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. Author manuscript; available in PMC: 2014 May 1.
Published in final edited form as: Alcohol Clin Exp Res. 2012 Dec 6;37(5):730–739. doi: 10.1111/acer.12032

Genome-wide significant association signals in IPO11-HTR1A region specific for alcohol and nicotine co-dependence

Lingjun Zuo 1,*, Xiang-Yang Zhang 2, Fei Wang 1, Chiang-Shan R Li 1, Lingeng Lu 3, Liefu Ye 4, Heping Zhang 3, John H Krystal 1, Hong-Wen Deng 5, Xingguang Luo 1,*
PMCID: PMC3610804  NIHMSID: NIHMS413350  PMID: 23216389

Abstract

Background

Alcohol and nicotine co-dependence can be considered as a more severe subtype of alcohol dependence. A portion of its risk may be attributable to genetic factors.

Methods

We searched for significant risk genomic regions specific for this disorder using a genome-wide association study (GWAS). A total of 8,847 subjects underwent gene-disease association analysis, including (i) a discovery cohort of 818 European-American cases with alcohol and nicotine co-dependence and 1,396 European-American controls, (ii) a replication cohort of 5,704 Australian family subjects with 907 affected offspring, and (iii) a replication cohort of 449 African-American cases and 480 African-American controls. Additionally, a total of 38,714 subjects of European or African descent in 18 independent cohorts with 10 other non-alcoholism neuropsychiatric disorders were analyzed as contrast. Furthermore, 90 unrelated HapMap CEU individuals, 93 European brain tissue samples and 80 European peripheral blood mononuclear cell (PBMC) samples underwent cis-acting expression quantitative locus (cis-eQTL) analysis.

Results

We identified a significant risk region for alcohol and nicotine co-dependence between IPO11 and HTR1A on chromosome 5q that was reported to be suggestively associated with alcohol dependence previously. In the European-American discovery cohort, 381 SNPs in this region were nominally associated with alcohol and nicotine co-dependence (p<0.05); 57 associations of them survived region- and cohort-wide correction (α=3.6×10−6); and the top SNP (rs7445832) was significantly associated with alcohol and nicotine co-dependence at the genome-wide significance level (p=6.2×10−9). Furthermore, associations for 34 and 11 SNPs were replicated in the Australian and African-American replication cohorts, respectively. Among these replicable associations, 4 reached genome-wide significance level in the meta-analysis of European-Americans and European-Australians: rs7445832 (p=9.6×10−10), rs13361996 (p=8.2×10−9), rs62380518 (p=2.3×10−8) and rs7714850 (p=3.4×10−8). Cis-eQTL analysis showed that many risk SNPs in this region had nominally significant cis-acting regulatory effects on HTR1A or IPO11 mRNA expression. Finally, no markers were significantly associated with any other neuropsychiatric disorder examined.

Conclusions

We speculate that this IPO11-HTR1A region might harbor a causal variant for alcohol and nicotine co-dependence.

Keywords: GWAS, alcohol and nicotine co-dependence, cis-eQTL, IPO11, HTR1A

Introduction

Alcohol and nicotine are the most commonly misused substances in the United States. Nearly 20 million Americans are alcohol abusing or dependent, and almost 50 million Americans smoke cigarettes (Substance Abuse and Mental Health Services Administration (SAMHSA), 2010). Alcohol dependence and nicotine dependence frequently co-occur in the same individuals. Furthermore, nicotine dependent individuals are four times more likely than the general population to be alcohol dependent, and people who drink are three times more likely than the general population to smoke (Grant et al., 2004). Identical twins are twice as likely as fraternal twins to become alcohol and nicotine dependent if the other twin is dependent (Carmelli et al., 1993; Swan et al., 1997). Alcohol and nicotine may enhance motivation to use either drug by activating common brain targets that are responsible for their reinforcing effects. They may also exert synergistic effects on behaviors which may contribute to their concurrent use.

Alcohol and nicotine co-dependence may represent a more severe subtype of alcohol dependence. A large number of risk loci have been associated with both alcohol dependence and nicotine dependence by candidate gene approach, including many genes that are involved in the dopaminergic, serotoninergic, GABAergic, glutamatergic, cholinergic, opioid and endocannabinoid systems. However, none of these genes have been confirmed by recent genome-wide association studies (GWASs) of alcohol dependence (Treutlein et al., 2009; Bierut et al., 2010; Edenberg et al., 2010; Heath et al., 2011; Johnson et al., 2011). Only two of them (CHRNA6-CHRNB3 (Thorgeirsson et al., 2010) and CHRNA5-CHRNA3-CHRNB4 (Liu et al., 2010)) were confirmed by meta-analysis GWASs of nicotine dependence. GWASs of alcohol dependence or alcohol consumption reported multiple other potential risk loci (Treutlein et al., 2009; Bierut et al., 2010; Edenberg et al., 2010; Heath et al., 2011; Schumann et al., 2011), so did most GWASs of nicotine dependence. To date, there has been only one GWAS (Lind et al., 2010) directly studying the phenotype of alcohol and nicotine co-dependence. That study identified three risk genes including ARHGAP10, MARK1 and DDX6. However, those findings have not been replicated independently yet.

