Multi-trait genomic prediction for nitrogen response indices in tropical maize hybrids

In maize breeding, genomic prediction may be an efficient tool for selecting single-crosses evaluated under abiotic stress conditions. In addition, a promising strategy is applying multiple-trait genomic prediction using selection indices (SIs), increasing genetics gains and reducing time per cycles...

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Bibliographic Details
Published inMolecular breeding Vol. 37; no. 6; pp. 1 - 14
Main Authors Lyra, Danilo Hottis, de Freitas Mendonça, Leandro, Galli, Giovanni, Alves, Filipe Couto, Granato, Ítalo Stefanine Correia, Fritsche-Neto, Roberto
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.06.2017
Springer Nature B.V
Subjects
Accuracy
Biomedical and Life Sciences
Biotechnology
Corn
Crop damage
Crop yield
Datasets
Genetics
Genomics
Grain
Hybrids
Kernels
Life Sciences
Molecular biology
Nitrogen
Nonlinearity
Plant biology
Plant breeding
Plant Genetics and Genomics
Plant Pathology
Plant Physiology
Plant Sciences
Stresses
Gaussian kernel
GBLUP
Genomic heritability
Abiotic stress
Single-trait genomic prediction
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Summary:In maize breeding, genomic prediction may be an efficient tool for selecting single-crosses evaluated under abiotic stress conditions. In addition, a promising strategy is applying multiple-trait genomic prediction using selection indices (SIs), increasing genetics gains and reducing time per cycles. In this study, we aimed (i) to compare accuracy of single- and multi-trait genomic prediction (STGP; MTGP) in two maize datasets, (ii) to evaluate prediction of four selection indices that could contribute to the selection of tropical maize hybrids under contrasting nitrogen conditions, and (iii) to compare the use of linear (GBLUP) and nonlinear (RKHS/GK) kernels in STGP and MTGP analyses. For either single-trait GBLUP and RKHS analyses, the highest values obtained for accuracy were 0.40 and 0.41 using harmonic mean (HM), respectively. From multi-trait GBLUP and GK, using the combination of selection indices in MTGP seems to be suitable, increasing the accuracy. Adding grain yield and plant height in MTGP showed a slight improvement in accuracy compared to STGP. In general, there was a modest benefit of using single-trait RKHS and GK multi-trait, rather than GBLUP.
ISSN:1380-3743
1572-9788
DOI:10.1007/s11032-017-0681-1