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. 2021 Jan;124(2):447-454.
doi: 10.1038/s41416-020-01083-1. Epub 2020 Oct 6.

Searching for causal relationships of glioma: a phenome-wide Mendelian randomisation study

Charlie N Saunders  1 , Alex J Cornish  2 , Ben Kinnersley  2 , Philip J Law  2 , Richard S Houlston  2 ; Collaborators
Collaborators, Affiliations

Searching for causal relationships of glioma: a phenome-wide Mendelian randomisation study

Charlie N Saunders et al. Br J Cancer. 2021 Jan.

Abstract

Background: The aetiology of glioma is poorly understood. Summary data from genome-wide association studies (GWAS) can be used in a Mendelian randomisation (MR) phenome-wide association study (PheWAS) to search for glioma risk factors.

Methods: We performed an MR-PheWAS analysing 316 phenotypes, proxied by 8387 genetic variants, and summary genetic data from a GWAS of 12,488 glioma cases and 18,169 controls. Causal effects were estimated under a random-effects inverse-variance-weighted (IVW-RE) model, with robust adjusted profile score (MR-RAPS), weighted median and mode-based estimates computed to assess the robustness of findings. Odds ratios per one standard deviation increase in each phenotype were calculated for all glioma, glioblastoma (GBM) and non-GBM tumours.

Results: No significant associations (P < 1.58 × 10-4) were observed between phenotypes and glioma under the IVW-RE model. Suggestive associations (1.58 × 10-4 < P < 0.05) were observed between leukocyte telomere length (LTL) with all glioma (ORSD = 3.91, P = 9.24 × 10-3) and GBM (ORSD = 4.86, P = 3.23 × 10-2), but the association was primarily driven by the TERT variant rs2736100. Serum low-density lipoprotein cholesterol and plasma HbA1C showed suggestive associations with glioma (ORSD = 1.11, P = 1.39 × 10-2 and ORSD = 1.28, P = 1.73 × 10-2, respectively), both associations being reliant on single genetic variants.

Conclusions: Our study provides further insight into the aetiological basis of glioma for which published data have been mixed.

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Conflict of interest statement

R.S.H. is a subject editor and a member of the Editorial Board of the BJC. The remaining authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Principles of Mendelian randomisation (MR) and the assumptions required to obtain an unbiased causal effect estimate.
The three assumptions are (1) genetic variants used as instrumental variables are only associated with the modifiable risk factor (X), (2) genetic variants are not associated with any measured or unmeasured confounders and (3) genetic variants only influence the risk of developing glioma (Y) through the modifiable risk factor (X). SNP single-nucleotide polymorphism.
Fig. 2
Fig. 2. Frequency histogram of percentage of variance explained (PVE).
This plot shows the PVE of single-nucleotide polymorphisms (SNP) used as instrumental variables for the 316 phenotypes.
Fig. 3
Fig. 3. Volcano plot of the odds ratio per standard deviation from random-effects inverse-variance-weighted (IVW) Mendelian randomisation analysis of 316 phenotypes with risk of all glioma.
Top dashed grey line corresponds to a Bonferroni-corrected P value of −log10 P value of 3.80 (1.58 × 10−4), indicating significant association. Bottom dashed grey line corresponds to −log10 P value of 1.30 (0.05), indicating a suggestive association. Vertical dashed grey lines correspond to log (OR = 1.5) and –log(OR = 1.5).
Fig. 4
Fig. 4. Volcano plot of the odds ratio per standard deviation from random-effects inverse-variance-weighted (IVW) Mendelian randomisation analysis of the 316 phenotypes with GBM risk.
Top dashed grey line corresponds to a Bonferroni-corrected P value of −log10 P value of 3.80 (P = 1.58 × 10−4), indicating significant association. Bottom dashed grey line corresponds to −log10 P value of 1.30 (P = 0.05), indicating a suggestive association. Vertical dashed grey lines correspond to log (OR = 1.5) and –log (OR = 1.5).
Fig. 5
Fig. 5. Volcano plot of the odds ratio per standard deviation from random-effects inverse-variance-weighted (IVW) Mendelian randomisation analysis of the 316 phenotypes with non-GBM risk.
Top dashed grey line corresponds to a Bonferroni-corrected P value of −log10 P value of 3.80 (P = 1.58 × 10−4), indicating significant association. Bottom dashed grey line corresponds to −log10 P value of 1.30 (P = 0.05) indicating a suggestive association. Vertical dashed grey lines correspond to log (OR = 1.5) and –log (OR = 1.5).
Fig. 6
Fig. 6. Forest plot showing the effect of alleles associated with longer leukocyte telomere length on all glioma risk.
Diamonds represents overall causal effects estimated using both random and fixed-effects inverse-variance-weighted (IVW) models, both with and without the TERT SNP (rs2736100). Confidence intervals indicated by diamond width. Vertical line denotes the null value (ORSD = 1).
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