In the present study, we searched for significant risk genomic regions for alcohol and nicotine co-dependence using a GWAS. A European-American cohort was used as the discovery one, and a European-Australian cohort and an African-American cohort were used as the replication ones. Additionally, we used three independent samples of European descent to detect expression quantitative trait locus (eQTL) signals in this risk genomic region, to see if the risk variants were functional. Finally, as contrast, we tested gene-disease associations in 18 additional independent cohorts with 10 other non-alcoholism neuropsychiatric disorders, to see whether the risk regions were specific for alcohol and nicotine co-dependence or not.

Materials and Methods

Subjects

A total of 8,847 subjects underwent gene-disease association analysis, including (i) a discovery cohort of 818 European-American cases with alcohol and nicotine co-dependence (476 males and 342 females; 38.3±10.2 years) and 1,396 European-American controls (422 males and 974 females; 39.4±10.4 years), (ii) a replication cohort of 5,704 European-Australian family subjects (1,856 families; 2,620 males and 3,084 females; 46.0±10.0 years; 907 affected offspring with alcohol and nicotine co-dependence including 366 females), and (iii) a replication cohort of 449 African-American cases (260 males and 189 females; 40.3±7.8 years) with alcohol and nicotine co-dependence and 480 African-American controls (170 males and 310 females; 39.6±8.6 years). Additionally, a total of 38,714 subjects of European or African descent in 18 independent case-control or family-based cohorts with 10 other neuropsychiatric disorders were analyzed. These neuropsychiatric disorders included schizophrenia, autism, attention deficit hyperactivity disorder (ADHD), major depression, bipolar disorder, Alzheimer’s disease, amyotrophic lateral sclerosis (ALS), early onset stroke, ischemic stroke, and Parkinson’s disease (Supplemental Tables S1a and S1b).

The European-American discovery cohort and the African-American replication cohort came from the dataset of the Study of Addiction - Genetics and Environment (SAGE) (dbGaP study accession phs000092.v1.p1) (Bierut et al., 2010), and the Australian replication cohort came from the dataset of the Australian twin-family study of alcohol use disorder (OZ-ALC) (dbGaP study accession phs000181.v1.p1) (Lind et al., 2010; Heath et al., 2011). (All subjects with alcohol and nicotine co-dependence in another dataset of the Collaborative Study on the Genetics of Alcoholism (COGA) (dbGaP: phs000125.v1.p1) (Edenberg et al., 2010) have been included in this SAGE dataset). These datasets were originally collected to study alcohol dependence alone. SAGE subjects were recruited from 8 different study sites in 7 states and the District of Columbia; the majority of subjects were recruited in Missouri (Bierut et al., 2010). All subjects were interviewed using the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) (Bucholz et al., 1994). Affected subjects met lifetime DSM-IV criteria for both alcohol and nicotine dependence (American Psychiatric Association, 1994). Affected subjects were excluded if they had schizophrenia or other psychotic illnesses. Controls were defined as individuals who had been exposed to alcohol and nicotine (and possibly to other drugs), but had never become dependent on these substances. Additionally, controls were also screened to exclude individuals with major axis I disorders, including schizophrenia, mood disorders, and anxiety disorders. The Australian subjects included twins and their parents, siblings, spouses, children and other family members. The index cases reported a history of alcohol dependence and nicotine dependence (DSM-IV). More detailed demographic information is available elsewhere (Edenberg et al., 2005; Bierut et al., 2010; Edenberg et al., 2010; Heath et al., 2011). The European-American discovery cohort and the African-American replication cohort were genotyped on the Illumina Human 1M beadchip and the Australian cohort was genotyped on the Illumina CNV370v1 beadchip.

Detailed demographic information, including sample sizes, ethnicity and diagnosis of the 18 cohorts with other neuropsychiatric disorders, is shown in Supplemental Table S1 or is available in dbGaP database (http://www.ncbi.nlm.nih.gov/gap). These subjects were genotyped on different Illumina or Affymetric microarray beadchip platforms. All subjects gave written informed consent to participating in protocols approved by the relevant institutional review boards (IRBs). All subjects were de-identified in this study that was approved by Yale IRB.

Imputation

After we identified a significant risk genomic region in the European-American discovery cohort, we imputed that entire region (1.5 Mb at Chr5: 61,708,573-63,257,546 from the transcript start site (TSS) of IPO11 to the TSS of HTR1A) in all samples of 21 cohorts using the same strategy as previously (Zuo et al., 2012). Rare variants with minor allele frequencies (MAF) < 0.05 were excluded.

Data analysis

Before the association analysis, we strictly cleaned the phenotype and genotype data of all datasets (see the Supplemental Materials and Methods). We tested gene-disease associations in the European-American discovery cohort first, to identify the significant risk genomic regions at genome-wide significance level, and then we imputed and carefully examined this region across 21 cohorts.

