scholarly journals Genetic Mapping and Genomic Prediction of Sclerotinia Stem Rot Resistance to Rapeseed/Canola (Brassica Napus L.) at Seedling Stage

2021 ◽  
Author(s):  
Jayanta Roy ◽  
Luis E. del Río Mendoza ◽  
Nonoy Bandillo ◽  
Phillip E. McClean ◽  
Mukhlesur Rahman
Keyword(s):  
Genomic Prediction ◽  
Predictive Ability ◽  
Resistance Breeding ◽  
Significant Snps ◽  
Stem Rot ◽  
Post Inoculation ◽  
Phenotypic Variance ◽  
Sclerotinia Stem Rot ◽  
Brassica Napus L ◽  
Genome Wide

Abstract The lack of complete host resistance and a complex resistance inheritance nature between rapeseed/canola and Sclerotinia sclerotiorum often limits the development of functional molecular markers that enable breeding for sclerotinia stem rot (SSR) resistance. However, genomics-assisted selection has the potential to accelerate the breeding for SSR resistance. Therefore, genome-wide association (GWA) mapping and genomic prediction (GP) was performed using a diverse panel of 337 rapeseed/canola genotypes. Three-weeks old seedlings were screened using the petiole inoculation technique (PIT). Days to wilt (DW) up to 2 weeks and lesion phenotypes (LP) at 3, 4, and 7 days post inoculation (dpi) were recorded. A strong correlation (r = -0.94) between DW and LP_4dpi implied that a single time point scoring at four days could be used as a proxy trait. GWA analyses using single-locus (SL) and multi-locus (ML) models identified a total of 35, and 219 significantly associated SNPs, respectively. Out of these, seventy-one SNPs were identified by a combination of the SL model and any of the ML models, at least two ML models, or two traits. These SNPs explained 1.4-13.3% of the phenotypic variance, and considered as significant, could be associated with SSR resistance. Eighty-one candidate genes with a function in disease resistance were associated with the significant SNPs. Six GP models resulted in moderate to high (0.45-0.68) predictive ability depending on SSR resistance traits. The resistant genotypes and significant SNPs will serve as valuable resources for future SSR resistance breeding. Our results also highlight the potential of genomic selection to improve rapeseed/canola breeding for SSR resistance.

scholarly journals Genome-wide association mapping and genomic prediction for adult stage sclerotinia stem rot resistance in Brassica napus (L) under field environments

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jayanta Roy ◽  
T. M. Shaikh ◽  
Luis del Río Mendoza ◽  
Shakil Hosain ◽  
Venkat Chapara ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Predictive Ability ◽  
Significant Snps ◽  
Genome Wide Association ◽  
Stem Rot ◽  
Lesion Length ◽  
Sclerotinia Stem Rot ◽  
Agar Plug ◽  
Genome Wide ◽  
Full Flowering

AbstractSclerotinia stem rot (SSR) is a fungal disease of rapeseed/canola that causes significant seed yield losses and reduces its oil content and quality. In the present study, the reaction of 187 diverse canola genotypes to SSR was characterized at full flowering stage using the agar plug to stem inoculation method in four environments. Genome-wide association study (GWAS) using three different algorithms identified 133 significant SNPs corresponding with 123 loci for disease traits like stem lesion length (LL), lesion width (LW), and plant mortality at 14 (PM_14D) and 21 (PM_21D) days. The explained phenotypic variation of these SNPs ranged from 3.6 to 12.1%. Nineteen significant SNPs were detected in two or more environments, disease traits with at least two GWAS algorithms. The strong correlations observed between LL and other three disease traits evaluated, suggest they could be used as proxies for SSR resistance phenotyping. Sixty-nine candidate genes associated with disease resistance mechanisms were identified. Genomic prediction (GP) analysis with all the four traits employing genome-wide markers resulted in 0.41–0.64 predictive ability depending on the model specifications. The highest predictive ability for PM_21D with three models was about 0.64. From our study, the identified resistant genotypes and stable significant SNP markers will serve as a valuable resource for future SSR resistance breeding. Our study also suggests that genomic selection holds promise for accelerating canola breeding progress by enabling breeders to select SSR resistance genotypes at the early stage by reducing the need to phenotype large numbers of genotypes.

scholarly journals Genome-Wide Association Mapping and Genomic Prediction for Adult Stage Sclerotinia Stem Rot Resistance in Brassica Napus (L) Under Field Environments

