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Crops are hosts to numerous plant pathogenic microorganisms. Maize has several major disease issues; thus, breeding multiple disease resistant (MDR) varieties is critical. While the genetic basis of resistance to multiple fungal pathogens has been studied in maize, less is known about the relationship between fungal and bacterial resistance. In this study, we evaluated a disease resistance introgression line (DRIL) population for the foliar disease Goss’s bacterial wilt and blight (GW) and conducted quantitative trait locus (QTL) mapping. We identified a total of ten QTL across multiple environments. We then combined our GW data with data on four additional foliar diseases (northern corn leaf blight, southern corn leaf blight, gray leaf spot, and bacterial leaf streak) and conducted multivariate analysis to identify regions conferring resistance to multiple diseases. We identified 20 chromosomal bins with putative multiple disease effects. We examined the five chromosomal regions (bins 1.05, 3.04, 4.06, 8.03, and 9.02) with the strongest statistical support. By examining how each haplotype effected each disease, we identified several regions associated with increased resistance to multiple diseases and three regions associated with opposite effects for bacterial and fungal diseases. In summary, we identified several promising candidate regions for multiple disease resistance in maize and specific DRILs to expedite interrogation.

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.

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.

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.

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.

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.

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.

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.

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.

Background A previous study reported a comprehensive quantitative trait locus (QTL) and genome wide association study (GWAS) of southern leaf blight (SLB) resistance in the maize Nested Association Mapping (NAM) panel. Since that time, the genomic resources available for such analyses have improved substantially. An updated NAM genetic linkage map has a nearly six-fold greater marker density than the previous map and the combined SNPs and read-depth variants (RDVs) from maize HapMaps 1 and 2 provided 28.5 M genomic variants for association analysis, 17 fold more than HapMap 1. In addition, phenotypic values of the NAM RILs were re-estimated to account for environment-specific flowering time covariates and a small proportion of lines were dropped due to genotypic data quality problems. Comparisons of original and updated QTL and GWAS results confound the effects of linkage map density, GWAS marker density, population sample size, and phenotype estimates. Therefore, we evaluated the effects of changing each of these parameters individually and in combination to determine their relative impact on marker-trait associations in original and updated analyses. Results Of the four parameters varied, map density caused the largest changes in QTL and GWAS results. The updated QTL model had better cross-validation prediction accuracy than the previous model. Whereas joint linkage QTL positions were relatively stable to input changes, the residual values derived from those QTL models (used as inputs to GWAS) were more sensitive, resulting in substantial differences between GWAS results. The updated NAM GWAS identified several candidate genes consistent with previous QTL fine-mapping results. Conclusions The highly polygenic nature of resistance to SLB complicates the identification of causal genes. Joint linkage QTL are relatively stable to perturbations of data inputs, but their resolution is generally on the order of tens or more Mbp. GWAS associations have higher resolution, but lower power due to stringent thresholds designed to minimize false positive associations, resulting in variability of detection across studies. The updated higher density linkage map improves QTL estimation and, along with a much denser SNP HapMap, greatly increases the likelihood of detecting SNPs in linkage with causal variants. We recommend use of the updated genetic resources and results but emphasize the limited repeatability of small-effect associations. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-1068) contains supplementary material, which is available to authorized users.