Brain-wide structural connectivity alterations under the control of Alzheimer risk genes.

Q4 Pharmacology, Toxicology and Pharmaceutics
Jingwen Yan, Vinesh Raja V, Zhi Huang, Enrico Amico, Kwangsik Nho, Shiaofeng Fang, Olaf Sporns, Yu-Chien Wu, Andrew Saykin, Joaquin Goni, Li Shen
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引用次数: 7

Abstract

Background: Alzheimer's disease is the most common form of brain dementia characterized by gradual loss of memory followed by further deterioration of other cognitive function. Large-scale genome-wide association studies have identified and validated more than 20 AD risk genes. However, how these genes are related to the brain-wide breakdown of structural connectivity in AD patients remains unknown.

Methods: We used the genotype and DTI data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. After constructing the brain network for each subject, we extracted three types of link measures, including fiber anisotropy, fiber length and density. We then performed a targeted genetic association analysis of brain-wide connectivity measures using general linear regression models. Age at scan and gender were included in the regression model as covariates. For fair comparison of the genetic effect on different measures, fiber anisotropy, fiber length and density were all normalized with mean as 0 and standard deviation as one.We aim to discover the abnormal brain-wide network alterations under the control of 34 AD risk SNPs identified in previous large-scale genome-wide association studies.

Results: After enforcing the stringent Bonferroni correction, rs10498633 in SLC24A4 were found to significantly associated with anisotropy, total number and length of fibers, including some connecting brain hemispheres. With a lower level of significance at 5e-6, we observed significant genetic effect of SNPs in APOE, ABCA7, EPHA1 and CASS4 on various brain connectivity measures.

阿尔茨海默病风险基因控制下的全脑结构连接改变。
背景:阿尔茨海默病是最常见的脑痴呆形式,其特征是逐渐丧失记忆,随后其他认知功能进一步恶化。大规模全基因组关联研究已经确定并验证了20多个AD风险基因。然而,这些基因如何与阿尔茨海默病患者全脑结构连通性的破坏相关仍然未知。方法:我们使用阿尔茨海默病神经影像学倡议(ADNI)数据库中的基因型和DTI数据。在构建每个受试者的大脑网络后,我们提取了三种类型的链路度量,包括纤维各向异性、纤维长度和密度。然后,我们使用一般线性回归模型对全脑连通性测量进行了有针对性的遗传关联分析。扫描时的年龄和性别作为协变量纳入回归模型。为了比较不同指标上的遗传效应,纤维各向异性、纤维长度和密度均归一化,均值为0,标准差为1。我们的目标是发现在先前大规模全基因组关联研究中发现的34个AD风险snp控制下的异常脑全网络改变。结果:在执行严格的Bonferroni校正后,发现SLC24A4中的rs10498633与各向异性、纤维总数和长度显著相关,包括一些连接大脑半球的纤维。我们观察到APOE、ABCA7、EPHA1和CASS4位点的snp对各种脑连通性测量的遗传影响显著,但在5e-6位点的显著性水平较低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Computational Biology and Drug Design
International Journal of Computational Biology and Drug Design Pharmacology, Toxicology and Pharmaceutics-Drug Discovery
CiteScore
1.00
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8
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