Statistical methods for human-pathogen genome-wide association studies

DNA sequencing and new analysis tools have driven progress in our understanding of the genetics behind infectious disease. Most studies investigate human genes for susceptibility or germ genes for infectiousness, but there is a strong argument for joined-up studies as different people tend to be vulnerable to different germs. If we ignore this, we risk missing vital clues to the way we can tackle disease.

Until now, joined-up studies have been hindered by the fact that testing all combinations of possible interactions between human and germ genetics requires trillions of tests and it is difficult to avoid false findings. I propose to extend new techniques and create new software to overcome this hurdle.

The techniques I develop can be used for joined-up studies to better understand common infections.