Next Article in Journal
Time to Spread Your Wings: A Review of the Avian Ancient DNA Field
Previous Article in Journal
Evolutionary Origins of Cancer Driver Genes and Implications for Cancer Prognosis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 8
Issue 7
10.3390/genes8070183
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genetic Contribution to Alcohol Dependence: Investigation of a Heterogeneous German Sample of Individuals with Alcohol Dependence, Chronic Alcoholic Pancreatitis, and Alcohol-Related Cirrhosis

by
Jens Treutlein
1,†,
Josef Frank
1,†,
Fabian Streit
1,
Céline S. Reinbold
2,3,
Dilafruz Juraeva
4,
Franziska Degenhardt
5,6,
Liz Rietschel
7,
Stephanie H. Witt
1,
Andreas J. Forstner
2,3,5,6,8,
Monika Ridinger
9,10,
Jana Strohmaier
1,
Norbert Wodarz
9,
Helene Dukal
1,
Jerome C. Foo
1,
Per Hoffmann
2,3,5,6,
Stefan Herms
2,3,5,6,
Stefanie Heilmann-Heimbach
5,6,
Michael Soyka
11,12,
Wolfgang Maier
13,
Wolfgang Gaebel
14,
Norbert Dahmen
15,
Norbert Scherbaum
16,
Bertram Müller-Myhsok
17,
Susanne Lucae
18,
Marcus Ising
19,
Felix Stickel
20,21,
Thomas Berg
22,
Ulla Roggenbuck
23,
Karl-Heinz Jöckel
23,
Henrike Scholz
24,
Ulrich S. Zimmermann
25,
Stephan Buch
26,
Wolfgang H. Sommer
27,28,
Rainer Spanagel
27,
Benedikt Brors
4,
Sven Cichon
2,3,
Karl Mann
28,
Falk Kiefer
28,
Jochen Hampe
26,
Jonas Rosendahl
29,
Markus M. Nöthen
5,6 and
Marcella Rietschel
1,*add Show full author list remove Hide full author list
1
Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
2
Human Genomics Research Group, Department of Biomedicine, University and University Hospital Basel, 4031 Basel, Switzerland
3
Institute of Medical Genetics and Pathology, University Hospital Basel, 4031 Basel, Switzerland
4
Division of Applied Bioinformatics (G200), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
5
Institute of Human Genetics, University of Bonn, 53127 Bonn, Germany
6
Department of Genomics, Life & Brain Center, University of Bonn, 53127 Bonn, Germany
7
University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
8
Department of Psychiatry (U.P.K.), University of Basel, CH-4002 Basel, Switzerland
9
Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany
10
Psychiatric Health Care Aargau, 5210 Windisch, Switzerland
11
Private Hospital Meiringen, 3860 Meiringen, Switzerland
12
Psychiatric Hospital, Ludwig Maximilians University, 80336 Munich, Germany
13
Department of Psychiatry, University of Bonn, 53105 Bonn, Germany
14
Department of Psychiatry and Psychotherapy, University of Düsseldorf, 40629 Düsseldorf, Germany
15
Department of Psychiatry, University of Mainz, 55131 Mainz, Germany
16
LVR-Hospital Essen, Department of Psychiatry and Psychotherapy, Medical Faculty, University of Duisburg-Essen, 45147 Essen, Germany
17
Department of Statistical Genetics, Max-Planck-Institute of Psychiatry, 80804 Munich, Germany
18
Department of Psychiatric Pharmacogenetics, Max-Planck-Institute of Psychiatry, 80804 Munich, Germany
19
Department of Molecular Psychology, Max-Planck-Institute of Psychiatry, 80804 Munich, Germany
20
Department of Gastroenterology and Hepatology, University Hospital of Zurich, 8091 Zurich, Switzerland
21
Hirslanden Private Hospital, 8032 Hirslanden Zürich, Switzerland
22
Section of Hepatology, University Hospital Leipzig, 04103 Leipzig, Germany
23
Institute for Medical Informatics, Biometry, and Epidemiology, University Hospital of Essen, 45122 Essen, Germany
24
Department of Animal Physiology, University of Cologne, 50674 Cologne, Germany
25
Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Dresden Technical University, 01307 Dresden, Germany
26
Medical Department 1, University Hospital Dresden, TU Dresden, 01307 Dresden, Germany
27
Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
28
Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
29
Department of Internal Medicine I, Martin Luther University Halle, 06120 Halle (Saale), Germany
 
add Show full affiliation list
 
remove Hide full affiliation list
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Genes 2017, 8(7), 183; https://doi.org/10.3390/genes8070183
Submission received: 27 June 2017 / Accepted: 11 July 2017 / Published: 17 July 2017
(This article belongs to the Section Human Genomics and Genetic Diseases)
Versions Notes

Abstract

:
The present study investigated the genetic contribution to alcohol dependence (AD) using genome-wide association data from three German samples. These comprised patients with: (i) AD; (ii) chronic alcoholic pancreatitis (ACP); and (iii) alcohol-related liver cirrhosis (ALC). Single marker, gene-based, and pathway analyses were conducted. A significant association was detected for the ADH1B locus in a gene-based approach (puncorrected = 1.2 × 10−6; pcorrected = 0.020). This was driven by the AD subsample. No association with ADH1B was found in the combined ACP + ALC sample. On first inspection, this seems surprising, since ADH1B is a robustly replicated risk gene for AD and may therefore be expected to be associated also with subgroups of AD patients. The negative finding in the ACP + ALC sample, however, may reflect genetic stratification as well as random fluctuation of allele frequencies in the cases and controls, demonstrating the importance of large samples in which the phenotype is well assessed.

