#### Send to
jQuery(document).ready( function () {
jQuery("#send_to_menu input[type='radio']").click( function () {
var selectedValue = jQuery(this).val().toLowerCase();
var selectedDiv = jQuery("#send_to_menu div." + selectedValue);
if(selectedDiv.is(":hidden")){
jQuery("#send_to_menu div.submenu:visible").slideUp();
selectedDiv.slideDown();
}
});
});
jQuery("#sendto").bind("ncbipopperclose", function(){
jQuery("#send_to_menu div.submenu:visible").css("display","none");
jQuery("#send_to_menu input[type='radio']:checked").attr("checked",false);
});

# A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization.

### Author information

- 1
- MRC Integrative Epidemiology Unit, University of Bristol, U.K.
- 2
- Center for Biomedicine, EURAC research, Bolzano, Italy.
- 3
- Population Health and Occupational Disease, NHLI, Imperial College, London, U.K.
- 4
- Department of Health Sciences, University of Leicester, Leicester, U.K.

### Abstract

Mendelian randomization (MR) uses genetic data to probe questions of causality in epidemiological research, by invoking the Instrumental Variable (IV) assumptions. In recent years, it has become commonplace to attempt MR analyses by synthesising summary data estimates of genetic association gleaned from large and independent study populations. This is referred to as two-sample summary data MR. Unfortunately, due to the sheer number of variants that can be easily included into summary data MR analyses, it is increasingly likely that some do not meet the IV assumptions due to pleiotropy. There is a pressing need to develop methods that can both detect and correct for pleiotropy, in order to preserve the validity of the MR approach in this context. In this paper, we aim to clarify how established methods of meta-regression and random effects modelling from mainstream meta-analysis are being adapted to perform this task. Specifically, we focus on two contrastin g approaches: the Inverse Variance Weighted (IVW) method which assumes in its simplest form that all genetic variants are valid IVs, and the method of MR-Egger regression that allows all variants to violate the IV assumptions, albeit in a specific way. We investigate the ability of two popular random effects models to provide robustness to pleiotropy under the IVW approach, and propose statistics to quantify the relative goodness-of-fit of the IVW approach over MR-Egger regression. © 2017 The Authors. Statistics in Medicine Published by JohnWiley & Sons Ltd.

© 2017 The Authors. Statistics in Medicine Published by JohnWiley & Sons Ltd.

#### KEYWORDS:

MR-Egger regression; Mendelian randomization; instrumental variables; meta-analysis; pleiotropy

### Comment in

- Misconceptions on the use of MR-Egger regression and the evaluation of the InSIDE assumption. [Int J Epidemiol. 2017]
- A note on the use of Egger regression in Mendelian randomization studies. [Int J Epidemiol. 2017]

- PMID:
- 28114746
- PMCID:
- PMC5434863
- DOI:
- 10.1002/sim.7221

- [Indexed for MEDLINE]

### Publication type, MeSH terms, Grant support

#### Publication type

#### MeSH terms

- Data Interpretation, Statistical
- Genetic Pleiotropy*
- Humans
- Mendelian Randomization Analysis*
- Meta-Analysis as Topic
- Models, Statistical