The north-western Indian Himalayas possesses vast diversity in common bean germplasm due to several years of natural adaptation and farmer’s selection. Systematic efforts have been made for the first time for the characterization and use of this huge diversity for the identification of genes/quantitative trait loci (QTLs) for yield and yield-contributing traits in common bean in India. A core set of 96 diverse common bean genotypes was characterized using 91 genome-wide genomic and genic simple sequence repeat (SSR) markers. The study of genetic diversity led to the identification of 691 alleles ranging from 2 to 21 with an average of 7.59 alleles/locus. The gene diversity (expected heterozygosity, He) varied from 0.31 to 0.93 with an average of 0.73. As expected, the genic SSR markers detected less allelic diversity than the random genomic SSR markers. The traditional clustering and Bayesian clustering (structural analysis) analyses led to a clear cut separation of a core set of 96 genotypes into two distinct groups based on their gene pools (Mesoamerican and Andean genotypes). Genome-wide association mapping for pods/plant, seeds/pod, seed weight, and yield/plant led to the identification of 39 significant marker–trait associations (MTAs) including 15 major, 15 stable, and 13 both major and stable MTAs. Out of 39 MTAs detected, 29 were new MTAs reported for the first time, whereas the remaining 10 MTAs were already identified in earlier studies and therefore declared as validation of earlier results. A set of seven markers was such, which were found to be associated with multiple (two to four) different traits. The important MTAs will be used for common bean molecular breeding programs worldwide for enhancing common bean yield.

Common bean (Phaseolus vulgaris L.) is considered as one of the principle grain legume crops grown in Western Himalayas of Jammu and Kashmir, India. This region holds great diversity of common bean germplasm. The purpose of present study was to characterize 109 common bean genotypes collected from different hotspots for morphological traits-plant growth (growth habit, growth type, and twinning habit); leaf (color, size, and shape); flower (color, stripping on outer petal); pod (shape in relation to suture, shape of cross-section, shape of distal part, and stringiness), seed (color and shape) traits, and quantitative morphological traits (seed weight, length, and breadth). The preliminary analysis of trait data showed wide variation for different morphological traits. Furthermore, diverse 60 genotypes were selected out of 109 genotypes and were evaluated for seed micronutrients (Fe, Zn, and Cu) and seed macronutrients (K, Ca, P, and Mg). The analysis of seed micronutrient and macronutrient data indicated substantial variation for these minerals in the germplasm.

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.

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 (He = 0.73) as compared to genotypes from Kashmir region (He = 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.

The core particle represents the catalytic portions of the 26S proteasomal complex. The genes encoding α- and β-subunits play a crucial role in protecting plants against various environmental stresses by controlling the quality of newly produced proteins. The 20S proteasome gene family has already been reported in model plants such as Arabidopsis and rice; however, they have not been studied in oilseed crops such as rapeseed (Brassica napus L.). In the present study, we identified 20S proteasome genes for α- (PA) and β-subunits (PB) in B. napus through systematically performed gene structure analysis, chromosomal location, conserved motif, phylogenetic relationship, and expression patterns. A total of 82 genes, comprising 35 BnPA and 47 BnPB of the 20S proteasome, were revealed in the B. napus genome. These genes were distributed on all 20 chromosomes of B. napus and most of these genes were duplicated on homoeologous chromosomes. The BnPA (α1-7) and BnPB (β1-7) genes were phylogenetically placed into seven clades. The pattern of expression of all the BnPA and BnPB genes was also studied using RNA-seq datasets under biotic and abiotic stress conditions. Out of 82 BnPA/PB genes, three exhibited high expression under abiotic stresses, whereas two genes were overexpressed in response to biotic stresses at both the seedling and flowering stages. Moreover, an additional eighteen genes were expressed under normal conditions. Overall, the current findings developed our understanding of the organization of the 20S proteasome genes in B. napus, and provided specific BnPA/PB genes for further functional research in response to abiotic and biotic stresses.