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Choice of shrinkage parameter and prediction of genomic breeding values in elite maize breeding populations
Author(s) -
Zhao Yusheng,
Gowda Manje,
Liu Wenxin,
Würschum Tobias,
Maurer Hans P.,
Longin Friedrich H.,
Ranc Nicolas,
Piepho Hans P.,
Reif Jochen C.
Publication year - 2013
Publication title -
plant breeding
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.583
H-Index - 71
eISSN - 1439-0523
pISSN - 0179-9541
DOI - 10.1111/pbr.12008
Subject(s) - biology , selection (genetic algorithm) , best linear unbiased prediction , genomic selection , plant breeding , population , genetic gain , grain yield , microbiology and biotechnology , agronomy , statistics , genetics , mathematics , genetic variation , computer science , machine learning , gene , single nucleotide polymorphism , genotype , demography , sociology
Genomic selection ( GS ) is a promising alternative to marker‐assisted selection particularly for quantitative traits. In this study, we examined the prediction accuracy of genomic breeding values by using ridge regression best linear unbiased prediction in combination with fivefold cross‐validation based on empirical data of a commercial maize breeding programme. The empirical data is composed of 930 testcross progenies derived from 11 segregating families evaluated at six environments for grain yield and grain moisture. Accuracy to predict genomic breeding values was affected by the choice of the shrinkage parameter λ 2 , by unbalanced family size, by size of the training population and to a lower extent by the number of markers. Accuracy of genomic breeding values was high suggesting that the selection gain can be improved implementing GS in elite maize breeding programmes.