One useful way of identifying overly influential outlier studies in meta-analyses and their direction of effect is through the calculation of SPRE (standardised predicted random-effects) statistics. SPRE statistics are precision-weighted residuals that capture the direction and extent to which genetic effects of different studies in a meta-analysis deviate from the average genetic effect at a variant of interest. Positive outliers have the potential to inflate average genetic effects in a meta-analysis whilst negative outliers might lower or change the direction of effect.
getspres facilitates calculation of SPRE statistics in R and provides forest plots that show corresponding SPRE statistic values for participating studies in meta-analyses.
An advantage of using the getspres package is that it provides a quantitative and visual view of heterogeneity at individual genetic variants in meta-analyses.
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Consider a GWAS meta-analysis (P), with S GWAS studies (s = 1,2,3,⋯,S) and V independently associated variants (v = 1,2,3,⋯,V). Data for each variant are analyzed using a random-effects model to estimate the average genetic effect and partition the variability in study effect-sizes into random sampling and heterogeneity components. Then, the standardized predicted random effect (SPRE) for the vth variant in the sth study is calculated as:
See the following references for more details:
Harbord, R. M., & Higgins, J. P. T. (2008). Meta-regression in Stata. Stata Journal 8: 493–519.
Magosi LE, Goel A, Hopewell JC, Farrall M, on behalf of the CARDIoGRAMplusC4D Consortium (2017) Identifying systematic heterogeneity patterns in genetic association meta-analysis studies. PLoS Genet 13(5): e1006755. https://doi.org/10.1371/journal.pgen.1006755.
# To install the release version from CRAN:
install.packages("getspres")
# Load libraries
library(getspres) # for calculating SPRE statistics
# To install the development version from GitHub:
# install devtools
install.packages("devtools")
# install getspres
library(devtools)
devtools::install_github("magosil86/getspres")
# Load libraries
library(getspres) # for calculating SPRE statistics
Load the getspres package in your current R session, and try some examples in the example workflow
# Load libraries
library(getspres) # for calculating SPRE statistics
For an overview of available functions in getspres, type ?getspres and ?plotspres at the R prompt.
To suggest a new feature, report a bug or ask for help, please provide a reproducible example at: https://github.com/magosil86/getspres/issues. Also see reprex to learn more about generating reproducible examples.
Lerato E Magosi, Anuj Goel, Jemma C Hopewell, Martin Farrall, on behalf of the CARDIoGRAMplusC4D Consortium, Identifying small-effect genetic associations overlooked by the conventional fixed-effect model in a large-scale meta-analysis of coronary artery disease, Bioinformatics, , btz590, https://doi-org.ezp-prod1.hul.harvard.edu/10.1093/bioinformatics/btz590
Harbord, R. M., & Higgins, J. P. T. (2008). Meta-regression in Stata. Stata Journal 8: 493–519.
Wolfgang Viechtbauer (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1-48. URL http://www.jstatsoft.org/v36/i03/.
Magosi LE, Goel A, Hopewell JC, Farrall M, on behalf of the CARDIoGRAMplusC4D Consortium (2017) Identifying systematic heterogeneity patterns in genetic association meta-analysis studies. PLoS Genet 13(5): e1006755. https://doi.org/10.1371/journal.pgen.1006755.
Lerato E. Magosi 
Lerato E. Magosi lmagosi@well.ox.ac.uk or magosil86@gmail.com
Lerato E Magosi, Jemma C Hopewell and Martin Farrall (2018). getspres: SPRE Statistics for Exploring Heterogeneity in Meta-Analysis. R package version 0.1.0.9000. https://magosil86.github.io/getspres/
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