Genetics of alcohol dependence: a review of clinical studies.

Neuropsychobiology 2014;70:77–94 DOI: 10.1159/000364826

Received: October 7, 2013 Accepted after revision: May 24, 2014 Published online: October 30, 2014

Genetics of Alcohol Dependence: A Review of Clinical Studies Jerzy Samochowiec a Agnieszka Samochowiec b Imke Puls d Przemyslaw Bienkowski c Björn H. Schott d, e a Department of Psychiatry, Pomeranian Medical University, and b Institute of Psychology, Department of Clinical Psychology, University of Szczecin, Szczecin, and c Institute of Psychiatry and Neurology, Warsaw, Poland; d Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, and e Leibniz Institute for Neurobiology, Magdeburg, Germany

Abstract Background/Aims: Alcohol dependence is a common severe psychiatric disorder with a multifactorial etiology. Since the completion of the human genome project and with the increased availability of high-throughput genotyping, multiple genetic risk factors for substance-related disorders, including alcohol dependence, have been identified, but not all results could be replicated. Methods: We systematically review the clinical literature on genetic risk factors for alcohol dependence and alcohol-related phenotypes, including candidate gene-based studies, linkage studies and genome-wide association studies (GWAS). Results: Irrespectively of the methodology employed, the most robust findings regarding genetic risk factors for alcohol dependence concern genetic variations that affect alcohol metabolism. GWAS confirm the importance of the alcohol dehydrogenase gene cluster on chromosome 4 in the genetic risk for alcohol dependence with

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multiple variants that exert a small, but cumulative influence. A single variant with strong influence on individual risk is the aldehyde dehydrogenase 2 ALDHD2*2 variant common in Asian populations. Other robust associations have been found with previously uncharacterized genes like KIAA0040, and such observations can lead to the identification of thus far unknown signaling pathways. Converging evidence also points to a role of glutamatergic, dopaminergic and serotonergic neurotransmitter signaling in the risk for alcohol dependence, but effects are small, and gene-environment interactions further increase the complexity. Conclusion: With few exceptions like ALDH2*2, the contribution of individual genetic variants to the risk for alcohol-related disorders is small. However, the concentration of risk variants within neurotransmitter signaling pathways may help to deepen our understanding of the underlying pathophysiology and thereby contribute to develop novel therapeutic strategies. © 2014 S. Karger AG, Basel

Jerzy Samochowiec and Agnieszka Samochowiec contributed equally to this work.

Björn H. Schott, MD, PhD Department of Psychiatry and Psychotherapy Campus Charité Mitte, Charité Universitätsmedizin Berlin Charitéplatz 1, DE–10117 Berlin (Germany) E-Mail bjoern.schott @ charite.de

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Key Words Alcohol-related phenotypes · Alcohol dependence · Genetic risk factors

While alcohol consumption at moderate levels is socially widely accepted in many cultures, excessive drinking ultimately leading to alcohol dependence is a severe health problem both at the individual and large-scale socioeconomic levels. According to World Health Organization recommendations, the limit of risky consumption is for men not to consume more than 4 standard drinks a day or 28 standard drinks a week and for women not to consume more than 2 standard drinks a day or 14 standard drinks a week. It is estimated that every fourth person in Europe aged 15–64 regularly consumes alcohol at harmful levels, and over 14 million people are alcohol dependent [1]. The US data from the 2001–2002 National Epidemiological Survey on Alcohol and Related Conditions showed a 12-month prevalence of 3.8% for alcohol dependence defined by DSM-IV: 5.4% in men and 2.3% in women [2]. The lifetime prevalence of alcohol dependence is 12.5% in the USA. Alcohol use disorders (AUDs) can be defined as ‘maladaptive patterns of alcohol use leading to clinically significant impairment or stress’ [3]. Diagnostic criteria, both from the Diagnostic and Statistical Manual of Mental Disorders [4] and from the International Classification of Diseases [5] divide AUDs into alcohol dependence and abuse/harmful use. The major change proposed for DSMV (and introduced in May 2013) has replaced the 2 separate DSM-IV (substance-specific) categories of dependence and abuse with a single (substance-specific) category, substance use disorder (SUD). The criteria for SUD merge the previous lists of 7 criteria for dependence and 4 criteria for abuse into a single list of 11 criteria. SUD is now additionally defined by the presence of craving, while the criterion of recurrent legal problems has been discarded. Severity of the illness is graded by the number of criteria met: 0–1, no diagnosis; 2–3, mild SUD; 4–5, moderate SUD; 6 or more, severe SUD. Tolerance and withdrawal remain as symptoms of SUDs [6]. Alcohol dependence is defined across all versions of the DSM, despite the recent changes in its definition, as a disorder characterized by physiological and psychological effects in individuals who consume large amounts of alcohol [7]. The discrepancy between the widespread occurrence of risky or harmful alcohol consumption and the frequent, yet substantially lower, prevalence of clinical alcohol dependence raises the question why certain individuals become dependent on alcohol, sometimes in a rather short time, while others do not seem to be affected by 78


