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Approved research

Enhancing resilience in psychosis through within and between-family polygenic risk scoring, Gene x Gene interactions and gene-environment (GxE) prediction models (REGENESIS)

Principal Investigator: Mr Justo Pinzon-Espinosa
Approved Research ID: 55392
Approval date: November 28th 2019

Lay summary

It is currently unknown how genes and experiences in life interact to bring about adverse mental health outcomes. Understanding the factors most predictive of such outcomes is important for developing appropriate and effective interventions aimed at preventing and treating them. We know that our genes and the environment (for example, where we live, exposure to traumatic events or our socioeconomic status) are related to the onset of psychiatric symptoms and illness. Genes and environment interact in a complex manner that we do not fully understand yet. For example, genes might make an individual more likely to be exposed to some environmental risk factors (e.g. drug use), and at the same time influence how someone responds to them (i.e. predisposition to addiction to a substance or to violence or hostility). Mental illnesses are not related to one but multiple genes across the genome. Genetic statistical techniques, such as Polygenic Risk Scoring (PRS), can quantify part of our genetic risk for psychiatric symptoms or illness. However, it still remains unclear why this genetic risk, as well as the impact that environmental factors have on mental health outcomes, exert such disparaging effects across individuals. In this six-month project we aim to leverage recently developed statistical methods to advance prediction of mental health outcomes (e.g. psychotic symptoms). We will do this by looking at the genes but also at environmental and lifestyle factors. In particular, we propose to: 1. Identify the contribution of genetic factors to the development of psychiatric symptoms using novel methods that minimize the contributions of confounders. 2. Identify modifiable environmental factors that impact mental health outcomes on top of an individual's genetic make-up. Correctly identifying these factors will hopefully allow the development of clinical tools to personally inform persons with symptoms on how changes in lifestyle may ameliorate the course of their symptoms.