High-density genetic map construction and QTL mapping to identify genes for blight defense- and yield-related traits in sesame (Sesamum indicum L.).

Sesame (Sesamum indicum L.) is an important oilseed crop widely cultivated in subtropical and tropical areas. Low genetic yield potential and susceptibility to disease contribute to low productivity in sesame. However, the genetic basis of sesame yield- and disease-related traits remains unclear. Here, we represent the construction of a high-density bin map of sesame using whole genome sequencing of an F2 population derived from 'Yizhi' and 'Mingdeng Zhima'. A total of 2766 Bins were categorized into 13 linkage groups. Thirteen significant QTLs were identified, including ten QTLs related to yield, two QTLs related to Sesame Fusarium wilt (SFW) disease, and one QTL related to seed color. Among these QTLs, we found that SFW-QTL1.1 and SFW-QTL1.2 were major QTLs related to Fusarium wilt disease, explaining more than 20% of the phenotypic variation with LOD > 6. SCC-QTL1.1 was related to seed coat color, explaining 52% of the phenotypic variation with LOD equal to 25.3. This suggests that seed color traits were controlled by a major QTL. Candidate genes related to Fusarium wilt disease and seed color in the QTLs were annotated. We discovered a significant enrichment of genes associated with resistance to late blight. These genes could be spectral disease resistance genes and may have a role in the regulation of Fusarium wilt disease resistance. Our study will benefit the implementation of marker-assisted selection (MAS) for the genetic improvement of disease resistance and yield-related traits in sesame.

High-content phenotypic analysis of a C. elegans recombinant inbred population identifies genetic and molecular regulators of lifespan.

Lifespan is influenced by complex interactions between genetic and environmental factors. Studying those factors in model organisms of a single genetic background limits their translational value for humans. Here, we mapped lifespan determinants in 85 C. elegans recombinant inbred advanced intercross lines (RIAILs). We assessed molecular profiles-transcriptome, proteome, and lipidome-and life-history traits, including lifespan, development, growth dynamics, and reproduction. RIAILs exhibited large variations in lifespan, which correlated positively with developmental time. We validated three longevity modulators, including rict-1, gfm-1, and mltn-1, among the top candidates obtained from multiomics data integration and quantitative trait locus (QTL) mapping. We translated their relevance to humans using UK Biobank data and showed that variants in GFM1 are associated with an elevated risk of age-related heart failure. We organized our dataset as a resource that allows interactive explorations for new longevity targets.

Mining Candidate Genes for Leaf Angle in Brassica napus L. by Combining QTL Mapping and RNA Sequencing Analysis.

Leaf angle (LA) is an important trait of plant architecture, and individuals with narrow LA can better capture canopy light under high-density planting, which is beneficial for increasing the overall yield per unit area. To study the genetic basis and molecular regulation mechanism of leaf angle in rapeseed, we carried out a series of experiments. Quantitative trait loci (QTL) mapping was performed using the RIL population, and seven QTLs were identified. Transcriptome analysis showed that the cell wall formation/biogenesis processes and biosynthesis/metabolism of cell wall components were the most enrichment classes. Most differentially expressed genes (DEGs) involved in the synthesis of lignin, xylan, and cellulose showed down-regulated expression in narrow leaf material. Microscopic analysis suggested that the cell size affected by the cell wall in the junction area of the stem and petiole was the main factor in leaf petiole angle (LPA) differences. Combining QTL mapping and RNA sequencing, five promising candidate genes BnaA01G0125600ZS, BnaA01G0135700ZS, BnaA01G0154600ZS, BnaA10G0154200ZS, and BnaC03G0294200ZS were identified in rapeseed, and most of them were involved in cell wall biogenesis and the synthesis/metabolism of cell wall components. The results of QTL, transcriptome analysis, and cytological analysis were highly consistent, collectively revealing that genes related to cell wall function played a crucial role in regulating the LA trait in rapeseed. The study provides further insights into LA traits, and the discovery of new QTLs and candidate genes is highly beneficial for genetic improvement.

Identification and mapping of late blight resistance QTLs in the wild tomato accession PI 224710 (Solanum pimpinellifolium).

