Search Results (475)

Wheat leaf rust (Lr), caused by Puccinia triticina Eriks. (Pt), is one of the most important diseases affecting wheat production worldwide. Using resistant wheat cultivars is the most economic and environmentally friendly way to control leaf rust. The Chinese wheat cultivar Xinong1163-4 has shown good resistance to Lr in field trials. To identify the genetic basis of Lr resistance in Xinong1163-4, 195 recombinant inbred lines (RILs) from the Xinong1163-4/Thatcher cross were phenotyped for Lr severity in three environments: the 2017/2018, 2018/2019, and 2019/2020 growing seasons in Baoding, Hebei Province. Bulked segregant analysis and simple sequence repeat markers were then used to identify the quantitative trait loci (QTLs) for Lr adult plant resistance (APR) in the population. As a result, six QTLs were detected, designated as QLr.hbau-1BL.1, QLr.hbau-1BL.2, and QLr.hbau-1BL.3. These QTLs were predicted to be novel. QLr.hbau-4BL, QLr.hbau-4BL.1, and QLr.hbau-3A were identified at similar physical positions to previously reported QTLs. Based on chromosome positions and molecular marker testing, QLr.hbau-1BL.3 shares similar flanking markers with Lr46. Lr46 is a non-race-specific APR gene for leaf rust, stripe rust, and powdery mildew. Similarly, QLr.hebau-4BL showed resistance to multiple diseases, including leaf rust, stripe rust, Fusarium head blight, and powdery mildew. The QTLs identified in this study, as well as their closely linked markers, can potentially be used for marker-assisted selection in wheat breeding. Full article

Soybeans are a primary source of plant-based protein, with seeds containing approximately 40% protein—a key quality trait. Selecting superior hybrid combinations and managing progeny effectively are crucial for developing high-protein soybean varieties. Using a recombinant inbred line population (RIL3613) derived from Dongnong L13 × Heihe 36 and its previously constructed high-density genetic linkage map, QTLs and QTL × environment interactions (QEIs) associated with seed protein content (SPC) were identified through the bi-parental population (BIP) model and multi-environment trials (MET) model in QTL IciMapping v4.2. Candidate genes were then predicted via sequence alignment and haplotype analysis between the parents. Finally, simulated breeding was conducted using the B4L function in the In Silico Breeding (ISB) module of the Blib platform to determine optimal breeding strategies across diverse environments. The analysis identified 19 QTLs associated with SPC and 97 QEIs linked to SPC. These QTLs collectively explained 84.442% of the phenotypic variance, with four QTLs exhibiting significant contributions. A key candidate gene, Glyma.12G231400, associated with soybean SPC, was predicted within the 38,995,090–39,293,825 bp interval on chromosome 12. Across 11 environments, three to six optimal breeding schemes were selected, all employing modified pedigree selection. These findings enhance our understanding of the genetic basis of soybean protein formation and provide technological support for molecular breeding for seed quality improvement. Full article

Soybean frogeye leaf spot (FLS) disease has been reported globally and is caused by the fungus Cercospora sojina, which affects the growth, seed yield, and quality of soybean. Among the 15 physiological microspecies of C. sojina soybean in China, Race 7 is one of the main pathogenic microspecies. A few genes are involved in resistance to FLS, and they cannot meet the need to design molecular breeding methods for disease resistance. In this study, a soybean recombinant inbred line (RIL3613) population and a germplasm resource (GP) population were planted at two sites, Acheng (AC) and Xiangyang (XY). Phenotypic data on the percentage of leaf area diseased (PLAD) in soybean leaves were obtained via image recognition technology after the inoculation of seven physiological species and full onset at the R3 stage. Quantitative trait loci (QTLs) and quantitative trait nucleotides (QTNs) were mapped via linkage analysis and genome-wide association studies (GWASs), respectively. The resistance genes of FLS were subsequently predicted in the linkage disequilibrium region of the collocated QTN. We identified 114 QTLs and 18 QTNs in the RIL3613 and GP populations, respectively. A total of 14 QTN loci were colocalized in the two populations, six of which presented high phenotypic contributions. Through haplotype–phenotype association analysis and expression quantification, three genes (Glyma.06G300100, Glyma.06G300600, and Glyma.13G172300) located near molecular markers AX-90524088 and AX-90437152 (QTNs) are associated with FLS Chinese Race 7, identifying them as potential candidate resistance genes. These results provide a theoretical basis for the genetic mining of soybean antigray spot No. 7 physiological species. These findings also provide a theoretical basis for understanding the genetic mechanism underlying FLS resistance in soybeans. Full article

