scholarly journals QTL mapping reveals complex genetic architecture of quantitative virulence in the wheat pathogen Zymoseptoria tritici

2016 ◽  
Author(s):  
E. L. Stewart ◽  
D. Croll ◽  
M. H. Lendenmann ◽  
A. Sanchez Vallet ◽  
F. E. Hartmann ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Quantitative Measure ◽  
Automated Image Analysis ◽  
Host Specialization ◽  
Zymoseptoria Tritici ◽  
Pathogen Virulence ◽  
High Throughput Phenotyping ◽  
Dense Panel ◽  
Mapping Populations ◽  
Accessory Chromosomes

SummaryWe conducted a comprehensive analysis of virulence in the fungal wheat pathogen Zymoseptoria tritici using QTL mapping. High throughput phenotyping based on automated image analysis allowed measurement of pathogen virulence on a scale and with a precision that was not previously possible. Across two mapping populations encompassing more than 520 progeny, 540,710 pycnidia were counted and their sizes and grey values were measured, yielding over 1.6 million phenotypes associated with pathogen reproduction. Large pycnidia were shown to produce more numerous and larger spores than small pycnidia. Precise measures of percent leaf area covered by lesions provided a quantitative measure of host damage. Combining these large and accurate phenotype datasets with a dense panel of RADseq genetic markers enabled us to genetically dissect pathogen virulence into components related to host damage and components related to pathogen reproduction. We show that different components of virulence can be under separate genetic control. Large-and small-effect QTLs were identified for all traits, with some QTLs specific to mapping populations, cultivars and traits and other QTLs shared among traits within the same mapping population. We associated the presence or absence of accessory chromosomes with several virulence traits, providing the first evidence for an important function associated with accessory chromosomes in this organism. A large-effect QTL involved in host specialization was identified on chromosome 7, leading to identification of candidate genes having a large effect on virulence.

scholarly journals Measuring Quantitative Virulence in the Wheat Pathogen Zymoseptoria tritici Using High-Throughput Automated Image Analysis

2014 ◽  
Vol 104 (9) ◽  
pp. 985-992 ◽  
Author(s):  
Ethan L. Stewart ◽  
Bruce A. McDonald
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Continuous Distribution ◽  
Automated Image Analysis ◽  
Host Specialization ◽  
Septoria Tritici Blotch ◽  
Septoria Tritici ◽  
Zymoseptoria Tritici

Zymoseptoria tritici, causal agent of Septoria tritici blotch on wheat, produces pycnidia in chlorotic and necrotic lesions on infected leaves. A high-throughput phenotyping method was developed based on automated digital image analysis that accurately measures the percentage of leaf area covered by lesions (PLACL) as well as pycnidia size and number. A seedling inoculation assay was conducted using 361 Z. tritici isolates originating from a controlled cross and two different winter wheat cultivars. Pycnidia size and density were found to be quantitative traits that showed a continuous distribution in the progeny. There was a weak correlation between pycnidia density and size (r = −0.27) and between pycnidia density and PLACL (r = 0.37). There were significant differences in PLACL and pycnidia density on resistant and susceptible cultivars. In all, >20% of the offspring exhibited significantly different pycnidia density on the two cultivars, consistent with host specialization. Automated image analysis provided greater accuracy and precision compared with traditional visual estimates of virulence. These results show that digital image analysis provides a powerful tool for measuring differences in quantitative virulence among strains of Z. tritici.

scholarly journals Next-generation re-sequencing as a tool for rapid bioinformatic screening of presence and absence of genes and accessory chromosomes across isolates of Zymoseptoria tritici

2015 ◽  
Vol 79 ◽  
pp. 71-75 ◽  
Author(s):  
Megan C. McDonald ◽  
Angela H. Williams ◽  
Andrew Milgate ◽  
Julie A. Pattemore ◽  
Peter S. Solomon ◽  
...  
Keyword(s):  
Next Generation ◽  
Zymoseptoria Tritici ◽  
Presence And Absence ◽  
Accessory Chromosomes

scholarly journals Measuring splash-dispersal of a major wheat pathogen in the field

2021 ◽  
Author(s):  
Petteri Karisto ◽  
Frédéric Suffert ◽  
Alexey Mikaberidze
Keyword(s):  
Plant Pathogen ◽  
Precision Agriculture ◽  
Plant Pathogens ◽  
Population Based ◽  
Dispersal Kernel ◽  
Statistical Testing ◽  
Automated Image Analysis ◽  
Zymoseptoria Tritici ◽  
Phenotypic Data ◽  
Plant Pathosystems