regular alcohol consumption. There is nowadays little doubt that alcohol dependence is a disorder of complex etiology with multiple risk factors contributing to it. The susceptibility to alcohol dependence is subject to considerable interindividual variability, and it is shaped by both including environmental (e.g. cultural, social political, religious, economic, legal including alcohol accessibility, price and social norms) and genetic factors [8–11]. The biological processes that make one person more susceptible to addiction than another are subject to intense investigations. Twin and adoption studies quantify the heritable component at 50–60% [12–14]. While in early adolescence the initiation and use of alcohol and other addictive drugs is primarily influenced by environmental, i.e. familial and social, factors, the role of environmental influences progressively decreases during the transition to adulthood, when the impact of genetic contribution reaches its peak [15]. This observation suggests that adolescence may be the optimal time point for educational interventions. A complex pattern of genetic associations indicates a combined contribution of many genes as well as genegene and gene-environment (GxE) interactions. To distinguish between environmental and genetic factors, adoption and twin studies have been conducted. Adoption studies demonstrate that adoptees more closely resemble their biological parents than their adoptive parents in terms of susceptibility to alcohol dependence [16– 19]. Twin studies reveal greater concordance between monozygotic as compared to dizygotic twins, highlighting a major genetic impact [10, 19–21]. (Note: importantly, not all monozygotic cotwins of alcohol-dependent patients are alcohol-dependent themselves, also highlighting the influence of environmental factors and GxE interactions in the etiology of the disorder.) A third line of evidence for a substantial genetic contribution to alcohol dependence comes from animal research. In selectively bred rodent lines, a considerable proportion of heritability in the development of addiction-related behavior has been observed, including strong preference for alcohol over water, willingness to work for alcohol, sensitivity to the hypnotic or activating effects of alcohol and to withdrawal, and demonstrations that alcohol is rewarding even in the presence of food and water [22–26]. A fourth line of evidence comes from early genetic studies in humans that demonstrated genetic variations in genes related to alcohol metabolism that affect the predisposition to alcohol dependence [27–30]. The environmental contributions to alcohol-related phenotypes include peer influence [31–35], availability Samochowiec /Samochowiec /Puls / Bienkowski /Schott


Introduction

Genetics of Alcohol Dependence

There are 3 main techniques used to identify genetic variations affecting alcohol dependence: candidate gene studies, linkage studies and genome-wide association studies (GWAS). The following review is based on a search for PubMed-indexed articles reporting such studies. Candidate gene studies of alcohol dependence were obtained from PubMed (http://www.ncbi.nlm.nih.gov/ pubmed) using the term ‘candidate gene’ combined with ‘alcohol dependence’, ‘alcohol use disorder’ or ‘alcohol addiction’. Similarly, we searched for PubMed-indexed articles describing linkage-based studies of genetic influences on alcohol dependence. Finally, we searched for PubMed-indexed articles describing GWAS of alcohol dependence by using the term ‘GWAS’ combined with ‘alcohol dependence’, ‘alcohol use disorder’ or ‘alcohol addiction’. In each thus identified article, the reference list was reviewed for similar studies. Only English-language articles published before July 2013 (December 2013 in case of GWAS) in peer-reviewed journals were accepted. Articles were identified and reviewed by 2 of the authors, and their final decision was made by consensus. In the case of GWAS of alcohol dependence, all relevant articles identified in PubMed met the above criteria and were included in this review.

Candidate Gene Studies

Genes Related to Alcohol Metabolism The earliest genetic association studies in alcohol dependence were candidate gene studies targeting coding variations in genes that metabolize alcohol. Alcohol is mostly metabolized in the liver, in a 2-step reaction: oxidation to acetaldehyde, which is further oxidized to acetate. The first step is primarily to be catalyzed by alcohol dehydrogenases (ADHs), a reaction accompanied by reduction of NAD+ to NADH. The second step is mainly to be catalyzed by aldehyde dehydrogenases (ALDHs), with reduction of another molecule of NAD+ to NADH. Humans have 7 ADHs, with ADH1 being most important for ethanol oxidation. ADH1 consists of 3 subunits, α, β and γ, which are encoded by the genes ADH1A, ADH1B and ADH1C. ALDHs constitute a group of enzymes, out of which ALDH2 is primarily involved in the oxidation of acetaldehyde. Variations in these genes have long been known to affect the risk for alcoholism [27–30, 61]. A variation in the ALDH2 gene has striking effects on alcohol metabolism. ALDH2 encodes the mitochondrially localized ALDH2. An amino acid substitution from glutamate to lysine at position 504 (NCBI accession No.: rs671), Neuropsychobiology 2014;70:77–94 DOI: 10.1159/000364826

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of alcohol [31, 32, 36–38], early first drink [32, 39] and marital status [32, 40]. Religious belief has been shown to affect alcohol initiation [32, 41]. The pattern of alcohol drinking is thought to contribute to the risk of alcohol dependence. The alcohol dependence risk grows almost linearly with the frequency of binge drinking [42]. Data from the National Epidemiologic Survey on Alcohol and Related Conditions confirm the finding [3, 42]. However, one should remember that the pattern of drinking can be modulated by both environmental and genetic factors. Risk loci for AUDs can be grouped into genes involved in alcohol pharmacokinetics and pharmacodynamics, and genes moderating addiction-related traits and behaviors such as sensation seeking, impulsivity, externalizing behaviors and disinhibition. Our insight into the causes of alcoholism is limited due to heterogeneity of its clinical features, including a frequent co-occurrence with other addictions and also with other psychiatric disorders, both internalizing (e.g. depression and anxiety) and externalizing (e.g. antisocial personality disorder, conduct disorder and attention deficit hyperactivity disorder) [43–46]. This heterogeneity may also be reflected by various AUD subtypes used in current typologies and in the diverse patterns of psychiatric and medical comorbidity observed among AUD patients. Empirically derived typologies typically identify 2–5 consistent subtypes classified by their most prominent symptoms: early onset, externalizing behavior, affective instability and antisocial personality disorder. Multidimensional typologies differentiate alcoholism by symptom severity. Typologies have commonly been derived from clinically based samples of alcohol-dependent persons, as the precise identification of genetic loci has, at least so far, proven to be too complex a task to accomplish [47–50]. The genetic contribution to the risk for AUDs is subject to nonmendelian transmission, and recent studies have concluded that there is a wide range of genetic factors that might influence the risk for AUDs [51–54]. Intermediate phenotypes – or endophenotypes – of AUDs have been identified that are believed to be biologically more directly related to the majority of the genetic risk factors compared to the disorder itself. At least 3 reliable endophenotypes have been identified for AUDs: (a) alcohol-induced skin flushing reaction associated with lower AUD risk; (b) a low response level to alcohol; (c) electrophysiological, psychological, neuroendocrine and neuroimaging phenotypes predictive of AUD and other types of addiction [55–60].

