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Song J, Mavraganis I, Shen W, Yang H, Zou J. Applying a non-GMO breeding approach with an identified natural variation to reduce food allergen Len c3 in Lens culinaris seeds. FRONTIERS IN PLANT SCIENCE 2024; 15:1355902. [PMID: 38742216 PMCID: PMC11090098 DOI: 10.3389/fpls.2024.1355902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 03/26/2024] [Indexed: 05/16/2024]
Abstract
Lentils (Lens culinaris) are produced in diverse agroecological regions and are consumed as one of the most important food legumes worldwide. Lentils possess a nutritional profile from a human health perspective that is not only nutrient dense but also offers a better balance between protein and carbohydrates. However, lentil causes food allergy, which has been a significant concern due to increased consumption in parts of the world. Len c3, a non-specific lipid transfer protein (LTP), was identified as one of the allergens in lentil seeds. In this study, we identified an LTP gene Lcu.2RBY.4g013600 that encodes the lentil allergen Len c3. We then focused on gene screening from a collection of natural accessions to search for natural mutations of the Len c3 allergen-encoding gene. A natural lentil line M11 was identified with mutations at LcLTP3b and low accumulation of vicilin through genomic-assisted approaches. Furthermore, we generated a pool of lentil germplasms with LcLTP3b mutation background through crossing the identified lentil plant M11 with two lentil cultivars, CDC Redmoon and CDC Gold. These generated lentil hybrids can be used as a breeding resource targeting at reducing allergen risk in lentil consumption.
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Affiliation(s)
| | | | | | | | - Jitao Zou
- Aquatic and Crop Resource Development Research Centre, National Research Council of Canada, Saskatoon, SK, Canada
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2
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Gebremedhin A, Li Y, Shunmugam ASK, Sudheesh S, Valipour-Kahrood H, Hayden MJ, Rosewarne GM, Kaur S. Genomic selection for target traits in the Australian lentil breeding program. FRONTIERS IN PLANT SCIENCE 2024; 14:1284781. [PMID: 38235201 PMCID: PMC10791954 DOI: 10.3389/fpls.2023.1284781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/07/2023] [Indexed: 01/19/2024]
Abstract
Genomic selection (GS) uses associations between markers and phenotypes to predict the breeding values of individuals. It can be applied early in the breeding cycle to reduce the cross-to-cross generation interval and thereby increase genetic gain per unit of time. The development of cost-effective, high-throughput genotyping platforms has revolutionized plant breeding programs by enabling the implementation of GS at the scale required to achieve impact. As a result, GS is becoming routine in plant breeding, even in minor crops such as pulses. Here we examined 2,081 breeding lines from Agriculture Victoria's national lentil breeding program for a range of target traits including grain yield, ascochyta blight resistance, botrytis grey mould resistance, salinity and boron stress tolerance, 100-grain weight, seed size index and protein content. A broad range of narrow-sense heritabilities was observed across these traits (0.24-0.66). Genomic prediction models were developed based on 64,781 genome-wide SNPs using Bayesian methodology and genomic estimated breeding values (GEBVs) were calculated. Forward cross-validation was applied to examine the prediction accuracy of GS for these targeted traits. The accuracy of GEBVs was consistently higher (0.34-0.83) than BLUP estimated breeding values (EBVs) (0.22-0.54), indicating a higher expected rate of genetic gain with GS. GS-led parental selection using early generation breeding materials also resulted in higher genetic gain compared to BLUP-based selection performed using later generation breeding lines. Our results show that implementing GS in lentil breeding will fast track the development of high-yielding cultivars with increased resistance to biotic and abiotic stresses, as well as improved seed quality traits.
