1
|
Oli P, Punetha S, Punetha A, Pant K, Bhatt ID. Mainstreaming Glycine soja (Himalayan soybean) a potential underutilized climate resilient crop for nutritional security in the Himalayan region. 3 Biotech 2025; 15:131. [PMID: 40255447 PMCID: PMC12006611 DOI: 10.1007/s13205-025-04299-1] [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: 07/20/2024] [Accepted: 03/31/2025] [Indexed: 04/22/2025] Open
Abstract
Growing challenges of climate change, agricultural sustainability and malnutrition demand climate-resilient nutrient dense crops to mitigate the consequences of climate change while sustaining agricultural productivity and ensuring nutritional security in the Himalayan regions. Glycine soja also known as Himalayan soybean or wild soybean is a wild relative of cultivated soybean (Glycine max) is a valuable underutilized, less explored, and nutritionally rich climate resilient crop offers promising solution to address these challenges. The present systematic review was conducted using bibliometric analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and total 3359 published scientific documents were analyzed. G. soja is a rich source of various nutrients such as protein, carbohydrate, vitamins, micronutrients and several bioactive compounds having potential role in disease prevention. The genetic diversity within G. soja presents considerable opportunities for crop improvement through gene flow with G. max utilizing biotechnological methods or breeding programs. The aim of the present study is to not only highlight the existing knowledge on its nutraceutical, stress resilience and crop improvement potential but it also emphasizes the research gaps including its de novo domestication, in-depth understanding of nutritional and stress resilience properties and the limitations of current biotechnological techniques in addressing agronomic challenges in G. soja cultivation and consumption. Mainstreaming and harnessing the potential of G. soja might help to achieve sustainable food systems, enhancing nutritional security and supporting climate-resilient agriculture in the Himalayan regions.
Collapse
Affiliation(s)
- Pooja Oli
- G.B. Pant National Institute of Himalayan Environment, Kosi-Katarmal, Uttarakhand India
| | - Shailaja Punetha
- G.B. Pant National Institute of Himalayan Environment, Kosi-Katarmal, Uttarakhand India
| | - Arjita Punetha
- G.B. Pant National Institute of Himalayan Environment, Kosi-Katarmal, Uttarakhand India
| | - Kanchan Pant
- H.N.B. Garhwal Central University, Swami Ram Teerth Campus, Tehri, Badshahi Thaul, Uttarakhand India
| | - Indra D. Bhatt
- G.B. Pant National Institute of Himalayan Environment, Kosi-Katarmal, Uttarakhand India
| |
Collapse
|
2
|
Wu P, Stich B, Hartje S, Muders K, Prigge V, Van Inghelandt D. Optimal implementation of genomic selection in clone breeding programs exemplified in potato: II. Effect of selection strategy and cross-selection method on long-term genetic gain. THE PLANT GENOME 2025; 18:e70000. [PMID: 39965909 PMCID: PMC11835509 DOI: 10.1002/tpg2.70000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2024] [Accepted: 01/01/2025] [Indexed: 02/20/2025]
Abstract
Different cross-selection (CS) methods incorporating genomic selection (GS) have been used in diploid species to improve long-term genetic gain and preserve diversity. However, their application to heterozygous and autotetraploid crops such as potato (Solanum tuberosum L.) is lacking so far. The objectives of our study were to (i) assess the effects of different CS methods and the incorporation of GS and genetic variability monitoring on both short- and long-term genetic gains compared to strategies using phenotypic selection (PS); (ii) evaluate the changes in genetic variability and the efficiency of converting diversity into genetic gain across different CS methods; and (iii) investigate the interaction effects between different genetic architectures and CS methods on long-term genetic gain. In our simulation results, implementing GS with optimal selected proportions had increased short- and long-term genetic gain compared to any PS strategy. The CS method considering additive and dominance effects to predict progeny mean based on simulated progenies (MEGV-O) achieved the highest long-term genetic gain among the assessed mean-based CS methods. Compared to MEGV-O and usefulness criteria (UC), the linear combination of UC and genome-wide diversity (called EUCD) maintained the same level of genetic gain but resulted in higher diversity and a lower number of fixed QTLs. Moreover, EUCD had a relatively high degree of efficiency in converting diversity into genetic gain. However, choosing the most appropriate weight to account for diversity in EUCD depends on the genetic architecture of the target trait and the breeder's objectives. Our results provide breeders with concrete methods to improve their potato breeding programs.