Genes Implicated in the Neurobiology of Addiction The positive reinforcing effects of alcohol are generally believed to be critically mediated by the mesolimbic reward pathway, a dopaminergic projection from the ventral tegmental area to the nucleus accumbens that is further modulated by several other neurotransmitters. In addition to its effect on the dopaminergic system, alcohol can affect the principal neurotransmitter glutamate and γ-aminobutyric acid (GABA) as well as neuromodulatory transmitters including acetylcholine, opioids and serotonin [69–74]. It is therefore conceivable that genetic variations of the dopaminergic system have been in the focus of candidate gene-based association studies for a long time. For example, genetic variations in the dopamine-metabolizing enzymes catechol-O-methyltransferase and monoamine oxidase A (MAOA) have been found to be associated with the risk for alcohol dependence [75–77], although these results could not be uniformly replicated [78–80]. Other 80


studies have linked genetic variations in the dopamine transporter 1 gene to the severity of withdrawal symptoms [81, 82]. One key problem in many candidate gene studies is the poor replicability, which becomes evident in markedly mixed results. Perhaps the best-known example is the association of the Taq1A allele of the dopamine D2 receptor gene (DRD2-Taq1A; rs1800497) with alcohol disorder, which was first reported in a small study in 1990 [83, 84]. Numerous groups have attempted to replicate this finding, but most replication studies have yielded negative or inconclusive results [85–90]. A recent meta-analysis has suggested a small but significant effect. The authors cautioned that the positive result might have been caused by publication bias [91], but a more recent meta-analysis by a different group has confirmed the association and largely ruled out publication bias [84]. The example of rs1800497 highlights another important problem of genetic association studies, namely that inconclusive results can result from linkage disequilibrium with other variants that might actually be causally related to a phenotype (Note: linkage disequilibrium refers to the nonrandom association and coupled inheritance of allelic variants at two loci that may or may not have a large genetic distance [92]). The so-called DRD2 Taq1A polymorphism is actually located downstream of the DRD2 gene, within the adjacent and closely linked ANKK1 (ankyrin repeat and kinase domain containing 1) gene region [93, 94]. Therefore, associations of Taq1A with alcohol dependence might result from linkage disequilibrium with other causative genetic variations in either DRD2 or ANKK1 or both. A recent family study showed that a different genetic variation in DRD2 (rs6277, a synonymous single nucleotide polymorphism) that affects mRNA stability [95] did indeed show an association with alcohol dependence, while rs1800497 was not significantly associated [96]. On the other hand, another family-based analysis showed that the association of the ANKK1/DRD2 region on chromosome 11q23 with alcohol dependence might in fact be strongest in a part of the ANKK1 gene that is not in linkage disequilibrium with DRD2 [35]. A further family and case-control study suggested that a gene cluster in this region consisting of NCAM1, TTC12 and ANKK1 might be involved in the etiology of alcohol dependence [97–99]. The complexity and the inconclusive character of these data point to a need for basic research to investigate the biological processes underlying these associations. Such investigations might also help to identify intermediate phenotypes that are more closely related to the genetic variations than a clinical phenotype per se. In the Samochowiec /Samochowiec /Puls / Bienkowski /Schott


called ALDH2*2, leads to a drastic activity reduction of the catalytically active subunit and additionally increases the turnover of the tetrameric enzyme. The ALDH2*2 variant is dominant, and the presence of at least 1 allele renders the ALDH2 catalytic activity (measured in vitro) below the usual limit of detection [62, 63]. In individuals carrying the ALDH2*2 allele, consumption of even small amounts of alcohol leads to accumulation of acetaldehyde to toxic levels resulting in symptoms like flushing, tachycardia and nausea, similar to the effects of alcohol in patients taking the ALDH2 inhibitor disulfiram. The genes of the ADHs that catalyze the first step in ethanol metabolism, clustered at chromosome 4, also harbor genetic variations that strongly affect the risk for alcohol-related disorders. After low to moderate intake of alcohol, the ADH1 enzyme which consists of subunits encoded by ADH1A, ADH1B and ADH1C genes plays the major role in alcohol metabolism [27–29, 64]. At higher (toxic) levels of alcohol, the ADH4 enzyme (encoded by the ADH4 gene on chromosome 4) becomes more important in alcohol metabolism [64]. Genetic variations of ADH4 have been linked to alcohol elimination [65], as well as the risk for alcohol dependence in several different populations [66–68]. ADH7, located in the esophagus and stomach lining, can contribute to ‘first pass’ metabolism and thereby influence blood alcohol concentration [27]. However, these successfully replicated findings do not explain risk differences in European populations in which the alleles with the strongest effects are rare.

Beyond the endogenous opioid system, research has provided evidence for NPY, which encodes the peptide neurotransmitter neuropeptide Y, as a candidate gene for AUD. NPY modulates alcohol dependence in a rat model [123–125], and NPY knockout mice show increased preference for alcohol [126]. In humans, some but not all genetic association studies have found evidence for an association between polymorphisms in the NPY gene on chromosome 7 and the risk for AUDs [127–130]. Three NPY receptor genes on chromosome 4 have also been investigated in relation to AUDs, yielding 2 positive associations: NPY2R was associated with alcohol dependence and alcohol withdrawal symptoms, and NPY5R was associated with seizures during alcohol withdrawal [130]. Other genetic association studies of peptide neurotransmitter systems have found evidence for an association of AUDs with polymorphisms of the brain-derived neurotrophic factor [131] and the tachykinin receptor 3 [132]. In light of the multitude of identified risk variants to date, it must be kept in mind that there are numerous examples of associations that cannot be replicated. This problem has occasionally led to scepticism about the usefulness of candidate-based association studies. Reasons contributing to this lack of reproducibility include poor study design, incorrect assumptions about the underlying genetic architecture and simple overinterpretations of the data. The most common errors in association studies of complex diseases relate to small sample sizes and resulting low power, poorly matched control groups, data-driven subgroup analyses (‘double dipping’) and insufficient statistical control for multiple testing. To some extent, a positive publication bias and unwarranted ‘candidate gene’ declaration after identifying an association in an arbitrary genetic region also contribute to poor replicability [133, 134]. Moreover, there might also be a biological reason for heterogeneous results of association studies between AUDs and genes related to neurotransmitter systems, namely the fact that most neural cells express multiple receptors that activate converging complex signaling cascades, and potential risk mechanisms at receptor level might be compensated at the level of intracellular signaling. Therefore, genes affecting intracellular signaling in response to receptor activation could also potentially affect the risk for alcohol dependence. For example, the gene encoding the nuclear factor of κ light polypeptide gene enhancer in B cells 1 (NFKB1), a transcription factor activated by growth factor signaling [135], and the gene encoding the matrix metallopeptidase 9, which plays a



