Department of Public Health and Primary Care,
Cardiovascular Epidemiology Unit,
Strangeways Research Laboratory, Cambridge, UK
Mendelian randomization:
The use of genetic variants as an instrumental variable for assessing causal associations in observational data
Presenting author: Stephen Burgess
Problem:
How to assess the causal effect of a factor on an outcome if the data available is observational, not
experimental?
?
Factor of interest
Difficulties:
Outcome
Confounding: association between factor of interest and competing risks means that those with
different levels of the factor of interest cannot be directly compared.
?
Factor of interest
Confounding:
If richer, healthier people have decreased
intake of LDL-C, then this may simply
mean that richer, healthier people have
lower incidence of CHD. LDL-C may be
a marker of good health, not a cause.
Outcome
Competing risk factors
Reverse causation: the factor may not only affect the outcome, but the outcome may also affect the
risk factor.
Factor of interest
Instrumental
variables:
(IVs)
?
Outcome
An instrumental variable is a variable which is:
Reverse causation:
If people with poor coronary health
decrease their intake of LDL-C in
response to subclinical disease (early
warning signs of disease), then an
association between LDL-C and CHD
will be induced.
1) associated with the factor of interest (so the instrument defines groups differing in the factor),
Instrumental variable:
Suppose there is a common genetic
variant which causes the body to retain
more LDL-C from the diet, dividing the
population into absorbers and nonabsorbers.
2) not associated with any other risk factor (so the instrument gives a fair test),
3) not associated with the outcome conditional on any risk factor (so the effect of the
instrument must be via the factor of interest).
1): association between
instrument and factor
Instrument
Competing risks
Population
3): no direct association
between instrument
and outcome
2): no association between
instrument and competing risks
Factor
Absorbers
Outcome
These conditions, as shown in the directed acyclic graph (DAG) above, ensure that instrumental
variable estimates are not biased by confounding.
Mendelian
Genetic variants are ideal candidates to be used as instrumental variables as genes are:
randomization: 1) generally specifically associated with biological factors,
2) determined at conception.
These characteristics motivate use and validity of genetic instrumental variables and ensure
estimates are not subject to bias due to reverse causation.
Estimation:
Example:
What is the causal association of lipid
levels on coronary heart disease (CHD)?
— observational injurious association of
low density cholesterol (LDL-C) and
protective association of high density
cholesterol (HDL-C) on CHD
If all associations are linear and not subject to interactions, the causal effect of a factor on an
outcome can be estimated by the ratio of:
βGY
regression coefficient of outcome (Y) on instrument (G)
regression coefficient of factor (X) on instrument (G)
= βGY / βGX = βXY
G
βGX
X
βXY
Y
Non-absorbers
All other factors
equal between groups
Compare outcome
between groups
We see from the diagram that the
groups defined by the instrumental
variable are similar to arms in a
randomized controlled trial.
Assumptions for analysis:
We assume that the instrument is only
associated with lipid levels. This analysis
would be invalid if, for example:
– the genetic variant was correlated
with another variant associated with,
say, triglyceride levels.
Current
work:
If cross-sectional data is available on a number of different factors, each of which has an associated instrumental variable, how can the network of
associations between the factors be efficiently estimated?
For example, if we are interested in the causal effect of lipid levels on CHD, and have measured instruments which affect LDL-C, HDL-C and
triglycerides, how would we estimate a causal association? What if we believe that LDL-C levels may affect triglyceride levels? Could we estimate a
direct effect of LDL-C on CHD, or an indirect effect of the increase in LDL-C on CHD via triglycerides? How would you account for structural
uncertainty in the model?
Take-home
message:
Current methods for instrumental variable analysis enable causal effects to be estimated in a limited and often unrealistic context, where an
instrumental variable is only associated with a single factor. More sophisticated methodology is required to estimate causal effects in a more
realistic situation, where a range of instruments are associated with a range of interacting factors.
Such analysis requires detailed cross-sectional observational and genetic data, and lots of it!
Contact details — E: sb452@medschl.cam.ac.uk, T: 01223 740002