Abstract
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).
1 Application
Application ID | Title |
12514 | The limits of predicting complex traits and diseases from genetic data |
1 Return
Return ID | App ID | Description | Archive Date |
3084 | 12514 | Causal associations between risk factors and common diseases inferred from GWAS summary data | 16 Dec 2020 |