For many disease-associated genetic variants, the functional mechanism through which the variant affects disease susceptibility is unknown. Because genetic variants also affect molecular phenotypes such as the transcriptome, methylome and proteome, studying ?omics? outcomes may lead to an improved understanding of disease processes. In particular, joint analysis of multi-omics data may enhance our knowledge of how genetic effects on these outcomes are coordinated in a multi-level molecular system to contribute to disease susceptibility. Since genetic effects on molecular phenotypes may further depend on tissue, cell type, or other conditions, the scientific community would benefit from continued development of methods to integrate multi- omics data across conditions or contexts. However, the large scale of the data coupled with unknown correlation structures across features or conditions makes such analyses challenging. In this project, we propose efficient methods to integrate summary statistics from multiple studies of genetic effects on complex and omics phenotypes. To improve upon existing multi-omics integrative approaches that take summary statistics as input, we expand joint analyses to more than three data types or conditions, and allow the sets of statistics to come from overlapping samples. Preliminary results presented in the application demonstrate that the proposed methods are computationally feasible and produce results that are consistent with current biological knowledge. Proposed applications of the methods have the potential to identify novel associations or provide new evidence for known associations between omics features and cancer risk. The success of this work will provide flexible methods and computational tools that can be applied to other diseases and settings.
Integrating data from multiple complex and omics phenotypes presents an opportunity to identify genetic variants with coordinated effects in a multi-level system. This proposal will develop tailored quantitative methods that integrate summary statistics from studies of complex traits and molecular phenotypes to inform analyses of susceptibility or other disease-related traits. By including more sets of summary statistics than allowed by existing methods, the proposed methods may expand functional understanding of disease processes, provide novel evidence to confirm suspected indicators of disease risk, or prioritize targets for treatment in diseases like cancer.
