| Title: | The genetics of multiple sclerosis in the UK |
| Lead Institution: | University of Cambridge |
| Principal investigator: | Professor Stephen Sawcer |
Application 4408
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About
Our primary aim is to identify genetic variants that influence the risk of developing multiple sclerosis. In collaboration with others from the International Multiple Sclerosis Genetics Consortium (IMSGC) and the Wellcome Trust Case Control Consortium (WTCCC) we have already identified some 114 associated variants; however, these are all common and only account for about a quarter of the heritability. We believe that our current project will provide a robust way to substantially expand the catalogue of relevant common variants and effectively explore the role of less common variants. Multiple sclerosis is one of the most common disabling neurological diseases to affect young adults in the UK. The disease results in chronic disability for the majority of those affected and is estimated to cost the country some £3 billion per year in terms of health care and lost productivity. Identifying associated genetic variants will provide unbiased insights into how and why the disease develops. We believe that such knowledge provides one of the most promising routes towards rational therapy which is both safe and effective. We have collected DNA from >15,000 UK patients with multiple sclerosis and plan to genotype these patient samples with the same chip that is being used for the Biobank genetic analysis project. This will allow us to systematically compare how each variant differs between those with and those without the disease. Since these variants come from all parts of the genome such a study is generally known as a Genome-Wide Association Study (GWAS). By considering the very large amount of control data available through the biobank we will have more power than any previous study. Since our primary aim is to use the data from the cohort as controls our main analysis will include the vast majority of the full cohort. Only those participants with self-reported/confirmed diagnosis of MS at baseline or where the genotyping data fail to pass quality control standards will be excluded. In additional analysis we will include those UK biobank individuals with confirmed MS in the cases group, we will also combine these new data with data from previously published studies.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 11311 | Fast and accurate Bayesian polygenic risk modeling with variational inference | Shadi Zabad (+2) | 2023 | American Journal of Human Genetics |
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