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Lawton M, Ben-Shlomo Y, Gkatzionis A, Hu MT, Grosset D, Tilling K. Two sample Mendelian Randomisation using an outcome from a multilevel model of disease progression. Eur J Epidemiol 2024:10.1007/s10654-023-01093-2. [PMID: 38281297 DOI: 10.1007/s10654-023-01093-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 12/21/2023] [Indexed: 01/30/2024]
Abstract
Identifying factors that are causes of disease progression, especially in neurodegenerative diseases, is of considerable interest. Disease progression can be described as a trajectory of outcome over time-for example, a linear trajectory having both an intercept (severity at time zero) and a slope (rate of change). A technique for identifying causal relationships between one exposure and one outcome in observational data whilst avoiding bias due to confounding is two sample Mendelian Randomisation (2SMR). We consider a multivariate approach to 2SMR using a multilevel model for disease progression to estimate the causal effect an exposure has on the intercept and slope. We carry out a simulation study comparing a naïve univariate 2SMR approach to a multivariate 2SMR approach with one exposure that effects both the intercept and slope of an outcome that changes linearly with time since diagnosis. The simulation study results, across six different scenarios, for both approaches were similar with no evidence against a non-zero bias and appropriate coverage of the 95% confidence intervals (for intercept 93.4-96.2% and the slope 94.5-96.0%). The multivariate approach gives a better joint coverage of both the intercept and slope effects. We also apply our method to two Parkinson's cohorts to examine the effect body mass index has on disease progression. There was no strong evidence that BMI affects disease progression, however the confidence intervals for both intercept and slope were wide.
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Affiliation(s)
- Michael Lawton
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Yoav Ben-Shlomo
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Apostolos Gkatzionis
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Michele T Hu
- Nuffield Department of Clinical Neurosciences, Oxford University and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Donald Grosset
- School of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
| | - Kate Tilling
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
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2
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Hemani G, Gkatzionis A, Tilling K, Davey Smith G. Sensitivity analyses gain relevance by fixing parameters observable during the empirical analyses. Genet Epidemiol 2023; 47:461-462. [PMID: 37417943 DOI: 10.1002/gepi.22530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 04/13/2023] [Accepted: 06/02/2023] [Indexed: 07/08/2023]
Affiliation(s)
- Gibran Hemani
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | | | - Kate Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
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Burgess S, Mason AM, Grant AJ, Slob EAW, Gkatzionis A, Zuber V, Patel A, Tian H, Liu C, Haynes WG, Hovingh GK, Knudsen LB, Whittaker JC, Gill D. Using genetic association data to guide drug discovery and development: Review of methods and applications. Am J Hum Genet 2023; 110:195-214. [PMID: 36736292 PMCID: PMC9943784 DOI: 10.1016/j.ajhg.2022.12.017] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Evidence on the validity of drug targets from randomized trials is reliable but typically expensive and slow to obtain. In contrast, evidence from conventional observational epidemiological studies is less reliable because of the potential for bias from confounding and reverse causation. Mendelian randomization is a quasi-experimental approach analogous to a randomized trial that exploits naturally occurring randomization in the transmission of genetic variants. In Mendelian randomization, genetic variants that can be regarded as proxies for an intervention on the proposed drug target are leveraged as instrumental variables to investigate potential effects on biomarkers and disease outcomes in large-scale observational datasets. This approach can be implemented rapidly for a range of drug targets to provide evidence on their effects and thus inform on their priority for further investigation. In this review, we present statistical methods and their applications to showcase the diverse opportunities for applying Mendelian randomization in guiding clinical development efforts, thus enabling interventions to target the right mechanism in the right population group at the right time. These methods can inform investigators on the mechanisms underlying drug effects, their related biomarkers, implications for the timing of interventions, and the population subgroups that stand to gain the most benefit. Most methods can be implemented with publicly available data on summarized genetic associations with traits and diseases, meaning that the only major limitations to their usage are the availability of appropriately powered studies for the exposure and outcome and the existence of a suitable genetic proxy for the proposed intervention.
