A Network-Based Framework for Mining High-Level Imaging Genetic Associations

Hong Liang, Xianglian Meng, Feng Chen, Qiushi Zhang, Jingwen Yan, Xiaohui Yao, Sungeun Kim, Lei Wang, Weixing Feng, Andrew J. Saykin, Jin Li, Li Shen

Research output: Chapter in Book/Report/Conference proceedingChapter


Genome-wide association studies (GWASs) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer's disease (AD). However, single marker level association may not be able to address the underlying biological interactions associated with disease mechanism. In this paper, we propose a network-based framework, guided by protein-protein interaction data, for mining high-level imaging genetic associations. Multilevel GWASs are conducted to investigate the genetic main effect on subcortical imaging measures. Network interface miner for multigenic interactions is employed to discover the trait prioritized subnetworks that are significantly associated with a trait. For several identified significant QT-subnetwork associations, we map the QTs to the imaging domain and perform functional annotation and network analysis for the genes in the subnetwork. The gene-level GWAS yielded significant hits within the APOE and APOC1 regions, which were previously implicated in AD. Pathway analysis was performed to make functional annotation for a few identified subnetworks and discovered several pathways related to degenerative diseases. The imaging results revealed that significant effects emerged on subcortical regions especially on hippocampus.

Original languageEnglish (US)
Title of host publicationImaging Genetics
PublisherElsevier Inc.
Number of pages16
ISBN (Electronic)9780128139691
ISBN (Print)9780128139684
StatePublished - Jan 1 2018


  • Imaging genetics
  • Network-based analysis
  • Pathway analysis
  • Subcortical quantitative trait

ASJC Scopus subject areas

  • Medicine (miscellaneous)

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    Liang, H., Meng, X., Chen, F., Zhang, Q., Yan, J., Yao, X., Kim, S., Wang, L., Feng, W., Saykin, A. J., Li, J., & Shen, L. (2018). A Network-Based Framework for Mining High-Level Imaging Genetic Associations. In Imaging Genetics (pp. 119-134). Elsevier Inc.. https://doi.org/10.1016/B978-0-12-813968-4.00007-9