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Peanut (Arachis hypogaea L.) is a great plant protein source for human diet since it has high protein content in the kernel. Therefore, seed protein content (SPC) is considered a major agronomic and quality trait in peanut breeding. However, few genetic loci underlying SPC have been identified in peanuts, and the underlying regulatory mechanisms remain unknown, limiting the effectiveness of breeding for high-SPC peanut varieties. In this study, a major QTL (qSPCB10.1) controlling peanut SPC was identified within a 2.3 Mb interval in chromosome B10 by QTL-seq using a recombinant inbred line population derived from parental lines with high and low SPCs, respectively. Sequence comparison, transcriptomic analysis, and annotation analysis of the qSPCB10.1 locus were performed. Six differentially expressed genes with sequence variations between two parents were identified as candidate genes underlying qSPCB10.1. Further locus interaction analysis revealed that qSPCB10.1 could not affect the seed oil accumulation unless qOCA08.1XH13 was present, a high seed oil content (SOC) allele for a major QTL underlying SOC. In summary, our study provides a basis for future investigation of the genetic basis of seed protein accumulation and facilitates marker-assisted selection for developing high-SPC peanut genotypes.

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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.

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Development of protein-enriched chickpea varieties necessitates an understanding of specific genes and key regulatory circuits that govern the synthesis of seed storage proteins (SSPs). Here, we demonstrated the novel involvement of Ca-miR164e-CaNAC100 in regulating SSP synthesis in chickpea. Ca-miRNA164e was significantly decreased during seed maturation, especially in high-protein accessions. The miRNA was found to directly target the transactivation conferring C-terminal region of a nuclear-localized transcription factor, CaNAC100 as revealed using RNA ligase-mediated-rapid amplification of cDNA ends and target mimic assays. The functional role of CaNAC100 was demonstrated through seed-specific overexpression (NACOE) resulting in significantly augmented seed protein content (SPC) consequential to increased SSP transcription. Further, NACOE lines displayed conspicuously enhanced seed weight but reduced numbers and yield. Conversely, a downregulation of CaNAC100 and SSP transcripts was evident in seed-specific overexpression lines of Ca-miR164e that culminated in significantly lowered SPC. CaNAC100 was additionally demonstrated to transactivate the SSP-encoding genes by directly binding to their promoters as demonstrated using electrophoretic mobility shift and dual-luciferase reporter assays. Taken together, our study for the first time established a distinct role of CaNAC100 in positively influencing SSP synthesis and its critical regulation by CamiR164e, thereby serving as an understanding that can be utilized for developing SPC-rich chickpea varieties.

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Soybean is one of the most economically important crops worldwide and an important source of unsaturated fatty acids and protein for the human diet. Consumer demand for healthy fats and oils is increasing, and the global demand for vegetable oil is expected to double by 2050. Identification of key genes that regulate seed fatty acid content can facilitate molecular breeding of high-quality soybean varieties with enhanced fatty acid profiles. Here, we analysed the genetic architecture underlying variations in soybean seed fatty acid content using 547 accessions, including mainly landraces and cultivars from northeastern China. Through fatty acid profiling, genome re-sequencing, population genomics analyses, and GWAS, we identified a SEIPIN homologue at the FA9 locus as an important contributor to seed fatty acid content. Transgenic and multiomics analyses confirmed that FA9 was a key regulator of seed fatty acid content with pleiotropic effects on seed protein and seed size. We identified two major FA9 haplotypes in 1295 resequenced soybean accessions and assessed their phenotypic effects in a field planting of 424 accessions. Soybean accessions carrying FA9H2 had significantly higher total fatty acid contents and lower protein contents than those carrying FA9H1 . FA9H2 was absent in wild soybeans but present in 13% of landraces and 26% of cultivars, suggesting that it may have been selected during soybean post-domestication improvement. FA9 therefore represents a useful genetic resource for molecular breeding of high-quality soybean varieties with specific seed storage profiles.

