Agrawal, Arpana

Aliev, Fazil

Bierut, Laura J

Derringer, Jamie

Dick, Danielle M

Edenberg, Howard J

Goate, Alison M

Grucza, Richard A

Hesselbrock, Victor M

Kramer, John R

Krueger, Robert F

Lin, Peng

Lynskey, Michael T

Neuman, Rosalind J

Nurnberger, John I, Jr.

Rice, John P

Saccone, Scott

Tischfield, Jay A

Digital Commons@Becker

Washington University School of MedicineDigital Commons@BeckerPosters2010: Disentangling the Genetics of Alcoholism:Understanding Pathophysiology and ImprovingTreatment2010Specificity of a cocaine-derived dopaminergicgenetic risk scoreJamie DerringerWashington University School of Medicine in St. LouisRobert F. KruegerWashington University School of Medicine in St. LouisDanielle M. DickVirginia Commonwealth UniversityFazil AlievVirginia Commonwealth UniversityRichard A. GruczaWashington University School of Medicine in St. LouisSee next page for additional authorsFollow this and additional works at: http://digitalcommons.wustl.edu/guzeposter2010Part of the Medicine and Health Sciences CommonsThis Poster is brought to you for free and open access by the 2010: Disentangling the Genetics of Alcoholism: Understanding Pathophysiology andImproving Treatment at Digital Commons@Becker. It has been accepted for inclusion in Posters by an authorized administrator of DigitalCommons@Becker. For more information, please contact engeszer@wustl.edu.Recommended CitationDerringer, Jamie; Krueger, Robert F.; Dick, Danielle M.; Aliev, Fazil; Grucza, Richard A.; Saccone, Scott; Agrawal, Arpana; Goate,Alison M.; Lin, Peng; Lynskey, Michael T.; Neuman, Rosalind J.; Rice, John P.; Edenberg, Howard J.; Nurnberger, John I. Jr.;Hesselbrock, Victor M.; Kramer, John R.; Tischfield, Jay A.; and Bierut, Laura J., "Specificity of a cocaine-derived dopaminergicgenetic risk score" (2010). Posters. Paper 21 Samuel B. Guze Symposium on Alcoholism.http://digitalcommons.wustl.edu/guzeposter2010/21AuthorsJamie Derringer, Robert F. Krueger, Danielle M. Dick, Fazil Aliev, Richard A. Grucza, Scott Saccone, ArpanaAgrawal, Alison M. Goate, Peng Lin, Michael T. Lynskey, Rosalind J. Neuman, John P. Rice, Howard J.Edenberg, John I. Nurnberger Jr., Victor M. Hesselbrock, John R. Kramer, Jay A. Tischfield, and Laura J.BierutThis poster is available at Digital Commons@Becker: http://digitalcommons.wustl.edu/guzeposter2010/21TABLE 1. GENES SELECTED FOR ANALYSISCurrent Study• Made use of:• Large, existing, genotyped sample,• Selected specifically for phenotype(s) of interest• Conducted intra-sample cross-validation• Focused analyses on dopamine system• Reduced likelihood of including “noise” SNPs• Increased ability to identify optimal SNP scoring setParticipants• 1591 unrelated individuals from the Study of Addiction: Genetics and Environment(SAGE) who reported having ever used cocaine.• SAGE participants drawn from three primary studies of cocaine (FSCD), alcohol(COGA), and nicotine dependence (COGEND)Measures• Cocaine, alcohol, nicotine, and marijuana dependence symptom counts• Assessed via the Semi-Structured Assessment for the Genetics of Alcoholism(SSAGA-II), which has demonstrated validity and reliability.• Marijuana symptom counts log-transformed [i.e., ln(symptoms+1)] to obtain normallydistributed residuals• Other substance symptom counts (i.e., cocaine, alcohol, nicotine) untransformedFIGURE 1. VARIANCE EXPLAINED BY TOP 4 TRAINING SAMPLE SNPsConclusions• Association between cocaine and dopamine at system level• Optimal risk score incorporated 4 SNPs from 4 separate genes• Cocaine-derived genetic risk score predicted cocaine (R2 = 0.546%, p = 0.037)• Did not predict alcohol, tobacco, or marijuana dependence severity (p > 0.78).