Genotype-Phenotype Correlations in Multiple Sclerosis

Project Details

Synopsis

MS is a complex polygenic and environmentally determined disease. MS risk has been linked to over 200 single nucleotide polymorphisms (SNPs), each with average increased odds of developing MS within the range of 1.1-1.3.  To date, the only replicated genetic modifier of any MS phenotype the main risk allele, HLA-DRB1*1501, which confers reduced age of onset.  The best evidence to-date for a genetic basis underlying phenotypic outcomes comes from a small number of cross-sectional genome-wide association studies a priori dedicated to a search for severity signals.  These studies have had modest success in cumulatively identifying 109 putative modulators disease outcome. Critically however, a functional dichotomy between genes involved in susceptibility and those that regulate severity has been reported, the latter having an over-representation of signals related to CNS and embryonic development, and cellular respiration. Therefore, strong preliminary evidence exists that genetic variation does influence phenotypic outcomes, however this remains to be validated. This PhD project will utilise genetic data that is linked to a global observational cohort to identify genotype-phenotype correlations with (1) clinical phenotypes (2) MRI phenotypes. Further, genetic variants will be incorporated into prognostic models to determine whether they exert an independent effect on disease outcomes.

Outcomes and impact

The identification of genetic predictors of MS phenotype will have a significant impact on MS management, with the capacity for rapid translation in to clinical practice through the development of a genetic test of disease outcome.  It will inform risk/benefit decision-making when selecting appropriate therapies for individuals, and thus maximise quality of life and reduce economic burden. Biologically, success in genetic analyses will provide insight into the molecular mechanisms of MS progression.

Research Environment

The proposed project will be undertaken using the MSBase Registry, an international, prospective, observational MS cohort study.  It currently contains over 50,000 longitudinal patient records, with over 230,000-patient years of follow-up.  Within the MSBase observational cohort study, we have formed a special interest group to examine genetic predictors of disease outcome. This group comprises 11 centres that have the capacity to undertake genetic studies.  Here, our cohort comprises 8,574 patients, with 60,000 patient-years of follow-up with visits occurring on average every 6 months.

Researchers

Collaborators

  • Prof Dana Horakova, and Prof Eva Havrdova, Charles University Prague, Czech Republic
  • Prof Guillermo Izquierdo, Hospital Universitario Virgen Macarena, Seville, Spain
  • Dr Fuencisla Matesanz Instituto de Parasitolog√≠a y Biomedicina L√≥pez Neyra, CSIC, Granada, Spain
  • A. Prof Nikolaos Patsopoulos, Brigham and the Women's Hospital, Harvard Medical School, Boston, MA, USA
  • Prof Jan Hillert, and Dr Ali Manouchehrinia Karolinska Institutet, Stockholm, Sweden
  • Prof Philip de Jager, Columbia University, New York City, NY, USA
  • A. Prof Tomas Kalincik, Department of Medicine, CORe Unit, University of Melbourne, VIC, Australia
  • Prof. Trevor Kilpatrick, Melbourne Neuroscience Institute, University of Melbourne, VIC, Australia
  • Prof Jeannette Lechner-Scott, University of Newcastle, Newcastle, NSW, Australia
  • Prof Mark Slee, Flinders University and Medical Centre, Adelaide, SA, Australia
  • Prof. Michael Barnett, Brain and Mind Centre, University of Sydney, NSW, Australia
  • Dr Steve Vucic and Prof David Booth, Westmead Institute, University of Sydney, NSW, Australia
  • Prof. Bruce Taylor, Menzies Research Institute, Univesity of Tasmania, TAS, Australia

Funding

  • The Royal Melbourne Hospital [MH2013-055]
  • MSBase Foundation Project Grant
  • CharityWorks for MS/MS Research Australia [MSRA12-062]

Research Outcomes

  1. Jokubaitis, V.G., Kalincik, T., Horakova, D., Havrdova, E., Kleinova, P., Izquierdo, G., Matesanz, F., Kilpatrick, T.J., Lechner-Scott, J., Slee, M. and Barnett, M., et al. Genotype-phenotype correlations in relapsing-remitting multiple sclerosis: making use of a robust severity phenotype. Multiple Sclerosis Journal 2017; 23, No. 1, pp. 59-59
  2. Jokubaitis, V.G., Kalincik, T., Horakova, D., Havrdova, E., Kleinova, P., Kucerova, K., Izquierdo, G., Matesanz, F., Lugaresi, A., Kilpatrick, T.J. and Lechner-Scott, J.,et al. Defining a robust disease severity phenotype for use in genetic association studies. Multiple Sclerosis Journal 2016; 22, pp. 168-169

Research Publications

  1. Jokubaitis, V.G. and Butzkueven, H., 2016. A genetic basis for multiple sclerosis severity: Red herring or real?. Molecular and cellular probes, 30(6), pp.357-365.
  2. Jokubaitis VG, Spelman T, Kalincik T, et al., Predictors of long-term disability accrual in relapse-onset multiple sclerosis. Annals of Neurology. 2016; 80(1): 89-100
  3. Mahukar S, Moldovan M, Suppiah V, et al., Response to interferon-beta treatment in multiple sclerosis patients: a genome-wide association study. The Pharmacogenomics Journal. 2017;17(4):312-318.
  4. Jokubaitis VG, Spelman T, Kalincik T, et al., Predictors of disability worsening in clinically isolated syndrome. Ann Clin Transl Neurol. 2015; 2(5): 479-91
  5. Jonas A, Thiem S, Kuhlmann T, et al., Axonally derived matrilin-2 induces proinflammatory responses that exacerbate autoimmune neuroinflammation. J Clin Invest. 2014; 124(11): 5042-56
  6. Jokubaitis VG, Gresle MM, Kemper DA et al., Endogenously regulated Dab2 worsen inflammatory injury in experimental autoimmune encephalomyelitis. Acta Neuropathol Commun. 2013; 1(32). doi: 10.1186/2051-5960-1-32.

Research Group

MS-BioGaPs



Faculty Research Themes

Neuroscience

School Research Themes

Neuroscience & Psychiatry



Key Contact

For further information about this research, please contact the research group leader.

Department / Centre

Medicine and Radiology

Node

Royal Melbourne Hospital

Unit / Centre

MS-BioGaPs