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- Materials and Methods
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Background: Alcohol dependence is a complex disease, and although linkage and candidate gene studies have identified several genes associated with the risk for alcoholism, these explain only a portion of the risk.
Methods: We carried out a genome-wide association study (GWAS) on a case–control sample drawn from the families in the Collaborative Study on the Genetics of Alcoholism. The cases all met diagnostic criteria for alcohol dependence according to the Diagnostic and Statistical Manual of Mental Disorders–Fourth Edition; controls all consumed alcohol but were not dependent on alcohol or illicit drugs. To prioritize among the strongest candidates, we genotyped most of the top 199 single nucleotide polymorphisms (SNPs) (p ≤ 2.1 × 10−4) in a sample of alcohol-dependent families and performed pedigree-based association analysis. We also examined whether the genes harboring the top SNPs were expressed in human brain or were differentially expressed in the presence of ethanol in lymphoblastoid cells.
Results: Although no single SNP met genome-wide criteria for significance, there were several clusters of SNPs that provided mutual support. Combining evidence from the case–control study, the follow-up in families, and gene expression provided strongest support for the association of a cluster of genes on chromosome 11 (SLC22A18, PHLDA2, NAP1L4, SNORA54, CARS, and OSBPL5) with alcohol dependence. Several SNPs nominated as candidates in earlier GWAS studies replicated in ours, including CPE, DNASE2B, SLC10A2, ARL6IP5, ID4, GATA4, SYNE1, and ADCY3.
Conclusions: We have identified several promising associations that warrant further examination in independent samples.
Alcohol dependence (alcoholism) is a major health and social issue affecting 4 to 5% of the United States population at any given time (Li et al., 2007), with a lifetime prevalence of 12.5% (Hasin et al., 2007). Alcohol dependence is characterized by serious problems in multiple domains. Family, twin, and adoption studies have consistently demonstrated a substantial genetic contribution to disease etiology (Kendler et al., 1994; McGue, 1999; Nurnberger et al., 2004; Pickens et al., 1991). Alcohol dependence is a complex disease in which both genetic and environmental factors affect susceptibility.
Strategies for identifying genes in which variations contribute to the risk for alcohol dependence have employed linkage analysis or candidate gene approaches (Edenberg and Foroud, 2006). These methods have led to the identification of several genes associated with alcohol dependence, including GABRA2 (Covault et al., 2004; Drgon et al., 2006; Edenberg et al., 2004; Enoch et al., 2006, 2008; Fehr et al., 2006; Lappalainen et al., 2005; Lind et al., 2008; Soyka et al., 2008), ADH4 (Edenberg and Foroud, 2006; Edenberg et al., 2006; Guindalini et al., 2005; Kuo et al., 2008; Luo et al., 2005b, 2006), GABRG3 (Dick et al., 2004), CHRM2 (Luo et al., 2005a; Wang et al., 2004), NFKB1 (Edenberg et al., 2008b), OPRK1 (Edenberg et al., 2008a; Xuei et al., 2006), PDYN (Xuei et al., 2006), NPY2R (Wetherill et al., 2008), ANKK1/DRD2 (Dick et al., 2007b); CHRNA5 (Wang et al., 2009); GRM8 (Chen et al., 2009), TACR3 (Foroud et al., 2008), and GABRR2 (Xuei et al., 2010). However, the effect of variation in each of these genes is small, and much of the genetic contribution to the risk for alcoholism remains to be discovered.
We completed a genome-wide association study (GWAS) to identify genes contributing to alcohol dependence, defined according to Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition (DSM-IV) criteria (American Psychiatric Association, 1994). Both the alcohol-dependent cases and the controls have completed a rigorous clinical evaluation, with controls being drinkers who are free from alcohol dependence or abuse by several diagnostic classification systems (DSM-IIIR, DSM-IV, ICD-10) (American Psychiatric Association, 1987, 1994; World Health Organization, 1993), and also free of dependence on illicit drugs. To prioritize among the most promising single nucleotide polymorphisms (SNPs) identified from the GWAS, we genotyped the top SNPs in a sample of alcohol-dependent families and performed family-based association analysis, and tested whether they fell within or near genes whose expression is affected by alcohol exposure and were expressed in brain.