Collapse
Affiliation(s)
- Po‐Ya Wu
- Institute of Quantitative Genetics and Genomics of PlantsHeinrich Heine UniversityDüsseldorfGermany
- Institute for Breeding Research on Agricultural CropsFederal Research Centre for Cultivated PlantsSanitzGermany
| | - Benjamin Stich
- Institute of Quantitative Genetics and Genomics of PlantsHeinrich Heine UniversityDüsseldorfGermany
- Institute for Breeding Research on Agricultural CropsFederal Research Centre for Cultivated PlantsSanitzGermany
- Cluster of Excellence on Plant Sciences (CEPLAS)Heinrich Heine UniversityDüsseldorfGermany
- Max Planck Institute for Plant Breeding ResearchKölnGermany
| | - Stefanie Hartje
- Böhm‐Nordkartoffel Agrarproduktion GmbH & Co. OHGLüneburgGermany
| | | | | | - Delphine Van Inghelandt
- Institute of Quantitative Genetics and Genomics of PlantsHeinrich Heine UniversityDüsseldorfGermany
- Institute for Breeding Research on Agricultural CropsFederal Research Centre for Cultivated PlantsSanitzGermany
- Department of GenebankLeibniz Institute of Plant Genetics and Crop Plant ResearchSanitzGermany
| |
Collapse
|
3
|
Potapova NA, Zorkoltseva IV, Zlobin AS, Shcherban AB, Fedyaeva AV, Salina EA, Svishcheva GR, Aksenovich TI, Tsepilov YA. Genome-Wide Association Study on Imputed Genotypes of 180 Eurasian Soybean Glycine max Varieties for Oil and Protein Contents in Seeds. PLANTS (BASEL, SWITZERLAND) 2025; 14:255. [PMID: 39861608 PMCID: PMC11768550 DOI: 10.3390/plants14020255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2024] [Revised: 01/13/2025] [Accepted: 01/15/2025] [Indexed: 01/27/2025]
Abstract
Soybean (Glycine max) is a leguminous plant with a broad range of applications, particularly in agriculture and food production, where its seed composition-especially oil and protein content-is highly valued. Improving these traits is a primary focus of soybean breeding programs. In this study, we conducted a genome-wide association study (GWAS) to identify genetic loci linked to oil and protein content in seeds, using imputed genotype data for 180 Eurasian soybean varieties and the novel "genotypic twins" approach. This dataset encompassed 87 Russian and European cultivars and 93 breeding lines from Western Siberia. We identified 11 novel loci significantly associated with oil and protein content in seeds (p-value < 1.5 × 10-6), including one locus on chromosome 11 linked to protein content and 10 loci associated with oil content (chromosomes 1, 5, 11, 16, 17, and 18). The protein-associated locus is located near a gene encoding a CBL-interacting protein kinase, which is involved in key biological processes, including stress response mechanisms such as drought and osmotic stress. The oil-associated loci were linked to genes with diverse functions, including lipid transport, nutrient reservoir activity, and stress responses, such as Sec14p-like phosphatidylinositol transfer proteins and Germin-like proteins. These findings suggest that the loci identified not only influence oil and protein content but may also contribute to plant resilience under environmental stress conditions. The data obtained from this study provide valuable genetic markers that can be used in breeding programs to optimize oil and protein content, particularly in varieties adapted to Russian climates, and contribute to the development of high-yielding, nutritionally enhanced soybean cultivars.
Collapse
Affiliation(s)
- Nadezhda A. Potapova
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, Lavrentiev Av. 10, 630090 Novosibirsk, Russia; (A.S.Z.); (A.B.S.); (E.A.S.); (G.R.S.); (Y.A.T.)
| | - Irina V. Zorkoltseva
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, Lavrentiev Av. 10, 630090 Novosibirsk, Russia; (A.S.Z.); (A.B.S.); (E.A.S.); (G.R.S.); (Y.A.T.)
- The Federal Research Center, Institute of Cytology and Genetics SB RAS, Lavrentiev Av. 10, 630090 Novosibirsk, Russia; (A.V.F.); (T.I.A.)
| | - Alexander S. Zlobin
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, Lavrentiev Av. 10, 630090 Novosibirsk, Russia; (A.S.Z.); (A.B.S.); (E.A.S.); (G.R.S.); (Y.A.T.)
| | - Andrey B. Shcherban
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, Lavrentiev Av. 10, 630090 Novosibirsk, Russia; (A.S.Z.); (A.B.S.); (E.A.S.); (G.R.S.); (Y.A.T.)