  1. Genome-wide association tests in the European-American discovery cohort: The allele frequencies of all cleaned markers across the genome were compared between cases and controls using genome-wide logistic regression analysis implemented in the program PLINK (Purcell et al., 2007). Diagnosis served as the dependent variable, alleles served as the independent variables, and sex, age, the first 10 principal components and the most significant marker (i.e., rs7445832) served as the covariates. The principal component scores of our samples were derived from all autosomal SNPs across the genome using principal component analysis (PCA) implemented in the software package EIGENSTRAT (Price et al., 2006). Each individual received scores on each principal component. These principal components reflected the population structure of our samples. The first principal component (PC1) separated the self-identified European-American and African-American subjects very well, which was highly consistent with a previous report (Bierut et al., 2010). The second principal component (PC2) separated the self-identified Hispanic subjects from the non-Hispanic subjects. Other principal components also accounted for very small fractions of the total variance. The first 10 principal component scores accounted for >95% of variance. These PCs serving as covariates in the regression model can control for the population stratification and admixture effects on association analysis. The p-values derived from these association analyses are illustrated in Figure 1. Furthermore, similar association analysis was performed on the imputed data (see below). The top-ranked (p<10−5) risk markers are listed in Table 1. To mitigate false positive rates, genome-wide associations in the discovery cohort were corrected for multiple testing by Bonferroni correction (α=5×10−8).

  2. Association tests for the imputed genotype data in all samples in 21 cohorts: To analyze the associations between neuropsychiatric diseases and all imputed markers in the case-control samples, we used the logistic regression analysis described above. For the family samples, we tested associations using the program FBAT (Horvath et al., 2001). Association results were corrected for multiple comparisons by the effective number of SNPs within the IPO11-HTR1A region and the number of cohorts examined (i.e., n=21). The effective marker numbers were calculated using the program SNPSpD (Li and Ji, 2005). In the present study, the effective genetic marker number was 669 in the IPO11-HTR1A region; thus, the region- and cohort-widely corrected α was set at 3.6×10−6. The associations that were replicable between the discovery and replication cohorts are shown in Tables 2 and 3. Meta-analysis was performed on these replicable associations, to derive the combined p values using the program METAL.

  3. Cis-acting genetic regulation of expression analysis in the lymphoblastoid cell lines: To examine relationships between all available SNPs in the IPO11-HTR1A region and mRNA expression levels of local genes (i.e., HTR1A and IPO11) in the lymphoblastoid cell lines, we performed cis-acting expression quantitative locus (cis-eQTL) analysis. Expression array data of 14,925 transcripts (14,072 genes) in 90 unrelated HapMap CEU individuals were assessed (Stranger et al., 2005). Differences in the distribution of mRNA expression levels between SNP genotypes were compared using a Wilcoxon-type trend test. The risk SNPs that were associated with disease in the discovery cohort and had p < 0.05 in this cis-eQTL analysis are shown in the Supplemental Table S2.

  4. Cis-eQTL analysis on all available SNPs in the IPO11-HTR1A region in the brain tissue samples and the peripheral blood mononuclear cell (PBMC) samples: To examine whether the SNPs in the risk region influence the local gene expression changes, we also tested the associations between the genotypes and the expression levels of exons and transcripts of local genes (i.e., HTR1A and IPO11) in two additional European samples (Table S2). Expression array data in 93 autopsy-collected frontal cortical brain tissue samples with no defined neuropsychiatric condition and 80 PBMC samples collected from living healthy donors obtained from a study (Heinzen et al., 2008) at Duke University were evaluated. Each of these associations was analyzed using a linear regression model by correcting for age, sex, source of tissues, and principle component scores of ancestry. The expression array data have been confirmed by quantitative RT-PCR previously (Heinzen et al., 2008).

Figure 1. Manhattan plot for the p-values in European-American discovery cohort.

Figure 1

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[Y-axis: −log0.05=1.3; −log10−5=5; −log(5×10−8)=7.3. X-axis: Chr1-22=Autosomes; X=ChrX; Y=ChrY; SNPs were ordered by physical distance within each chromosome/region]

Table 1.

P-values for top-ranked (p<10−5) risk SNPs in IPO11-HTR1A region in the discovery cohort

