1. Theme 2.- Development of improved RTBvarieties: Breeding ApplicationSelection of superior potato progenitors forrealizing heterosis supported by high throughputgenotyping and Genomic selectionMerideth BonierbaleRTB Annual MeetingSept. 29, 2014

2. Heterosis Superior performance of hybrids overparents Expressed as an increase in biomass,yield, fertility, resistance to pathogens, ortolerance to climatic stress Maximizing heterosis implies an increasein heterozygocity and a number of multi-allelicloci 3. ObjectiveIdentify parents withinpotentially heterotic genepoolsto exploit heterosis for thedevelopment of high perfomancetetraploid hybrids 4. Expected outputNew high-yielding hybrids thatcombine factors for adaptation toresource-limiting environmentswith end-user preference traitsin reduced time frame 5. Linkage to RTB FlagshipContributes to accelerate CIP Delivery FlagshipAGILE, RESILIENT AND PRECOCIOUS POTATOVARIETIES (70-90 days) through the applicationof HTP genotyping, GWAS and S to expeditegains for yield, earliness, drought and heattolerance, and long critical photoperiod.Discovery Flagship: Next GenerationBreeding Systems 6. Research OutcomeNARS and ARI will have access tosuperior progenitors, abdto tools(MAS, selection based on GEBV) toselect robust and stable candidatevarieties 7. Expectation Shprtem breeding cycles in such away that the rate of genetic gainper unit of time and cost can bezccelerated. 8. Materials: Two advanced tetraploidpotato breeding populations bred atCIP Pilot study of HeterosisPopulationB3PopulationLTVR Yield gainsthroughHeterosis CombiningLate blight +Virus ResistanceAdaptation tolowland tropicsHeat toleranceResistance tovirusesNew HybridPopulationLTVRB3Field resistanceto late blightAdaptation tohighland tropics 9. MethodologyGenotype population samples using theSolCAP Infinium SNP array (8303 SNP)Apply GWAS to identify markers associatedwith adaptation traitsApply GS to select best parents based onGEBVGenerate a prediction model fromtraining populationCompute GEBVsPerform cross validation of thepredictiona 10. ****B3 Populationn=103LTVR Populationn=101*Genepool selection: 101 LTVR and 103B3 breeding lines Genotyped with 52 SSR Neighbor Joining Treeconstructed using DARWIN LTVR and B3 grouped into two distinct clusters High diversity is observed within populations 11. TraitsTrait Component traitsVariableLong criticalphotoperiodTuberinitiationTuber induction (Scale 1 (the least) -9(the most)Stolon lengthand number1= very short -9 very long1= few 9 manyBulking (Marketable tuber number / total tubernumber) * (Number of tuberized plants/total number of plants) *100HeatToleranceHeat defects 1=no defects- 9= >80% of tuber defects 12. TraitsTrait ComponenttraitsVariableDroughttoleranceRoot traits Root lengthNumber of rootsRoot fresh and dry weightRoot angleStolon traits Stolon numberStolon diameterStolon fresh and dry weightTuber traits Tuber numberTuber fresh and dry weightHarvest Index Tuber Fresh Weight (FW) / Totalbiomass (FW)* X 100*Total biomass= FW(leaf + stem+ stolon + tuber +root 13. TraitsTrait ComponenttraitsVariableDroughttolerancePhysiologicaltraitsChlorophyll content(SPAD)Canopy temperatureCanopy reflectance(NDVI)Biochemical Metabolite profiling and NIRS 14. Progress: Genotyping 276 breeding lines genotyped withSNP 150 LTVR , 50 B3 , and 76 unter-populationhybrids Genotypes assigned to breeding linesusing fitTetra package of R V1.0(AAAA, AAAB, AABB, ABBB, BBBB) 4738 SNP markers retained afterquality controlCluster 2 :B3 =37/43LTVR = 5/135LTVRxB3=4/24 15. Population Structure Structure estimated using 120 SNP (12 /chromosome) in135 LTVR, 43 B3 and 24 inter-population hybrids LTVR and B3 breeding lines were consistently assignedto Cluster 1 or 2 (some intermixing) while most LTVR x B3hybrids appeared in the intermixing zone .1.000.900.800.700.600.500.400.300.200.100.00Cluster 1 :LTVR =103/135B3 = 4/43LTVRxB3 hybrids=4/24Cluster 2 :B3 =37/43LTVR = 5/135LTVRxB3=4/24Intermixing zoneB3 =4/43LTVR = 27/135LTVRxB3=16/24 16. Phenotyping (Training population =171 breeding lines454035302520151050Tuber Induction(40 DAPEm)3.0 3.8 4.6 5.4 6.2 7.0 7.8 8.6 9.0Number of breeding linesTuber Induction (1-9)1= noinduction2= noinduction4= veryweak5= weak 9= strong45352515454035302520151050Stolon Number(75 DAP)Stolon Length(75 DAP)2.0 2.9 3.9 4.8 5.8 6.7 7.7 8.6 9.0Number of breeding linesStolon Length (1-9)A1 5 9B1 5 95-51.8 2.7 3.5 4.3 5.2 6.0 6.8 7.6 9.0Number of breedinglinesStolon Number(1-9) 17. Integrative