This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
We have used a genetical genomic approach, in conjunction with phenotypic analysis of alcohol consumption, to identify candidate genes that predispose to varying levels of alcohol intake by HXB/BXH recombinant inbred rat strains. In addition, in two populations of humans, we assessed genetic polymorphisms associated with alcohol consumption using a custom genotyping array for 1,350 single nucleotide polymorphisms (SNPs). Our goal was to ascertain whether our approach, which relies on statistical and informatics techniques, and non-human animal models of alcohol drinking behavior, could inform interpretation of genetic association studies with human populations.
In the HXB/BXH recombinant inbred (RI) rats, correlation analysis of brain gene expression levels with alcohol consumption in a two-bottle choice paradigm, and filtering based on behavioral and gene expression quantitative trait locus (QTL) analyses, generated a list of candidate genes. A literature-based, functional analysis of the interactions of the products of these candidate genes defined pathways linked to presynaptic GABA release, activation of dopamine neurons, and postsynaptic GABA receptor trafficking, in brain regions including the hypothalamus, ventral tegmentum and amygdala. The analysis also implicated energy metabolism and caloric intake control as potential influences on alcohol consumption by the recombinant inbred rats. In the human populations, polymorphisms in genes associated with GABA synthesis and GABA receptors, as well as genes related to dopaminergic transmission, were associated with alcohol consumption.
Our results emphasize the importance of the signaling pathways identified using the non-human animal models, rather than single gene products, in identifying factors responsible for complex traits such as alcohol consumption. The results suggest cross-species similarities in pathways that influence predisposition to consume alcohol by rats and humans. The importance of a well-defined phenotype is also illustrated. Our results also suggest that different genetic factors predispose alcohol dependence versus the phenotype of alcohol consumption.
The term "genetical genomics" is entering the common parlance of researchers, denoting the combined use of genetic marker information and transcriptome analysis. Complex trait phenotyping can be fruitfully combined with genetical genomic analysis to ascertain the candidate genes and gene product interaction pathways which significantly influence the variation in expression of a phenotype of interest. We and others have utilized the genetical genomics and phenomic approaches to identify genes and pathways important in genetically influenced complex traits such as obesity, respiratory function and "addictive" behavior [
The genus/species
We have also taken the opportunity to directly compare the candidate genes and pathways for alcohol consumption that we identified using the rodent model, to candidate genes in human populations. We used the "Addictions Array" [
Male rats from well-characterized HXB/BXH recombinant inbred (RI) rat strains [
Data on alcohol consumption were gathered on 23 HXB/BXH strains and the two progenitor strains. The number of rats per strain ranged from 9 to 12, with 242 total rats being utilized to measure alcohol consumption. In the first week (week 0) of treatment, rats were given 10% ethanol as their only choice of fluid. For the next seven weeks (week 1 - week 7), the rats were given a choice of two bottles, one with water and one with a 10% (v/v) ethanol solution [
Behavioral QTLs (bQTLs) were calculated for the alcohol consumption phenotype using strain means for average daily alcohol consumption in grams per kilogram during week 2 of the two-bottle choice period. Individual values were not included if they were more than two standard deviations from the strain mean. The recently developed STAR Consortium SNP set of genetic markers was used in this QTL analysis [
Naive (non alcohol-exposed) male HXB/BXH RI rats were used for microarray analysis. Rats were group-housed and individual rats were quickly anesthetized with isofluorane/air and decapitated according to a protocol approved by the UCSD IACUC. Brains were rapidly removed, sectioned sagittally into two hemispheres, and frozen on dry ice or in liquid nitrogen. The right half-hemispheres were promptly shipped on dry ice to the University of Colorado, where they were kept at -80°C until used for RNA extraction. CodeLink Rat Whole Genome Bioarrays were obtained from G.E. Healthcare/Amersham Biosciences (Piscataway, NJ).
RNA from the right halves of brains of five to seven rats per strain (26 RI strains and the progenitor strains) was used for these experiments (our prior studies and those of others [
Each CodeLink Rat Whole Genome Bioarray used in these experiments has ~34,000 probes for rat transcripts and expressed sequence tags (ESTs), plus a number of positive and negative control probes. Using the protocol supplied by the manufacturer, double-stranded cDNA was synthesized from the total RNA (4 μg of total RNA from an individual rat brain was used for each array) and was used to obtain biotin-labeled cRNA by an
Raw intensity values were obtained from the CodeLink processing software. Entire arrays were examined for quality control purposes both before and after normalization. Before normalization, arrays were examined by boxplots and coefficient of variation (CV) plots for consistency, along with background levels and proportion of probes within each category for the CodeLink quality flags. After normalization, samples were examined using hierarchical clustering and pair-wise scatter plots to identify samples that severely deviated from other samples within the same strain (such samples were eliminated from further analysis).
