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Human Molecular Genetics Advance Access originally published online on June 1, 2006
Human Molecular Genetics 2006 15(14):2192-2199; doi:10.1093/hmg/ddl144
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Intronic variants in the dopa decarboxylase (DDC) gene are associated with smoking behavior in European-Americans and African-Americans

Yi Yu1, Carolien Panhuysen1,2, Henry R. Kranzler4, Victor Hesselbrock4, Bruce Rounsaville5, Roger Weiss7,8, Kathleen Brady9, Lindsay A. Farrer1,2,3 and Joel Gelernter5,6,*

1 Department of Medicine (Genetics Program), 2 Department of Biostatistics, 3 Department of Neurology, Genetics and Genomics and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA, USA, 4 Departments of Psychiatry and Neurobiology, University of Connecticut Health Center, Farmington, CT, USA, 5 Department of Psychiatry, VA CT Healthcare Center and 6 Department of Neurobiology, Yale University School of Medicine, USA, 7 Alcohol and Drug Abuse Treatment Program, McLean Hospital, Belmont, MA, USA, 8 Department of Psychiatry, Harvard Medical School, Boston, MA, USA and 9 Department of Psychiatry, Medical University of South Carolina, Charleston, SC, USA

* To whom correspondence should be addressed at: Department of Psychiatry, Yale University School of Medicine, Division of Human Genetics in Psychiatry, VA CT 116A2, 950 Campbell Avenue, West Haven, CT 06516, USA. Fax: +1 2039373897; Email: joel.gelernter{at}yale.edu

Received March 23, 2006; Revised May 16, 2006; Accepted May 29, 2006


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 MATERIALS AND METHODS
 REFERENCES
 
We report here a study considering association of alleles and haplotypes at the DOPA decarboxylase (DDC) locus with the DSM-IV diagnosis of nicotine dependence (ND) or a quantitative measure for ND using the Fagerstrom Test for Nicotine Dependence (FTND). We genotyped 18 single nucleotide polymorphisms (SNPs) spanning a region of ~210 kb that includes DDC and the genes immediately flanking DDC in 1590 individuals from 621 families of African-American (AA) or European-American (EA) ancestry. Evidence of association (family-based tests) was observed with several SNPs for both traits (0.0002≤P≤0.04). The most significant result was obtained for the relationship of FTND score to SNP rs12718541 (AA families: P=0.002; EA families: P=0.03; all families: P=0.0002) which is in the same intron as the splice site for a neuronal isoform of human DDC lacking exons 10–15. Haplotype analysis did not reveal any SNP combination with stronger evidence for association than rs12718541 alone. Although sequence analysis suggests that rs12718541 may be an intronic splicing enhancer, further studies are needed to determine whether a direct link exists between an alternatively spliced form of DDC and predisposition to ND. These findings confirm a previous report of association of DDC with ND, localize the causative variants to the 3' end of the coding region and extend the association to multiple population groups.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 MATERIALS AND METHODS
 REFERENCES
 
Nicotine dependence (ND) is one of the most prevalent substance dependencies in the world; about one-third of the world adult population are tobacco smokers (1). In 2000, approximately 4.83 million premature deaths worldwide were attributed to smoking (2). In the same year, in the USA, there were an estimated 435 000 smoking-related deaths, representing 18.1% of the total adult mortality (3). Gaining insight into the genetic mechanisms that increase risk for smoking is therefore of great public health importance.

Nicotine addiction is both psychological and physiological (4), primarily induced by the rewarding effects of nicotine (5). Comparable to other substance-dependence disorders, ND is believed to be a complex trait determined by a combination of genetic and environmental factors. Twin and adoption studies have shown that genetic factors play an important role in both smoking initiation and smoking persistence (6–9). The heritability of regular tobacco use was estimated to be 61% in a large Swedish twin study, and the heritability of ND was estimated to be 60% in a study from the population-based Virginia Twin Registry (10,11). Recently, in a large Dutch twin study (12), ND was estimated to be influenced more strongly by genetic (75%) than environmental (25%) factors. The sibling recurrence risk ({lambda}s) of nicotine addiction was estimated to be >2 in a sample of nuclear families in China (13). Despite its high heritability, the specific genetic mechanisms involved in the pathogenesis of ND remain elusive. Possible linkages with several chromosomal regions have been reported in four independent data sets (14–17), but no genes have been positively identified by this approach.

