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

By Zhihong Zhu, Zhili Zheng, Futao Zhang, Yang Wu, M. Trzaskowski, Robert M. Maier, M. Robinson, John J. McGrath, Peter M. Visscher, Naomi R. Wray and 1 more. Nature Communications, 2017

942 citations recorded by the source corpus.

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). Genetic methods are useful to test whether risk factors are causal for or consequence of disease. Here, Zhu et al. develop a generalized summary-based Mendelian Randomization (GSMR) method which uses summary-level data from GWAS to test for causal associations of health risk factors with common diseases.

Subfields: Epidemiology, Genetics, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/2ecc919aaa4c8b3a526d2ddd906aae5f9255fa4a

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Citations in the corpus

Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, methodology): “Other software packages are available such as MendelianRandomization (96) and gsmr (62) and in Stata, mrrobust (100).”
Bayesian weighted Mendelian randomization for causal inference based on summary statistics (2018, methodology): “Based on the idea of MR, the effective random assignment criterion requires Gj to be independent of the confounder U ; The exclusion restriction criterion assumes the SNPs only affect the outcome Y through the exposure X (Greenland, 2000; Clarke and Windmeijer, 2012).”
Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms. (2020, positive): “We used the GSMR method ( 29 ) to perform Mendelian randomization (MR) using near-independent [linkage disequilibrium (LD) R 2 = 0.05, where R 2 is the coefficient of determination] cis-pQTLs for IL17RA .”
Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis (2020, background): “Importantly, such univariable MR approaches that are more robust to horizontal pleiotropy notably do not, with a few exceptions [56,70], allow simultaneous statistical adjustment for multiple traits.”
Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets (2023, positive): “Of note, our MR analysis revealed that the association of CXCL5 was restricted to UC (unadjusted P = 2.3 × 10 −6 , GSMR), with no significant association in Crohn’s disease (CD; unadjusted P = 0.4) (Fig.”