June 2013
Volume 54, Issue 15
ARVO Annual Meeting Abstract  |   June 2013
Genetic interaction mapping of AMD identifies potential gene-disease associations
Author Affiliations & Notes
  • Lee Kiang
    Kellogg Eye Center, University of Michigan, Ann Arbor, MI
  • Jillian Huang
    Kellogg Eye Center, University of Michigan, Ann Arbor, MI
  • Ryan Tsuchida
    Kellogg Eye Center, University of Michigan, Ann Arbor, MI
  • Kanishka Jayasundera
    Kellogg Eye Center, University of Michigan, Ann Arbor, MI
  • Footnotes
    Commercial Relationships Lee Kiang, None; Jillian Huang, None; Ryan Tsuchida, None; Kanishka Jayasundera, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 6172. doi:
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      Lee Kiang, Jillian Huang, Ryan Tsuchida, Kanishka Jayasundera; Genetic interaction mapping of AMD identifies potential gene-disease associations. Invest. Ophthalmol. Vis. Sci. 2013;54(15):6172.

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      © ARVO (1962-2015); The Authors (2016-present)

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Purpose: Systems biology networks provide a way to rapidly visualize, analyze and model gene functions, interactions and expression. Using a network-driven approach for Age-Related Macular Degeneration (AMD), we sought to identify key regulatory genes of the disease network, potential biomarkers and therapeutic targets, and to assess the genetic interactions underlying differential response to therapy and environmental modifers such as smoking.

Methods: We used visANT, a web-based program, to extract molecular networks based on physical and functional relationships to individual genes. The initial network was created by inputting 16 ‘seed’ genes or nodes confirmed to be associated with AMD (identified in >2 GWAS studies) and querying for internal connections among nodes. We defined ‘hub’ genes as those with highest degree of interaction with other network genes and ‘inferred’ genes as nodes through which seed genes are connected. To determine key biological processes of the network, we clustered genes based on a hierarchical algorithm that evaluates degree centrality and betweenness of nodes based on the shortest path between node pairs. We queried each cluster for the most significant GO terms, and examined biological pathways linked to the network through KEGG pathways. Because smoking is a well-established risk factor for AMD, we mapped a set of 5 genes that are overexpressed in smokers compared to controls, onto our gene network, in order to infer intermediate genes which might link smoking to AMD.

Results: Ten inferred genes link seed genes well-established to play a role in AMD by one degree of separation (ALB, LRP1, UBC, C4B, HNF4A, EP300, GRB2, THBS1, ONECUT1, EFEMP2). Five inferred genes (ABL1, SMAD3, KPNA1, CTNNB1 and KDR) link smoking to AMD. Several inferred genes have been implicated in AMD pathogenesis, such as ABL1 (required for apoptosis of lipofuscin-laden RPE in an experimental model). Cluster analysis revealed our gene sets were involved in immune response, cholesterol metabolism, angiogenesis, and apolipoprotein binding.

Conclusions: Via genetic interaction mapping, we inferred genes known to play a role in AMD, as well as genes not previously associated with the disease. Network mapping is valuable in revealing key players in AMD, with the potential of identifying integral hub genes, which may include novel biomarkers for disease diagnosis, surveillance and progression, and new therapeutic targets.

Keywords: 412 age-related macular degeneration • 539 genetics  

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