| 1. |
- Haworth, Simon, et al.
(författare)
-
Assessment and visualization of phenome-wide causal relationships using genetic data : an application to dental caries and periodontitis
- 2021
-
Ingår i: European Journal of Human Genetics. - : Springer Nature. - 1018-4813 .- 1476-5438. ; 29, s. 300-308
-
Tidskriftsartikel (refereegranskat)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. 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.
|
|
| 2. |
- Haworth, Simon, et al.
(författare)
-
Inference and visualization of phenome-wide causal relationships using genetic data : an application to dental caries and periodontitis
- 2019
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background: 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 or complicated by horizontal pleiotropy. The recently described latent causal variable (LCV) approach provides an alternative method for casual inference which might be useful in hypothesis-free experiments.Methods: 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 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.Results: The 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.Conclusions: The 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.
|
|
| 3. |
- Ong, Jue-Sheng, et al.
(författare)
-
Assessment of moderate coffee consumption and risk of epithelial ovarian cancer : a Mendelian randomization study
- 2018
-
Ingår i: International Journal of Epidemiology. - : Oxford University Press (OUP). - 1464-3685 .- 0300-5771. ; 47:2, s. 450-459
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Coffee consumption has been shown to be associated with various health outcomes in observational studies. However, evidence for its association with epithelial ovarian cancer (EOC) is inconsistent and it is unclear whether these associations are causal.Methods: We used single nucleotide polymorphisms associated with (i) coffee and (ii) caffeine consumption to perform Mendelian randomization (MR) on EOC risk. We conducted a two-sample MR using genetic data on 44 062 individuals of European ancestry from the Ovarian Cancer Association Consortium (OCAC), and combined instrumental variable estimates using a Wald-type ratio estimator.Results: For all EOC cases, the causal odds ratio (COR) for genetically predicted consumption of one additional cup of coffee per day was 0.92 [95% confidence interval (CI): 0.79, 1.06]. The COR was 0.90 (95% CI: 0.73, 1.10) for high-grade serous EOC. The COR for genetically predicted consumption of an additional 80 mg caffeine was 1.01 (95% CI: 0.92, 1.11) for all EOC cases and 0.90 (95% CI: 0.73, 1.10) for high-grade serous cases.Conclusions: We found no evidence indicative of a strong association between EOC risk and genetically predicted coffee or caffeine levels. However, our estimates were not statistically inconsistent with earlier observational studies and we were unable to rule out small protective associations.
|
|
