Genomic Prediction of Gene Bank Wheat Landraces

Authors

José Crossa, International Maize and Wheat improvement Center (CIMMYT)Follow
Diego Jarquin, University of Nebraska-LincolnFollow
Jorge Franco, Universidad de la República (Udelar), Paysandú, UruguayFollow
Paulino Pérez-Rodríguez, Colegio de Post-Graduados, Montecillos, Edo. de MexicoFollow
Juan Burgueño, International Maize and Wheat Improvement Center (CIMMYT)
Carolina Saint-Pierre, International Maize and Wheat Improvement Center (CIMMYT)
Prashant Vikram, International Maize and Wheat Improvement Center (CIMMYT)
Carolina Sansaloni, International Maize and Wheat Improvement Center (CIMMYT)
Cesar Petroli, International Maize and Wheat Improvement Center (CIMMYT)
Denis Akdemir, Cornell UniversityFollow
Clay Sneller, Ohio State UniversityFollow
Matthew Reynolds, International Maize and Wheat Improvement Center (CIMMYT)
Maria Tattaris, International Maize and Wheat Improvement Center (CIMMYT)
Thomas Payne, International Maize and Wheat Improvement Center (CIMMYT)
Carlos Guzman, International Maize and Wheat Improvement Center (CIMMYT)
Roberto J. Peña, International Maize and Wheat Improvement Center (CIMMYT)
Peter Wenzl, International Maize and Wheat Improvement Center (CIMMYT)
Sukhwinder Singh, International Maize and Wheat Improvement CenterFollow

Document Type

Article

Date of this Version

2016

Citation

G3: Genes Genomes Genetics, 6 (July 2016), pp 1819-1834. doi: 10.1534/g3.116.029637

Comments

Copyright © 2016 Crossa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License

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. 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.