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Theoretical and Applied Genetics

, Volume 126, Issue 1, pp 69–82 | Cite as

Comparisons of single-stage and two-stage approaches to genomic selection

  • Torben Schulz-Streeck
  • Joseph O. Ogutu
  • Hans-Peter PiephoEmail author
Original Paper

Abstract

Genomic selection (GS) is a method for predicting breeding values of plants or animals using many molecular markers that is commonly implemented in two stages. In plant breeding the first stage usually involves computation of adjusted means for genotypes which are then used to predict genomic breeding values in the second stage. We compared two classical stage-wise approaches, which either ignore or approximate correlations among the means by a diagonal matrix, and a new method, to a single-stage analysis for GS using ridge regression best linear unbiased prediction (RR-BLUP). The new stage-wise method rotates (orthogonalizes) the adjusted means from the first stage before submitting them to the second stage. This makes the errors approximately independently and identically normally distributed, which is a prerequisite for many procedures that are potentially useful for GS such as machine learning methods (e.g. boosting) and regularized regression methods (e.g. lasso). This is illustrated in this paper using componentwise boosting. The componentwise boosting method minimizes squared error loss using least squares and iteratively and automatically selects markers that are most predictive of genomic breeding values. Results are compared with those of RR-BLUP using fivefold cross-validation. The new stage-wise approach with rotated means was slightly more similar to the single-stage analysis than the classical two-stage approaches based on non-rotated means for two unbalanced datasets. This suggests that rotation is a worthwhile pre-processing step in GS for the two-stage approaches for unbalanced datasets. Moreover, the predictive accuracy of stage-wise RR-BLUP was higher (5.0–6.1 %) than that of componentwise boosting.

Keywords

Genomic Selection Standard Variety Boost Regression Tree Adjusted Means Mixed Model Framework 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

BLUP

Best linear unbiased prediction

GEBV

Genomic estimated breeding value

GS

Genomic selection

RCBD

Randomized complete block design

REML

Restricted maximum likelihood

RR-BLUP

Ridge regression BLUP

SNP

Single nucleotide polymorphism

Notes

Acknowledgments

We thank AgReliant Genetics for providing the datasets. This research was funded by AgReliant Genetics and the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr “Synbreed—Synergistic plant and animal breeding” (Grant ID: 0315526). Three anonymous referees are thanked for very useful and constructive comments.

Conflict of interest

The authors declare that they have no competing interests.

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Torben Schulz-Streeck
    • 1
  • Joseph O. Ogutu
    • 1
  • Hans-Peter Piepho
    • 1
    Email author
  1. 1.Bioinformatics Unit, Institute of Crop ScienceUniversity of HohenheimStuttgartGermany

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