Comprehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat.

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.

Delineating Marker-Trait Associations for Fusarium Wilt in Chickpea Using the Axiom® CicerSNP Array.

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 50K Axiom® CicerSNP Array and trait data of FW together led to the identification of 26 significant (P ≤ 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.

Proteomics for abiotic stresses in legumes: present status and future directions.

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.

Comprehensive biochemical approach for understanding the interaction between host "common bean" and pathogen "Colletotrichum lindemuthianum" causing bean anthracnose.

Anthracnose (ANT) caused by Colletotrichum lindemuthianum is the most devastating seed-borne fungal disease of common bean. In response to fungal infections, it is hypothesized that pathogen-plant interactions typically cause hypersensitive reactions by producing reactive oxygen species, hydrogen peroxide and lipid peroxidation of cell membranes. esent study was conducted by inoculating susceptible bean genotype "SB174" and resistant bean genotype "E10" with pathogen "C. lindemuthianum". Defense-related enzymes (ascorbate peroxidase, peroxidase, lipid peroxidase, and catalase) and C-based compounds (total phenols and flavonoids) were studied using the detached bean leaf method. Comparative defense response was studied in different plant tissues (pod, stem, and seed) in susceptible and resistant bean genotypes under uninoculated and pathogen-inoculated conditions. The host‒pathogen interaction was studied at mock inoculation, 2, 4 and 6 days after inoculation (dai). Comparing the pathogen-inoculated bean leaves to water-treated bean leaves, defense enzymes as well as total phenols and flavonoids exhibited differential expression. In a comparative study, the enzyme activity also displayed differential biochemical responses in pods, stems and seeds in both contrasting genotypes. For example, 5.1-fold (pod), 1.5-fold (stem) and 1.06-fold (seed) increases in ascorbate peroxidase activity were observed in the susceptible genotype at 6 dai compared to mock inoculation. Similarly, catalase activity in pods was upregulated (1.47-fold) in the resistant genotype and downregulated (1.30-fold) in the susceptible genotype at 6 dai. The study revealed that defense-related antioxidative enzymes, phenols and flavonoids are fine-tuned to detoxify important reactive oxygen species (ROS) molecules, induce systemic resistance and are successfully controlled in common bean plants against pathogen invasion.

Delineating meta-quantitative trait loci for anthracnose resistance in common bean (Phaseolus vulgaris L.).

Anthracnose, caused by the fungus Colletotrichum lindemuthianum, is one of the devastating disease affecting common bean production and productivity worldwide. Several quantitative trait loci (QTLs) for anthracnose resistance have been identified. In order to make use of these QTLs in common bean breeding programs, a detailed meta-QTL (MQTL) analysis has been conducted. For the MQTL analysis, 92 QTLs related to anthracnose disease reported in 18 different earlier studies involving 16 mapping populations were compiled and projected on to the consensus map. This meta-analysis led to the identification of 11 MQTLs (each involving QTLs from at least two different studies) on 06 bean chromosomes and 10 QTL hotspots each involving multiple QTLs from an individual study on 07 chromosomes. The confidence interval (CI) of the identified MQTLs was found 3.51 times lower than the CI of initial QTLs. Marker-trait associations (MTAs) reported in published genome-wide association studies (GWAS) were used to validate nine of the 11 identified MQTLs, with MQTL4.1 overlapping with as many as 40 MTAs. Functional annotation of the 11 MQTL regions revealed 1,251 genes including several R genes (such as those encoding for NBS-LRR domain-containing proteins, protein kinases, etc.) and other defense related genes. The MQTLs, QTL hotspots and the potential candidate genes identified during the present study will prove useful in common bean marker-assisted breeding programs and in basic studies involving fine mapping and cloning of genomic regions associated with anthracnose resistance in common beans.

SSR markers in revealing extent of genetic diversity and phylogenetic relationships among chickpea core collection accessions for Western Himalayas.

BACKGROUND: The exploration of genetic diversity is the key source of germplasm conservation and potential to broaden its genetic base. The globally growing demand for chickpea suggests superior/climate-resilient varieties, which in turn necessitates the germplasm characterization to unravel underlying genetic variation. METHODOLOGY AND RESULTS: A chickpea core collection comprising of diverse 192 accessions which include cultivated Cicer arietinum, and wild C. reticulatum, C. echinospermum, and C. microphyllum species were investigated to analyze their genetic diversity and relationship, by assaying 33 unlinked simple sequence repeat (SSR) markers. The results amplified a total of 323 alleles (Na), ranging from 2 to 8 with an average of 4.25 alleles per locus. Expected heterozygosity (He) differed from 0.46 to 0.86 with an average of 0.68. Polymorphic information content (PIC) ranged from 0.73 to 0.98 with an average of 0.89. Analysis of molecular variance (AMOVA) showed that most of the variation was among individuals (87%). Cluster analysis resulted in the formation of four distinct clusters. Cluster I represented all cultivated and clusters II, III, and IV comprised a heterogeneous group of cultivated and wild chickpea accessions. CONCLUSION: We report considerable diversity and greater resolving power of SSR markers for assessing variability and interrelationship among the chickpea accessions. The chickpea core is expected to be an efficient resource for breeders for broadening the chickpea genetic base and could be useful for selective breeding of desirable traits and in the identification of target genes for genomics-assisted breeding.

High-throughput phenotyping for crop improvement in the genomics era.

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.