Improved imputation of summary statistics for realistic settings

Abstract

MotivationSummary statistics imputation can be used to infer association summary statistics of an already conducted, genotype-based meta-analysis to higher ge-nomic resolution. This is typically needed when genotype imputation is not feasible for some cohorts. Oftentimes, cohorts of such a meta-analysis are variable in terms of (country of) origin or ancestry. This violates the assumption of current methods that an external LD matrix and the covariance of the Z-statistics are identical.\n\nResultsTo address this issue, we present variance matching, an extention to the existing summary statistics imputation method, which manipulates the LD matrix needed for summary statistics imputation. Based on simulations using real data we find that accounting for ancestry admixture yields noticeable improvement only when the total reference panel size is > 1000. We show that for population specific variants this effect is more pronounced with increasing FST.