Skip Navigation


Human Molecular Genetics Advance Access originally published online on May 6, 2005
Human Molecular Genetics 2005 14(12):1691-1698; doi:10.1093/hmg/ddi177
This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
14/12/1691    most recent
ddi177v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (15)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Ma, J. Z.
Right arrow Articles by Li, M. D.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Ma, J. Z.
Right arrow Articles by Li, M. D.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org

Haplotype analysis indicates an association between the DOPA decarboxylase (DDC) gene and nicotine dependence

Jennie Z. Ma1, Joke Beuten1, Thomas J. Payne2, Randolph T. Dupont3, Robert C. Elston4 and Ming D. Li1,*

1Program in Genomics and Bioinformatics on Drug Addiction, Department of Psychiatry, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA, 2The ACT Tobacco Center, University of Mississippi Schools of Dentistry and Medicine, Jackson, MS, USA, 3Department of Criminology and Criminal Justice, University of Memphis, Memphis, TN, USA and 4Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA

* To whom correspondence should be addressed at: MSC 7792, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA. Tel: +1 2105670830; Fax: +1 2105670853; Email: lim2{at}uthscsa.edu

Received April 1, 2005; Revised April 21, 2005; Accepted April 29, 2005


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
DOPA decarboxylase (DDC; also known as L-amino acid decarboxylase; AADC) is involved in the synthesis of dopamine, norepinephrine and serotonin. Because the mesolimbic dopaminergic system is implicated in the reinforcing effects of many drugs, including nicotine, the DDC gene is considered a plausible candidate for involvement in the development of vulnerability to nicotine dependence (ND). Further, this gene is located within the 7p11 region that showed a ‘suggestive linkage’ to ND in our previous genome-wide scan in the Framingham Heart Study population. In the present study, we tested eight single nucleotide polymorphisms (SNPs) within DDC for association with ND, which was assessed by smoking quantity (SQ), the heaviness of smoking index (HSI) and the Fagerström test for ND (FTND) score, in a total of 2037 smokers and non-smokers from 602 nuclear families of African- or European-American (AA or EA, respectively) ancestry. Association analysis for individual SNPs using the PBAT-GEE program indicated that SNP rs921451 was significantly associated with two of the three adjusted ND measures in the EA sample (P=0.01–0.04). Haplotype-based association analysis revealed a protective T–G–T–G haplotype for rs921451–rs3735273–rs1451371–rs2060762 in the AA sample, which was significantly associated with all three adjusted ND measures after correction for multiple testing (min Z=–2.78, P=0.006 for HSI). In contrast, we found a high-risk T–G–T–G haplotype for a different SNP combination in the EA sample, rs921451–rs3735273–rs1451371–rs3757472, which showed a significant association after Bonferroni correction with the SQ and FTND score (max Z=2.73, P=0.005 for FTND). In summary, our findings provide the first evidence for the involvement of DDC in the susceptibility to ND and, further, reveal the racial specificity of its impact.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Cigarette smoking is a highly prevalent and harmful behavior. Annually, tobacco smoking is responsible for approximately three million deaths world-wide (1Go,2Go), with an estimated 435 000 deaths in the USA alone (3Go). More than 98% of tobacco users are cigarette smokers. Although a minority of tobacco smokers do not smoke daily, most do and are physically dependent on nicotine, the primary addictive component of cigarette smoke (4Go). Like all substance-dependence disorders, nicotine dependence (ND) has a substantial heritable component. Over the last few decades, many large-sample twin studies have concluded that genetics contributes to the risk of becoming a regular smoker (5Go). Meta-analysis of its polygenic heritability based on 17 published twin studies indicated that the weighted mean heritability for ND is 0.56 (6Go). A familial aggregation study among siblings of nuclear families also suggested that genetics plays an important role in determining the vulnerability to ND (7Go). Further evidence comes from biochemical and pharmacological findings showing that genetic factors contribute to differential behavioral and physiological manifestations of nicotine administration among various inbred mouse strains (8Go–10Go).

The mesolimbic dopaminergic system of the brain has been implicated in the reinforcing effects of nicotine and other substances (11Go,12Go). In laboratory animals, nicotine increases the release of dopamine and enhances energy metabolism via stimulation of the basal ganglia, especially in the ventral tegmental area and nucleus accumbens, similar to the effects of other addictive drugs such as cocaine, amphetamines and morphine (13Go,14Go). Imaging studies of the human brain have revealed an association between dopamine activity and smoking. For example, [11C]raclopride positron emission tomography (PET) studies have shown increased dopamine release and L-DOPA uptake in smokers relative to non-smokers (15Go,16Go). Because of its importance in brain reward mechanisms, polymorphisms within genes involved in the dopaminergic system are reasonable candidates for involvement in the etiology of ND and other substance-dependence disorders.

