Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption | Zendy

Jack Bowden | Zendy; Fabiola Del Greco M | Zendy; Cosetta Minelli | Zendy; Qingyuan Zhao | Zendy; Debbie A. Lawlor | Zendy; Nuala A. Sheehan | Zendy; John R. Thompson | Zendy; George Davey Smith | Zendy

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Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption

Author(s) -

Jack Bowden,

Fabiola Del Greco M,

Cosetta Minelli,

Qingyuan Zhao,

Debbie A. Lawlor,

Nuala A. Sheehan,

John R. Thompson,

George Davey Smith

Publication year - 2018

Publication title -

international journal of epidemiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.406

H-Index - 208

eISSN - 1464-3685

pISSN - 0300-5771

DOI - 10.1093/ije/dyy258

Subject(s) - mendelian randomization , econometrics , statistics , sample size determination , weighting , mathematics , variance (accounting) , regression , instrumental variable , causality (physics) , computer science , genetic variants , biology , genetics , medicine , physics , accounting , radiology , quantum mechanics , gene , genotype , business

Two-sample summary-data Mendelian randomization (MR) incorporating multiple genetic variants within a meta-analysis framework is a popular technique for assessing causality in epidemiology. If all genetic variants satisfy the instrumental variable (IV) and necessary modelling assumptions, then their individual ratio estimates of causal effect should be homogeneous. Observed heterogeneity signals that one or more of these assumptions could have been violated.

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