Joint Use of Genome, Pedigree, and Their Interaction with Environment for Predicting the Performance of Wheat Lines in New Environments

Authors

Réka Howard, University of Nebraska-LincolnFollow
Daniel Gianola, University of Wisconsin-Madison
Osval Montesinos-Lopez, Universidad de Colima
Philomin Juliana, CIMMYT
Ravi Singh, CIMMYT
Jesse Poland, Kansas State University
Sandesh Shrestha, Kansas State University
Paulino Pérez-Rodriguez, Colegio de Postgraduados, Montecillos
José Crossa, CIMMYTFollow
Diego Jarquin, University of Nebraska-LincolnFollow

ORCID IDs

Réka Howard

Daniel Gianola

Osval Montesinos-López

Philomin Juliana

Jesse Poland

Paulino Pérez-Rodríguez

José Crossa

Diego Jarquín

Date of this Version

2019

Citation

G3 Genes | Genomes | Genetics Volume 9 September 2019

Comments

Copyright © 2019 Howard et al.

Open access

doi: https://doi.org/10.1534/g3.119.400508

Abstract

Genome-enabled prediction plays an essential role in wheat breeding because it has the potential to increase the rate of genetic gain relative to traditional phenotypic and pedigree-based selection. Since the performance of wheat lines is highly influenced by environmental stimuli, it is important to accurately model the environment and its interaction with genetic factors in prediction models. Arguably, multi-environmental best linear unbiased prediction (BLUP) may deliver better prediction performance than single-environment genomic BLUP. We evaluated pedigree and genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree information as prediction inputs in two different validation schemes. All models included main effects, but some considered interactions between the different types of pedigree and genomic covariates via Hadamard products of similarity kernels. Pedigree models always gave better prediction of new lines in observed environments than genome-based models when only main effects were fitted. However, for all traits, the highest predictive abilities were obtained when interactions between pedigree, genomes, and environments were included. When new lines were predicted in unobserved environments, in almost all trait/year combinations, the marker main-effects model was the best. These results provide strong evidence that the different sources of genetic information (molecular markers and pedigree) are not equally useful at different stages of the breeding pipelines, and can be employed differentially to improve the design and prediction of the outcome of future breeding programs.