My research group uses computational and statistical methods on biobanks and electronic health record (EHR) data to dissect the ethology and heterogeneity of Major Depressive Disorder (MDD). MDD will be the second highest cause of morbidity by 2020 according to the World Health Organisation, with a lifetime prevalence of 20%. However, research efforts have not discovered quantitative measures for its diagnosis, nor pharmaceutical interventions that are widely effective. This makes studying MDD a difficult and interesting problem that requires innovative and interdisciplinary solutions. Molecular understanding of the disease through genetics and genomics, in combination with deep-phenotyping of both clinical features and environmental factors, is the way forward. In particular, we focus on how genetic variants interact with both the internal metabolic environment and external stressors in conferring disease risk and heterogeneity. Our ultimate goal is to use large-scale phenotyping, genetics and multi-omics approaches to identify symptomatic profiles and biological markers to improve diagnosis, monitoring and treatment of the disease.