TY - JOUR
T1 - Multienvironment and multitrait genomic selection models in unbalanced early-generation wheat yield trials
AU - Ward, Brian P.
AU - Brown-Guedira, Gina
AU - Tyagi, Priyanka
AU - Kolb, Frederic L.
AU - van Sanford, David A.
AU - Sneller, Clay H.
AU - Griffey, Carl A.
N1 - Publisher Copyright:
© Crop Science Society of America.
PY - 2019/3/1
Y1 - 2019/3/1
N2 - 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.
AB - 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.
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U2 - 10.2135/cropsci2018.03.0189
DO - 10.2135/cropsci2018.03.0189
M3 - Article
AN - SCOPUS:85061985787
SN - 0011-183X
VL - 59
SP - 491
EP - 507
JO - Crop Science
JF - Crop Science
IS - 2
ER -