Meta-Analyses of QTLs Associated with Protein and Oil Contents and Compositions in Soybean [Glycine max (L.) Merr.] Seed

Advances in genomic tools for plant breeding: harnessing DNA molecular markers, genomic selection, and genome editing

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.

Single- and multiple-trait quantitative trait locus analyses for seed oil and protein contents of soybean populations with advanced breeding line background

Soybean seed oil and protein contents are negatively correlated, posing challenges to enhance both traits simultaneously. Previous studies have identified numerous oil and protein QTLs via single-trait QTL analysis. Multiple-trait QTL methods were shown to be superior but have not been applied to seed oil and protein contents. Our study aimed to evaluate the effectiveness of single- and multiple-trait multiple interval mapping (ST-MIM and MT-MIM, respectively) for these traits using three recombinant inbred line populations from advanced breeding line crosses tested in four environments. Using original and simulated data, we found that MT-MIM did not outperform ST-MIM for our traits with high heritability (H2 > 0.84). Empirically, MT-MIM confirmed only five out of the seven QTLs detected by ST-MIM, indicating single-trait analysis was sufficient for these traits. All QTLs exerted opposite effects on oil and protein contents with varying protein-to-oil additive effect ratios (-0.4 to -4.8). We calculated the economic impact of the allelic variations via estimated processed values (EPV) using the National Oilseed Processors Association (NOPA) and High Yield + Quality (HY + Q) methods. Oil-increasing alleles had positive effects on both EPVNOPA and EPVHY+Q when the protein-to-oil ratio was low (-0.4 to -0.7). However, when the ratio was high (-4.1 to -4.8), oil-increasing alleles increased EPVNOPA and decreased EPVHY+Q, which penalizes low protein meal. In conclusion, single-trait QTL analysis is adequately effective for high heritability traits like seed oil and protein contents. Additionally, the populations' elite pedigrees and varying protein-to-oil ratios provide potential lines for further yield assessment and direct integration into breeding programs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01489-2.

Meta QTL analysis for dissecting abiotic stress tolerance in chickpea

BACKGROUND

Chickpea is prone to many abiotic stresses such as heat, drought, salinity, etc. which cause severe loss in yield. Tolerance towards these stresses is quantitative in nature and many studies have been done to map the loci influencing these traits in different populations using different markers. This study is an attempt to meta-analyse those reported loci projected over a high-density consensus map to provide a more accurate information on the regions influencing heat, drought, cold and salinity tolerance in chickpea.

RESULTS

A meta-analysis of QTL reported to be responsible for tolerance to drought, heat, cold and salinity stress tolerance in chickpeas was done. A total of 1512 QTL responsible for the concerned abiotic stress tolerance were collected from literature, of which 1189 were projected on a chickpea consensus genetic map. The QTL meta-analysis predicted 59 MQTL spread over all 8 chromosomes, responsible for these 4 kinds of abiotic stress tolerance in chickpea. The physical locations of 23 MQTL were validated by various marker-trait associations and genome-wide association studies. Out of these reported MQTL, CaMQAST1.1, CaMQAST4.1, CaMQAST4.4, CaMQAST7.8, and CaMQAST8.2 were suggested to be useful for different breeding approaches as they were responsible for high per cent variance explained (PVE), had small intervals and encompassed a large number of originally reported QTL. Many putative candidate genes that might be responsible for directly or indirectly conferring abiotic stress tolerance were identified in the region covered by 4 major MQTL- CaMQAST1.1, CaMQAST4.4, CaMQAST7.7, and CaMQAST6.4, such as heat shock proteins, auxin and gibberellin response factors, etc. CONCLUSION: The results of this study should be useful for the breeders and researchers to develop new chickpea varieties which are tolerant to drought, heat, cold, and salinity stresses.

Meta-analysis of identified genomic regions and candidate genes underlying salinity tolerance in rice (Oryza sativa L.)

Rice output has grown globally, yet abiotic factors are still a key cause for worry. Salinity stress seems to have the more impact on crop production out of all abiotic stresses. Currently one of the most significant challenges in paddy breeding for salinity tolerance with the help of QTLs, is to determine the QTLs having the best chance of improving salinity tolerance with the least amount of background noise from the tolerant parent. Minimizing the size of the QTL confidence interval (CI) is essential in order to primarily include the genes responsible for salinity stress tolerance. By considering that, a genome-wide meta-QTL analysis on 768 QTLs from 35 rice populations published from 2001 to 2022 was conducted to identify consensus regions and the candidate genes underlying those regions responsible for the salinity tolerance, as it reduces the confidence interval (CI) to many folds from the initial QTL studies. In the present investigation, a total of 65 MQTLs were extracted with an average CI reduced from 17.35 to 1.66 cM including the smallest of 0.01 cM. Identification of the MQTLs for individual traits and then classifying the target traits into correlated morphological, physiological and biochemical aspects, resulted in more efficient interpretation of the salinity tolerance, identifying the candidate genes and to understand the salinity tolerance mechanism as a whole. The results of this study have a huge potential to improve the rice genotypes for salinity tolerance with the help of MAS and MABC.

Hotspot Regions of Quantitative Trait Loci and Candidate Genes for Ear-Related Traits in Maize: A Literature Review

In this study, hotspot regions, QTL clusters, and candidate genes for eight ear-related traits of maize (ear length, ear diameter, kernel row number, kernel number per row, kernel length, kernel width, kernel thickness, and 100-kernel weight) were summarized and analyzed over the past three decades. This review aims to (1) comprehensively summarize and analyze previous studies on QTLs associated with these eight ear-related traits and identify hotspot bin regions located on maize chromosomes and key candidate genes associated with the ear-related traits and (2) compile major and stable QTLs and QTL clusters from various mapping populations and mapping methods and techniques providing valuable insights for fine mapping, gene cloning, and breeding for high-yield and high-quality maize. Previous research has demonstrated that QTLs for ear-related traits are distributed across all ten chromosomes in maize, and the phenotypic variation explained by a single QTL ranged from 0.40% to 36.76%. In total, 23 QTL hotspot bins for ear-related traits were identified across all ten chromosomes. The most prominent hotspot region is bin 4.08 on chromosome 4 with 15 QTLs related to eight ear-related traits. Additionally, this study identified 48 candidate genes associated with ear-related traits. Out of these, five have been cloned and validated, while twenty-eight candidate genes located in the QTL hotspots were defined by this study. This review offers a deeper understanding of the advancements in QTL mapping and the identification of key candidates associated with eight ear-related traits. These insights will undoubtedly assist maize breeders in formulating strategies to develop higher-yield maize varieties, contributing to global food security.

