1 / 20

Complex Adaptive Systems and Human Health: Statistical Approaches in Pharmacogenomics

Complex Adaptive Systems and Human Health: Statistical Approaches in Pharmacogenomics. Kim E. Zerba, Ph.D. Bristol-Myers Squibb FDA/Industry Statistics Workshop Statistics: From Theory to Regulatory Acceptance 18-19 September 2003 Bethesda, Maryland. Disclaimer

mimi
Télécharger la présentation

Complex Adaptive Systems and Human Health: Statistical Approaches in Pharmacogenomics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Complex Adaptive Systems and Human Health: Statistical Approaches in Pharmacogenomics Kim E. Zerba, Ph.D. Bristol-Myers Squibb FDA/Industry Statistics Workshop Statistics: From Theory to Regulatory Acceptance 18-19 September 2003 Bethesda, Maryland Disclaimer The views presented are my own and do not necessarily represent those of Bristol-Myers Squibb

  2. Outline • Complex Adaptive Systems and Human Health • Approach and Some Key Statistical Issues with Genetic Polymorphisms in Pharmacogenomics • Where Do We Go from Here?

  3. Protein Phenotype Disease RNA Gene Gene DNA The Genetic Paradigm

  4. Non-Infectious Human Disease Load Complex, Multifactorial > 98% Simple, Monogenic < 2%

  5. UNIQUE (Initial Conditions) Complex Adaptive Systems and Human Health • Each individual is a complex • adaptive system and the • fundamental unit of organization UNIQUE FUTURE ENVIRONMENTAL NORM OF HISTORY REACTION Risk of Disease - INDIVIDUAL • Health or disease is • an emergent feature based on • interactions among many • agents, including genes and • environments PHYSIOLOGICAL FITNESS, HEALTH NOW + TIME-SPACE CONTINUUM • Agents participate in dynamic • network and are notdirectcauses BLOOD HAEMOSTASIS PRESSURE • Network organized hierarchically • and heterarchically into fields REGULATION • Fields are domains of relational • order among agents LIPID CARBOHYDRATE METABOLISM METABOLISM • Stronger relationships within fields, • weaker relationships among fields GENOME TYPE • Unique genome type provides initial • conditions and capacity for change • Context and time are key to understanding influence of genetic variation See : Zerba and Sing, 1993, Current Opinion in Lipidology 4: 152-162, Zerba et al. 2000, Human Genetics 107: 466-475 for more detail

  6. ? 2 Complex Adaptive Systems Approach to PGx Endpoints ? 1 Biomarkers 3 ? Genes

  7. Some Key Statistical Issues for Pharmacogenomics Studies Using Genetic Polymorphisms • Gene/Polymorphism Selection • Linkage Disequilibrium • Admixture and Population Stratification • Invariance • Context Dependence • Time

  8. Gene/Polymorphism Selection • Genome Scan • Genes not identified a priori • Genotyping • 25K - 500K polymorphisms genotyped • foreachsubject (not practical yet) • DNA Pooling • 25K - 1.5 million polymorphisms • Case-control allele frequency differences for each polymorphism • Candidate Genes

  9. P F Candidate Genes Candidate Gene Region F Si Unknown and unmeasured functional polymorphism One of numerous non-functional polymorphisms • Assume that any association of Si with phenotype, P, is because of linkage disequilibrium between F and Si PFS = pFpS + DFS

  10. SNP + - Admixture Population I Population II + + + - - + - - - + + - + - + - + + - - + p+ = 0.8 p+ = 0.2 + - - + - Admixed Population + - - + a = proportion of population I = 0.5 p+ = 0.5

  11. Consider two subpopulations, I and II: For each subpopulation, there is linkage equilibrium between a disease allele, F, and a marker allele, S, PFISI = pFIpSI; PFIISII = pFIIpSII; DFISI = DFIISII = 0. In the admixed population (I + II), there is linkage disequilibrium between F and S, PFS = pFpS + a(1-a)(pFI - pFII)(pSI - pSII) Admixture Linkage Disequilibrium Marker Allele Frequency Difference Subpopulations Proportions Disease Allele Frequency Difference

  12. Admixture and Population Stratification • Admixture linkage disequilibrium dissipates quickly in a randomly mating population • Common clinical trial feature: > 1 ethnic group • Population stratification • Ethnicity is a confounder • Population stratification can create linkage disequilibrium just like admixture only spurious • Type I or Type II error inflation

  13. False-Positive Endpoint Association Example Not considered in analysis • Unbalanced design • Unequal numbers of each group: aI = 0.67 • Marker allele: p+ = 0.8 in ethnic group I • p+ = 0.2 in ethnic group II • Disease risk: pF = 0.8 for ethnic group I • pF = 0.2 for ethnic group II

  14. Population Genetic Structure and the Search for Functional Mutations: Quantitative Traits FREQUENCY Aa AA aa Phenotype (Biomarker) ? SCALE ? Genotype Functional Mutation? SNP FREQUENCYandSCALEcontribute to inferences about SNP-phenotype associations: Analysis of Variance Approach SSR = fi(Yi - Y)2

  15. Population Stratification and Genotype Frequencies A a SNP Aa • Stratification can result in decreased heterozygote frequencies relative to expectation: PAa = 2pApa - 2DA (DA positive in example) PAA Paa Ethnic Group II Ethnic Group I Average Genotype Frequencies PAa aa AA pa pA

  16. DA --> + • Population stratification can result in overestimation of quantitative phenotypic variation associated with genetic variation relative to Hardy-Weinberg equilibrium expectation + 0 Sum of Squares Bias - - + 0 DA

  17. e2 e3 e4 AA 112 Cys Cys Arg AA 158 Cys Arg Arg SNP SNP Invariance, Context and Time An example from Apolipoprotein E Biology • Molecular weight: 34 kD • Synthesized in most organs • liver, brain, gonads, kidney, spleen, muscle • Key physiological role in lipid transport • ligand for the LDL (ApoB-E) receptor • Structural gene on chromosome 19 • polymorphic with three common alleles 5’ 3’ Note: combination of SNPs involved

  18. Invariance Nancy, France N = 223 Rochester, MN, USA N=226 Quebec, Canada N = 201 Munster, Germany N = 1000 Helsinki, Finland N=207 Alleles 2 3 4 Cholesterol (mg/dL) From Sing et al. (1996) Genetic architecture of common multifactorial diseases, pp. 211-232 In:Chadwick and Cardew (eds.) Variation in the human genome, Ciba Foundation Symposium 197, John Wiley & Sons, New York

  19. Context and Time 2 A Changes in ApoE Additive Genetic Variance with Age Rochester, MN Males, N=1035 Bootstrap Significance Tests 16 70 60 -4 12 50 8 40 Age Window Midpoint Variance x 10 (years) 30 4 20 0 10 10 20 30 40 50 60 70 10 20 30 40 50 60 70 Age Window Midpoint (years) + 0.05 > P <0.10 P < 0.05 From Zerba et al. 1996, Genetics 143: 463-478.

  20. Where Do We Go From Here? Some Additional Statistical Challenges • Study design in genetic setting • Genetic stratification • Genomic control • Ascertainment bias correction in choice of which polymorphisms to study • Contexts/Interactions-- which ones are important? • New analytical methods needed • Combinations of SNPs within and among genes and environments may be involved • Haplotype Reconstruction • Combinatorial Partitioning • Missing genotypes for individual polymorphisms • Sampling vs technical variability in DNA pooling studies • Multiplicity-- p-value adjustment not a trivial problem

More Related