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IntroductionAgronomic traits are key components of wheat yield. Exploitation of the major underlying quantitative trait loci (QTLs) can improve the yield potential in wheat breeding.MethodsIn this study, we constructed a recombinant inbred line (RIL) population from Mingxian 169 (MX169) and Pindong 34 (PD34) to determine the QTLs for grain length (GL), grain width (GW), grain length-to-width ratio (LWR), plant height (PH), spike length (SL), grain number per spike (GNS), and the thousand grain weight (TGW) across four environments using wheat 90K SNP array.ResultsA QTL associated with TGW, i.e., QTGWpd.swust-6BS, was identified on chromosome 6B, which explained approximately 14.1%–16.2% of the phenotypic variation. In addition, eight QTLs associated with GL were detected across six chromosomes in four different test environments. These were QGLpd.swust-1BL, QGLpd.swust-2BL, QGLpd.swust-3BL.1, QGLpd.swust-3BL.2, QGLpd.swust-5DL, QGLpd.swust-6AL, QGLpd.swust-6DL.1, and QGLpd.swust-6DL.2. They accounted for 9.0%–21.3% of the phenotypic variation. Two QTLs, namely, QGWpd.swust-3BS and QGWpd.swust-6DL, were detected for GW on chromosomes 3B and 6D, respectively. These QTLs explained 12.8%–14.6% and 10.8%–15.2% of the phenotypic variation, respectively. In addition, two QTLs, i.e., QLWRpd.swust-7AS.1 and QLWRpd.swust-7AS.2, were detected on chromosome 7A for the grain LWR, which explained 10.9%–11.6% and 11.6%–11.2% of the phenotypic variation, respectively. Another QTL, named QGNSpd-swust-6DS, was discovered on chromosome 6D, which determines the GNS and which accounted for 11.4%–13.8% of the phenotypic variation. Furthermore, five QTLs associated with PH were mapped on chromosomes 2D, 3A, 5A, 6B, and 7B. These QTLs were QPHpd.swust-2DL, QPHpd.swust-3AL, QPHpd.swust-5AL, QPHpd.swust-6BL, and QPHpd.swust-7BS, which accounted for 11.3%–19.3% of the phenotypic variation. Lastly, a QTL named QSLpd.swust-3AL, conferring SL, was detected on chromosome 3A and explained 16.1%–17.6% of the phenotypic variation. All of these QTLs were defined within the physical interval of the Chinese spring reference genome.DiscussionThe findings of this study have significant implications for the development of fine genetic maps, for genomic breeding, and for marker-assisted selection to enhance wheat grain yield.

IntroductionStripe rust is a global disease of wheat. Identification of new resistance genes is key to developing and growing resistant varieties for control of the disease. Wheat line PI 660122 has exhibited a high level of stripe rust resistance for over a decade. However, the genetics of stripe rust resistance in this line has not been studied. A set of 239 recombinant inbred lines (RILs) was developed from a cross between PI 660122 and an elite Chinese cultivar Zhengmai 9023.MethodsThe RIL population was phenotyped for stripe rust response in three field environments and genotyped with the Wheat 15K single-nucleotide polymorphism (SNP) array.ResultsA total of nine quantitative trait loci (QTLs) for stripe rust resistance were mapped to chromosomes 1B (one QTL), 2B (one QTL), 4B (two QTLs), 4D (two QTLs), 6A (one QTL), 6D (one QTL), and 7D (one QTL), of which seven QTLs were stable and designated as QYrPI660122.swust-4BS, QYrPI660122.swust-4BL, QYrPI660122.swust-4DS, QYrPI660122.swust-4DL, QYrZM9023.swust-6AS, QYrZM9023.swust-6DS, and QYrPI660122.swust-7DS. QYrPI660122.swust-4DS was a major all-stage resistance QTL explaining the highest percentage (10.67%–20.97%) of the total phenotypic variation and was mapped to a 12.15-cM interval flanked by SNP markers AX-110046962 and AX-111093894 on chromosome 4DS.DiscussionThe QTL and their linked SNP markers in this study can be used in wheat breeding to improve resistance to stripe rust. In addition, 26 lines were selected based on stripe rust resistance and agronomic traits in the field for further selection and release of new cultivars.

Glioma is well known as the most aggressive and prevalent primary malignant tumor in the central nervous system. Molecular subtypes and prognosis biomarkers remain a promising research area of gliomas. Notably, the aberrant expression of mesenchymal (MES) subtype related long non-coding RNAs (lncRNAs) is significantly associated with the prognosis of glioma patients. In this study, MES-related genes were obtained from The Cancer Genome Atlas (TCGA) and the Ivy Glioblastoma Atlas Project (Ivy GAP) data sets of glioma, and MES-related lncRNAs were acquired by performing co-expression analysis of these genes. Next, Cox regression analysis was used to establish a prognostic model, that integrated ten MES-related lncRNAs. Glioma patients in TCGA were divided into high-risk and low-risk groups based on the median risk score; compared with the low-risk groups, patients in the high-risk group had shorter survival times. Additionally, we measured the specificity and sensitivity of our model with the ROC curve. Univariate and multivariate Cox analyses showed that the prognostic model was an independent prognostic factor for glioma. To verify the predictive power of these candidate lncRNAs, the corresponding RNA-seq data were downloaded from the Chinese Glioma Genome Atlas (CGGA), and similar results were obtained. Next, we performed the immune cell infiltration profile of patients between two risk groups, and gene set enrichment analysis (GSEA) was performed to detect functional annotation. Finally, the protective factors DGCR10 and HAR1B, and risk factor SNHG18 were selected for functional verification. Knockdown of DGCR10 and HAR1B promoted, whereas knockdown of SNHG18 inhibited the migration and invasion of gliomas. Collectively, we successfully constructed a prognostic model based on a ten MES-related lncRNAs signature, which provides a novel target for predicting the prognosis for glioma patients.

Glioma is one of the most common malignant tumors of the central nervous system, and its prognosis is extremely poor. Aberrant methylation of lncRNA promoter region is significantly associated with the prognosis of glioma patients. In this study, we investigated the potential impact of methylation of lncRNA promoter region in glioma patients to establish a signature of nine lncRNA methylated genes for determining glioma patient prognosis. Methylation data and clinical follow-up data were obtained from The Cancer Genome Atlas (TCGA). The multistep screening strategy identified nine lncRNA methylated genes that were significantly associated with the overall survival (OS) of glioma patients. Subsequently, we constructed a risk signature that containing nine lncRNA methylated genes. The risk signature successfully divided the glioma patients into high-risk and low-risk groups. Compared with the low-risk group, the high-risk group had a worse prognosis, higher glioma grade, and older age. Furthermore, we identified two lncRNAs termed PCBP1-AS1 and LINC02875 that may be involved in the malignant progression of glioma cells by using the TCGA database. Loss-of-function assays confirmed that knockdown of PCBP1-AS1 and LINC02875 inhibited the proliferation, migration, and invasion of glioma cells. Therefore, the nine lncRNA methylated genes signature may provide a novel predictor and therapeutic target for glioma patients.