TY - JOUR
T1 - Searching for causal relationships of glioma
T2 - a phenome-wide Mendelian randomisation study
AU - Saunders, Charlie N
AU - Cornish, Alex J
AU - Kinnersley, Ben
AU - Law, Philip J
AU - Houlston, Richard S
AU - Collaborators
A2 - Johansen, Christoffer
PY - 2021/1/19
Y1 - 2021/1/19
N2 - 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.
AB - 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.
KW - Brain Neoplasms/genetics
KW - Genetic Variation
KW - Genome-Wide Association Study
KW - Glioma/genetics
KW - Humans
KW - Mendelian Randomization Analysis
KW - Risk Factors
UR - http://www.scopus.com/inward/record.url?scp=85092192018&partnerID=8YFLogxK
U2 - 10.1038/s41416-020-01083-1
DO - 10.1038/s41416-020-01083-1
M3 - Journal article
C2 - 33020596
SN - 0007-0920
VL - 124
SP - 447
EP - 454
JO - British Journal of Cancer
JF - British Journal of Cancer
IS - 2
ER -