Network-guided interaction mining for the blood pressure phenotype of unrelated individuals in genetic analysis workshop 19

Adeline Lo, Michael Agne, Jonathan Auerbach, Rachel Fan, Shaw Hwa Lo, Pei Wang, Tian Zheng

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Interactions between genes are an important part of the genetic architecture of complex diseases. In this paper, we use literature-guided individual genes known to be associated with type 2 diabetes (referred to as "seed genes") to create a larger list of genes that share implied or direct networks with these seed genes. This larger list of genes are known to interact with each other, but whether they interact in ways to influence hypertension in individuals presents an interesting question. Using Genetic Analysis Workshop data on individuals with diabetes, for which only case-control labels of hypertension are known, we offer a foray into identification of diabetes-related gene interactions that are associated with hypertension. We use the approach of Lo et al. (Proc Natl Acad Sci USA 105: 12387-12392, 2008), which creates a score to identify pairwise significant gene associations. We find that the genes GCK and PAX4, formerly known to be found within similar coexpression and pathway networks but without specific direct interactions, do, in fact, show significant joint interaction effects for hypertension.

Original languageEnglish
Article number13
JournalBMC Proceedings
Volume10
DOIs
StatePublished - 2016

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