Cardiometabolic conditions poses a huge and increasing burden not only for the affected individuals but also for the society. Large genetic studies have been successful in identifying genetic loci associated with the risk of developing cardiometabolic conditions, however, for the vast majority of the identified genetic loci the underlying causal genes remains unknown highly limiting the biological insight. Thus, there is a need for new statistical approaches that better disentangle the causal molecular aetiology underlying complex medical conditions which is strongly needed for future improved preventive, diagnostic and therapeutic options.

Technological advancements in biotechnology has led to great improvement in the cost-effectiveness of measure circulating biomolecules within the bloodstream entailing that large biobanks how have detailed omics-profile on thousands of individuals. Although this is a rich resource, there is an untapped potential for collecting and combining different multi-omics data sets. However, the challenge is, how to efficiently integrate and combine these different types of omics data, and how do we extract biological interpretable meaning out of such complex data?

We aim to develop MoPS, a statistical framework that provide a robust approach for multi-omics data integration. Our framework builds upon Bayesian Linear Regression models that we already have demonstrated has superior performance for statistical genetic fine mapping, prediction of genetic predisposition of common complex disease, and for gene set enrichment analyses. Now, we want to further expand this framework to also include integration of multi-omic datasets.

We develop and validate our statistical methods framework to provide biomarker results that are biologically interpretable and offer improved primary and secondary disease risk stratification. We strongly believe that our framework will also identify new disease-specific biomarkers, potentially pointing toward new drug targets. While there has been significant focus on disease stratification based on individuals' genetic profiles (polygenic scores), the time has now come to integrate other omics data into the risk scoring.