Abstract

Motivation: 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. Results: 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. Availability and Implementation: STAMS is implemented as an R package and is freely available at https://simtk.org/projects/stams. Contact: rbaltman@stanford.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Keywords

Genome-wide association studyComputer scienceComputational biologyContext (archaeology)ENCODEProbabilistic logicGenetic associationString (physics)GenePhenotypeData miningGeneticsBiologyArtificial intelligenceSingle-nucleotide polymorphismMathematicsGenotype

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Publication Info

Year
2016
Type
article
Volume
32
Issue
24
Pages
3815-3822
Citations
19
Access
Closed

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Sara Hillenmeyer, Lea K. Davis, Eric R. Gamazon et al. (2016). STAMS: STRING-assisted module search for genome wide association studies and application to autism. Bioinformatics , 32 (24) , 3815-3822. https://doi.org/10.1093/bioinformatics/btw530

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DOI
10.1093/bioinformatics/btw530