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ExploiBng Trait CorrelaBons for Next-GeneraBon Grain Yield and End-Use Quality Improvement of U.S. Hard Winter Wheat Scog Haley 1 , Jessica Moore 1 , Susan Latshaw 1 , Craig Morris 2 , Jesse Poland 3 1 Soil and Crop Sciences Dept., Colorado State University, Fort Collins CO USA 2 USDA-ARS Western Wheat Quality Laboratory, Pullman WA USA 3 Dept. of Plant Pathology, Kansas State University, Manhagan KS USA Scog D. Haley, Ph.D. Winter Wheat Breeder Colorado State University email: [email protected] website: wheat.colostate.edu Contact 1. Butler D, Cullis B, Gilmour A, Gogel B (2009) ASReml-R reference manual, Version 3. hgp://www.vsni.co.uk/soHware/asreml-r/ . 2. Endelman J (2011) Ridge regression and other kernels for genomic selecFon with R package rrBLUP. The Plant Genome 4: 250-255. 3. Glaubitz JC, Casstevens TM, Lu F, Harriman J, Elshire RJ, Sun Q, Buckler ES (2014) TASSEL-GBS: A high capacity genotyping by sequencing analysis pipeline. PLoS ONE 9(2): e90346. 4. Jia Y, Jannink JL (2012) MulFple-trait genomic selecFon methods increase geneFc value predicFon accuracy. GeneFcs 192: 1513-1522. 5. Money D, Gardner K, Migicovsky Z, Schwaninger H, Zhong G-Y, Myles S (2015) LinkImpute: fast and accurate genotype imputaFon for nonmodel organisms. G3: Genes, Genomes, GeneFcs 5(11):2383-2390. 6. Poland JA., Brown PJ, Sorrells ME, Jannink J-L (2012) Development of high-density geneFc maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PloS ONE 7: e32253. 7. R Core Team (2016) R: A language and environment for staFsFcal compuFng., R 3.3.2 ed. R FoundaFon for StaFsFcal CompuFng, Vienna, Austria. hgps://www.r-project.org . References Grain yield and quality improvement are important wheat breeding objecFves. Early predicFon of breeding values via genomic selecFon (GS) may foster more rapid geneFc gains in applied culFvar development programs. Whole-genome single nucleoFde polymorphisms (SNPs) discovered using genotyping-by-sequencing (GBS) were used to assess predicFon accuracy in single-trait and mulF-trait predicFon models for end-use quality and field-based traits. Single-trait cross-validaFon accuracies for quality traits ranged from 0.31 for grain protein to 0.64 for mixograph mixing Fme, yet few improvements were observed for mulF- trait GS for quality traits. MulF-trait GS for grain yield with secondary traits grain protein and grain protein deviaFon was highly effecFve at increasing GS predicFon accuracy, with accuracies over 0.70. MulF-trait GS shows promise for leveraging phenotypes from applied culFvar development programs to increase the rate of gain through breeding. Abstract GBS-Based SNP Marker Discovery • Over 24,000 SNPs were idenFfied in the SNP discovery among the total of 11,528 individuals in the panel (Fig. 4). Overall a lower coverage of the D genome was observed, as has been reported previously. • Following mean imputaFon using rrBLUP, 9,488 SNPs were retained for GS analysis with the test individuals. PredicFon Accuracy • Strong posiFve and negaFve phenotypic correlaFons were observed within the panel for the end-use quality traits (Fig. 5). RelaFvely few improvements in predicFon accuracy were observed, however, with incorporaFon of a secondary trait in the model (Table 1). Mixing Fme and tolerance were notable excepFons. • Single-trait GS predicFon accuracies for yield, grain protein, and grain protein deviaFon were low to moderate (Fig. 6). IncorporaFon of grain protein and grain protein deviaFon as secondary traits significantly improved GS predicFon accuracy for grain yield. AddiFon of a group of 245 unphenotyped individuals into the model (“forward”, Fig. 6) resulted in a similar predicFon accuracy as with the original panel. RaBonale and ObjecBves Phenotypes • Laboratory end-use quality data and yield trial data from the CSU Wheat Breeding Program since 2006 were assembled for analysis. • End-use quality characterisFcs included grain hardness and protein, break flour yield, mixograph mixing Fme and tolerance, and pup-loaf bake water absorpFon and loaf volume (3,743 records). • Yield, grain protein, and grain protein deviaFon (residuals from protein vs yield regression) were obtained from yield trials (6,819 records). GBS-SNP Marker Discovery • GBS libraries were constructed using genomic DNA extracted in a 96- well format using the King Fisher Flex PurificaFon System (Thermo Fisher ScienFfic, Waltham, MA) and the restricFon enzymes PstI and MspI using a protocol modified from Poland et al. (2012). • Sequencing was performed at 96- and 192-plex on an Illumina Hi-Seq 2500 (Univ. Missouri, Columbia MO), and 384-plex on an Illumina Hi- Seq 4000 (Univ. Illinois, Urbana IL). • SNP calls were made with the TASSEL-GBSv2 Pipeline (Glaubitz et al. 2014) using the InternaFonal Wheat Genome Sequencing ConsorFum Whole Genome Assembly v0.4 as a reference. SNPs were filtered for F > 0.9 and then subject to LD-kNNi imputaFon (Money et al. 2015). StaFsFcal Analysis • Best linear unbiased predictors (BLUPs) for genotypes (n=771 for quality traits, n=681 for field traits) for each phenotype were obtained using the ASreml-R package (VSN Intl. Ltd., Hemel Hempstead UK; Butler et al. 2009 ) in R (R Core Team 2016) based on an across- locaFon model with genotypes, years, and locaFons as random effects. • Single-trait GS was done using the rrBLUP package (Endelman 2011) in R and mulF-trait GS (Jia and Jannink 2012) was done using the ASreml- R package. The G inverse relaFonship matrix was calculated in rrBLUP (max.missing value=0.3) for use in the call to ASreml-R. Materials and Methods Secondary Trait Primary Trait Single Trait GS Mixing Time Flour Yield Mixing Tolerance Grain Hardness Loaf Volume Bake AbsorpFon Grain Protein 0.23 0.21 0.24 -0.14 0.27 0.24 0.24 Bake AbsorpFon 0.31 0.25 0.15 0.32 0.25 0.21 --- Loaf Volume 0.50 0.39 -0.04 0.30 0.12 --- 0.28 Grain Hardness 0.53 0.17 -0.28 0.15 --- 0.54 0.36 Mixing Tolerance 0.54 0.65 0.43 --- 0.50 0.49 0.33 Flour Yield 0.62 -0.01 --- 0.05 0.25 0.65 -0.21 Mixing Time 0.64 --- 0.40 0.74 0.56 0.65 0.36 Since the early 1980s, the land area planted to U.S. hard winter wheat and the share of U.S. wheat in export markets have declined dramaFcally. Improved profitability of other crops, staFc domesFc flour consumpFon, and increasingly compeFFve global markets are factors underlying these trends. In order to reverse these trends, modern approaches must be implemented that foster increased rates of geneFc gain for producFon- related traits simultaneously with improved funcFonal value for end-use quality. Using a mulF-year, mulF-environment panel of genotypes and phenotypes collected within an applied wheat culFvar development program, our objecFves were to: a) esFmate single-trait GS predicFon accuracies for producFon-related and quality-related characterisFcs, and b) assess the uFlity of mulF-trait GS for increasing predicFon accuracy of key target traits. Results and Discussion Figure 1. Combine harvest of field yield trials. Figure 2. Computerized mixograph for dough mixing properFes. Figure 3. Pup-loaf bake test for loaf volume and water absorpFon. Table 1. Genomic selecFon predicFon accuracies for end-use quality traits. Highlighted cells represent increased mulF-trait GS accuracy relaFve to single-trait GS accuracy. Figure 4. DistribuFon of GBS-based SNPs across the wheat genome. Figure 5. Phenotypic correlaFons among end-use quality traits. Figure 6. Genomic selecFon predicFon accuracies for grain yield and related traits grain protein and grain protein deviaFon (GPD).

