Department of Economics and Business Economics

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

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DOI

  • Zhihong Zhu, Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.
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  • Zhili Zheng, The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China.
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  • Futao Zhang, Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.
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  • Yang Wu, Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.
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  • Maciej Trzaskowski, Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.
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  • Robert Maier, Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.
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  • Matthew R Robinson, Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.
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  • John J McGrath
  • Peter M Visscher, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia.
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  • Naomi R Wray, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia.
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  • Jian Yang, Institute 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
Article number224
JournalNature Communications
Volume9
Issue1
Number of pages12
ISSN2041-1723
DOIs
Publication statusPublished - 2018

    Research areas

  • AGE, BODY-MASS INDEX, CORONARY-HEART-DISEASE, EPIDEMIOLOGY, GENOME-WIDE ASSOCIATION, INSTRUMENTS, LIFE-STYLE, MACULAR DEGENERATION, MENDELIAN RANDOMIZATION, TYPE-2 DIABETES-MELLITUS

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