www.creal.cat

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?