Genomic Selection for Quantitative Adult Plant Stem Rust Resistance in Wheat

Quantitative adult plant resistance (APR) to stem rust (Puccinia graminis f. sp. tritici) is an important breeding target in wheat (Triticum aestivum L.) and a potential target for genomic selection (GS). To evaluate the relative importance of known APR loci in applying GS, we characterized a set of CIMMYT germplasm at important APR loci and on a genome‐wide profile using genotyping‐by‐sequencing (GBS). Using this germplasm, we describe the genetic architecture and evaluate prediction models for APR using data from the international Ug99 stem rust screening nurseries. Prediction models incorporating markers linked to important APR loci and seedling phenotype scores as fixed effects were evaluated along with the classic prediction models: Multiple linear regression (MLR), Genomic best linear unbiased prediction (G‐BLUP), Bayesian Lasso (BL), and Bayes Cπ (BCπ). We found the Sr2 region to play an important role in APR in this germplasm. A model using Sr2 linked markers as fixed effects in G‐BLUP was more accurate than MLR with Sr2 linked markers (p‐value = 0.12), and ordinary G‐BLUP (p‐value = 0.15). Incorporating seedling phenotype information as fixed effects in G‐BLUP did not consistently increase accuracy. Overall, levels of prediction accuracy found in this study indicate that GS can be effectively applied to improve stem rust APR in this germplasm, and if genotypes at Sr2 linked markers are available, modeling these genotypes as fixed effects could lead to better predictions.

[1]  Jean-Luc Jannink,et al.  Imputation of Unordered Markers and the Impact on Genomic Selection Accuracy , 2013, G3: Genes, Genomes, Genetics.

[2]  José Crossa,et al.  Genomic‐Enabled Prediction Based on Molecular Markers and Pedigree Using the Bayesian Linear Regression Package in R , 2010, The plant genome.

[3]  Rohan L. Fernando,et al.  Extension of the bayesian alphabet for genomic selection , 2011, BMC Bioinformatics.

[4]  J. Doležel,et al.  An accurate DNA marker assay for stem rust resistance gene Sr2 in wheat , 2011, Theoretical and Applied Genetics.

[5]  R. Bernardo.  Prediction of maize single-cross performance using RFLPs and information from related hybrids , 1994 .

[6]  M. Goddard,et al.  Genomic selection. , 2007, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[7]  J. Parlevliet.  Durability of resistance against fungal, bacterial and viral pathogens; present situation , 2002, Euphytica.

[8]  Aaron J. Lorenz,et al.  Genomic Selection in Plant Breeding , 2011 .

[9]  Adrian E. Raftery,et al.  Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .

[10]  P. Dyck.  The association of a gene for leaf rust resistance with the chromosome 7D suppressor of stem rust resistance in common wheat , 1987 .

[11]  M. McMullen,et al.  A unified mixed-model method for association mapping that accounts for multiple levels of relatedness , 2006, Nature Genetics.

[12]  P. VanRaden,et al.  Efficient methods to compute genomic predictions. , 2008, Journal of dairy science.

[13]  B. Keller,et al.  A Putative ABC Transporter Confers Durable Resistance to Multiple Fungal Pathogens in Wheat , 2009, Science.

[14]  José Crossa,et al.  Genomic Selection in Wheat Breeding using Genotyping‐by‐Sequencing , 2012 .

[15]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[16]  H. Kang,et al.  Variance component model to account for sample structure in genome-wide association studies , 2010, Nature Genetics.

[17]  Adrian E. Raftery,et al.  mclust Version 4 for R : Normal Mixture Modeling for Model-Based Clustering , Classification , and Density Estimation , 2012 .

[18]  M. Sorrells,et al.  Genomic Selection for Crop Improvement , 2009 .

[19]  M. Goddard,et al.  Prediction of total genetic value using genome-wide dense marker maps. , 2001, Genetics.

[20]  D. Cox,et al.  An Analysis of Transformations , 1964 .