SNP Position risk allele OR p-values SNP Position risk allele OR p-values
rs7445832 62622057 A 1.53 6.2×10−9 rs6873148 62695568 A 1.52 1.0×10−6
rs1494578 62622185 G 1.47 4.7×10−7 rs6873152 62695579 A 1.52 1.0×10−6
rs10471577 62623023 C 1.47 4.7×10−7 rs10939966 62696069 C 1.37 6.8×10−6
rs7702856 62630122 G 1.47 4.3×10−7 rs10805382 62696495 C 1.38 5.1×10−6
rs4700575 62631732 G 1.48 2.8×10−7 rs10939967 62696691 C 1.52 1.0×10−6
rs346425 62632000 T 1.36 3.2×10−6 rs73119677 62698022 G 1.53 9.0×10−7
rs73761305 62632087 T 1.43 4.1×10−6 rs4590141 62698797 C 1.37 7.0×10−6
rs1319474 62634947 G 1.46 7.4×10−7 rs55701004 62699567 C 1.37 7.0×10−6
rs1017776 62635216 G 1.47 6.7×10−7 rs6860119 62700122 T 1.53 7.9×10−7
rs1462460 62649397 T 1.53 8.3×10−6 rs6860278 62700179 T 1.53 7.9×10−7
rs7444332 62680629 C 1.47 5.9×10−7 rs6860501 62700377 T 1.53 7.9×10−7
rs4403132 62681092 T 1.39 4.8×10−6 rs73119687 62701503 T 1.53 7.9×10−7
rs7718679 62687582 C 1.54 6.7×10−7 rs6893950 62704479 T 1.51 4.2×10−6
rs73119652 62688162 T 1.54 5.1×10−7 rs62380518 63020823 A 1.68 1.5×10−7
rs57363006 62689707 G 1.50 2.8×10−6 rs74829400 63027037 T 1.64 1.2×10−7
rs57361220 62689740 T 1.50 2.8×10−6 rs62380521 63028029 C 1.41 8.1×10−6
rs59544801 62689880 T 1.52 1.1×10−6 rs6887027 63046909 C 1.38 9.9×10−6
rs7714594 62690438 A 1.53 6.1×10−7 rs62380555 63070602 C 1.47 1.5×10−6
rs7735086 62692417 A 1.52 1.0×10−6 rs7714850 63072093 C 1.43 2.7×10−7
rs7735004 62692558 G 1.38 6.2×10−6 rs10042862 63072461 G 1.47 1.5×10−6
rs7735451 62692702 A 1.52 1.0×10−6 rs13354185 63073747 A 1.48 1.1×10−6
rs4302532 62694427 C 1.55 4.4×10−7 rs72766222 63074411 A 1.46 7.3×10−6
rs4455508 62694538 A 1.54 5.7×10−7 rs13361996 63075996 A 1.65 6.9×10−8
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All markers are in Hardy-Weinberg Equilibrium (HWE), common variants and ordered by chromosome position; all risk alleles are minor alleles. The markers underlined are non-imputed markers. The bold are the genome-wide significant markers with p < 5×10-8 in meta-analysis (see Table 2).

Table 2.

P-values for replicable risk SNPs between European-American discovery and Australian replication cohorts

SNP Position Risk Allele European-Americans Australians Meta-analysis
OR p OR p z p
rs6861297 62212972 T 1.62 0.024 2.67 0.008 2.57 0.010
rs6884324 62213017 C 1.60 0.026 2.67 0.008 2.54 0.011
rs350306 62469018 T 1.38 0.012 1.56 0.005 3.18 0.001
rs1494622 62491395 T 1.27 0.021 1.17 0.009 2.92 0.004
rs1494623 62493473 C 1.20 0.037 1.18 0.004 2.82 0.005
rs691234 62496771 C 1.37 0.003 1.23 0.013 3.48 0.001
rs690957 62502634 C 1.38 0.008 1.67 0.001 3.20 0.001
rs681342 62507733 T 1.28 0.012 1.18 0.015 3.04 0.002
rs181156 62509099 A 1.39 0.002 1.23 0.007 3.66 2.6×10−4
rs350309 62509354 T 1.38 0.002 1.23 0.007 3.66 2.6×10−4
rs350311 62510417 C 1.39 0.003 1.17 0.019 3.43 0.001
rs350312 62511201 A 1.37 0.004 1.17 0.019 3.34 0.001
rs690816 62514088 G 1.38 0.003 1.17 0.020 3.43 0.001
rs114705639 62536627 A 1.35 0.022 1.46 0.019 2.71 0.007
rs17481124 62574713 G 1.23 0.048 1.21 0.048 2.30 0.021
rs72758793 62588647 A 1.37 0.018 1.80 0.046 2.62 0.009
rs55860379 62593868 A 1.36 0.010 1.78 0.034 2.85 0.004
rs72760718 62615268 G 1.35 0.016 1.80 0.027 2.67 0.008
rs7445832 62622057 A 1.53 6.2×10−9 1.15 0.049 6.12 9.6×10−10
rs9291778 62622713 A 1.51 4.0×10−5 2.08 0.012 4.50 6.7×10−6
rs56051136 62630434 A 1.41 0.003 3.33 0.001 3.51 4.6×10−4
rs60685959 62630435 C 1.41 0.003 3.33 0.001 3.51 4.6×10−4
rs73761304 62632086 T 1.37 4.7×10−4 2.37 0.001 4.14 3.4×10−5
rs6882265 62702209 T 1.28 0.001 1.58 0.026 3.82 1.3×10−4
rs62380518 63020823 A 1.68 1.5×10−7 1.33 0.028 5.59 2.3×10−8
rs7714850 63072093 C 1.43 2.7×10−7 1.27 0.042 5.52 3.4×10−8
rs10042862 63072461 G 1.47 1.5×10−6 1.24 0.039 5.18 2.2×10−7
rs13354185 63073747 A 1.48 1.1×10−6 1.33 0.034 5.21 1.9×10−7
rs13361996 63075996 A 1.65 6.9×10−8 1.78 0.002 5.76 8.2×10−9
rs17180095 63104352 C 1.52 0.001 1.64 0.033 3.69 2.2×10−4
rs10061598 63113748 T 1.14 0.048 1.59 0.033 2.51 0.012
rs989049 63114801 C 1.19 0.012 1.73 0.039 2.94 0.003
rs2202266 63117281 G 1.14 0.049 1.79 0.039 2.44 0.015
rs1478493 63119942 G 1.26 0.008 1.53 0.011 3.60 3.2×10−4
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The bold are the genome-wide significant markers with p < 5×10-8 in meta-analysis.

