Search claims · PlantClaims

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Including physiological traits and/or interaction effects improved grain yield prediction accuracies in four environments compared with genomic-only models.

Taxon: Soft Wheatt0
Treatment: physiological traits and/or interaction effectse3
Outcome: Prediction accuracies of grain yielde3
Direction: improvede3
Comparator: models with only genomic datae3
Setting: four environmentse3

Verbatim

t0
“Soft Wheat Panel”
✓ located verbatim in the source · title, characters 95–111

e3
“Prediction accuracies of grain yield evaluated in four environments improved when physiological traits and/or interaction effects (genotype × environment or physiology × environment) were included in the model compared to models with only genomic data”
✓ located verbatim in the source · abstract, characters 920–1171

Title

Increased Prediction Accuracy Using Combined Genomic Information and Physiological Traits in A Soft Wheat Panel Evaluated in Multi-Environments

Jia Guo; Sumit Pradhan; Dipendra Shahi; et al. Scientific Reports 2020. doi:10.1038/s41598-020-63919-3

Other claims from this paper (3)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Prediction accuracy against the whole dataset model was 0.80 for tested hybrids.

Taxon: maizeZea mayst0
Material: CV0-Predicted EGe0
Outcome: Prediction accuracye0
Direction: improvede0
Value: 0.80e0
Comparator: whole-dataset modele0

Verbatim

e0
“Prediction accuracy improved to 0.80 (CV0-Predicted EG) and 0.60 (CV00-Predicted EG) when compared to the whole-dataset model that used the genomic relationships and the environmental gradient of all environments in the study”
✓ located verbatim in the source · abstract, characters 1981–2206

t0
“maize”
✓ located verbatim in the source · title, characters 46–51

Title

Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models

Seth A. Tolley; Luiz F. Brito; Diane R. Wang; et al. Frontiers in Genetics 2023. doi:10.3389/fgene.2023.1221751

Other claims from this paper (7)

Association Yield heritability was greater in higher yielding environments than lower yielding ones.
Association Genetic variability increased in higher yielding environments.
Observation Five yield associated SNPs with the largest effects were selected per environment.
Observation Many candidate genes near selected SNPs had reported stress response roles.
Method / resource Cross year prediction accuracy was 0.34 for previously tested hybrids against a BLUP baseline.
Method / resource Cross year prediction accuracy was 0.21 for untested hybrids against a BLUP baseline.
Method / resource Prediction accuracy against the whole dataset model was 0.60 for untested hybrids.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Prediction accuracy against the whole dataset model was 0.60 for untested hybrids.

Taxon: maizeZea mayst0
Material: CV00-Predicted EGe0
Outcome: Prediction accuracye0
Direction: improvede0
Value: 0.60e0
Comparator: whole-dataset modele0

Verbatim

e0
“Prediction accuracy improved to 0.80 (CV0-Predicted EG) and 0.60 (CV00-Predicted EG) when compared to the whole-dataset model that used the genomic relationships and the environmental gradient of all environments in the study”
✓ located verbatim in the source · abstract, characters 1981–2206

t0
“maize”
✓ located verbatim in the source · title, characters 46–51

Title

Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models

Seth A. Tolley; Luiz F. Brito; Diane R. Wang; et al. Frontiers in Genetics 2023. doi:10.3389/fgene.2023.1221751

Other claims from this paper (7)

Association Yield heritability was greater in higher yielding environments than lower yielding ones.
Association Genetic variability increased in higher yielding environments.
Observation Five yield associated SNPs with the largest effects were selected per environment.
Observation Many candidate genes near selected SNPs had reported stress response roles.
Method / resource Cross year prediction accuracy was 0.34 for previously tested hybrids against a BLUP baseline.
Method / resource Cross year prediction accuracy was 0.21 for untested hybrids against a BLUP baseline.
Method / resource Prediction accuracy against the whole dataset model was 0.80 for tested hybrids.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The rank correlation is higher for the model with synthetic covariates than for models without them.

