Typically, items on a questionnaire gauging personality traits, or symptoms used as indicators of psychiatric disorders such as depression, are combined to obtain a composite score (e.g. sum-score or case-control status). Geneticists subsequently use such composite scores to identify genetic variants that are associated to the trait of interest, using a method called the genome-wide association study (GWAS). However, the items or symptoms underlying a trait may not be caused by the same set of genetic variants.Therefore, we studied 12 individual questionnaire items that are often used as indicators of neuroticism. Neuroticism is a stable personality trait that correlates with psychiatric traits like anxiety, substance abuse, and major depressive disorder. The results indicated that the 12 items are genetically quite different. In addition, we identified two sets of items that are genetically very similar. This suggests that analyzing individual items or symptoms, or genetically homogeneous sets of items or symptoms, may provide additional information compared to using a composite score. In turn, this may help advance our understanding of the biological mechanisms underlying personality traits and psychiatric disorders.
Causes of individual differences in cognitive and mental health
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.
|Lead investigator:||Danielle Posthuma|
|Lead institution:||VU University Amsterdam|