Multienvironment and multitrait genomic selection models in unbalanced early-generation wheat yield trials

Brian P. Ward, Gina Brown-Guedira, Priyanka Tyagi, Frederic L. Kolb, David A. van Sanford, Clay H. Sneller, Carl A. Griffey

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

The majority of studies evaluating genomic selection (GS) for plant breeding have used single-trait, single-site models that ignore genotype x environment interaction (GEI) effects. However, such studies do not accurately reflect the complexities of many applied breeding programs, and previous papers have found that models that incorporate GEI effects and multiple traits can increase the accuracy of genomic estimated breeding values (GEBVs). This study’s goal was to test GS methods for prediction in scenarios that simulate earlygeneration yield testing by correcting for field spatial variation, and fitting multienvironment and multitrait models on data for 14 traits of varying heritability evaluated in unbalanced designs across four environments. Corrections for spatial variation increased across-environment trait heritability by 25%, on average, but had little effect on model predictive ability. Results between all models were generally equivalent when predicting the performance of newly introduced genotypes. However, models incorporating GEI information and multiple traits increased prediction accuracy by up to 9.6% for low-heritability traits when phenotypic data were sparsely collected across environments. The results suggest that GS models using multiple traits and incorporating GEI effects may best be suited to predicting line performance in new environments when phenotypic data have already been collected across a subset of the total testing environments.

Original languageEnglish
Pages (from-to)491-507
Number of pages17
JournalCrop Science
Volume59
Issue number2
DOIs
StatePublished - Mar 1 2019

Bibliographical note

Funding Information:
Genotyping costs for this project were supported by the Agriculture and Food Research Initiative Competitive Grant 2017-67007-25939 from the USDA National Institute of Food and Agriculture. The lead author was supported by the Virginia Agricultural Council Grant 617, and by funding from the Virginia Small Grains Board. The authors thank Professor Jim Holland of the USDA-ARS for his assistance in revising this manuscript.

Publisher Copyright:
© Crop Science Society of America.

ASJC Scopus subject areas

  • Agronomy and Crop Science

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