Two decades ago, in an effort to better understand the factors underlying multiple sclerosis risk, we initiated a research program focusing on the discovery of DNA affecting risk to this chronic and debilitating disease. We propose now to expand this program through a multi-disciplinary approach that will identify genetic determinants of disease expression and progression. Our proposal harnesses the power of neuro-imaging information to bridge genetics with clinical expression of the disease (i.e. the phenotype), representing therefore an important step to promote evidence-based personalized medicine in a complex neurological disease.
Genome wide association studies have contributed greatly to the understanding of multiple sclerosis (MS) pathogenesis through the identification of 110 non-HLA genetic associations and deconstruction of the MHC genomic effects on disease risk. Progress in mapping additional risk-genes for this chronic, debilitating disease of the central nervous system is likely to be immediate. However, all large genetic studies to date have focused on susceptibility and not clinical expression or course of MS; genetic contributors to progression remain to be revealed. We hypothesize that allelic susceptibility variants act within functional networks that confer cumulative physiological effects on the phenotype, and that genetic burdens within distinct functional pathways will be reflected mechanistically in MRI metrics associates with neuronal damage and consequent disability. We propose two main research goals, bridging hypothesis driven genetic experiments with measurable indicators of neuronal loss to identify the genetic underpinnings of clinical progression. Specific Aim 1 describes the development and empirical validation of global genetic burden scoring statistics that incorporate mechanistic annotations for the stratification of patients into functional disease sub-types according to the ontological enrichment of the genetic risk variants each individual carries. Specific Aim 2 takes advantage of novel high-field MRI sequences to describe a limited number of correlated quantitative MRI phenotypes reflecting a specific aspect of the biology of progression, namely grey and white matter volumes of the brain and spinal cord. Although this proposal relies on a variety of ambitious and challenging methodological and analytical approaches, it is important to emphasize that we have extensive experience in clinical, imaging, and laboratory aspects of MS, together with data management, bio-informatics, and statistical analysis. The demonstration of even a modest functional effect of a known gene or group of genes on the course of MS could help elucidate fundamental disease mechanisms and yield a major therapeutic opportunity. Equally exciting is the potential for a better understanding of disease heterogeneity and rational reclassification on the basis of genotype scores, molecular pathways, and shared genetics with other diseases.