We have post doc positions available for statisticians who want to analyse genetic, genomic and immunological data to understand and investigate potential treatments for type 1 diabetes. http://bit.ly/dsoct14
who won a prize at the recent joint meeting of BCGES (Bloomsbury Centre for Genetic Epidemiology) and SEGEG (South of England Genetic Epidemiology Group) for her poster on colocalisation analysis using multiple diseases in a shared controls design.
Haldane’s Sieve: Olly Burren writes about our latest preprint on arXiv, a method for relating GWAS summary statistics to functionally defined gene sets which doesn’t require access to raw genotyping data.
Our group has a manuscript just out in Diabetes in which we have investigated the role of type 1 interferon signalling in the pathogenesis of the autoimmune disease T1D. The work was led by Ricardo Ferreira and Hui Guo. Type 1 interferon (IFN) signalling is a evolutionarily conserved biological pathway that plays a major role in the defense against viral infections. Every mammal expresses IFN genes and birds, amphibians and fish also express functionally homologous molecules. However, a side effect of the IFN responses is that they can also cause bystander tissue damage and can also lead to the activation of an autoimmune response. In fact, in humans chronically activated IFN signalling has been recently implicated in the aetiology of several systemic autoimmune diseases such as systemic lupus erythematosus (SLE) or vasculitis. Importantly, in T1D, genetic evidence from genome-wide association studies has pointed to an important role of this biological pathway in this disease, including the identification of IFIH1, a major sensor of viral infections, as a susceptibility gene.
We just advertised a new position (two years, in the first instance), at postdoc or senior postdoc level. This is an opportunity to develop and apply statistics to support the DIL’s aim of understanding the mechanism through which genetic variation can influence risk of type 1 diabetes. We use extensive molecular biological phenotyping both of healthy individuals who carry genetic susceptibility variants and, within the context of intervention trials, of individuals with new onset diabetes. We are located in the Cambridge Biomedical Research Campus and have strong collaborative links with the MRC Biostatistics Unit under its recently appointed Director, Professor Sylvia Richardson.
I am a co-author on another paper about colocalisation posted on arXiv. It’s a novel approach, using Bayesian inference based on Approximate Bayes Factors derived from p values, making colocalisation testing much more practical when data is not often as open access as claimed. My co-author, Vincent Plagnol, has written a nice post about it on Haldane’s Sieve. The software to conduct these tests is in the coloc package, v2.0 now available on CRAN.
We have revised our paper on arXiv detailing some work on colocalisation analysis, a method to determine whether two traits share a common causal variant.
I have been following the debate about open peer review: not just reviewers for traditional journals signing their reviews, but the idea of community-sourced peer review: you publish your paper when you are happy with it, other scientists comment and point out weaknesses, you revise it appropriately and publicly. This all sounds like a great idea, and is part of why this paper was first published on arXiv. I really care about the appropriate use of statistical methods for colocalisation and think the topic of data integration is an important one. Having the paper on arXiv has been useful for sharing my paper with others, and for giving a reference url in talks. But, although some people have told me they read it, and I know some are using the software, no one has given me any criticism of the paper itself.