GeneImp: Fast Imputation to Large Reference Panels Using Genotype Likelihoods from Ultralow Coverage Sequencing. Issue 1 (1st May 2017)

• Record Type: Journal Article
• Authors: Spiliopoulou, Athina; Colombo, Marco; Orchard, Peter; Agakov, Felix; McKeigue, Paul
• Is Part Of: Genetics. Volume 206:Issue 1(2017)
• Page Start: 91
• Page End: 104
• Publication Date: 2017-05-01
• Subjects: genotype imputation -- genotype likelihood -- imputation from genotype likelihoods -- GeneImp -- phasing -- no prephasing; Genetics -- Periodicals; 576.5
• DOI: 10.1534/genetics.117.200063
• Languages: English
• ISSNs: 0016-6731

Abstract

Abstract: We address the task of genotype imputation to a dense reference panel given genotype likelihoods computed from ultralow coverage sequencing as inputs. In this setting, the data have a high-level of missingness or uncertainty, and are thus more amenable to a probabilistic representation. Most existing imputation algorithms are not well suited for this situation, as they rely on prephasing for computational efficiency, and, without definite genotype calls, the prephasing task becomes computationally expensive. We describe GeneImp, a program for genotype imputation that does not require prephasing and is computationally tractable for whole-genome imputation. GeneImp does not explicitly model recombination, instead it capitalizes on the existence of large reference panels—comprising thousands of reference haplotypes—and assumes that the reference haplotypes can adequately represent the target haplotypes over short regions unaltered. We validate GeneImp based on data from ultralow coverage sequencing (0.5×), and compare its performance to the most recent version of BEAGLE that can perform this task. We show that GeneImp achieves imputation quality very close to that of BEAGLE, using one to two orders of magnitude less time, without an increase in memory complexity. Therefore, GeneImp is the first practical choice for whole-genome imputation to a dense reference panel when prephasing cannot be applied, for instance, in datasets produced via ultralow coverage sequencing. A related future application for GeneImp is whole-genome imputation based on the off-target reads from deep whole-exome sequencing.