affymetrix 428™ scanner are performing with sufficient precision, and we produced useful information for planning efficient future experiments.

The mixed linear model (MLM) is an advanced statistical technique applicable to many fields of science. The multivariate MLM can be used to model longitudinal data, such as repeated ratings of disease resistance taken across time. In this study, using an example data set from a multi-environment trial of northern leaf blight disease on 290 maize lines with diverse levels of resistance, multivariate MLM analysis was performed and its utility was examined. In the population and environments tested, genotypic effects were highly correlated across disease ratings and followed an autoregressive pattern of correlation decay. Because longitudinal data are often converted to the univariate measure of area under the disease progress curve (AUDPC), comparisons between univariate MLM analysis of AUDPC and multivariate MLM analysis of longitudinal data were made. Univariate analysis had the advantage of simplicity and reduced computational demand, whereas multivariate analysis enabled a comprehensive perspective on disease development, providing the opportunity for unique insights into disease resistance. To aid in the application of multivariate MLM analysis of longitudinal data on disease resistance, annotated program syntax for model fitting is provided for the software ASReml.

The availability of the rice genome sequence enabled the global characterization of nucleotide-binding site (NBS)-leucine-rich repeat (LRR) genes, the largest class of plant disease resistance genes. The rice genome carries approximately 500 NBS-LRR genes that are very similar to the non-Toll/interleukin-1 receptor homology region (TIR) class (class 2) genes of Arabidopsis but none that are homologous to the TIR class genes. Over 100 of these genes were predicted to be pseudogenes in the rice cultivar Nipponbare, but some of these are functional in other rice lines. Over 80 other NBS-encoding genes were identified that belonged to four different classes, only two of which are present in dicotyledonous plant sequences present in databases. Map positions of the identified genes show that these genes occur in clusters, many of which included members from distantly related groups. Members of phylogenetic subgroups of the class 2 NBS-LRR genes mapped to as many as ten different chromosomes. The patterns of duplication of the NBS-LRR genes indicate that they were duplicated by many independent genetic events that have occurred continuously through the expansion of the NBS-LRR superfamily and the evolution of the modern rice genome. Genetic events, such as inversions, that inhibit the ability of recently duplicated genes to recombine promote the divergence of their sequences by inhibiting concerted evolution.

Resistance gene homologue (RGH) sequences have been developed into useful genetic markers for marker-assisted selection (MAS) of disease resistant Theobroma cacao. A plasmid library of amplified fragments was created from seven different cultivars of cacao. Over 600 cloned recombinant amplicons were evaluated. From these, 74 unique RGHs were identified that could be placed into 11 categories based on sequence analysis. Primers specific to each category were designed. The primers specific for a single RGH category amplified fragments of equal length from the seven different cultivars used to create the library. However, these fragments exhibited single-strand conformational polymorphism (SSCP), which allowed us to map six of the RGH categories in an F(2) population of T. cacao. RGHs 1, 4 and 5 were in the same linkage group, with RGH 4 and 5 separated by less than 4 cM. As SSCP can be efficiently performed on our automated sequencer, we have developed a convenient and rapid high throughput assay for RGH alleles.