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Peixoto MA, Leach KA, Jarquin D, Flannery P, Zystro J, Tracy WF, Bhering L, Resende MFR. Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program. FRONTIERS IN PLANT SCIENCE 2024; 15:1293307. [PMID: 38726298 PMCID: PMC11080654 DOI: 10.3389/fpls.2024.1293307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 03/26/2024] [Indexed: 05/12/2024]
Abstract
Sweet corn breeding programs, like field corn, focus on the development of elite inbred lines to produce commercial hybrids. For this reason, genomic selection models can help the in silico prediction of hybrid crosses from the elite lines, which is hypothesized to improve the test cross scheme, leading to higher genetic gain in a breeding program. This study aimed to explore the potential of implementing genomic selection in a sweet corn breeding program through hybrid prediction in a within-site across-year and across-site framework. A total of 506 hybrids were evaluated in six environments (California, Florida, and Wisconsin, in the years 2020 and 2021). A total of 20 traits from three different groups were measured (plant-, ear-, and flavor-related traits) across the six environments. Eight statistical models were considered for prediction, as the combination of two genomic prediction models (GBLUP and RKHS) with two different kernels (additive and additive + dominance), and in a single- and multi-trait framework. Also, three different cross-validation schemes were tested (CV1, CV0, and CV00). The different models were then compared based on the correlation between the estimated breeding values/total genetic values and phenotypic measurements. Overall, heritabilities and correlations varied among the traits. The models implemented showed good accuracies for trait prediction. The GBLUP implementation outperformed RKHS in all cross-validation schemes and models. Models with additive plus dominance kernels presented a slight improvement over the models with only additive kernels for some of the models examined. In addition, models for within-site across-year and across-site performed better in the CV0 than the CV00 scheme, on average. Hence, GBLUP should be considered as a standard model for sweet corn hybrid prediction. In addition, we found that the implementation of genomic prediction in a sweet corn breeding program presented reliable results, which can improve the testcross stage by identifying the top candidates that will reach advanced field-testing stages.
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Affiliation(s)
- Marco Antônio Peixoto
- Laboratório de Biometria, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
- Department of Horticultural Sciences, University of Florida, Gainesville, FL, United States
| | - Kristen A. Leach
- Department of Horticultural Sciences, University of Florida, Gainesville, FL, United States
| | - Diego Jarquin
- Department of Agronomy, University of Florida, Gainesville, FL, United States
| | - Patrick Flannery
- Department of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, Madison, WI, United States
| | - Jared Zystro
- Organic Seed Alliance, Port Townsend, WA, United States
| | - William F. Tracy
- Department of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, Madison, WI, United States
| | - Leonardo Bhering
- Laboratório de Biometria, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
| | - Márcio F. R. Resende
- Department of Horticultural Sciences, University of Florida, Gainesville, FL, United States
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Fradgley NS, Bentley AR, Gardner KA, Swarbreck SM, Kerton M. Maintenance of UK bread baking quality: Trends in wheat quality traits over 50 years of breeding and potential for future application of genomic-assisted selection. THE PLANT GENOME 2023; 16:e20326. [PMID: 37057385 DOI: 10.1002/tpg2.20326] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/22/2023] [Accepted: 02/28/2023] [Indexed: 06/19/2023]
Abstract
Improved selection of wheat varieties with high end-use quality contributes to sustainable food systems by ensuring productive crops are suitable for human consumption end-uses. Here, we investigated the genetic control and genomic prediction of milling and baking quality traits in a panel of 379 historic and elite, high-quality UK bread wheat (Triticum eastivum L.) varieties and breeding lines. Analysis of the panel showed that genetic diversity has not declined over recent decades of selective breeding while phenotypic analysis found a clear trend of increased loaf baking quality of modern milling wheats despite declining grain protein content. Genome-wide association analysis identified 24 quantitative trait loci (QTL) across all quality traits, many of which had pleiotropic effects. Changes in the frequency of positive alleles of QTL over recent decades reflected trends in trait variation and reveal where progress has historically been made for improved baking quality traits. It also demonstrates opportunities for marker-assisted selection for traits such as Hagberg falling number and specific weight that do not appear to have been improved by recent decades of phenotypic selection. We demonstrate that applying genomic prediction in a commercial wheat breeding program for expensive late-stage loaf baking quality traits outperforms phenotypic selection based on early-stage predictive quality traits. Finally, trait-assisted genomic prediction combining both phenotypic and genomic selection enabled slightly higher prediction accuracy, but genomic prediction alone was the most cost-effective selection strategy considering genotyping and phenotyping costs per sample.
