Omics advances and integrative approaches for the simultaneous improvement of seed oil and protein content in soybean (<i>Glycine max</i>L.)

Kumar, Virender; Vats, Sanskriti; Kumawat, Surbhi; Bisht, Ashita; Bhatt, Vacha; Shivaraj, S. M.; Padalkar, Gunashri; Goyal, Vinod; Zargar, Sajad Majeed; Gupta, S. K.; Kumawat, Giriraj; Chandra, Subhash; Chalam, V. Celia; Ratnaparkhe, Milind B.; Gill, B. S.; Jean, Martine; Patil, Gunvant; Vuong, Tri D.; Rajcan, Istvan; Deshmukh, Rupesh; Belzile, François; Sharma, Tilak Raj; Nguyen, Henry T.; Sonah, Humira

doi:10.1080/07352689.2021.1954778

Cited by 18 publications

(10 citation statements)

References 157 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Soybean has emerged as one of the most important crops globally since it is an excellent source of edible oil and protein [1]. Soybean seeds have about 18-21% oil and 40-45% protein content, and are majorly utilized as food, animal feed, cooking oil, and industrial biofuel [2]. It is a major oilseed crop in the global market, contributing more than 50% to global vegetable oil production [3].…”

Section: Introductionmentioning

confidence: 99%

“…The domestication of soybeans has led to the selection of large seeds, which resulted in less protein and more oil content than the wild soybean Glycine soja [7]. The soybean oil and protein are important traits; however, these traits are negatively correlated, which is a major bottleneck for soybean improvement [2,8,9]. These traits also correlate with other seed composition and agronomical traits.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pinpointing Genomic Regions and Candidate Genes Associated with Seed Oil and Protein Content in Soybean through an Integrative Transcriptomic and QTL Meta-Analysis

Kumar

Goyal

Mandlik

et al. 2022

Cells

Self Cite

View full text Add to dashboard Cite

Soybean with enriched nutrients has emerged as a prominent source of edible oil and protein. In the present study, a meta-analysis was performed by integrating quantitative trait loci (QTLs) information, region-specific association and transcriptomic analysis. Analysis of about a thousand QTLs previously identified in soybean helped to pinpoint 14 meta-QTLs for oil and 16 meta-QTLs for protein content. Similarly, region-specific association analysis using whole genome re-sequenced data was performed for the most promising meta-QTL on chromosomes 6 and 20. Only 94 out of 468 genes related to fatty acid and protein metabolic pathways identified within the meta-QTL region were found to be expressed in seeds. Allele mining and haplotyping of these selected genes were performed using whole genome resequencing data. Interestingly, a significant haplotypic association of some genes with oil and protein content was observed, for instance, in the case of FAD2-1B gene, an average seed oil content of 20.22% for haplotype 1 compared to 15.52% for haplotype 5 was observed. In addition, the mutation S86F in the FAD2-1B gene produces a destabilizing effect of (ΔΔG Stability) −0.31 kcal/mol. Transcriptomic analysis revealed the tissue-specific expression of candidate genes. Based on their higher expression in seed developmental stages, genes such as sugar transporter, fatty acid desaturase (FAD), lipid transporter, major facilitator protein and amino acid transporter can be targeted for functional validation. The approach and information generated in the present study will be helpful in the map-based cloning of regulatory genes, as well as for marker-assisted breeding in soybean.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pinpointing Genomic Regions and Candidate Genes Associated with Seed Oil and Protein Content in Soybean through an Integrative Transcriptomic and QTL Meta-Analysis

Kumar

Goyal

Mandlik

et al. 2022

Cells

Self Cite

View full text Add to dashboard Cite

show abstract

“…The millions of crossing events in germplasm during evolution increased the mapping resolution of GWAS. While GWAS is used widely for many plant species (see Kumar et al 2021;Saini et al 2022b), relatively few studies on the traits contributing to resistance to different biotic stresses have been reported in soybean (Table 2). Recently, GWAS for sclerotinia stem rot using 22,048 SNPs identi ed 18 marker trait associations (MTAs) and 243 underlying candidate genes ).…”

