This study bridges the gap between high-resolution genetic dissection and predictive breeding, providing a practical framework to accelerate oil yield improvement in rapeseed.
Abstract
Seed oil content (SOC) is a key determinant of oil yield in rapeseed, but translating high-resolution QTL and functional gene information into effective breeding selection remains challenging. Here, we integrated high-quality genome assembly, QTL fine mapping, gene function validation, and QTL-informed genomic prediction to improve the SOC in rapeseed. The improved, chromosome-scale genome of the semi-winter cultivar NY7 served as a reliable reference for fine mapping via its bidirectional introgression populations. Seven major QTLs were rapidly fine-mapped into 117 kb ~ 358 kb intervals; each increased the SOC by 2% ~ 6%. Integrated transcriptomic and haplotype analyses revealed eight candidate genes, highlighting BnaDIR1.C2 as the hub gene underlying qOC.C2–1. Functional validation confirmed the positive regulation of BnaDIR1.C2 in SOC. Moreover, genomic prediction models incorporating QTL-weighted markers substantially improved the prediction performance by an average of 20.34% across different populations. This study bridges the gap between high-resolution genetic dissection and predictive breeding, providing a practical framework to accelerate oil yield improvement in rapeseed.
Seed oil content is one of the primary breeding objectives in soybean. However, the genetic basis of seed oil content remains largely unexplored. In this study, we employed a recombinant inbred line (RIL) population consisting of 201 lines derived from a cross between Feidong and Guanyun, to identify quantitative trait loci (QTLs) for seed oil content in soybean under five environments. High broad-sense heritability (92.34%) indicates a substantial genetic contribution to phenotypic variation in this population and set of environments. A high-density genetic map containing 11,220 SNP markers was constructed, spanning 2507.62 cM, with an average distance of 0.22 cM between markers. By jointly using five QTL mapping methods, a total of 24 QTLs for seed oil content were identified, including eight stable QTLs detected in at least three environments and seven QTLs identified by all five methods. Out of these QTLs, qOil-5-4, qOil-9-2 and qOil-10-3 were considered as the major and stable QTLs. Within these three major and stable QTL intervals, except that Glyma.05g244100 (GmMFT/GmST05) is an experimentally validated gene, the other six genes that were highly expressed during seed development stages were identified as putative candidate genes requiring further validation. In conclusion, the major stable QTLs and candidate genes identified in this study facilitate the dissection of the genetic architecture underlying seed oil content and hold great potential for application in developing new high-oil soybean cultivars after further fine mapping and validation in independent genetic backgrounds.
Shu-Guang Li, Meng-Meng Fu, Ya-Qi Wang et al.· Agronomy· 0 citations
Rice bran oil (RBO) is highly valued for its beneficial fatty acid profile and wide applications in the food, pharmaceutical and skincare industries. It offers numerous health benefits, including reduced bad cholesterol and antioxidant activity, due to its high levels of vitamin E and γ-oryzanol. Enhancing oil composition and understanding the complex genetic architecture underlying lipid metabolism are, therefore, critical for improving RBO quality. The complexity of quantitative trait loci (QTL) and their environmental interactions has constrained their application in rice breeding. However, Meta-QTL (M-QTL) analysis can streamline this QTL information, identifying stable QTL and candidate genes for introgression. In this study, a M-QTL approach was employed to identify stable QTLs influencing RBO quality. Fourteen independent linkage mapping studies were integrated to construct a high-density consensus map spanning 3029.6 cM with 1523 markers. The analysis identified 19 stable M-QTLs and 133 putative candidate genes associated with lipid metabolism. KEGG pathway analysis highlighted 20 key genes essential for lipid biosynthesis, while haplotype analysis and gene–metabolite interaction mapping provided further insight into the regulation of lipid content in RBO. This work provides a foundation for developing rice varieties with enhanced lipid content, supporting the growing global demand for high-quality rice.
