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Pesquisa Agropecuária Brasileira

versão impressa ISSN 0100-204Xversão On-line ISSN 1678-3921

Resumo

COSTA, Jaquicele Aparecida da et al. Genomic prediction with the additive-dominant model by dimensionality reduction methods. Pesq. agropec. bras. [online]. 2020, vol.55, e01713.  Epub 07-Dez-2020. ISSN 1678-3921.  https://doi.org/10.1590/s1678-3921.pab2020.v55.01713.

The objective of this work was to evaluate the application of different dimensionality reduction methods in the additive-dominant model and to compare them with the genomic best linear unbiased prediction (G-BLUP) method. The dimensionality reduction methods evaluated were: principal components regression (PCR), partial least squares (PLS), and independent components regression (ICR). A simulated data set composed of 1,000 individuals and 2,000 single-nucleotide polymorphisms was used, being analyzed in four scenarios: two heritability levels × two genetic architectures. To help choose the number of components, the results were evaluated as to additive, dominant, and total genomic information. In general, PCR showed higher accuracy values than the other methods. However, none of the methodologies are able to recover true genomic heritabilities and all of them present biased estimates, under- or overestimating the genomic genetic values. For the simultaneous estimation of the additive and dominance marker effects, the best alternative is to choose the number of components that leads the dominance genomic value to a higher accuracy.

Palavras-chave : dominance effect; G-BLUP; independent components regression; partial least squares; principal components regression.

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