Approved research

Causes of individual differences in cognitive and mental health

Lay summary

The main goal of our study is to quantify and understand the role of genetic variants, the environment (including lifestyle), and their interaction on outcomes related to cognitive health. In doing so we will combine expertise of statistical genetics, medical genetics, bioinformatics and functional genomics. We are specifically interested in the following health-relevant outcomes from the U.K. Biobank data: cognitive function (incl. normal function and dementia), mental health (incl. depression, neuroticism, personality, smoking, and alcohol drinking), and brain MRI. Our research will contribute to quantifying and understanding how several risk factors (e.g. lifestyle, environment, genes), both separately and in combination, influence cognitive health as well as the comorbidities between different cognitive health outcomes. Our study will consist of a combination of methods, including: - Genome-wide association studies (GWAS) that aim to identify individual genetic variants associated with a particular outcome. - Comorbidity analyses, using e.g. meta-analytic techniques, LD score regression or BOLD-GREML methods to quantify the extent of genetic overlap between particular outcomes - Gene-set analyses (e.g. using MAGMA and INRICH tools) and bioinformatic secondary analyses to understand genetic findings in terms of their biological function - Heterogeneity analyses to determine genetic subgroups of individuals - Annotation of genetic findings using external information from e.g. expression or quantitative proteomics data - Gene-by-environment correlation and interaction analyses to quantify the relevance of the interplay between genes and environment (including lifestyle) on outcomes related to cognitive health We aim to use all available observations in the UKB that are currently released and will be released in the future, and that have been successfully genotyped and have measures of relevant outcomes. 

Scope extension: As the analysis of mental health phenotypes is a multi-faceted challenge, we will utilize multiple overlapping approaches for methods development. Among others, these approaches will include simulations of phenotypes and/or genotypes based on the real UKB data, developing and disseminating methodological resources based on the UKB data, comparison of methods performance for mental health phenotypes versus "simpler" non-psychiatric measures, and direct application of methods to mental health outcomes. We intend to develop and refine methods that will aid in both understanding etiology (e.g. identifying causal genetic/environmental factors) and moving these insights forward into clinical applications (e.g. drug target prediction).