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format_radial

Source: R/format_radial.R
format_radial.Rd

A function which restructures summary GWAS data for downstream two-sample Mendelian randomization analyses. Where variant identification numbers are not provided, an index vector is generated corresponding to the ordering of variants provided.

Usage

format_radial(BXG, BYG, seBXG, seBYG, RSID)

Arguments

BXG

A numeric vector of beta-coefficient values for genetic associations with the first variable (exposure).

BYG

A numeric vector of beta-coefficient values for genetic associations with the second variable (outcome).

seBXG

The standard errors corresponding to the beta-coefficients BXG.

seBYG

The standard errors corresponding to the beta-coefficients BYG.

RSID

A vector of names for genetic variants included in the analysis. If variant IDs are not provided (RSID = "NULL"), a vector of ID numbers will be generated.

Value

The function provides a data frame containing the following columns:

SNP

The identification number for each variant

beta.exposure

The association estimate for the genetic variant with respect to the exposure

beta.outcome

The association estimate for the genetic variant with respect to the outcome

se.exposure

The standard error for the variant-exposure association beta.exposure

se.outcome

The standard error for the variant-outcome association beta.outcome

References

Bowden, J., et al., Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. International Journal of Epidemiology, 2018. 47(4): p. 1264-1278.

Author

Wes Spiller; Jack Bowden; Tom Palmer.

Examples

ldl.dat <- data_radial[data_radial[,10]<5e-8,]
ldl.fdat <- format_radial(ldl.dat[,6], ldl.dat[,9],
                          ldl.dat[,15], ldl.dat[,21], ldl.dat[,1])
head(ldl.fdat)
#>          SNP beta.exposure beta.outcome se.exposure se.outcome
#> 1 rs10903129        -0.033       -0.012 0.003692528 0.01366904
#> 2  rs1998013        -0.380       -0.150 0.021953470 0.09647707
#> 3  rs4587594        -0.049        0.017 0.003842235 0.01509245
#> 4  rs6603981         0.034        0.012 0.004444080 0.01698989
#> 5   rs646776         0.160        0.094 0.004375672 0.01724356
#> 6  rs1010167        -0.025       -0.028 0.003969023 0.01897288
class(ldl.fdat)
#> [1] "data.frame" "rmr_format"