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Affiliation(s)
- Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| | - Amy M Mason
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Andrew J Grant
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Eric A W Slob
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | | | - Verena Zuber
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK; UK Dementia Research Institute at Imperial College, Imperial College London, London, UK
| | - Ashish Patel
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Haodong Tian
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Cunhao Liu
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - William G Haynes
- Novo Nordisk Research Centre Oxford, Novo Nordisk, Oxford, UK; Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - G Kees Hovingh
- Department of Vascular Medicine, Academic Medical Center, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands; Global Chief Medical Office, Novo Nordisk, Copenhagen, Denmark
| | - Lotte Bjerre Knudsen
- Chief Scientific Advisor Office, Research and Early Development, Novo Nordisk, Copenhagen, Denmark
| | - John C Whittaker
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; Chief Scientific Advisor Office, Research and Early Development, Novo Nordisk, Copenhagen, Denmark
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Gkatzionis A, Burgess S, Newcombe PJ. Statistical methods for cis-Mendelian randomization with two-sample summary-level data. Genet Epidemiol 2023; 47:3-25. [PMID: 36273411 PMCID: PMC7614127 DOI: 10.1002/gepi.22506] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 08/28/2022] [Accepted: 08/29/2022] [Indexed: 02/03/2023]
Abstract
Mendelian randomization (MR) is the use of genetic variants to assess the existence of a causal relationship between a risk factor and an outcome of interest. Here, we focus on two-sample summary-data MR analyses with many correlated variants from a single gene region, particularly on cis-MR studies which use protein expression as a risk factor. Such studies must rely on a small, curated set of variants from the studied region; using all variants in the region requires inverting an ill-conditioned genetic correlation matrix and results in numerically unstable causal effect estimates. We review methods for variable selection and estimation in cis-MR with summary-level data, ranging from stepwise pruning and conditional analysis to principal components analysis, factor analysis, and Bayesian variable selection. In a simulation study, we show that the various methods have comparable performance in analyses with large sample sizes and strong genetic instruments. However, when weak instrument bias is suspected, factor analysis and Bayesian variable selection produce more reliable inferences than simple pruning approaches, which are often used in practice. We conclude by examining two case studies, assessing the effects of low-density lipoprotein-cholesterol and serum testosterone on coronary heart disease risk using variants in the HMGCR and SHBG gene regions, respectively.
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Affiliation(s)
- Apostolos Gkatzionis
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- MRC Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Paul J. Newcombe
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
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Mitchell RE, Hartley AE, Walker VM, Gkatzionis A, Yarmolinsky J, Bell JA, Chong AHW, Paternoster L, Tilling K, Smith GD. Strategies to investigate and mitigate collider bias in genetic and Mendelian randomisation studies of disease progression. PLoS Genet 2023; 19:e1010596. [PMID: 36821633 PMCID: PMC9949638 DOI: 10.1371/journal.pgen.1010596] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
Genetic studies of disease progression can be used to identify factors that may influence survival or prognosis, which may differ from factors that influence on disease susceptibility. Studies of disease progression feed directly into therapeutics for disease, whereas studies of incidence inform prevention strategies. However, studies of disease progression are known to be affected by collider (also known as "index event") bias since the disease progression phenotype can only be observed for individuals who have the disease. This applies equally to observational and genetic studies, including genome-wide association studies and Mendelian randomisation (MR) analyses. In this paper, our aim is to review several statistical methods that can be used to detect and adjust for index event bias in studies of disease progression, and how they apply to genetic and MR studies using both individual- and summary-level data. Methods to detect the presence of index event bias include the use of negative controls, a comparison of associations between risk factors for incidence in individuals with and without the disease, and an inspection of Miami plots. Methods to adjust for the bias include inverse probability weighting (with individual-level data), or Slope-Hunter and Dudbridge et al.'s index event bias adjustment (when only summary-level data are available). We also outline two approaches for sensitivity analysis. We then illustrate how three methods to minimise bias can be used in practice with two applied examples. Our first example investigates the effects of blood lipid traits on mortality from coronary heart disease, while our second example investigates genetic associations with breast cancer mortality.
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Affiliation(s)
- Ruth E. Mitchell
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - April E. Hartley
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Venexia M. Walker
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Apostolos Gkatzionis
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - James Yarmolinsky
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Joshua A. Bell
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Amanda H. W. Chong
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Lavinia Paternoster
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Kate Tilling
