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Bioinformatics. 2016 Dec 15;32(24):3815-3822. Epub 2016 Aug 19.

STAMS: STRING-assisted module search for genome wide association studies and application to autism.

Author information

Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA.
Vanderbilt Genetics Institute.
Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA.
Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
Institute for Juvenile Research, Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA.
Departments of Bioengineering and Genetics, Stanford University, Stanford, CA, USA.



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 CONTACT: rbaltman@stanford.eduSupplementary information: Supplementary data are available at Bioinformatics online.

[Indexed for MEDLINE]
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