QTLs and Candidate Genes for Seed Protein Content in Two Recombinant Inbred Line Populations of Soybean

This study aimed to discover the quantitative trait loci (QTL) associated with a high seed protein content in soybean and unravel the potential candidate genes. We developed two recombinant inbred line populations: YS and SI, by crossing Saedanbaek (high protein) with YS2035-B-91-1-B-1 (low protein) and Saedanbaek with Ilmi (low protein), respectively, and evaluated the protein content for three consecutive years. Using single-nucleotide polymorphism (SNP)-marker-based linkage maps, four QTLs were located on chromosomes 15, 18, and 20 with high logarithm of odds values (5.9-55.0), contributing 5.5-66.0% phenotypic variance. In all three experimental years, qPSD20-1 and qPSD20-2 were stable and identified in overlapping positions in the YS and SI populations, respectively. Additionally, novel QTLs were identified on chromosomes 15 and 18. Considering the allelic sequence variation between parental lines, 28 annotated genes related to soybean seed protein-including starch, lipid, and fatty acid biosynthesis-related genes-were identified within the QTL regions. These genes could potentially affect protein accumulation during seed development, as well as sucrose and oil metabolism. Overall, this study offers insights into the genetic mechanisms underlying a high soybean protein content. The identified potential candidate genes can aid marker-assisted selection for developing soybean lines with an increased protein content.

Impact of foliar spray with Se, nano-Se and sodium sulfate on growth, yield and metabolic activities of red kidney bean

Sulfur (S) is an essential microelement for plants. Based on the chemical similarity between Se and S, selenium may affects sulphur uptake by plants. This work aimed at investigating the effect of foliar spray with sodium selenate, gum arabic coated selenium nanoparticles (GA-SeNPs ≈ 48.22 nm) and sodium sulfate on red kidney bean (Phaseolus vulgaris L.) plants. Each treatment was used at 0.0, 1, 5, 10 and 50 µM, alone or combination of sodium sulfate with either Se or nano-Se, each at 0.5, 2.5 and 5 µM concentrations. The effect of foliar spray on vegetative growth, seed quality, and some metabolic constituents of red kidney bean (Phaseolus vulgaris L.) plants were investigated. Selenium nanoparticles have been synthesized through the green route using gum arabic (as a stabilizing and coating agent. Foliar application of different concentrations of Se, nano-Se, Na2SO4 up to 10 μM and their interaction were effective in increasing the growth criteria (i.e. shoot and root lengths, plant fresh and dry weights, number of leaves and photosynthetic area (cm2 plant-1).There was also a significant increase in photosynthetic pigment contents, yield (i.e., 100-seed weight), total carbohydrate, crude proteins and mineral contents in both leaf as compared to their untreated control plants. Furthermore, interaction between sodium sulfate with nano-Se or Se, each at 5 µM significantly increased the vegetative growth, 100-seed weight, and pigment contents in leaves and improved the nutritional value and quality of red kidney bean seeds.

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

Soybean seed protein content (PC) and oil content (OC) have important economic value. Detecting the loci/gene related to PC and OC is important for the marker-assisted selection (MAS) breeding of soybean. To detect the stable and new loci for PC and OC, a total of 320 soybean accessions collected from the major soybean-growing countries were used to conduct a genome-wide association study (GWAS) by resequencing. The PC ranged from 37.8% to 46.5% with an average of 41.1% and the OC ranged from 16.7% to 22.6% with an average of 21.0%. In total, 23 and 29 loci were identified, explaining 3.4%-15.4% and 5.1%-16.3% of the phenotypic variations for PC and OC, respectively. Of these, eight and five loci for PC and OC, respectively, overlapped previously reported loci and the other 15 and 24 loci were newly identified. In addition, nine candidate genes were identified, which are known to be involved in protein and oil biosynthesis/metabolism, including lipid transport and metabolism, signal transduction, and plant development pathway. These results uncover the genetic basis of soybean protein and oil biosynthesis and could be used to accelerate the progress in enhancing soybean PC and OC.

Identification of an important QTL for seed oil content in soybean

Seed oil content is one of the most important quantitative traits in soybean (Glycine max) breeding. Here, we constructed a high-density single nucleotide polymorphism linkage map using two genetically similar parents, Heinong 84 and Kenfeng 17, that differ dramatically in their seed oil contents, and performed quantitative trait loci (QTL) mapping of seed oil content in a recombinant inbred line (RIL) population derived from their cross. We detected five QTL related to seed oil content distributed on five chromosomes. The QTL for seed oil content explained over 10% of the phenotypic variation over two years. This QTL was mapped to an interval containing 20 candidate genes, including a previously reported gene, soybean RING Finger 1a (RNF1a) encoding an E3 ubiquitin ligase. Notably, two short sequences were inserted in the GmRNF1a coding region of KF 17 compared to that of HN 84, resulting in a longer protein variant in KF 17. Our results thus provide information for uncovering the genetic mechanisms determining seed oil content in soybean, as well as identifying an additional QTL and highlighting GmRNF1a as candidate gene for modulating seed oil content in soybean.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01384-2.

Genetic dissection reveals the complex architecture of amino acid composition in soybean seeds

Five loci related to soybean protein and amino acid contents were colocated by performing linkage mapping and GWAS. The haplotype analysis showed that Glyma.08G109100 may be useful to improve the soybean seed composition. Soybean (Glycine max (L.) Merr.) seeds are good protein sources. Although genetic variation is abundant, natural variation in seed amino acids and their derived traits is lacking across soybean accessions. Here, we determined the contents of protein and 17 amino acids, obtained 36 derived traits based on the protein and total amino acid contents, and derived 34 traits based on seven amino acid family groups. Furthermore, we performed a linkage analysis of the contents of 17 amino acids and 73 amino acid-derived traits based on the recombinant inbred line (RIL)-derived Kefeng No. 1 × Nannong 1138-2. Six hundred thirty-nine quantitative trait loci (QTLs) were identified, explaining 6.07-39.00% of the phenotypic variation. Among these loci, five were detected in diverse soybean accessions using a genome-wide association study. A network analysis revealed that some loci that were significantly associated with multiple amino acids were tightly linked on chromosome 8 based on linkage disequilibrium values, which also further confirmed the results of the correlation analysis among amino acid traits. Through a combination of a genome-wide association study, linkage analysis, qRT-PCR, and genomic polymorphism comparison, Glyma.08G109100 on chromosome 8, which may affect amino acid contents, was selected. The haplotype analysis showed that Hap-T of Glyma.08G109100 may be useful to improve the contents of protein and 16 amino acids in soybean. This study provides new insights into the genetic basis of the amino acid composition in soybean seeds and may facilitate marker-based breeding of soybean with improved nutritional value.

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

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.

Delineating meta-quantitative trait loci for anthracnose resistance in common bean (Phaseolus vulgaris L.)

