| Application: | 16406, Causes of individual differences in cognitive and mental health |
| Title: | Genetic mapping and evolutionary analysis of human-expanded cognitive networks |
| Size: | 992272 B |
| Archived: | 30 Oct 2020 |
| Stability: | Complete |
| Personal: | No individual-level data |
Return 2711
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Notes
Default-mode network (DMN) is a key functional network of the human brain. In this study, we conducted genome-wide association analysis to find regions of the genome linked to variation in the functional activity of the DMN, and we examined whether human-accelerated genes (HAR genes) were specifically enriched in the observed genome regions. The fMRI amplitude of the ICA-based resting-state network resembling the DMN (described as NETMAT amplitudes 25(01) in http://big.stats.ox.ac.uk; UK Biobank field ID: 25754) was taken as the phenotype of interest. GWAS analysis revealed 2 associated genomic loci and 12 genes to be related to variations in DMN amplitude. Using MAGMA linear-regression-based gene-set analysis, we found that the set of brain-expressed HAR genes to be significantly associated with the phenotypic variation in DMN amplitude. Taking the normalized DMN amplitude (corrected for the mean amplitude across all networks) as the phenotype of interest showed similar results. Our findings thus suggest that genetic changes in recent human brain evolution may play a role in the neural activity of the DMN to support increasing human cognitive properties.Application 16406
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: | Professor Danielle Posthuma |
| Lead institution: | VU University Amsterdam |
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1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 2712 | Genetic mapping and evolutionary analysis of human-expanded cognitive networks | Wei Y et al. | 2019 | Nat Commun |
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