Late blight (LB), caused by oomycete Phytophthora infestans, is one of the most destructive diseases of the cultivated tomato, Solanum lycopersicum. Since new and aggressive clonal lineages of P. infestans, many of which overcoming formerly effective fungicides or host resistance genes, have continued to emerge, it is crucial to identify, characterize, and utilize new sources of host resistance in tomato breeding. A recent screening of tomato germplasm identified Solanum pimpinellifolium accession PI 224710 with very strong resistance to several current P. infestans clonal lineages. The present study aimed to identify and characterize QTLs associated with LB resistance in PI 224710. Disease screening of a large F2 population (n = 1721), derived from a cross between PI 224710 and LB-susceptible tomato breeding line Fla. 8059, followed by F3 progeny testing, resulted in the identification of 43 highly-resistant and 27 highly-susceptible F2 individuals. A selective genotyping approach, using 469 non-identical SNP markers, resulted in the construction of a genetic linkage map and identification of three LB-resistance QTLs on chromosomes 6, 9 and 10 of PI 224710. A comparison of the QTLs genomic locations with the tomato physical map resulted in the identification of several candidate genes, which might be underpinning the LB-resistance QTLs in PI 224710. The identified markers associated with the LB-resistance QTLs can be utilized in breeding programs to transfer resistance from PI 224710 into tomato breeding lines and hybrid cultivars via marker-assisted breeding; they also can be used to develop near-isogenic lines for fine mapping of the QTLs. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-024-01498-1.

An ultra-dense linkage map identified quantitative trait loci corresponding to fruit quality- and size-related traits in red goji berry.

Goji berries are a small-fruited shrub with industrial importance whose fruit considered beneficial in both fresh and dried forms. Current germplasms of goji berries include small fruits with a short shelf life, less sweet and bitter taste, and a lack of appropriate genetic information. This study aimed to employ whole genome resequencing to generate an ultra-dense bin linkage map and to elucidate the genetic basis of goji fruit quality and size using quantitative trait loci (QTL) mapping analysis in a cross-pollinated hybrid population. To achieve this goal, human sensory tests were carried out to determine the bitter taste (BT) and sweet taste (ST), and to quantify the soluble solid content (SSC), fruit firmness (FF), and fruit size-related traits of fresh goji fruits over three or four years. The results revealed that the goji bin linkage map based on resequencing spanned a total length of 966.42 cM and an average bin interval of 0.03 cM. Subsequent variant calling and ordering resulted in 3,058 bins containing 35,331 polymorphic markers across 12 chromosomes. A total of 99 QTLs, with individual loci in different environments explaining a phenotypic variance of 1.21-16.95% were identified for the studied traits. Ten major effects, including colocalized QTLs corresponding to different traits, were identified on chromosomes 1, 3, 5, 6, 7, and 8, with a maximum Logarithm of Odds (LOD) of 29.25 and 16.95% of explained phenotypic variance (PVE). In addition, four stable loci, one for FF, one for fruit weight (FW), and two for fruit shape index (FSI), were mainly mapped on chromosomes 5, 6, and 7, elucidating 2.10-16.95% PVE. These findings offer valuable insights into the genetic architecture of goji fruit traits along with identified specific loci and markers to further improve and develop sweeter, less bitter and larger fruited goji berry cultivars with extended shelf life.

QTL mapping for seed vigor-related traits under artificial aging in common wheat in two introgression line (IL) populations.

Background: Seed vigor recognized as a quantitative trait is of particular importance for agricultural production. However, limited knowledge is available for understanding genetic basis of wheat seed vigor. Methods: The aim of this study was to identify quantitative trait loci (QTL) responsible for 10 seed vigor-related traits representing multiple aspects of seed-vigor dynamics during artificial aging with 6 different treatment times (0, 24, 36, 48, 60, and 72 h) under controlled conditions (48 °C, 95% humidity, and dark). The mapping populations were two wheat introgression lines (IL-1 and IL-2) derived from recipient parent (Lumai 14) and donor parent (Shaanhan 8675 or Jing 411). Results: A total of 26 additive QTLs and 72 pairs of epistatic QTLs were detected for wheat seed-vigor traits. Importantly, chromosomes 1B and 7B contained several co-located QTLs, and chromosome 2A had a QTL-rich region near the marker Xwmc667, indicating that these QTLs may affect wheat seed vigor with pleiotropic effects. Furthermore, several possible consistent QTLs (hot-spot regions) were examined by comparison analysis of QTLs detected in this study and reported previously. Finally, a set of candidate genes for wheat seed vigor were predicted to be involved in transcription regulation, carbohydrate and lipid metabolism. Conclusion: The present findings lay new insights into the mechanism underlying wheat seed vigor, providing valuable information for wheat genetic improvement especially marker-assisted breeding to increase seed vigor and consequently achieve high grain yield despite of further investigation required.

