Digital Imaging Combined with Genome-Wide Association Mapping Links Loci to Plant-Pathogen Interaction Traits

Fordyce, Rachel; Soltis, Nicole E.; Caseys, Céline; Gwinner, Raoni; Corwin, Jason A.; Atwell, Susanna; Copeland, Daniel; Feusier, Julie E.; Subedy, Anushriya; Eshbaugh, Robert; Kliebenstein, Daniel J.

doi:10.1104/pp.18.00851

Cited by 39 publications

(33 citation statements)

References 80 publications

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“…There is evidence in other pathosystems that different disease symptoms represent differences in the underlying genetics of resistance in the plant [34] or virulence in the pathogen [35]. Considering individual components of disease symptoms rather than simply assigning a single overall value, on an ordinal or percentage scale for example, has led to the discovery of new genetic loci responsible for pathogen virulence [35] and plant resistance [36].…”

Section: Discussionmentioning

confidence: 99%

Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning

et al. 2019

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Plant disease poses a serious threat to global food security. Accurate, high-throughput methods of quantifying disease are needed by breeders to better develop resistant plant varieties and by researchers to better understand the mechanisms of plant resistance and pathogen virulence. Northern leaf blight (NLB) is a serious disease affecting maize and is responsible for significant yield losses. A Mask R-CNN model was trained to segment NLB disease lesions in unmanned aerial vehicle (UAV) images. The trained model was able to accurately detect and segment individual lesions in a hold-out test set. The mean intersect over union (IOU) between the ground truth and predicted lesions was 0.73, with an average precision of 0.96 at an IOU threshold of 0.50. Over a range of IOU thresholds (0.50 to 0.95), the average precision was 0.61. This work demonstrates the potential for combining UAV technology with a deep learning-based approach for instance segmentation to provide accurate, high-throughput quantitative measures of plant disease.

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Section: Discussionmentioning

confidence: 99%

Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning

et al. 2019

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“…Genetic analyses have also shown links between clock genes and plant defenses, with perturbation of expression of the clock genes CCA1, LHY, or ELF3 in Arabidopsis, increasing susceptibility to bacterial, fungal, and oomycete attack (Bhardwaj et al 2011;Wang et al 2011;Zhang et al 2013;Lu et al 2017b) and the silencing of ZTL expression rendering wild tobacco more susceptible to a generalist herbivore (Li et al 2018). Genome-wide association mapping in Arabidopsis identified LHY and LUX alleles as associated with Botrytis cinera infection traits such as lesion eccentricity and size (Corwin et al 2016;Fordyce et al 2018). Despite these clear links between the clock and immune responses, whether allelic variation in clock genes of cultivated plants affects biotic stress responses remains to be determined.…”

Section: Variation In Clock Genes Enables Plant Adaptation To Stressmentioning

confidence: 99%

Circadian Rhythms in Plants

Creux

Harmer

2019

Cold Spring Harb Perspect Biol

150

113

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The circadian oscillator is a complex network of interconnected feedback loops that regulates a wide range of physiological processes. Indeed, variation in clock genes has been implicated in an array of plant environmental adaptations, including growth regulation, photoperiodic control of flowering, and responses to abiotic and biotic stress. Although the clock is buffered against the environment, maintaining roughly 24-h rhythms across a wide range of conditions, it can also be reset by environmental cues such as acute changes in light or temperature. These competing demands may help explain the complexity of the links between the circadian clock network and environmental response pathways. Here, we discuss our current understanding of the clock and its interactions with light and temperature-signaling pathways.

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“…Network analysis has also been used with genome-wide association studies (GWASs) to enhance the biological interpretation and prioritize candidate GWAS loci [48][49][50][51][52][53][54][55][56][57]. This approach is gaining in popularity because GWAS results on their own do not provide the necessary context to interpret findings, as pathways affected by a genetic locus are often not immediately apparent.…”

Section: Identifying Network Across Molecular Scalesmentioning

confidence: 99%

Plant Networks as Traits and Hypotheses: Moving Beyond Description

Marshall‐Colón

Kliebenstein

2019

Trends in Plant Science

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Biology relies on the central thesis that the genes in an organism encode molecular mechanisms that combine with stimuli and raw materials from the environment to create a final phenotypic expression representative of the genomic programming. While conceptually simple, the genotype-to-phenotype linkage in a eukaryotic organism relies on the interactions of thousands of genes and an environment with a potentially unknowable level of complexity. Modern biology has moved to the use of networks in systems biology to try to simplify this complexity to decode how an organism's genome works. Previously, biological networks were basic ways to organize, simplify, and analyze data. However, recent advances are allowing networks to move beyond description and become phenotypes or hypotheses in their own right. This review discusses these efforts, like mapping responses across biological scales, including relationships among cellular entities, and the direct use of networks as traits or hypotheses. Biological Networks The modern molecular synthesis proposes that the genes in an organism encode molecular mechanisms that combine with signals and raw materials from the environment to create the final phenotypic expression. This genotype-to-phenotype linkage is conceptually simple, but in reality a eukaryotic organism has thousands of genes and the environment has an unknown and potentially unknowable level of complexity. This complexity has led to current efforts to utilize systems analysis to decode how an organism's genome works to create the optimal phenotype for a given environment. One simplification in the systems biology toolkit is to use biological networks to organize, simplify, and analyze data. These networks can be used to map responses across biological scales, including relationships among cellular entities, such as protein-protein interactions, gene-gene coexpression, and protein:DNA binding. The description of biological processes with networks has uncovered regulatory factors that exert control over many biological processes [1-3], regulatory motifs that shed light on signal transduction [4-7], and putative roles for genes of unknown function [8-10]. The construction of gene regulatory networks (GRNs) provides insight about the regulationdirect, indirect, or hierarchicalin a network by layering DNA-binding data (predicted or observed) onto coexpression networks. However, the predominant use of biological networks has been to describe relationships among molecules at a snapshot in time or to focus on the potential role of hub genes, yielding important insights but with significant limitations [4]. For example, downstream functional analysis of hub genes identified by network analysis can result in no obvious physiological phenotype, potentially due to functional redundancy, or the network may not translate to another experiment [11]. While some of this difficulty is caused by the available data being limiting for complete network inference, there is a key need to improve our ability to derive functional and pred...

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Digital Imaging Combined with Genome-Wide Association Mapping Links Loci to Plant-Pathogen Interaction Traits

Cited by 39 publications

References 80 publications

Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning

Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning

Circadian Rhythms in Plants

Plant Networks as Traits and Hypotheses: Moving Beyond Description

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