Taxon: sorghumSorghume4
Outcome: rank correlatione2
Direction: highere2
Comparator: others without SCe1
Setting: Ethiopiae4

Verbatim

e1
“According to the MSEPD, models accounting for SC improve predictive accuracy of genotype performances in the three of the variance-covariance structures compared to others without SC”
✓ located verbatim in the source · abstract, characters 1513–1695

e2
“The rank correlation was also higher for the model with the SC”
✓ located verbatim in the source · abstract, characters 1697–1759

e4
“Our approach indicates improvement in predictive accuracy with SC in the context of genotype-by-location interactions of a sorghum breeding in Ethiopia”
✓ located verbatim in the source · abstract, characters 1920–2071

Title

Accuracy of prediction from multi-environment trials for new locations using pedigree information and environmental covariates: the case of sorghum (Sorghum bicolor (L.) Moench) breeding

Diriba Tadese; Hans‑Peter Piepho; Jens Hartung Theoretical and Applied Genetics 2024. doi:10.1007/s00122-024-04684-z

Other claims from this paper (4)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The GBLUP-GE model with 19 climatic factors predicted grain yield with accuracy 0.331, outperforming GBLUP or BayesB.

Taxon: maizeZea mayse5
Treatment: GE interaction effects of 19 climatic factorse3
Outcome: prediction accuraciese2
Value: 0.331e2
Qualifier: GYe1
Comparator: traditional GBLUP or BayesB modelse1

Verbatim

e1
“outperformed the traditional GBLUP or BayesB models in predicting GMC or GY”
✓ located verbatim in the source · abstract, characters 860–935

e2
“achieving prediction accuracies of 0.731 and 0.331, respectively”
✓ located verbatim in the source · abstract, characters 965–1029

e3
“incorporating GE interaction effects of 19 climatic factors associated with day length, transpiration, temperature, and radiation”
✓ located verbatim in the source · abstract, characters 687–816

e5
“generated from 475 maize inbred lines”
✓ located verbatim in the source · abstract, characters 517–554

Title

Accurate genomic prediction for grain yield and grain moisture content of maize hybrids using multi‐environment data

Jingxin Wang; Liwei Liu; Kunhui He; et al. Journal of Integrative Plant Biology 2025. doi:10.1111/jipb.13857

Other claims from this paper (6)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Cross year prediction accuracy was 0.21 for untested hybrids against a BLUP baseline.

Taxon: maizeZea mayst0
Material: CV00-Predicted EGe0
Outcome: Prediction accuracye0
Value: 0.21e0
Comparator: Best Linear Unbiased Prediction (BLUPs)e0

Verbatim

e0
“Prediction accuracy was 0.34 for cross validation across years (CV0-Predicted EG) and 0.21 for cross validation across years with only untested hybrids (CV00-Predicted EG) when compared to Best Linear Unbiased Prediction (BLUPs) that did not utilize genotypic or environmental relationships”
✓ located verbatim in the source · abstract, characters 1689–1979

t0
“maize”
✓ located verbatim in the source · title, characters 46–51

Title

Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models

Seth A. Tolley; Luiz F. Brito; Diane R. Wang; et al. Frontiers in Genetics 2023. doi:10.3389/fgene.2023.1221751

Other claims from this paper (7)

Association Yield heritability was greater in higher yielding environments than lower yielding ones.
Association Genetic variability increased in higher yielding environments.
Observation Five yield associated SNPs with the largest effects were selected per environment.
Observation Many candidate genes near selected SNPs had reported stress response roles.
Method / resource Cross year prediction accuracy was 0.34 for previously tested hybrids against a BLUP baseline.
Method / resource Prediction accuracy against the whole dataset model was 0.80 for tested hybrids.
Method / resource Prediction accuracy against the whole dataset model was 0.60 for untested hybrids.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The GBLUP-GE model with 19 climatic factors predicted grain moisture content with accuracy 0.731, outperforming GBLUP or BayesB.

Taxon: maizeZea mayse5
Treatment: GE interaction effects of 19 climatic factorse3
Outcome: prediction accuraciese2
Value: 0.731e2
Qualifier: GMCe1
Comparator: traditional GBLUP or BayesB modelse1

Verbatim

e1
“outperformed the traditional GBLUP or BayesB models in predicting GMC or GY”
✓ located verbatim in the source · abstract, characters 860–935

e2
“achieving prediction accuracies of 0.731 and 0.331, respectively”
✓ located verbatim in the source · abstract, characters 965–1029

e3
“incorporating GE interaction effects of 19 climatic factors associated with day length, transpiration, temperature, and radiation”
✓ located verbatim in the source · abstract, characters 687–816

e5
“generated from 475 maize inbred lines”
✓ located verbatim in the source · abstract, characters 517–554

Title

Accurate genomic prediction for grain yield and grain moisture content of maize hybrids using multi‐environment data

Jingxin Wang; Liwei Liu; Kunhui He; et al. Journal of Integrative Plant Biology 2025. doi:10.1111/jipb.13857

Other claims from this paper (6)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Cross year prediction accuracy was 0.34 for previously tested hybrids against a BLUP baseline.

