Brown midrib mutant and genome-wide association analysis uncover lignin genes for disease resistance in maize.

Brown midrib (BMR) maize (Zea mays L.) harbors mutations that result in lower lignin levels and higher feed digestibility, making it a desirable silage market class for ruminant nutrition. Northern leaf blight (NLB) epidemics in upstate New York highlighted the disease susceptibility of commercially grown BMR maize hybrids. We found the bm1, bm2, bm3, and bm4 mutants in a W64A genetic background to be more susceptible to foliar fungal (NLB, gray leaf spot [GLS], and anthracnose leaf blight [ALB]) and bacterial (Stewart's wilt) diseases. The bm1, bm2, and bm3 mutants showed enhanced susceptibility to anthracnose stalk rot (ASR), and the bm1 and bm3 mutants were more susceptible to Gibberella ear rot (GER). Colocalization of quantitative trait loci (QTL) and correlations between stalk strength and disease traits in recombinant inbred line families suggest possible pleiotropies. The role of lignin in plant defense was explored using high-resolution, genome-wide association analysis for resistance to NLB in the Goodman diversity panel. Association analysis identified 100 single and clustered single-nucleotide polymorphism (SNP) associations for resistance to NLB but did not implicate natural functional variation at bm1-bm5. Strong associations implicated a suite of diverse candidate genes including lignin-related genes such as a ß-glucosidase gene cluster, hct11, knox1, knox2, zim36, lbd35, CASP-like protein 8, and xat3. The candidate genes are targets for breeding quantitative resistance to NLB in maize for use in silage and nonsilage purposes.

Validation and Application of a Low-Cost Sorting Device for Fumonisin Reduction in Maize.

Fumonisin mycotoxins are a persistent challenge to human and livestock health in tropical and sub-tropical maize cropping systems, and more efficient methods are needed to reduce their presence in food systems. We constructed a novel, low-cost device for sorting grain, the "DropSort", and tested its effectiveness on both plastic kernel models and fumonisin-contaminated maize. Sorting plastic kernels of known size and shape enabled us to optimize the sorting performance of the DropSort. The device sorted maize into three distinct fractions as measured by bulk density and 100-kernel weight. The level of fumonisin was lower in the heaviest fractions of maize compared to the unsorted samples. Based on correlations among fumonisin and bulk characteristics of each fraction, we found that light fraction 100-kernel weight could be an inexpensive proxy for unsorted fumonisin concentration. Single kernel analysis revealed significant relationships among kernel fumonisin content and physical characteristics that could prove useful for future sorting efforts. The availability of a low-cost device (materials~USD 300) that can be used to reduce fumonisin in maize could improve food safety in resource-limited contexts in which fumonisin contamination remains a pressing challenge.

Low-cost grain sorting technologies to reduce mycotoxin contamination in maize and groundnut.

The widespread contamination of foods by mycotoxins continues to be a public health hazard in sub-Saharan Africa, with maize and groundnut being major sources of contamination. This study was undertaken to assess the hypothesis that grain sorting can be used to reduce mycotoxin contamination in grain lots by removing toxic kernels. We tested a set of sorting principles and methods for reducing mycotoxin levels in maize and groundnut from a variety of genotypes and environments. We found that kernel bulk density (KBD) and 100-kernel weight (HKW) were associated with the levels of aflatoxins (AF) and fumonisins (FUM) in maize grain. A low-cost sorter prototype (the 'DropSort' device) that separated maize grain based on KBD and HKW was more effective in reducing FUM than AF. We then evaluated the effectiveness of DropSorting when combined with either size or visual sorting. Size sorting followed by DropSorting was the fastest method for reducing FUM to under 2 ppm, but was not effective in reducing AF levels in maize grain to under 20 ppb, especially for heavily AF-contaminated grain. Analysis of individual kernels showed that high -AF maize kernels had lower weight, volume, density, length, and width and higher sphericity than those with low AF. Single kernel weight was the most significant predictor of AF concentration. The DropSort excluded kernels with lower single kernel weight, volume, width, depth, and sphericity. We also found that visual sorting and bright greenish-yellow fluorescence sorting of maize single kernels were successful in separating kernels based on AF levels. For groundnut, the DropSort grouped grain based on HKW and did not significantly reduce AF concentrations, whereas size sorting and visual sorting were much more effective.

Maize Introgression Library Provides Evidence for the Involvement of liguleless1 in Resistance to Northern Leaf Blight.

