GCnet: Using Granger causality to explore the dynamic causality relations among genes as-sociated with intellectual disability in human brain

TL;DR

This paper applies Granger causality analysis to gene expression time series data during brain development to identify causal gene relationships related to intellectual disability, aiding in understanding its molecular basis.

Contribution

It introduces a novel approach using Granger causality to analyze gene expression dynamics and identify new genes associated with intellectual disability.

Findings

Identified new candidate genes related to ID.

Constructed gene networks revealing causal relationships.

Prioritized genes linked to Mowat Wilson Syndrome.

Abstract

Intellectual disability (ID) is defined by an IQ under 70, in addition to deficits in two or more adaptive behaviors that affect everyday living. Throughout history, individuals with ID have often been margin-alized from society and continue to suffer significantly even in modern times. A varying proportion of ID cases are attributable to genetic causes. Identifying the causal relation among these ID-associated genes and their gene expression pattern during brain development process would gain us a better understanding of the molecular basis of ID. In this paper, we interpret gene expression data collected at different time points during the in vitro brain development process as time series and further introduce Granger causality test to evaluate the dynamic dependence relations among genes. These evaluations are used as input to construct gene expression network and extract the pathological information associated to ID including identi-fying new genes that can be critically related to the disease. To demonstrate our methods, we pro-vide a priority list of new genes that are most likely associated with Mowat Wilson Syndrome via monitoring the community structure of ZEB2 in our Granger causality network constructed based on the Kutsche dataset (Kutsche, et al., 2018).