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Gene-Set Enrichment with Mathematical Biology (GEMB)

View ORCID ProfileAmy L Cochran, View ORCID ProfileDaniel B Forger, View ORCID ProfileSebastian Zöllner, View ORCID ProfileMelvin G McInnis
doi: https://doi.org/10.1101/554212
Amy L Cochran
1Department of Biostatistics and Medical Informatics, University of Wisconsin – Madison
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Daniel B Forger
2Department of Mathematics, University of Michigan
3Department of Computational Medicine and Bioinformatics, University of Michigan
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Sebastian Zöllner
4Department of Biostatistics, University of Michigan
5Department of Psychiatry, University of Michigan
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Melvin G McInnis
5Department of Psychiatry, University of Michigan
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Abstract

Background Multiple genes can contribute to disease risk. Common pathways can be identified among risk genes with gene-set analysis, which measures association between a set of pathway-related genes and the disorder of interest. Pathway-related genes, however, are often broadly defined having differential contributions to diverse pathway functions. Mathematical models of biology can predict the relative contribution of a gene to a specific function of a pathway. Our goal was to use model predictions to strengthen gene to disorder connections by identifying a specific function common among risk genes.

Results We present a method (GEMB) to enrich gene-set analyses with models from mathematical biology. The method combines gene weights from model predictions and gene ranks from genome-wide association studies into a weighted gene-set test. We use the test to examine the hypothesis that intracellular calcium ion concentrations contribute to bipolar disorder, using publicly-available summary data from the Psychiatric Genetics Consortium (n=41,653; ~9 million SNPs).

Conclusions With our method, we can strengthen inferences from a P-value of 0.081 to 1.7×10−4 by moving from a general calcium signaling pathway to a specific model-predicted function. This dramatic difference demonstrates that incorporating math biology into gene-set analysis can strengthen gene to disease connections.

Footnotes

  • ↵* cochran4{at}wisc.edu

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted February 18, 2019.
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Gene-Set Enrichment with Mathematical Biology (GEMB)
Amy L Cochran, Daniel B Forger, Sebastian Zöllner, Melvin G McInnis
bioRxiv 554212; doi: https://doi.org/10.1101/554212
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Gene-Set Enrichment with Mathematical Biology (GEMB)
Amy L Cochran, Daniel B Forger, Sebastian Zöllner, Melvin G McInnis
bioRxiv 554212; doi: https://doi.org/10.1101/554212

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