title: Genomic Selection in Multi-environment Crop Trials.
abstract: Genomic selection in crop breeding introduces modeling challenges not found in animal studies. These include the need to accommodate replicate plants for each line, consider spatial variation in field trials, address line by environment interactions, and capture nonadditive effects. Here, we propose a flexible single-stage genomic selection approach that resolves these issues. Our linear mixed model incorporates spatial variation through environment-specific terms, and also randomization-based design terms. It considers marker, and marker by environment interactions using ridge regression best linear unbiased prediction to extend genomic selection to multiple environments. Since the approach uses the raw data from line replicates, the line genetic variation is partitioned into marker and nonmarker residual genetic variation (i.e., additive and nonadditive effects). This results in a more precise estimate of marker genetic effects. Using barley height data from trials, in 2 different years, of up to 477 cultivars, we demonstrate that our new genomic selection model improves predictions compared to current models. Analyzing single trials revealed improvements in predictive ability of up to 5.7%. For the multiple environment trial (MET) model, combining both year trials improved predictive ability up to 11.4% compared to a single environment analysis. Benefits were significant even when fewer markers were used. Compared to a single-year standard model run with 3490 markers, our partitioned MET model achieved the same predictive ability using between 500 and 1000 markers depending on the trial. Our approach can be used to increase accuracy and confidence in the selection of the best lines for breeding and/or, to reduce costs by using fewer markers.
authors.affiliation: Division of Plant Sciences, University of Dundee at the James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK.
authors.forename: Helena
authors.initials: H
authors.lastname: Oakey
authors.affiliation: National Institute for Applied Statistics Research Australia, University of Wollongong, NSW, 2522, Australia.
authors.forename: Brian
authors.initials: B
authors.lastname: Cullis
authors.affiliation: Rothamsted Research, Harpenden, Hertfordshire AL5 3JQ, UK.
authors.forename: Robin
authors.initials: R
authors.lastname: Thompson
authors.affiliation: Department of Cell and Molecular Sciences, The James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK.
authors.forename: Jordi
authors.initials: J
authors.lastname: Comadran
authors.affiliation: Division of Plant Sciences, University of Dundee at the James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK c.halpin@dundee.ac.uk r.waugh@dundee.ac.uk.
authors.forename: Claire
authors.identifier: 0000-0002-1808-8130
authors.initials: C
authors.lastname: Halpin
authors.affiliation: Division of Plant Sciences, University of Dundee at the James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK Department of Cell and Molecular Sciences, The James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK c.halpin@dundee.ac.uk r.waugh@dundee.ac.uk.
authors.forename: Robbie
authors.initials: R
authors.lastname: Waugh
mesh_terms: D000465:Algorithms; D001947:Breeding; D018556:Crops, Agricultural / Q000235:genetics*; D004777:Environment; D059647:Gene-Environment Interaction; D056726:Genetic Association Studies; D018745:Genome, Plant; D023281:Genomics / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing; D040582:Inheritance Patterns; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D019655:Quantitative Trait, Heritable; D015203:Reproducibility of Results; D012641:Selection, Genetic
keywords: GEBV; GenPred; barley; genomic selection; multi-environment trial; random ridge regression; shared data resource