. 2021 Jul 9;50(3):893-901.

doi: 10.1093/ije/dyaa216.

High-throughput multivariable Mendelian randomization analysis prioritizes apolipoprotein B as key lipid risk factor for coronary artery disease

Verena Zuber^{1

2}, Dipender Gill¹, Mika Ala-Korpela^{3

4}, Claudia Langenberg⁵, Adam Butterworth^{6

7

8

9

10

11}, Leonardo Bottolo^{2

12

13}, Stephen Burgess^{2

6}

Affiliations

¹ Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
² MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
³ Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu, Finland.
⁴ NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland.
⁵ MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
⁶ Department of Public Health and Primary Care, British Heart Foundation Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK.
⁷ British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
⁸ National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK.
⁹ National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK.
¹⁰ Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
¹¹ Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK.
¹² Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
¹³ Alan Turing Institute, London, UK.

PMID: 33130851
PMCID: PMC8271202
DOI: 10.1093/ije/dyaa216

High-throughput multivariable Mendelian randomization analysis prioritizes apolipoprotein B as key lipid risk factor for coronary artery disease

Verena Zuber et al. Int J Epidemiol. 2021.

. 2021 Jul 9;50(3):893-901.

doi: 10.1093/ije/dyaa216.

Authors

Verena Zuber^{1

2}, Dipender Gill¹, Mika Ala-Korpela^{3

4}, Claudia Langenberg⁵, Adam Butterworth^{6

7

8

9

10

11}, Leonardo Bottolo^{2

12

13}, Stephen Burgess^{2

6}

Affiliations

¹ Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
² MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
³ Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu, Finland.
⁴ NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland.
⁵ MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
⁶ Department of Public Health and Primary Care, British Heart Foundation Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK.
⁷ British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
⁸ National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK.
⁹ National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK.
¹⁰ Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
¹¹ Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK.
¹² Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
¹³ Alan Turing Institute, London, UK.

PMID: 33130851
PMCID: PMC8271202
DOI: 10.1093/ije/dyaa216

Abstract

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.

Keywords: Lipoproteins; Mendelian randomization; apolipoprotein B; blood lipids; coronary artery disease; metabolomics; risk factor selection.

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Figures

**Figure 1**
Diagram illustrating multivariable Mendelian randomization for selecting causal risk factors for coronary artery disease (CAD) from a large number of candidate risk factors, e.g. metabolites measured using nuclear magnetic resonance (NMR) spectroscopy. G, genetic variants; $M_{1}, \dots, M_{30}$ , metabolites as risk factors; *CAD*, coronary artery disease as outcome; U, confounders

**Figure 2**
Schematic diagram of the study design and results for the main, supplementary and sensitivity analyses. Selected risk factors are those which had a empirical P-value of less than 0.05 after correction for multiple testing

**Figure 3**
Estimates of genetic associations with coronary artery disease (CAD) risk (y-axis) against genetic associations with apolipoprotein B (ApoB, x-axis) for each genetic variant from the primary analysis using CARDIoGRAMplusC4D and UK Biobank. Outliers removed from the analysis are highlighted as diamonds (◆) and their annotated gene-region is displayed

See this image and copyright information in PMC

References

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High-throughput multivariable Mendelian randomization analysis prioritizes apolipoprotein B as key lipid risk factor for coronary artery disease

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High-throughput multivariable Mendelian randomization analysis prioritizes apolipoprotein B as key lipid risk factor for coronary artery disease

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