Aims/hypothesis The aim of this study was to use Mendelian randomisation (MR) to identify
the causal risk factors for type 2 diabetes. Methods We first conducted a review of meta …

Causal inference is essential across the biomedical, behavioural and social sciences. By
progressing from confounded statistical associations to evidence of causal relationships …

This paper provides guidelines for performing Mendelian randomization investigations. It is
aimed at practitioners seeking to undertake analyses and write up their findings, and at …

Host genetics are known to influence the gut microbiome, yet their role remains poorly
understood. To robustly characterize these effects, we performed a genome-wide …

Mendelian randomisation uses genetic variation as a natural experiment to investigate the
causal relations between potentially modifiable risk factors and health outcomes in …

Abstract Mendelian randomization-Egger (MR-Egger) is an analysis method for Mendelian
randomization using summarized genetic data. MR-Egger consists of three parts:(1) a test …

With the increasing availability of large-scale GWAS summary data on various traits,
Mendelian randomization (MR) has become commonly used to infer causality between a …

MendelianRandomization is a software package for the R open-source software
environment that performs Mendelian randomization analyses using summarized data. The …

Mendelian randomization (MR) is an epidemiological technique that uses genetic variants to
distinguish correlation from causation in observational data. The reliability of a MR …

Mendelian randomization analyses are often performed using summarized data. The causal
estimate from a one‐sample analysis (in which data are taken from a single data source) …