Mendelian randomization: genetic anchors for causal inference in epidemiological studies

By George Davey Smith, Gibran Hemani. Human Molecular Genetics, 2014

4,051 citations recorded by the source corpus.

Observational epidemiological studies are prone to confounding, reverse causation and various biases and have generated findings that have proved to be unreliable indicators of the causal effects of modifiable exposures on disease outcomes. Mendelian randomization (MR) is a method that utilizes genetic variants that are robustly associated with such modifiable exposures to generate more reliable evidence regarding which interventions should produce health benefits. The approach is being widely applied, and various ways to strengthen inference given the known potential limitations of MR are now available. Developments of MR, including two-sample MR, bidirectional MR, network MR, two-step MR, factorial MR and multiphenotype MR, are outlined in this review. The integration of genetic information into population-based epidemiological studies presents translational opportunities, which capitalize on the investment in genomic discovery research.

View Paper: https://www.semanticscholar.org/paper/e86fe9b56bac5e159a1b18d9e5003ec06cd14314

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Citations in the corpus

Orienting the causal relationship between imprecisely measured traits using GWAS summary data (2017, methodology): “If multiple instruments are available for an hypothesised exposure, which is increasingly typical for complex traits that are analysed in large GWAS consortia, then techniques can be applied to mitigate these issues [16].”
Insulin resistance: Genetic associations with depression and cognition in population based cohorts (2019, unknown): “However, the PRS approach is liable to yield substantive false positive rates due to horizontal pleiotropy, the phenomenon whereby a gene (or genes) influences multiple traits (Davey Smith and Hemani, 2014).”
Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score (2018, background): “Empirically, there is generally a lack of confounding of genetic variants with factors that confound exposures in conventional observational epidemiological studies [19].”
Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression (2018, background): “Much attention has focused on detecting and adjusting for one specific source of violation referred to as horizontal pleiotropy.(8,9) This occurs when SNPs exert a direct effect on the outcome through pathways other than the exposure.”
Two-Sample Instrumental Variable Analyses Using Heterogeneous Samples (2017, methodology): “For a more comprehensive textbook treatment of the classical IV estimators, we refer the reader to Davidson and MacKinnon (1993).”
Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption (2017, methodology): “Using germline genetic variants as instrumental variables of modifiable exposure phenotypes can strengthen causal inference in observational studies by applying the principles of Mendelian randomisation (MR).(1,2) This method has already been used to address causality in several exposureoutcome comb”
The MR-Base platform supports systematic causal inference across the human phenome (2018, methodology): “MR ( Davey Smith and Ebrahim, 2003 ; Davey Smith and Hemani, 2014 ) uses genetic variation to mimic the design of randomised controlled trials (RCT) (although for interpretive caveats see Holmes et al., 2017 ).”
Multivariable Mendelian Randomization and Mediation. (2020, methodology): “There are a number of sensitivity analyses that can be used to try to identify pleiotropic effects in the MR analysis (Chen et al. 2008; Davey Smith and Hemani 2014; Bowden et al. 2015; Bowden et al. 2016; Bowden et al. 2017; Hartwig et al. 2017; Spiller et al. 2018).”