Gene-set Enrichment with Mathematical Biology (GEMB). Issue 10 (9th October 2020)

• Record Type: Journal Article
• Authors: Cochran, Amy L; Nieser, Kenneth J; Forger, Daniel B; Zöllner, Sebastian; McInnis, Melvin G
• Is Part Of: GigaScience. Volume 9:Issue 10(2020)
• Publication Date: 2020-10-09
• Subjects: mathematical biology -- gene ontology -- genetic enrichment -- gene-set analysis -- bipolar disorder -- calcium signaling; Information storage and retrieval systems -- Research -- Periodicals; Biology -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; Database management -- Periodicals; 570.285
• DOI: 10.1093/gigascience/giaa091
• Languages: English
• ISSNs: 2047-217X

Abstract

Abstract: Background: 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 differential contributions of each gene. We extend this concept further—defining gene contributions based on biophysical properties—by leveraging mathematical models of biology to predict the effects of genetic perturbations on a particular downstream function. Results: 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. We demonstrate in simulation how such a method can improve statistical power. To this effect, we identify a gene set, weighted by model-predicted contributions to intracellular calcium ion concentration, that is significantly related to bipolar disorder in a small dataset ( P = 0.04; n = 544). We reproduce this finding using publicly available summary data from the Psychiatric Genomics Consortium ( P = 1.7 × 10 −4 ; n = 41, 653). By contrast, an approach using a general calcium signaling pathway did not detect a significant association with bipolar disorder ( P = 0.08). The weighted gene-set approach based on intracellular calcium ion concentration did not detect a significant relationship with schizophrenia ( P = 0.09; n = 65, 967) or major depression disorder ( P = 0.30; n = 500, 199). Conclusions: Together, these findings show how incorporating math biology into gene-set analyses might help to identify biological functions that underlie certain polygenic disorders.