Haile, T.

Pozniak, C.

English

eCommons@USASK

Comparison of genomic selection models for wheat breeding

University of Saskatchewan Research Archive

Comparison of Genomic Selection Models for Wheat BreedingTeketel Haile                                                                             Ph.D. Candidate Department of Plant Sciences/Crop Development Centre   Genomic or Genomewide Selection (GS)• MAS without identifying markers associated with a trait• Considers all markers without significance test• Prediction of genomic estimated breeding values (GEBVs) • Captures major and small effect QTL• Unbiased marker effect estimates• No multiple testing Genomic Selection Procedures PhenotypesMarker GenotypesMarker GenotypesTrain ModelTraining population                        Selection candidatesSelection+Genomic Selection• Widely used in livestock breeding programs- Long generation interval- Milk production on bulls, meat quality • Improved genetic gain  • Growing interest in crop breeding programs• Insufficient information for practical application• Empirical studies are necessary to validate GS in wheat breedingObjectives:1) To evaluate single and multiple trait GS models for wheat breeding. 2) To examine prediction accuracy when modelling G×E interaction.Hypothesis:• GS has the potential to predict GEBVs with accuracy sufficient to allow selection without repeated phenotyping.Single and Multiple Trait Prediction• 231 Spring bread wheat lines • Genotyped using the wheat 90K iSelect assay• 18K polymorphic SNPs used for analysis• Traits- Days to heading   - Days to maturity    - Plant height- Grain yield                    - Test weight               - 1000-kernel weight   - Grain protein - Falling number      - SDS sedimentationStatistical Methods1) Single Trait Models- Ridge regression BLUP- Genomic BLUP- Bayesian Lasso- Bayesian ridge regression- BayesA- BayesB- BayesC - RKHS- RKHS-KA2) Multiple Trait Models- MT-BayesA- MT-BayesA matrix - MT-BayesA scale• Models were fitted in RBGLR package (Perez and de los Campos, 2014)rrBLUP package (Endelman, 2011)C programs (Jiang et al., 2015)Fivefold Cross-Validation• Divide the population into five groups • Use four to train the model and one to validate• Accuracy in each fold is the correlation between GEBVs and phenotypes of individuals in the validation• Repeated until each group is used as validation• Averages of the fivefold reportedModel PredictionValidationModelling G×E Interaction• 81 spring bread wheat lines• Three approaches using G-BLUP (Lopez-Cruz et al., 2015)1) M×E interaction model (M×E)2) Across-environment (AcrossEnv)3) Single-environment (SingleEnv)• 80% TP : 20% validation • Prediction was made for grain yield • Two cross-validation schemesCross-Validation SchemesCV1: Prediction for newly developed linesE1 E2 E3 E4 E5Line 1 Y11 Y12 Y13 Y14 Y15Line 2 Y21 Y22 Y23 Y24 Y25Line 3 NA NA NA NA NALine 4 Y41 Y42 Y43 Y44 Y45Line 5 Y51 Y52 Y53 Y54 Y55CV2: Prediction for incomplete field trialsE1 E2 E3 E4 E5Line 1 Y11 NA Y13 Y14 Y15Line 2 Y21 Y22 NA Y24 Y25Line 3 Y31 Y32 Y33 Y34 NALine 4 Y41 Y42 Y43 NA Y45Line 5 NA Y52 Y53 Y54 Y55Jarquín et al. 2014Resultsr(GEBV:Phenotype)Trait00.10.20.30.40.50.60.70.80.91HD HT MAT YLD TWT TKW PRO FN SDSRR-BLUP G-BLUP BA BB BC BL BRR RKHS RKHS-KASingle Trait Prediction Accuracy0.82 0.62 0.58 0.51 0.57 0.84 0.64 0.57 0.84Multiple Trait Prediction Accuracy00.10.20.30.40.50.60.70.80.91HD HT MAT YLD TWT TKW PRO FN SDSST-BayesA MT-BayesA MT-BayesA Matrix MT-BayesA ScalarTraitr(GEBV:Phenotype)00.10.20.30.40.50.60.70.80.91KER-11 KER-12 KER-13 KER-14 SWC-12 SWC-13 SWC-14r(GEBV:Phenotype)Environment00.10.20.30.40.50.60.70.80.91SingleEnv AcrossEnv MxEr(GEBV:Phenotype)Modelling GxE Interaction (Yield)CV1CV2KER: Kernen   SWC: Swift CurrentConclusion• No difference among single trait prediction models.• Multiple trait prediction accuracy was similar or lower than single trait prediction accuracy.• No relationship between trait heritability and accuracy.• No benefit of modelling G×E interaction.• Accuracies obtained in this study are encouraging.• In wheat, GS can be implemented using G-BLUP.Acknowledgements• Dr. Curtis Pozniak• Advisory Committee Dr. Aaron BeattieDr. Fiona BuchananDr. Pierre HuclDr. Yuguang Bai• Durum field crew• Durum molecular lab crew• Wheat quality lab crew • Durum professional staffThank You!

https://harvest.usask.ca/bitstream/10388/8761/1/T.%20Haile%20and%20C.%20Pozniak%2c%202017.pdf

Comparison of genomic selection models for wheat breeding

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