November 10, 2020

Gene-set Enrichment with Mathematical Biology (GEMB)

A method that incorporates predictions from mathematical models to relate genes to disorders

Dr. Amy Cochran provides a summary of this research, published in October 2020 in GigaScience

Genes offer clues into which biological functions, when they are disrupted, contribute to bipolar disorder. The hope is that this information could be used to develop treatments to target these functions. In psychiatry, hundreds to thousands of genes can contribute to the risk of developing many psychiatric disorders, including bipolar disorder. With so many relevant genes, it is difficult to pinpoint which biological functions contribute to the disorder.   

Prechter Program Cochran
Amy Cochran, Ph.D.

To overcome this challenge, a collection of genes can be analyzed rather than the typical approach of one gene at a time. The key is not to pick just any collection, but rather a collection of genes that contributes to a specific biological pathway. That way, if the collection is found to contribute to the disorder, then the associated biological pathway is implicated.  

These collections, however, are often still too broad. Genes from a one pathway can contribute differently to a biological function, whereas genes from other pathways may contribute to the function. A function such as neuronal firing rate, for example, involves pathways related to action potentials, calcium signaling, monoamine transmission, among others. Broad definitions may weaken inferences if ultimately a specific function contributes to the disease. Mathematical models of the underlying biology could provide even further detail about a gene’s role in a specific biological function. 

A recent study, led by Amy Cochran, Ph.D., Assistant Professor of Mathematics and of Population Health Sciences at the University of Wisconsin, introduces a simple method, called Gene-Set Enrichment with Mathematical Biology or GEMB. The method incorporates predictions from mathematical models to relate genes to disorders. They used GEMB to test whether average intracellular calcium ion concentrations contribute to bipolar disorder. From genetic data on over 40,000 individuals, they found that intracellular calcium ion contributions does indeed contribute significantly to bipolar disorder. In other words, how we move calcium ions in and out of our cells may determine whether or not we develop bipolar disorder.  

This finding is significant in several regards. First, without a GEMB, a traditional approach did not find that calcium signaling contributes to bipolar disorder, hiding the potential importance of intracellular calcium ion concentrations.  

Second, it replicates what we had first found in a sample of 544 individuals in the Heinz C. Prechter Bipolar Research Program. Replication is one of the great challenges of genetic studies.  

Third, this finding is specific to bipolar disorder. That is, even though bipolar disorder shares risk genes with schizophrenia and major depressive disorder, GEMB did not find that intracellular Ca2+ concentration contributes to schizophrenia or major depressive disorder. Intracellular calcium ion concentrations may confer a unique risk to bipolar disorder.  

Such promising results should motivate interest in GEMB from several communities. In psychiatry, for one, the genetic basis of many disorders remains elusive despite high heritability. In computational biology, for another, researchers want to empirically test the relevance of a model to a particular disorder. The method is designed to be simple and can be used with publicly-available repositories of genetic data and models. There should thus be few barriers to its use, hopefully leading to many future discoveries.