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

Zhihong Zhu, Zhili Zheng, Futao Zhang, Yang Wu, Maciej Trzaskowski, Robert Maier, Matthew R. Robinson, John J. McGrath, Peter M. Visscher, Naomi R. Wray and Jian Yang ()
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Zhihong Zhu: The University of Queensland
Zhili Zheng: The University of Queensland
Futao Zhang: The University of Queensland
Yang Wu: The University of Queensland
Maciej Trzaskowski: The University of Queensland
Robert Maier: The University of Queensland
Matthew R. Robinson: The University of Queensland
John J. McGrath: The University of Queensland
Peter M. Visscher: The University of Queensland
Naomi R. Wray: The University of Queensland
Jian Yang: The University of Queensland

Nature Communications, 2018, vol. 9, issue 1, 1-12

Abstract: Abstract 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).

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-017-02317-2

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DOI: 10.1038/s41467-017-02317-2

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