Efficient Exact Maximum a Posteriori Computation for Bayesian SNP Genotyping in Polyploids

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Efficient Exact Maximum a Posteriori Computation for Bayesian SNP Genotyping in Polyploids

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Title: Efficient Exact Maximum a Posteriori Computation for Bayesian SNP Genotyping in Polyploids
Author: Mollinari, Marcelo; Garcia, Antonio Augusto Franco; Serang, Oliver R

Note: Order does not necessarily reflect citation order of authors.

Citation: Serang, Oliver, Marcelo Mollinari, and Antonio Augusto Franco Garcia. 2012. Efficient exact maximum a posteriori computation for bayesian SNP genotyping in polyploids. PLoS ONE 7(2): e30906.
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Abstract: The problem of genotyping polyploids is extremely important for the creation of genetic maps and assembly of complex plant genomes. Despite its significance, polyploid genotyping still remains largely unsolved and suffers from a lack of statistical formality. In this paper a graphical Bayesian model for SNP genotyping data is introduced. This model can infer genotypes even when the ploidy of the population is unknown. We also introduce an algorithm for finding the exact maximum a posteriori genotype configuration with this model. This algorithm is implemented in a freely available web-based software package SuperMASSA. We demonstrate the utility, efficiency, and flexibility of the model and algorithm by applying them to two different platforms, each of which is applied to a polyploid data set: Illumina GoldenGate data from potato and Sequenom MassARRAY data from sugarcane. Our method achieves state-of-the-art performance on both data sets and can be trivially adapted to use models that utilize prior information about any platform or species.
Published Version: doi:10.1371/journal.pone.0030906
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3281906/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:9361510

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