Metasubtract: an R-package to analytically produce leave-one-out meta-analysis GWAS summary statistics.

Abstract

statistics from a meta-analysis of genome-wide association studies (meta-GWAS) can be used for many follow-up analyses. One valuable application is the creation of polygenic scores. However, if polygenic scores are calculated in a validation cohort that was part of the meta-GWAS consortium, this cohort is not independent and analyses will therefore yield inflated results. The R package 'MetaSubtract' was developed to subtract the results of the validation cohort from meta-GWAS summary statistics analytically. The statistical formulas for a meta-analysis were inverted to compute corrected summary statistics of a meta-GWAS leaving one (or more) cohort(s) out. These formulas have been implemented in MetaSubtract for different meta-analyses methods (fixed effects inverse variance or square root sample size weighted z-score) accounting for no, single or double genomic control correction. Results obtained by MetaSubtract correlate very well to those calculated using the traditional way, i.e. by performing a meta-analysis leaving out the validation cohort. In conclusion, MetaSubtract allows researchers to compute meta-GWAS summary statistics that are independent of the GWAS results of the validation cohort without requiring access to the cohort level GWAS results of the corresponding meta-GWAS consortium. https://cran.r-project.org/web/packages/MetaSubtract. Supplementary data are available at Bioinformatics online.