Genetic analysis of protein content and oil content in soybean by genome-wide association study

Edible Insects from the Perspective of Sustainability-A Review of the Hazards and Benefits

The increasing global population, projected to exceed 9.1 billion by 2050, presents a critical challenge for sustainable food production. Edible insects have emerged as a promising alternative protein source due to their high nutritional value, low environmental footprint, and efficient resource utilization. This review explores the opportunities and challenges of integrating edible insects into food systems. Benefits include their high protein content and quality, low greenhouse gas emissions, low-cost production, and ability to thrive on organic waste. Furthermore, edible insect cultivation requires significantly less land and water compared to traditional livestock. Edible insects are nutritionally rich, containing substantial amounts of essential amino acids, unsaturated fatty acids, and minerals. However, barriers to widespread adoption persist, such as cultural perceptions, regulatory hurdles, potential allergenicity, and biological and chemical contamination. Furthermore, standardizing rearing practices and ensuring food safety are critical for broader adoption. While edible insects represent a nutritious, low-cost food and feed, there are a lot of variables that have not been fully investigated. Only after further research, promising results, and solutions that are relatively easy to apply might edible insects be considered a sustainable food source. Considering the challenges that may arise by 2050, more intensive research is highly advised.

Genomic and phenomic prediction for soybean seed yield, protein, and oil

Developments in genomics and phenomics have provided valuable tools for use in cultivar development. Genomic prediction (GP) has been used in commercial soybean [Glycine max L. (Merr.)] breeding programs to predict grain yield and seed composition traits. Phenomic prediction (PP) is a rapidly developing field that holds the potential to be used for the selection of genotypes early in the growing season. The objectives of this study were to compare the performance of GP and PP for predicting soybean seed yield, protein, and oil. We additionally conducted genome-wide association studies (GWAS) to identify significant single-nucleotide polymorphisms (SNPs) associated with the traits of interest. The GWAS panel of 292 diverse accessions was grown in six environments in replicated trials. Spectral data were collected at two time points during the growing season. A genomic best linear unbiased prediction (GBLUP) model was trained on 269 accessions, while three separate machine learning (ML) models were trained on vegetation indices (VIs) and canopy traits. We observed that PP had a higher correlation coefficient than GP for seed yield, while GP had higher correlation coefficients for seed protein and oil contents. VIs with high feature importance were used as covariates in a new GBLUP model, and a new random forest model was trained with the inclusion of selected SNPs. These models did not outperform the original GP and PP models. These results show the capability of using ML for in-season predictions for specific traits in soybean breeding and provide insights on PP and GP inclusions in breeding programs.

Genome-Wide Association Study on Imputed Genotypes of 180 Eurasian Soybean Glycine max Varieties for Oil and Protein Contents in Seeds

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.

Network analysis combined with genome-wide association study helps identification of genes related to amino acid contents in soybean

BACKGROUND

Additional to total protein content, the amino acid (AA) profile is important to the nutritional value of soybean seed. The AA profile in soybean seed is a complex quantitative trait controlled by multiple interconnected genes and pathways controlling the accumulation of each AA. With a total of 621 soybean germplasm, we used three genome-wide association study (GWAS)-based approaches to investigate the genomic regions controlling the AA content and profile in soybean. Among those approaches, the GWAS network analysis we implemented takes advantage of the relationships between specific AAs to identify the genetic control of AA profile.

RESULTS

For Approach I, GWAS were performed for the content of 24 single AAs under all environments combined. Significant SNPs grouping into 16 linkage disequilibrium (LD) blocks from 18 traits were identified. For Approach II, the individual AAs were grouped by five families according to their metabolic pathways and were examined based on the sum, ratios, and interactions of AAs within the same biochemical family. Significant SNPs grouping into 35 LD blocks were identified, with SNPs associated with traits from the same biochemical family often positioned on the same LD blocks. Approach III, a correlation-based network analysis, was performed to assess the empirical relationships among AAs. Two groups were described by the network topology, Group 1: Ala, Gly, Lys, available Lys (Alys), and Thr and Group 2: Ile and Tyr. Significant SNPs associated with a ratio of connected AAs or a ratio of a single AA to its fully or partially connected metabolic groups were identified within 9 LD blocks for Group 1 and 2 LD blocks for Group 2. Among 40 identified QTL for AA or AA-derived traits, three genomic regions were novel in terms of seed composition traits (oil, protein, and AA content). An additional 24 regions had previously not been specifically associated with the AA content.

CONCLUSIONS

Our results confirmed loci identified from previous studies but also suggested that network approaches for studying AA contents in soybean seed are valuable. Three genomic regions (Chr 5: 41,754,397-41,893,109 bp, Chr 9: 1,537,829-1,806,586 bp, and Chr 20: 31,554,795-33,678,257 bp) were significantly identified by all three approaches. Yet, the majority of associations between a genomic region and an AA trait were approach- and/or environment-specific. Using a combination of approaches provides insights into the genetic control and pleiotropy among AA contents, which can be applied to mechanistic understanding of variation in AA content as well as tailored nutrition in cultivars developed from soybean breeding programs.

Assessment of genetic diversity by phenological traits, field performance, and Start Codon Targeted (SCoT) polymorphism marker of seventeen soybean genotypes (Glycine max L.)

