5Gs for crop genetic improvement

5Gs

for

crop

genetic

improvement

Rajeev

K

Varshney

,

Pallavi

Sinha

,

Vikas

K

Singh

,

Arvind

Kumar

,

Qifa

Zhang

and

Jeffrey

L

Bennetzen

Hereweproposea5Gbreedingapproachforbringing

much-neededdisruptivechangestocropimprovement.These5Gs

areGenomeassembly,Germplasmcharacterization,Gene

functionidentification,Genomicbreeding(GB),andGene

editing(GE).Inourview,itisimportanttohavegenome

assembliesavailableforeachcropandadeepcollectionof

germplasmcharacterizedatsequencingandagronomiclevels

foridentificationofmarker-traitassociationsandsuperior

haplotypes.Systemsbiologyandsequencing-basedmapping

approachescanbeusedtoidentifygenesinvolvedinpathways

leadingtotheexpressionofatrait,therebyprovidingdiagnostic

markersfortargettraits.Thesegenes,markers,haplotypes,

andgenome-widesequencingdatamaybeutilizedinGBand

GEmethodologiesincombinationwitharapidcyclebreeding

strategy. Addresses

1_{CenterofExcellenceinGenomics&SystemsBiology,International} CropsResearchInstitutefortheSemi-AridTropics(ICRISAT), Hyderabad, 502324, India

2_{International Rice Research Institute, South Asia Hub, ICRISAT,} Hyderabad,502324,India

3_{IRRISouthAsiaRegionalCenter,NSRTCCampus,G.T.Road,} Collectry Farm, P.O. Industrial Estate, Varanasi, 221006, India 4_{National Key Laboratory of Crop Genetic Improvement, Huazhong} AgriculturalUniversity,Wuhan,430070,China

5

DepartmentofGenetics,UniversityofGeorgia,Athens,GA30602,USA Corresponding author: Varshney, Rajeev K ([email protected])

CurrentOpinioninPlantBiology2020, 56:190–196

This review comes from a themed issue on AGRI

Edited by DavidEdwards

For a complete overview see the Issueand the Editorial Available online 28th January 2020

https://doi.org/10.1016/j.pbi.2019.12.004

1369-5266/ã2019TheAuthor(s).PublishedbyElsevierLtd.Thisisan open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).

Introduction

Dramatic and rapid climate change will cause extreme weather, including droughts, floods and other disasters. Foodproductionwill suffer greatlyfromthese changes. Thenearly80percentoftheworld’spopulationthatare poorandliveinruralareastypicallyrelyonlocal agricul-turefor theirsurvival[1].Ithasbeenpredictedthat,on average,globalyieldsofmajorcropswillbereduced6.0% inwheat,3.2%inrice,7.4%inmaize,and3.1%insoybean for every degree Celsius increase in global mean

temperature [2]. In this regard, the CGIAR system (https://www.cgiar.org/) initiated a ‘Two Degree Initia-tiveforFoodandAgriculture’.Thisinitiativeistargeted on assisting 200 million small scale food producers across the globe to adapt at the speed and scale neededforthecurrentpaceofclimatechange.Improving access to climate-smart technologies and practices, including this development of climate-resilient high yielding varieties and their rapid availability to farmers’ fields, will provide an opportunity to achieve climatesmartsolutions [3].

Crop improvement for food and nutritional security, especiallyinthecontextofcontinuouspopulationgrowth andsuchchallengesasclimatechangeandwaterscarcity, have become important global concerns [4]. Facing these threats, current crop breeding strategies will not yield a sufficient rate of crop improvement to meet demands in theshort-term or long-term future. Hence, weproposea5Gbreedingstrategytodramatically accel-eratecrop geneticimprovement. The 1st Gis Genome assemblyforeachcropspecies,the2ndGisGermplasm characterizedatgenomicandagronomiclevels,the3rdG is Gene function identification, the 4th G is Genomic breedingmethodologies, andthe5thGis Geneediting technologies(Figure 1).

Inthefollowingsections,wedescribethe5Gsfor enhanc-ingcropimprovement.Weconcludewithadiscussionof thecurrentchallengesand opportunitiesfor integrating the5Gsintocrop improvement.

1st

G:

Genome

assembly

Advancesinnext-generationsequencing(NGS) technol-ogies coupled with improved genome assembly algo-rithms have facilitated the de novo assemblies of >264 plantgenomes,includingsuchcropsasrice,maize,wheat, barley, soybean, cotton, sorghum, tomato, pigeonpea, chickpea,and groundnut. Thequality of thesegenome assemblies varies tremendously, from nearly finished genomesto draftgenomeswithhundredsofunoriented sequence scaffolds. A few meet the platinum genome standard, including assemblies with full-chromosome scaffolds and haplotypes resolved across the entire genome,preferablyincludingstronglinkstothegenetic map.However,mostplant genomeassemblies aredraft genomes.Recentadvancementsinsequencing technolo-gies,particularlylong readgeneration andphysical map linkages,cannowoftengeneratechromosome-scale,fully

phaseddiploidgenomeassembliesforanyspeciesatthe platinum genomelevel[5].