Table 3.

P-values for replicable risk SNPs between European-American discovery and African-American replication cohorts

SNP Position Risk Allele European-American African-American Meta-analysis
OR P OR P OR P
rs10514949 62068672 C 1.15 0.042 0.78 0.049 1.05 0.376
rs58617906 62332704 A 1.24 0.020 0.61 0.032 1.12 0.185
rs690957 62502634 C 1.38 0.008 0.64 0.047 1.16 0.177
rs10042968 63069474 G 1.41 8.1×10−6 1.54 0.041 1.42 8.0×10−7
rs7700448 63124338 A 1.17 0.025 0.81 0.048 1.05 0.407
rs17795292 63125355 G 1.17 0.018 0.81 0.036 1.04 0.424
rs35393059 63125728 C 1.17 0.020 0.81 0.044 1.05 0.369
rs13159097 63125873 A 1.17 0.022 0.81 0.044 1.05 0.381
rs6876878 63127666 A 1.17 0.016 0.81 0.045 1.06 0.333
rs2365875 63128094 G 1.17 0.019 0.80 0.040 1.05 0.363
rs10939982 63128760 G 1.17 0.018 0.82 0.048 1.05 0.378
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Results

We scanned the genome in the European-American discovery cohort and identified a significant risk region between HTR1A and IPO11 on chromosome 5q at genome-wide significance level (Figures 1, 2 and 3), with the most significant SNP rs7445832 (p=6.2×10−9). We examined the 10Mb range surrounding this SNP, which covered the entire IPO11-HTR1A region (1.5Mb), in the discovery cohort, and found a total of 13 SNPs that had association signals for alcohol and nicotine co-dependence with p<10−4 (i.e., 6.2×10−9 ≤ p ≤ 9.1×10−5). These SNPs were concentrated within a narrow region (0.5Mb) surrounding the most significant SNP between IPO11 and HTR1A (Figure 2A).

Figure 2. Regional association plots.

Figure 2

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[Left Y-axis corresponds to −log(p) value; right Y-axis corresponds to recombination rates; quantitative color gradient corresponds to r2; red squares represent peak SNPs. (A) regional association plot in European-American discovery cohort for a 10Mb region around the peak association SNP (rs7445832); (B) regional association plot in European-American discovery cohort for a 1Mb region around the peak association SNP (rs7445832) [without conditioning on rs7445832]; (C) regional association plot in European-American discovery cohort for a 1Mb region around the peak association SNP (rs7445832) [conditional on rs7445832]; (D) regional association plot in Australian replication cohort for a 1Mb region around the peak association SNP (rs7445832)]

Figure 3. QQ-plot for the p-values in European-American discovery cohort.

Figure 3

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[X-axis: expected −log(P) values; Y-axis: observed −log(P) values; P-values correspond to associations between SNPs and alcohol and nicotine co-dependence; λ=1.03]

We further examined the entire IPO11-HTR1A region (1.5Mb) in multiple populations, and detected many association and functional signals (Tables 1, 2, 3 and S2). In the European-American discovery cohort, among 2,726 SNPs including 261 originally-genotyped SNPs and 2,465 imputed SNPs, 381 SNPs were nominally associated with alcohol and nicotine co-dependence (p<0.05) (Table 4); 57 SNPs were significantly associated with alcohol and nicotine co-dependence after region- and cohort-wide correction (α=3.6×10−6). As mentioned, one of the SNP showed evidence for genome-wide significance (rs7445832; p=6.2×10−9). All risk alleles of these markers were minor alleles (f<0.5). If conditional on the most significant SNP (i.e., rs7445832), all of the associations with other SNPs became less significant (all p>10−4; Figure 2C). In the Australian replication cohort, 100 SNPs were nominally associated with alcohol and nicotine co-dependence (0.001 ≤ p ≤ 0.049; data not shown). Thirty-four associations in the discovery cohort (6.2×10−9 ≤ p ≤ 0.049) were replicated in the Australian replication cohort (0.001 ≤ p ≤ 0.049) (Table 2 and Figure 2D), with the same directions of gene effects in both cohorts. Meta-analysis showed that all of these 34 replicable SNPs were associated with disease (9.6×10−10 ≤ p ≤ 0.021; Table 2), including four genome-wide significant SNPs, i.e., rs7445832 (p=9.6×10−10), rs13361996 (p=8.2×10−9), rs62380518 (p=2.3×10−8) and rs7714850 (p=3.4×10−8). In the African-American replication cohort, 77 SNPs were nominally associated with alcohol and nicotine co-dependence (0.002 ≤ p ≤ 0.049; data not shown). Eleven risk SNPs in the discovery cohort (8.1×10−6 ≤ p ≤ 0.042) were also risk SNPs in the African-American replication cohort (0.032 ≤ p ≤ 0.049) (Table 3). However, all of these 11 SNPs but one had opposite directions of gene effects between the discovery cohort and the African-American cohort. Meta-analysis showed that only this exceptional one SNP was associated with disease (rs10042968: OR=1.41, p=8.1×10−6 in European-Americans; OR=1.54, p=0.041 in African-Americans; OR=1.42, p=8.0×10−7 in meta-analysis; Table 3). Among these SNPs, rs690957 was a risk SNP across three cohorts (p=0.008, 0.004 and 0.047 in European-Americans, European-Australians and African-Americans, respectively). Rs690957 was also the most significant one in European-Australians (Table 4). In other 18 independent cohorts, 9-261 SNPs were nominally associated with diseases, but none of them survived region- and cohort-wide correction for multiple comparisons (Table 4).

