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Convergence of Genetic Findings for Nicotine Dependence, Lung Cancer and COPD

Convergence of Genetic Findings for Nicotine Dependence, Lung Cancer and COPD. Laura Jean Bierut, MD Washington University. Financial Disclosure. Patent on genetic variants that predict addiction – “Markers of Addiction”.

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Convergence of Genetic Findings for Nicotine Dependence, Lung Cancer and COPD

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  1. Convergence of Genetic Findings for Nicotine Dependence, Lung Cancer and COPD Laura Jean Bierut, MD Washington University

  2. Financial Disclosure • Patent on genetic variants that predict addiction – “Markers of Addiction”. • Consultant for Pfizer in 2008 for genetic studies for smoking cessation. • Funding of studies is through the National Institutes of Health

  3. Genetic Studies of Complex Diseases A Retelling of the Emperor’s New Clothes Laura Jean Bierut, MD Washington University

  4. Table of Contents Chapter 1: What is the utility of linkage analysis in complex diseases? Chapter 2: How to interpret all the previous genetic findings? Chapter 3: What have we learned from Genome Wide Association Studies of schizophrenia, bipolar disorder, depression, alcoholism and autism? Do we have any findings? Chapter 4: What is the best phenotype to study? Chapter 5: What does gene environment interaction really mean? Chapter 6: What is the power to detect gene environment interaction? Chapter 7: Should we move into studying diverse populations? Chapter 8: Don’t get me started Chapter 9: The Happy Ending

  5. Prologue

  6. Model of Nicotine Dependence - A many step process Never Use Initiation First puff – First cigarette Does everyone who uses nicotine become addicted? Smoker 100 cigarettes lifetime Nicotine Dependence

  7. U.S. Population Screening andNicotine Dependence No Symptoms 3,051 Screened 53,742 50.9% 19.2% Initiated Smoking 27,372 Smoked 100+ Cigarettes 15,881 Some Symptoms 5,596 58.0% 35.2% 44.3% Nicotine Dependence 7,028 Collaborative Genetic Study of Nicotine Dependence

  8. Novel Gene in Dependence • a5-a3-b4 nicotinic receptor gene cluster is involved in the development of nicotine dependence. • How did we get there?

  9. NICSNP Project NICSNP is a large scale genome wide association study and candidate gene study of nicotine dependence. • Collaborative Genetic Study of Nicotine Dependence Principal Investigator: Laura Jean Bierut (P01 CA 089392) • The Genetics of Vulnerability to Nicotine Addiction Principal Investigator: Pamela Madden (R01 DA 012854) • Genes for Smoking in Related and Unrelated Individuals Principal Investigator: Ovide Pomerleau (R01 DA 017640) • Pharmacokinetics of Nicotine in Twins Principal Investigator: Gary Swan (R01 DA 011170) NIDA Phenotypic Repository John Rice Perlegen Sciences Dennis Ballinger

  10. Fagerström Test for Nicotine Dependence Heatherton TF, Kozlowski LT, Frecker RC, Fagerström KO. (1991). The Fagerstrom Test For Nicotine Dependence: A revision of the Fagerström Tolerance Questionnaire. British Journal of Addiction 86:1119-1127.

  11. Case and Control Phenotype Definition Case: Nicotine dependent defined by a Fagerström Test for Nicotine Dependence (FTND) > 4 Control: Individual who has smoked 100 or more cigarettes and never had any symptoms of nicotine dependence (Lifetime FTND = 0). Heatherton et al., 1991

  12. Results from Candidate Gene Study Saccone et al., 2007

  13. Results from Candidate Gene Study Saccone et al., 2007

  14. SNPs highly correlated with rs16969968 Findings for Nicotine Dependence rs16969968 Saccone et al., 2007

  15. SNPs highly correlated with rs16969968 Findings for Nicotine Dependence rs16969968 Saccone et al., 2007 Bierut et al., 2008 Sherva et al., 2008 Weiss et al., 2008 Stevens et al., 2008 rs1317286 Berrettini et al., 2008 rs1051730 Saccone et al., 2007 Thorgeirsson et al., 2008 Amos et al., 2008 Spitz et al., 2008

  16. Results from Candidate Gene Study The correlation between rs16969968 and rs578776 is < 0.2. There are two distinct findings in the nicotinic gene cluster associated with nicotine dependence. Saccone et al., 2007

  17. Genetic Association and the Nicotinic Receptors - Chromosome 15 rs578776 Saccone et al., 2007

  18. Genetic Association and the Nicotinic Receptors - Chromosome 15 rs578776 Saccone et al., 2007 Bierut et al., 2008 Weiss et al., 2008 Stevens et al., 2008 rs6495308 Berrettini et al.,2008

  19. Nature, 2008 Nature, 2008 Nature Genetics, 2008

  20. PLOS Genetics, 2009

  21. SNPs highly correlated with rs16969968 Findings for Nicotine Dependence,Lung Cancer, COPD rs16969968 Saccone et al., 2007 Bierut et al., 2008 Sherva et al., 2008 Weiss et al., 2008 Stevens et al., 2008 rs1317286 Berrettini et al., 2008 rs8034191 Amos et al., 2008 Hung et al., 2008 Liu et al., 2008 Pillai et al., 2009 rs1051730 Saccone et al., 2007 Thorgeirsson et al., 2008 Amos et al., 2008 Hung et al., 2008 Thorgeirsson et al., 2008 Liu et al., 2008 Pillai et al., 2009

  22. SNPs highly correlated with rs578776Findings for Nicotine Dependence and Lung Cancer rs578776 Saccone et al., 2007 Bierut et al., 2008 Weiss et al., 2008 Stevens et al., 2008 Hung et al., 2008 Liu et al., 2008 rs6495308 Berrettini et al.,2008

  23. Genetic Association Data for Nicotine Dependence and Lung Cancer

  24. Prologue - The Smoke is Clearing There are at least two distinct genetic variants on chromosome 15 associated with nicotine dependence and smoking quantity. These same variants are associated with lung cancer and COPD. Is the mechanism of action related to a change in protein structure and expression? Big Question: Is the association with lung cancer and COPD only an indirect effect through smoking or both an indirect and direct effect?