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Affiliation(s)
- Alem Gebremedhin
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Yongjun Li
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | | | - Shimna Sudheesh
- 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
| | | | - Sukhjiwan Kaur
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
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3
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Hafeez A, Ali B, Javed MA, Saleem A, Fatima M, Fathi A, Afridi MS, Aydin V, Oral MA, Soudy FA. Plant breeding for harmony between sustainable agriculture, the environment, and global food security: an era of genomics-assisted breeding. PLANTA 2023; 258:97. [PMID: 37823963 DOI: 10.1007/s00425-023-04252-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 09/22/2023] [Indexed: 10/13/2023]
Abstract
MAIN CONCLUSION Genomics-assisted breeding represents a crucial frontier in enhancing the balance between sustainable agriculture, environmental preservation, and global food security. Its precision and efficiency hold the promise of developing resilient crops, reducing resource utilization, and safeguarding biodiversity, ultimately fostering a more sustainable and secure food production system. Agriculture has been seriously threatened over the last 40 years by climate changes that menace global nutrition and food security. Changes in environmental factors like drought, salt concentration, heavy rainfalls, and extremely low or high temperatures can have a detrimental effects on plant development, growth, and yield. Extreme poverty and increasing food demand necessitate the need to break the existing production barriers in several crops. The first decade of twenty-first century marks the rapid development in the discovery of new plant breeding technologies. In contrast, in the second decade, the focus turned to extracting information from massive genomic frameworks, speculating gene-to-phenotype associations, and producing resilient crops. In this review, we will encompass the causes, effects of abiotic stresses and how they can be addressed using plant breeding technologies. Both conventional and modern breeding technologies will be highlighted. Moreover, the challenges like the commercialization of biotechnological products faced by proponents and developers will also be accentuated. The crux of this review is to mention the available breeding technologies that can deliver crops with high nutrition and climate resilience for sustainable agriculture.
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Affiliation(s)
- Aqsa Hafeez
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan
| | - Baber Ali
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan.
| | - Muhammad Ammar Javed
- Institute of Industrial Biotechnology, Government College University, Lahore, 54000, Pakistan
| | - Aroona Saleem
- Institute of Industrial Biotechnology, Government College University, Lahore, 54000, Pakistan
| | - Mahreen Fatima
- Faculty of Biosciences, Cholistan University of Veterinary and Animal Sciences, Bahawalpur, 63100, Pakistan
| | - Amin Fathi
- Department of Agronomy, Ayatollah Amoli Branch, Islamic Azad University, Amol, 46151, Iran
| | - Muhammad Siddique Afridi
- Department of Plant Pathology, Federal University of Lavras (UFLA), Lavras, MG, 37200-900, Brazil
| | - Veysel Aydin
- Sason Vocational School, Department of Plant and Animal Production, Batman University, Batman, 72060, Turkey
| | - Mükerrem Atalay Oral
- Elmalı Vocational School of Higher Education, Akdeniz University, Antalya, 07058, Turkey
| | - Fathia A Soudy
- Genetics and Genetic Engineering Department, Faculty of Agriculture, Benha University, Moshtohor, 13736, Egypt
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4
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Zhao H, Lin Z, Khansefid M, Tibbits JF, Hayden MJ. Genomic prediction and selection response for grain yield in safflower. Front Genet 2023; 14:1129433. [PMID: 37051598 PMCID: PMC10083426 DOI: 10.3389/fgene.2023.1129433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/13/2023] [Indexed: 03/29/2023] Open
Abstract
In plant breeding programs, multiple traits are recorded in each trial, and the traits are often correlated. Correlated traits can be incorporated into genomic selection models, especially for traits with low heritability, to improve prediction accuracy. In this study, we investigated the genetic correlation between important agronomic traits in safflower. We observed the moderate genetic correlations between grain yield (GY) and plant height (PH, 0.272–0.531), and low correlations between grain yield and days to flowering (DF, −0.157–0.201). A 4%–20% prediction accuracy improvement for grain yield was achieved when plant height was included in both training and validation sets with multivariate models. We further explored the selection responses for grain yield by selecting the top 20% of lines based on different selection indices. Selection responses for grain yield varied across sites. Simultaneous selection for grain yield and seed oil content (OL) showed positive gains across all sites with equal weights for both grain yield and oil content. Combining g×E interaction into genomic selection (GS) led to more balanced selection responses across sites. In conclusion, genomic selection is a valuable breeding tool for breeding high grain yield, oil content, and highly adaptable safflower varieties.