• Individual effects of SNPs did not replicate across samples• Training sample: p = 0.012 - 0.032• Testing sample: p = 0.14 - 0.59• Only significant in aggregate• Narrow SNP selection criteria limited inclusion of spurious SNPs in risk score• Decreased the “noise” in true score “measurement”• Provides greater power• Detected 4 SNPs accounting for 0.55% of replication sample variance in cocaine• (Compare to recent genome-wide schizophrenia score12, explaining ~3% ofvariance with >37,000 SNPs)TABLE 2. TRAINING SAMPLE TOP SNPsUSED TO ESTIMATE TESTING SAMPLE GENETIC RISK SCORESResults• Top 4 training sample SNPs (Table 2) explained:• 0.546% variance in testing sample cocaine dependence symptoms (p < 0.037);• 0.004% variance in alcohol (p = 0.854);• 0.010% variance in nicotine (p = 0.781);• 0.003% variance in marijuana (p = 0.879)• Top 4 SNPs in 4 different genes• Linkage disequilibrium (LD) unlikely to affect resultsAnalyses• Sample split in half randomly creating “training” sample and “testing” sample• Halves did not differ on covariates or phenotypes• Dependence symptom counts residualized over covariates:• sex; age in quartiles; primary study source; ancestry (i.e., PC1 and PC2)• SNPs coded for number of minor alleles• Missing SNPs imputed as 2*MAF• Association tests run in training sample between cocaine symptoms and each SNP• SNPs incorporated one at a time to calculate testing sample score, in order ofascending training sample p-values:(1) Score = ∑ (N Minor Alleles for SNP i*B SNP i)• SNPs (weighted by training sample regression weights) added to the score until testingsample variance explained began decreasing• Specificity investigated by correlating score with alcohol, tobacco, and marijuana intesting sampleSPECIFICITY OF A COCAINE-DERIVED DOPAMINERGIC GENETIC RISK SCOREJaime Derringer1, Robert F. Krueger1, Danielle M. Dick2, Fazil Aliev2, Richard A. Grucza1, Scott Saccone1,Arpana Agrawal1, Alison M. Goate1, Peng Lin1, Michael T. Lynskey1, Rosalind J. Neuman1, John P. Rice1, Howard J. Edenberg3,John I. Nurnberger Jr.3, Victor M. Hesselbrock4, John R. Kramer5, Jay A. Tischfield6, GENEVA consortium, Laura J. Bierut11Washington University; 2Virginia Commonwealth University; 3Indiana University; 4University of Connecticut; 5University of Iowa; 6Rutgers UniversityGenotyping• DNA obtained from blood samples• Genotyping conducted at Johns Hopkins University Center for Inherited DiseaseResearch (CIDR) using Illumina Human IM Bead Chip.• Quality control procedures included:• Assessment of population structure, missing call rates, Mendelian errors, duplicationerrors, gender and chromosomal anomalies, hidden relatedness, batch effects, andHardy-Weinberg disequilibrium;• Removal of duplicates, related subjects, and outliers;• Median missing call rate < 0.05%;• 95% SNPs had <1.4% missingness• 948,142 SNPs passed quality control.References1. Ford JD, Gelernter J, DeVoe JS, et al. (2009) Association of psychiatric and substance use disordercomorbidity with cocaine dependence severity and treatment utilization in cocaine-dependent individuals. Drugand Alcohol Dependence 99:193-203.2. Kendler KS, Karkowski LM, Neale MC, Prescott CA (2000) Illicit psychoactive substance use, heavy use,abuse, and dependence in a US population-based sample of male twins. Arch Gen Psychiatry 57:261-9.3. Kendler KS, Prescott CA (1998) Cocaine use, abuse and dependence in a population-based sample of femaletwins. Br J Psychiatry 173:345-50.4. van den Bree MB, Johnson EO, Neale MC, Pickens RW (1998) Genetic and environmental influences on druguse and abuse/dependence in male and female twins. Drug and Alcohol Dependence 52:231-41.5. Kendler K, Myers J, Prescott C (2007) Specificity of genetic and environmental risk factors for symptoms ofcannabis, cocaine, alcohol, caffeine, and nicotine dependence. Arch Gen Psychiatry 64:1313-20.6.  