QTL-Seq identified a genomic region on chromosome 1 for soil-salinity tolerance in F2 progeny of Thai salt-tolerant rice donor line "Jao Khao".

Introduction: Owing to advances in high-throughput genome sequencing, QTL-Seq mapping of salt tolerance traits is a major platform for identifying soil-salinity tolerance QTLs to accelerate marker-assisted selection for salt-tolerant rice varieties. We performed QTL-BSA-Seq in the seedling stage of rice from a genetic cross of the extreme salt-sensitive variety, IR29, and "Jao Khao" (JK), a Thai salt-tolerant variety. Methods: A total of 462 F2 progeny grown in soil and treated with 160 mM NaCl were used as the QTL mapping population. Two high- and low-bulk sets, based on cell membrane stability (CMS) and tiller number at the recovery stage (TN), were equally sampled. The genomes of each pool were sequenced, and statistical significance of QTL was calculated using QTLseq and G prime (G') analysis, which is based on calculating the allele frequency differences or Δ(SNP index). Results: Both methods detected the overlapping interval region, wherein CMS-bulk was mapped at two loci in the 38.41-38.85 Mb region with 336 SNPs on chromosome 1 (qCMS1) and the 26.13-26.80 Mb region with 1,011 SNPs on chromosome 3 (qCMS3); the Δ(SNP index) peaks were -0.2709 and 0.3127, respectively. TN-bulk was mapped at only one locus in the overlapping 38.26-38.95 Mb region on chromosome 1 with 575 SNPs (qTN1) and a Δ(SNP index) peak of -0.3544. These identified QTLs in two different genetic backgrounds of segregating populations derived from JK were validated. The results confirmed the colocalization of the qCMS1 and qTN1 traits on chromosome 1. Based on the CMS trait, qCMS1/qTN1 stably expressed 6%-18% of the phenotypic variance in the two validation populations, while qCMS1/qTN1 accounted for 16%-20% of the phenotypic variance in one validation population based on the TN trait. Conclusion: The findings confirm that the CMS and TN traits are tightly linked to the long arm of chromosome 1 rather than to chromosome 3. The validated qCMS-TN1 QTL can be used for gene/QTL pyramiding in marker-assisted selection to expedite breeding for salt resistance in rice at the seedling stage.

QTL Mapping and Candidate Gene Analysis for Starch-Related Traits in Tartary Buckwheat (Fagopyrum tataricum (L.) Gaertn).

Starch is the main component that determines the yield and quality of Tartary buckwheat. As a quantitative trait, using quantitative trait locus (QTL) mapping to excavate genes associated with starch-related traits is crucial for understanding the genetic mechanisms involved in starch synthesis and molecular breeding of Tartary buckwheat varieties with high-quality starch. Employing a recombinant inbred line population as research material, this study used QTL mapping to investigate the amylose, amylopectin, and total starch contents across four distinct environments. The results identified a total of 20 QTLs spanning six chromosomes, which explained 4.07% to 14.41% of the phenotypic variation. One major QTL cluster containing three stable QTLs governing both amylose and amylopectin content, qClu-4-1, was identified and located in the physical interval of 39.85-43.34 Mbp on chromosome Ft4. Within this cluster, we predicted 239 candidate genes and analyzed their SNP/InDel mutations, expression patterns, and enriched KEGG pathways. Ultimately, five key candidate genes, namely FtPinG0004897100.01, FtPinG0002636200.01, FtPinG0009329200.01, FtPinG0007371600.01, and FtPinG0005109900.01, were highlighted, which are potentially involved in starch synthesis and regulation, paving the way for further investigative studies. This study, for the first time, utilized QTL mapping to detect major QTLs controlling amylose, amylopectin, and total starch contents in Tartary buckwheat. The QTLs and candidate genes would provide valuable insights into the genetic mechanisms underlying starch synthesis and improving starch-related traits of Tartary buckwheat.

