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).

Scope extension, April 2024:

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 primarily interested in the following health-relevant outcomes from the U.K. Biobank data: cognitive function (including normal function and dementia), mental health (including depression, neuroticism, personality, smoking, and alcohol use), and brain MRI.

A second goal is to develop statistical methods that will aid in the understanding of how genetic and environmental factors influence mental health. Because these influences are typically small, heterogeneous, and carried out by complex biological mechanisms, we will develop methods and resources to investigate pleiotropic effects of genes/variants on multiple aspects of cognitive and physical health and to characterize the effects of genetic variants via aggregation in genes, gene-sets, and biological pathways. Due to the challenges inherent in the study of complex cognitive phenotypes, we will develop/test our methods using additional phenotypes with less error-prone measurement properties (e.g. blood pressure, height) in order to refine these tools for application to cognitive and psychiatric outcomes.

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).

A third goal is to apply the expertise and methodology from our work in mental health towards general genetic understanding of human health and behavior. Recent research has shown that mental and physical health and behavior are not separate spheres, but grounded in the same biological (and environmental) origins. To understand their comorbidity and uncover their biological relatedness, we will develop and apply statistical genetic methods more broadly to complex human physical, physiological, medical, and mental health traits. We will investigate the genetic origins of such traits individually as well as their mutual etiological overlap.