Abstract

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.

Keywords

Mendelian randomizationPleiotropyBiologyGeneticsTraitMendelian inheritanceOutlierEvolutionary biologyStatisticsPhenotypeMathematicsGenetic variantsGeneComputer scienceGenotype

MeSH Terms

DiseaseGenetic PleiotropyGenetic Predisposition to DiseaseGenetic VariationHumans

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Publication Info

Year
2018
Type
article
Volume
50
Issue
5
Pages
693-698
Citations
8490
Access
Closed

Citation Metrics

8490
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898
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Cite This

Marie Verbanck, Chia‐Yen Chen, Benjamin M. Neale et al. (2018). Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nature Genetics , 50 (5) , 693-698. https://doi.org/10.1038/s41588-018-0099-7

Identifiers

DOI
10.1038/s41588-018-0099-7
PMID
29686387
PMCID
PMC6083837

Data Quality

Data completeness: 86%