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Biology and genetics of substance abuse

Biology and genetics of substance abuse. Tomas Drgon PhD NIDA/NIH Baltimore MD. Decreased brain function in amphetamine abusers (DAT imaging). Volkow et al (2001) Am. J. Psychiatry 158:377-382. Annual cost to society. Heritability Billion $ Addictions 0.4 544.11

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Biology and genetics of substance abuse

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  1. Biology and genetics of substance abuse Tomas Drgon PhD NIDA/NIH Baltimore MD

  2. Decreased brain function in amphetamine abusers (DAT imaging) Volkow et al (2001) Am. J. Psychiatry 158:377-382

  3. Annual cost to society Heritability Billion $ Addictions 0.4 544.11 Alzheimer + dementias 0.53 170.86 Pain (w migraine) 0.4 150.8 Head and spinal cord injury 0.05 94.41 Anxiety disorders 0.3 82.63 Schizophrenia 0.7 57.08 Depressive illnesses 0.4 53.14 Developmental disorders 0.33 35.68 Stroke 0.1 27.03 Parkinson disease 0.1 15.96 Multiple sclerosis 0.4 7.62 Seizures 0.6 1.04 Huntington disease 1 0.23 Overall 1240.59 Uhl and Grow (2004) Arch Gen Psychiatry 61:223-229.

  4. Heritability • Family studies • Twin studies

  5. Heritability • Family studies • Twin studies

  6. Alcohol Dependence Dick et al 2006 Ann Clin Psychiatry 18: 223–231.

  7. Agrawal 2006 Addiction 101, 801–812 Cannabis

  8. Cocaine Agrawal et al (2004) American Journal of Medical Genetics Part B (Neuropsychiatric Genetics) 129B:125–128

  9. Nicotine/alcohol Swan et al (1996) Journal of Substance Abuse 8:19-31

  10. Polysubstance dependence Kendler et al (2007) Arch Gen Psychiatry 64:1313-20

  11. Polysubstance dependence Kendler et al (2006) Psychol Med. 36: 955–962.

  12. Brains of addicts are different from brains of non-addicts But where they the same to start with??

  13. Thompson PM (2001) Nature Neuroscience 4:1253-1258

  14. Thompson PM (2001) Nature Neuroscience 4:1253-1258

  15. Substance dependent individuals display less prefrontal and temporal grey matter than controls (Liu et al1998; Franklin et al 2002) prefrontal

  16. Brains of addicts are different from brains of non-addicts But where they the same to start with?? Not necessarily….

  17. Genetic architecture • From intake and metabolism to brain and reward, example – flushing syndrome • From oligo-genic to polygenic, example – flushing syndrome

  18. 25-50 million deaths in Europe 30%-60% of European population

  19. Substances and targets • Cocaine – DAT • Marijuana – cannabinoid receptors • Amphetamine – VMAT2 (?) • Caffeine – adenosine receptors • Nicotine – acetylcholine receptors • Alcohol - GABRA4(??)

  20. Current working model of genetic architecture for substance dependence in the population Genetic a2 Environment e2 Working ideas: Substantial overall genetic influences. Polygenic genetic architecture with small effects at each locus. Additive models provide first approximations.

  21. Analysis • Assumptions • Method (Affy chips) • Pre-planned analysis • Nominal statistics • Genomic Clustering • Convergence • Systems biology approach/Functional enrichment

  22. Analysis • Assumptions • There are variants that make individuals vulnerable to substance abuse regardless of • Substance (alcohol, polysubstance, amphetamine, nicotine) • population (Caucasian-American, African-American, Japanese, Taiwanese) • These variants are common • The effects of these variants are additive

  23. Analysis • In other words: the variants coming from this screen will NOT be • Drug specific • Population specific • Rare • Interactive

  24. Samples • Samples in this analysis: • EA and AA polysubstance abusers (n=1600/5000) • COGA alcoholics (n=280) • Taiwan amphetamine abusers (n=380) • Japan amphetamine abusers (n=200) • Related samples: • Duke University Smoking cessation (n=400) • U Penn Smoking dependence and cessation (n=200) • U Rhode Island Smoking cessation (n=200)

  25. 500K chip 500 000 SNPs

  26. 1M chip 1 000 000 SNPs 1 000 000 CNVs

  27. Each spot represents a hybridization intensity of a SNP or a CNV probe. These can be used in binary mode to identify presence or absence of an allele in an individual, or in a quantitative mode to assay allele/CNV frequency in a pool of individuals.

  28. Principal Component Analysis • No anticipation of structure in the data • No hypothesis • Separates overall variance in data to variance in certain directions

  29. Nominal t statistics • Only used to rank data • Expected effect sizes small = not expected to survive Bonferronni correction • Focus on type II error, not type I error. • Actual statistical significance will be tested empirically (Monte Carlo)

  30. Bayes theorem.

  31. Bayes theorem.

  32. Bayes theorem.

  33. Bayes theorem.

  34. Genomic clustering of positive SNPs • Assuming there is LD, true positives should cluster together in the genome • SNP dense areas are more likely to have clusters of positive SNPs • Significance of clustering tested empirically (Monte Carlo)

  35. www.hapmap.org

  36. www.hapmap.org

  37. Convergence of positive SNPs between independent samples • Assuming all the detected differences are noise, the probability of a SNP being positive in two independent sub-populations is low (can be calculated) • Significance of convergence tested empirically (Monte Carlo)

  38. Histogram of nominal t-values

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