Genetic Underpinnings of Brain Structural Connectome for Young Adults

Abstract

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. Supplementary materials for this article are available online.

Keywords:

• Bayesian shrinkage
• Brain connectivity
• Expectation-maximization
• Imaging genetics
• Network response

Supplementary Materials

The supplementary materials contain additional figures, ROI information and results for data application and simulations.

Disclosure Statement

There are no competing interests to declare from the authors.

Acknowledgments

The authors would like to thank the Editor, the Associate Editor, and anonymous Reviewers for their constructive comments and suggestions which significantly helped improve this article.

Additional information

Funding

This work was partially supported by the National Institutes of Health (NIH) grants RF1AG068191, P30AG066508, R01MH118927, and R01AG066970.