A Mendelian randomization study of the role of lipoprotein subfractions in coronary artery disease

  1. Qingyuan Zhao  Is a corresponding author
  2. Jingshu Wang
  3. Zhen Miao
  4. Nancy R Zhang
  5. Sean Hennessy
  6. Dylan S Small
  7. Daniel J Rader
  1. University of Cambridge, United Kingdom
  2. University of Chicago, United States
  3. University of Pennsylvania, United States

Abstract

Recent genetic data can offer important insights into the roles of lipoprotein subfractions and particle sizes in preventing coronary artery disease (CAD), as previous observational studies have often reported conflicting results. We used the LD score regression to estimate the genetic correlation of 77 subfraction traits with traditional lipid profile and identified 27 traits that may represent distinct genetic mechanisms. We then used Mendelian randomization (MR) to estimate the causal effect of these traits on the risk of CAD. In univariable MR, the concentration and content of medium high-density lipoprotein (HDL) particles showed a protective effect against CAD. The effect was not attenuated in multivariable analyses. Multivariable MR analyses also found that small HDL particles and smaller mean HDL particle diameter may have a protective effect. We identified four genetic markers for HDL particle size and CAD. Further investigations are needed to fully understand the role of HDL particle size.

Data availability

GWAS data used in the data are publicly available. Details can be found in Table 1.

The following previously published data sets were used

Article and author information

Author details

  1. Qingyuan Zhao

    Statistical Laboratory, University of Cambridge, Cambridge, United Kingdom
    For correspondence
    qyzhao@statslab.cam.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9902-2768
  2. Jingshu Wang

    Department of Statistics, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Zhen Miao

    Genomics and Computational Biology, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3255-9517
  4. Nancy R Zhang

    Department of Statistics, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Sean Hennessy

    Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Dylan S Small

    Department of Statistics, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4928-2646
  7. Daniel J Rader

    Department of Medicine, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.

Funding

No external funding was received for this work.

Copyright

© 2021, Zhao et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,210
    views
  • 265
    downloads
  • 27
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Qingyuan Zhao
  2. Jingshu Wang
  3. Zhen Miao
  4. Nancy R Zhang
  5. Sean Hennessy
  6. Dylan S Small
  7. Daniel J Rader
(2021)
A Mendelian randomization study of the role of lipoprotein subfractions in coronary artery disease
eLife 10:e58361.
https://doi.org/10.7554/eLife.58361

Share this article

https://doi.org/10.7554/eLife.58361

Further reading

    1. Genetics and Genomics
    2. Neuroscience
    Thomas P Spargo, Lachlan Gilchrist ... Alfredo Iacoangeli
    Research Article

    Continued methodological advances have enabled numerous statistical approaches for the analysis of summary statistics from genome-wide association studies. Genetic correlation analysis within specific regions enables a new strategy for identifying pleiotropy. Genomic regions with significant ‘local’ genetic correlations can be investigated further using state-of-the-art methodologies for statistical fine-mapping and variant colocalisation. We explored the utility of a genome-wide local genetic correlation analysis approach for identifying genetic overlaps between the candidate neuropsychiatric disorders, Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia, Parkinson’s disease, and schizophrenia. The correlation analysis identified several associations between traits, the majority of which were loci in the human leukocyte antigen region. Colocalisation analysis suggested that disease-implicated variants in these loci often differ between traits and, in one locus, indicated a shared causal variant between ALS and AD. Our study identified candidate loci that might play a role in multiple neuropsychiatric diseases and suggested the role of distinct mechanisms across diseases despite shared loci. The fine-mapping and colocalisation analysis protocol designed for this study has been implemented in a flexible analysis pipeline that produces HTML reports and is available at: https://github.com/ThomasPSpargo/COLOC-reporter.

    1. Chromosomes and Gene Expression
    2. Genetics and Genomics
    Arkadiy K Golov, Alexey A Gavrilov ... Sergey V Razin
    Research Article

    The enhancer-promoter looping model, in which enhancers activate their target genes via physical contact, has long dominated the field of gene regulation. However, the ubiquity of this model has been questioned due to evidence of alternative mechanisms and the lack of its systematic validation, primarily owing to the absence of suitable experimental techniques. In this study, we present a new MNase-based proximity ligation method called MChIP-C, allowing for the measurement of protein-mediated chromatin interactions at single-nucleosome resolution on a genome-wide scale. By applying MChIP-C to study H3K4me3 promoter-centered interactions in K562 cells, we found that it had greatly improved resolution and sensitivity compared to restriction endonuclease-based C-methods. This allowed us to identify EP300 histone acetyltransferase and the SWI/SNF remodeling complex as potential candidates for establishing and/or maintaining enhancer-promoter interactions. Finally, leveraging data from published CRISPRi screens, we found that most functionally verified enhancers do physically interact with their cognate promoters, supporting the enhancer-promoter looping model.