NOFIA

This is an internal page providing additional information about our long-term vision about "A Novel Framework for the Integrated Analysis of large-scale biomedical data" (NOFIA). We have applied for funding NOFIA within an ERC Consolidator Grant.

Summary

Vast amounts of financial and human resources have been invested into clinical and genomic profiling of large cohorts creating enormous amounts of data. While genome-wide association studies (GWAS) have already successfully revealed new candidate loci that potentially affect human disease or related phenotypes, they still fail to predict a significant portion of the heritable component of phenotypic variability. We believe that part of this failure may be overcome by developing novel analysis concepts and methodologies.

The main goal of this proposal is to develop and apply a new analysis framework for the integrated analysis of large-scale medical data. Such data include molecular phenotypes as well as large collections of organismal or clinical observables. Molecular phenotypes, like expression- or metabolomics-profiles are now becoming available for many cohorts, but efficient methods to integrate these data into association studies are still missing. We propose to adapt and extend the modular technologies we have developed in recent years in order to address this challenge. Specifically, we plan to (1) perform modular analyses generating meta-phenotypes of metabolomics, transcriptomics and large-scale clinical data from genotyped individuals in order to facilitate the identification of genetic variants associated with these traits, (2) perform coupled co-module decompositions for the unsupervised integrated analysis of distinct large sets of molecular and clinical phenotypes in order to generate modular links between the various types of data, and (3) develop predictive models using (co )modules as features and explore practical applications aimed at predicting disease risks or response to treatment with better accuracy than classical approaches based on individual biomarkers.

Our work will synthesize our expertise with modular analysis (including our well-established state-of-the-art tools) and our ample experience with GWAS. While our methodological developments will be set within concrete bio-medical questions and applied to real data from the Cohorte Lausannoise and other large data collections, they will be relevant for a large field of data-driven bio-medical research.

Support

I have strong support from my colleagues Prof. Peter Vollenweider (PI of CoLaus, see Media:letter_PV) and Prof. Martin Preisig (PI of PsyCoLaus, see Media:letter_MP).