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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Soremekun O, Karhunen V, He Y, Rajasundaram S, Liu B, Gkatzionis A, Soremekun C, Udosen B, Musa H, Silva S, Kintu C, Mayanja R, Nakabuye M, Machipisa T, Mason A, Vujkovic M, Zuber V, Soliman M, Mugisha J, Nash O, Kaleebu P, Nyirenda M, Chikowore T, Nitsch D, Burgess S, Gill D, Fatumo S. Lipid traits and type 2 diabetes risk in African ancestry individuals: A Mendelian Randomization study. EBioMedicine 2022; 78:103953. [PMID: 35325778 PMCID: PMC8941323 DOI: 10.1016/j.ebiom.2022.103953] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 03/08/2022] [Accepted: 03/08/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Dyslipidaemia is highly prevalent in individuals with type 2 diabetes mellitus (T2DM). Numerous studies have sought to disentangle the causal relationship between dyslipidaemia and T2DM liability. However, conventional observational studies are vulnerable to confounding. Mendelian Randomization (MR) studies (which address this bias) on lipids and T2DM liability have focused on European ancestry individuals, with none to date having been performed in individuals of African ancestry. We therefore sought to use MR to investigate the causal effect of various lipid traits on T2DM liability in African ancestry individuals. METHODS Using univariable and multivariable two-sample MR, we leveraged summary-level data for lipid traits and T2DM liability from the African Partnership for Chronic Disease Research (APCDR) (N = 13,612, 36.9% men) and from African ancestry individuals in the Million Veteran Program (Ncases = 23,305 and Ncontrols = 30,140, 87.2% men), respectively. Genetic instruments were thus selected from the APCDR after which they were clumped to obtain independent instruments. We used a random-effects inverse variance weighted method in our primary analysis, complementing this with additional sensitivity analyses robust to the presence of pleiotropy. FINDINGS Increased genetically proxied low-density lipoprotein cholesterol (LDL-C) and total cholesterol (TC) levels were associated with increased T2DM liability in African ancestry individuals (odds ratio (OR) [95% confidence interval, P-value] per standard deviation (SD) increase in LDL-C = 1.052 [1.000 to 1.106, P = 0.046] and per SD increase in TC = 1.089 [1.014 to 1.170, P = 0.019]). Conversely, increased genetically proxied high-density lipoprotein cholesterol (HDL-C) was associated with reduced T2DM liability (OR per SD increase in HDL-C = 0.915 [0.843 to 0.993, P = 0.033]). The OR on T2DM per SD increase in genetically proxied triglyceride (TG) levels was 0.884 [0.773 to 1.011, P = 0.072] . With respect to lipid-lowering drug targets, we found that genetically proxied 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) inhibition was associated with increased T2DM liability (OR per SD decrease in genetically proxied LDL-C = 1.68 [1.03-2.72, P = 0.04]) but we did not find evidence of a relationship between genetically proxied proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibition and T2DM liability. INTERPRETATION Consistent with MR findings in Europeans, HDL-C exerts a protective effect on T2DM liability and HMGCR inhibition increases T2DM liability in African ancestry individuals. However, in contrast to European ancestry individuals, LDL-C may increase T2DM liability in African ancestry individuals. This raises the possibility of ethnic differences in the metabolic effects of dyslipidaemia in T2DM. FUNDING See the Acknowledgements section for more information.
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Affiliation(s)
- Opeyemi Soremekun
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Ville Karhunen
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland; Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
| | - Yiyan He
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Skanda Rajasundaram
- Kellogg College, University of Oxford, Oxford, UK; Faculty of Medicine, Imperial College London, London, UK
| | - Bowen Liu
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, UK
| | - Apostolos Gkatzionis
- MRC Integrative Epidemiology Unit, University of Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, UK
| | - Chisom Soremekun
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Brenda Udosen
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Hanan Musa
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Sarah Silva
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda; Department of Non-communicable Disease Epidemiology (NCDE), London School of Hygiene and Tropical Medicine, London, UK
| | - Christopher Kintu
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Richard Mayanja
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Mariam Nakabuye
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Tafadzwa Machipisa
- Department of Medicine, University of Cape Town & Groote Schuur Hospital, Cape Town, South Africa; Department of Medicine, Hatter Institute for Cardiovascular Diseases Research in Africa (HICRA) & Cape Heart Institute (CHI), University of Cape Town, South Africa; Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON L8L 2X2, Canada
| | - Amy Mason
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, UK
| | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Verena Zuber
- Department of Epidemiology and Biostatistics, Medical School Building, St Mary's Hospital, Imperial College London, London, UK
| | - Mahmoud Soliman
- Discipline of Pharmaceutical Chemistry, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | | | - Oyekanmi Nash
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | | | | | - Tinashe Chikowore
- Department of Pediatrics, MRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; Faculty of Health Sciences, Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Dorothea Nitsch
- Department of Non-communicable Disease Epidemiology (NCDE), London School of Hygiene and Tropical Medicine, London, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, UK; Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, Medical School Building, St Mary's Hospital, Imperial College London, London, UK; Novo Nordisk Research Centre Oxford, Old Road Campus, Oxford, UK
| | - Segun Fatumo
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda; MRC/UVRI and LSHTM, Entebbe, Uganda; Department of Non-communicable Disease Epidemiology (NCDE), London School of Hygiene and Tropical Medicine, London, UK.
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Gkatzionis A, Burgess S, Conti DV, Newcombe PJ. Bayesian variable selection with a pleiotropic loss function in Mendelian randomization. Stat Med 2021; 40:5025-5045. [PMID: 34155684 PMCID: PMC8446304 DOI: 10.1002/sim.9109] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 04/17/2021] [Accepted: 06/07/2021] [Indexed: 01/04/2023]
Abstract
Mendelian randomization is the use of genetic variants as instruments to assess the existence of a causal relationship between a risk factor and an outcome. A Mendelian randomization analysis requires a set of genetic variants that are strongly associated with the risk factor and only associated with the outcome through their effect on the risk factor. We describe a novel variable selection algorithm for Mendelian randomization that can identify sets of genetic variants which are suitable in both these respects. Our algorithm is applicable in the context of two-sample summary-data Mendelian randomization and employs a recently proposed theoretical extension of the traditional Bayesian statistics framework, including a loss function to penalize genetic variants that exhibit pleiotropic effects. The algorithm offers robust inference through the use of model averaging, as we illustrate by running it on a range of simulation scenarios and comparing it against established pleiotropy-robust Mendelian randomization methods. In a real-data application, we study the effect of systolic and diastolic blood pressure on the risk of suffering from coronary heart disease (CHD). Based on a recent large-scale GWAS for blood pressure, we use 395 genetic variants for systolic and 391 variants for diastolic blood pressure. Both traits are shown to have significant risk-increasing effects on CHD risk.