Anthracnose, caused by the fungus Colletotrichum lindemuthianum, is one of the devastating disease affecting common bean production and productivity worldwide. Several quantitative trait loci (QTLs) for anthracnose resistance have been identified. In order to make use of these QTLs in common bean breeding programs, a detailed meta-QTL (MQTL) analysis has been conducted. For the MQTL analysis, 92 QTLs related to anthracnose disease reported in 18 different earlier studies involving 16 mapping populations were compiled and projected on to the consensus map. This meta-analysis led to the identification of 11 MQTLs (each involving QTLs from at least two different studies) on 06 bean chromosomes and 10 QTL hotspots each involving multiple QTLs from an individual study on 07 chromosomes. The confidence interval (CI) of the identified MQTLs was found 3.51 times lower than the CI of initial QTLs. Marker-trait associations (MTAs) reported in published genome-wide association studies (GWAS) were used to validate nine of the 11 identified MQTLs, with MQTL4.1 overlapping with as many as 40 MTAs. Functional annotation of the 11 MQTL regions revealed 1,251 genes including several R genes (such as those encoding for NBS-LRR domain-containing proteins, protein kinases, etc.) and other defense related genes. The MQTLs, QTL hotspots and the potential candidate genes identified during the present study will prove useful in common bean marker-assisted breeding programs and in basic studies involving fine mapping and cloning of genomic regions associated with anthracnose resistance in common beans.

Meta-analysis of QTLs and candidate genes associated with seed germination in rice (Oryza sativa L.)

Seed germination is one of the critical stages of plant life, and many quantitative trait loci (QTLs) control this complex trait. Meta-analysis of QTLs is a powerful computational technique for estimating the most stable QTLs regardless of the population's genetic background. Besides, this analysis effectively narrows down the confidence interval (CI) to identify candidate genes (CGs) and marker development. In the current study, a comprehensive genome-wide meta-analysis was performed on QTLs associated with germination in rice. This analysis was conducted based on the data reported over the last two decades. In this case, various analyses were performed, including seed germination rate, plumule length, radicle length, germination percentage, coleoptile length, coleorhiza length, radicle fresh weight, germination potential, and germination index. A total of 67 QTLs were projected onto a reference map for these traits and then integrated into 32 meta-QTLs (MQTLs) to provide a genetic framework for seed germination. The average CI of MQTLs was considerably reduced from 15.125 to 8.73 cM compared to the initial QTLs. This situation identified 728 well-known functionally characterized genes and novel putative CGs for investigated traits. The fold change calculation demonstrated that 155 CGs had significant changes in expression analysis. In this case, 112 and 43 CGs were up-regulated and down-regulated during germination, respectively. This study provides an overview and compares genetic loci controlling traits related to seed germination in rice. The findings can bridge the gap between QTLs and CGs for seed germination.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-022-01232-1.

Ensuring Global Food Security by Improving Protein Content in Major Grain Legumes Using Breeding and 'Omics' Tools

Grain legumes are a rich source of dietary protein for millions of people globally and thus a key driver for securing global food security. Legume plant-based 'dietary protein' biofortification is an economic strategy for alleviating the menace of rising malnutrition-related problems and hidden hunger. Malnutrition from protein deficiency is predominant in human populations with an insufficient daily intake of animal protein/dietary protein due to economic limitations, especially in developing countries. Therefore, enhancing grain legume protein content will help eradicate protein-related malnutrition problems in low-income and underprivileged countries. Here, we review the exploitable genetic variability for grain protein content in various major grain legumes for improving the protein content of high-yielding, low-protein genotypes. We highlight classical genetics-based inheritance of protein content in various legumes and discuss advances in molecular marker technology that have enabled us to underpin various quantitative trait loci controlling seed protein content (SPC) in biparental-based mapping populations and genome-wide association studies. We also review the progress of functional genomics in deciphering the underlying candidate gene(s) controlling SPC in various grain legumes and the role of proteomics and metabolomics in shedding light on the accumulation of various novel proteins and metabolites in high-protein legume genotypes. Lastly, we detail the scope of genomic selection, high-throughput phenotyping, emerging genome editing tools, and speed breeding protocols for enhancing SPC in grain legumes to achieve legume-based dietary protein security and thus reduce the global hunger risk.

POWR1 is a domestication gene pleiotropically regulating seed quality and yield in soybean

Seed protein, oil content and yield are highly correlated agronomically important traits that essentially account for the economic value of soybean. The underlying molecular mechanisms and selection of these correlated seed traits during soybean domestication are, however, less known. Here, we demonstrate that a CCT gene, POWR1, underlies a large-effect protein/oil QTL. A causative TE insertion truncates its CCT domain and substantially increases seed oil content, weight, and yield while decreasing protein content. POWR1 pleiotropically controls these traits likely through regulating seed nutrient transport and lipid metabolism genes. POWR1 is also a domestication gene. We hypothesize that the TE insertion allele is exclusively fixed in cultivated soybean due to selection for larger seeds during domestication, which significantly contributes to shaping soybean with increased yield/seed weight/oil but reduced protein content. This study provides insights into soybean domestication and is significant in improving seed quality and yield in soybean and other crop species.

Multiomics approach reveals a role of translational machinery in shaping maize kernel amino acid composition

Maize (Zea mays) seeds are a good source of protein, despite being deficient in several essential amino acids. However, eliminating the highly abundant but poorly balanced seed storage proteins has revealed that the regulation of seed amino acids is complex and does not rely on only a handful of proteins. In this study, we used two complementary omics-based approaches to shed light on the genes and biological processes that underlie the regulation of seed amino acid composition. We first conducted a genome-wide association study to identify candidate genes involved in the natural variation of seed protein-bound amino acids. We then used weighted gene correlation network analysis to associate protein expression with seed amino acid composition dynamics during kernel development and maturation. We found that almost half of the proteome was significantly reduced during kernel development and maturation, including several translational machinery components such as ribosomal proteins, which strongly suggests translational reprogramming. The reduction was significantly associated with a decrease in several amino acids, including lysine and methionine, pointing to their role in shaping the seed amino acid composition. When we compared the candidate gene lists generated from both approaches, we found a nonrandom overlap of 80 genes. A functional analysis of these genes showed a tight interconnected cluster dominated by translational machinery genes, especially ribosomal proteins, further supporting the role of translation dynamics in shaping seed amino acid composition. These findings strongly suggest that seed biofortification strategies that target the translation machinery dynamics should be considered and explored further.

Integrating omics approaches to discover and prioritize candidate genes involved in oil biosynthesis in soybean

Soybean is a major source of edible protein and oil. Oil content is a quantitative trait that is significantly determined by genetic and environmental factors. Over the past 30 years, a large volume of soybean genetic, genomic, and transcriptomic data have been accumulated. Nevertheless, integrative analyses of such data remain scarce, in spite of their importance for crop improvement. We hypothesized that the co-occurrence of genomic regions for oil-related traits in different studies may reveal more stable regions encompassing important genetic determinants of oil content and quality in soybean. We integrated publicly available data, obtained with distinct techniques, to discover and prioritize candidate genes involved in oil biosynthesis and regulation in soybean. We detected key fatty acid biosynthesis genes (e.g., BCCP2 and ACCase, FADs, KAS family proteins) and several transcription factors, which are likely regulators of oil biosynthesis. In addition, we identified new candidates for seed oil accumulation and quality, such as Glyma.03G213300 and Glyma.19G160700, which encode a translocator protein homolog and a histone acetyltransferase, respectively. Further, oil and protein genomic hotspots are strongly associated with breeding and not with domestication, suggesting that soybean domestication prioritized other traits. The genes identified here are promising targets for breeding programs and for the development of soybean lines with increased oil content and quality.