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Affiliation(s)
- Huanhuan Zhao
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- *Correspondence: Huanhuan Zhao,
| | - Zibei Lin
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Majid Khansefid
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Josquin F. Tibbits
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Matthew J. Hayden
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
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5
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Neupane S, Wright DM, Martinez RO, Butler J, Weller JL, Bett KE. Focusing the GWAS Lens on days to flower using latent variable phenotypes derived from global multienvironment trials. THE PLANT GENOME 2023; 16:e20269. [PMID: 36284473 DOI: 10.1002/tpg2.20269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 08/25/2022] [Indexed: 05/10/2023]
Abstract
Adaptation constraints within crop species have resulted in limited genetic diversity in some breeding programs and areas where new crops have been introduced, for example, for lentil (Lens culinaris Medik.) in North America. An improved understanding of the underlying genetics involved in phenology-related traits is valuable knowledge to aid breeders in overcoming limitations associated with unadapted germplasm and expanding their genetic diversity by introducing new, exotic material. We used a large, 18 site-year, multienvironment dataset phenotyped for phenology-related traits across nine locations and over 3 yr along with accompanying latent variable phenotypes derived from a photothermal model and principal component analysis (PCA) of days from sowing to flower (DTF) data for a lentil diversity panel (324 accessions), which has also been genotyped with an exome capture array. Genome-wide association studies (GWAS) on DTF across multiple environments helped confirm associations with known flowering-time genes and identify new quantitative trait loci (QTL), which may contain previously unknown flowering time genes. Additionally, the use of latent variable phenotypes, which can incorporate environmental data such as temperature and photoperiod as both GWAS traits and as covariates, strengthened associations, revealed additional hidden associations, and alluded to potential roles of the associated QTL. Our approach can be replicated with other crop species, and the results from our GWAS serve as a resource for further exploration into the complex nature of phenology-related traits across the major growing environments for cultivated lentil.
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Affiliation(s)
- Sandesh Neupane
- Dep. of Plant Sciences, Univ. of Saskatchewan, Saskatoon, SK, S7N 5A8, Canada
| | - Derek M Wright
- Dep. of Plant Sciences, Univ. of Saskatchewan, Saskatoon, SK, S7N 5A8, Canada
| | - Raul O Martinez
- School of Natural Sciences, Univ. of Tasmania, Hobart, TAS, 7001, Australia
| | - Jakob Butler
- School of Natural Sciences, Univ. of Tasmania, Hobart, TAS, 7001, Australia
| | - James L Weller
- School of Natural Sciences, Univ. of Tasmania, Hobart, TAS, 7001, Australia
| | - Kirstin E Bett
- Dep. of Plant Sciences, Univ. of Saskatchewan, Saskatoon, SK, S7N 5A8, Canada
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Sehgal D, Dhakate P, Ambreen H, Shaik KHB, Rathan ND, Anusha NM, Deshmukh R, Vikram P. Wheat Omics: Advancements and Opportunities. PLANTS (BASEL, SWITZERLAND) 2023; 12:426. [PMID: 36771512 PMCID: PMC9919419 DOI: 10.3390/plants12030426] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/07/2022] [Accepted: 12/14/2022] [Indexed: 06/18/2023]
Abstract
Plant omics, which includes genomics, transcriptomics, metabolomics and proteomics, has played a remarkable role in the discovery of new genes and biomolecules that can be deployed for crop improvement. In wheat, great insights have been gleaned from the utilization of diverse omics approaches for both qualitative and quantitative traits. Especially, a combination of omics approaches has led to significant advances in gene discovery and pathway investigations and in deciphering the essential components of stress responses and yields. Recently, a Wheat Omics database has been developed for wheat which could be used by scientists for further accelerating functional genomics studies. In this review, we have discussed various omics technologies and platforms that have been used in wheat to enhance the understanding of the stress biology of the crop and the molecular mechanisms underlying stress tolerance.
