Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2012 Nov;2(11):1427-36.
doi: 10.1534/g3.112.003699. Epub 2012 Nov 1.

Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments

Affiliations

Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments

Vanessa S Windhausen et al. G3 (Bethesda). 2012 Nov.

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 F(2)-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.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Heat map of the kinship matrix of 255 lines assigned to 8 breeding populations (Experiment 1).
Figure 2
Figure 2
(A) Second-order smoothing spline fits of LD (r2) vs. the distance in mega base pairs (Mbp) between markers on the same chromosome, within the La Posta Sequía (1), Zimbabwe (2), and Entomology (3) breeding population. (B) Second-order smoothing spline fits of proportion of marker pairs with equal linkage phase vs. the distance in marker base pairs between markers on the same chromosome. The horizontal line indicates a linkage phase of 0.5.

References

    1. Albrecht T., Wimmer V., Auinger H.-J., Erbe M., Knaak C., et al. , 2011. Genome-based prediction of testcross values in maize. TAG 123: 339–350 - PubMed
    1. Araus J. L., Slafer G. A., Royo C., Serret M. D., 2008. Breeding for yield potential and stress adaptation in cereals. Crit. Rev. Plant Sci. 27: 377–412
    1. Bernardo R., 1991. Correlation between testcross performance of lines at early and late selfing generations. Theor. Appl. Genet. 82: 17–21 - PubMed
    1. Bernardo R., Yu J., 2007. Prospects for genomewide selection for quantitative traits in maize. Crop Sci. 47: 1082–1090
    1. Burgueño J., Campos G. D. L., Weigel K., Crossa J., 2012. Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci. 52: 707–719

Publication types

LinkOut - more resources