Genetic Analysis of Vaccine Adverse Effects

Transcription

1 Genetic Analysis of Vaccine Adverse Effects Jason H. Moore, Ph.D. Frank Lane Research Scholar in Computational Genetics Professor of Genetics and Community and Family Medicine Dartmouth Medical School, NH Affiliate Associate Professor of Computer Science University of New Hampshire, NH Adjunct Associate Professor of Computer Science University of Vermont, VT Adjunct Investigator Translational Genomics Research Institute Jason H. Moore

2 July 3, 2000

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5 Science, December 21, 2007

6 Genetic Architecture of Vaccine Adverse Effects 1) How many genes? 2) What is their variability? 3) What is their impact?

7 RISK OF VACCINE ADVERSE EFFECTS EXERCISE DNA Repair ARSENIC DIET Oxidative Signaling Cell Cycle Methylation Metabolism STRESS SMOKING Adapted from Sing et al., (2003) Jason H. Moore

8 Biological Epistasis

9 Jason H. Moore Statistical Epistasis

10 Biological vs. Statistical Epistasis Moore, Nature Genetics (2005) Moore and Williams, BioEssays (2005) Tyler et al., BioEssays (2009)???? Jason H. Moore

11 One SNP at a time approach 10% 90% Jason H. Moore Complex systems approach

12 Evaluating SNP Subsets SNP 4 DD Dd dd SNP 2 SNP 2 SNP 2 SNP 3 CC Cc cc SNP 1 SNP 1 SNP 1 AA Aa aa BB Bb bb AA Aa aa AA Aa aa BB Bb bb BB Bb bb Empty Cells Jason H. Moore

13 Jason H. Moore Multifactor Dimensionality Reduction (MDR) Ritchie et al., American Journal of Human Genetics (2001) Moore, Expert Review of Molecular Diagnostics (2004) Moore et al., Journal of Theoretical Biology (2006)

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16 N > 20,000 Downloads

17 Asthma Atrial fibrillation Autism Bladder cancer Breast cancer Example MDR Applications > 150 published studies Cardiovascular disease Lung cancer Oral cancer Pancreatic cancer Prostate cancer Rheumatoid arthritis Systemic sclerosis Type II diabetes Jason H. Moore

18 Jason H. Moore MTHFR: 5,10-methylenetetrahydrofolate reductase IRF1: Interferon regulatory factor 1

19 MDR Analysis of Adverse Effects of Smallpox Vaccination in preparation Accuracy = 0.79 Sensitivity = 0.71 Specificity = 0.86 P < OR = 23.8 (95% CI ) CTH: cystathionase CCR2: chemokine receptor 2 Jason H. Moore

20 Genome-Wide Association Studies (GWAS)

21 50 Research Groups 14,000 cases and 3,000 shared controls 500,000 SNPs Seven complex human diseases: bipolar disorder (BD) coronary artery disease (CAD) Crohn s disease (CD) hypertension (HT) rheumatoid arthritis (RA) type 1 diabetes (T1D) type 2 diabetes (T2D)

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23 Traditional Approach: Assumes Simplicity Disease Disease Disease SNP 1 SNP 2 SNP 3 Disease Disease Disease SNP 1 SNP 2 SNP 3 Disease Disease Disease SNP 1 SNP 2 SNP 3 Jason H. Moore

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25 Question Does failure to replicate a genetic association warrant dismissal? Jason H. Moore

26 Hypothesis Failure to replicate a genetic association may provide important clues about genetic architecture. Jason H. Moore

27 Five different epistasis models Six different heritabilities N=1600 cases and controls Two functional SNPs Estimated power to replicate SNP2 after changing allele frequency of SNP1 Jason H. Moore

28 Jason H. Moore p (A) = 0.2 p (a) = 0.8

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30 Conclusion SNPs that fail to replicate should be assessed for gene-gene interaction. Jason H. Moore

31 Genome-Wide Analysis of Epistasis Challenges

32 Genome-Wide Analysis of Epistasis

33 Selecting SNP Subsets Test all subsets of 1 SNP: n = 3*10 5 Test all subsets of 2 SNPs: n 4*10 10 Test all subsets of 3 SNPs: n 4*10 15 Test all subsets of 4 SNPs: n 3*10 20 Test all subsets of 5 SNPs: n 2*10 25 Test all subsets of 6 SNPs: n 1*10 30 Test all subsets of 20 SNPs: n 1*10 91 Jason H. Moore

34 Computing Time Required (assuming 1 million PCs each processing 1 model/sec.) Test all subsets of 1 SNP: 1 second Test all subsets of 2 SNPs: 11 hours Test all subsets of 3 SNPs: 127 years Test all subsets of 4 SNPs: 9513 millennia Test all subsets of 5 SNPs: eternity Test all subsets of 6 SNPs: eternity Test all subsets of 20 SNPs: eternity Jason H. Moore

35 Barcelona Supercomputer

36 MDR Y = α+βx Neural Networks

37 Expert Knowledge is Critical

38 Implications of Epistasis for Personal Genetics

39 Personalized Genetics

40 Personalized Genetics

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42 Summary Genetics will play an important role in vaccine adverse effects GWAS provide some of the data we need Success will depend on our assumptions about the complexity of the problem New analytical tools that embrace complexity are needed Jason H. Moore

43 Graduate Students Acknowledgments Richard Cowper, Casey Greene, Krissy Pattin, Sarah Pedergrass, Nadia Penrod, Nima Pouladi, Chantel Sloan, Anna Tyler, Ryan Urbanowicz Staff Peter Andrews, Tom Caldwell, Jeff Kiralis, Pete Schmitt, Paul Thompson NIH grants R01 AI59694, R01 LM compgen.blogspot.com Jason H. Moore