Last week we published a major paper in the American Journal of Human Genetics. This is one of the main projects I’ve been working on at MCRI and it is fantastic to finally have it out.

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.