Project 12: Integration of multi-omics data and disease-related phenotypes for better disease risk prediction

Pradeep Eranti

Université de Paris
Advisors: Florence Demenais & Emmanuelle Bouzigon

Project description

Advancements in high-throughput technologies have enabled the large-scale generation of biological data and aids in the understanding of the disease mechanism on a system-level. A comprehensive system-level view can be obtained through systematic integration of complementary information from multiple molecular profiles, besides utilizing the information present in public databases. Network-based methods, which integrate biological knowledge, i.e. molecular interactions among proteins/genes, and the results (summary statistics) from genome-wide association studies (GWAS), have been proven successful in discovering sets of interacting genes (gene modules) enriched in disease association signals and also in generating new biological hypothesis. Furthermore, integrating multiple omics datasets can improve our understanding of the complex mechanisms underlying multifactorial diseases, such as asthma. However, the integration of multiple omics datasets in the framework of network analysis is challenging.

This project aims to propose methodologies for the network analysis of multiple omics data associated with the disease. The work performed in this project will result in the identification of biological pathways shared by omics data underlying the disease process (eg, asthma and asthma-related phenotypes) and in the discovery of genes having a key role in the disease to be potentially used as drug targets. Furthermore, the project can allow improving disease risk prediction by integrating polygenic scores derived from omics data together with clinical risk factors and thus contribute to the development of personalized prevention strategies.


  1. Comparison of machine learning approaches for disease network dis-covery
    Host: ETH Zürich
    Planned date: November 2020 – January 2021
  2. Computation of polygenic risk scores using multi-omics data
    Host: STACC
    Planned date: December 2021 – February 2022