Genetically Predicted Blood DNA Methylation Reveals Putative Regulatory Signals Associated with ALS Risk

Abstract

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder whose genetic architecture and underlying molecular mechanisms remain incompletely understood, particularly in sporadic disease. To investigate whether genetically regulated DNA methylation may help interpret ALS susceptibility, we conducted a methylome-wide association study (MWAS) of genetically predicted blood DNA methylation using the PrediXcan framework. CpG-specific prediction models developed in the ARIES and Understanding Society cohorts were applied to ALS genome-wide association study (GWAS) summary statistics from 27,205 cases and 110,881 controls of European ancestry. In total, genetically predicted methylation at 192,378 unique CpG sites was evaluated using S-PrediXcan. At a nominal threshold of p < 0.05, 3,741 CpGs were associated with ALS risk using ARIES models and 13,127 using Understanding Society models. After Bonferroni correction, 25 CpGs across eight genomic regions remained significantly associated with ALS risk. These included signals near established ALS and ALS-frontotemporal dementia genes and loci, including C9orf72, TBK1, SCFD1, and MOB3B, as well as three CpGs mapping to WHAMM at 15q25.2, a region not previously implicated in ALS by GWAS. Predicted methylation was positively associated with ALS risk at 18 CpGs and inversely associated at seven. Complementary transcriptome-wide association analyses using GTEx v8 whole-blood gene-expression prediction models identified 11 genes associated with ALS risk after Bonferroni correction, including convergent methylation and expression signals at C9orf72. These findings add a regulatory dimension to ALS genetic studies by prioritizing CpG sites, genes, and genomic regions through which inherited variation may influence disease susceptibility. PrediXcan-based MWAS therefore provides a complementary strategy for refining genetic association signals into biologically testable candidates and identifying regulatory mechanisms for further functional investigation.