Search

Northern leaf blight [NLB; caused by Exserohilum turcicum (Pass) K.J. Leonard and E.G. Suggs] is an important fungal disease of maize (Zea mays L.) in the United States and worldwide. The IBM population, an advanced intercross recombinant inbred line population derived from a cross between the lines Mo17 and B73, was evaluated in three environments (Aurora, NY, in 2006 and 2007 and Clayton, NC in 2007) for two traits related to NLB resistance, weighted mean disease (WMD) and incubation period (IP), and for days to anthesis (DTA). Two WMD quantitative trait loci (QTL) in bins 2.00/2.01 and 4.08 were detected from the overall analysis; of these, only the QTL in bin 4.08 was detected in all three environments analyzed separately. Likewise, only one IP QTL, in bin 2.02, was detected in all three environments and from the overall analysis. Several environment-specific QTL for each trait were also detected. Several DTA QTL were detected with the strongest effect detected in bin 8.05. Correlations between disease resistance traits and days to anthesis were uniformly low. The results from this study were compared to those of previous studies that used the IBM population to identify QTL for two other maize foliar diseases, southern leaf blight {causal agent Cochliobolus heterostrophus (Drechs.) Drechs. [anamorph = Bipolaris maydis (Nisikado and Miyake) Shoemaker; synonym = Helminthosporium maydis (Nisikado and Miyake)]} and gray leaf spot [causal agent Cercospora zeae-maydis (Tehon and E.Y. Daniets)]. Although we did not find QTL conferring resistance to all three diseases, significant correlations between resistances to these diseases in the IBM population were identified, implying the existence of loci (and possibly genes) affecting resistance to all three diseases. doi: 10.2135/cropsci2009.02.0066

The success of a breeding program depends on having adequate diversity in the germplasm. However, as advanced breeding stocks and materials are generated, one casualty is the diversity itself. As a result, breeding programs in many crop species have reached a point of diminishing returns and it is feared that unless new diversity is infused into the breeding germplasm, we face catastrophic reductions in crop productivity if the climate turns adverse. Although some scientists favor transgenic approaches, a 'back to nature' approach to genetic diversity may prove faster and more effective. Wild and exotic relatives of crop plants hold a wealth of alleles that, if we can find them, can help break yield barriers and enhance tolerance to stresses. Many approaches, based largely on quantitative trait loci genetics, have been proposed and used for this purpose, but most are either highly laborious or discover relevant variation inefficiently. Here, we propose a gene-centered approach, dubbed MAGIC (mutant-assisted gene identification and characterization), that uses Mendelian mutants or other genetic variants in a trait of interest as reporters to identify novel genes and variants for that trait. MAGIC is similar to enhancer--suppressor screens, but rather than relying on variation created in the laboratory, it reveals variation created and refined by nature over millions of years of evolution. This approach could be an effective tool for exploring novel variation and a valuable means to harness natural diversity and define genetic networks.

Gray leaf spot [GLS, causal agent Cercospora zeae-maydis (Tehon and E. Y. Daniels)] is an important fungal disease of maize in the U.S. and worldwide. The IBM population, an advanced intercross recombinant inbred line population derived from a cross between the maize lines Mo17 (resistant) and B73 (susceptible), was evaluated in three environments (Andrews, NC in 2005, 2006, and 2007) for resistance to GLS and for days from planting to anthesis (DTA). A conventional recombinant inbred line population derived from the same two parents (the 'Stuber' population) was also assessed for GLS resistance in two environments (Andrews NC, 2004 and 2005). Quantitative trait loci (QTL) for GLS resistance were detected in each population. Five significant QTL were detected in the IBM population in bins 1.05, 2.04, 4.05, 9.03, and 9.05. In each case the QTL were localized to regions less than 3 centiMorgans (cM). Two QTL for GLS resistance were identified in the Stuber population in bins 2.04 and 7.05. The GLS QTL in bin 2.04 was previously identified as a QTL for southern leaf blight resistance in the IBM population. These results were compared with results from five previous GLS QTL studies and two potential GLS QTL 'hotspots' were identified in bins 1.05-1.06 and 2.03-2.05. As expected, QTL were identified with much more precision in the IBM population compared to the Stuber population and to previous studies. There was no significant correlation between disease resistance and days to anthesis. Three DTA QTL were detected in bins 4.09, 8.05, and 9.02, which did not co-localize with GLS QTL.

Fusarium verticillioides (Sacc.) Nirenberg (synonym F. moniliforme Sheldon) (teleomorph: Gibberella moniliformis) and F. proliferatum (Matsushima) Nirenberg (teleomorph: G. intermedia) are fungal pathogens of maize (Zea mays L.) that cause ear rot and contaminate grain with fumonisins, mycotoxins that can harm animals and humans. The objective of this study was to identify quantitative trait loci (QTL) for resistance to Fusarium ear rot and fumonisin contamination in two maize populations, comprised of 213 B[C.sub.1][F.sub.1:2] families from the first backcross of GE440 to FR1064 (GEFR) and 143 recombinant inbred lines from the cross of NC300 to B104 (NCB). QTL mapping was used to study the genetic relationships between resistances to ear rot and fumonisin contamination and to investigate consistency of QTL across populations. In the GEFR population, seven QTL explained 47% of the phenotypic variation for mean ear rot resistance and nine QTL with one epistatic interaction explained 67% of the variation for mean fumonisin concentration. In the NCB population, five QTL explained 31% of the phenotypic variation for mean ear rot resistance and six QTL and three epistatic interactions explained 81% of the phenotypic variation for mean fumonisin concentration. Eight QTL in the GEFR population and five QTL in the NCB population affected both disease traits. At least three ear rot and two fumonisin contamination resistance QTL mapped to similar positions in the two populations. Two QTL, localized to chromosomes four and five, appeared to be consistent for both traits across both populations.

Abstract 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 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 non-specific 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.