1. Introduction

Genetic influences play a major role in the development of alcohol use disorders as formal genetic studies in twins and epidemiological samples have shown [1,2,3]. Candidate studies and genome-wide association studies (GWAS) have identified numerous candidate genes for alcohol dependence (AD) and alcohol consumption (AC).
In candidate gene studies, the most consistently reported associations for both traits have been obtained for the genes alcohol dehydrogenase 1B (ADH1B; chr4q23) [4], alcohol dehydrogenase 1C (ADH1C; chr4q23) [5], and aldehyde dehydrogenase 2 (ALDH2; chr12q24) [6].
The advantage of GWAS studies which screen the entire genome with millions of variants is that they facilitate gene identification in novel biological contexts. Indeed, recent successes have been achieved for complex traits with low heritability, such as depression [7] in large samples comprising more than one hundred thousand individuals. Previous GWAS of AD have been conducted in samples with a lower number of individuals [8,9,10]. However, they have also most consistently identified variants in the ADH gene cluster. The up-to-now largest GWAS of AC which contained more than one hundred thousand individuals from the UK Biobank, and identified ten loci reaching genome-wide significance, also showed the best finding in the ADH1B/ADH1C region (rs145452708, p = 8.93 × 10−29) [11]. Additionally, earlier GWAS of AC identified rs1229984 and other variants at the chr4q22/q23 region in/near the ADH gene cluster [12,13,14].
A promising approach to mitigating the burden of multiple testing, which limits the single-marker approach, is to analyze the aggregated contribution of variants in single genes and in functionally related gene groups (e.g., biological pathways), under the assumption that these contain a large number of variants with a disruptive influence on gene/pathway function.
In the present study, multi-marker analyses were performed in order to detect new genes and pathways for AD and/or to confirm prior reported genes and pathways. To increase sample size in order to maximize statistical power, individuals with alcohol-related somatic disease were also included. Gene-wide significance for the ADH1B gene was detected in the combined sample. However, the findings demonstrated that very large sample sizes are warranted to overcome heterogeneity and/or random genetic fluctuation.

2. Materials and Methods

2.1. Participants

The cohort comprised individuals from three previously reported German samples: (i) 1331 AD patients and 1934 controls [9]; (ii) 1110 patients with chronic alcoholic pancreatitis (ACP) [15]; and (iii) 400 patients with alcohol-related liver cirrhosis (ALC) [16]. A control sample (n = 1750) for the ACP + ALC cases was drawn from the Heinz Nixdorf Recall (HNR) study [17].
The study was approved by the ethics committee II of Medical Faculty Mannheim of Heidelberg University study number 2012-361N-MA, and was carried out in accordance with the Declaration of Helsinki. All subjects provided written informed consent prior to inclusion.
Alcohol dependence (AD) case-control subsample: A detailed description of the sample is provided elsewhere [9]. All patients were of self-reported German ancestry, and fulfilled Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) criteria for AD [18]. The patients were recruited from consecutive admissions to psychiatric units at university hospitals participating in the German Addiction Research Network [19]. These five study centers are located in the following areas of central and southern Germany: Mannheim, Bonn/Essen/Düsseldorf/Homburg, Regensburg, Munich, and Mainz. Controls were drawn from the following three population-based epidemiological cohorts: (i) KORA-gen [20]; (ii) PopGen [21]; and (iii) HNR [17]. Further controls were drawn at random from a Munich community sample screened using the Composite International Diagnostic Interview. The AD case-control subsample is part of the Psychiatric Genomics Consortium (PGC) [22]. The GWAS data from the samples can be made available within the context of research collaborations.
Chronic alcoholic pancreatitis (ACP) patients: A diagnosis of ACP was assigned in patients with a history of ≥2 years ingestion of ≥80 g alcohol/day (men), or ≥60 g/day (women). Most patients exceeded these cut-offs for level and/or duration. The cohort included patients from a number of European countries [15]. However, only German ACP patients were included in the present analyses. These individuals were recruited in Berlin, Dresden, Erlangen, Heidelberg, Greifswald, Leipzig, Magdeburg, Mannheim, and Munich.
Alcohol-related liver cirrhosis (ALC) patients: A detailed description of the criteria used to define case status is provided in [16]. ALC patients presented with clinically-diagnosed, or biopsy-confirmed, cirrhosis and a ≥10-year history of a past and/or present alcohol consumption level of ≥80 g/day (men), or ≥60 g/day (women). In all cases, other causes of cirrhosis were excluded. ALC cases were recruited from university hospital departments of hepatology and gastroenterology in: (i) Germany (Bonn, Regensburg, Dresden, Leipzig, Kiel, Frankfurt); (ii) Austria (Salzburg); and (iii) Switzerland (Bern).
Controls for ACP and ALC patients: Controls were drawn from the HNR study [17].

2.2. Genotyping

AD case-control subsample: Patient samples were individually genotyped using the Illumina HumanHap550, Human610Quad, or Human660w Quad BeadChips (Illumina, Inc., San Diego, CA, USA). Controls were genotyped using the Illumina HumanHap550 Bead Chip.
ACP patient subsample, ALC patient subsample, and control sample for ACP and ALC patients: The individuals were genotyped using the Illumina Omni Express BeadChip.

2.3. Quality Control

Quality control (QC) and single marker association testing were performed using PLINK v1.9 [23]. Prior to QC, the genotype data of the three samples were merged. QC of the merged data was performed in accordance with the protocol of the Schizophrenia Working Group of the Psychiatric Genomics Consortium [24]. The analyses were restricted to autosomal single nucleotide polymorphisms (SNPs) only. In the combined sample, genetic outliers were identified using principal component analysis (PCA). Outlier status was defined as the presence of data points located more than 6 standard deviations from the mean on any of the first 20 principal components. The respective individuals were excluded from further analysis. After the removal of PCA outliers, the first and second principal components showed a nominally significant association with AD, and were included as covariates in all association analyses. As the present sample was comparatively small, a stringent Hardy–Weinberg Equilibrium (HWE) test cutoff of p > 0.05 was applied to the controls of the combined sample in order to optimize the quality of the genotyping clusters for the purposes of multimarker analysis. A total of 6525 (out of 6894) individuals and 257,866 variants passed all filters. Of the 6525 individuals, 2841 were cases (1331 AD patients, 1110 ACP patients, 400 ALC patients), and 3684 were controls (1934 controls from the AD subsample, and 1750 controls for the combined ACP and ALC patient sample). After linkage disequilibrium (LD)-based pruning (Variance Inflation Factor of 10), a total of 194,024 SNPs remained for the combined sample and subsample analyses.

2.4. Statistical Analysis

The single marker analysis was conducted using PLINK v1.9 [23]. This involved logistic regression, an additive model of inheritance and correction for population stratification by including the first two principal components as covariates. Gene-based and pathway-based analysis was conducted using MAGMA v1.04 [25]. For the pathway analysis, output files from the gene-based analysis were used as input. SNPs were assigned to a gene if the variant was located within the gene sequence or within 20 kb of the transcript. If a variant was located within a region shared by more than one gene, the variant was assigned to all of the respective genes. Version 5.1 of the Reactome database set was retrieved from the Molecular Signatures Database [26]. The Reactome v5.1 set comprises 674 pathways.
For the post hoc analysis, genotypes for the ADH variant rs1789891 were counted using the “hardy” option in PLINK. Deviation from HWE (exact test) was calculated using the DeFinetti program [27]. SNAP [28] was used to generate the regional association plot. The database dbSNP [29] was used to retrieve the nucleotide triplet for the amino acid exchange.

