Hunting, shooting and fishing... Shooting –Prior hypothesis Hunting –“Promising area” Fishing –Angling –Trawling –Floundering

Hunting, shooting and fishing... Shooting –Prior hypothesis Hunting –“Promising area” Fishing –Angling –Trawling –Floundering G*E suspected –Prior publication G known, E not –E * (top SNPs) E known, G not –E * (GWAS) Neither G nor E is known as a risk factor for asthma

Hunting, shooting and fishing... Single test –p<0.05 or tests –p<0.001 ? 550,000 tests –p<10 -6 / ? 25 million tests –p<10 -9 ? –plus biology…? G*E suspected –Prior publication G known, E not –E * (top SNPs) E known, G not –E * (GWAS) Neither G nor E is known as a risk factor for asthma

Hunting, shooting and fishing... 1-stage test –interaction analysis 1-stage test (?) –interaction analysis 2-stage test (?) –screen then confirm 2-stage test –screen then confirm –plus biology…? G*E suspected –Prior publication G known, E not –E * (top SNPs) E known, G not –E * (GWAS) Neither G nor E is known as a risk factor for asthma

Association of chr17q21 variant rs with childhood asthma / wheezy bronchitis Trend p = 2 x MAF = 44% PARF = 27%

Association of chr17q21 variant rs with childhood asthma / wheezy bronchitis Trend p = 2 x MAF = 44% PARF = 27%

PersonsChromosomes GGGgggG+G- CasesnnnD+ab ControlsnnnD-cd Logistic regression (G=0,1,2)Odds ratio ad/bc Same results for per-allele odds ratio and significance

Formal case-control interaction analysis (1) ExposedGGGgggE+G+G- CasesnnnD+ab ControlsnnnD-cd Unexp.GGGgggE-G+G- CasesnnnD+ef ControlsnnnD-gh Interaction OR = (ad/bc) / (eh/fg) = (adfg) / (bceh)

Formal case-control interaction analysis (2) CasesGGGgggD+G+G- ExposednnnE+ab Unexp.nnnE-ef ControlsGGGgggD-G+G- ExposednnnE+cd Unexp.nnnE-gh Interaction OR = (af/be) / (ch/dg) = (adfg) / (bceh)

Case-only approach to G*E interaction analysis CasesGGGgggD+G+G- ExposednnnE+ab Unexp.nnnE-ef Interaction OR = (af/be) / 1 Assumes no association between exposure and genotype in undiseased (Mendelian randomization). Gains statistical power as no error in (ch/dg) term. Not statistically independent of (af/be) / (ch/dg)

Two-stage case-control interaction analysis D+ & D-GGGgggAllG+G- Exposedn+nn+nn+nE+a+cb+d Unexp.n+nn+nn+nE-e+gf+h The “screening” OR = (a+c)(f+h)/(b+d)(e+g), an “average” of G*E associations across cases & controls is statistically independent of the interaction OR. No assumption of Mendelian randomization. Gains statistical power in GWAS if used to select SNPs for formal interaction testing in 2nd stage at p<0.01.

Interaction analysis with multi-level exposures D+ & D-GGGgggAllG+G- Exposedn+nn+nn+nE+a+cb+d Unexp.n+nn+nn+nE-e+gf+h The “screening” OR = (a+c)(f+h)/(b+d)(e+g) (or the “case-only” OR in a case-only design) can also be derived by logistic regression: Modelling exposure as a function of genotype … or … Modelling G (0,1) as a function of exposure (0,1).

Interaction with multi-level exposures (step 1) D+ & D-GGGgggAllG+G- High3 Medium2 Low1 None0 Test for association of G with E by logistic regression among cases and controls combined: Modelling G (0,1) as a function of exposure (0-3).

Interaction with multi-level exposures (step 2) Compare association of G with E between cases and controls, for SNPs with “promising” screening ORs: Model G (presence of effect allele or genotype) as a function of level of exposure (0,1,2...k) in cases Model G (presence of effect allele or genotype) as a function of level of exposure (0,1,2...k) in controls Calculate difference between betas for E=1, 2, 3 … k within each study (exposure definition is consistent). Pool results for these differences (log interaction ORs) across studies (but exposure definition or groupings may not be consistent between studies).

Hunting, shooting and fishing... 1-stage test –interaction analysis 1-stage test (?) –interaction analysis 2-stage test (?) –screen then confirm 2-stage test –screen then confirm –plus biology…? G*E suspected –Prior publication G known, E not –E * (top SNPs) E known, G not –E * (GWAS) Neither G nor E is known as a risk factor for asthma

Analytical strategy: interactions (1) How many asthma-related SNPs should be tested for interactions with: –each other (G*G interactions)? –environment and lifestyle factors (G*E)? Should biological candidate genes be prioritised for G*E interactions? –If so, only for biologically plausible interaction effects? Should interactions be tested only for disease outcomes/subgroups that are associated with the genotypes? –At what p value cut-off for the main effect?

Analytical strategy: interactions (2) Should interactions be tested only for the genetic model (additive, dominant or recessive) that best fits phenotype? –Probably, to maximise statistical power Should interactions be tested in all cohorts (with relevant exposure data) even if the environmental effect is non-significant in one or more cohorts? –Yes, if we want to meta-analyse eventually Should we seek “replication” of new G*E interactions before publication? –Yes, if single study. No, if meta-analysis.

Why study interactions? “Antidote to determinism” –Genetic susceptibility, programming –Lifecourse approach to disease causation More certain identification of causes –Interaction RR larger than overall RR –Bias and confounding more easily excluded Guidance for public health policy –“Safety for susceptibles”

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