Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers.
The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.
Crossa
José
J
School of Land Crop and Food Sciences, University of Queensland, 4072, Sta. Lucia, Queensland, Australia. j.crossa@cgiar.org
Campos
Gustavo de Los
Gde L
Pérez
Paulino
P
Gianola
Daniel
D
Burgueño
Juan
J
Araus
José Luis
JL
Makumbi
Dan
D
Singh
Ravi P
RP
Dreisigacker
Susanne
S
Yan
Jianbing
J
Arief
Vivi
V
Banziger
Marianne
M
Braun
Hans-Joachim
HJ
D001499:Bayes Theorem; D001947:Breeding; D003627:Data Interpretation, Statistical; D005819:Genetic Markers; D014644:Genetic Variation; D005838:Genotype; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D019655:Quantitative Trait, Heritable; D012098:Reproduction / Q000235:genetics; D012641:Selection, Genetic; D014908:Triticum / Q000235:genetics*; D003313:Zea mays / Q000235:genetics*
D005819:Genetic Markers
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