3. Results

A single marker analysis in the combined sample generated no genome-wide significant findings (Table S1). The top five variants were: (i) rs10392 (p = 8.336 × 10−7; Odds Ratio (OR) = 1.252), located in PPP1R16B (protein phosphatase 1 regulatory subunit 16B), and near FAM83D (family with sequence similarity 83 member D); (ii) rs454510 (3.334 × 10−6; OR = 1.206) near zinc finger protein 697 (ZNF697); (iii) rs2028201 (4.928 × 10−6; OR = 1.183) in spermatogenesis associated serine rich 2 like (SPATS2L); (iv) intergenic variant rs926544 (p = 1.133 × 10−5; OR = 0.8113) and (v) rs1789891 (p = 1.315 × 10−5; OR = 1.232) near alcohol dehydrogenase 1B (ADH1B) and near alcohol dehydrogenase 1C (ADH1C).
In the gene-based approach, only one finding achieved genome-wide significance. This was the association with alcohol dehydrogenase 1B (ADH1B) (puncorrected = 1.2 × 10−6; pcorrected = 0.020; Table 1). The Bonferroni corrected gene-based genome-wide significance threshold was 2.9 × 10−6 (0.05/16853 genes). In the combined genotype data, 16,853 genes were represented. Table 2 shows the variants of the ADH1B locus that were included in the gene-based analysis. This association was driven by the AD subsample (AD: puncorrected = 6.5 × 10−10; ACP + ALC: puncorrected = 0.69).
The ADH variant which made the strongest contribution to the gene-wide significance of ADH1B was rs1789891, which is located within ±20 kb of ADH1B and ADH1C and contributes to the gene-based p-values of both genes (Figure S1). The rs1789891 association was driven by the AD subsample (combined sample: p = 1.315 × 10−5, ORA-allele = 1.232; ACP + ALC subsample: p = 0.6392, ORA-allele = 1.033; AD subsample: p = 1.642 × 10−8, ORA-allele = 1.469) (Table S1). The rs1789891 “risk” allele was A and the “protective” allele was C.
In the control population of the combined cohort, the rs1789891 allele frequency was 0.154 for the A-allele. The A-allele frequency of the controls was 0.141 for the AD subsample and 0.169 for the ACP + ALC subsample (Table 3), the latter being similar to that reported in the 1000 Genomes Phase 3 data (A-allele: 0.167; CEU subpopulation) [30].
In the pathway analysis of the combined sample, the top finding was the “Ethanol_Oxidation” gene set, which contains ADH1B (puncorrected = 2.2 × 10−4; pcorrected = 0.15; Table 4).

4. Discussion

To facilitate both the identification of new genes and pathways for AD and the replication of previous results, the present study combined the cohorts of three previous investigations in order to increase sample size to increase statistical power. The only association to withstand correction for multiple testing was the association with ADH1B in the gene-based test. This association was attributable to the AD subsample, and no association was detected in the ACP + ALC subsample. On first inspection, the lack of association with ADH1B in the combined ACP + ALC sample may seem surprising, since this gene is one of most consistently reported genes for AD and AC, and achieved genome-wide significance in the present AD subsample. In the AD patient subsample, the frequency of the A allele was higher (19.4% vs. 17.9%), and in the controls lower (14.1% vs. 16.9%) than in the ACP + ALC subsample. The lack of association may be attributable to random or systematic genetic differences within the patient and/or control samples.

4.1. Patients

  • For ACP and ALC, no explicit diagnosis of AD was required, and these patients may therefore differ in terms of genetic disposition. However, the ACP and ALC patients were recruited from a clinic specialized in the treatment of alcohol-induced somatic disorders. Furthermore, in each patient, the respective disorder had been induced by excessive alcohol consumption, and the majority of patients were unable to abstain from alcohol despite the assignment of the somatic diagnosis.
  • The differing distribution of rs1789891 in the AD and ACP + ALC samples is non-random. ADH1B metabolizes alcohol to acetaldehyde, and research suggests that the adverse effects of acetaldehyde inhibit further drinking [31,32,33]. Alleles that confer an increased rate of alcohol metabolism may also contribute to tissue damage [34]. This was illustrated in a recent study from Japan, which analyzed rs1229984 (Arg48His) in ADH1B. The ADH1B 48His variant leads to an increased level of acetaldehyde and is thus protective in terms of AD development. The authors found that the ADH1B 48His variant was overrepresented in patients with alcoholic liver cirrhosis and chronic alcoholic calcific pancreatitis [35]. ADH1B_48His has a low frequency in Europeans [36,37,38], was not present in our genotyping arrays, and could not be imputed with sufficient imputation quality (R2 = 0.44).
In the present analyses, the ADH1B variant with the lowest p-value was rs1789891 (p = 1.315 × 10−5). The rs1789891 variant is located between the genes ADH1B and ADH1C. Although both genes are expressed in the liver [39] and pancreas [40], rs1789891 has no known function according to the NCBI Phenotype-Genotype Integrator [41]. However, rs1789891 is in high LD with the functional variants rs1693482 (Arg272Gln) and rs698 (Ile350Val) in the gene ADH1C (Figure S2). These two ADH1C missense variants Arg272Gln and Ile350Val typically occur together (r2 = 1.0), and result in two different forms of ADH1C: (i) the ADH1C isoenzyme gamma1 (ADH1C*1), in which arginine is present at amino acid position 272 and isoleucine at amino acid position 350; and (ii) the ADH1C isoenzyme gamma2 (ADH1C*2), in which glutamine is present at amino acid position 272 and valine at amino acid position 350. Although ADH1C_Arg272/Ile350 (which correspond to the rs1789891 C-allele) confers a rapid rate of ethanol oxidation [42,43], its effect on the rate of alcohol metabolism is weaker than that of ADH1B_48His. However, imputation of rs1693482 and rs698 showed that the associations with rs1693482 (p = 1.43 × 10−5) and rs698 (p = 1.798 × 10−5) were weaker than with rs1789891 (p = 1.315 × 10−5). Thus, the issue of whether the association with rs1789891 is mainly attributable to LD with rs1693482 and rs698, or whether further variants in this region are implicated, remains unclear. Variants rs1693482/rs698 could nevertheless be the contributory factor in terms of organ damage [44,45,46], since they are in LD with rs1789891.
Two plausible hypotheses can be formulated to explain how the products of the ADH reaction may increase the risk of tissue damage in the pancreas and liver. First, acetaldehyde accumulation in response to chronic alcohol ingestion has been implicated in the etiology of liver cirrhosis, pancreatitis, brain damage, cardiomyopathy, fetal alcohol syndrome, and various forms of cancer [34]. Whereas the allele or genotype differences in some studies were non-significant, several investigations have reported a lower frequency of alcoholism-susceptibility alleles or genotypes in patients with alcoholic liver disease or alcoholic pancreatitis (reviewed in [35]). The cytotoxic acetaldehyde that is formed as an intermediate in the metabolism of ethanol is reported to induce morphological changes in the pancreas of experimental animals [47]. In addition, acetaldehyde has reported fibrogenic effects in the liver [48]. The likely molecular mechanism through which acetaldehyde causes organ damage is the promotion of adduct formation, which leads to protein and DNA damage [48].
Second, nicotinamide adenine dinucleotide (NAD+) is an intermediate electron carrier in the cytosolic ADH-mediated metabolism of alcohol to acetaldehyde. In this reaction, NAD+ is reduced to NADH by two electrons. In a subsequent step, the electrons of NADH are transferred to O2 in the mitochondrial respiratory chain, which captures H+ to yield H2O. Ethanol metabolism therefore increases the O2 requirement of hepatocytes, and may result in hepatocyte hypoxia [49]. This may lead in turn to organ damage.

