Advancing Soybean Improvement: Multi-Omics Strategies, Cutting-Edge Techniques and Bioinformatics Innovations

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Genetics, Genomics and Biotechnology".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 4580

Special Issue Editor


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Guest Editor
Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, ON K1A 0C6, Canada
Interests: genomics/proteomics and transcriptomics of soybean; cell and molecular biology; CRISPR; plant biotechnology; genetics and genomics; DNA-based markers; allele-specific marker developments; systems biology; molecular breeding; host–pathogen interactions; bioinformatics; computational biology; QTL and GWAS analysis; time of flowering and maturity (genetics of photoperiod sensitivity) in soybean; soybean seed protein content; food allergy; microbiology

Special Issue Information

Dear Colleagues,

This Special Issue, “Advancing Soybean Improvement: Multi-Omics Strategies, Cutting-Edge Techniques and Bioinformatics Innovations”, focuses on the application of multi-omics approaches and bioinformatics tools in soybean research with the ultimate goal of enhancing soybean improvement efforts. Soybean is one of the most important legume crops, providing a significant source of protein and oil for human and animal consumption. However, soybean production faces numerous challenges, including biotic and abiotic stresses, which can significantly impact yield and quality.

This Special Issue aims to highlight the latest advances in multi-omics approaches, including genomics, transcriptomics, proteomics, and metabolomics, and their integration with bioinformatics tools in soybean research. The Special Issue will cover a wide range of topics, including, but not limited to, the identification and functional characterization of genes and pathways associated with important agronomic traits; understanding the molecular mechanisms underlying soybean responses to biotic and abiotic stresses; the exploration of soybean genetic diversity and population genomics; and the utilization of bioinformatics tools for data integration, analysis, and visualization in soybean research.

Contributions to this Special Issue may include original research articles, reviews, and perspectives that provide novel insights, methodologies, and applications of multi-omics approaches and bioinformatics in soybean improvement. The Special Issue will serve as a valuable resource for researchers, scientists, and practitioners working in the field of soybean research, plant breeding, genomics, and bioinformatics, and contribute to the advancement of soybean improvement strategies through cutting-edge multi-omics approaches and bioinformatics tools.

Dr. Bahram Samanfar
Guest Editor

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Keywords

  • glycine max
  • multi-omics approaches
  • bioinformatics tools
  • genomics research
  • transcriptomics analysis
  • proteomics profiling
  • metabolomics investigations
  • soybean improvement strategies
  • biotic stress responses
  • abiotic stress tolerance
  • molecular mechanism elucidation
  • genetic diversity assessment
  • population genomics studies
  • data integration techniques
  • advanced data analysis methods

Published Papers (3 papers)

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Research

17 pages, 2819 KiB  
Article
Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity
by Marina Ćeran, Vuk Đorđević, Jegor Miladinović, Marjana Vasiljević, Vojin Đukić, Predrag Ranđelović and Simona Jaćimović
Plants 2024, 13(7), 975; https://doi.org/10.3390/plants13070975 - 28 Mar 2024
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Abstract
To overcome the different challenges to food security caused by a growing population and climate change, soybean (Glycine max (L.) Merr.) breeders are creating novel cultivars that have the potential to improve productivity while maintaining environmental sustainability. Genomic selection (GS) is an [...] Read more.
To overcome the different challenges to food security caused by a growing population and climate change, soybean (Glycine max (L.) Merr.) breeders are creating novel cultivars that have the potential to improve productivity while maintaining environmental sustainability. Genomic selection (GS) is an advanced approach that may accelerate the rate of genetic gain in breeding using genome-wide molecular markers. The accuracy of genomic selection can be affected by trait architecture and heritability, marker density, linkage disequilibrium, statistical models, and training set. The selection of a minimal and optimal marker set with high prediction accuracy can lower genotyping costs, computational time, and multicollinearity. Selective phenotyping could reduce the number of genotypes tested in the field while preserving the genetic diversity of the initial population. This study aimed to evaluate different methods of selective genotyping and phenotyping on the accuracy of genomic prediction for soybean yield. The evaluation was performed on three populations: recombinant inbred lines, multifamily diverse lines, and germplasm collection. Strategies adopted for marker selection were as follows: SNP (single nucleotide polymorphism) pruning, estimation of marker effects, randomly selected markers, and genome-wide association study. Reduction of the number of genotypes was performed by selecting a core set from the initial population based on marker data, yet maintaining the original population’s genetic diversity. Prediction ability using all markers and genotypes was different among examined populations. The subsets obtained by the model-based strategy can be considered the most suitable for marker selection for all populations. The selective phenotyping based on makers in all cases had higher values of prediction ability compared to minimal values of prediction ability of multiple cycles of random selection, with the highest values of prediction obtained using AN approach and 75% population size. The obtained results indicate that selective genotyping and phenotyping hold great potential and can be integrated as tools for improving or retaining selection accuracy by reducing genotyping or phenotyping costs for genomic selection. Full article
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10 pages, 2652 KiB  
Article
Inheritance of Early Stomatal Closure Trait in Soybean: Ellis × N09-13890 Population
by Avat Shekoofa, Victoria Moser, Kripa Dhakal, Isha Poudel and Vince Pantalone
Plants 2023, 12(18), 3227; https://doi.org/10.3390/plants12183227 - 11 Sep 2023
Viewed by 959
Abstract
Drought conditions exhibit various physiological and morphological changes in crops and thus reduce crop growth and yield. In order to mitigate the negative impacts of drought stress on soybean (Glycine max L. Merr.) production, identification and selection of genotypes that are best [...] Read more.
Drought conditions exhibit various physiological and morphological changes in crops and thus reduce crop growth and yield. In order to mitigate the negative impacts of drought stress on soybean (Glycine max L. Merr.) production, identification and selection of genotypes that are best adapted to limited water availability in a specific environmental condition can be an effective strategy. This study aimed to assess the inheritance of early stomatal closure traits in soybeans using a population of recombinant inbred lines (RILs) derived from a cross between N09-13890 and Ellis. Thirty soybean lines were subjected to progressive water-deficit stress using a dry-down experiment. The experiment was conducted from June to November 2022 at the West Tennessee Research and Education Center (WTREC), University of Tennessee in Jackson, TN, under controlled environment conditions. This study identified significant differences among soybean lines in their early stomatal closure thresholds. The fraction of transpirable soil water (FTSW) thresholds among 30 tested lines ranged from 0.18 to 0.80, at which the decline in transpiration with soil drying was observed. Almost 65% of the RILs had FTSW threshold values between 0.41 to 0.80. These results, indicating inheritance, are supportive of the expression of early stomatal closure trait in progeny lines at a high level in cultivar development for water-deficit stress conditions. Thus, identifying the differences in genotypes of water use and their response to water-deficit stress conditions can provide a foundation for selecting new cultivars that are best adapted to arid and semi-arid agricultural production systems. Full article
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20 pages, 3565 KiB  
Article
Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits
by Mohsen Yoosefzadeh-Najafabadi, Sepideh Torabi, Dan Tulpan, Istvan Rajcan and Milad Eskandari
Plants 2023, 12(14), 2659; https://doi.org/10.3390/plants12142659 - 16 Jul 2023
Cited by 1 | Viewed by 2001
Abstract
Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci [...] Read more.
Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research. Full article
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