Although numerous rice plant height-related genes have been cloned and functionally characterized in recent years, a gap between the identified genes and their utilization in breeding still exists. Here, we developed a linear regression model by pyramiding plant height-related alleles to predict rice plant height and confirmed that it can be used in rice breeding. In our study, we firstly identified 22 plant height-associated molecular markers from 218 markers in an association mapping population which consisted of 273 rice varieties. Linear regression analysis revealed a positive correlation between rice plant height and the number of plant height-increasing alleles derived from these 22 molecular markers. Subsequently, linear regression models were developed using 2–10 loci based on the genotype and phenotype data of the association mapping population. The predictive accuracy of the model was tested using a recombinant inbred line (RIL) population consisting of 219 lines, and it revealed the trend that predictive accuracy increased with more loci in a certain range of less than five loci. If the prediction model was built based on 5–10 loci, it yielded an average absolute error from 11.05 to 11.96 cm, which was smaller than absolute error induced by environmental factors (5.72 cm to 12.79 cm). The reliable prediction of rice plant height by this model highlights its value as a practical tool for optimizing rice breeding strategies. Additionally, the linear regression model developed in this study not only can facilitate plant height manipulation but also will inspire other design breeding techniques in other crops or other traits. Full article

To identify the key genes conferring resistance to soybean cyst nematode race 4 (SCN4, Heterodera glycines), this study utilized 280 recombinant inbred lines (RILs) derived from the resistant cultivar Huipizhiheidou (HPD) and the susceptible cultivar Jindou23 (JD23). Through phenotypic characterization and a genome-wide association study (GWAS), a genomic region (Gm18:1,223,546–1,782,241) on chromosome 18 was mapped, yielding 14 candidate genes. GmPDH1 was validated as a critical resistance gene using reverse transcription quantitative PCR (RT-qPCR) and Kompetitive Allele Specific PCR (KASP) marker M0526. RT-qPCR revealed that GmPDH1 expression in HPD roots was upregulated 9 days post-inoculation with SCN4 compared to uninoculated controls. KASP genotyping showed that marker M0526 efficiently distinguished between resistant and susceptible plants in natural populations: 71.05% of the resistant accessions exhibited resistant or moderately resistant genotypes, whereas 81.03% of the susceptible accessions showed susceptible or highly susceptible genotypes. Functional validation demonstrated that overexpression of GmPDH1 significantly enhanced SCN4 resistance in the susceptible cultivars JD23 and Jack, whereas CRISPR/Cas9-mediated knockout of GmPDH1 in HPD attenuated its resistance. This study confirmed GmPDH1 as a key gene governing SCN4 resistance and developed an efficient molecular marker, M0526, providing theoretical insights and technical tools for dissecting nematode resistance mechanisms and advancing soybean disease-resistant breeding. Full article