Protein Biofortification in Lentils (Lens culinaris Medik.) Toward Human Health

Lentil (Lens culinaris Medik.) is a nutritionally dense crop with significant quantities of protein, low-digestible carbohydrates, minerals, and vitamins. The amino acid composition of lentil protein can impact human health by maintaining amino acid balance for physiological functions and preventing protein-energy malnutrition and non-communicable diseases (NCDs). Thus, enhancing lentil protein quality through genetic biofortification, i.e., conventional plant breeding and molecular technologies, is vital for the nutritional improvement of lentil crops across the globe. This review highlights variation in protein concentration and quality across Lens species, genetic mechanisms controlling amino acid synthesis in plants, functions of amino acids, and the effect of antinutrients on the absorption of amino acids into the human body. Successful breeding strategies in lentils and other pulses are reviewed to demonstrate robust breeding approaches for protein biofortification. Future lentil breeding approaches will include rapid germplasm selection, phenotypic evaluation, genome-wide association studies, genetic engineering, and genome editing to select sequences that improve protein concentration and quality.

Meta-QTL Analysis in Rice and Cross-Genome Talk of the Genomic Regions Controlling Nitrogen Use Efficiency in Cereal Crops Revealing Phylogenetic Relationship

The phenomenal increase in the use of nitrogenous fertilizers coupled with poor nitrogen use efficiency is among the most important threats to the environment, economic, and social health. During the last 2 decades, a number of genomic regions associated with nitrogen use efficiency (NUE) and related traits have been reported by different research groups, but none of the stable and major effect QTL have been utilized in the marker-assisted introgression/pyramiding program. Compiling the data available in the literature could be very useful in identifying stable and major effect genomic regions associated with the root and NUE-related trait improving the rice grain yield. In the present study, we performed meta-QTL analysis on 1,330 QTL from 29 studies published in the past 2 decades. A total of 76 MQTL with a stable effect over different genetic backgrounds and environments were identified. The significant reduction in the confidence interval of the MQTL compared to the initial QTL resulted in the identification of annotated and putative candidate genes related to the traits considered in the present study. A hot spot region associated with correlated traits on chr 1, 4, and 8 and candidate genes associated with nitrate transporters, nitrogen content, and ammonium uptake on chromosomes 2, 4, 6, and 8 have been identified. The identified MQTL, putative candidate genes, and their orthologues were validated on our previous studies conducted on rice and wheat. The research-based interventions such as improving nitrogen use efficiency via identification of major genomic regions and candidate genes can be a plausible, simple, and low-cost solution to address the challenges of the crop improvement program.

Training set design in genomic prediction with multiple biparental families

Genomic selection is a powerful tool to reduce the cycle length and enhance the genetic gain of complex traits in plant breeding. However, questions remain about the optimum design and composition of the training set. In this study, we used 944 soybean [Glycine max (L.) Merr.] recombinant inbred lines from eight families derived through a partial-diallel mating design among five parental lines. The cross-validated prediction accuracies for the six traits seed yield, 1,000-seed weight, protein yield, plant height, protein content, and oil content were high, ranging from 0.79 to 0.87. We investigated among-family predictions, making use of the special mating design with different degrees of relatedness among families. Generally, the prediction accuracy decreased from full-sibs to half-sib families to unrelated families. However, half-sib and unrelated families also showed substantial variation in their prediction accuracy for a given family, which appeared to be caused at least in part by the shared segregation of quantitative trait loci in both the training and prediction sets. Combining several half-sib families in composite training sets generally led to an increase in the prediction accuracy compared with the best family alone. The prediction accuracy increased with the size of the training set, but for comparable prediction accuracy, substantially more half-sibs were required than full-sibs. Collectively, our results highlight the potential of genomic selection for soybean breeding and, in a broader context, illustrate the importance of the targeted design of the training set.

Global analysis of lysine acetylation in soybean leaves

Protein lysine acetylation (Kac) is an important post-translational modification in both animal and plant cells. Global Kac identification has been performed at the proteomic level in various species. However, the study of Kac in oil and resource plant species is relatively limited. Soybean is a globally important oil crop and resouce plant. In the present study, lysine acetylome analysis was performed in soybean leaves with proteomics techniques. Various bioinformatics analyses were performed to illustrate the structure and function of these Kac sites and proteins. Totally, 3148 acetylation sites in 1538 proteins were detected. Motif analysis of these Kac modified peptides extracted 17 conserved motifs. These Kac modified protein showed a wide subcellular location and functional distribution. Chloroplast is the primary subcellular location and cellular component where Kac proteins were localized. Function and pathways analyses indicated a plenty of biological processes and metabolism pathways potentially be influenced by Kac modification. Ribosome activity and protein biosynthesis, carbohydrate and energy metabolism, photosynthesis and fatty acid metabolism may be regulated by Kac modification in soybean leaves. Our study suggests Kac plays an important role in soybean physiology and biology, which is an available resource and reference of Kac function and structure characterization in oil crop and resource plant, as well as in plant kingdom.

Genetic mapping high protein content QTL from soybean 'Nanxiadou 25' and candidate gene analysis

BACKGROUND

Soybean is a globally important legume crop that provides a primary source of high-quality vegetable protein and oil. Seed protein content (SPC) is a valuable quality trait controlled by multiple genes in soybean.

RESULTS

In this study, we performed quantitative trait loci (QTL) mapping, QTL-seq, and RNA sequencing (RNA-seq) to reveal the genes controlling protein content in the soybean by using the high protein content variety Nanxiadou 25. A total of 50 QTL for SPC distributed on 14 chromosomes except chromosomes 4, 12, 14, 17, 18, and 19 were identified by QTL mapping using 178 recombinant inbred lines (RILs). Among these QTL, the major QTL qSPC_20-1 and qSPC_20-2 on chromosome 20 were repeatedly detected across six tested environments, corresponding to the location of the major QTL detected using whole-genome sequencing-based QTL-seq. 329 candidate DEGs were obtained within the QTL region of qSPC_20-1 and qSPC_20-2 via gene expression profile analysis. Nine of which were associated with SPC, potentially representing candidate genes. Clone sequencing results showed that different single nucleotide polymorphisms (SNPs) and indels between high and low protein genotypes in Glyma.20G088000 and Glyma.16G066600 may be the cause of changes in this trait.

CONCLUSIONS

These results provide the basis for research on candidate genes and marker-assisted selection (MAS) in soybean breeding for seed protein content.

