Genome-wide association studies (GWAS) have identified dozens of genetic variants related to brain structure. These studies have focused on a few aggregate measures since GWAS are computationally demanding. However, the brain is complex and can be described using millions of measures. We recently developed methods to perform such genome-wide and brain-wide association studies and applied this in several cohort studies. We would like to replicate our findings in the UK Biobank by jointly meta-analyzing the results. It fits well with the purpose of the UK Biobank in two ways:
1) It build upon this major resource by providing a range of novel neuroimaging biomarkers, which will be made available to other researchers.
2) It will hopefully identify novel genetic variants that are important for brain structure and diseases of the brain (e.g., Alzheimer's disease, schizophrenia) We will analyze brain images to calculate million of measures that describe the structure of the brain. Next, we will perform genome-wide screens of millions of genetic variants to identify ones that affect brain structure. The full cohort of individuals with both brain imaging and genetic data available.
|Return ID||App ID||Description||Archive Date|
|3342||23509||GenNet framework: interpretable neural networks for phenotype prediction||15 Apr 2021|
|3865||23509||Normative brain volumetry derived from different reference populations: impact on single-subject diagnostic assessment in dementia||28 Sep 2021|
|3343||GenNet framework: interpretable neural networks for phenotype prediction||van Hilten et al||2014||bioRxiv (2020)|