Whole Brain and Brain Regional Coexpression Network Interactions Associated with Predisposition to Alcohol Consumption

Lauren A Vanderlinden,Laura M Saba,Michael F Miles,Boris Tabakoff,Paula L Hoffman,Katerina Kechris,Hoon Ryu

doi:10.1371/journal.pone.0068878

Abstract

To identify brain transcriptional networks that may predispose an animal to consume alcohol, we used weighted gene coexpression network analysis (WGCNA). Candidate coexpression modules are those with an eigengene expression level that correlates significantly with the level of alcohol consumption across a panel of BXD recombinant inbred mouse strains, and that share a genomic region that regulates the module transcript expression levels (mQTL) with a genomic region that regulates alcohol consumption (bQTL). To address a controversy regarding utility of gene expression profiles from whole brain, vs specific brain regions, as indicators of the relationship of gene expression to phenotype, we compared candidate coexpression modules from whole brain gene expression data (gathered with Affymetrix 430 v2 arrays in the Colorado laboratories) and from gene expression data from 6 brain regions (nucleus accumbens (NA); prefrontal cortex (PFC); ventral tegmental area (VTA); striatum (ST); hippocampus (HP); cerebellum (CB)) available from GeneNetwork. The candidate modules were used to construct candidate eigengene networks across brain regions, resulting in three “meta-modules”, composed of candidate modules from two or more brain regions (NA, PFC, ST, VTA) and whole brain. To mitigate the potential influence of chromosomal location of transcripts and cis-eQTLs in linkage disequilibrium, we calculated a semi-partial correlation of the transcripts in the meta-modules with alcohol consumption conditional on the transcripts' cis-eQTLs. The function of transcripts that retained the correlation with the phenotype after correction for the strong genetic influence, implicates processes of protein metabolism in the ER and Golgi as influencing susceptibility to variation in alcohol consumption. Integration of these data with human GWAS provides further information on the function of polymorphisms associated with alcohol-related traits.

Highlights

The concept of networks is critical to understanding biology at a systems level [1,2,3]
To identify modules associated with a predisposition to alcohol consumption, we calculated a Pearson correlation coefficient and its associated p-value between each eigengene and each alcohol consumption dataset from the 2 independent studies of 2BC alcohol consumption [18,19]
To be as inclusive as possible, we considered behavioral QTL (bQTL) for alcohol consumption reported by Belknap and Atkins [17], which were based on a meta-analysis of alcohol preference studies of mapping populations derived from C57BL/6 and DBA/2 strains

Summary

Introduction

The concept of networks is critical to understanding biology at a systems level [1,2,3]. The same approach can be applied to transcriptional networks comprising gene coexpression modules Such analysis allows for the description of genetically-regulated pathways that are associated with a complex phenotype, and take gene-gene interactions into account [13,14]. This approach has the potential to identify common signaling pathways that are associated with a trait in different populations, even if different individual genes/transcripts are associated with the trait in each population

Methods

Results

Discussion

Conclusion