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Affiliation(s)
- Deepmala Sehgal
- International Maize and Wheat Improvement Center (CIMMYT), El Batán, Texcoco 56237, Mexico
- Syngenta, Jealott’s Hill International Research Centre, Bracknell, Berkshire RG42 6EY, UK
| | - Priyanka Dhakate
- National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi 110076, India
| | - Heena Ambreen
- School of Life Sciences, University of Sussex, Brighton BN1 9RH, UK
| | - Khasim Hussain Baji Shaik
- Faculty of Agriculture Sciences, Georg-August-Universität, Wilhelmsplatz 1, 37073 Göttingen, Germany
| | - Nagenahalli Dharmegowda Rathan
- Indian Agricultural Research Institute (ICAR-IARI), New Delhi 110012, India
- Corteva Agriscience, Hyderabad 502336, Telangana, India
| | | | - Rupesh Deshmukh
- Department of Biotechnology, Central University of Haryana, Mahendragarh 123031, Haryana, India
| | - Prashant Vikram
- Bioseed Research India Ltd., Hyderabad 5023324, Telangana, India
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7
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Nielsen KME, Duddu HSN, Bett KE, Shirtliffe SJ. UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies. PLANTS (BASEL, SWITZERLAND) 2022; 11:2691. [PMID: 36297713 PMCID: PMC9611424 DOI: 10.3390/plants11202691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/06/2022] [Accepted: 10/09/2022] [Indexed: 06/16/2023]
Abstract
Plant growth rate is an essential phenotypic parameter for quantifying potential crop productivity. Under field conditions, manual measurement of plant growth rate is less accurate in most cases. Image-based high-throughput platforms offer great potential for rapid, non-destructive, and objective estimation of plant growth parameters. The aim of this study was to assess the potential for quantifying plant growth rate using UAV-based (unoccupied aerial vehicle) imagery collected multiple times throughout the growing season. In this study, six diverse lines of lentils were grown in three replicates of 1 m2 microplots with six biomass collection time-points throughout the growing season over five site-years. Aerial imagery was collected simultaneously with each manual measurement of the above-ground biomass time-point and was used to produce two-dimensional orthomosaics and three-dimensional point clouds. Non-linear logistic models were fit to multiple data collection points throughout the growing season. Overall, remotely detected vegetation area and crop volume were found to produce trends comparable to the accumulation of dry weight biomass throughout the growing season. The growth rate and G50 (days to 50% of maximum growth) parameters of the model effectively quantified lentil growth rate indicating significant potential for image-based tools to be used in plant breeding programs. Comparing image-based groundcover and vegetation volume estimates with manually measured above-ground biomass suggested strong correlations. Vegetation area measured from a UAV has utility in quantifying lentil biomass and is indicative of leaf area early in the growing season. For mid- to late-season biomass estimation, plot volume was determined to be a better estimator. Apart from traditional traits, the estimation and analysis of plant parameters not typically collected in traditional breeding programs are possible with image-based methods, and this can create new opportunities to improve breeding efficiency mainly by offering new phenotypes and affecting selection intensity.
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Affiliation(s)
- Karsten M. E. Nielsen
- Department of Plant Sciences, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada
| | - Hema S. N. Duddu
- Agriculture Agri-Food Canada, 107 Science Place, Saskatoon, SK S7N 0X2, Canada
| | - Kirstin E. Bett
- Department of Plant Sciences, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada
| | - Steve J. Shirtliffe
- Department of Plant Sciences, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada
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8
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Guerra‐Garcia A, Haile T, Ogutcen E, Bett KE, von Wettberg EJ. An evolutionary look into the history of lentil reveals unexpected diversity. Evol Appl 2022; 15:1313-1325. [PMID: 36051460 PMCID: PMC9423085 DOI: 10.1111/eva.13467] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 07/12/2022] [Accepted: 08/01/2022] [Indexed: 11/29/2022] Open
Abstract
The characterization and preservation of genetic variation in crops is critical to meeting the challenges of breeding in the face of changing climates and markets. In recent years, the use of single nucleotide polymorphisms (SNPs) has become routine, allowing us to understand the population structure, find divergent lines for crosses, and illuminate the origin of crops. However, the focus on SNPs overlooks other forms of variation, such as copy number variation (CNVs). Lentil is the third most important cold‐season legume and was domesticated in the Fertile Crescent. We genotyped 324 accessions that represent its global diversity, and using both SNPs and CNVs, we dissected the population structure and genetic variation, and identified candidate genes. Eight clusters were detected, most of them located in or near the Fertile Crescent, even though different clusters were present in distinct regions. The cluster from South Asia was particularly differentiated and presented low diversity, contrasting with the clusters from the Mediterranean and the northern temperate. Accessions from North America were mainly assigned to one cluster and were highly diverse, reflecting the efforts of breeding programs to integrate variation. Thirty‐three genes were identified as candidates under selection and among their functions were sporopollenin synthesis in pollen, a component of chlorophyll B reductase that partially determines the antenna size, and two genes related to the import system of chloroplasts. Eleven percent of all lentil genes and 21% of lentil disease resistance genes were affected by CNVs. The gene categories overrepresented in these genes were “enzymes,” “Cell Wall Organization,” and “external stimuli response.” All the genes found in the latter were associated with pathogen response. CNVs provided information about population structure and might have played a role in adaptation. The incorporation of CNVs in diversity studies is needed for a broader understanding of how they evolve and contribute to domestication.
