The Cochrane Collaboration Colloquium: The Human Genome Epidemiology Network: making sense of 10,000,000 postulated genetic risk factors

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    The Cochrane Collaboration Colloquium: The Human Genome Epidemiology Network: making sense of 10,000,000 postulated genetic risk factors - Presentation Transcript

    1. Plenary 3 Co-chairs: Sally Green and Helen Whelton Pembroke
    2. The Human Genome Epidemiology Network (HuGENet): making sense of 10,000,000 postulated genetic risk factors John P.A. Ioannidis Department of Hygiene and Epidemiology University of Ioannina School of Medicine, Ioannina, Greece and Tufts University School of Medicine, Boston, USA
    3. Human Genome Epidemiology Network (HuGENet)
      • Global collaboration of individuals and organizations to assess population impact of genomics and how it can be used to improve health and prevent disease
      • Genotype prevalence
      • Gene - disease association
      • Gene - gene interactions
      • Gene - environment interactions
      • Assessment of Genetic tests
      “ Systematic application of epidemiologic methods and approaches to assess the impact of human genetic variation on health and disease” Khoury, Little and Burke, HuGE 2004 HuGE problem: 25,000 genes, their combinations and interactions with risk factors
    4. From Genetics to Genomics
      • Genetic Disorders
      • Mendelian Disorders
      • Disease burden: 5%
      • Mutations/One Gene
      • High Disease Risk
      • Environment +/-
      • “ Genetic Services”
      • Genetic Information
      • All Diseases
      • Disease Burden: 95%
      • Variants/MultiGenes
      • Low Disease Risk
      • Environment +++
      • General Practice
    5. Counting fish in the sea of gene-disease associations Ioannidis, Trends Mol Med 2003 Candidate analyses 1 quadrillion Investigators >10 Genetic contrasts >10 Subgroups >10 Outcomes >10 Diseases >1000 Gene variants >10000000 Parameter Multiplier
    6. Major postulated problems of human genome epidemiology
      • Small sample sizes
      • Small effect sizes
      • Large number of biological factors
      • Main effects vs. interactions of genes
      • Interaction with environmental exposures
      • Potential variability across populations
      • Old-epidemiology problems: confounding (population stratification), misclassification
      • Questionable replication validity
    7. Background issues
      • Assay development
      • Minimizing measurement error
      • Minimizing design error
      • Standardization and harmonization
      • Validation
      • Biological rationale and plausibility
      • Clinical and public health use
    8. Small sample size
    9. Small genetic effects Ioannidis , Trikalinos, and Khoury , Am J Epidemiol 2006
    10. Number of Published Human Genome Epidemiology (HuGE) Papers* 2001-2005
      • Year Prevalence Associations Interactions
      • 2001 308 2141 436
      • 2002 349 2799 569
      • 2003 328 3021 600
      • 2004 430 3772 664
      • 2005 418 4569 911
      * Data from HuGE Published Literature Database
    11. Epidemiologic Studies/Platforms Analyses and Publications Knowledge Integration Knowledge Dissemination Network of Networks/ P3G Guide Health Services & Inform Public Policy Guide Research Agenda The HuGENet “Road Map”
    12. HuGENet “Network of Networks” Nat Genet Jan 2006
    13. Examples of Network-based HuGE Study Platforms
      • Disease Consortium Teams Subjects
      • Parkinson GEO-PD 18 10,000
      • Osteoporosis GENOMOS 10 30,000
      • Preterm birth PREGENIA 10 20,000
      • Lymphoma INTERLYMPH 15 20,000
      • Lung cancer ILLCO 30 51,000
      • Head & Neck INHANCE 13 28,000
      • Melanoma GENOMEL 12 3,000
      • Pancreatic Ca PACGENE 10 5,000
    14. Study Platforms: Methodologic Implications
      • Assembling Teams
      • Overall Project Design
      • Harmonization vs standardization
      • Outcome definitions and ascertainment
      • Risk factor definitions and ascertainment
      • Gene Selection and Measurement of genotypes
      • Other biological markers
      • Integrating and understanding the environmental variables (see. Davey-Smith et al. IJE 2006)
    15. Non-replicated diminishing effects Ioannidis et al , Nature Genetics 2001
    16. Breast cancer: a “null” field for common genetic variants? Ioannidis , JNCI 2006 and Pharoah et al. JNCI 2006
    17. The other side: don’t give up early
    18. H: heterogeneity R/F: difference in first vs. subsequent D1-D3: small-study bias diagnostics RS/FS: significant findings (with/without first studies) Ioannidis et al, Lancet 2003
    19. Succession of early extremes: the Proteus phenomenon Ioannidis and Trikalinos, J Clin Epidemiol 2005
    20. Language bias and global science Pan et al. PLoS Med 2005
    21. Measurement error: insight from a collaborative analysis
      • Of 18 teams of investigators participating in the collaborative analysis of alpha-synuclein REP-I variation and Parkinson’s disease risk, we found that 7 had to be excluded from the main analyses because of laboratory error exceeding 10% and/or overt violation of HWE in the controls
      • Two other teams who had published an inverse association apparently had miscoded the alleles in their databases.
      Maraganore et al , JAMA 2006
    22. Let us add the environment Hunter D. Nat Rev Genet 2005
    23. Genomic Tests: a Public Health Issue
      • Can potentially affect a lot of people (especially pharmacogenomics)
      • Potential for enhancing and targeting prevention efforts
      • Implementation and access
      • Provider and public education
      • Monitoring impact on population health
    24. “ Genomic profiling to promote a healthy lifestyle: not ready for prime time” Haga S et al. Nat Genet 2003
      • For each intended use
        • A nalytic Validity
        • C linical Validity
        • C linical Utility
      • Define test, disorder, and setting
      Knowledge Integration Across Disciplines: Evaluation of Genetic Tests http://www.cdc.gov/genomics/activities/fbr.htm
    25.  
    26.  
    27. Research on genetic risk factors
      • Single studies: purely hypothesis-generating, important to register data, regardless of results
      • Meta-analyses of group data: increasing certainty when several thousand subjects available
      • Large-scale evidence from consortia or meta-analysis of individual-level data: evolving gold standard?
    28. Plenary 3 Co-chairs: Sally Green and Helen Whelton Pembroke

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