ECTRIMS eLearning

A multiple sclerosis CD4+ T cell methylation quantitative trait loci (meQTL) reference map: a novel data resource identifies the proximal and distal functional consequences of 19 MS susceptibility loci
Author(s): ,
T. Roostaei
Affiliations:
Columbia University Medical Center, New York, NY
,
H.-U. Klein
Affiliations:
Columbia University Medical Center, New York, NY
,
N. Patsopoulos
Affiliations:
Brigham & Women’s Hospital and Harvard Medical School, Boston, MA, United States
,
H.L. Weiner
Affiliations:
Brigham & Women’s Hospital and Harvard Medical School, Boston, MA, United States
P.L. De Jager
Affiliations:
Columbia University Medical Center, New York, NY
ECTRIMS Learn. De Jager P. 10/12/18; 228163; P1785
Philip L. De Jager
Philip L. De Jager
Contributions
Abstract

Abstract: P1785

Type: Poster Sessions

Abstract Category: N/A

MS is a polygenic disease with more than 200 independent risk loci. The molecular consequences of these loci are not fully understood. These variants may alter the epigenome, the conformation of chromatin that leads to changes in RNA expression and immune function. To investigate systematically, we generated a dataset of DNA methylation profiles from blood CD4+ T cells of 180 MS patients to determine the effects of both individual MS risk variants and aggregate measures of genetic susceptibility in the form of polygenic scores on local and distal DNA methylation.
After quality control, data from 769,634 CpG sites (measured using the IlluminaEPIC array) from 156 patients with European ancestry were used for analysis. Linear regression was performed to assess the association between DNA methylation and imputed genotypes, accounting for the effects of covariates including age and sex.
We found evidence for the influence of cis (±1Mb) genetic effects on the methylation of 107,925 CpGs (FDR-adjusted p< 0.05). Colocalization analysis was performed on 3,090 meQTLs in ±100Kb of the lead MS variants to find loci which likely have a shared causal effect on MS risk and cis-DNA methylation. 3 MS loci showed strong evidence of colocalization (posterior probability >0.95) with meQTL effects on CpGs in the body of TNFSF14, ANKRD55 and IL6ST and the transcription start site (TSS) of CYP24A1 genes. 16 additional MS loci showed colocalization evidence with posterior probability >0.8 with meQTLs close to genes including RGS14, AHI1, ZNF767P, RMI2, TBX6 and NCF4.
Our trans-meQTL analysis revealed a colocalized effect for MS rs3809627 locus on chr16 and methylation of 3 CpGs on chr4 close to PRDM8 gene. PRDM8 is previously shown to be differentially methylated in MS CD8+ T cells. Our finding relating the gene to MS genetic risk suggests a causal role for PRDM8 in MS.
We also investigated the effects of aggregate genetic scores on DNA methylation. Although results were nonsignificant for non-MHC polygenic score, MHC score was associated with methylation levels of 55 CpGs (FDR p< 0.05/2), 2 of which were outside the MHC region, in the gene body of PRKCA and TSS of EMILIN3. Linkage to PRKCA has previously been suggested in familial MS, and our finding supports its role in MS pathobiology.
Overall, our analyses identify functional consequences of a large proportion of MS susceptibility variants and help prioritize genes and loci for investigation in future MS pathophysiology studies.
Disclosure: The authors have nothing to disclose.

Abstract: P1785

Type: Poster Sessions

Abstract Category: N/A

MS is a polygenic disease with more than 200 independent risk loci. The molecular consequences of these loci are not fully understood. These variants may alter the epigenome, the conformation of chromatin that leads to changes in RNA expression and immune function. To investigate systematically, we generated a dataset of DNA methylation profiles from blood CD4+ T cells of 180 MS patients to determine the effects of both individual MS risk variants and aggregate measures of genetic susceptibility in the form of polygenic scores on local and distal DNA methylation.
After quality control, data from 769,634 CpG sites (measured using the IlluminaEPIC array) from 156 patients with European ancestry were used for analysis. Linear regression was performed to assess the association between DNA methylation and imputed genotypes, accounting for the effects of covariates including age and sex.
We found evidence for the influence of cis (±1Mb) genetic effects on the methylation of 107,925 CpGs (FDR-adjusted p< 0.05). Colocalization analysis was performed on 3,090 meQTLs in ±100Kb of the lead MS variants to find loci which likely have a shared causal effect on MS risk and cis-DNA methylation. 3 MS loci showed strong evidence of colocalization (posterior probability >0.95) with meQTL effects on CpGs in the body of TNFSF14, ANKRD55 and IL6ST and the transcription start site (TSS) of CYP24A1 genes. 16 additional MS loci showed colocalization evidence with posterior probability >0.8 with meQTLs close to genes including RGS14, AHI1, ZNF767P, RMI2, TBX6 and NCF4.
Our trans-meQTL analysis revealed a colocalized effect for MS rs3809627 locus on chr16 and methylation of 3 CpGs on chr4 close to PRDM8 gene. PRDM8 is previously shown to be differentially methylated in MS CD8+ T cells. Our finding relating the gene to MS genetic risk suggests a causal role for PRDM8 in MS.
We also investigated the effects of aggregate genetic scores on DNA methylation. Although results were nonsignificant for non-MHC polygenic score, MHC score was associated with methylation levels of 55 CpGs (FDR p< 0.05/2), 2 of which were outside the MHC region, in the gene body of PRKCA and TSS of EMILIN3. Linkage to PRKCA has previously been suggested in familial MS, and our finding supports its role in MS pathobiology.
Overall, our analyses identify functional consequences of a large proportion of MS susceptibility variants and help prioritize genes and loci for investigation in future MS pathophysiology studies.
Disclosure: The authors have nothing to disclose.

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