4.2. Controls

Random fluctuation seems very likely when looking at allele frequencies in the control samples. A detailed inspection of rs1789891 allele frequencies in the present control subsamples (data not shown) was therefore performed. Despite the fact that the control sample used for the ALC + ACP samples was drawn from the same study as a subcohort of the AD controls, it displayed a higher frequency of the AD risk allele A (16.9%) than the corresponding control subcohort used for AD (13.9%).

5. Conclusions

The aim of the present study was to identify new genes and pathways for AD, and to confirm previously reported findings, by combining three previously investigated samples. No novel data were generated. The fact that the previously reported association with ADH1B was not observed in the ACP + ALC subsample may reflect genetic stratification in cases and/or random fluctuation in allele frequencies in controls. This finding demonstrates that even strong signals can be blurred, if samples are small and heterogeneous with possible opposing effects. Our finding therefore stresses the necessity for, and central importance of samples that are large and well characterized, such as those investigated within the context of the Psychiatric Genomics Consortium. The present authors are optimistic that as has been the case with other psychiatric disorders, the possibilities offered by GWAS will ultimately generate major contributions to our understanding of the genetic background of the alcohol use disorders.

Supplementary Materials

The following are available online at www.mdpi.com/2073-4425/8/7/183/s1. Figure S1: Regional association plot of the alcohol dehydrogenase gene region in the combined sample; Figure S2: Linkage disequilibrium between rs1789891 and rs1229984, rs1693482, and rs698. Table S1: Top SNPs from the single marker analysis in the combined sample and their respective p-values in the subsamples.

Acknowledgments

The study was supported by the German Federal Ministry of Education and Research (BMBF) within the context of the following two projects: (i) the Integrated Network IntegraMent (Integrated Understanding of Causes and Mechanisms in Mental Disorders; grant 01ZX1314A to S.C. and M.M.N.; grant 01ZX1314G to Ma.R.); and (ii) SysMedAlcoholism (Alcohol Addiction: A Systems-Oriented Approach; grant 01ZX1311A to Ma.R., M.M.N., R.S.). The latter two projects are being conducted under the auspices of the BMBF e:Med Systems Medicine program. The study was also supported by the German Research Foundation (DFG; grant FOR2107; RI908/11-1 to Ma.R.; NO246/10-1 to M.M.N.). K.M. was supported by BMBF grant 01EB0410. B.B. was supported by BMBF grants 01GS0896, 01GS08149, and 01GS08153. M.M.N. is a member of the DFG-funded Excellence-Cluster ImmunoSensation 3, and received support from the Alfried Krupp von Bohlen und Halbach-Stiftung. U.S.Z. was supported by the BMBF grants 01ZX1311H and 01ZX1611H, and by DFG grant ZI 1119/4-1. Support was also provided by the DFG Exzellenzinitiative II to J.S. H.S. was supported by DFG Heisenberg grant Scho 656/7-2. N.D. was supported by grants from the DFG and BMBF. The present work was also supported by grants from the Swiss National Funds (SNF no. 310030_169196) and the Swiss Foundation for Alcohol Research (SSA) to F.S. W.G. has received symposia support from Janssen-Cilag GmbH, Neuss, Lilly Deutschland GmbH, Bad Homburg, and Servier, Munich, and is a member of the Faculty of the Lundbeck International Neuroscience Foundation (LINF), Denmark. Mo.R has received honoraria from Lundbeck Switzerland and the Lundbeck Institute for membership of advisory boards and participation in expert meetings, and from Lundbeck and Lilly Suisse for workshops and presentations. N.W. has received funding from the DFG and BMBF, and speaker’s honoraria and travel expenses from Janssen-Cilag and Essex Pharma. N.W. has also participated in industry sponsored multi-center randomized trials conducted by D&A pharma and Lundbeck. N.S. has received honoraria from Abbvie, Sanofi-Aventis, Reckitt Benckiser, Indivior, Lundbeck, and Janssen-Cilag for advisory board membership and the preparation of lectures, manuscripts, and educational materials. Since 2013, N.S. has also participated in clinical trials financed by Reckitt Benckiser and Indivior. N.D. has received support from Astra Zeneca and Janssen-Cilag, and honoraria for expert opinions for organizations, courts, insurance providers, and private persons. We thank Christine Schmäl for her critical reading of the manuscript, and members and collaborators of the PanEuropean Working Group on Chronic Alcoholic Pancreatitis for the provision of data from their pancreatitis cases.

