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

• Published in: Molecular 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
• Language: English
• 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
• DOI: 10.1007/s11032-017-0681-1
• ISSN: 1380-3743

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.