Genomic Prediction of Resistance to Tan Spot, Spot Blotch and Septoria Nodorum Blotch in Synthetic Hexaploid Wheat

• Published in: International journal of molecular sciences Vol. 24; no. 13; p. 10506
• Main Authors: García-Barrios, Guillermo, Crossa, José, Cruz-Izquierdo, Serafín, Aguilar-Rincón, Víctor Heber, Sandoval-Islas, J Sergio, Corona-Torres, Tarsicio, Lozano-Ramírez, Nerida, Dreisigacker, Susanne, He, Xinyao, Singh, Pawan Kumar, Pacheco-Gil, Rosa Angela
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.06.2023; MDPI
• Subjects: Accuracy; Analysis; Breeding; breeding values; Disease; Gene frequency; Genetic diversity; Genome; Genomes; genomic selection; Genomics; Genotype; Humans; Models, Genetic; Parastagonospora nodorum; pedigree and genomic relationship matrices; Performance evaluation; Phenotype; Plant Breeding; Plant Diseases - genetics; Population genetics; Population studies; Predictions; Spot blotch; Statistical analysis; Tan spot; Triticum - genetics; Wheat; wheat diseases; Mexico; Australia; United States > US; genomic selection; breeding values; wheat diseases; pedigree and genomic relationship matrices
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms241310506

Genomic prediction combines molecular and phenotypic data in a training population to predict the breeding values of individuals that have only been genotyped. The use of genomic information in breeding programs helps to increase the frequency of favorable alleles in the populations of interest. This study evaluated the performance of BLUP (Best Linear Unbiased Prediction) in predicting resistance to tan spot, spot blotch and Septoria nodorum blotch in synthetic hexaploid wheat. BLUP was implemented in single-trait and multi-trait models with three variations: (1) the pedigree relationship matrix (A-BLUP), (2) the genomic relationship matrix (G-BLUP), and (3) a combination of the two matrices (A+G BLUP). In all three diseases, the A-BLUP model had a lower performance, and the G-BLUP and A+G BLUP were statistically similar ( ≥ 0.05). The prediction accuracy with the single trait was statistically similar ( ≥ 0.05) to the multi-trait accuracy, possibly due to the low correlation of severity between the diseases.