Katyayni Kanth, Zeba Quaiyum, B. D. Prasad et al.· Scientific Reports· 0 citations
Soil salinity is a major constraint limiting rice productivity, particularly at the reproductive stage. To elucidate the genetic basis of reproductive stage salinity tolerance, this study validated and fine-mapped quantitative trait loci (QTL) for yield-related traits using a salinity-tolerant line SL506, identified through screening of Nona Bokra–CSSLs in a Koshihikari background. In 2016, the F2 population derived from SL506/Koshihikari was evaluated under long-term salt stress; three QTLs associated with plant dry weight, panicle number, and grain weight were detected on chromosome 2. In 2023, validation analysis using F3 individuals confirmed the presence of these QTLs. Subsequent fine-mapping using F4 near-isogenic lines (NILs) delimited the QTL to a 1.7 Mb interval and high-resolution mapping using an F5 recombinant population progressively refined it to a 473 kb genomic region containing 69 annotated genes. Variant Effect Predictor analysis identified 15 deleterious nonsynonymous variants (SIFT < 0.05) in six candidate genes. Based on annotated gene functions, predicted variant effects, and their membership in stress-related gene families, OsPP2C24, OsFbox102, and OsWAK14 were suggested as the most promising candidate genes underlying qPDW2. These findings provide insights into the genetic basis of reproductive-stage salinity tolerance from valuable resources for the future improvement of salt tolerance and yield stability in rice.
Farjana Rauf, H. Trần, T. Nguyen et al.· Agronomy· 0 citations
Introduction Mungbean [Vigna radiata (L.) R. Wilczek] has emerged as a highly valued crop owing to its unique functional properties. Improving seed quality represents a major goal in mungbean breeding; however, the systematic identification of quantitative trait loci (QTLs) governing key seed quality traits remains limited. Methods In this study, a recombinant inbred line (RIL) population derived from a cross between Zhonglv1 and HB211 was developed, and phenotypic data were collected across three distinct environments. Three complementary QTL mapping methods—composite interval mapping (CIM), inclusive composite interval mapping (ICIM), and multiple QTL mapping (MQM)—were utilized to detect genomic regions associated with protein, starch, oil, and moisture content. Results and discussion In total, 162 QTLs were identified, including 44, 30, 43, and 45 QTLs were identified for protein, starch, oil, and moisture content, respectively. Among these, stable QTLs were consistently detected across multiple environments and methods, highlighting their potential utility in breeding applications. The putative candidate genes underlying these robust QTLs were further annotated, providing preliminary insights into the genetic control of seed quality traits. This study establishes a comprehensive genetic framework for future functional studies and the development of improved mungbean varieties with enhanced seed quality.
Chunxiu Ren, Lipan Qiao, Jiaqi Qiao et al.· Frontiers in Plant Science· 0 citations
Cowpea (Vigna unguiculata [L.] Walp.) is a resilient grain legume and an important global source of dietary protein, yet the genetic and environmental basis of phenological and canopy development, as well as grain composition, remains incompletely characterized across production environments. In this study, we evaluated a cowpea multi-parent advanced generation intercross (MAGIC) population along an environmental gradient in California (with contrasting daylengths, temperatures, and soil types) using agronomic, grain compositional, and uncrewed aerial vehicle (UAV) and rover-enabled phenotyping. Near-infrared spectroscopy (NIRS) enabled assessment of grain compositional traits, while sensing-enabled time-series imaging captured canopy and reproductive dynamics. Quantitative trait locus (QTL) mapping identified 267 QTL, and genome-wide association studies (GWAS) detected 1,973 marker-trait associations. Integrating QTL mapping and GWAS results identified two major genomic hotspots affecting multiple traits. A chromosome 9 hotspot (5.8–6.0 Mb) was associated with flowering time and co-localized with sensing-enabled measures of flower and pod counts, plant height, and vegetation fraction, indicating broad effects on phenological and canopy development. A chromosome 8 hotspot (37.3–37.9 Mb) contained co-localized signals for seed weight, protein, starch, phytate, and moisture. A total of 22 prioritized candidate genes were identified within these and other loci with multi-environment QTL and GWAS support. Genomic predictive abilities were moderate to high for most traits and scenarios, with multi-trait MegaLMM outperforming RR-BLUP. Together, these results define major genomic regions controlling cowpea phenology, canopy development, and grain composition, and provide targets and strategies for breeding cowpea cultivars with favorable and environmentally resilient productivity and grain composition. Significance Statement To dissect the genetic basis of cowpea productivity, adaptation, and grain composition, and how performance for these traits varies and can be predicted across environments, we combined multi-environment phenotyping, including sensing of canopy and reproductive traits, with quantitative genetic analyses in a multi-parental population. We identified genomic hotspots for seed size/composition and reproductive phenology and an across-environment predictive advantage for multi-trait vs. single-trait genomic prediction. Overall, these findings support the comprehensive improvement of cowpea.
Jonathan M. Berlingeri, Sassoum Lo, Margaret Riggs et al.· bioRxiv· 0 citations
