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. ?
|Return ID||App ID||Description||Archive Date|
|2885||16406||Conditional and interaction gene-set analysis reveals novel functional pathways for blood pressure||26 Nov 2020|
|2709||16406||GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia||30 Oct 2020|
|2711||16406||Genetic mapping and evolutionary analysis of human-expanded cognitive networks||30 Oct 2020|
|2886||16406||Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence||26 Nov 2020|
|723||16406||Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence||17 Oct 2017|
|2710||16406||Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk||30 Oct 2020|
|3202||16406||Item-level Analyses Reveal Genetic Heterogeneity in Neuroticism||11 Mar 2021|
|1969||16406||Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways||4 Feb 2020|