Briefly, we developed a statistical method that can infer the genetic types of a particular group of immune system genes, based on other genetic information nearby. This will be an important tool in allowing large-scale studies of these genes and their effect on human diseases.
The genes we have targetted are those that encode proteins called killer-cell immunoglobulin-like receptors (KIRs). These are either known to play a role, or we have good evidence to suspect a role, in autoimmune diseases, resistance to viruses, reproductive conditions and cancer. What makes these genes particularly difficult to study is that they vary a lot between different individuals. They vary so much that the standard methods for measuring them in the lab are very expensive and time-consuming. The huge advances in genomic technology of recent times don’t work so well for these genes, which means they have largely been ‘ignored’ in most of the large, high-profile studies.
Our statistical method aims to change this. We use nearby genetic variation that can easily be measured (SNPs), and a statistical model that relates these to the genes, to create a method that can effectively ‘measure’ these genes cheaply and accurately.
Our method, called KIR*IMP, is available online as a web implementation and is free for researchers.