Genotyping Polyploids from Messy Sequencing Data

Gerard et al. highlight several issues encountered when genotyping polyploid organisms from next-generation sequencing data, including allelic bias, overdispersion, and outlying observations. They present modeling solutions and software to account for these issues... Detecting and quantifying the di...

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Vydáno v:Genetics (Austin) Ročník 210; číslo 3; s. 789 - 807
Hlavní autoři: Gerard, David, Ferrão, Luis Felipe Ventorim, Garcia, Antonio Augusto Franco, Stephens, Matthew
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Genetics Society of America 01.11.2018
Témata:
Bayesian analysis
Bias
Binomial distribution
Bioinformatics
Chromosomes
Computer simulation
Gene sequencing
Genetics
Genomes
Genotype & phenotype
Genotyping
Investigations
Methods
Next-generation sequencing
Organisms
Polyploidy
Potatoes
Rice
Sweet potatoes
GBS
RAD-Seq
read-mapping bias
hierarchical modeling
sequencing
ISSN:1943-2631, 0016-6731, 1943-2631
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Shrnutí:Gerard et al. highlight several issues encountered when genotyping polyploid organisms from next-generation sequencing data, including allelic bias, overdispersion, and outlying observations. They present modeling solutions and software to account for these issues... Detecting and quantifying the differences in individual genomes (i.e., genotyping), plays a fundamental role in most modern bioinformatics pipelines. Many scientists now use reduced representation next-generation sequencing (NGS) approaches for genotyping. Genotyping diploid individuals using NGS is a well-studied field, and similar methods for polyploid individuals are just emerging. However, there are many aspects of NGS data, particularly in polyploids, that remain unexplored by most methods. Our contributions in this paper are fourfold: (i) We draw attention to, and then model, common aspects of NGS data: sequencing error, allelic bias, overdispersion, and outlying observations. (ii) Many datasets feature related individuals, and so we use the structure of Mendelian segregation to build an empirical Bayes approach for genotyping polyploid individuals. (iii) We develop novel models to account for preferential pairing of chromosomes, and harness these for genotyping. (iv) We derive oracle genotyping error rates that may be used for read depth suggestions. We assess the accuracy of our method in simulations, and apply it to a dataset of hexaploid sweet potato (Ipomoea batatas). An R package implementing our method is available at https://cran.r-project.org/package=updog.
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ISSN:1943-2631
0016-6731
1943-2631
DOI:10.1534/genetics.118.301468