Plant disease resistance is largely governed by complex genetic architecture. In maize, few disease resistance loci have been characterized. Near-isogenic lines are a powerful genetic tool to dissect quantitative trait loci. We analyzed an introgression library of maize (Zea mays) near-isogenic lines, termed a nested near-isogenic line library for resistance to northern leaf blight caused by the fungal pathogen Setosphaeria turcica The population was comprised of 412 BC5F4 near-isogenic lines that originated from 18 diverse donor parents and a common recurrent parent, B73. Single nucleotide polymorphisms identified through genotyping by sequencing were used to define introgressions and for association analysis. Near-isogenic lines that conferred resistance and susceptibility to northern leaf blight were comprised of introgressions that overlapped known northern leaf blight quantitative trait loci. Genome-wide association analysis and stepwise regression further resolved five quantitative trait loci regions, and implicated several candidate genes, including Liguleless1, a key determinant of leaf architecture in cereals. Two independently-derived mutant alleles of liguleless1 inoculated with S. turcica showed enhanced susceptibility to northern leaf blight. In the maize nested association mapping population, leaf angle was positively correlated with resistance to northern leaf blight in five recombinant inbred line populations, and negatively correlated with northern leaf blight in four recombinant inbred line populations. This study demonstrates the power of an introgression library combined with high density marker coverage to resolve quantitative trait loci. Furthermore, the role of liguleless1 in leaf architecture and in resistance to northern leaf blight has important applications in crop improvement.

Genotypic and phenotypic characterization of a large, diverse population of maize near-isogenic lines.

Genome-wide association (GWA) studies can identify quantitative trait loci (QTL) putatively underlying traits of interest, and nested association mapping (NAM) can further assess allelic series. Near-isogenic lines (NILs) can be used to characterize, dissect and validate QTL, but the development of NILs is costly. Previous studies have utilized limited numbers of NILs and introgression donors. We characterized a panel of 1270 maize NILs derived from crosses between 18 diverse inbred lines and the recurrent inbred parent B73, referred to as the nested NILs (nNILs). The nNILs were phenotyped for flowering time, height and resistance to three foliar diseases, and genotyped with genotyping-by-sequencing. Across traits, broad-sense heritability (0.4-0.8) was relatively high. The 896 genotyped nNILs contain 2638 introgressions, which span the entire genome with substantial overlap within and among allele donors. GWA with the whole panel identified 29 QTL for height and disease resistance with allelic variation across donors. To date, this is the largest and most diverse publicly available panel of maize NILs to be phenotypically and genotypically characterized. The nNILs are a valuable resource for the maize community, providing an extensive collection of introgressions from the founders of the maize NAM population in a B73 background combined with data on six agronomically important traits and from genotyping-by-sequencing. We demonstrate that the nNILs can be used for QTL mapping and allelic testing. The majority of nNILs had four or fewer introgressions, and could readily be used for future fine mapping studies.

Dominant, Heritable Resistance to Stewart's Wilt in Maize Is Associated with an Enhanced Vascular Defense Response to Infection with Pantoea stewartii.

Vascular wilt bacteria such as Pantoea stewartii, the causal agent of Stewart's bacterial wilt of maize (SW), are destructive pathogens that are difficult to control. These bacteria colonize the xylem, where they form biofilms that block sap flow leading to characteristic wilting symptoms. Heritable forms of SW resistance exist and are used in maize breeding programs but the underlying genes and mechanisms are mostly unknown. Here, we show that seedlings of maize inbred lines with pan1 mutations are highly resistant to SW. However, current evidence suggests that other genes introgressed along with pan1 are responsible for resistance. Genomic analyses of pan1 lines were used to identify candidate resistance genes. In-depth comparison of P. stewartii interaction with susceptible and resistant maize lines revealed an enhanced vascular defense response in pan1 lines characterized by accumulation of electron-dense materials in xylem conduits visible by electron microscopy. We propose that this vascular defense response restricts P. stewartii spread through the vasculature, reducing both systemic bacterial colonization of the xylem network and consequent wilting. Though apparently unrelated to the resistance phenotype of pan1 lines, we also demonstrate that the effector WtsE is essential for P. stewartii xylem dissemination, show evidence for a nutritional immunity response to P. stewartii that alters xylem sap composition, and present the first analysis of maize transcriptional responses to P. stewartii infection.

Emotion dysregulation and posttraumatic stress disorder: a test of the incremental role of difficulties regulating positive emotions.

