title: STAMS: STRING-assisted module search for genome wide association studies and application to autism.
abstract: Analyzing genome wide association data in the context of biological pathways helps us understand how genetic variation influences phenotype and increases power to find associations. However, the utility of pathway-based analysis tools is hampered by undercuration and reliance on a distribution of signal across all of the genes in a pathway. Methods that combine genome wide association results with genetic networks to infer the key phenotype-modulating subnetworks combat these issues, but have primarily been limited to network definitions with yes/no labels for gene-gene interactions. A recent method (EW_dmGWAS) incorporates a biological network with weighted edge probability by requiring a secondary phenotype-specific expression dataset. In this article, we combine an algorithm for weighted-edge module searching and a probabilistic interaction network in order to develop a method, STAMS, for recovering modules of genes with strong associations to the phenotype and probable biologic coherence. Our method builds on EW_dmGWAS but does not require a secondary expression dataset and performs better in six test cases.
  
  
  
  We show that our algorithm improves over EW_dmGWAS and standard gene-based analysis by measuring precision and recall of each method on separately identified associations. In the Wellcome Trust Rheumatoid Arthritis study, STAMS-identified modules were more enriched for separately identified associations than EW_dmGWAS (STAMS P-value 3.0 × 10-4; EW_dmGWAS- P-value = 0.8). We demonstrate that the area under the Precision-Recall curve is 5.9 times higher with STAMS than EW_dmGWAS run on the Wellcome Trust Type 1 Diabetes data.
  
  
  
  STAMS is implemented as an R package and is freely available at https://simtk.org/projects/stams CONTACT: rbaltman@stanford.eduSupplementary information: Supplementary data are available at Bioinformatics online.
authors.affiliation: Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA.
authors.forename: Sara
authors.initials: S
authors.lastname: Hillenmeyer
authors.affiliation: Vanderbilt Genetics Institute.|Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA.
authors.forename: Lea K
authors.initials: LK
authors.lastname: Davis
authors.affiliation: Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA.|Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
authors.forename: Eric R
authors.initials: ER
authors.lastname: Gamazon
authors.affiliation: Institute for Juvenile Research, Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA.
authors.forename: Edwin H
authors.initials: EH
authors.lastname: Cook
authors.affiliation: Vanderbilt Genetics Institute.|Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA.
authors.forename: Nancy J
authors.initials: NJ
authors.lastname: Cox
authors.affiliation: Departments of Bioengineering and Genetics, Stanford University, Stanford, CA, USA.
authors.forename: Russ B
authors.initials: RB
authors.lastname: Altman
mesh_terms: D000465:Algorithms; D001321:Autistic Disorder / Q000235:genetics*; D019295:Computational Biology / Q000379:methods; D053263:Gene Regulatory Networks; D055106:Genome-Wide Association Study; D006801:Humans; D010641:Phenotype