Yan Jingwen, Raja V Vinesh, Huang Zhi, Amico Enrico, Nho Kwangsik, Fang Shiaofeng, Sporns Olaf, Wu Yu-Chien, Saykin Andrew, Goni Joaquin, Shen Li
Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, 719 Indiana Ave, Indianapolis, USA.
Electrical and Computing Engineering, Indiana University Purdue University Indianapolis, 46202 Indianapolis, USA.
Int J Comput Biol Drug Des. 2020;13(1):58-70. doi: 10.1504/ijcbdd.2020.10026789. Epub 2020 Feb 7.
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.
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.
After enforcing the stringent Bonferroni correction, rs10498633 in 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 and on various brain connectivity measures.
阿尔茨海默病是最常见的脑痴呆形式,其特征是记忆力逐渐丧失,随后其他认知功能进一步衰退。大规模全基因组关联研究已经鉴定并验证了20多个阿尔茨海默病风险基因。然而,这些基因如何与阿尔茨海默病患者全脑结构连接性的破坏相关尚不清楚。
我们使用了阿尔茨海默病神经影像倡议(ADNI)数据库中的基因型和弥散张量成像(DTI)数据。在为每个受试者构建脑网络后,我们提取了三种类型的连接测量指标,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接性测量指标进行了靶向基因关联分析。扫描时的年龄和性别作为协变量纳入回归模型。为了公平比较不同测量指标上的遗传效应,纤维各向异性、纤维长度和密度均以均值为0、标准差为1进行标准化。我们旨在发现先前大规模全基因组关联研究中鉴定的34个阿尔茨海默病风险单核苷酸多态性(SNP)控制下的全脑网络异常改变。
在实施严格的Bonferroni校正后,发现位于……的rs10498633与纤维各向异性、纤维总数和长度显著相关,包括一些连接脑半球的纤维。在5e - 6的显著性水平较低时,我们观察到位于……和……的SNP对各种脑连接性测量指标有显著的遗传效应。