Background and Objectives: Literature provides support for the role of emotion dysregulation in the development and course of posttraumatic stress disorder (PTSD) among women victims of intimate partner violence (IPV). However, a dearth of studies have examined the contribution of emotion dysregulation stemming from positive emotions to PTSD. Extending research, the current study examined (1) the bivariate association of difficulties regulating positive emotions to PTSD symptom severity, and (2) the incremental role of difficulties regulating positive emotions in PTSD symptom severity beyond difficulties regulating negative emotions. Design: Participants were 210 women victims of IPV involved in the criminal justice system because of their partners' arrest (M age = 36.14, 48.6% African American). Methods: Participants completed empirically-supported self-report measures assessing difficulties regulating positive and negative emotions and PTSD symptom severity. Results: Difficulties regulating positive and negative emotions (overall and across each of the specific dimensions) were significantly positively associated with PTSD symptom severity. Moreover, difficulties regulating positive emotions demonstrated an incremental relation to PTSD symptom severity beyond the variance accounted for by difficulties regulating negative emotions. Conclusions: Our findings suggest the potential utility of targeting difficulties regulating positive emotions in interventions for PTSD among women victims of IPV.

Diverse Components of Resistance to Fusarium verticillioides Infection and Fumonisin Contamination in Four Maize Recombinant Inbred Families.

The fungus Fusarium verticillioides can infect maize ears, causing Fusarium ear rot (FER) and contaminating the grain with fumonisins (FUM), which are harmful to humans and animals. Breeding for resistance to FER and FUM and post-harvest sorting of grain are two strategies for reducing FUM in the food system. Kernel and cob tissues have been previously associated with differential FER and FUM. Four recombinant inbred line families from the maize nested associated mapping population were grown and inoculated with F. verticillioides across four environments, and we evaluated the kernels for external and internal infection severity as well as FUM contamination. We also employed publicly available phenotypes on innate ear morphology to explore genetic relationships between ear architecture and resistance to FER and FUM. The four families revealed wide variation in external symptomatology at the phenotypic level. Kernel bulk density under inoculation was an accurate indicator of FUM levels. Genotypes with lower kernel density-under both inoculated and uninoculated conditions-and larger cobs were more susceptible to infection and FUM contamination. Quantitative trait locus (QTL) intervals could be classified as putatively resistance-specific and putatively shared for ear and resistance traits. Both types of QTL mapped in this study had substantial overlap with previously reported loci for resistance to FER and FUM. Ear morphology may be a component of resistance to F. verticillioides infection and FUM accumulation.

Millimeter-Level Plant Disease Detection From Aerial Photographs via Deep Learning and Crowdsourced Data.

Computer vision models that can recognize plant diseases in the field would be valuable tools for disease management and resistance breeding. Generating enough data to train these models is difficult, however, since only trained experts can accurately identify symptoms. In this study, we describe and implement a two-step method for generating a large amount of high-quality training data with minimal expert input. First, experts located symptoms of northern leaf blight (NLB) in field images taken by unmanned aerial vehicles (UAVs), annotating them quickly at low resolution. Second, non-experts were asked to draw polygons around the identified diseased areas, producing high-resolution ground truths that were automatically screened based on agreement between multiple workers. We then used these crowdsourced data to train a convolutional neural network (CNN), feeding the output into a conditional random field (CRF) to segment images into lesion and non-lesion regions with accuracy of 0.9979 and F1 score of 0.7153. The CNN trained on crowdsourced data showed greatly improved spatial resolution compared to one trained on expert-generated data, despite using only one fifth as many expert annotations. The final model was able to accurately delineate lesions down to the millimeter level from UAV-collected images, the finest scale of aerial plant disease detection achieved to date. The two-step approach to generating training data is a promising method to streamline deep learning approaches for plant disease detection, and for complex plant phenotyping tasks in general.

Image set for deep learning: field images of maize annotated with disease symptoms.

OBJECTIVES: Automated detection and quantification of plant diseases would enable more rapid gains in plant breeding and faster scouting of farmers' fields. However, it is difficult for a simple algorithm to distinguish between the target disease and other sources of dead plant tissue in a typical field, especially given the many variations in lighting and orientation. Training a machine learning algorithm to accurately detect a given disease from images taken in the field requires a massive amount of human-generated training data. DATA DESCRIPTION: This data set contains images of maize (Zea mays L.) leaves taken in three ways: by a hand-held camera, with a camera mounted on a boom, and with a camera mounted on a small unmanned aircraft system (sUAS, commonly known as a drone). Lesions of northern leaf blight (NLB), a common foliar disease of maize, were annotated in each image by one of two human experts. The three data sets together contain 18,222 images annotated with 105,705 NLB lesions, making this the largest publicly available image set annotated for a single plant disease.