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Affiliation(s)
- Nick S Fradgley
- Genetics and Pre-Breeding Department, National Institute of Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, UK
| | - Alison R Bentley
- Genetics and Pre-Breeding Department, National Institute of Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, UK
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, México
| | - Keith A Gardner
- Genetics and Pre-Breeding Department, National Institute of Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, UK
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, México
| | - Stéphanie M Swarbreck
- Genetics and Pre-Breeding Department, National Institute of Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, UK
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Gill HS, Brar N, Halder J, Hall C, Seabourn BW, Chen YR, St Amand P, Bernardo A, Bai G, Glover K, Turnipseed B, Sehgal SK. Multi-trait genomic selection improves the prediction accuracy of end-use quality traits in hard winter wheat. THE PLANT GENOME 2023; 16:e20331. [PMID: 37194433 DOI: 10.1002/tpg2.20331] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 02/16/2023] [Accepted: 03/01/2023] [Indexed: 05/18/2023]
Abstract
Improvement of end-use quality remains one of the most important goals in hard winter wheat (HWW) breeding. Nevertheless, the evaluation of end-use quality traits is confined to later development generations owing to resource-intensive phenotyping. Genomic selection (GS) has shown promise in facilitating selection for end-use quality; however, lower prediction accuracy (PA) for complex traits remains a challenge in GS implementation. Multi-trait genomic prediction (MTGP) models can improve PA for complex traits by incorporating information on correlated secondary traits, but these models remain to be optimized in HWW. A set of advanced breeding lines from 2015 to 2021 were genotyped with 8725 single-nucleotide polymorphisms and was used to evaluate MTGP to predict various end-use quality traits that are otherwise difficult to phenotype in earlier generations. The MTGP model outperformed the ST model with up to a twofold increase in PA. For instance, PA was improved from 0.38 to 0.75 for bake absorption and from 0.32 to 0.52 for loaf volume. Further, we compared MTGP models by including different combinations of easy-to-score traits as covariates to predict end-use quality traits. Incorporation of simple traits, such as flour protein (FLRPRO) and sedimentation weight value (FLRSDS), substantially improved the PA of MT models. Thus, the rapid low-cost measurement of traits like FLRPRO and FLRSDS can facilitate the use of GP to predict mixograph and baking traits in earlier generations and provide breeders an opportunity for selection on end-use quality traits by culling inferior lines to increase selection accuracy and genetic gains.
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Affiliation(s)
- Harsimardeep S Gill
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Navreet Brar
- 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
| | - Cody Hall
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Bradford W Seabourn
- USDA-ARS, CGAHR, Hard Winter Wheat Quality Laboratory, Manhattan, Kansas, USA
| | - Yuanhong R Chen
- USDA-ARS, CGAHR, Hard Winter Wheat Quality Laboratory, Manhattan, Kansas, USA
| | - Paul St Amand
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, USA
| | - Amy Bernardo
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, USA
| | - Guihua Bai
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, USA
| | - Karl Glover
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Brent Turnipseed
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Sunish K Sehgal
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
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Önder H, Sitskowska B, Kurnaz B, Piwczyński D, Kolenda M, Şen U, Tırınk C, Çanga Boğa D. Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population. Animals (Basel) 2023; 13:3070. [PMID: 37835676 PMCID: PMC10572056 DOI: 10.3390/ani13193070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
The objective of our study was to evaluate the predictive ability of a multi-trait genomic prediction model that accounts for interactions between marker effects to estimate heritability and genetic correlations of traits including 305-day milk yield, milk fat percentage, milk protein percentage, milk lactose percentage, and milk dry matter percentage in the Polish Holstein Friesian cow population. For this aim, 14,742 SNP genotype records for 586 Polish Holstein Friesian dairy cows from Poland were used. Single-Trait-ssGBLUP (ST) and Multi-Trait-ssGBLUP (MT) methods were used for estimation. We examined 305-day milk yield (MY, kg), milk fat percentage (MF, %), milk protein percentage (MP, %), milk lactose percentage (ML, %), and milk dry matter percentage (MDM, %). The results showed that the highest marker effect rank correlation was found between milk fat percentage and milk dry matter. The weakest marker effect rank correlation was found between ML and all other traits. Obtained accuracies of this study were between 0.770 and 0.882, and 0.773 and 0.876 for MT and ST, respectively, which were acceptable values. All estimated bias values were positive, which is proof of underestimation. The highest heritability value was obtained for MP (0.3029) and the lowest heritability value was calculated for ML (0.2171). Estimated heritability values were low for milk yield and milk composition as expected. The strongest genetic correlation was estimated between MDM and MF (0.4990) and the weakest genetic correlation was estimated between MY and ML (0.001). The genetic relations with milk yield were negative and can be ignored as they were not significant. In conclusion, multi-trait genomic prediction can be more beneficial than single-trait genomic prediction.
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Affiliation(s)
- Hasan Önder
- Department of Animal Science, Ondokuz Mayis University, Samsun 55139, Türkiye;
| | - Beata Sitskowska
- Department of Animal Biotechnology and Genetic, Faculty of Animal Breeding and Biology, Bydgoszcz University of Science and Technology, 85084 Bydgoszcz, Poland; (B.S.); (D.P.); (M.K.)
| | - Burcu Kurnaz
- Department of Animal Science, Ondokuz Mayis University, Samsun 55139, Türkiye;
| | - Dariusz Piwczyński
- Department of Animal Biotechnology and Genetic, Faculty of Animal Breeding and Biology, Bydgoszcz University of Science and Technology, 85084 Bydgoszcz, Poland; (B.S.); (D.P.); (M.K.)
| | - Magdalena Kolenda
- Department of Animal Biotechnology and Genetic, Faculty of Animal Breeding and Biology, Bydgoszcz University of Science and Technology, 85084 Bydgoszcz, Poland; (B.S.); (D.P.); (M.K.)