In preparation for normalization, probes were removed from the datasets if they were one of the negative or positive controls placed on the array by the manufacturer. Next, individual values were eliminated based on the quality flags assigned by the CodeLink Expression Analysis Software. Values were eliminated if they were flagged as M (spot was identified to be defective through image inspection at manufacturing), C (spot has a high level of background contamination), I (spot has an irregular shape), or S (spot has a high number of saturated pixels). Values were retained if they were flagged G (spot is good) or L (spot is below local background noise). In addition, to be able to take the log base 2 transformation of the background adjusted intensity values, all background adjusted intensity values below zero were replaced with the value 0.00001. The data were then normalized using a cyclic LOESS (locally weighted scatterplot smoothing) procedure executed in R which accounted for the missing intensity values. Normalization and quality control were executed in R using the affy, codelink, and limma packages [
After normalization of the expression data, several filters were applied in order to identify candidate genes for alcohol preference in the HXB/BXH RI strains.
Probes were eliminated from further analysis if they did not represent a valid gene. Each probe is represented by a 30-mer base sequence. These 30-mers were compared to the most recent (November 2004) assembly of the rat genome using the BLAST-like alignment tool (BLAT) available from the UCSC website [
Expression QTL (eQTL) were established using the HXB/BXH RI brain gene expression data. For this analysis, 26 strains (2 progenitor strains and 24 RI strains) were represented among 124 samples (data from two strains for which genotyping was not available were not included). The quality control measures and normalization procedures described above were utilized. The STAR marker set described in the behavioral QTL methods section was also utilized in this analysis. Because more strains were included in this analysis than in the bQTL analysis, 1,184 unique strain distribution patterns were represented in this marker set.
To calculate eQTLs, mean expression levels within strains were used as phenotypic values in a QTL analysis implemented in QTLReaper, which is written in C and compiled as a Python module. A weighted marker regression analysis was used within this program to calculate likelihood ratio statistic (LRS) scores for each marker. LRS scores were transformed to LOD scores for convenience by dividing by 4.61. The regression is weighted to account for the different number of arrays within strains used to calculate strain means. The weight is based on the repeatability of the transcript intensity and number of arrays used to calculate the strain mean [
Probes whose eQTL 95% confidence interval for genomic location overlapped the chromosomal location of any of the four behavioral QTLs were retained for further consideration.
A broad-sense heritability was calculated for each probe in the expression data used in the correlation analysis. Probes were retained if they had a broad sense heritability ≥ 50%.
Detection limits are calculated by the CodeLink software as the median intensity of the local background plus 1.5 local background standard deviations. Probes were eliminated if values were below detection limits for ≥ 50% of the samples.
The correlation of alcohol consumption and gene expression was modeled using a joint mixed model for both the expression and alcohol consumption data,
Once a list of candidate genes was established, the proportion of genetic variance in alcohol consumption among strains that was explained by gene expression was examined. A multivariate genetic model for alcohol consumption was constructed using a linear regression with strain means for both gene expression and alcohol consumption. Genes were selected for inclusion using a forward stepwise selection process with a significance criterion of 0.01 for entrance into the model. A forward selection method was chosen over a backward selection method in this instance because of the large number of potential covariates (candidate genes). The proportion of genetic variance explained was determined for each candidate gene individually and for the combination of transcripts in the final multivariate genetic model which described the strain variation in alcohol consumption.
In order to determine the functions of the candidate gene products, and assemble them into pathways, a literature search was performed using PubMed. The gene symbols (NCBI) and gene product names were used as key words.
In order to replicate differences in gene expression associated with alcohol consumption, gene expression profiles were analyzed using Affymetrix GeneChip Rat Gene 1.0 ST arrays (Affymetrix, Santa Clara, CA, USA), which utilize probesets that span the coding region of each gene. RNA from brains of five male rats from HXB RI strain 23 (low alcohol consumption) and five male rats from HXB RI strain 26 (high alcohol consumption) was used for these studies. These particular strains were chosen because the expression levels of all of the candidate genes (assayed on the CodeLink arrays (Amersham Biosciences)) showed an appropriate direction of differential expression, compared to the correlation analysis, although not all of the differences in the candidate gene expression were statistically significant between these particular strains. RNA (0.3 μg per rat) was processed and hybridized to the Affymetrix arrays (cDNA from one rat to each individual array) according to the manufacturer's protocol. Arrays were labeled and scanned as previously described [
Subjects for the human genetics association study were recruited as part of the WHO/ISBRA Study on State and Trait Markers of Alcohol Use and Dependence [
Genotype data were obtained from a custom array using the Illumina Goldengate SNP technology assay platform. This array includes a panel of markers designed to extract full haplotype information for 130 candidate genes associated with alcoholism, other drug addictions, and mood and anxiety disorders. The design and performance of this array are described in detail by Hodgkinson et al. [
The main phenotypic outcome measure of this analysis was alcohol consumption reported in grams per kilogram of body weight per day. The total amount of alcohol consumed in the last 30 days was calculated based on information that was collected in the WHO/ISBRA questionnaire on beverage-specific frequency and quantity of drinking and this total was divided by 30 to obtain the daily average.