There are many plausible candidate genes for ND and several (including DRD2, CYP2A6 and SLC6A4) have been studied intensively. Despite some consistency of findings across studies, causative variants remain elusive. The gene encoding DOPA decarboxylase (DDC), located on chromosome 7p11 (18), is an attractive candidate. DDC catalyzes the conversion of substrates to two important neurotransmitters, namely L-DOPA to dopamine and 5-hydroxytryptophan to serotonin (19), and is regulated both pre- and post-translationally (20). Polymorphisms in the DDC gene have been associated with schizophrenia (21), bipolar affective disorders (22,23) and attention deficit hyperactivity disorder (24), but these findings have not been consistently replicated (25,26). A recent study by Ma et al. (27) showed association between long-range DDC haplotypes and three correlated quantitative traits [smoking quantity, heaviness of smoking index and the Fagerstrom Test for Nicotine Dependence (FTND) score] for ND in families ascertained through an affected sib-pair (ASP) for ND.

The goal of the present study was to examine the association between DDC and measures of smoking behavior in a multi-population family-based sample and, if possible, identify causative variants for susceptibility to ND. To localize the source of the association signal, we also genotyped markers at flanking genetic loci: ZNFN1A1 [zinc finger protein, subfamily 1A, 1 (Ikaros)], FIGNL1 (fidgetin-like 1) and GRB10 (growth factor receptor-bound protein 10).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
MATERIALS AND METHODS
 REFERENCES
 
Single-single nucleotide polymorphism association analyses
We evaluated 18 single nucleotide polymorphisms (SNPs) in the DDC region. Detailed information about these SNPs is summarized in Table 1. ND was significantly associated with SNP 4 (rs2060761) in the African-American (AA) families (P=0.002), a result that survived correction for multiple testing (Table 1). ND was also nominally associated with adjacent SNP 3 (rs10230343) in the European-American (EA) families (P=0.04). Consistent with the fact that associations with different SNPs were observed in the two populations in this part of the gene, none of the SNPs was associated with ND in the total group of families. More consistent and robust evidence for association was obtained for the quantitative measure of tobacco dependence. The FTND score was significantly associated with SNP 7 (rs12718541) in AA families (P=0.002) and was nominally associated in EA families (P=0.03). Moreover, the pattern of association (excess transmission of the A allele from parents to offspring with high FTND scores) was the same in both population groups (P=0.0002 for all families combined). An analogous but only nominally significant pattern of association was observed for SNP 13 (rs921451) in AA (P=0.04) and EA (P=0.02) families, but the association was significant after correction in the entire group of families (P=0.002). Adjacent SNPs 14 (rs1470747) and 15 (rs11238214) also showed only nominal evidence for association with FNTD score in the combined families (P=0.03 and P=0.02, respectively) (Table 2).


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  		Table 1. Characteristics of SNPs in the DDC region on chromosome 7

 


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  		Table 2. Results of family based association with SNPs in the DDC region

 
Haplotype block structure and linkage disequilibrium analysis
Figure 1 shows pairwise linkage disequilibrium (LD) (D') values for the 18 SNPs in the 210 kb sequence encompassing DDC, including a portion of the gene proximal (GRB10) and the entire gene distal (FIGNL1) to DDC. Although adjacent markers displayed strong LD, three haplotype blocks were revealed in each population. A 16 kb block containing SNP 3 (rs10230343 located in FIGNL1) and SNP 4 (rs2060761 located in the 3' end of DDC) was identified in the EA group. A larger 18 kb block containing SNP 5 (rs11575542) was evident in the AA group. An 8 kb block, containing intergenic SNPs 12 (rs4947644), 13 (rs921451) and 14 (rs1470747), was the same in both populations. A third block containing SNPs 7 (rs12718541) and 8 (rs1451371) and covering 2 kb within intron 9 of DDC was discerned in the AA group, whereas in the EA sample, the analogous middle block extends over 32 kb from intron 6 to intron 11 of DDC and includes SNPs 6–10 (rs732215, rs12718541, rs1451371, rs3823674 and rs1470750).