| | - Uğur Şen
- Department of Agricultural Biotechnology, Ondokuz Mayis University, Samsun 55139, Türkiye;
| | - Cem Tırınk
- Department of Animal Science, Iğdır University, Iğdır 76000, Türkiye;
| | - Demet Çanga Boğa
- Department of Chemistry and Chemical Processing, Osmaniye Korkut Ata University, Osmaniye 80050, Türkiye;
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Mora-Poblete F, Maldonado C, Henrique L, Uhdre R, Scapim CA, Mangolim CA. Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach. FRONTIERS IN PLANT SCIENCE 2023; 14:1153040. [PMID: 37593046 PMCID: PMC10428628 DOI: 10.3389/fpls.2023.1153040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 07/12/2023] [Indexed: 08/19/2023]
Abstract
Maize (Zea mays L.), the third most widely cultivated cereal crop in the world, plays a critical role in global food security. To improve the efficiency of selecting superior genotypes in breeding programs, researchers have aimed to identify key genomic regions that impact agronomic traits. In this study, the performance of multi-trait, multi-environment deep learning models was compared to that of Bayesian models (Markov Chain Monte Carlo generalized linear mixed models (MCMCglmm), Bayesian Genomic Genotype-Environment Interaction (BGGE), and Bayesian Multi-Trait and Multi-Environment (BMTME)) in terms of the prediction accuracy of flowering-related traits (Anthesis-Silking Interval: ASI, Female Flowering: FF, and Male Flowering: MF). A tropical maize panel of 258 inbred lines from Brazil was evaluated in three sites (Cambira-2018, Sabaudia-2018, and Iguatemi-2020 and 2021) using approximately 290,000 single nucleotide polymorphisms (SNPs). The results demonstrated a 14.4% increase in prediction accuracy when employing multi-trait models compared to the use of a single trait in a single environment approach. The accuracy of predictions also improved by 6.4% when using a single trait in a multi-environment scheme compared to using multi-trait analysis. Additionally, deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches. A complementary genome-wide association study identified associations with 26 candidate genes related to flowering time traits, and 31 marker-trait associations were identified, accounting for 37%, 37%, and 22% of the phenotypic variation of ASI, FF and MF, respectively. In conclusion, our findings suggest that deep learning models have the potential to significantly improve the accuracy of predictions, regardless of the approach used and provide support for the efficacy of this method in genomic selection for flowering-related traits in tropical maize.
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Affiliation(s)
| | - Carlos Maldonado
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile
| | - Luma Henrique
- Department of Agronomy, State University of Maringá, Paraná, Brazil
| | - Renan Uhdre
- Department of Agronomy, State University of Maringá, Paraná, Brazil
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Yadav RK, Tripathi MK, Tiwari S, Tripathi N, Asati R, Chauhan S, Tiwari PN, Payasi DK. Genome Editing and Improvement of Abiotic Stress Tolerance in Crop Plants. Life (Basel) 2023; 13:1456. [PMID: 37511831 PMCID: PMC10381907 DOI: 10.3390/life13071456] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/25/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023] Open
Abstract
Genome editing aims to revolutionise plant breeding and could assist in safeguarding the global food supply. The inclusion of a 12-40 bp recognition site makes mega nucleases the first tools utilized for genome editing and first generation gene-editing tools. Zinc finger nucleases (ZFNs) are the second gene-editing technique, and because they create double-stranded breaks, they are more dependable and effective. ZFNs were the original designed nuclease-based approach of genome editing. The Cys2-His2 zinc finger domain's discovery made this technique possible. Clustered regularly interspaced short palindromic repeats (CRISPR) are utilized to improve genetics, boost biomass production, increase nutrient usage efficiency, and develop disease resistance. Plant genomes can be effectively modified using genome-editing technologies to enhance characteristics without introducing foreign DNA into the genome. Next-generation plant breeding will soon be defined by these exact breeding methods. There is abroad promise that genome-edited crops will be essential in the years to come for improving the sustainability and climate-change resilience of food systems. This method also has great potential for enhancing crops' resistance to various abiotic stressors. In this review paper, we summarize the most recent findings about the mechanism of abiotic stress response in crop plants and the use of the CRISPR/Cas mediated gene-editing systems to improve tolerance to stresses including drought, salinity, cold, heat, and heavy metals.
