Some methodological considerations in mendelian randomization studies
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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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Some Methodological Considerations in Mendelian Randomization Studies

Eric J. TchetgenTchetgen

Depts of Epidemiology and Biostatistics


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


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


    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


    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


    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


    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


    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


    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


    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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