Title: Identifying genetic factors of progression and treatment response in autoimmune diseases using genetics of biomarkers and Bayesian shrinkage prediction
Funder: The Academy of Medical Sciences

Abstract: Aim: To identify genetic markers predictive of clinically useful traits in autoimmune diseases, such as progression and response to treatment, where large sample sizes are difficult to collect, and conventional genome-wide association studies (GWAS) have low power. To achieve this aim, I will increase the power to detect genetic associations by reducing the size of the hypothesis space in two ways: 1. Collapse the GWAS data into a smaller number of genetic proxies for relevant intermediate traits, and 2. Learn from data which genetic proxies are most informative through the application of state-of-the-art Bayesian sparse prediction models. I will demonstrate the strength of this approach over conventional GWAS on important traits in autoimmune disease: treatment response in rheumatoid arthritis (RA), and residual insulin secretion in type 1 diabetes (T1D). Key objectives: 1. Compute locus-specific genotypic risk scores (genetic proxies) for relevant intermediate traits (autoimmune diseases, cytokines, proteins, gene expressions) using published GWAS results and my GENOSCORES platform. 2. Identify relevant biological pathways to prioritise genetic proxies for genes and biomarkers most likely to be mediating genetic associations with autoimmune traits. 3. Implement a pathway-informed Bayesian sparse model and evaluate its predictive performance on the two target traits. Evaluation will entail model comparison with a pathway-agnostic Bayesian sparse model and a baseline clinical model (no genetics), and comparison of findings to conventional GWAS. This approach will advance current methods for genetic studies, with the potential to discover novel shared mechanisms underlying heterogeneity in autoimmune disease.

Lay abstract: For many people with rheumatoid arthritis (RA), disease-modifying drugs are used to control the inflammation of the joints and limit long-term joint damage. However, for all classes of drugs, 30-40% of patients do not respond well. As a result, treatment remains a frustrating trial-and-error process, with associated pain and joint damage for the patient. People with type 1 diabetes (T1D) require lifelong insulin injections, because an autoimmune process destroys their body’s specialised cells that produce insulin. Recent studies have shown that, despite this destructive process, some people with T1D still produce small amounts of insulin for years after their diagnosis. This is important because these people have less severe diabetes. The aim of this project is to investigate if differences in people’s genetic makeup predict (i) how well they respond to RA treatments and (ii) how much insulin they produce in T1D. To achieve this, we will use a novel approach. We will exploit genetic studies of other relevant traits to construct genetic scores and use advanced statistical methods to identify which scores are most predictive. We will construct scores for molecular markers of the immune system and for autoimmune diseases, including RA and T1D. This work will help us identify genetic factors that influence RA treatment response and T1D insulin production. This is the first step to understanding the biological mechanisms involved, which could ultimately help choose the most appropriate therapy and develop new treatments and new approaches to prevent or delay the onset of disease.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: INFLAMMATORY AND IMMUNE SYSTEM