Loading in 5 sec....

Some Methodological Considerations in Mendelian Randomization StudiesPowerPoint Presentation

Some Methodological Considerations in Mendelian Randomization Studies

- 103 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about 'Some Methodological Considerations in Mendelian Randomization Studies' - finola

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

### 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 Then the IV estimandof

“Average effect in the compliers “

- Suppose all variables are binary and the following monotonicity assumption holds:
“FTO -> BMI” same direction for all individuals.

”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 regression 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 regression

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

Download Presentation

Connecting to Server..