Integrating Genomics and Multi-Omics to Study Cardiovascular Disease risk
The Cardiovascular Genomics Research Group aims to understand the genetic basis of cardiovascular disease and its risk factors, uncover shared biological pathways and disease mechanisms, and identify new therapeutic targets. Our research centres on genome-wide association studies integrated with deep phenotyping from large-scale population and biobank datasets. Using state-of-the-art statistical and computational approaches, we combine human genetics with imaging, biomarker, clinical, transcriptomic, and proteomic data to elucidate the biological mechanisms underlying cardiovascular disease and related traits. Ultimately, our goal is to translate genetic discoveries into biological insight, enabling improved risk prediction, prevention strategies, and the development of targeted, personalised interventions.
What We Study
Cardiovascular disease (CVD) emerges from complex interactions between genetic, molecular, and environmental factors. Our research seeks to uncover the genetic architecture that underpins cardiovascular disease and its major risk factors, including atherosclerosis and hypertension, by leveraging large-scale population and biobank datasets. We focus on identifying genome-wide determinants of early, subclinical atherosclerosis and on dissecting shared genetic and proteomic pathways that link cardiometabolic risk factors to clinical outcomes such as coronary artery disease and stroke. Beyond the cardiovascular system, our work also explores the genetic basis of liver health–related traits—including liver fat, iron, and inflammation—recognising their emerging role in cardiometabolic disease. Together, these efforts aim to connect genetic variation to early disease processes and meaningful clinical outcomes, ultimately informing improved prevention and targeted intervention strategies.
Our research focuses on:
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The genetic architecture of cardiovascular disease and related cardio-metabolic traits/risk factors.
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Subclinical atherosclerosis markers, including carotid intima–media thickness and carotid plaque
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Shared biological pathways linking atherosclerosis, hypertension, and metabolic disease
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Shared genetic landscape of liver–heart axis, including liver fat, iron overload, inflammation, and their impact on cardiovascular disease
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Population-specific genetic risk, with a particular emphasis on underrepresented populations (South Asians)
Why It Matters
Cardiovascular disease (CVD) remains the leading cause of death and disability worldwide. Large-scale association studies have identified hundereds of genomic loci associated with CVDs and its risk factors. Despite these major advances in cardiovascular genomics, many associations identified through genome-wide association studies remain poorly understood, limiting their translation into biological and clinical insight. In addition, large-scale genomic studies continue to underrepresent diverse populations, restricting the generalizability of genetic discoveries. Leveraging large-scale population-level resources, our work aims to address these challenges by improving causal inference in cardiovascular genetics, integrating multi-omics data to help interpret GWAS signals, and potentially identifying biologically meaningful targets for disease prevention and therapy. Through collaboration and data sharing, we hope to contribute to more equitable and impactful cardiovascular science.
Analytical approach
We use a comprehensive, omics-driven and data-intensive analytical approach to study the genetic basis of disease. This includes genome-wide association studies (GWAS) to identify genetic loci, Mendelian randomization (MR) to infer potential causal relationships between molecular traits and disease outcomes, and transcriptomic and proteomic analyses to link genetic variation to gene expression and protein regulation. We further apply post-GWAS methods such as fine-mapping, colocalization with eQTL and pQTL data, transcriptome-wide association studies (TWAS), SMR/HEIDI, and gene and pathway prioritisation. By integrating data from large-scale biobanks and international consortia, these approaches enable us to move from genetic association to biological interpretation.
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