GRABSEEDS: extraction of plant organ traits through image analysis.

BACKGROUND: Phenotyping of plant traits presents a significant bottleneck in Quantitative Trait Loci (QTL) mapping and genome-wide association studies (GWAS). Computerized phenotyping using digital images promises rapid, robust, and reproducible measurements of dimension, shape, and color traits of plant organs, including grain, leaf, and floral traits. RESULTS: We introduce GRABSEEDS, which is specifically tailored to extract a comprehensive set of features from plant images based on state-of-the-art computer vision and deep learning methods. This command-line enabled tool, which is adept at managing varying light conditions, background disturbances, and overlapping objects, uses digital images to measure plant organ characteristics accurately and efficiently. GRABSEED has advanced features including label recognition and color correction in a batch setting. CONCLUSION: GRABSEEDS streamlines the plant phenotyping process and is effective in a variety of seed, floral and leaf trait studies for association with agronomic traits and stress conditions. Source code and documentations for GRABSEEDS are available at: https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS .

The construction of a maize-teosinte introgression population and quantitative trait loci analysis of their 21 agronomic traits.

Teosinte is a progenitor species of maize (Zea mays ssp. mays) that retains a significant reservoir of genetic resources unaltered via the domestication process. To harness and explore the genetic reservoirs inherent in teosinte, we used the cultivated publicly inbred line H95 and wild species PI566673 (Zea mays ssp. mexicana) to develop a set of introgression lines (ILs), including 366 BC2F5 lines. Using these lines, 12481 high-quality polymorphic homozygous single nucleotide polymorphisms were converted into 2358 bin markers based on Genotyping by Target Sequencing technology. The homozygous introgression ratio in the ILs was approximately 12.1 % and the heterozygous introgression ratio was approximately 5.7 %. Based on the population phenotypic data across 21 important agronomic traits collected in Sanya and Beijing, 185 and 156 quantitative trait loci (QTLs) were detected in Sanya and Beijing, respectively, with 64 stable QTLs detected in both locations. We detected 12 QTL clusters spanning 10 chromosomes consisting of diverse QTLs related to yield traits such as grain size and weight. In addition, we identified useful materials in the ILs for further gene cloning of related variations. For example, some heterogeneous inbred families with superior genetic purity, shorter target heterozygotes, and some ILs exhibit clear morphological variation associated with plant growth, development, and domestication, manifesting traits such as white stalks, sharp seeds, and cob shattering. In conclusion, our results provide a robust foundation for delving into the genetic reservoirs of teosinte, presenting a wealth of genetic resources and offering insight into the genetic architecture underlying maize agronomic traits.

Modular control of vertebrate axis segmentation in time and space.

How the timing of development is linked to organismal size is a longstanding question. Although numerous studies have reported a correlation of temporal and spatial traits, the developmental or selective constraints underlying this link remain largely unexplored. We address this question by studying the periodic process of embryonic axis segmentation in-vivo in Oryzias fish. Interspecies comparisons reveal that the timing of segmentation correlates to segment, tissue and organismal size. Segment size in turn scales according to tissue and organism size. To probe for underlying causes, we genetically hybridised two closely related species. Quantitative analysis in ~600 phenotypically diverse F2 embryos reveals a decoupling of timing from size control, while spatial scaling is preserved. Using developmental quantitative trait loci (devQTL) mapping we identify distinct genetic loci linked to either the control of segmentation timing or tissue size. This study demonstrates that a developmental constraint mechanism underlies spatial scaling of axis segmentation, while its spatial and temporal control are dissociable modules.

Fine Mapping and Candidate Gene Analysis of Two Major Quantitative Trait Loci, qFW2.1 and qFW3.1, Controlling Fruit Weight in Pepper (Capsicum annuum).

