genes have been shown to be important risk factors for AD in EAs and AAs in our initial study (7
). In this study, we present the first comprehensive evidence that these genes (specifically ADH
1C and ADH
7) also moderate risk for DD.
In our sample, genotypes of all markers were in HWE in controls, but some were in HWD in cases, indicating the existence of associations between genes and disease (6
). The genotypewise case–control association analysis also showed association of these genes with DD, both before and after controlling for admixture effects. However, we noted that many associations from these conventional analyses became non-significant after correction for multiple testing, indicating that these analyses often led to information loss. To preserve this information, we applied DTR. The HWD test, case–control comparison and structured association (SA) analyses cannot correct for interaction effects between markers and between genes. These issues can also be well addressed by DTR (7
DTR is a powerful method, and in using it, we detected associations that were not seen using the aforementioned conventional association methods. Several features, more details of which were discussed in Luo et al
), make DTR more powerful than other conventional association methods in many circumstances. First, it was possible to combine cases and controls, and EAs and AAs, in a single DTR model, thereby increasing the sample size and statistical power (while simultaneously controlling for potential stratification effects from phenotype and population variance). Second, different variables, including different genotypes and diplo-types from different genes, were entered into a single DTR model to avoid the multiple tests that inflate Type 1 error. Third, because DTR does not assume the presence of HWE, analysis was possible using this approach in the presence of deviation from HWE [e.g. for diplotype probabilities predicted by the program PHASE (18
)]. Under HWD, alleles at a locus or multilocus haplotypes at a gene are not independent of each other, which may invalidate allelewise and haplotype-wise analyses (6
), and thus both analyses were not presented in the present study; however, because genotypewise and diplotypewise analyses may be valid and informative methods, we used them in the present study. Fourth, in the present study, the maximum proportion of individuals with unambiguous diplotypes (i.e. P
= 1) in a single gene was only 37%. These diplotype data with uncertain phase can be analyzed by DTR. Fifth, age and sex were included in the DTR model to control for potential confounding effects. In the present study, the average age of the controls was 9 years less than for the cases, so a certain proportion of control subjects might still develop substance dependence in their lifetimes. Using such subjects as a control group for a study of substance dependence could reduce power but not produce false-positive results. The positive findings in the present study should be reliable—any bias from this age difference would tend to decrease the significance of the results but not lead to spurious findings. Potential confounding effects by age were controlled in the DTR analysis. Ancestry proportions were also included in the DTR model to control for population stratification and admixture effects. AD was not included in the DTR model as a covariate because of the multicollinearity issue resulting from the strong associations between AD and the ADH
). Sixth, DTR takes into account gene–gene interactions, which is a more powerful approach than single-gene analysis (21
). Diplotypes incorporate the LD information from different markers, and the interactions between diplotypes (diplotype–diplotype interactions from different genes are more representative of gene–gene interactions than marker–marker interactions) were considered in the DTR model. Finally, DTR is able to account for LD effects and, additionally, cis
-acting functional effects. On the basis of these considerations, findings obtained through application of DTR have a high likelihood of being valid.
DTR showed that in EAs and/or AAs, genotypes of ADH
SNP1 and ADH
SNP9 and diplotypes at the ADH
1C, and ADH
7 genes were associated with phenotype (the risk genotypes and diplotypes exerted effects in the same direction for both DD and DD-only). Further, most associations evidenced by DTR analysis showed a higher degree of statistical significance than those based on conventional association methods. In summary, our findings by DTR analysis include the following: (i) Some associations were observed in both populations. This is expected for functional variants and could also occur when the same functional variants are important in both populations, and the markers studied are in similar LD with such variants in both populations. (ii) Some associations were population-specific or even showed opposite effects in the two populations. This is understandable because the diplotype frequency distributions are population-specific in the genetically distinct EAs and AAs, and even the same diplotypes could have different frequencies in these two populations, e.g. ADH1A^
AGA/TCG is quite rare in AAs ( f
= 0.057), but more common in EAs ( f
= 0.181). The common diplotypes in one population and the rare diplotypes in another population might be in LD with the opposite allele phases of either the same or possibly different unknown risk-influencing variants and could thus exert opposite effects. This is analogous to the situation in which the major allele and the minor allele of the same marker have opposite effects. Additionally, as explored by Luo et al
), gene–gene interaction effects can be population-specific; of course, the admixture effects could be population-specific also (e.g. AAs show more admixture effects than EAs). These gene–gene interaction effects and admixture effects could affect the direction of association of a certain diplotype in two distinct populations, which could explain why the ADH1C^
AGT/GAC is common in both EAs ( f
= 0.353) and AAs ( f
= 0.275) but has opposite effects in these two populations. (iii) Of particular note here, ADH
SNP9 genotype was suggestively associated with DD-only and only in AAs (P
= 0.088). We cannot exclude the possibility that the failure to reach statistical significance is due to insufficient statistical power.
