| Title: | Multiomics integration for the diagnosis and prediction of complex disorders and their comorbidities |
| Lead Institution: | Weill Cornell Medicine - Qatar |
| Principal investigator: |
Application 588633
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About
The scientific rationale for this project is the following: Complex Disorders, such as diabetes, heart and kidney disease, or cancers are the result of a complex interplay between genetic predisposition, lifestyle, and environmental factors. While associations between risk factors and disease endpoints are key to developing new treatments and diagnosis options, a deeper understanding of the biological pathways that drive the disease is key to advances in personalized health management. Multiomics traits, such as genomics, transcriptomics, epigenetics, proteomics, metabolomics, and all kinds of imaging modalities are key readouts to characterize disease-related pathways and play the role of intermediate phenotypes that connect the disease drivers to the disease outcomes. The scientific objective of this project is to connect and integrate all available multiomics traits to disease risk factors, other intermediate traits, and disease outcomes and comorbidities. Scientific questions include: What are the genetic variants that associate with the different multiomics traits? Which multiomics traits are associated with a specific disease or disease comorbidity? How do the multiomics traits correlate with each other and do they reflect specific perturbed biological pathways? Which multiomics traits can predict disease outcomes, how specific are they, and what can be learned about their role in causing the disease?1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 20697 | Size-Resolved Lipoprotein Fatty Acid Content as a Novel Nuclear Magnetic Resonance-Derived Trait Specifically Associates with Genetic Variants That Control Fatty Acid Metabolism | Aziz Belkadi (+4) | 2026 | Journal of Proteome Research |
Enabling scientific discoveries that improve human health