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Genomic prediction of gene bank wheat landraces

Author: Crossa, J.
Author: Jarquin, D.
Author: Franco, J.
Author: Pérez-Rodríguez, P.
Author: Burgueño, J.
Author: Saint Pierre, C.
Author: Vikram, P.
Author: Sansaloni, C.P.
Author: Petroli, C.D.
Author: Akdemir, D.
Author: Sneller, C.
Author: Reynolds, M.P.
Author: Tattaris, M.
Author: Payne, T.S.
Author: Guzman, C.
Author: Peña-Bautista, R.J.
Author: Wenzl, P.
Author: Sukhwinder Singh
Year: 2016
URI: http://hdl.handle.net/10883/17051
Abstract: This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH) and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G×E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% testing (TST) (TRN20-TST80) sets, and (2) two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections were formed. Accounting for population structure decreased prediction accuracy by 15%-20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections but slightly lower for Iranian collections. Prediction accuracy when incorporating G×E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G×E term. For Iranian landraces, accuracies were 0.55 for the G×E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm into elite materials.
Format: PDF
Language: English
Publisher: Genetics Society of America
Copyright: CIMMYT 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.
Type: Article
Place of Publication: USA
Issue: 7
Volume: 6
Volume: 1819-1834
DOI: 10.1534/g3.116.029637
Keywords: Gene Bank Accessions
Keywords: Genomic Prediction
Keywords: Cross-Validations
Keywords: Reference Core Subsets
Keywords: Accession × Environment Interaction
Keywords: AxE
Country of Focus: MEXICO
Agrovoc: GENE BANKS
Agrovoc: GENOMES
Agrovoc: FORECASTING
Agrovoc: GENOTYPE ENVIRONMENT INTERACTION
Related Datasets: http://genomics.cimmyt.org/mexican_iranian/traverse/
Journal: G3: Genes, Genomes, Genetics


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  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP

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