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Cowpea (Vigna unguiculata L. Walp., 2n = 2x = 22) is a protein-rich crop that complements staple cereals for humans and serves as fodder for livestock. It is widely grown in Africa and other developing countries as the primary source of protein in the diet; therefore, it is necessary to identify the protein-related loci to improve cowpea breeding. In the current study, we conducted a genome-wide association study (GWAS) on 161 cowpea accessions (151 USDA germplasm plus 10 Arkansas breeding lines) with a wide range of seed protein contents (21.8~28.9%) with 110,155 high-quality whole-genome single-nucleotide polymorphisms (SNPs) to identify markers associated with protein content, then performed genomic prediction (GP) for future breeding. A total of seven significant SNP markers were identified using five GWAS models (single-marker regression (SMR), the general linear model (GLM), Mixed Linear Model (MLM), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK), which are located at the same locus on chromosome 8 for seed protein content. This locus was associated with the gene Vigun08g039200, which was annotated as the protein of the thioredoxin superfamily, playing a critical function for protein content increase and nutritional quality improvement. In this study, a genomic prediction (GP) approach was employed to assess the accuracy of predicting seed protein content in cowpea. The GP was conducted using cross-prediction with five models, namely ridge regression best linear unbiased prediction (rrBLUP), Bayesian ridge regression (BRR), Bayesian A (BA), Bayesian B (BB), and Bayesian least absolute shrinkage and selection operator (BL), applied to seven random whole genome marker sets with different densities (10 k, 5 k, 2 k, 1 k, 500, 200, and 7), as well as significant markers identified through GWAS. The accuracies of the GP varied between 42.9% and 52.1% across the seven SNPs considered, depending on the model used. These findings not only have the potential to expedite the breeding cycle through early prediction of individual performance prior to phenotyping, but also offer practical implications for cowpea breeding programs striving to enhance seed protein content and nutritional quality.

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Utilizing a combinatorial approach of quantitative trait locus (QTL)-Seq and candidate gene-based association mapping, the QTLs and genes responsible for seed protein content (SPC), a major quality trait in chickpea, were identified. Whole genome re-sequencing based QTL-Seq analysis of bulked recombinant inbred lines from a mapping population contrasting for SPC led to the identification of two QTLs [0.94 Mb on Linkage Group (LG)5 and 1.16 Mb on LG6] encompassing three SNPs, displaying the highest ΔSNP index. These highly significant SNPs and their associated genes were validated in 211 chickpea mini-core accessions varying in SPC, revealing a tightly associated marker affecting CaREN1 (ROP1 ENHANCER1) and explaining a phenotypic variation of 23%. This SNP was subsequently converted into a cost effective allele-specific PCR-based marker that could be utilized for rapid screening of SPC during marker assisted breeding. Furthermore, in planta functional validation via knockdown of CaREN1 transcripts led to significant reduction in SPC of chickpea. This decrease in seed protein is likely due to disruption in the formation of CaREN1 protein complexes comprising chaperones, phosphopeptide-binding proteins, and GTPases that mediate folding, transport and accumulation of seed storage proteins, as indicated through affinity purification-mass spectrometry. Taken together, our data will expedite tailoring of chickpea cultivars with augmented SPC.

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Drought is one of the most destructive abiotic stresses that impact the growth, physiology, yield, and nutritional quality of seeds of crop plants. In modern agriculture, the use of nanoparticles can be beneficial due to their large surface area and higher potentiality to enter into the plant leaf during foliar application. This study aims to evaluate the effects of foliar spray containing varying doses (0, 100, and 200 ppm) of the nano-iron (Fe3O4) on the growth, physiology, yield, and seed nutritional quality of soybean under drought (40% of field capacity, FC) and well-watered (80% of FC) conditions. Leaf water status, chlorophyll content of leaves, the height of the plant, fresh leaf weight, fresh stem weight, fresh petiole weight, total dry weight, seed yield, and protein and oil content in soybean seeds were found to be suppressed by the applied drought stress. Under both drought (40% of FC) and controlled well-watered (80% of FC) conditions, the foliar application of nano-iron substantially improved the growth, physiology, yield, and quality of soybean seeds. The nanoparticles at 200 ppm increased soybean seed yield by 40.12 and 32.60% in drought and well-watered conditions, respectively, compared to the untreated plants. Furthermore, nano-iron increased the oil content of soybean seeds by 10.14 and 7.87% under drought and well-watered conditions, respectively, compared to the untreated control. Our results indicate that the application of nano-iron improved drought tolerance, yield, and seed quality of soybean, so exogenous foliar sprays of 200 ppm Fe3O4 were more effective than the other treatments in enhancing drought tolerance and can be utilized to reduce losses caused by drought stress in soybean-growing areas.

