Beyene, Yoseph

Burgueño, Juan

Crossa, Jose

Gowda, Manje

Olsen, Michael

Perez, P.

Prasanna, Boddupalli M.

Robbins, Kelly

Crossa, José

Pérez, P.

Boddupalli, P.M.

CGSpace

Yoseph Beyene, M. Gowda, J. Crossa, M. Olsen; K. Robbins, P. Perez, J. Burgueno, and BM. Prasanna Implementation  of Genomic Selection in CIMMYT –Africa Maize Breeding ProgramPresentation to the EiB Breeding Optimization Community of Practice Monthly Meeting,  12 May  2022CIMMYT Maize Breeding Pipelines in ESACode Pipeline description Target Countries Est. Area (M ha)EA-PP1 Early/intermediate-maturing, white maize varieties with drought-tolerance, NUE and resistance to GLS, TLB, Ear rots, MSV and MLN, and suitable for food use in Eastern African tropical rainfed dry/wet mid-altitude areas.Ethiopia, Kenya, Uganda, Tanzania (Northern)3.17EA-PP2 Late-maturing, white maize varieties with drought tolerance, NUE, and resistance to GLS, TLB, MSV, common rust, and ear rots, adapted to Eastern African tropical rainfed wet, upper mid-altitude areas, and used mainly for food purposes.Ethiopia, Kenya, Uganda, Tanzania (Southern)3.38EA-PP3 Late-maturing, multiple stress-tolerant white maize varieties for the Eastern African tropical rainfed highlands and used mainly for food purposes.Ethiopia, Kenya, Uganda, Tanzania (Southern)1.75SA-PP1 Intermediate/late maturing, nitrogen-use efficient (NUE), drought- and heat-tolerant white maize varieties for the Southern African tropical rainfed mid-altitude/transition areas and used mainly for food purposes.Mozambique, Zimbabwe, Malawi, Zambia, Tanzania (Southern); drought-prone smallholder farm areas in South Africa3.74SA-PP2 Early-maturing, drought-, heat- and low soil pH stress-tolerant white maize varieties for the Southern African dry/wet lowland and mid-altitude areas and used mainly for food purposes.Zimbabwe, Malawi, Zambia, Tanzania (Southern), drought-prone smallholder farmers, about 2-3% of total maize area)2.0314 million hectaresCIMMYT Maize Breeding: Stage-Gate Process➢ Stage 1 – First testcross evaluation; one tester; 2 reps, 3-5 sites➢ Stage 2 – Selected lines (10-15% S.I.) from Stage 1 trials; 3 testers; 2 reps, 8-10 sites➢ Stage 3 – Selected lines from Stage 2 trials (15% S.I.); Cross with 5 testers; 2 reps, 10-15 locations➢ Stage 4 (Regional On-station Trials) – Best products from Stage 3; 2 rows, 3 reps, 25-35 locations➢ Stage 5 (Regional On-Farm Trials) – 30-50 on-farm trials per Product Profile; Farmers’ preferences➢ Final Product Advancement Meeting to identify products/pre-commercial hybrids to be announcedto the partners through CIMMYT WebsiteCriteria for Product AdvancementStage 1 to Stage 2 [Line Development Team]▪ Line entering Stage 1 TC are pre-selected for disease resistance (e.g., MSV, MLN) using markers.▪ GEBV of the line analyzed▪ Hybrid performance (yield under optimal, and abiotic and biotic stresses)▪ Selection intensity of 10-15% applied▪ Independent culling/selection index▪ Number of lines used in Stage 1 varies from 1000 to 1500.Stage 2 to Stage 3 [Line Development Team]▪ GCA of the line with testers▪ Hybrid performance at more locations relative to Stage 1 ( optimal; abiotic and biotic stresses)▪ Sites with low heritability (<0.10) discarded▪ Selection intensity of 10-15% applied.