DOPA decarboxylase (DDC), also known as aromatic L-amino acid decarboxylase (AADC), catalyzes the conversion of L-DOPA to dopamine and of 5-hydroxytryptophan to serotonin (17Go). In addition, DDC is thought to be the sole enzyme responsible for the synthesis of the trace amines 2-phenylethylamine, p-tyramine and tryptamine, considered to act as neuromodulators. The DDC gene, which maps to chromosome 7p11, is ~85 kb and contains 15 exons (18Go,19Go).

Although DDC is not a rate-limiting enzyme in the synthesis of dopamine, norepinephrine and serotonin, it is regulated at both pre- and post-translational levels (20Go). This fact, in conjunction with its functions in the biosynthesis of dopamine and serotonin, makes DDC a plausible candidate in the etiology of major psychiatric and drug abuse disorders. Genetic polymorphisms within DDC have been investigated for potential association with schizophrenia (21Go,22Go), unipolar and bipolar affective disorder (23Go–25Go) and attention deficit hyperactivity disorder (26Go), among others. However, to date no study has been reported examining a possible association between the presence of DDC variants and substance dependence, including ND. Further support for investigating the role of DDC in the vulnerability to ND is based on its location within a genomic region on chromosome 7 that showed a ‘suggestive linkage’ with smoking quantity (SQ) in our previous genome-wide linkage studies in the Framingham Heart Study (FHS) population (Fig. 1A) (27Go,28Go). Such linkage of SQ to chromosome 7 was further confirmed by our group in a genome-wide permutation linkage analysis on the same FHS cohort (29Go). In this study, we analyzed eight SNPs selected almost evenly along the DDC gene in 602 nuclear families of American-American (AA) or European-American (EA) ancestry to determine whether there exists an association of DDC with ND.



View larger version (21K):
[in this window]
[in a new window]
  		Figure 1. Schematic view of the chromosome 7 linkage region showing the position and structure of DDC and the SNPs selected for association analysis. (A) Linkage analysis results for log-transformed SQ in the FHS population (27Go,28Go). The linkage results were generated using SIBPAL of SAGE. (B) Relative position of a number of microsatellite markers and genes, including DDC, within the 7p linkage region. Microsatellite markers D7S2846 and D7S1818, used in the linkage analysis, are marked in bold and underlined. (C) DDC structure and positions of the SNPs (presented from 5' to 3' of the gene). Exons are represented as open boxes, with the size of the exon in the box, and introns are depicted by a horizontal line between the exons. SNP rs921451 showing a significant association with ND in the single-SNP analysis is marked in bold, and the SNPs used for haplotype analysis are underlined.

 

   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Individual SNP analysis using the PBAT-GEE program revealed no significant association with the three adjusted ND measures in the pooled sample. Given the potential genetic differences in ND across racial groups (30Go), we also analyzed the AA and EA samples separately, finding a significant association for SNP rs921451 with age- and sex-adjusted SQ and heaviness of smoking index (HSI) score under different genetic models in the EA sample (P=0.01–0.04; Table 1). This association remained significant only for the adjusted SQ under the additive model after correcting for multiple testing based on the SNP spectral decomposition approach. In the AA sample, no significant associations for single SNPs with ND measures were found.


View this table:
[in this window]
[in a new window]
  		Table 1. P-values for association of single SNPs within DDC with three ND measures in pooled, AA and EA samples
 
The pair-wise D' values of the eight SNPs within DDC were generally above 0.86, with some differences noted between the AA and EA samples (Fig. 2). According to the criteria of Gabriel et al. (31Go), two haplotype blocks across DDC were revealed from the linkage disequilibrium (LD) data for these eight SNPs. A first block containing SNPs rs998850 and rs3735273 of 10 kb was the same in both ethnic groups. The second block differed between the AA and EA samples: in the AA sample two SNPs (rs1451371 and rs732215) were assigned to this block which covered 8 kb whereas in the EA sample this second block extended over 47 kb, including SNPs rs1470750, rs1451371, rs7322155, rs3757472 and rs2060762.



View larger version (26K):
[in this window]
[in a new window]
  		Figure 2. LD structure of eight SNPs within DDC in the AA and EA samples. Regions of high LD (D'=1 and LOD>2) are shown in dark gray. Markers with lower LD (0.21<D'<1 and LOD>2) are shown in light gray with the intensity decreasing with decreased D' value. Regions of low LD and low LOD scores (LOD<2) are shown in white. The numbers indicate the D' statistic value between the corresponding two SNPs. Haplotype blocks were defined by the Haploview program with the option of using block definitions proposed by Gabriel et al. (31Go).