The Egyptian-farmed soybeans have a wide range of genetic diversity which is most important in plant improvement programs in order to develop new higher yielding soybean genotypes. The present study is designed to determine the genetic variability among seventeen genotypes of cultivated soybean (Glycine max L.) by examining the phenotypic level at the seedling stage, field performance over two years 2022/2023 and genetically using Start Codon Targeted (SCoT) markers. Results indicated that the SCoT markers, 100 seed weight, and tip angle (TA) traits were positively correlated with H2L12, DR 101, H15L5, and H117 genotypes. In addition, the number of branches per plant and plant height were associated with H113, H32, Crowford, H129, and D7512035. Furthermore, the length of the first internode (LFI), root width (RW), root length (RL), and shoot length (SL) were more associated with Giza 111, NC105, and Hutcheson. The hierarchical cluster analysis (HCA) and its associated heatmap explored the differences among the genotypes. It showed that all examined parameters were clustered into four distinct clusters. The obtained results showed that genotypes NC105, H30, D75_12035, and H2L12 have promising phenological and morphological traits besides tracking the inheritance of nearby genes surrounding the ATG translation start codon since they are in a monoclades. The obtained results will help the breeder plan appropriate selection strategies for improving seed yield in soybeans through hybridization from divergent clusters.

Genome-Wide Association and RNA-Seq Analyses Reveal a Potential Candidate Gene Related to Oil Content in Soybean Seeds

Soybean is a crucial crop globally, serving as a significant source of unsaturated fatty acids and protein in the human diet. However, further enhancements are required for the related genes that regulate soybean oil synthesis. In this study, 155 soybean germplasms were cultivated under three different environmental conditions, followed by phenotypic identification and genome-wide association analysis using simplified sequencing data. Genome-wide association analysis was performed using SLAF-seq data. A total of 36 QTLs were significantly associated with oil content (-log10(p) > 3). Out of the 36 QTLs associated with oil content, 27 exhibited genetic overlap with previously reported QTLs related to oil traits. Further transcriptome sequencing was performed on extreme high-low oil soybean varieties. Combined with transcriptome expression data, 22 candidate genes were identified (|log2FC| ≥ 3). Further haplotype analysis of the potential candidate genes showed that three potential candidate genes had excellent haplotypes, including Glyma.03G186200, Glyma.09G099500, and Glyma.18G248900. The identified loci harboring beneficial alleles and candidate genes likely contribute significantly to the molecular network's underlying marker-assisted selection (MAS) and oil content.

GWAS and WGCNA Analysis Uncover Candidate Genes Associated with Oil Content in Soybean

Soybean vegetable oil is an important source of the human diet. However, the analysis of the genetic mechanism leading to changes in soybean oil content is still incomplete. In this study, a total of 227 soybean materials were applied and analyzed by a genome-wide association study (GWAS). There are 44 quantitative trait nucleotides (QTNs) that were identified as associated with oil content. A total of six, four, and 34 significant QTN loci were identified in Xiangyang, Hulan, and Acheng, respectively. Of those, 26 QTNs overlapped with or were near the known oil content quantitative trait locus (QTL), and 18 new QTNs related to oil content were identified. A total of 594 genes were located near the peak single nucleotide polymorphism (SNP) from three tested environments. These candidate genes exhibited significant enrichment in tropane, piperidine, and pyridine alkaloid biosynthesiss (ko00960), ABC transporters (ko02010), photosynthesis-antenna proteins (ko00196), and betalain biosynthesis (ko00965). Combined with the GWAS and weighted gene co-expression network analysis (WGCNA), four candidate genes (Glyma.18G300100, Glyma.11G221100, Glyma.13G343300, and Glyma.02G166100) that may regulate oil content were identified. In addition, Glyma.18G300100 was divided into two main haplotypes in the studied accessions. The oil content of haplotype 1 is significantly lower than that of haplotype 2. Our research findings provide a theoretical basis for improving the regulatory mechanism of soybean oil content.

Integrating Genome-Wide Association Study, Transcriptome and Metabolome Reveal Novel QTL and Candidate Genes That Control Protein Content in Soybean

Protein content (PC) is crucial to the nutritional quality of soybean [Glycine max (L.) Merrill]. In this study, a total of 266 accessions were used to perform a genome-wide association study (GWAS) in three tested environments. A total of 23,131 high-quality SNP markers (MAF ≥ 0.02, missing data ≤ 10%) were identified. A total of 40 association signals were significantly associated with PC. Among them, five novel quantitative trait nucleotides (QTNs) were discovered, and another 32 QTNs were found to be overlapping with the genomic regions of known quantitative trait loci (QTL) related to soybean PC. Combined with GWAS, metabolome and transcriptome sequencing, 59 differentially expressed genes (DEGs) that might control the change in protein content were identified. Meantime, four commonly upregulated differentially abundant metabolites (DAMs) and 29 commonly downregulated DAMs were found. Remarkably, the soybean gene Glyma.08G136900, which is homologous with Arabidopsis hydroxyproline-rich glycoproteins (HRGPs), may play an important role in improving the PC. Additionally, Glyma.08G136900 was divided into two main haplotype in the tested accessions. The PC of haplotype 1 was significantly lower than that of haplotype 2. The results of this study provided insights into the genetic mechanisms regulating protein content in soybean.