Efficient Computation of Ridge‐Regression Best Linear Unbiased Prediction in Genomic Selection in Plant Breeding

Computational efficiency of procedures for genomic selection is an important issue when cross‐validation is used for model selection and evaluation. Moreover, limited computational resources may be a bottleneck when processing large datasets. This paper reviews several options for computing ridge‐regression best linear unbiased prediction (RR‐BLUP) in genomic selection and compares their computational efficiencies when using a mixed model package. Attention is also given to the problem of singular genetic variance‐covariance. Annotated code is provided for implementing and evaluating the methods using the MIXED procedure of SAS. It is concluded that a recently proposed method based on a spectral decomposition of the variance‐covariance matrix of the data is preferable compared to established methods because of its superior computational efficiency and applicability also for singular genetic variance‐covariance.