Bayesian statistical approaches to identification of shared genetic signals

A number of genes have been found associated with certain clinical outcome of interest from multiple studies. Identification of shared causal genes from these studies is crucial to understand the aetiology of certain diseases and the underlying causal pathways.

To date, several statistical methods have been developed in the research field. However, there is a lack of comprehensive review of these approaches in the literature to guide researchers in health data science. In this project, we will be investigating state-of-the-art methods by using summary statistics from large-scale association studies, e.g. UK Biobank, The Genetic Investigation of ANthropometric Traits (GIANT). In particular, we will examine the performance of statistical colocalizaiton and fine-mapping. Despite the different underlying assumptions between fine-mapping and colocalization, we see similarities of the two approaches. Since each of the methods has their own strengths and limitations, we aim to develop a Bayesian approach, taking forward strengths of both, to address shared genetic signals. Examination and validation of our method will be carried out via a number of simulations. We will also develop statistical software for applications of our method in the Comprehensive R Archive Network (CRAN) and make it publicly accessible.