Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments

Vanessa S. Windhausen, Gary N. Atlin, John M. Hickey, Jose Crossa, Jean-Luc Jannink, Mark E. Sorrells, Babu Raman, Jill E. Cairns, Amsal Tarekegne, Kassa Semagn, Yoseph Beyene, Pichet Grudloyma, Frank Technow, Christian Riedelsheimer, Albrecht E. Melchinger*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F-2-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F2-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set.

Original languageEnglish
Pages (from-to)1427-1436
Number of pages10
JournalG3: Genes | Genomes | Genetics
Volume2
Issue number11
DOIs
Publication statusPublished - 1 Nov 2012

Keywords

  • COMPLEX TRAITS
  • PEDIGREE
  • GENOMEWIDE SELECTION
  • QUANTITATIVE TRAITS
  • DENSE MOLECULAR MARKERS
  • IMPROVEMENT
  • MODELS
  • ACCURACY
  • REGRESSION
  • GENETIC VALUES

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