Dissecting Symptomatology and Fumonisin Contamination Produced by Fusarium verticillioides in Maize Ears.

The fungus Fusarium verticillioides can infect maize ears, contaminating the grain with mycotoxins, including fumonisins. This global public health threat can be managed by breeding maize varieties that are resistant to colonization by F. verticillioides and by sorting grain after harvest to reduce fumonisin levels in food systems. Here, we employed two F. verticillioides inoculation techniques representing distinct infection pathways to dissect ear symptomatology and morphological resistance mechanisms in a diverse panel of maize inbred lines. The "point" method involved penetrating the ear with a spore-coated toothpick and the "inundative" method introduced a liquid spore suspension under the husk of the ear. We evaluated quantitative and qualitative indicators of external and internal symptom severity as low-cost proxies for fumonisin contamination, and found that kernel bulk density was predictive of fumonisin levels (78 to 84% sensitivity; 97 to 99% specificity). Inundative inoculation resulted in greater disease severity and fumonisin contamination than point inoculation. We also found that the two inoculation methods implicated different ear tissues in defense, with cob morphology being a more important component of resistance under point inoculation. Across both inoculation methods, traits related to cob size were positively associated with disease severity and fumonisin content. Our work demonstrates that (i) the use of diverse modes of inoculation is necessary for combining complementary mechanisms of genetic resistance, (ii) kernel bulk density can be used effectively as a proxy for fumonisin levels, and (iii) trade-offs may exist between yield potential and resistance to fumonisin contamination.

Determinants of Virulence and In Vitro Development Colocalize on a Genetic Map of Setosphaeria turcica.

Generating effective and stable strategies for resistance breeding requires an understanding of the genetics of host-pathogen interactions and the implications for pathogen dynamics and evolution. Setosphaeria turcica causes northern leaf blight (NLB), an important disease of maize for which major resistance genes have been deployed. Little is known about the evolutionary dynamics of avirulence (AVR) genes in S. turcica. To test the hypothesis that there is a genetic association between avirulence and in vitro development traits, we (i) created a genetic map of S. turcica, (ii) located candidate AVRHt1 and AVRHt2 regions, and (iii) identified genetic regions associated with several in vitro development traits. A cross was generated between a race 1 and a race 23N strain, and 221 progeny were isolated. Genotyping by sequencing was used to score 2,078 single-nucleotide polymorphism markers. A genetic map spanning 1,981 centimorgans was constructed, consisting of 21 linkage groups. Genetic mapping extended prior evidence for the location and identity of the AVRHt1 gene and identified a region of interest for AVRHt2. The genetic location of AVRHt2 colocalized with loci influencing radial growth and mycelial abundance. Our data suggest a trade-off between virulence on Ht1 and Ht2 and the pathogen's vegetative growth rate. In addition, in-depth analysis of the genotypic data suggests the presence of significant duplication in the genome of S. turcica.

Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning.

Northern leaf blight (NLB) can cause severe yield loss in maize; however, scouting large areas to accurately diagnose the disease is time consuming and difficult. We demonstrate a system capable of automatically identifying NLB lesions in field-acquired images of maize plants with high reliability. This approach uses a computational pipeline of convolutional neural networks (CNNs) that addresses the challenges of limited data and the myriad irregularities that appear in images of field-grown plants. Several CNNs were trained to classify small regions of images as containing NLB lesions or not; their predictions were combined into separate heat maps, then fed into a final CNN trained to classify the entire image as containing diseased plants or not. The system achieved 96.7% accuracy on test set images not used in training. We suggest that such systems mounted on aerial- or ground-based vehicles can help in automated high-throughput plant phenotyping, precision breeding for disease resistance, and reduced pesticide use through targeted application across a variety of plant and disease categories.

Determinants of recent aflatoxin exposure among pregnant women in rural Zimbabwe.

