Principal Investigator: Dr Ruth Dobson
Institution: Queen Mary, University of LondonTags: 43101, epstein-barr-virus, fracture, genetics/genotyping, Multiple sclerosis, obesity, vitamin D
This research aims to compare vitamin D status, vitamin D supplementation behaviours, and other factors potentially affecting vitamin D status between people with multiple sclerosis (MS) and those without MS across the UK. We aim to examine risk factors known to be associated with MS development including smoking behaviour, infectious mononucleosis and obesity, and how these interact with the vitamin D status of people with MS.
Vitamin D deficiency has been linked to MS development in a number of large studies, however we do not know how many people with MS in the UK are taking vitamin D supplements, or even what their vitamin D levels are. We do not know if there is any variation across the country with respect to either vitamin D levels, supplementation, or other factors that are known to affect vitamin D levels. We also do not know if genetic factors known to affect vitamin D levels significantly affect these levels in the MS population in the UK, or if other factors are more important.
The UK biobank will allow us to study a large group of people who have been diagnosed with MS, and look at both vitamin D levels, factors that may affect these, and also possible consequences of low vitamin D such as fractures and osteoporosis. It will also allow us to examine some of the potential genetic effects on vitamin D levels and MS susceptibility in this population.
We anticipate that analysing the depth of data that the UK biobank can provide will take up to 18 months. We anticipate that this project will inform a future trial of vitamin D supplementation in MS in the UK – this work is part of a project grant funded by the MS Society of Great Britain to help inform future studies.
Project extension – January 2020
In addition to the above, we would now like to systematically interrogate all environmental risk factors for Multiple Sclerosis and determine whether, and how, the effects of these risk factors are modified by an individual’s genotype. Specifically, we would like to examine both hypothesis-driven and unbiased, ‘genome-wide’ gene-environment interactions. To do this we will use a combination of contemporary techniques in statistical genetics, including polygenic risk score profiling, genome-wide interaction study (GWIS) analysis, and Mendelian Randomization. Furthermore, we would like to extend our understanding of how genetic variation contributes to MS risk by examining both imputed genotype data and available exome sequencing data. In addition to conventional analytic techniques testing, we aim to develop novel computational methods to build and tune polygenic risk score profiles using machine-learning approaches.
Last updated Jan 15, 2020