GWAS of yield stability and GxE in soybean
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Illumina NextSeq 500
ORGANISM(S): Glycine Max
SUBMITTER: David Hyten Lab
PROVIDER: PRJEB43548 | EVA | 2021-03-10
REPOSITORIES: EVA
Dataset's files
Items per page: 5 1 - 4 of 4 |
Similar Datasets
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 aphids are phloem-feeding pests that can cause significant yield losses in soybean plants. Soybean aphids thrive on susceptible soybean lines but not on resistant lines. We used microarrays to characterize the soybean plant's transcriptional defense against aphids in two related cultivars, a susceptible line and a resistant line with the Rag1 aphid-resistance gene. We measured trancript levels in leaves after one and seven days of aphid infestation.
2012-01-31 | GSE35427 | GEO
Project description:Soybean is a rich source of protein and oil and a primary feedstock for biodiesel production. Previous research on soybean indicated that protein, oil and yield are controlled quantitatively in soybean seeds. However, genetic mechanisms controlling seed composition and yield in soybean remain unknown. We used Affymetrix Soybean GeneChips® to identify genes that are differentially expressed between developing seeds of the Minsoy and Archer soybean varieties, which differ in seed weight, yield, protein content and oil content. Some of the differentially expressed genes identified in this study may play important roles in controlling these traits.
2015-04-29 | GSE21598 | GEO
Project description:Soybean aphids are phloem-feeding pests that can cause significant yield losses in soybean plants. Soybean aphids thrive on susceptible soybean lines but not on resistant lines. Aphids do not normally kill their host and colonize plants for long periods of time, up to several months in soybean. However, our knowledge of plant responses to long-term aphid colonization is very limited. We used microarrays to characterize the soybean plant's transcriptional response against aphids in two related cultivars, a susceptible line and a resistant line with the Rag1 aphid-resistance gene. We measured transcript levels in leaves after 21 days of aphid infestation.
2018-06-15 | GSE115790 | GEO
Project description:Purpose: Soybean aphid (Aphis glycines Matsumura; SBA) is major pest of soybean (Glycine max) in the United States of America. One previous study on soybean, soybean-aphid interactions showed that avirulent (biotype 1) and virulent (biotype 2) biotypes can co-occur and potentially interact on resistant and susceptible soybean resulting induced susceptibility. The main objective of this research was to employ RNA sequencing technique to characterize the induced susceptibility effect in which initial feeding by virulent aphids can increase the suitability of avirulent aphids in both susceptible and resistant cultivars. Methods: The data in this submission come from the green house experiment using two genotypes of soybean: susceptible soybean cultivar was LD12-15838R and the resistant cultivar was LD12-15813Ra (with Rag1 gene) and two aphid populations: biotype 1 (avirulent) and biotype 2 (virulent biotype 2). RNA was extracted from the leave samples from resistant and susceptible cultivars treated with no aphids, biotype 2: biotype1 collected at day 1 and no aphids, biotype 2: biotype1 and no aphids: biotype1 at day 11 using PureLink RNA mini kit (Invitrogen, USA). RNA samples were treated with TURBOTM DNase (Invitrogen, USA) to remove any DNA contamination following the manufacturer’s instructions. Assessment of the isolated RNA integrity was performed by 1% agarose gel electrophoresis, and RNA concentration was measured by Nanodrop 2000 (Thermo Fisher Scientific, USA). Three replicates from these treatments in resistant and susceptible cultivars were pooled in equimolar concentration. RNAseq library construction was prepared using Illumina’s TruSeq Stranded mRNA Kit v1 (San Diego, CA). The libraries were quantified by QuBit dsDNA HS Assay (Life Technologies, Carlsbad, CA) and pooled in equimolar concentrations. The libraries were sequenced on an Illumina NextSeq 500 using a NextSeq 500/550 High Output Reagent Cartridge v2 (San Diego, CA) at 75 cycles. Results: A total of 10 RNA libraries were prepared and sequenced with the sequencing depth ranging from 24,779,816 to 29,72,4913. Total reads of 266,535,654 were subjected to FastQC analysis to determine the data quality using various quality metrics such as mean quality scores, per sequence quality scores, per sequence GC content, and sequence length distribution. The phred quality scores per base for all the samples were higher than 30. The GC content ranged from 45 to 46% 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 90.4% to 92.9%. Among the mapped reads, 85.8% to 91.9% reads were uniquely mapped. Conclusions: The objective of this study is to characterize the mechanism of induced susceptibility in soybean via transcriptional response study of soybean in presence of biotype 1 and biotype 2 soybean aphids using RNA-Seq. The data resulted from this study might provide insights into the interactions between soybean and soybean aphids and identify genes, their regulation and enriched pathways that may be associated with resistance or susceptibility to A. glycines.