SCOPE: Aflatoxins (AFs) are toxic secondary metabolites of Aspergillus species that contaminate staple foods such as maize and groundnuts. AF exposure during pregnancy has been associated with adverse birth outcomes in limited-scale surveys in sub-Saharan Africa. The objective of this study was to describe the determinants of AF exposure, using urinary aflatoxin M1 (AFM1) biomarkers and data generated by the Sanitation Hygiene Infant Nutrition Efficacy (SHINE) trial for rural Zimbabwean women in early pregnancy. Sanitation Hygiene Infant Nutrition Efficacy is a large, cluster-randomized community-based trial in Zimbabwe designed to investigate the independent and combined effects of nutrition and hygiene interventions on early child growth. METHODS AND RESULTS: Urine samples collected from 1580 pregnant women in rural Zimbabwe at median gestational age of 13.9 wk were measured for AFM1. AFM1 was detected in 30% of samples (median of exposed, 162 pg AFM1/mg creatinine; range 30-6046 pg AFM1/mg). In multivariable ordinal logistic models, geographical location (p<0.001), seasonality (p < 0.001) and dietary practices (p = 0.011) were significant predictors of urinary AFM1. CONCLUSION: This is the largest AF biomarker survey conducted in Zimbabwe, and demonstrated frequent exposure in pregnant women with clear temporal and spatial variability in AF biomarker levels.

A remorin gene is implicated in quantitative disease resistance in maize.

KEY MESSAGE: Quantitative disease resistance is used by plant breeders to improve host resistance. We demonstrate a role for a maize remorin ( ZmREM6.3 ) in quantitative resistance against northern leaf blight using high-resolution fine mapping, expression analysis, and mutants. This is the first evidence of a role for remorins in plant-fungal interactions. Quantitative disease resistance (QDR) is important for the development of crop cultivars and is particularly useful when loci also confer multiple disease resistance. Despite its widespread use, the underlying mechanisms of QDR remain largely unknown. In this study, we fine-mapped a known quantitative trait locus (QTL) conditioning disease resistance on chromosome 1 of maize. This locus confers resistance to three foliar diseases: northern leaf blight (NLB), caused by the fungus Setosphaeria turcica; Stewart's wilt, caused by the bacterium Pantoea stewartii; and common rust, caused by the fungus Puccinia sorghi. The Stewart's wilt QTL was confined to a 5.26-Mb interval, while the rust QTL was reduced to an overlapping 2.56-Mb region. We show tight linkage between the NLB QTL locus and the loci conferring resistance to Stewart's wilt and common rust. Pleiotropy cannot be excluded for the Stewart's wilt and the common rust QTL, as they were fine-mapped to overlapping regions. Four positional candidate genes within the 243-kb NLB interval were examined with expression and mutant analysis: a gene with homology to an F-box gene, a remorin gene (ZmREM6.3), a chaperonin gene, and an uncharacterized gene. The F-box gene and ZmREM6.3 were more highly expressed in the resistant line. Transposon tagging mutants were tested for the chaperonin and ZmREM6.3, and the remorin mutant was found to be more susceptible to NLB. The putative F-box is a strong candidate, but mutants were not available to test this gene. Multiple lines of evidence strongly suggest a role for ZmREM6.3 in quantitative disease resistance.

Mental health problems in young male offenders with and without sex offences: a comparison based on the MAYSI-2.

BACKGROUND: There is a need for better knowledge about the relationship between sexual offending by young people and mental health problems. AIM: This study aimed to compare mental health problems between young people who commit sexual offences and those who do not. METHODS: After completion of the Massachusetts Youth Screening Instrument-Version 2 (MAYSI-2), 334 young people who, according to MAYSI-2 information, had committed a sex offence were compared with 334 young people whose MAYSI-2 data suggested that they had not committed a sex offence. They were matched for age, race/ethnicity, type of facility and adjudication status. We also examined the young sex offenders for within group differences. RESULTS: The young sex offenders were less likely to report anger-irritability or substance misuse than the comparison youths. Within the sex offender group, older juveniles were more likely to report alcohol and drug use problems than younger ones, Caucasians were more likely to report anger and suicidal ideation than their non-Caucasian peers, those detained were more likely to report alcohol and drug use problems and somatic complaints than those on probation, and convicted youths were more likely to report alcohol and drug use problems and anger-irritability than those awaiting trial. CONCLUSIONS: Juvenile sexual offending seems less likely to be committed in the context of an anti-social lifestyle than other offending. Important findings among young sex offenders are their higher levels of mental health problems among those detained and convicted than among those on probation or awaiting trial. Assessment of the mental health of young sex offenders seems to be even more important the further they are into the justice system. Copyright © 2015 John Wiley & Sons, Ltd.

Resistance to gray leaf spot of maize: genetic architecture and mechanisms elucidated through nested association mapping and near-isogenic line analysis.