AbstractCapacity for dispersal is a fundamental fitness component of plant pathogens. Empirical characterization of plant pathogen dispersal is of prime importance for understanding how plant pathogen populations change in time and space. We measured dispersal of Zymoseptoria tritici in natural environment. Primary disease gradients were produced by rain-splash driven dispersal and subsequent transmission via asexual pycnidiospores from infected source. To achieve this, we inoculated field plots of wheat (Triticum aestivum) with two distinct Z. tritici strains and a 50/50 mixture of the two strains. We measured effective dispersal of the Z. tritici population based on pycnidia counts using automated image analysis. The data were analyzed using a spatially-explicit mathematical model that takes into account the spatial extent of the source. We employed robust bootstrapping methods for statistical testing and adopted a two-dimensional hypotheses test based on the kernel density estimation of the bootstrap distribution of parameter values. Genotyping of re-isolated pathogen strains with strain-specific PCR-reaction further confirmed the conclusions drawn from the phenotypic data. The methodology presented here can be applied to other plant pathosystems.We achieved the first estimates of the dispersal kernel of the pathogen in field conditions. The characteristic spatial scale of dispersal is tens of centimeters – consistent with previous studies in controlled conditions. Our estimation of the dispersal kernel can be used to parameterize epidemiological models that describe spatial-temporal disease dynamics within individual wheat fields. The results have the potential to inform spatially targeted control of crop diseases in the context of precision agriculture.

scholarly journals Measuring splash-dispersal of a major wheat pathogen in the field

2021 ◽  
Author(s):  
Petteri Karisto ◽  
Frédéric Suffert ◽  
Alexey Mikaberidze
Keyword(s):  
Plant Pathogen ◽  
Plant Pathogens ◽  
Statistical Testing ◽  
Field Conditions ◽  
Automated Image Analysis ◽  
Accurate Estimation ◽  
Zymoseptoria Tritici ◽  
Phenotypic Data ◽  
Plant Disease Management ◽  
Dispersal Kernels

Capacity for dispersal is a fundamental fitness component of plant pathogens. Characterization of plant pathogen dispersal is important for understanding how pathogen populations change in time and space. We devised a systematic approach to measure and analyze rain splash-driven dispersal of plant pathogens in field conditions, using the major fungal wheat pathogen Zymoseptoria tritici as a case study. We inoculated field plots of wheat (Triticum aestivum) with two distinct Z. tritici strains. Next, we measured disease intensity as counts of fruiting bodies (pycnidia) using automated image analysis. These measurements characterized primary disease gradients, which we used to estimate effective dispersal of the pathogen population. Genotyping of re-isolated pathogen strains with strain-specific PCR-reaction confirmed the conclusions drawn from phenotypic data. Consistently with controlled environment studies, we found that the characteristic scale of dispersal is tens of centimeters. We analyzed the data using a spatially-explicit mathematical model that incorporates the spatial extent of the source, rather than assuming a point source, which allows for a more accurate estimation of dispersal kernels. We employed bootstrapping methods for statistical testing and adopted a two-dimensional hypotheses test based on kernel density estimation, enabling more robust inference compared to standard methods. We report the first estimates of dispersal kernels of the pathogen in field conditions. However, repeating the experiment in other environments would lead to more robust conclusions. We put forward advanced methodology that paves the way to further measurements of plant pathogen dispersal in field conditions, which can inform spatially targeted plant disease management.

QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat

2006 ◽  
Vol 19 (2) ◽  
pp. 163-177 ◽  
Author(s):  
N. Kumar ◽  
P. L. Kulwal ◽  
H. S. Balyan ◽  
P. K. Gupta
Keyword(s):  
Qtl Mapping ◽  
Bread Wheat ◽  
Mapping Populations

scholarly journals ScreenSeed as a novel high throughput seed germination phenotyping method

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicolas Merieux ◽  
Pierre Cordier ◽  
Marie-Hélène Wagner ◽  
Sylvie Ducournau ◽  
Sophie Aligon ◽  
...  
Keyword(s):  
Arabidopsis Thaliana ◽  
Image Analysis ◽  
Seed Germination ◽  
High Throughput ◽  
Automated Image Analysis ◽  
Experimental Protocol ◽  
Visual Scoring ◽  
Radicle Protrusion ◽  
High Throughput Phenotyping ◽  
Fast Procedure

AbstractA high throughput phenotyping tool for seed germination, the ScreenSeed technology, was developed with the aim of screening genotype responsiveness and chemical drugs. This technology was presently used with Arabidopsis thaliana seeds to allow characterizing seed samples germination behavior by incubating seeds in 96-well microplates under defined conditions and detecting radicle protrusion through the seed coat by automated image analysis. This study shows that this technology provides a fast procedure allowing to handle thousands of seeds without compromising repeatability or accuracy of the germination measurements. Potential biases of the experimental protocol were assessed through statistical analyses of germination kinetics. Comparison of the ScreenSeed procedure with commonly used germination tests based upon visual scoring displayed very similar germination kinetics.

