Blood lipids and prostate cancer: a Mendelian randomization analysis

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Genetic risk scores were used as unconfounded instruments for specific lipid traits (Mendelian randomization) to assess whether circulating lipids causally influence prostate cancer risk. Data from 22,249 prostate cancer cases and 22,133 controls from 22 studies within the international PRACTICAL consortium were analyzed. Allele scores based on single nucleotide polymorphisms ( SNPs) previously reported to be uniquely associated with each of low‐density lipoprotein ( LDL), high‐density lipoprotein ( HDL), and triglyceride ( TG) levels, were first validated in an independent dataset, and then entered into logistic regression models to estimate the presence (and direction) of any causal effect of each lipid trait on prostate cancer risk. There was weak evidence for an association between the LDL genetic score and cancer grade: the odds ratio ( OR) per genetically instrumented standard deviation ( SD) in LDL, comparing high‐ (≥7 Gleason score) versus low‐grade (<7 Gleason score) cancers was 1.50 (95% CI: 0.92, 2.46; P = 0.11). A genetically instrumented SD increase in TGs was weakly associated with stage: the OR for advanced versus localized cancer per unit increase in genetic risk score was 1.68 (95% CI: 0.95, 3.00; P = 0.08). The rs12916‐T variant in 3‐hydroxy‐3‐methylglutaryl‐CoA reductase ( HMGCR) was inversely associated with prostate cancer ( OR: 0.97; 95% CI: 0.94, 1.00; P = 0.03). In conclusion, circulating lipids, instrumented by our genetic risk scores, did not appear to alter prostate cancer risk. We found weak evidence that higher LDL and TG levels increase aggressive prostate cancer risk, and that a variant in HMGCR (that mimics the LDL lowering effect of statin drugs) reduces risk. However, inferences are limited by sample size and evidence of pleiotropy.

Related collections

Most cited references 39

Record: found
Abstract: found
Article: found

Is Open Access

Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

Jack Bowden, George Davey Smith, Stephen Burgess (2016)

Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.

0 comments Cited 2290 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data

Stephen Burgess, Adam S. Butterworth, Simon Thompson (2013)

Genome-wide association studies, which typically report regression coefficients summarizing the associations of many genetic variants with various traits, are potentially a powerful source of data for Mendelian randomization investigations. We demonstrate how such coefficients from multiple variants can be combined in a Mendelian randomization analysis to estimate the causal effect of a risk factor on an outcome. The bias and efficiency of estimates based on summarized data are compared to those based on individual-level data in simulation studies. We investigate the impact of gene–gene interactions, linkage disequilibrium, and ‘weak instruments’ on these estimates. Both an inverse-variance weighted average of variant-specific associations and a likelihood-based approach for summarized data give similar estimates and precision to the two-stage least squares method for individual-level data, even when there are gene–gene interactions. However, these summarized data methods overstate precision when variants are in linkage disequilibrium. If the P-value in a linear regression of the risk factor for each variant is less than , then weak instrument bias will be small. We use these methods to estimate the causal association of low-density lipoprotein cholesterol (LDL-C) on coronary artery disease using published data on five genetic variants. A 30% reduction in LDL-C is estimated to reduce coronary artery disease risk by 67% (95% CI: 54% to 76%). We conclude that Mendelian randomization investigations using summarized data from uncorrelated variants are similarly efficient to those using individual-level data, although the necessary assumptions cannot be so fully assessed.

0 comments Cited 1336 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?

George Davey Smith, Shah Ebrahim (2003)

Associations between modifiable exposures and disease seen in observational epidemiology are sometimes confounded and thus misleading, despite our best efforts to improve the design and analysis of studies. Mendelian randomization-the random assortment of genes from parents to offspring that occurs during gamete formation and conception-provides one method for assessing the causal nature of some environmental exposures. The association between a disease and a polymorphism that mimics the biological link between a proposed exposure and disease is not generally susceptible to the reverse causation or confounding that may distort interpretations of conventional observational studies. Several examples where the phenotypic effects of polymorphisms are well documented provide encouraging evidence of the explanatory power of Mendelian randomization and are described. The limitations of the approach include confounding by polymorphisms in linkage disequilibrium with the polymorphism under study, that polymorphisms may have several phenotypic effects associated with disease, the lack of suitable polymorphisms for studying modifiable exposures of interest, and canalization-the buffering of the effects of genetic variation during development. Nevertheless, Mendelian randomization provides new opportunities to test causality and demonstrates how investment in the human genome project may contribute to understanding and preventing the adverse effects on human health of modifiable exposures.

