Temperature-sensitive genic male sterile (TGMS) line Beijing Sterility 366 (BS366) has been utilized in hybrid breeding for a long time, but the molecular mechanism underlying male sterility remains unclear. Expression arrays, small RNA, and degradome sequencing were used in this study to explore the potential role of miRNA in the cold-induced male sterility of BS366. Microspore observation showed defective cell plates in dyads and tetrads and shrunken microspores at the vacuolated stage. Differential regulation of Golgi vesicle transport, phragmoplast formation, sporopollenin biosynthesis, pollen exine formation, and lipid metabolism were observed between cold and control conditions. Pollen development was significantly represented in the 352 antagonistic miRNA-target pairs in the integrated analysis of miRNA and mRNA profiles. The specific cleavage of ARF17 and TIR1 by miR160 and miR393 were found in the cold-treated BS366 degradome, respectively. Thus, the cold-mediated miRNAs impaired cell plate formation through repression of Golgi vesicle transport and phragmoplast formation. The repressed expression of ARF17 and TIR1 impaired pollen exine formation. The results of this study will contribute to our understanding of the roles of miRNAs in male sterility in wheat.

The accurate delineation of mega-environments (MEs) and the rigorous evaluation of test locations are critical for optimizing regional variety trial schemes, particularly when addressing unbalanced datasets from multi-year, multi-location wheat (Triticum aestivum L.) trials. This study aimed at refining the regional wheat trial framework in the Huanghuai Winter Wheat Region (HWWR) of China using an integrated BLUP-GGE biplot approach, which combines best linear unbiased prediction (BLUP) values with genotype main effect plus genotype-by-environment interaction (GGE) biplot analysis to account for temporal variability and experimental error. We systematically evaluated the BLUP-GGE biplot approach, focusing on its goodness of fit and its ability to resolve inter-location relationships. We further assessed test location representativeness, discriminating ability, and overall desirability via the BLUP-GGE biplot, and contrasted ME delineation outcomes between the traditional “which-won-where” polygon method and the test location clustering-based approach. The BLUP-GGE biplot explained 72.9% of total phenotypic variation, with all location vectors displaying positive correlations (maximum angle = 88.8°), confirming the ecological homogeneity of the target region and yielding robust evaluation results. Based on the ideal tester view, Puyang was identified as the most desirable location, followed by Zhumadian, Shangqiu, and Huixian, while Lianyungang and Suqian exhibited relatively poor comprehensive performance. MEs delineated by the “which-won-where” method showed strong inter-ME correlations and insufficient differentiation, whereas the location clustering-based method markedly enhanced inter-ME discrimination (maximum vector angle > 60°), stably partitioning the HWWR into three distinct MEs with clear cultivar–ME interaction patterns: ME1 (Lianyungang, Suqian, Fuyang, Suzhou, Guoyang, Huixian, Huai’an, Xinmaqiao, Huayin, and Yangling), ME2 (Luoyang, Xinxiang, Zhumadian, Shangqiu, Puyang, and Luohe), and ME3 (Baoji, Xuzhou, Yuanyang, Sheyang, and Xingyang). This study confirms the superiority of the BLUP-GGE biplot for analyzing unbalanced multi-year multi-environment trial data and validates a robust clustering strategy for ME delineation. The findings provide a scientific basis for optimizing wheat regional trial systems and facilitating precise cultivar deployment in the HWWR, and offer a reference for analogous studies on other crops or ecological regions.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), threatens global wheat production. Breeding resistant varieties is a key to disease control. In this study, 198 modern wheat varieties were phenotyped with the prevalent Pst races CYR33 and CYR34 at the seedling stage and with mixed Pst races at the adult-plant stage. Seven stable resistance varieties with infection type (IT) ≤ 2 and disease severity (DS) ≤ 20% were found, including five Chinese accessions (Zhengpinmai8, Zhengmai1860, Zhoumai36, Lantian36, and Chuanmai32), one USA accession (GA081628-13E16), and one Pakistani accession (Pa12). The genotyping applied a 55K wheat single-nucleotide polymorphism (SNP) array. A genome-wide association study (GWAS) identified 14 QTL using a significance threshold of p ≤ 0.001, which distributed on chromosomes 1B (4), 1D (2), 2B (4), 6B, 6D, 7B, and 7D (4 for CYR33, 7 for CYR34, 3 for mixed Pst races), explaining 6.04% to 18.32% of the phenotypic variance. Nine of these QTL were potentially novel, as they did not overlap with the previously reported Yr or QTL loci within a ±5.0 Mb interval (consistent with genome-wide LD decay). The haplotypes and resistance effects were evaluated to identify the favorable haplotype for each QTL. Candidate genes within the QTL regions were inferred based on their transcription levels following the stripe rust inoculation. These resistant varieties, QTL haplotypes, and favorable alleles will aid in wheat breeding for stripe rust resistance.