Table 4.

Associations between IPO-11-HTR1A gene region and different neuropsychiatric disorders

Human Diseases Dataset # SNP # (total) SNP # (p<0.05) Minimal p value Most sig. SNP Gene Minor allele frequency
Affected Unaffected
AD+ND 1 2726 381 6.2×10−9 rs7445832 Sig. region 0.289 0.211
AD+ND 2 2605 100 0.001 rs690957 Intergenic 0.084 0.076
AD+ND 3 2901 77 0.002 rs1353270 Intergenic 0.184 0.126
ADHD 4 2716 67 2.8×10−4 rs1478498 Intergenic 0.229 0.236
Schizophrenia 5 2087 216 0.001 rs923963 Intergenic 0.372 0.326
Schizophrenia 6 1997 62 0.001 rs9283703 Intergenic 0.392 0.493
Schizophrenia 7 2013 126 0.001 rs7707596 Sig. region 0.059 0.094
Autism 8 2587 86 0.003 rs1319474 Sig. region 0.128 0.134
Major Depression 9 2743 143 0.005 rs16892399 Intergenic 0.255 0.226
Bipolar Disorder 10 2015 9 0.018 rs35509126 Intergenic 0.101 0.068
Bipolar Disorder 11 2015 164 0.001 rs347670 Sig. region 0.093 0.060
Bipolar Disorder 12 1948 95 0.002 rs260991 Intergenic 0.052 0.117
Alzheimer’s Disease 13 2678 170 3.0×10−4 rs4449492 Intergenic 0.473 0.457
Alzheimer’s disease 14 1570 175 0.001 rs1160346 Intergenic 0.216 0.119
ALS 15 2492 143 0.002 rs1422301 Sig. region 0.197 0.281
Early Onset Stroke 16 2559 79 0.002 rs16891019 Intergenic 0.196 0.140
Early Onset Stroke 17 2817 144 0.001 rs56280615 Sig. region 0.054 0.118
Ischemic Stroke 18 2435 261 1.2×10−4 rs13186191 Intergenic 0.319 0.184
Parkinson’s Disease 19 2614 95 0.001 rs1851333 Intergenic 0.109 0.135
Parkinson’s Disease 20 2572 149 3.1×10−4 rs34606485 Sig. region 0.070 0.132
Parkinson’s Disease 21 2683 95 0.004 rs6888308 Intergenic 0.324 0.278
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AD + ND, alcohol and nicotine codependence

Only the most significant risk markers are listed. Dataset # refers to Table S1a. “Sig. Region”, a 0.5Mb significant risk region for alcohol and nicotine co-dependence (see Figure 2A).

Cis-eQTL analysis showed that, among the risk SNPs for alcohol and nicotine co-dependence, 30 SNPs had nominal cis-acting regulatory effects on expression of HTR1A or IPO11 mRNA in the brain, PBMC or lymphoblastoid cell lines (2.3×10−13 ≤ p ≤ 0.05); among all of the 65 SNPs within this region that were genotyped for eQTL analysis, 43 (66.2%) were risk markers for alcohol and nicotine co-dependence (6.2×10−9 ≤ p ≤ 0.048) (Table S2). Cis-regulatory effects on IPO11 expression were much stronger than those on HTR1A expression. All of the risk alleles for alcohol and nicotine co-dependence increased the expression of HTR1A. However, some of the risk alleles increased the expression of IPO11 but the others decreased it.

Additionally, a total of 2,058 SNPs in ARHGAP10, MARK1, DDX6, KIAA1409, CTBP2, GRM3, TBC1D2B, BACH2 and CNTNA that were significant risk genes for alcohol dependence, alcohol and nicotine co-dependence or nicotine dependence identified by Lind et al. (2010) were also tested in our samples. We listed all p values <0.01 in the Supplemental Table S3. We found that none of these markers were significantly associated with alcohol and nicotine co-dependence in our samples after Bonferroni correction.

Discussion

In the European-American population, we identified a genome-wide significant risk marker at the IPO11-HTR1A region specific for alcohol and nicotine co-dependence. The region surrounding this marker was enriched with many association signals and functional signals. We speculated that this region might harbor a causal variant for alcohol and nicotine co-dependence.

