Genomic signatures of selection for resistance to stripe rust in Austrian winter wheat

Abstract

Key message We combined quantitative and population genetic methods to identify loci under selection for adult plant resistance to stripe rust in an Austrian winter wheat breeding population from 2008 to 2018. Abstract Resistance to stripe rust, a foliar disease caused by the fungus P. striiformis f. sp. tritici, in wheat (Triticum aestivum L.) is both qualitatively and quantitatively controlled. Resistance genes confer complete, race-specific resistance but are easily overcome by evolving pathogen populations, while quantitative resistance is controlled by many small- to medium-effect loci that provide incomplete yet more durable protection. Data on resistance loci can be applied in marker-assisted selection and genomic prediction frameworks. We employed genome-wide association to detect loci associated with stripe rust and selection testing to identify regions of the genome that underwent selection for stripe rust resistance in an Austrian winter wheat breeding program from 2008 to 2018. Genome-wide association mapping identified 150 resistance loci, 62 of which showed significant evidence of selection over time. The breeding population also demonstrated selection for resistance at the genome-wide level.

Comparison of linear and semi-parametric models incorporating genomic, pedigree, and associated loci information for the prediction of resistance to stripe rust in an Austrian winter wheat breeding program

“…Here we analyzed a historical stripe rust dataset from the winter wheat breeding program of Saatzucht Donau GmbH & CoKG (Probstdorf, Austria), as described previously by Morales et al ( 2021 ). Briefly, 20,529 genotypes were evaluated for adult stripe rust resistance on a 1 (most resistant) to 9 (most susceptible) scale in 71 trials across 53 locations from 2008 to 2018, where the majority (60/71 trials) of the trials were naturally infected by Pst (Morales et al 2021 ). The phenotypic dataset is highly unbalanced, with most genotypes only evaluated in one plot in one trial (Morales et al 2021 ).…”

“…Briefly, 20,529 genotypes were evaluated for adult stripe rust resistance on a 1 (most resistant) to 9 (most susceptible) scale in 71 trials across 53 locations from 2008 to 2018, where the majority (60/71 trials) of the trials were naturally infected by Pst (Morales et al 2021 ). The phenotypic dataset is highly unbalanced, with most genotypes only evaluated in one plot in one trial (Morales et al 2021 ). Within-trial spatial variation in stripe rust severity was adjusted using the “SpATS” package (Rodríguez-Álvarez et al 2018 ) in R Core Team ( 2020 ), Morales et al ( 2021 ).…”

“…The phenotypic dataset is highly unbalanced, with most genotypes only evaluated in one plot in one trial (Morales et al 2021 ). Within-trial spatial variation in stripe rust severity was adjusted using the “SpATS” package (Rodríguez-Álvarez et al 2018 ) in R Core Team ( 2020 ), Morales et al ( 2021 ). Within and across years, a mixed model was fit with the spatially-adjusted stripe rust plot values as the response, genotype as a fixed effect, and trial as a random effect using the “breedR” package (Muñoz and Sanchez 2020 ) in R Core Team ( 2020 ) and the genotype best linear unbiased estimates (BLUEs) were then extracted from the model for further analysis (Morales et al 2021 ).…”

“…Here, we tested the predictive ability of linear and semi-parametric models incorporating genomic, pedigree, and QTL information on the prediction of stripe rust resistance under cross-validation and various cross-population frameworks using a historical dataset on more than 5000 Austrian winter wheat breeding lines evaluated over 11 years, largely under natural Pst infection (Morales et al 2021 ). Linear models included genomic and pedigree-based best linear unbiased prediction (GBLUP and PBLUP, respectively) (Meuwissen et al 2001 ; Endelman and Jannink 2012 ) and non-parametric models included genomic and pedigree-based reproducing kernel Hilbert spaces prediction (GRKHS and PRKHS, respectively) (Gianola et al 2006 ; Gianola and Van Kaam 2008 ; González-Camacho et al 2012 ).…”

“…Linear models included genomic and pedigree-based best linear unbiased prediction (GBLUP and PBLUP, respectively) (Meuwissen et al 2001 ; Endelman and Jannink 2012 ) and non-parametric models included genomic and pedigree-based reproducing kernel Hilbert spaces prediction (GRKHS and PRKHS, respectively) (Gianola et al 2006 ; Gianola and Van Kaam 2008 ; González-Camacho et al 2012 ). The QTL used as prediction model covariates in this study had been previously identified via genome-wide association (GWA) in this material (Morales et al 2021 ).…”