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Affiliation(s)
- Apostolos Gkatzionis
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - David V. Conti
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
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8
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Gill D, Georgakis MK, Walker VM, Schmidt AF, Gkatzionis A, Freitag DF, Finan C, Hingorani AD, Howson JM, Burgess S, Swerdlow DI, Davey Smith G, Holmes MV, Dichgans M, Scott RA, Zheng J, Psaty BM, Davies NM. Mendelian randomization for studying the effects of perturbing drug targets. Wellcome Open Res 2021; 6:16. [PMID: 33644404 PMCID: PMC7903200 DOI: 10.12688/wellcomeopenres.16544.2] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/01/2021] [Indexed: 12/11/2022] Open
Abstract
Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline.
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Affiliation(s)
- Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Centre for Pharmacology and Therapeutics, Department of Medicine, Imperial College London, London, UK
- Novo Nordisk Research Centre, Oxford, UK
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, London, UK
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals NHS Foundation Trust, London, UK
| | - Marios K. Georgakis
- Institute for Stroke and Dementia Research (ISD), University Hospital of Ludwig-Maximilians-University (LMU), Munich, Germany
| | - Venexia M. Walker
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - A. Floriaan Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Apostolos Gkatzionis
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Daniel F. Freitag
- Bayer Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Acceleratorversity College London, London, UK
- UCL Hospitals, NIHR Biomedical Research Centre, London, UK
| | - Aroon D. Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Acceleratorversity College London, London, UK
- UCL Hospitals, NIHR Biomedical Research Centre, London, UK
| | | | - Stephen Burgess
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Daniel I. Swerdlow
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK
| | - Michael V. Holmes
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital of Ludwig-Maximilians-University (LMU), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- German Centre for Neurodegenerative Diseases (DZNE), Munich, Germany
| | | | - Jie Zheng
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Neil M. Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
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9
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Gill D, Georgakis MK, Walker VM, Schmidt AF, Gkatzionis A, Freitag DF, Finan C, Hingorani AD, Howson JM, Burgess S, Swerdlow DI, Davey Smith G, Holmes MV, Dichgans M, Scott RA, Zheng J, Psaty BM, Davies NM. Mendelian randomization for studying the effects of perturbing drug targets. Wellcome Open Res 2021; 6:16. [PMID: 33644404 PMCID: PMC7903200 DOI: 10.12688/wellcomeopenres.16544.1] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/21/2021] [Indexed: 08/17/2023] Open
Abstract
Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline.
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Affiliation(s)
- Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Centre for Pharmacology and Therapeutics, Department of Medicine, Imperial College London, London, UK
- Novo Nordisk Research Centre, Oxford, UK
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, London, UK
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals NHS Foundation Trust, London, UK
| | - Marios K. Georgakis
- Institute for Stroke and Dementia Research (ISD), University Hospital of Ludwig-Maximilians-University (LMU), Munich, Germany
| | - Venexia M. Walker
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - A. Floriaan Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Apostolos Gkatzionis
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Daniel F. Freitag
- Bayer Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Acceleratorversity College London, London, UK
- UCL Hospitals, NIHR Biomedical Research Centre, London, UK
| | - Aroon D. Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Acceleratorversity College London, London, UK
- UCL Hospitals, NIHR Biomedical Research Centre, London, UK
| | | | - Stephen Burgess
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Daniel I. Swerdlow
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK
| | - Michael V. Holmes
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital of Ludwig-Maximilians-University (LMU), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- German Centre for Neurodegenerative Diseases (DZNE), Munich, Germany
| | | | - Jie Zheng
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Neil M. Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
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10
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Ponsford MJ, Gkatzionis A, Walker VM, Grant AJ, Wootton RE, Moore LS, Fatumo S, Mason AM, Zuber V, Willer C, Rasheed H, Brumpton B, Hveem K, Kristian Damås J, Davies N, Olav Åsvold B, Solligård E, Jones S, Burgess S, Rogne T, Gill D. Cardiometabolic Traits, Sepsis, and Severe COVID-19: A Mendelian Randomization Investigation. Circulation 2020; 142:1791-1793. [PMID: 32966752 PMCID: PMC7594537 DOI: 10.1161/circulationaha.120.050753] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Mark J. Ponsford
- Immunodeficiency Centre of Wales, University Hospital Wales, Heath Park, Cardiff, United Kingdom (M.J.P.)
- Division of Immunology, Infection, and Inflammation, Tenovus Institute, Cardiff University, United Kingdom (M.J.P., S.J.)
| | - Apostolos Gkatzionis
- Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, United Kingdom (A.G., A.J.G., V.Z., S.B.)
| | - Venexia M. Walker
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, United Kingdom (V.M.W., R.E.W., H.R., B.B., N.D.)
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia (V.M.W.)
| | - Andrew J. Grant
- Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, United Kingdom (A.G., A.J.G., V.Z., S.B.)
| | - Robyn E. Wootton
- Immunodeficiency Centre of Wales, University Hospital Wales, Heath Park, Cardiff, United Kingdom (M.J.P.)
