Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases

By M. Verbanck, Chia-Yen Chen, Benjamin M. Neale, R. Do. Nature Genetics, 2018

7,287 citations recorded by the source corpus.

Horizontal pleiotropy occurs when the variant has an effect on disease outside of its effect on the exposure in Mendelian randomization (MR). Violation of the ‘no horizontal pleiotropy’ assumption can cause severe bias in MR. We developed the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test to identify horizontal pleiotropic outliers in multi-instrument summary-level MR testing. We showed using simulations that the MR-PRESSO test is best suited when horizontal pleiotropy occurs in <50% of instruments. Next we applied the MR-PRESSO test, along with several other MR tests, to complex traits and diseases and found that horizontal pleiotropy (i) was detectable in over 48% of significant causal relationships in MR; (ii) introduced distortions in the causal estimates in MR that ranged on average from –131% to 201%; (iii) induced false-positive causal relationships in up to 10% of relationships; and (iv) could be corrected in some but not all instances. The MR-PRESSO test detects and corrects horizontal pleiotropy in multi-instrument Mendelian randomization (MR) analyses. Applying the MR-PRESSO test to 4,250 MR tests of complex traits and diseases finds horizontal pleiotropy in >48% of causal relationships.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/457abaf3b42faa2d603c6f69c5396e12e00f52e1

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In Genetics

1. J. Bowden
2. George Davey Smith
3. Gibran Hemani
4. Stephen Burgess
5. Dylan S. Small
6. Qingyuan Zhao
7. Jingshu Wang
8. Philip C. Haycock

Citations in the corpus

• Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score (2018, background): “Addressing the issue of bias due to pleiotropy has attracted lots of attention in the summary-data MR literature [7, 8, 26, 34, 51, 52], but no solid statistical underpinning has yet been given.”
• The MR-Base platform supports systematic causal inference across the human phenome (2018, methodology): “…for correlated SNPs ( Yavorska and Burgess, 2017 ), a method for the detection and correction of outliers in IVW linear regression (MR-PRESSO, Verbanck et al., 2018 ), methods for fitting and visualising radial IVW and radial MR-Egger models ( Bowden et al., 2017b ) and a method for…”
• Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, methodology): “based heterogeneity and outlier test is proposed within MR-PRESSO (61), which performs similarly to modified second-order”
• Mendelian randomization for cardiovascular diseases: principles and applications (2023, neutral): “• MR-PRESSO ( Pleiotropy RESidual Sum and Outlier ): can identify and remove outliers but has a high false-positive rate with several in-valid instrumental variables.”
• Bayesian weighted Mendelian randomization for causal inference based on summary statistics (2018, background): “To name a few, PRESSO has been proposed to handle horizontal pleiotropy (Verbanck et al., 2018).”