Bayesian integrative analysis and prediction with application to atherosclerosis cardiovascular disease

Research output: Contribution to journalArticlepeer-review


The problem of associating data from multiple sources and predicting an outcome simultaneously is an important one in modern biomedical research. It has potential to identify multidimensional array of variables predictive of a clinical outcome and to enhance our understanding of the pathobiology of complex diseases. Incorporating functional knowledge in association and prediction models can reveal pathways contributing to disease risk. We propose Bayesian hierarchical integrative 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 exploring other risk factors of atherosclerotic cardiovascular disease (ASCVD), are used for integrative analysis of clinical, demographic, and genomics data to identify genetic variants, genes, and gene pathways likely contributing to 10-year ASCVD risk in healthy adults. Our findings revealed several genetic variants, genes, and gene pathways that are highly associated with ASCVD risk, with some already implicated in cardiovascular disease (CVD) risk. Extensive simulations demonstrate the merit of joint association and prediction models over two-stage methods: association followed by prediction.

Original languageEnglish (US)
Pages (from-to)124-139
Number of pages16
JournalBiostatistics (Oxford, England)
Issue number1
Early online dateMay 10 2021
StatePublished - Dec 12 2022

Bibliographical note

Publisher Copyright:
© The Author 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail:


  • Bayesian variable selection
  • Biological information
  • Cardiovascular disease
  • Factor analysis
  • Integrative analysis
  • Joint association and prediction

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't


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