[21]  C. Carlson,et al.  Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. , 2004, American journal of human genetics.

[22]  R. Singh,et al.  Molecular genetic characterization of the Lr34/Yr18 slow rusting resistance gene region in wheat , 2006, Theoretical and Applied Genetics.

[23]  R. Singh,et al.  QTL mapping of slow-rusting, adult plant resistance to race Ug99 of stem rust fungus in PBW343/Muu RIL population , 2013, Theoretical and Applied Genetics.

[24]  R. Bernardo,et al.  Genomewide selection when major genes are known , 2014 .

[25]  R. Ward,et al.  Current status, likely migration and strategies to mitigate the threat to wheat production from race Ug99 (TTKS) of stem rust pathogen , 2006 .

[26]  W. Spielmeyer,et al.  Identification and Validation of Markers Linked to Broad‐Spectrum Stem Rust Resistance Gene Sr2 in Wheat (Triticum aestivum L.) , 2003 .

[27]  R. Doerge,et al.  Empirical threshold values for quantitative trait mapping. , 1994, Genetics.

[28]  H. Piepho.  Ridge Regression and Extensions for Genomewide Selection in Maize , 2009 .

[29]  S. Knapp,et al.  A Genomic Scan for Selection Reveals Candidates for Genes Involved in the Evolution of Cultivated Sunflower (Helianthus annuus)[W] , 2008, The Plant Cell Online.

[30]  R. Ward,et al.  Characterization of Seedling Infection Types and Adult Plant Infection Responses of Monogenic Sr Gene Lines to Race TTKS of Puccinia graminis f. sp. tritici. , 2007, Plant disease.

[31]  A. Roelfs,et al.  Greenhouse evaluation of the adult plant resistance of Sr2 to wheat stem rust. , 1980 .

[32]  Long-Xi Yu,et al.  Association mapping and gene–gene interaction for stem rust resistance in CIMMYT spring wheat germplasm , 2011, Theoretical and Applied Genetics.

[33]  B. Keller,et al.  Identification of QTL associated with durable adult plant resistance to stem rust race Ug99 in wheat cultivar ‘Pavon 76’ , 2012, Euphytica.

[34]  E. C. Stakman,et al.  Identification of physiologic races of Puccinia graminis var. tritici. , 1944 .

[35]  J. Poland,et al.  Development of High-Density Genetic Maps for Barley and Wheat Using a Novel Two-Enzyme Genotyping-by-Sequencing Approach , 2012, PloS one.

[36]  Robert J. Elshire,et al.  A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species , 2011, PloS one.

[37]  R. Park.  Stem rust of wheat in Australia , 2007 .

[38]  B. Keller,et al.  Gene-specific markers for the wheat gene Lr34/Yr18/Pm38 which confers resistance to multiple fungal pathogens , 2009, Theoretical and Applied Genetics.

[39]  Meng Li,et al.  Genetics and population analysis Advance Access publication July 13, 2012 , 2012 .

[40]  A. Hallauer,et al.  Quantitative Genetics in Maize Breeding , 1981 .

[41]  R. Fernando,et al.  Deregressing estimated breeding values and weighting information for genomic regression analyses , 2009, Genetics Selection Evolution.

[42]  Ky L. Mathews,et al.  Genomic Prediction of Genetic Values for Resistance to Wheat Rusts , 2012 .

[43]  G. Casella,et al.  The Bayesian Lasso , 2008 .

[44]  R. Singh,et al.  Detection of Virulence to Wheat Stem Rust Resistance Gene Sr31 in Puccinia graminis. f. sp. tritici in Uganda. , 2000, Plant disease.

[45]  Jeffrey B. Endelman,et al.  Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP , 2011 .

[46]  R. F. Peterson,et al.  A DIAGRAMMATIC SCALE FOR ESTIMATING RUST INTENSITY ON LEAVES AND STEMS OF CEREALS , 1948 .