Genomic Selection in Multi-environment Crop Trials

A - Papers appearing in refereed journals

Oakey, H., Cullis, B. R., Thompson, R., Comadran, J., Halpin, C. and Waugh, R. 2016. Genomic Selection in Multi-environment Crop Trials. G3. 6 (5), pp. 1313-1326.

AuthorsOakey, H., Cullis, B. R., Thompson, R., Comadran, J., Halpin, C. and Waugh, R.

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.

KeywordsMulti-environment trial; Genomic selection; Random ridge regression; GEBV; Barley; GENPRED; Shared data resource
Year of Publication2016
Journal citation6 (5), pp. 1313-1326
Digital Object Identifier (DOI)doi:10.1534/g3.116.027524
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeThe BBSRC Sustainable Bioenergy Centre (BSBEC): Perennial Bioenergy Crops Programme [2009-2015]
Publisher's version
Output statusPublished
Publication dates
Online11 Mar 2016
Publication process dates
Accepted05 Mar 2016
Copyright licenseCC BY
PublisherGenetics Society of America (GSA)

Permalink -

21 total views
12 total downloads
0 views this month
0 downloads this month
Download files as zip