Approved research

Multivariate approaches for the identification of novel environmental and genetic risk factors for neuropsychiatric disorders, skin cancers and bone-related outcomes.

Lay summary

Our study aims to apply multivariate methods to study the associations of numerous environmental and genetic risk factors using agnostic approaches. To test our developed methodologies we will use various phenotypes that assumed to be correlated and may share a common genetic background. We will focus independently to neuropsychiatric diseases, skin cancers and bone related phenotypes. The findings will be used to inform gene-environment interactions and assess potential dose-response relationship for toxic and nutritional factors. Our novel findings (both genetic and environmental) will be used to develop prediction models combined with already known traditional factors The proposed research is entirely congruent with the stated aim of UK Biobank to improve the prevention, diagnosis and treatment of a wide range of illnesses. We will examine the environmental and genetic risk factors aiming to identify novel robust associations with and their potential interactions for a range of diseases. These findings will have major impact in the prevention and diagnosis of the investigated medical conditions, as they will help to identify subsets of populations at high risk. Also, the observed associations will assist the understanding of the mechanism of diseases, providing new insights to the drug development process. Our study will examine the association of environmental risk factors with the aforementioned outcomes, using a recently emerged state-of-the-art approach to simultaneously study multiple environmental risk factors. Through this approach, we aim to replicate already known associations and to examine additional associations that were not previously considered or are supported by little evidence. Additionally, our study will examine the role of genetic polymorphisms and their gene-environment interactions to the pathogenesis of the diseases under consideration. Finally, these findings will be used to construct a prediction model for our studied diseases. We will use the full cohort and the available genetic information.