Genetic loci and causal genes for seed fatty acids accumulation across multiple environments and genetic backgrounds in soybean

Soybean is a major oil crop in the world, and fatty acids are the predominant components for oil bio-synthesis and catabolism metabolisms and also are the most important energy resources for organisms. In view of this, two recombinant inbred line (RIL) populations (ZL-RIL and ZQ-RIL) and one natural population were evaluated for five individual seed fatty acid contents (palmitic acid, stearic acid, oleic acid, linoleic acid, and linolenic acid) under four different environments, simultaneously. In total, sixteen additive QTL clusters were identified in ZL-RIL population, and fifteen were stably expressed across multiple environments or had pleiotropic effects on various fatty acid contents. Furthermore, five and five of these 16 QTL clusters were verified in ZQ-RIL population and natural population, respectively. Among these consistent and stable QTL clusters, one QTL cluster controlling fatty acid on chromosome 5 with pleiotropic effect was identified under all of the environments in ZL-RIL and ZQ-RIL populations and also was validated in the natural population. Meanwhile, another stable QTL cluster was detected on chromosome 9 with pleiotropic effect under multiple environments in ZL-RIL population and was further verified by the natural population. More importantly, some causal genes, such as the genes on chromosome 9, involving in the fatty acid catabolism process were found in these stable QTL clusters, and some of them, such as Gm09G042000, Gm09G041500, and Gm09G047200 on chromosome 9, showed different expressions in ZL-RIL parents (Zheng92116 and Liaodou14) based on the transcriptome sequencing analysis at different seed developmental stages. Thus, the study results provided insights into the genetic basis and molecular markers for regulating seed fatty acid contents in soybean breeding program.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01227-y.

Identification of SNP markers associated with soybean fatty acids contents by genome-wide association analyses

Composition of fatty acids (FAs) in soybean seed is important for the quality and uses of soybean oil. Using gas chromatography, we have measured soybean FAs profiles of 621 soybean accessions (maturity groups I through IV) grown in five different environments; Columbus, OH (2015), Wooster, OH (2014 and 2015), Plymouth, NC (2015), and Urbana, IL (2015). Using publicly available SoySNP50K genotypic data and the FA profiles from this study, a genome-wide association analysis was completed with a compressed mixed linear model to identify 43 genomic regions significantly associated with a fatty acid at a genome wide significance threshold of 5%. Among these regions, one and three novel genomic regions associated with palmitic acid and stearic acid, respectively, were identified across all five environments. Additionally, nine novel environment-specific FA-related genomic regions were discovered providing new insights into the genetics of soybean FAs. Previously reported FA-related loci, such as FATB1a, SACPD-C, and KASIII, were also confirmed in this study. Our results will be useful for future functional studies and marker-assisted breeding for soybean FAs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01216-1.

Meta-QTL and ortho-MQTL analyses identified genomic regions controlling rice yield, yield-related traits and root architecture under water deficit conditions

Meta-QTL (MQTL) analysis is a robust approach for genetic dissection of complex quantitative traits. Rice varieties adapted to non-flooded cultivation are highly desirable in breeding programs due to the water deficit global problem. In order to identify stable QTLs for major agronomic traits under water deficit conditions, we performed a comprehensive MQTL analysis on 563 QTLs from 67 rice populations published from 2001 to 2019. Yield and yield-related traits including grain weight, heading date, plant height, tiller number as well as root architecture-related traits including root dry weight, root length, root number, root thickness, the ratio of deep rooting and plant water content under water deficit condition were investigated. A total of 61 stable MQTLs over different genetic backgrounds and environments were identified. The average confidence interval of MQTLs was considerably refined compared to the initial QTLs, resulted in the identification of some well-known functionally characterized genes and several putative novel CGs for investigated traits. Ortho-MQTL mining based on genomic collinearity between rice and maize allowed identification of five ortho-MQTLs between these two cereals. The results can help breeders to improve yield under water deficit conditions.

Characterization of the Common Genetic Basis Underlying Seed Hilum Size, Yield, and Quality Traits in Soybean

Developing high yielding cultivars with outstanding quality traits are perpetual objectives throughout crop breeding operations. Confoundingly, both of these breeding objectives typically involve working with complex quantitative traits that can be affected by genetic and environmental factors. Establishing correlations of these complex traits with more easily identifiable and highly heritable traits can simplify breeding processes. In this study, two parental soybean genotypes contrasting in seed hilum size, yield, and seed quality, as well as 175 F₉ recombinant inbred lines (RILs) derived from these parents, were grown in 3 years. The h² _b of four hilum size, two quality and two yield traits, ranged from 0.72 to 0.87. The four observed hilum size traits exhibited significant correlation (P < 0.05) with most of seed yield and quality traits, as indicated by correlation coefficients varying from -0.35 to 0.42, which suggests that hilum size could be considered as a proxy trait for soybean yield and quality. Interestingly, among 53 significant quantitative trait loci (QTLs) with logarithm of odds (LOD) values ranging from 2.51 to 6.69 and accounting for 6.40-16.10% of genetic variation, three loci encoding hilum size, qSH6.2, qSH8, and qSH10, colocated with QTLs for seed yield and quality traits, demonstrating that genes impacting seed hilum size colocalize in part with genes acting on soybean yield and quality. As a result of the breeding efforts and field observations described in this work, it is reasonable to conclude that optimizing hilum size through selection focused on a few QTLs may be useful for breeding new high yielding soybean varieties with favorable quality characteristics.

Genome-wide association mapping for protein, oil and water-soluble protein contents in soybean

As a globally important legume crop, soybean provides excellent sources of protein and oil for human and livestock nutrition. Improving seed protein and oil contents has always been an important objective in soybean breeding. Water-soluble protein plays a significant role in the processing and efficacy of soybean protein. Here, a genome-wide association study (GWAS) of seed compositions (protein, oil, and water-soluble protein contents) was conducted using 211 diverse soybean accessions genotyped with a 355 K SoySNP array. Three, four, and five QTLs were identified related to the protein, oil, and water-soluble protein contents, respectively. Furthermore, five QTLs (qPC-15-1, qOC-8-1, qOC-12-1, qOC-20-1 and qWSPC-8-1) were detected in multiple environments. Analysis of the favorable alleles for oil and water-soluble protein contents showed that qOC-8-1 (qWSPC-8-1) exerted inverse effects on oil and water-soluble protein synthesis. Relative expression analysis suggested that Glyma.15G049200 in qPC-15-1 affects protein synthesis and Glyma.08G107800 in qOC-8-1 and qWSPC-8-1 might be involved in oil and water-soluble protein synthesis, producing opposite effects. The candidate genes and significant SNPs detected in the present study will allow a deeper understanding of the genetic basis for the regulation of protein, oil and water-soluble protein contents and provide important information that could be utilized in marker-assisted selection for soybean quality improvement.