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case of DRD2 and ANKK1, it has meanwhile been demonstrated that the two molecules interact at protein levels and that ANKK1 is upregulated by the dopamine agonist apomorphine [100, 101]. Beyond the dopaminergic system, numerous candidate gene studies of alcohol dependence and alcohol-related phenotypes have focused on molecules involved in neurotransmitter pathways interacting with dopaminergic transmission. These include genes involved in cholinergic neurotransmission, such as the muscarinic cholinergic receptor 2 (CHRM2) [102] and the α5-subunit of the nicotinergic cholinergic receptor (CHRNA5) [103–105], components of the GABAergic system like the α2-subunit of the GABA-A receptor (GABRA2) [106] and GABA receptor genes [107–110], genes coding for components of glutamatergic transmission like the metabotropic glutamate receptor 8 (GRM8) [111], genes involved in serotonergic neurotransmission like the solute carrier family 6 member 4 (SLC6A4) gene that encodes the serotonin transporter [112, 113], and components of the endocannabinoid system such as cannabinoid receptor 1. In addition to these genetic variants, which all affect small-molecule neurotransmitters, numerous genetic association studies have been conducted on genes related to peptide neurotransmitters. Most notably, the opioid system has repeatedly been found to be implicated in alcohol addiction [114–118]. There are 3 main opioid receptors and 3 main genes encoding endogenous ligands. OPRM1 encodes the μ-receptor, and its primary ligand β-endorphin is cleaved from the proopiomelanocortin protein, which is encoded by the POMC gene. OPRK1 encodes the κ-opioid receptor, and PDYN encodes the primary endogenous κ-opioid agonist (pro-)dynorphin. The δ-opioid receptor is encoded by OPRD1, and its primary ligand metenkephalin is synthesized as a propeptide, proenkephalin A, which is encoded by the PENK gene [119]. The μ-opioid receptor gene OPRM1, and especially the coding variation Asn40Asp (rs1799971), has been extensively studied in relation to alcohol dependence [85]. Results have been inconclusive and partly conflicting, and a meta-analysis concluded that there was no reliable evidence for association [120]. On the other hand, a more recent meta-analysis has provided evidence for a role of the Asn40Asp variation in the therapeutic response to the μ-opioid antagonist naltrexone for prevention of relapse [121]. The κ-opioid system has also been linked to AUDs with both the PDYN gene coding for the κ-opioid agonist prodynorphin and the κ-opioid receptor gene OPRK1 showing a genetic association with alcohol dependence [122].

Gene-Environment Interactions The association of single molecular-genetic variations with psychiatric disorders including AUDs is modulated by nongenetic influences, and the interplay between specific candidate genes and environmental factors has been subject to numerous GxE interaction studies [32]. Environmental determinants that enhance the risk for AUD differ in both proximity to the disorder and its mechanism. Important risks for alcohol dependence include prenatal exposure to alcohol, growing up in a home with an alcoholic parent and being poorly monitored by one’s parents [137]. Other environmental factors exert their influence on an individual’s risk later in life. For instance, genetic effects on drinking are greater in urban versus rural residential settings [36, 37], as social restrictions inhibit development of genetically determined patterns of behavior. Findings from a Collaborative Study on the Genetics of Alcoholism (COGA) further suggest that variation within an AUD susceptibility gene (GABRA2; rs279871) also depends on a person’s marital status, with individuals carrying the high risk variant being less likely to be married. Marital status also abated the influence of other variants within GABRA2 [108]. GxE interactions have been reported for several gene variants that have previously been found to be associated with alcohol dependence including genes with inconsistent results in association studies like the DRD2/ANKK1 gene region, 5-HTTLPR, MAOA, catechol-O-methyltransferase, GABRA2 and the corticotropin-releasing hormone receptor 1 (CRHR1) [138–142]. Therefore, GxE interactions might contribute to the complexity of genetic associations, and systematic assessment of environmental factors might help to explain the difficulties in replicating certain results. Notably, GxE interactions are not limited to genes with weak associations, but even genetic variations with strong influence on alcohol-related behaviors and disorders exert their influences in the interaction with environmental factors. In fact, one of the most robust GxE interactions was observed in carriers of the ALDH2*2 variant. A substantial GxE interaction with respect to ALDH2 in the Japanese population was reported by Higuchi et al. [30]. The protective effect of the ALDH2*2 allele seemed to decrease substantially from 1979 to 1992 when the proportion of Japanese AUD patients heterozy82


gous for the ALDH2*2 soared from 2.5 to 13% [30]. Given the short time interval of observation, a hypothetical shift in allele frequencies within the Japanese population could not explain this result. More likely, the protective effect of the ALDH2*2 allele was reduced due to environmental influences, most likely social changes favoring alcohol consumption. In the same study, no alcohol-dependent patients homozygous for the ALDH2*2 were identified, although 120 patients would have been expected based on the allele frequency. It was concluded that the protective influence of the ALDH2 *2 allele was so substantial in homozygotes that further modulation by environmental influences did not occur [30].

