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Thapa S, Gill HS, Halder J, Rana A, Ali S, Maimaitijiang M, Gill U, Bernardo A, St Amand P, Bai G, Sehgal SK. Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat. THE PLANT GENOME 2024:e20470. [PMID: 38853339 DOI: 10.1002/tpg2.20470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 04/07/2024] [Accepted: 04/14/2024] [Indexed: 06/11/2024]
Abstract
Fusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end-use quality. Phenotyping of FHB resistance traits, Fusarium-damaged kernels (FDK), and deoxynivalenol (DON), is either prone to human biases or resource expensive, hindering the progress in breeding for FHB-resistant cultivars. Though genomic selection (GS) can be an effective way to select these traits, inaccurate phenotyping remains a hurdle in exploiting this approach. Here, we used an artificial intelligence (AI)-based precise FDK estimation that exhibits high heritability and correlation with DON. Further, GS using AI-based FDK (FDK_QVIS/FDK_QNIR) showed a two-fold increase in predictive ability (PA) compared to GS for traditionally estimated FDK (FDK_V). Next, the AI-based FDK was evaluated along with other traits in multi-trait (MT) GS models to predict DON. The inclusion of FDK_QNIR and FDK_QVIS with days to heading as covariates improved the PA for DON by 58% over the baseline single-trait GS model. We next used hyperspectral imaging of FHB-infected wheat kernels as a novel avenue to improve the MT GS for DON. The PA for DON using selected wavebands derived from hyperspectral imaging in MT GS models surpassed the single-trait GS model by around 40%. Finally, we evaluated phenomic prediction for DON by integrating hyperspectral imaging with deep learning to directly predict DON in FHB-infected wheat kernels and observed an accuracy (R2 = 0.45) comparable to best-performing MT GS models. This study demonstrates the potential application of AI and vision-based platforms to improve PA for FHB-related traits using genomic and phenomic selection.
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Affiliation(s)
- Subash Thapa
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Harsimardeep S Gill
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Jyotirmoy Halder
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Anshul Rana
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Shaukat Ali
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Maitiniyazi Maimaitijiang
- Department of Geography & Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, South Dakota, USA
| | - Upinder Gill
- Department of Plant Pathology, North Dakota State University, Fargo, North Dakota, USA
| | - Amy Bernardo
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, USA
| | - Paul St Amand
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, USA
| | - Guihua Bai
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, USA
| | - Sunish K Sehgal
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
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Ganaparthi VR, Wechter P, Levi A, Branham SE. Mapping and validation of Fusarium wilt race 2 resistance QTL from Citrullus amarus line USVL246-FR2. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:91. [PMID: 38555543 PMCID: PMC10982098 DOI: 10.1007/s00122-024-04595-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 03/07/2024] [Indexed: 04/02/2024]
Abstract
KEY MESSAGE Fon race 2 resistant QTLs were identified on chromosomes 8 and 9. Families homozygous for resistance alleles at a haplotype of three KASP markers had 42% lower disease severity than those with susceptible alleles in an independent, interspecific validation population confirming their utility for introgression of Fusarium wilt resistance. Fusarium oxysporum f. sp. niveum (Fon) race 2 causes Fusarium wilt in watermelon and threatens watermelon production worldwide. Chemical management options are not effective, and no resistant edible watermelon cultivars have been released. Implementation of marker-assisted selection to develop resistant cultivars requires identifying sources of resistance and the underlying quantitative trait loci (QTL), developing molecular markers associated with the QTL, and validating marker-phenotype associations with an independent population. An intraspecific Citrullus amarus recombinant inbred line population from a cross of resistant USVL246-FR2 and susceptible USVL114 was used for mapping Fon race 2 resistance QTL. KASP markers were developed (N = 51) for the major QTL on chromosome 9 and minor QTL on chromosomes 1, 6, and 8. An interspecific F2:3 population was developed from resistance donor USVL246-FR2 (C. amarus) and a susceptible cultivar 'Sugar Baby' (Citrullus lanatus) to validate the utility of the markers for introgression of resistance from the wild crop relative into cultivated watermelon. Only 16 KASP markers segregated in the interspecific C. amarus/lanatus validation population. Four markers showed significant differences in the separation of genotypes based on family-mean disease severity, but together explained only 16% of the phenotypic variance. Genotypes that inherited homozygous resistant parental alleles at three KASP markers had 42% lower family-mean disease severity than homozygous susceptible genotypes. Thus, haplotype analysis was more effective at predicting the mean disease severity of families than single markers. The haplotype identified in this study will be valuable for developing Fon race 2 resistant watermelon cultivars.
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Affiliation(s)
| | - Patrick Wechter
- Coastal Research and Education Center, Clemson University, Charleston, SC, USA
| | - Amnon Levi
- USDA, US Vegetable Laboratory, ARS, 2700 Savannah Highway, Charleston, SC, 29414, USA
| | - Sandra E Branham
- Coastal Research and Education Center, Clemson University, Charleston, SC, USA.
