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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

1
Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA.
2
Vanderbilt Genetics Institute.
3
Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA.
4
Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
5
Institute for Juvenile Research, Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA.
6
Departments of Bioengineering and Genetics, Stanford University, Stanford, CA, USA.

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.eduSupplementary information: Supplementary data are available at Bioinformatics online.

PMID:
27542772
PMCID:
PMC5167061
DOI:
10.1093/bioinformatics/btw530
[Indexed for MEDLINE]
Free PMC Article

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