- The Federal Research Center, Institute of Cytology and Genetics SB RAS, Lavrentiev Av. 10, 630090 Novosibirsk, Russia; (A.V.F.); (T.I.A.)
| | - Anna V. Fedyaeva
- The Federal Research Center, Institute of Cytology and Genetics SB RAS, Lavrentiev Av. 10, 630090 Novosibirsk, Russia; (A.V.F.); (T.I.A.)
| | - Elena A. Salina
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, Lavrentiev Av. 10, 630090 Novosibirsk, Russia; (A.S.Z.); (A.B.S.); (E.A.S.); (G.R.S.); (Y.A.T.)
- The Federal Research Center, Institute of Cytology and Genetics SB RAS, Lavrentiev Av. 10, 630090 Novosibirsk, Russia; (A.V.F.); (T.I.A.)
| | - Gulnara R. Svishcheva
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, Lavrentiev Av. 10, 630090 Novosibirsk, Russia; (A.S.Z.); (A.B.S.); (E.A.S.); (G.R.S.); (Y.A.T.)
- The Federal Research Center, Institute of Cytology and Genetics SB RAS, Lavrentiev Av. 10, 630090 Novosibirsk, Russia; (A.V.F.); (T.I.A.)
- Institute of General Genetics RAS, Gubkin St. 3, 119333 Moscow, Russia
| | - Tatiana I. Aksenovich
- The Federal Research Center, Institute of Cytology and Genetics SB RAS, Lavrentiev Av. 10, 630090 Novosibirsk, Russia; (A.V.F.); (T.I.A.)
| | - Yakov A. Tsepilov
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, Lavrentiev Av. 10, 630090 Novosibirsk, Russia; (A.S.Z.); (A.B.S.); (E.A.S.); (G.R.S.); (Y.A.T.)
- The Federal Research Center, Institute of Cytology and Genetics SB RAS, Lavrentiev Av. 10, 630090 Novosibirsk, Russia; (A.V.F.); (T.I.A.)
| |
Collapse
|
4
|
Stupar RM, Locke AM, Allen DK, Stacey MG, Ma J, Weiss J, Nelson RT, Hudson ME, Joshi T, Li Z, Song Q, Jedlicka JR, MacIntosh GC, Grant D, Parrott WA, Clemente TE, Stacey G, An YC, Aponte‐Rivera J, Bhattacharyya MK, Baxter I, Bilyeu KD, Campbell JD, Cannon SB, Clough SJ, Curtin SJ, Diers BW, Dorrance AE, Gillman JD, Graef GL, Hancock CN, Hudson KA, Hyten DL, Kachroo A, Koebernick J, Libault M, Lorenz AJ, Mahan AL, Massman JM, McGinn M, Meksem K, Okamuro JK, Pedley KF, Rainey KM, Scaboo AM, Schmutz J, Song B, Steinbrenner AD, Stewart‐Brown BB, Toth K, Wang D, Weaver L, Zhang B, Graham MA, O'Rourke JA. Soybean genomics research community strategic plan: A vision for 2024-2028. THE PLANT GENOME 2024; 17:e20516. [PMID: 39572930 PMCID: PMC11628913 DOI: 10.1002/tpg2.20516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 08/09/2024] [Accepted: 08/16/2024] [Indexed: 12/11/2024]
Abstract
This strategic plan summarizes the major accomplishments achieved in the last quinquennial by the soybean [Glycine max (L.) Merr.] genetics and genomics research community and outlines key priorities for the next 5 years (2024-2028). This work is the result of deliberations among over 50 soybean researchers during a 2-day workshop in St Louis, MO, USA, at the end of 2022. The plan is divided into seven traditional areas/disciplines: Breeding, Biotic Interactions, Physiology and Abiotic Stress, Functional Genomics, Biotechnology, Genomic Resources and Datasets, and Computational Resources. One additional section was added, Training the Next Generation of Soybean Researchers, when it was identified as a pressing issue during the workshop. This installment of the soybean genomics strategic plan provides a snapshot of recent progress while looking at future goals that will improve resources and enable innovation among the community of basic and applied soybean researchers. We hope that this work will inform our community and increase support for soybean research.
