Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors

Abstract

Finding individual-level data for adequately-powered Mendelian randomization analyses may be problematic. As publicly-available summarized data on genetic associations with disease outcomes from large consortia are becoming more abundant, use of published data is an attractive analysis strategy for obtaining precise estimates of the causal effects of risk factors on outcomes. We detail the necessary steps for conducting Mendelian randomization investigations using published data, and present novel statistical methods for combining data on the associations of multiple (correlated or uncorrelated) genetic variants with the risk factor and outcome into a single causal effect estimate. A two-sample analysis strategy may be employed, in which evidence on the gene-risk factor and gene-outcome associations are taken from different data sources. These approaches allow the efficient identification of risk factors that are suitable targets for clinical intervention from published data, although the ability to assess the assumptions necessary for causal inference is diminished. Methods and guidance are illustrated using the example of the causal effect of serum calcium levels on fasting glucose concentrations. The estimated causal effect of a 1 standard deviation (0.13 mmol/L) increase in calcium levels on fasting glucose (mM) using a single lead variant from the CASR gene region is 0.044 (95 % credible interval -0.002, 0.100). In contrast, using our method to account for the correlation between variants, the corresponding estimate using 17 genetic variants is 0.022 (95 % credible interval 0.009, 0.035), a more clearly positive causal effect.

Fig. 1

Schematic diagram outlining the Mendelian randomization approach

Fig. 2

Associations with a range of covariates of weighted allele scores based on genetic variants associated with calcium levels for: (top) 17 variants in and around the CASR gene region; (bottom) 10 variants in different gene regions. Estimates are coefficients for the difference in the covariate measured in standard deviations per unit increase in the allele score [a unit increase in the allele score is scaled to be associated with a 1 standard deviation (0.13 mmol/L) increase in calcium levels]. Coefficients are obtained from the EPIC-InterAct dataset using linear regression with adjustment for age, sex and centre. Lines are 95 % confidence intervals

Fig. 3

Association of genetic variants with fasting glucose (mM) obtained from publicly-available data from MAGIC consortium against association with calcium levels (mmol/L) obtained from EPIC-InterAct per calcium-increasing allele for: (top) 17 variants in and around the CASR gene region; (bottom) the subset of 6 variants in and around the CASR gene region associated with calcium levels ($p<0.1$). Lines represent 95 % confidence intervals