The extent and genetic basis of inverse comorbidity
There are many cases when several diseases are seen in a single individual. Such cases are very troublesome for doctors as they need to cure several diseases at once. Many studies analysed why some diseases come together and we already know that, in part, this is explained by common genes between several diseases. On the other hand, there are cases, when the presence of one disease "protects" a patient from having another disease. For instance, people with tuberculosis almost never have allergy, and people with Alzheimer's disease usually do not have cancer. We don't really know why one disease prevents another, but we can hypothesise that this is related to genetic variants, some of which are in favour of one disease, but protect us from another disease.
The aim of this project is to find genetic variants that predispose to one disease but protect from another disease. First, we will make a list of disease pairs, which are very rarely seen together in a single person. Then, we will check is there are genetic variants that would predict the high chances of one disease and low chances for another disease. We will pull together many such variants to develop genetic scores (called polygenic risk scores) for antagonistic diseases. Finally, we will test if the presence of one disease, and not anything else, is the real cause of the absence of another disease.
The project will help discover new drugs and new tools for diagnostics. If we can identify genes that are responsible for exclusion of one disease if there is another, we can find ways of disease prevention. Results of our study may also improve clinical diagnostics and reduce healthcare costs by avoiding unnecessary clinical tests - knowing that if there is one disease, another is unlikely to happen, doctors will be better equipped to decide the optimal ways to carry out medical diagnostics. Also, our genetic scores may become useful as an additional biomarker of predicting risks of diseases; they may inform doctors about potential low risks of some complications and additional diseases, especially in older people where multiple overlapping diseases are often seen. Thus, we expect that results of the project may have a large positive social and healthcare impact.
The expected project duration is 36 months.