Abstract
L.), which is difficult to assay because it is strongly influenced by the environment and depends on the natural occurrence of multiple pathogens. Here we report a study of the efficacy of GS for predicting ascochyta blight resistance in pea, as represented by ascochyta blight disease score (ASC), and using nucleotide polymorphism data acquired through genotyping-by-sequencing. The effects on prediction accuracy of different GS models and different thresholds for missing genotypic data (which modified the number of single nucleotide polymorphisms used in the analysis) were compared using cross-validation. Additionally, the inclusion of marker × environment interactions in a genomic best linear unbiased prediction (GBLUP) model was evaluated. Finally, different ways of combining trait data from two field trials using bivariate, spatial, and single-stage analyses were compared to results obtained using a mean value. The best prediction accuracy achieved for ASC was 0.56, obtained using GBLUP analysis with a mean value for ASC and data quality threshold of 70% (i.e., missing SNP data in <30% of lines). GBLUP and Bayesian Reproducing kernel Hilbert spaces regression (RKHS) performed slightly better than the other models trialed, whereas different missing data thresholds made minimal differences to prediction accuracy. The prediction accuracies of individual, randomly selected, testing/training partitions were highly variable, highlighting the effect that the choice of training population has on prediction accuracy. The inclusion of marker × environment interactions did not increase the prediction accuracy for lines which had not been phenotyped, but did improve the results of prediction across environments. GS is potentially useful for pea breeding programs pursuing ascochyta blight resistance, both for predicting breeding values for lines that have not been phenotyped, and for providing enhanced estimated breeding values for lines for which trait data is available.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 7
Claim · model-written summary
The best pea ascochyta score prediction accuracy was 0.56 using GBLUP, a mean score and a 70% data quality threshold.
- Taxon
- Pea
Lathyrus oleraceust0 - Treatment
- GBLUP analysise0
- Outcome
- prediction accuracye0
- Value
- 0.56e0
- Qualifier
- data quality threshold of 70%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The best prediction accuracy achieved for ASC was 0.56, obtained using GBLUP analysis with a mean value for ASC and data quality threshold of 70%”
t0“Pea”
Claim · model-written summary
GBLUP slightly outperformed other genomic selection models.
- Taxon
- PeaLathyrus oleraceust0
- Outcome
- performed slightly bettere0
- Direction
- slightly bettere0
- Comparator
- other models trialede0
Not reportedValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“GBLUP and Bayesian Reproducing kernel Hilbert spaces regression (RKHS) performed slightly better than the other models trialed”
t0“Pea”
Claim · model-written summary
RKHS slightly outperformed other genomic selection models.
- Taxon
- PeaLathyrus oleraceust0
- Outcome
- performed slightly bettere0
- Direction
- slightly bettere0
- Comparator
- other models trialede0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“GBLUP and Bayesian Reproducing kernel Hilbert spaces regression (RKHS) performed slightly better than the other models trialed”
t0“Pea”
Claim · model-written summary
Missing-data thresholds changed prediction accuracy minimally.
- Taxon
- PeaLathyrus oleraceust0
- Outcome
- prediction accuracye0
- Direction
- minimal differencese0
- Comparator
- different missing data thresholdse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“different missing data thresholds made minimal differences to prediction accuracy”
t0“Pea”
Claim · model-written summary
Prediction accuracy varied greatly among random training and testing partitions.
- Taxon
- PeaLathyrus oleraceust0
- Outcome
- prediction accuraciese0
- Direction
- highly variablee0
- Comparator
- testing/training partitionse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The prediction accuracies of individual, randomly selected, testing/training partitions were highly variable”
t0“Pea”
Claim · model-written summary
Marker by environment interactions did not improve accuracy for unphenotyped lines.
- Taxon
- PeaLathyrus oleraceust0
- Treatment
- marker × environment interactionse0
- Outcome
- prediction accuracye0
- Direction
- did not increasee0
- Comparator
- lines which had not been phenotypede0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The inclusion of marker × environment interactions did not increase the prediction accuracy for lines which had not been phenotyped”
t0“Pea”
Claim · model-written summary
Marker by environment interactions improved prediction across environments.
- Taxon
- PeaLathyrus oleraceust0
- Outcome
- results of predictione0
- Direction
- improvee0
- Setting
- across environmentse0
Not reportedValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“but did improve the results of prediction across environments”
t0“Pea”