title: Genomic prediction for rust resistance in diverse wheat landraces.
abstract: We have demonstrated that genomic selection in diverse wheat landraces for resistance to leaf, stem and strip rust is possible, as genomic breeding values were moderately accurate. Markers with large effects in the Bayesian analysis confirmed many known genes, while also discovering many previously uncharacterised genome regions associated with rust scores. Genomic selection, where selection decisions are based on genomic estimated breeding values (GEBVs) derived from genome-wide DNA markers, could accelerate genetic progress in plant breeding. In this study, we assessed the accuracy of GEBVs for rust resistance in 206 hexaploid wheat (Triticum aestivum) landraces from the Watkins collection of phenotypically diverse wheat genotypes from 32 countries. The landraces were genotyped for 5,568 SNPs using an Illumina iSelect 9 K bead chip assay and phenotyped for field-based leaf rust (Lr), stem rust (Sr) and stripe rust (Yr) responses across multiple years. Genomic Best Linear Unbiased Prediction (GBLUP) and a Bayesian Regression method (BayesR) were used to predict GEBVs. Based on fivefold cross-validation, the accuracy of genomic prediction averaged across years was 0.35, 0.27 and 0.44 for Lr, Sr and Yr using GBLUP and 0.33, 0.38 and 0.30 for Lr, Sr and Yr using BayesR, respectively. Inclusion of PCR-predicted genotypes for known rust resistance genes increased accuracy more substantially when the marker was diagnostic (Lr34/Sr57/Yr18) for the presence-absence of the gene rather than just linked (Sr2). Investigation of the impact of genetic relatedness between validation and reference lines on accuracy of genomic prediction showed that accuracy will be higher when each validation line had at least one close relationship to the reference lines. Overall, the prediction accuracies achieved in this study are encouraging, and confirm the feasibility of genomic selection in wheat. In several instances, estimated marker effects were confirmed by published literature and results of mapping experiments using Watkins accessions.
authors: School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia, hans.daetwyler@depi.vic.gov.au.
authors: Hans D
authors: HD
authors: Daetwyler
authors: Urmil K
authors: UK
authors: Bansal
authors: Harbans S
authors: HS
authors: Bariana
authors: Matthew J
authors: MJ
authors: Hayden
authors: Ben J
authors: BJ
authors: Hayes
mesh_terms: D001487:Basidiomycota / Q000502:physiology*; D001947:Breeding; D060467:Disease Resistance / Q000235:genetics*; D060146:Ecotype; D059647:Gene-Environment Interaction; D005819:Genetic Markers; D018745:Genome, Plant / Q000235:genetics; D023281:Genomics / Q000379:methods*; D005838:Genotype; D040582:Inheritance Patterns / Q000235:genetics; D010802:Phylogeny; D010935:Plant Diseases / Q000235:genetics* / Q000276:immunology* / Q000382:microbiology; D025341:Principal Component Analysis; D019655:Quantitative Trait, Heritable; D012044:Regression Analysis; D015203:Reproducibility of Results; D014908:Triticum / Q000235:genetics* / Q000382:microbiology*
chemical_list: D005819:Genetic Markers