John R. Thompson

Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome

Mendelian randomisation (MR) estimates causal effects of modifiable phenotypes on an outcome by using genetic variants as instrumental variables, but its validity relies on the ...

Using pseudo‐data to correct for publication bias in meta‐analysis

Abstract In many ways, adjustment for publication bias in meta‐analysis parallels adjustment for ascertainment bias in genetic studies. We investigate a previously published sim...

A framework for the investigation of pleiotropy in two‐sample summary data Mendelian randomization

Mendelian randomization (MR) uses genetic data to probe questions of causality in epidemiological research, by invoking the Instrumental Variable (IV) assumptions. In recent yea...

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

Abstract Background Two-sample summary data Mendelian randomization (MR) incorporating multiple genetic variants within a meta-analysis framework is a popular technique for asse...

Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic

: Care must be taken to assess the NOME assumption via the IGX2 statistic before implementing standard MR-Egger regression in the two-sample summary data context. If IGX2 is suf...

Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors

Improving the visualisation, interpretation and analysis of two-sample summary data Mendelian randomization via the radial plot and radial regression

Abstract Background Summary data furnishing a two-sample Mendelian randomization study are often visualized with the aid of a scatter plot, in which single nucleotide polymorphi...