GWAS for Plant Height and Ear Height in Maize under Well-Watered and Water-Stressed Conditions
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PROVIDER: PRJEB90981 | ENA |
REPOSITORIES: ENA
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Project description:12plex_pea_2013_02 - 12plex_pea_2013_02_f - What is the effect of a moderate water stress on seed filling (reserve accumulation) and nitrogen remobilisation in pea (Pisum sativum) - Pea plants (genotype Cameor) were subjected to a moderate water stress at the beggining of the seed filling period (12 Days After Pollination) of the second flowering node for a period of 8 days. Samples were collected from Well Watered (WW) plants at the beginning of the stress imposition (point A, T=0), and from Water-Stressed (WS) and WW control plants at the end of the drought period (point B, T=+8). Samples named SEED consisted of seeds from the pod of the second flowering node (seed-WW-A, seed-WW-B and Seed-WS-B). Samples named LEAF consisted of the leaves and stem sections from the two vegetative nodes below the first flowering node (leaf-WW-A, Leaf-WW-B and Leaf-WS-B). Each sample consited of a pool of 3 individual plants and 4 repetitions per condition were carried out.
2017-02-01 | GSE93630 | GEO
Project description:12plex_pea_2013_02 - 12plex_pea_2013_02_g - What is the effect of a moderate water stress on seed filling (reserve accumulation) and nitrogen remobilisation in pea (Pisum sativum) - Pea plants (genotype Cameor) were subjected to a moderate water stress at the beggining of the seed filling period (12 Days After Pollination) of the second flowering node for a period of 8 days. Samples were collected from Well Watered (WW) plants at the beginning of the stress imposition (point A, T=0), and from Water-Stressed (WS) and WW control plants at the end of the drought period (point B, T=+8). Samples named SEED consisted of seeds from the pod of the second flowering node (seed-WW-A, seed-WW-B and Seed-WS-B). Samples named LEAF consisted of the leaves and stem sections from the two vegetative nodes below the first flowering node (leaf-WW-A, Leaf-WW-B and Leaf-WS-B). Each sample consited of a pool of 3 individual plants and 4 repetitions per condition were carried out.
2017-04-05 | GSE97237 | GEO
Project description:IntroductionArtemisinin is a secondary metabolite well-known for its use in the treatment of malaria. It also displays other antimicrobial activities which further increase its interest. At present, Artemisia annua is the sole commercial source of the substance, and its production is limited, leading to a global deficit in supply. Furthermore, the cultivation of A. annua is being threatened by climate change. Specifically, drought stress is a major concern for plant development and productivity, but, on the other hand, moderate stress levels can elicit the production of secondary metabolites, with a putative synergistic interaction with elicitors such as chitosan oligosaccharides (COS). Therefore, the development of strategies to increase yield has prompted much interest. With this aim, the effects on artemisinin production under drought stress and treatment with COS, as well as physiological changes in A. annua plants are presented in this study.MethodsPlants were separated into two groups, well-watered (WW) and drought-stressed (DS) plants, and in each group, four concentrations of COS were applied (0, 50,100 and 200 mg•L-1). Afterwards, water stress was imposed by withholding irrigation for 9 days.ResultsTherefore, when A. annua was well watered, COS did not improve plant growth, and the upregulation of antioxidant enzymes hindered the production of artemisinin. On the other hand, during drought stress, COS treatment did not alleviate the decline in growth at any concentration tested. However, higher doses improved the water status since leaf water potential (YL) improved by 50.64% and relative water content (RWC) by 33.84% compared to DS plants without COS treatment. Moreover, the combination of COS and drought stress caused damage to the plant's antioxidant enzyme defence, particularly APX and GR, and reduced the amount of phenols and flavonoids. This resulted in increased ROS production and enhanced artemisinin content by 34.40% in DS plants treated with 200 mg•L-1 COS, compared to control plants.ConclusionThese findings underscore the critical role of ROS in artemisinin biosynthesis and suggest that COS treatment may boost artemisinin yield in crop production, even under drought conditions.