Figure 1441
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  		Figure 1. Locations and LD map structure of SNPs in the DDC and flanking gene regions. The LD maps for the EA and AA family samples are shown, respectively, above and below the gene structure diagram. The measure of LD (D') among all possible pairs of SNPs (identified by number using the scheme in Table 1) is shown graphically according to the shade of red where white represents very low D' and dark red represents very high D'. High D' estimates associated with a large confidence interval (most likely due to one of the alleles being rare) are denoted by blue squares. Starting from the top-left, the gene structures for ZNFN1A1, FIGNL1, DDC and GRB10 including intergenic regions (white), introns (green) and exons (purple) are shown on the diagonal.

 
Haplotype analyses
To assess whether a particular marker profile accounted for or clarified the associations with ND and FTND score, we evaluated three-marker haplotypes using a sliding window approach. This analysis did not reveal any haplotypic association for ND in the EA or total group of families. Several SNP combinations were associated with ND in the AA families (Table 3). The most significant of these combinations (rs1110701, rs10230343 and rs2060761) remained significant after correction for multiple testing (global P=0.001). Inspection of specific haplotypes revealed that this association is explained entirely by rs2060761, because the same alleles for rs1110701 and rs10230343 are part of both risk (P=0.0002) and protective (P=0.007) haplotypes. Significantly associated haplotypes for FTND score were identified in both AA and EA families; however, in the combined group of families, only the overlapping SNP combinations rs11575542–rs732215–rs12718541 (global P=0.001) and rs732215–rs12718541–rs1451371 (global P=0.002) remained significant after adjustment for multiple testing (Table 4). Similar to the result with ND, this association can be explained entirely by rs12718541, because the same alleles for rs11575542, rs732215 and rs1451371 are part of both risk (P=0.00006 and P=0.0006) and protective haplotypes (P=0.01 and P=0.03). Although haplotype G-T-C of the SNP combination rs11575334–rs4947644–rs921451 was also significantly associated with FTND score (P=0.0005), this result must be interpreted cautiously because the global P-value for this combination (0.04) is not significant after correction for multiple testing. We also analyzed haplotypes derived from the combination of SNPs rs2060761, rs12718541, rs11575334 and rs921451, which ‘tag’ all the LD blocks and intervening regions in both the EA and AA groups. Results from these analyses were not significant in either population group or the combined sample of families.


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  		Table 3. Haplotypes associated with ND in the AA families

 


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  		Table 4. Haplotypes associated with FTND score in the AA, EA and combined families

 
Discussion
Our study confirms a previously reported association between DDC polymorphisms and smoking behavior (27) and extends this finding in several important and meaningful ways. First, by testing a large number of polymorphic SNPs, including several in the genes flanking DDC, we excluded the possibility that the association signal is a result of LD with a neighboring gene. Second, the most significant evidence of association in the Ma et al. study was obtained with the FTND score and two four-SNP haplotypes spanning the entire DDC gene region. These haplotypes share the first three SNPs but showed opposite patterns of association in AA and EA families in their data set (uncorrected haplotype P=0.009 and 0.005 for these groups, respectively; global P-values were not reported). In contrast, our SNP and haplotype analyses strongly suggest that the variant directly influencing smoking behavior is located in intron 9 at or very proximate to rs12718541. Notably, Ma and coworkers did not test this SNP and both their study and the present one yielded negative results with another intron 9 SNP (rs1451371). Thirdly, our observation of the same pattern of association with the same SNP in both AA and EA families is consistent with the hypothesis that at least one genetic mechanism underlying the association with DDC is common to these two populations.

