Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials. Issue 10 (30th July 2021)

• Record Type: Journal Article
• Authors: Montesinos-López, Abelardo; Runcie, Daniel E; Ibba, Maria Itria; Pérez-Rodríguez, Paulino; Montesinos-López, Osval A; Crespo, Leonardo A; Bentley, Alison R; Crossa, José
• Editors: de Koning, D -J
• Is Part Of: G3. Volume 11:Issue 10(2021)
• Publication Date: 2021-07-30
• Subjects: wheat -- wheat quality -- multi-trait analysis -- multi-environment analysis -- genomic prediction -- GenPred -- shared data resource; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1093/g3journal/jkab270
• Languages: English
• ISSNs: 2160-1836

Abstract

Abstract: 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.