Identifying the downstream effects of disease-associated single nucleotide polymorphisms (SNPs) is challenging: the causal gene is often unknown or it is unclear how the SNP affects the causal gene, making it difficult to design experiments that reveal functional consequences. To help overcome this problem, we performed the largest expression quantitative trait locus (eQTL) meta-analysis so far reported in non-transformed peripheral blood samples of 5,311 individuals, with replication in 2,775 individuals. We identified and replicated trans-eQTLs for 233 SNPs (reflecting 103 independent loci) that were previously associated with complex traits at genome-wide significance. Although we did not study specific patient cohorts, we identified trait-associated SNPs that affect multiple trans-genes that are known to be markedly altered in patients: for example, systemic lupus erythematosus (SLE) SNP rs49170141 altered C1QB and five type 1 interferon response genes, both hallmarks of SLE2-4. Subsequent ChIP-seq data analysis on these trans-genes implicated transcription factor IKZF1 as the causal gene at this locus, with DeepSAGE RNA-sequencing revealing that rs4917014 strongly alters 3’ UTR levels of IKZF1. Variants associated with cholesterol metabolism and type 1 diabetes showed similar phenomena, indicating that large-scale eQTL mapping provides insight into the downstream effects of many trait-associated variants.

SummaryOverweight and obesity affect ~1.5 billion people worldwide, and are major risk factors for type-2 diabetes (T2D), cardiovascular disease and related metabolic and inflammatory disturbances.1,2 Although the mechanisms linking adiposity to its clinical sequelae are poorly understood, recent studies suggest that adiposity may influence DNA methylation,3–6 a key regulator of gene expression and molecular phenotype.7 Here we use epigenome-wide association to show that body mass index (BMI, a key measure of adiposity) is associated with widespread changes in DNA methylation (187 genetic loci at P<1x10-7, range P=9.2x10-8 to 6.0x10-46; N=10,261 samples). Genetic association analyses demonstrate that the alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause. We find the methylation loci are enriched for functional genomic features in multiple tissues (P<0.05), and show that sentinel methylation markers identify gene expression signatures at 38 loci (P<9.0x10-6, range P=5.5x10-6 to 6.1x10-35, N=1,785 samples). The methylation loci identified highlight genes involved in lipid and lipoprotein metabolism, substrate transport, and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future type-2 diabetes (relative risk per 1SD increase in Methylation Risk Score: 2.3 [2.07-2.56]; P=1.1x10-54). Our results provide new insights into the biologic pathways influenced by adiposity, and may enable development of new strategies for prediction and prevention of type-2 diabetes and other adverse clinical consequences of obesity.

Large-scale cis-and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression