Imaging genomics

Abstract

Purpose of review: Imaging genomics is an emerging field that is rapidly identifying genes that influence the brain, cognition, and risk for disease. Worldwide, thousands of individuals are being scanned with high-throughput genotyping (genome-wide scans), and new imaging techniques [high angular resolution diffusion imaging and resting state functional magnetic resonance imaging (MRI)] that provide fine-grained measures of the brain's structural and functional connectivity. Along with clinical diagnosis and cognitive testing, brain imaging offers highly reproducible measures that can be subjected to genetic analysis.

Recent findings: Recent studies of twin, pedigree, and population-based datasets have discovered several candidate genes that consistently show small to moderate effects on brain measures. Many studies measure single phenotypes from the images, such as hippocampal volume, but voxel-wise genomic methods can plot the profile of genetic association at each 3D point in the brain. This exploits the full arsenal of imaging statistics to discover and replicate gene effects.

Summary: Imaging genomics efforts worldwide are now working together to discover and replicate many promising leads. By studying brain phenotypes closer to causative gene action, larger gene effects are detectable with realistic sample sizes obtainable from meta-analysis of smaller studies. Imaging genomics has broad applications to dementia, mental illness, and public health.

Figure 1. Genome-wide association study of temporal lobe structure

The GWA study identified a common glutamate receptor variant (GRIN2b) with suggestive evidence of association with temporal lobe volume and increased risk for Alzheimer’s disease in 740 brain MRI scans. The plot on the left is a standard output from the PLINK software [58] – it shows the significance of association between the image-derived measure of interest (here temporal lobe volume) and variants at each of 600 000 locations on the genome. By performing genetic association to assess the effects of variants in this SNP at millions of points in a brain image, associations were detected and mapped in 3D (right). Adapted from [57].