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Affiliation(s)
- Rakesh Kumar Yadav
- Department of Genetics & Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Manoj Kumar Tripathi
- Department of Genetics & Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
- Department of Plant Molecular Biology & Biotechnology, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Sushma Tiwari
- Department of Genetics & Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
- Department of Plant Molecular Biology & Biotechnology, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Niraj Tripathi
- Directorate of Research Services, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur 482004, India
| | - Ruchi Asati
- Department of Genetics & Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Shailja Chauhan
- Department of Genetics & Plant Breeding, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
| | - Prakash Narayan Tiwari
- Department of Plant Molecular Biology & Biotechnology, College of Agriculture, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior 474002, India
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Hashem M, Sandhu KS, Ismail SM, Börner A, Sallam A. Validation and marker-assisted selection of DArT-genomic regions associated with wheat yield-related traits under normal and drought conditions. Front Genet 2023; 14:1195566. [PMID: 37292145 PMCID: PMC10245129 DOI: 10.3389/fgene.2023.1195566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 05/10/2023] [Indexed: 06/10/2023] Open
Abstract
Quantitative trait loci (QTL) is one of the most important steps in marker-assisted selection. Few studies have validated quantitative trait loci for marker-assisted selection of yield traits under drought stress conditions in wheat. A set of 138 highly diverse wheat genotypes were tested under normal and drought stress conditions for 2 years. Plant height, heading date, spike length, grain number per spike, grain yield per spike, and 1000-kernel weight were scored. High genetic variation was found among genotypes in all traits scored under both conditions in the 2 years. The same panel was genotyped using a diversity-array technology (DArT) marker, and a genome-wide association study was performed to find alleles associated with yield traits under all conditions. A set of 191 significant DArT markers were identified in this study. The results of the genome-wide association study revealed eight common markers in wheat that were significantly associated with the same traits under both conditions in the 2 years. Out of the eight markers, seven were located on the D genome except one marker. Four validated markers were located on the 3D chromosome and found in complete linkage disequilibrium. Moreover, these four markers were significantly associated with the heading date under both conditions and the grain yield per spike under drought stress condition in the 2 years. This high-linkage disequilibrium genomic region was located within the TraesCS3D02G002400 gene model. Furthermore, of the eight validated markers, seven were previously reported to be associated with yield traits under normal and drought conditions. The results of this study provided very promising DArT markers that can be used for marker-assisted selection to genetically improve yield traits under normal and drought conditions.
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Affiliation(s)
- Mostafa Hashem
- Department of Genetics, Faculty of Agriculture, Assiut University, Assuit, Egypt
| | | | - Saleh M. Ismail
- Soils and Water Department, Faculty of Agriculture, Assiut University, Assiut, Egypt
| | - Andreas Börner
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Ahmed Sallam
- Department of Genetics, Faculty of Agriculture, Assiut University, Assuit, Egypt
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
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Azizinia S, Mullan D, Rattey A, Godoy J, Robinson H, Moody D, Forrest K, Keeble-Gagnere G, Hayden MJ, Tibbits JFG, Daetwyler HD. Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes. FRONTIERS IN PLANT SCIENCE 2023; 14:1167221. [PMID: 37275257 PMCID: PMC10233148 DOI: 10.3389/fpls.2023.1167221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 04/14/2023] [Indexed: 06/07/2023]
Abstract
Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400-1,900) were measured across 8 years (2012-2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5-0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69-0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle.
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Affiliation(s)
- Shiva Azizinia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | | | | | | | | | | | - Kerrie Forrest
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | | | - Matthew J. Hayden
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | - Josquin FG. Tibbits
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Hans D. Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
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Kumar M, Kumar S, Sandhu KS, Kumar N, Saripalli G, Prakash R, Nambardar A, Sharma H, Gautam T, Balyan HS, Gupta PK. GWAS and genomic prediction for pre-harvest sprouting tolerance involving sprouting score and two other related traits in spring wheat. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:14. [PMID: 37313293 PMCID: PMC10248620 DOI: 10.1007/s11032-023-01357-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 01/26/2023] [Indexed: 06/15/2023]
Abstract
In wheat, a genome-wide association study (GWAS) and genomic prediction (GP) analysis were conducted for pre-harvest sprouting (PHS) tolerance and two of its related traits. For this purpose, an association panel of 190 accessions was phenotyped for PHS (using sprouting score), falling number, and grain color over two years and genotyped with 9904 DArTseq based SNP markers. GWAS for main-effect quantitative trait nucleotides (M-QTNs) using three different models (CMLM, SUPER, and FarmCPU) and epistatic QTNs (E-QTNs) using PLINK were performed. A total of 171 M-QTNs (CMLM, 47; SUPER, 70; FarmCPU, 54) for all three traits, and 15 E-QTNs involved in 20 first-order epistatic interactions were identified. Some of the above QTNs overlapped the previously reported QTLs, MTAs, and cloned genes, allowing delineating 26 PHS-responsive genomic regions that spread over 16 wheat chromosomes. As many as 20 definitive and stable QTNs were considered important for use in marker-assisted recurrent selection (MARS). The gene, TaPHS1, for PHS tolerance (PHST) associated with one of the QTNs was also validated using the KASP assay. Some of the M-QTNs were shown to have a key role in the abscisic acid pathway involved in PHST. Genomic prediction accuracies (based on the cross-validation approach) using three different models ranged from 0.41 to 0.55, which are comparable to the results of previous studies. In summary, the results of the present study improved our understanding of the genetic architecture of PHST and its related traits in wheat and provided novel genomic resources for wheat breeding based on MARS and GP. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01357-5.
