• Jonathan Mill, Exeter, UK


  • Paul Hop, The Netherlands


Ammar Al-Chalabi, Ahmad Al Kheilfat, Andrea Calvo, Antonia Ratti, Vincenzo Silani


Despite success in identifying genetic variants associated with ALS, in many cases there remains uncertainty about the specific involved in disease pathogenesis and how their function is regulated. Insights into the functional complexity of the genome have also focused attention on the probable role of non-sequence-based genomic variation in health and disease. Of particular interest are epigenetic processes that regulate gene expression via modifications to DNA, histone proteins, and chromatin. DNA methylation is the best-characterized epigenetic modification, stably influencing gene expression via disruption of transcription factor binding and recruitment of methyl-binding proteins that initiate chromatin compaction and gene silencing. Despite being traditionally regarded as a mechanism of transcriptional repression, DNA methylation is actually associated with both increased and decreased gene expression, and other genomic functions including alternative splicing and promoter usage. The availability of high-throughput profiling methods for quantifying DNA methylation across the genome at single-base resolution in large numbers of samples has enabled researchers to perform epigenome-wide association studies (EWAS) aimed at identifying methylomic variation associated with environmental exposure and disease; however, these studies are inherently more complex to design and interpret than GWAS. The dynamic nature of epigenetic processes means that unlike in genetic epidemiology a range of potentially important confounding factors need to be considered, including tissue or cell type, age, sex, lifestyle exposures, and reverse causation. This WG will identify epigenetic variation in clinical cases, discordant MZ twins, and post-mortem brain tissue, integrating methylomic data with genetic and available environmental data.


  1. Perform an EWAS meta-analysis (ALS cases compared to controls) across all samples with available DNA methylation data;
  2. Increased explained variance: combining PRS with PMRS (polymethylation risk score), and combining a genetic relationship matrix and methylation matrix to see if explained variance increases in ALS;
  3. Examine DNA methylation differences in genetically-identical monozygotic twins discordant for ALS;
  4. Identify epigenetic variation associated with ALS mutations and elevated ALS polygenic risk score;
  5. C9orf72 EWAS;
  6. Integrate genetic and epigenetic data using Bayesian colocalization approaches and SMR;
  7. Derive environmental/exposure variables from DNA methylation data (e.g. smoking, epigenetic age) to examine associations in ALS;
  8. Assess epigenetic variation in ALS brain samples and matched control samples, examining overlap with blood-derived DNA methylation data;
  9. Associations between DNA methylation profiles (and imputed WBC) and disease phenotype (survival, age at onset). Interaction with WG-1 Phenotying;
  10. Identification of DNA methylation subtypes in relation to ALS mutations using various clustering/ grouping algorithms on methylation data (analogous to the field of oncology)