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.
Highlights
The prediction process in Genome Wide Selection (GWS) (Meuwissen et al, 2001) presents statistical problems related to high dimensionality and multicollinearity, which affect the accuracy of methods based on ordinary least squares (OLS) (Desta and Ortiz, 2014)
Resende et al (2012) reported that the statistical methodologies applied to GWS could be divided into three groups: methods based on explicit regression, implicit regression, and the dimensionality reduction methods
The dimensionality reduction methods, the Principal Component Regression (PCR), and the Independent Component Regression (ICR) are highlighted when compared to the other methods applied to GWS as they present great applicability and relatively simple theory
Summary
The prediction process in Genome Wide Selection (GWS) (Meuwissen et al, 2001) presents statistical problems related to high dimensionality (number of markers greater than the number of individual phenotypic observations) and multicollinearity (highly correlated markers), which affect the accuracy of methods based on ordinary least squares (OLS) (Desta and Ortiz, 2014). In this context, methodologies to solve such statistical challenges have gained prominence in GWS research. Le Floch et al (2012) presented the criterion for choosing the optima number based on this assertion
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