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Although the overall number of subjects was modest for a GWAS, the sample studied here had extensive phenotypic characterization and a strong contrast in substance dependence between affected and unaffected subjects. The affected subjects all met DSM-IV criteria for alcohol dependence, whereas the controls did not meet criteria for alcohol dependence or abuse or harmful use, nor were they dependent on other illicit drugs. Many of the affected subjects (56.5%) were also dependent upon an illicit drug; while it is possible that risk for a more general substance dependence contributed to the association signal, testing for this was not judged to be feasible, given the sample size and reduction in power that would result. None of the SNPs tested met conventional criteria for genome-wide significance, probably because of the modest size of the sample. Therefore, it was important to use other lines of evidence to prioritize potential candidates. Regions in which several SNPs provided evidence for association were found. Evidence of association, with similar estimates of effect size (odds ratios), was found for many SNPs when the sample of alcohol-dependent individuals was dichotomized and only those with early-onset alcohol dependence were defined as cases. Early onset generally marks a group of more severe cases, often with comorbid psychiatric problems. Analyses of this subgroup have yielded smaller p values, despite the reduced sample size, in some previous studies (Agrawal et al., 2006; Dick et al., 2007a; Edenberg et al., 2008b; Foroud et al., 2008). We assessed segregation within alcohol-dependent families as a means to prioritize among our SNP results. We also used gene expression data as additional lines of support, testing whether a gene in or nearest the SNP was expressed in human brain samples. We tested whether the expression of the gene in LCLs was altered by ethanol exposure; 80% of the genes differentially expressed in LCLs after ethanol exposure are also expressed in at least 1 of the 9 brain regions we analyzed (Edenberg et al., in preparation).
Overall, the convergence of evidence supports a region on chromosome 11 as the strongest candidate. It contained many of the top SNPs from the case control sample, and several SNPs genotyped in the family sample support the association of this region with alcohol dependence and the early onset of dependence (Table 2; Fig. 2A,B; Table S1). This region contains 6 genes: SLC22A18 (solute carrier family 22, member 18, a poly-specific organic cation transporter), PHLDA2 (pleckstrin homology-like domain, family A, member 2, important in regulating placental growth), NAPIL4 (nucleosome assembly protein 1-like 4, a likely histone chaperone), CARS (cysteinyl-tRNA synthetase), OSBPL5 (oxysterol-binding protein-like protein 5, an intracellular lipid receptor that interacts with retinoic acid and estradiol), and SNORA54 (small nucleolar RNA, H/ACA box 54). The 4 genes that were assayed (PHLDA2, NAPIL4, CARS, OSBPL5) are expressed in the human brain. In LCLs, expression of SLC22A18 and PHLDA2 are increased 9 to 14% by ethanol exposure (FDR <0.05), while expression of NAP1L4 is decreased 8% by ethanol (FDR <10−5; Edenberg et al., in preparation). These lines of evidence suggest that 1 or more of these genes is a good candidate for affecting the risk for alcoholism. Although not previously considered candidates for affecting the risk for alcoholism, several of these genes have functions that might relate to alcoholism, such as growth regulation, cation transport, and lipid signaling. Careful dissection of a quantitative trait locus (QTL) hotspot on mouse distal chromosome 1 (Mozhui et al., 2008) that includes QTLs affecting alcohol dependence (Buck et al., 2002), alcohol withdrawal (Crabbe, 1996), and alcohol-induced locomotor activity (Downing et al., 2003) showed that genes located in the QTL had a trans effect on expression in nervous tissue of a set of aminoacyl tRNA synthetases (Mozhui et al., 2008). As noted by Mozhui and colleagues (2008), the nervous system depends heavily on finely tuned protein metabolism in dendrites and axons (Chang et al., 2006; Malgaroli et al., 2006). Thus, CARS is a reasonable candidate.
One of the top-ranked SNPs was in the promoter region of BBX (Table 2); this SNP was also significant in the family sample, for both alcohol dependence and early onset (Table 2). Several additional SNPs just upstream of BBX were associated with both phenotypes in the GWAS (Table S1). BBX is widely expressed in human tissues and encodes the human homolog of Drosophila Bobbysox, an HMG-BOX transcription factor. The potential role of BBX in affecting risk for alcohol dependence is likely to be complex; BBX mRNA levels are themselves significantly increased by alcohol exposure (13% increase in lymphoblastoid cell lines, FDR = 9 × 10−5; Table 2). A SNP in intron 1 of KCNMA1 provided evidence in subjects of both European and African ancestry, and in the family sample (Table 2), KCNMA1 is expressed in brain, and encodes potassium large conductance calcium-activated channel, subfamily M, alpha member 1, a protein important in controlling neuronal excitability.
