Abstract

With distinct advantages in power over behavioral phenotype, brain imaging traits have become emerging endophenotypes to dissect molecular contribution to behaviors and neuropsychiatric illness. Among different imaging features, brain structural connectivity (i.e. structural connectome) which summarizes whole brain anatomical neural connections is one of the most cutting edge while under-investigated traits; and the genetic influence on the shifts of structural connectivity remains highly elusive.  Relying on a landmark imaging genetics study for young adults, we develop a biologically plausible brain network response shrinkage model to comprehensively characterize the relationship between high dimensional genetic variants and the structural connectome phenotype. Under a unified Bayesian framework, we accommodate the topology of brain network and biological architecture within genome; and eventually establish a mechanistic mapping between genetic biomarkers and the associated brain sub-network units. An efficient expectation-maximization algorithm is developed to estimate the model parameters and ensure computing feasibility. We show the superiority of our method in extensive simulations. In the application to the Human Connectome Project Young Adult  (HCP-YA) data, we establish the genetic underpinnings which are highly interpretable under functional annotation and brain tissue eQTL analysis, for the brain white matter tract sub-networks concentrating on hippocampus and between hemispheres.