 
Haplotype-based association analysis was performed for different combinations of SNPs within DDC in the AA and EA samples. In the AA sample, we found a major haplotype T–G–T–G (with a frequency of 31.8%) for rs921451–rs375723–rs1451371–rs2060762 that showed a significant inverse association with all three adjusted ND measures (Z=–2.78 to –2.01; P=0.006–0.04; Table 2). These inverse associations remained significant after Bonferroni correction for testing of five major haplotypes under the dominant model. In the EA sample, this T–G–T–G haplotype was also significantly related to all three adjusted ND measures (Z=2.01–2.06; P=0.04–0.045); however, these associations were no longer significant after Bonferroni correction (the adjusted P-value at the 0.05 significance level for four major haplotypes in the EA sample is 0.0125; Table 2).


View this table:
[in this window]
[in a new window]
  		Table 2. Z- and permutation P-values for association of major DDC haplotypes for rs921451–rs3735273–rs1451371–rs2060762 with ND measures
 
Additional haplotype analysis based on a different SNP combination (rs921451–rs375723–rs1451371–rs3757472) revealed a major T–G–T–G haplotype, with a frequency of 10.8% in the EA sample, that was positively associated with all three adjusted ND measures (Z=2.73–2.18; P=0.005–0.015; Table 3). After Bonferroni correction for testing of five major haplotypes, the association between this T–G–T–G haplotype and both the SQ and Fagerström test for ND (FTND) score remained significant under the dominant model. This SNP combination was not significant in the AA sample.


View this table:
[in this window]
[in a new window]
  		Table 3. Z- and permutation P-values for association of major DDC haplotypes of rs921451–rs3735273–rs1451371–rs3757472 with ND measures
 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 REFERENCES
 
Over the past years, considerable effort has been expended in identifying the genetic bases of ND (reviewed in 28,32–34). We previously provided evidence for a ‘suggestive linkage’ of smoking quantity to chromosome 7 in the FHS population (27Go,28Go). This was subsequently confirmed by a genome-wide permutation linkage analysis on the same dataset (29Go). On the basis of these linkage results and with an understanding of the biological functions of the products encoded by the genes within the 7p11 linkage region, we selected DDC for further investigation regarding a possible association with ND.

It has long been known that the mesolimbic dopaminergic system plays a critical role in the reinforcing effects of many drugs, including nicotine. Considering the central role of DDC in the synthesis of dopamine and serotonin, this gene represents a plausible candidate for key involvement in major psychiatric and substance-dependence disorders. Thus far, several studies have been conducted investigating a potential association of DDC variants with unipolar and bipolar disorders, schizophrenia and attention deficit hyperactivity disorder (21Go,23Go–26Go). The results have been controversial, and most of the studies have failed to reveal an association. No study reported to date has examined the role of DDC variants in substance-dependence disorders, including ND. We now provide the first evidence for an association with ND and a likely involvement of DDC in the etiology of ND.

Single SNP-based association analysis revealed that after correction for multiple testing, rs921451 is significantly related to SQ in the EA sample. Further, we identified a major haplotype T–G–T–G (frequency of 31.8%) for rs921451–rs375723–rs1451371–rs2060762, which showed a significant negative association, even after Bonferroni correction, with all three adjusted ND measures in the AA sample. In the EA sample, on the other hand, a different SNP combination (rs921451–rs375723–rs1451371–rs3757472) revealed a major haplotype T–G–T–G (frequency of 10.8%) that was positively associated with both SQ and the FTND score after Bonferroni correction. These findings are consistent with the theoretical expectation that haplotype-based analysis is more powerful than single-marker analysis (35Go,36Go).

It is of considerable interest that the protective T–G–T–G haplotype identified in the AA sample and the high-risk T–G–T–G haplotype in the EA sample share the first three SNPs; only the SNP at the 3' end of the DDC gene is different between the two ethnic groups. This suggests that the region toward the 3' end of the gene, where rs2060762 and rs3757472 are located, may harbor causative variants leading to different physiological effects of DDC on the vulnerability to ND. Since these two SNPs are intronic, we are not able to prove an immediately functional significance of the SNPs in the involvement of DDC on ND, nor are we able to explain the ethnic-specific effect of the gene as shown by the protective versus high-risk haplotypes in the AA and EA samples, respectively. Further analysis of additional SNPs within the region in strong LD with rs2060762 and rs3757472 may identify the underlying causative variant(s) and facilitate our understanding of the involvement of DDC in the etiology of ND. On the basis of these haplotype findings, including the ethnic specificity and chromosomal location across DDC, we suggest that these five SNPs should be considered as tagging-SNPs within the gene for future association analysis in AA and EA populations.

A strength of this study is the number of participants. A relatively small sample size is commonly cited as a reason for failure to replicate findings across multiple studies (37Go–39Go). Further, in comparing the effects of DDC on the vulnerability to ND, we provide supportive evidence for the postulated interracial genetic differences (40Go,41Go). The relatively large number of participants in each sub-sample reduces the potential impact of population heterogeneity with regard to lifestyle, social and cultural influences and socioeconomic status, leading to greater power to detect potential associations for genetic variants with ND. Additionally, our association analyses for all ND measures were adjusted for age, sex and race in the pooled sample, and for age and sex in each racial sample to reduce the influence of confounding factors. Such adjustments are essential, as documented in both animal and human studies of nicotine administration and smoking behavior (6Go,12Go,32Go).

In conclusion, the present study is the first to provide evidence for the association of allelic variants of DDC with ND across EA and AA populations. We identified a common protective haplotype (T–G–T–G; 31.8%) for ND in the AA sample and a different, high-risk haplotype (T–G–T–G; 10.8%) for ND in the EA sample. These findings support the hypothesis that DDC plays a significant role in the etiology of ND and suggest a haplotype specificity of DDC across different races.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
REFERENCES
 
Subjects and smoking phenotypes
Participants in the present study were of EA or AA ancestry and were recruited during 1999–2004 in the USA from the states of Tennessee, Mississippi and Arkansas. Extensive clinical data are available, including demographics (e.g. sex, age, race, relationships, weight, height, years of education and marital status), medical history, smoking history and current smoking behavior, ND and selected personality traits. All participants provided informed consent, and this study was approved by all participating Institutional Review Boards.

Three measures were used to assess ND: SQ (number of cigarettes smoked per day), HSI (0–6 scale) and FTND score (0–10 points) (42Go). Our primary reasons for examining all three measures were: (a) the current lack of consensus regarding the best approach to assess ND as a phenotype and (b) to permit maximum cross-reference with previous studies of ND. The SQ provides a simple, quantifiable index of the amount of consumption (using a 0–3 point compressed format), whereas HSI includes one item addressing quantity (SQ) and another item assessing urgency (i.e. ‘How soon after you wake up do you smoke your first cigarette?’). The FTND score includes the HSI and other indicators of propensity to smoke in various situations. The FTND has been accepted as a standard in both clinical and research settings, although recent evidence suggests ND is a broader and more complex construct than it was previously considered to be (43Go,44Go). Thus, while it is premature to endorse other measures that have received interesting but limited support, we believe it is prudent to examine our genetic findings with respect to the three aforementioned measures. A detailed description of demographic and clinical characteristics for the participants in the study is presented in Table 4.


View this table:
[in this window]
[in a new window]
  		Table 4. Clinical characteristics of pooled, EA and AA samples
 
DNA samples, SNP selection and genotyping
DNA was extracted from peripheral blood samples using kits from Qiagen, Inc. (Valencia, CA, USA). The eight SNPs examined within DDC were selected from the NCBI SNP database with the goal of providing as uniform coverage of the gene as possible. Data on these SNPs, including location within the gene, chromosomal position, minor allele frequency and primer/probe sequences, are summarized in Table 5 and Figure 1C. All SNPs were genotyped using the TaqMan assay in a 384-well microplate format (Applied Biosystems, Inc., Foster City, CA, USA), as described previously (45Go). Briefly, 15 ng of DNA was amplified in a total volume of 7 µl containing an MGB probe and 2.5 µl of TaqMan universal PCR master mix. Amplification reaction conditions were 2 min at 50°C and 10 min at 95°C, followed by 40 cycles of 95°C for 25 s and 60°C for 1 min. Allelic discrimination analysis was performed on the Prism 9700 Sequence Detection System (ABI, Foster City, CA, USA). To ensure the quality of the genotyping, SNP-specific control samples were added to each 384-well reaction plate.


View this table:
[in this window]
[in a new window]
  		Table 5. Positions, nucleotide variation, minor allele frequency and primer/probe sequences of eight SNPs within the DDC
 
Statistical analysis
Genotyping consistency for Mendelian inheritance was determined using the PedCheck program (46Go). A total of 80 genotyping inconsistencies (66 in the AA sample and 14 in the EA sample) were identified out of ~16 300 assays for eight SNPs across all DNA samples (i.e. <0.5% of the global assay) and were excluded from all subsequent statistical analyses. Pair-wise LD between all SNP markers was assessed using the Haploview program (47Go). Associations between individual SNPs and ND measures were determined by the PBAT program (a multivariate extension of the Family Based Association Test, FBAT) (48Go) using generalized estimating equations (49Go). Associations between each ND measure and haplotypes from various SNP combinations were examined using the haplotype FBAT program (50Go), with the option of computing P-values of the Z-statistics with the use of Monte Carlo sampling under the null distribution of no linkage and no association.

Three genetic models (i.e. additive, dominant and recessive) were tested for individual and multi-locus SNPs (i.e. haplotype) with sex, age and race as covariates in the pooled samples and with sex and age as covariates in the AA and EA samples. The three ND measures (SQ, HSI and FTND scores) were analyzed individually. All significant associations were corrected for multiple testing according to the SNP spectral decomposition (SNPSpD) approach (51Go) for individual SNP analysis and by applying a Bonferroni correction by dividing the significance level by the number of major haplotypes (frequency >5.0%) for haplotype-based association analysis.


   ACKNOWLEDGEMENTS
 
We acknowledge the invaluable contributions of personal information and blood samples by all participants in the study. Clinical and research staff at The University of Texas Health Science Center at San Antonio, The University of Tennessee Health Science Center and the ACT Center of The University of Mississippi Medical Center have been involved in clinical recruitment and genetic analysis, and we thank them for their dedicated work. This project is funded by a grant from the National Institute on Drug Abuse to M.D.L. (DA-12844), grants from the National Center for Research Resources (RR03655) and the National Institute of General Medical Sciences (GM28356) to R.C.E. and general support for T.J.P. and Karen Crews via a grant from The Partnership for a Healthy Mississippi to the University of Mississippi School of Dentistry.

Conflict of Interest statement. None declared.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 

  1. Twombly, R. (2002) World Health Organization takes on ‘tobacco epidemic’. J. Natl. Cancer Inst., 94, 644–646.[Free Full Text]

  2. WHO (2002) The World Health Report 2002. World Health Organization.

  3. Mokdad, A.H., Marks, J.S., Stroup, D.F. and Gerberding, J.L. (2004) Actual causes of death in the United States, 2000. JAMA, 291, 1238–1245.[Abstract/Free Full Text]

  4. Benowitz, N.L. (1988) Drug therapy. Pharmacologic aspects of cigarette smoking and nicotine addiction. N. Engl. J. Med., 319, 1318–1330.[Web of Science][Medline]

  5. Sullivan, P.F. and Kendler, K.S. (1999) The genetic epidemiology of smoking. Nicotine Tob. Res., 1(Suppl. 2), S51–S57 (discussion S69–S70).

  6. Li, M.D., Cheng, R., Ma, J.Z. and Swan, G.E. (2003) A meta-analysis of estimated genetic and environmental effects on smoking behavior in male and female adult twins. Addiction, 98, 23–31.[CrossRef][Web of Science][Medline]

  7. Niu, T., Chen, C., Ni, J., Wang, B., Fang, Z., Shao, H. and Xu, X. (2000) Nicotine dependence and its familial aggregation in Chinese. Int. J. Epidemiol., 29, 248–252.[Abstract/Free Full Text]

  8. Hatchell, P.C. and Collins, A.C. (1980) The influence of genotype and sex on behavioral sensitivity to nicotine in mice. Psychopharmacology (Berl), 71, 45–49.[CrossRef][Medline]

  9. Marks, M.J., Stitzel, J.A. and Collins, A.C. (1989) Genetic influences on nicotine responses. Pharmacol. Biochem. Behav., 33, 667–678.[CrossRef][Web of Science][Medline]

  10. Robinson, S.F., Marks, M.J. and Collins, A.C. (1996) Inbred mouse strains vary in oral self-selection of nicotine. Psychopharmacology (Berl), 124, 332–339.[CrossRef][Medline]

  11. Uhl, G.R., Vandenbergh, D.J., Rodriguez, L.A., Miner, L. and Takahashi, N. (1998) Dopaminergic genes and substance abuse. Adv. Pharmacol., 42, 1024–1032.

  12. Picciotto, M.R. (2003) Nicotine as a modulator of behavior: beyond the inverted U. Trends Pharmacol. Sci., 24, 493–499.[CrossRef][Medline]

  13. Corrigall, W.A., Franklin, K.B., Coen, K.M. and Clarke, P.B. (1992) The mesolimbic dopaminergic system is implicated in the reinforcing effects of nicotine. Psychopharmacology (Berl), 107, 285–289.[CrossRef][Medline]

  14. Pontieri, F.E., Tanda, G., Orzi, F. and Di Chiara, G. (1996) Effects of nicotine on the nucleus accumbens and similarity to those of addictive drugs. Nature, 382, 255–257.[CrossRef][Medline]

  15. Salokangas, R.K., Vilkman, H., Ilonen, T., Taiminen, T., Bergman, J., Haaparanta, M., Solin, O., Alanen, A., Syvalahti, E. and Hietala, J. (2000) High levels of dopamine activity in the basal ganglia of cigarette smokers. Am. J. Psychiatry, 157, 632–634.[Abstract/Free Full Text]

  16. Brody, A.L., Olmstead, R.E., London, E.D., Farahi, J., Meyer, J.H., Grossman, P., Lee, G.S., Huang, J., Hahn, E.L. and Mandelkern, M.A. (2004) Smoking-induced ventral striatum dopamine release. Am. J. Psychiatry, 161, 1211–1218.[Abstract/Free Full Text]

  17. Christenson, J.G., Dairman, W. and Udenfriend, S. (1972) On the identity of DOPA decarboxylase and 5-hydroxytryptophan decarboxylase (immunological titration-aromatic L-amino acid decarboxylase–serotonin–dopamine–norepinephrine). Proc. Natl Acad. Sci. USA, 69, 343–347.[Abstract/Free Full Text]

  18. Craig, S.P., Thai, A.L., Weber, M. and Craig, I.W. (1992) Localisation of the gene for human aromatic L-amino acid decarboxylase (DDC) to chromosome 7p13->p11 by in situ hybridisation. Cytogenet. Cell Genet., 61, 114–116.[Web of Science][Medline]

  19. Sumi-Ichinose, C., Ichinose, H., Takahashi, E., Hori, T. and Nagatsu, T. (1992) Molecular cloning of genomic DNA and chromosomal assignment of the gene for human aromatic L-amino acid decarboxylase, the enzyme for catecholamine and serotonin biosynthesis. Biochemistry, 31, 2229–2238.[CrossRef][Medline]

  20. Berry, M.D., Juorio, A.V., Li, X.M. and Boulton, A.A. (1996) Aromatic L-amino acid decarboxylase: a neglected and misunderstood enzyme. Neurochem. Res., 21, 1075–1087.[Web of Science][Medline]

  21. Borglum, A.D., Hampson, M., Kjeldsen, T.E., Muir, W., Murray, V., Ewald, H., Mors, O., Blackwood, D. and Kruse, T.A. (2001) Dopa decarboxylase genotypes may influence age at onset of schizophrenia. Mol. Psychiatry, 6, 712–717.[CrossRef][Web of Science][Medline]

  22. Zhang, B., Jia, Y., Yuan, Y., Yu, X., Xu, Q. and Shen, Y. (2004) No association between polymorphisms in the DDC gene and paranoid schizophrenia in a northern Chinese population. Psychiatr. Genet., 14, 161–163.[CrossRef][Web of Science][Medline]

  23. Borglum, A.D., Bruun, T.G., Kjeldsen, T.E., Ewald, H., Mors, O., Kirov, G., Russ, C., Freeman, B., Collier, D.A. and Kruse, T.A. (1999) Two novel variants in the DOPA decarboxylase gene: association with bipolar affective disorder. Mol. Psychiatry, 4, 545–551.[CrossRef][Web of Science][Medline]

  24. Jahnes, E., Muller, D.J., Schulze, T.G., Windemuth, C., Cichon, S., Ohlraun, S., Fangerau, H., Held, T., Maier, W., Propping, P. et al. (2002) Association study between two variants in the DOPA decarboxylase gene in bipolar and unipolar affective disorder. Am. J. Med. Genet., 114, 519–522.[CrossRef][Web of Science][Medline]

  25. Borglum, A.D., Kirov, G., Craddock, N., Mors, O., Muir, W., Murray, V., McKee, I., Collier, D.A., Ewald, H., Owen, M.J. et al. (2003) Possible parent-of-origin effect of Dopa decarboxylase in susceptibility to bipolar affective disorder. Am. J. Med. Genet. B: Neuropsychiatr. Genet., 117, 18–22.[Medline]

  26. Hawi, Z., Foley, D., Kirley, A., McCarron, M., Fitzgerald, M. and Gill, M. (2001) Dopa decarboxylase gene polymorphisms and attention deficit hyperactivity disorder (ADHD): no evidence for association in the Irish population. Mol. Psychiatry, 6, 420–424.[CrossRef][Web of Science][Medline]

  27. Li, M.D., Ma, J.Z., Cheng, R., Dupont, R., Williams, K.C., Payne, T.J. and Elston, R.C. (2003) A genome wide scan to identify loci for smoking quantity in the Framingham Heart Study populations. BMC Genet., 4(Suppl. I), S103.

  28. Li, M.D., Ma, J.Z. and Beuten, J. (2004) Progress in searching for susceptibility loci and genes for smoking-related behaviour. Clin. Genet., 66, 382–392.[CrossRef][Web of Science][Medline]

  29. Wang, D., Ma, J.Z. and Li, M.D. (2005) Mapping and verification of susceptibility loci for smoking quantity using permutation linkage analysis. Pharmacogenomics J., in press.

  30. Benowitz, N.L., Perez-Stable, E.J., Fong, I., Modin, G., Herrera, B. and Jacob, P., III (1999) Ethnic differences in N-glucuronidation of nicotine and cotinine. J. Pharmacol. Exp. Ther., 291, 1196–1203.[Abstract/Free Full Text]

  31. Gabriel, S.B., Schaffner, S.F., Nguyen, H., Moore, J.M., Roy, J., Blumenstiel, B., Higgins, J., DeFelice, M., Lochner, A., Faggart, M. et al. (2002) The structure of haplotype blocks in the human genome. Science, 296, 2225–2229.[Abstract/Free Full Text]

  32. Tyndale, R.F. (2003) Genetics of alcohol and tobacco use in humans. Ann. Med., 35, 94–121.[CrossRef][Web of Science][Medline]

  33. Lerman, C. and Berrettini, W. (2003) Elucidating the role of genetic factors in smoking behavior and nicotine dependence. Am. J. Med. Genet. B: Neuropsychiatr. Genet., 118, 48–54.[Medline]

  34. Munafo, M.R., Clark, T.G., Johnstone, E.C., Murphy, F.G. and Walton, R.T. (2004) The genetics basis for smoking behavior: a systematic review and meta-analysis. Nicotine Tob Res., 6, 583–597.[CrossRef][Web of Science][Medline]

  35. Niu, T., Qin, Z.S., Xu, X. and Liu, J.S. (2002) Bayesian haplotype inference for multiple linked single-nucleotide polymorphisms. Am. J. Hum. Genet., 70, 157–169.[CrossRef][Web of Science][Medline]

  36. Clark, A.G. (2004) The role of haplotypes in candidate gene studies. Genet. Epidemiol., 27, 321–333.[CrossRef][Web of Science][Medline]

  37. Cardon, L.R. and Bell, J.I. (2001) Association study designs for complex diseases. Nat. Rev. Genet., 2, 91–99.[CrossRef][Web of Science][Medline]

  38. Hirschhorn, J.N., Lohmueller, K., Byrne, E. and Hirschhorn, K. (2002) A comprehensive review of genetic association studies. Genet. Med., 4, 45–61.[Web of Science][Medline]

  39. Ioannidis, J.P., Trikalinos, T.A., Ntzani, E.E. and Contopoulos-Ioannidis, D.G. (2003) Genetic associations in large versus small studies: an empirical assessment. Lancet, 361, 567–571.[CrossRef][Web of Science][Medline]

  40. Burchard, E.G., Ziv, E., Coyle, N., Gomez, S.L., Tang, H., Karter, A.J., Mountain, J.L., Perez-Stable, E.J., Sheppard, D. and Risch, N. (2003) The importance of race and ethnic background in biomedical research and clinical practice. N. Engl. J. Med., 348, 1170–1175.[Free Full Text]

  41. Bamshad, M., Wooding, S., Salisbury, B.A. and Stephens, J.C. (2004) Deconstructing the relationship between genetics and race. Nat. Rev. Genet., 5, 598–609.[Web of Science][Medline]

  42. Heatherton, T.F., Kozlowski, L.T., Frecker, R.C. and Fagerstrom, K.O. (1991) The Fagerstrom test for nicotine dependence: a revision of the Fagerstrom tolerance questionnaire. Br. J. Addict., 86, 1119–1127.[CrossRef][Web of Science][Medline]

  43. Moolchan, E.T., Radzius, A., Epstein, D.H., Uhl, G., Gorelick, D.A., Cadet, J.L. and Henningfield, J.E. (2002) The Fagerstrom test for nicotine dependence and the diagnostic interview schedule: do they diagnose the same smokers? Addict. Behav., 27, 101–113.[CrossRef][Web of Science][Medline]

  44. Swan, G.E., Hudmon, K.S., Jack, L.M., Hemberger, K., Carmelli, D., Khroyan, T.V., Ring, H.Z., Hops, H., Andrews, J.A., Tildesley, E. et al. (2003) Environmental and genetic determinants of tobacco use: methodology for a multidisciplinary, longitudinal family-based investigation. Cancer Epidemiol. Biomarkers Prev., 12, 994–1005.[Abstract/Free Full Text]

  45. Beuten, J., Ma, J.Z., Payne, T.J., Dupont, R.T., Crews, K.M., Somes, G., Williams, N.J., Elston, R.C. and Li, M.D. (2005) Single- and multilocus allelic variants within the GABAB receptor subunit 2 (GABAB2) gene are significantly associated with nicotine dependence. Am. J. Hum. Genet., 76, 859–864.[CrossRef][Web of Science][Medline]

  46. O'Connell, J.R. and Weeks, D.E. (1998) PedCheck: a program for identification of genotype incompatibilities in linkage analysis. Am. J. Hum. Genet., 63, 259–266.[CrossRef][Web of Science][Medline]

  47. Barrett, J.C., Fry, B., Maller, J. and Daly, M.J. (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics, 21, 263–265.[Abstract/Free Full Text]

  48. Laird, N.M., Horvath, S. and Xu, X. (2000) Implementing a unified approach to family-based tests of association. Genet. Epidemiol., 19(Suppl. 1), S36–S42.

  49. Lange, C., Silverman, E.K., Xu, X., Weiss, S.T. and Laird, N.M. (2003) A multivariate family-based association test using generalized estimating equations: FBAT-GEE. Biostatistics, 4, 195–206.[Abstract]

  50. Horvath, S., Xu, X., Lake, S.L., Silverman, E.K., Weiss, S.T. and Laird, N.M. (2004) Family-based tests for associating haplotypes with general phenotype data: application to asthma genetics. Genet. Epidemiol., 26, 61–69.[CrossRef][Web of Science][Medline]

  51. Nyholt, D.R. (2004) A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am. J. Hum. Genet., 74, 765–769.[CrossRef][Web of Science][Medline]


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Eur Respir JHome page
M. Quaak, C. P. van Schayck, A. M. Knaapen, and F. J. van Schooten
Genetic variation as a predictor of smoking cessation success. A promising preventive and intervention tool for chronic respiratory diseases?
Eur. Respir. J., March 1, 2009; 33(3): 468 - 480.
[Abstract] [Full Text] [PDF]

Home page
 Arch Gen PsychiatryHome page
X. Chen, V. S. Williamson, S.-S. An, J. M. Hettema, S. H. Aggen, M. C. Neale, and K. S. Kendler
Cannabinoid Receptor 1 Gene Association With Nicotine Dependence
Arch Gen Psychiatry, July 1, 2008; 65(7): 816 - 823.
[Abstract] [Full Text] [PDF]

Home page
 Hum Mol GenetHome page
J. Nussbaum, Q. Xu, T. J. Payne, J. Z. Ma, W. Huang, J. Gelernter, and M. D. Li
Significant association of the neurexin-1 gene (NRXN1) with nicotine dependence in European- and African-American smokers
Hum. Mol. Genet., June 1, 2008; 17(11): 1569 - 1577.
[Abstract] [Full Text] [PDF]

Home page
 Hum Mol GenetHome page
S. F. Saccone, A. L. Hinrichs, N. L. Saccone, G. A. Chase, K. Konvicka, P. A.F. Madden, N. Breslau, E. O. Johnson, D. Hatsukami, O. Pomerleau, et al.
Cholinergic nicotinic receptor genes implicated in a nicotine dependence association study targeting 348 candidate genes with 3713 SNPs
Hum. Mol. Genet., January 1, 2007; 16(1): 36 - 49.
[Abstract] [Full Text] [PDF]

Home page
 Hum Mol GenetHome page
J. Gelernter, Y. Yu, R. Weiss, K. Brady, C. Panhuysen, B.-z. Yang, H. R. Kranzler, and L. Farrer
Haplotype spanning TTC12 and ANKK1, flanked by the DRD2 and NCAM1 loci, is strongly associated to nicotine dependence in two distinct American populations
Hum. Mol. Genet., December 15, 2006; 15(24): 3498 - 3507.
[Abstract] [Full Text] [PDF]

Home page
 Hum Mol GenetHome page
Y. Yu, C. Panhuysen, H. R. Kranzler, V. Hesselbrock, B. Rounsaville, R. Weiss, K. Brady, L. A. Farrer, and J. Gelernter
Intronic variants in the dopa decarboxylase (DDC) gene are associated with smoking behavior in European-Americans and African-Americans
Hum. Mol. Genet., July 15, 2006; 15(14): 2192 - 2199.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
14/12/1691    most recent
ddi177v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (15)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Ma, J. Z.
Right arrow Articles by Li, M. D.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Ma, J. Z.
Right arrow Articles by Li, M. D.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?