Selection of GmSWEET39 for oil and protein improvement in soybean

Soybean [Glycine max (L.) Merr.] was domesticated from wild soybean (G. soja Sieb. and Zucc.) and has been further improved as a dual-use seed crop to provide highly valuable oil and protein for food, feed, and industrial applications. However, the underlying genetic and molecular basis remains less understood. Having combined high-confidence bi-parental linkage mapping with high-resolution association analysis based on 631 whole sequenced genomes, we mapped major soybean protein and oil QTLs on chromosome15 to a sugar transporter gene (GmSWEET39). A two-nucleotide CC deletion truncating C-terminus of GmSWEET39 was strongly associated with high seed oil and low seed protein, suggesting its pleiotropic effect on protein and oil content. GmSWEET39 was predominantly expressed in parenchyma and integument of the seed coat, and likely regulates oil and protein accumulation by affecting sugar delivery from maternal seed coat to the filial embryo. We demonstrated that GmSWEET39 has a dual function for both oil and protein improvement and undergoes two different paths of artificial selection. A CC deletion (CC-) haplotype H1 has been intensively selected during domestication and extensively used in soybean improvement worldwide. H1 is fixed in North American soybean cultivars. The protein-favored (CC+) haplotype H3 still undergoes ongoing selection, reflecting its sustainable role for soybean protein improvement. The comprehensive knowledge on the molecular basis underlying the major QTL and GmSWEET39 haplotypes associated with soybean improvement would be valuable to design new strategies for soybean seed quality improvement using molecular breeding and biotechnological approaches.

Three-dimensional genetic networks among seed oil-related traits, metabolites and genes reveal the genetic foundations of oil synthesis in soybean

Although the biochemical and genetic basis of lipid metabolism is clear in Arabidopsis, there is limited information concerning the relevant genes in Glycine max (soybean). To address this issue, we constructed three-dimensional genetic networks using six seed oil-related traits, 52 lipid metabolism-related metabolites and 54 294 SNPs in 286 soybean accessions in total. As a result, 284 and 279 candidate genes were found to be significantly associated with seed oil-related traits and metabolites by phenotypic and metabolic genome-wide association studies and multi-omics analyses, respectively. Using minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) analyses, six seed oil-related traits were found to be significantly related to 31 metabolites. Among the above candidate genes, 36 genes were found to be associated with oil synthesis (27 genes), amino acid synthesis (four genes) and the tricarboxylic acid (TCA) cycle (five genes), and four genes (GmFATB1a, GmPDAT, GmPLDα1 and GmDAGAT1) are already known to be related to oil synthesis. Using this information, 133 three-dimensional genetic networks were constructed, 24 of which are known, e.g. pyruvate-GmPDAT-GmFATA2-oil content. Using these networks, GmPDAT, GmAGT and GmACP4 reveal the genetic relationships between pyruvate and the three major nutrients, and GmPDAT, GmZF351 and GmPgs1 reveal the genetic relationships between amino acids and seed oil content. In addition, GmCds1, along with average temperature in July and the rainfall from June to September, influence seed oil content across years. This study provides a new approach for the construction of three-dimensional genetic networks and reveals new information for soybean seed oil improvement and the identification of gene function.

Biofabrication of zinc oxide nanoparticles from Melia azedarach and its potential in controlling soybean seed-borne phytopathogenic fungi

Present study, report the biofabrication of zinc oxide nanoparticles from aqueous leaf extract of Melia azedarach (MaZnO-NPs) through solution combustion method and their novel application in preventing the growth of seed-borne fungal pathogens of soybean (Cladosporium cladosporioides and Fusarium oxysporum). The standard blotter method was employed to isolate fungi and was identified through molecular techniques. The characterization of MaZnO-NPs was carried out by UV–Vis spectroscopy, Fourier Transform Infrared Spectroscopy (FT-IR), X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM) equipped with Energy Dispersive Spectroscopy (EDS) and Transmission Electron Microscopy (TEM). The physicochemical characterization confirmed the particles were of high purity and nano size (30–40 nm) with a hexagonal shape. The synthesized MaZnO-NPs inhibited the growth of C. cladosporioides and F. oxysporum in a dose dependent manner. Biomass, ergosterol, lipid peroxidation, intracellular reactive oxygen species and membrane integrity determination upon MaZnO-NPs treatment offered significant activities there by confirming the mechanism of action against the test pathogens. In conclusion, due to the effectiveness of MaZnO-NPs in controlling the growth of C. cladosporioides and F. oxysporum, the synthesized MaZnO-NPs provides insight towards their potential application in agriculture and food industries.

Genetic Analysis of High Protein Content in 'AC Proteus' Related Soybean Populations Using SSR, SNP, DArT and DArTseq Markers

Key message: Several AC Proteus derived genomic regions (QTLs, SNPs) have been identified which may prove useful for further development of high yielding high protein cultivars and allele-specific marker developments. High seed protein content is a trait which is typically difficult to introgress into soybean without an accompanying reduction in seed yield. In a previous study, 'AC Proteus' was used as a high protein source and was found to produce populations that did not exhibit the typical association between high protein and low yield. Five high x low protein RIL populations and a high x high protein RIL population were evaluated by either quantitative trait locus (QTL) analysis or bulk segregant analyses (BSA) following phenotyping in the field. QTL analysis in one population using SSR, DArT and DArTseq markers found two QTLs for seed protein content on chromosomes 15 and 20. The BSA analyses suggested multiple genomic regions are involved with high protein content across the five populations, including the two previously mentioned QTLs. In an alternative approach to identify high protein genes, pedigree analysis identified SNPs for which the allele associated with high protein was retained in seven high protein descendants of AC Proteus on chromosomes 2, 17 and 18. Aside from the two identified QTLs (five genomic regions in total considering the two with highly elevated test statistic, but below the statistical threshold and the one with epistatic interactions) which were some distance from Meta-QTL regions and which were also supported by our BSA analysis within five populations. These high protein regions may prove useful for further development of high yielding high protein cultivars.

A Combined Linkage and GWAS Analysis Identifies QTLs Linked to Soybean Seed Protein and Oil Content

Soybean is an excellent source of vegetable protein and edible oil. Understanding the genetic basis of protein and oil content will improve the breeding programs for soybean. Linkage analysis and genome-wide association study (GWAS) tools were combined to detect quantitative trait loci (QTL) that are associated with protein and oil content in soybean. Three hundred and eight recombinant inbred lines (RILs) containing 3454 single nucleotide polymorphism (SNP) markers and 200 soybean accessions, including 94,462 SNPs and indels, were applied to identify QTL intervals and significant SNP loci. Intervals on chromosomes 1, 15, and 20 were correlated with both traits, and QTL qPro15-1, qPro20-1, and qOil5-1 reproducibly correlated with large phenotypic variations. SNP loci on chromosome 20 that overlapped with qPro20-1 were reproducibly connected to both traits by GWAS (p < 10-4). Twenty-five candidate genes with putative roles in protein and/or oil metabolisms within two regions (qPro15-1, qPro20-1) were identified, and eight of these genes showed differential expressions in parent lines during late reproductive growth stages, consistent with a role in controlling protein and oil content. The new well-defined QTL should significantly improve molecular breeding programs, and the identified candidate genes may help elucidate the mechanisms of protein and oil biosynthesis.