Collapse
Affiliation(s)
- Robert M. Stupar
- Department of Agronomy and Plant GeneticsUniversity of MinnesotaSt. PaulMinnesotaUSA
| | - Anna M. Locke
- USDA‐ARS Soybean & Nitrogen Fixation Research UnitRaleighNorth CarolinaUSA
| | - Doug K. Allen
- USDA‐ARS Donald Danforth Plant Science CenterSt. LouisMissouriUSA
| | - Minviluz G. Stacey
- Division of Plant Science and TechnologyUniversity of MissouriColumbiaMissouriUSA
| | - Jianxin Ma
- Department of AgronomyPurdue UniversityWest LafayetteIndianaUSA
| | - Jackie Weiss
- Smithbucklin for the United Soybean BoardSt. LouisMissouriUSA
| | - Rex T. Nelson
- USDA‐ARS Corn Insects and Crop Genetics Research UnitAmesIowaUSA
| | | | - Trupti Joshi
- Division of Plant Science and TechnologyUniversity of MissouriColumbiaMissouriUSA
- MU Institute for Data Science and InformaticsUniversity of Missouri–ColumbiaColumbiaMissouriUSA
| | - Zenglu Li
- Department of Crop and Soil Sciences, and Institute of Plant Breeding, Genetics and GenomicsUniversity of GeorgiaAthensGeorgiaUSA
| | - Qijian Song
- USDA‐ARS Soybean Genomics and Improvement Laboratory, Beltsville Agricultural Research CenterBeltsvilleMarylandUSA
| | | | - Gustavo C. MacIntosh
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular BiologyIowa State UniversityAmesIowaUSA
| | - David Grant
- USDA‐ARS Corn Insects and Crop Genetics Research UnitAmesIowaUSA
- Department of AgronomyIowa State UniversityAmesIowaUSA
| | - Wayne A. Parrott
- Department of Crop and Soil Sciences, and Institute of Plant Breeding, Genetics and GenomicsUniversity of GeorgiaAthensGeorgiaUSA
- Center for Applied Genetic TechnologiesUniversity of GeorgiaAthensGeorgiaUSA
| | - Tom E. Clemente
- Department of Agronomy & HorticultureUniversity of NebraskaLincolnNebraskaUSA
| | - Gary Stacey
- Division of Plant Science and TechnologyUniversity of MissouriColumbiaMissouriUSA
| | | | | | | | - Ivan Baxter
- Donald Danforth Plant Science CenterSt. LouisMissouriUSA
| | | | | | - Steven B. Cannon
- USDA‐ARS Corn Insects and Crop Genetics Research UnitAmesIowaUSA
| | - Steven J. Clough
- USDA‐ARS Soybean/Maize Germplasm, Pathology and Genetics Research UnitUrbanaIllinoisUSA
| | | | - Brian W. Diers
- Department of Crop SciencesUniversity of IllinoisUrbanaIllinoisUSA
| | - Anne E. Dorrance
- Department of Plant PathologyThe Ohio State UniversityWoosterOhioUSA
| | | | - George L. Graef
- Department of Agronomy & HorticultureUniversity of NebraskaLincolnNebraskaUSA
| | - C. Nathan Hancock
- Department of Biological, Environmental, and Earth SciencesUniversity of South Carolina AikenAikenSouth CarolinaUSA
| | - Karen A. Hudson
- USDA‐ARS Crop Production and Pest Control Research UnitWest LafayetteIndianaUSA
| | - David L. Hyten
- Department of Agronomy & HorticultureUniversity of NebraskaLincolnNebraskaUSA
| | - Aardra Kachroo
- Department of Plant PathologyUniversity of KentuckyLexingtonKentuckyUSA
| | - Jenny Koebernick
- Department of Crop, Soil and Environmental SciencesAuburn UniversityAuburnAlabamaUSA
| | - Marc Libault
- Division of Plant Science and TechnologyUniversity of MissouriColumbiaMissouriUSA
| | - Aaron J. Lorenz
- Department of Agronomy and Plant GeneticsUniversity of MinnesotaSt. PaulMinnesotaUSA
| | - Adam L. Mahan
- USDA‐ARS Soybean/Maize Germplasm, Pathology and Genetics Research UnitUrbanaIllinoisUSA
| | | | - Michaela McGinn
- Smithbucklin for the United Soybean BoardSt. LouisMissouriUSA
| | - Khalid Meksem
- Department of Plant, Soil, and Agricultural SystemsSouthern Illinois UniversityCarbondaleIllinoisUSA
| | - Jack K. Okamuro
- USDA‐ARS Crop Production and ProtectionBeltsvilleMarylandUSA
| | - Kerry F. Pedley
- USDA‐ARS Foreign Disease‐Weed Science Research UnitFt. DetrickMarylandUSA
| | | | - Andrew M. Scaboo
- Division of Plant Science and TechnologyUniversity of MissouriColumbiaMissouriUSA
| | - Jeremy Schmutz
- DOE Joint Genome InstituteLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
- HudsonAlpha Institute of BiotechnologyHuntsvilleAlabamaUSA
| | - Bao‐Hua Song
- Department of Biological SciencesUniversity of North Carolina at CharlotteCharlotteNorth CarolinaUSA
| | | | | | | | - Dechun Wang
- Department of Plant, Soil and Microbial SciencesMichigan State UniversityEast LansingMichiganUSA
| | - Lisa Weaver
- Smithbucklin for the United Soybean BoardSt. LouisMissouriUSA
| | - Bo Zhang
- School of Plant and Environmental SciencesVirginia Polytechnic Institute and State UniversityBlacksburgVirginiaUSA
| | | | | |
Collapse
|
5
|
Kumar R, Das SP, Choudhury BU, Kumar A, Prakash NR, Verma R, Chakraborti M, Devi AG, Bhattacharjee B, Das R, Das B, Devi HL, Das B, Rawat S, Mishra VK. Advances in genomic tools for plant breeding: harnessing DNA molecular markers, genomic selection, and genome editing. Biol Res 2024; 57:80. [PMID: 39506826 PMCID: PMC11542492 DOI: 10.1186/s40659-024-00562-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Accepted: 10/28/2024] [Indexed: 11/08/2024] Open