We examined results in our case control sample for genes that were previously associated with alcoholism in our family-based sample. Among the best-supported findings was the ANKK1/DRD2 region (Dick et al., 2007b), in which 24 SNPs were nominally associated with dependence and 22 with early onset; SNPs extending through TTC12 were also significant, supporting results from Yang and colleagues (2007). Three GWAS SNPs in NFKB1 were nominally associated with early-onset alcoholism, as were more extending upstream, consistent with our previous work (Edenberg et al., 2008). Thirty-two SNPs in GRM8 (Chen et al., 2009), encoding metabotropic glutamate receptor 8, were associated with early onset of dependence. There was also nominal support for PDYN (Xuei et al., 2006), CHRNA3 and CHRNA5 (Wang et al., 2009), and (among AA only) CHRM2 (Wang et al., 2004). In the ADH gene cluster (Edenberg et al., 2006), SNPs in ADH4 were not significant in this GWAS, although 15 SNPs (particularly in the region between ADH1B and ADH1A) were. Genes encoding GABAA receptors continued to provide evidence for association with alcoholism. A SNP in GABRR2 was among those with the smallest p value in this GWAS (6 × 10−5); that SNP was not itself significant in the earlier family study, although other SNPs in the gene were (Xuei et al., 2010). Although GABRA2 has been associated with alcohol dependence (Edenberg et al., 2004) in many samples (Bauer et al., 2007; Covault et al., 2004; Drgon et al., 2006; Enoch et al., 2006, 2008; Fehr et al., 2006; Lappalainen et al., 2005; Soyka et al., 2008), there was no evidence in this case–control GWAS, although 5 SNPs in GABRG1 were nominally associated with alcoholism and 11 with early onset (cf Covault et al., 2008). Six SNPs in GABRG3 (Dick et al., 2004) were associated with alcohol dependence in the EA sample, and 17 SNPs in the much smaller AA sample. In GABRA1 (Dick et al., 2006), we found 3 SNPs associated with alcohol dependence and 4 with early onset. Many SNPs in and near GABRG2 were also associated with dependence (and early onset). Thus, overall, there continues to be evidence that variations in GABAA receptors affect alcohol dependence.
Several SNPs nominated as candidates in earlier GWAS studies replicated in ours. Johnson and colleagues (2006) reported 51 regions, containing 181 SNPs, that provided the strongest evidence for association in a GWAS of alcohol dependence using pooled samples of 120 cases and 160 controls drawn from the COGA study (we do not know the overlap with the present study). Among the 68 of those SNPs for which we had data, 4 provided evidence of replication (p < 0.02; Table 3). CPE encodes carboxypeptidase E, present in the central nervous system (Lynch et al., 1990), which catalyzes an important step in the processing of peptide hormones and neurotransmitters (Hook et al., 2008). A pair of closely linked SNPs in DNASE2B, encoding deoxyribonuclease II beta, provided evidence for replication, and other SNPs in that region provided further support. SLC10A2, which encodes a sodium/bile acid cotransporter, also replicated; expression of SLC10A2 is regulated in part by retinol (Neimark et al., 2004). Three other SNPs replicated when the subgroup with early-onset alcoholism was analyzed: rs35164 just downstream of CDH11 (a type II classical cadherin), rs1927384, which lies between FGF14 (fibroblast growth factor 14) and TPP2 (tripeptidyl peptidase II), and rs6729553 in DNAH6 (axonemal dynein heavy chain 6, a microtubule-associated motor protein important in retrograde axonal organelles (Schnapp and Reese, 1989)).