Dackis CA, Gold MS (1985) New concepts in cocaine addiction: the dopamine depletion hypothesis. NeurosciBiobehav Rev, 9:469-77.7.  Haile CN, Kosten TR, Kosten TA (2007) Genetics of dopamine and its contribution to cocaine addiction. BehavGenet 37:119-45.8.   Kuhar MJ, Ritz MC, Boja JW (1991) The dopamine hypothesis of the reinforcing properties of cocaine. TrendsNeurosci 14:299-302.9.   Beuming T, Kniazeff J, Bergmann M, et al. (2008) The binding sites for cocaine and dopamine in the dopaminetransporter overlap. Nat Neurosci 11:780-9.10. Volkow ND, Wang GJ, Fischman MW, et al. (1997) Relationship between subjective effects of cocaine anddopamine transporter occupancy. Nature 386:827-30.11. Derringer J, Krueger RF, Dick DM, et al. (accepted) Predicting sensation seeking from dopamine genes: Acandidate system approach. Psychol Sci.12. The International Schizophrenia Consortium (2009) Common polygenic variation contributes to risk ofschizophrenia and bipolar disorder. Nature 460:748-52.Acknowledgements: HG004422, HG004446, AA008401, CA089392, DA013423, HG004438,HHSN268200782096C, DA024722Contact: Jaime Derringer (derringer@wustl.edu)Gene selection• Genes included if:• Autosomal;• Definite, direct effect on dopamine• Identified N=8 genes (see Table 1)• N=273 SNPs on Illumina 1M Chip• (Genes & SNPs identical to ones in association study of sensation seeking usingpartially overlapping sample11)SNP Gene Chr Function Allele MAF B Z p B Z prs1611131 DBH 9 Intron G 0.23 -0.4 -2.5 0.012 -0.1 -0.9 0.348rs5326 DRD1 5 UTR-5 A 0.14 0.5 2.4 0.015 0.1 0.5 0.588rs9817063 DRD3 3 NearGene-3 C 0.45 0.3 2.2 0.026 0.2 1.5 0.142rs1079597 DRD2 11 Intron A 0.16 0.4 2.2 0.032 0.2 1.4 0.159Training sample Testing sampleBackground• Cocaine dependence highly comorbid with psychiatric and other substance disorders1• 40-80% variance in cocaine dependence from additive genetic factors2-4• Most genetic variance in cocaine dependence shared with other substances5• Dopamine implicated as primary neurotransmitter system involved in responses tococaine exposure6-8• Cocaine competitively inhibits dopamine transportation by binding to overlapping siteson dopamine transporter9• Administration of typical dose blocks majority of dopamine transporter sites10• Blocking sites results in increased synaptic dopamine, contributing to reinforcing andaddictive properties of cocaineGene Location SNPs Role in Dopamine (DA)DRD3 3q13.3 32 codes D3 subtype of DA receptorsSLC6A3 5p15.3 35 transporter, mediates DA reuptakeDRD1 5q35.1 9 codes D1 subtype of DA receptorsDDC 7p12.2 81 protein coded converts L-DOPA to DADBH 9q34 37 converts DA to norepinephrineDRD4 11p15.5 4 codes D4 subtype of DA receptorsDRD2 11q23 40 codes D2 subtype of DA receptorsCOMT 22q11.21 35 affects catecholamine degradation

Specificity of a cocaine-derived dopaminergic genetic risk score

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Specificity of a cocaine-derived dopaminergic genetic risk score

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