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Approved research

Identifying genetic factors for brain ageing

Principal Investigator: Dr Qiang Chen
Approved Research ID: 51830
Approval date: September 30th 2019

Lay summary

Ageing is associated with changes in both brain structure and function, and genetic factors can affect brain ageing in a different way in different brain regions. Although age-related changes in brain volume and brain function have been extensively studied, more investigations to identify genetic causal variants on these phenotypes are still warranted. Discovering such variants is crucial to identify individual at-risk of cognitive decline and diseases associated with ageing, and to inform specific strategies of prevention and treatment. Magnetic resonance imaging (MRI) is a noninvasive technique to measure brain structure and function. Previous studies have shown that MRI can detect changes in brain structure and function, associated with ageing. Therefore, MRI data can be used to assess the genetic architecture of brain ageing. In UK Biobank, there are over 8000 individuals with both genetic and brain imaging data, which represent a unique resource to investigate the genetic mechanisms underlying brain ageing. Among six brain imaging modalities, we will use T1 weighted-image for Voxel Based Morphometry (VBM) analysis, resting-state functional MRI for connectivity analysis, and task functional MRI for activation analysis. Using such MRI data (structural, functional, and resting state), we plan to run a genome-wide association study (GWAS) to detect the genetic variants that interact with age in affecting brain structure and function. Of note, the availability of RNA-seq data from post-mortem brains at the Lieber Institute will represent an important asset to substantiate our findings. Specifically, Summary-data-based Mendelian Randomization (SMR) and Transcriptome-Wide Association (TWAS) studies will allow us to evaluate the causal variants that are associated with brain ageing. Finally, we will be able to replicate and validate our results using the related MRI and genetic data, available at the Lieber Institute. The results of this study will help researchers to better understanding how the human brain ageing and how brain systems respond to accumulating pathological burden related to brain degeneration disease. Therefore more precise genetic components could be targeted in therapeutic interventions.