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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. [ABSTRACT FROM AUTHOR]

Background: Rp1 is a complex locus of maize, which carries a set of genes controlling race-specific resistance to the common rust fungus, Puccinia sorghi. The resistance response includes the "Hypersensitive response" (HR), a rapid response triggered by a pathogen recognition event that includes localized cell death at the point of pathogen penetration and the induction of pathogenesis associated genes. The Rp1-D21gene is an autoactive allelic variant at the Rp1 locus, causing spontaneous activation of the HR response, in the absence of pathogenesis. Previously we have shown that the severity of the phenotype conferred by Rp1-D21 is highly dependent on genetic background. Results: In this study we show that the phenotype conferred by Rp1-D21 is highly dependent on temperature, with lower temperatures favoring the expression of the HR lesion phenotype. This temperature effect was observed in all the 14 genetic backgrounds tested. Significant interactions between the temperature effects and genetic background were observed. When plants were grown at temperatures above 30°C, the spontaneous HR phenotype conferred by Rp1-D21 was entirely suppressed. Furthermore, this phenotype could be restored or suppressed by alternately reducing and increasing the temperature appropriately. Light was also required for the expression of this phenotype. By examining the expression of genes associated with the defense response we showed that, at temperatures above 30°C, the Rp1-D21 phenotype was suppressed at both the phenotypic and molecular level. Conclusions: We have shown that the lesion phenotype conferred by maize autoactive resistance gene Rp1-D21 is temperature sensitive in a reversible manner, that the temperature-sensitivity phenotype interacts with genetic background and that the phenotype is light sensitive. This is the first detailed demonstration of this phenomenon in monocots and also the first demonstration of the interaction of this effect with genetic background. The use of temperature shifts to induce a massive and synchronous HR in plants carrying the Rp1-D21 genes will be valuable in identifying components of the defense response pathway. [ABSTRACT FROM AUTHOR]

Background: Accurate characterization of complex plant phenotypes is critical to assigning biological functions to genes through forward or reverse genetics. It can also be vital in determining the effect of a treatment, genotype, or environmental condition on plant growth or susceptibility to insects or pathogens. Although techniques for characterizing complex phenotypes have been developed, most are not cost effective or are too imprecise or subjective to reliably differentiate subtler differences in complex traits like growth, color change, or disease resistance. Results: We designed an inexpensive imaging protocol that facilitates automatic quantification of two-dimensional visual phenotypes using computer vision and image processing algorithms applied to standard digital images. The protocol allows for non-destructive imaging of plants in the laboratory and field and can be used in suboptimal imaging conditions due to automated color and scale normalization. We designed the web-based tool PhenoPhyte for processing images adhering to this protocol and demonstrate its ability to measure a variety of two-dimensional traits (such as growth, leaf area, and herbivory) using images from several species (Arabidopsis thaliana and Brassica rapa). We then provide a more complicated example for measuring disease resistance of Zea mays to Southern Leaf Blight. Conclusions: PhenoPhyte is a new cost-effective web-application for semi-automated quantification of two-dimensional traits from digital imagery using an easy imaging protocol. This tool's usefulness is demonstrated for a variety of traits in multiple species. We show that digital phenotyping can reduce human subjectivity in trait quantification, thereby increasing accuracy and improving precision, which are crucial for differentiating and quantifying subtle phenotypic variation and understanding gene function and/or treatment effects. [ABSTRACT FROM AUTHOR]

Background: Studies on host-pathogen interactions in a range of pathosystems have revealed an array of mechanisms by which plants reduce the efficiency of pathogenesis. While R-gene mediated resistance confers highly effective defense responses against pathogen invasion, quantitative resistance is associated with intermediate levels of resistance that reduces disease progress. To test the hypothesis that specific loci affect distinct stages of fungal pathogenesis, a set of maize introgression lines was used for mapping and characterization of quantitative trait loci (QTL) conditioning resistance to Setosphaeria turcica, the causal agent of northern leaf blight (NLB). To better understand the nature of quantitative resistance, the identified QTL were further tested for three secondary hypotheses: (1) that disease QTL differ by host developmental stage; (2) that their performance changes across environments; and (3) that they condition broad-spectrum resistance. Results: Among a set of 82 introgression lines, seven lines were confirmed as more resistant or susceptible than B73. Two NLB QTL were validated in BC4F2 segregating populations and advanced introgression lines. These loci, designated qNLB1.02 and qNLB1.06, were investigated in detail by comparing the introgression lines with B73 for a series of macroscopic and microscopic disease components targeting different stages of NLB development. Repeated greenhouse and field trials revealed that qNLB1.06Tx303 (the Tx303 allele at bin 1.06) reduces the efficiency of fungal penetration, while qNLB1.02B73 (the B73 allele at bin 1.02) enhances the accumulation of callose and phenolics surrounding infection sites, reduces hyphal growth into the vascular bundle and impairs the subsequent necrotrophic colonization in the leaves. The QTL were equally effective in both juvenile and adult plants; qNLB1.06Tx303 showed greater effectiveness in the field than in the greenhouse. In addition to NLB resistance, qNLB1.02B73 was associated with resistance to Stewart's wilt and common rust, while qNLB1.06Tx303 conferred resistance to Stewart's wilt. The nonspecific resistance may be attributed to pleiotropy or linkage. Conclusions: Our research has led to successful identification of two reliably-expressed QTL that can potentially be utilized to protect maize from S. turcica in different environments. This approach to identifying and dissecting quantitative resistance in plants will facilitate the application of quantitative resistance in crop protection. [ABSTRACT FROM AUTHOR]