Results from DTR analysis suggest that the ADH gene cluster may harbor risk loci for DD. Risk alleles can be fine-mapped using the J-value. If a marker is close to a risk allele for AD, but not close to a DD risk allele (i.e. if the allele affects risk for AD, but not DD), the J-value of this marker should be highest for AD, intermediate for DD (which includes co-morbid AD) and lowest in DD-only (; ). For example, the J-value at ADH1B^Arg/His (SNP16: previously called ADH2*1/2) is extremely high for AD (11.667 in EAs; 1.000 in AAs), intermediate in DD (1.185 in EAs; 1.000 in AAs) and very low in DD-only (0.008 in EAs; 0.001 in AAs), suggesting that the marker is close to a risk allele for AD, but not for DD. We expected to identify markers that, if they affected risk for DD, did so through co-morbid AD, rather than through a direct effect on DD. Conversely, if a marker is close to a risk allele for DD, but not close to one for AD, the J-value of this marker would be high in DD-only, intermediate in DD (which is co-morbid with AD) and low in AD. Although not anticipated, we observed several associations of this kind as well, including those involving SNPs 1, 11, 20 and 22 in EAs and SNPs 9, 14, 17 and 21 in AAs. If a marker is close to risk alleles both for AD and DD (i.e. the allele affects risk for both phenotypes), the J-value of this marker should be high in all groups: AD, DD and DD-only. We observed this situation for SNP13 in EAs and SNPs 10 and 22 in AAs. This finding suggests that some risk alleles for AD and DD may be located in similar positions in the ADH gene cluster; others may be located in different positions. Some risk alleles for DD are close to some well-known functional variants, such as ADH1B^Arg/Cys (SNP14: previously called ADH2*1/3), ADH1C^Ile/Val (SNP17: previously called ADH3*1/2) and ADH7^Ala/Gly (SNP22).
All of the earlier-mentioned conventional association analyses, the DTR association analysis and the fine-mapping analysis suggest that risk for DD is affected by multiple ADH
genes, consistent with their multigenic etiology, i.e. minor effects of different genes with additive effects on risk for DD. There are several possible explanations for the associations between ADH
genes and DD: (i) the associations are completely driven by the actual associations between these genes and AD, because many patients with DD have co-morbid AD. Alcohol helps to enhance the effects of many drugs. As a specific example, cocaine and alcohol are metabolized to cocaethylene (22
), which has biological properties similar to cocaine but is longer acting. Many cocaine abusers therefore prefer to use cocaine together with alcohol, which contributes to the high rate of co-morbidity of AD and CD. Thus, variants affecting risk for AD may indirectly affect risk for DD by modifying alcohol’s enhancing effect on other drugs. However, we also observed many associations between these genes and DD-only (i.e. without co-morbid AD), which argues against indirect effects as a general explanation for these observations. (ii) The susceptibility to DD attributable to these genes is shared with AD susceptibility (i.e. the genes contribute independently to risk for the two phenotypes); for example, these alcohol-metabolizing genes may directly modulate risk for DD via specific pathways independent of alcohol metabolism. These pathways may overlap with the mechanism by which disulfiram exerts its treatment effect on CD. (iii) The findings could also be false positives; however, the statistical significance levels we observed argue in favor of other explanations.