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Affiliation(s)
| | - Teketel Haile
- Department of Plant Sciences University of Saskatchewan Saskatoon SK Canada
| | - Ezgi Ogutcen
- Conservatoire et Jardin Botaniques de la Ville de Genève Geneva Switzerland
| | - Kirstin E. Bett
- Department of Plant Sciences University of Saskatchewan Saskatoon SK Canada
| | - Eric J. von Wettberg
- Plant and Soil Science and Gund Institute for the Environment University of Vermont Burlington VT USA
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9
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Meher PK, Rustgi S, Kumar A. Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results. Heredity (Edinb) 2022; 128:519-530. [PMID: 35508540 DOI: 10.1038/s41437-022-00539-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 04/19/2022] [Accepted: 04/19/2022] [Indexed: 11/09/2022] Open
Abstract
We evaluated the performances of three BLUP and five Bayesian methods for genomic prediction by using nine actual and 54 simulated datasets. The genomic prediction accuracy was measured using Pearson's correlation coefficient between the genomic estimated breeding value (GEBV) and the observed phenotypic data using a fivefold cross-validation approach with 100 replications. The Bayesian alphabets performed better for the traits governed by a few genes/QTLs with relatively larger effects. On the contrary, the BLUP alphabets (GBLUP and CBLUP) exhibited higher genomic prediction accuracy for the traits controlled by several small-effect QTLs. Additionally, Bayesian methods performed better for the highly heritable traits and, for other traits, performed at par with the BLUP methods. Further, genomic BLUP (GBLUP) was identified as the least biased method for the GEBV estimation. Among the Bayesian methods, the Bayesian ridge regression and Bayesian LASSO were less biased than other Bayesian alphabets. Nonetheless, genomic prediction accuracy increased with an increase in trait heritability, irrespective of the sample size, marker density, and the QTL type (major/minor effect). In sum, this study provides valuable information regarding the choice of the selection method for genomic prediction in different breeding programs.
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Affiliation(s)
- Prabina Kumar Meher
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-12, India.
| | - Sachin Rustgi
- Department of Plant and Environmental Sciences, Clemson University Pee Dee Research and Education Center, Darlington, SC, USA.
| | - Anuj Kumar
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-12, India
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10
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Budhlakoti N, Kushwaha AK, Rai A, Chaturvedi KK, Kumar A, Pradhan AK, Kumar U, Kumar RR, Juliana P, Mishra DC, Kumar S. Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops. Front Genet 2022; 13:832153. [PMID: 35222548 PMCID: PMC8864149 DOI: 10.3389/fgene.2022.832153] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 01/10/2022] [Indexed: 12/17/2022] Open
Abstract
Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.
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Affiliation(s)
- Neeraj Budhlakoti
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Anil Rai
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - K K Chaturvedi
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anuj Kumar
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Uttam Kumar
- Borlaug Institute for South Asia (BISA), Ludhiana, India
| | | | | | - D C Mishra
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sundeep Kumar
- ICAR- National Bureau of Plant Genetic Resources, New Delhi, India
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11
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Bari MAA, Zheng P, Viera I, Worral H, Szwiec S, Ma Y, Main D, Coyne CJ, McGee RJ, Bandillo N. Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction. Front Genet 2022; 12:707754. [PMID: 35003202 PMCID: PMC8740293 DOI: 10.3389/fgene.2021.707754] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/26/2021] [Indexed: 11/13/2022] Open
Abstract
Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder's toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction's potential to a set of 482 pea (Pisum sativum L.) accessions-genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components-for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.