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MAIN CONCLUSION: TaATLa1 was identified to respond to nitrogen deprivation through transcriptome analysis of wheat seedlings. TaATLa1 specifically transports Gln, Glu, and Asp, and affects the biomass of Arabidopsis and wheat. Nitrogen is an essential macronutrient and plays a crucial role in wheat production. Amino acids, the major form of organic nitrogen, are remobilized by amino acid transporters (AATs) in plants. AATs are commonly described as central components of essential developmental processes and yield formation via taking up and transporting amino acids in plants. However, few studies have reported the detailed biochemical properties and biological functions of these AATs in wheat. In this study, key genes encoding AATs were screened from transcriptome analysis of wheat seedlings treated with normal nitrogen (NN) and nitrogen deprivation (ND). Among them, 21 AATs were down-regulated and eight AATs were up-regulated under ND treatment. Among the homoeologs, TaATLa1.1-3A, TaATLa1.1-3B, and TaATLa1.1-3D (TaATLa1.1-3A, -3B, and -3D), belonging to amino acid transporter-like a (ATLa) subfamily, were significantly down-regulated in response to ND in wheat, and accordingly were selected for functional analyses. The results demonstrated that TaATLa1.1-3A, -3B, and -3D effectively transported glutamine (Gln), glutamate (Glu), and aspartate (Asp) in yeast. Overexpression of TaAILa1.1-3A, -3B, and -3D in Arabidopsis thaliana L. significantly increased amino acid content in leaves, storage protein content in seeds and the plant biomass under NN. Knockdown of TaATLa1.1-3A, -3B, and -3D in wheat seedlings resulted in a significant block of amino acid remobilization and growth inhibition. Taken together, TaATLa1.1-3A, -3B, and -3D contribute substantially to Arabidopsis and wheat growth. We propose that TaATLa1.1-3A, -3B, and -3D may participate in the source-sink translocation of amino acid, and they may have profound implications for wheat yield improvement.

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Rapeseed (Brassica napus L.) is an important oil crop and has the potential to serve as a highly productive source of protein. This protein exhibits an excellent amino acid composition and has high nutritional value for humans. Seed protein content (SPC) and seed oil content (SOC) are two complex quantitative and polygenic traits which are negatively correlated and assumed to be controlled by additive and epistatic effects. A reduction in seed glucosinolate (GSL) content is desired as GSLs cause a stringent and bitter taste. The goal here was the identification of genomic intervals relevant for seed GSL content and SPC/SOC. Mapping by sequencing (MBS) revealed 30 and 15 new and known genomic intervals associated with seed GSL content and SPC/SOC, respectively. Within these intervals, we identified known but also so far unknown putatively causal genes and sequence variants. A 4 bp insertion in the MYB28 homolog on C09 shows a significant association with a reduction in seed GSL content. This study provides insights into the genetic architecture and potential mechanisms underlying seed quality traits, which will enhance future breeding approaches in B. napus.

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Northeast China is a major soybean production region in China. A representative sample of the Northeast China soybean germplasm population (NECSGP) composed of 361 accessions was evaluated for their seed protein content (SPC) in Tieling, Northeast China. This SPC varied greatly, with a mean SPC of 40.77%, ranging from 36.60 to 46.07%, but it was lower than that of the Chinese soybean landrace population (43.10%, ranging from 37.51 to 50.46%). The SPC increased slightly from 40.32-40.97% in the old maturity groups (MG, MGIII + II + I) to 40.93-41.58% in the new MGs (MG0 + 00 + 000). The restricted two-stage multi-locus genome-wide association study (RTM-GWAS) with 15,501 SNP linkage-disequilibrium block (SNPLDB) markers identified 73 SPC quantitative trait loci (QTLs) with 273 alleles, explaining 71.70% of the phenotypic variation, wherein 28 QTLs were new ones. The evolutionary changes of QTL-allele structures from old MGs to new MGs were analyzed, and 97.79% of the alleles in new MGs were inherited from the old MGs and 2.21% were new. The small amount of new positive allele emergence and possible recombination between alleles might explain the slight SPC increase in the new MGs. The prediction of recombination potentials in the SPC of all the possible crosses indicated that the mean of SPC overall crosses was 43.29% (+2.52%) and the maximum was 50.00% (+9.23%) in the SPC, and the maximum transgressive potential was 3.93%, suggesting that SPC breeding potentials do exist in the NECSGP. A total of 120 candidate genes were annotated and functionally classified into 13 categories, indicating that SPC is a complex trait conferred by a gene network.

