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

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  • Oliver Serang
  • Marcelo Mollinari
  • Antonio Augusto Franco Garcia

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.

Suggested Citation

  • Oliver Serang & Marcelo Mollinari & Antonio Augusto Franco Garcia, 2012. "Efficient Exact Maximum a Posteriori Computation for Bayesian SNP Genotyping in Polyploids," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-13, February.
    • Handle: RePEc:plo:pone00:0030906
    DOI: 10.1371/journal.pone.0030906
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    Cited by:

    1. David Gerard, 2023. "Double reduction estimation and equilibrium tests in natural autopolyploid populations," Biometrics, The International Biometric Society, vol. 79(3), pages 2143-2156, September.
    2. Ivone de Bem Oliveira & Rhewter Nunes & Lucia Mattiello & Stela Barros-Ribeiro & Isabela Pavanelli Souza & Alexandre Siqueira Guedes Coelho & Rosane Garcia Collevatti, 2019. "Research and partnership in studies of sugarcane using molecular markers: a scientometric approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 335-355, April.

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