The computational genomics group uses large-scale, high-throughput genomic data to investigate how DNA sequence variants contribute to human disease. Our research engages sophisticated statistical methodology and the use of high performance computing resources for novel analyses and methods development. We are committed to both open-source and reproducible research practices.
The last decade has seen a dramatic increase in our understanding of the role of genetic variation in common diseases. In particular, genome-wide association (GWA) studies have catalogued thousands of common genetic variants that affect human diseases. However, the intermediate molecular mechanisms by which this genetic variation predisposes individuals to disease are still poorly understood, impeding the development of effective therapeutic interventions.
Research has shown that most GWA variants are not located in protein coding regions and are instead highly enriched for regulatory regions of the genome. This suggests that for most variants, the functional mechanism by which they affect disease susceptibility is via gene regulation. Thus, characterization of the regulatory architecture of the human genome is essential, not only for understanding basic biology, but also for interpreting GWAS loci. The expression levels of most transcripts are highly heritable and expression quantitative trait loci (eQTL) can be mapped using GWA type approaches. However, the lack of comprehensive eQTL data from a wide variety of human tissues and cell types has resulted in eQTL databases that are biased towards the small group of tissues that are more readily accessible. The pathophysiology of most common diseases is restricted to a limited number of tissue types or organ systems. Therefore, to understand the mechanisms of disease susceptibility and develop preventative and targeted therapies, we ultimately require knowledge of genetic control of regulatory variation in many different tissues.
Broadly, our research is focused on three areas:
1. The investigation of differences in the genetic regulation of genes across tissues and cell types. We are interested in whether this can explain some of the tissue specific pathology of disease.
2. The development of novel methodology to provide flexible and efficient approaches to help determine the biological mechanisms underlying human disease. In silico methods are first used as a stepping-stone, prior to collaborating with colleagues to design molecular genomics studies to delineate the exact mechanisms.
3. The development of computational tools and apps to integrate methods and data to aid in the clinical diagnosis of disease using genomic information.
**Please be aware that these apps will load large datasets (up to 300MB)**
Endometriosis eQTLs browser (currently unavailable)