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Affiliation(s)
- Manoj Kumar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Sachin Kumar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | | | - Neeraj Kumar
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC USA
| | - Gautam Saripalli
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD USA
| | - Ram Prakash
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Akash Nambardar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Hemant Sharma
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Tinku Gautam
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Harindra Singh Balyan
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Pushpendra Kumar Gupta
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
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Singh J, Chhabra B, Raza A, Yang SH, Sandhu KS. Important wheat diseases in the US and their management in the 21st century. FRONTIERS IN PLANT SCIENCE 2023; 13:1010191. [PMID: 36714765 PMCID: PMC9877539 DOI: 10.3389/fpls.2022.1010191] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/28/2022] [Indexed: 05/27/2023]
Abstract
Wheat is a crop of historical significance, as it marks the turning point of human civilization 10,000 years ago with its domestication. Due to the rapid increase in population, wheat production needs to be increased by 50% by 2050 and this growth will be mainly based on yield increases, as there is strong competition for scarce productive arable land from other sectors. This increasing demand can be further achieved using sustainable approaches including integrated disease pest management, adaption to warmer climates, less use of water resources and increased frequency of abiotic stress tolerances. Out of 200 diseases of wheat, 50 cause economic losses and are widely distributed. Each year, about 20% of wheat is lost due to diseases. Some major wheat diseases are rusts, smut, tan spot, spot blotch, fusarium head blight, common root rot, septoria blotch, powdery mildew, blast, and several viral, nematode, and bacterial diseases. These diseases badly impact the yield and cause mortality of the plants. This review focuses on important diseases of the wheat present in the United States, with comprehensive information of causal organism, economic damage, symptoms and host range, favorable conditions, and disease management strategies. Furthermore, major genetic and breeding efforts to control and manage these diseases are discussed. A detailed description of all the QTLs, genes reported and cloned for these diseases are provided in this review. This study will be of utmost importance to wheat breeding programs throughout the world to breed for resistance under changing environmental conditions.
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Affiliation(s)
- Jagdeep Singh
- Department of Crop, Soil & Environmental Sciences, Auburn University, Auburn, AL, United States
| | - Bhavit Chhabra
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD, United States
| | - Ali Raza
- College of Agriculture, Oil Crops Research Institute, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Seung Hwan Yang
- Department of Integrative Biotechnology, Chonnam National University, Yeosu, Republic of Korea
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11
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Jubair S, Domaratzki M. Crop genomic selection with deep learning and environmental data: A survey. Front Artif Intell 2023; 5:1040295. [PMID: 36703955 PMCID: PMC9871498 DOI: 10.3389/frai.2022.1040295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/22/2022] [Indexed: 01/12/2023] Open
Abstract
Machine learning techniques for crop genomic selections, especially for single-environment plants, are well-developed. These machine learning models, which use dense genome-wide markers to predict phenotype, routinely perform well on single-environment datasets, especially for complex traits affected by multiple markers. On the other hand, machine learning models for predicting crop phenotype, especially deep learning models, using datasets that span different environmental conditions, have only recently emerged. Models that can accept heterogeneous data sources, such as temperature, soil conditions and precipitation, are natural choices for modeling GxE in multi-environment prediction. Here, we review emerging deep learning techniques that incorporate environmental data directly into genomic selection models.
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Affiliation(s)
- Sheikh Jubair
- Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada,*Correspondence: Sheikh Jubair ✉
| | - Mike Domaratzki
- Department of Computer Science, University of Western Ontario, London, ON, Canada
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12
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Subedi M, Ghimire B, Bagwell JW, Buck JW, Mergoum M. Wheat end-use quality: State of art, genetics, genomics-assisted improvement, future challenges, and opportunities. Front Genet 2023; 13:1032601. [PMID: 36685944 PMCID: PMC9849398 DOI: 10.3389/fgene.2022.1032601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 12/20/2022] [Indexed: 01/06/2023] Open
Abstract
Wheat is the most important source of food, feed, and nutrition for humans and livestock around the world. The expanding population has increasing demands for various wheat products with different quality attributes requiring the development of wheat cultivars that fulfills specific demands of end-users including millers and bakers in the international market. Therefore, wheat breeding programs continually strive to meet these quality standards by screening their improved breeding lines every year. However, the direct measurement of various end-use quality traits such as milling and baking qualities requires a large quantity of grain, traits-specific expensive instruments, time, and an expert workforce which limits the screening process. With the advancement of sequencing technologies, the study of the entire plant genome is possible, and genetic mapping techniques such as quantitative trait locus mapping and genome-wide association studies have enabled researchers to identify loci/genes associated with various end-use quality traits in wheat. Modern breeding techniques such as marker-assisted selection and genomic selection allow the utilization of these genomic resources for the prediction of quality attributes with high accuracy and efficiency which speeds up crop improvement and cultivar development endeavors. In addition, the candidate gene approach through functional as well as comparative genomics has facilitated the translation of the genomic information from several crop species including wild relatives to wheat. This review discusses the various end-use quality traits of wheat, their genetic control mechanisms, the use of genetics and genomics approaches for their improvement, and future challenges and opportunities for wheat breeding.