scholarly journals Modeling Allelic Diversity of Multi-parent Mapping Populations Affects Detection of Quantitative Trait Loci

2021 ◽  
Author(s):  
Sarah Odell ◽  
Asher I Hudson ◽  
Sébastien Praud ◽  
Pierre Dubreuil ◽  
Marie-Helene Tixier ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Flowering Time ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Association Studies ◽  
Allelic Diversity ◽  
Genome Wide Association Studies ◽  
Trait Loci ◽  
Mapping Populations

The search for quantitative trait loci (QTL) that explain complex traits such as yield and flowering time has been ongoing in all crops. Methods such as bi-parental QTL mapping and genome-wide association studies (GWAS) each have their own advantages and limitations. Multi-parent advanced generation intercross (MAGIC) populations contain more recombination events and genetic diversity than bi-parental mapping populations and reduce the confounding effect of population structure that is an issue in association mapping populations. Here we discuss the results of using a MAGIC population of doubled haploid (DH) maize lines created from 16 diverse founders to perform QTL mapping. We compare three models that assume bi-allelic, founder, and ancestral haplotype allelic states for QTL. The three methods have different power to detect QTL for a variety of agronomic traits. Although the founder approach finds the most QTL, there are also QTL unique to each method, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time QTL, qDTA8, which contains vgt1, suggests a potential epistatic interaction and highlights the strengths and weaknesses of each method. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and show the limitations of binary SNP data for identifying multi-allelic QTL.

scholarly journals An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5727 ◽  
Author(s):  
Jeffrey C. Berry ◽  
Noah Fahlgren ◽  
Alexandria A. Pokorny ◽  
Rebecca S. Bart ◽  
Kira M. Veley
Keyword(s):  
High Throughput ◽  
Linear Models ◽  
Correction Method ◽  
Automated Image Analysis ◽  
Plant Phenotyping ◽  
Image Brightness ◽  
Automated Method ◽  
Color Profile ◽  
High Throughput Phenotyping ◽  
High Throughput Imaging

High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.

scholarly journals Ranking Quantitative Resistance to Septoria tritici Blotch in Elite Wheat Cultivars Using Automated Image Analysis

2018 ◽  
Vol 108 (5) ◽  
pp. 568-581 ◽  
Author(s):  
Petteri Karisto ◽  
Andreas Hund ◽  
Kang Yu ◽  
Jonas Anderegg ◽  
Achim Walter ◽  
...  
Keyword(s):  
Image Analysis ◽  
Quantitative Resistance ◽  
Major Gene ◽  
Growing Season ◽  
Automated Image Analysis ◽  
Septoria Tritici Blotch ◽  
Septoria Tritici ◽  
Wheat Cultivars ◽  
Zymoseptoria Tritici ◽  
Breeding Programs

Quantitative resistance is likely to be more durable than major gene resistance for controlling Septoria tritici blotch (STB) on wheat. Earlier studies hypothesized that resistance affecting the degree of host damage, as measured by the percentage of leaf area covered by STB lesions, is distinct from resistance that affects pathogen reproduction, as measured by the density of pycnidia produced within lesions. We tested this hypothesis using a collection of 335 elite European winter wheat cultivars that was naturally infected by a diverse population of Zymoseptoria tritici in a replicated field experiment. We used automated image analysis of 21,420 scanned wheat leaves to obtain quantitative measures of conditional STB intensity that were precise, objective, and reproducible. These measures allowed us to explicitly separate resistance affecting host damage from resistance affecting pathogen reproduction, enabling us to confirm that these resistance traits are largely independent. The cultivar rankings based on host damage were different from the rankings based on pathogen reproduction, indicating that the two forms of resistance should be considered separately in breeding programs aiming to increase STB resistance. We hypothesize that these different forms of resistance are under separate genetic control, enabling them to be recombined to form new cultivars that are highly resistant to STB. We found a significant correlation between rankings based on automated image analysis and rankings based on traditional visual scoring, suggesting that image analysis can complement conventional measurements of STB resistance, based largely on host damage, while enabling a much more precise measure of pathogen reproduction. We showed that measures of pathogen reproduction early in the growing season were the best predictors of host damage late in the growing season, illustrating the importance of breeding for resistance that reduces pathogen reproduction in order to minimize yield losses caused by STB. These data can already be used by breeding programs to choose wheat cultivars that are broadly resistant to naturally diverse Z. tritici populations according to the different classes of resistance.

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