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Some Methodological Considerations in Mendelian Randomization Studies

Some Methodological Considerations in Mendelian Randomization Studies. Eric J. Tchetgen Tchetgen Depts of Epidemiology and Biostatistics. What is Mendelian Randomization.

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Some Methodological Considerations in Mendelian Randomization Studies

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  1. Some Methodological Considerations in Mendelian Randomization Studies Eric J. TchetgenTchetgen Depts of Epidemiology and Biostatistics

  2. What is Mendelian Randomization • Use genotypes as instrumental variables (IVs) to estimate the causal health effects of phenotypes influenced by those genotypes • MR methodology relies on strong assumptions • Consider a recent study by Kivimaki et al AJE 2011 • Causal DAG of Valid IV ? FTO BMI MD Unmeasured trait

  3. More formal interpretationSuppose all variables are binary “Average effect in the compliers “ • Suppose all variables are binary and the following monotonicity assumption holds: “FTO -> BMI” same direction for all individuals. • Then the IV estimandof ”BMI->MD” =“FTO->MD” / “FTO->BMI” =The causal effect of BMI on MD in the subpopulation of individuals for whom “FTO -> BMI” is not zero “Average effect in the exposed” • If the causal effect ”BMI->MD” is the same for individuals with a high BMI regardless of their FTO status, • Then the IV estimand of ”BMI->MD” =“FTO->MD” / “FTO->BMI” =The causal effect of BMI on MD among individuals with high BMI

  4. More formal interpretationSuppose all variables are binary “Population Average effect ” • If in subpopulation with a given BMI, the causal effect ”BMI->MD” is independent of their FTO status, • Then the IV estimand of ”BMI->MD” =“FTO->MD” / “FTO->BMI” =The average causal effect of BMI on MD in the entire population

  5. Is the IV the causal gene?Suppose all variables are binary • “Average effect in the compliers “ • Provided monoticity of causal gene and relation of FTO with (BMI,MD) only through KIAA1005 ”BMI->MD” =“FTO->MD” / “FTO->BMI” =The causal effect of BMI on MD amongst the subpopulation of individuals for whom “KIAA1005 -> BMI” is not zero • “Average effect in the compliers and population Average effect “ • equal to IV estimand as long as respective homogeneity assumption hold for the causal gene FTO ? Gene in LD KIAA1005 BMI MD Unmeasured trait

  6. Most GWAS are case-control studies • Over sampling of cases introduces selection bias which induces violation of the IV assumption • This connects to recent interest into methods for repurposing case-control samples • Simple solution is to reweight sample to break the link between Diabetes and selection into case control sampling • Matched density sampling, i.e. within risk sets, more complicated weighting scheme but can be done (Walter et al, 2012, in progress) Case-control sample DIABETES ? FTO BMI MD Unmeasured trait

  7. Timing may be everything • BMI is a lifecourse exposure , do we measure BMI at a time where it matters for MD . This is generally more severe than classical measurement error • If we use either BMI(1) or BMI(2) alone , FTO is no longer be a valid IV, so –called exclusion restriction may not hold. • Sometimes, people use the average of BMI(1) and BMI(2), this implicitly assumes that the effects are of the same magnitude • Can use Robins Structural Nested models for average effect (Glymour et al, 2012, in progress) ? ? FTO BMI(1) BMI(2) MD Unmeasured trait

  8. Survival analysis should be more powerful than binary regression • Modeling time to MD should generally be more powerful than cumulative risk analysis • Robins’ Structural nested AFT model an option, but can be difficult to implement with administrative censoring • Structural Cox regression can be used to obtain a “compliers “ hazards ratio. (TchetgenTchetgen, 2012, in progress) • Alternatively Structural nested additive hazards model can be used. (TchetgenTchetgen and Glymour, 2012, in progress) ? FTO BMI MD Unmeasured trait

  9. Credible Mendelian Randomization • The strong assumptions needed to identify the causal effects of a phenotype on a disease via MR will often not hold exactly • These assumptions are not routinely systematically evaluated in MR applications , although such evaluation could add to the credibility of MR • Approaches to Falsify an IV (Glymour, TchetgenTchetgen, Robins, AJE,2012): • Leverage prior causal assumption such as the known direction of confounding • Identify modifying subgroups • Instrumental inequality tests • Overidentification tests

  10. MR Collaborators • Maria Glymour • Liming Liang • Laura Kubzansky, • Stefan Walter • James Robins • Shun-Chiao Chang • Eric Rimm • Marilyn Cornelis, • KarestanKoenen • Ichiro Kawachi • StijnVansteelandt

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