Verena Zuber, Dipender Gill, Mika Ala-Korpela, Claudia Langenberg, Adam Butterworth, Leonardo Bottolo, Stephen Burgess, High-throughput multivariable Mendelian randomization analysis prioritizes apolipoprotein B as key lipid risk factor for coronary artery disease, International Journal of Epidemiology, Volume 50, Issue 3, June 2021, Pages 893–901, https://doi.org/10.1093/ije/dyaa216
High-throughput multivariable Mendelian randomization analysis prioritizes apolipoprotein B as key lipid risk factor for coronary artery disease
|Author:||Zuber, Verena1,2; Gill, Dipender1; Ala-Korpela, Mika3,4;|
1Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
2MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
3Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu, Finland
4NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
5MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
6Department of Public Health and Primary Care, British Heart Foundation Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
7British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
8National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
9National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
10Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
11Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
12Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK
13Alan Turing Institute, London, UK
14MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge , Cambridge, UK
15Department of Public Health and Primary Care, British Heart Foundation Cardiovascular Epidemiology Unit , University of Cambridge, Cambridge, UK
|Online Access:||PDF Full Text (PDF, 0.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021101450976
Oxford University Press,
|Publish Date:|| 2021-10-14
Background: Genetic variants can be used to prioritize risk factors as potential therapeutic targets via Mendelian randomization (MR). An agnostic statistical framework using Bayesian model averaging (MR-BMA) can disentangle the causal role of correlated risk factors with shared genetic predictors. Here, our objective is to identify lipoprotein measures as mediators between lipid-associated genetic variants and coronary artery disease (CAD) for the purpose of detecting therapeutic targets for CAD.
Methods: As risk factors we consider 30 lipoprotein measures and metabolites derived from a high-throughput metabolomics study including 24 925 participants. We fit multivariable MR models of genetic associations with CAD estimated in 453 595 participants (including 113 937 cases) regressed on genetic associations with the risk factors. MR-BMA assigns to each combination of risk factors a model score quantifying how well the genetic associations with CAD are explained. Risk factors are ranked by their marginal score and selected using false-discovery rate (FDR) criteria. We perform supplementary and sensitivity analyses varying the dataset for genetic associations with CAD.
Results: In the main analysis, the top combination of risk factors ranked by the model score contains apolipoprotein B (ApoB) only. ApoB is also the highest ranked risk factor with respect to the marginal score (FDR <0.005). Additionally, ApoB is selected in all sensitivity analyses. No other measure of cholesterol or triglyceride is consistently selected otherwise.
Conclusions: Our agnostic genetic investigation prioritizes ApoB across all datasets considered, suggesting that ApoB, representing the total number of hepatic-derived lipoprotein particles, is the primary lipid determinant of CAD.
International journal of epidemiology
|Pages:||893 - 901|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3121 General medicine, internal medicine and other clinical medicine
This work was supported by the UK Medical Research Council (MC_UU_00002/7). S.B. and V.Z. are supported by Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant Number 204623/Z/16/Z). D.G. was supported by the Wellcome Trust 4i Programme (203928/Z/16/Z) and British Heart Foundation Centre of Research Excellence (RE/18/4/34215) at Imperial College London. M.A.K was supported by a research grant from the Sigrid Juselius Foundation, Finland. This work was supported by core funding from: the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (RG/13/13/30194; RG/18/13/33946), the National Institute for Health Research (Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust) and Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. L.B. was supported by the BHF-Turing Cardiovascular Data Science Awards 2017 and the Alan Turing Institute under the Engineering and Physical Sciences Research Council grant EP/N510129/1. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.
© The Author(s) 2020. Published by Oxford University Press on behalf of the International Epidemiological Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.