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Page 1: Poster Print Size: Exploing Trait Correlaons for Next ...iwgs2017.boku.ac.at/wp/wp-content/uploads/abstracts/366/poster.pdf · the built-in color themes in ... Exploing Trait Correlaons

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ExploiBngTraitCorrelaBonsforNext-GeneraBonGrainYieldandEnd-UseQualityImprovementofU.S.HardWinterWheat

ScogHaley1,JessicaMoore1,SusanLatshaw1,CraigMorris2,JessePoland31SoilandCropSciencesDept.,ColoradoStateUniversity,FortCollinsCOUSA

2USDA-ARSWesternWheatQualityLaboratory,PullmanWAUSA3Dept.ofPlantPathology,KansasStateUniversity,ManhaganKSUSA

ScogD.Haley,Ph.D.WinterWheatBreederColoradoStateUniversityemail:[email protected]:wheat.colostate.edu

Contact1.  ButlerD,CullisB,GilmourA,GogelB(2009)ASReml-Rreferencemanual,Version3.hgp://www.vsni.co.uk/soHware/asreml-r/.2.  EndelmanJ(2011)RidgeregressionandotherkernelsforgenomicselecFonwithRpackagerrBLUP.ThePlantGenome4:250-255.3.  GlaubitzJC,CasstevensTM,LuF,HarrimanJ,ElshireRJ,SunQ,BucklerES(2014)TASSEL-GBS:Ahighcapacitygenotypingby

sequencinganalysispipeline.PLoSONE9(2):e90346.4.  JiaY,JanninkJL(2012)MulFple-traitgenomicselecFonmethodsincreasegeneFcvaluepredicFonaccuracy.GeneFcs192:

1513-1522.5.  MoneyD,GardnerK,MigicovskyZ,SchwaningerH,ZhongG-Y,MylesS(2015)LinkImpute:fastandaccurategenotypeimputaFonfor

nonmodelorganisms.G3:Genes,Genomes,GeneFcs5(11):2383-2390.6.  PolandJA.,BrownPJ,SorrellsME,JanninkJ-L(2012)Developmentofhigh-densitygeneFcmapsforbarleyandwheatusinganovel

two-enzymegenotyping-by-sequencingapproach.PloSONE7:e32253.7.  RCoreTeam(2016)R:AlanguageandenvironmentforstaFsFcalcompuFng.,R3.3.2ed.RFoundaFonforStaFsFcalCompuFng,

Vienna,Austria.hgps://www.r-project.org.

References

GrainyieldandqualityimprovementareimportantwheatbreedingobjecFves.EarlypredicFonofbreedingvaluesviagenomicselecFon(GS)mayfostermorerapidgeneFcgainsinappliedculFvardevelopmentprograms.Whole-genomesinglenucleoFdepolymorphisms(SNPs)discoveredusinggenotyping-by-sequencing(GBS)wereusedtoassesspredicFonaccuracyinsingle-traitandmulF-traitpredicFonmodelsforend-usequalityandfield-basedtraits.Single-traitcross-validaFonaccuraciesforqualitytraitsrangedfrom0.31forgrainproteinto0.64formixographmixingFme,yetfewimprovementswereobservedformulF-traitGSforqualitytraits.MulF-traitGSforgrainyieldwithsecondarytraitsgrainproteinandgrainproteindeviaFonwashighlyeffecFveatincreasingGSpredicFonaccuracy,withaccuraciesover0.70.MulF-traitGSshowspromiseforleveragingphenotypesfromappliedculFvardevelopmentprogramstoincreasetherateofgainthroughbreeding.

AbstractGBS-BasedSNPMarkerDiscovery•  Over24,000SNPswereidenFfiedintheSNPdiscoveryamongthetotal

of11,528individualsinthepanel(Fig.4).OverallalowercoverageoftheDgenomewasobserved,ashasbeenreportedpreviously.

•  FollowingmeanimputaFonusingrrBLUP,9,488SNPswereretainedforGSanalysiswiththetestindividuals.

PredicFonAccuracy•  StrongposiFveandnegaFvephenotypiccorrelaFonswereobserved

withinthepanelfortheend-usequalitytraits(Fig.5).RelaFvelyfewimprovementsinpredicFonaccuracywereobserved,however,withincorporaFonofasecondarytraitinthemodel(Table1).MixingFmeandtolerancewerenotableexcepFons.

•  Single-traitGSpredicFonaccuraciesforyield,grainprotein,andgrainproteindeviaFonwerelowtomoderate(Fig.6).IncorporaFonofgrainproteinandgrainproteindeviaFonassecondarytraitssignificantlyimprovedGSpredicFonaccuracyforgrainyield.AddiFonofagroupof245unphenotypedindividualsintothemodel(“forward”,Fig.6)resultedinasimilarpredicFonaccuracyaswiththeoriginalpanel.

RaBonaleandObjecBves

Phenotypes•  Laboratoryend-usequalitydataandyieldtrialdatafromtheCSU

WheatBreedingProgramsince2006wereassembledforanalysis.•  End-usequalitycharacterisFcsincludedgrainhardnessandprotein,

breakflouryield,mixographmixingFmeandtolerance,andpup-loafbakewaterabsorpFonandloafvolume(3,743records).

