Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials
| Autor: | Osval A. Montesinos-López, Alison R. Bentley, Paulino Pérez-Rodríguez, Abelardo Montesinos-López, José Crossa, Maria Itria Ibba, Daniel E. Runcie, Leonardo Crespo |
|---|---|
| Přispěvatelé: | de Koning, D-J |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
AcademicSubjects/SCI01140
Genotype AcademicSubjects/SCI00010 wheat quality shared data resource Bivariate analysis QH426-470 Biology AcademicSubjects/SCI01180 multi-trait analysis Genetic correlation Correlation Genetic Models wheat Multi trait Statistics Genetics Selection Genetic Selection Molecular Biology Genetics (clinical) genomic prediction Triticum Investigation multi-environment analysis Models Genetic Human Genome Genomics Heritability Correlation value Plant Breeding GenPred Phenotype Grain yield AcademicSubjects/SCI00960 Predictive modelling |
| Zdroj: | G3: Genes|Genomes|Genetics G3 (Bethesda, Md.), vol 11, iss 10 G3: Genes, Genomes, Genetics, Vol 11, Iss 10 (2021) |
| ISSN: | 2160-1836 |
| Popis: | Implementing genomic-based prediction models in genomic selection requires an understanding of the measures for evaluating prediction accuracy from different models and methods using multi-trait data. In this study, we compared prediction accuracy using six large multi-trait wheat data sets (quality and grain yield). The data were used to predict 1 year (testing) from the previous year (training) to assess prediction accuracy using four different prediction models. The results indicated that the conventional Pearson’s correlation between observed and predicted values underestimated the true correlation value, whereas the corrected Pearson’s correlation calculated by fitting a bivariate model was higher than the division of the Pearson’s correlation by the squared root of the heritability across traits, by 2.53–11.46%. Across the datasets, the corrected Pearson’s correlation was higher than the uncorrected by 5.80–14.01%. Overall, we found that for grain yield the prediction performance was highest using a multi-trait compared to a single-trait model. The higher the absolute genetic correlation between traits the greater the benefits of multi-trait models for increasing the genomic-enabled prediction accuracy of traits. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|