To elucidate the genetic basis of thousand-kernel weight (TKW) related to fundamental traits such as kernel length (KL), kernel width (KW), and kernel diameter ratio (KDR) at the individual quantitative trait loci (QTL) level, both unconditional QTL analysis and conditional QTL analysis for TKW were analyzed using a recombinant inbred line (RIL) population, along with a simplified physical map. A total of 37 unconditional QTLs and 34 conditional QTLs were identified. Six QTLs exhibited independent effects from individual traits (KL, KW, or KDR), while 18 QTLs showed common influences from two or three of these traits simultaneously. Additionally, 26 pairs of epistatically interacting QTLs involving 16 loci were detected. Subsequently, fine mapping of the stable and major-effect QTL QTkw1B was carried out using the derived near-isogenic lines (NILs), ultimately locating it within the interval of 698.15–700.19 Mb on chromosome 1B of the KN9204 genome. The conditional QTL analysis and genetic effect analysis based on NILs both indicated that the increase in TKW was primarily contributed by kernel length. The QTL identified in the present study through the combination of conditional and unconditional QTL mapping could increase the understanding of the genetic interrelationships between TKW and kernel size traits at the individual QTL level and provide a theoretical basis for subsequent candidate gene mining. Full article

Grain yield establishment is a complex progress and the genetic basis of one of the most important yield components, 100-kernel weight, remains largely unknown. Here, we employed a double haploid (DH) population containing 477 lines which was developed from a cross of two maize elite inbred lines, PHBA6 and Chang7-2, to identify quantitative trait loci (QTL) that related to 100-kernel weight. The phenotypes of the DH population were acquired over three years in two different locations, while the DH lines were genotyped by next-generation sequencing technology of massively parallel 3ʹ end RNA sequencing (MP3RNA-seq). Eventually, 28,874 SNPs from 436 DH lines were preserved after SNP calling and filtering and a genetic map with a length of 837 cM was constructed. Then, single environment QTL analysis was performed using the R/qtl program, and it was found that a total of 17 QTLs related to 100-kernel weight were identified and distributed across the whole genome except chromosomes 5 and 6. The total phenotypic variation explained by QTLs detected in three different environments (BJ2016, BJ2107, and HN2018) was 22.2%, 32.9%, and 51.38%, respectively. Among these QTLs, three of them were identified across different environments as environmentally stable QTLs and explained more than 10% of the phenotypic variance each. Together, the results provided in this study preliminarily revealed the genetic basis of 100-kernel weight and will enhance molecular breeding for key agronomic kernel-related traits in maize. Full article

High grain quality is a key target in wheat breeding and is influenced by genetic and environmental factors. This study evaluated 94 recombinant inbred lines (RILs) from a Pamyati Azieva × Paragon (PA × P) mapping population grown in two regions in Kazakhstan to assess the genetic basis of six grain quality traits: the test weight per liter (TWL, g/L), grain protein content (GPC, %), gluten content (GC, %), gluten deformation index in flour (GDI, unit), sedimentation value in a 2% acetic acid solution (SV, mL), and grain starch content (GSC, %). A correlation analysis revealed a trade-off between protein and starch accumulation and an inverse relationship between grain quality and yield components. Additionally, GPC exhibited a negative correlation with yield per square meter (YM2), underscoring the challenge of simultaneously improving grain quality and yield. With the use of the QTL Cartographer statistical package, 71 quantitative trait loci (QTLs) were identified for the six grain quality traits, including 20 QTLs showing stability across multiple environments. Notable stable QTLs were detected for GPC on chromosomes 4A, 5B, 6A, and 7B and for GC on chromosomes 1D and 6A, highlighting their potential for marker-assisted selection (MAS). A major QTL found on chromosome 1D (QGDI-PA × P.ipbb-1D.1, LOD 19.4) showed a strong association with gluten deformation index, emphasizing its importance in improving flour quality. A survey of published studies on QTL identification in common wheat suggested the likely novelty of 12 QTLs identified for GDI (five QTLs), TWL (three QTLs), SV, and GSC (two QTLs each). These findings underscore the need for balanced breeding strategies that optimize grain composition while maintaining high productivity. With the use of SNP markers associated with the identified QTLs for grain quality traits, the MAS approach can be implemented in wheat breeding programs. Full article

