Whole-genome analyses of whole-brain data: working within an expanded search space

Abstract

Large-scale comparisons of patients and healthy controls have unearthed genetic risk factors associated with a range of neurological and psychiatric illnesses. Meanwhile, brain imaging studies are increasing in size and scope, revealing disease and genetic effects on brain structure and function, and implicating neural pathways and causal mechanisms. With the advent of global neuroimaging consortia, imaging studies are now well powered to discover genetic variants that reliably affect the brain. Genetic analyses of brain measures from tens of thousands of people are being extended to test genetic associations with signals at millions of locations in the brain, and connectome-wide, genome-wide scans can jointly screen brain circuits and genomes; these analyses and others present new statistical challenges. There is a growing need for the community to establish and enforce standards in this developing field to ensure robust findings. Here we discuss how neuroimagers and geneticists have formed alliances to discover how genetic factors affect the brain. The field is rapidly advancing with ultra-high-resolution imaging and whole-genome sequencing. We recommend a rigorous approach to neuroimaging genomics that capitalizes on its recent successes and ensures the reliability of future discoveries.

Figure 1

(a) Voxel-wise genetic association analysis. This kind of analysis involves a genome-wide search at each voxel in the brain, after aligning all subjects’ images to a common template. (b) Extending this method to study brain connections, Jahanshad et al. described connectome-wide searches. They combined diffusion-based MRI tractography and cortical parcellations to perform GWAS at all connections between cortical regions of interest. Artificial Manhattan plots are illustrated here, with thresholds shown based on a single GWAS. Despite the vast number of tests, promising findings emerged, even after correction, from these whole-connectome genetic screens.

Figure 2

Testing genetic associations in an image. As a greater percentage of the voxels in the brain image are tested for associations (x axis), the minimum uncorrected P value will increase (y axis), even if the SNPs have no effect (that is, the null hypothesis is true). To correct for this, the genome-wide significance threshold can be divided by the effective number of independent tests on the image, based on P values that actually occur in simulated null data with the same spatial coherence.

Figure 3