Efficient Design for Mendelian Randomization Studies: Subsample and 2-Sample Instrumental Variable Estimators

By Brandon L. Pierce, Stephen Burgess. American Journal of Epidemiology, 2013

1,223 citations recorded by the source corpus.

Mendelian randomization (MR) is a method for estimating the causal relationship between an exposure and an outcome using a genetic factor as an instrumental variable (IV) for the exposure. In the traditional MR setting, data on the IV, exposure, and outcome are available for all participants. However, obtaining complete exposure data may be difficult in some settings, due to high measurement costs or lack of appropriate biospecimens. We used simulated data sets to assess statistical power and bias for MR when exposure data are available for a subset (or an independent set) of participants. We show that obtaining exposure data for a subset of participants is a cost-efficient strategy, often having negligible effects on power in comparison with a traditional complete-data analysis. The size of the subset needed to achieve maximum power depends on IV strength, and maximum power is approximately equal to the power of traditional IV estimators. Weak IVs are shown to lead to bias towards the null when the subsample is small and towards the confounded association when the subset is relatively large. Various approaches for confidence interval calculation are considered. These results have important implications for reducing the costs and increasing the feasibility of MR studies.

Subfields: Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e3ebfee85665829bb5a1e2db735a45c702743777

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Citations in the corpus

Two-Sample Instrumental Variable Analyses Using Heterogeneous Samples (2017, background): “…in which one measures the genotypes and the exposure and the other one measures the genotypes and the disease than to find a single GWAS that measures all three types of variables (Pierce and Burgess, 2013, Davey Smith and Hemani, 2014, Burgess et al., 2015, Gamazon et al., 2015, Lawlor, 2016).”
A framework for the investigation of pleiotropy in two‐sample summary data Mendelian randomization (2017, methodology): “This is referred to as two-sample summary data MR [5, 6].”
Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data (2013, methodology): “This is known as a two-sample IV analysis [Pierce and Burgess, 2013].”
A review of instrumental variable estimators for Mendelian randomization (2015, methodology): “Simulations have shown that a subsample IV analysis with exposure data on only 10% of participants may retain 90% of the power of the full-sample IV analysis.(128) Estimates and confidence intervals with a single IV can be calculated using the ratio method and Fieller’s theorem (Section 3.”
Orienting the causal relationship between imprecisely measured traits using GWAS summary data (2017, methodology): “We performed two sample MR [30] for each association in the direction of causality inferred by the Stieger test.”
Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, methodology): “MR analyses that use only summary data have been called summary MR (SMR) (30) and two-sample MR (2SMR) (7).”
Meta‐analysis and Mendelian randomization: A review (2019, background): “The Galbraith radial plot has additionally been repurposed for detecting outlying variants in MR.28”
Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects (2015, neutral): “If the genetic associations with the risk factors and with the outcome are measured in the same data set, this will lead to bias in the direction of the observational association (25), whereas if the genetic associations with the risk factors and with the outcome come from different sources (known a”
The MR-Base platform supports systematic causal inference across the human phenome (2018, methodology): “The MendelianRandomization R package ( Yavorska and Burgess, 2017 ) is a standalone tool comprising several 2SMR methods and, in addition to the methods that we have implemented, we make it easy to import data from MR-Base into that R package.”
Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator (2016, background): “…were obtained, then the resulting analysis suffers from bias and inflated type 1 error rates when the included variants are “weak” (i.e., they do not explain a substantial proportion of variation in the exposure in the dataset under analysis) (Burgess et al., 2011; Pierce and Burgess, 2013).”