Background/Objectives: Sweet taste receptor TAS1R2 is expressed in skeletal muscle, yet its role in muscle metabolism remains poorly understood. Methods: Here, we leverage the BXD recombinant inbred mouse panel and Tas1r2 whole-body knockout (bKO) models to investigate the transcriptional impact of Tas1r2 deficiency on skeletal muscle function. Results: A gene network analysis revealed significant overlap in transcriptomic signatures between BXD strains with low Tas1r2 expression (BXD L_Tas1r2) and bKO muscle, particularly in pathways regulating oxidative phosphorylation, cytoplasmic ribosome function, and proteostasis. Notably, Tas1r2 expression negatively correlated with genes involved in fatty acid metabolism, suggesting its role in lipid utilization. Under high-fat diet (HFD) conditions, BXD_HFD L_Tas1r2 mice exhibited further enrichment in pathways linked to proteasome degradation, oxidative stress, and interleukin signaling, amplifying the transcriptomic convergence with bKO models. Key transcription factors (Mlxipl, Nfic, Rxrb) exhibited altered regulatory patterns under dietary stress, indicating that TAS1R2 influences metabolic adaptability through transcriptional reprogramming. Conclusions: Given that human TAS1R2 variants rarely result in complete loss of function (LOF), the BXD panel provides an effective dose-dependent model to bridge the gap between knockout phenotypes and human SNP carriers. Our findings establish TAS1R2 as a metabolic regulator in skeletal muscle and highlight the utility of genetically diverse mouse populations in dissecting gene-diet interactions relevant to human metabolic diseases. Full article

Brassica crops, cultivated as vegetables, oilseeds, and forages, are vital economic resources in agricultural production. However, black rot caused by Xanthomonas campestris pv. campestris (Xcc) poses a significant threat to the production of these crops. This study aimed to enhance the resistance resource pool for Brassica crops by evaluating 29 inbred lines and 52 commercial cultivars of B. rapa through an inoculation test. Among these, 11 inbred lines, such as ‘E5’ and ‘LW’, and 8 commercial cultivars, such as ‘QX’ and ‘SY’, demonstrated high resistance. We constructed a genetic segregating population (P₁, P₂, F₁, F₂) using the highly resistant line ‘E5’ and the highly susceptible line ‘E4’ as parents. Utilizing a major gene plus polygenic mixed inheritance model for genetic analysis, our findings indicate that the resistance to black rot in ‘E5’ is governed by a pair of additive-dominant polygenes, and the main gene heritability is 93.43%. Furthermore, transmission electron microscopy examination revealed numerous autophagic structures in the xylem parenchyma cells of the highly resistant line ‘E5’, while the highly susceptible line exhibited cell necrosis, indicating that the resistant material might protect mesophyll cells and adjacent structures through programmed cell death. This research contributes novel genetic materials for breeding disease-resistant varieties, enhances our understanding of Xcc invasion mechanisms and host defense traits, and establishes a theoretical framework for the effective prevention and control of these diseases. Full article

Leaf rust (LR) is a devastating foliar disease that impacts common wheat (Triticum aestivum L.) globally. For optimal disease protection, wheat cultivars should possess adult plant resistance (APR) to leaf rust. In the current study, the objective was to map quantitative trait loci (QTL) related to leaf rust resistance. This was achieved by using 193 recombinant inbred line (RIL) populations which were developed from the cross between N. Strampelli and Huixianhong. Four trials were conducted in China (three in Baoding, Hebei province, and one in Zhoukou, Henan province) to assesses the leaf rust response of the RILs and parental lines. The wheat 660K SNP array and additional SSR markers were used to genotype the RIL populations. Through inclusive composite interval mapping (ICIM), three QTL related to leaf rust (LR) resistance were detected. ICIM was also employed to reevaluate previously published data in order to identify QTL with pleiotropic effects. To determine the physical positions, the flanking sequences of all SNP probes were compared against the Chinese Spring wheat reference sequence through BLAST searches. Three leaf rust resistance loci, two on chromosome 2A and 5B, were contributed by N. Strampelli. QLr.hbau-2AL.1 was detected in three leaf rust environments with phenotypic variance explained (PVE of 12.2–17%); QLr.hbau-2AL.2 was detected in two environments with 12.5–13.2% of the PVE; and QLr.hbau-5BL was detected in all leaf rust environments with phenotypic variance explained (PVE) of 17.8–19.1%. QLr.hbau-5BL exhibited potentially pleiotropic responses to multiple diseases. The QTL and the associated flanking markers discovered in this study could prove valuable for purposes such as fine mapping, the exploration of candidate genes, and marker-assisted selection (MAS). Full article

