Genome Wide Association Study and Genomic Selection of Amino Acid Concentrations in Soybean Seeds
Ontology highlight
ABSTRACT:
INSTRUMENT(S): -
ORGANISM(S): Glycine Max
SUBMITTER: National Soybean Improvement Center Shijiazhuang Sub-Center
PROVIDER: PRJEB34546 | EVA | 2019-09-24
REPOSITORIES: EVA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| soybean_gwas.accessioned.vcf.gz | Vcf | |||
| soybean_gwas.accessioned.vcf.gz.csi | Vcf | |||
| soybean_gwas.accessioned.vcf.gz.tbi | Vcf | |||
| soybean_gwas.vcf.gz | Vcf | |||
| soybean_gwas.vcf.gz.tbi | Vcf |
Items per page: 5 1 - 5 of 5 |
Similar Datasets
Project description:We report total mRNA library using Illumina Nova Seq high-throughput sequencing platform platform for analysis of transcriptome between the two relatives of the soybean ie weedy and cultivar growth types in F7 generation derived from the crossing of wild and cultivated soybean.
2021-11-12 | GSE188519 | GEO
Project description:Background: Cowpea (Vigna unguiculata L. Walp) is an important food and fodder legume of the semiarid tropics and subtropics worldwide, especially in sub-Saharan Africa. High density genetic linkage maps are needed for marker assisted breeding but are not available for cowpea. A single feature polymorphism (SFP) is a microarray-based marker which can be used for high throughput genotyping and high density mapping. Results: Here we report detection and validation of SFPs in cowpea using a readily available soybean (Glycine max) genome array. Robustified projection pursuit (RPP) was used for statistical analysis using RNA as a surrogate for DNA. Using a 15% outlying score cut-off, 1058 potential SFPs were enumerated between two parents of a recombinant inbred line (RIL) population segregating for several important traits including drought tolerance, Fusarium and brown blotch resistance, grain size and photoperiod sensitivity. Sequencing of 25 putative polymorphism-containing amplicons yielded a SFP probe set validation rate of 68%. Conclusions: We conclude that the Affymetrix soybean genome array is a satisfactory platform for identification of some 1000's of SFPs for cowpea. This study provides an example of extension of genomic resources from a well supported species to an orphan crop. Presumably, other legume systems are similarly tractable to SFP marker development using existing legume array resources. Keywords: Polymorphism discovery, array based genotyping
2008-02-21 | GSE10284 | GEO
Project description:Purpose: Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae) and soybean cyst nematode, Heterodera glycines Ichinohe, (SCN) are the two most economically important pests of soybean, Glycine max (L.) Merr., in the Midwest. Although the soybean aphid is an aboveground pest and SCN is a belowground pest there is evidence that concomitant infestations result in improved SCN reproduction. This study is aimed to characterize the three-way interactions among soybean, soybean aphid and SCN using demographic and genetic datasets. Results: More than 1.1 billion reads (61.4 GB) of transcriptomic data were yielded from 47 samples derived from the experiment using whole roots of G. max. The phred quality scores per base for all the samples were higher than 30. The GC content ranged from 43 to 45% and followed the normal distribution. After trimming, more than 99% of the reads were retained as the clean and good quality reads. Upon mapping these reads, we obtained high mapping rate ranging from 73.8% to 94.3%. Among the mapped reads, 67.1% to 87.6% reads were uniquely mapped. Conclusions: The comprehensive understanding of these transcriptome data would help in understanding the molecular interactions among soybean, A. glycines, and H. glycines. The use of multifaceted bioinformatics approaches could facilitate finding candidate genes and their function that might play a crucial role in various pathways for host resistance against both soybean aphids and SCN. For differential gene expression analysis, EdgeR, limma, and DEseq2 could be used. Apart from standalone tools like iDEP, Galaxy (https://usegalaxy.org), CyVerse (http://www.cyverse.org), and MeV (http://mev.tm4.org) could also be used for both analysis and visualization of RNA- seq data.
