Integrating Genome-Wide Genetic Variations and Monocyte Expression Data Reveals Trans-Regulated Gene Modules in Humans

Abstract

One major expectation from the transcriptome in humans is to characterize the biological basis of associations identified by genome-wide association studies. So far, few cis expression quantitative trait loci (eQTLs) have been reliably related to disease susceptibility. Trans-regulating mechanisms may play a more prominent role in disease susceptibility. We analyzed 12,808 genes detected in at least 5% of circulating monocyte samples from a population-based sample of 1,490 European unrelated subjects. We applied a method of extraction of expression patterns—independent component analysis—to identify sets of co-regulated genes. These patterns were then related to 675,350 SNPs to identify major trans-acting regulators. We detected three genomic regions significantly associated with co-regulated gene modules. Association of these loci with multiple expression traits was replicated in Cardiogenics, an independent study in which expression profiles of monocytes were available in 758 subjects. The locus 12q13 (lead SNP rs11171739), previously identified as a type 1 diabetes locus, was associated with a pattern including two cis eQTLs, RPS26 and SUOX, and 5 trans eQTLs, one of which (MADCAM1) is a potential candidate for mediating T1D susceptibility. The locus 12q24 (lead SNP rs653178), which has demonstrated extensive disease pleiotropy, including type 1 diabetes, hypertension, and celiac disease, was associated to a pattern strongly correlating to blood pressure level. The strongest trans eQTL in this pattern was CRIP1, a known marker of cellular proliferation in cancer. The locus 12q15 (lead SNP rs11177644) was associated with a pattern driven by two cis eQTLs, LYZ and YEATS4, and including 34 trans eQTLs, several of them tumor-related genes. This study shows that a method exploiting the structure of co-expressions among genes can help identify genomic regions involved in trans regulation of sets of genes and can provide clues for understanding the mechanisms linking genome-wide association loci to disease.

Type 1 Diabetes Mellitus and Cardiovascular Disease

“…194 In addition, genome-wide association studies have identified several single-nucleotide polymorphisms associated with ESRD in some but not all studies. [195][196][197][198] Prevention of DKD remains challenging. Although microalbuminuria and macroalbuminuria are attractive therapeutic targets for CVD prevention, there are no specific interventions directed at the kidney that prevent DKD.…”

From Markers to Molecular Mechanisms: Type 1 Diabetes in the Post-GWAS Era

“…A significant advantage for T1D is that much of the disease risk appears to be conferred by genes involved in immune system processes, particularly leukocyte function, and a wide range of leukocytes can be accessed via the peripheral blood using commercially available kits that facilitate subset purification from whole blood or buffy coat. A proof of principle for this approach was provided by the integration of SNP genotyping and peripheral blood monocyte expression data in which allelic variation at the T1D susceptibility locus 12q13 (ERBB3; lead SNP rs11171739) was associated with differences in expression of two nearby (cis) genes, RPS26 and SUOX, and 5 trans genes, including MADCAM1 [167]. There is significant evidence that modulation of MAdCAM-1 expression affects the pathogenesis of T1D; it is increased in expression on vascular endothelium adjacent to and within the inflamed islets of NOD mice [168], and its blockade reduces the incidence of spontaneous diabetes [169] and diabetes transferred by a T cell clone in mice [170].…”

Genetic Regulation of Adipose Gene Expression and Cardio-Metabolic Traits

“…20 Studies of gene expression in circulating monocytes or whole-blood cells have also provided evidence of trans regulation of gene expression with linkage to traits relevant to lipid metabolism, type 1 diabetes, hypertension, celiac disease, and cancer. 21,22 In light of the widespread use of mice to help validate and gain mechanistic understanding of genes in GWAS loci, commonalities and differences in the regulatory networks of mice and humans are of clear relevance for studies of common diseases. 6 A previous comparison between mouse and human adipose transcriptional networks detected a shared core-network module enriched for genes involved in the inflammatory and immune response causally associated with obesity-related traits.…”