Orienting the causal relationship between imprecisely measured traits using GWAS summary data

Inference about the causal structure that induces correlations between two traits can be achieved by combining genetic associations with a mediation-based approach, as is done in the causal inference test (CIT). However, we show that measurement error in the phenotypes can lead to the CIT inferring...

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Bibliographic Details
Published in:PLoS genetics Vol. 13; no. 11; p. e1007081
Main Authors: Hemani, Gibran, Tilling, Kate, Davey Smith, George
Format: Journal Article
Language:English
Published: United States Public Library of Science 17.11.2017
Public Library of Science (PLoS)
Subjects:
Analysis
Bias
Biology and life sciences
Computer and Information Sciences
DNA Methylation
Gene expression
Gene Expression Regulation
Genetic Association Studies - methods
Genetic research
Genome-Wide Association Study - methods
Genomics
Measurement
Mendelian Randomization Analysis - methods
Methods
Methylation
Phenotype
Physical Sciences
Research and Analysis Methods
Sample Size
Social Sciences
ISSN:1553-7404, 1553-7390, 1553-7404
Online Access:Get full text
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Summary:Inference about the causal structure that induces correlations between two traits can be achieved by combining genetic associations with a mediation-based approach, as is done in the causal inference test (CIT). However, we show that measurement error in the phenotypes can lead to the CIT inferring the wrong causal direction, and that increasing sample sizes has the adverse effect of increasing confidence in the wrong answer. This problem is likely to be general to other mediation-based approaches. Here we introduce an extension to Mendelian randomisation, a method that uses genetic associations in an instrumentation framework, that enables inference of the causal direction between traits, with some advantages. First, it can be performed using only summary level data from genome-wide association studies; second, it is less susceptible to bias in the presence of measurement error or unmeasured confounding. We apply the method to infer the causal direction between DNA methylation and gene expression levels. Our results demonstrate that, in general, DNA methylation is more likely to be the causal factor, but this result is highly susceptible to bias induced by systematic differences in measurement error between the platforms, and by horizontal pleiotropy. We emphasise that, where possible, implementing MR and appropriate sensitivity analyses alongside other approaches such as CIT is important to triangulate reliable conclusions about causality.
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The authors have declared that no competing interests exist.
ISSN:1553-7404
1553-7390
1553-7404
DOI:10.1371/journal.pgen.1007081