Abstract
Conventional pre-genomics breeding methodologies have significantly improved crop yields since the mid-twentieth century. Genomics provides breeders with advanced tools for whole-genome study, enabling a direct genotype-phenotype analysis. This shift has led to precise and efficient crop development through genomics-based approaches, including molecular markers, genomic selection, and genome editing. Molecular markers, such as SNPs, are crucial for identifying genomic regions linked to important traits, enhancing breeding accuracy and efficiency. Genomic resources viz. genetic markers, reference genomes, sequence and protein databases, transcriptomes, and gene expression profiles, are vital in plant breeding and aid in the identification of key traits, understanding genetic diversity, assist in genomic mapping, support marker-assisted selection and speeding up breeding programs. Advanced techniques like CRISPR/Cas9 allow precise gene modification, accelerating breeding processes. Key techniques like Genome-Wide Association study (GWAS), Marker-Assisted Selection (MAS), and Genomic Selection (GS) enable precise trait selection and prediction of breeding outcomes, improving crop yield, disease resistance, and stress tolerance. These tools are handy for complex traits influenced by multiple genes and environmental factors. This paper explores new genomic technologies like molecular markers, genomic selection, and genome editing for plant breeding showcasing their impact on developing new plant varieties.
Collapse
Affiliation(s)
- Rahul Kumar
- ICAR Research Complex for NEH Region, Tripura Centre, Lembucherra, Agartala, 799210, Tripura, India.
| | | | - Burhan Uddin Choudhury
- ICAR Research Complex for NEH Region, Tripura Centre, Lembucherra, Agartala, 799210, Tripura, India
| | - Amit Kumar
- ICAR Research Complex for NEH Region, Umiam, 793103, Meghalaya, India
| | | | - Ramlakhan Verma
- ICAR-National Rice Research Institute, Cuttack, 753006, Odisha, India
| | | | - Ayam Gangarani Devi
- ICAR Research Complex for NEH Region, Tripura Centre, Lembucherra, Agartala, 799210, Tripura, India
| | - Bijoya Bhattacharjee
- ICAR Research Complex for NEH Region, Tripura Centre, Lembucherra, Agartala, 799210, Tripura, India
| | - Rekha Das
- ICAR Research Complex for NEH Region, Tripura Centre, Lembucherra, Agartala, 799210, Tripura, India
| | - Bapi Das
- ICAR Research Complex for NEH Region, Tripura Centre, Lembucherra, Agartala, 799210, Tripura, India
| | | | - Biswajit Das
- ICAR Research Complex for NEH Region, Tripura Centre, Lembucherra, Agartala, 799210, Tripura, India
| | - Santoshi Rawat
- Department of Food Science and Technology, College of Agriculture, G.B.P.U.A.&T., Pantnagar, India
| | | |
Collapse
|
6
|
Haidar S, Hooker J, Lackey S, Elian M, Puchacz N, Szczyglowski K, Marsolais F, Golshani A, Cober ER, Samanfar B. Harnessing Multi-Omics Strategies and Bioinformatics Innovations for Advancing Soybean Improvement: A Comprehensive Review. PLANTS (BASEL, SWITZERLAND) 2024; 13:2714. [PMID: 39409584 PMCID: PMC11478702 DOI: 10.3390/plants13192714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2024] [Revised: 09/26/2024] [Accepted: 09/26/2024] [Indexed: 10/20/2024]
Abstract
Soybean improvement has entered a new era with the advent of multi-omics strategies and bioinformatics innovations, enabling more precise and efficient breeding practices. This comprehensive review examines the application of multi-omics approaches in soybean-encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics. We first explore pre-breeding and genomic selection as tools that have laid the groundwork for advanced trait improvement. Subsequently, we dig into the specific contributions of each -omics field, highlighting how bioinformatics tools and resources have facilitated the generation and integration of multifaceted data. The review emphasizes the power of integrating multi-omics datasets to elucidate complex traits and drive the development of superior soybean cultivars. Emerging trends, including novel computational techniques and high-throughput technologies, are discussed in the context of their potential to revolutionize soybean breeding. Finally, we address the challenges associated with multi-omics integration and propose future directions to overcome these hurdles, aiming to accelerate the pace of soybean improvement. This review serves as a crucial resource for researchers and breeders seeking to leverage multi-omics strategies for enhanced soybean productivity and resilience.
