When there are duplicate summary sets for a particular exposure-outcome combination, this function keeps the exposure-outcome summary set with the highest expected statistical power. This can be done by dropping the duplicate summary sets with the smaller sample sizes. Alternatively, the pruning procedure can take into account instrument strength and outcome sample size. The latter is useful, for example, when there is considerable variation in SNP coverage between duplicate summary sets (e.g. because some studies have used targeted or fine mapping arrays). If there are a large number of SNPs available to instrument an exposure, the outcome GWAS with the better SNP coverage may provide better power than the outcome GWAS with the larger sample size.

power_prune(dat, method = 1, dist.outcome = "binary")



Results from harmonise_data().


Should the duplicate summary sets be pruned on the basis of sample size alone (method = 1) or a combination of instrument strength and sample size (method = 2)? Default set to 1. When set to 1, the duplicate summary sets are first dropped on the basis of the outcome sample size (smaller duplicates dropped). If duplicates are still present, remaining duplicates are dropped on the basis of the exposure sample size (smaller duplicates dropped). When method is set to 2, duplicates are dropped on the basis of instrument strength (amount of variation explained in the exposure by the instrumental SNPs) and sample size, and assumes that the SNP-exposure effects correspond to a continuous trait with a normal distribution (i.e. exposure cannot be binary). The SNP-outcome effects can correspond to either a binary or continuous trait. If the exposure is binary then method=1 should be used.


The distribution of the outcome. Can either be "binary" or "continuous". Default set to "binary".


data.frame with duplicate summary sets removed