Fruit weight is an important agronomic trait in pepper production and is closely related to yield. At present, many quantitative trait loci (QTL) related to fruit weight have been found in pepper; however, the genes affecting fruit weight remain unknown. We analyzed the fruit weight-related quantitative traits in an intraspecific Capsicum annuum cross between the cultivated species blocky-type pepper, cv. Qiemen, and the bird pepper accession, "129-1" (Capsicum annuum var. glatriusculum), which was the wild progenitor of C. annuum. Using the QTL-seq combined with the linkage-based QTL mapping approach, QTL detection was performed; and two major effects of QTL related to fruit weight, qFW2.1 and qFW3.1, were identified on chromosomes 2 and 3. The qFW2.1 maximum explained 12.28% of the phenotypic variance observed in two F2 generations, with the maximum LOD value of 11.02, respectively; meanwhile, the qFW3.1 maximum explained 15.50% of the observed phenotypic variance in the two F2 generations, with the maximum LOD value of 11.36, respectively. qFW2.1 was narrowed down to the 1.22 Mb region using homozygous recombinant screening from BC2S2 and BC2S3 populations, while qFW3.1 was narrowed down to the 4.61Mb region. According to the transcriptome results, a total of 47 and 86 differentially expressed genes (DEGs) in the candidate regions of qFW2.1 and qFW3.1 were identified. Further, 19 genes were selected for a qRT-PCR analysis based on sequence difference combined with the gene annotation. Finally, Capana02g002938 and Capana02g003021 are the most likely candidate genes for qFW2.1, and Capana03g000903 may be a candidate gene for qFW3.1. Taken together, our results identified and fine-mapped two major QTL for fruit weight in pepper that will facilitate marker-assistant breeding for the manipulation of yield in pepper.

The genetic architecture of the pepper metabolome and the biosynthesis of its signature capsianoside metabolites.

Capsicum (pepper) is among the most economically important species worldwide, and its fruits accumulate specialized metabolites with essential roles in plant environmental interaction and human health benefits as well as in conferring their unique taste. However, the genetics underlying differences in metabolite presence/absence and/or accumulation remain largely unknown. In this study, we carried out a genome-wide association study as well as generating and characterizing a novel backcross inbred line mapping population to determine the genetic architecture of the pepper metabolome. This genetic analysis provided over 1,000 metabolic quantitative trait loci (mQTL) for over 250 annotated metabolites. We identified 92 candidate genes involved in various mQTLs. Among the identified loci, we described and validated a gene cluster of eleven UDP-glycosyltransferases (UGTs) involved in monomeric capsianoside biosynthesis. We additionally constructed the gene-by-gene-based biosynthetic pathway of pepper capsianoside biosynthesis, including both core and decorative reactions. Given that one of these decorative pathways, namely the glycosylation of acyclic diterpenoid glycosides, contributes to plant resistance, these data provide new insights and breeding resources for pepper. They additionally provide a blueprint for the better understanding of the biosynthesis of species-specific natural compounds in general.

Genetics and plasticity of white leaf mark variegation in white clover (Trifolium repens L.).

BACKGROUND AND AIMS: Leaf variegation is common in plants and confers diverse adaptive functions. However, its genetic underpinnings remain largely unresolved; this is particularly true for variegation that arises through modified leaf tissue structure that affects light reflection. White clover is naturally polymorphic for structure-based white leaf mark variegation. It therefore provides a useful system to examine the genetic basis of this phenotype, and to assess potential costs to photosynthetic efficiency resulting from modified leaf structures. This study sought to map the loci controlling the white leaf mark in white clover and evaluate the relationship between white leaf mark, leaf thickness, and photosynthetic efficiency. METHODS: We generated a high-density genetic linkage map from an F3 mapping population, employing reference genome-based SNP markers. White leaf mark was quantified through detailed phenotypic evaluations alongside leaf thickness to test how tissue thickness may affect the variegation phenotype. Quantitative trait locus (QTL) mapping was performed to characterize their genetic bases. Photosynthetic efficiency measurements were used to test for physiological trade-offs between variegation and photosynthetic output. KEY RESULTS: The V locus, a major gene responsible for the white leaf mark polymorphism, was mapped to the distal end of chromosome 5, and several modifier loci were also mapped that contribute additively to variegation intensity. The presence and intensity of white leaf mark was associated with greater leaf thickness; however, increased variegation did not detectably affect photosynthetic efficiency. CONCLUSIONS: We have successfully mapped the major locus governing the white leaf mark in white clover, along with several modifier loci, revealing a complex basis for this structure-based variegation. The apparent absence of compromised photosynthesis in variegated leaves challenges the notion that variegation creates fitness trade-offs between photosynthetic efficiency and other adaptive functions. This finding suggests that other factors may maintain the white leaf mark polymorphism in white clover.