▪ Lines selected for recycling Stage 3 to Stage 4 [Product Development Team; Advancement Committee]▪ Hybrid performance at more locations (10-15) relative to Stage 2 (optimal; abiotic and biotic stresses)▪ Sites with low heritability (<0.10) discarded▪ Selection intensity of 15% appliedTrait Packages and Advancement Thresholds1 = top priority; 2 = lower priority.Basic Traits Product target PHB30G19, DK777, H517Yield>10% greater than commercial checks)1Grain color White 1Maturity Equivalent to best check 1Plant height ±10 cm of best check 2Ear height ±10 cm of best check 2Grain moisture Equivalent to best check 2Drought tolerance >10% > mean of checks 1Nitrogen use efficiency (NUE)>10% > mean of checks 2TLB resistance <4.0 (1-9 scale) 1GLS resistance <4.0 (1-9 scale) 1MSV resistance <3.0 (1-9 scale) 1Common rust resistance <4.0 (1-9 scale) 2Fusarium ear rot resistanceLess than 10% incidence 1Bare tips (poor husk cover)Less than 10% incidence 1MLN resistance10 % > of the mean of checks1Value-added traits Product target Market priorityStriga tolerance>10% or equal to the best trait checks2FAW resistance≤5 leaf damage; ≤3 ear damage (1-9 scale)2Sampling TPEs Maize Lethal Necrosis (MLN)Managed drought stress Optimum water and recommended fertilizers environmentTurcicum leaf blightGS being applied at CIMMYT maize breeding to accelerate the rate of genetic gain in two ways1. Rapid cycle genomic selection (RCGS)2. Predict the performance of un-phenotyped genotypes at an early stage of testing  Rapid generation advancement using GS• 34 bi-parental populations (total =6252 F2:3, each with aprox. 184 progenies)• Each pop. phenotyped in 2-4 managed water-stressed, 3-4 well-watered environments, and genotyped with low and high-density markers• Genetic gain studies completed for 18 populationsSelf selected PlantsSelf selected PlantsSelf selected PlantsCycle 1recombine selected PlantsCycle 2Self or recombine selected PlantsCycle 3Crossed with TesterF4Inbred  A x Inbred BF1F2F3 = Cycle 0 F3 x TesterF5F6Evaluate TC under target environmentsPhenotypic Selection Cycle 0 Cycle 1 Cycle 2 Cycle 3Genotype, Select Best Families Based on Phenotypic and Genotypic DataGenotyping and model building GEBVQC genotypingDevelop DH linesRapid cycle of GS workflowGenetic gain studyPolymorphism screeningTraining set: TC evaluation under Optimum, Drought and Diseases OptimumDiseasesDroughtRCGS results: Gain in grain yield under drought environments in SSA0.00.51.01.52.02.53.03.5Grain yield (Mg/ha)PopulationsCycle0 Cycle1 Cycle2 Cycle3Genetic gain from conventional breeding in Africa 32 kg ha-1 yr-1 (B. Masuka, 2017)Genetic gain from GS is 2 times higher than the gains from conventional breeding reported in sub-Saharan Africa. Overall gain in GY : 70.5  kg ha-1 year-1Beyene et al. (2015) Crop Sci. 55:154–163Yield (t/ha)0123456789Pop1-GWS Pop2-GWS Pop3-GWS Pop4-GWS Pop5-GWSTop 5 hybrids Parents Commercial checksGain over the commercial checks= 8.7 to 20.8%Gain over the parents = 9.0 to 91.1%• Gain over the commercial checks= 47.3 to 98.2%• Gain over the parents = 17.2 to 57.3%00.511.522.5Pop1-GWS Pop2-GWS Pop3-GWS Pop4-GWS Pop5-GWSYield (t/ha)OptimumDrought stressPerformance of hybrids developed from lines derived  via RCGS Beyene et. al 2016Lines developed through RCGS are being  used as the parent of allocated hybrids to partnersLine Parent in # allocated hybrids CKLMARS1C3S50268 1CKLMARS1C3S50080 2CKLMARS1C3S50113 3CKLMARS1C3S50140 2CKLMARS1C3S50137 1DT tolerant  and susceptible hybrids at Kiboko-2021Genomic selection using “test half-predict-half” strategy # lines genotyped (Stage I)= 2511# DH lines phenotyped (test crosses) =855# sites=2 optimum and one managed drought• The phenotypic data were used to predict the remaining untested linesCorrelation (observed and predicted): Optimum: 0.84 and Drought: 0.92 Genomic selection using test half-predict-half strategy –incorporating pedigree4.005.006.007.008.009.0010.0011.000 2 4 6 8 10 12 14GY PredGY ObsObs vs Pred - Markers45678910113 5 7 9 11 13GY  PredGY ObsObs vs Pred – Markers + PedigreeUnder Optimumr = 0.85 r = 0.89 22.533.544.50 1 2 3 4 5 6 7GY PredGY ObsObs vs Pred – Markers + Pedigree1.522.533.544.50 1 2 3 4 5 6 7GY PredGY ObsObs vs Pred - MarkersUnder drought stressr = 0.72 r = 0.77 Cross-validated prediction accuracy within and across testersManagement/traitTester 1 Tester 2Across testers111 742 853GY-WW 0.16±0.12 0.60±0.03 0.67±0.05GY-WS 0.22±0.18 0.64±0.07 0.65±0.05MOI-WW 0.16±0.14 0.58±0.01 0.65±0.04MOI- WS 0.44±0.16 0.61±0.06 0.57±0.05AD-WW 0.41 ±0.13 0.70 ±0.07 0.75 ±0.04AD- WS 0.49±0.20 0.63±0.04 0.67±0.05PH-WW 0.14±0.12 0.65±0.03 0.70±0.03PH-WS 0.17±0.12 0.72±0.04 0.72±0.04Advancement of lines based on GEBV and PS:Population CML536/LPS-F64# DH genotyped 166# DH lines phenotyped 88# lines selected based on Phenotype 21# lines selected based on GEBV 19Check GY(t/ha)_Opt GY(t/ha)_MD MOI-Opt PH_OptH517 6.5 1.8 16.4 267.4Pioneer 30G19 6.2 2.9 17.9 254.5WH505 7.6 3.1 17.9 257.3Heritability 0.64 0.52 0.3 0.8Genotype Variance 0.70 0.21 0.3 79.8GenxLoc Variance 0.13 0.3 16.2Residual Variance 2.06 0.39 4.0 81.5Grand Mean 6.81 3.10 18.2 248.8LSD 1.43 0.93 1.4 11.6CV 21.07 20.27 10.9 3.6n Replicates 2 2 2 2n Locations 3 1 3 3SN nameObserved GY_BLUE_optimalPredicted GY_BLUE__optimalObserved GY_BLUE_droughtPredicted GY_BLUE_drought1 CKDHL152921 9.1 7.6 3.1 3.12 CKDHL152857 8.8 7.6 4 43 CKDHL152610 8.4 7.7 4.7 3.84 CKDHL152563 8.4 7.7 4.2 3.65 CKDHL152554 8.3 7.7 3.6 3.36 CKDHL152653 8.3 7.8 3.3 3.37 CKDHL152616 8.2 7.3 4.3 4.18 CKDHL152617 8.2 7.2 3.8 3.49 CKDHL152821 8.2 7.3 3 3.110 CKDHL152733 8.1 7.5 4.2 3.411 CKDHL152658 8.1 7.2 3.4 3.212 CKDHL152638 8.1 7.5 3.6 3.113 CKDHL152976 8 7.5 3.3 3.414 CKDHL152591 8 7.6 3.7 3.615 CKDHL152906 7.6 7.2 3.6 3.216 CKDHL152751 7.5 7.3 3.6 3.817 CKDHL152769 7.4 7.2 4.7 3.818 CKDHL153005 7.4 7.1 4 3.719 CKDHL152929 7.3 7 3.9 3.820 CKDHL152866 7.1 7.2 4.3 3.721 CKDHL152962 7 7 4.3 3.51 CKDHL152820 NA 7.6 NA 3.52 CKDHL152994 NA 7.5 NA 3.73 CKDHL152529 NA 7.5 NA 3.54 CKDHL152590 NA 7.5 NA 3.35 CKDHL152811 NA 7.4 NA 3.86 CKDHL152682 NA 7.4 NA 3.47 CKDHL152579 NA 7.4 NA 3.58 CKDHL152927 NA 7.4 NA 3.59 CKDHL152759 NA 7.4 NA 3.910 CKDHL152890 NA 7.4 NA 3.311 CKDHL152689 NA 7.4 NA 3.112 CKDHL152632 NA 7.3 NA 3.313 CKDHL152773 NA 7.3 NA 3.914 CKDHL152862 NA 7 NA 3.915 CKDHL152813 NA 7 NA 3.816 CKDHL152879 