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Affiliation(s)
- Madhav Subedi
- Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Griffin Campus, Griffin, GA, United States
| | - Bikash Ghimire
- Department of Plant Pathology, University of Georgia, Griffin Campus, Griffin, GA, United States
| | - John White Bagwell
- Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Griffin Campus, Griffin, GA, United States
| | - James W. Buck
- Department of Plant Pathology, University of Georgia, Griffin Campus, Griffin, GA, United States
| | - Mohamed Mergoum
- Department of Crop and Soil Sciences, University of Georgia, Griffin Campus, Griffin, GA, United States,*Correspondence: Mohamed Mergoum,
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13
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Atanda SA, Steffes J, Lan Y, Al Bari MA, Kim JH, Morales M, Johnson JP, Saludares R, Worral H, Piche L, Ross A, Grusak M, Coyne C, McGee R, Rao J, Bandillo N. Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea. THE PLANT GENOME 2022; 15:e20260. [PMID: 36193571 DOI: 10.1002/tpg2.20260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 08/10/2022] [Indexed: 06/16/2023]
Abstract
Multi-trait genomic selection (MT-GS) has the potential to improve predictive ability by maximizing the use of information across related genotypes and genetically correlated traits. In this study, we extended the use of sparse phenotyping method into the MT-GS framework by split testing of entries to maximize borrowing of information across genotypes and predict missing phenotypes for targeted traits without additional phenotyping expenditure. Using 300 advanced breeding lines from North Dakota State University (NDSU) pulse breeding program and ∼200 USDA accessions that were evaluated for 10 nutritional traits, our results show that the proposed sparse phenotyping aided MT-GS can further improve predictive ability by >12% across traits compared with univariate (UNI) genomic selection. The proposed strategy departed from the previous reports that weak genetic correlation is a limitation to the advantage of MT-GS over UNI genomic selection, which was evident in the partially balanced phenotyping-enabled MT-GS. Our results point to heritability and genetic correlation between traits as possible metrics to optimize and further improve the estimation of model parameters, and ultimately, prediction performance. Overall, our study offers a new approach to optimize the prediction performance using the MT-GS and further highlight strategy to maximize the efficiency of GS in a plant breeding program. The sparse-testing-aided MT-GS proposed in this study can be further extended to multi-environment, multi-trait GS to improve prediction performance and further reduce the cost of phenotyping and time-consuming data collection process.
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Affiliation(s)
| | - Jenna Steffes
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Yang Lan
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Md Abdullah Al Bari
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Jeong-Hwa Kim
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Mario Morales
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Josephine P Johnson
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Rica Saludares
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Hannah Worral
- North Central Research Extension Center, NDSU, 5400 Hwy. 83, South Minot, ND, 58701, USA
| | - Lisa Piche
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Andrew Ross
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Mike Grusak
- Edward T. Schafer Agricultural Research Center, USDA-ARS, 1616 Albrecht Blvd. N, Fargo, ND, 58102-2765, USA
| | - Clarice Coyne
- USDA-ARS Plant Germplasm Introduction and Testing, Washington State Univ., Pullman, WA, 99164, USA
| | - Rebecca McGee
- USDA-ARS, Grain Legume Genetics and Physiology Research, Pullman, WA, 99164, USA
- Dep. of Horticulture, Washington State Univ., Pullman, WA, 99164, USA
| | - Jiajia Rao
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Nonoy Bandillo
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
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14
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Xu Y, Zhang X, Li H, Zheng H, Zhang J, Olsen MS, Varshney RK, Prasanna BM, Qian Q. Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. MOLECULAR PLANT 2022; 15:1664-1695. [PMID: 36081348 DOI: 10.1016/j.molp.2022.09.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 05/12/2023]
Abstract
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.
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Affiliation(s)
- Yunbi Xu
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; CIMMYT-China Tropical Maize Research Center, School of Food Science and Engineering, Foshan University, Foshan, Guangdong 528231, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China.
| | - Xingping Zhang
- Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China
| | - Huihui Li
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, Hainan 572024, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Jianan Zhang
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang, Hebei 050035, China
| | - Michael S Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Rajeev K Varshney
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Australia
| | - Boddupalli M Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Qian Qian
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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15
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Ballén-Taborda C, Lyerly J, Smith J, Howell K, Brown-Guedira G, Babar MA, Harrison SA, Mason RE, Mergoum M, Murphy JP, Sutton R, Griffey CA, Boyles RE. Utilizing genomics and historical data to optimize gene pools for new breeding programs: A case study in winter wheat. Front Genet 2022; 13:964684. [PMID: 36276956 PMCID: PMC9585219 DOI: 10.3389/fgene.2022.964684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 08/05/2022] [Indexed: 11/13/2022] Open
Abstract
With the rapid generation and preservation of both genomic and phenotypic information for many genotypes within crops and across locations, emerging breeding programs have a valuable opportunity to leverage these resources to 1) establish the most appropriate genetic foundation at program inception and 2) implement robust genomic prediction platforms that can effectively select future breeding lines. Integrating genomics-enabled1 breeding into cultivar development can save costs and allow resources to be reallocated towards advanced (i.e., later) stages of field evaluation, which can facilitate an increased number of testing locations and replicates within locations. In this context, a reestablished winter wheat breeding program was used as a case study to understand best practices to leverage and tailor existing genomic and phenotypic resources to determine optimal genetics for a specific target population of environments. First, historical multi-environment phenotype data, representing 1,285 advanced breeding lines, were compiled from multi-institutional testing as part of the SunGrains cooperative and used to produce GGE biplots and PCA for yield. Locations were clustered based on highly correlated line performance among the target population of environments into 22 subsets. For each of the subsets generated, EMMs and BLUPs were calculated using linear models with the ‘lme4’ R package. Second, for each subset, TPs representative of the new SC breeding lines were determined based on genetic relatedness using the ‘STPGA’ R package. Third, for each TP, phenotypic values and SNP data were incorporated into the ‘rrBLUP’ mixed models for generation of GEBVs of YLD, TW, HD and PH. Using a five-fold cross-validation strategy, an average accuracy of r = 0.42 was obtained for yield between all TPs. The validation performed with 58 SC elite breeding lines resulted in an accuracy of r = 0.62 when the TP included complete historical data. Lastly, QTL-by-environment interaction for 18 major effect genes across three geographic regions was examined. Lines harboring major QTL in the absence of disease could potentially underperform (e.g., Fhb1 R-gene), whereas it is advantageous to express a major QTL under biotic pressure (e.g., stripe rust R-gene). This study highlights the importance of genomics-enabled breeding and multi-institutional partnerships to accelerate cultivar development.
