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Nick Martin Queensland Institute of Medical Research, Brisbane

Genetic susceptibility to substance abuse. Nick Martin Queensland Institute of Medical Research, Brisbane. Translational Medicine Canberra November 4, 2010. Alcohol dependence in the US. Many substances. Alcohol Nicotine Caffeine Cannabis Opioids Gambling.

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Nick Martin Queensland Institute of Medical Research, Brisbane

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  1. Genetic susceptibility to substance abuse Nick Martin Queensland Institute of Medical Research, Brisbane Translational Medicine Canberra November 4, 2010

  2. Alcohol dependence in the US

  3. Many substances • Alcohol • Nicotine • Caffeine • Cannabis • Opioids • Gambling

  4. Genetic Epidemiology:4 Stages of Genetic Mapping • Are there genes influencing this trait? • Genetic epidemiological studies • Where are those genes? • Linkage analysis • What are those genes? • Association analysis • What can we do with them ? • Translational medicine

  5. C D E A Variance components Additive Genetic Effects Dominance Genetic Effects Unique Environment Shared Environment c a e d Phenotype P = eE + aA + cC + dD

  6. Designs to disentangle G + E • Family studies – G + C confounded • MZ twins alone – G + C confounded • MZ twins reared apart – rare, atypical, selective placement ? • Adoptions – increasingly rare, atypical, selective placement ? • MZ and DZ twins reared together • Extended twin design

  7. MZ twins reared apart - note the same way of supporting their cans of beer

  8. Designs to disentangle G + E • Family studies – G + C confounded • MZ twins alone – G + C confounded • MZ twins reared apart – rare, atypical, selective placement ? • Adoptions – increasingly rare, atypical, selective placement ? • MZ and DZ twins reared together • Extended twin design

  9. Identity at marker loci - except for rare mutation MZ and DZ twins: determining zygosity using ABI Profiler™ genotyping (9 STR markers + sex) MZ DZ DZ

  10. Twincorrelations for alcohol dependence factor score

  11. Three scenarios Twin Correlation Causes of Variation

  12. ACE Model for twin data 1 MZ=1.0 / DZ=0.5 E C A A C E e c a a c e PT1 PT2

  13. Sources of variation in alcohol dependence Non-shared environment Additive genetic Shared environment

  14. Extended Twin Design Truett, et al (1994) Behavior Genetics, 24: 35-49

  15. Smoking: extended twin kinship data from Virginia & Australia

  16. 4 Stages of Genetic Mapping • Are there genes influencing this trait? • Genetic epidemiological studies • Where are those genes? • Linkage analysis • What are those genes? • Association analysis • What can we do with them ? • Translational medicine

  17. Linkage Markers…

  18. Linkage for MaxCigs24 in Australia and Finland AJHG, in press

  19. But overall, the results of linkage studies have been disappointing, so we have moved to -

  20. Association • Looks for correlation between specific alleles and phenotype (trait value, disease risk)

  21. Variation: Single Nucleotide Polymorphisms

  22. High density SNP arrays – up to 1 million SNPs

  23. Genome-Wide Association Studies 500 000 - 1. 000 000 SNPsHuman Genome - 3,1x109 Base Pairs

  24. Bipolar GWAS of 10,648 samples >1.7 million genotyped and (high confidence) imputed SNPs 5 x 10-8 X Ankryin-G (ANK3) CACNA1C Sample Cases Controls P-value STEP 7.4% 5.8% 0.0013 WTCCC 7.6% 5.9% 0.0008 EXT 7.3% 4.7% 0.0002 Total 7.5% 5.6% 9.1×10-9 Sample Case Controls P-value STEP 35.7% 32.4% 0.0015 WTCCC 35.7% 31.5% 0.0003 EXT 35.3% 33.7% 0.0108 Total 35.6% 32.4% 7×10-8 Ferreira et al (Nature Genetics, 2008)

  25. Published Genome-Wide Associations through 6/2010, 904 published GWA at p<5x10-8 for 165 traits NHGRI GWA Catalog www.genome.gov/GWAStudies

  26. GWAS for Smoking Thorgeirsson et al., 2010 Furberg et al., 2010 Liu et al., 2010

  27. Genome Wide Association with Cigarettes per DayA Proxy for Nicotine Dependence Chromosome 15 contains the strongest genetic contribution to the risk of developing nicotine dependence. Furberg et al., 2010

  28. mRNA expression of CHRNA5 in brain D398N (rs16969968) Variant Lowers Receptor Response to Nicotine Agonists Decreased a4b2a5 receptor response yields increased nicotine dependence risk Bierut et al., 2008 From the lab of Jerry Stitzel Minor allele of rs588765 is associated with increased mRNA expression of CHRNA5 in human frontal cortex. This SNP explains 42% of the variance in mRNA expression. Wang et al., 2009a Wang et al., 2009b From the lab of Alison Goate Additional work see Smith et al and Saddee, 2010 p=1.11x10-9

  29. Genetic risk of nicotine dependence at rs1051730 in CHRNA5 changes across birth cohort in the US, corresponding to changes in public policy. • Increasing social restrictions may strengthen the genetic component contributing to smoking.

  30. What do modest genetic effects mean? • Many genes are involved in disease, which is consistent with genetic risk in the 1.1 range. • If there are rare variants associated with disease, they must be very strong for us to detect them. • No one gene will predict disease. • So we need to be clever – pathway analysis

  31. Pathway (Ingenuity) analysis of GWAS for smoking Vink et al, 2009 American Journal of Human Genetics 84: 367-79, 2009

  32. What is the best phenotype to study? • The best phenotype is one that is most associated with genetic variants. • P value = sample size and genetic risk. • To improve the p value you can – • Increase the sample size • Increase the genetic effect

  33. Meta-Analysis of Genomewide Association Studies Manolio T. N Engl J Med 2010;363:166-176

  34. International research – the telcon Progress W.Coast USA 7am E.Coast USA 10am UK 3pm Europe 4pm Brisbane midnight

  35. Alcohol Dependence Meta-Analysis

  36. Samples Treutlein et al., 2009 Bierut et al., 2009 Heath et al., in review

  37. Meta-analysis Results Top findings show similar results in the US and Australian samples.

  38. Association findings with SNPs in the ADH region Birley et al. 2009

  39. GWAS for esophageal ca ADH1B ALDH2

  40. Opioid Dependence: Candidate Genes and G x E Effects(Comorbidity and Trauma Study) Elliot C. Nelson, M.D. Washington University

  41. Sample Ascertainment Cases (N = 1500) Clients currently receiving maintenance treatment for heroin dependence in NSW Neighborhood controls (N = 1500) Recruited via posters placed in employment centres, GP offices, and handed out at locations near the clinics or via ads in local newspapers; little or no recreational opioid use

  42. Psychiatric Disorders Including Licit Drug Dependence

  43. Non-opioid Illicit Drug Dependence

  44. Genotypic data analyses • Two-stages planned: 1) 136 Opioid receptor gene SNPs: OPRD1 21; OPRM1 93; OPRK1 22; 2) SNPs from other genes

  45. Cases vs ATR controls – OPRD1 SNPs Levran et al. 2008 (Kreek lab) most significant hits (3 of 9) with Goldman Addiction chip

  46. Future Directions • GWAS genotyping pending including further cases from Perth (N~1000) and US (N~1500) - N~4000 total cases

  47. 4 Stages of Genetic Mapping • Are there genes influencing this trait? • Genetic epidemiological studies • Where are those genes? • Linkage analysis • What are those genes? • Association analysis • What can we do with them ? • Translational medicine

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