Meta-analysis of grain iron and zinc associated QTLs identified hotspot chromosomal regions and positional candidate genes for breeding biofortified rice

Biofortification of staple crops with essential micronutrients is the sustainable way to overcome the hidden hunger. A large number of quantitative trait loci (QTL) linked with grain micronutrient contents have been reported in different mapping studies. Identification of consistent QTLs across diverse genetic backgrounds is useful for candidate gene analysis and marker assisted selection of target traits. In this study, an up to date meta-analysis of grain iron and zinc associated QTLs was performed and 48 meta-QTLs (MQTLs) distributed across 12 rice chromosomes were identified. The 95% confidence intervals of identified genomic regions were significantly narrower than the average of their corresponding original QTLs. A total of 9308 genes/transcripts physically located within or near MQTL regions were retrieved and through prioritization of candidate genes (CGs) 663 non-redundant iron and zinc CGs were selected and studied in detailed. Several functionally characterized iron and zinc homoeostasis related genes e.g OsATM3, OsDMAS1, OsFRO2, OsNAS1-3, OsVIT2, OsYSL16, OsZIP3 and OsZIP7 were also included in our MQTL analysis. More than 64% genes were enriched with zinc and iron binding gene ontology terms and were involved in oxidation reduction process, carbohydrate metabolic process, regulation of transcription, trans-membrane transport, response to oxidative stress, cell redox homeostasis and proteolysis etc. In-silico transcriptomic analysis of rice identified 260 CGs which were regulated in response to iron and zinc stresses. We also identified at least 37 genes which were differentially expressed under both stress conditions and majority of these have not been studied in detailed before. Our results strongly indicate that majority of the MQTLs identified in this study are hotspots for grain iron and zinc concentration and are worth of intensive functional studies in near future.

Selection of candidate genes controlling veraison time in grapevine through integration of meta-QTL and transcriptomic data

BACKGROUND

High temperature during grape berry ripening impairs the quality of fruits and wines. Veraison time, which marks ripening onset, is a key factor for determining climatic conditions during berry ripening. Understanding its genetic control is crucial to successfully breed varieties more adapted to a changing climate. Quantitative trait loci (QTL) studies attempting to elucidate the genetic determinism of developmental stages in grapevine have identified wide genomic regions. Broad scale transcriptomic studies, by identifying sets of genes modulated during berry development and ripening, also highlighted a huge number of putative candidates.

RESULTS

With the final aim of providing an overview about available information on the genetic control of grapevine veraison time, and prioritizing candidates, we applied a meta-QTL analysis for grapevine phenology-related traits and checked for co-localization of transcriptomic candidates. A consensus genetic map including 3130 markers anchored to the grapevine genome assembly was compiled starting from 39 genetic maps. Two thousand ninety-three QTLs from 47 QTL studies were projected onto the consensus map, providing a comprehensive overview about distribution of available QTLs and revealing extensive co-localization especially across phenology related traits. From 141 phenology related QTLs we generated 4 veraison meta-QTLs located on linkage group (LG) 1 and 2, and 13 additional meta-QTLs connected to the veraison time genetic control, among which the most relevant were located on LG 14, 16 and 18. Functional candidates in these intervals were inspected. Lastly, taking advantage of available transcriptomic datasets, expression data along berry development were integrated, in order to pinpoint among positional candidates, those differentially expressed across the veraison transition.

CONCLUSION

Integration of meta-QTLs analysis on available phenology related QTLs and data from transcriptomic dataset allowed to strongly reduce the number of candidate genes for the genetic control of the veraison transition, prioritizing a list of 272 genes, among which 78 involved in regulation of gene expression, signal transduction or development.

Artificial selection on GmOLEO1 contributes to the increase in seed oil during soybean domestication

Increasing seed oil content is one of the most important breeding goals for soybean due to a high global demand for edible vegetable oil. However, genetic improvement of seed oil content has been difficult in soybean because of the complexity of oil metabolism. Determining the major variants and molecular mechanisms conferring oil accumulation is critical for substantial oil enhancement in soybean and other oilseed crops. In this study, we evaluated the seed oil contents of 219 diverse soybean accessions across six different environments and dissected the underlying mechanism using a high-resolution genome-wide association study (GWAS). An environmentally stable quantitative trait locus (QTL), GqOil20, significantly associated with oil content was identified, accounting for 23.70% of the total phenotypic variance of seed oil across multiple environments. Haplotype and expression analyses indicate that an oleosin protein-encoding gene (GmOLEO1), colocated with a leading single nucleotide polymorphism (SNP) from the GWAS, was significantly correlated with seed oil content. GmOLEO1 is predominantly expressed during seed maturation, and GmOLEO1 is localized to accumulated oil bodies (OBs) in maturing seeds. Overexpression of GmOLEO1 significantly enriched smaller OBs and increased seed oil content by 10.6% compared with those of control seeds. A time-course transcriptomics analysis between transgenic and control soybeans indicated that GmOLEO1 positively enhanced oil accumulation by affecting triacylglycerol metabolism. Our results also showed that strong artificial selection had occurred in the promoter region of GmOLEO1, which resulted in its high expression in cultivated soybean relative to wild soybean, leading to increased seed oil accumulation. The GmOLEO1 locus may serve as a direct target for both genetic engineering and selection for soybean oil improvement.

Genome-wide association study of seed protein, oil and amino acid contents in soybean from maturity groups I to IV

KEY MESSAGE

Genomic regions associated with seed protein, oil and amino acid contents were identified by genome-wide association analyses. Geographic distributions of haplotypes indicate scope of improvement of these traits. Soybean [Glycine max (L.) Merr.] protein and oil are used worldwide in feed, food and industrial materials. Increasing seed protein and oil contents is important; however, protein content is generally negatively correlated with oil content. We conducted a genome-wide association study using phenotypic data collected from five environments for 621 accessions in maturity groups I-IV and 34,014 markers to identify quantitative trait loci (QTL) for seed content of protein, oil and several essential amino acids. Three and five genomic regions were associated with seed protein and oil contents, respectively. One, three, one and four genomic regions were associated with cysteine, methionine, lysine and threonine content (g kg-1 crude protein), respectively. As previously shown, QTL on chromosomes 15 and 20 were associated with seed protein and oil contents, with both exhibiting opposite effects on the two traits, and the chromosome 20 QTL having the most significant effect. A multi-trait mixed model identified trait-specific QTL. A QTL on chromosome 5 increased oil with no effect on protein content, and a QTL on chromosome 10 increased protein content with little effect on oil content. The chromosome 10 QTL co-localized with maturity gene E2/GmGIa. Identification of trait-specific QTL indicates feasibility to reduce the negative correlation between protein and oil contents. Haplotype blocks were defined at the QTL identified on chromosomes 5, 10, 15 and 20. Frequencies of positive effect haplotypes varied across maturity groups and geographic regions, providing guidance on which alleles have potential to contribute to soybean improvement for specific regions.

