University of Pennsylvania, Philadelphia, PA 19104, USA.
The Catholic University of Korea, Seoul 06591, Korea.
Genes (Basel). 2022 Aug 24;13(9):1520. doi: 10.3390/genes13091520.
Brain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer's disease (AD). The individual level SNP-QT signals are high dimensional and typically have small effect sizes, making them hard to be detected and replicated. To overcome this limitation, this work proposes a new approach that identifies high-level imaging genetic associations through applying multigraph clustering to the SNP-QT association maps. Given an SNP set and a brain QT set, the association between each SNP and each QT is evaluated using a linear regression model. Based on the resulting SNP-QT association map, five SNP-SNP similarity networks (or graphs) are created using five different scoring functions, respectively. Multigraph clustering is applied to these networks to identify SNP clusters with similar association patterns with all the brain QTs. After that, functional annotation is performed for each identified SNP cluster and its corresponding brain association pattern. We applied this pipeline to an AD imaging genetic study, which yielded promising results. For example, in an association study between 54 AD SNPs and 116 amyloid QTs, we identified two SNP clusters with one responsible for amyloid beta clearances and the other regulating amyloid beta formation. These high-level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts.
脑影像遗传学研究旨在探讨影像定量特征（QTs）与遗传因素（如单核苷酸多态性，SNP）之间的关联，从而为阿尔茨海默病（AD）的发病机制提供重要的见解。个体水平的 SNP-QT 信号具有高维性，且通常具有较小的效应大小，这使得它们难以被检测和复制。为了克服这一局限性，本研究提出了一种新方法，通过将多图聚类应用于 SNP-QT 关联图，来识别高级别的影像遗传学关联。给定一组 SNP 和一组大脑 QT，使用线性回归模型评估每个 SNP 与每个 QT 之间的关联。基于所得的 SNP-QT 关联图，使用五个不同的评分函数分别创建五个 SNP-SNP 相似性网络（或图）。对这些网络应用多图聚类，以识别与所有大脑 QT 具有相似关联模式的 SNP 簇。之后，对每个鉴定出的 SNP 簇及其对应的大脑关联模式进行功能注释。我们将此方法应用于 AD 影像遗传学研究，得到了有前景的结果。例如，在 54 个 AD SNP 与 116 个淀粉样蛋白 QT 之间的关联研究中，我们鉴定出两个 SNP 簇，其中一个与淀粉样蛋白β清除有关，另一个则与淀粉样蛋白β形成有关。这些高级别的发现有可能为相关的遗传途径和大脑回路提供有价值的见解，这有助于在独立队列中为更详细的影像遗传学研究形成新的假设。
