Determination of optimal number of independent components in yield traits in rice

da Costa, Jaquicele AparecidaAzevedo, Camila FerreiraNascimento, MoysésSilva, Fabyano Fonseca ede Resende, Marcos Deon VilelaNascimento, Ana Carolina Campana

ABSTRACT: The principal component regression (PCR) and the independent component regression (ICR) are dimensionality reduction methods and extremely important in genomic prediction. These methods require the choice of the number of components to be inserted into the model. For PCR, there are formal criteria; however, for ICR, the adopted criterion chooses the number of independent components (ICs) associated to greater accuracy and requires high computational time. In this study, seven criteria based on the number of principal components (PCs) and methods of variable selection to guide this choice in ICR are proposed and evaluated in simulated and real data. For both datasets, the most efficient criterion and that drastically reduced computational time determined that the number of ICs should be equal to the number of PCs to reach a higher accuracy value. In addition, the criteria did not recover the simulated heritability and generated biased genomic values.

1. independent component regression
2. principal component regression
3. plant breeding
4. genomic prediction
5. Oryza sativa L.