Common Bean (Phaseolus vulgaris L.) Accumulates Most S-Methylcysteine as Its γ-Glutamyl Dipeptide

The common bean (Phaseolus vulgaris) constitutes an excellent source of vegetable dietary protein. However, there are sub-optimal levels of the essential amino acids, methionine and cysteine. On the other hand, P. vulgaris accumulates large amounts of the γ-glutamyl dipeptide of S-methylcysteine, and lower levels of free S-methylcysteine and S-methylhomoglutathione. Past results suggest two distinct metabolite pools. Free S-methylcysteine levels are high at the beginning of seed development and decline at mid-maturation, while there is a biphasic accumulation of γ-glutamyl-S-methylcysteine, at early cotyledon and maturation stages. A possible model involves the formation of S-methylcysteine by cysteine synthase from O-acetylserine and methanethiol, whereas the majority of γ-glutamyl-S-methylcysteine may arise from S-methylhomoglutathione. Metabolite profiling during development and in genotypes differing in total S-methylcysteine accumulation showed that γ-glutamyl-S-methylcysteine accounts for most of the total S-methylcysteine in mature seed. Profiling of transcripts for candidate biosynthetic genes indicated that BSAS4;1 expression is correlated with both the developmental timing and levels of free S-methylcysteine accumulated, while homoglutathione synthetase (hGS) expression was correlated with the levels of γ-glutamyl-S-methylcysteine. Analysis of S-methylated phytochelatins by liquid chromatography and high resolution tandem mass spectrometry revealed only small amounts of homophytochelatin-2 with a single S-methylcysteine. The mitochondrial localization of phytochelatin synthase 2—predominant in seed, determined by confocal microscopy of a fusion with the yellow fluorescent protein—and its spatial separation from S-methylhomoglutathione may explain the lack of significant accumulation of S-methylated phytochelatins.

Meta-analysis and transcriptome profiling reveal hub genes for soybean seed storage composition during seed development

Soybean is an important crop providing edible oil and protein source. Soybean oil and protein contents are quantitatively inherited and significantly affected by environmental factors. In this study, meta-analysis was conducted based on soybean physical maps to integrate quantitative trait loci (QTLs) from multiple experiments in different environments. Meta-QTLs for seed oil, fatty acid composition, and protein were identified. Of them, 11 meta-QTLs were located on hot regions for both seed oil and protein. Next, we selected 4 chromosome segment substitution lines with different seed oil and protein contents to characterize their 3 years of phenotype selection in the field. Using strand-specific RNA-sequencing analysis, we profile the time-course transcriptome patterns of soybean seeds at early maturity, middle maturity, and dry seed stages. Pairwise comparison and K-means clustering analysis revealed 7,482 differentially expressed genes and 45 expression patterns clusters. Weighted gene coexpression network analysis uncovered 46 modules of gene expression patterns. The 2 most significant coexpression networks were visualized, and 7 hub genes were identified that were involved in soybean oil and seed storage protein accumulation processes. Our results provided a transcriptome dataset for soybean seed development, and the candidate hub genes represent a foundation for further research.

Meta-QTL analysis of seed iron and zinc concentration and content in common bean (Phaseolus vulgaris L.)

Twelve meta-QTL for seed Fe and Zn concentration and/or content were identified from 87 QTL originating from seven population grown in sixteen field trials. These meta-QTL include 2 specific to iron, 2 specific to zinc and 8 that co-localize for iron and zinc concentrations and/or content. Common bean (Phaseolus vulgaris L.) is the most important legume for human consumption worldwide and it is an important source of microelements, especially iron and zinc. Bean biofortification breeding programs develop new varieties with high levels of Fe and Zn targeted for countries with human micronutrient deficiencies. Biofortification efforts thus far have relied on phenotypic selection of raw seed mineral concentrations in advanced generations. While numerous quantitative trait loci (QTL) studies have been conducted to identify genomic regions associated with increased Fe and Zn concentration in seeds, these results have yet to be employed for marker-assisted breeding. The objective of this study was to conduct a meta-analysis from seven QTL studies in Andean and Middle American intra- and inter-gene pool populations to identify the regions in the genome that control the Fe and Zn levels in seeds. Two meta-QTL specific to Fe and two meta-QTL specific to Zn were identified. Additionally, eight Meta QTL that co-localized for Fe and Zn concentration and/or content were identified across seven chromosomes. The Fe and Zn shared meta-QTL could be useful candidates for marker-assisted breeding to simultaneously increase seed Fe and Zn. The physical positions for 12 individual meta-QTL were identified and within five of the meta-QTL, candidate genes were identified from six gene families that have been associated with transport of iron and zinc in plants.

Maize Combined Insect Resistance Genomic Regions and Their Co-localization With Cell Wall Constituents Revealed by Tissue-Specific QTL Meta-Analyses

Combinatorial insect attacks on maize leaves, stems, and kernels cause significant yield losses and mycotoxin contaminations. Several small effect quantitative trait loci (QTL) control maize resistance to stem borers and storage pests and are correlated with secondary metabolites. However, efficient use of QTL in molecular breeding requires a synthesis of the available resistance information. In this study, separate meta-analyses of QTL of maize response to stem borers and storage pests feeding on leaves, stems, and kernels along with maize cell wall constituents discovered in these tissues generated 24 leaf (LIR), 42 stem (SIR), and 20 kernel (KIR) insect resistance meta-QTL (MQTL) of a diverse genetic and geographical background. Most of these MQTL involved resistance to several insect species, therefore, generating a significant interest for multiple-insect resistance breeding. Some of the LIR MQTL such as LIR4, 17, and 22 involve resistance to European corn borer, sugarcane borer, and southwestern corn borer. Eleven out of the 42 SIR MQTL related to resistance to European corn borer and Mediterranean corn borer. There KIR MQTL, KIR3, 15, and 16 combined resistance to kernel damage by the maize weevil and the Mediterranean corn borer and could be used in breeding to reduce insect-related post-harvest grain yield loss and field to storage mycotoxin contamination. This meta-analysis corroborates the significant role played by cell wall constituents in maize resistance to insect since the majority of the MQTL contain QTL for members of the hydroxycinnamates group such as p-coumaric acid, ferulic acid, and other diferulates and derivates, and fiber components such as acid detergent fiber, neutral detergent fiber, and lignin. Stem insect resistance MQTL display several co-localization between fiber and hydroxycinnamate components corroborating the hypothesis of cross-linking between these components that provide mechanical resistance to insect attacks. Our results highlight the existence of combined-insect resistance genomic regions in maize and set the basis of multiple-pests resistance breeding.

Impact of Environment on the Biomass Composition of Soybean (Glycine max) seeds

Factors including genetics, fertilization, and climatic conditions, can alter the biomass composition of soybean seeds, consequently impacting their market value and usage. This study specifically determined the content of protein and oil, as well as the composition of proteinogenic amino acids and fatty acids in seeds from 10 diverse soybean cultivars grown in four different sites. The results highlighted that different environments produce a different composition for the 10 cultivars under investigation. Specifically, the levels of oleic and linoleic acids, important contributors to oil stability, were negatively correlated. Although the protein and oil contents were higher in some locations, their "quality" was lower in terms of composition of essential amino acids and oleic acid, respectively. Finally, proteinogenic histidine and glutamate were the main contributors to the separation between Central and Northern growing sites. Taken together, these results can guide future breeding and engineering efforts aiming to develop specialized soybean lines.