A cross-platform analysis of 14,177 expression quantitative trait loci derived from lymphoblastoid cell lines

Abstract

Gene expression levels can be an important link DNA between variation and phenotypic manifestations. Our previous map of global gene expression, based on ∼400K single nucleotide polymorphisms (SNPs) and 50K transcripts in 400 sib pairs from the MRCA family panel, has been widely used to interpret the results of genome-wide association studies (GWASs). Here, we more than double the size of our initial data set with expression data on 550 additional individuals from the MRCE family panel using the Illumina whole-genome expression array. We have used new statistical methods for dimension reduction to account for nongenetic effects in estimates of expression levels, and we have also included SNPs imputed from the 1000 Genomes Project. Our methods reduced false-discovery rates and increased the number of expression quantitative trait loci (eQTLs) mapped either locally or at a distance (i.e., in cis or trans) from 1534 in the MRCA data set to 4452 (with <5% FDR). Imputation of 1000 Genomes SNPs further increased the number of eQTLs to 7302. Using the same methods and imputed SNPs in the newly acquired MRCE data set, we identified eQTLs for 9000 genes. The combined results identify strong local and distant effects for transcripts from 14,177 genes. Our eQTL database based on these results is freely available to help define the function of disease-associated variants.

Human validation of genes associated with a murine atherosclerotic phenotype

“…We obtained summary statistics from GWAS on CAD 8 and large artery stroke 9 for SNPs ±50kb from the 5' and 3' gene borders of the 659 studied murine genes (Supplemental Material). We used these as input for a gene-based analysis using VEGAS which assigned an empirical p-value (after permutations) to each of the 659 genes based on the p-value of the SNPs in and ±50 kb around the genes of interest 10 .…”

“…Therefore, we identified eQTLs through two approaches. First, we queried three online resources (table SVI) for eQTLs in lymphoblastoid cells 7,8 , monocytes 9 , subcutaneous adipose tissue 7 , and skin tissue 7 . Secondly, we performed eQTL analyses in 7 tissue types that could be relevant for disease development in the STAGE study 10 .…”

“…This has opened up the possibility to agnostically assess the effects of genome-wide variation on human traits and disease 7 . Indeed, meta-analyses of human genome-wide association studies (GWAS) have identified many risk loci for CAD 8 and large artery ischemic stroke (LAS) 9 . These GWAS provide the unique opportunity to validate the putative disease-causing genes identified through murine models in humans.…”

SNPs located at CpG sites modulate genome-epigenome interaction

“…We used the significant association of random SNP/CpG site pairs as an upper bound of false discoveries, a strategy used in expression QTL analysis. 12 While that approach overestimates the true false discovery rate (FDR) due to ignoring any potential true trans-meQTLs, it is easier to compute and preserve all family structure and covariates. Using 130 million pairs, we estimated that the nominal P value of 10 −9 as equivalent to the FDR of 0.000 004, and the nominal P value of 10 −200 as FDR < 7.7 × 10 −9 (no random pairs with P < 10 −200 ).…”

Neurodegenerative Diseases: Phenome to Genome Analysis

“…The three components critical to validating eQTL results include i) a SNP marker; ii) the gene expression levels, as measured by a probe or sequence information; and iii) a measure of the statistical association between the two in a study population, such as the P-value. The eQTL browser provides an approach to query the eQTL database [52].…”