If results from a meta-GWAS are used for validation in one of the cohorts that was included in the meta-analysis, this will yield biased (i.e. too optimistic) results. The validation cohort needs to be independent from the meta-GWAS results. MetaSubtract will subtract the results of the respective cohort from the meta-GWAS results analytically without having to redo the meta-GWAS analysis using the leave-one-out methodology. It can handle different meta-analyses methods and takes into account if single or double genomic control correction was applied to the original meta-analysis. It can be used for whole GWAS, but also for a limited set of SNPs or other genetic markers.
The core of this package is the function
meta.subtract. The merging of cohort results with the meta-GWAS summary statistics is done based on marker identification code (e.g. rs-number or CHR:POSITION:TYPE). The script will check for allele flips and strand mismatches. If after allele flip and strand correction the alleles still don't match, the statistics for those markers will be set to missing in the cohort results (implying that the meta-GWAS results are not corrected) and a list of corresponding marker identification codes will be saved to a file with the name of the cohort's results file extended with '.allele_mismatch.txt'
Ilja M. Nolte
Maintainer: Ilja M. Nolte <email@example.com>
Nolte et al. (2017). Missing heritability: is the gap closing? An analysis of 32 complex traits in the Lifelines Cohort Study. Eur J Hum Genet. 2017;25:877-885.
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