Genetic Linkage Studies

Genetic linkage studies provide an unbiased methodology toward identifying genetic variations that affect the risk for a complex genetic disease such as alcohol dependence. The linkage approach is employed to identify genetic risk variants for a disorder in families with several affected members. Linkage studies test markers distributed across the genome, and if one such marker variant is located near a causal genetic variation, family members with the phenotype of interest would be expected to share larger numbers of the same marker allele than those without the phenotype. This provides information about the broad chromosomal region in which the causal variants are located, but, in most cases, not about specific genes. The linkage method is most effective in phenotypes with a mendelian inheritance pattern, which is rare in psychiatric disorders where common variants are unlikely to have large effects [143]. On the other hand, linkage studies are well suited for detecting genetic variants associated with a major risk increase (at least 5- to 10-fold) [144]. Although nowadays largely of historic interest, linkage studies have provided some useful information regarding chromosomal risk loci for alcohol dependence. The earliest whole-genome linkage studies on alcohol dependence were reported in 1998 [145, 146]. Large-scale linkage studies like the COGA [146] or the study by Long et al. [145] in a relatively homogeneous Native American population have pointed to a risk locus on chromosome 4q, which contains the ADH region. Additional evidence for a risk locus on chromosome 4q was found in the Irish Affected Sib Pair Study of Alcohol Dependence that additionally provided weaker, suggestive evidence for linkage on chromosomes 1q, 13q and 22q for alcohol dependence, on 2q, 9q and 19p for symptom count [147]. FurSamochowiec /Samochowiec /Puls / Bienkowski /Schott


role in the remodeling of the perineuronal extracellular matrix during synaptic plasticity [136], have both been found to be associated with the risk for alcohol dependence.

Genome-Wide Association Studies

Recent advances in large-scale array-based genotyping have opened up the possibility to conduct GWAS that by now largely replaced linkage studies as the method of choice for detecting genetic risk loci in an unbiased manner. These studies combine several advantages of linkage studies and candidate gene-based association studies: (a) the coverage of the entire genome without the need for strong a priori hypotheses and of candidate gene approaches; (b) the high resolution providing information about single genes; (c) the possibility to include sporadic and not only familial cases. In GWAS, several hundred thousands or millions of markers across the entire genome are analyzed in order to identify differences in allele or genotype frequencies between individuals positive or negative for a phenotype of interest. Since their introduction, GWAS have proven to be successful in identifying numerous genetic factors contributing to the risk for complex somatic diseases like breast cancer [152, 153], colorectal cancer [154], type 2 diabetes [155, 156] or heart disease [157], but also for psychiatric disorders like schizophrenia or bipolar disorder [158–160]. The GWAS approach is not without limitations, however. Due to the risk of false positives resulting from the extraordinary amount of multiple testing inherent to the method, conservative statistical corrections of the significance level are required that are likely to result in false-negative data. To overcome this limitation, very Genetics of Alcohol Dependence

large sample sizes are required to obtain the necessary statistical power. Moreover, GWAS are designed to identify relatively common polymorphisms associated with the risk for a disease. Rare genetic variants with potentially high influence on individual risk might thus be overlooked by GWAS, but can be still captured by linkage studies. Regarding alcohol-related disorders, GWAS have identified a number of chromosomal regions and candidate genes for AUDs, alcohol-related phenotypes and comorbid disorders. Johnson et al. [161] published in 2006 a pioneering GWAS of alcohol dependence from the COGA. The study was performed on 100,000 markers in 120 patients and 160 healthy controls. Fifty-one clusters of polymorphisms were identified that provided information about novel candidate genes for alcohol dependence, including genes involved in gene regulation, cell signaling, brain development and cell adhesion. Among these were the genes of the cell adhesion molecules cadherin 11 (CDH11) and cadherin 13 (CDH13). In the following years, several GWAS have been conducted to identify risk factors for alcohol dependence (table 1, first part) as well as genetic modifiers of alcohol consumption in the healthy population (table 1, second part). Additionally, a few GWAS have been aimed to identify associations with alcohol-related phenotypes, including both clinical features like withdrawal severity and basic behavioral and neurophysiological measures (table 1, third part). Several risk loci identified by GWAS confirm previously observed associations of genes involved in alcohol metabolism with a risk for alcohol dependence and drinking behavior. For example, Frank et al. [162] detected genome-wide significance for marker rs1789891 in the ADH gene cluster on chromosome 4q in a sample of 1,333 patients and 2,168 controls. This finding is well in line with prior results of linkage analyses reporting an association of this chromosomal region with alcohol dependence and alcohol-related phenotypes [147]. rs1789891 is also in high linkage disequilibrium with the functional ADHC1 Arg272Gln polymorphism that has been shown to affect alcohol oxidation in vitro. Further genome-wide evidence for a role of the ADH gene on the chromosome 4q cluster in the risk of alcohol dependence was provided by Park et al. [163], who additionally found genome-wide significance for the functional ALDH2 rs671 that has repeatedly been associated with alcohol consumption levels and risk for alcohol dependence (see above). This association was also confirmed at the genome-wide level in a 2-stage GWAS for alcohol use in a Japanese population performed by Takeuchi et al. [164]. For both, nominally Neuropsychobiology 2014;70:77–94 DOI: 10.1159/000364826

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ther loci identified in the COGA that were replicated in a follow-up [148] include chromosomes 1 and 7. Other linkage studies have focused on disease-related phenotypes rather than on the diagnosis per se. An analysis combining the diagnostic phenotype with an electrophysiological variable (amplitude of the P300, an eventrelated potential related to the processing of rare or novel events) also pointed to a risk locus on chromosome 4q [149]. A quantitative trait, maximum drinks in a 24-hour period, also showed linkage on chromosome 4q [150], on chromosome 9 for age of onset, on chromosomes 1 and 11 for initial response to alcohol, on chromosomes 1, 6 and 22 for tolerance, on chromosomes 12 and 18 for maximum drinks, and on chromosome 2 for withdrawal symptoms [151]. As these linkage studies only provide information about chromosomal regions associated with a disorder, a challenge for future research remains the identification of the specific risk genes at these chromosomal loci.

Authors, year

Number of patients/controls

Identified gene loci

GWAS of alcohol dependence Johnson et al. COGA [161], 2006

120 patients, 160 controls

51 gene loci remain significant after Monte Carlo simulationbased correction, including CDH11, CDH13 FDR threshold less conservative than in later GWAS

Treutlein et al. [182], 2009

n.a.

