Upcoming SlideShare
Loading in...5

Like this? Share it with your network








Total Views
Views on SlideShare
Embed Views



0 Embeds 0

No embeds



Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

Slides Presentation Transcript

  • 1. Challenges of an Epidemiologist Working in Genomics Wendy Post, MD, MS Associate Professor of Medicine and Epidemiology Cardiology Division Johns Hopkins University
  • 2.
    • “ There is a need to bridge the chasm between geneticists and traditional epidemiologists who are now wondering how they can apply GWAS technology to their studies”.
    • Teri Manolio 5/8/07
  • 3. Nature Genetics 2006;38(6):644-51 (epub Apr 30 2006).
  • 4. CAPON Association with adjusted QT interval Results of a genome wide association study in KORA S4 and 2 replication cohorts n n KORA S4 3366 4.9 msec 36% < 10 -7 Cohort N Effect MAF Adjusted p KORA F3 2646 7.9 msec 36% < 10 -11 FHS 1805 4.0 msec 39% 0.004 Arking DE, Pfeufer A, Post W et al. Nature Genetics ; published online Apr 30 2006. *QT- adjusted for age, gender and heart rate
  • 5.  
  • 6.  
  • 7.
    • Heritability of Left Ventricular Mass
        • The Framingham Heart Study
    • Wendy S. Post; Martin G. Larson; Richard H. Myers; Maurizio Galderisi; Daniel Levy
    • Hypertension. 1997;30:1025-1028.
    • © 1997 American Heart Association, Inc.
    • From the National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Mass (W.S.P., M.G.L., R.H.M., M.G., D.L.); the Division of Cardiology, Beth Israel Hospital, Boston, Mass (D.L.); the Department of Neurology (R.H.M.), Division of Epidemiology and Preventive Medicine (M.G.L., D.L.), Boston University School of Medicine; the National Heart, Lung, and Blood Institute, Bethesda, Md (D.L.), University of Naples, Italy (M.G.); and the Division of Cardiology, Johns Hopkins Hospital, Baltimore, Md (W.S.P.).
  • 8. Confusing genetics nomenclature
    • Before rs numbers the snp names kept changing
      • Makes it hard to compare results to prior studies in PubMed
      • rs numbers (RefSNP accession ID- db SNP)
        • db SNP- reference database (
    • Forward strand versus reverse strand
      • Nucleotide names
    • Dominant model versus recessive model
      • Relative to major or minor allele?
        • AB+AB vs BB
    • Remembering my biochemistry
      • Untranslated exon?
        • Exon= region of DNA transcribed into the final mRNA
  • 9. Complicated authorship issues
    • Collaboration is key
      • Phenotypers
      • Statisticians
      • Bioinformatics
      • Genotypers
    • Collaboration with other cohorts for replication/validation
    • Order of authorship on manuscripts is not straightforward
      • Decide before the work is done
  • 10. What covariates to put in the model?
    • Epidemiologists “worry” a lot about confounding.
    • Confounders are associated with the outcome (phenotype) and the predictor (genotype).
      • most of our traditional confounders are not associated with genotype.
    • Might want to add covariates for “precision”
    • How much of the variability in the phenotype is explained by genotype after including known predictors in the model?
  • 11. Choosing appropriate control groups
    • Epidemiology 101
    • Cases and controls need to be collected in a similar fashion
    • similar ancestry
    • similar environmental exposures
  • 12. Dealing with population stratification
    • How big of an issue is it really?
    • Should we use AIMs or self described race/ethnicity?
      • AIM (ancestral informative markers)
        • allele frequencies of snps differ based on parental population
        • Can estimate the ancestral proportion of an individual
      • Self described race/ethnicity
    • When can we combine racial/ethnic groups for analyses when there is no statistical interaction?
  • 13. Gene-environment and gene-gene interactions
    • Complex disorders
      • Multiple genes and environmental interactions
    • Tests for interactions
      • Multiple testing issues
      • Power
    • How to combine multiple genes/snps into same prediction model
  • 14. Multiple testing issues
    • Fishing expedition
      • Traditionally in epidemiology, seen as “poor science”
      • GWAS is a really big, sophisticated, fishing expedition
        • Fishing in Alaska for seven different kinds of salmon, instead of fishing on the LI sound.
  • 15. What p value do we use?
    • Bonferroni adjustment seems overly conservative
      • False negatives
    • False Discovery Rate
    • Need for replication/validation
      • What cutpoint do we use to move results forward?
  • 16. Other issues
    • Lack of reproducibility
      • False positives versus
        • differences in environmental exposures or haplotype structure
        • different study design
    • HWE
      • Relative frequency of alleles for a snp are stable in the population (not changing over successive generations).
        • p 2 , 2pq, q 2
    • What genetic model to test
      • 2df, additive, dominant, recessive
        • Again, issues of multiple testing arise
  • 17. To patent or not to patent our results
    • Epidemiologists rarely patent findings
    • History of new scientific discoveries in genetics acquiring patents
    • Could hinder scientific progress?
  • 18. Ann. Int. Med. 49:556-567, 1958