  With distinct advantages in power over behavioral phenotypes, brain imaging
traits have become emerging endophenotypes to dissect molecular contributions
to behaviors and neuropsychiatric illnesses. Among different imaging features,
brain structural connectivity (i.e., structural connectome), which summarizes
the anatomical connections between different brain regions, is one of the most
cutting-edge while under-investigated traits; and the genetic influence on the
structural connectome variation 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 the 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 and ensure computing feasibility.
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 tracts connecting the hippocampus and two cerebral hemispheres. We also
show the superiority of our method in extensive simulations.