Genetic association studies for complex traits must be interpreted cautiously (28,29). Often, highly significant findings are not replicated, thus calling into question the veracity or generalizability of the original report. These concerns are lessened in our study for several reasons. First, the pattern of association for our most significant finding (with the A allele of rs12718541 for FTND score) was the same in two distinct population samples. Second, our results are unlikely to be spurious due to population stratification because we defined our major population groups using a clustering approach and genetic marker information obtained from a 10 cM genome scan (30) and considered them separately, using family-based controls. Third, we applied a very stringent correction for multiple testing. Fourth, our results were obtained from a family sample ascertained through probands meeting DSM-IV criteria for cocaine or opioid dependence, whereas the DDC association had already been demonstrated in families ascertained on the basis of ND (27). This is the first association with this gene to be documented in a sample not ascertained for familial ND. This is important because samples ascertained for ND should be enriched for genes of relatively high attributable {lambda}, but that is not the case for the present sample. Our sample was, however, ascertained for other substance dependence, which raises the possibility that it, too, might be enriched for ND-related genes, although possibly in a less biased way. This possibility is supported by the high rate of ND observed in the sample (overall prevalence=60.9%). Our ascertainment also raises the possibility that the association of rs12718541 with FTND score may be confounded by association with dependence to other substances. To test this possibility, we repeated the association analyses for FTND after adjusting the raw scores for cocaine and opioid dependence. These analyses yielded very similar results and did not alter any of the conclusions (data not shown). In any event, evidence for association between DDC and smoking behavior in independent studies inspires confidence that these findings will lead to a better understanding of the biological basis of ND in the general population. Fifth, assignment of ND status was made using a reliable standardized instrument (31). Finally, genetic analyses of the quantitative FTND score were done on residuals adjusted for age and sex, thus accounting in part for secular trends in smoking behavior.

Our study also revealed two secondary association signals. Rs921451 was significantly associated with FTND score in both population groups in our study (combined sample P=0.002) and previously, as part of a haplotype in the Ma et al. study (27). This variant is located in intron 1 (Fig. 2). Our analysis suggested that a particular haplotype (G-T-C) of SNP combination rs11575334–rs4947644–rs921451, which extends over the portion of DDC including introns 1–4, increases tobacco dependence risk as reflected in the FNTD score. A DNA sequence upstream of DDC that possesses enhancer-like properties and is essential for normal neuron-specific expression has been identified in Drosophila (32). However, two observations make it unlikely that a polymorphism in this region is responsible for the observed association with FTND score. First, the association with the G-T-C haplotype was evident primarily in the EA sample, and the global P-value for this SNP combination was marginally significant before correction for multiple testing (Table 4). Second, LD block analysis suggests that the association with rs921451 may be due to LD between this SNP and rs12718541 (D'=0.78 in AA, D'=0.77 in EA). To test this hypothesis, we examined the association between FTND score and the haplotype consisting of these two SNPs. The observation that the risk allele for rs12718541, but not for rs921451, was contained in a deleterious haplotype suggests that there is only one site associated with FTND score in our sample.


Figure 1442
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  		Figure 2. DDC gene structure. Approximate positions of SNPs (identified by number using the scheme in Table 1) are indicated with arrows. Exons (not drawn to scale) are represented as numbered boxes and are separated by introns which are shaded gray. The sizes (in base pairs) of exons and introns are indicated above and below the gene structure, respectively. The alternative exon (A10) reported by Vassilacopoulou et al. (35) and the position of the SNP most strongly associated with FTND score (rs12718541) are highlighted in red.

