Microarray analysis of Bay-0 x Sha recombinant inbred lines at four seed germination stages

Seed germination is characterized by a constant change of gene expression across different time points. These changes are related to specific processes, which eventually determine the onset of seed germination. To get a better understanding on the regulation of gene expression during seed germination, we measured gene expression levels of Arabidopsis thaliana Bay x Sha recombinant inbred lines (RILs) at four important seed germination stages (primary dormant, after-ripened, six-hour after imbibition, and radicle protrusion stage) using. We mapped the eQTL of the gene expression and the result displayed the distinctness of the eQTL landscape for each stage. We found several eQTL hotspots across stages associated with the regulation of expression of a large number of genes. Together, we have revealed that the genetic regulation of gene expression is dynamic along the course of seed germination

Microarray analysis of Bay-0 x Sha recombinant inbred lines at four seed germination stages

Seed germination is characterized by a constant change of gene expression across different time points. These changes are related to specific processes, which eventually determine the onset of seed germination. To get a better understanding on the regulation of gene expression during seed germination, we measured gene expression levels of Arabidopsis thaliana Bay x Sha recombinant inbred lines (RILs) at four important seed germination stages (primary dormant, after-ripened, six-hour after imbibition, and radicle protrusion stage) using. We mapped the eQTL of the gene expression and the result displayed the distinctness of the eQTL landscape for each stage. We found several eQTL hotspots across stages associated with the regulation of expression of a large number of genes. Together, we have revealed that the genetic regulation of gene expression is dynamic along the course of seed germination

Supplemental Material for Hartanto et al., 2020

Figure S1. Density distribution of the absolute eQTL effect, -log(p), and explained phenotypic variance (R2) for local and distant eQTLs. Figure S2. The histogram of the number of distant eQTLs per marker location for the PD2 (A) and RP4 (B) hotspot. Supplementary Table 1. Gene ontology enrichment for genes with distinctive expression patterns during seed germination. Supplementary Table 2. Distant eQTL hotspots of the four seed germination stages Supplementary Table 3. The mean rank and standard deviation of candidate genes as the most likely causal genes for the RP4 hotspot across different thresholds Supplementary Table 4. The mean rank and standard deviation of candidate genes as the most likely causal genes for the PD2 hotspot across different thresholds. Supplementary Table 5. The location and type of SNPs on candidate genes for the RP4 eQTL hotspot and MUM2. Supplementary Table 6. The location and type of SNPs on candidate genes for the PD2 eQTL hotspot. Supplementary Table 7. The list of genetic markers used for QTL mapping. Supplementary Table 8. The genetic map of Bay-0 x Sha parents and the RIL population. Supplementary Table 9. Gene expression levels of Bay-0 x Sha parents and the RIL population. Supplementary Table 10. Phenotype measurements of Bay-0 x Sha parents and the RIL population. Supplementary Table 11. Metabolite measurements of Bay-0 x Sha parents and the RIL population. Supplementary Table 12. Differentially expressed genes between any of two consecutive stages. Supplementary Table 13. The list of expression QTL. Supplementary Table 14. The list of phenotype QTL. Supplementary Table 15. The list of metabolite QTL

WormQTL2 : an interactive platform for systems genetics in Caenorhabditis elegans

Quantitative genetics provides the tools for linking polymorphic loci to trait variation. Linkage analysis of gene expression is an established and widely applied method, leading to the identification of expression quantitative trait loci (eQTLs). (e)QTL detection facilitates the identification and understanding of the underlying molecular components and pathways, yet (e)QTL data access and mining often is a bottleneck. Here, we present WormQTL2, a database and platform for comparative investigations and meta-analyses of published (e)QTL data sets in the model nematode worm C. elegans. WormQTL2 integrates six eQTL studies spanning 11 conditions as well as over 1000 traits from 32 studies and allows experimental results to be compared, reused and extended upon to guide further experiments and conduct systems-genetic analyses. For example, one can easily screen a locus for specific cis-eQTLs that could be linked to variation in other traits, detect gene-by-environment interactions by comparing eQTLs under different conditions, or find correlations between QTL profiles of classical traits and gene expression. WormQTL2 makes data on natural variation in C. elegans and the identified QTLs interactively accessible, allowing studies beyond the original publications. Database URL: www.bioinformatics.nl/WormQTL2/