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Upland cotton (Gossypium hirsutum) is the world's leading fiber crop and one of the most important oilseed crops. Genetic improvement of cotton has primarily focused on fiber yield and quality. However, there is an increased interest and demand for enhanced cottonseed traits, including protein, oil, fatty acids, and amino acids for broad food, feed and biofuel applications. As a byproduct of cotton production, cottonseed is an important source of edible oil in many countries and could also be a vital source of protein for human consumption. The focus of cotton breeding on high yield and better fiber quality has substantially reduced the natural genetic variation available for effective cottonseed quality improvement within Upland cotton. However, genetic variation in cottonseed oil and protein content exists within the genus of Gossypium and cultivated cotton. A plethora of genes and quantitative trait loci (QTLs) (associated with cottonseed oil, fatty acids, protein and amino acids) have been identified, providing important information for genetic improvement of cottonseed quality. Genetic engineering in cotton through RNA interference and insertions of additional genes of other genetic sources, in addition to the more recent development of genome editing technology has achieved considerable progress in altering the relative levels of protein, oil, fatty acid profile, and amino acids composition in cottonseed for enhanced nutritional value and expanded industrial applications. The objective of this review is to summarize and discuss the cottonseed oil biosynthetic pathway and major genes involved, genetic basis of cottonseed oil and protein content, genetic engineering, genome editing through CRISPR/Cas9, and QTLs associated with quantity and quality enhancement of cottonseed oil and protein.

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Soybean (Glycine max (L.) Merr.) is among the most valuable crops based on its nutritious seed protein and oil. Protein quality, evaluated as the ratio of glycinin (11S) to ß-conglycinin (7S), can play a role in food and feed quality. To help uncover the underlying differences between high and low protein soybean varieties, we performed differential expression analysis on high and low total protein soybean varieties and high and low 11S soybean varieties grown in four locations across Eastern and Western Canada over three years (2018-2020). Simultaneously, ten individual differential expression datasets for high vs. low total protein soybeans and ten individual differential expression datasets for high vs. low 11S soybeans were assessed, for a total of 20 datasets. The top 15 most upregulated and the 15 most downregulated genes were extracted from each differential expression dataset and cross-examination was conducted to create shortlists of the most consistently differentially expressed genes. Shortlisted genes were assessed for gene ontology to gain a global appreciation of the commonly differentially expressed genes. Genes with roles in the lipid metabolic pathway and carbohydrate metabolic pathway were differentially expressed in high total protein and high 11S soybeans in comparison to their low total protein and low 11S counterparts. Expression differences were consistent between East and West locations with the exception of one, Glyma.03G054100. These data are important for uncovering the genes and biological pathways responsible for the difference in seed protein between high and low total protein or 11S cultivars.

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Genomic selection and marker-assisted recurrent selection have been applied to improve quantitative traits in many cross-pollinated crops. However, such selection is not feasible in self-pollinated crops owing to laborious crossing procedures. In this study, we developed a simulation-based selection strategy that makes use of a trait prediction model based on genomic information to predict the phenotype of the progeny for all possible crossing combinations. These predictions are then used to select the best cross combinations for the selection of the given trait. In our simulated experiment, using a biparental initial population with a heritability set to 0.3, 0.6, or 1.0 and the number of quantitative trait loci set to 30 or 100, the genetic gain of the proposed strategy was higher or equal to that of conventional recurrent selection method in the early selection cycles, although the number of cross combinations of the proposed strategy was considerably reduced in each cycle. Moreover, this strategy was demonstrated to increase or decrease seed protein content in soybean recombinant inbred lines using SNP markers. Information on 29 genomic regions associated with seed protein content was used to construct the prediction model and conduct simulation. After two selection cycles, the selected progeny had significantly higher or lower seed protein contents than those from the initial population. These results suggest that our strategy is effective in obtaining superior progeny over a short period with minimal crossing and has the potential to efficiently improve the target quantitative traits in self-pollinated crops.

