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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #285218
Title: Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments

Author
item WEBER, VANESSA - University Of Hohenheim
item ATLIN, GARY - International Maize & Wheat Improvement Center (CIMMYT)
item HICKEY, JOHN - International Maize & Wheat Improvement Center (CIMMYT)
item CROSSA, JOSE - International Maize & Wheat Improvement Center (CIMMYT)
item Jannink, Jean-Luc
item SORRELLS, MARK - Cornell University
item RAMAN, BABU - International Maize & Wheat Improvement Center (CIMMYT)
item CAIRNS, JILL - International Maize & Wheat Improvement Center (CIMMYT)
item TAREKEGNE, AMSAL - International Maize & Wheat Improvement Center (CIMMYT)
item SEMAGN, KASSA - International Maize & Wheat Improvement Center (CIMMYT)
item BEYENE, YOSEPH - International Maize & Wheat Improvement Center (CIMMYT)
item GRUDLOYMA, PICHET - Nakhon Sawan Field Crops Research Center
item TECHNOW, FRANK - University Of Hohenheim
item RIEDELSHEIMER, CHRISTIAN - University Of Hohenheim
item MELCHINGER, ALBRECH - University Of Hohenheim

Submitted to: Genes, Genomes, and Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/14/2012
Publication Date: 11/1/2012
Citation: Weber, V.S., Atlin, G.A., Hickey, J.M., Crossa, J., Jannink, J., Sorrells, M.E., Raman, B., Cairns, J.E., Tarekegne, A., Semagn, K., Beyene, Y., Grudloyma, P., Technow, F., Riedelsheimer, C., Melchinger, A.E. 2012. Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments. Genes, Genomes, and Genomics. 2(11):1427-1436.

Interpretive Summary: Genomic selection uses data from a large training set of lines from a breeding program that has both phenotype and genotype data. The trained model can be used to predict the performance of lines that have not been phenotyped. Here we tried to predict performance of progeny that were not closely related to the training population and found very low prediction accuracy. We also evaluated prediction within the training population itself and found that it was very strongly affected by the population structure of the training set. That is, lines in the training set could be grouped to maximize within-group relatedness and minimize between-group relatedness. For any given individual, the prediction model essentially predicted the mean of the group that the individual was in. These results led to recommendations on how to prepare for using genomic selection within a breeding program.

Technical 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 F2-derived lines from each of five populations. While 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 based on 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 (i) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (ii) a detailed analysis of the population structure prior to performing cross validation, and (iii) larger training sets with strong genetic relationship to the validation set.