Initial sample: 487 patients, 1,358 controls; follow-up sample: 1,024 patients, 966 controls

Genome-wide significance for 2 intergenic loci on chromosome 2q35: rs7590720, rs1344694 in the combined sample 15 nominally significant SNPs were replicated, including SNPs in CDH13 and ADH1C

Edenberg et al. [169], 2010

COGA

Initial sample: 1,192 patients, 692 controls; follow-up in 292 pedigrees

No SNP with genome-wide significance Initial sample and follow-up sample provide converging evidence for a gene cluster on chromosome 11, encompassing SLC22A18, PHLDA2, NAP1L4, SNORA54, CARS, OSBPL5

Bierut et al. [168], 2010

SAGE (includes COGA, FSCD, COGEND)

1,897 patients, 1,932 controls; 2 replication samples

No SNP with replicated genome-wide significance Replicated nominal significance for GABRA2 SNPs

Lind et al. [200], 2010

OZALC

Initial sample: 1,224 patients, 1,162 controls from the Australian Twin Registry; replication sample: 555 patients, 2,768 controls from the Netherlands

No replicated genome-wide significance Meta-analysis of initial and replication samples provides evidence for risk variants in genes coding ion channels and cell adhesion molecules

Kendler et al. [186], 2011

MGS2

3,169 controls from the MGS2 study screened for symptoms of alcohol dependence

No genome-wide significance Most significant loci KCNMA1 (Caucasians) and SLC35B4 (African-Americans)

Wang et al. [166], 2011

COGA, OZALC

272 nuclear families from 116 pedigrees (COGA); replication in the OZALC cohort (twin study)

Low density GWAS with 11,120 SNPs and less conservative threshold (p < 10 – 3) Genome-wide significance at p < 10 – 8 for DSCMAL1 Further replicated loci include TPARP, CYFIP2, THEMIS, PSG11

Wang et al. [167], 2011

COGA, SAGE, OZALC

COGA: 1,025 patients, 569 controls; SAGE: 637 patients, 1,032 controls; replication in OZALC: 1,650 patients, 1,684 controls from 778 nuclear families

No genome-wide significance Replicated novel associations with KIAA0040, THSD7B, NRD1 Confirmed association with PKNOX2

Frank et al. [162], 2012

n.a.

1,333 patients, 2,168 controls

Genome-wide significance for rs1789891 in ADH1 gene cluster, in LD with functional ADH1C Arg272Gln, nominally replicated in COGA sample Feasibility of polygenic score analysis was demonstrated

Zuo et al. [172, 173], 2011, 2012

COGA, SAGE


Genome-wide significance for KIAA0040 (chromosome 1q) Replicated association for 90-Mb region around the PHF3PTP4A1 locus on chromosome 6q12

Biernacka et al. [201], 2013

SAGE


Gene set enrichment analysis, not aimed at single genome-wide significant loci Pathway analysis points to potential involvement of ketone metabolism and ligand-receptor interactions

Zuo et al. [202], 2013

COGA, SAGE

Initial sample: 1,409 patients, 1,518 controls; replication sample: 6,438 European-Australian family subjects including 1,645 alcoholdependent patients

Genome-wide significance for NKAIN1-SERINC2 in subjects of European, but not African descent 471 SNPs nominally associated, 53 survived region- and cohortwide correction, 92 were replicated

Zuo et al. [203], 2013

SAGE, COGA

Initial sample: 818 European-American patients with alcohol and nicotine codependence, 1,396 controls; 2 replication samples

Genome-wide significant association of combined alcohol and nicotine dependence with rs7445832 in IPO11-HTR1A region on chromosome 5q 4 genome-wide significant SNPs in IPO11-HTR1A region revealed by meta-analysis of discovery and replication samples

Park et al. [163], 2013

n.a.

Initial sample: 117 alcohol-dependent patients, 279 controls; replication sample: 504 patients, 471 controls

Genome-wide significane for rs1442492 and rs10516441 in ADH7 gene region and ALDH2 rs671 Multiple nominally significant SNPs in ADH gene cluster on 4q22–q23

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Study name


Samochowiec /Samochowiec /Puls / Bienkowski /Schott


Table 1. Overview of GWAS on alcohol-related disorders

Table 1 (continued) Authors, year

Study name

Number of patients/controls

Identified gene loci

Quillen et al. [165], 2014

n.a.

352 males, 243 females from an isolated rural Chinese population

Genome-wide significance for ALDH2*2 ALDH2*2 also shows genome-wide significant association with maximum alcohol intake and flushing response

Gelernter et al. [171], 2014

GCD

Initial sample: 379 European-Americans, 3,318 African-Americans; replication sample: 1,746 European-Americans, 803 African-Americans; further replication in SAGE sample and in a German sample; total n = 16,087 subjects

Replicated genome-wide significance for ADH1B, ADH1C Novel genome-wide significant risk variant rs1437396 between MTIF2 and CCDC88A

GWAS of alcohol consumption Schumann et al. n.a. [204], 2011

26,316 individuals from 12 population-based samples

Genome-wide significance for AUTS2 rs6943555 Nominal significant association with RASGRF2 rs26907

Baik et al. [205], 2011

n.a.

Initial sample: 1,721 males; replication sample: 1,113 males

Replicated genome-wide significance for SNPs on chromosome 12q24, including C12ORF51 rs2074356, C12orf51 in LD with ALDH2, CCDC63, MYL2 Genome-wide significance, but no replication for SNPs in CCDC63, OAS3, CUX2 and RPH3A

Takeuchi et al. [164], 2011

n.a.

Initial sample: 733 ever-drinkers, 729 nondrinkers; replication sample: 2,794 drinkers, 1,521 occasional drinkers, 1,351 nondrinkers

genome-wide significance for ALDH2 rs671 in both discovery and replication sample replicated nominal significance for ADH1B rs1229984

Chen et al. [206], 2012

n.a.

Initial sample: 904 Caucasians, 3 replication samples (n = 1,972, 761, 2,955)

Genome-wide significance for SNP clusters in ANKRD7, CYTL1 Most significant SNPs ANKRD7 rs4295599, CYTL1 rs16836497

Heath et al. [170], 2011

n.a.

