MODELO OCULTO DE MARKOV PARA IMPUTAÇÃO DE GENÓTIPOS DE MARCADORES MOLECULARES: uma aplicação no mapeamento de QTL utilizando a abordagem bayesiana - MEDEIROS, Elias Silva de; LEANDRO, Roseli Aparecida (Brazilian Journal of Biometrics, 2019)
Several are the quantitative traits that are significantly influenced by genetic factors. Genetic mapping has been used to identify regions of the genome that contribute directly to the development of these characteristics. Molecular marker experiments can provide in their data set a sequence of unmeasured genotypic data. These missing data may be the result of genotyping errors or non-informative markers. Consequently, this missing information about the genotypes in the markers is a problem that can lead to difficulties in the study of genome mapping. Thus, this paper aims to impute the genotypes of the markers, proposing in the modeling the Hidden Markov Model (HMM) to infer this data. For the application of this methodology, a set of microsatellite molecular marker data was considered. The HMM approach to imputation of the missing genotypes in the markers presented significant results through simulations, since the measures to evaluate the imputation accuracy in this study, evidenced a good performance regarding the imputation of these genotypes. In addition, the methodology proposed in this article becomes an alternative to imputation in the markers, which can be employed in SNPs t Single Nucleotide Polymorphisms obtained from genotyping by sequencing (GBS) for species that do not yet have the sequenced genome, resulting in a reduction in genotyping costs, especially for the implementation of genomic selection.