Collapse
Affiliation(s)
- Siwar Haidar
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, ON K1A 0C6, Canada; (S.H.)
- Department of Biology, Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Julia Hooker
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, ON K1A 0C6, Canada; (S.H.)
- Department of Biology, Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Simon Lackey
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, ON K1A 0C6, Canada; (S.H.)
- Department of Biology, Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Mohamad Elian
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, ON K1A 0C6, Canada; (S.H.)
- Department of Biology, Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Nathalie Puchacz
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, ON K1A 0C6, Canada; (S.H.)
| | - Krzysztof Szczyglowski
- Agriculture and Agri-Food Canada, London Research and Development Centre, London, ON N5V 4T3, Canada
| | - Frédéric Marsolais
- Agriculture and Agri-Food Canada, London Research and Development Centre, London, ON N5V 4T3, Canada
| | - Ashkan Golshani
- Department of Biology, Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Elroy R. Cober
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, ON K1A 0C6, Canada; (S.H.)
| | - Bahram Samanfar
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, ON K1A 0C6, Canada; (S.H.)
- Department of Biology, Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON K1S 5B6, Canada
| |
Collapse
|
7
|
Wartha CA, Lorenz AJ. Genomic predictions of genetic variances and correlations among traits for breeding crosses in soybean. Heredity (Edinb) 2024; 133:173-185. [PMID: 38997517 PMCID: PMC11350137 DOI: 10.1038/s41437-024-00703-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 07/14/2024] Open
Abstract
Parental selection is perhaps the most critical decision a breeder makes, establishing the foundation of the entire program for years to come. Cross selection based on predicted mean and genetic variance can be further expanded to multiple-trait improvement by predicting the genetic correlation (r G ) between pairs of traits. Our objective was to empirically assess the ability to predict the family mean, genetic variance, superior progeny mean and genetic correlation through genomic prediction in a soybean population. Data made available through the Soybean Nested Association Mapping project included phenotypic data on seven traits (days to maturity, lodging, oil, plant height, protein, seed size, and seed yield) for 39 families. Training population composition followed a leave-one-family-out cross-validation scheme, with the validation family genetic parameters predicted using the remaining families as the training set. The predictive abilities for family mean and superior progeny mean were significant for all traits while predictive ability of genetic variance was significant for four traits. We were able to validate significant predictive abilities ofr G for 18 out of 21 (86%) pairwise trait combinations (P < 0.05). The findings from this study support the use of genome-wide marker effects for predictingr G in soybean biparental crosses. If successfully implemented in breeding programs, this methodology could help to increase the rate of genetic gain for multiple correlated traits.
Collapse
Affiliation(s)
- Cleiton A Wartha
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, USA
| | - Aaron J Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, USA.
| |
Collapse
|
8
|
Song Q, Quigley C, He R, Wang D, Nguyen H, Miranda C, Li Z. Development and implementation of nested single-nucleotide polymorphism (SNP) assays for breeding and genetic research applications. THE PLANT GENOME 2024; 17:e20491. [PMID: 39034885 DOI: 10.1002/tpg2.20491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/18/2024] [Accepted: 06/20/2024] [Indexed: 07/23/2024]
Abstract
SoySNP50K and SoySNP6K are commonly used for soybean (Glycine max) genotyping. The SoySNP50K assay has been used to genetically analyze the entire USDA Soybean Germplasm Collection, while the SoySNP6K assay, containing a subset of 6000 single-nucleotide polymorphisms (SNPs) from SoySNP50K, has been used for quantitative trait loci mapping of different traits. To meet the needs for genomic selection, selection of parents for crosses, and characterization of breeding populations, especially early selection of ideal offspring from thousands of lines, we developed two assays, SoySNP3K and SoySNP1K, containing 3072 and 1252 SNPs, respectively, based on SoySNP50K and SoySNP6K mark sets. These two assays also contained the trait markers reported or contributed by soybean breeders. The SNPs in the SoySNP3K are a subset from SoySNP6K, while the SNPs in the SoySNP1K are a subset from SoySNP3K. These SNPs were chosen to reduce the SNP number in the large linkage blocks while capturing as much of the haplotype diversity as possible. They are highly polymorphic and of high quality. The mean minor allele frequencies of the SNPs in the southern and northern US elites were 0.25 and 0.27 for SoySNP3K, respectively, and 0.29 and 0.33 for SoySNP1K. The selected SNPs are a valuable source for developing targeted amplicon sequencing assay or beadchip assay in soybean. SoySNP3K and SoySNP1K assays are commercialized by Illumina Inc. and AgriPlex Genomics, respectively. Together with SoySNP50K and SoySNP6K, a series of nested assays with different marker densities will serve as additional low-cost genomic tools for genetic, genomic, and breeding research.