2021 ◽  
Author(s):  
Jayanta Roy ◽  
T M Shaikh ◽  
Luis E. del Río Mendoza ◽  
Shakil Hosain ◽  
Venkat Chapara ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Significant Snps ◽  
Genome Wide Association ◽  
Stem Rot ◽  
Lesion Length ◽  
Sclerotinia Stem Rot ◽  
Agar Plug ◽  
Genome Wide ◽  
Full Flowering ◽  
Gp Model

Abstract Sclerotinia stem rot (SSR) is a fungal disease of rapeseed/canola that causes significant seed yield losses and reduces its oil content and quality. In the present study, the reaction of 187 diverse canola genotypes to SSR was characterized at full flowering stage using the agar plug to stem inoculation method in four environments. Genome-wide association study (GWAS) using three different algorithms identified 133 significant SNPs corresponding with 123 loci for disease traits like stem lesion length (LL), lesion width (LW), and plant mortality at 14 (PM_14D) and 21 (PM_21D) days. The explained phenotypic variation of these SNPs ranged from 3.6–12.1%. Nineteen significant SNPs were detected in two or more environments, disease traits with at least two GWAS algorithms. The strong correlations observed between LL and other three disease traits evaluated, suggest they could be used as proxies for SSR resistance phenotyping. Sixty-nine candidate genes associated with disease resistance mechanisms were identified. Genomic prediction (GP) analysis with all the four traits employing genome-wide markers resulted in 0.43–0.63 prediction accuracy. However, the prediction efficiency was further improved 0.55–0.88 when we integrated the GWAS information in the GP model. From our study, the identified resistant genotypes and stable significant SNP markers will serve as a valuable resource for future SSR resistance breeding. Our study also suggests that genomic selection holds promise for accelerating canola breeding progress by enabling breeders to select SSR resistance genotypes at the early stage by reducing the need to phenotype large numbers of genotypes.

scholarly journals Modeling first order additive × additive epistasis improves accuracy of genomic prediction for sclerotinia stem rot resistance in canola

2021 ◽  
Author(s):  
Mark C Derbyshire ◽  
Yuphin Khentry ◽  
Anita Severn‐Ellis ◽  
Virginia Mwape ◽  
Nur Shuhadah Mohd Saad ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Stem Rot ◽  
Sclerotinia Stem Rot ◽  
First Order

scholarly journals Genome-Wide Analyses and Prediction of Resistance to MLN in Large Tropical Maize Germplasm

Genes ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 16 ◽  
Author(s):  
Christine Nyaga ◽  
Manje Gowda ◽  
Yoseph Beyene ◽  
Wilson T. Muriithi ◽  
Dan Makumbi ◽  
...  
Keyword(s):  
Disease Severity ◽  
Genomic Prediction ◽  
Defense Responses ◽  
Minor Effect ◽  
Phenotypic Variance ◽  
Progress Curve ◽  
Genome Wide ◽  
Chlorotic Mottle Virus ◽  
Maize Chlorotic Mottle Virus ◽  
Chlorotic Mottle

Maize lethal necrosis (MLN), caused by co-infection of maize chlorotic mottle virus and sugarcane mosaic virus, can lead up to 100% yield loss. Identification and validation of genomic regions can facilitate marker assisted breeding for resistance to MLN. Our objectives were to identify marker-trait associations using genome wide association study and assess the potential of genomic prediction for MLN resistance in a large panel of diverse maize lines. A set of 1400 diverse maize tropical inbred lines were evaluated for their response to MLN under artificial inoculation by measuring disease severity or incidence and area under disease progress curve (AUDPC). All lines were genotyped with genotyping by sequencing (GBS) SNPs. The phenotypic variation was significant for all traits and the heritability estimates were moderate to high. GWAS revealed 32 significantly associated SNPs for MLN resistance (at p < 1.0 × 10−6). For disease severity, these significantly associated SNPs individually explained 3–5% of the total phenotypic variance, whereas for AUDPC they explained 3–12% of the total proportion of phenotypic variance. Most of significant SNPs were consistent with the previous studies and assists to validate and fine map the big quantitative trait locus (QTL) regions into few markers’ specific regions. A set of putative candidate genes associated with the significant markers were identified and their functions revealed to be directly or indirectly involved in plant defense responses. Genomic prediction revealed reasonable prediction accuracies. The prediction accuracies significantly increased with increasing marker densities and training population size. These results support that MLN is a complex trait controlled by few major and many minor effect genes.

scholarly journals Across-population genomic prediction in grapevine opens up promising prospects for breeding