  25. Chapter 1 • What is the utility of linkage analysis in complex diseases?

  26. Linkage Analysis

  27. Biologic Psychiatry Genome search meta-analysis results for all independent genome scans on smoking behavior (3404 families with 10,235 genotyped subjects). Significance levels corresponding to nominal (p < 0.05), suggestive (p < 0.0085), and genome wide (p < 0.00042) significance are shown by the horizontal lines.

  28. Meta-analysis of 32 Genome-wide Linkage Studies of SchizophreniaNYM Ng, DF Levinson, SV Faraone, BK Suarez, LE Delisi, T Arinami, B Riley, T Paunio, AE Pulver, Irmansyah, PA Holmans, M Escamilla, DB Wildenauer, NM Williams, C Laurent, BJ Mowry, et al Mol Psychiatry. 2009 Aug;14(8):774-85.

  29. Meta-Analysis of 23 Type 2 Diabetes Linkage Studies from the International Type 2 Diabetes Linkage Analysis ConsortiumWeihua Guan, Anna Pluzhnokov, Nancy J. Cox, Michael Boehnke for the International Type 2 Diabetes Linkage Analysis Consortium Human Heredity 2008;66(1):35-49. TCF7L2

  30. Science 1996

  31. Linkage analysis has little power to localize genetic regions for complex diseases • Linkage analysis is great to localize genetic regions for Mendelian disorders such as rare illnesses that are transmitted in families. • There is very limited power for linkage analysis to detect genetic regions that are associated with complex illnesses.

  32. Chapter 2 • How to interpret all the previous genetic findings?

  33. 2005 – Time Zero • 2005 was the start of new generation genetic studies with genome wide association studies. Complement Factor H Polymorphism in Age-Related Macular Degeneration Robert J. Klein,Caroline Zeiss,Emily Y. Chew,Jen-Yue Tsai,Richard S. Sackler, Chad Haynes,Alice K. Henning,John Paul SanGiovanni,Shrikant M. Mane,Susan T. Mayne,Michael B. Bracken,Frederick L. Ferris,Jurg Ott,Colin Barnstable,Josephine Hoh Science, 2005 April 15;308(5720):362-4

  34. Genes reported associated with diabetes mellitus type 2 Number of Studies Number of Genes genes

  35. Genes reported associated with schizophrenia Number of Studies Number of Genes genes

  36. Influence of Life Stress on Depression: Moderation by a Polymorphism in the 5-HTT Gene Avshalom Caspi,Karen Sugden,Terrie E. Moffitt,Alan Taylor,Ian W. Craig,HonaLee Harrington,Joseph McClay,Jonathan Mill,Judy Martin,Antony Braithwaite,Richie Poulton

  37. Interaction Between the Serotonin Transporter Gene (5-HTTLPR), Stressful Life Vents, and Risk of Depression: A Meta-AnalysisNeil Risch; Richard Herrel; Thomas Lehner; Kung-Yee Liang; Lindon Eaves; Josephine Hohn; Andrea Griem; Maria Kovacs; Jurg Ott; Kathleen Reis-Merikangas

  38. Logistic Regression Analyses of Risk of Depression for 14 Studies Risch, N. et al. JAMA 2009;301:2462-2471.

  39. 2005 – Time Zero • The new paradigm of genetic studies with large scale genome wide association studies has led to an explosion of genetic findings related to illnesses. • Findings for complex diseases prior to GWAS studies are suspect.

  40. Chapter 3 • What have we learned from Genome Wide Association Studies of schizophrenia, bipolar disorder, depression, alcoholism and autism? • Do we have any findings?

  41. Don’t let the p values fool you • The number of genetic variants tested is in the range of 500,000 to 1 million. • P values at 10-5, 10-6 are common. A p value of 10-7 is starting to be interesting.

  42. Negative results are also a finding.

  43. Genetic effects are modest • Genetic risks for complex diseases are modest. • A genetic risk (OR) of 1.3 is large. • Most genetic risks are in the 1.1 to 1.2 range or less. This is true for most complex diseases in medicine. Alcoholism, schizophrenia, bipolar disorder, lung cancer, diabetes mellitus (type II).

  44. 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. • Prediction of disease will remain difficult.

  45. Chapter 4 • What is the best phenotype to study?

  46. Best phenotype is one that is 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

  47. Does complex phenotyping help? • Given that the genetic effect is modest, we will need very large sample sizes to detect an effect. (What is large? 50,000 individuals) • If large sample sizes are needed, then the phenotyping must be simple and standardized. • If there are complex phenotypes with complex measurements, then the genetic effect must be very large to compensate for the smaller studied population.

  48. Chen et al., under review

  49. Chapter 5 • What does gene environment interaction really mean?

  50. Gene Environment Interaction • Genetic effect may differ in varying environments. • Common environmental variables include – parental monitoring, peer smoking, childhood sexual abuse, other childhood adversity.

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