Imperial College London
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DrCosettaMinelli

Faculty of Medicine, National Heart & Lung Institute

Emeritus Reader in Medical Statistics
 
 
 
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Contact

 

cosetta.minelli1 Website

 
 
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Location

 

G 49Emmanuel Kaye BuildingRoyal Brompton Campus

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Summary

 

Publications

Citation

BibTex format

@article{Minelli:2021:ije/dyab084,
author = {Minelli, C and Del, Greco FM and van, der Plaat DA and Bowden, J and Sheehan, NA and Thompson, J},
doi = {ije/dyab084},
journal = {International Journal of Epidemiology},
pages = {1651--1659},
title = {The use of two-sample methods for Mendelian randomization analyses on single large datasets},
url = {http://dx.doi.org/10.1093/ije/dyab084},
volume = {50},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BackgroundWith genome-wide association data for many exposures and outcomes now available from large biobanks, one-sample Mendelian randomization (MR) is increasingly used to investigate causal relationships. Many robust MR methods are available to address pleiotropy, but these assume independence between the gene-exposure and gene-outcome association estimates. Unlike in two-sample MR, in one-sample MR the two estimates are obtained from the same individuals, and the assumption of independence does not hold in the presence of confounding.MethodsWith simulations mimicking a typical study in UK Biobank, we assessed the performance, in terms of bias and precision of the MR estimate, of the fixed-effect and (multiplicative) random-effects meta-analysis method, weighted median estimator, weighted mode estimator and MR-Egger regression, used in both one-sample and two-sample data. We considered scenarios differing by the: presence/absence of a true causal effect; amount of confounding; and presence and type of pleiotropy (none, balanced or directional).ResultsEven in the presence of substantial correlation due to confounding, all two-sample methods used in one-sample MR performed similarly to when used in two-sample MR, except for MR-Egger which resulted in bias reflecting direction and magnitude of the confounding. Such bias was much reduced in the presence of very high variability in instrument strength across variants ( of 97%).ConclusionsTwo-sample MR methods can be safely used for one-sample MR performed within large biobanks, expect for MR-Egger. MR-Egger is not recommended for one-sample MR unless the correlation between the gene-exposure and gene-outcome estimates due to confounding can be kept low, or the variability in instrument strength is very high.
AU  - Minelli,C
AU  - Del,Greco FM
AU  - van,der Plaat DA
AU  - Bowden,J
AU  - Sheehan,NA
AU  - Thompson,J
DO  - ije/dyab084
EP  - 1659
PY  - 2021///
SN  - 0300-5771
SP  - 1651
TI  - The use of two-sample methods for Mendelian randomization analyses on single large datasets
T2  - International Journal of Epidemiology
UR  - http://dx.doi.org/10.1093/ije/dyab084
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000728182600030&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://academic.oup.com/ije/article/50/5/1651/6252978
UR  - http://hdl.handle.net/10044/1/108065
VL  - 50
ER  -
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