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Affiliation(s)
- Md Abdullah Al Bari
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
| | - Ping Zheng
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Indalecio Viera
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
| | - Hannah Worral
- NDSU North Central Research Extension Center, Minot, ND, United States
| | - Stephen Szwiec
- NDSU North Central Research Extension Center, Minot, ND, United States
| | - Yu Ma
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Dorrie Main
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Clarice J Coyne
- USDA-ARS Plant Germplasm Introduction and Testing, Washington State University, Pullman, WA, United States
| | - Rebecca J McGee
- USDA-ARS Grain Legume Genetics and Physiology Research, Pullman, WA, United States
| | - Nonoy Bandillo
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
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Gela T, Ramsay L, Haile TA, Vandenberg A, Bett K. Identification of anthracnose race 1 resistance loci in lentil by integrating linkage mapping and genome-wide association study. THE PLANT GENOME 2021; 14:e20131. [PMID: 34482633 DOI: 10.1002/tpg2.20131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 06/08/2021] [Indexed: 05/24/2023]
Abstract
Anthracnose, caused byColletotrichum lentis, is a devastating disease of lentil (Lens culinaris Medik.) in western Canada. Growing resistant lentil cultivars is the most cost-effective and environmentally friendly approach to prevent seed yield losses that can exceed 70%. To identify loci conferring resistance to anthracnose race 1 in lentil, biparental quantitative trait loci (QTL) mapping of two recombinant inbred line (RIL) populations was integrated with a genome-wide association study (GWAS) using 200 diverse lentil accessions from a lentil diversity panel. A major-effect QTL (qAnt1.Lc-3) conferring resistance to race 1 was mapped to lentil chromosome 3 and colocated on the lentil physical map for both RIL populations. Clusters of candidate nucleotide-binding leucine-rich repeat (NB-LRR) and other defense-related genes were uncovered within the QTL region. A GWAS detected 14 significant single nucleotide polymorphism (SNP) markers associated with race 1 resistance on chromosomes 3, 4, 5, and 6. The most significant GWAS SNPs on chromosome 3 supported qAnt1.Lc-3 and delineated a region of 1.6 Mb containing candidate resistance genes. The identified SNP markers can be directly applied in marker-assisted selection (MAS) to accelerate the introgression of race 1 resistance in lentil breeding.
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Affiliation(s)
- Tadesse Gela
- Crop Development Centre, Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, Saskatchewan, S7N 5A8, Canada
| | - Larissa Ramsay
- Crop Development Centre, Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, Saskatchewan, S7N 5A8, Canada
| | - Teketel A Haile
- Crop Development Centre, Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, Saskatchewan, S7N 5A8, Canada
| | - Albert Vandenberg
- Crop Development Centre, Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, Saskatchewan, S7N 5A8, Canada
| | - Kirstin Bett
- Crop Development Centre, Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, Saskatchewan, S7N 5A8, Canada
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Ubbens J, Parkin I, Eynck C, Stavness I, Sharpe AG. Deep neural networks for genomic prediction do not estimate marker effects. THE PLANT GENOME 2021; 14:e20147. [PMID: 34596363 DOI: 10.1002/tpg2.20147] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 07/09/2021] [Indexed: 06/13/2023]
Abstract
Genomic prediction is a promising technology for advancing both plant and animal breeding, with many different prediction models evaluated in the literature. It has been suggested that the ability of powerful nonlinear models, such as deep neural networks, to capture complex epistatic effects between markers offers advantages for genomic prediction. However, these methods tend not to outperform classical linear methods, leaving it an open question why this capacity to model nonlinear effects does not seem to result in better predictive capability. In this work, we propose the theory that, because of a previously described principle called shortcut learning, deep neural networks tend to base their predictions on overall genetic relatedness rather than on the effects of particular markers such as epistatic effects. Using several datasets of crop plants [lentil (Lens culinaris Medik.), wheat (Triticum aestivum L.), and Brassica carinata A. Braun], we demonstrate the network's indifference to the values of the markers by showing that the same network, provided with only the locations of matches between markers for two individuals, is able to perform prediction to the same level of accuracy.
