Genomic prediction in CIMMYT maize and wheat breeding programs

dc.creatorCrossa, J.
dc.creatorPerez, P.
dc.creatorHickey, J.M.
dc.creatorBurgueño, J.
dc.creatorOrnella, L.
dc.creatorCeron Rojas, J.J.
dc.creatorZhang, X.
dc.creatorDreisigacker, S.
dc.creatorBabu, R.
dc.creatorLi, Y.
dc.creatorBonnett, D.
dc.creatorMathews, K.
dc.date2014-03-05T17:59:55Z
dc.date2014-03-05T17:59:55Z
dc.date2014
dc.date.accessioned2023-07-17T19:57:33Z
dc.date.available2023-07-17T19:57:33Z
dc.identifier0018-067X
dc.identifierhttp://hdl.handle.net/10883/3441
dc.identifier10.1038/hdy.2013.16
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7509195
dc.descriptionGenomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype_environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related biparental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.
dc.description48-60
dc.formatPDF
dc.languageEnglish
dc.publisherSpringer Nature
dc.publisherhttp://www.nature.com/hdy/journal/vaop/ncurrent/abs/hdy201316a.html
dc.rightsCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.
dc.rightsOpen Access
dc.source112
dc.sourceHeredity
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectBayesian LASSO
dc.subjectInternational Maize and Wheat Improvement Center
dc.subjectCIMMYT
dc.subjectGenomic Selection
dc.subjectReproducing Kernel Hilbert Space Regression
dc.subjectBAYESIAN THEORY
dc.subjectARTIFICIAL SELECTION
dc.subjectGENOTYPE ENVIRONMENT INTERACTION
dc.subjectSTATISTICAL METHODS
dc.titleGenomic prediction in CIMMYT maize and wheat breeding programs
dc.typeArticle
dc.coverageHarlow (United Kingdom)


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