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Genome-wide association studies generate very large datasets that requirescalable analysis algorithms. In this report we describe the GEDI softwarepackage, which implements efficient algorithms for performing several commontasks in the analysis of population genotype data, including genotype errordetection and correction, imputation of both randomly missing and untypedgenotypes, and genotype phasing. Experimental results show that GEDI achieveshigh accuracy with a runtime scaling linearly with the number of markers andsamples. The open source C++ code of GEDI, released under the GNU GeneralPublic License, is available for download athttp://dna.engr.uconn.edu/software/GEDI/

GEDI: Scalable Algorithms for Genotype Error Detection and Imputation