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Affiliation(s)
- Jordan Ubbens
- Global Institute for Food Security (GIFS), University of Saskatchewan, Saskatoon, SK, S7N 0W9, Canada
| | - Isobel Parkin
- Agriculture and Agri-Food Canada, Saskatoon, SK, S7N 0X2, Canada
| | - Christina Eynck
- Agriculture and Agri-Food Canada, Saskatoon, SK, S7N 0X2, Canada
| | - Ian Stavness
- Global Institute for Food Security (GIFS), University of Saskatchewan, Saskatoon, SK, S7N 0W9, Canada
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, S7N 0W9, Canada
| | - Andrew G Sharpe
- Global Institute for Food Security (GIFS), University of Saskatchewan, Saskatoon, SK, S7N 0W9, Canada
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Guerra-García A, Gioia T, von Wettberg E, Logozzo G, Papa R, Bitocchi E, Bett KE. Intelligent Characterization of Lentil Genetic Resources: Evolutionary History, Genetic Diversity of Germplasm, and the Need for Well-Represented Collections. Curr Protoc 2021; 1:e134. [PMID: 34004055 DOI: 10.1002/cpz1.134] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The genetic and phenotypic characterization of crops allows us to elucidate their evolutionary and domestication history, the genetic basis of important traits, and the use of variation present in landraces and wild relatives to enhance resilience. In this context, we aim to provide an overview of the main genetic resources developed for lentil and their main outcomes, and to suggest protocols for continued work on this important crop. Lens culinaris is the third-most-important cool-season grain and its use is increasing as a quick-cooking, nutritious, plant-based source of protein. L. culinaris was domesticated in the Fertile Crescent, and six additional wild taxa (L. orientalis, L. tomentosus, L. odemensis, L. lamottei, L. ervoides, and L. nigricans) are recognized. Numerous genetic diversity studies have shown that wild relatives present high levels of genetic variation and provide a reservoir of alleles that can be used for breeding programs. Furthermore, the integration of genetics/genomics and breeding techniques has resulted in identification of quantitative trait loci and genes related to attributes of interest. Genetic maps, massive genotyping, marker-assisted selection, and genomic selection are some of the genetic resources generated and applied in lentil. In addition, despite its size (∼4 Gbp) and complexity, the L. culinaris genome has been assembled, allowing a deeper understanding of its architecture. Still, major knowledge gaps exist in lentil, and a deeper understanding and characterization of germplasm resources, including wild relatives, is critical to lentil breeding and improvement. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Recording of lentil seed descriptors Basic Protocol 2: Lentil seed imaging Basic Protocol 3: Lentil seed increase Basic Protocol 4: Recording of primary lentil seed INCREASE descriptors.
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Affiliation(s)
- Azalea Guerra-García
- Department of Plant Sciences, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Tania Gioia
- School of Agriculture, Forestry, Food and Environmental Sciences, University of Basilicata, Potenza, Italy
| | - Eric von Wettberg
- Department of Plant and Soil Sciences and Gund Institute for the Environment, University of Vermont, Burlington, Vermont
| | - Giuseppina Logozzo
- School of Agriculture, Forestry, Food and Environmental Sciences, University of Basilicata, Potenza, Italy
| | - Roberto Papa
- Dipartimento di Scienze Agrarie, Alimentari ed Ambientali, Università Politecnica delle Marche, Ancona, Italy
| | - Elena Bitocchi
- Dipartimento di Scienze Agrarie, Alimentari ed Ambientali, Università Politecnica delle Marche, Ancona, Italy
| | - Kirstin E Bett
- Department of Plant Sciences, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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Diaz LM, Arredondo V, Ariza-Suarez D, Aparicio J, Buendia HF, Cajiao C, Mosquera G, Beebe SE, Mukankusi CM, Raatz B. Genetic Analyses and Genomic Predictions of Root Rot Resistance in Common Bean Across Trials and Populations. FRONTIERS IN PLANT SCIENCE 2021; 12:629221. [PMID: 33777068 PMCID: PMC7994901 DOI: 10.3389/fpls.2021.629221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 02/16/2021] [Indexed: 06/12/2023]
Abstract
Root rot in common bean is a disease that causes serious damage to grain production, particularly in the upland areas of Eastern and Central Africa where significant losses occur in susceptible bean varieties. Pythium spp. and Fusarium spp. are among the soil pathogens causing the disease. In this study, a panel of 228 lines, named RR for root rot disease, was developed and evaluated in the greenhouse for Pythium myriotylum and in a root rot naturally infected field trial for plant vigor, number of plants germinated, and seed weight. The results showed positive and significant correlations between greenhouse and field evaluations, as well as high heritability (0.71-0.94) of evaluated traits. In GWAS analysis no consistent significant marker trait associations for root rot disease traits were observed, indicating the absence of major resistance genes. However, genomic prediction accuracy was found to be high for Pythium, plant vigor and related traits. In addition, good predictions of field phenotypes were obtained using the greenhouse derived data as a training population and vice versa. Genomic predictions were evaluated across and within further published data sets on root rots in other panels. Pythium and Fusarium evaluations carried out in Uganda on the Andean Diversity Panel showed good predictive ability for the root rot response in the RR panel. Genomic prediction is shown to be a promising method to estimate tolerance to Pythium, Fusarium and root rot related traits, indicating a quantitative resistance mechanism. Quantitative analyses could be applied to other disease-related traits to capture more genetic diversity with genetic models.