Collapse
Affiliation(s)
- Qijian Song
- USDA-ARS, Soybean Genomics & Improvement Laboratory, Beltsville, Maryland, USA
| | - Charles Quigley
- USDA-ARS, Soybean Genomics & Improvement Laboratory, Beltsville, Maryland, USA
| | - Ruifeng He
- USDA-ARS, Soybean Genomics & Improvement Laboratory, Beltsville, Maryland, USA
| | - Dechun Wang
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA
| | - Henry Nguyen
- Molecular Genetics and Soybean Genomics Laboratory, Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
| | - Carrie Miranda
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, USA
| | - Zenglu Li
- Institute of Plant Breeding, Genetics and Genomics/Department of Crop and Soil Sciences, University of Georgia, Athens, Georgia, USA
| |
Collapse
|
9
|
Potapova NA, Zlobin AS, Leonova IN, Salina EA, Tsepilov YA. The BLUP method in evaluation of breeding values of Russian spring wheat lines using micro- and macroelements in seeds. Vavilovskii Zhurnal Genet Selektsii 2024; 28:456-462. [PMID: 39027122 PMCID: PMC11253017 DOI: 10.18699/vjgb-24-51] [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: 11/29/2023] [Revised: 03/06/2024] [Accepted: 03/12/2023] [Indexed: 07/20/2024] Open
Abstract
Genomic selection is a technology that allows for the determination of the genetic value of varieties of agricultural plants and animal breeds, based on information about genotypes and phenotypes. The measured breeding value (BV) for varieties and breeds in relation to the target trait allows breeding stages to be thoroughly planned and the parent forms suitable for crossing to be chosen. In this work, the BLUP method was used to assess the breeding value of 149 Russian varieties and introgression lines (4 measurements for each variety or line, 596 phenotypic points) of spring wheat according to the content of seven chemical elements in the grain - K, Ca, Mg, Mn, Fe, Zn, Cu. The quality of the evaluation of breeding values was assessed using cross-validation, when the sample was randomly divided into five parts, one of which was chosen as a test population. The following average values of the Pearson correlation were obtained for predicting the concentration of trace elements: K - 0.67, Ca - 0.61, Mg - 0.4, Mn - 0.5, Fe - 0.38, Zn - 0.46, Cu - 0.48. Out of the 35 models studied, the p-value was below the nominal significant threshold (p-value < 0.05) for 28 models. For 11 models, the p-value was significant after correction for multiple testing (p-value < 0.001). For Ca and K, four out of five models and for Mn two out of five models had a p-value below the threshold adjusted for multiple testing. For 30 varieties that showed the best varietal values for Ca, K and Mn, the average breeding value was 296.43, 785.11 and 4.87 mg/kg higher, respectively, than the average breeding value of the population. The results obtained show the relevance of the application of genomic selection models even in such limited-size samples. The models for K, Ca and Mn are suitable for assessing the breeding value of Russian wheat varieties based on these characteristics.
