Toward understanding the genetics of alcohol drinking through transcriptome meta-analysis

Abstract

Much evidence from studies in humans and animals supports the hypothesis that alcohol addiction is a complex disease with both hereditary and environmental influences. Molecular determinants of excessive alcohol consumption are difficult to study in humans. However, several rodent models show a high or low degree of alcohol preference, which provides a unique opportunity to approach the molecular complexities underlying the genetic predisposition to drink alcohol. Microarray analyses of brain gene expression in three selected lines, and six isogenic strains of mice known to differ markedly in voluntary alcohol consumption provided >4.5 million data points for a meta-analysis. A total of 107 arrays were obtained and arranged into six experimental data sets, allowing the identification of 3,800 unique genes significantly and consistently changed between all models of high or low amounts of alcohol consumption. Several functional groups, including mitogen-activated protein kinase signaling and transcription regulation pathways, were found to be significantly overrepresented and may play an important role in establishing a high level of voluntary alcohol drinking in these mouse models. Data from the general meta-analysis was further filtered by a congenic strain microarray set, from which cis-regulated candidate genes for an alcohol preference quantitative trait locus on chromosome 9 were identified: Arhgef12, Carm1, Cryab, Cox5a, Dlat, Fxyd6, Limd1, Nicn1, Nmnat3, Pknox2, Rbp1, Sc5d, Scn4b, Tcf12, Vps11, and Zfp291 and four ESTs. The present study demonstrates the use of (i) a microarray meta-analysis to analyze a behavioral phenotype (in this case, alcohol preference) and (ii) a congenic strain for identification of cis regulation.

Fig. 1.

Compatibility of data sets for meta-analysis. (A) Number of transcripts regulated in the same direction between any two (left y axis) or all four (right y axis) data sets (P < 0.0001, χ²). n.s., not significant. (B) Frequency distribution of z test P values (x axis) is shown. The solid line represents theoretical chance distribution. IS, Inbred strain.

Fig. 2.

Visual representation of microarray data. Columns represent microarray databases used in the meta-analysis, and rows represent transcripts. The filtered criterion was Q < 0.01, and genes were sorted by effect size. (A) Transcripts (2,697) are listed from positive to negative Cohen's d values (effect size). Red indicates a positive effect size and higher expression in preferring mice, and green indicates a negative effect size and lower expression in preferring mice. Black indicates an effect size near zero. (B) The 75 unique transcripts with the highest absolute average effect size (Q < 0.05, ∣d∣ > 1.94). STS, short-term selection; IS, Inbred strains.

Fig. 3.

Complexity of functional group interaction. Genes present in at least three overrepresented functional groups/pathways from BioCarta, KEGG, and Gene Ontology are shown. Larger font sizes represent either smaller P values for functional groups/pathways (from overrepresentation analysis) or larger effect size for individual genes (P < 0.001, Q < 0.01, and ∣d∣ > 0.5 for all genes). Lines connect gene symbols with relevant functional groups. Arrows indicate pathway connections. For full gene names, see Table 2.

Fig. 4.

Congenic 9 expression analysis. Meta-analysis results were filtered by significant cis-regulation on chromosome 9. Gene names and their physical location are indicated on the left, and effect size and direction are shown on the right. Asterisks indicate a correlation between gene expression and the preference for drinking phenotype in a panel of BXD recombinant inbred strains (5) generated with the WebQTL (www.genenetwork.org) Integrative Neuroscience Initiative on Alcoholism Brain mRNA M430 (April 2005 release) PDNN (Positional Dependent Nearest Neighbor) database [referred to as INIA Brain mRNA M430 (Apr05) PDNN by WebQTL] (7).