Gene-set Enrichment with Mathematical Biology (GEMB)

Area of Science:

• Genetics
• Computational Biology
• Psychiatric Genomics

Background:

• Gene-set analyses typically assess disease associations using gene networks.
• Existing methods often overlook the biophysical properties of genes.
• This study proposes incorporating mathematical models to define gene contributions.

Purpose of the Study:

• To develop a weighted gene-set test combining model-predicted gene weights and genome-wide association study ranks.
• To enhance statistical power in identifying gene associations with diseases.
• To investigate the role of intracellular calcium ion concentration in bipolar disorder.

Main Methods:

• Developed a novel weighted gene-set test integrating gene weights from mathematical biology models and gene ranks from genome-wide association studies.
• Applied the method to identify gene sets associated with intracellular calcium ion concentration and bipolar disorder.
• Validated findings using independent datasets, including data from the Psychiatric Genomics Consortium.

Main Results:

• The weighted gene-set approach identified a significant association between intracellular calcium ion concentration and bipolar disorder (P = 1.7 × 10-4; n = 41,653).
• A standard calcium signaling pathway analysis did not yield significant results for bipolar disorder (P = 0.08).
• The method did not find significant associations for schizophrenia or major depressive disorder.

Conclusions:

• Incorporating mathematical biology into gene-set analyses can improve the identification of biological functions underlying polygenic disorders.
• Specific gene sets, like those involved in intracellular calcium ion concentration, may be crucial for understanding bipolar disorder.
• This approach offers a powerful tool for dissecting the genetic architecture of complex diseases.