Introduction

In this chapter, we consider extensions to a simple Mendelian randomization analysis to include data from multiple studies. We provide methods for combining the information provided by each study in an efficient way to produce a single causal estimate. Also, we consider how to combine summarized data on genetic associations from multiple variants in a single study.

Key points from chapter

  • A pooled causal effect estimate can be obtained by combining study-level, summary-level or individual-level data.
  • A single causal effect can be estimated from published data on genetic associations with the exposure and with the outcome, either taken from a single study or from separate sources.
  • If the same genetic variants have been measured in several studies, the parameters of genetic association can be pooled in a hierarchical model across studies.
  • Studies with common genetic variants can contribute to a pooled causal effect estimate even if data on one of the exposure or the outcome has not been measured.

Relevant papers to chapter:

S. Burgess, S.G. Thompson, CRP CHD Genetics Collaboration. Methods for meta-analysis of individual participant data from Mendelian randomization studies with binary outcomes. Stat. Meth. Med. Res. 2012.

S. Burgess, S.G. Thompson, CRP CHD Genetics Collaboration. Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables. Statist. Med. 2010; 29(12):1298-1311.

Section 9.4 (Summary-level meta-analysis). S. Burgess, A. Butterworth, S.G. Thompson. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 2013; 32(27):4726-4747.

Section 9.4 (Summary-level meta-analysis) and Section 9.8.2 (Two-sample Mendelian randomization). S. Burgess, R.A. Scott, N.J. Timpson, G. Davey Smith. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur. J. Epidemiol. 2015.

Section 9.8.2 (Two-sample Mendelian randomization). B.L. Pierce, S. Burgess. Efficient design for Mendelian randomization studies: subsample and two-sample instrumental variable estimators. Am. J. Epidemiol. 2013; 178(7):1177-1184.