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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.

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BACKGROUND: Diethyl aminoethyl hexanoate (DA-6), a plant growth regulator, has many beneficial effects on agricultural production. DA-6 has been applied to many plant species, but the molecular mechanism by which spraying DA-6 after anthesis regulates wheat grain filling is still unknown. RESULTS: In this study, we used four DA-6 concentrations: C0 (0 g/L), C2 (2 g/L), C4 (4 g/L), and C6 (6 g/L). The results showed that C4 and C6 led to a significantly higher 1000-grain weight and seed protein content than C0 during two wheat growing seasons. We then subjected samples at 24 days after anthesis (at which point the grain weight increased rapidly) to transcriptome analysis. Flag leaf (L), seed (S), and stem (T) samples under C6 and C0 were used for RNA-seq. The seed samples under C6 compared with C0 (S6vsS0) presented the most differentially expressed genes (DEGs; 2164). Plant hormone signal transduction (p = 1.97 × 10- 4), protein processing in the endoplasmic reticulum (ER; p = 9.04 × 10- 11) and starch and sucrose metabolism (p = 1.90 × 10- 10) pathways were the most markedly enriched pathways in the flag leaves, stems, and seeds, respectively. DEGs involved in sucrose synthesis in the flag leaves, protein processing in ER in the stems, and starch synthesis and protein processing in ER in the seeds were significantly upregulated under C6 compared with C0. CONCLUSIONS: Overall, we propose a model for spraying DA-6 after anthesis to regulate metabolic pathways in wheat, which provides new insights into wheat in response to DA-6.

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Soybean is a globally important legume crop that provides a primary source of high-quality vegetable protein and oil. Seed protein and oil content are two valuable quality traits controlled by multiple genes in soybean. In this study, the restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS) procedure was performed to dissect the genetic architecture of seed protein and oil content in a diverse panel of 279 soybean accessions from the Yangtze and Huaihe River Valleys in China. We identified 26 quantitative trait loci (QTLs) for seed protein content and 23 for seed oil content, including five associated with both traits. Among these, 39 QTLs corresponded to previously reported QTLs, whereas 10 loci were novel. As reported previously, the QTL on chromosome 20 was associated with both seed protein and oil content. This QTL exhibited opposing effects on these traits and contributed the most to phenotype variation. From the detected QTLs, 55 and 51 candidate genes were identified for seed protein and oil content, respectively. Among these genes, eight may be promising candidate genes for improving soybean nutritional quality. These results will facilitate marker-assisted selective breeding for soybean protein and oil content traits.

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BACKGROUND: Seeds of domesticated barley are grouped into two distinct types, which differ in morphology. Caryopses covered by adaxial (palea) and abaxial (lemma) hulls that tightly adhere to the pericarp at maturity give rise to hulled seeds whereas caryopses without adhering hulls give rise to naked seeds. The naked caryopsis character is an essential trait regarding the end use of barley. RESULTS: To uncover the genetic basis of the trait, a genome-wide association study (GWAS) has been performed in a panel comprising 222 2-rowed and 303 6-rowed spring barley landrace accessions. In addition to the well-described Nud locus on chromosome 7H, three novel loci showed strong associations with the trait: the first locus on 2H was specifically detected in 6-rowed accessions, the second locus on 3H was found in 2-rowed accessions from Eurasia and the third locus on 6H was revealed in 6-rowed accessions from Ethiopia. PCR analysis of naked accessions also confirmed the absence of a 17 kb region harboring the Nud gene on chromosome 7H for all but one naked accession. The latter was characterized by a slightly variant phenotype of the caryopsis. CONCLUSION: Our findings provide evidence of the pervasiveness of the 17 kb deletion in spring barley from different geographic regions and at the same time reveal genomic footprints of selection in naked barley, which follow both geographic and morphological patterns.

