5 citations · 13 across the 10 of their papers we have counts for
10 papers
Bayesian Mendelian randomization testing of interval causal null hypotheses: ternary decision rules and loss function calibration
Linyi Zou, Teresa Fazia, Hui Guo +1
Our approach to Mendelian Randomization (MR) analysis is designed to increase reproducibility of causal effect "discoveries" by: (i) using a Bayesian approach to inference; (ii) re…
Discovery methods for systematic analysis of causal molecular networks in modern omics datasets
Jack Kelly, Carlo Berzuini, Bernard Keavney +2
With the increasing availability and size of multi-omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring u…
Bayesian Mendelian randomization with study heterogeneity and data partitioning for large studies
Linyi Zou, Hui Guo, Carlo Berzuini
Background: Mendelian randomization (MR) is a useful approach to causal inference from observational studies when randomised controlled trials are not feasible. However, study hete…
Overlapping-sample Mendelian randomisation with multiple exposures: A Bayesian approach
Linyi Zou, Hui Guo, Carlo Berzuini
Background: Mendelian randomization (MR) has been widely applied to causal inference in medical research. It uses genetic variants as instrumental variables (IVs) to investigate pu…
Mendelian Randomization with Incomplete Exposure Data: a Bayesian Approach
Teresa Fazia, Leonardo Egidi, Burcu Ayoglu +9
We expand Mendelian Randomization (MR) methodology to deal with randomly missing data on either the exposure or the outcome variable, and furthermore with data from nonindependent…
Bayesian Mendelian Randomization identifies disease causing proteins via pedigree data, partially observed exposures and correlated instruments
Teresa Fazia, Leonardo Egidi, Burcu Ayoglu +7
Background In a study performed on multiplex Multiple Sclerosis (MS) Sardinian families to identify disease causing plasma proteins, application of Mendelian Randomization (MR) met…
Bayesian Mendelian Randomization
Carlo Berzuini, Hui Guo, Stephen Burgess +1
Our Bayesian approach to Mendelian Randomisation uses multiple instruments to assess the putative causal effect of an exposure on an outcome. The approach is robust to violations o…
Stochastic Mechanistic Interaction
Carlo Berzuini, A. Philip Dawid
We propose a fully probabilistic formulation of the notion of mechanistic interaction (interaction in some fundamental mechanistic sense) between the effects of putative (possibly…
Direct genetic effects and their estimation from matched case-control data
Carlo Berzuini, Stijn Vansteelandt, Luisa Foco +2
In genetic association studies, a single marker is often associated with multiple, correlated phenotypes (e.g., obesity and cardiovascular disease, or nicotine dependence and lung…
Deep determinism and the assessment of mechanistic interaction between categorical and continuous variables
Carlo Berzuini, A. Philip Dawid
Our aim is to detect mechanistic interaction between the effects of two causal factors on a binary response, as an aid to identifying situations where the effects are mediated by a…