Evidence map›Paper›PMID 19046436›Full record

ArticleBMC bioinformatics2008

Inference of haplotypic phase and missing genotypes in polyploid organisms and variable copy number genomic regions.

Shu-Yi Su, Jonathan White, David J Balding, Lachlan J M Coin

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.2field-weighted citation impact, top 12% of its field
1 · What the graph read from 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.

2 · The registry

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3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 33 citations in OpenAlex.

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  6. Review
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  8. Article
  9. A sequential Monte Carlo framework for haplotype inference in CNV/SNP genotype data.EURASIP journal on bioinformatics & systems biology · 2014
    Article
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  14. Inferring combined CNV/SNP haplotypes from genotype data.Bioinformatics (Oxford, England) · 2010
    Article
4 · The record

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5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Shu-Yi SuDepartment of Epidemiology and Public Health, Imperial College, London, W2 1PG, UK. lshu-yi.su@imperial.ac.uk
Jonathan White
David J Balding
Lachlan J M Coin
Imperial College London · GB

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundThe power of haplotype-based methods for association studies, identification of regions under selection, and ancestral inference, is well-established for diploid organisms. For polyploids, however, the difficulty of determining phase has limited such approaches. Polyploidy is common in plants and is also observed in animals. Partial polyploidy is sometimes observed in humans (e.g. trisomy 21; Down's syndrome), and it arises more frequently in some human tissues. Local changes in ploidy, known as copy number variations (CNV), arise throughout the genome. Here we present a method, implemented in the software polyHap, for the inference of haplotype phase and missing observations from polyploid genotypes. PolyHap allows each individual to have a different ploidy, but ploidy cannot vary over the genomic region analysed. It employs a hidden Markov model (HMM) and a sampling algorithm to infer haplotypes jointly in multiple individuals and to obtain a measure of uncertainty in its inferences.

resultsIn the simulation study, we combine real haplotype data to create artificial diploid, triploid, and tetraploid genotypes, and use these to demonstrate that polyHap performs well, in terms of both switch error rate in recovering phase and imputation error rate for missing genotypes. To our knowledge, there is no comparable software for phasing a large, densely genotyped region of chromosome from triploids and tetraploids, while for diploids we found polyHap to be more accurate than fastPhase. We also compare the results of polyHap to SATlotyper on an experimentally haplotyped tetraploid dataset of 12 SNPs, and show that polyHap is more accurate.

conclusionWith the availability of large SNP data in polyploids and CNV regions, we believe that polyHap, our proposed method for inferring haplotypic phase from genotype data, will be useful in enabling researchers analysing such data to exploit the power of haplotype-based analyses.

Indexed as

Genome, HumanGenotypePolyploidyChromosomes, Human, XComputer SimulationGene DosageGenetics, PopulationGenetic VariationHaplotypesHumansMalePolymorphism, Single NucleotideSoftware

Identifiers

PMID19046436
PMCPMC2647950
OpenAlexW2038635328

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LicenceCC BY
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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.