1. Genetics and Genomics

Evaluating the effect of metabolic traits on oral and oropharyngeal cancer risk using Mendelian randomization

  1. Mark Gormley  Is a corresponding author
  2. Tom Dudding
  3. Steven J Thomas
  4. Jessica Tyrrell
  5. Andrew R Ness
  6. Miranda Pring
  7. Danny Legge
  8. George Davey Smith
  9. Rebecca C Richmond
  10. Emma E Vincent
  11. Caroline Bull
  1. University of Bristol, United Kingdom
  2. University of Exeter, United Kingdom
https://doi.org/10.7554/eLife.82674
Share this article
Cite this article
  1. Mark Gormley
  2. Tom Dudding
  3. Steven J Thomas
  4. Jessica Tyrrell
  5. Andrew R Ness
  6. Miranda Pring
  7. Danny Legge
  8. George Davey Smith
  9. Rebecca C Richmond
  10. Emma E Vincent
  11. Caroline Bull
(2023)
Evaluating the effect of metabolic traits on oral and oropharyngeal cancer risk using Mendelian randomization
eLife 12:e82674.
https://doi.org/10.7554/eLife.82674
  2. Download BibTeX
  3. Download .RIS

Abstract

A recent World Health Organization report states that at least 40% of all cancer cases may be preventable, with smoking, alcohol consumption and obesity identified as three of the most important modifiable lifestyle factors. Given the significant decline in smoking rates, particularly within developed countries, other potentially modifiable risk factors for head and neck cancer warrant investigation. Obesity and related metabolic disorders such as type 2 diabetes and hypertension have been associated with head and neck cancer risk in multiple observational studies. However, adiposity has also been correlated with smoking, with bias, confounding or reverse causality possibly explaining these findings. To overcome the challenges of observational studies, we conducted two-sample Mendelian randomization (inverse variance weighted (IVW) method) using genetic variants which were robustly associated with adiposity, glycaemic and blood pressure traits in genome-wide association studies (GWAS). Outcome data was taken from the largest available GWAS of 6,034 oral and oropharyngeal cases, with 6,585 controls. We found limited evidence of a causal effect of genetically proxied body mass index (OR IVW = 0.89, 95%CI 0.72-1.09, p = 0.26 per 1 SD in BMI (4.81 kg/m2)) on oral and oropharyngeal cancer risk. Similarly, there was limited evidence for related traits including type 2 diabetes and hypertension.

Data availability

Summary-level analysis was conducted using publicly available GWAS data as cited. Full summary statistics for the GAME-ON outcome data GWAS can be accessed via dbGAP (OncoArray: Oral and Pharynx Cancer; study accession number: phs001202.v1.p1, August 2017) at: https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001202.v1.p1) (Lesseur et al., 2016). This data is also available via the IEU OpenGWAS project (https://gwas.mrcieu.ac.uk/).All exposure data used in this study is publicly available from the relevant studies as described below. Data for BMI, WC and WHR GWAS was downloaded from the Genetic Investigation of ANthropometric Traits (GIANT) consortiumhttps://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files (Pulit et al., 2019; Shungin et al., 2015) and UK Biobank (http://www.ukbiobank.ac.uk). T2D data was downloaded from the DIAMANTE (DIAbetes Meta-ANalysis of Trans-Ethnic association studies) consortium from: https://kp4cd.org/node/169 (Vujkovic et al., 2020). Data for FG, FI and HbA1c, were obtained from GWAS published by the MAGIC (Meta-Analyses of Glucose and Insulin-Related Traits) Consortium, available for download from: https://magicinvestigators.org/downloads/ (Lagou et al., 2021),. Finally, data for SBP and DBP were extracted from a GWAS meta-analysis of participants in UK Biobank (and UK Biobank (http://www.ukbiobank.ac.uk) and the International Consortium of Blood Pressure Genome Wide Association Studies (ICBP), available via dbGAP (International Consortium for Blood Pressure (ICBP), study accession number: phs000585.v2.p1, October 2016) at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000585.v2.p1 (Evangelou et al., 2018).Instrument-risk factor analysis outcome summary-level data were derived from the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN) and UK Biobank and UK Biobank (http://www.ukbiobank.ac.uk) for alcoholic drinks per week https://conservancy.umn.edu/handle/11299/201564 (Liu et al., 2019) and the comprehensive smoking index (Wootton et al., 2019). Data for risk tolerance and educational attainment were taken from Social Science Genetic Association Consortium (SSGAC) data available from http://www.thessgac.org/data (Karlsson Linner et al., 2019; J. Lee et al., 2018). MR analyses were conducted using the 'TwoSampleMR' package in R (version 3.5.3). A copy of the code and all data files used in this study are available at GitHub (https://github.com/MGormley12/metabolic_trait_hnc_mr.git).

