Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score

By Qingyuan Zhao, Jingshu Wang, Gibran Hemani, J. Bowden, Dylan S. Small. Annals of Statistics, 2018

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Mendelian randomization (MR) is a method of exploiting genetic variation to unbiasedly estimate a causal effect in presence of unmeasured confounding. MR is being widely used in epidemiology and other related areas of population science. In this paper, we study statistical inference in the increasingly popular two-sample summary-data MR. We show a linear model for the observed associations approximately holds in a wide variety of settings when all the genetic variants satisfy the exclusion restriction assumption, or in genetic terms, when there is no pleiotropy. In this scenario, we derive a maximum profile likelihood estimator with provable consistency and asymptotic normality. However, through analyzing real datasets, we find strong evidence of both systematic and idiosyncratic pleiotropy in MR, echoing some recent discoveries in statistical genetics. We model the systematic pleiotropy by a random effects model, where no genetic variant satisfies the exclusion restriction condition exactly. In this case propose a consistent and asymptotically normal estimator by adjusting the profile score. We then tackle the idiosyncratic pleiotropy by robustifying the adjusted profile score. We demonstrate the robustness and efficiency of the proposed methods using several simulated and real datasets.

Subfields: Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6b74a0218fdab2fa25bb80932f64a8dd50cd5124

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Citations in the corpus

Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, methodology): “Modified second-order weights are also incorporated into the MR-RAPS (54) estimator.”
The MR-Base platform supports systematic causal inference across the human phenome (2018, methodology): “…) (https://cran.r-project.org/ web/packages/MendelianRandomization/), RadialMR ( Bowden et al., 2017b ) (https://github.com/ wspiller/radialmr), MR-PRESSO ( Verbanck et al., 2018 ) (https://github.com/rondolab/MR-PRESSO) and mr.raps ( Zhao et al., 2018 ) (https://github.com/qingyuanzhao/mr.raps).”
Bayesian weighted Mendelian randomization for causal inference based on summary statistics (2018, methodology): “(9) Then, according to LRVB, we can obtain the accurate estimate Σ̂ of the covariance matrix of sufficient statistics in BWMR by Σ̂ = −(H−V−1)−1 = (I−VH)−1V. (10) In summary, from VEM algorithm, we obtain the estimate of β as the mean of latent variable β under variational posterior distribution aft”
The confidence interval method for selecting valid instrumental variables (2019, result): “These results are similar to those found in the two- sample summary data analysis of Zhao et al. (2019), who found profile score, RAPS, IVW and weighted median estimates of 0.601 (0.054), 0.402 (0.106), 0.514 (0.102) and 0.472 (0.176), respectively in their analysis with 160 SNPs as potential…”
Two-Sample Instrumental Variable Analyses Using Heterogeneous Samples (2017, neutral): “Two-sample (summary data) 160 MR-RAPS (Zhao et al., 2018) 0.”
Meta‐analysis and Mendelian randomization: A review (2019, methodology): “and the dilution towards zero approximated by the relation F − 1 =F.(5,11) For example, an F of 100 or 20 would indicate a likely 1% or 5% dilution in the IVW estimate,”