xQTLImp: efficient and accurate xQTL summary statistics imputation

Abstract

MotivationQuantitative trait locus (QTL) analysis of multiomic molecular traits, such as gene transcription (eQTL), DNA methylation (mQTL) and histone modification (haQTL), has been widely used to infer the effects of genomic variation on multiple levels of molecular activities. However, the power of xQTL (various types of QTLs) detection is largely limited by missing association statistics due to missing genotypes and limited effective sample size. Existing hidden Markov model (HMM)-based imputation approaches require individual-level genotypes and molecular traits, which are rarely available. No available implementation exists for the imputation of xQTL summary statistics when individual-level data are missed.\n\nResultsWe present xQTLImp, a C++ software package specifically designed for efficient imputation of xQTL summary statistics based on multivariate Gaussian approximation. Experiments on a single-cell eQTL dataset demonstrates that a considerable amount of novel significant eQTL associations can be rediscovered by xQTLImp.\n\nAvailabilitySoftware is available at https://github.com/hitbc/xQTLimp.\n\nContactydwang@hit.edu.cn or jiajiepeng@nwpu.edu.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.