 
We also detected significant association between the DSM-IV diagnosis of ND and rs2060761, which is located in intron 14. This finding does not appear to be confounded with rs12718541 (Table 3) and may reflect a different mechanism influencing smoking behavior. The FTND score emphasizes cigarette craving upon waking from nighttime sleep and overall ‘heaviness’ of smoking and thus may provide a stronger measure of physical dependence; the DSM classification of ND emphasizes continued smoking despite adverse consequences, desire to cut down and mood changes during withdrawal and may tap other domains such as awareness of dependence, behaviors resulting from that awareness and psychiatric symptomatology (33). Rs2060761 is located in the 3'-UTR region of DDC. In many genes, 3'-UTR binding factors control translation by regulating diverse steps such as ribosome binding, scanning, initiation and elongation (34). However, the association between rs2060761 and ND, which was evident in the AA sample only and was based on a contribution from approximately one-half of the AA families (due to the requirement in FBAT that qualitative trait analysis include at least one discordant sib pair per family), is clearly less robust than the association between rs12718541 and FTND score and needs confirmation in independent samples.

The strongest evidence of association in this study was found between FNTD score and rs12718541. This SNP is located in DDC intron 9, about 12 903 bp from exon 9 and 2583 bp from exon 10 (Fig. 2). Curiously, none of the other SNPs tested in the coding region, including rs1451371 (which is in the same intron) and rs11575542 (a non-synonymous coding SNP in exon 14), showed evidence for association. Insight into the lack of association with other intergenic SNPs and the mechanism of action may be gleaned from a recent report of a neuronal isoform of human DDC mRNA that lacks exons 10–15 but includes an alternative exon 10 that is located within intron 9 (35). The position of the splice site relative to rs12718541 is shown in Figure 2. Alternative splicing of DDC mRNA might render a functional change or provide plasticity in the modulation of the rate of DDC mRNA expression and translation (35–38). Inspection of the DDC sequence suggests that when the rs12718541 G allele is present, the complementary strand has the intron sequence ‘CCCC,’ a C-rich motif that is predicted to be an intronic splicing enhancer (ISE) in mammals (39). Although no specific ISE has been reported in the human DDC gene, cis-regulatory sequences responsible for alternative splicing of the DDC gene have been reported in Drosophila (40). Analysis of the GABA (A) receptor {gamma}2 gene revealed a distal downstream ISE sequence regulating alternative splicing, suggesting ISE may be a common regulatory mechanism in the translation of neuronal proteins (41). On the basis of this model, the rs12718541 A allele would be predicted to disrupt the ISE sequence and decrease the efficiency of splicing and therefore influence predisposition to ND. Additional molecular functional studies are needed to investigate the details of mechanism of this association.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 MATERIALS AND METHODS
REFERENCES
 
Subject recruitment and assessment
Families included in this study were recruited at four sites in the US: Yale University School of Medicine (APT Foundation, New Haven, CT, USA), University of Connecticut Health Center (Farmington, CT, USA), McLean Hospital (Harvard Medical School, Belmont, MA, USA) and Medical University of South Carolina (Charleston, SC, USA). Subjects were ascertained as ASPs that met DSM-IV criteria for cocaine or opioid dependence (30,42). Individuals with a primary diagnosis of a major psychotic illness (schizophrenia or schizoaffective disorder) were excluded. Once an ASP was enrolled, additional siblings and parents were recruited whenever possible regardless of affection status. Tobacco use played no role in proband selection or pedigree extension. Subjects were classified as AA or EA based on a Bayesian model-based clustering method using genetic marker information as described previously (30,42). DNA was obtained from immortalized cell lines in most cases, but for some subjects, DNA was obtained directly from blood or saliva. Subjects gave informed consent as approved by the institutional review board at each clinical site, and a certificate of confidentiality for the work was issued by NIH (NIDA).

