Identification and Validation of Major QTLs, Epistatic Interactions, and Candidate Genes for Soybean Seed Shape and Weight Using Two Related RIL Populations

Genome-wide association study of drought tolerance in wheat (Triticum aestivum L.) identifies SNP markers and candidate genes

Drought stress poses a severe threat to global wheat production, necessitating an in-depth exploration of the genetic basis for drought tolerance associated traits. This study employed a 90 K SNP array to conduct a genome-wide association analysis, unravelling genetic determinants of key traits related to drought tolerance in wheat, namely plant height, root length, and root and shoot dry weight. Using the mixed linear model (MLM) method on 125 wheat accessions subjected to both well-watered and drought stress treatments, we identified 53 SNPs significantly associated with stress susceptibility (SSI) and tolerance indices (STI) for the targeted traits. Notably, chromosomes 2A and 3B stood out with ten and nine associated markers, respectively. Across 17 chromosomes, 44 unique candidate genes were pinpointed, predominantly located on the distal ends of 1A, 1B, 1D, 2A, 3A, 3B, 4A, 6A, 6B, 7A, 7B, and 7D chromosomes. These genes, implicated in diverse functions related to plant growth, development, and stress responses, offer a rich resource for future investigation. A clustering pattern emerged, notably with seven genes associated with SSI for plant height and four genes linked to both STI of plant height and shoot dry weight, converging on specific regions of chromosome arms of 2AS and 3BL. Additionally, shared genes encoding polygalacturonase, auxilin-related protein 1, peptide deformylase, and receptor-like kinase underscored the interconnectedness between plant height and shoot dry weight. In conclusion, our findings provide insights into the molecular mechanisms governing wheat drought tolerance, identifying promising genomic loci for further exploration and crop improvement strategies.

Construction of a genetic map and QTL mapping of seed size traits in soybean

Soybean seed size and seed shape traits are closely related to plant yield and appearance quality. In this study, 186 individual plants of the F2 generation derived from crosses between Changjiang Chun 2 and JiYu 166 were selected as the mapping population to construct a molecular genetic linkage map, and the phenotypic data of hundred-grain weight, seed length, seed width, and seed length-to-width ratio of soybean under three generations of F2 single plants and F2:3 and F2:4 lines were combined to detect the QTL (quantitative trait loci) for the corresponding traits by ICIM mapping. A soybean genetic map containing 455 markers with an average distance of 6.15 cM and a total length of 2799.2 cM was obtained. Forty-nine QTLs related to the hundred-grain weight, seed length, seed width, and seed length-to-width ratio of soybean were obtained under three environmental conditions. A total of 10 QTLs were detected in more than two environments with a phenotypic variation of over 10%. Twelve QTL clusters were identified on chromosomes 1, 2, 5, 6, 8, 13, 18, and 19, with the majority of the overlapping intervals for hundred-grain weight and seed width. These results will lay the theoretical and technical foundation for molecularly assisted breeding in soybean seed weight and seed shape. Eighteen candidate genes that may be involved in the regulation of soybean seed size were screened by gene functional annotation and GO enrichment analysis.

Molecular and genetic insights into secondary metabolic regulation underlying insect-pest resistance in legumes

Insect pests pose a major threat to agricultural production, resulting in significant economic losses for countries. A high infestation of insects in any given area can severely reduce crop yield and quality. This review examines the existing resources for managing insect pests and highlights alternative eco-friendly techniques to enhance insect pest resistance in legumes. Recently, the application of plant secondary metabolites has gained popularity in controlling insect attacks. Plant secondary metabolites encompass a wide range of compounds such as alkaloids, flavonoids, and terpenoids, which are often synthesized through intricate biosynthetic pathways. Classical methods of metabolic engineering involve manipulating key enzymes and regulatory genes to enhance or redirect the production of secondary metabolites in plants. Additionally, the role of genetic approaches, such as quantitative trait loci mapping, genome-wide association (GWAS) mapping, and metabolome-based GWAS in insect pest management is discussed, also, the role of precision breeding, such as genome editing technologies and RNA interference for identifying pest resistance and manipulating the genome to develop insect-resistant cultivars are explored, highlighting the positive contribution of plant secondary metabolites engineering-based resistance against insect pests. It is suggested that by understanding the genes responsible for beneficial metabolite compositions, future research might hold immense potential to shed more light on the molecular regulation of secondary metabolite biosynthesis, leading to advancements in insect-resistant traits in crop plants. In the future, the utilization of metabolic engineering and biotechnological methods may serve as an alternative means of producing biologically active, economically valuable, and medically significant compounds found in plant secondary metabolites, thereby addressing the challenge of limited availability.

