Structured association analysis leads to insight into Saccharomyces cerevisiaegene regulation by finding multiple contributing eQTL hotspots associated with functional gene modules

Abstract

BackgroundAssociation analysis using genome-wide expression quantitative trait locus (eQTL) data investigates the effect that genetic variation has on cellular pathways and leads to the discovery of candidate regulators. Traditional analysis of eQTL data via pairwise statistical significance tests or linear regression does not leverage the availability of the structural information of the transcriptome, such as presence of gene networks that reveal correlation and potentially regulatory relationships among the study genes. We employ a new eQTL mapping algorithm, GFlasso, which we have previously developed for sparse structured regression, to reanalyze a genome-wide yeast dataset. GFlasso fully takes into account the dependencies among expression traits to suppress false positives and to enhance the signal/noise ratio. Thus, GFlasso leverages the gene-interaction network to discover the pleiotropic effects of genetic loci that perturb the expression level of multiple (rather than individual) genes, which enables us to gain more power in detecting previously neglected signals that are marginally weak but pleiotropically significant.ResultsWhile eQTL hotspots in yeast have been reported previously as genomic regions controlling multiple genes, our analysis reveals additional novel eQTL hotspots and, more interestingly, uncovers groups of multiple contributing eQTL hotspots that affect the expression level of functional gene modules. To our knowledge, our study is the first to report this type of gene regulation stemming from multiple eQTL hotspots. Additionally, we report the results from in-depth bioinformatics analysis for three groups of these eQTL hotspots: ribosome biogenesis, telomere silencing, and retrotransposon biology. We suggest candidate regulators for the functional gene modules that map to each group of hotspots. Not only do we find that many of these candidate regulators contain mutations in the promoter and coding regions of the genes, in the case of the Ribi group, we provide experimental evidence suggesting that the identified candidates do regulate the target genes predicted by GFlasso.ConclusionsThus, this structured association analysis of a yeast eQTL dataset via GFlasso, coupled with extensive bioinformatics analysis, discovers a novel regulation pattern between multiple eQTL hotspots and functional gene modules. Furthermore, this analysis demonstrates the potential of GFlasso as a powerful computational tool for eQTL studies that exploit the rich structural information among expression traits due to correlation, regulation, or other forms of biological dependencies.

BRG1 variant rs1122608 on chromosome 19p13.2 confers protection against stroke and regulates expression of pre-mRNA-splicing factor SFRS3

“…The eQTL analysis is a strategy for discovering the complex patterns correlating genotypic data of genetic variants to global microarray gene expression data from a population of individuals (Curtis et al 2013). By searching the University of Michigan Center for Statistical Genetics eQTL database (http://www.sph.umich.edu/csg/liang/imputation/), we found that the common risk allele G of SNP rs1122608 was negatively associated with the expression level of the SFRS3 gene (Affymetrix HG U133 Plus 2.0 probe:232392_at, effect = −0.344, P = 0.00041) (Dixon et al 2007).…”

A Fully Automated Parallel-Processing R Package for High-Dimensional Multiple-Phenotype Analysis Considering Population Structure

“…A total of 692 SNPs was determined based on the GAMMAR p-value< 5 × 10 −5 (Figure 2). We next divided the whole yeast genome into 603 20-kb bins, and then SNPs with the smallest GAMMAR p-values were selected in each bin for comparison with the previous yeast eQTL studies [21,22]. We determined that 117 trans-eQTLs had 59 eGenes on three chromosomes.…”

“…Collectively, we defined the six bins as trans-regulatory hotspots and 59 eGenes as putative regulators. Of the 59 total eGenes, nine had been previously identified [21,22,24] (Table 1). In four previous studies, MATing type protein ALPHA 1; III:190000 (MATALPHA1) was reported to be a casual regulator [21,[25][26][27], and Killer toxin REsistant 33; XIV:360000 (KRE33) was recently identified as a putative causal regulator [24].…”

Mapping eQTL Networks with Mixed Graphical Markov Models

“…In chromosome V, around the locus of URA3 we find a cisacting eQTL that has a trans-acting effect on URA1 (chromosome XI) and URA4 (chromosome XII), the three of them taking part in the biosynthesis of pyrimidines (Yvert et al 2003;Curtis et al 2013). As can be easily seen from Figure 6, and consistent with previous observations (Yvert et al 2003), few of the eQTLs directly affect transcription factors, such as the ARR1 gene in chromosome XI, or RNA-binding proteins, such as NOP8 in chromosome V.…”

“…This eQTL is cisassociated with the gene MATALPHA1, which is expressed in haploids of the alpha-mating type and has been previously reported as a candidate regulator of the rest of genes associated with this locus (Yvert et al 2003;Curtis et al 2013). This locus is trans-associated with two other genes in the same chromosome (HMLALPHA1, HMRA1) and to a set of genes distributed throughout the genome [STE2, STE3, STE6, AFB1, BAR1, MF(ALPHA)1, MFA2], which are all involved in the regulation of mating-type-specific transcription.…”