ArticleGene2015
High-accuracy haplotype imputation using unphased genotype data as the references.
Article in Gene, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Missing genotype imputation in non-model species using self-organizing maps.Molecular ecology resources · 2025Article
- A Pipeline for Phasing and Genotype Imputation on Mixed Human Data (Parents-Offspring Trios and Unrelated Subjects) by Reviewing Current Methods and Software.Life (Basel, Switzerland) · 2022Review
- Chromosome-Range Whole-Genome High-Throughput Experimental Haplotyping by Single-Chromosome Microdissection.Methods in molecular biology (Clifton, N.J.) · 2017Article
- Data supporting the high-accuracy haplotype imputation using unphased genotype data as the references.Data in brief · 2016Article
- References for Haplotype Imputation in the Big Data Era.Molecular biology (Los Angeles, Calif.) · 2015Article
- The mirror RNA expression pattern in human tissues.Precision medicine · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Enormously growing genomic datasets present a new challenge on missing data imputation, a notoriously resource-demanding task. Haplotype imputation requires ethnicity-matched references. However, to date, haplotype references are not available for the majority of populations in the world. We explored to use existing unphased genotype datasets as references; if it succeeds, it will cover almost all of the populations in the world. The results showed that our HiFi software successfully yields 99.43% accuracy with unphased genotype references. Our method provides a cost-effective solution to breakthrough the bottleneck of limited reference availability for haplotype imputation in the big data era.
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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.