Table 3. Replication of SNPs Detected in a Pooling Study
|SNP||Chr||Position||GENE||Alcohol dependence (p value)||Alcohol dependence odds ratio||Early onset alcohol dependence (p value)||Early onset alcohol dependence odds ratio|| |
|rs1506700||1||84,649,319||DNASE2B||0.007||0.80||0.001||0.73||DLAD DNase II-like acid DNase|
|rs3121147||1||84,649,571||DNASE2B||0.009||0.80||0.001||0.73||DLAD DNase II-like acid DNase|
|rs6729553||2||84,763,346||DNAH6||0.302||0.90||0.050||0.79||Dynein, axonemal, heavy chain 6|
|rs1370687||4||166,609,805||CPE||0.001||1.36||0.027||1.27||CPE carboxypeptidase E|
|rs1927384||13||101,943,751||FGF14||0.188||0.86||0.037||0.75||Between FGF14 and TPP2|
|rs279929||13||102,538,720||SLC10A2||0.016||1.26||0.015||1.31||Flank SLC10A2 solute carrier family 10 (sodium/bile acid cotransporter family), member 2|
|rs35164||16||63,532,702||CDH11||0.314||0.91||0.022||0.78||Flank CDH11 cadherin 11, type 2|
Treutlein and colleagues (2009) recently reported results from a GWAS of German alcoholics. Their cases were male alcoholics who had been hospitalized for treatment or prevention of severe withdrawal. We had data for 114 of their top 140 SNPs (121 at p < 10−4 in their study and 19 they nominated from rodent studies); 14 were significant in our primary analysis of alcohol dependence; 11 of these were also significant in our smaller subset of early-onset alcoholics (Table 4). Only one of these, rs13273672 in GATA4, was among the 15 SNPs for which Treutlein et al. reported confirmation in their follow-up (Treutlein et al., 2009). Among the SNPs for which we had replication, 6 have the same risk allele; these lie in or near ARL6IP5, ID4, GATA4, SYNE1, ADCY3, and PRKCA; 3 of these (GATA4, ADCY3, SYNE1) were among the top 6 with our early-onset phenotype. These are all good candidate genes; 2 regulate transcription (ID4, GATA4), 2 (ADCY3, PRKCA) regulate important second messenger systems, one (ARL6IP5) inhibits the glutamate transporter EAAC1, and one (SYNE1) is associated with autosomal recessive spinocerebellar ataxia 8. PRKCA expression is lower in the nucleus accumbens of alcohol-preferring P rats after operant ethanol self-administration (Rodd et al., 2008) and is reduced by chronic alcohol in vertebrae of Sprague–Dawley rats after chronic binge exposure to ethanol (Himes et al., 2008). We have confirming evidence for 3 of these (PRKCA, ADCY3, ARL6IP5) in our African American sample.
Table 4. Replication of SNPs Detected in a GWAS of German Alcoholics
|SNP||Chr||Location (bp)||Gene||Treutlein et al.||COGA results – EA||COGA results – AA||Gene name|
|Risk allele||p value||Dependence (p value)||OR||Risk allele||Early onset p value||OR||MAF||Dependence (p value)||OR||MAF|
|rs420033||1||111,097,197||KCNA3||A||2.7E-05||9.6E-03||0.8||G||7.7E-03||0.7||0.26||6.6E-01||1.2||0.06||Potassium voltage-gated channel, shaker-related subfamily, member 3|
|rs17799872||2||24,898,461||ADCY3||A||2.1E-03||4.4E-02||1.4||A||1.9E-02||1.5||0.08||3.1E-03||3.1||0.07||Adenylate cyclase 3|
|rs11706542||3||28,303,587||CMC1||T||4.3E-05||2.0E-02||0.8||C||4.1E-02||0.8||0.28||6.8E-01||0.9||0.12||COX assembly mitochondrial protein homolog|
|rs1606388||3||28,309,831||CMC1||T||5.2E-05||1.5E-02||0.8||G||3.2E-02||0.8||0.28||2.0E-01||0.7||0.08||COX assembly mitochondrial protein homolog|
|rs6549184||3||69,232,967||ARL6IP5||A||7.6E-05||1.6E-02||1.7||A||6.4E-02||1.6||0.04||1.1E-04||2.1||0.25||ADP-ribosylation-like factor 6 interacting protein 5|
|rs12527834||6||19,929,760||ID4||G||3.7E-05||2.1E-02||1.4||G||7.7E-02||1.4||0.10||2.7E-01||1.5||0.04||Inhibitor of DNA binding 4, dominant negative helix-loop-helix protein|
|rs214959||6||152,744,514||SYNE1||A||4.7E-05||2.8E-02||1.2||A||1.8E-02||1.3||0.50||9.3E-01||1.0||0.12||Spectrin repeat containing, nuclear envelope 1|
|rs2188594||7||20,645,811||ABCB5||A||9.3E-05||6.9E-03||0.7||G||2.2E-02||0.7||0.18||4.6E-01||0.9||0.49||ATP-binding cassette, subfamily B (MDR/TAP), member 5|
|rs6958596||7||20,661,260||ABCB5||T||1.4E-03||4.6E-03||0.7||C||1.7E-02||0.7||0.16||2.6E-01||0.8||0.21||ATP-binding cassette, subfamily B (MDR/TAP), member 5|
|rs13273672*||8||11,649,790||GATA4||C||2.2E-03||2.2E-02||1.2||C||1.6E-03||1.4||0.31||2.9E-01||0.8||0.35||GATA binding protein 4|
|rs753708||13||100,219,370||TMTC4||C||7.9E-05||3.1E-02||0.7||A||2.5E-02||0.6||0.07||3.2E-01||1.3||0.12||transmembrane and tetratricopeptide repeat containing 4|
|rs12603061||17||62,242,660||PRKCA||A||7.8E-05||1.2E-01||1.1||A||4.7E-02||1.2||0.48||3.3E-02||0.7||0.46||Protein kinase C, alpha|
|rs8082983||18||55,306,698||CCBE1||G||9.0E-05||1.7E-02||0.8||A||2.3E-02||0.8||0.35||2.1E-01||0.8||0.49||Collagen and calcium binding EGF domains 1|
