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

Some Methodological Considerations in Mendelian Randomization Studies

Eric J. TchetgenTchetgen

Depts of Epidemiology and Biostatistics


What is mendelian randomization

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 interpretation suppose all variables are binary

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 interpretation suppose all variables are binary1

    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

    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

    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

    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

    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

    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

    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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