DNA Methylation Signatures of Multiple Sclerosis Occur Independently of Known Genetic Risk and Are Primarily Attributed to B Cells and Monocytes.

Epigenetic mechanisms can regulate how DNA is expressed independently of sequence and are known to be associated with various diseases. Among those epigenetic mechanisms, DNA methylation (DNAm) is influenced by genotype and the environment, making it an important molecular interface for studying disease etiology and progression. In this study, we examined the whole blood DNA methylation profiles of a large group of people with (pw) multiple sclerosis (MS) compared to those of controls. We reveal that methylation differences in pwMS occur independently of known genetic risk loci and show that they more strongly differentiate disease (AUC = 0.85, 95% CI 0.82-0.89, p = 1.22 × 10-29) than known genetic risk loci (AUC = 0.72, 95% CI: 0.66-0.76, p = 9.07 × 10-17). We also show that methylation differences in MS occur predominantly in B cells and monocytes and indicate the involvement of cell-specific biological pathways. Overall, this study comprehensively characterizes the immune cell-specific epigenetic architecture of MS.

scTEM-seq: Single-cell analysis of transposable element methylation to link global epigenetic heterogeneity with transcriptional programs.

Global changes in DNA methylation are observed in development and disease, and single-cell analyses are highlighting the heterogeneous regulation of these processes. However, technical challenges associated with single-cell analysis of DNA methylation limit these studies. We present single-cell transposable element methylation sequencing (scTEM-seq) for cost-effective estimation of average DNA methylation levels. By targeting high-copy SINE Alu elements, we achieve amplicon bisulphite sequencing with thousands of loci covered in each scTEM-seq library. Parallel transcriptome analysis is also performed to link global DNA methylation estimates with gene expression. We apply scTEM-seq to KG1a acute myeloid leukaemia (AML) cells, and primary AML cells. Our method reveals global DNA methylation heterogeneity induced by decitabine treatment of KG1a cells associated with altered expression of immune process genes. We also compare global DNA methylation estimates to expression of transposable elements and find a predominance of negative correlations. Finally, we observe co-ordinated upregulation of many transposable elements in a sub-set of decitabine treated cells. By linking global DNA methylation heterogeneity with transcription, scTEM-seq will refine our understanding of epigenetic regulation in cancer and beyond.

Capturing SNP Association across the NK Receptor and HLA Gene Regions in Multiple Sclerosis by Targeted Penalised Regression Models.

Conventional genome-wide association studies (GWASs) of complex traits, such as Multiple Sclerosis (MS), are reliant on per-SNP p-values and are therefore heavily burdened by multiple testing correction. Thus, in order to detect more subtle alterations, ever increasing sample sizes are required, while ignoring potentially valuable information that is readily available in existing datasets. To overcome this, we used penalised regression incorporating elastic net with a stability selection method by iterative subsampling to detect the potential interaction of loci with MS risk. Through re-analysis of the ANZgene dataset (1617 cases and 1988 controls) and an IMSGC dataset as a replication cohort (1313 cases and 1458 controls), we identified new association signals for MS predisposition, including SNPs above and below conventional significance thresholds while targeting two natural killer receptor loci and the well-established HLA loci. For example, rs2844482 (98.1% iterations), otherwise ignored by conventional statistics (p = 0.673) in the same dataset, was independently strongly associated with MS in another GWAS that required more than 40 times the number of cases (~45 K). Further comparison of our hits to those present in a large-scale meta-analysis, confirmed that the majority of SNPs identified by the elastic net model reached conventional statistical GWAS thresholds (p < 5 × 10-8) in this much larger dataset. Moreover, we found that gene variants involved in oxidative stress, in addition to innate immunity, were associated with MS. Overall, this study highlights the benefit of using more advanced statistical methods to (re-)analyse subtle genetic variation among loci that have a biological basis for their contribution to disease risk.