| S-EPMC10272596 | biostudies-literature
Project description:Maize grain yield can be greatly reduced when flowering time coincides with drought conditions, which delays silking and consequently increases the anthesis-silking interval. Although the genetic basis of delayed flowering time under water-stressed conditions has been elucidated in maize-maize populations, little is known in this regard about maize-teosinte populations. Here, 16 quantitative trait loci (QTL) for three flowering-time traits, namely days to anthesis, days to silk, and the anthesis-silking interval, were identified in a maize-teosinte introgression population under well-watered and water-stressed conditions; these QTL explained 3.98-32.61% of phenotypic variations. Six of these QTL were considered to be sensitive to drought stress, and the effect of any individual QTL was small, indicating the complex genetic nature of drought resistance in maize. To resolve which genes underlie the six QTL, 11 candidate genes were identified via colocalization analysis of known associations with flowering-time-related drought traits. Among the 11 candidate genes, five were found to be differentially expressed in response to drought stress or under selection during maize domestication, and thus represented the most likely candidates underlying the drought-sensitive QTL. The results lay a foundation for further studies of the genetic mechanisms of drought resistance and provide valuable information for improving drought resistance during maize breeding.Supplementary informationThe online version contains supplementary material available at 10.1007/s11032-023-01413-0.
| S-EPMC10435433 | biostudies-literature
Project description:Plant height (PH) and ear height (EH) are important traits associated with biomass, lodging resistance, and grain yield in maize. There were strong effects of genotype x environment interaction (GEI) on plant height and ear height of maize. In this study, 203 maize inbred lines were grown at five locations across China's Spring and Summer corn belts, and plant height (PH) and ear height (EH) phenotype data were collected and grouped using GGE biplot. Five locations fell into two distinct groups (or mega environments) that coincide with two corn ecological zones called Summer Corn Belt and Spring Corn Belt. In total, 73,174 SNPs collected using GBS sequencing platform were used as genotype data and a recently released multi-environment GWAS software package IIIVmrMLM was employed to identify QTNs and QTN x environment (corn belt) interaction (QEIs); 12 and 11 statistically significant QEIs for PH and EH were detected respectively and their phenotypic effects were further partitioned into Add*E and Dom*E components. There were 28 and 25 corn-belt-specific QTNs for PH and EH identified, respectively. The result shows that there are a large number of genetic loci underlying the PH and EH GEIs and IIIVmrMLM is a powerful tool in discovering QTNs that have significant QTN-by-Environment interaction. PH and EH candidate genes were annotated based on transcriptomic analysis and haplotype analysis. EH related-QEI S10_135 (Zm00001d025947, saur76, small auxin up RNA76) and PH related-QEI S4_4 (Zm00001d049692, mads32, encoding MADS-transcription factor 32), and corn-belt specific QTNs including S10_4 (Zm00001d023333, sdg127, set domain gene127) and S7_1 (Zm00001d018614, GLR3.4, and glutamate receptor 3.4 or Zm00001d018616, DDRGK domain-containing protein) were reported, and the relationship among GEIs, QEIs and phenotypic plasticity and their biological and breeding implications were discussed.
