Genetic Underpinnings of Brain Structural Connectome for Young Adults

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.

脑影像特征相较行为表型具备显著优势，已成为解析行为与神经精神疾病分子贡献的新兴内表型（endophenotypes）。在各类影像特征中，脑结构连接（即结构连接组，structural connectome）——该指标可概括不同脑区间的解剖学连接——是当前最前沿却尚未被充分研究的特征之一；而遗传对结构连接组变异的影响仍极不明确。本研究依托一项针对青年群体的标志性影像遗传学研究，构建了具备生物学合理性的脑网络响应收缩模型，以全面刻画高维遗传变异与结构连接组表型之间的关联。在统一的贝叶斯（Bayesian）框架下，我们整合了脑网络的拓扑结构与基因组内的生物学架构，并最终建立了遗传生物标志物与相关脑子网单元之间的机制性映射关系。我们开发了高效的期望-最大化（expectation-maximization）算法以实现模型估计并保障计算可行性。在对人类连接组计划青年成人（Human Connectome Project Young Adult, HCP-YA）数据集的应用中，我们针对连接海马与两侧大脑半球的脑白质纤维束，建立了可通过功能注释与脑组织表达数量性状基因座（expression quantitative trait locus, eQTL）分析实现高度解释性的遗传基础。本研究还通过大量仿真实验验证了所提方法的优越性。本文的补充材料可在线获取。