title: Network-guided interaction mining for the blood pressure phenotype of unrelated individuals in genetic analysis workshop 19.
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 U S A 105: 12387-12392, 2008), which creates a score to identify pairwise significant gene associations. We find that the genesGCKandPAX4, 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.
authors.affiliation: Department of Politics, Princeton University, 130 Corwin Hall, Princeton, NJ 08544-1012 USA.
authors.forename: Adeline
authors.initials: A
authors.lastname: Lo
authors.affiliation: Department of Statistics, Columbia University, New York, 10027 USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Agne
authors.affiliation: Department of Statistics, Columbia University, New York, 10027 USA.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Auerbach
authors.affiliation: Department of Statistics, Columbia University, New York, 10027 USA.
authors.forename: Rachel
authors.initials: R
authors.lastname: Fan
authors.affiliation: Department of Statistics, Columbia University, New York, 10027 USA.
authors.forename: Shaw-Hwa
authors.initials: SH
authors.lastname: Lo
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029 USA.
authors.forename: Pei
authors.initials: P
authors.lastname: Wang
authors.affiliation: Department of Statistics, Columbia University, New York, 10027 USA.
authors.forename: Tian
authors.initials: T
authors.lastname: Zheng