•  Yield,grainprotein,andgrainproteindeviaFon(residualsfromproteinvsyieldregression)wereobtainedfromyieldtrials(6,819records).

GBS-SNPMarkerDiscovery•  GBSlibrarieswereconstructedusinggenomicDNAextractedina96-

wellformatusingtheKingFisherFlexPurificaFonSystem(ThermoFisherScienFfic,Waltham,MA)andtherestricFonenzymesPstIandMspIusingaprotocolmodifiedfromPolandetal.(2012).

•  Sequencingwasperformedat96-and192-plexonanIlluminaHi-Seq2500(Univ.Missouri,ColumbiaMO),and384-plexonanIlluminaHi-Seq4000(Univ.Illinois,UrbanaIL).

•  SNPcallsweremadewiththeTASSEL-GBSv2Pipeline(Glaubitzetal.2014)usingtheInternaFonalWheatGenomeSequencingConsorFumWholeGenomeAssemblyv0.4asareference.SNPswerefilteredforF>0.9andthensubjecttoLD-kNNiimputaFon(Moneyetal.2015).

StaFsFcalAnalysis•  Bestlinearunbiasedpredictors(BLUPs)forgenotypes(n=771for

qualitytraits,n=681forfieldtraits)foreachphenotypewereobtainedusingtheASreml-Rpackage(VSNIntl.Ltd.,HemelHempsteadUK;Butleretal.2009)inR(RCoreTeam2016)basedonanacross-locaFonmodelwithgenotypes,years,andlocaFonsasrandomeffects.

•  Single-traitGSwasdoneusingtherrBLUPpackage(Endelman2011)inRandmulF-traitGS(JiaandJannink2012)wasdoneusingtheASreml-Rpackage.TheGinverserelaFonshipmatrixwascalculatedinrrBLUP(max.missingvalue=0.3)foruseinthecalltoASreml-R.

MaterialsandMethodsSecondaryTrait

PrimaryTrait

SingleTraitGS

MixingTime

FlourYield

MixingTolerance

GrainHardness

LoafVolume

BakeAbsorpFon

GrainProtein 0.23 0.21 0.24 -0.14 0.27 0.24 0.24

BakeAbsorpFon 0.31 0.25 0.15 0.32 0.25 0.21 ---

LoafVolume 0.50 0.39 -0.04 0.30 0.12 --- 0.28

GrainHardness 0.53 0.17 -0.28 0.15 --- 0.54 0.36

MixingTolerance 0.54 0.65 0.43 --- 0.50 0.49 0.33

FlourYield 0.62 -0.01 --- 0.05 0.25 0.65 -0.21

MixingTime 0.64 --- 0.40 0.74 0.56 0.65 0.36

Sincetheearly1980s,thelandareaplantedtoU.S.hardwinterwheatandtheshareofU.S.wheatinexportmarketshavedeclineddramaFcally.Improvedprofitabilityofothercrops,staFcdomesFcflourconsumpFon,andincreasinglycompeFFveglobalmarketsarefactorsunderlyingthesetrends.Inordertoreversethesetrends,modernapproachesmustbeimplementedthatfosterincreasedratesofgeneFcgainforproducFon-relatedtraitssimultaneouslywithimprovedfuncFonalvalueforend-usequality.UsingamulF-year,mulF-environmentpanelofgenotypesandphenotypescollectedwithinanappliedwheatculFvardevelopmentprogram,ourobjecFveswereto:

a)esFmatesingle-traitGSpredicFonaccuraciesforproducFon-relatedandquality-relatedcharacterisFcs,and

b)assesstheuFlityofmulF-traitGSforincreasingpredicFonaccuracyofkeytargettraits.

ResultsandDiscussion

Figure1.Combineharvestoffieldyieldtrials.

Figure2.ComputerizedmixographfordoughmixingproperFes.

Figure3.Pup-loafbaketestforloafvolumeandwaterabsorpFon.

Table1.GenomicselecFonpredicFonaccuraciesforend-usequalitytraits.HighlightedcellsrepresentincreasedmulF-traitGSaccuracyrelaFvetosingle-traitGSaccuracy.

Figure4.DistribuFonofGBS-basedSNPsacrossthewheatgenome.

Figure5.PhenotypiccorrelaFonsamongend-usequalitytraits.

Figure6.GenomicselecFonpredicFonaccuraciesforgrainyieldandrelatedtraitsgrainproteinandgrainproteindeviaFon(GPD).

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