Section: Genome-wide Association Mapping For Biotic Stress Resistancementioning

confidence: 99%

“…It is aptly known as a 'wonder crop' due to its rich nutritional composition of protein (40%) and edible oil (20%), providing nutritional security to a vast population around the globe. It has diverse applications as food, biofuel, and animal feed (Kumar et al 2021; SoyStat 2022). Its excellent source of protein provides all the essential amino acids, with major proteins including glycinin, conglycinin lectin, and trypsin inhibitors.…”

Section: Introductionmentioning

confidence: 99%

Multi-omics assisted breeding for biotic stress resistance in soybean: challenges and opportunities

Bisht

Saini

Kaur

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Biotic stress is a critical factor limiting soybean growth and development. Soybean responses to biotic stresses such as insects, nematodes, and fungal, bacterial, and viral pathogens are governed by complex regulatory and defense mechanisms. Next-generation sequencing has availed research techniques and strategies in genomics and post-genomics. This review summarizes the available information on marker resources, quantitative trait loci, and marker trait associations involved in regulating biotic stress responses of soybean. We discuss the differential expression of related genes and proteins reported in different transcriptomics and proteomics studies and the role of signaling pathways and metabolites reported in metabolomic studies. Recent advances in omics technologies offer opportunities to reshape and improve biotic stress resistance in soybean by altering gene regulation and/or other regulatory networks. We recommend using ‘integrated omics’ to understand how soybean responds to different biotic stresses. We discuss the potential challenges of integrating multi-omics for functional analysis of genes and their regulatory networks and the development of biotic stress-resistant cultivars. This review will help direct soybean breeding programs to develop resistance against different biotic stresses.

show abstract

“…Soybean is one of the most important raw materials for oil and feed ( Herman et al, 2018 ; Kumar et al, 2021 ; Wei et al, 2021a ). Differences in soybean varieties lead to significant differences in their protein, fat, and other constituent contents ( Wang et al, 2020a , b ).…”

Section: Introductionmentioning

confidence: 99%

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

et al. 2022

View full text Add to dashboard Cite

Different soybean varieties vary greatly in their nutritional value and composition. Screening for superior varieties is also essential for the development of the soybean seed industry. The objective of the paper was to analyze the feasibility of terahertz (THz) frequency-domain spectroscopy and chemometrics for soybean variety identification. Meanwhile, a grey wolf optimizer-support vector machine (GWO-SVM) soybean variety identification model was proposed. Firstly, the THz frequency-domain spectra of experimental samples (6 varieties, 270 in total) were collected. Principal component analysis (PCA) was used to analyze the THz spectra. After that, 203 samples from the calibration set were used to establish a soybean variety identification model. Finally, 67 samples from the test set were used for prediction validation. The experimental results demonstrated that THz frequency-domain spectroscopy combined with GWO-SVM could quickly and accurately identify soybean varieties. Compared with discriminant partial least squares (DPLS) and particles swarm optimization support vector machine, GWO-SVM combined with the second derivative could establish a better soybean variety identification model. The overall correct identification rate of its prediction set was 97.01%.

show abstract

Omics advances and integrative approaches for the simultaneous improvement of seed oil and protein content in soybean (Glycine maxL.)

Cited by 18 publications

References 157 publications

Pinpointing Genomic Regions and Candidate Genes Associated with Seed Oil and Protein Content in Soybean through an Integrative Transcriptomic and QTL Meta-Analysis

Pinpointing Genomic Regions and Candidate Genes Associated with Seed Oil and Protein Content in Soybean through an Integrative Transcriptomic and QTL Meta-Analysis

Multi-omics assisted breeding for biotic stress resistance in soybean: challenges and opportunities

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

Contact Info

Product

Resources

About