Abstract
Genomic prediction (GP) has been evaluated in durum wheat breeding programs for several years, but prediction accuracy (PA) remains insufficient for some traits. Recently, multivariate (MV) analysis has gained much attention due to its potential to significantly improve PA. In this study, PA was evaluated for several agronomic traits using a univariate (UV) model in durum wheat, subsequently, different multivariate genomic prediction models were performed to attempt to increase PA. The panel was phenotyped for 10 agronomic traits over two consecutive crop seasons and under two different field conditions: high nitrogen and well-watered (HNW), and low nitrogen and rainfed (LNR). Multivariate GP was implemented using two cross-validation (CV) schemes: MV-CV1, testing the model for each target trait using only the markers, and MV-CV2, testing the model for each target trait using additional phenotypic information. These two MV-CVs were applied in two different analyses: modelling the same trait under both HNW and LNR conditions, and modelling grain yield together with the five most genetically correlated traits. PA for all traits in HNW was higher than LNR for the same trait, except for the trait yellow index. Among all traits, PA ranged from 0.34 (NDVI in LNR) to 0.74 (test weight in HNW). In modelling the same traits in both HNW and LNR, MV-CV1 produced improvements in PA up to 12.45% (NDVI in LNR) compared to the univariate model. By contrast, MV-CV2 increased PA up to 56.72% (thousand kernel weight in LNR). The MV-CV1 scheme did not improve PA for grain yield when it was modelled with the five most genetically correlated traits, whereas MV-CV2 significantly improved PA by up to ~18%. This study demonstrated that increases in prediction accuracy for agronomic traits can be achieved by modelling the same traits in two different field conditions using MV-CV2. In addition, the effectiveness of MV-CV2 was established when grain yield was modelled with additional correlated traits.
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Claims 7
Claim · model-written summary
Prediction accuracy was higher under HNW than LNR for all traits except yellow index.
Not reportedTaxon
Verbatim
e0“PA for all traits in HNW was higher than LNR for the same trait, except for the trait yellow index.”
Claim · model-written summary
The lowest reported prediction accuracy was 0.34 for NDVI under LNR.
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Verbatim
e0“PA ranged from 0.34 (NDVI in LNR) to 0.74 (test weight in HNW).”
Claim · model-written summary
The highest reported prediction accuracy was 0.74 for test weight under HNW.
Not reportedTaxon
Verbatim
e0“PA ranged from 0.34 (NDVI in LNR) to 0.74 (test weight in HNW).”
Claim · model-written summary
MV-CV1 improved NDVI prediction accuracy under LNR by up to 12.45% versus univariate prediction.
Not reportedTaxon
Verbatim
e0“MV-CV1 produced improvements in PA up to 12.45% (NDVI in LNR) compared to the univariate model.”
Claim · model-written summary
MV-CV2 improved thousand kernel weight prediction accuracy under LNR by up to 56.72%.
Not reportedTaxon
Verbatim
e0“MV-CV2 increased PA up to 56.72% (thousand kernel weight in LNR).”
Claim · model-written summary
MV-CV1 did not improve grain yield prediction accuracy when modeled with correlated traits.
- Outcome
- PA for grain yielde0
- Direction
- did not improvee0
- Comparator
- five most genetically correlated traitse0
Not reportedTaxon
Verbatim
e0“The MV-CV1 scheme did not improve PA for grain yield when it was modelled with the five most genetically correlated traits”
Claim · model-written summary
MV-CV2 significantly improved grain yield prediction accuracy by up to about 18%.
Not reportedTaxon
Verbatim
e0“MV-CV2 significantly improved PA by up to ~18%.”