Gray leaf spot (GLS), caused by Cercospora zeae-maydis and Cercospora zeina, is one of the most important diseases of maize worldwide. The pathogen has a necrotrophic lifestyle and no major genes are known for GLS. Quantitative resistance, although poorly understood, is important for GLS management. We used genetic mapping to refine understanding of the genetic architecture of GLS resistance and to develop hypotheses regarding the mechanisms underlying quantitative disease resistance (QDR) loci. Nested association mapping (NAM) was used to identify 16 quantitative trait loci (QTL) for QDR to GLS, including seven novel QTL, each of which demonstrated allelic series with significant effects above and below the magnitude of the B73 reference allele. Alleles at three QTL, qGLS1.04, qGLS2.09, and qGLS4.05, conferred disease reductions of greater than 10%. Interactions between loci were detected for three pairs of loci, including an interaction between iqGLS4.05 and qGLS7.03. Near-isogenic lines (NILs) were developed to confirm and fine-map three of the 16 QTL, and to develop hypotheses regarding mechanisms of resistance. qGLS1.04 was fine-mapped from an interval of 27.0 Mb to two intervals of 6.5 Mb and 5.2 Mb, consistent with the hypothesis that multiple genes underlie highly significant QTL identified by NAM. qGLS2.09, which was also associated with maturity (days to anthesis) and with resistance to southern leaf blight, was narrowed to a 4-Mb interval. The distance between major leaf veins was strongly associated with resistance to GLS at qGLS4.05. NILs for qGLS1.04 were treated with the C. zeae-maydis toxin cercosporin to test the role of host-specific toxin in QDR. Cercosporin exposure increased expression of a putative flavin-monooxygenase (FMO) gene, a candidate detoxification-related gene underlying qGLS1.04. This integrated approach to confirming QTL and characterizing the potential underlying mechanisms advances the understanding of QDR and will facilitate the development of resistant varieties.

Unraveling genomic complexity at a quantitative disease resistance locus in maize.

Multiple disease resistance has important implications for plant fitness, given the selection pressure that many pathogens exert directly on natural plant populations and indirectly via crop improvement programs. Evidence of a locus conditioning resistance to multiple pathogens was found in bin 1.06 of the maize genome with the allele from inbred line "Tx303" conditioning quantitative resistance to northern leaf blight (NLB) and qualitative resistance to Stewart's wilt. To dissect the genetic basis of resistance in this region and to refine candidate gene hypotheses, we mapped resistance to the two diseases. Both resistance phenotypes were localized to overlapping regions, with the Stewart's wilt interval refined to a 95.9-kb segment containing three genes and the NLB interval to a 3.60-Mb segment containing 117 genes. Regions of the introgression showed little to no recombination, suggesting structural differences between the inbred lines Tx303 and "B73," the parents of the fine-mapping population. We examined copy number variation across the region using next-generation sequencing data, and found large variation in read depth in Tx303 across the region relative to the reference genome of B73. In the fine-mapping region, association mapping for NLB implicated candidate genes, including a putative zinc finger and pan1. We tested mutant alleles and found that pan1 is a susceptibility gene for NLB and Stewart's wilt. Our data strongly suggest that structural variation plays an important role in resistance conditioned by this region, and pan1, a gene conditioning susceptibility for NLB, may underlie the QTL.

Tissue-specific components of resistance to Aspergillus ear rot of maize.

Aspergillus flavus and other Aspergillus spp. infect maize and produce aflatoxins. An important control measure is the use of resistant maize hybrids. There are several reports of maize lines that are resistant to aflatoxin accumulation but the mechanisms of resistance remain unknown. To gain a better understanding of resistance, we dissected the phenotype into 10 components: 4 pertaining to the response of silk, 4 pertaining to the response of developing kernels, and 2 pertaining to the response of mature kernels to inoculation with A. flavus. In order to challenge different tissues and to evaluate multiple components of resistance, various inoculation methods were used in experiments in vitro and under field conditions on a panel of diverse maize inbred lines over 3 years. As is typical for this trait, significant genotype-environment interactions were found for all the components of resistance studied. There was, however, significant variation in maize germplasm for susceptibility to silk and kernel colonization by A. flavus as measured in field assays. Resistance to silk colonization has not previously been reported. A significant correlation of resistance to aflatoxin accumulation with flowering time and kernel composition traits (fiber, ash, carbohydrate, and seed weight) was detected. In addition, correlation analyses with data available in the literature indicated that lines that flower later in the season tend to be more resistant. We were not able to demonstrate that components identified in vitro were associated with reduced aflatoxin accumulation in the field.

Multivariate analysis of maize disease resistances suggests a pleiotropic genetic basis and implicates a GST gene.

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.