Rice tillering is an important trait that is genetically and environmentally co-regulated. Nitorgen is one of the key nutrients affecting tillering, and straw return further affects tiller development by altering soil heterogeneity. In order to analyze the genetic regulation mechanism of rice tillering and its interactions with the environment, 124 recombinant inbred line (RIL) populations derived from two superior Peijiu lines, 9311 and PA64s, were used as materials in this study, and the dynamic tillering phenotypes were measured under three treatments (no nitrogen application, nitrogen application, and nitrogen + straw return) for two consecutive years. Using an existing genetic map, we conducted single-environment, multi-environment, and meta-QTL analyses to systematically identify tiller-related genetic loci and their environmental interactions. The main findings were as follows: (1) A total of 57 QTLs were identified in the single-environment QTL analysis, of which 44 were unreported new QTLs. Four QTLs showed temporal pleiotropy, ten QTLs contributed more than 10% to the phenotypes under the no-N treatment, and five QTLs contributed more than 10% under the straw return treatment. Among them, the phenotypic contribution of mks1-355 (qD1tn1-3) and mks1-352 (qD2TN1-2) both exceeded 40%. (2) Multi-environmental QTL analysis detected 15 QTLs. Of these, qmD1TN1 (mks1-356) showed no environmental interaction effect, while qmD1TN12 (mks12-267), qmD2TN1 (mks1-334), qmD2TN3-1 (mks3-105), and qmD5TN6 (mks6-71) exhibited antagonistic pleiotropy, suggesting that these QTL need to be considered for environmental specificity in breeding. (3) Meta-QTL analysis localized 52 MQTLs, of which MQTL3.1 and MQTL6.8 contained 82 and 59 candidate genes, respectively, and no reported tiller-related genes were found. (4) mks1-355 (qD1tn1-3), mks1-352 (qD2TN1-2), and mks1-356 (qmD1TN1) may be located in the same genetic locus, and their phenotypic contributions were more than 40%. These QTLs were detected stably for two consecutive years, and they may be the main effector QTLs in tillering that are less affected by the environment. Further analysis revealed that these QTLs corresponded to MQTL1.6, which contains 56 candidate genes. Of these, the expression level of OsSPL2 gene in the parental line 9311 was significantly higher than that of PA64s, and there were polymorphic differences in the coding region. It was hypothesized that OsSPL2 was the main effector gene of this QTL. This study provides important genetic resources for mining candidate genes related to tillering and nitrogen efficiency in rice and lays a theoretical foundation for directional breeding and molecular marker development in specific environments. Full article

The Multi-Parent Advanced Generation Inter-Cross (MAGIC) population is a powerful tool for dissecting the genetic architecture controlling natural variation in complex traits. In this work, the natural variation available in Arabidopsis thaliana MAGIC lines was evaluated by mapping quantitative trait loci (QTLs) for primary root length (PRL), lateral root number (LRN), lateral root length (LRL), adventitious root number (ARN), and adventitious root length (ARL). We analyzed the differences in the root structure of 139 MAGIC lines by measuring PRL, LRN, LRL, ARN, and ARL. Through QTL mapping, we identified new potential genes that may be responsible for these traits. Furthermore, we detected single-nucleotide polymorphisms (SNPs) in the coding regions of candidate genes in the founder accessions and the recombinant inbred lines (RILs). We identified a significant region on chromosome 1 associated with AR formation. This region encompasses 316 genes, many of which are involved in auxin and gibberellin signaling and homeostasis. We discovered SNPs in the coding regions of these candidate genes in the founder accessions that may contribute to natural variation in AR characteristics. Additionally, we found a novel gene that encodes a protein from the hydroxyproline-rich glycoprotein family, which exhibits differential SNPs in accessions with contrasting AR formation. This study provides genetic insights into the natural variation in AR numbers using MAGIC lines linked to hormone-related genes. Full article

