Evidence map›Paper›PMID 21705746›Full record

ArticleGenetics2011

Imputation of missing genotypes from sparse to high density using long-range phasing.

Hans D Daetwyler, George R Wiggans, Ben J Hayes, John A Woolliams, Mike E Goddard

Abstract read
In one paragraph

Article in Genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  13. Exome sequence genotype imputation in globally diverse hexaploid wheat accessions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2017
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  18. Identification of key ancestors of modern germplasm in a breeding program of maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2014
    Article
  19. Accuracy of genotype imputation in Nelore cattle.Genetics, selection, evolution : GSE · 2014
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Hans D DaetwylerBiosciences Research Division, Department of Primary Industries, Bundoora 3083, Australia. hans.daetwyler@dpi.vic.gov.au
George R Wiggans
Ben J Hayes
John A Woolliams
Mike E Goddard

Funding

Biotechnology and Biological Sciences Research Council BBS/E/D/05191133
6 · The paper itself

Abstract

Related individuals share potentially long chromosome segments that trace to a common ancestor. We describe a phasing algorithm (ChromoPhase) that utilizes this characteristic of finite populations to phase large sections of a chromosome. In addition to phasing, our method imputes missing genotypes in individuals genotyped at lower marker density when more densely genotyped relatives are available. ChromoPhase uses a pedigree to collect an individual's (the proband) surrogate parents and offspring and uses genotypic similarity to identify its genomic surrogates. The algorithm then cycles through the relatives and genomic surrogates one at a time to find shared chromosome segments. Once a segment has been identified, any missing information in the proband is filled in with information from the relative. We tested ChromoPhase in a simulated population consisting of 400 individuals at a marker density of 1500/M, which is approximately equivalent to a 50K bovine single nucleotide polymorphism chip. In simulated data, 99.9% loci were correctly phased and, when imputing from 100 to 1500 markers, more than 87% of missing genotypes were correctly imputed. Performance increased when the number of generations available in the pedigree increased, but was reduced when the sparse genotype contained fewer loci. However, in simulated data, ChromoPhase correctly imputed at least 12% more genotypes than fastPHASE, depending on sparse marker density. We also tested the algorithm in a real Holstein cattle data set to impute 50K genotypes in animals with a sparse 3K genotype. In these data 92% of genotypes were correctly imputed in animals with a genotyped sire. We evaluated the accuracy of genomic predictions with the dense, sparse, and imputed simulated data sets and show that the reduction in genomic evaluation accuracy is modest even with imperfectly imputed genotype data. Our results demonstrate that imputation of missing genotypes, and potentially full genome sequence, using long-range phasing is feasible.

Indexed as

GenotypePolymorphism, Single NucleotideAllelesAnimalsCattleCrossing Over, GeneticFemaleGenetic DriftGenetic MarkersGenetics, PopulationGenome-Wide Association StudyMaleQuantitative Trait LociGenetic Markers

Identifiers

PMID21705746
PMCPMC3176129

What OpenQuestion holds

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Registered trials

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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.