|rs11702690||21||42,280,737||ZNF295||G||2.0E-05||7.8E-02||1.3||G||4.6E-02||1.4||0.08||8.6E-01||0.9||0.02||Zinc finger protein 295|
A recently submitted manuscript (Bierut et al., 2010) describes results from the Study of Addiction: Genetics and Environment Consortium, which has performed a GWAS using the phenotype of alcohol dependence in a sample of cases and controls ascertained through 3 different studies (dbGaP accession phs000092.v1.p1). One of the 3 studies was COGA, with 612 EA alcohol-dependent individuals and 413 EA controls included in both our analysis and the SAGE analyses. Two other studies also provided EA cases: one recruited cases with nicotine dependence through a population screening design (n = 343 alcohol-dependent EA cases) and the other recruited cases with cocaine dependence through treatment centers (n = 278 alcohol-dependent EA cases). While 52% of EA COGA subjects also reported another substance dependence, the inclusion of cases in the SAGE analysis recruited for different primary diagnoses will likely introduce a number of novel genes contributing to alcohol dependence and another comorbid condition, either nicotine dependence or cocaine dependence. For this reason, while we might hope for some commonality between the results from the COGA and SAGE studies because of the overlapping samples, it is not surprising that in practice results of GWAS from each study are quite different.
Overall, although we did not detect any SNP that met genome-wide significance, we have assembled several different lines of evidence to prioritize SNPs and genes from among the results for further study. In a multi-stage follow-up to the GWAS, we: a) analyzed a set of top SNPs by analyzing transmission in a family sample, which provided additional support for some of the SNPs; b) determined which among the top SNPs were expressed in human brain; and c) determined which top SNPs are affected by exposure to alcohol in human LCLs. The convergence of evidence from the GWAS, family-based association analyses and the response of genes to ethanol exposure provides a set of interesting candidate genes for further analyses in larger samples.
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- Materials and Methods
- Supporting Information
We thank Kim Doheny and Elizabeth Pugh from CIDR and Justin Paschall from the NCBI dbGaP staff for valuable assistance with genotyping and quality control in developing the dataset available at dbGaP.
The Collaborative Study on the Genetics of Alcoholism (COGA), Principal Investigators B. Porjesz, V. Hesselbrock, H. Edenberg, L. Bierut, includes ten different centers: University of Connecticut (V. Hesselbrock); Indiana University (H.J. Edenberg, J. Nurnberger Jr., T. Foroud); University of Iowa (S. Kuperman, J. Kramer); SUNY Downstate (B. Porjesz); Washington University in St Louis (L. Bierut, A. Goate, J. Rice, K. Bucholz); University of California at San Diego (M. Schuckit); Howard University (R. Taylor); Rutgers University (J. Tischfield); Southwest Foundation (L. Almasy), and Virginia Commonwealth University (D. Dick). A. Parsian and M. Reilly are the NIAAA Staff Collaborators. We continue to be inspired by our memories of Henri Begleiter and Theodore Reich, founding PI and Co-PI of COGA, and also owe a debt of gratitude to other past organizers of COGA, including Ting-Kai Li (currently a consultant with COGA), P. Michael Conneally, and Raymond Crowe, for their critical contributions. This national collaborative study is supported by NIH Grant U10AA008401 from NIAAA and the National Institute on Drug Abuse (NIDA).
Funding support for GWAS genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research, was provided by the National Institute on Alcohol Abuse and Alcoholism, the NIH GEI (U01HG004438), and the NIH contract “High throughput genotyping for studying the genetic contributions to human disease” (HHSN268200782096C). Family-based genotyping was performed using the facilities of the Center for Medical Genomics at Indiana University School of Medicine, which is supported in part by the Indiana Genomics Initiative of Indiana University (INGEN®); INGEN is supported in part by The Lilly Endowment, Inc.
Brain tissues were received from the New South Wales Tissue Resource Centre, which is supported by the National Health and Medical Research Council of Australia, The University of Sydney, Prince of Wales Medical Research Institute, Neuroscience Institute of Schizophrenia and Allied Disorders, National Institute of Alcohol Abuse and Alcoholism (Grant R01 AA12725) and NSW Department of Health.