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.

相较于行为表型，脑影像特征具有显著的统计效力优势，现已成为解析行为与神经精神疾病分子机制的新兴内表型（endophenotype）。在各类影像特征中，脑结构连接（即结构连接组（structural connectome））——其概括了不同脑区间的解剖连接关系——是当前最具前沿性却研究不足的特征之一；而遗传因素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年轻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，我们同时纳入脑网络拓扑结构与基因组内的生物学架构，最终建立了遗传生物标志物与对应脑子网单元之间的机制性映射关系。为保障计算可行性，我们开发了高效的期望最大化（expectation-maximization, EM）算法以完成模型参数估计。在对人类连接组计划年轻成人数据集（Human Connectome Project Young Adult, HCP-YA）的应用中，我们针对连接海马体与双侧大脑半球的脑白质纤维束，通过功能注释与脑组织表达数量性状位点（expression quantitative trait locus, eQTL）分析，得到了具备高度可解释性的遗传基础。此外，我们通过大规模模拟实验验证了所提方法的优越性。