Poster Presentation GENEMAPPERS 2026

Unravelling the Molecular Landscape of Multiple Sclerosis Through Integrated Multi-Omics (#50)

Yuanhao Yang 1 , Eleanor Sabey 2 , Xin Lin 2 , Valery Fuh Ngwa 2 , Kathryn Burdon 2 , Ingrid Van Der Mei 2 , William Reay 2 , Kaylene M Young 2 , Bruce V Taylor 2 , Jake Gratten 1 , Yuan Zhou 2
  1. Mater Research Institute, Brisbane, QLD, Australia
  2. University of Tasmania, Hobart, TAS, Australia

A major challenge in the clinical management of multiple sclerosis (MS) is the absence of reliable blood-based biomarkers for early diagnosis, prognosis, and treatment evaluation. We hypothesise that integrated multi-omics analyses can elucidate the molecular architecture underlying MS onset and progression, paving the way for biomarker discovery.

In this ongoing project, we apply two complementary multi-omics approaches: (1) integration of large-scale MS onset (>40K cases; >60K controls) and severity (N=12.6K) GWAS with up to six omics layers from blood & brain tissues and cell types, using a novel summary-statistics-based framework; (2) integration of longitudinal multi-omics data with deep clinical phenotyping in the AusLong MS cohort (>250 cases; >15-year follow-up) from the pre-clinical stage to established disease.

Our pilot analyses, integrating current GWAS datasets (14.8K MS; 26.7K controls) with blood & brain eQTL (N=31.6K and 2.7K) and pQTL (N=54.2K and 376) data, identified 130 blood-based and 49 brain-based gene-protein interactions associated with MS onset, including several genes (ICA1L, TYMP) with consistent associations across tissues and specific immune cell types. Integration with in-house mouse brain spatial transcriptomics further revealed significant heritability enrichment for MS severity in the cortex, hippocampus, and amygdala, patterns not observed for MS onset.

We have generated genomic, longitudinal epigenomic, lipidomic, and metabolomic data from AusLong (baseline, 5, 10, 15-years). Pilot lipidomic and metabolomics results showed 147 metabolites and 136 lipids that were differentially abundant between MS cases and controls. Additionally, 4 metabolites and 10 lipids were significantly associated with disability progression (PFDR < 0.05). Proteomic and glycomic profiling is currently underway, alongside the development of AI driven models for longitudinal multi-omics integration. These integrated analyses are expected to yield an atlas of molecular changes associated with MS onset and progression, enabling the identification of accessible blood biomarkers to inform precision management and therapeutic targeting.