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Affiliation(s)
- Carolina Ballén-Taborda
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
- Pee Dee Research and Education Center, Clemson University, Florence, SC, United States
| | - Jeanette Lyerly
- Crop and Soil Sciences Department, North Carolina State University, Raleigh, NC, United States
| | - Jared Smith
- U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS), Raleigh, NC, United States
| | - Kimberly Howell
- U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS), Raleigh, NC, United States
| | - Gina Brown-Guedira
- Crop and Soil Sciences Department, North Carolina State University, Raleigh, NC, United States
- U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS), Raleigh, NC, United States
| | - Md. Ali Babar
- Agronomy Department, University of Florida, Gainesville, FL, United States
| | - Stephen A. Harrison
- School of Plant, Environmental and Soil Sciences, Louisiana State University, Baton Rouge, LA, United States
| | - Richard E. Mason
- College of Agricultural Sciences, Colorado State University, Fort Collins, CO, United States
| | - Mohamed Mergoum
- Department of Crop and Soil Sciences, University of Georgia, Griffin, GA, United States
| | - J. Paul Murphy
- Crop and Soil Sciences Department, North Carolina State University, Raleigh, NC, United States
| | - Russell Sutton
- Department of Soil and Crop Sciences, Texas A&M University, Commerce, TX, United States
| | - Carl A. Griffey
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Richard E. Boyles
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
- Pee Dee Research and Education Center, Clemson University, Florence, SC, United States
- *Correspondence: Richard E. Boyles,
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16
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Singh J, Sharma D, Brar GS, Sandhu KS, Wani SH, Kashyap R, Kour A, Singh S. CRISPR/Cas tool designs for multiplex genome editing and its applications in developing biotic and abiotic stress-resistant crop plants. Mol Biol Rep 2022; 49:11443-11467. [PMID: 36002653 DOI: 10.1007/s11033-022-07741-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 05/22/2022] [Accepted: 06/22/2022] [Indexed: 11/28/2022]
Abstract
Crop plants are prone to several yield-reducing biotic and abiotic stresses. The crop yield reductions due to these stresses need addressing to maintain an adequate balance between the increasing world population and food production to avoid food scarcities in the future. It is impossible to increase the area under food crops proportionately to meet the rising food demand. In such an adverse scenario overcoming the biotic and abiotic stresses through biotechnological interventions may serve as a boon to help meet the globe's food requirements. Under the current genomic era, the wide availability of genomic resources and genome editing technologies such as Transcription Activator-Like Effector Nucleases (TALENs), Zinc Finger Nucleases (ZFNs), and Clustered-Regularly Interspaced Palindromic Repeats/CRISPR-associated proteins (CRISPR/Cas) has widened the scope of overcoming these stresses for several food crops. These techniques have made gene editing more manageable and accessible with changes at the embryo level by adding or deleting DNA sequences of the target gene(s) from the genome. The CRISPR construct consists of a single guide RNA having complementarity with the nucleotide fragments of the target gene sequence, accompanied by a protospacer adjacent motif. The target sequence in the organism's genome is then cleaved by the Cas9 endonuclease for obtaining a desired trait of interest. The current review describes the components, mechanisms, and types of CRISPR/Cas techniques and how this technology has helped to functionally characterize genes associated with various biotic and abiotic stresses in a target organism. This review also summarizes the application of CRISPR/Cas technology targeting these stresses in crops through knocking down/out of associated genes.
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Affiliation(s)
- Jagmohan Singh
- Division of Plant Pathology, Indian Agricultural Research Institute, 110012, New Delhi, India.,Guru Angad Dev Veterinary and Animal Science University, KVK, Barnala, India
| | - Dimple Sharma
- Department of Food Science and Human Nutrition, Michigan State University, 48824, East Lansing, MI, USA
| | - Gagandeep Singh Brar
- Department of Biological Sciences, North Dakota State University, 58102, Fargo, ND, USA
| | - Karansher Singh Sandhu
- Department of Crop and Soil Sciences, Washington State University, 99163, Pullman, WA, USA
| | - Shabir Hussain Wani
- Mountain Research Center for Field Crops, Sher-e-Kashmir University of Agricultural Sciences and Technology Srinagar, Khudwani, Srinagar, Jammu, Kashmir, India
| | - Ruchika Kashyap
- Department of Agronomy, Horticulture, and Plant Sciences, South Dakota State University, 57007, Brookings, SD, USA
| | - Amardeep Kour
- Regional Research Station, Punjab Agricultural University, 151001, Bathinda, Punjab, India
| | - Satnam Singh
- Regional Research Station, Punjab Agricultural University, 151203, Faridkot, Punjab, India.
