  Cardiovascular diseases (CVD), including atherosclerosis CVD (ASCVD), are
multifactorial diseases that present a major economic and social burden
worldwide. Tremendous efforts have been made to understand traditional risk
factors for ASCVD, but these risk factors account for only about half of all
cases of ASCVD. It remains a critical need to identify nontraditional risk
factors (e.g., genetic variants, genes) contributing to ASCVD. Further,
incorporating functional knowledge in prediction models have the potential to
reveal pathways associated with disease risk. We propose Bayesian hierarchical
factor analysis models that associate multiple omics data, predict a clinical
outcome, allow for prior functional information, and can accommodate clinical
covariates. The models, motivated by available data and the need for other risk
factors of ASCVD, are used for the integrative analysis of clinical,
demographic, and multi-omics data to identify genetic variants, genes, and gene
pathways potentially contributing to 10-year ASCVD risk in healthy adults. Our
findings revealed several genetic variants, genes and gene pathways that were
highly associated with ASCVD risk. Interestingly, some of these have been
implicated in CVD risk. The others could be explored for their potential roles
in CVD. Our findings underscore the merit in joint association and prediction
models.
