Profiling gene expression in the brain for insight into neurological disease

Abstract

Autism Spectrum Disorder (ASD) has a strong, yet heterogeneous, genetic component. Among the various methods that are being developed to help reveal the underlying molecular aetiology of the disease, one approach that is gaining popularity is the combination of gene expression and clinical genetic data, often using the SFARI Gene database, which comprises lists of curated genes considered to have causative roles in ASD when mutated in patients. We built a gene co-expression network to study the relationship between ASD-specific transcriptomic data and SFARI genes and analysed it at different levels of granularity, first as individual genes, then as clusters of genes, and finally as the complete co-expression network.

No significant evidence was found of association between SFARI genes and differential gene expression patterns when comparing ASD samples to a control group, nor statistical enrichment of SFARI genes in gene co-expression network clusters that have a strong correlation with ASD diagnosis. However, classification models that incorporate topological information from the whole ASD-specific gene co-expression network can predict novel SFARI candidate genes that share features of existing SFARI genes and have support for roles in ASD in the literature. We demonstrate that only co-expression network analyses that integrate information from the whole network are able to reveal signatures linked to ASD diagnosis. These analyses successfully identify novel candidate genes associated with ASD where individual gene or cluster analyses fail.

We also find a statistically significant association between the level of expression of SFARI genes and their SFARI Gene Score which confounds downstream analysis of ASD gene expression data. We present a novel approach to correct for this that is generalisable to other situations where analysis is affected by continuous sources of bias.