Understanding the genes that underpin the immune system is vital for studying the causes and effects of many diseases. In particular, autoimmune diseases are often associated with specific variants of such genes. This project focuses on genes in the major histocompatibility complex (MHC), the killer cell immunoglobulin-like receptor region (KIR), and other genes related to the immune system, such as the complement system (CS) and heat shock proteins (HSP). Traditional approaches to typing these genes are very expensive and time-consuming, meaning that studying them in the large cohorts (like UK Biobank) that are typical for modern genetics studies is not feasible for most researchers. Over three years, we will develop new methods and generate data that will make it possible to study the relationships between variants of these genes and disease more cheaply, and more accurately.
A cheap, rapid and effective means for typing these variants is to use imputation – the statistical prediction of the variants of genes from genotype data, which is relatively cheap to obtain. Imputation methods rely on reference panels. These are datasets where we know both the genotypes (the cheaply obtained data) and the gene variants (the data we wish to be able to impute in the future). They let us observe how patterns in the genotypes relate to the gene variants. Our group was the first to develop such methods, and we used them to type the main genes in the MHC for the first release of the UK Biobank data. The larger and more representative the reference panel, the better these methods perform.
We will expand and develop methods for direct typing (not imputation) of HLA/KIR/MHC/CS/HSP variants from sequence data. We will use these new methods to create massive reference panels that can be used by imputation methods.
We will use these reference panels in developing new imputation methods to determine the types of immune-related genes very cheaply so they can be studied in very large samples.
We will apply the methods we develop to genetic studies of psoriasis and multiple sclerosis. We will make the new data and methods we develop available to other scientists to use in their studies, with the hope that this will lead to better diagnosis and treatment of autoimmune and communicable diseases.