Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat

Philomin Juliana, Ravi P. Singh, Pawan K. Singh, Jose Crossa, Julio Huerta-Espino, Caixia Lan, Sridhar Bhavani, Jessica E. Rutkoski, Jesse A. Poland, Gary C. Bergstrom, Mark E. Sorrells

Research output: Contribution to journalArticlepeer-review

81 Scopus citations


Key message: Genomic prediction for seedling and adult plantresistance to wheat rusts was compared to prediction using few markers as fixed effects in a least-squares approach and pedigree-based prediction. Abstract: The unceasing plant-pathogen arms race and ephemeral nature of some rust resistance genes have been challenging for wheat (Triticum aestivum L.) breeding programs and farmers. Hence, it is important to devise strategies for effective evaluation and exploitation of quantitative rust resistance. One promising approach that could accelerate gain from selection for rust resistance is ‘genomic selection’ which utilizes dense genome-wide markers to estimate the breeding values (BVs) for quantitative traits. Our objective was to compare three genomic prediction models including genomic best linear unbiased prediction (GBLUP), GBLUP A that was GBLUP with selected loci as fixed effects and reproducing kernel Hilbert spaces-markers (RKHS-M) with least-squares (LS) approach, RKHS-pedigree (RKHS-P), and RKHS markers and pedigree (RKHS-MP) to determine the BVs for seedling and/or adult plant resistance (APR) to leaf rust (LR), stem rust (SR), and stripe rust (YR). The 333 lines in the 45th IBWSN and the 313 lines in the 46th IBWSN were genotyped using genotyping-by-sequencing and phenotyped in replicated trials. The mean prediction accuracies ranged from 0.31–0.74 for LR seedling, 0.12–0.56 for LR APR, 0.31–0.65 for SR APR, 0.70–0.78 for YR seedling, and 0.34–0.71 for YR APR. For most datasets, the RKHS-MP model gave the highest accuracies, while LS gave the lowest. GBLUP, GBLUP A, RKHS-M, and RKHS-P models gave similar accuracies. Using genome-wide marker-based models resulted in an average of 42% increase in accuracy over LS. We conclude that GS is a promising approach for improvement of quantitative rust resistance and can be implemented in the breeding pipeline.
Original languageEnglish (US)
Pages (from-to)1415-1430
Number of pages16
JournalTheoretical and Applied Genetics
Issue number7
StatePublished - Jul 1 2017
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-13

ASJC Scopus subject areas

  • Genetics
  • Agronomy and Crop Science
  • Biotechnology


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