Abstract

To date, thousands of genetic variants have been associated with disease susceptibility. However, the clinical implications and biological mechanisms underlying these genetic associations remain largely unknown and underexploited. My research program proposes two integrated roadmaps for translating genetic associations detected across multiple diseases into new clinical applications and new actionable biology. The roadmap towards clinical application (Aim 1) focuses on developing the most accurate and transferable (across populations) genomic risk predictors for common diseases. These optimal genomic predictors will improve identification of disease subtypes and help personalise prevention and treatment. Another aim towards clinical translation includes training of clinicians to fully understand the benefit and limitations of genomic prediction technologies. The roadmap towards actionable biology has two arms. The first arm focuses on developing new statistical models with an increased ability to discover, beyond statistical associations, the causal genetic variation for common diseases (Aim 2). This step is critical for designing the future gene-editing experiments needed to firmly identify causal genes for various diseases. The second arm focuses on identifying molecular endpoints affected by disease-causing genetic variation. The latter step will leverage many publicly available genomic datasets, including a sample of 500 first-generation admixed individuals (The Australian Bridge Study; ABS) recruited as part of this program (Aim 3). The ABS will be a unique genomic resource with maximized genetic diversity for studying the effect of genetic liability to common diseases on allele-specific gene expression and other cellular phenotypes. Altogether, my ambitious research program will develop the next generation of methods for analysing an ever-growing volume of genomic data (generated across the world), train the next generation of statistical geneticists and healthcare practitioners who will apply and deploy these methods in the clinic, and make fundamental discoveries regarding the genetic causes of inter-individual differences in disease risk and progression.

Vision

To achieve personalised healthcare by tailoring prevention, diagnostic and cure of chronic disease to each person’s unique genome. To realise this vision, three major challenges must be addressed:

1. Transitioning from detecting disease-associated genetic variation, which is the current paradigm underlying genome-wide association studies (GWAS), towards identifying disease-causing genes
2. Building a mechanistic understanding of what and how genes affect disease susceptibility and severity; and
3. Creating accurate, reliable and equitable genetic risk predictors for common disease.