A phenome-wide association study for cardiac diseases using Mendelian randomization (MR) and Artificial intelligence (AI).
Approved Research ID: 81679
Approval date: April 20th 2022
Cardiovascular diseases have been known to associate with genetic predisposition, however, the pleiotropic effects of a single genetic variant or group of genetic variants on various disease phenotypes have not been well understood. Phenome-wide association studies (PheWAS) are defined as a statistical approach to test for the association of a genetic variant or accumulative effects of genetic variants and a wide range of phenotypes. PheWAS has been widely used as the large genetic data set has recently become available. PheWAS is particularly useful in situations where we currently have an incomplete understanding of disease mechanisms. Moreover, PheWAS approaches were based on genotypes that are fixed from birth, so it is less affected by environmental confounding factors and reverse causality. This research project aims to investigate the phenotypes that were associated with a polygenic risk score for cardiovascular diseases, and to study whether there is a causal association of cardiovascular disease-polygenic risk score (PRS) and the phenotypes using mendelian randomization (MR). MR is a widely used epidemiological method that utilizes genetic variants to investigate casual association of a risk factor and an outcome. In MR, genetic variants that satisfy all the assumptions are used as an instrumental variable (IV). Assumption of MR includes that genetic variants are associated with risk factors and IV are not directly associated with confounders of the risk factors, nor does it affect the outcome directly. The goal of the research project is to discover the causal risk factors for cardiovascular diseases and to construct a predictive model for new diagnostic tests or treatments. The project duration is about 36 months. Clinical implications include understanding the genetic causes of cardiovascular diseases and applying such information for clinical practice. Public health impacts are concerned with discovering and predicting causative risk factors for cardiovascular diseases that can be a cornerstone of timely clinical management.
The current aim of the study is to identify the causal risk factors for cardiac diseases by investigating various phenotypes with cardiac diseases-polygenic risk score (PRS) using a phenome-wide association study (PheWAS) approach. We also study the causal association of cardiac diseases and such phenotypes using MR and develop AI predictive models.
The additional aim of the study is to identify causal relationship between genetic data and mortality and make risk model to predict mortality using clinical data, genetic data, and ECG data.