8,754 individuals including 2,062 patients

No genome-wide significance Convergent evidence for association of ANKS1A rs2140418 and TMEM108 rs10935045 with drinking severity

Kapoor et al. [207], 2013

COGA, SAGE

2,322 subjects from 118 extended families; 2,593 subjects from a case-control study

No genome-wide significance Meta-analysis suggests association of LMO1 and PLCL1 with maximum number of drinks Nominal association of AUTS2, INADL, C15ORF32 and HIP1 with measures of alcohol consumption in meta-analysis

Pan et al. [208], 2013

COGA, SAGE

1,059 subjects from COGA study, 1,628 subjects from SAGE study; replication in OZALC sample

Genome-wide significant association of rs11128951 near SGOL1 with maximum number of drinks Meta-analysis additionally shows association of rs17144687 near DTWD2, rs12108602 near NDST4 and rs2128158 in KCNB2 with maximum number of drinks

2,322 subjects from 118 extended families; replication in SAGE and OZALC samples

Genome-wide significance for 3 SNPs in C15ORF53 gene with alcohol dependence symptom count, albeit only nominally significant after inflation correction

GWAS of alcohol-related phenotypes Wang et al. COGA [176], 2013 Wang et al. [209], 2012

COGA

461 patients, 408 controls

No genome-wide significance 4 SNPs nominally associated with alcohol withdrawal severity: rs770182 (near EFNA5, chromosome 5q21); rs10975990, rs10758821 and rs1407862 (KDM4C, 9p24.1)

Joslyn et al. [179], 2010

n.a.

367 healthy individuals with positive family history for alcohol dependence

Gene set enrichment analysis, not aimed at single genome-wide significant loci 156 loci pointing to 173 genes, mostly in neuronal signaling pathways, influence alcohol level of response

Zlojutro et al. [177], 2011

COGA

1,192 patients, 692 controls; replication in 262 pedigrees (1,195 individuals)

No genome-wide significance, ARID5A rs4907240 and HTR7 rs7916403 nominally associated with novelty-related theta oscillations HTR7 rs7916403 nominally associated with diagnosis of alcohol dependence



85


FDR = False discovery rate; n.a. = not available; SNP = single-nucleotide polymorphism; SAGE = Study of Addiction: Genetics and Environment; FSCD = Family Study of Cocaine Dependence; COGEND = Collaborative Genetic Study of Nicotine Dependence; OZALC = Australian Twin-Family Study of Alcohol Use Disorder; MGS2 = Molecular Genetics of Schizophrenia; GCD = GWAS discovery samples; LD = linkage disequilibrium.

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sults of transcriptome-wide expression analyses showing that the transcripts of these genes were involved in regulatory mechanisms of other alcohol-related candidate genes [172, 173]. At the system level, it has been suggested that the related endophenotypes are more directly related to the molecular effect of a gene variant than the clinical phenotype [174, 175]. A potential endophenotype solely based on clinical criteria is the symptom count as a crude, but straightforward measure of disease severity that can be applied in both patients and healthy controls. In a GWAS directed at alcohol dependence symptom count, Wang et al. [176] found converging evidence for a role of the thus far poorly characterized C15ORF53 gene in the etiology of alcohol dependence-related phenotypes. Other endophenotypes show a weaker statistical association with the clinical phenotype of interest, but might be more directly related to the underlying pathophysiological mechanisms. For example, novelty-related brain oscillations in the theta band have previously been found to be linked to alcohol dependence. Zlojutro et al. [177] performed a 2-stage GWAS on event-related theta oscillations in 1,192 patients and 692 controls, using 262 pedigrees (1,095 individuals) as a replication sample. They identified genetic variations in the ARID5A gene (rs4907240) and in the gene coding for the serotonin receptor 7 (HTR7, rs7916403) to be associated with novelty-related theta activity.

From Genes to Pathways

The genes associated with alcohol dependence in both GWAS and replicated candidate-based association studies are, at first sight, a rather heterogeneous group, with their products being involved in diverse biological functions, including (peripheral) alcohol metabolism, neurotransmission, intracellular signaling, but also seemingly unrelated processes like immune function. For example, the KIAA0040 risk variant identified in a GWAS by Zuo et al. [172] and replicated in the meta-analysis by Wang et al. [167], encodes an HLA1-DR11-restricted T cell epitope with yet unknown function [178]. However, expression quantitative trait locus analysis has demonstrated that the expression of the KIAA0040 gene is associated with the expression of multiple other genes involved in neurotransmission, including several genes previously implicated in alcohol dependence and other psychiatric disorders, such as DRD2-TTC12, DRD3, HTR1B, GRM5 and OPRD1 [172]. Further evidence for a role of genes Samochowiec /Samochowiec /Puls / Bienkowski /Schott


classified drinking behavior (nondrinkers vs. occasional drinkers vs. drinkers) and level of alcohol consumption, ALDH2 rs671 came out as the strongest predictor with genome-wide significance. The most recent replication of the prominent role for ALDH2 in the modulation of drinking behavior in East Asians comes from a GWAS by Quillen et al. [165], which was performed in an isolated rural Chinese sample. In addition to these highly expected findings, GWAS have also helped to identify novel risk loci for alcohol dependence and related phenotypes, albeit resulting in more ambiguous results (see table 1 for a summary). While some studies did not yield any genetic variations with genome-wide significance, others identified several candidate loci, many of which could, however, not be reliably replicated subsequently. A reason for the inconclusive results of most GWAS might be insufficient statistical power resulting from required corrections for multiple testing. Means to circumvent this problem include metaanalyses of the literature and reanalyses of pre-existing original data, both of which increase statistical power. For example, Wang et al. [166, 167] combined data sets from the SAGE, the COGA and the OZALC GWAS [168–170] and identified several gene loci not reported in the initial publications (table 1, first part). In a very recent GWAS, Gelernter et al. [171] included a total of 16,087 subjects, both newly recruited and from pre-existing samples. The study yielded strong genome-wide associations with variants in the ADH gene cluster and additionally identified a putatively regulatory variant rs1437396, located between the MTIF2 and CCDC88A genes as a novel risk variant with replicated genome-wide significance [171]. A further scientific challenge posed by GWAS is due to the identification of numerous associations that have been observed with genetic variants that are either extragenic or located in thus far poorly characterized genes. To understand the biological processes that underlie the observed associations, it is therefore necessary to further characterize the impact of the identified variants at both the molecular and system levels. At a molecular level, expression quantitative trait locus studies allow the identification of both cis and trans regulatory associations of genetic variations with gene expression levels. Zuo et al. [172, 173] combined the GWAS approach with expression quantitative trait locus in the analysis of data from 2,090 alcohol-dependent patients and 2,026 controls. The observed associations of variants in PHD finger protein 3 gene (PHF3), protein tyrosine phosphatase type IVA, member 1 gene (PTP4A1) region [173], and KIAA0040 [172] genes with alcohol dependents corresponded to re-