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17
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Tanin MJ, Saini DK, Sandhu KS, Pal N, Gudi S, Chaudhary J, Sharma A. Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding. Sci Rep 2022; 12:13680. [PMID: 35953529 PMCID: PMC9372038 DOI: 10.1038/s41598-022-18149-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 08/05/2022] [Indexed: 12/03/2022] Open
Abstract
In wheat, a meta-analysis was performed using previously identified QTLs associated with drought stress (DS), heat stress (HS), salinity stress (SS), water-logging stress (WS), pre-harvest sprouting (PHS), and aluminium stress (AS) which predicted a total of 134 meta-QTLs (MQTLs) that involved at least 28 consistent and stable MQTLs conferring tolerance to five or all six abiotic stresses under study. Seventy-six MQTLs out of the 132 physically anchored MQTLs were also verified with genome-wide association studies. Around 43% of MQTLs had genetic and physical confidence intervals of less than 1 cM and 5 Mb, respectively. Consequently, 539 genes were identified in some selected MQTLs providing tolerance to 5 or all 6 abiotic stresses. Comparative analysis of genes underlying MQTLs with four RNA-seq based transcriptomic datasets unravelled a total of 189 differentially expressed genes which also included at least 11 most promising candidate genes common among different datasets. The promoter analysis showed that the promoters of these genes include many stress responsiveness cis-regulatory elements, such as ARE, MBS, TC-rich repeats, As-1 element, STRE, LTR, WRE3, and WUN-motif among others. Further, some MQTLs also overlapped with as many as 34 known abiotic stress tolerance genes. In addition, numerous ortho-MQTLs among the wheat, maize, and rice genomes were discovered. These findings could help with fine mapping and gene cloning, as well as marker-assisted breeding for multiple abiotic stress tolerances in wheat.
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Affiliation(s)
- Mohammad Jafar Tanin
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India.
| | - Dinesh Kumar Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India
| | - Karansher Singh Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99163, USA
| | - Neeraj Pal
- Department of Molecular Biology and Genetic Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India
| | - Santosh Gudi
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India
| | - Jyoti Chaudhary
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, Uttar Pradesh, India
| | - Achla Sharma
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India
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18
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Tanin MJ, Saini DK, Sandhu KS, Pal N, Gudi S, Chaudhary J, Sharma A. Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding. Sci Rep 2022; 12:13680. [PMID: 35953529 DOI: 10.1101/2022.06.24.497482] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 08/05/2022] [Indexed: 05/20/2023] Open
Abstract
In wheat, a meta-analysis was performed using previously identified QTLs associated with drought stress (DS), heat stress (HS), salinity stress (SS), water-logging stress (WS), pre-harvest sprouting (PHS), and aluminium stress (AS) which predicted a total of 134 meta-QTLs (MQTLs) that involved at least 28 consistent and stable MQTLs conferring tolerance to five or all six abiotic stresses under study. Seventy-six MQTLs out of the 132 physically anchored MQTLs were also verified with genome-wide association studies. Around 43% of MQTLs had genetic and physical confidence intervals of less than 1 cM and 5 Mb, respectively. Consequently, 539 genes were identified in some selected MQTLs providing tolerance to 5 or all 6 abiotic stresses. Comparative analysis of genes underlying MQTLs with four RNA-seq based transcriptomic datasets unravelled a total of 189 differentially expressed genes which also included at least 11 most promising candidate genes common among different datasets. The promoter analysis showed that the promoters of these genes include many stress responsiveness cis-regulatory elements, such as ARE, MBS, TC-rich repeats, As-1 element, STRE, LTR, WRE3, and WUN-motif among others. Further, some MQTLs also overlapped with as many as 34 known abiotic stress tolerance genes. In addition, numerous ortho-MQTLs among the wheat, maize, and rice genomes were discovered. These findings could help with fine mapping and gene cloning, as well as marker-assisted breeding for multiple abiotic stress tolerances in wheat.
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Affiliation(s)
- Mohammad Jafar Tanin
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India.
| | - Dinesh Kumar Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India
| | - Karansher Singh Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99163, USA
| | - Neeraj Pal
- Department of Molecular Biology and Genetic Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India
| | - Santosh Gudi
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India
| | - Jyoti Chaudhary
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, Uttar Pradesh, India
| | - Achla Sharma
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India
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19
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Gill T, Gill SK, Saini DK, Chopra Y, de Koff JP, Sandhu KS. A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:156-183. [PMID: 36939773 PMCID: PMC9590503 DOI: 10.1007/s43657-022-00048-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/29/2022] [Accepted: 02/11/2022] [Indexed: 02/04/2023]
Abstract
During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.
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Affiliation(s)
- Taqdeer Gill
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Simranveer K. Gill
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Dinesh K. Saini
- grid.412577.20000 0001 2176 2352Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Yuvraj Chopra
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Jason P. de Koff
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Karansher S. Sandhu
- grid.30064.310000 0001 2157 6568Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
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