- Division of Immunology, Infection, and Inflammation, Tenovus Institute, Cardiff University, United Kingdom (M.J.P., S.J.)
- Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, United Kingdom (A.G., A.J.G., V.Z., S.B.)
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, United Kingdom (V.M.W., R.E.W., H.R., B.B., N.D.)
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia (V.M.W.)
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (L.S.P.M.), Imperial College London, United Kingdom
- Department of Epidemiology and Biostatistics, School of Public Health (V.Z., D.G.), Imperial College London, United Kingdom
- Chelsea and Westminster National Health Service Foundation Trust, London, United Kingdom (L.S.P.M.)
- Imperial Biomedical Research Centre, Imperial College London and Imperial College National Health Service Healthcare Trust, United Kingdom (L.S.P.M.)
- Department of Non-Communicable Diseases Epidemiology, London School of Hygiene and Tropical Medicine, United Kingdom (S.F.)
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, United Kingdom (A.M.M., S.B.)
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, United Kingdom (A.M.M.)
- Departments of Internal Medicine, Human Genetics and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor (C.W.)
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing (H.R., B.B., K.H., N.D., B.O.Å.), Norwegian University of Science and Technology, Trondheim
- Gemini Center for Sepsis Research, Department of Circulation and Medical Imaging (E.S., T.R., E.S.), Norwegian University of Science and Technology, Trondheim
- Department of Thoracic Medicine (B.B.), St Olavs Hospital, Trondheim University Hospital, Norway
- Department of Research, Innovation and Education (K.H.), St Olavs Hospital, Trondheim University Hospital, Norway
- Department of Infectious Diseases (J.K.D.), St Olavs Hospital, Trondheim University Hospital, Norway
- Department of Endocrinology (B.O.Å.), St Olavs Hospital, Trondheim University Hospital, Norway
- Clinic of Anesthesia and Intensive Care (E.S.), St Olavs Hospital, Trondheim University Hospital, Norway
- Centre of Molecular Inflammation Research, Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim (J.K.D.)
- Novo Nordisk Research Centre Oxford, Old Road Campus, United Kingdom (D.G.)
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, United Kingdom (D.G.)
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals National Health Service Foundation Trust, London, United Kingdom (D.G.)
| | - Luke S.P. Moore
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (L.S.P.M.), Imperial College London, United Kingdom
- Chelsea and Westminster National Health Service Foundation Trust, London, United Kingdom (L.S.P.M.)
- Imperial Biomedical Research Centre, Imperial College London and Imperial College National Health Service Healthcare Trust, United Kingdom (L.S.P.M.)
| | - Segun Fatumo
- Department of Non-Communicable Diseases Epidemiology, London School of Hygiene and Tropical Medicine, United Kingdom (S.F.)
| | - Amy M. Mason
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, United Kingdom (A.M.M., S.B.)
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, United Kingdom (A.M.M.)
| | - Verena Zuber
- Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, United Kingdom (A.G., A.J.G., V.Z., S.B.)
- Department of Epidemiology and Biostatistics, School of Public Health (V.Z., D.G.), Imperial College London, United Kingdom
| | - Cristen Willer
- Departments of Internal Medicine, Human Genetics and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor (C.W.)
| | - Humaira Rasheed
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, United Kingdom (V.M.W., R.E.W., H.R., B.B., N.D.)
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing (H.R., B.B., K.H., N.D., B.O.Å.), Norwegian University of Science and Technology, Trondheim
| | - Ben Brumpton
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, United Kingdom (V.M.W., R.E.W., H.R., B.B., N.D.)
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing (H.R., B.B., K.H., N.D., B.O.Å.), Norwegian University of Science and Technology, Trondheim
- Department of Thoracic Medicine (B.B.), St Olavs Hospital, Trondheim University Hospital, Norway
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing (H.R., B.B., K.H., N.D., B.O.Å.), Norwegian University of Science and Technology, Trondheim
- Department of Research, Innovation and Education (K.H.), St Olavs Hospital, Trondheim University Hospital, Norway
| | - Jan Kristian Damås
- Department of Infectious Diseases (J.K.D.), St Olavs Hospital, Trondheim University Hospital, Norway
- Centre of Molecular Inflammation Research, Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim (J.K.D.)
| | - Neil Davies
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, United Kingdom (V.M.W., R.E.W., H.R., B.B., N.D.)
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing (H.R., B.B., K.H., N.D., B.O.Å.), Norwegian University of Science and Technology, Trondheim
| | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing (H.R., B.B., K.H., N.D., B.O.Å.), Norwegian University of Science and Technology, Trondheim
- Department of Endocrinology (B.O.Å.), St Olavs Hospital, Trondheim University Hospital, Norway
| | - Erik Solligård
- Gemini Center for Sepsis Research, Department of Circulation and Medical Imaging (E.S., T.R., E.S.), Norwegian University of Science and Technology, Trondheim
- Clinic of Anesthesia and Intensive Care (E.S.), St Olavs Hospital, Trondheim University Hospital, Norway
| | - Simon Jones
- Division of Immunology, Infection, and Inflammation, Tenovus Institute, Cardiff University, United Kingdom (M.J.P., S.J.)
| | - Stephen Burgess
- Medical Research Council Biostatistics Unit, School of Clinical Medicine, University of Cambridge, United Kingdom (A.G., A.J.G., V.Z., S.B.)