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Soybean is globally cultivated primarily for its protein and oil. The protein and oil contents of the seeds are quantitatively inherited traits determined by the interaction of numerous genes. In order to gain a better understanding of the molecular foundation of soybean protein and oil content for the marker-assisted selection (MAS) of high quality traits, a population of 185 soybean germplasms was evaluated to identify the quantitative trait loci (QTLs) associated with the seed protein and oil contents. Using specific length amplified fragment sequencing (SLAF-seq) technology, a total of 12,072 single nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) ≥ 0.05 were detected across the 20 chromosomes (Chr), with a marker density of 78.7 kbp. A total of 31 SNPs located on 12 of the 20 soybean chromosomes were correlated with seed protein and oil content. Of the 31 SNPs that were associated with the two target traits, 31 beneficial alleles were identified. Two SNP markers, namely rs15774585 and rs15783346 on Chr 07, were determined to be related to seed oil content both in 2015 and 2016. Three SNP markers, rs53140888 on Chr 01, rs19485676 on Chr 13, and rs24787338 on Chr 20 were correlated with seed protein content both in 2015 and 2016. These beneficial alleles may potentially contribute towards the MAS of favorable soybean protein and oil characteristics.

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Pigeonpea is an important source of dietary protein to over a billion people globally, but genetic enhancement of seed protein content (SPC) in the crop has received limited attention for a long time. Use of genomics-assisted breeding would facilitate accelerating genetic gain for SPC. However, neither genetic markers nor genes associated with this important trait have been identified in this crop. Therefore, the present study exploited whole genome re-sequencing (WGRS) data of four pigeonpea genotypes (~ 12X coverage) to identify sequence-based markers and associated candidate genes for SPC. By combining a common variant filtering strategy on available WGRS data with knowledge of gene functions in relation to SPC, 108 sequence variants from 57 genes were identified. These genes were assigned to 19 GO molecular function categories with 56% belonging to only two categories. Furthermore, Sanger sequencing confirmed presence of 75.4% of the variants in 37 genes. Out of 30 sequence variants converted into CAPS/dCAPS markers, 17 showed high level of polymorphism between low and high SPC genotypes. Assay of 16 of the polymorphic CAPS/dCAPS markers on an F2 population of the cross ICP 5529 (high SPC) × ICP 11605 (low SPC), resulted in four of the CAPS/dCAPS markers significantly (P < 0.05) co-segregated with SPC. In summary, four markers derived from mutations in four genes will be useful for enhancing/regulating SPC in pigeonpea crop improvement programs.

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BACKGROUND: Cotton germplasm resources contain beneficial alleles that can be exploited to develop germplasm adapted to emerging environmental and climate conditions. Accessions and lines have traditionally been characterized based on phenotypes, but phenotypic profiles are limited by the cost, time, and space required to make visual observations and measurements. With advances in molecular genetic methods, genotypic profiles are increasingly able to identify differences among accessions due to the larger number of genetic markers that can be measured. A combination of both methods would greatly enhance our ability to characterize germplasm resources. Recent efforts have culminated in the identification of sufficient SNP markers to establish high-throughput genotyping systems, such as the CottonSNP63K array, which enables a researcher to efficiently analyze large numbers of SNP markers and obtain highly repeatable results. In the current investigation, we have utilized the SNP array for analyzing genetic diversity primarily among cotton cultivars, making comparisons to SSR-based phylogenetic analyses, and identifying loci associated with seed nutritional traits. RESULTS: The SNP markers distinctly separated G. hirsutum from other Gossypium species and distinguished the wild from cultivated types of G. hirsutum. The markers also efficiently discerned differences among cultivars, which was the primary goal when designing the CottonSNP63K array. Population structure within the genus compared favorably with previous results obtained using SSR markers, and an association study identified loci linked to factors that affect cottonseed protein content. CONCLUSIONS: Our results provide a large genome-wide variation data set for primarily cultivated cotton. Thousands of SNPs in representative cotton genotypes provide an opportunity to finely discriminate among cultivated cotton from around the world. The SNPs will be relevant as dense markers of genome variation for association mapping approaches aimed at correlating molecular polymorphisms with variation in phenotypic traits, as well as for molecular breeding approaches in cotton.

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