Prior to the univariate analyses, the SNP dataset was subjected to strict quality control standards. Individual SNPs were eliminated if they were not in Hardy-Weinberg equilibrium (FDR<0.01) or if they had minor allele frequency less than 5%. Subjects were eliminated if their genotype was not determined for at least 80% of the SNPs according to standards set forth in Hodgkinson et al. [
Since the subjects came from two distinct geographical locations, it is reasonable to consider that there may be some genetic differences between these populations. Population stratification with respect to the outcome, alcohol consumption, was tested against the AIMs using the concept outlined in Pritchard and Rosenberg [
The association between alcohol consumption and each individual SNP was tested in an ANOVA using a genotype model.
Haplotype blocks were determined in Haploview [
A backwards selection linear regression was used to create a multivariate model that potentially could include effects for all genes that were significant in the univariate analysis. To start the model selection procedure, the best fit univariate model for each gene, either haplotype-based or SNP-based, according to the Bayesian Information Criterion [BIC, 30], was entered into the model. An exit criterion of
A multivariate model was also created using the candidate genes and the covariates shown in Additional file
Each candidate gene was tested for association with each of the covariates that remained in the final multivariate model. Each gene by covariate pair was tested independently. Genes were represented using their best fit univariate model as described above. For the binary covariates, a Fisher's exact test was used for association. For continuous covariates, an ANOVA model was used for association. All
Figure
Four genomic regions were identified as having significant or suggestive associations with alcohol consumption in the HXB/BXH rats (Figure
After quality control
As noted in methods, a number of filters were used to identify a list of potential candidate genes for alcohol preference in the HXB/BXH RI rats. Starting with the quality controlled, normalized dataset, we initially determined probes that could be matched with exonic regions of the rat genome (or with a homologous human or mouse sequence). There are 14,622 unique rat RefSeq DNA IDs specified by the UCSC Genome Browser. Of these, 8,433 genes (10,162 probes) are included on the CodeLink Whole Genome Rat Array. We identified 4,966 additional probes on the CodeLink array that were associated with either a human or mouse homolog gene (RefSeq DNA ID) whose sequence matched an area of the rat genome. Of these 15,128 probes, 12,466 (82%) were present (by our criteria) in the brain samples that we analyzed. Significant or suggestive eQTLs could be identified for 2,164 probes (17%), and, of these, 378 probes had an eQTL that overlapped one of the bQTLs for alcohol consumption in these rats. Transcripts with eQTLs that overlap bQTLs were considered to represent likely candidate genes for the alcohol consumption behavior [
Candidate Genes for Alcohol Consumption by HXB/BXH RI Rats.
|
|
|
|
|
|
|
|
|
|
|
| Cckbr | cholecystokinin B receptor | 1 (163.17) | 1 (168.95) | 139.73-179.97 | 6.3 (<0.001) | 1 (159-194) | 0.61 (0.0023) | 0.82 | 100% |
|
|
|
||||||||
| Coq7 | demethyl-Q 7 (Coenzyme q (ubiquinone) biosynthetic enzyme (DHPB methyltransferase) 7) | 1 (176.76) | 1 (177.64) | 152.25-179.75 | 9.0 (<0.001) | 0.55 (0.0059) | 0.93 | 100% | |
|
|
|
||||||||
| Fgfr2 | fibroblast growth factor receptor 2 isoform c | 1 (189.48) | 1 (184.63) | 173.38-184.63 | 20.7 (<0.001) | 0.58 (0.0029) | 0.99 | 93% | |
|
|
|
||||||||
| Ptpre | protein tyrosine phosphatase receptor type E | 1 (195.16) | 1 (195.13) | 184.63-196.57 | 13.1 (<0.001) | -0.43 (0.0348) | 0.95 | 100% | |
|
|
|
||||||||
| Abat | 4-aminobutyrate aminotransferase | 10 (7.04) | 1 (132.04) | 12.84-249.11 | 4.2 (0.027) | -0.51 (0.0267) | 0.56 | 100% | |
|
|
|
||||||||
| Tpst1 | tyrosylprotein sulfotransferase 1 (predicted) | 12 (27.59) | 1 (15.55) | 9.02-205.67 | 3.1 (0.096) | 0.51 (0.0265) | 0.54 | 99% | |