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Affiliation(s)
- Lucy Milena Diaz
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Victoria Arredondo
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Daniel Ariza-Suarez
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Johan Aparicio
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Hector Fabio Buendia
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Cesar Cajiao
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Gloria Mosquera
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Stephen E. Beebe
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Clare Mugisha Mukankusi
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Kampala, Uganda
| | - Bodo Raatz
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
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Krishnappa G, Savadi S, Tyagi BS, Singh SK, Mamrutha HM, Kumar S, Mishra CN, Khan H, Gangadhara K, Uday G, Singh G, Singh GP. Integrated genomic selection for rapid improvement of crops. Genomics 2021; 113:1070-1086. [PMID: 33610797 DOI: 10.1016/j.ygeno.2021.02.007] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/08/2020] [Accepted: 02/15/2021] [Indexed: 11/15/2022]
Abstract
An increase in the rate of crop improvement is essential for achieving sustained food production and other needs of ever-increasing population. Genomic selection (GS) is a potential breeding tool that has been successfully employed in animal breeding and is being incorporated into plant breeding. GS promises accelerated breeding cycles through a rapid selection of superior genotypes. Numerous empirical and simulation studies on GS and realized impacts on improvement in the crop yields are recently being reported. For a holistic understanding of the technology, we briefly discuss the concept of genetic gain, GS methodology, its current status, advantages of GS over other breeding methods, prediction models, and the factors controlling prediction accuracy in GS. Also, integration of speed breeding and other novel technologies viz. high throughput genotyping and phenotyping technologies for enhancing the efficiency and pace of GS, followed by its prospective applications in varietal development programs is reviewed.
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Affiliation(s)
| | | | | | | | | | - Satish Kumar
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | - Hanif Khan
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | | | - Gyanendra Singh
- Indian Institute of Wheat and Barley Research, Karnal, India
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Kumar J, Sen Gupta D. Prospects of next generation sequencing in lentil breeding. Mol Biol Rep 2020; 47:9043-9053. [PMID: 33037962 DOI: 10.1007/s11033-020-05891-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 10/03/2020] [Indexed: 11/28/2022]
Abstract
Lentil is an important food legume crop that has large and complex genome. During past years, considerable attention has been given on the use of next generation sequencing for enriching the genomic resources including identification of SSR and SNP markers, development of unigenes, transcripts, and identification of candidate genes for biotic and abiotic stresses, analysis of genetic diversity and identification of genes/ QTLs for agronomically important traits. However, in other crops including pulses, next generation sequencing has revolutionized the genomic research and helped in genomic assisted breeding rapidly and cost effectively. The present review discuss current status and future prospects of the use NGS based breeding in lentil.
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Affiliation(s)
- Jitendra Kumar
- Division of Crop Improvement, ICAR-Indian Institute of Pulses Research, Kalyanpur, Kanpur, 208024, India.
| | - Debjyoti Sen Gupta
- Division of Crop Improvement, ICAR-Indian Institute of Pulses Research, Kalyanpur, Kanpur, 208024, India
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