Several pieces of evidence supported our conclusion. First, within 10Mb range surrounding this genome-wide significant risk SNP, all association signals for alcohol and nicotine co-dependence with p<10−4 were concentrated within a narrow region surrounding this SNP. This region was completely located between HTR1A and IPO11. It is, thus, highly likely that the putative causal variant for alcohol and nicotine co-dependence was located within this region. Second, many risk SNPs in this region had significant cis-acting regulatory effects on mRNA expression both in the PBMC and in the brain, increasing the possibility that the IPO11-HTR1A region plays a direct functional role in the disorder. Third, many associations discovered in European-Americans were replicated in European-Australians, and meta-analysis showed that four SNPs reached the genome-wide significance level. Some associations in European-Americans were also replicated in African-Americans. Finally, this region was specific for alcohol and nicotine co-dependence, not for any other non-alcoholism neuropsychiatric disorder examined. This region has been suggestively associated with alcohol dependence (75.6% nicotine dependence) in the same dataset before (p=2.3×10−6 by Bierut et al. 2010; p=2.8×10−7 by Zuo et al. 2011), but not genome-wide significant (α=5×10−8). The association was genome-wide significant only in the subgroup with alcohol and nicotine co-dependence (p=6.2×10−9), which might suggest that this region is associated with a more severe subtype of alcohol dependence.

It is worth noting that the “causal” variants may not be identical to the “risk” markers, which is actually a common limitation of most association studies. There were other reasons for this inconsistency between the “causal” variants and the “risk” markers implicated in the current study. First, none of the risk SNPs presented here were non-synonymous. Rather, they appeared to have implications for risk and function by virtue of their being in LD with a putative causal variant and/or due to their location in the regulatory region that may in turn regulated transcription of the causal variant. Second, the SNPs employed by GWAS are common, but not rare, variants. Numerous studies have shown that many gene-disease associations are not due to a single common variant, but rather due to a constellation of more rare, regionally concentrated, disease-causing variants. Thus, the signals of association credited to our common SNPs might be synthetic associations resulting from the contributions of multiple rare SNPs in the IPO11-HTR1A region, which needs to be identified by sequencing. Third, the associations in the European-American discovery cohort, the associations in the replication cohorts, and the functional signals in the eQTL analysis did not perfectly match, which was probably because these risk markers were not the causal variants per se, but rather in LD with a common putative causal variant. Fourth, current evidence, including the effect sizes and the significance strength of associations, was not sufficient to fine-map the putative causal variant to any one of the four genome-wide significant risk markers, although the most significant one (i.e., rs7445832) was most likely. Sequencing is warranted to detect the actual causal variant. Finally, after conditioning on rs7445832, all association signals for other markers were significantly reduced, which might suggest there exists only one putative causal locus in this region.

Our study is the first to detect the association between HTR1A and alcohol and nicotine co-dependence at a genome-wide significance level. HTR1A is located in 5q11.2-q13. It encodes the 5-HT1A receptor that binds the endogenous neurotransmitter serotonin (5-hydroxytryptamine, 5-HT). This receptor is a G protein-coupled receptor (GPCR) that is coupled to Gi/Go and mediates inhibitory neurotransmission. In the central nervous system, 5-HT1A receptors exist in the cerebral cortex, hippocampus, septum, amygdala, and raphe nucleus in high densities. The activation of 5-HT1A receptor has been shown to increase dopamine release in the medial prefrontal cortex, striatum, and hippocampus, to impair cognition, learning, and memory by inhibiting the release of glutamate and acetylcholine in various areas of the brain, or to increase impulsivity and inhibition of addictive behaviors. This activation is therefore likely to be related to the development of alcohol dependence or nicotine dependence. This is consistent with our findings that the risk alleles of the variants in the IPO11-HTR1A region for alcohol and nicotine co-dependence increased the expression of HTR1A. Additionally, a well-known and functional promoter SNP of HTR1A, C-1016G (rs6295), displays differential binding to repressors and affects transcription (Lemonde et al., 2003; Strobel et al., 2003). Its minor allele G has been reported to increase risk for alcohol dependence (Lee et al., 2009) or increase the relapse rate of alcohol dependence (Wojnar et al., 2006), which is consistent with our conclusion that minor alleles in this region are risk alleles.

IPO11 is a flanking gene of HTR1A. It encodes the importin 11 that is a member of the karyopherin/importin-beta family of transport receptors. This receptor mediates nucleocytoplasmic transport of protein and RNA cargoes (Plafker and Macara, 2000). It has been reported that, in mice, IPO11 expression was significantly regulated by ethanol in the prefrontal cortex (Kerns et al., 2005) and in the whole embryos (Zhou et al., 2011). In the present study, we found many alcohol and nicotine co-dependence-associated markers had significant cis-acting regulatory effects on IPO11 mRNA expression both in the brain and the PBMC. Thus, IPO11 might play important roles in alcohol and nicotine co-dependence too.

Among CHRNA6-CHRNB3 and CHRNA5-CHRNA3-CHRNB4 regions that have been widely associated to both alcohol and nicotine dependence before (Edenberg et al., 2004; Bierut et al., 2007; Saccone et al., 2007; Ray et al., 2009; Liu et al., 2010; Thorgeirsson et al., 2010), we only found that CHRNA6^rs6474421 was modestly associated with alcohol and nicotine co-dependence in the European-American discovery cohort (p=0.005). Furthermore, this modest association was not replicated in the Australian and African-American replication cohorts, nor did the marker make the top-ranked gene list in the present study, consistent with previous results using the same SAGE dataset (Bierut et al., 2010; Wang et al., 2011). Additionally, the risk genes identified by Lind et al. (2010) were not significantly associated with alcohol and nicotine co-dependence in our samples after Bonferroni correction. Critical difference between the study of Lind et al. (2010) and ours might result from the sample heterogeneity. Finally, in the present study, only the region between the TSS of IPO11 and the TSS of HTR1A was studied. The 5′ regulatory regions, which boundaries are hard to defined, of both genes were excluded. Some information in these 5′ regulatory regions might be lost.