2019-04-26 | GSE129626 | GEO
Project description:The soybean aphid, a plant sap sucking insect, is an important soybean pest in the USA causing significant yield losses. The Rag2 gene of soybean provides resistance to soybean aphid biotypes I and II. Transcriptomic analyses were performed on near isogenic lines (NILs) with the Rag2 allele for aphid resistance or rag2 for susceptibility at the Rag2 locus. Soybeans were infested with soybean aphids and leaves were collected at 0, 4, 8, 24, and 48 hours after infestation. RNA were extracted and a high throughput RNA-seq approach was used to examine mRNA expression in Rag2 and rag2 soybean leaves. The expression of ~43,000 genes was detected in both the Rag2 and rag2 leaves. Statistical analysis identified 2361 genes significantly regulated between the resistant and susceptible lines at different times after aphid infestation. Genes found up-regulated in the Rag2 line were annotated as involved in the cell wall, secondary and hormone metabolism, as well as in stress, signaling and transcriptional responses. Genes found up-regulated in the rag2 line were annotated as involved in photosynthesis and carbon metabolism. Interestingly, mRNAs of 2 genes (unknown and mitochondrial protease) located within the Rag2 locus were expressed significantly higher in the resistant genotype. The expression of the putative NBS-LRR resistant gene present in the Rag2 locus was not different between the two soybean lines. However, another NBL-LRR gene located just at the border of the Rag2 locus was and, therefore, may be involved in the differential resistance to aphid infestation exhibited by the two NIL genotypes analyzed.
2016-04-08 | GSE67779 | GEO
Project description:To investigate the effect of genotype on soybean transcriptomic response to different soybean cyst nematode races
2024-10-29 | GSE233370 | GEO
Project description:Changes in the performance of genotypes in different environments are defined as genotype 3 environment (G3E) interactions. In grapevine (Vitis vinifera), complex interactions between different genotypes and climate, soil and farming practices yield unique berry qualities. However, the molecular basis of this phenomenon remains unclear. To dissect the basis of grapevine G3E interactions we characterized berry transcriptome plasticity, the genome methylation landscape and within-genotype allelic diversity in two genotypes cultivated in three different environments over two vintages. We identified, through a novel data-mining pipeline, genes with expression profiles that were: unaffected by genotype or environment, genotype-dependent but unaffected by the environment, environmentally-dependent regardless of genotype, and G3E-related. The G3E-related genes showed different degrees of within-cultivar allelic diversity in the two genotypes and were enriched for stress responses, signal transduction and secondary metabolism categories. Our study unraveled the mutual relationships between genotypic and environmental variables during G3E interaction in a woody perennial species, providing a reference model to explore how cultivated fruit crops respond to diverse environments. Also, the pivotal role of vineyard location in determining the performance of different varieties, by enhancing berry quality traits, was unraveled.
2018-04-09 | GSE97960 | GEO
Project description:Changes in the performance of genotypes in different environments are defined as genotype 3 environment (G3E) interactions. In grapevine (Vitis vinifera), complex interactions between different genotypes and climate, soil and farming practices yield unique berry qualities. However, the molecular basis of this phenomenon remains unclear. To dissect the basis of grapevine G3E interactions we characterized berry transcriptome plasticity, the genome methylation landscape and within-genotype allelic diversity in two genotypes cultivated in three different environments over two vintages. We identified, through a novel data-mining pipeline, genes with expression profiles that were: unaffected by genotype or environment, genotype-dependent but unaffected by the environment, environmentally-dependent regardless of genotype, and G3E-related. The G3E-related genes showed different degrees of within-cultivar allelic diversity in the two genotypes and were enriched for stress responses, signal transduction and secondary metabolism categories. Our study unraveled the mutual relationships between genotypic and environmental variables during G3E interaction in a woody perennial species, providing a reference model to explore how cultivated fruit crops respond to diverse environments. Also, the pivotal role of vineyard location in determining the performance of different varieties, by enhancing berry quality traits, was unraveled.
2018-04-09 | GSE97578 | GEO
Project description:Soybean is a rich source of protein and oil and a primary feedstock for biodiesel production. Previous research on soybean indicated that protein, oil and yield are controlled quantitatively in soybean seeds. However, genetic mechanisms controlling seed composition and yield in soybean remain unknown. We used Affymetrix Soybean GeneChips® to identify genes that are differentially expressed between developing seeds of the Minsoy and Archer soybean varieties, which differ in seed weight, yield, protein content and oil content. Some of the differentially expressed genes identified in this study may play important roles in controlling these traits. The soybean plants of two soybean varieties Minsoy and Archer and two recombinant inbred lines from the cross that are similar in maturity but differ in yield were grown in St Paul, Minnesota during the summers of 2007 and 2008. In 2007, each line was planted as a single row. In 2008, a randomized complete block (RCB) design was used and each line had 3 replicates planted 1-2 weeks apart. Within each replicate, two rows per line were planted. Seeds were harvested at three developmental stages, namely, seed length = 2 mm, 3.5 mm, and 5-6 mm, which correspond approximately to soybean reproductive stages R4, R5 and early R6, respectively. In 2007 three independent samples were collected for each line and developmental stage. In 2008, two seed samples (one from each row) were collected for each line at each stage within each replicate. The pairs of seed samples were then pooled. Thus, three sets of independent tissue samples were collected for RNA extraction and hybridization on Affymetrix microarrys.
2015-04-29 | E-GEOD-21598 | biostudies-arrayexpress