Abstract
Gene expression quantitative trait loci (eQTL) are useful for identifying single nucleotide polymorphisms (SNPs) associated with diseases. At times, a genetic variant may be associated with a master regulator involved in the manifestation of a disease. The downstream target genes of the master regulator are typically co-expressed and share biological function. Therefore, it is practical to screen for eQTLs by identifying SNPs associated with the targets of a transcript-regulator (TR). We used a multivariate regression with the gene expression of known targets of TRs and SNPs to identify TReQTLs in European (CEU) and African (YRI) HapMap populations. A nominal p-value of <1×10−6 revealed 234 SNPs in CEU and 154 in YRI as TReQTLs. These represent 36 independent (tag) SNPs in CEU and 39 in YRI affecting the downstream targets of 25 and 36 TRs respectively. At a false discovery rate (FDR) = 45%, one cis-acting tag SNP (within 1 kb of a gene) in each population was identified as a TReQTL. In CEU, the SNP (rs16858621) in Pcnxl2 was found to be associated with the genes regulated by CREM whereas in YRI, the SNP (rs16909324) was linked to the targets of miRNA hsa-miR-125a. To infer the pathways that regulate expression, we ranked TReQTLs by connectivity within the structure of biological process subtrees. One TReQTL SNP (rs3790904) in CEU maps to Lphn2 and is associated (nominal p-value = 8.1×10−7) with the targets of the X-linked breast cancer suppressor Foxp3. The structure of the biological process subtree and a gene interaction network of the TReQTL revealed that tumor necrosis factor, NF-kappaB and variants in G-protein coupled receptors signaling may play a central role as communicators in Foxp3 functional regulation. The potential pleiotropic effect of the Foxp3 TReQTLs was gleaned from integrating mRNA-Seq data and SNP-set enrichment into the analysis.
Highlights
Phenotypic differences between populations have been shown to be associated with variation in genes, the epigenome, the environment and quantitative traits
Analysis Strategy Typical expression quantitative trait loci analyses take the form of a strategy where a single gene is used as a response variable and individual single nucleotide polymorphisms (SNPs) the predictor variable to determine if there is association of a particular phenotype with a variant
We used a multivariate approach to leverage the covariance of the gene expression of downstream targets (DSTs) of a transcript-regulator (TR) to perform genome-wide associations for SNPs that are potentially linked to changes in gene expression across genotypes
Summary
Phenotypic differences between populations have been shown to be associated with variation in genes, the epigenome, the environment and quantitative traits. Gene expression has been used as a quantitative phenotypic trait to locate regions in the genome that have polymorphisms governing differential transcription within populations [1,2,3,4]. This type of inference termed expression quantitative trait loci (eQTL) analysis has been used in genome-wide association studies (GWAS) to map single nucleotide polymorphisms (SNPs) to regions that affect gene expression [5]. The advantage of understanding the contribution of genetic variations on the expression of genes has major implications on the manner in which pharmaceuticals are personalized for an individual and how complex diseases are investigated.
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