|
|
|||||||||
| Tmem2 | transmembrane protein 2 | 1 (225.04) | 1 (223.19) | 39.64-254.91 | 3.7 (0.065) | 1 (213-238) | 0.53 (0.0095) | 0.85 | 100% |
|
|
|||||||||
| Mboat2 | membrane bound O-acyltransferase domain | 6 (42.49) | 6 (38.84) | 18.53-131.90 | 3.5 (0.072) | 6 (33-53) | 0.43 (0.0455) | 0.73 | 100% |
|
|
|
||||||||
| Dynlrb1 | dynein light chain roadblock-type 1 | 3 (145.72) | 6 (38.84) | 18.53-115.00 | 3.6 (0.095) | 0.44 (0.0456) | 0.66 | 100% | |
|
|
|
||||||||
| Tnk2 | tyrosine kinase, no-receptor, 2 | 11 (69.93) | 6 (25.68) | 0.27-38.84 | 4.0 (0.010) | -0.50 (0.0212) | 0.68 | 100% | |
|
|
|
||||||||
| Nek3 | NIMA (never in mitosis gene a)-related expressed | 16 (74.52) | 6 (37.98) | 25.56-37.98 | 4.1 (0.008) | -0.43 (0.0499) | 0.69 | 97% | |
|
|
|
||||||||
| Afap1l1 | actin filament associated protein 1-like 1 | 18 (57.71) | 6 (33.80) | 25.68-50.93 | 3.3 (0.090) | 0.49 (0.0227) | 0.74 | 100% | |
|
|
|
||||||||
| Mc4r | melanocortin 4 receptor | 18 (63.39) | 6 (33.80) | 33.80-50.93 | 3.7 (0.006) | 0.46 (0.0417) | 0.59 | 96% | |
|
|
|
||||||||
| Tubb6 | tubulin beta 6 | 18 (63.92) | 6 (33.80) | 33.80-124.56 | 3.2 (0.097) | 0.47 (0.0344) | 0.63 | 98% | |
|
|
|
||||||||
| Spire1 | spire homolog 1 | 18 (64.02) | 6 (33.80) | 18.53-98.70 | 3.4 (0.025) | 0.53 (0.0109) | 0.77 | 100% | |
|
|
|||||||||
| Mogat2 | monoacyglycerol O-acyltransferase 2 | 1 (156.52) | 12 (34.31) | 29.92-38.52 | 3.5 (0.028) | 12 (25-46) | -0.45 (0.0353) | 0.78 | 98% |
|
|
|
||||||||
| F2rl1 | proteinase-activated receptor-2 G protein coupled | 2 (25.89) | 12 (38.52) | 36.18-43.30 | 2.8 (0.059) | 0.53 (0.0224) | 0.52 | 52% | |
|
|
|
||||||||
| P2rx4 | P2X purinoceptor 4 (ATP receptor) | 12 (34.94) | 12 (34.50) | 13.40-34.50 | 5.1 (0.003) | -0.70 (0.0004) | 0.80 | 100% | |
|
|
|
||||||||
| Rab35 | RAB35 member RAS oncogene family | 12 (42.23) | 12 (41.98) | 38.21-43.30 | 9.0 (<0.001) | 0.45 (0.0296) | 0.91 | 100% | |
|
|
|
||||||||
| Pop5 | processing of precursor 5 ribonuclease P/MRP | 12 (42.64) | 12 (41.98) | 36.18-43.30 | 4.4 (0.025) | -0.54 (0.0125) | 0.68 | 100% | |
All transcripts listed displayed an eQTL/bQTL overlap, showed a significant correlation with alcohol consumption, and passed the filters for heritability and detectability of expression.
The chromosomal locations, as well as the location of the bQTLs and eQTLs for the 20 probes, are shown in Table
The transcript that explains the greatest proportion of genetic variance in alcohol consumption in the HXB/BXH RI strains is
The results obtained using the CodeLink arrays were assessed using a different gene expression measurement platform. The analysis of the Affymetrix Array data for the expression levels of the candidate genes in brains of rats from two HXB/BXH strains are shown in Table
Replication Study of Candidate Genes
|
|
|
||||||||
|
|
|
|
|
|
|
|
|
|
|
|
|
|||||||||
| Afap1l1 | 6.44 | 6.69 | 0.84 | 0.0344 | 1.22 | 1.58 | 0.78 | 0.0199 | Yes |
|
|
|||||||||
| Cckbr | 7.92 | 8.31 | 0.76 | 0.0063 | 2.25 | 2.84 | 0.66 | 0.0110 | Yes |
|
|
|||||||||
| P2rx4 | 8.45 | 7.52 | 1.90 | 0.0122 | 2.53 | 1.50 | 2.04 | <.0001 | Yes |
|
|
|||||||||
| Tnk2 | 8.80 | 8.68 | 1.09 | 0.3473 | 4.17 | 3.59 | 1.50 | 0.0214 | Yes |
|
|
|||||||||
| Tubb6 | 6.92 | 7.00 | 0.94 | 0.5960 | 0.30 | 0.69 | 0.76 | 0.0400 | Yes |
|
|
|||||||||
| Ptpre | 7.70 | 7.63 | 1.05 | 0.2589 | 1.93 | 0.77 | 2.23 | <.0001 | Yes |
|
|
|||||||||
| Mogat2 | 5.26 | 5.10 | 1.12 | 0.3124 | 0.63 | -0.63 | 2.39 | 0.0005 | Yes |
|
|
|||||||||
| Nek3 | 6.05 | 5.90 | 1.10 | 0.1681 | -0.36 | -0.54 | 1.13 | 0.0388 | Yes |
|
|
|||||||||
| Tmem2 | 7.25 | 7.32 | 0.95 | 0.3860 | 2.47 | 2.82 | 0.78 | 0.0086 | Yes |
|
|
|||||||||
| Mc4r | 6.01 | 5.82 | 0.87 | 0.0360 | -0.39 | -0.26 | 0.91 | 0.2058 | Yes |
|
|
|||||||||
| F2rl1 | 6.16 | 6.26 | 0.94 | 0.3674 | -2.10 | -1.72 | 0.77 | 0.3943 | Yes |
|
|
|||||||||
| Abat | 10.52 | 10.35 | 1.13 | 0.0669 | 4.71 | 4.66 | 1.04 | 0.4464 | Yes |
|
|
|||||||||
| Mboat2 | 8.73 | 8.46 | 1.21 | 0.0797 | 0.95 | 1.31 | 0.78 | 0.0051 | No |
|
|
|||||||||
