Alzheimer s disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. These loci only explain a small proportion of the genetic risk for developing AD, indicating that undiscovered loci remain. Although there are very few individuals with Alzheimer s in UK Biobank, this study leveraged participant reports of their parents AD diagnoses to create a proxy measure of inherited genetic risk. Combining this sample with other studies of clinically diagnosed AD (71,880 cases and 383,378 controls in total) resulted in greater statistical power to identify associated genomic loci. Altogether we identified 29 disease-associated loci (9 novel and 20 validating previous findings), implicating 215 potential causative genes. The associated genes are most active in immune-related tissues and cell types (i.e. spleen, liver, and microglia). Gene-set analyses indicated that the associated genes are involved in biological mechanisms such as lipid-related processes and degradation of amyloid precursor proteins. This link was already known in relation to the APOE gene, the most robust genetic risk factor for AD, but these findings strengthen the hypothesis that AD is caused by an interplay between inflammation and lipids in the brain. This study also showed genetic correlations between AD and multiple health-related outcomes, including a protective effect of cognitive ability against the risk of developing AD. These results are a step forward in identifying the genetic factors that contribute to AD and add novel insights into its neurobiology.
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|