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, United Kingdom (A.M.M., S.B.)
| | - Tormod Rogne
- Gemini Center for Sepsis Research, Department of Circulation and Medical Imaging (E.S., T.R., E.S.), Norwegian University of Science and Technology, Trondheim
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health (V.Z., D.G.), Imperial College London, United Kingdom
- Novo Nordisk Research Centre Oxford, Old Road Campus, United Kingdom (D.G.)
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, United Kingdom (D.G.)
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals National Health Service Foundation Trust, London, United Kingdom (D.G.)
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11
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Khandaker GM, Zuber V, Rees JMB, Carvalho L, Mason AM, Foley CN, Gkatzionis A, Jones PB, Burgess S. Shared mechanisms between coronary heart disease and depression: findings from a large UK general population-based cohort. Mol Psychiatry 2020; 25:1477-1486. [PMID: 30886334 PMCID: PMC7303009 DOI: 10.1038/s41380-019-0395-3] [Citation(s) in RCA: 126] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 01/29/2019] [Accepted: 02/14/2019] [Indexed: 12/15/2022]
Abstract
While comorbidity between coronary heart disease (CHD) and depression is evident, it is unclear whether the two diseases have shared underlying mechanisms. We performed a range of analyses in 367,703 unrelated middle-aged participants of European ancestry from UK Biobank, a population-based cohort study, to assess whether comorbidity is primarily due to genetic or environmental factors, and to test whether cardiovascular risk factors and CHD are likely to be causally related to depression using Mendelian randomization. We showed family history of heart disease was associated with a 20% increase in depression risk (95% confidence interval [CI] 16-24%, p < 0.0001), but a genetic risk score that is strongly associated with CHD risk was not associated with depression. An increase of 1 standard deviation in the CHD genetic risk score was associated with 71% higher CHD risk, but 1% higher depression risk (95% CI 0-3%; p = 0.11). Mendelian randomization analyses suggested that triglycerides, interleukin-6 (IL-6), and C-reactive protein (CRP) are likely causal risk factors for depression. The odds ratio for depression per standard deviation increase in genetically-predicted triglycerides was 1.18 (95% CI 1.09-1.27; p = 2 × 10-5); per unit increase in genetically-predicted log-transformed IL-6 was 0.74 (95% CI 0.62-0.89; p = 0.0012); and per unit increase in genetically-predicted log-transformed CRP was 1.18 (95% CI 1.07-1.29; p = 0.0009). Our analyses suggest that comorbidity between depression and CHD arises largely from shared environmental factors. IL-6, CRP and triglycerides are likely to be causally linked with depression, so could be targets for treatment and prevention of depression.
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Affiliation(s)
- Golam M Khandaker
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Verena Zuber
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Jessica M B Rees
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Livia Carvalho
- Department of Clinical Pharmacology, Queen Mary University of London, London, UK
| | - Amy M Mason
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | | | - Peter B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
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12
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Allara E, Morani G, Carter P, Gkatzionis A, Zuber V, Foley CN, Rees JM, Mason AM, Bell S, Gill D, Lindström S, Butterworth AS, Di Angelantonio E, Peters J, Burgess S. Genetic Determinants of Lipids and Cardiovascular Disease Outcomes: A Wide-Angled Mendelian Randomization Investigation. Circ Genom Precis Med 2019; 12:e002711. [PMID: 31756303 PMCID: PMC6922071 DOI: 10.1161/circgen.119.002711] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 11/15/2019] [Indexed: 12/28/2022]
Abstract
BACKGROUND Evidence from randomized trials has shown that therapies that lower LDL (low-density lipoprotein)-cholesterol and triglycerides reduce coronary artery disease (CAD) risk. However, there is still uncertainty about their effects on other cardiovascular outcomes. We therefore performed a systematic investigation of causal relationships between circulating lipids and cardiovascular outcomes using a Mendelian randomization approach. METHODS In the primary analysis, we performed 2-sample multivariable Mendelian randomization using data from participants of European ancestry. We also conducted univariable analyses using inverse-variance weighted and robust methods, and gene-specific analyses using variants that can be considered as proxies for specific lipid-lowering medications. We obtained associations with lipid fractions from the Global Lipids Genetics Consortium, a meta-analysis of 188 577 participants, and genetic associations with cardiovascular outcomes from 367 703 participants in UK Biobank. RESULTS For LDL-cholesterol, in addition to the expected positive associations with CAD risk (odds ratio [OR] per 1 SD increase, 1.45 [95% CI, 1.35-1.57]) and other atheromatous outcomes (ischemic cerebrovascular disease and peripheral vascular disease), we found independent associations of genetically predicted LDL-cholesterol with abdominal aortic aneurysm (OR, 1.75 [95% CI, 1.40-2.17]) and aortic valve stenosis (OR, 1.46 [95% CI, 1.25-1.70]). Genetically predicted triglyceride levels were positively associated with CAD (OR, 1.25 [95% CI, 1.12-1.40]), aortic valve stenosis (OR, 1.29 [95% CI, 1.04-1.61]), and hypertension (OR, 1.17 [95% CI, 1.07-1.27]), but inversely associated with venous thromboembolism (OR, 0.79 [95% CI, 0.67-0.93]) and hemorrhagic stroke (OR, 0.78 [95% CI, 0.62-0.98]). We also found positive associations of genetically predicted LDL-cholesterol and triglycerides with heart failure that appeared to be mediated by CAD. CONCLUSIONS Lowering LDL-cholesterol is likely to prevent abdominal aortic aneurysm and aortic stenosis, in addition to CAD and other atheromatous cardiovascular outcomes. Lowering triglycerides is likely to prevent CAD and aortic valve stenosis but may increase thromboembolic risk.
