Brabec Jeffrey L, Lara Montana Kay, Tyler Anna L, Mahoney J Matthew
Department of Neurological Sciences, University of Vermont, Burlington, VT, United States.
The Jackson Laboratory, Bar Harbor, ME, United States.
Front Genet. 2021 Apr 6;12:625246. doi: 10.3389/fgene.2021.625246. eCollection 2021.
Alzheimer's disease (AD) is a debilitating neurodegenerative disorder. Since the advent of the genome-wide association study (GWAS) we have come to understand much about the genes involved in AD heritability and pathophysiology. Large case-control meta-GWAS studies have increased our ability to prioritize weaker effect alleles, while the recent development of has provided a mechanism by which we can use machine learning to reprioritize GWAS hits in the functional context of relevant brain tissues like the hippocampus and amygdala. In parallel with these developments, groups like the Alzheimer's Disease Neuroimaging Initiative (ADNI) have compiled rich compendia of AD patient data including genotype and biomarker information, including derived volume measures for relevant structures like the hippocampus and the amygdala. In this study we wanted to identify genes involved in AD-related atrophy of these two structures, which are often critically impaired over the course of the disease. To do this we developed a combined score prioritization method which uses the cumulative distribution function of a gene's functional and positional score, to prioritize top genes that not only segregate with disease status, but also with hippocampal and amygdalar atrophy. Our method identified a mix of genes that had previously been identified in AD GWAS including , , and () and several others that have not been identified in AD genetic studies, but play integral roles in AD-effected functional pathways including , , and . Our findings support the viability of our novel combined score as a method for prioritizing region- and even cell-specific AD risk genes.
阿尔茨海默病（AD）是一种使人衰弱的神经退行性疾病。自从全基因组关联研究（GWAS）出现以来，我们对AD遗传易感性和病理生理学中涉及的基因有了很多了解。大型病例对照meta-GWAS研究提高了我们对弱效应等位基因进行优先级排序的能力，而最近[此处原文缺失相关内容]的发展提供了一种机制，通过该机制我们可以利用机器学习在海马体和杏仁核等相关脑组织的功能背景下对GWAS命中结果重新进行优先级排序。与这些进展同时，像阿尔茨海默病神经影像倡议（ADNI）这样的团队已经汇编了丰富的AD患者数据纲要，包括基因型和生物标志物信息，包括海马体和杏仁核等相关结构的衍生体积测量值。在本研究中，我们想要确定与这两个结构的AD相关萎缩有关的基因，这两个结构在疾病过程中常常严重受损。为此，我们开发了一种综合评分优先级排序方法，该方法使用基因功能和位置评分的累积分布函数，对不仅与疾病状态，而且与海马体和杏仁核萎缩相关的顶级基因进行优先级排序。我们的方法识别出了一系列基因，其中包括先前在AD GWAS中已被识别的[此处原文缺失相关基因]以及其他几个在AD基因研究中尚未被识别，但在受AD影响的功能途径（包括[此处原文缺失相关内容]）中起不可或缺作用的基因。我们的研究结果支持了我们新的综合评分作为一种对区域甚至细胞特异性AD风险基因进行优先级排序方法的可行性。