WormQTL2: an interactive platform for systems genetics in Caenorhabditis elegans

Quantitative genetics provides the tools for linking polymorphic loci to trait variation. Linkage analysis of gene expression is an established and widely applied method, leading to the identification of expression quantitative trait loci (eQTLs). (e)QTL detection facilitates the identification and understanding of the underlying molecular components and pathways, yet (e)QTL data access and mining often is a bottleneck. Here, we present WormQTL2, a database and platform for comparative investigations and meta-analyses of published (e)QTL data sets in the model nematode worm C. elegans. WormQTL2 integrates six eQTL studies spanning 11 conditions as well as over 1000 traits from 32 studies and allows experimental results to be compared, reused and extended upon to guide further experiments and conduct systems-genetic analyses. For example, one can easily screen a locus for specific cis-eQTLs that could be linked to variation in other traits, detect gene-by-environment interactions by comparing eQTLs under different conditions, or find correlations between QTL profiles of classical traits and gene expression. WormQTL2 makes data on natural variation in C. elegans and the identified QTLs interactively accessible, allowing studies beyond the original publications

Network Analysis Prioritizes DEWAX and ICE1 as the Candidate Genes for Major eQTL Hotspots in Seed Germination of Arabidopsis thaliana

Seed germination is characterized by a constant change of gene expression across different time points. These changes are related to specific processes, which eventually determine the onset of seed germination. To get a better understanding on the regulation of gene expression during seed germination, we performed a quantitative trait locus mapping of gene expression (eQTL) at four important seed germination stages (primary dormant, after-ripened, six-hour after imbibition, and radicle protrusion stage) using Arabidopsis thaliana Bay x Sha recombinant inbred lines (RILs). The mapping displayed the distinctness of the eQTL landscape for each stage. We found several eQTL hotspots across stages associated with the regulation of expression of a large number of genes. Interestingly, an eQTL hotspot on chromosome five collocates with hotspots for phenotypic and metabolic QTLs in the same population. Finally, we constructed a gene co-expression network to prioritize the regulatory genes for two major eQTL hotspots. The network analysis prioritizes transcription factors DEWAX and ICE1 as the most likely regulatory genes for the hotspot. Together, we have revealed that the genetic regulation of gene expression is dynamic along the course of seed germination

Network Analysis Prioritizes DEWAX and ICE1 as the Candidate Genes for Major eQTL Hotspots in Seed Germination of Arabidopsis thaliana

Seed germination is characterized by a constant change of gene expression across different time points. These changes are related to specific processes, which eventually determine the onset of seed germination. To get a better understanding on the regulation of gene expression during seed germination, we performed a quantitative trait locus mapping of gene expression (eQTL) at four important seed germination stages (primary dormant, after-ripened, six-hour after imbibition, and radicle protrusion stage) using Arabidopsis thaliana Bay x Sha recombinant inbred lines (RILs). The mapping displayed the distinctness of the eQTL landscape for each stage. We found several eQTL hotspots across stages associated with the regulation of expression of a large number of genes. Interestingly, an eQTL hotspot on chromosome five collocates with hotspots for phenotypic and metabolic QTL in the same population. Finally, we constructed a gene co-expression network to prioritize the regulatory genes for two major eQTL hotspots. The network analysis prioritizes transcription factors DEWAX and ICE1 as the most likely regulatory genes for the hotspot. Together, we have revealed that the genetic regulation of gene expression is dynamic along the course of seed germination