| Coq7 | 8.22 | 8.02 | 1.15 | 0.2095 | 2.60 | 3.76 | 0.45 | <.0001 | No |
|
|
|||||||||
| Dynlrb1 | 11.27 | 11.23 | 1.03 | 0.7064 | 5.96 | 6.00 | 0.97 | 0.4726 | No |
|
|
|||||||||
| Tpst1 | 7.70 | 7.56 | 1.10 | 0.1642 | 3.12 | 3.77 | 0.64 | 0.0004 | No |
|
|
|||||||||
| Fgfr2 | 9.44 | 9.41 | 1.02 | 0.7038 | -1.10 | 4.25 | 0.02 | <.0001 | No |
|
|
|||||||||
| Pop5 | 7.53 | 7.55 | 0.98 | 0.9056 | 2.58 | 2.45 | 1.09 | 0.1509 | No |
|
|
|||||||||
| Rab35 | 8.91 | 8.87 | 1.03 | 0.7719 | 4.18 | 4.28 | 0.93 | 0.0107 | No |
|
|
|||||||||
| Spire1 | 9.78 | 9.74 | 1.03 | 0.3554 | 1.50 | 3.26 | 0.30 | <.0001 | No |
Expression levels of the identified candidate genes were determined in brains (n = 5 per strain) of rats from two of the RI strains, HXB RI 26 (high alcohol consumption) and HXB RI 23 (low alcohol consumption) using CodeLink Rat Whole Genome Bioarrays and Affymetrix GeneChip Rat Gene 1.0ST arrays. These strains were chosen because the expression levels of all candidate genes (on CodeLink arrays) that were significantly correlated with alcohol consumption when all the RI strains were analyzed, showed an appropriate direction of differential expression in these two strains compared to the correlation analysis, even if the difference in expression between these 2 particular strains was not statistically significant. Details on analysis, normalization and statistical comparisons are provided in the text. Values represent mean of the log base 2 transformed expression data. "Ratio" is the HXB23/HXB26 ratio of the unlogged intensity values. P values are from the two-sample t-test, and "match direction" refers to the comparison between the two array experiments.
Of the original 606 subjects in the WHO/ISBRA data from Montreal, 545 were self-reported Caucasian. Alcohol consumption in the Montreal population ranged from abstainers (0 g/kg/day) to heavy drinkers (maximum, 8.4 g/kg/day; see Additional file
Out of the 1,350 gene-related SNPs, 98 SNPs were not genotyped in any of the Montreal subjects (98 in Sydney subjects) and 88 were not informative across the Montreal sample (49 in the Sydney sample). Further evaluation of the remaining SNPs resulted in 121 SNPs being eliminated because they were not in Hardy-Weinberg equilibrium (HWE) in the Montreal sample (156 SNPs in the Sydney sample) (FDR<0.01). An additional 203 SNPs were eliminated in the Montreal sample (203 in Sydney) that had a minor allele frequency (MAF) that was less than 5% (46 SNPs in the Montreal population (79 in Sydney) did not meet HWE and had a MAF<5%). In addition, 110 subjects from Montreal and 61 subjects from Sydney were eliminated for having a call rate that was less than 80%, based on the criteria described in Hodgkinson et al. [
There was no evidence for population stratification at each site with respect to the outcome, i.e., alcohol consumption (chi-square = 344, df = 352,
Table
Genetic (SNP) Associations with Alcohol Consumption: WHO/ISBRA Subjects
|
|
|
|
|
|
|
|
|
|
|
|||||||
|
|
|||||||
| rs2241165 | intronic region of GAD1 | 2 | 171386625 | C/T | 0.25/0.23 | 0.0001 | 0.0400 |
|
|
|||||||
| rs701492 | intronic or downstream region of GAD1 | 2 | 171410726 | T/C | 0.31/0.31 | <0.0001 | 0.0018 |
|
|
|||||||
| rs7578661 | intronic region of GAD1 | 2 | 171423379 | G/C | 0.31/0.31 | <0.0001 | 0.0018 |
|
|
|||||||
| rs1389752 | intronic region of MPDZ | 9 | 13225287 | A/T | 0.14/0.15 | <0.0001b | 0.0130b |
|
|
|||||||
|
|
|||||||
|
|
|||||||
| rs8030094 | intronic region of CHRM5 | 15 | 32124174 | A/G | 0.18/NA | 0.0002 | 0.0326 |
|
|
|||||||
| rs10051667 | intronic region of GABRB2 | 5 | 160830906 | C/T | 0.09/NA | <0.0001 | 0.0110 |
|
|
|||||||
| rs9607272 | intronic region of MAPK1 and TUBA8c | 22 | 20466398 | G/T | 0.19/NA | <0.0001 | 0.0062 |
|
|
|||||||
| rs879606 | upstream region of PPP1R1B | 17 | 35035375 | A/G | 0.13/NA | <0.0001 | 0.0062 |
Significant results from univariate association analysis with alcohol consumption. Shown are the eight SNPs that showed a significant (FDR<0.05) association with alcohol consumption (g/kg/day) in a genotype model.