NA 7.2 NA 3.817 CKDHL152777 NA 7 NA 3.718 CKDHL152849 NA 7.2 NA 3.719 CKDHL152778 NA 7.2 NA 3.6Comparison of GS vs PS Stage1 TC:50% Phenotyping + 50% genotypingBeyene et al (2019). Front. Plant Sci. 10:1502. doi: 10.3389/fpls.2019.01502OptimumDroughtCategory # lines # testers # of hybridsAll stage II hybrids 348 3 1042Hybrids advance through  phenotype 176 3 526Hybrids advance through  GEBV 172 3 516GS:PS cost ratio 0.68Entry PedigreeAdvancementOptimum (GY/ha) % increaseDrought (GY/t) AD %MOI TLB % ER20 (CML543/CML566)//CKDHL150421 Phenotype 9.5 20.4 3.9 72.3 19.2 2.1 3.197 (CML395/CML444)//CKDHL1500213 GEBV 9.4 19.1 3.8 75.2 20.1 2.2 1.386 (CML543/CML566)//CKDHL1500261 GEBV 9.1 14.7 5.1 73.0 20.0 2.1 1.358 (CML395/CML444)//CKDHL150399 Phenotype 9.0 13.6 3.8 72.9 19.7 2.2 2.755 (CML395/CML444)//CKDHL1500041 Phenotype 8.9 12.8 3.8 72.7 19.5 2.5 1.876 (CML395/CML444)//CKDHL150431 GEBV 8.9 12.7 3.3 73.0 20.6 2.0 0.577 (CML543/CML566)//CKDHL150431 GEBV 8.8 11.0 4.3 74.3 21.6 2.3 5.5126 (CML322/CML543)//CKDHL1500215 GEBV 8.7 10.0 4.4 70.7 19.5 2.0 3.956 (CML543/CML566)//CKDHL1500041 Phenotype 8.7 9.8 3.6 74.2 18.2 2.0 4.665 (CML543/CML566)//CKDHL150342 Phenotype 8.7 9.6 3.5 73.1 19.1 2.5 1.698 (CML543/CML566)//CKDHL1500213 GEBV 8.7 9.1 3.7 76.0 21.3 2.5 0.921 (CML322/CML543)//CKDHL150421 Phenotype 8.5 7.6 3.2 70.1 19.5 2.0 0.811 (CML543/CML566)//CKDHL150339 Phenotype 8.5 7.5 4.8 73.6 20.4 2.4 1.9137 WH505 7.9 3.3 73.9 18.8 2.3 1.0136 PH 30G19 7.9 3.6 67.8 20.7 2.0 4.1139 DK 777 7.1 3.9 71.9 18.4 2.6 5.5138 H516 6.7 2.2 69.9 18.2 2.0 6.6140 DK 8031 5.7 2.1 66.9 16.9 2.2 8.7Heritability 0.6 0.5 0.9 0.6 0.6 0.4Grand Mean 7.5 3.6 72.6 19.7 2.5 3.3LSD 1.3 1.4 1.4 1.8 0.7 4.2CV 16.3 18.6 1.9 9.1 16.1 113.6n Locations 5 1 5 5 2 4Performance of the top hybrids (advanced via phenotypic and GEBV) for grain yield and other agronomic traits compared to the best check under optimum and drought conditionsGS using historical Data – Prediction across YearsScenario 1 2017 data (N)Training set (TRN): 2017 data + x % of 2018)Testing set (TST): (100-x) % 2018923923 (0%) 14231065 (10%) 12811035 (30%) 9961635 (50%) 7121919 (70%) 4272204 (90%) 142Scenario 22017+ 2018 pooled data(N)Training set: 2017 + 2018 + x % of 2019Testing set: (100-x) % 201923462346 (0%) 7222418 (10%) 6502563 (30%) 5052707 (50%) 3612851 (70%) 2172996 (90%) 117Beyene et.al 2021Year Number of lines phenotyped and genotyped records Management 2017 923 Optimum and manged drought 2018 1423 Optimum and manged drought 2019 722 Optimum and manged drought Total 30685173 markers  (after QC)Phenotypic distribution of GY, AD and PH under optimum (top) and managed drought (bottom) conditionsPrediction accuracies for grain yield using one-year data to predict another year’s data and converting 10%, 30%, 50%, 70% and 90% of the data from the TST to TRN Results of Predication Across Years when two years data used to predict third-year data   2017+2018 data use as training set to predict 2019 Beyene et .al, 2021Genetic gain estimation  Grain yield (2011-2020)Year Optimum Drought2011 45 502012 44 442013 39 392014 43 432015 51 512016 53 532017 54 542018 65 652019 44 442020 38 38Total 476 481Genetic gain (%) 1.61 2.4OptimumDroughtSimulation