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Semagn K, Henriquez MA, Iqbal M, Brûlé-Babel AL, Strenzke K, Ciechanowska I, Navabi A, N’Diaye A, Pozniak C, Spaner D. Identification of Fusarium head blight sources of resistance and associated QTLs in historical and modern Canadian spring wheat. FRONTIERS IN PLANT SCIENCE 2023; 14:1190358. [PMID: 37680355 PMCID: PMC10482112 DOI: 10.3389/fpls.2023.1190358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 07/18/2023] [Indexed: 09/09/2023]
Abstract
Fusarium head blight (FHB) is one the most globally destructive fungal diseases in wheat and other small grains, causing a reduction in grain yield by 10-70%. The present study was conducted in a panel of historical and modern Canadian spring wheat (Triticum aestivum L.) varieties and lines to identify new sources of FHB resistance and map associated quantitative trait loci (QTLs). We evaluated 249 varieties and lines for reaction to disease incidence, severity, and visual rating index (VRI) in seven environments by artificially spraying a mixture of four Fusarium graminearum isolates. A subset of 198 them were genotyped with the Wheat 90K iSelect single nucleotide polymorphisms (SNPs) array. Genome-wide association mapping performed on the overall best linear unbiased estimators (BLUE) computed from all seven environments and the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map of 26,449 polymorphic SNPs out of the 90K identified sixteen FHB resistance QTLs that individually accounted for 5.7-10.2% of the phenotypic variance. The positions of two of the FHB resistance QTLs overlapped with plant height and flowering time QTLs. Four of the QTLs (QFhb.dms-3B.1, QFhb.dms-5A.5, QFhb.dms-5A.7, and QFhb.dms-6A.4) were simultaneously associated with disease incidence, severity, and VRI, which accounted for 27.0-33.2% of the total phenotypic variance in the combined environments. Three of the QTLs (QFhb.dms-2A.2, QFhb.dms-2D.2, and QFhb.dms-5B.8) were associated with both incidence and VRI and accounted for 20.5-22.1% of the total phenotypic variance. In comparison with the VRI of the checks, we identified four highly resistant and thirty-three moderately resistant lines and varieties. The new FHB sources of resistance and the physical map of the associated QTLs would provide wheat breeders valuable information towards their efforts in developing improved varieties in western Canada.
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Affiliation(s)
- Kassa Semagn
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, Canada
| | - Maria Antonia Henriquez
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB, Canada
| | - Muhammad Iqbal
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, Canada
| | | | - Klaus Strenzke
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, Canada
| | - Izabela Ciechanowska
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, Canada
| | - Alireza Navabi
- Department of Plant Agriculture, Crop Science Building, University of Guelph, Guelph, ON, Canada
| | - Amidou N’Diaye
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK, Canada
| | - Curtis Pozniak
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK, Canada
| | - Dean Spaner
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, Canada
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Garcia-Abadillo J, Morales L, Buerstmayr H, Michel S, Lillemo M, Holzapfel J, Hartl L, Akdemir D, Carvalho HF, Isidro-Sánchez J. Alternative scoring methods of fusarium head blight resistance for genomic assisted breeding. FRONTIERS IN PLANT SCIENCE 2023; 13:1057914. [PMID: 36714712 PMCID: PMC9876611 DOI: 10.3389/fpls.2022.1057914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/24/2022] [Indexed: 06/18/2023]
Abstract
Fusarium head blight (FHB) is a fungal disease of wheat (Triticum aestivum.L) that causes yield losses and produces mycotoxins which could easily exceed the limits of the EU regulations. Resistance to FHB has a complex genetic architecture and accurate evaluation in breeding programs is key to selecting resistant varieties. The Area Under the Disease Progress Curve (AUDPC) is one of the commonly metric used as a standard methodology to score FHB. Although efficient, AUDPC requires significant costs in phenotyping to cover the entire disease development pattern. Here, we show that there are more efficient alternatives to AUDPC (angle, growing degree days to reach 50% FHB severity, and FHB maximum variance) that reduce the number of field assessments required and allow for fair comparisons between unbalanced evaluations across trials. Furthermore, we found that the evaluation method that captures the maximum variance in FHB severity across plots is the most optimal approach for scoring FHB. In addition, results obtained on experimental data were validated on a simulated experiment where the disease progress curve was modeled as a sigmoid curve with known parameters and assessment protocols were fully controlled. Results show that alternative metrics tested in this study captured key components of quantitative plant resistance. Moreover, the new metrics could be a starting point for more accurate methods for measuring FHB in the field. For example, the optimal interval for FHB evaluation could be predicted using prior knowledge from historical weather data and FHB scores from previous trials. Finally, the evaluation methods presented in this study can reduce the FHB phenotyping burden in plant breeding with minimal losses on signal detection, resulting in a response variable available to use in data-driven analysis such as genome-wide association studies or genomic selection.
