Testing and correcting for weak and pleiotropic instruments in two‐sample multivariable Mendelian randomization
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
- Teacher spread
- 0.306 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Multivariable Mendelian randomization (MVMR) is a form of instrumental variable analysis which estimates the direct effect of multiple exposures on an outcome using genetic variants as instruments. Mendelian randomization and MVMR are frequently conducted using two‐sample summary data where the association of the genetic variants with the exposures and outcome are obtained from separate samples. If the genetic variants are only weakly associated with the exposures either individually or conditionally, given the other exposures in the model, then standard inverse variance weighting will yield biased estimates for the effect of each exposure. Here, we develop a two‐sample conditional F ‐statistic to test whether the genetic variants strongly predict each exposure conditional on the other exposures included in a MVMR model. We show formally that this test is equivalent to the individual level data conditional F ‐statistic, indicating that conventional rule‐of‐thumb critical values of 10, can be used to test for weak instruments. We then demonstrate how reliable estimates of the causal effect of each exposure on the outcome can be obtained in the presence of weak instruments and pleiotropy, by repurposing a commonly used heterogeneity Q ‐statistic as an estimating equation. Furthermore, the minimized value of this Q ‐statistic yields an exact test for heterogeneity due to pleiotropy. We illustrate our methods with an application to estimate the causal effect of blood lipid fractions on age‐related macular degeneration.
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The record
- Venue
- Statistics in Medicine
- Topic
- Genetic Associations and Epidemiology
- Field
- Biochemistry, Genetics and Molecular Biology
- Canadian institutions
- —
- Funders
- Medical Research CouncilMedical Research Council CanadaWellcome Trust
- Keywords
- Mendelian randomizationStatisticsPleiotropyTest statisticInstrumental variableStatisticMathematicsEconometricsSample size determinationResamplingStatistical hypothesis testingBiologyGeneticsGenetic variants
- Has abstract in OpenAlex
- yes