Prioritizing candidate eQTL causal genes in Arabidopsis using RANDOM FORESTS.

Expression quantitative trait locus mapping has been widely used to study the genetic regulation of gene expression in Arabidopsis thaliana. As a result, a large amount of expression quantitative trait locus data has been generated for this model plant; however, only a few causal expression quantitative trait locus genes have been identified, and experimental validation is costly and laborious. A prioritization method could help speed up the identification of causal expression quantitative trait locus genes. This study extends the machine-learning-based QTG-Finder2 method for prioritizing candidate causal genes in phenotype quantitative trait loci to be used for expression quantitative trait loci by adding gene structure, protein interaction, and gene expression. Independent validation shows that the new algorithm can prioritize 16 out of 25 potential expression quantitative trait locus causal genes within the top 20% rank. Several new features are important in prioritizing causal expression quantitative trait locus genes, including the number of protein-protein interactions, unique domains, and introns. Overall, this study provides a foundation for developing computational methods to prioritize candidate expression quantitative trait locus causal genes. The prediction of all genes is available in the AraQTL workbench (https://www.bioinformatics.nl/AraQTL/) to support the identification of gene expression regulators in Arabidopsis.

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.

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/.