ArticleBMC bioinformatics2008
Inference of haplotypic phase and missing genotypes in polyploid organisms and variable copy number genomic regions.
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.
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14 citing papers in PubMed, 33 citations in OpenAlex.
- PolyHaplotyper: haplotyping in polyploids based on bi-allelic marker dosage data.BMC bioinformatics · 2022Article
- Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species.Frontiers in plant science · 2022Review
- Assembly of whole-chromosome pseudomolecules for polyploid plant genomes using outbred mapping populations.Nature genetics · 2020Article
- Expected Genotype Quality and Diploidized Marker Data from Genotyping-by-Sequencing of Urochloa spp. Tetraploids.The plant genome · 2019Article
- Allele phasing is critical to revealing a shared allopolyploid origin of Medicago arborea and M. strasseri (Fabaceae).BMC evolutionary biology · 2018Article
- Tools for Genetic Studies in Experimental Populations of Polyploids.Frontiers in plant science · 2018Review
- Assessing accuracy of imputation using different SNP panel densities in a multi-breed sheep population.Genetics, selection, evolution : GSE · 2016Article
- SHEsisPlus, a toolset for genetic studies on polyploid species.Scientific reports · 2016Article
- A sequential Monte Carlo framework for haplotype inference in CNV/SNP genotype data.EURASIP journal on bioinformatics & systems biology · 2014Article
- A population model for genotyping indels from next-generation sequence data.Nucleic acids research · 2013Article
- LDx: estimation of linkage disequilibrium from high-throughput pooled resequencing data.PloS one · 2012Article
- Inferring haplotypes of copy number variations from high-throughput data with uncertainty.G3 (Bethesda, Md.) · 2011Article
- cnvHap: an integrative population and haplotype-based multiplatform model of SNPs and CNVs.Nature methods · 2010Article
- Inferring combined CNV/SNP haplotypes from genotype data.Bioinformatics (Oxford, England) · 2010Article
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4 authors at 1 institution in 1 country.
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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.
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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.