Principal Investigator: Dr Michael Greicius
Stanford University, Stanford, USATags: 45420, gene-network, GWAS, imaging-genetics, resting-state fmri
Our project has two main goals: (i) identify genes and causal genetic variants that regulates the variance of resting-state fMRI phenotypes; (ii) develop multivariate approaches taking into account the potential joint effects between genes and/or covariations between brain regions.
Resting-state fMRI leverages the fact that even at rest the brain remains functionally active. In this passive state, every brain region shows ongoing low-frequency fluctuations of neural activity that can be detected with fMRI. Critically, these low-frequency fluctuations are strongly temporally correlated across brain regions. Measures of temporal correlation between two brain regions are used to define functional connectivity and, when applied across several brain regions are used to define resting-state networks (RSNs).
Recent imaging genetic studies have emphasized the heritability of resting-state fMRI associated phenotypes. Our aim is to go a step further and identify the underlying genetic variants which regulate these phenotypes.
The functional connectivity within RSNs has been shown to discriminate controls and people with brain disorders. Our study will enable to understand the molecular mechanisms at stake in the RSNs variability across individuals.
This meets the UK Biobank’s stated purpose because characterizing the genes that regulates the resting-state phenotypes will contribute to build a reference set of genes the variability in the general population. This will in turn help to better identify and understand the potential genetic patterns underlying diseases for which the variability in RSNs has been shown to be a biomarker, such as the default mode network which distinguishes Alzheimer’s disease from healthy aging.
Project extension – 10/09/2019
We will expand our search by performing the largest subpopulation genetic association of different neurodegenerative diseases and by evaluating the interaction of genetic risk/protective factors and neurodegenerative genotypes. This extension of our current scope will allow us to (1) fill in the current knowledge gap of subpopulation-specific genetic risk for different neurodegenerative diseases, (2) improve the power of genetic association studies, and (3) identify novel genetic variants associated with different neurodegenerative diseases and their respective imaging phenotypes.
Last updated Sep 12, 2019