Supplementary Material

Supplementary methods&TableS1-S4

Acknowledgments

We thank for Dr. Weissbecker’s helpful comments. This work was supported in part by National Institute on Drug Abuse (NIDA) grants K01 DA029643 and R01DA016750, National Institute on Alcohol Abuse and Alcoholism (NIAAA) grants R01 AA016015 and R21 AA020319 and the National Alliance for Research on Schizophrenia and Depression (NARSAD) Award 17616 (L.Z.). We thank NIH GWAS Data Repository, the Contributing Investigator(s) who contributed the phenotype and genotype data from his/her original study (e.g., Drs. Bierut, Edenberg, Heath, Singleton, Hardy, Foroud, Myers, Gejman, Faraone, Sonuga-Barke, Sullivan, Nurnberger, Devlin, Monaco, etc.), and the primary funding organization that supported the contributing study. Funding and other supports for phenotype and genotype data were provided through the National Institutes of Health (NIH) Genes, Environment and Health Initiative (GEI) (U01HG004422, U01HG004436 and U01HG004438); the GENEVA Coordinating Center (U01HG004446); the NIAAA (U10AA008401, R01AA013320, P60AA011998); the NIDA (R01DA013423); the National Cancer Institute (P01 CA089392); the Division of Neuroscience, the NIA National Institute of Neurological Disorders and Stroke (NINDS); the NINDS Human Genetics Resource Center DNA and Cell Line Repository; the NIH contract “High throughput genotyping for studying the genetic contributions to human disease” (HHSN268200782096C); the Center for Inherited Disease Research (CIDR); a Cooperative Agreement with the Division of Adult and Community Health, Centers for Disease Control and Prevention; the NIH Office of Research on Women’s Health (ORWH) (R01NS45012); the Department of Veterans Affairs; the University of Maryland General Clinical Research Center (M01RR165001), the National Center for Research Resources, NIH; the National Institute of Mental Health (R01MH059160, R01MH59565, R01MH59566, R01MH59571, R01MH59586, R01MH59587, R01MH59588, R01MH60870, R01MH60879, R01MH61675, R01MH62873, R01MH081803, R01MH67257, R01MH81800, U01MH46276, U01MH46282, U01MH46289, U01MH46318, U01MH79469, U01MH79470 and R01MH67257); the NIMH Genetics Initiative for Bipolar Disorder; the Genetic Association Information Network (GAIN); the Genetic Consortium for Late Onset Alzheimer’s Disease; the Autism Genome Project, the MARC: Risk Mechanisms in Alcoholism and Comorbidity; the Molecular Genetics of Schizophrenia Collaboration; the Medical Research Council (G0601030) and the Wellcome Trust (075491/Z/04), University of Oxford; the Netherlands Scientific Organization (904-61-090, 904-61-193, 480-04-004, 400-05-717, NWO Genomics, SPI 56-464-1419) the Centre for Neurogenomics and Cognitive Research (CNCR-VU); Netherlands Study of Depression and Anxiety (NESDA) and the Netherlands Twin Register (NTR); and the European Union (EU/WLRT-2001-01254), ZonMW (geestkracht program, 10-000-1002). Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the Genetic Consortium for Late Onset Alzheimer’s Disease, the GENEVA Coordinating Center (U01 HG004446), and the National Center for Biotechnology Information. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research, and GlaxoSmithKline, R&D Limited. The datasets used for the analyses described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/sites/entrez?Db=gap through dbGaP accession numbers listed in Table S1. XL had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Footnotes

Conflict of Interest: I have read the journal’s policy and have the following conflicts. Dr. Krystal has been a paid consultant for Aisling Capital, LLC, AstraZeneca Pharmaceuticals, Brintnall & Nicolini, Inc., Easton Associates, Gilead Sciences, Inc., GlaxoSmithKline, Janssen Pharmaceuticals, Lundbeck Research USA, Medivation, Inc., Merz Pharmaceuticals, MK Medical Communications, F. Hoffmann-La Roche Ltd, SK Holdings Co., Ltd, Sunovion Pharmaceuticals, Inc., Takeda Industries and Teva Pharmaceutical Industries, Ltd. He serves as a member of Scientific Advisory Boards for Abbott Laboratories, Bristol-Myers Squibb, Eisai, Inc., Eli Lilly and Co., Forest Laboratories, Inc., Lohocla Research Corporation, Mnemosyne Pharmaceuticals, Inc., Naurex, Inc., Pfizer Pharmaceuticals and Shire Pharmaceuticals. He is the Editor for Biological Psychiatry, a member of Board of Directors of Coalition for Translational Research in Alcohol and Substance Use Disorders, and the President Elect for American College of Neuropsychopharmacology. He also gets support from Tetragenex Pharmaceuticals.

Other authors have no conflict of interest.

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Supplementary Materials

Supplementary methods&TableS1-S4
NIHMS413350-supplement-Supplementary_methods_TableS1-S4.doc (457KB, doc)

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