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EXPERIENCES IN POOLING OF DATA: OPPORTUNITIES FOR HARMONIZATION OF I4C COHORTS
Manolis Kogevinas, MD, PhD CREAL, Barcelona and National School of Public Health, Athens
Outline
• Ad hoc and wider collaborations• New cohorts, need to harmonise without
restricting new ideas• Pooling or meta-analysis
ad hoc collaborations between mother-child cohorts
Most collaborations are ad hoc around a specific project with specific hypotheses and normally short term (max 5 years)
– NewGeneris: genotoxicity-biomarkers– Obelix: obesity– Earnest; nutrition and programming– Mobi-Kids: mobile phones, late childhood, young adults– HiWate: water contaminants– ESCAPE: air-pollution– EnviroGenoMarkers: environment and omics
Mother-Child Birth Cohorts and Biobanks in NewGeneris
Micronuclei, a biomarker predictive of future cancer risk
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NewGeneris. Adjusted mean birth weight (grams) and 95% confidence intervals per acrylamide Hb adduct
quartiles in cord blood (M Pedersen, ISEE2011)
Adjusted for gestational age and country; n=903
Two initiatives on coordination of European birth cohorts funded by the EU (FP7)•Enrieco: environmental health•Chicos: all exposures and policy
Main task coordination, but also several case studies
Inventory of European cohorts with data on environmental exposures. ENRIECO project
(www.enrieco.org)
Expand ENRIECO/CHICOS worldwide
• Harmonization and websearch achieved in Europe
• Know-how developed• Expand worldwide (using I4C as platform for
doing this?)• Funding: EU, RO1, other?
New cohorts/1
• Necessary for I4C objectives (numbers)• Increases exposure variation (and protocol
variation)• Extremely positive commitment for long term
funding• Extremely positive commitment to evaluate
environmental exposures
New cohorts: questionnaires
• Development of new protocols and questionnaires coordinated with older cohorts, but allowing innovation– Basic exposure and outcome variables and
biological material should be combinable– e.g. asthma/allergies: inclusion of main ISAAC
questions absolutely necessary but some cohorts may also include new more detailed questions
During your entire lifetime, have you ever smoked a total of 100 cigarettes or more (which is 5 or more packs)?
YES 1NO(SKIP TO B19) 2DK(SKIP TO B19) 8
Did you ever smoke cigarettes regularly, that is, at least one per day for six months or longer?
YES 1NO(SKIP TO B19) 2DK(SKIP TO B19) 8
New cohorts: questionnaires-cultural adaptation
New cohorts: mother–child cohorts are complex projects
Absolute need for multidisciplinary group leading each cohort including a very strong group in epidemiology
Pooling or meta-analysis?
Pooling or meta-analysis? • Some analyses can be done through meta-analysis,
i.e. without tranfer of raw data• Depending on hypotheses and adjustment factors
individual cohorts may need to run hundreds of prespecified models that should then be meta-analysed
• Some analyses impossible to do through meta-analysis, e.g. Gene-environment interactions or pathway analyses
• Ethical considerations for data transfer
Genomewide Association Study of Asthma- GABRIEL Consortium (Moffatt, NEJM 2010)
One or two stages analytical approach to detect GxE interactions
1. a global genome-wide analysis2. a two-stage approach-first G x exposure (Murcray AJE
2009): - evaluate association of SNPs with exposure; select SNPs with p-value<10E-05- then GxE
Association testsAssociation tests
Logistic Regression-GE
When information on the exposure is available both in cases and controls
Test of GxE interaction effect
LRT (1 df-χ²) :
H1 : Logit P(M|G,E) = β0 + βE E + βG G + βGE GE
H0 : Logit P(M|G,E) = β0’ + βE’ E + βG’ G
2 Step-Analysis to identify genes involved in GxEMurcray et al. Am J Epidemiol 2008
Step 1: Screening test: to find SNPs most likely to be involved in a GxE interaction by testing for G-E
association Case only analysis (combined case/control sample )
For each of N SNPs: LR Test for association between G and E → Select m SNPs with P < 1
Step 2: Case-Control analysis LR Test for GxE applied to m SNPs selected at step 1 → Significance based on P < /m
Comparison with classical one-step approach applied to case-controls
→ Significance based on P < /N
Power for one-step and two-step analyses to detect GxE
for varying levels of interaction effect size
10,000 markers and 500 cases/500 controls
Murcray et al. Am J Epidemiol 2009;169:219-26
Metabolism of benzo[a]pyrene by CYPs and other drug-metabolizing enzymes (IARC 2010)
Pathway analysis: Gene set enrichment analysis (GSEA) -example European
adult asthma cohorts
• 488,058 variants included • mapping 16,656 genes• 258 gene sets evaluated
Pathway analysis, example European adult asthma cohorts
Gene set name Source Gene set p-value
Gene set FDR q value
Sign. genes
Selected gene #
All gene #
Cell adhesion molecules
KEGG ≤0.001 0.018 71 122 134
Tight junction KEGG ≤0.001 0.028 60 118 136
Type I diabetes mellitus
KEGG ≤0.001 0.168 28 42 45
Wnt signalling pathway
KEGG ≤0.001 0.175 71 136 149
Antigen processing and presentation
KEGG 0.002 0.228 27 64 83
Expand ENRIECO/CHICOS worldwide
• Harmonization and websearch achieved in Europe
• Know-how developed• Expand worldwide (using I4C as platform for
doing this?)• Funding: EU, RO1, other?