aMinor and major allele designation is according to Ensembl release 50.
bThis SNP was significant when both genders are combined; in male subjects, the FDR for this SNP was 0.09. All other SNPs shown were significant when males only were tested.
cThe localization of this SNP is ambiguous. Its presence in MAPK1 and the overlapping gene, TUBA8 (tubulin alpha 8) is of particular interest, given the findings of differences in expression of cytoskeletal genes in the HXB/BXH rat strains.
We determined the haplotype block that contained each significant SNP. For GAD1 in the Montreal sample, three SNPs significantly associated with alcohol consumption were located within the same known haplotype block (Additional file
The final genetic multivariate model for Montreal male subjects included the recessive effect of haplotype 2 (see Additional file
Multivariate Models with Covariates for Alcohol Consumption
|
|
||||
|
|
|
|
|
|
| Intercept | -0.59 | 0.13 | -4.59 | <.0001 |
| recessive haplotype 2 - MPDZ | 1.02 | 0.24 | 4.17 | <.0001 |
| rs701492 - GAD1 (AA) | 0.70 | 0.10 | 6.94 | <.0001 |
| rs701492 - GAD1 (AB) | -0.03 | 0.07 | -0.42 | 0.6714 |
| rs701492 - GAD1 (BB) | referent | |||
| Age | 0.01 | 0.00 | 4.50 | <.0001 |
| Alcohol dependence in past year | 1.04 | 0.10 | 10.73 | <.0001 |
| Alcohol abuse in past year | 0.73 | 0.10 | 7.67 | <.0001 |
| Family history of depression (1st degree relative) | 0.50 | 0.09 | 5.45 | <.0001 |
| Familial depression and alcohol dependence* | -0.85 | 0.14 | -6.09 | <.0001 |
| Used medication other than antidepressants in last month | 0.26 | 0.07 | 3.91 | 0.0001 |
|
|
||||
|
|
|
|
|
|
| Intercept | 0.24 | 0.06 | 4.12 | <.0001 |
| CHRM5 (recessive) | 2.10 | 0.35 | 5.95 | <.0001 |
| GABRB2 (dominant) | 3.03 | 0.43 | 7.00 | <.0001 |
| PPP1R1B (recessive) | 2.97 | 0.32 | 9.22 | <.0001 |
| Alcohol dependence in past year | 0.58 | 0.11 | 5.06 | <.0001 |
| Alcohol abuse in past year | 0.58 | 0.11 | 5.14 | <.0001 |
Shown are the coefficients associated with the final multivariate models with covariates for alcohol consumption as outcome. Final models were chosen using a backward selection process with an exit criterion of p > 0.001.
* Familial depression is defined as major depression in the subject and in a first-degree relative.
We also analyzed the relationship of phenotypic characteristics with alcohol consumption levels by males in Sydney and Montreal. As expected, our data showed that alcohol consumption was positively correlated with alcohol dependence in both populations (Additional file
The reasons for differential intake of ethanol by individuals in a population, be it humans or other animals, have been the subject of an immense amount of research. The current dogma posits that both environmental and genetic factors contribute to individual differences in alcohol consumption [
Epidemiologic studies do indicate that the higher the levels of alcohol intake by an individual, the higher is the propensity for an individual to become alcohol dependent [
What emanates as a conclusion from our genetical genomic/phenomic approach for the search for "candidate genes" that influence non-dependent alcohol intake is that a group of genes/gene products that can be linked to systems which, in the rat (and human), control appetite and satiety, play an important role in variations in the non-dependent alcohol intake phenotype (see
Function of Replicated Candidate Gene Products
|
|
|
|
| + |
|
Melanocortin 4 receptor; activation by melanocortin (MSH) decreases caloric intake. Mc4r localized on GABA neurons. Mc4r agonist reduced ethanol consumption in association with food consumption [ |
|
|
||
| + |
|
Cholecystokinin 2 receptor; Cholecystokinin (CCK) is localized in some GABA interneurons, and activation of Cck2r can facilitate GABA release. CCK is a satiety hormone. Cck2r null mutants show low alcohol consumption [ |
|
|
||
| - |
|
Monoacylglycerol O-acyltransferase 2; esterase affecting mono- and di-acylglycerol. |
|
|
||
| - |
|
4-aminobutyrate aminotransferase (GABA aminotransferase); GABA degrading enzyme. |
|
|
||
| - |
|
P2x purinoceptor 4; located on GABA neuron terminals in VTA and is a cation channel for Ca2+. Enhances GABA release by presynaptic action. Inhibited by ethanol [ |
|
|
||
| - |
|
Receptor type protein tyrosine phosphatase epsilon; inhibits voltage-gated K+ channels on GABA interneurons [ |
|
|
||
| + |
|
Actin filament associated protein; interacts with dynamin, involved in endocytosis. |
|
|
||
| + |
|
Tubulin B6; cytoplasmic dynein interacts with tubulin, involved in microtubule movement [ |
|
|
||
| - |
|
Protein tyrosine kinase Ack; Cdc42-associated kinase, located pre and post synaptically; influences endocytosis [ |
|
|
||
| + |
|
Protease-activated receptor 2(PAR2); activates PI3-kinase, activation of Cdc42 required. Increases Ca2+ signaling. In Glu and GABA neurons [ |
Figure
However, animals and humans ingest food not only for nourishment, but also for the rewarding properties of food, and motivational mechanisms are important for generating responses needed for food-seeking and consumption behavior. The neuronal pathways and neurotransmitter substances that regulate food intake for energy homeostasis, and for the rewarding properties of food, are intimately connected but not identical. Particularly relevant to our current studies are the hypothalamic nuclei with connections to the ventral tegmental area (VTA) that generate and carry information on hunger and satiety, and initiate the cascade of events resulting in "wanting/liking" food or other "rewards" [