Data imbalance is common in multi-environment trials (METs) and complicates genotype and environment evaluations. To address this, linear mixed models (LMMs) and factor-analytic linear mixed models (FA-LMMs) were employed to estimate best linear unbiased predictions (BLUPs) for genotype effects. The highly unbalanced wheat grain yield data from the 2021–2025 national regional trials in the Northern Winter Wheat Region (NWWR) served as our case study. These BLUP values were then integrated into the genotype plus genotype-by-environment interaction (GGE) biplot framework to construct BLUP-GGE biplots. Subsequently, these biplots were systematically compared in terms of genotype evaluation, environment evaluation, and mega-environment delineation. Based on Akaike Information Criterion (AIC) and cross-validation, FA2 was chosen as the representative FA-LMM for comparison with LMM. For genotype evaluation, the two biplots produced highly consistent rankings. Based on the FA-LMM output, the top five genotypes were BH5318, BH2605, BH7868, JD39, and ZM8729. For mega-environment delineation, both approaches consistently identified one major and one minor mega-environment, with eight and three environments, respectively. The FA-LMM-based biplot provided clearer separation between them. For environment evaluation, the two biplots showed highly significant correlations for discriminating ability, representativeness, and desirability index. Luannan ranked first, followed by Zunhua, Baodi, Gu’an, Changping, and Shunyi, Xushui and Wuqing were intermediate, while Taiyuan, Tunliu, and Qixian performed relatively poorly. In summary, both methods are effective and reliable for analyzing highly unbalanced MET data, and show high consistency in genotype and environment evaluation. However, the FA-LMM-based BLUP-GGE biplot provides superior goodness-of-fit and more precise mega-environment delineation.

Molecular characterization of wheat varieties is of high importance for wheat breeding. In the present study, 1883 Chinese wheat cultivars released between 1971 and 2025 were genotyped using a wheat 90K SNP array. Analysis of genetic diversity indicated a consistent A > B > D pattern, with the D subgenome showing the lowest genetic diversity and fastest LD decay (4.2 Mb), indicating diminished genetic diversity driven by breeding and selection. Population structure analysis revealed ten genetic clusters with clear ecological preferences, whereas genetic differentiation was weak (FST < 0.05) and gene flow extensive (Nem > 1), indicating the co-existence of strong ecological adaptation and frequent interregional germplasm exchange. Selective sweeps were identified on chromosome 5A (TaVRN1) and 2D (Rht8/RNHL-D1) regions controlling vernalization, plant height, and stress tolerance. Despite high gene flow, these loci for variety adaptiveness remained differentiated, highlighting the impact of artificial selection. Average pairwise genetic similarity was 0.56, but only a minority of cultivar pairs exceeded 0.98, especially in the Northeastern Spring Wheat and Middle-Lower Yangtze River Facultative Wheat regions, revealing severe genetic homogeneity from the excessive use of core parents. Nucleotide diversity increased to a peak in 2011–2015. These findings indicate that Chinese wheat cultivars still harbor significant potential for genetic improvement, highlighting the critical need to integrate exotic germplasm into core breeding parents to drive future advancements.