Article thumbnail

Genomic Prediction of Gene Bank Wheat Landraces

By José Crossa, Diego Jarquin, Jorge Franco, Paulino Pérez-Rodríguez, Juan Burgueño, Carolina Saint-Pierre, Prashant Vikram, Carolina Sansaloni, Cesar Petroli, Denis Akdemir, Clay Sneller, Matthew Reynolds, Maria Tattaris, Thomas Payne, Carlos Guzman, Roberto J. Peña, Peter Wenzl and Sukhwinder Singh

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

Topics: Gene bank accessions, genomic prediction, cross-validations, reference core subsets, A · E: accession · environment interaction, GenPred, shared data resources, genomic selection, Agricultural Science, Agriculture, Agronomy and Crop Sciences, Botany, Computational Biology, Genetics and Genomics, Genomics, Horticulture, Life Sciences, Other Plant Sciences, Plant Biology, Plant Sciences
Publisher: DigitalCommons@University of Nebraska - Lincoln
Year: 2016
DOI identifier: 10.1534/g3.116.029637
OAI identifier: oai:digitalcommons.unl.edu:agronomyfacpub-1890

Suggested articles


To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.