Taxon: maizeZea mayst0
Material: CV0-Predicted EGe0
Outcome: Prediction accuracye0
Value: 0.34e0
Comparator: Best Linear Unbiased Prediction (BLUPs)e0

Verbatim

e0
“Prediction accuracy was 0.34 for cross validation across years (CV0-Predicted EG) and 0.21 for cross validation across years with only untested hybrids (CV00-Predicted EG) when compared to Best Linear Unbiased Prediction (BLUPs) that did not utilize genotypic or environmental relationships”
✓ located verbatim in the source · abstract, characters 1689–1979

t0
“maize”
✓ located verbatim in the source · title, characters 46–51

Title

Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models

Seth A. Tolley; Luiz F. Brito; Diane R. Wang; et al. Frontiers in Genetics 2023. doi:10.3389/fgene.2023.1221751

Other claims from this paper (7)

Association Yield heritability was greater in higher yielding environments than lower yielding ones.
Association Genetic variability increased in higher yielding environments.
Observation Five yield associated SNPs with the largest effects were selected per environment.
Observation Many candidate genes near selected SNPs had reported stress response roles.
Method / resource Cross year prediction accuracy was 0.21 for untested hybrids against a BLUP baseline.
Method / resource Prediction accuracy against the whole dataset model was 0.80 for tested hybrids.
Method / resource Prediction accuracy against the whole dataset model was 0.60 for untested hybrids.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Predicted selection gains were slightly higher with MTM and RRM.

Taxon: maizeZea mayst0
Outcome: predicted selection gainse0
Direction: slightly highere0
Comparator: MTM and RRMe0

Verbatim

e0
“In general, estimates of broad-sense heritability, selective accuracy, and predicted selection gains were slightly higher when obtained using MTM and RRM.”
✓ located verbatim in the source · abstract, characters 1148–1302

t0
“maize”
✓ located verbatim in the source · title, characters 106–111

Title

Multiple-trait, random regression, and compound symmetry models for analyzing multi-environment trials in maize breeding

Igor Ferreira Coelho; Marco Antônio Peixoto; Jeniffer Santana Pinto Coelho Evangelista; et al. PLoS ONE 2020. doi:10.1371/journal.pone.0242705

Other claims from this paper (6)

Method / resource Selected MTM, RRM and CSM fit heterogeneous residuals.
Method / resource RRM genetic effects were represented by second-order Legendre polynomials.
Observation Maize hybrids showed genetic variability for grain yield.
Method / resource Broad-sense heritability estimates were slightly higher with MTM and RRM.
Method / resource Selective accuracy estimates were slightly higher with MTM and RRM.
Recommendation Authors favored RRM for parsimonious analysis and prediction in untested environments.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Broad-sense heritability estimates were slightly higher with MTM and RRM.

Taxon: maizeZea mayst0
Outcome: broad-sense heritabilitye0
Direction: slightly highere0
Comparator: MTM and RRMe0

Verbatim

e0
“In general, estimates of broad-sense heritability, selective accuracy, and predicted selection gains were slightly higher when obtained using MTM and RRM.”
✓ located verbatim in the source · abstract, characters 1148–1302

t0
“maize”
✓ located verbatim in the source · title, characters 106–111

Title

Multiple-trait, random regression, and compound symmetry models for analyzing multi-environment trials in maize breeding

Igor Ferreira Coelho; Marco Antônio Peixoto; Jeniffer Santana Pinto Coelho Evangelista; et al. PLoS ONE 2020. doi:10.1371/journal.pone.0242705

Other claims from this paper (6)

Method / resource Selected MTM, RRM and CSM fit heterogeneous residuals.
Method / resource RRM genetic effects were represented by second-order Legendre polynomials.
Observation Maize hybrids showed genetic variability for grain yield.
Method / resource Selective accuracy estimates were slightly higher with MTM and RRM.
Method / resource Predicted selection gains were slightly higher with MTM and RRM.
Recommendation Authors favored RRM for parsimonious analysis and prediction in untested environments.

“accuracy of multi-environment models”

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