| S-EPMC10716222 | biostudies-literature
Project description:Soybean (Glycine max) productivity is significantly reduced by drought stress. Breeders are aiming to improve soybean grain yields both under well-watered (WW) and drought-stressed (DS) conditions, however, little is known about the genetic architecture of yield-related traits. Here, a panel of 188 soybean germplasm was used in a genome wide association study (GWAS) to identify single nucleotide polymorphism (SNP) markers linked to yield-related traits including pod number per plant (PN), biomass per plant (BM) and seed weight per plant (SW). The SLAF-seq genotyping was conducted on the population and three phenotype traits were examined in WW and DS conditions in four environments. Based on best linear unbiased prediction (BLUP) data and individual environmental analyses, 39 SNPs were significantly associated with three soybean traits under two conditions, which were tagged to 26 genomic regions by linkage disequilibrium (LD) analysis. Of these, six QTLs qPN-WW19.1, qPN-DS8.8, qBM-WW1, qBM-DS17.4, qSW-WW4 and qSW-DS8 were identified controlling PN, BM and SW of soybean. There were larger proportions of favorable haplotypes for locus qPN-WW19.1 and qSW-WW4 rather than qBM-WW1, qBM-DS17.4, qPN-DS8.8 and qSW-DS8 in both landraces and improved cultivars. In addition, several putative candidate genes such as Glyma.19G211300, Glyma.17G057100 and Glyma.04G124800, encoding E3 ubiquitin-protein ligase BAH1, WRKY transcription factor 11 and protein zinc induced facilitator-like 1, respectively, were predicted. We propose that the further exploration of these locus will facilitate accelerating breeding for high-yield soybean cultivars.
| S-EPMC10593458 | biostudies-literature
Project description:In this study, protein was extracted from maize kernel at 10 and 25 DAP with three biological replicates. All maize kernel samples under the two water treatments were collected at 09:00 h for proteomics analysis.
2021-06-16 | PXD026755 |
Project description:One of the most important applications of genomic selection in maize breeding is to predict and identify the best untested lines from biparental populations, when the training and validation sets are derived from the same cross. Nineteen tropical maize biparental populations evaluated in multienvironment trials were used in this study to assess prediction accuracy of different quantitative traits using low-density (~200 markers) and genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs), respectively. An extension of the Genomic Best Linear Unbiased Predictor that incorporates genotype × environment (GE) interaction was used to predict genotypic values; cross-validation methods were applied to quantify prediction accuracy. Our results showed that: (1) low-density SNPs (~200 markers) were largely sufficient to get good prediction in biparental maize populations for simple traits with moderate-to-high heritability, but GBS outperformed low-density SNPs for complex traits and simple traits evaluated under stress conditions with low-to-moderate heritability; (2) heritability and genetic architecture of target traits affected prediction performance, prediction accuracy of complex traits (grain yield) were consistently lower than those of simple traits (anthesis date and plant height) and prediction accuracy under stress conditions was consistently lower and more variable than under well-watered conditions for all the target traits because of their poor heritability under stress conditions; and (3) the prediction accuracy of GE models was found to be superior to that of non-GE models for complex traits and marginal for simple traits.
| S-EPMC4815582 | biostudies-other
Project description:Drought significantly influences maize morphology and yield potential. The elucidation of the genetic mechanisms of yield components and morphological traits, and tightly linked molecular markers under drought stress are thus of great importance in marker assisted selection (MAS) breeding. Here, we identified 32 QTLs for grain weight per ear, kernel ratio, and ear height-to-plant height ratio across two F2:3 populations under both drought and non-drought conditions by single-environment mapping with composite interval mapping (CIM), of which 21 QTLs were mapped under water-stressed conditions. We identified 29 QTLs by joint analysis of all environments with mixed-linear-model-based composite interval mapping (MCIM), 14 QTLs involved in QTL-by-environment interactions, and 11 epistatic interactions. Further analysis simultaneously identified 20 stable QTLs (sQTLs) by CIM and MCIM could be useful for genetic improvement of these traits via QTL pyramiding. Remarkably, bin 1.07-1.10/6.05/8.03/8.06 exhibited four pleiotropic sQTLs that were consistent with phenotypic correlations among traits, supporting the pleiotropy of QTLs and playing important roles in conferring growth and yield advantages under contrasting watering conditions. These findings provide information on the genetic mechanisms responsible for yield components and morphological traits that are affected by different watering conditions. Furthermore, these alleles provide useful targets for MAS.
| S-EPMC6977450 | biostudies-literature