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Affiliation(s)
- J. Garcia-Abadillo
- Department of Biotechnology and Plant Biology - Centre for Biotechnology and Plant Genomics (CBGP) - Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - L. Morales
- Department of Agrobiotechnology, Institute of Biotechnology in Plant Production, University of Natural Resources and Life Sciences Vienna (BOKU), Tulln an der Donau, Austria
| | - H. Buerstmayr
- Department of Agrobiotechnology, Institute of Biotechnology in Plant Production, University of Natural Resources and Life Sciences Vienna (BOKU), Tulln an der Donau, Austria
| | - S. Michel
- Department of Agrobiotechnology, Institute of Biotechnology in Plant Production, University of Natural Resources and Life Sciences Vienna (BOKU), Tulln an der Donau, Austria
| | - M. Lillemo
- Department of Plant Sciences, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | | | - L. Hartl
- Bavarian State Research Center for Agriculture, Institute for Crop Science and Plant Breeding, Freising, Germany
| | - D. Akdemir
- CIBMTR (Center for International Blood and Marrow Transplant Research), National Marrow Donor Program/Be The Match, Minneapolis, MN, United States
| | - H. F. Carvalho
- Department of Biotechnology and Plant Biology - Centre for Biotechnology and Plant Genomics (CBGP) - Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - J. Isidro-Sánchez
- Department of Biotechnology and Plant Biology - Centre for Biotechnology and Plant Genomics (CBGP) - Universidad Politécnica de Madrid (UPM), Madrid, Spain
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Crop Improvement: Where Are We Now? BIOLOGY 2022; 11:biology11101373. [PMID: 36290279 PMCID: PMC9598755 DOI: 10.3390/biology11101373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 11/17/2022]
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Semagn K, Crossa J, Cuevas J, Iqbal M, Ciechanowska I, Henriquez MA, Randhawa H, Beres BL, Aboukhaddour R, McCallum BD, Brûlé-Babel AL, N'Diaye A, Pozniak C, Spaner D. Comparison of single-trait and multi-trait genomic predictions on agronomic and disease resistance traits in spring wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:2747-2767. [PMID: 35737008 DOI: 10.1007/s00122-022-04147-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 05/28/2022] [Indexed: 06/15/2023]
Abstract
This study performed comprehensive analyses on the predictive abilities of single-trait and two multi-trait models in three populations. Our results demonstrated the superiority of multi-traits over single-trait models across seven agronomic and four to seven disease resistance traits of different genetic architecture. The predictive ability of multi-trait and single-trait prediction models has not been investigated on diverse traits evaluated under organic and conventional management systems. Here, we compared the predictive abilities of 25% of a testing set that has not been evaluated for a single trait (ST), not evaluated for multi-traits (MT1), and evaluated for some traits but not others (MT2) in three spring wheat populations genotyped either with the wheat 90K single nucleotide polymorphisms array or DArTseq. Analyses were performed on seven agronomic traits evaluated under conventional and organic management systems, four to seven disease resistance traits, and all agronomic and disease resistance traits simultaneously. The average prediction accuracies of the ST, MT1, and MT2 models varied from 0.03 to 0.78 (mean 0.41), from 0.05 to 0.82 (mean 0.47), and from 0.05 to 0.92 (mean 0.67), respectively. The predictive ability of the MT2 model was significantly greater than the ST model in all traits and populations except common bunt with the MT1 model being intermediate between them. The MT2 model increased prediction accuracies over the ST and MT1 models in all traits by 9.0-82.4% (mean 37.3%) and 2.9-82.5% (mean 25.7%), respectively, except common bunt that showed up to 7.7% smaller accuracies in two populations. A joint analysis of all agronomic and disease resistance traits further improved accuracies within the MT1 and MT2 models on average by 21.4% and 17.4%, respectively, as compared to either the agronomic or disease resistance traits, demonstrating the high potential of the multi-traits models in improving prediction accuracies.
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Affiliation(s)
- Kassa Semagn
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada.
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico, DF, Mexico
| | | | - Muhammad Iqbal
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Izabela Ciechanowska
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Maria Antonia Henriquez
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB, R6M 1Y5, Canada
| | - Harpinder Randhawa
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB, T1J 4B1, Canada
| | - Brian L Beres
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB, T1J 4B1, Canada
| | - Reem Aboukhaddour
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB, T1J 4B1, Canada
| | - Brent D McCallum
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB, R6M 1Y5, Canada
| | - Anita L Brûlé-Babel
- Department of Plant Science, University of Manitoba, 66 Dafoe Road, Winnipeg, MB, R3T 2N2, Canada
| | - Amidou N'Diaye
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
| | - Curtis Pozniak
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
| | - Dean Spaner
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada.
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