Author Contributions

Mo.R., M.S., W.M., W.G., N.D., N.S., B.M.-M., H.S., U.S.Z., S.B., W.H.S., K.M., S.C., F.K., J.H., N.W., and R.S. conceived and designed the experiments; F.D., S.H.W., H.D., P.H., S.H., S.H.-H., S.L., M.I., F.S., T.B., U.R. and K.-H.J. performed the experiments; J.F., J.S., J.C.F., C.S.R., and F.S. analyzed the data; D.J. and B.B. contributed reagents/materials/analysis tools; Ma.R., L.R., J.T., J.R., A.J.F., and M.M.N. prepared the manuscript.

Conflicts of Interest

Norbert Scherbaum has received honoraria from Sanofi-Aventis, Reckitt-Benckiser/Indivior, Lundbeck, and Janssen-Cilag for advisory board participation, lectures, and the preparation of manuscripts and educational materials. In the past three years, he has participated in clinical trials financed by the pharmaceutical industry (Reckitt & Benckiser/Indivior). These funding sources had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or and the decision to publish the results.

References

  1. Goldman, D.; Oroszi, G.; Ducci, F. The genetics of addictions: Uncovering the genes. Nat. Rev. Genet. 2005, 6, 521–532. [Google Scholar] [CrossRef] [PubMed]
  2. Stickel, F.; Moreno, C.; Hampe, J.; Morgan, M.Y. The genetics of alcohol dependence and alcohol-related liver disease. J. Hepatol. 2017, 66, 195–211. [Google Scholar] [CrossRef] [PubMed]
  3. Reilly, M.T.; Noronha, A.; Goldman, D.; Koob, G.F. Genetic studies of alcohol dependence in the context of the addiction cycle. Neuropharmacology 2017, 122, 3–21. [Google Scholar] [CrossRef] [PubMed]
  4. Li, D.; Zhao, H.; Gelernter, J. Strong association of the alcohol dehydrogenase 1B gene (ADH1B) with alcohol dependence and alcohol-induced medical diseases. Biol. Psychiatry 2011, 70, 504–512. [Google Scholar] [CrossRef] [PubMed]
  5. Li, D.; Zhao, H.; Gelernter, J. Further clarification of the contribution of the ADH1C gene to vulnerability of alcoholism and selected liver diseases. Hum. Genet. 2012, 131, 1361–1374. [Google Scholar] [CrossRef] [PubMed]
  6. Li, D.; Zhao, H.; Gelernter, J. Strong protective effect of the aldehyde dehydrogenase gene (ALDH2) 504lys (*2) allele against alcoholism and alcohol-induced medical diseases in Asians. Hum. Genet. 2012, 131, 725–737. [Google Scholar] [CrossRef] [PubMed]
  7. Wray, N.R.; Ripke, S.; Mattheisen, M.; Trzaskowski, M.; Byrne, E.M.; Abdellaoui, A.; Adams, M.J.; Agerbo, E.; Air, T.M.; Andlauer, T.F.M.; et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depressive disorder. Nature 2017. submitted. [Google Scholar]
  8. Gelernter, J.; Kranzler, H.R.; Sherva, R.; Almasy, L.; Koesterer, R.; Smith, A.H.; Anton, R.; Preuss, U.W.; Ridinger, M.; Rujescu, D.; et al. Genome-wide association study of alcohol dependence: Significant findings in African- and European-Americans including novel risk loci. Mol. Psychiatry 2014, 19, 41–49. [Google Scholar] [CrossRef] [PubMed]
  9. Frank, J.; Cichon, S.; Treutlein, J.; Ridinger, M.; Mattheisen, M.; Hoffmann, P.; Herms, S.; Wodarz, N.; Soyka, M.; Zill, P.; et al. Genome-wide significant association between alcohol dependence and a variant in the ADH gene cluster. Addict. Biol. 2012, 17, 171–180. [Google Scholar] [CrossRef] [PubMed]
  10. Park, B.L.; Kim, J.W.; Cheong, H.S.; Kim, L.H.; Lee, B.C.; Seo, C.H.; Kang, T.C.; Nam, Y.W.; Kim, G.B.; Shin, H.D.; et al. Extended genetic effects of ADH cluster genes on the risk of alcohol dependence: From GWAS to replication. Hum. Genet. 2013, 132, 657–668. [Google Scholar] [CrossRef] [PubMed]
  11. Clarke, T.-K.; Adams, M.J.; Davies, G.; Howard, D.M.; Hall, L.S.; Padmanabhan, S.; Murray, A.D.; Smith, B.H.; Campbell, A.; Hayward, C.; et al. Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N = 112,117). bioRxiv 2017. [Google Scholar] [CrossRef]
  12. Xu, K.; Kranzler, H.R.; Sherva, R.; Sartor, C.E.; Almasy, L.; Koesterer, R.; Zhao, H.; Farrer, L.A.; Gelernter, J. Genomewide Association Study for Maximum Number of Alcoholic Drinks in European Americans and African Americans. Alcohol. Clin. Exp. Res. 2015, 39, 1137–1147. [Google Scholar] [CrossRef] [PubMed]
  13. Kapoor, M.; Wang, J.C.; Wetherill, L.; Le, N.; Bertelsen, S.; Hinrichs, A.L.; Budde, J.; Agrawal, A.; Bucholz, K.; Dick, D.; et al. A meta-analysis of two genome-wide association studies to identify novel loci for maximum number of alcoholic drinks. Hum. Genet. 2013, 132, 1141–1151. [Google Scholar] [CrossRef] [PubMed]
  14. McKay, J.D.; Truong, T.; Gaborieau, V.; Chabrier, A.; Chuang, S.C.; Byrnes, G.; Zaridze, D.; Shangina, O.; Szeszenia-Dabrowska, N.; Lissowska, J.; et al. A genome-wide association study of upper aerodigestive tract cancers conducted within the INHANCE consortium. PLoS Genet. 2011, 7, e1001333. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Derikx, M.H.; Kovacs, P.; Scholz, M.; Masson, E.; Chen, J.M.; Ruffert, C.; Lichtner, P.; Te Morsche, R.H.; Cavestro, G.M.; Férec, C.; et al. PanEuropean Working group on Alcoholic Chronic Pancreatitis Members and Collaborators. Polymorphisms at PRSS1-PRSS2 and CLDN2-MORC4 loci associate with alcoholic and non-alcoholic chronic pancreatitis in a European replication study. Gut 2015, 64, 1426–1433. [Google Scholar] [CrossRef] [PubMed]
  16. Buch, S.; Stickel, F.; Trépo, E.; Way, M.; Herrmann, A.; Nischalke, H.D.; Brosch, M.; Rosendahl, J.; Berg, T.; Ridinger, M.; et al. A genome-wide association study confirms PNPLA3 and identifies TM6SF2 and MBOAT7 as risk loci for alcohol-related cirrhosis. Nat. Genet. 2015, 47, 1443–1448. [Google Scholar] [CrossRef] [PubMed]
  17. Heinz Nixdorf Recall (HNR) MehrGenerationenStudie. Available online: https://www.uni-due.de/recall-studie/ (accessed on 31 March 2017).
  18. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 4th ed.; American Psychiatric Association: Washington, DC, USA, 1994; ISBN 0-89042-061-0. [Google Scholar]
  19. Forschungsverbünde. German Addiction Research Network (GARN). Available online: http://www.bw-suchtweb.de (accessed on 3 April 2017).
  20. HelmholtzZentrum München, Instititut für Genetische Epidemiologie. Available online: https://www.helmholtz-muenchen.de/ige/forschung/kora-gen/index.html (accessed on 31 March 2017).
  21. Biobank PopGen, Institut für Epidemiologie, Christian-Albrechts-Universität zu Kiel. Available online: https://www.epidemiologie.uni-kiel.de/biobanking/biobank-popgen (accessed on 31 March 2017).
  22. Psychiatric Genomics Consortium. Available online: http://www.med.unc.edu/pgc (accessed on 3 April 2017).
