TABLE 2.

Regression and classification genomic selection models for stripe rust infection type (IT) and disease severity (SEV) in winter wheat.

Model	Type	Description	References
rrBLUP	Regression	Linear ridge regression model using untransformed phenotypes	Endelman (2011)
SQRT rrBLUP	Regression	Linear ridge regression model using square-root (SQRT) transformation	Endelman (2011)
LOG rrBLUP	Regression	Linear ridge regression model using logarithmic (LOG) transformation	Endelman (2011)
BC rrBLUP	Regression	Linear ridge regression model using Box-Cox (BC) transformation	Endelman (2011)
GLM	Regression	Generalized linear model (GLM) with a Poisson distribution	Hastie et al. (2016)
SVMR	Regression	Non-parametric regression support vector machine (SVMR) using a radial kernel	Karatzoglou et al. (2019)
BOR	Classification	Bayesian ordinal regression (BOR) model using the full-scale IT (0–9) and SEV (0–100%)	Pérez and de los Campos, (2014)
BOR 3-Class	Classification	Bayesian ordinal regression (BOR) model using the reduced three class scale IT (0–2) and SEV (0–2)	Pérez and de los Campos, (2014)
BOR 2-Class	Classification	Bayesian ordinal regression (BOR) model using the reduced two class scale IT (0–1) and SEV (0–1)	Pérez and de los Campos, (2014)
SVM	Classification	Non-parametric classification support vector machine (SVM) using a radial kernel using the full- scale IT (0–9) and SEV (0–100%)	Karatzoglou et al. (2019)
SVM 3-Class	Classification	Non-parametric classification support vector machine (SVM) using a radial kernel using the reduced three class scale IT (0–2) and SEV (0–2)	Karatzoglou et al. (2019)
SVM 2-Class	Classification	Non-parametric classification support vector machine (SVM) using a radial kernel using the reduced two class scale IT (0–1) and SEV (0–1)	Karatzoglou et al. (2019)