The following previously published data sets were used
    1. Vujkovic M
    2. et al
    (2020) DIAMANTE (European) T2D GWAS
    DIAMANTE (DIAbetes Meta-ANalysis of Trans-Ethnic association studies).
    https://kp4cd.org/node/169

Article and author information

Author details

  1. Mark Gormley

    MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
    For correspondence
    mark.gormley@bristol.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5733-6304

  2. Tom Dudding

    Bristol Dental Hospital and School, University of Bristol, Bristol, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  3. Steven J Thomas

    Bristol Dental Hospital and School, University of Bristol, Bristol, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  4. Jessica Tyrrell

    Medical School, University of Exeter, Exeter, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  5. Andrew R Ness

    University Hospitals Bristol and Weston NHS Foundation Trust, University of Bristol, Bristol, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  6. Miranda Pring

    Bristol Dental Hospital and School, University of Bristol, Bristol, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  7. Danny Legge

    Translational Health Sciences, University of Bristol, Bristol, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3897-5861

  8. George Davey Smith

    MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1407-8314

  9. Rebecca C Richmond

    MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  10. Emma E Vincent

    MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8917-7384

  11. Caroline Bull

    MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2176-5120

Funding

Wellcome Trust (220530/Z/20/Z)

  • Mark Gormley

Diabetes UK (SBF004\1079)

  • Jessica Tyrrell

National Institute for Health and Care Research (RP-PG-0707-10034)

  • Andrew R Ness

Cancer Research UK (C18281/A20919)

  • Andrew R Ness

National Institute of Dental and Craniofacial Research (R01 DE025712 and 1X01HG007780-0)

  • Andrew R Ness

Diabetes UK (17/0005587)

  • Emma E Vincent

World Cancer Research Fund (IIG_2019_2009)

  • Emma E Vincent

Medical Research Council (MC_UU_00011/1,MC_UU_00011/5,MC_UU_00011/6,MC_UU_00011/7)

  • George Davey Smith

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: Publicly available summary level data only used in this study. Application entitled "Investigating aetiology, associations and causality in diseases of the head and neck" (Project ID: 40644) covers use of all UK Biobank data in this study and dbGaP application made for accessing OncoArray: Oral and Pharynx Cancer; study accession number: phs001202.v1.p1 data entitled "Investigating risk factors in head and neck cancer using Mendelianrandomization" (Project ID 24266).

Copyright

© 2023, Gormley et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,189
    views
  • 168
    downloads
  • 18
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Mark Gormley
  2. Tom Dudding
  3. Steven J Thomas
  4. Jessica Tyrrell
  5. Andrew R Ness
  6. Miranda Pring
  7. Danny Legge
  8. George Davey Smith
  9. Rebecca C Richmond
  10. Emma E Vincent
  11. Caroline Bull
(2023)
Evaluating the effect of metabolic traits on oral and oropharyngeal cancer risk using Mendelian randomization
eLife 12:e82674.
https://doi.org/10.7554/eLife.82674

Share this article

https://doi.org/10.7554/eLife.82674

Categories and tags

Research organism

Further reading

    1. Genetics and Genomics

    Maf-family bZIP transcription factor NRL interacts with RNA-binding proteins and R-loops in retinal photoreceptors

    Ximena Corso Diaz, Xulong Liang ... Anand Swaroop
    Research Article

    RNA-binding proteins (RBPs) perform diverse functions including the regulation of chromatin dynamics and the coupling of transcription with RNA processing. However, our understanding of their actions in mammalian neurons remains limited. Using affinity purification, yeast-two-hybrid and proximity ligation assays, we identified interactions of multiple RBPs with neural retina leucine (NRL) zipper, a Maf-family transcription factor critical for retinal rod photoreceptor development and function. In addition to splicing, many NRL-interacting RBPs are associated with R-loops, which form during transcription and increase during photoreceptor maturation. Focusing on DHX9 RNA helicase, we demonstrate that its expression is modulated by NRL and that the NRL–DHX9 interaction is positively influenced by R-loops. ssDRIP-Seq analysis reveals both stranded and unstranded R-loops at distinct genomic elements, characterized by active and inactive epigenetic signatures and enriched at neuronal genes. NRL binds to both types of R-loops, suggesting an epigenetically independent function. Our findings suggest additional functions of NRL during transcription and highlight complex interactions among transcription factors, RBPs, and R-loops in regulating photoreceptor gene expression in the mammalian retina.

    1. Computational and Systems Biology
    2. Genetics and Genomics

    Discovering root causal genes with high-throughput perturbations

    Eric V Strobl, Eric Gamazon
    Research Article

    Root causal gene expression levels – or root causal genes for short – correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high-throughput perturbations with single-cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.

    1. Chromosomes and Gene Expression
    2. Genetics and Genomics

    Transcription: The plusses and minuses of DNA torsion

    Steven Henikoff, David L Levens
    Insight

    A new method for mapping torsion provides insights into the ways that the genome responds to the torsion generated by RNA polymerase II.