The products of many of the candidate genes identified in this study can affect GABAergic neuronal activity. One of the most studied systems in this regard, and one suggested to link the homeostatic and reward pathways associated with food intake [
The cholecystokinin 2 (CCK2) receptor is expressed not only in the hypothalamus, but also in other brain regions, such as the VTA or amygdala, where it is localized presynaptically on GABA neurons [
Another identified candidate gene may further regulate the excitability of GABA neurons and GABA release.
On the other hand, another of the candidate genes, Abat, codes for the enzyme 4-aminobutyrate (GABA) aminotransferase, which degrades GABA. This gene is expressed at lower levels in animals with higher ethanol consumption, and this situation could contribute to enhanced GABA levels in GABAergic synapses in the high alcohol-consuming rats.
The effects on GABAergic activity described above could contribute to a decreased hypothalamic GABAergic inhibitory effect on VTA dopaminergic activity in the high alcohol-consuming rats. Furthermore, the products of a number of the candidate genes can more directly affect the activity of midbrain dopamine neurons. MSH, administered into the VTA, increases dopamine release in the nucleus accumbens via actions on VTA melanocortin 4 receptors [
Recent work has shown that the quantities of ethanol consumed by rats in a two-bottle choice paradigm, can, in fact, enhance the electrophysiological activity of VTA dopaminergic neurons [
Another intriguing aspect of our candidate gene list is the presence of a number of genes coding for products that can affect postsynaptic aspects of GABA receptor trafficking. These candidate genes and gene products are related to rho GTPases, which affect the cytoskeleton, membrane trafficking and cell adhesion through their regulation of actin dynamics. For example, Cdc42 is a member of the rho GTPase family that can stimulate actin polymerization, affecting specific steps of vesicle trafficking such as those involved in endocytosis [
Two of the candidate genes that we identified are Cdc42-associated proteins.
The products of some other candidate genes are associated with actin organization and microtubule activity, which is also important for the trafficking of receptors and other proteins. Afap1l1 codes for an actin filament associated protein. The expression of this gene is positively correlated with alcohol consumption. Microtubules are involved not only in receptor transport, but also in the movement of gephyrin to and from the synaptic plasma membrane [
Overall, our analysis of the functional pathways defined by the identification of the candidate genes in the HXB/BXH rat strains focuses attention on GABAergic and dopaminergic activity that may set a tone that predisposes to (or against) voluntary alcohol consumption. We are proposing that, in rats, a lower inherent GABAergic tone generated by reduction in presynaptic release and a more responsive GABA receptor desensitization system predisposes to higher alcohol consumption in a free choice experimental paradigm. There are numerous studies showing that administration of agonists or antagonists of GABA or dopamine receptors can alter alcohol consumption or self-administration by rats [e.g., [
Although we have focused attention on neuronal systems which mediate the animals' recognition of the energy status of the body and the rewarding properties of caloric substances, it should be clearly stated that the candidate genes we identified, and their products, do play important roles in other anatomically-defined neural systems. A case in point may be that dopaminergic neurons which innervate the dorsal striatum (cell bodies in the substantia nigra) may have as much to do with appetitive behavior as the systems we describe innervating the nucleus accumbens from the A10 nucleus (VTA) [
The ultimate goal of genetic studies using animal models of alcohol consumption is to identify candidate genes that influence a human's level of alcohol consumption. It should be stressed at this point, that the phenotype we utilized for our genetic association studies with humans was also the quantitative measure of alcohol intake. This phenotype was chosen to allow proper comparison with our studies with the rats. We also performed a separate analysis of the genetic association of the phenotype of alcohol dependence defined by both DSM IV and ICD-10 criteria with the panel of 1,350 SNP marker included on the "Addiction Array" [
