Department of Economics and Business Economics

Causal associations between risk factors and common diseases inferred from GWAS summary data

Research output: Research - peer-reviewJournal article

Documents

DOI

  • Zhihong Zhu
    Zhihong ZhuInstitute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.
  • Zhili Zheng
    Zhili ZhengThe Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China.
  • Futao Zhang
    Futao ZhangInstitute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.
  • Yang Wu
    Yang WuInstitute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.
  • Maciej Trzaskowski
    Maciej TrzaskowskiInstitute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.
  • Robert Maier
    Robert MaierInstitute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.
  • Matthew R Robinson
    Matthew R RobinsonInstitute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.
  • John J McGrath
  • Peter M Visscher
    Peter M VisscherQueensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia.
  • Naomi R Wray
    Naomi R WrayQueensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia.
  • Jian Yang
    Jian YangInstitute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia. jian.yang@uq.edu.au.Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia. jian.yang@uq.edu.au.

Health risk factors such as body mass index (BMI) and serum cholesterol are associated with many common diseases. It often remains unclear whether the risk factors are cause or consequence of disease, or whether the associations are the result of confounding. We develop and apply a method (called GSMR) that performs a multi-SNP Mendelian randomization analysis using summary-level data from genome-wide association studies to test the causal associations of BMI, waist-to-hip ratio, serum cholesterols, blood pressures, height, and years of schooling (EduYears) with common diseases (sample sizes of up to 405,072). We identify a number of causal associations including a protective effect of LDL-cholesterol against type-2 diabetes (T2D) that might explain the side effects of statins on T2D, a protective effect of EduYears against Alzheimer's disease, and bidirectional associations with opposite effects (e.g., higher BMI increases the risk of T2D but the effect of T2D on BMI is negative).

Original languageEnglish
JournalNature Communications
Volume9
Pages (from-to)224
ISSN2041-1723
DOIs
StatePublished - 15 Jan 2018

    Research areas

  • Journal Article

See relations at Aarhus University Citationformats

ID: 120617753