ArticleGenetics2017
GeneImp: Fast Imputation to Large Reference Panels Using Genotype Likelihoods from Ultralow Coverage Sequencing.
Article in Genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data Integration, Imputation, and Meta-analysis for Genome-Wide Association Studies.Methods in molecular biology (Clifton, N.J.) · 2022Pooled it
- Advances in haplotype phasing and genotype imputation.Nature reviews. Genetics · 2026Review
- Imputation of ancient canid genomes reveals inbreeding history over the past 10,000 years.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Assessing Genotype Imputation Methods for Low-Coverage Sequencing Data in Populations With Differing Relatedness and Inbreeding Levels.Molecular ecology resources · 2025Article
- High performance imputation of structural and single nucleotide variants using low-coverage whole genome sequencing.Genetics, selection, evolution : GSE · 2025Article
- Low-coverage whole genome sequencing for a highly selective cohort of severe COVID-19 patients.GigaByte (Hong Kong, China) · 2024Article
- Review: Computational analysis of human skeletal remains in ancient DNA and forensic genetics.iScience · 2023Review
- Imputation of ancient human genomes.Nature communications · 2023Article
- Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing.Genetics, selection, evolution : GSE · 2022Article
- A joint use of pooling and imputation for genotyping SNPs.BMC bioinformatics · 2022Article
- Article
- Best practices for analyzing imputed genotypes from low-pass sequencing in dogs.Mammalian genome : official journal of the International Mammalian Genome Society · 2022Article
- Genotyping, the Usefulness of Imputation to Increase SNP Density, and Imputation Methods and Tools.Methods in molecular biology (Clifton, N.J.) · 2022Article
- An autoencoder-based deep learning method for genotype imputation.Frontiers in artificial intelligence · 2022Article
- Constructing germline research cohorts from the discarded reads of clinical tumor sequences.Genome medicine · 2021Article
- Rapid genotype imputation from sequence with reference panels.Nature genetics · 2021Article
- Efficient phasing and imputation of low-coverage sequencing data using large reference panels.Nature genetics · 2021Article
- imputeqc: an R package for assessing imputation quality of genotypes and optimizing imputation parameters.BMC bioinformatics · 2020Article
- Optimal sequencing depth design for whole genome re-sequencing in pigs.BMC bioinformatics · 2019Article
- Protocols, Methods, and Tools for Genome-Wide Association Studies (GWAS) of Dental Traits.Methods in molecular biology (Clifton, N.J.) · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.