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

Abstract

BackgroundHypothesis-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 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. MethodsWe 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 a causal architecture plot. We apply this process to body mass index and lipid traits as exemplars of traits where there is strong prior expectation for causal effects and dental caries and periodontitis as exemplars of traits where there is a need for causal inference. ResultsThe results for lipids and BMI suggest that these traits are best viewed as creating consequences 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. ConclusionsThe automated process is available as part of the MASSIVE pipeline from 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 way visualizing the overall causal map of the human phenome. Key messages1) The latent causal variable approach uses summary statistics from genome-wide association studies to estimate a parameter termed genetic causality proportion.2) Systematic estimation of genetic causality proportion for many pairs of traits provides an alternative method for phenome-wide causal inference with some theoretical and practical advantages compared to phenome-wide Mendelian randomization.3) Using this approach, we confirm that lipid traits are an upstream risk factor for other traits and diseases, and we identify that dental diseases are predominantly a downstream consequence of other traits rather than a cause of poor systemic health.4) The method assumes no bidirectional causality and no confounding by environmental correlates of genotypes, so care is needed when these assumptions are not met. 5)We developed an automated and accessible pipeline for estimating phenome-wide causal relationships and generating interactive visual summaries.

Is periodontitis a risk factor for ischaemic stroke, coronary artery disease and subclinical atherosclerosis? A Mendelian randomization study

“…Periodontitis and cardiovascular disease are both multifactorial diseases, manifesting in individuals following many years of exposure to risk factors. Therefore, it is possible that the observed association between the two diseases is confounded by a range of other influences [ 49 ]. For example, smoking is an important risk factor shared by both periodontitis and cardiovascular disease [ 50 , 51 ].…”

Inference of causal relationships between sleep-related traits and 1,527 phenotypes using genetic data

“…22 If the genetic correlation is nominally significant, then a latent variable L is introduced into the model to assess causality between trait A and trait B, assuming that L is the causal component that mediates the genetic correlation between both traits (see below). 17,18 We corrected for multiple testing using Benjamini-Hochberg’s False Discovery Rate (FDR < 5%).…”

“…Therefore, care is required when these assumptions are not met. 18 Moreover, the most attractive features of this method include that it is robust to sample overlap, has higher statistical power than MR and is unconfounded by horizontal pleiotropy. 18 Multiple testing in GCP was corrected for using Benjamini-Hochberg’s False Discovery Rate (FDR < 5%).…”

“…Values between 0 and 1 or 0 and -1 would indicate partial genetic causality. 17,18 Although causality is often thought of as a binary characteristic, the idea of partial genetic causality is consistent with both a causal relationship between two complex traits and the notion of distinct genetic components underlying complex traits (e.g. a disease could be caused by both, direct genetic effects, and genetic predisposition to one or more environmental exposures).…”

“…Besides pleiotropy, MR can also become limited by weakly-powered discovery GWAS studies, which increase the likelihood of false-positive findings. 17,18 Given the limitations of MR, alternative methods such as Latent Causal Variable (LCV) have been developed. 17 LCV mediates the genetic correlation between two traits through a latent variable that has a causal effect on each trait.…”