Barley (Hordeum vulgare L.) is one of the most important cereal crops in the world, and its production is important to humans. Barley spike morphology is highly correlated with yield and is also a complex multigene-controlled quantitative trait. To date, a considerable number of spike-related quantitative trait loci (QTLs) have been reported in barley, but the large physical distances between most of them and the lack of follow-up studies have made it difficult to use them in molecular-assisted breeding in barley. To explore more novel and yield-enhancing spike QTLs, in this study, a high-density genetic linkage map was developed based on a population of 172 F_2:12 recombinant inbred lines (RILs) developed from a cross between the barley variety Yongjiabaidamai (YJ) and Hua 30 (H30), and used to map the spike length (SL), rachis node number (SRN), and spike density (SD). A total of 50 additive QTLs (LOD > 3) were mapped in four environments, four of them being stable and major QTLs. The qSL2-5 overlaps with the zeo1 gene, comparing the gene sequences of both parents and combining with previous studies, zeo1 was determined to be the SL regulatory gene in qSL2-5. The qSRN2-1 overlaps with vrs1, but it has not been previously reported that vrs1 affects SRN. Notably, two novel QTLs, one each on chromosomes 2H (qSL2-1) and 5H (qSL5-1), respectively, were first identified in this study. The qSL2-1 has only 0.06 Mb and contains three high-confidence genes. In addition, this study explored the relationship between three spike traits, and found that SL was affected by both SRN and SD, while there was almost no relationship between SRN and SD. We also explored the effect of these QTLs on grain weight per spike (GWPS) to assess their effect on yield and found that qSRN2-1 and qSL5-1 had a greater effect on GWPS, suggesting that they are potential loci to increase yield. Full article

Genomic selection in Eucalyptus enables the identification of superior genotypes, thereby reducing breeding cycles and increasing selection intensity. However, its efficiency may be compromised due to the complex structures of breeding populations, which arise from the use of multiple parents from different species. In this context, partial inbred lines have emerged as a viable alternative to enhance efficiency and generate productive clones. This study aimed to apply genomic selection to a self-fertilized population of different Eucalyptus spp. Our objective was to predict the genomic breeding values (GEBVs) of individuals lacking phenotypic information, with a particular focus on inbred line development. The studied population comprised 662 individuals, of which 600 were phenotyped for diameter at breast height (DBH) at 36 months in a field experiment. The remaining 62 individuals were located in a hybridization orchard and lacked phenotypic data. All individuals, including progeny and parents, were genotyped using 10,132 SNP markers. Genomic prediction was conducted using four frequentist models—GBLUP, GBLUP dominant additive, HBLUP, and ABLUP—and five Bayesian models—BRR, BayesA, BayesB, BayesC, and Bayes LASSO—using k-fold cross-validation. Among the GS models, GBLUP exhibited the best overall performance, with a predictive ability of 0.48 and an R² of 0.21. For mean squared error, the Bayes LASSO presented the lowest error (3.72), and for the other models, the MSE ranged from 3.72 to 15.50. However, GBLUP stood out as it presented better precision in predicting individual performance and balanced performance in the studied parameter. These results highlight the potential of genomic selection for use in the genetic improvement of Eucalyptus through inbred lines. In addition, our model facilitates the identification of promising individuals and the acceleration of breeding cycles, one of the major challenges in Eucalyptus breeding programs. Consequently, it can reduce breeding program production costs, as it eliminates the need to implement experiments in large planted areas while also enhancing the reliability in selection of genotypes. Full article