0 comments Cited 885 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Journal ID (nlm-ta): Cancer Med

Journal ID (iso-abbrev): Cancer Med

Journal ID (doi): 10.1002/(ISSN)2045-7634

Journal ID (publisher-id): CAM4

Title: Cancer Medicine

Publisher: John Wiley and Sons Inc. (Hoboken )

ISSN (Electronic): 2045-7634

Publication date (Electronic): 19 March 2016

Publication date Collection: June 2016

Volume: 5

Issue: 6 ( doiID: 10.1002/cam4.2016.5.issue-6 )

Pages: 1125-1136

Affiliations

[ ¹ ] School of Social and Community MedicineUniversity of Bristol BristolUnited Kingdom

[ ² ] MRC/University of Bristol Integrative Epidemiology UnitUniversity of Bristol BristolUnited Kingdom

[ ³ ] Integrative Cancer Epidemiology ProgrammeUniversity of Bristol BristolUnited Kingdom

[ ⁴ ] IGFs and Metabolic Endocrinology Group School of Clinical Sciences North BristolUniversity of Bristol Bristol BS10 5NBUnited Kingdom

[ ⁵ ] Department of Medicine Division of Endocrinology, EpidemiologyBiostatistics and Occupational Health McGill University Montreal QuebecCanada

[ ⁶ ] Department of Twin ResearchKing's College London London SE1 7EHUnited Kingdom

[ ⁷ ]The Institute of Cancer Research London SM2 5NGUnited Kingdom

[ ⁸ ]The Royal Marsden NHS Foundation Trust London SW3 6JJUnited Kingdom

[ ⁹ ] Centre for Cancer Genetic Epidemiology Department of Public Health and Primary CareUniversity of Cambridge Strangeways Laboratory Worts Causeway CambridgeUnited Kingdom

[ ¹⁰ ] Warwick Medical SchoolUniversity of Warwick Coventry CV4 7ALUnited Kingdom

[ ¹¹ ] Institute of Population HealthUniversity of Manchester Manchester M13 9PLUnited Kingdom

[ ¹² ] Cancer Epidemiology CentreThe Cancer Council Victoria 615 St Kilda Road Melbourne VictoriaAustralia

[ ¹³ ] Centre for Epidemiology and Biostatistics Melbourne School of Population and Global HealthThe University of Melbourne VictoriaAustralia

[ ¹⁴ ] Department of Medical Epidemiology and BiostatisticsKarolinska Institute StockholmSweden

[ ¹⁵ ] Department of Preventive Medicine Keck School of MedicineUniversity of Southern California/Norris Comprehensive Cancer Center Los Angeles California

[ ¹⁶ ] Department of Medical Biochemistry and GeneticsUniversity of Turku and Tyks Microbiology and Genetics Department of Medical GeneticsTurku University Hospital TurkuFinland

[ ¹⁷ ] Institute of Biomedical Technology/BioMediTechUniversity of Tampere and FimLab Laboratories TampereFinland

[ ¹⁸ ] Department of Clinical Biochemistry Herlev and Gentofte HospitalCopenhagen University Hospital Herlev Ringvej 75 Herlev DK‐2730Denmark

[ ¹⁹ ] Cancer Epidemiology Nuffield Department of Population HealthUniversity of Oxford OxfordUnited Kingdom

[ ²⁰ ] Surgical Oncology (Uro‐Oncology: S4)University of Cambridge Addenbrooke's Hospital Box 279, Hills Road CambridgeUnited Kingdom

[ ²¹ ] Cancer Research UK Cambridge Research InstituteLi Ka Shing Centre CambridgeUnited Kingdom

[ ²² ] Department of Applied Health ResearchUniversity College London 1‐19 Torrington Place London WC1E 7HBUnited Kingdom

[ ²³ ] Cambridge Institute of Public HealthUniversity of Cambridge Forvie Site, Robinson Way Cambridge CB2 0SRUnited Kingdom

[ ²⁴ ] Division of Public Health SciencesFred Hutchinson Cancer Research Center Seattle Washington

[ ²⁵ ] Department of EpidemiologySchool of Public Health University of Washington Seattle Washington

[ ²⁶ ]International Epidemiology Institute 1455 Research Blvd., Suite 550 Rockville 20850 Maryland