Major genetic locus with pleiotropism determined seed-related traits in cultivated and wild soybeans

KEY MESSAGE

Here, a novel pleiotropic QTL qSS14 simultaneously regulating four seed size traits and two consistently detected QTLs qSW17 and qSLW02 were identified across multiple years. Seed-related traits were the key agronomic traits that have been artificially selected during the domestication of wild soybean. Identifying the genetic loci and genes that regulate seed size could clarify the genetic variations in seed-related traits and provide novel insights into high-yield soybean breeding. In this study, we used a high-density genetic map constructed by F₁₀ RIL populations from a cross between Glycine max and Glycine soja to detect additive QTLs for seven seed-related traits over the last three years. As a result, we identified one novel pleiotropic QTL, qSS14, that simultaneously controlled four seed size traits (100-seed weight, seed length, seed width, and seed thickness) and two consistently detected QTLs, qSW17, and qSLW02, in multiple years of phenotypic data. Furthermore, we predicted two, two and three candidate genes within these three critical loci based on the parental resequencing data and gene function annotations. And the relative expression of four candidate genes GLYMA_14G155100, GLYMA_17G061000, GLYMA_02G273100, and GLYMA_02G273300 showed significant differences among parents and the extreme materials through qRT-PCR analysis. These findings could facilitate the determination of beneficial genes in wild soybean and contribute to our understanding of the soybean domestication process.

Identification of major QTLs for soybean seed size and seed weight traits using a RIL population in different environments

INTRODUCTION

The seed weight of soybean [Glycine max (L.) Merr.] is one of the major traits that determine soybean yield and is closely related to seed size. However, the genetic basis of the synergistic regulation of traits related to soybean yield is unclear.

METHODS

To understand the molecular genetic basis for the formation of soybean yield traits, the present study focused on QTLs mapping for seed size and weight traits in different environments and target genes mining.

RESULTS

A total of 85 QTLs associated with seed size and weight traits were identified using a recombinant inbred line (RIL) population developed from Guizao1×B13 (GB13). We also detected 18 environmentally stable QTLs. Of these, qSL-3-1 was a novel QTL with a stable main effect associated with seed length. It was detected in all environments, three of which explained more than 10% of phenotypic variance (PV), with a maximum of 15.91%. In addition, qSW-20-3 was a novel QTL with a stable main effect associated with seed width, which was identified in four environments. And the amount of phenotypic variance explained (PVE) varied from 9.22 to 21.93%. Five QTL clusters associated with both seed size and seed weight were summarized by QTL cluster identification. Fifteen candidate genes that may be involved in regulating soybean seed size and weight were also screened based on gene function annotation and GO enrichment analysis.

DISCUSSION

The results provide a biologically basic reference for understanding the formation of soybean seed size and weight traits.

