1. Cell Biology
  2. Chromosomes and Gene Expression
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Predicting the effect of statins on cancer risk using genetic variants from a Mendelian randomization study in the UK Biobank

  1. Paul Carter
  2. Mathew Vithayathil
  3. Siddhartha Kar
  4. Rahul Potluri
  5. Amy M Mason
  6. Susanna C Larsson
  7. Stephen Burgess  Is a corresponding author
  1. Department of Public Health and Primary Care, University of Cambridge, United Kingdom
  2. MRC Cancer Unit, University of Cambridge, United Kingdom
  3. MRC Integrative Epidemiology Unit, University of Bristol, United Kingdom
  4. Population Health Sciences, Bristol Medical School, University of Bristol, United Kingdom
  5. ACALM Study Unit, Aston Medical School, United Kingdom
  6. Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Sweden
  7. Department of Surgical Sciences, Uppsala University, Sweden
  8. MRC Biostatistics Unit, University of Cambridge, United Kingdom
Research Article
Cite this article as: eLife 2020;9:e57191 doi: 10.7554/eLife.57191
4 figures, 1 table and 4 additional files

Figures

Power calculations for polygenic and gene-specific analyses, displaying the Mendelian randomization estimate that can be detected with 80% power assuming a sample size of 367,703 individuals for site-specific cancers.
Figure 2 with 8 supplements
Gene-specific Mendelian randomization estimates (odds ratio with 95% confidence interval per one standard deviation increase in lipid fraction) for variants in gene regions representing targets of lipid-lowering treatments.

Estimates are scaled to a one standard deviation increase in LDL-cholesterol for the HMGCR, PCSK9, LDLR, and NPC1L1 regions, and a one standard deviation increase in triglycerides for the APOC3 and LPL regions. A: associations with overall cancer for each gene region in turn. B: associations with site-specific cancers for variants in the HMGCR gene region.

Figure 2—figure supplement 1
Gene-specific Mendelian randomization estimates (odds ratio with 95% confidence interval per one standard deviation increase in LDL-cholesterol) for variants in the PCSK9 gene region.
Figure 2—figure supplement 2
Gene-specific Mendelian randomization estimates (odds ratio with95%confidence interval per one standard deviation increase in LDL-cholesterol) for variants in theLDLRgene region.
Figure 2—figure supplement 3
Gene-specific Mendelian randomization estimates (odds ratio with 95% confidence interval per one standard deviation increase in LDL-cholesterol) for variants in theNPC1L1gene region.
Figure 2—figure supplement 4
Gene-specific Mendelian randomization estimates (odds ratio with 95% confidence interval per one standard deviation increase in LDL-cholesterol) for variants in theAPOC3gene region.
Figure 2—figure supplement 5
Gene-specific Mendelian randomization estimates (odds ratio with 95% confidence interval per one standard deviation increase in LDL-cholesterol) for variants in theLPLgene region.
Figure 2—figure supplement 6
Genetic associations with LDL-cholesterol (standard deviation units) plotted against genetic associations with overall cancer (log odds ratios) for six variants in the HMGCR gene region.
Figure 2—figure supplement 7
Gene-specific Mendelian randomization estimates (odds ratio with 95% confidence interval per one standard deviation increase in LDL-cholesterol) for variants in the HMGCR gene region excluding self-reported outcomes.
Figure 2—figure supplement 8
Gene-specific Mendelian randomization estimates (odds ratio with 95% confidence interval per one standard deviation increase in LDL-cholesterol) for variants in the HMGCR gene region excluding those with a cancer diagnosis other than site-specific cancer under analysis.
Figure 3 with 2 supplements
Multivariable Mendelian randomization estimates for HDL-cholesterol, LDL-cholesterol, and triglycerides (odds ratio with 95% confidence interval per one standard deviation increase in lipid fraction) from polygenic analyses including all lipid-associated variants.
Figure 3—figure supplement 1
Multivariable Mendelian randomization estimates for HDL-cholesterol, LDL-cholesterol, and triglycerides (odds ratio with 95% confidence interval per one standard deviation increase in lipid fraction) from polygenic analyses including all lipid-associated variants excluding self-reported outcomes.
Figure 3—figure supplement 2
Multivariable Mendelian randomization estimates for HDL-cholesterol, LDL-cholesterol, and triglycerides (odds ratio with 95% confidence interval per one standard deviation increase in lipid fraction) from polygenic analyses including all lipid-associated variants excluding those with a cancer diagnosis other than the site-specific cancer under analysis.
Figure 4 with 3 supplements
Univariable Mendelian randomization estimates for total cholesterol (odds ratio with 95% confidence interval per one standard deviation increase in lipid fraction) from polygenic analyses including all lipid-associated variants.
Figure 4—figure supplement 1
Univariable Mendelian randomization estimates for total cholesterol (odds ratio with 95% confidence interval per one standard deviation increase in lipid fraction) from polygenic analyses including all lipid-associated variants excluding self-reported outcomes.
Figure 4—figure supplement 2
Univariable Mendelian randomization estimates for total cholesterol (odds ratio with 95% confidence interval per one standard deviation increase in lipid fraction) from polygenic analyses including all lipid-associated variants excluding those with a cancer diagnosis other than the site-specific cancer under analysis.
Figure 4—figure supplement 3
Scatterplot to assess heterogeneity of genetic associations with total cholesterol (horizontal axis, standard deviation units) against genetic associations with overall cancer (vertical axis, log odds ratios).

Error bars represent 95% confidence intervals. Solid diagonal line represents the inverse-variance weighted estimate.

Tables

Table 1
Baseline characteristics of the UK Biobank participants included in this study and the numbers of outcome events.
Characteristic or cancer site/typeMean (SD) or N (%)
Sample size367,703 (100)
Female198,904 (54.1)
Age at baseline57.2 (8.1)
Body mass index27.3 (4.8)
Systolic blood pressure137.6 (18.6)
Diastolic blood pressure82.0 (10.1)
Smoking status (current/ex/ never)*37,866 (10.3)/185,704 (50.5)/143,777 (39.1)
Alcohol status (current/ex/ never)*342,797 (93.2)/12,732 (3.5)/11,646 (3.2)
History of type 2 diabetes15,834 (4.3)
Overall cancer75,037 (20.4)
Breast13,666 (6.9)
Prostate7872 (4.7)
Lung2838 (0.8)
Bowel5486 (1.5)
Melanoma4869 (1.3)
Non-Hodgkin’s lymphoma2296 (0.6)
Kidney1310 (0.4)
Head/neck1615 (0.4)
Brain810 (0.2)
Bladder2588 (0.7)
Pancreas1264 (0.3)
Uterus1931 (1.0)
Leukaemia1403 (0.4)
Esophagus843 (0.2)
Ovaries1520 (0.8)
Gastric736 (0.2)
Liver324 (0.1)
Myeloma656 (0.2)
Thyroid375 (0.1)
Biliary387 (0.1)
Cervix1928 (1.0)
Testes735 (0.4)
  1. *Excluding 356 participants with smoking status absent and 528 participants with alcohol consumption status absent.

    For sex-specific cancers, this is the percentage of individuals of the relevant sex.

Data availability

All data generated or analysed during this study are publicly-available and/or provided in the supporting files. The UK Biobank can be accessed online at http://biobank.ctsu.ox.ac.uk/crystal/. The Global Lipids Genetics Consortium data can be accessed at http://csg.sph.umich.edu/willer/public/lipids2013/.

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