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A study was conducted to collect and characterize the indigenous Kala zeera (Bunium persicum Boiss. Fedtsch.) accessions from the hills of northwestern Himalayan sates of Jammu and Kashmir, Himachal Pradesh and Uttarakhand. Around 1000 accessions were collected during this exploration mission, out of which diversity of 252 accessions was established through morphological characterization. The morpho-agronomic characterization and the analysis of trait data revealed significant variability in number of branches per plant, number of umbelets per umbel, number of seeds per plant, seed yield per plant and 1000 seed weight. The collection and characterization of these diverse accessions can prove useful in future Kala zeera improvement programmes in the world as this is the first such comprehensive report of the crop from northwestern Himalayan region of India. [ABSTRACT FROM AUTHOR]

Micronutrient deficiency is a widespread food-related health problem around the world. The present study was conducted to evaluate a set of 63 advanced breeding lines of bread wheat (Triticum aestivum L.) for grain iron (GFe) and grain zinc (GZn) concentrations, and to characterise the germplasm set via simple sequence repeat (SSR) markers (both genic and random). Substantial variation was found for both micronutrients. GFe concentration ranged from 28.9 to 67.4 mg kg-1 and GZn from 26.3 to 56.6 mg kg-1.Molecular characterisation with six genic and 20 random SSR markers detected 168 alleles with an average of 3.170 alleles per locus. Analysis of genotypic data based on division into two subpopulations revealed 165 alleles with an average of 3.113 alleles per locus in the low GFe--GZn subpopulation, whereas in the high GFe--GZn subpopulation, 149 alleles with an average of 2.811 alleles per locus were detected. Genic SSRs detected a higher average number of alleles (3.273 alleles per locus) than random SSRs (3.143 alleles per locus). Hierarchical clustering using genic markers alone clustered the whole germplasm set into two distinct groups: one possessing low GFe--GZn genotypes, the other with high GFe--GZn genotypes. Study of marker--trait associations (MTAs) identified seven new MTAs (six for GZn and one for GFe) and validated one MTA for GZn concentration. The promising genotypes andMTAs identified during the study will prove useful in wheat bio-fortification programs in the future. [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]

Fusarium wilt (FW) caused by Fusarium oxysporum f. sp. ciceri is a devastating disease of chickpea (Cicer arietinum). To identify promising resistant genotypes and genomic loci for FW resistance, a core set of 179 genotypes of chickpea was tested for FW reactions at the seedling and reproductive stages under field conditions and controlled conditions in the greenhouse. Our results revealed that at the seedling stage, most of the genotypes were resistant, whereas at the reproductive stage, most of the genotypes were susceptible. Genotyping using a 5OK Axiom® CicerSNP Array and trait data of FW together led to the identification of 26 significant (P 5 E-05) marker-trait associations (MTAs) for FW resistance. Among the 26 MTAs, 12 were identified using trait data recorded in the field (three at the seedling and nine at the reproductive stage), and 14 were identified using trait data recorded under controlled conditions in the greenhouse (six at the seedling and eight at the reproductive stage). The phenotypic variation explained by these MTAs varied from 11.75 to 15.86%, with an average of 13.77%. Five MTAs were classified as major, explaining more than 15% of the phenotypic variation for FW, and two were declared stable, being identified in two environments. One of the promising stable and major MTAs (Affx_123280060) detected in field conditions at the reproductive stage was also detected in greenhouse conditions at the seedling and reproductive stages. The stable and major (> 15% PVE) MTAs can be used in chickpea breeding programs. [ABSTRACT FROM AUTHOR]

Legumes are the most important crop plants in agriculture, contributing 27% of the world's primary food production. However, productivity and production of Legumes is reduced due to increasing environmental stress. Hence, there is a pressing need to understand the molecular mechanism involved in stress response and legumes adaptation. Proteomics provides an important molecular approach to investigate proteins involved in stress response. Both the gel-based and gel-free-based techniques have significantly contributed to understanding the proteome regulatory network in leguminous plants. In the present review, we have discussed the role of different proteomic approaches (2-DE, 2 D-DIGE, ICAT, iTRAQ, etc.) in the identification of various stress-responsive proteins in important leguminous crops, including soybean, chickpea, cowpea, pigeon pea, groundnut, and common bean under variable abiotic stresses including heat, drought, salinity, waterlogging, frost, chilling and metal toxicity. The proteomic analysis has revealed that most of the identified differentially expressed proteins in legumes are involved in photosynthesis, carbohydrate metabolism, signal transduction, protein metabolism, defense, and stress adaptation. The proteomic approaches provide insights in understanding the molecular mechanism of stress tolerance in legumes and have resulted in the identification of candidate genes used for the genetic improvement of plants against various environmental stresses. Identifying novel proteins and determining their expression under different stress conditions provide the basis for effective engineering strategies to improve stress tolerance in crop plants through marker-assisted breeding. [ABSTRACT FROM AUTHOR]

Highlights • Importance of phenomics in view of the genomics revolution and ways to alleviate the phenomics bottleneck. • Summary of phenomics initiatives and platforms developed world-wide. • Use of phenomics platforms in genetic dissection of targeted traits for crop improvement. • Root phenomics to alleviate the root phenotyping bottleneck. • Phenomics databases and the need for data sharing for crop improvement. Abstract Tremendous progress has been made with continually expanding genomics technologies to unravel and understand crop genomes. However, the impact of genomics data on crop improvement is still far from satisfactory, in large part due to a lack of effective phenotypic data; our capacity to collect useful high quality phenotypic data lags behind the current capacity to generate high-throughput genomics data. Thus, the research bottleneck in plant sciences is shifting from genotyping to phenotyping. This article review the current status of efforts made in the last decade to systematically collect phenotypic data to alleviate this 'phenomics bottlenecks' by recording trait data through sophisticated non-invasive imaging, spectroscopy, image analysis, robotics, high-performance computing facilities and phenomics databases. These modern phenomics platforms and tools aim to record data on traits like plant development, architecture, plant photosynthesis, growth or biomass productivity, on hundreds to thousands of plants in a single day, as a phenomics revolution. It is believed that this revolution will provide plant scientists with the knowledge and tools necessary for unlocking information coded in plant genomes. Efforts have been also made to present the advances made in the last 10 years in phenomics platforms and their use in generating phenotypic data on different traits in several major crops including rice, wheat, barley, and maize. The article also highlights the need for phenomics databases and phenotypic data sharing for crop improvement. The phenomics data generated has been used to identify genes/QTL through QTL mapping, association mapping and genome-wide association studies (GWAS) for genomics-assisted breeding (GAB) for crop improvement. [ABSTRACT FROM AUTHOR]