A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice

Improving grain yield potential in rice is an important step toward addressing global food security challenges. The meta-QTL analysis offers stable and robust QTLs irrespective of the genetic background of mapping populations and phenotype environment and effectively narrows confidence intervals (CI) for candidate gene (CG) mining and marker-assisted selection improvement. To achieve these aims, a comprehensive bibliographic search for grain yield traits (spikelet fertility, number of grains per panicle, panicles number per plant, and 1000-grain weight) QTLs was conducted, and 462 QTLs were retrieved from 47 independent QTL research published between 2002 and 2022. QTL projection was performed using a reference map with a cumulative length of 2,945.67 cM, and MQTL analysis was conducted on 313 QTLs. Consequently, a total of 62 MQTLs were identified with reduced mean CI (up to 3.40 fold) compared to the mean CI of original QTLs. However, 10 of these MQTLs harbored at least six of the initial QTLs from diverse genetic backgrounds and environments and were considered the most stable and robust MQTLs. Also, MQTLs were compared with GWAS studies and resulted in the identification of 16 common significant loci modulating the evaluated traits. Gene annotation, gene ontology (GO) enrichment, and RNA-seq analyses of chromosome regions of the stable MQTLs detected 52 potential CGs including those that have been cloned in previous studies. These genes encode proteins known to be involved in regulating grain yield including cytochrome P450, zinc fingers, MADs-box, AP2/ERF domain, F-box, ubiquitin ligase domain protein, homeobox domain, DEAD-box ATP domain, and U-box domain. This study provides the framework for molecular dissection of grain yield in rice. Moreover, the MQTLs and CGs identified could be useful for fine mapping, gene cloning, and marker-assisted selection to improve rice productivity.

An enhancing effect attributed to a nonsynonymous mutation in SOYBEAN SEED SIZE 1, a SPINDLY-like gene, is exploited in soybean domestication and improvement

Soybean (Glycine max) was domesticated from its wild relative Glycine soja. One-hundred-seed weight is one of the most important domesticated traits determining soybean yield; however, its underlying genetic basis remains elusive. We characterized a soybean seed size 1 (sss1) mutant featuring large seeds compared to its wild-type background. Positional cloning revealed that the candidate gene GmSSS1 encoded a SPINDLY homolog and was co-located in a well-identified quantitative trait locus (QTL)-rich region on chromosome 19. Knocking out GmSSS1 resulted in small seeds, while overexpressing GmSSS1/Gmsss1 induced large seeds. Modulating GmSSS1/Gmsss1 in transgenic plants can positively influence cell expansion and cell division. Relative to GmSSS1, one mutation leading to an E to Q substitution at the 182^nd residue in Gmsss1 conferred an enhancing effect on seed weight. GmSSS1 underwent diversification in wild-type and cultivated soybean, and the alleles encoding the Gmsss1-type substitution of 182^nd -Q, which originated along the central and downstream parts of the Yellow River, were selected and expanded during soybean domestication and improvement. We cloned the causative gene for the sss1 mutant, which is linked with a seed weight QTL, identified an elite allele of this gene for increasing seed weight, and provided new insights into soybean domestication and breeding.

Genetic Diversity and Association Analysis for Carotenoid Content among Sprouts of Cowpea (Vigna unguiculata L. Walp)

The development and promotion of biofortified foods plants are a sustainable strategy for supplying essential micronutrients for human health and nutrition. We set out to identify quantitative trait loci (QTL) associated with carotenoid content in cowpea sprouts. The contents of carotenoids, including lutein, zeaxanthin, and β-carotene in sprouts of 125 accessions were quantified via high-performance liquid chromatography. Significant variation existed in the profiles of the different carotenoids. Lutein was the most abundant (58 ± 12.8 mg/100 g), followed by zeaxanthin (14.7 ± 3.1 mg/100 g) and β-carotene (13.2 ± 2.9 mg/100 g). A strong positive correlation was observed among the carotenoid compounds (r ≥ 0.87), indicating they can be improved concurrently. The accessions were distributed into three groups, following their carotenoid profiles, with accession C044 having the highest sprout carotenoid content in a single cluster. A total of 3120 genome-wide SNPs were tested for association analysis, which revealed that carotenoid biosynthesis in cowpea sprouts is a polygenic trait controlled by genes with additive and dominance effects. Seven loci were significantly associated with the variation in carotenoid content. The evidence of variation in carotenoid content and genomic regions controlling the trait creates an avenue for breeding cowpea varieties with enhanced sprouts carotenoid content.