Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones

Authors

C. Saint Pierre, International Maize and Wheat Improvement Center
Juan Burgueño, Biometrics and Statistics Unit, International Maize and Wheat Improvement Center, México
Jose Crossa, Maize and Wheat Improvement CenterFollow
G. Fuentes Dávila, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias
P. Figueroa López, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias
E. Solís Moya, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias
J. Ireta Moreno, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias
V. M. Hernández Muela, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias
V. M. Zamora Villa, Universidad Autónoma Agraria Antonio Narro, México
P. Vikram, International Maize and Wheat Improvement Center
K. Mathews, International Maize and Wheat Improvement Center
C. Sansaloni, International Maize and Wheat Improvement Center
D. Sehgal, International Maize and Wheat Improvement Center
Diego Jarquin, University of Nebraska-LincolnFollow
P. Wenzl, International Maize and Wheat Improvement Center
Sukhwinder Singh, International Maize and Wheat Improvement CenterFollow

Document Type

Article

Date of this Version

2016

Citation

Scientific Reports 6:27312 DOI: 10.1038/srep27312

Comments

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license

Abstract

Genomic and pedigree predictions for grain yield and agronomic traits were carried out using high density molecular data on a set of 803 spring wheat lines that were evaluated in 5 sites characterized by several environmental co-variables. Seven statistical models were tested using two random cross-validations schemes. Two other prediction problems were studied, namely predicting the lines’ performance at one site with another (pairwise-site) and at untested sites (leave-one-site-out). Grain yield ranged from 3.7 to 9.0 t ha⁻¹ across sites. The best predictability was observed when genotypic and pedigree data were included in the models and their interaction with sites and the environmental co-variables. The leave-one-site-out increased average prediction accuracy over pairwise-site for all the traits, specifically from 0.27 to 0.36 for grain yield. Days to anthesis, maturity, and plant height predictions had high heritability and gave the highest accuracy for prediction models. Genomic and pedigree models coupled with environmental co-variables gave high prediction accuracy due to high genetic correlation between sites. This study provides an example of model prediction considering climate data along-with genomic and pedigree information. Such comprehensive models can be used to achieve rapid enhancement of wheat yield enhancement in current and future climate change scenario.