A careful look at the range of physiological function of the alcohol-metabolizing enzymes also provides support for the existence of direct genetic effects on DD risk, without invoking mediation by effects on alcohol use. In addition to catalyzing the oxidation of ethanol, ADH and aldehyde dehydrogenase (ALDH) enzymes may be involved in the metabolic pathways of several neurotransmitters, including serotonin, norepinephrine and dopamine (23
). For example, (i) 5-hydroxytryptamine (5-HT) is metabolized by monoamine oxidase to 5-hydroxyindole-3-acetaldehyde (5HIAL), which is either oxidized to the major metabolite 5-hydroxyindole-3-acetic acid (5HIAA) by ALDH or reduced to the minor metabolite 5-hydroxytryptophol (5HTOL) by alcohol dehydrogenase (ADH) (24
). Hypo-potentiated γADH (mainly) and βADH (partially) inhibit the turnover of 5-HIAL to 5-HTOL (25
). Elevated ALDH2 activity potentiates the oxidization of 5-HIAL to 5-HIAA and decreases the production of 5-HTOL reduced from 5-HIAL (26
). Low levels of 5-HTOL may predispose to self-administration of cocaine, a potential exogenous competitor for 5-HTOL, thereby compensating for the reduced effects of 5-HTOL, which could lead to CD. Inhibiting ALDH2 activity with disulfiram (Antabuse) or cyanamide (calcium carbimide, Dipsan) inhibits the oxidization of 5-HIAL to 5-HIAA and increases the production of 5-HTOL from 5-HIAL (26
), which could reduce cocaine self-administration and might be the mechanism by which disulfiram exerts therapeutic effects in CD. (ii) πADH can catalyze the reduction of norepinephrine aldehydes. Increased πADH activity could lead to a very high turnover of norepinephrine aldehydes (27
). Cocaine, which functions as a norepinephrine re-uptake inhibitor, can activate the nor-adrenergic system (28
). Plasma epinephrine and norepinephrine concentrations were significantly increased in response to cocaine injection (28
). Intravenous opioids also stimulate norepinephrine and acetylcholine release in cerebrospinal fluid (29
). Therefore, self-administration of cocaine or opioids could elevate norepinephrine aldehydes, which could reinforce these behaviors, thereby contributing to the development of DD.
These neurotransmission signaling systems may also modulate the reinforcing or rewarding effects of abusable drugs through other neurotransmitter systems, providing a key psychomotor mechanism of the development of DD. For example, (i) the psychomotor stimulant effects of cocaine are most often thought to be mediated through enhanced dopamine or serotonin neurotransmission in mesolimbic areas of the brain (30
). Blockade of the D3 receptor in the mesolimbic system with SB-277011A, a novel D3-selective antagonist, attenuates cocaine-enhanced brain stimulation reward, cocaine-induced conditioned place preference and cocaine-induced reinstatement of cocaine-seeking behavior in rats (33
). Dopamine signaling in the nucleus accumbens (NAc) is also thought to play an important role in regulating drug-taking and drug-seeking behaviors (34
). Dopamine D1 receptor agonists and D2 receptor antagonists attenuate reinstatement of cocaine-seeking in rats through D1-like and D2 dopamine receptors in the NAc (35
). (ii) Cocaine binding to the dopamine transporter (DAT), serotonin transporter (SERT) and norepinephrine transporter (NET) strongly inhibits the reuptake of these biogenic amines (38
). These neurotransmitters play important roles in the pharmacological effects of cocaine, including the pleasurable properties, rewarding and reinforcing effects, and may also produce neurotoxic effects (39
). Altered activities of ADH enzymes determined by ADH
gene variation may thus modulate risk for DD via a number of different neurotransmitter signaling pathways.
In summary, we have presented the first comprehensive evidence for association of ADH
variants with DD, irrespective of co-morbidity with AD. Associations of some of the alcohol-metabolizing enzymes with AD are well known and in fact are some of the oldest and most consistent finds in psychiatric genetics. Associations with DD [except for ADH4
, which we reported previously (5
)] have not been reported. Such associations might be expected on the basis of the high rate of co-morbidity of AD and DD or through the modulating effects of polymorphic variation in the genes encoding these enzymes on the effects of alcohol, even in the absence of AD. However, our results, which in some cases show stronger association with DD than AD, are not fully explained by recourse to the effects on alcohol metabolism. These associations may be better explained by physiological effects of alcohol-metabolizing enzymes on neurotransmission or indirect effects through personality (41
). These results provide a novel window into the genetic risk mechanisms for DD and new support for the role of the polymorphic variation in genes encoding alcohol-metabolizing enzymes for substance dependence risk. As for any such novel results, these findings require independent replication to support their validity.