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Wheat is one of the most important cereal crops in the world. Cold stress is a major constraint in production of wheat grown in cold climate regions. In this study, we conducted a comprehensive assessment of cold stress tolerance in wheat genotypes through field screening, cell membrane stability through electrolyte leakage assay and biochemical profiling. A core set comprising 4560 genotypes was evaluated for two years (2021–2022), revealing substantial genetic variation for cold stress tolerance. Most genotypes exhibited moderate tolerance, while a smaller proportion showed susceptibility to cold stress. Based on the cold screening data in the field, a mini‐core set of 350 genotypes was selected for membrane stability analysis using electrical conductivity assays. Significant differences were observed in membrane stability among the genotypes, indicating the presence of genetic variation for this trait. Furthermore, a mini‐core set was narrowed down to 50 diverse candidate genotypes that were subsequently profiled for various biochemicals, including reactive oxygen species (ROS) like lipid peroxidation (MDA) and hydrogen peroxide (H202), osmoprotectant (proline) and enzymatic antioxidants including ascorbate peroxidase (APX), superoxide dismutase (SOD), guaiacol peroxidase (GPX), and catalase (CAT). Correlation analysis of the biochemicals revealed negative associations between antioxidants and reactive oxygen species (ROS), highlighting their role in mitigating oxidative damage under cold stress. This study enhances our understanding of the physiological and biochemical mechanisms underlying cold stress tolerance in wheat. The identified genotypes with superior cold stress tolerance can serve as valuable genetic resources for wheat breeding. [ABSTRACT FROM AUTHOR]

Wheat, an important cereal crop globally, faces major challenges due to increasing global population and changing climates. The production and productivity are challenged by several biotic and abiotic stresses. There is also a pressing demand to enhance grain yield and quality/nutrition to ensure global food and nutritional security. To address these multifaceted concerns, researchers have conducted numerous meta-QTL (MQTL) studies in wheat, resulting in the identification of candidate genes that govern these complex quantitative traits. MQTL analysis has successfully unraveled the complex genetic architecture of polygenic quantitative traits in wheat. Candidate genes associated with stress adaptation have been pinpointed for abiotic and biotic traits, facilitating targeted breeding efforts to enhance stress tolerance. Furthermore, high-confidence candidate genes (CGs) and flanking markers to MQTLs will help in marker-assisted breeding programs aimed at enhancing stress tolerance, yield, quality and nutrition. Functional analysis of these CGs can enhance our understanding of intricate trait-related genetics. The discovery of orthologous MQTLs shared between wheat and other crops sheds light on common evolutionary pathways governing these traits. Breeders can leverage the most promising MQTLs and CGs associated with multiple traits to develop superior next-generation wheat cultivars with improved trait performance. This review provides a comprehensive overview of MQTL analysis in wheat, highlighting progress, challenges, validation methods and future opportunities in wheat genetics and breeding, contributing to global food security and sustainable agriculture. [ABSTRACT FROM AUTHOR]

Background: Yellow or stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici (Pst) is an important disease of wheat that threatens wheat production. Since developing resistant cultivars offers a viable solution for disease management, it is essential to understand the genetic basis of stripe rust resistance. In recent years, meta-QTL analysis of identified QTLs has gained popularity as a way to dissect the genetic architecture underpinning quantitative traits, including disease resistance. Results: Systematic meta-QTL analysis involving 505 QTLs from 101 linkage-based interval mapping studies was conducted for stripe rust resistance in wheat. For this purpose, publicly available high-quality genetic maps were used to create a consensus linkage map involving 138,574 markers. This map was used to project the QTLs and conduct meta-QTL analysis. A total of 67 important meta-QTLs (MQTLs) were identified which were refined to 29 high-confidence MQTLs. The confidence interval (CI) of MQTLs ranged from 0 to 11.68 cM with a mean of 1.97 cM. The mean physical CI of MQTLs was 24.01 Mb, ranging from 0.0749 to 216.23 Mb per MQTL. As many as 44 MQTLs colocalized with marker–trait associations or SNP peaks associated with stripe rust resistance in wheat. Some MQTLs also included the following major genes- Yr5, Yr7, Yr16, Yr26, Yr30, Yr43, Yr44, Yr64, YrCH52, and YrH52. Candidate gene mining in high-confidence MQTLs identified 1,562 gene models. Examining these gene models for differential expressions yielded 123 differentially expressed genes, including the 59 most promising CGs. We also studied how these genes were expressed in wheat tissues at different phases of development. Conclusion: The most promising MQTLs identified in this study may facilitate marker-assisted breeding for stripe rust resistance in wheat. Information on markers flanking the MQTLs can be utilized in genomic selection models to increase the prediction accuracy for stripe rust resistance. The candidate genes identified can also be utilized for enhancing the wheat resistance against stripe rust after in vivo confirmation/validation using one or more of the following methods: gene cloning, reverse genetic methods, and omics approaches. [ABSTRACT FROM AUTHOR]

The diverse microclimatic belts of the Western Himalayan region of India are considered hot spots for genetic diversity of common bean (Phaseolus vulgaris L.). Western Himalayan beans are known for various agronomically superior/important traits including unique aroma, taste and cooking quality. In the present study, 25 unlinked genomic simple sequence repeat (SSR) markers distributed across the common bean genome were used to assess the genetic/allelic diversity among and within populations belonging to the Jammu and Kashmir regions of the Western Himalayas. These two regions are considered most important hot-spots for common bean diversity in western-Himalayas. The analysis of genotypic data of SSR markers revealed a total of 263 alleles with an average of 10.52 alleles per locus. The genetic diversity analysis revealed higher variability in bean landraces belonging to Jammu region (H e = 0.73) as compared to genotypes from Kashmir region (H e = 0.647) and some exotic genotypes (0.71). The genotypes were also phenotyped for four important nutritional traits and the analysis of trait data revealed that sugar content was highest in common bean genotypes from Jammu region, while protein, starch and phenol content were highest in exotic common bean genotypes. Therefore, the superiority of common bean germplasm from Jammu region may be due to a higher level of allelic diversity, more private alleles and higher sugar content. The diverse genotypes based on genotypic data and trait performance will prove useful in future breeding programs aimed at enhancing nutritional contents of common bean varieties. We collected common bean germplasm from two hot-spot regions in the Western Himalayas and evaluated it acrossfour nutritional traits: sugar, starch, protein and phenol contents. We present a comparison of germplasm from these two hot-spots based on trait data and allelic diversity detected by 25 SSR markers. [ABSTRACT FROM AUTHOR]