Mapping of dynamic quantitative trait loci for plant height in a RIL population of foxtail millet (Setaria italica L.).

Plant height (PH) is a crucial trait for strengthening lodging resistance and boosting yield in foxtail millet. To identify quantitative trait loci (QTL) and candidate genes associated with PH, we first developed a genetic map using a recombinant inbred line (RIL) population derived from a cross between Aininghuang and Jingu 21. Then, PH phenotyping data and four variations of best linear unbiased prediction (BLUP) were collected from nine environments and three development stages. Next, QTL mapping was conducted using both unconditional and conditional QTL methods. Subsequently, candidate genes were predicted via transcriptome analysis of parental samples at three developmental stages. The results revealed that the genetic map, based on re-sequencing, consisted of 4,360 bin markers spanning 1,016.06 cM with an average genetic distance of 0.23 cM. A total of 19 unconditional QTL, accounting for 5.23%-35.36% of the phenotypic variation explained (PVE), which included 7 major and 4 stable QTL, were identified. Meanwhile, 13 conditional QTL, explaining 5.88%-40.35% of PVE, including 5 major and 3 stable QTL, were discovered. Furthermore, four consistent and stable QTL were identified. Finally, eight candidate genes were predicted through RNA-seq and weighted gene co-expression network analysis (WGCNA). Those findings provide a crucial foundation for understanding the genetic mechanisms underlying PH development and facilitate molecular marker-assisted breeding of ideal plant types in foxtail millet.

QTL Mapping of Yield, Agronomic, and Nitrogen-Related Traits in Barley (Hordeum vulgare L.) under Low Nitrogen and Normal Nitrogen Treatments.

Improving low nitrogen (LN) tolerance in barley (Hordeum vulgare L.) increases global barley yield and quality. In this study, a recombinant inbred line (RIL) population crossed between "Baudin × CN4079" was used to conduct field experiments on twenty traits of barley yield, agronomy, and nitrogen(N)-related traits under LN and normal nitrogen (NN) treatments for two years. This study identified seventeen QTL, comprising eight QTL expressed under both LN and NN treatments, eight LN-specific QTL, and one NN-specific QTL. The localized C2 cluster contained QTL controlling yield, agronomic, and N-related traits. Of the four novel QTL, the expression of the N-related QTL Qstna.sau-5H and Qnhi.sau-5H was unaffected by N treatment. Qtgw.sau-2H for thousand-grain weight, Qph.sau-3H for plant height, Qsl.sau-7H for spike length, and Qal.sau-7H for awn length were identified to be the four stable expression QTL. Correlation studies revealed a significant negative correlation between grain N content and harvest index (p < 0.01). These results are essential for barley marker-assisted selection (MAS) breeding.

QTL mapping for pod quality and yield traits in snap bean (Phaseolus vulgaris L.).

Pod quality and yield traits in snap bean (Phaseolus vulgaris L.) influence consumer preferences, crop adoption by farmers, and the ability of the product to be commercially competitive locally and globally. The objective of the study was to identify the quantitative trait loci (QTL) for pod quality and yield traits in a snap × dry bean recombinant inbred line (RIL) population. A total of 184 F6 RILs derived from a cross between Vanilla (snap bean) and MCM5001 (dry bean) were grown in three field sites in Kenya and one greenhouse environment in Davis, CA, USA. They were genotyped at 5,951 single nucleotide polymorphisms (SNPs), and composite interval mapping was conducted to identify QTL for 16 pod quality and yield traits, including pod wall fiber, pod string, pod size, and harvest metrics. A combined total of 44 QTL were identified in field and greenhouse trials. The QTL for pod quality were identified on chromosomes Pv01, Pv02, Pv03, Pv04, Pv06, and Pv07, and for pod yield were identified on Pv08. Co-localization of QTL was observed for pod quality and yield traits. Some identified QTL overlapped with previously mapped QTL for pod quality and yield traits, with several others identified as novel. The identified QTL can be used in future marker-assisted selection in snap bean.

Three novel QTLs for FHB resistance identified and mapped in spring wheat PI672538 by bulked segregant analysis of the recombinant inbred line.

