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Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption.

Abstract：

Background:Mendelian randomization (MR) is being increasingly used to strengthen causal inference in observational studies. Availability of summary data of genetic associations for a variety of phenotypes from large genome-wide association studies (GWAS) allows straightforward application of MR using summary data methods, typically in a two-sample design. In addition to the conventional inverse variance weighting (IVW) method, recently developed summary data MR methods, such as the MR-Egger and weighted median approaches, allow a relaxation of the instrumental variable assumptions. Methods:Here, a new method - the mode-based estimate (MBE) - is proposed to obtain a single causal effect estimate from multiple genetic instruments. The MBE is consistent when the largest number of similar (identical in infinite samples) individual-instrument causal effect estimates comes from valid instruments, even if the majority of instruments are invalid. We evaluate the performance of the method in simulations designed to mimic the two-sample summary data setting, and demonstrate its use by investigating the causal effect of plasma lipid fractions and urate levels on coronary heart disease risk. Results:The MBE presented less bias and lower type-I error rates than other methods under the null in many situations. Its power to detect a causal effect was smaller compared with the IVW and weighted median methods, but was larger than that of MR-Egger regression, with sample size requirements typically smaller than those available from GWAS consortia. Conclusions:The MBE relaxes the instrumental variable assumptions, and should be used in combination with other approaches in sensitivity analyses.

journal_name

Int J Epidemiol

journal_title

International journal of epidemiology

authors

Hartwig FP,Davey Smith G,Bowden J

doi

10.1093/ije/dyx102

subject

Has Abstract

pub_date

2017-12-01 00:00:00

pages

1985-1998

issue

6

eissn

0300-5771

issn

1464-3685

pii

3957932

journal_volume

46

pub_type

杂志文章

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