Identifying Multimodal Intermediate Phenotypes Between Genetic Risk Factors and Disease Status in Alzheimer's Disease.
Identifying Multimodal Intermediate Phenotypes Between Genetic Risk Factors and Disease Status in Alzheimer's Disease.
复制标题
识别阿尔茨海默病遗传风险因素和疾病状态之间的多模式中间表型
DOI：
10.1007/s12021-016-9307-8
复制
发表时间：
2016-10
期刊：
影响因子：
3
通讯作者：
Alzheimer’s Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Hao X;Yao X;Yan J;Risacher SL;Saykin AJ;Zhang D;Shen L;Alzheimer’s Disease Neuroimaging Initiative
神经影像遗传学已经引起了越来越多的关注和兴趣，这被认为是检查遗传变异影响的有力策略（即，单核苷酸多态性（SNP））对人脑结构或功能的影响。在最近的研究中，通常使用单变量或多变量回归分析方法来捕获遗传变异与数量性状（QT）（例如脑成像表型）之间的有效关联。所识别的成像QT，虽然与某些遗传标记相关，但可能不是所有疾病特异性的。一个有用的，但未充分探索的方案可能是只发现那些与遗传标记和疾病状态相关的QT，以揭示从基因型到表型再到症状的链。此外，多模态脑成像表型是从不同的角度提取的，并且在多模态中一致显示的成像标记物可以为疾病的机制理解提供更多的见解（即，阿尔茨海默病（AD））。在这项工作中，我们提出了一个总体框架，利用多模态脑成像表型作为中间性状，桥梁遗传风险因素和多类疾病状态。我们应用我们提出的方法来探索众所周知的AD风险SNP APOE rs 429358和三种基线脑成像模式（即，结构磁共振成像（MRI）、氟脱氧葡萄糖正电子发射断层扫描（FDG-PET）和F-18 florbetapir PET扫描淀粉样蛋白成像（AV 45））。实验结果表明，我们提出的方法不仅有助于提高成像遗传关联的性能，而且还发现了跨多模态的鲁棒和一致的感兴趣区域（ROI），以指导疾病诱导的解释。
Neuroimaging genetics has attracted growing attention and interest, which is thought to be a powerful strategy to examine the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on structures or functions of human brain. In recent studies, univariate or multivariate regression analysis methods are typically used to capture the effective associations between genetic variants and quantitative traits (QTs) such as brain imaging phenotypes. The identified imaging QTs, although associated with certain genetic markers, may not be all disease specific. A useful, but underexplored, scenario could be to discover only those QTs associated with both genetic markers and disease status for revealing the chain from genotype to phenotype to symptom. In addition, multimodal brain imaging phenotypes are extracted from different perspectives and imaging markers consistently showing up in multimodalities may provide more insights for mechanistic understanding of diseases (i.e., Alzheimer’s disease (AD)). In this work, we propose a general framework to exploit multi-modal brain imaging phenotypes as intermediate traits that bridge genetic risk factors and multi-class disease status. We applied our proposed method to explore the relation between the well-known AD risk SNP APOE rs429358 and three baseline brain imaging modalities (i.e., structural magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET) and F-18 florbetapir PET scans amyloid imaging (AV45)) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The empirical results demonstrate that our proposed method not only helps improve the performances of imaging genetic associations, but also discovers robust and consistent regions of interests (ROIs) across multi-modalities to guide the disease-induced interpretation.
登录
查看更多内容
影响因子：
14
作者：
Brookmeyer, Ron;Johnson, Elizabeth;Arrighi, H. Michael
通讯作者：
Arrighi, H. Michael
影响因子：
158.5
作者：
Reiman, EM;Caselli, RJ;Osborne, D
通讯作者：
Osborne, D
影响因子：
5.7
作者：
Ge, Tian;Feng, Jianfeng;Hibar, Derrek P.;Thompson, Paul M.;Nichols, Thomas E.
通讯作者：
Nichols, Thomas E.
影响因子：
2.1
作者：
Beck, Amir;Teboulle, Marc
通讯作者：
Teboulle, Marc
影响因子：
5.7
作者：
Gray, Katherine R.;Aljabar, Paul;Heckemann, Rolf A.;Hammers, Alexander;Rueckert, Daniel
通讯作者：
Rueckert, Daniel