|Impact of genetic risk loci in multiple sclerosis on expression of proximal genes|| |
|T James1, M Lindén1, M Huss2, M Brandi3, M Khademi1, J Tegnér4, D Gomez-Cabrero4, I Kockum1, T Olsson1|
|1Karolinska Institutet, Clinical Neuroscience, Stockholm, Sweden, 2Stockhom Univiersity, Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm, Sweden, 3Royal Institute of Technology, Science for Life Labratory, Stockholm, Sweden, 4Karolinska Institutet, Department of Medicine, Stockholm, Sweden|
|Background: The major genetic susceptibility factors for multiple sclerosis (MS) are different alleles in the HLA-DRB1 and HLA-A genes, but more than 100 non-HLA genetic loci have also been associated with MS. To be able to understand the impact of genetics on MS pathogenesis and disease biology, it is of great importance to investigate the relationship between MS risk loci and gene expression.|
Objectives: The aim of this study was to study expression quantitative trait loci (eQTL) for genes mapping within 400 kb of established MS risk loci.
Methods: We sequenced the transcriptome in peripheral blood mononuclear cells (PBMCs) from a total of 183 individuals with MS and other neurological diseases using 100bp paired-end reads with an average sequencing depth of 36 Mreads. We estimated expression levels of genes in proximity of established MS risk loci using trimmed mean of M-values. These were correlated to genotypes from 109 MS susceptibility loci and four HLA variants (DRB1*15:01, 03:01, 13:02 and A*02:01). Genotyping was carried out using the Immunochip, HLA alleles were imputed using HLA*IMP:02. We used a generalized linear model, assuming negative binomial distribution of the expression data to correlate this to number of risk alleles assuming an additive genetic model. Gender, experimental batch, disease state and treatment were included as covariates. We selected eQTL associations based on the significance of permutation-based p-values, strength of the correlation and false discovery rate.
We validated our findings in public RNA-seq data from lymphoblastic cell lines (LCLs) from 248 individuals with genotypes from the 1000 Genomes Project, and in sorted CD4+, CD8+, CD19+ and CD14+ cells from 57 individuals.
Results: We identified 28 non-HLA and 11 HLA eQTL signals in PBMC. Eleven of the non-HLA and many of the HLA eQTLs were replicated in the expression data from LCL.
We confirmed previously observed eQTL effects, such as rs1021156 affecting FAM164A expression. We also report several new eQTLs such as rs1920296 that affects IQCB1 and rs4794058 that affects TBKBP1 and MRPL45P2. We further show that rs12946510 affects ORMDL3 transcription in CD19+ cells, and rs7595717 affects PLEK expression in CD4+ cells.
Conclusions: With this study we have gained further insight into which genes are affected by MS associated polymorphisms, confirming that these are not necessarily the genes that are located closest to the associated polymorphism.
PhD Ingrid Kockum , Karolinska Institutet , Stockholm , SE
Assigned in sessions:
11.09.2014, 15:30-17:00, Poster viewing, poster sessions, P1, Poster Session 1 (P001-P490) and Coffee Break, Hall C