Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, 7925, South Africa.
Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, 7925, South Africa.
Sci Rep. 2020 Jan 29;10(1):1433. doi: 10.1038/s41598-020-58291-1.
Variations in the human genome have been found to be an essential factor that affects susceptibility to Alzheimer's disease. Genome-wide association studies (GWAS) have identified genetic loci that significantly contribute to the risk of Alzheimers. The availability of genetic data, coupled with brain imaging technologies have opened the door for further discoveries, by using data integration methodologies and new study designs. Although methods have been proposed for integrating image characteristics and genetic information for studying Alzheimers, the measurement of disease is often taken at a single time point, therefore, not allowing the disease progression to be taken into consideration. In longitudinal settings, we analyzed neuroimaging and single nucleotide polymorphism datasets obtained from the Alzheimer's Disease Neuroimaging Initiative for three clinical stages of the disease, including healthy control, early mild cognitive impairment and Alzheimer's disease subjects. We conducted a GWAS regressing the absolute change of global connectivity metrics on the genetic variants, and used the GWAS summary statistics to compute the gene and pathway scores. We observed significant associations between the change in structural brain connectivity defined by tractography and genes, which have previously been reported to biologically manipulate the risk and progression of certain neurodegenerative disorders, including Alzheimer's disease.
人类基因组的变异被发现是影响阿尔茨海默病易感性的一个重要因素。全基因组关联研究(GWAS)已经确定了一些遗传位点,这些遗传位点显著增加了患阿尔茨海默病的风险。遗传数据的可用性,加上脑成像技术,为进一步的发现开辟了道路,使用了数据集成方法和新的研究设计。尽管已经提出了用于整合图像特征和遗传信息来研究阿尔茨海默病的方法,但疾病的测量通常只在一个时间点进行,因此,不能考虑疾病的进展。在纵向研究中,我们分析了来自阿尔茨海默病神经影像学倡议的神经影像学和单核苷酸多态性数据集,用于疾病的三个临床阶段,包括健康对照、早期轻度认知障碍和阿尔茨海默病患者。我们进行了全基因组关联研究,将全局连通性指标的绝对变化回归到遗传变异上,并使用全基因组关联研究的汇总统计数据来计算基因和途径得分。我们观察到通过轨迹定义的结构脑连接的变化与先前报道的生物操纵某些神经退行性疾病(包括阿尔茨海默病)风险和进展的基因之间存在显著关联。