MSA Alliance

Project Background

Multiple system atrophy (MSA) is a neurodegenerative disease in which effective treatments are unavailable. Genetic factors are an important component of variance in any biological system, although MSA is largely considered sporadic. In the late 1990's Parkinson's disease (PD) was also considered to have little, if any, genetic etiology. However, at least 40 genes have been implicated in monogenic forms of parkinsonism in the past 20 years. Currently, cohorts of mutation carriers, biomarker and neuroimaging studies, and clinical trials are in devopment for three of those genetic targets, namely alpha-synuclein (SNCA), leucine-rich repeat kinase (LRRK2) and glucocerebrosidase (GBA). Meta-analyses of candidate gene and genome-wide association studies (GWAS) in idiopathic PD have successfully generalized the findings and identified novel loci. However, very large samples sizes were required to provide compelling evidence; initial GWAS for discovery were underpowered and replication still remains challenging . A GWAS of progressive supranuclear palsy, another sporadic form of parkinsonism, has also been successful. Autopsy-confirmed patients in the discovery sample helped assure disease homogeneity so far fewer cases were necessary. In MSA a differential diagnosis is quite challenging, the clinical heterogeneity of cerebellar and parkinsonian subtypes is broad, and the first GWAS was clearly underpowered. Nevertheless, continued exploration of GWAS and other candidate gene loci are warranted. While there are only a small number of families with multi-incident MSA, a similar meta-analysis of relatively rare genetic variability (MAF < 0.001) identified by high-throughput sequencing (HTS) technologies would be worthwhile.

Aims

Gene identification in MSA can be achieved by combining global resources, expertise and innovative methods, and may be facilitated by the following aims:

  1. The ethical/legal framework for Institutional/Investigator collaboration
  2. A web forum for project communication amongst research professionals including a meta-data directory of expertise, resources and data
  3. Steering groups to advise on data harmonization and acquisition
  4. A genetic analyses workshop at the International Congress on MSA, to discuss opportunities, methods and set priorities
  5. Distributed clinicogenetic studies using combined datasets