  23. Chang, C.C.; Chow, C.C.; Tellier, L.C.; Vattikuti, S.; Purcell, S.M.; Lee, J.J. Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience 2015, 4, 7. [Google Scholar] [CrossRef] [PubMed]
  24. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 2014, 511, 421–427. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. De Leeuw, C.A.; Mooij, J.M.; Heskes, T.; Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 2015, 11, e1004219. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Liberzon, A.; Birger, C.; Thorvaldsdóttir, H.; Ghandi, M.; Mesirov, J.P.; Tamayo, P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015, 1, 417–425. [Google Scholar] [CrossRef] [PubMed]
  27. DeFinetti Program. Available online: http://ihg.gsf.de/cgi-bin/hw/hwa1.pl (accessed on 10 October 2016).
  28. Broad Institute SNAP for SNP Annotation and Proxy Search. Available online: http://archive.broadinstitute.org/mpg/snap/ (accessed on 29 March 2017).
  29. NCBI dbSNP Database. Available online: https://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=1693482 (accessed on 3 April 2017).
  30. 1000 Genomes Data. A Deep Catalog of Human Genetic Variation, rs1789891 Allele Population Frequencies. Available online: http://phase3browser.1000genomes.org/Homo_sapiens/Variation/Population?db=core;r=4:100249919-100250919;v=rs1789891;vdb=variation;vf=1222186 (accessed on 31 March 2017).
  31. Hurley, T.D.; Edenberg, H.J. Genes encoding enzymes involved in ethanol metabolism. Alcohol. Res. 2012, 34, 339–344. [Google Scholar] [PubMed]
  32. Borràs, E.; Coutelle, C.; Rosell, A.; Fernández-Muixi, F.; Broch, M.; Crosas, B.; Hjelmqvist, L.; Lorenzo, A.; Gutiérrez, C.; Santos, M.; et al. Genetic polymorphism of alcohol dehydrogenase in europeans: The ADH2*2 allele decreases the risk for alcoholism and is associated with ADH3*1. Hepatology 2000, 31, 984–989. [Google Scholar] [CrossRef] [PubMed]
  33. Polimanti, R.; Gelernter, J. ADH1B: From alcoholism, natural selection, and cancer to the human phenome. Am. J. Med. Genet. B Neuropsychiatr. Genet. 2017. [Google Scholar] [CrossRef] [PubMed]
  34. Eriksson, C.J. The role of acetaldehyde in the actions of alcohol (update 2000). Alcohol. Clin. Exp. Res. 2001, 25, 15S–32S. [Google Scholar] [CrossRef] [PubMed]
  35. Yokoyama, A.; Mizukami, T.; Matsui, T.; Yokoyama, T.; Kimura, M.; Matsushita, S.; Higuchi, S.; Maruyama, K. Genetic polymorphisms of alcohol dehydrogenase-1B and aldehyde dehydrogenase-2 and liver cirrhosis, chronic calcific pancreatitis, diabetes mellitus, and hypertension among Japanese alcoholic men. Alcohol. Clin. Exp. Res. 2017, 41, 171–178. [Google Scholar] [CrossRef] [PubMed]
  36. Bierut, L.J.; Goate, A.M.; Breslau, N.; Johnson, E.O.; Bertelsen, S.; Fox, L.; Agrawal, A.; Bucholz, K.K.; Grucza, R.; Hesselbrock, V.; et al. ADH1B is associated with alcohol dependence and alcohol consumption in populations of European and African ancestry. Mol. Psychiatry 2012, 17, 445–450. [Google Scholar] [CrossRef] [PubMed]
  37. Treutlein, J.; Frank, J.; Kiefer, F.; Rietschel, M. ADH1B Arg48His allele frequency map: Filling in the gap for Central Europe. Biol. Psychiatry 2014, 75, e15. [Google Scholar] [CrossRef] [PubMed]
  38. The ALlele FREquency Database. Available online: https://alfred.med.yale.edu/ (accessed on 31 March 2017).
  39. The Human Protein Atlas. Available online: http://www.proteinatlas.org/ENSG00000248144-ADH1C/tissue (accessed on 20 June 2017).
  40. Chiang, C.P.; Wu, C.W.; Lee, S.P.; Chung, C.C.; Wang, C.W.; Lee, S.L.; Nieh, S.; Yin, S.J. Expression pattern, ethanol-metabolizing activities, and cellular localization of alcohol and aldehyde dehydrogenases in human pancreas: Implications for pathogenesis of alcohol-induced pancreatic injury. Alcohol. Clin. Exp. Res. 2009, 33, 1059–1068. [Google Scholar] [CrossRef] [PubMed]
  41. NCBI Phenotype-Genotype Integrator. Available online: https://www.ncbi.nlm.nih.gov/gap/phegeni (accessed on 31 March 2017).
  42. Edenberg, H.J. The genetics of alcohol metabolism: Role of alcohol dehydrogenase and aldehyde dehydrogenase variants. Alcohol. Res. Health 2007, 30, 5–13. [Google Scholar] [PubMed]
  43. Martínez, C.; Galván, S.; Garcia-Martin, E.; Ramos, M.I.; Gutiérrez-Martín, Y.; Agúndez, J.A. Variability in ethanol biodisposition in whites is modulated by polymorphisms in the ADH1B and ADH1C genes. Hepatology 2010, 51, 491–500. [Google Scholar] [CrossRef] [PubMed]
  44. He, L.; Deng, T.; Luo, H.S. Alcohol dehydrogenase 1C (ADH1C) gene polymorphism and alcoholic liver cirrhosis risk: A meta analysis. Int. J. Clin. Exp. Med. 2015, 8, 11117–11124. [Google Scholar] [PubMed]
  45. Fang, F.; Pan, J.; Su, G.H.; Xu, L.X.; Li, G.; Li, Z.H.; Zhao, H.; Wang, J. Association between alcohol dehydrogenase 1C gene *1/*2 polymorphism and pancreatitis risk: a meta-analysis. Genet. Mol. Res. 2015, 14, 15267–15275. [Google Scholar] [CrossRef] [PubMed]
  46. Zhong, Y.; Cao, J.; Zou, R.; Peng, M. Genetic polymorphisms in alcohol dehydrogenase, aldehyde dehydrogenase and alcoholic chronic pancreatitis susceptibility: A meta-analysis. Gastroenterol. Hepatol. 2015, 38, 417–425. [Google Scholar] [CrossRef] [PubMed]
  47. Vonlaufen, A.; Wilson, J.S.; Pirola, R.C.; Apte, M.V. Role of alcohol metabolism in chronic pancreatitis. Alcohol. Res. Health 2007, 30, 48–54. [Google Scholar] [PubMed]
  48. Setshedi, M.; Wands, J.R.; Monte, S.M. Acetaldehyde adducts in alcoholic liver disease. Oxid. Med. Cell. Longev. 2010, 3, 178–185. [Google Scholar] [CrossRef] [PubMed]
  49. Zakhari, S. Overview: How is alcohol metabolized by the body? Alcohol. Res. Health 2006, 29, 245–254. [Google Scholar] [PubMed]
Table
Table 1. Results of the gene-based analysis in the combined sample, and the respective p-values in the subsamples. Genes with puncorrected < 1.0 × 10−4 are shown. If a variant was located within a region shared by more than one gene, the variant was assigned to all of the respective genes.
Table 1. Results of the gene-based analysis in the combined sample, and the respective p-values in the subsamples. Genes with puncorrected < 1.0 × 10−4 are shown. If a variant was located within a region shared by more than one gene, the variant was assigned to all of the respective genes.
GenePosition (hg18)p-Value in the Combined Sample (2841 Cases, 3684 Controls)p-Value in the ACP + ALC Subsample (1510 Cases, 1750 Controls)p-Value in the AD Subsample (1331 Cases, 1934 Controls)