Diagnosis
Subjects were interviewed using an electronic version of the Semi-structured Assessment for Drug Dependence and Alcoholism (SSADDA) instrument (31). Diagnostic data were transferred electronically to a centralized database at Boston University. Classification of subjects as ND was carried out using computerized algorithms that applied the DSM-IV diagnostic criteria for the disorder (43). Previously, we estimated the overall reliability ({kappa}) of the SSADDA for the diagnosis of ND to be 0.85, with test–retest reliability {kappa}=0.97 (based on 120 subjects) and inter-rater reliability {kappa}=0.77 (based on 173 subjects) (31). The FTND score (range of 0–10 points) was derived from the SSADDA, in which the questions were embedded (44). Both the DSM diagnosis of ND and the FTND score are common assessment tools for tobacco dependence diagnosis, and they appear to measure different aspects of the tobacco dependence process (33). Characteristics of the participants in this study are presented in Table 5.


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  		Table 5. Sample characteristics

 
SNP selection and genotyping
The coding portion of the gene contains 15 exons and is 85 kb in length (18,45). Eleven SNPs within DDC were selected from the NCBI SNP database (http://www.ncbi.nlm.nih.gov/projects/SNP/) with a uniform coverage of the gene. An additional seven SNPs extending into the genes flanking DDC were selected based on their information content and map position; they also cover LD blocks as defined for the samples of Caucasians and Yorubans included in the HapMap Project (http://www.hapmap.org) (46). The locations of SNPs across this interval are shown in Figure 1. SNPs were genotyped with a fluorogenic 5' nuclease assay method, i.e. the TaqMan technique (47), using the ABI PRISM 7900 Sequence Detection System (ABI, Foster City, CA, USA). All SNP assays were performed in duplicate and discrepant genotypes were discarded. All SNPs had minor allele frequency >10% and were in Hardy–Weinberg equilibrium in a set of unrelated healthy subjects from each population.

Statistical analysis
The PedCheck program was used to detect Mendelian inconsistencies in the genotype data (48). A total of nine genotyping inconsistencies were identified out of 28 620 assays for 18 SNPs across all DNA samples (i.e. <0.03% of all assays) and these results were excluded from all subsequent statistical analyses.

Because several of the markers have population-specific allele frequencies and given the potential for genetic heterogeneity in ND, separate analyses were conducted for each population. To reduce potential confounding, the FTND score was adjusted for age and sex in each population sample by computing standardized residuals using SAS (version 9.0). Pairwise LD between all markers was assessed using the Haploview program (49) and haplotype blocks were discerned according to the criteria of Gabriel et al. (50). Association of individual SNPs with ND and adjusted FTND scores was evaluated using the Family-Based Association Test (FBAT) program (51) assuming an additive model. P-values were estimated by the Monte Carlo sampling method under the null hypothesis of no linkage and no association. Haplotype analyses were performed using HBAT, the haplotype extension routine in the FBAT program (52) using a three-SNP sliding window approach. Haplotype analysis was also performed on a group of SNPs which tag the LD blocks determined empirically for each of the population groups.

To reduce the potential of spurious finding due to having tested multiple individual markers, we applied a Bonferroni correction to the empirically derived P-values.Considering that 18 markers were evaluated in our study, the adjusted nominal P-value is 0.003 (i.e. 0.05/18). Haplotype results were considered significant on the basis of the global P-value, which takes into account the testing of all possible haplotypes for a particular combination of SNPs. Bonferroni correction was also applied to these global P-values to adjust for the number of windows tested (i.e. 0.05/16=0.003).


   ACKNOWLEDGEMENTS
 
We thank the families who volunteered to participate in this research study. This work was supported by NIH (NIDA) grants R01 DA12690, R01 DA12849, K24 DA15105 and GCRC grant M01 RR06192. Ann Marie Lacobelle provided excellent technical assistance. John Farrell provided excellent database support and Dr Marsha Wilcox assisted with some statistical analyses. We also thank the Rutgers University Cell and DNA Repository, the contractor for the NIDA Center for Genetic Studies, co-directed by Dr Jay Tischfield and Dr John Rice.

Conflict of Interest statement. None declared.


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 ABSTRACT
 INTRODUCTION
 RESULTS
 MATERIALS AND METHODS
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