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Affiliation(s)
- Elias Allara
- Department of Public Health and Primary Care, BHF Cardiovascular Epidemiology Unit (E.A., P.C., J.M.B.R., A.M.M., S. Bell, A.S.B., E.D.A., J.P., S. Burgess), University of Cambridge, United Kingdom
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics (E.A., S. Bell, A.S.B., E.D.A.), University of Cambridge, United Kingdom
| | - Gabriele Morani
- Dipartimento di Scienze del Sistema Nervoso e del Comportamento, Università degli studi di Pavia, Italy (G.M.)
| | - Paul Carter
- Department of Public Health and Primary Care, BHF Cardiovascular Epidemiology Unit (E.A., P.C., J.M.B.R., A.M.M., S. Bell, A.S.B., E.D.A., J.P., S. Burgess), University of Cambridge, United Kingdom
| | - Apostolos Gkatzionis
- MRC Biostatistics Unit (A.G., V.Z., C.N.F., S. Burgess), University of Cambridge, United Kingdom
| | - Verena Zuber
- MRC Biostatistics Unit (A.G., V.Z., C.N.F., S. Burgess), University of Cambridge, United Kingdom
- Department of Epidemiology and Biostatistics, Imperial College London (V.Z., D.G.)
| | - Christopher N. Foley
- MRC Biostatistics Unit (A.G., V.Z., C.N.F., S. Burgess), University of Cambridge, United Kingdom
| | - Jessica M.B. Rees
- Department of Public Health and Primary Care, BHF Cardiovascular Epidemiology Unit (E.A., P.C., J.M.B.R., A.M.M., S. Bell, A.S.B., E.D.A., J.P., S. Burgess), University of Cambridge, United Kingdom
- Edinburgh Clinical Trials Unit, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, United Kingdom (J.M.B.R.)
| | - Amy M. Mason
- Department of Public Health and Primary Care, BHF Cardiovascular Epidemiology Unit (E.A., P.C., J.M.B.R., A.M.M., S. Bell, A.S.B., E.D.A., J.P., S. Burgess), University of Cambridge, United Kingdom
- National Institute for Health Research Cambridge Biomedical Research Centre (A.M.M, A.S.B., E.D.A), University of Cambridge, United Kingdom
| | - Steven Bell
- Department of Public Health and Primary Care, BHF Cardiovascular Epidemiology Unit (E.A., P.C., J.M.B.R., A.M.M., S. Bell, A.S.B., E.D.A., J.P., S. Burgess), University of Cambridge, United Kingdom
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics (E.A., S. Bell, A.S.B., E.D.A.), University of Cambridge, United Kingdom
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, Imperial College London (V.Z., D.G.)
| | - Sara Lindström
- Department of Epidemiology, University of Washington, Seattle (S.L.)
| | - Adam S. Butterworth
- Department of Public Health and Primary Care, BHF Cardiovascular Epidemiology Unit (E.A., P.C., J.M.B.R., A.M.M., S. Bell, A.S.B., E.D.A., J.P., S. Burgess), University of Cambridge, United Kingdom
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics (E.A., S. Bell, A.S.B., E.D.A.), University of Cambridge, United Kingdom
- National Institute for Health Research Cambridge Biomedical Research Centre (A.M.M, A.S.B., E.D.A), University of Cambridge, United Kingdom
- Health Data Research UK, Cambridge, UK (A.S.B., E.D.A., J.P.)
| | - Emanuele Di Angelantonio
- Department of Public Health and Primary Care, BHF Cardiovascular Epidemiology Unit (E.A., P.C., J.M.B.R., A.M.M., S. Bell, A.S.B., E.D.A., J.P., S. Burgess), University of Cambridge, United Kingdom
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics (E.A., S. Bell, A.S.B., E.D.A.), University of Cambridge, United Kingdom
- National Institute for Health Research Cambridge Biomedical Research Centre (A.M.M, A.S.B., E.D.A), University of Cambridge, United Kingdom
- Health Data Research UK, Cambridge, UK (A.S.B., E.D.A., J.P.)
