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

Su, Shu-Yi, White, Jonathan, Balding, David J. and Coin, Lachlan J. M. (2008) Inference of haplotypic phase and missing genotypes in polyploid organisms and variable copy number genomic regions. BMC Bioinformatics, 9 513.1-513.9. doi:10.1186/1471-2105-9-513


Author Su, Shu-Yi
White, Jonathan
Balding, David J.
Coin, Lachlan J. M.
Title Inference of haplotypic phase and missing genotypes in polyploid organisms and variable copy number genomic regions
Journal name BMC Bioinformatics   Check publisher's open access policy
ISSN 1471-2105
Publication date 2008-12-01
Sub-type Article (original research)
DOI 10.1186/1471-2105-9-513
Open Access Status DOI
Volume 9
Start page 513.1
End page 513.9
Total pages 9
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
Formatted abstract
Background: The 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.

Results: In 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.

Conclusion: With 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. 
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Institute for Molecular Bioscience - Publications
 
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