Alcohol Dehydrogenase 1B (ADH1B)chr4: 100426552-1004815811.209 × 10−60.687766.5258 × 10−10
Family with Sequence Similarity 83 Member D (FAM83D)chr20: 36968369-370351174.2443 × 10−60.00700160.34143
Alcohol Dehydrogenase 1C (ADH1C)chr4: 100456672-1005129409.9601 × 10−60.866488.5406 × 10−10
Zinc finger protein 697 (ZNF697)chr1: 119943523-1200119132.3488 × 10−50.00114350.0087101
Ras Homolog Family Member T2 (RHOT2)chr16: 638134-6841723.006 × 10−50.0252740.00029276
Crystallin Zeta (CRYZ)chr1: 74923772-749913153.0976 × 10−50.0519740.00084007
X-ray repair cross complementing 5 (XRCC5)chr2: 216662378-2167992483.4649 × 10−50.275612.7093 × 10−5
COX14 Cytochrome C Oxidase Assembly Factor (COX14)chr12: 48772167-488205013.7703 × 10−50.00170510.0044189
Rhomboid, Veinlet-Like 1 (RHBDL1)chr16: 646076-6882684.5926 × 10−50.0265910.00033002
LOC440330 (LOC440330)chr16: 648519-6897724.5926 × 10−50.0265910.00033002
STIP1 homology and U-box containing protein 1 (STUB1)chr16: 650116-6927694.5926 × 10−50.0265910.00033002
Jumonji domain containing 8 (JMJD8)chr16: 651668-6944404.62 × 10−50.0267320.00035589
WD Repeat Domain 24 (WDR24)chr16: 654703-7004014.62 × 10−50.0267320.00035589
F-Box and Leucine-Rich Repeat Protein 16 (FBXL16)chr16: 662503-7158094.62 × 10−50.0267320.00035589
Olfactomedin Like 2A (OLFML2A)chr9: 126559258-1266369824.8774 × 10−50.000446530.015794
AD: Alcohol dependence; ACP: Chronic alcoholic pancreatitis; ALC: Alcohol-related liver cirrhosis.
Table
Table 2. Variants used as input for the gene-based analysis of ADH1B (±20 kb; chr4:100426552-100481581; hg18). Single marker association puncorrected-values shown, as calculated using logistic regression in PLINK.
Table 2. Variants used as input for the gene-based analysis of ADH1B (±20 kb; chr4:100426552-100481581; hg18). Single marker association puncorrected-values shown, as calculated using logistic regression in PLINK.
SNP (Genotypes)p-Value in the Combined Sample (2841 Cases, 3684 Controls); [OR]; Genotype Counts : All (MAF), Affected (MAF), Unaffected (MAF)p-Value in the ACP + ALC Subsample (1510 Cases, 1750 Controls); [OR]; Genotype Counts: All (MAF), Affected (MAF), Unaffected (MAF)p-Value in the AD Subsample (1331 Cases, 1934 Controls); [OR]; Genotype Counts: All (MAF), Affected (MAF), Unaffected (MAF)
rs1159918 (TT/TG/GG)0.64770.62560.271
[1.018][0.9732][1.061]
731/2941/2846 (0.3378)360/1486/1407 (0.3391)371/1455/1439 (0.3364)
324/1285/1227 (0.3408)169/679/657 (0.3379)155/606/570 (0.3441)
407/1656/1619 (0.3354)191/807/750 (0.3401)216/849/869 (0.3312)
rs1229982 (TT/TG/GG)0.23060.68650.1655
[0.947][0.9738][0.9153]
262/2034/4229 (0.196)127/1015/2118 (0.1946)135/1019/2111 (0.1974)
104/884/1853 (0.1922)52/485/973 (0.195)52/399/880 (0.189)
158/1150/2376 (0.199)75/530/1145 (0.1943)83/620/1231 (0.2032)
rs9307239 (TT/TC/CC)0.54490.56950.6132
[0.9781][0.9707][0.974]
1084/3105/2334 (0.4042)581/1556/1121 (0.4171)503/1549/1213 (0.3913)
485/1307/1047 (0.401)278/688/542 (0.4125)207/619/505 (0.3881)
599/1798/1287 (0.4066)303/868/579 (0.4211)296/930/708 (0.3935)
rs1789891 (AA/AC/CC)1.315 × 10−50.63921.642 × 10−8
[1.232][1.033][1.469]
200/1782/4507 (0.1681)106/910/2209 (0.174)94/872/2298 (0.1624)
102/850/1879 (0.1862)49/440/1011 (0.1793)53/410/868 (0.1938)
98/932/2628 (0.1542)57/470/1198 (0.1693)41/462/1430 (0.1407)
rs2173201 (AA/AC/CC)0.00050630.22310.0001093
[0.8584][0.9274][0.7823]
316/2286/3923 (0.2236)172/1164/1924 (0.2313)144/1122/1999 (0.2159)
127/933/1781 (0.2089)81/512/917 (0.2232)46/421/864 (0.1927)
189/1353/2142 (0.2349)91/652/1007 (0.2383)98/701/1135 (0.2319)
SNP: Single nucleotide polymorphism; BP: Base pair; OR: Odds ratio. MAF: Minor allele frequency.
Table
Table 3. Genotype counts, allele frequencies, and Hardy–Weinberg Equilibrium (HWE) p-values for rs1789891 in the combined sample, and the respective p-values in the subsamples. Genotype counts for rs1789891 are shown; number of individuals with the respective rs1789891 genotype is shown.
Table 3. Genotype counts, allele frequencies, and Hardy–Weinberg Equilibrium (HWE) p-values for rs1789891 in the combined sample, and the respective p-values in the subsamples. Genotype counts for rs1789891 are shown; number of individuals with the respective rs1789891 genotype is shown.
SampleSubgroupGenotype Counts AA/AC/CCFrequency of rs1789891 Risk Allele for AD (A-allele); Frequency of rs1789891 Protective Allele for AD (C-Allele)p HWE
Combined sample (2831 cases, 3658 controls)all200/1782/45070.168; 0.8320.14
affected102/850/18790.186; 0.8140.62
unaffected98/932/26280.154; 0.8460.16
ACP + ALC subsample (1500 cases, 1725 controls)all106/910/22090.174; 0.8260.30
affected49/440/10110.179; 0.8210.86
unaffected57/470/11980.169; 0.8310.20
ACP subsample (1101 cases)affected35/320/7460.177; 0.8230.92
ALC subsample (399 cases)affected14/120/2650.185; 0.8150.87
AD subsample (1331 cases, 1933 controls)all94/872/22980.162; 0.8380.30
affected53/410/8680.194; 0.8060.60
unaffected41/462/14300.141; 0.8590.57
Table
Table 4. Results of the genome-wide pathway analysis of the combined sample, and the respective values in the subsamples. Pathways with puncorrected < 1 × 10−2 are shown. If a variant was located within a region shared by more than one gene, the variant was assigned to all of the respective genes.
Table 4. Results of the genome-wide pathway analysis of the combined sample, and the respective values in the subsamples. Pathways with puncorrected < 1 × 10−2 are shown. If a variant was located within a region shared by more than one gene, the variant was assigned to all of the respective genes.
PathwayNGENESBETA of the Combined SampleBETA_STD of the Combined SampleSE in the Combined Samplep–Value in the Combined Sample (2841 Cases, 3684 Controls)p-Value in the ACP + ALC Subsample (1510 Cases, 1750 Controls)p-Value in the AD Subsample (1331 Cases, 1934 Controls)
Ethanol Oxidation101.280.03120.3640.000216940.0615619.15 × 10−5
Organic Cation Anion Zwitterion Transport130.8440.02340.2670.000777460.0259140.37297
Amino Acid Transport across the Plasma Membrane280.4290.01750.1610.00376990.413150.52029
Basigin Interactions230.4280.01580.1610.00386180.507050.17262
Recruitment of NUMA to Mitotic Centrosomes90.7460.01720.2850.00449780.0737730.27771
Trafficking and Processing of Endosomal TLR90.7390.01710.30.00682390.353930.313
BETA: Regression coefficient; STD: Standard deviation; SE: Standard error.