2019-05-18 | GSE125103 | GEO
Project description:Soybean oil consumption is increasing worldwide and parallels the obesity epidemic in the U.S. Rich in unsaturated fats, especially linoleic acid, soybean oil is assumed to be healthy, and yet it induces obesity, diabetes, insulin resistance and fatty liver in mice. The genetically modified soybean oil Plenish came on the U.S. market in 2014: it is low in linoleic acid and similar to olive oil in fatty acid composition. Here we show that Plenish induces less obesity than conventional soybean oil: metabolomics, proteomics and a transgenic mouse model implicate oxylipin metabolites of omega-6 and omega-3 fatty acids (linoleic and α-linolenic acid, respectively), which are generated by target genes of nuclear receptor HNF4α. While Plenish induces less insulin resistance than conventional soybean oil, it results in hepatomegaly and liver dysfunction as does olive oil. Altering the fatty acid profile of soybeans could help reduce obesity but may also cause liver complications.
2017-03-20 | GSE79408 | GEO
Project description:Background:; Cowpea (Vigna unguiculata L. Walp) is an important food and fodder legume of the semiarid tropics and subtropics worldwide, especially in sub-Saharan Africa. High density genetic linkage maps are needed for marker assisted breeding but are not available for cowpea. A single feature polymorphism (SFP) is a microarray-based marker which can be used for high throughput genotyping and high density mapping. Results:; Here we report detection and validation of SFPs in cowpea using a readily available soybean (Glycine max) genome array. Robustified projection pursuit (RPP) was used for statistical analysis using RNA as a surrogate for DNA. Using a 15% outlying score cut-off, 1058 potential SFPs were enumerated between two parents of a recombinant inbred line (RIL) population segregating for several important traits including drought tolerance, Fusarium and brown blotch resistance, grain size and photoperiod sensitivity. Sequencing of 25 putative polymorphism-containing amplicons yielded a SFP probe set validation rate of 68%. Conclusions:; We conclude that the Affymetrix soybean genome array is a satisfactory platform for identification of some 1000âs of SFPs for cowpea. This study provides an example of extension of genomic resources from a well supported species to an orphan crop. Presumably, other legume systems are similarly tractable to SFP marker development using existing legume array resources. SUBMITTER_CITATION: Detection and validation of single feature polymorphisms in cowpea (Vigna unguiculata L. Walp) using a soybean genome array Sayan Das, Prasanna R. Bhat, Chinta Sudhakar, Jeffrey D. Ehlers, Steve Wanamaker, Philip A. Roberts, Xinping Cui, Timothy J. Close BMC Genomics 2008, 9:107 Experiment Overall Design: Expression data were generated by hybridizing cowpea cRNA to the soybean genome array. A statistical method called robustified projection pursuit (RPP) was used for Single Feature Polymorphism(SFP) analysis. Only the values from the PM probes were utilized. The use of RNA as a surrogate for genomic DNA eliminated interference from highly repetitive DNA as a technical impediment to SFP detection. An important aspect of the RPP method is that it first utilizes a probe set level analysis to identify SFP-containing probe sets and then chooses individual probes from within each SFP-containing probe set. The net result is the identification of probes that directly overlay polymorphic sequences. Experiment Overall Design: Separate comparisons were made between two genotypes (with two replicates each) for unstressed and drought stressed treatments, resulting in two SFP lists. In the context of SFPs, there is no necessity to have separate stress and control lists; in fact it would be simpler and less costly to have only one SFP list from highly complex RNA made by blending stressed and unstressed RNA. In our case, two separate lists were available as a consequence of another study not described here which compared gene expression patterns in stressed and control plants (data not shown). At 15% outlying score cut-off, we detected 488 SFP probes in stressed and 661 SFP probes in unstressed treatments. The union of these two lists contained 1058 SFP probes and the intersection contained 91. A total of 37 primer pairs targeting 37 putative SFP probe sets were initially tested, of which 25 yielded single amplicons of the expected sizes from both parents. These 25 amplicons targeted 14 probe sets selected from the intersection of the two SFP probe set lists and 11 from the remaining SFP probe sets. 9 of the 14 SFP probe sets (64%) from the intersection list were validated at the DNA sequence level and 8 of the other 11 (73%) were validated.