2021 ◽  
Author(s):  
Charlotte Brault ◽  
Vincent Segura ◽  
Patrice This ◽  
Loïc Le Cunff ◽  
Timothée Flutre ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Predictive Ability ◽  
Vitis Vinifera L ◽  
Individual Values ◽  
Population Genomic ◽  
Genome Wide ◽  
Diallel Mating Design ◽  
Genetic Value ◽  
Berry Composition ◽  
Half Diallel

Crop breeding involves two selection steps: choosing progenitors and selecting offspring within progenies. Genomic prediction, based on genome-wide marker estimation of genetic values, could facilitate these steps. However, its potential usefulness in grapevine (Vitis vinifera L.) has only been evaluated in non-breeding contexts mainly through cross-validation within a single population. We tested across-population genomic prediction in a more realistic breeding configuration, from a diversity panel to ten bi-parental crosses connected within a half-diallel mating design. Prediction quality was evaluated over 15 traits of interest (related to yield, berry composition, phenology and vigour), for both the average genetic value of each cross (cross mean) and the genetic values of individuals within each cross (individual values). Genomic prediction in these conditions was found useful: for cross mean, average per-trait predictive ability was 0.6, while per-cross predictive ability was halved on average, but reached a maximum of 0.7. Mean predictive ability for individual values within crosses was 0.26, about half the within-half-diallel value taken as a reference. For some traits and/or crosses, these across-population predictive ability values are promising for implementing genomic selection in grapevine breeding. This study also provided key insights on variables affecting predictive ability. Per-cross predictive ability was well predicted by genetic distance between parents and when this predictive ability was below 0.6, it was improved by training set optimization. For individual values, predictive ability mostly depended on trait-related variables (magnitude of the cross effect and heritability). These results will greatly help designing grapevine breeding programs assisted by genomic prediction.

scholarly journals Detection of Sclerotinia Stem Rot on Oilseed Rape (Brassica napus L.) Leaves Using Hyperspectral Imaging

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1764 ◽  
Author(s):  
Wenwen Kong ◽  
Chu Zhang ◽  
Feng Cao ◽  
Fei Liu ◽  
Shaoming Luo ◽  
...  
Keyword(s):  
Brassica Napus ◽  
Hyperspectral Imaging ◽  
Oilseed Rape ◽  
Stem Rot ◽  
Sclerotinia Stem Rot ◽  
Brassica Napus L

Detection of Sclerotinia Stem Rot on Oilseed Rape (Brassica napus L.) Based on Laser- Induced Breakdown Spectroscopy

2019 ◽  
Vol 62 (1) ◽  
pp. 123-130
Author(s):  
Fei Liu ◽  
Fei Liu ◽  
Tingting Shen ◽  
Jian Wang ◽  
Yong He ◽  
...  
Keyword(s):  
Oilseed Rape ◽  
Nearest Neighbor ◽  
Emission Lines ◽  
Stem Rot ◽  
Laser Induced Breakdown Spectroscopy ◽  
Sclerotinia Stem Rot ◽  
K Nearest Neighbor ◽  
Brassica Napus L ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Abstract. In this study, a novel approachser-induced breakdown spectroscopy (LIBS) is proposed to rapidly diagnose stem rot (SSR) in oilseed rape ( L.). A rapid diagnostic method is important to prevent this worldwide disease and promote growth of oilseed rape. A total of 120 fresh leaves, including 60 healthy and 60 SSR-infected leaves, were collected to acquire LIBS spectra. Robust baseline estimation (RBE) and wavelet transform (WT) were applied to preprocess the raw LIBS spectra for baseline correction and denoising. K-nearest neighbor (KNN), radial basis function neural network (RBFNN), random forest (RF), and extreme learning machine (ELM) methods combining full LIBS spectra were chosen to establish classification models to identify healthy and SSR-infected leaves, and the ELM model obtained classified accuracy of more than 80.00% in the prediction set. Twenty-four emission lines were selected by second-derivative spectra as the most relevant to distinguish healthy and SSR-infected leaves. The ELM model using the optimal emission lines improved the classified accuracy to more than 85% and the specificity to 95.00%. Compared with full-spectra models, the number of variables in the models based on optimal wavelengths was reduced from 22,036 to 24, a reduction of 99.89%. This study indicates that LIBS combined with appropriate chemometric m. Keywords: Chemometrics, Laser-induced breakdown spectroscopy, Oilseed rape, Sclerotinia stem rot.

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