We devise novel statistical methods for large scale genetic data to gain insight in to the epidemiology and biological mechanisms of complex traits. All of our tools are shared freely with the scientific community through open source software.
Group research: As part of the PGC team, we conducted the largest genome-wide association study (GWAS) of schizophrenia to date, uncovering 108 genetic loci robustly associated with schizophrenia risk. We’re also conducting similar work to discover the first genetic risk loci for ADHD. Much of our research focuses on the development of new statistical approaches to understand the impact of genetic variation on human disease and variation. These efforts include the development of LD Score regression, an approach to delineate between confounding bias and polygenic inheritance in genome-wide association data, and C-alpha – a novel test for rare variants that is robust to variants conferring both risk and protective effects.
Commitment to sharing among the scientific community: We firmly believe in sharing consented genetic data and study results broadly, and believe there is tremendous value in enabling the wider scientific community the opportunity to independently investigate and data. For details of our Data Sharing Policy, read here. We are also strong advocates of open access publication and commit to posting all of our manuscripts to an open access preprint server such as bioRxiv.org. Please read more about our publication policy here.
Group structure and culture: Our research activities fall under three main umbrellas: data, methods and software. We have an experienced research scientist overseeing each of these areas to provide additional guidance to our postdocs and students and to help steer the various projects. We encourage more senior group members to take on mentorship of new trainees, and actively seek opportunities to connect people within the group through new collaborations that enable them to learn from one another.
