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脑连接表型的遗传影响:对两个特定年龄队列的研究

Genetic Influence Underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts.

作者信息

Cong Shan, Yao Xiaohui, Xie Linhui, Yan Jingwen, Shen Li

机构信息

Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

Department of Electrical and Computer Engineering, School of Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN, United States.

出版信息

Front Genet. 2022 Feb 7;12:782953. doi: 10.3389/fgene.2021.782953. eCollection 2021.

Abstract

Human brain structural connectivity is an important imaging quantitative trait for brain development and aging. Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. However, the underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear. This study analyzes diffusion-weighted imaging data from two age-specific neuroimaging cohorts, extracts structural connectome topological network measures, performs genome-wide association studies of the measures, and examines the causality of genetic influences on phenotypic outcomes mediated via connectivity measures. Our empirical study has yielded several significant findings: 1) It identified genetic makeup underlying structural connectivity changes in the human brain connectome for both age groups. Specifically, it revealed a novel association between the minor allele (G) of rs7937515 and the decreased network segregation measures of the left middle temporal gyrus across young and elderly adults, indicating a consistent genetic effect on brain connectivity across the lifespan. 2) It revealed rs7937515 as a genetic marker for body mass index in young adults but not in elderly adults. 3) It discovered brain network segregation alterations as a potential neuroimaging biomarker for obesity. 4) It demonstrated the hemispheric asymmetry of structural network organization in genetic association analyses and outcome-relevant studies. These imaging genetic findings underlying brain connectome warrant further investigation for exploring their potential influences on brain-related complex diseases, given the significant involvement of altered connectivity in neurological, psychiatric and physical disorders.

摘要

人类脑结构连接性是大脑发育和衰老的一项重要成像定量特征。将网络连接性映射到表型变异,为理解详细的脑拓扑结构、功能及功能障碍之间的关系提供了基本见解。然而,从基因到脑连接组,再到表型结果的潜在神经生物学机制,以及该机制是否随时间变化,仍不清楚。本研究分析了来自两个特定年龄神经成像队列的扩散加权成像数据,提取了结构连接组拓扑网络指标,对这些指标进行全基因组关联研究,并检验了通过连接性指标介导的遗传影响对表型结果的因果关系。我们的实证研究得出了几个重要发现:1)确定了两个年龄组人类脑连接组中结构连接性变化的基因组成。具体而言,揭示了rs7937515的次要等位基因(G)与青年和老年成年人左颞中回网络分离指标降低之间的新关联,表明在整个生命周期中对脑连接性存在一致的遗传效应。2)揭示rs7937515是青年成年人而非老年成年人身体质量指数的遗传标记。3)发现脑网络分离改变是肥胖的一种潜在神经成像生物标志物。4)在遗传关联分析和与结果相关的研究中证明了结构网络组织的半球不对称性。鉴于连接性改变在神经、精神和身体疾病中的显著参与,这些基于脑连接组的成像遗传学发现值得进一步研究,以探索它们对脑相关复杂疾病的潜在影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b1a/8884108/c2e70fae7c14/fgene-12-782953-g001.jpg

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