PT  - JOURNAL ARTICLE
AU  - Amy L Cochran
AU  - Daniel B Forger
AU  - Sebastian Zöllner
AU  - Melvin G McInnis
TI  - Gene-Set Enrichment with Mathematical Biology (GEMB)
AID  - 10.1101/554212
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 554212
4099  - http://biorxiv.org/content/early/2019/02/18/554212.short
4100  - http://biorxiv.org/content/early/2019/02/18/554212.full
AB  - 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.