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

By Gibran Hemani, K. Tilling, George Davey Smith. PLoS Genetics, 2017

2,215 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/48340f84c7966160da37e0a566a5dc2ed8729397

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In Genetics

  1. J. Bowden
  2. George Davey Smith
  3. Gibran Hemani
  4. Stephen Burgess
  5. Philip C. Haycock
  6. S. Thompson
  7. E. Sanderson
  8. Nicholas J. Timpson

In Molecular Biology

  1. J. Bowden
  2. George Davey Smith
  3. Gibran Hemani
  4. Guangyan Zhou
  5. Zhiqiang Pang
  6. Tom R. Gaunt
  7. Hashem A. Shihab

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