Causal effect estimation from data simulating instrument correlations, instrument strength, and horizontal pleiotropy.

Causal effect estimation from data simulating instrument correlations, instrument strength, and horizontal pleiotropy.

<p><b>(A)</b> Causal diagram for the simulation of two exposures <i>X</i><sub>1</sub> and <i>X</i><sub>2</sub> for an outcome <i>Y</i>, with two shared instruments <i>E</i><sub>1</sub> and <i>...

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Bibliographic Details
Main Author: Mariyam Khan (20161530) (author)
Other Authors: Adriaan-Alexander Ludl (9321107) (author), Sean Bankier (15152851) (author), Johan L. M. Björkegren (7759127) (author), Tom Michoel (29810) (author)
Published: 2024
Subjects:
Medicine
Genetics
Molecular Biology
Virology
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
variance small even
structural equation models
multivariate mendelian randomization
linkage disequilibrium ),
coronary artery disease
individual causal effects
direct causal effects
confirm causal roles
pleiotropic regulatory effects
extensive simulations confirmed
predicting causal genes
wide significant loci
disease risk loci
multiple nearby genes
multiple colocated genes
multiple exposures collectively
locus still require
div >< p
>- eqtl associations
predict causal gene
instrumental set condition
correlated instruments satisfies
considering risk loci
causal genes
multiple exposures
causal variants
multiple tissues
candidate genes
regulatory pleiotropy
instrumental variables
highly correlated
extensive degree
twelve loci
gwas loci
genomic loci
within tissues
theoretical results
statistical technique
starnet study
standard mr
single model
seven vascular
phactr1 </
metabolic tissues
limited number
horizontal pleiotropy
given locus
genetic variants
genetic instruments
field dictates
classical result
cis </
bias introduced
arterial tissues
among others
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