| | - James Peters
- Department of Public Health and Primary Care, BHF Cardiovascular Epidemiology Unit (E.A., P.C., J.M.B.R., A.M.M., S. Bell, A.S.B., E.D.A., J.P., S. Burgess), University of Cambridge, United Kingdom
- Health Data Research UK, Cambridge, UK (A.S.B., E.D.A., J.P.)
| | - Stephen Burgess
- Department of Public Health and Primary Care, BHF Cardiovascular Epidemiology Unit (E.A., P.C., J.M.B.R., A.M.M., S. Bell, A.S.B., E.D.A., J.P., S. Burgess), University of Cambridge, United Kingdom
- MRC Biostatistics Unit (A.G., V.Z., C.N.F., S. Burgess), University of Cambridge, United Kingdom
| | - INVENT consortium*
- Department of Public Health and Primary Care, BHF Cardiovascular Epidemiology Unit (E.A., P.C., J.M.B.R., A.M.M., S. Bell, A.S.B., E.D.A., J.P., S. Burgess), University of Cambridge, United Kingdom
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics (E.A., S. Bell, A.S.B., E.D.A.), University of Cambridge, United Kingdom
- MRC Biostatistics Unit (A.G., V.Z., C.N.F., S. Burgess), University of Cambridge, United Kingdom
- National Institute for Health Research Cambridge Biomedical Research Centre (A.M.M, A.S.B., E.D.A), University of Cambridge, United Kingdom
- Dipartimento di Scienze del Sistema Nervoso e del Comportamento, Università degli studi di Pavia, Italy (G.M.)
- Health Data Research UK, Cambridge, UK (A.S.B., E.D.A., J.P.)
- Department of Epidemiology and Biostatistics, Imperial College London (V.Z., D.G.)
- Edinburgh Clinical Trials Unit, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, United Kingdom (J.M.B.R.)
- Department of Epidemiology, University of Washington, Seattle (S.L.)
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Abstract
BACKGROUND Selection bias affects Mendelian randomization investigations when selection into the study sample depends on a collider between the genetic variant and confounders of the risk factor-outcome association. However, the relative importance of selection bias for Mendelian randomization compared with other potential biases is unclear. METHODS We performed an extensive simulation study to assess the impact of selection bias on a typical Mendelian randomization investigation. We considered inverse probability weighting as a potential method for reducing selection bias. Finally, we investigated whether selection bias may explain a recently reported finding that lipoprotein(a) is not a causal risk factor for cardiovascular mortality in individuals with previous coronary heart disease. RESULTS Selection bias had a severe impact on bias and Type 1 error rates in our simulation study, but only when selection effects were large. For moderate effects of the risk factor on selection, bias was generally small and Type 1 error rate inflation was not considerable. Inverse probability weighting ameliorated bias when the selection model was correctly specified, but increased bias when selection bias was moderate and the model was misspecified. In the example of lipoprotein(a), strong genetic associations and strong confounder effects on selection mean the reported null effect on cardiovascular mortality could plausibly be explained by selection bias. CONCLUSIONS Selection bias can adversely affect Mendelian randomization investigations, but its impact is likely to be less than other biases. Selection bias is substantial when the effects of the risk factor and confounders on selection are particularly large.
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Affiliation(s)
- Apostolos Gkatzionis
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
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Burgess S, Zuber V, Gkatzionis A, Foley CN. Modal-based estimation via heterogeneity-penalized weighting: model averaging for consistent and efficient estimation in Mendelian randomization when a plurality of candidate instruments are valid. Int J Epidemiol 2018; 47:1242-1254. [PMID: 29846613 PMCID: PMC6124628 DOI: 10.1093/ije/dyy080] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/19/2018] [Indexed: 11/14/2022] Open
Abstract
Background A robust method for Mendelian randomization does not require all genetic variants to be valid instruments to give consistent estimates of a causal parameter. Several such methods have been developed, including a mode-based estimation method giving consistent estimates if a plurality of genetic variants are valid instruments; i.e. there is no larger subset of invalid instruments estimating the same causal parameter than the subset of valid instruments. Methods We here develop a model-averaging method that gives consistent estimates under the same 'plurality of valid instruments' assumption. The method considers a mixture distribution of estimates derived from each subset of genetic variants. The estimates are weighted such that subsets with more genetic variants receive more weight, unless variants in the subset have heterogeneous causal estimates, in which case that subset is severely down-weighted. The mode of this mixture distribution is the causal estimate. This heterogeneity-penalized model-averaging method has several technical advantages over the previously proposed mode-based estimation method. Results The heterogeneity-penalized model-averaging method outperformed the mode-based estimation in terms of efficiency and outperformed other robust methods in terms of Type 1 error rate in an extensive simulation analysis. The proposed method suggests two distinct mechanisms by which inflammation affects coronary heart disease risk, with subsets of variants suggesting both positive and negative causal effects. Conclusions The heterogeneity-penalized model-averaging method is an additional robust method for Mendelian randomization with excellent theoretical and practical properties, and can reveal features in the data such as the presence of multiple causal mechanisms.
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Affiliation(s)
- Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Verena Zuber
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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