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Crop Science Abstract - Crop Breeding & Genetics

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


This article in CS

  1. Vol. 52 No. 3, p. 1093-1104
    Received: Nov 11, 2011

    * Corresponding author(s): piepho@uni-hohenheim.de
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  1. H. P. Piepho *a,
  2. J. O. Ogutua,
  3. T. Schulz-Streecka,
  4. B. Estaghviroua,
  5. A. Gordillob and
  6. F. Technowc
  1. a Bioinformatics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstrasse 23, 70599 Stuttgart, Germany
    b AgReliant Genetics, LLC, 4640 East State Road 32, Lebanon, IN 46052
    c Institute of Plant Breeding, University of Hohenheim, Fruwirthstrasse 21, 70599 Stuttgart, Germany


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.

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