Abstract
Understanding how gene expression translates to and affects human behavior is one of the ultimate goals of neuroscience. In this paper, we present a pipeline based on Mapper, a topological simplification tool, to analyze gene co-expression data. We first validate the method by reproducing key results from the literature on the Allen Human Brain Atlas and the correlations between resting-state fMRI and gene co-expression maps. We then analyze a dopamine-related gene set and find that co-expression networks produced by Mapper return a structure that matches the well-known anatomy of the dopaminergic pathway. Our results suggest that network based descriptions can be a powerful tool to explore the relationships between genetic pathways and their association with brain function and its perturbation due to illness and/or pharmacological challenges.
Highlights
The human brain is a highly complex organ whose function emerges from the integration of cellular, anatomical, and functional circuits (Bassett & Gazzaniga, 2011)
We present three different applications of Mapper to the microarray Allen Human Brain Atlas (AHBA) data set: (i) the replication of the gene co-expression analysis originally presented by the Allen Institute for Brain Science (Hawrylycz et al, 2012); (ii) co-expression analysis of the gene list identified by Richiardi et al (2015) that links gene co-expression and brain resting-state function; and (iii) topological co-expression analysis of the genes in the dopamine pathway
The gene expression data for all three gene lists we considered come from the microarray data of the Allen Human Brain Atlas
Summary
The human brain is a highly complex organ whose function emerges from the integration of cellular, anatomical, and functional circuits (Bassett & Gazzaniga, 2011). Genetic studies have investigated the association of genetic variants with a variety of brain disorders in large population studies (Ripke et al, 2014; Wray et al, 2018). Imaging-genetic studies provided additional insights by exploring the effect of genetic variants and expression of gene sets on normal and pathological brains. They are, not without limitations (Bogdan et al, 2017); notably, they focus on the association between gene expression networks and brain phenotype in a limited number, or even single, brain regions, for example, the prefrontal cortex
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