Gene-Set Enrichment with Mathematical Biology

Abstract

Gene-set analyses measure the association between a disease of interest and a set of genes related to a biological pathway. These analyses often incorporate gene network properties to account for the differential contributions of each gene. Extending this concept further, mathematical models of biology can be leveraged to define gene interactions based on biophysical principles by predicting the effects of genetic perturbations on a particular downstream function. We present a method that combines gene weights from model predictions and gene ranks from genome-wide association studies into a weighted gene-set test. Using publicly-available summary data from the Psychiatric Genetics Consortium (n=41,653; ~9) million SNPs), we examine an a priori hypothesis that intracellular calcium ion concentrations contribute to bipolar disorder. In this case study, we are able to strengthen inferences from a P-value of 0.081 to 1.7x10-4 by moving from a general calcium signaling pathway to a specific model-predicted function.