for Recycling Lines at Early Stage of TestingSimulations results comparing the current recycling at Stage 3 vs recycling after Stage 1 and 2 testing using data from EA-PP1. Recycling after Stage 1 or Stage 2 could deliver increase genetic gain by 17% and 9% compared to recycling at Stage 3, respectivelyFAG FAGAFA FAA0.2 0.3 0.4 0.5 0.6 0.2 0.3 0.4 0.5 0.67.67.88.08.28.47.67.88.08.28.4Save proportionMean of selected genotypesExperimentExp1Exp2Exp3Exp4Exp5Sparse phenotyping to sample TPE• Phenotypic data for 2018 Stage II trials (900 hybrids)• Evaluated at 5 locations in Kenya • Genotypic data for lines used in stage II trials• Different experimental layout for spare testing• 4 types of  analysis:• Factorial  analysis (FA), • FA +  CoP • FA+ Marker data • FA+ Marker + CoP Conclusion: By saving 30% the phenotypic cost, 90% of the best hybrids were common between complete phenotyping and sparse phenotyping The horizontal black line is the mean of the hybrids selected under complete phenotyping (8.36 t/ha)Incorporating a selection index for selecting parents for recycling ( DESIRE software )Line Merit RankCKDHL1715901 1.96 1CKDHL1715896 1.77 2CKDHL1715915 1.34 3CKDHL1715480 -1.33 196CKDHL1715260 -1.49 197CKDHL1720872 -1.34 198Example of  genetic meritTraits used for SI H2GY_Managed_Drought 0.83GY_Managed_Low_Nitrogen 0.46GY_Optimal 0.86pER 0.90pSL 0.87pBHC 0.85Eturc1 0.81MOI 0.88PH 0.95EH 0.97GLS 0.73ASI 0.92Expected changes in mean between the full population of lines versus a selected top 20% the lines using index.Retrospective analysis for identifying optimal number of environment to sample TPEResults from cross validation to know the accuracy between real (across the entire TPE) and estimated BV when selecting a given number of environments.Optimizing recycling through retrospective analysis to identify locations  with high heritability and high genetic varianceResults from single environment heritability across 3 years of data. We assume that environments with highest H2 and genetic variance represent the best locations for applying selectionOption 3To phenotypeSet Lines Testers TC1 48 1-2-3 1442 85 1 853 85 2 854 85 3 85Total 399To predictSet Lines Testers TC1 85 2-3 1702 85 1-3 1703 85 1-2 170Total 510Spearman correlation between observed and predicted line-breeding value (n=50). Set 1 line crossed with all testers, set 2-4 lines crossed with one tester, predicted with the other two testers.Results of sparse  testcrossing Lessons learned 1. Proper planning and coordination among • Conventional/ molecular breeder/Biometrician/ quantitative geneticist 2. Reliable service provider for efficient data turnaround time from leaf sampling to genotyping and data analysis.3. Reasonable database for phenotypic and genotypic data storage/Links phenotypes with genotypes4. Use of common genotyping platform to facilitate data sharing, reducing duplication and increase efficiencyThank you for your interest!

Implementation of genomic selection in CIMMYT –Africa Maize Breeding Program

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Implementation of genomic selection in CIMMYT –Africa Maize Breeding Program

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