Integration of multi-organ imaging phenotypes, clinical phenotypes, and genomic data
Approved Research ID: 32133
Approval date: January 28th 2019
There is evidence for a strong relationship between cardiovascular and neurological diseases but the genetic basis of this association has not been explored and studies performed thus far have not provided biomarkers of early disease progression. This research aims to examine pleiotropic associations between brain and heart image-derived phenotypes using candidate genes associated with late-onset neurological and cardiovascular-diseases. This means that we are looking at genes that are linked with heart and brain related traits and trying to understand the relationship between these diseases. Apart from having the potential to greatly advance our current understanding of the common genetic mechanisms of cardiovascular and neurological disease, this effort can also lead in improved diagnosis and development of better treatment modalities. We will develop advanced statistical models and machine learning algorithms that can integrate image-derived phenotypes with genome-wide genotype information to meet the stated aims of the project.
This research is based on two critical knowledge gaps: (1) There is evidence for a strong relationship between cardiovascular and neuropathological diseases but the genetic basis of this association has not been explored and (2) Although brain imaging data has been combined with polygenic data to identify common genetic variants associated with neurological diseases, existing cohorts used in these studies cannot provide biomarkers of early disease progression. This research aims to understand the genetic basis of associations between cognitive and cardiovascular-related traits. We propose to integrate multi-organ imaging data along with clinical and genetic data from UK Biobank.
In addition to the linkage between cardiovascular and neuropathological diseases, we would like to expand our disease focus to more broadly evaluate pleiotropy relationships across the phenome. As we have explored the two disease classes (cardiovascular and neuropathological) through the use of Phenome-wide Association Studies (PheWAS), we have identified many pleiotropic relationships that link these two disease areas with many other disease classes as well. In future analyses in this project, we would like to select different disease classes as the anchor group by which to perform PheWAS to look for pleiotropy relationships.