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

Autor(es)

Serang, Oliver ^{[1, 2]} ; Mollinari, Marcelo ^[3] ; Franco Garcia, Antonio Augusto ^[3]

Afiliação do(s) autor(es)

^[1] Harvard Univ, Sch Med, Dept Neurobiol, Boston, MA 02115 - USA

^[2] Childrens Hosp Boston, Dept Pathol, Boston, MA - USA

^[3] Univ Sao Paulo ESALQ, Dept Genet, Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: PLoS One; v. 7, n. 2 FEB 17 2012.
• Citações Web of Science: 41

Resumo

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. (AU)

• Processo FAPESP: 08/52197-4 - Genomic-assisted breeding of sugarcane: using molecular markers for understanding the genetic architecture of quantitative traits and to implement marker assisted selection
• Beneficiário: Anete Pereira de Souza
• Modalidade de apoio: Auxílio à Pesquisa - Programa BIOEN - Temático
• Processo FAPESP: 08/54402-4 - Desenvolvimento de um modelo para construção de mapas genéticos em autopoliploides, com aplicações em cana-de-açúcar
• Beneficiário: Marcelo Mollinari
• Modalidade de apoio: Bolsas no Brasil - Doutorado