A statistical framework for revealing signaling pathways perturbed by DNA variants

Nucleic Acids Research
Roni Wilentzik, Irit Gat-Viks

Abstract

Much of the inter-individual variation in gene expression is triggered via perturbations of signaling networks by DNA variants. We present a novel probabilistic approach for identifying the particular pathways by which DNA variants perturb the signaling network. Our procedure, called PINE, relies on a systematic integration of established biological knowledge of signaling networks with data on transcriptional responses to various experimental conditions. Unlike previous approaches, PINE provides statistical aspects that are critical for prioritizing hypotheses for followup experiments. Using simulated data, we show that higher accuracy is attained with PINE than with existing methods. We used PINE to analyze transcriptional responses of immune dendritic cells to several pathogenic stimulations. PINE identified statistically significant genetic perturbations in the pathogen-sensing signaling network, suggesting previously uncharacterized regulatory mechanisms for functional DNA variants.

References

May 1, 1996·American Journal of Public Health·M Aickin, H Gensler
Dec 11, 1999·Nucleic Acids Research·M Kanehisa, S Goto
Mar 27, 2004·Neuroinformatics·Jintao WangKenneth F Manly
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Dec 9, 2004·International Immunology·Kiyoshi Takeda, Shizuo Akira
Apr 7, 2006·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Irit Gat-ViksRon Shamir
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Nov 12, 2010·Nucleic Acids Research·David CroftLincoln Stein

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Citations

Apr 27, 2016·Current Opinion in Biotechnology·Deborah ChasmanSushmita Roy
Mar 11, 2016·PLoS Computational Biology·Yoo-Ah KimTeresa M Przytycka
Mar 5, 2020·Frontiers in Genetics·Wujuan ZhongXiaojing Zheng

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Methods Mentioned

BETA
transcription profiling
genotyping

Software Mentioned

KEGG
PINE In NEtworks
PINE
MetaReg
NanoString nCounter system
InCircuit
Reactome
Ingenuity Knowledge Base

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