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OUR RESEARCH
By using genetic and molecular approaches, we investigate the underlying processes leading to the development of complex cardiovascular disorders (CVDs), which are major contributors to morbidity and mortality worldwide. We have interests into how common noncoding gene variants control gene expression and drive network signalling pathways leading to vascular atheroma and heart valve fibrocalcification. We use prioritization of potential novel therapeutic targets and causal inference to track the molecular phenome of CVDs. |
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Functional Genomics and Molecular Pathway Characterization
One key central question we are probing is how common noncoding gene variants associated with complex trait disorders alter transcription factors (TFs), drivers of cell identity and fate, binding to DNA. Several algorithms are used in the lab to predict and prioritize causal variants based on their ability to modify TF binding sites (TFBS). Sequence information-based approaches as well as machine-learning algorithms are used to prioritize gene variants that may alter TF’s functions. Atomic-resolution data derived from published crystallographic studies are used to model potential molecular processes whereby a variant may affect protein-DNA interaction. We are interested into how these data may help predict experimental binding of TFs to DNA and their functional impacts. We explore several techniques such as DNA-binding, reporter assays and chromatin immunoprecipitation to functionally validate our models. At the cell level, we are probing how altered gene expression affects the function of vascular and valve cells. We exploit several cell and molecular assays to identify the interactome of dysregulated genes and its impact on cell function. |
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Molecular quantitative traits
Multi ‘omics’ data, including several molecular quantitative traits (QTLs), offer opportunities for multidimensional mapping and causal inference. We are using several epigenetic and expression QTLs to further assess how gene variants impact the molecular phenome of CVDs. Integration of expression and protein QTLs with genetic association data allows causal inference by Mendelian Randomization (MR). By using MR, we are interested into how causal inference may help identify key causal genes in network prioritized pathways. Furthermore, by using Phenome-wide association analyses, we investigate how pleiotropy is acting on CVDs. For instance, how pleiotropy is manifested in inflammatory disorders and CVDs and affect longevity. |