Summary and Future Perspectives

Considerable progress has been made in the research of genetic contributions to alcohol dependence. For several genetic variations, a prominent role in the risk for developing alcohol-related disorders is largely undisputed. Most prominently, several functional variations in genes involved in alcohol metabolism, namely the ADH and ALDH genes, play a major role in modulating alcohol-related behaviors and thereby affect the individual risk for AUD. This is particularly true for the ALDH2*2 variant that strongly predicts drinking behavior in East Asian populations, with effect sizes only comparable to the robust association of APOE/TOMM40 with dementia [188]. In populations of European ancestry, in which the coding variations in ALDH are far less common, the greatest proportion of the individual genetic risk is yet unexplained and probably reflects the accumulating effects of multiple genes with minor individual impact. However, the importance of alcohol-metabolizing enzymes becomes evident as most robust associations in Europeans have been observed in the ADH gene cluster. With recent advances in high-throughput genotyping Genetics of Alcohol Dependence

and bioinformatics, GWAS in large samples become feasible that allow the robust detection of variants beyond the metabolism-related genes [171]. Additionally, rare genetic variations that are insufficiently detected by GWAS as well as gene-gene and GxE interactions [189– 191] are likely to contribute heritability, phenomena that are actually common to the genetics of most psychiatric disorders [192]. A more comprehensive characterization of the genetic susceptibility to AUD will most likely require (a) a more refined characterization of alcohol-related phenotypes (e.g. subtypes of alcohol dependence with higher vs. lower heritability) and (b) advanced analytic techniques that make use of both array and next-generation sequencing technologies [193]. A comprehensive discussion of studies applying these approaches is beyond the scope of this review, but we refer to recent review papers that cover GWAS of alcohol dependence [182, 186, 194–196]. A potential future challenge might be posed by the change in diagnostic criteria with the publication of DSM-V [6]. While merging the diagnostic criteria into a single entity ‘substance use disorders’ with varying degrees of severity might ease the definition of endophenotypes or of semiquantitative approaches like symptom count [176], great care must be taken when comparing studies based on DSM-V with previously published DSM-IV-based work. Also, GxE interactions must be considered [189–191], although keeping in mind that the GxE approach is one of the most challenging and complex of all conceptual frameworks applied in the genetic investigation of AUDs [197]. From the statistical point of view, lack of power is a weakness of most genetic investigations in psychiatry, a problem that is actually shared by many disciplines within neurosciences and contributes to poor replicability of data [198]; very large samples are required to overcome this essential limitation. Preliminary findings must be confirmed in independent studies, and much work remains to be done to elucidate the neural mechanisms underlying the genetic associations. Nevertheless, owing to the emergence of new technologies allowing to study even larger sample sizes, progress will be swift. The future will involve studies of epigenetic factors, copy number variants and other rare genetic variations, and gene expression data. As we learn more about the genetic framework of AUD, we find that different genetic variants lead to varying responses to treatment options, which might improve our ability to design individualized therapy concepts [135, 199]. One example for the potential pharmacogeNeuropsychobiology 2014;70:77–94 DOI: 10.1159/000364826

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involved in neurotransmission and intracellular signaling comes from a gene set enrichment analysis (GSEA) on GWAS data performed by Joslyn et al. [179]. GSEA is a computational method that assesses differential distribution of a priori defined sets of genes between populations of interest. Joslyn et al. observed that level of alcohol response phenotypes (i.e. self-report on the effects of ethanol, subjective high assessment, body sway [180]) showed a systematic distribution difference in 173 genes, including CDH13 and XRCC5, which had been identified as susceptibility genes for ethanol sensitivity and alcohol dependence elsewhere [161, 181, 182]. Multiple genes identified in the GSEA were related to glutamatergic signaling, a finding well in line with previous results of animal studies and candidate gene analyses [183, 184]. Additionally, the muscarinic receptor (CHRM2) has been replicated in 2 GWAS [185, 186] and several other well-replicated candidate genes (i.e. >10 studies), such as GABRA2 [168], MAOA [166], GRIN2B [179] and ANKK1 [186], have emerged in recent GWAS, further confirming the role of the cholinergic, dopaminergic and GABAergic transmitter systems. Many of these variants do not only affect the risk for AUD, but also have an influence on other substance-related disorders (e.g. GABRA2, CHRM2, NFKB1) [68, 106, 132, 135, 187].

netic approaches is the association of the OPRM1 Asn40Asp variation with the efficacy of the opioid antagonist naltrexone on relapse prevention [117], although it remains to be established whether this association is of clinical significance. Irrespective of the actual magnitude of the effect of OPRM1 Asn40Asp, this observation should stimulate large-scale studies investigating the role of genetic markers in the response to pharmacotherapy, with the possibility to perform GWAS on treatment outcomes being a promising approach in the near future. More generally, from a clinical perspective, distinct phenotype characterization of a vast number of individuals including data on AUDs and alcohol-related phenotypes, comorbid psychiatric and medical disorders as well

as therapy response is needed to facilitate the identification of patients that might benefit from certain therapeutic lines. The above considerations refer both to pharmacotherapeutic and to psychotherapeutic approaches. The identification of reliable genetic markers predicting the response to psychotherapy, medications and their combination remains a great scientific challenge for the future. Acknowledgements This work was supported by the Deutsche Forschungsgemeinschaft (SFB 779, TP A8). We thank Claudia Hägele for her helpful comments on the paper.

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