Gene-set Enrichment with Mathematical Biology (GEMB).

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 contrib

SUBMITTER: Cochran AL 

PROVIDER: S-EPMC7546080 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature