Understanding the genetic architecture of complex diseases
Approved Research ID: 61553
Approval date: January 11th 2021
Since the first GWAS was published in 2005 there are more than 170000 SNPs found to be associated with traits. The contribution of increased sample size to the detection of loci associated with the studied phenotype is crucial. By integrating our own data with UK biobank data we seek to pursue GWAS in larger sample sizes of increased power.
We first focus on Myasthenia Gravis (MG), an autoimmune disorder of the neuromuscular junction that could cause ptosis, diplopia, fatigue etc. Previous studies that have identified genes associated with MG only explain part of the heritability and are not in full concordance. Additionally, Myasthenia Gravis is a quite uncommon disease with incidence of ~3/100,000 and prevalence of ~20/100,000, thus there is great need to integrate data from multiple sources in order to really increase the statistical power to detect causal variants. Here, we are aiming to use the UK biobank data and MG datasets already available to our team to perform the largest MG GWAS and the first CNV analysis of MG in order to explore the structural variants contribution to the susceptibility of MG.
We are planning to conclude the analysis of MG data in about 18 months after acquiring the data and proceed with the analyses of additional phenotypes. Based on the preliminary results using the datasets available to our team, we are confident that the additional UK Biobank samples will significantly help to increase the power to detect causal variants. Our team also aims to pursue similar analyses in the future for additional phenotypes of interest to our group and for which we have existing data available.