Share and Cite

MDPI and ACS Style

Treutlein, J.; Frank, J.; Streit, F.; Reinbold, C.S.; Juraeva, D.; Degenhardt, F.; Rietschel, L.; Witt, S.H.; Forstner, A.J.; Ridinger, M.; et al. Genetic Contribution to Alcohol Dependence: Investigation of a Heterogeneous German Sample of Individuals with Alcohol Dependence, Chronic Alcoholic Pancreatitis, and Alcohol-Related Cirrhosis. Genes 2017, 8, 183. https://doi.org/10.3390/genes8070183

AMA Style

Treutlein J, Frank J, Streit F, Reinbold CS, Juraeva D, Degenhardt F, Rietschel L, Witt SH, Forstner AJ, Ridinger M, et al. Genetic Contribution to Alcohol Dependence: Investigation of a Heterogeneous German Sample of Individuals with Alcohol Dependence, Chronic Alcoholic Pancreatitis, and Alcohol-Related Cirrhosis. Genes. 2017; 8(7):183. https://doi.org/10.3390/genes8070183

Chicago/Turabian Style

Treutlein, Jens, Josef Frank, Fabian Streit, Céline S. Reinbold, Dilafruz Juraeva, Franziska Degenhardt, Liz Rietschel, Stephanie H. Witt, Andreas J. Forstner, Monika Ridinger, and et al. 2017. "Genetic Contribution to Alcohol Dependence: Investigation of a Heterogeneous German Sample of Individuals with Alcohol Dependence, Chronic Alcoholic Pancreatitis, and Alcohol-Related Cirrhosis" Genes 8, no. 7: 183. https://doi.org/10.3390/genes8070183

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop