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Published in final edited form as: Neurosci Lett. 2008 May 24;440(3):280–283. doi: 10.1016/j.neulet.2008.05.073

Genetic variants in the cocaine- and amphetamine-regulated transcript gene (CARTPT) and cocaine dependence

Falk W Lohoff 1,*, Paul J Bloch 1, Andrew E Weller 1, Aleksandra H Nall 1, Glenn A Doyle 1, Russell J Buono 3, Thomas N Ferraro 1, Kyle M Kampman 2, Helen M Pettinati 2, Charles A Dackis 2, David W Oslin 2, Charles P O'Brien 2, Wade H Berrettini 1
PMCID: PMC2507865  NIHMSID: NIHMS59006  PMID: 18572320

Abstract

Dopaminergic brain systems have been implicated to play a major role in drug reward, thus making genes involved in these circuits plausible candidates for susceptibility to substance use disorders. The cocaine- and amphetamine-regulated transcript peptide (CARTPT) is involved in reward and feeding behavior and has functional characteristics of an endogenous psychostimulant. In this study we tested the hypothesis that variation in the CARTPT gene increases susceptibility to cocaine dependence in individuals of African descent. Genotypes of three HapMap tagging SNPs (rs6894758; rs11575893; rs17358300) across the CARTPT gene region were obtained in cocaine dependent individuals (n=348) and normal controls (n=256). All subjects were of African descent. There were no significant differences in allele, genotype or haplotype frequencies between cases and controls for any of the tested SNPs. Our results do not support an association of the CARTPT gene with cocaine dependence; however, additional studies using larger samples, comprehensive SNP coverage, and different populations are necessary to conclusively rule out CARTPT as a contributing factor in the etiology of cocaine dependence.

Keywords: genetics, association study, haplotype, addiction, substance abuse, cocaine

Introduction

Genetic studies estimate that 65−78% of the vulnerability risk for cocaine dependence is heritable [24, 25]; however, identification of genetic susceptibility factors has been difficult due to the complex mode of inheritance and clinical heterogeneity. Dopaminergic brain systems have been implicated to play a major role in drug reward [21], thus making genes involved in these circuits plausible candidates for influencing susceptibility to substance use disorders. In fact, several genes coding for the dopaminergic system have been investigated in cocaine dependence including genes for the dopamine receptor D2 (DRD2) [15, 31, 33, 36] the dopamine receptor D3 (DRD3) [6, 13, 31], the dopamine receptor D4 (DRD4) [4] and the dopamine transporter (DAT) [16, 17, 37]. The results of all these studies have been conflicting with some positive reports and some negative findings, possibly due to small sample sizes and the complex genetic nature of cocaine dependence.

The cocaine- and amphetamine-regulated transcript (CART) is a novel neuropeptide thought be involved in reward and feeding behaviors [11, 22, 27]. Several lines of evidence suggest a role of the CART peptide in the actions of psychostimulant drugs of abuse. CARTPT is predominately expressed in target regions of the mesocorticolimbic dopamine system, such as the nucleus accumbens, amygdala and orbitofrontal cortex [10, 20]. Experiments in rats show that CART mRNA expression is upregulated after acute cocaine self administration in these brain regions [8]; however, the results remain controversial since others have failed to replicated these findings [29, 44] and perhaps a binge-dosing regimen might be required to consistently increase CART expression following cocaine exposure [5, 12, 19]. Interestingly, this dosing regimen reflects closer the use of cocaine in humans and in fact postmortem brain studies of cocaine users demonstrate increased levels of CART mRNA [1, 42].

Genetic variation in the gene encoding the CART peptide (CARTPT) might influence expression and/or function of the peptide, which might have an effect on the degree of the rewarding and reinforcing properties of cocaine. In this study, we tested the hypothesis that variation in the CARTPT gene increases susceptibility to cocaine dependence in individuals of African descent.

Materials and methods

DNA samples from cocaine dependent individuals of African descent (n=348; 72% males, mean age: 43) were collected during clinical studies of cocaine dependence at the University of Pennsylvania Treatment Research Center. Subjects were at least 18 years of age and were all assessed with the Structured Clinical Interview for DSM Disorders (SCID) and urine drug screens were obtained. All patients had a clinical diagnosis of cocaine dependence as defined by DSM-IV. Exclusion criteria were all psychiatric axis I disorders except alcohol dependence/abuse and nicotine dependence. Family history was not obtained and ethnicity was determined by self report. Confidentiality of the participants' clinical data and genetic data was ensured by using a dual coding system that provides a “firewall” mechanism between the identifier on the DNA and clinical data. Control samples from individuals of African descent (n=256; 29% males, mean age: 40) were collected at the University of Pennsylvania, Thomas Jefferson University and through the National Institute of Mental Health Genetics Initiative (www.nimhgenetics.org). Control individuals had no history of substance use disorders or other psychiatric illness as determined by semi-structured interviews. Control subjects were not assessed with a urine drug screen and ethnicity determination was by self-report. Peripheral blood samples were obtained and genomic DNA was extracted from peripheral leukocytes by standard procedures. All protocols were approved by the Institutional Review Boards and written informed consent was obtained for all participating individuals.

The CARTPT gene contains 3 exons and spans 1879 bp (NCBI accession NM_004291). SNPs for genotyping were selected using the tagging SNP algorithm based on available HapMap data with a minor allele frequency (MAF) greater then 0.15 in Yoruban population and a pairwise linkage disequilibrium (LD) r2 cutoff of >0.8 (SNP1: rs6894758; SNP2: rs11575893; SNP3: rs17358300). Genotyping of these three HapMap tagging SNPs across the CARTPT gene region was performed using the Applied Biosystems Inc. (ABI) “Assays-on-demand” (ABI, Foster City, CA, USA) SNP genotyping assay as per manufacturers protocol. This genotyping method uses TaqMan technology and a real-time sequence detection system. Briefly, two primers and an internal labeled TaqMan probe are combined with the 5’−3’ nuclease activity of Taq DNA polymerase. During the PCR reaction a fluorescent reporter is released only when probe hybridization and amplification of the target sequence have occurred. Measurement of this fluorescence intensity offers a sensitive method to determine the presence or absence of specific sequences. Reactions were performed in 5 μl volumes and contained 2 ng DNA, 1x Taqman Universal Mastermix (Applied Biosystems), 200 nM of each probe and 900 nM of each primer. Thermal cycler conditions were as follows: 10 min at 95 °C and 40 cycles of 15 sec at 95°C and 1 min at 60°C. End-point fluorescence was measured after cycling. Alleles were assigned using SDS 2.1 software (Applied Biosystems). Genotyping success rates were between 98.8 and 99.5%. Genotyping quality was confirmed by genotyping 10% duplicates for cases and controls. Concordance rates of genotypes were 99.8%. Genotypes and allele frequencies were compared between groups using Chi square contingency analysis. A two-tailed type I error rate of 5% was chosen for the analysis. LD between markers and haplotypes were estimated using the COCAPHASE program [9].

Results

There were no significant differences in allele, genotype or haplotype frequencies between cases and controls for any of the tested SNPs (Table 1). Hardy-Weinberg equilibrium (HWE) was calculated separately for cases and controls and no significant deviation was observed for any of the markers (rs6894758: cases p=0.229, controls p=0.803; rs11575893: cases p=0.098, controls p=0.235; rs17358300: cases p=0.601, controls p=0.928). Allele frequencies were similar to those published in the HapMap database for Yoruba in Ibadan, Nigeria. There was low to moderate LD between markers: D'=1 between all SNPs and r2 ranging from 0.11 to 0.47 between markers. Power to detect an effect size of 1.5 was moderate to good (0.78−0.86) as estimated by the Quanto program [14].

Table 1.

Genotype and Allele Frequencies of Variations in the CARTPT Gene in Cases and Controls

SNP Sample n Genotype frequency P*a Allele frequency P*b
rs6894758 T/T T/C C/C f(T)
Cocaine 348 0.405 0.486 0.109 0.705 0.648 0.561
Controls 256 0.438 0.453 0.109 0.664
rs11575893 C/C C/T T/T f(C)
Cocaine 344 0.610 0.360 0.029 0.891 0.791 0.693
Controls 256 0.598 0.367 0.035 0.781
rs17358300 C/C C/T T/T f(C)
Cocaine 346 0.497 0.408 0.095 0.881 0.701 0.670
Controls 256 0.477 0.426 0.098 0.689
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*

type-I error rates for comparison of genotypea and alleleb frequencies between cocaine dependent individuals and controls.

Discussion

Our study failed to observe a statistically significant association for any of the genotyped SNPs in the CARTPT gene and cocaine dependence. Haplotype analysis also failed to show any significant association with disease. These results provide no support for the possibility that polymorphisms in CARTPT play a major role in susceptibility to cocaine dependence; however, our study has limitations which must be carefully considered before excluding CARTPT as a candidate gene for cocaine dependence.

The sample size used in this study was reasonably powered to detect risk alleles of major effect size, but we had limited power to detect risk alleles of small effects which could explain our negative results. Sample sizes in the thousands are necessary to detect risk alleles of small effects. On the other hand this increase in sample size might bring an increase in genetic heterogeneity and might contribute to undetected population stratification influencing outcomes of association analyses [28]. Undetected differences in population structure can mimic the signal of association and lead to false positive results or real effects that are missed [39]. This is a particular concern for analyses of samples of African-American descent, since recent studies indicate larger genetic admixture than previously thought [35, 38, 43, 47]. Possible ways to control for these stratification issues are the use of genomic controls [3, 7] and/or the use of a family-based association design, a strategy that matches the genotype of an affected offspring with parental alleles not inherited by the offspring [41]. Ultimately, replication studies and additional studies in different population are necessary to comprehensively evaluate CARTPT in cocaine dependence.

Besides these potential issues of genetic heterogeneity and population stratification, it is also important to consider limitations of clinical heterogeneity. All patients were diagnosed according to DSM-IV criteria and the diagnosis of cocaine dependence was confirmed using a urine drug screen; however, comorbid substance use of alcohol and nicotine might have differed between patients. In addition, the control subjects were assessed using semi-structured interviews but did not undergo urine drug testing. While drug testing is useful in establishing a diagnosis, it might not be useful for assessment of controls since it does not rule out past exposure or substance use. Unreported or minimized substance abuse in the control population is thus an important limitation that needs to be considered; however, even under the assumption that the prevalence of cocaine addiction of 0.5−1% was present in our control group, this factor might have only a minor influence when comparing the control group to the group of cocaine cases.

Another potential reason why we failed to detect an association in this study could be related to the LD structure of the gene. Our results indicate low to moderate LD between the markers, thus limiting the ability to conclusively rule out other variation which might play a relevant role. The low LD between markers is surprising, given the close proximity of the SNPs in this very small gene (1879bp). Comparison of the LD structure of available HapMap data for Caucasians, Chinese, Japanese and Yorubin populations confirms low LD across populations. This might indicate the phenomenon of gene conversion [2] and high genetic recombination rates (“recombination hotspot”). Homologous recombination hotspots are DNA sites exhibiting increased frequency of recombination and may be regulated primarily by discrete DNA sites and proteins that interact with those sites [45]. Recent studies revealed that recombination hotspots are a common feature of the human genome [30, 32] and contribute to the block-like pattern of haplotypes [18]. Characterization and understanding of the molecular mechanisms of these hotspots is of critical importance for designing better strategies in association studies of complex diseases [23, 26, 34, 40, 46]. Our results indicate that the CARTPT gene falls into one of these hotspots and no information is available on the biological relevance of this complex genomic structure. The complexity of the small genomic region of the CARTPT gene might require large scale sequencing of cases and controls in order to detect rare variations affecting illness and to evaluate this genomic region comprehensively.

In summary, our results do not support an association of the CARTPT gene with cocaine dependence; however, additional studies using larger samples, comprehensive SNP coverage, and different populations are necessary to conclusively rule out CARTPT as a contributing factor in the etiology of cocaine dependence.

Acknowledgements

This work was supported by the Center for Neurobiology and Behavior, Department of Psychiatry, University of Pennsylvania. Financial support is gratefully acknowledged from National Institutes of Health grants MH59553, MH63876 (W.H.B.), T32MH014654-29A1 (F.W.L.), NIDA grants P60-051186 (C.P.O.) and P50-12756 (H.M.P), the VISN4 Mental Illness Research and Clinical Center grant from the Veterans Affairs Administration (D.W.O.), grants from the National Alliance for Research on Schizophrenia and Depression (W.H.B. and F.W.L.), a grant from the Tzedakah Foundation (W.H.B.), the Daland Fellowship Award by the American Philosophical Society (F.W.L.), the McCabe Foundation (to F.W.L) and the APIRE/AstraZeneca Young Minds in Psychiatry Award (to F.W.L.) and a grant from Philip and Marcia Cohen (W.H.B.). We thank Candice Schwebel for technical assistance. Most importantly, we thank the subjects who have participated in and contributed to these studies.

The NIMH control subjects were collected by the NIMH Schizophrenia Genetics Initiative 'Molecular Genetics of Schizophrenia II' (MGS-2) collaboration. The investigators and coinvestigators are: ENH/Northwestern University, Evanston, IL, MH059571 – Pablo V. Gejman, MD (Collaboration Coordinator; PI), Alan R. Sanders, MD; Emory University School of Medicine, Atlanta, GA, MH59587 – Farooq Amin, MD (PI); Louisiana State University Health Sciences Center; New Orleans, LA, MH067257 – Nancy Buccola APRN, BC, MSN (PI); University of California-Irvine, Irvine, CA, MH60870 – William Byerley, MD (PI); Washington University, St Louis, MO, U01, MH060879 – C. Robert Cloninger, MD (PI); University of Iowa, Iowa, IA, MH59566 – Raymond Crowe, MD (PI), Donald Black, MD; University of Colorado, Denver, CO, MH059565 – Robert Freedman, MD (PI); University of Pennsylvania, Philadelphia, PA, MH061675 – Douglas Levinson, MD (PI); University of Queensland, QLD, Australia, MH059588 – Bryan Mowry, MD (PI); Mt Sinai School of Medicine, New York, NY, MH59586 – Jeremy Silverman, PhD (PI).

Footnotes

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Conflict of Interest Statement / Disclosure

The authors report no biomedical financial interests or potential conflicts of interest.

References

  • 1.Albertson DN, Pruetz B, Schmidt CJ, Kuhn DM, Kapatos G, Bannon MJ. Gene expression profile of the nucleus accumbens of human cocaine abusers: evidence for dysregulation of myelin. J Neurochem. 2004;88:1211–9. doi: 10.1046/j.1471-4159.2003.02247.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Ardlie K, Liu-Cordero SN, Eberle MA, Daly M, Barrett J, Winchester E, Lander ES, Kruglyak L. Lower-than-expected linkage disequilibrium between tightly linked markers in humans suggests a role for gene conversion. Am J Hum Genet. 2001;69:582–9. doi: 10.1086/323251. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Bacanu SA, Devlin B, Roeder K. The power of genomic control. Am J Hum Genet. 2000;66:1933–44. doi: 10.1086/302929. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Ballon N, Leroy S, Roy C, Bourdel MC, Olie JP, Charles-Nicolas A, Krebs MO, Poirier MF. Polymorphisms TaqI A of the DRD2, BalI of the DRD3, exon III repeat of the DRD4, and 3′ UTR VNTR of the DAT: association with childhood ADHD in male African-Caribbean cocaine dependents? Am J Med Genet B Neuropsychiatr Genet. 2007;144:1034–41. doi: 10.1002/ajmg.b.30540. [DOI] [PubMed] [Google Scholar]
  • 5.Brenz Verca MS, Widmer DA, Wagner GC, Dreyer J. Cocaine-induced expression of the tetraspanin CD81 and its relation to hypothalamic function. Mol Cell Neurosci. 2001;17:303–16. doi: 10.1006/mcne.2000.0942. [DOI] [PubMed] [Google Scholar]
  • 6.Comings DE, Gonzalez N, Wu S, Saucier G, Johnson P, Verde R, MacMurray JP. Homozygosity at the dopamine DRD3 receptor gene in cocaine dependence. Mol Psychiatry. 1999;4:484–7. doi: 10.1038/sj.mp.4000542. [DOI] [PubMed] [Google Scholar]
  • 7.Devlin B, Roeder K. Genomic control for association studies. Biometrics. 1999;55:997–1004. doi: 10.1111/j.0006-341x.1999.00997.x. [DOI] [PubMed] [Google Scholar]
  • 8.Douglass J, Daoud S. Characterization of the human cDNA and genomic DNA encoding CART: a cocaine- and amphetamine-regulated transcript. Gene. 1996;169:241–5. doi: 10.1016/0378-1119(96)88651-3. [DOI] [PubMed] [Google Scholar]
  • 9.Dudbridge F. Pedigree disequilibrium tests for multilocus haplotypes. Genet Epidemiol. 2003;25:115–21. doi: 10.1002/gepi.10252. [DOI] [PubMed] [Google Scholar]
  • 10.Elias CF, Lee CE, Kelly JF, Ahima RS, Kuhar M, Saper CB, Elmquist JK. Characterization of CART neurons in the rat and human hypothalamus. J Comp Neurol. 2001;432:1–19. doi: 10.1002/cne.1085. [DOI] [PubMed] [Google Scholar]
  • 11.Fagergren P, Hurd Y. CART mRNA expression in rat monkey and human brain: relevance to cocaine abuse. Physiol Behav. 2007;92:218–25. doi: 10.1016/j.physbeh.2007.05.027. [DOI] [PubMed] [Google Scholar]
  • 12.Fagergren P, Hurd YL. Mesolimbic gender differences in peptide CART mRNA expression: effects of cocaine. Neuroreport. 1999;10:3449–52. doi: 10.1097/00001756-199911080-00034. [DOI] [PubMed] [Google Scholar]
  • 13.Freimer M, Kranzler H, Satel S, Lacobelle J, Skipsey K, Charney D, Gelernter J. No association between D3 dopamine receptor (DRD3) alleles and cocaine dependence. Addict Biol. 1996;1:281–7. doi: 10.1080/1355621961000124896. [DOI] [PubMed] [Google Scholar]
  • 14.Gauderman WJ. Sample size requirements for association studies of gene-gene interaction. Am J Epidemiol. 2002;155:478–84. doi: 10.1093/aje/155.5.478. [DOI] [PubMed] [Google Scholar]
  • 15.Gelernter J, Kranzler H, Satel SL. No association between D2 dopamine receptor (DRD2) alleles or haplotypes and cocaine dependence or severity of cocaine dependence in European- and African-Americans. Biol Psychiatry. 1999;45:340–5. doi: 10.1016/s0006-3223(97)00537-4. [DOI] [PubMed] [Google Scholar]
  • 16.Gelernter J, Kranzler HR, Satel SL, Rao PA. Genetic association between dopamine transporter protein alleles and cocaine-induced paranoia. Neuropsychopharmacology. 1994;11:195–200. doi: 10.1038/sj.npp.1380106. [DOI] [PubMed] [Google Scholar]
  • 17.Guindalini C, Howard M, Haddley K, Laranjeira R, Collier D, Ammar N, Craig I, O'Gara C, Bubb VJ, Greenwood T, Kelsoe J, Asherson P, Murray RM, Castelo A, Quinn JP, Vallada H, Breen G. A dopamine transporter gene functional variant associated with cocaine abuse in a Brazilian sample. Proc Natl Acad Sci U S A. 2006;103:4552–7. doi: 10.1073/pnas.0504789103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.HapMap-Consortium A haplotype map of the human genome. Nature. 2005;437:1299–1320. doi: 10.1038/nature04226. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Hunter RG, Vicentic A, Rogge G, Kuhar MJ. The effects of cocaine on CART expression in the rat nucleus accumbens: a possible role for corticosterone. Eur J Pharmacol. 2005;517:45–50. doi: 10.1016/j.ejphar.2005.05.025. [DOI] [PubMed] [Google Scholar]
  • 20.Hurd YL, Fagergren P. Human cocaine- and amphetamine-regulated transcript (CART) mRNA is highly expressed in limbic- and sensory-related brain regions. J Comp Neurol. 2000;425:583–98. doi: 10.1002/1096-9861(20001002)425:4<583::aid-cne8>3.0.co;2-#. [DOI] [PubMed] [Google Scholar]
  • 21.Hyman SE, Malenka RC, Nestler EJ. Neural Mechanisms Of Addiction: The Role of Reward-Related Learning and Memory. Annual Review of Neuroscience. 2006;29:565–598. doi: 10.1146/annurev.neuro.29.051605.113009. [DOI] [PubMed] [Google Scholar]
  • 22.Jaworski JN, Jones DC. The role of CART in the reward/reinforcing properties of psychostimulants. Peptides. 2006;27:1993–2004. doi: 10.1016/j.peptides.2006.03.034. [DOI] [PubMed] [Google Scholar]
  • 23.Jorde LB. Linkage Disequilibrium and the Search for Complex Disease Genes. Genome Res. 2000;10:1435–1444. doi: 10.1101/gr.144500. [DOI] [PubMed] [Google Scholar]
  • 24.Kendler KS, Karkowski LM, Neale MC, Prescott CA. Illicit psychoactive substance use, heavy use, abuse, and dependence in a US population-based sample of male twins. Arch Gen Psychiatry. 2000;57:261–9. doi: 10.1001/archpsyc.57.3.261. [DOI] [PubMed] [Google Scholar]
  • 25.Kendler KS, Prescott CA. Cocaine use, abuse and dependence in a population-based sample of female twins. Br J Psychiatry. 1998;173:345–50. doi: 10.1192/bjp.173.4.345. [DOI] [PubMed] [Google Scholar]
  • 26.Kruglyak L. Prospects for whole-genome linkage disequilibrium mapping of common disease genes. Nat Genet. 1999;22:139–144. doi: 10.1038/9642. [DOI] [PubMed] [Google Scholar]
  • 27.Kuhar MJ, Adams S, Dominguez G, Jaworski J, Balkan B. CART peptides. Neuropeptides. 2002;36:1–8. doi: 10.1054/npep.2002.0887. [DOI] [PubMed] [Google Scholar]
  • 28.Marchini J, Cardon LR, Phillips MS, Donnelly P. The effects of human population structure on large genetic association studies. Nat Genet. 2004;36:512–517. doi: 10.1038/ng1337. [DOI] [PubMed] [Google Scholar]
  • 29.Marie-Claire C, Laurendeau I, Canestrelli C, Courtin C, Vidaud M, Roques B, Noble F. Fos but not Cart (cocaine and amphetamine regulated transcript) is overexpressed by several drugs of abuse: a comparative study using real-time quantitative polymerase chain reaction in rat brain. Neurosci Lett. 2003;345:77–80. doi: 10.1016/s0304-3940(03)00307-0. [DOI] [PubMed] [Google Scholar]
  • 30.McVean GAT, Myers SR, Hunt S, Deloukas P, Bentley DR, Donnelly P. The Fine-Scale Structure of Recombination Rate Variation in the Human Genome. Science. 2004;304:581–584. doi: 10.1126/science.1092500. [DOI] [PubMed] [Google Scholar]
  • 31.Messas G, Meira-Lima I, Turchi M, Franco O, Guindalini C, Castelo A, Laranjeira R, Vallada H. Association study of dopamine D2 and D3 receptor gene polymorphisms with cocaine dependence. Psychiatr Genet. 2005;15:171–4. doi: 10.1097/00041444-200509000-00006. [DOI] [PubMed] [Google Scholar]
  • 32.Myers S, Bottolo L, Freeman C, McVean G, Donnelly P. A Fine-Scale Map of Recombination Rates and Hotspots Across the Human Genome. Science. 2005;310:321–324. doi: 10.1126/science.1117196. [DOI] [PubMed] [Google Scholar]
  • 33.Noble EP, Blum K, Khalsa ME, Ritchie T, Montgomery A, Wood RC, Fitch RJ, Ozkaragoz T, Sheridan PJ, Anglin MD, et al. Allelic association of the D2 dopamine receptor gene with cocaine dependence. Drug Alcohol Depend. 1993;33:271–85. doi: 10.1016/0376-8716(93)90113-5. [DOI] [PubMed] [Google Scholar]
  • 34.Ott J. Predicting the range of linkage disequilibrium. PNAS. 2000;97:2–3. doi: 10.1073/pnas.97.1.2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Parra EJ, Kittles RA, Argyropoulos G, Pfaff CL, Hiester K, Bonilla C, Sylvester N, Parrish-Gause D, Garvey WT, Jin L, McKeigue PM, Kamboh MI, Ferrell RE, Pollitzer WS, Shriver MD. Ancestral proportions and admixture dynamics in geographically defined African Americans living in South Carolina. Am J Phys Anthropol. 2001;114:18–29. doi: 10.1002/1096-8644(200101)114:1<18::AID-AJPA1002>3.0.CO;2-2. [DOI] [PubMed] [Google Scholar]
  • 36.Persico AM, Bird G, Gabbay FH, Uhl GR. D2 dopamine receptor gene TaqI A1 and B1 restriction fragment length polymorphisms: enhanced frequencies in psychostimulant-preferring polysubstance abusers. Biol Psychiatry. 1996;40:776–84. doi: 10.1016/0006-3223(95)00483-1. [DOI] [PubMed] [Google Scholar]
  • 37.Persico AM, Vandenbergh DJ, Smith SS, Uhl GR. Dopamine transporter gene polymorphisms are not associated with polysubstance abuse. Biol Psychiatry. 1993;34:265–7. doi: 10.1016/0006-3223(93)90081-n. [DOI] [PubMed] [Google Scholar]
  • 38.Pfaff CL, Parra EJ, Bonilla C, Hiester K, McKeigue PM, Kamboh MI, Hutchinson RG, Ferrell RE, Boerwinkle E, Shriver MD. Population structure in admixed populations: effect of admixture dynamics on the pattern of linkage disequilibrium. Am J Hum Genet. 2001;68:198–207. doi: 10.1086/316935. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Pritchard JK, Donnelly P. Case-control studies of association in structured or admixed populations. Theoretical Population Biology. 2001;60:227–237. doi: 10.1006/tpbi.2001.1543. [DOI] [PubMed] [Google Scholar]
  • 40.Reich DE, Cargill M, Bolk S, Ireland J, Sabeti PC, Richter DJ, Lavery T, Kouyoumjian R, Farhadian SF, Ward R, Lander ES. Linkage disequilibrium in the human genome. Nature. 2001;411:199–204. doi: 10.1038/35075590. [DOI] [PubMed] [Google Scholar]
  • 41.Spielman RS, Ewens WJ. The TDT and other family-based tests for linkage disequilibrium and association. Am J Hum Genet. 1996;59:983–9. [PMC free article] [PubMed] [Google Scholar]
  • 42.Tang WX, Fasulo WH, Mash DC, Hemby SE. Molecular profiling of midbrain dopamine regions in cocaine overdose victims. J Neurochem. 2003;85:911–24. doi: 10.1046/j.1471-4159.2003.01740.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Tian C, Hinds DA, Shigeta R, Kittles R, Ballinger DG, Seldin MF. A genomewide single-nucleotide-polymorphism panel with high ancestry information for African American admixture mapping. Am J Hum Genet. 2006;79:640–9. doi: 10.1086/507954. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Vrang N, Larsen PJ, Kristensen P. Cocaine-amphetamine regulated transcript (CART) expression is not regulated by amphetamine. Neuroreport. 2002;13:1215–8. doi: 10.1097/00001756-200207020-00029. [DOI] [PubMed] [Google Scholar]
  • 45.Wahls W, Smith G. A heteromeric protein that binds to a meiotic homologous recombination hot spot: correlation of binding and hot spot activity. Genes Dev. 1994;8:1693–1702. doi: 10.1101/gad.8.14.1693. [DOI] [PubMed] [Google Scholar]
  • 46.Zhang K, Qin ZS, Liu JS, Chen T, Waterman MS, Sun F. Haplotype Block Partitioning and Tag SNP Selection Using Genotype Data and Their Applications to Association Studies. Genome Res. 2004;14:908–916. doi: 10.1101/gr.1837404. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Zhu X, Cooper RS, Elston RC. Linkage analysis of a complex disease through use of admixed populations. Am J Hum Genet. 2004;74:1136–53. doi: 10.1086/421329. [DOI] [PMC free article] [PubMed] [Google Scholar]

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