[ ²⁷ ]Mayo Clinic Rochester Minnesota

[ ²⁸ ] Department of UrologyUniversity Hospital UlmGermany

[ ²⁹ ]Institute of Human Genetics University Hospital UlmGermany

[ ³⁰ ]Brigham and Women's Hospital/Dana‐Farber Cancer Institute 45 Francis Street‐ ASB II‐3 Boston Massachusetts 02115

[ ³¹ ]Washington University St. Louis Missouri

[ ³² ] International Hereditary Cancer Center Department of Genetics and PathologyPomeranian Medical University SzczecinPoland

[ ³³ ] Division of Genetic Epidemiology Department of MedicineUniversity of Utah School of Medicine Salt Lake CityUtah

[ ³⁴ ] Division of Clinical Epidemiology and Aging ResearchGerman Cancer Research Center (DKFZ) HeidelbergGermany

[ ³⁵ ] Division of Preventive OncologyGerman Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT) HeidelbergGermany

[ ³⁶ ]German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) HeidelbergGermany

[ ³⁷ ] Division of Cancer Prevention and ControlH. Lee Moffitt Cancer Center Magnolia Dr. Tampa Florida 12902

[ ³⁸ ] Molecular Medicine Center and Department of Medical Chemistry and BiochemistryMedical University Sofia 2 Zdrave St Sofia 1431Bulgaria

[ ³⁹ ] Australian Prostate Cancer Research Centre‐Qld Institute of Health and Biomedical Innovation and School of Biomedical SciencesQueensland University of Technology BrisbaneAustralia

[ ⁴⁰ ] Department of GeneticsPortuguese Oncology Institute PortoPortugal

[ ⁴¹ ] Biomedical Sciences Institute (ICBAS)Porto University PortoPortugal

[ ⁴² ]The University of Surrey Surrey GU2 7XHUnited Kingdom

[ ⁴³ ] National Institute for Health ResearchBristol Nutrition Biomedical Research Unit BristolUnited Kingdom

Author notes

[*] [* ] Correspondence

Richard M. Martin, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom. Tel: 01179287321; Fax: 0117 928 7236; E‐mail: richard.martin@ 123456bristol.ac.uk

[†]

These authors contributed equally to the work.

[‡]

Additional cohort members are listed in the supporting information.

Article

Publisher ID: CAM4695

DOI: 10.1002/cam4.695

PMC ID: 4924371

PubMed ID: 26992435

SO-VID: 2294fdc6-df5d-420f-bf8a-9be88cad7a3e

License:

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

History

Date received : 13 November 2015

Date revision received : 03 February 2016

Date accepted : 08 February 2016

Page count

Pages: 12

Funding

Funded by: Wellcome Trust

Funded by: Medical Research Council

Funded by: University of Bristol

Funded by: Canadian Institutes of Health Research

Funded by: European Commission’s Seventh Framework Programme

Funded by: Cancer Research UK

Funded by: National Institute for Health Research

Funded by: National Institutes of Health

Award ID: 1 U19 CA 148537‐01

Funded by: Cancer Post‐Cancer GWAS initiative

Funded by: Biomedical Research Centre

Funded by: Institute of Cancer Research

Funded by: Royal Marsden NHS Foundation Trust

Award ID: WT083431MA

Award ID: C18281/A19169

Award ID: MC_UU_12013/1‐9

Award ID: HEALTH‐F2‐2009‐223175

Award ID: C5047/A7357

Award ID: C1287/A10118

Award ID: C5047/A3354

Award ID: C5047/A10692

Award ID: C16913/A6135

Funded by: The Institute of Cancer Research

Custom metadata

source-schema-version-number 2.0

component-id cam4695

cover-date June 2016

details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_NLMPMC version:4.9.1 mode:remove_FC converted:28.06.2016

ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: cholesterol,mendelian randomization,prostate cancer,statins

Data availability:

ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: cholesterol, mendelian randomization, prostate cancer, statins

Comments

Comment on this article

scite_

Cited by 38

See all cited by

- Version 1
- Version 1

Blood lipids and prostate cancer: a Mendelian randomization analysis

Read this article at

Abstract

Related collections

UCL: UN SDG 03 Good Health and Well-Being

Most cited references 39

Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data

'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?

Author and article information

Journal

Affiliations

Author notes

Article

History

Page count

Funding

Categories

Custom metadata

Comments

Comment on this article

Similar content 517

Cited by 38