Gresle, Melissa, Jordan, Margaret, Spelman, Tim, Stankovich, Jim, Johnson, Laura, Laverick, Louise, Hamlett, Alison, Baxter, Alan, Butzkeuven, Helmut, and Field, Judith (2015) Multiple Sclerosis risk SNPs modify gene expression in immune cells. Multiple Sclerosis Journal, 21 (14). p. 3.

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Abstract

Background: Population based genome wide association studies have identified 110 single nucleotide polymorphisms (SNPs) that are associated with an increase in MS risk. These SNPs are all common, and have odds ratios of between 1.1 and 1.4. Most are found in non-protein coding regions, and their functions are largely unknown.

Objectives: Importantly, recent work has shown that some non-coding SNPs can function by changing immune gene expression levels as a quantitative trait, termed expression quantitative trait loci (eQTL). We conducted studies to evaluate the effects of MS risk SNPs on gene expression in four main immune cell types.

Methods: We isolated monocytes, B-cells, CD4- and CD8- T-cells from untreated relapsing MS cases (n=79) and healthy controls (n=101). To test for cis-eQTL associations, we selected all genes within +/-500kb of an MS risk SNP (2500 pairs in total). The Illumina Immunochip was used to genotype for MS risk SNPs, and gene expression was measured for each cell type by microarray.

Results: We have identified MS risk eQTL associations in each immune cell type, some of which are cell type specific. We also present preliminary data showing that some MS risk SNPs could exert differential effects on gene expression in cases compared to controls. Here we report likely disease state specific eQTLs for all cell types with the top associations being: RNF26/rs9736016, B cells; MACROD1/rs694739, CD8 cells; SLC25A41/rs1077667, CD4 cells; GPR18/rs4772201, monocyte cells.

Conclusions: We have shown that MS risk SNPs contribute to immune heterogeneity. It is hoped that through an understanding of the functions of individual common risk variants, it may be possible to uncover the processes and cell types that are most important for conveying the genetic risk of MS.

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