Dataset Information

Tracing QTLs for Leaf Blast Resistance and Agronomic Performance of Finger Millet (Eleusine coracana (L.) Gaertn.) Genotypes through Association Mapping and in silico Comparative Genomics Analyses.

ABSTRACT: Finger millet is one of the small millets with high nutritive value. This crop is vulnerable to blast disease caused by Pyricularia grisea, which occurs annually during rainy and winter seasons. Leaf blast occurs at early crop stage and is highly damaging. Mapping of resistance genes and other quantitative trait loci (QTLs) for agronomic performance can be of great use for improving finger millet genotypes. Evaluation of one hundred and twenty-eight finger millet genotypes in natural field conditions revealed that leaf blast caused severe setback on agronomic performance for susceptible genotypes, most significant traits being plant height and root length. Plant height was reduced under disease severity while root length was increased. Among the genotypes, IE4795 showed superior response in terms of both disease resistance and better agronomic performance. A total of seven unambiguous QTLs were found to be associated with various agronomic traits including leaf blast resistance by association mapping analysis. The markers, UGEP101 and UGEP95, were strongly associated with blast resistance. UGEP98 was associated with tiller number and UGEP9 was associated with root length and seed yield. Cross species validation of markers revealed that 12 candidate genes were associated with 8 QTLs in the genomes of grass species such as rice, foxtail millet, maize, Brachypodium stacei, B. distachyon, Panicum hallii and switchgrass. Several candidate genes were found proximal to orthologous sequences of the identified QTLs such as 1,4-β-glucanase for leaf blast resistance, cytokinin dehydrogenase (CKX) for tiller production, calmodulin (CaM) binding protein for seed yield and pectin methylesterase inhibitor (PMEI) for root growth and development. Most of these QTLs and their putatively associated candidate genes are reported for first time in finger millet. On validation, these novel QTLs may be utilized in future for marker assisted breeding for the development of fungal resistant and high yielding varieties of finger millet.

SUBMITTER: Ramakrishnan M

PROVIDER: S-EPMC4944987 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Drought is one of the most significant abiotic stresses that affects the growth and productivity of crops worldwide. Finger millet [Eleusine coracana (L.) Gaertn.] is a C4 crop with high nutritional value and drought tolerance. However, the drought stress tolerance genetic mechanism of finger millet is largely unknown. In this study, transcriptomic (RNA-seq) and proteomic (iTRAQ) technologies were combined to investigate the finger millet samples treated with drought at different stages to determine drought response mechanism. A total of 80,602 differentially expressed genes (DEGs) and 3,009 differentially expressed proteins (DEPs) were identified in the transcriptomic and proteomic levels, respectively. An integrated analysis, which combined transcriptome and proteome data, revealed the presence of 1,305 DEPs were matched with the corresponding DEGs (named associated DEGs-DEPs) when comparing the control to samples which were treated with 19 days of drought (N1-N2 comparison group), 1,093 DEGs-DEPs between control and samples which underwent rehydration treatment for 36 hours (N1-N3 comparison group) and 607 DEGs-DEPs between samples which were treated with drought for 19 days and samples which underwent rehydration treatment for 36 hours (N2-N3 comparison group). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis identified 80 DEGs-DEPs in the N1-N2 comparison group, 49 DEGs-DEPs in the N1-N3 comparison group, and 59 DEGs-DEPs in the N2-N3 comparison group, which were associated with drought stress. The DEGs-DEPs which were drought tolerance-related were enriched in hydrolase activity, glycosyl bond formation, oxidoreductase activity, carbohydrate binding and biosynthesis of unsaturated fatty acids. Co-expression network analysis revealed two candidate DEGs-DEPs which were found to be centrally involved in drought stress response. These results suggested that the coordination of the DEGs-DEPs was essential to the enhanced drought tolerance response in the finger millet.

Project description:Finger millet, an orphan crop, possesses immense potential in mitigating climate change and could offer threefold security in terms of food, fodder, and nutrition. It is mostly cultivated as a subsistence crop in the marginal areas of plains and hills. Considering the changes in climate inclusive of recurrent weather vagaries witnessed every year, it is crucial to select stable, high-yielding, area-specific, finger millet cultivars. Sixty finger millet varieties released across the country were evaluated over six consecutive rainy seasons from 2011 to 2016 at the Agricultural Research Station, Vizianagaram. The genotype × environment interaction (GEI) was found to be significant in the combined ANOVA. Furthermore, the Additive Main effects and Multiplicative Interaction (AMMI) analysis asserted that genotypes and the GEI effects accounted for approximately 89% of the total variation. Strong positive associations were observed in an estimated set of eleven stability parameters which were chosen to identify stable genotypes. Furthermore, Non-parametric and Parametric Simultaneous Selection indices (NP-SSI and P-SSI) were calculated utilizing AMMI-based stability parameter (ASTAB), modified AMMI stability value (MASV), and Modified AMMI Stability Index (MASI) to identify stable high yielders. Both methods had inherent difficulties in ranking genotypes for SSI. To overcome this, the initial culling [i.e., SSI with culling strategy (C-SSI)] of genotypes was introduced for stability. In the C-SSI method, the top ten genotypes were above-average yielders, while those with below-average yield were observed in NP-SSI and P-SSI methods. Similarly, the estimation of best linear unbiased prediction (BLUP)-based simultaneous selections, such as harmonic mean of genotypic values (HMGV), relative performance of genotypic values (RPGV), and harmonic mean of relative performance of genotypic values (HMRPGV), revealed that none of the top ten entries had below-average yield. The study has proven that C-SSI and BLUP-based methods were equally worthy in the selection of high-yielding genotypes with stable performance. However, the C-SSI approach could be the best method to ensure that genotypes with a considerable amount of stability are selected. The multi-year trial SSI revealed that entries Indaf-9, Sri Chaitanya, PR-202, and A-404; and VL324 and VL146 were ascertained to be the most stable high-yielding genotypes among medium-to-late and early maturity groups, respectively.

Dataset Information

Tracing QTLs for Leaf Blast Resistance and Agronomic Performance of Finger Millet (Eleusine coracana (L.) Gaertn.) Genotypes through Association Mapping and in silico Comparative Genomics Analyses.

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