Phenotyping for QTL identification: A case study of resistance to Plasmopara viticola and Erysiphe necator in grapevine

Possamai, Tyrone; Wiedemann‐Merdinoglu, Sabine

doi:10.3389/fpls.2022.930954

Cited by 15 publications

(11 citation statements)

References 141 publications

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“…Our study implemented cutting-edge breeding methodologies to develop grapevine cultivars with enhanced resistance to pests, leveraging recent breakthroughs in DL based plant phenomics and ML based genomic selection. Specifically, we employed GWAS to map the genetic basis of pest resistance in grapes 43,44 . Subsequently, genomic selection is utilized to selectively breed individuals harboring pest-resistant alleles.…”

Section: Discussionmentioning

confidence: 99%

Deep learning based genomic breeding of pest-resistant grapevine

Gan,

Liu,

Zhang

et al. 2024

Preprint

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Crop pests have profoundly deleterious effects on crop yield and food security. However, conventional pest control depends heavily on the utilization of insecticides, which develops strong pesticide resistance and concerns of food safety. Crop and their wild relatives display diverse levels of pest resistance, indicating the feasibility for breeding of pest-resistant crop varieties. In this study, we integrate deep learning (DL)/machine learning (ML) algorithms, plant phenomics and whole genome sequencing (WGS) data to conduct genomic selection (GS) of pest-resistance in grapevine. We employ deep convolutional neural networks (DCNN) to accurately calculate the severity of damage by pests on grape leaves, which achieves a classification accuracy of 95.3% (Visual Geometry Group 16, VGG16, for binary trait) and a correlation coefficient of 0.94 in regression analysis (DCNN with Pest Damage Score, DCNN-PDS, for continuous trait). We apply DL models to predict and integrate phenotype (both binary and continuous) along with WGS data from 231 grape accessions, conducting Genome-Wide Association Studies (GWAS). This analysis detects a total of 69 QTLs, encompassing 139 candidate genes involved in pathways associated with pest resistance, including jasmonic acid (JA), salicylic acid (SA), ethylene, and other related pathways. Furthermore, through the combination with transcriptome data, we identify specific pest-resistant genes, such as ACA12 and CRK3, which play distinct roles in resisting herbivore attacks. Machine learning-based GS demonstrates a high accuracy (95.7%) and a strong correlation (0.90) in predicting the leaf area damaged by pests as binary and continuous traits in grapevine, respectively. In general, our study highlights the power of DL/ML in plant phenomics and GS, facilitating genomic breeding of pest-resistant grapevine.

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Section: Discussionmentioning

confidence: 99%

Deep learning based genomic breeding of pest-resistant grapevine

Gan,

Liu,

Zhang

et al. 2024

Preprint

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“…One of the interesting outcomes of the positive linear correlation obtained between the quantification of sporangia and sporulated area (SA) is that image analysis can be used for accurately estimating the sporangia production. This can help to reduce the amount of work needed for the phenotypic evaluation of the interaction between P. viticola and grapevine, which is time consuming and a limiting factor in the research of several features of downy mildew [ 25 , 26 ]. Quantifying the sporangia produced by P. viticola is important for biological and epidemiological reasons, because it is a fitness component and it can be used to estimate the inoculum produced by the pathogen to infect new plants and tissues [ 27 , 28 , 29 , 30 ].…”

Section: Discussionmentioning

confidence: 99%

Influence of Nitrogen on Grapevine Susceptibility to Downy Mildew

Marcianò

Ricciardi

Maddalena

et al. 2023

Plants

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Downy mildew, caused by the obligate parasite Plasmopara viticola, is one of the most important threats to viticulture. The exploitation of resistant and susceptibility traits of grapevine is one of the most promising ways to increase the sustainability of disease management. Nitrogen (N) fertilization is known for influencing disease severity in the open field, but no information is available on its effect on plant-pathogen interaction. A previous RNAseq study showed that several genes of N metabolism are differentially regulated in grapevine upon P. viticola inoculation, and could be involved in susceptibility or resistance to the pathogen. The aim of this study was to evaluate if N fertilization influences: (i) the foliar leaf content and photosynthetic activity of the plant, (ii) P. viticola infectivity, and (iii) the expression of the candidate susceptibility/resistance genes. Results showed that N level positively correlated with P. viticola infectivity, confirming that particular attention should be taken in vineyard to the fertilization, but did not influence the expression of the candidate genes. Therefore, these genes are manipulated by the pathogen and can be exploited for developing new, environmentally friendly disease management tools, such as dsRNAs, to silence the susceptibility genes or breeding for resistance.

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“…RP can be proposed as a new parameter for the evaluation of P. viticola adaptation to plant resistance in addition to disease severity, which is commonly used as a phenotyping tool for the identification of resistant traits in grapevine [ 28 ]. In fact, it easily estimates the measure in which the growth of the strain is affected by the plant considering, at the same time, the virulence level of the strain evaluated on a susceptible reference.…”

Section: Discussionmentioning

confidence: 99%

Pathogen Adaptation to American (Rpv3-1) and Eurasian (Rpv29) Grapevine Loci Conferring Resistance to Downy Mildew

Fassolo

Lecchi

Marcianò

et al. 2022

Plants

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Durable resistance is a key objective in genetic improvement for disease resistance in grapevines, which must survive for years in the field in the presence of adaptable pathogen populations. In this study, the adaptation of 72 Northern Italian isolates of Plasmopara viticola, the downy mildew agent, has been investigated into Bianca, possessing Rpv3-1, the most frequently exploited resistance locus for genetic improvement, and Mgaloblishvili, a Vitis vinifera variety possessing the newly discovered Rpv29 locus. Infection parameters (latency period, infection frequency, and disease severity) and oospore production and viability were evaluated and compared to those of Pinot noir, the susceptible reference. The expected levels of disease control were achieved by both resistant cultivars (>90% on Bianca; >25% on Mgaloblishvili), despite the high frequency of isolates able to grow on one (28%) or both (46%) accessions. The disease incidence and severity were limited by both resistant cultivars and the strains able to grow on resistant accessions showed signatures of fitness penalties (reduced virulence, infection frequency, and oospore density). Together, these results indicate an adequate pathogen control but suitable practices must be adopted in the field to prevent the diffusion of the partially adapted P. viticola strains to protect resistance genes from erosion.

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Phenotyping for QTL identification: A case study of resistance to Plasmopara viticola and Erysiphe necator in grapevine

Cited by 15 publications

References 141 publications

Deep learning based genomic breeding of pest-resistant grapevine

Deep learning based genomic breeding of pest-resistant grapevine

Influence of Nitrogen on Grapevine Susceptibility to Downy Mildew

Pathogen Adaptation to American (Rpv3-1) and Eurasian (Rpv29) Grapevine Loci Conferring Resistance to Downy Mildew

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