2008-04-07 | E-GEOD-10284 | biostudies-arrayexpress
Project description:Melatonin is a well-known agent that plays multiple roles in animals. Its possible function in plants is less clear. In the present study, we tested the effect of melatonin (N-acetyl-5-methoxytryptamine) on soybean growth and development. Both spraying of leaves and seed-coating with melatonin significantly promoted soybean growth as judged from leaf size and plant height. This enhancement was also observed in soybean production and their fatty acid content. Melatonin increased pod number, seed number and seed weight. However, the 100-seed weight was not influenced by melatonin application. Melatonin also improved soybean tolerance to salt and drought stresses. Transcriptome analysis revealed that melatonin up-regulated the expression of many genes and alleviated the inhibitory effects of salt stress on gene expressions. Further detailed analysis of the affected pathways documents that melatonin likely achieved its promotional roles in soybean through enhancement of genes involved in cell division, photosynthesis, carbohydrate metabolism, fatty acid biosynthesis and ascorbate metabolism. Our results demonstrate that melatonin has significant potential for improving of soybean growth and seed production. Further study should uncover more about the molecular mechanisms of melatonin’s function in soybeans and other crops.
2014-08-31 | GSE57960 | GEO
Project description:Proton toxicity is one of the major environmental stresses limiting crop production, and becomes increasingly serious because of anthropogenic activities. To understand acid tolerance mechanisms, the plant growth, mineral nutrient accumulation and global transcriptome changes in soybean (Glycine max) in response to long-term acid stress were investigated. Results showed that acid stress significantly inhibited soybean root growth, but exhibited slight effects on the shoot growth. Moreover, concentrations of essential mineral nutrients were significantly affected by acid stress, mainly dependent on soybean organs and mineral nutrient types. The concentrations of phosphorus (P) and molybdenum (Mo) in both leaves and roots, nitrogen (N) and potassium (K) in roots and magnesium (Mg) in leaves were significantly decreased, respectively. Whereas, the concentrations of calcium (Ca), sulfate (S) and iron (Fe) were increased in both leaves and roots. Transcriptome analyses in soybean roots resulted in identifying 419 up-regulated and 555 down-regulated genes under acid conditions. A total of 38 differentially expressed genes (DEGs) were involved in mineral nutrient transportation. Among them, all the detected five GmPTs and GmZIPs, two GmAMTs and GmKUP genes, together with GmIRT1, GmNramp5, GmVIT2.1, GmSKOR, GmTPK5 and GmHKT1, were significantly suppressed. Moreover, the genes encoding transcription factors (e.g., GmSTOP2s and a GmPHL1), and genes involved in pH stat metabolic pathways were significantly up-regulated by low pH stress in soybean roots. Taken together, it strongly suggested that maintaining pH stat and mineral nutrient homeostasis are adaptive strategies of soybean responses to acid stress, which might be regulated by a complex signaling network.
2019-06-24 | GSE129320 | GEO
Project description:We report the genome-wide small RNA of soybean early maturation seed coat parenchyma compartment soybean early maturation seeds using Illumina high-throughput sequencing technology.
2011-12-22 | GSE34638 | GEO
Project description:Aluminum (Al) toxicity is an important restraint to soybean (Glycine max L. Merr.) production on acid soils. However, little is known about the genes underlying Al tolerance in soybean. We used microarrays to detail the global programme of gene expression under control and Al stress in two soybean at 6, 12, and 24 h.
2021-12-30 | GSE116796 | GEO
Project description:Research conducted, including the rationale: Weeds reduce yield in soybeans through incompletely defined mechanisms. The effects of weeds on the soybean transcriptome were evaluated in field conditions during four separate gR1.fastqing seasons. Methods: RNASeq data were collected from 6 biological samples of soybeans gR1.fastqing with or without weeds. Weed species and the methods to maintain weed free controls varied between years to mitigate treatment effects and to allow detection of general soybeans weed responses. Key results: Soybean plants were not visibly nutrient or water stressed. We identified 55 consistently down-regulated genes in weedy plots. Many of the down-regulated genes were heat shock genes. Fourteen genes were consistently up-regulated. Several transcription factors including a PHYTOCHROME INTERACTING FACTOR 3-like gene (PIF3) were included among the up-regulated genes. Gene set enrichment analysis indicated roles for increased oxidative stress and jasmonic acid signaling responses during weed stress. Main conclusion: The relationship of this weed-induced PIF3 gene to genes involved in shade avoidance responses in arabidopsis provide evidence that this gene may be important in the response of soybean to weeds. These results suggest the weed-induced PIF3 gene will be a target for manipulating weed tolerance in soybean.
2021-03-23 | GSE59875 | GEO