Assessment and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis

Abstract

Hypothesis-free Mendelian randomization studies provide a way to assess the causal relevance of a trait across the human phenome but can be limited by statistical power, sample overlap or complicated by horizontal pleiotropy. The recently described latent causal variable (LCV) approach provides an alternative method for causal inference which might be useful in hypothesis-free experiments across human phenome. We developed an automated pipeline for phenome-wide tests using the LCV approach including steps to estimate partial genetic causality, filter to a meaningful set of estimates, apply correction for multiple testing and then present the findings in a graphical summary termed causal architecture plot. We apply this pipeline to body mass index (BMI) and lipid traits as exemplars of traits where there is strong prior expectation for causal effects, and to dental caries and periodontitis as exemplars of traits where there is a need for causal inference. The results for lipids and BMI suggest that these traits are best viewed as contributing factors on a multitude of traits and conditions, thus providing additional evidence that supports viewing these traits as targets for interventions to improve health. On the other hand, caries and periodontitis are best viewed as a downstream consequence of other traits and diseases rather than a cause of ill health. The automated pipeline is implemented in the Complex-Traits Genetics Virtual Lab (https://vl.genoma.io) and results are available in https://view.genoma.io. We propose causal architecture plots based on phenome-wide partial genetic causality estimates as a new way visualizing the overall causal map of the human phenome.

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Fig. 1: Use of a latent variable for causal inference.
Fig. 2: Interpretation of causal architecture plots.
Fig. 3: Comparison of the causal architecture of lipid traits.
Fig. 4: Comparison of the causal architecture of BMI, DMFS/dentures (caries), and periodontitis.

Data availability

No participant-level data were accessed to produce this article. The sources of GWAS summary statistics and reference used to perform analysis are described in full in the methods. Links and references for specific datasets are available at https://view.genoma.io.

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Acknowledgements

SH is funded by a National Institute for Health Research (NIHR) Academic Clinical Fellowship. MER is funded NHMRC and Australian Research Council (ARC), through a NHMRC-ARC Dementia Research Development Fellowship (GNT1102821). PFK is funded by an Australian Government Research Training Program Ph.D. Scholarship and QIMR Berghofer Postgraduate Top-Up Scholarship. GCP is funded by an Australia Research Council Discovery Early Career Researcher Award (DE180100976).

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Correspondence to Gabriel Cuellar-Partida.

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GC-P contributed to this study while employed by the University of Queensland, but he is now an employee of 23andMe Inc. He still maintains CTG-VL along PFK, MER and LDH. All other authors declare no conflict of interest.

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Haworth, S., Kho, P.F., Holgerson, P.L. et al. Assessment and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis. Eur J Hum Genet (2020). https://doi.org/10.1038/s41431-020-00734-4

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