There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Yogasudha Veturi |
| Institution: | Pennsylvania State University |
| Department: | Biobehavioral Health / Statistics |
| Country: | |
| Proposed Analysis: | We propose to understand between-sex genetic heterogeneity in the stress-cognitive decline relationship using Bayesian multivariate random effect interaction models implemented on cortisol levels and cognitive assessments. We will also identify areas of the brain associated with sex-specific neurodegeneration using extensions of same interaction models implemented on voxel-level functional neuroimaging data. Our past work has suggested genetic heterogeneity between sexes for obesity-related risk factors for cognitive decline (e.g., systolic blood pressure and waist-hip-ratio). Several small effect causal loci can cumulatively influence sex differences among individuals with cognitive decline. We developed methods that can incorporate genotype-by-sex random-effect interactions (GBS-REI) in a hierarchical Bayesian framework. These methods can decompose conditional SNP effects into three components: (i) shared across sexes, (ii) male-specific and (iii) female-specific deviations from the shared component and offer opportunities to study sex-specific genetic architectures of stress and cognitive decline. Our models can estimate average correlation of marker effects between sexes, proportion of non-zero effects as well as offer SNP-specific correlation of effects. Importantly, these can yield unbiased estimates of between-sex effect correlation. This is in stark contrast to obtaining a simple correlation of effects from sex-stratified models, which is heavily biased towards zero. To our knowledge, no other existing models can offer such a rich variety of genetic summaries for male, female and shared SNP effects. We also developed powerful region-based score tests that can test for the statistical significance of the between-sex correlation of effects on a gene-by-gene or pathway-by-pathway basis. These methods can be used to understand between-sex genetic heterogeneity in the stress-cognitive decline relationship as well as understand the regions of the brain driving the genetic heterogeneity. |
| Additional Investigators | |
| Investigator's Name: | Hyebin Song |
| Proposed Analysis: | We are developing hierarchical statistical models that can integrate longitudinal fMRI data with multi-omic data to predict risk of AD. |
| Investigator's Name: | Varun Kartikey |
| Proposed Analysis: | Performing QC on the fMRI and genomic ADNI data to set the stage for implementing novel statistical methods. |