The overall impression that is generated by cursorily examining the results of our studies with rats and humans, and our prior studies with mice, is that little evidence may have been produced to indicate that identical genes or gene products predispose free choice alcohol intake in rodents or humans. What may be missed is the fact that certain identical neurobiologic pathways have been identified in all of these investigations. In rodents, one can posit that neurobiologic systems that participate in sensing and transducing information about the rewarding or aversive properties of foodstuffs play an important role in oral consumption of ethanol in a free-choice paradigm. Polymorphisms in the loci of genes involved in such an appetitive pathway are also associated with quantitative measures of alcohol intake in humans [
Our results also cast some light on the influence of genetic polymorphisms on levels of alcohol drinking vs alcohol dependence in humans. Epidemiologic studies suggest a strong correlation between levels of ethanol consumed and the diagnosis of alcohol dependence [
A recent genome-wide association study of alcohol dependence in humans used genes identified by differential mRNA expression in alcohol-consuming rats as a means of attempting to generate more credence in the candidate genes with modest statistical support for association with alcohol dependence in the analysis of the human data [
The genetical genomics approach, in combination with phenomics, is a powerful method for determination of candidate genes that contribute to the predisposition to alcohol consumption in rats. Informatics-based analysis of the function of candidate gene products led to the consideration that GABAergic function, and particularly GABA release modulated by peptidergic, purinergic and endogenous cannabinoid systems, as well as GABA receptor trafficking, are important components of the genetic/biochemical pathways that contribute to alcohol drinking. Our results highlight the need to identify neuronal signaling networks based on candidate genes, rather than focusing on individual genes and gene products, when attempting to understand the genetic basis of complex behavioral traits. The comparison of rat and human genetic contributors to the trait of alcohol consumption suggested that one can extrapolate from pathways - not necessarily specific genes - found in animals to begin to elucidate cross-species similarities in the genetic basis of behavior. Our results also emphasize the importance of carefully defining phenotypes for genetical genomic approaches; in this case, although high levels of alcohol drinking are phenotypically correlated with alcohol dependence, the genetic factors that contribute to the full range of alcohol consumption versus alcohol dependence in humans are distinct.
BT planned and supervised all experiments, analyzed data and drafted the manuscript. LS carried out eQTL and bQTL analyses, correlation and all statistical analyses. MPrintz provided data and tissue from the HXB/BXH rats, participated in alcohol consumption studies, and analyzed alcohol consumption data. PF participated in HXB/BXH rat alcohol consumption experiments and analyzed data. CH performed studies using the addiction array. DG supervised addiction array studies and analyzed data. GK conceived, organized and supervised the HXB/BXH alcohol consumption studies. HNR organized and supervised the HXB/BXH rat alcohol consumption studies and analyzed data. KK performed transcription factor and transcription factor binding site analysis. RLB provided data and tissue from P and NP rats. NH performed genotyping of HXB/BXH RI strains. MH performed genotyping of HXB/BXH RI strains. MPravenec provided tissue from HXB/BXH rats. JM provided genotype data from HXB/BXH RI rats. LL recruited subjects from Montreal and provided data and DNA. MD supervised studies and recruited subjects in Montreal. KMC recruited subjects in Australia and provided data and DNA. JW supervised human studies and recruited subjects in Australia. JS recruited subjects in Australia and analyzed data. BG created questionnaire for WHO/ISBRA study and generated algorithms for data analysis. PLH analyzed microarray data and drafted manuscript. WHO/ISBRA Investigators are included as authors on all studies using data from the WHO/ISBRA study, by formal agreement.
Click here for file
The authors thank Janet Hopkins for performance of assays for measuring mRNA levels; Ron Smith, Laura Breen and Joseph Gatewood for technical assistance with behavioral data collection and Dr. Samuel P. Hazen for expert advice on database management. WHO/ISBRA Study on State and Trait Markers of Alcoholism is an abbreviation for World Health Organization/International Society for Biomedical Research on Alcoholism Study on State and Trait Markers of Alcohol Use and Dependence Investigators. This work was supported in part by NIAAA, NIH (AA013162 (BT); AA006420 (GK); AA016649-INIA Project (PH); AA016663-INIA Project (BT); AA013522-INIA Project (RLB); AA013517-INIA Project (GK); AA-U01-Developmental Grant - INIA Project (M Printz); AA16922 (KK)); NHLBI, NIH (HL35018 (M Printz)); Banbury Fund (BT); Pearson Center for Alcoholism and Addiction Research (GK); National Health and Medical Research Council of Australia (WHO/ISBRA Sydney); La Fondation Jean Lapointe (WHO/ISBRA Montreal); National Health Research Development Program, Canada (WHO/ISBRA Montreal); Wellcome Trust Program for Cardiovascular Functional Genomics (JM); Ministry of Education of the Czech Republic (1M6837805002(M Pravenec)) and Howard Hughes Medical Institute (M Pravenec).