GIGI2: A Fast Approach for Parallel Genotype Imputation in Large Pedigrees

Abstract MotivationImputation of untyped SNPs has become important in Genome-wide Association Studies (GWAS). There has also been a trend towards analyzing rare variants, driven by the decrease of genome sequencing costs. Rare variants are enriched in pedigrees that have many cases or extreme phenotypes. This is especially the case for large pedigrees, which makes family-based designs ideal to detect rare variants associated with complex traits. The costs of performing relatively large family-based GWAS can be significantly reduced by fully sequencing only a fraction of the pedigree and performing imputation on the remaining subjects. The program GIGI can efficiently perform imputation in large pedigrees but can be time consuming. Here, we implement GIGI’s imputation approach in a new program, GIGI2, which performs imputation with computational time reduced by at least 25x on one thread and 120x on eight threads. The memory usage of GIGI2 is reduced by at least 30x. This reduction is achieved by implementing better memory layout and a better algorithm for solving the Identity by Descent graphs, as well as with additional features, including multithreading. We also make GIGI2 available as a webserver based on the same framework as the Michigan Imputation Server. AvailabilityGIGI2 is freely available online at https://cse-git.qcri.org/eullah/GIGI2 and the websever is at https://imputation.qcri.org/ Contactmsaad@hbku.edu.qa