Tools for Drought PhenotypingTemperaturedifferencesand NDVI of56 potatoclones Canopy temperature and NDVI may help recognizedrought tolerant genotypes Leaf temperatures under drought strongly increasedafter 10 and 20 days after witholding water (DAWW) Drought strongly affects NDVI after 30 days ofwithholding water 18. GWAS Model Tested:Mixed Linear Regression y = X + Zg + Considers structure as co-variate Incorporates kinship to estimate geneticvariance Statistical package in R 19. GWAS: Manhattan Plot : AssociatedSNP/chromosome543210-log(p)Stolon length (75 DAP) Long Days-WarmconditionsChromosomeChr01Chr02Chr03Chr04Chr05Chr06Chr07Chr08Chr09Chr10Chr11Chr1211 significant QTL 20. 543210-log(p)Marketable yield (75 DAP) Long Days-WarmconditionsChromosomeChr01Chr02Chr03Chr04Chr05Chr06Chr07Chr08Chr09Chr10Chr11Chr12GWAS: Associated SNP/chromosome2 significant QTL 21. GWAS: Position of SNP associated with photoperiodresponse and adaptation to warm conditionsChromosomeTuberInductionStolonNumberStolonlengthBulking75 DAPMarketableyield 75DAPHeat-defectIIIIIIIVVVIVIIVIIIIXXXIXII 22. GS Model Tested:G BLUP ModelRestricted Maximum Likelihood (REML) methodsPackage rrBLUP V4.3 of RVariance componentsestimated with 2 dofferentrelationship matrices Additive relationship matrx A model Euclidian distance Gaussian kernel model 23. Correlation between Genomic EstimatingBreeding Values (GEBV) and Phenotypic Valuesr (Additive _ GEBV = 0.89r (Gaussian kernel _GEBV) = 0.941.51.00.50.0-0.5-1.0r (Additive_GEBV) = 0.33r (Gaussian kernel_GEBV= 0.35Additive based GEBVGaussian kernel basedGEBV0 2 4 6 8 10GEBVPhenotypic data3.53.02.52.01.51.00.50.0-0.5-1.0-1.5-2.0Additive based GEBVGaussian kernel basedGEBV0 2 4 6 8 10GEBVPhenotypic dataInference Populationn=41Training Population n = 130Trait: Tuber Induction 24. Correlation between GEBV and PhenotypicValuesTraining Population n=130 Trait : Stolon lengthInference Populationn=415.04.03.02.01.00.0-1.0-2.0-3.0r (Additive_GEBV) = 0.89r (Gaussian kernel_GEBV) = 0.94Additive based GEBVGaussian kernel basedGEBV2.52.01.51.00.50 2 4 6 8 10GEBVPhenotypic data0.0-0.5-1.0-1.5r (Additive _GEBV) = 0.41r (Gaussian kernel_GEBV=Additive based GEBVGaussian kernel basedGEBV0 2 4 6 8GEBVPhenotypic data 25. Lessons learnedGWAS is a good approach to study thearchitecture of complex traits and detectunderlying major genes but is unable tocapture minor gene effectsAllows tagging genes for thedevelopment of markers to assistselection, as a complementary tool forbreeding programs 26. Lessons learnedGenomic Selection addresses smalleffect genes, but several factors influenceits performancePopulation size affects predictionaccuracyThe GS model used may not be themost suitable for predicting GEBV forthe traits under study and other modelsshould be tested,e.g., Bayesian LASSO 27. Lessons learnedSince population size is critical andphenotyping is a key informant in GS,efficient and mass screening methodshave been identified.Single Node Cuttings for assessmentof tuberization under long photoperiod,or canopy temperature and NDVI todifferentiate drought resistancetolerance represent efficient andfeasible mass screening methods. 28. CollaboratorsElisa MIhovilovichAwais KhanMaria CarazaDavid de KoeyerMariela Aponte453525155Stolon Number(75 DAP)-51.8 2.7 3.5 4.3 5.2 6.0 6.8 7.6 9.0Evelyn Farfan Number of breeding linesStolon Number(1-9) 29. Ongoing work Reference population has beenincreased to 360 breeding lines (almost3-fold and experiments will beperformed in replicated trialsA known gene for photoperiodresponse on chr V, StCDF1, (CyclingDOF factor ) is being amplified in asample of the training population set tolook for variants toward markerassisted selectionDevelop impact pathway: RBM. CRPLinkages 30. CollaboratorsElisa MIhovilovichAwais KhanMaria CarazaDavid de KoeyerMariela Aponte453525155Stolon Number(75 DAP)-51.8 2.7 3.5 4.3 5.2 6.0 6.8 7.6 9.0Evelyn Farfan Number of breeding linesStolon Number(1-9) 31. Thank you 32. Genomic Selection estimates marker effectsacross the whole genome of a breedingpopulation based on the prediction modeldeveloped in a training population . A trainingpopulation is a group of individuals (breedinglines) that are both phenotyped and genotyped .The Breeding populationthat can be used for validation) includes thedescendants of a Training Population or a newvariety that is related to the training population,and is only genotyped not phenotyped , unlessthe breeder would like to validate thepredictionsAccura