title: Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments.
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.
authors.affiliation: Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Fruwirthstrasse 21, Stuttgart, Germany.
authors.forename: Vanessa S
authors.initials: VS
authors.lastname: Windhausen
authors.forename: Gary N
authors.initials: GN
authors.lastname: Atlin
authors.forename: John M
authors.initials: JM
authors.lastname: Hickey
authors.forename: Jose
authors.initials: J
authors.lastname: Crossa
authors.forename: Jean-Luc
authors.initials: JL
authors.lastname: Jannink
authors.forename: Mark E
authors.initials: ME
authors.lastname: Sorrells
authors.forename: Babu
authors.initials: B
authors.lastname: Raman
authors.forename: Jill E
authors.initials: JE
authors.lastname: Cairns
authors.forename: Amsal
authors.initials: A
authors.lastname: Tarekegne
authors.forename: Kassa
authors.initials: K
authors.lastname: Semagn
authors.forename: Yoseph
authors.initials: Y
authors.lastname: Beyene
authors.forename: Pichet
authors.initials: P
authors.lastname: Grudloyma
authors.forename: Frank
authors.initials: F
authors.lastname: Technow
authors.forename: Christian
authors.initials: C
authors.lastname: Riedelsheimer
authors.forename: Albrecht E
authors.initials: AE
authors.lastname: Melchinger
mesh_terms: D000704:Analysis of Variance; D001947:Breeding; D002678:Chimera / Q000235:genetics*; D004777:Environment; D014644:Genetic Variation; D018745:Genome, Plant; D015233:Models, Statistical; D011336:Probability; D019655:Quantitative Trait, Heritable; D003313:Zea mays / Q000235:genetics*