Orphanet J Rare Dis 16, 41 (2021). https://doi.org/10.1186/s13023-021-01684-w.
The Junior Members of the Society for Biochemistry and Molecular Biology (GBM), are organizing a lecture series on “Rare Diseases” in January and February 2021.
The Junior GBM are a small group of Molecular Life Science students who, since their foundation in 2013, have been organizing both internal and public events that are part of their interests in a scientific context and allow them to gain access to current research projects alongside their studies.
In collaboration with Inke König and Damian Gola from our University and colleagues from Munich and Tartu we examined how polygenic risk scores (PRS) trained in population-specific but European data sets perform in other European sub-populations in distinguishing between coronary artery disease patients and healthy individuals.
We found that PRS have the highest performance in their corresponding population testing data sets, whereas their performance significantly drops if applied to testing data sets from different European populations.
This result has direct impact on the clinical usability of PRS for risk prediction: a population effect must be kept in mind when applying risk estimation models which are based on additional genetic information even for individuals from different European populations of the same ethnicity.
Please have a look at the explainer video from Circulation: Genomic and Precision Medicine: link to video.
Coronary artery disease (CAD) is a major burden for patients and health care systems worldwide. The most common cause of CAD is atherosclerosis, an inflammatory disease gradually obstructing arteries, with life-threatening effects in the coronary circulation. Often, the circulation adapts through collateral artery formation, leading to significantly improved long-term post-ischemic outcome. Hence, timely determination of the collateral profile presents a pivotal factor in the Personalized Medicine of CAD. However, the coronary collateral circulation (CCC) development is not well predicted by traditional CAD risk factors. Moreover, manifold inconsistencies are still apparent in CCC research, mainly associated with the difficulty of quantifying CCC accurately and reproducibly.
The overarching objective of PROGRESS is to develop a tool for more accurate, reproducible and automated prediction of patients’ potential to develop CCC, which could be used for a more efficient CAD patient management.
Our hypothesis is that patients’ potential to develop CCC is, in part, determined by genetics. Thus, uncovering the genetic risk variants holds the potential of predicting CCC formation. To overcome previous difficulties of CCC research, we harness Artificial Intelligence (AI)-based angiogram image and genetics analyses aiming to improve risk stratification and management of CAD patients, based on their CCC formation profile, followed by timely application of therapeutic approaches in order to stimulate CCC formation and thus improve survival rates of patients after diagnosis. We have collected well-powered CAD cohorts with genetic and imaging data. AI-based image analysis will aid in phenotyping CCC and also to generate post-hoc surrogate functional parameters (validated against a cohort of invasively phenotyped patients) in an unbiased fashion. This provides the basis for a genome-wide association study (GWAS) on CCC performed in large detection and validation cohorts.