Collapse
Affiliation(s)
- N A Potapova
- Kurchatov Genomic Center of ICG SB RAS, Novosibirsk, Russia Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Moscow, Russia Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical-Biological Agency, Moscow, Russia
| | - A S Zlobin
- Kurchatov Genomic Center of ICG SB RAS, Novosibirsk, Russia
| | - I N Leonova
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - E A Salina
- Kurchatov Genomic Center of ICG SB RAS, Novosibirsk, Russia
| | - Y A Tsepilov
- Kurchatov Genomic Center of ICG SB RAS, Novosibirsk, Russia
| |
Collapse
|
10
|
Menke E, Steketee CJ, Song Q, Schapaugh WT, Carter TE, Fallen B, Li Z. Genetic mapping reveals the complex genetic architecture controlling slow canopy wilting in soybean. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:107. [PMID: 38632129 PMCID: PMC11024021 DOI: 10.1007/s00122-024-04609-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 03/23/2024] [Indexed: 04/19/2024]
Abstract
In soybean [Glycine max (L.) Merr.], drought stress is the leading cause of yield loss from abiotic stress in rain-fed US growing areas. Only 10% of the US soybean production is irrigated; therefore, plants must possess physiological mechanisms to tolerate drought stress. Slow canopy wilting is a physiological trait that is observed in a few exotic plant introductions (PIs) and may lead to yield improvement under drought stress. Canopy wilting of 130 recombinant inbred lines (RILs) derived from Hutcheson × PI 471938 grown under drought stress was visually evaluated and genotyped with the SoySNP6K BeadChip. Over four years, field evaluations of canopy wilting were conducted under rainfed conditions at three locations across the US (Georgia, Kansas, and North Carolina). Due to the variation in weather among locations and years, the phenotypic data were collected from seven environments. Substantial variation in canopy wilting was observed among the genotypes in the RIL population across environments. Three QTLs were identified for canopy wilting from the RIL population using composite interval mapping on chromosomes (Chrs) 2, 8, and 9 based on combined environmental analyses. These QTLs inherited the favorable alleles from PI 471938 and accounted for 11, 10, and 14% of phenotypic variation, respectively. A list of 106 candidate genes were narrowed down for these three QTLs based on the published information. The QTLs identified through this research can be used as targets for further investigation to understand the mechanisms of slow canopy wilting. These QTLs could be deployed to improve drought tolerance through a targeted selection of the genomic regions from PI 471938.
Collapse
Affiliation(s)
- Ethan Menke
- Institute of Plant Breeding, Genetics, and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, USA
| | - Clinton J Steketee
- Institute of Plant Breeding, Genetics, and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, USA
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD, USA
| | | | - Thomas E Carter
- Department of Crop and Soil Sciences, North Carolina State University and USDA-ARS, Raleigh, NC, USA
| | - Benjamin Fallen
- Department of Crop and Soil Sciences, North Carolina State University and USDA-ARS, Raleigh, NC, USA
| | - Zenglu Li
- Institute of Plant Breeding, Genetics, and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, USA.
| |
Collapse
|
11
|
Singh V, Krause M, Sandhu D, Sekhon RS, Kaundal A. Salinity stress tolerance prediction for biomass-related traits in maize (Zea mays L.) using genome-wide markers. THE PLANT GENOME 2023; 16:e20385. [PMID: 37667417 DOI: 10.1002/tpg2.20385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/18/2023] [Accepted: 08/14/2023] [Indexed: 09/06/2023]
Abstract
Maize (Zea mays L.) is the third most important cereal crop after rice (Oryza sativa) and wheat (Triticum aestivum). Salinity stress significantly affects vegetative biomass and grain yield and, therefore, reduces the food and silage productivity of maize. Selecting salt-tolerant genotypes is a cumbersome and time-consuming process that requires meticulous phenotyping. To predict salt tolerance in maize, we estimated breeding values for four biomass-related traits, including shoot length, shoot weight, root length, and root weight under salt-stressed and controlled conditions. A five-fold cross-validation method was used to select the best model among genomic best linear unbiased prediction (GBLUP), ridge-regression BLUP (rrBLUP), extended GBLUP, Bayesian Lasso, Bayesian ridge regression, BayesA, BayesB, and BayesC. Examination of the effect of different marker densities on prediction accuracy revealed that a set of low-density single nucleotide polymorphisms obtained through filtering based on a combination of analysis of variance and linkage disequilibrium provided the best prediction accuracy for all the traits. The average prediction accuracy in cross-validations ranged from 0.46 to 0.77 across the four derived traits. The GBLUP, rrBLUP, and all Bayesian models except BayesB demonstrated comparable levels of prediction accuracy that were superior to the other modeling approaches. These findings provide a roadmap for the deployment and optimization of genomic selection in breeding for salt tolerance in maize.
Collapse
Affiliation(s)
- Vishal Singh
- Plants, Soils, and Climate, College of Agricultural and Applied Sciences, Utah State University, Logan, Utah, USA
- ICAR-Indian Institute of Maize Research, Ludhiana, Punjab, India
| | - Margaret Krause
- Plants, Soils, and Climate, College of Agricultural and Applied Sciences, Utah State University, Logan, Utah, USA
| | - Devinder Sandhu
- US Salinity Laboratory (USDA-ARS), Riverside, California, USA
| | - Rajandeep S Sekhon
- Department of Genetics and Biochemistry, Clemson University, Clemson, South Carolina, USA
| | - Amita Kaundal
- Plants, Soils, and Climate, College of Agricultural and Applied Sciences, Utah State University, Logan, Utah, USA
| |
Collapse
|