Introduction: Fusarium head blight (FHB) has a large influence on both the yield and quality of wheat grain worldwide. Host resistance is the most effective method for controlling FHB, but unfortunately, very few genetic resources on FHB resistance are available; therefore, identifying novel resistance genes or quantitative trait loci (QTLs) is valuable. Methods: Here, a recombinant inbred line (RIL) population containing 451 lines derived from the cross L661/PI672538 was sown in four different environments (2019CZa, 2019CZb, 2021QL and 2021WJ). Results: Five QTLs, consisting of two previously reported QTLs (FhbL693a and FhbL693b) and three new QTLs (FhbL693c, FhbL693d and FhbL693e), were identified. Further investigation revealed that FhbL693b, FhbL693c and FhbL693d could be detected in all four environments, and FhbL693a and FhbL693e were detected only in 2019CZb and 2021WJ, respectively. Among the QTLs, the greatest contribution (10.5%) to the phenotypic variation effect (PVE) was FhbL693d in 2021WJ, while the smallest (1.2%) was FhbL693e and FhbL693a in 2019CZb. The selection of 5Dindel-4 for FhbL693d, 4Aindel-7 for FhbL693c and 3Bindel-24 for FhbL693b decreased the number of damaged spikelets by 2.1, and a new line resistant to FHB named H140-2 was developed by marker-assisted selection (MAS). Discussion: These results could help to further improve FHB resistance in the future.

Genetic and Physiological Insights into Salt Resistance in Rice through Analysis of Germination, Seedling Traits, and QTL Identification.

Understanding the genetic basis of salt resistance in crops is crucial for agricultural productivity. This study investigates the phenotypic and genetic basis of salt stress response in rice (Oryza sativa L.), focusing on germination and seedling traits. Under salt stress conditions, significant differences were observed in seed germination and seedling traits between parental LH99 (Indica rice LuHui 99) and SN265 (japonica rice ShenNong 265). Transgressive segregation was evident within the RIL population, indicating complex genetic interactions. Nine QTLs were detected at germination and seedling stages under salt stress, namely qSGE5 and qSGE7 for seed germination energy (SGE); qSGP7 for seed germination percentage (SGP); qSSH7, qSSH9-1, and qSSH9-2 for seeding height (SSH); qSRN6 for root number (SRN); and qSDW6 and qSDW9 for dry weight (SDW). Among them, qSSH9-1 and qSDW9 were localized in the same interval, derived from the salt-resistant parent SN265. PCA revealed distinct trait patterns under salt stress, captured by six PCs explaining 81.12% of the total variance. PC composite scores were used to localize a QTL associated with early salt resistance in rice qESC9, which was located in the same interval as qSSH9-1 and qSDW9, and was subsequently unified under the name qESC9, an important QTL for early-growth salt tolerance in rice. Correlation analysis also confirmed a relationship between alleles of qESC9 and the resistance to salt, underscoring the critical role this locus plays in the determination of overall salt tolerance in rice. Physiological analyses of extreme phenotype lines highlighted the importance of ion exclusion mechanisms in salt-resistant lines, while salt-susceptible lines exhibited elevated oxidative stress and impaired antioxidant defense, contributing to cellular damage. This comprehensive analysis sheds light on the genetic and physiological mechanisms underlying salt stress response in rice, providing valuable insights for breeding programs aimed at enhancing salt resistance in rice.

Leveraging Multiomics Insights and Exploiting Wild Relatives' Potential for Drought and Heat Tolerance in Maize.

Climate change, particularly drought and heat stress, may slash agricultural productivity by 25.7% by 2080, with maize being the hardest hit. Therefore, unraveling the molecular nature of plant responses to these stressors is vital for the development of climate-smart maize. This manuscript's primary objective was to examine how maize plants respond to these stresses, both individually and in combination. Additionally, the paper delved into harnessing the potential of maize wild relatives as a valuable genetic resource and leveraging AI-based technologies to boost maize resilience. The role of multiomics approaches particularly genomics and transcriptomics in dissecting the genetic basis of stress tolerance was also highlighted. The way forward was proposed to utilize a bunch of information obtained through omics technologies by an interdisciplinary state-of-the-art forward-looking big-data, cyberagriculture system, and AI-based approach to orchestrate the development of climate resilient maize genotypes.