Theavailabilityofagenomeassemblyprovidesan oppor-tunity to develop genomics tools and technologies for suchapplicationsastraitdiscoveryandmolecular breed-ing. All genetic variation can be described, including SNPs, insertions, deletions, transversions,copy number variationsandepigeneticchanges[6].Thesevariantsare usefulinthedevelopmentofcustomizedSNParrays[7], thatcanbeutilizedfordevelopmentofsaturatedgenetic mapsandQTLidentification.Sequencevariant informa-tiondefineshaplotypes[8],whichcanthenbeemployed forovercomingortakingadvantageoflinkage disequilib-riuminabreedingprogram[9].Genomeassembly infor-mationisalsovitalfordevelopingageneexpressionatlas, proteome maps, metabolome maps, and epigenome maps.

With the ongoing and deep reductions in sequencing costs,large-scalere-sequencingprojectshavebeen initi-ated in several crops. For instance, 3010, 994 and 429 germplasm accessions have been re-sequenced in rice[10],pearlmillet[11],andchickpea[12], respec-tively.Suchprojectsgenerate‘bigdata’thatposestorage andcomputationalchallenges.Thesechallengesinclude compilation, curation, complex dataanalyses, visualiza-tion, retrieval and sharing [13]. To accelerate use of genomesequenceinformationinnext-generation breed-ing,customizedinformaticsplatformsareneeded.Inthis context,someinitiatives/platformssuchasSNPSeek(for rice) (https://snp-seek.irri.org/_snp.zul),Genomic Open-source Breeding Informatics Initiative (GOBII) (http:// cbsugobii05.tc.cornell.edu/wordpress/)andExcellencein Breeding Platform (EiB) (https://excellenceinbreeding. org/) have become available. These platforms will be vital to breeders for mining superior alleles/haplotypes,

thus identifying the most-suitable parental lines for breeding populations.

2nd

G:

Germplasm

characterization

During thecourse of crop domesticationand breeding, cultivargeneticdiversityisnarrowedforalltraits[14],but nationalandinternational‘genebanks’(germplasm repos-itories/germplasmbanks)providearichsourceofdiverse alleles that may be vital for future crop improvement. The1750plantgermplasmbanksworldwidehold7.4 millionaccessions(www.fao.org),but<2%ofthese mate-rialshavebeenusedas plant geneticresources(PGRs), althoughthesefewuseshaveledtomajorcrop improve-ments[15].Oneofthereasonsbehindthislimiteduse ofPGRsistheoverwhelmingnumberofaccessionsthat havenotraitorothergeneticinformation.Therefore,we proposecharacterizationofasmanyaccessionsaspossible atbothgenomicandagronomiclevels.Ifthephenotyping isperformedatspecificnurserylocations,andwith com-munity-establishedcriteria,theprovidedinformationwill allowdeepgenomewideassociationstudies(GWAS)and identificationofGXEeffects.Thisprovidesthe informa-tion todeterminethepotential agronomicvalue of par-ticular alleles and accessions that will allow informed decision-makingin breedingprograms.

WhileNGS-basedapproacheshaveallowed comprehen-sivesequencingoflargegermplasmcollectionsinseveral crops,field phenotypinglagsdramatically. For instance, whole-genome re-sequencing (WGRS) hasinvestigated 3010 rice accessions [10], while genotyping-by-sequencing (GBS) has been utilized to characterize 44624 wheat breeding lines [16] and 20000 wild and domesticated barley accessions [17]. These studies are initial examplesof howgenomicsand informatics tech-nologies can characterize large crop germplasm collec-tions [18]. These studies are providing genome-wide

Figure1

Genome assembly available for a given crop species

Germplasm characterized at genome and agronomic level

Gene identification and functional characterization

Genomic breeding approaches using genomic knowledge and technologies Gene editing for improving plant performance 3G 2G 1G 4G 5G Genome Germplasm Genes Genomic breeding Gene editing 5Gs

Current Opinion in Plant Biology

variantinformationandinsightsonpopulationstructure, crop domestication, and so on. However, for mining usefulgeneticinformation,itisimperativetophenotype the collections. NGS technologies together with some phenotypinghavebeenutilizedin afew cropsfor iden-tificationofmarker-traitassociations,includingrice[19], foxtail millet [20], pigeonpea [21], pearl millet [11], cotton[22],rapeseed[23],chickpea[12]andgrape[24]. Thesestudieshaveprovidedinformationonthegenetic architecture of agriculturally important traits and the identificationofvaluableallelesformorphological, agro-nomic, developmentaland quality-related traits. In the future,sequencingofentiregermplasmcollections pres-entingenebanksandassociationwithphenotypesshould beaprimarycomponentforallcrop-breedingprograms. Large-scale germplasm characterization also provides informationonthepresenceofhaplotypesataparticular locusforagiventraitthatcanbeusedinhaplotype-based breeding strategies ([25], see later) or the genomic selection approach. Similarly, deleterious effect muta-tions(genetic load)canalsobeidentified[26],andthen canbepurgedbymarker-assistedselectionorgene edit-ing, as suggested by Johnsson et al. [27]. Eventually, superior parental lines will be identified with the best alleles at each locus, including minimum genetic load, and introduced into breeding programs with a plan to optimizethebestalleliccombinations.Asanearly step towardsthisoptimalgoal,currenthaplotypeinformation can be used to select parents for nested association mapping(NAM)and multi-parentadvanced generation inter-cross (MAGIC) populations for high-resolution gene:traitdiscovery.

Recentadvances in genomicshave led to the develop-ment of various sequencing-based rapid trait mapping approachessuchasBSR-Seq[28],MutMap[29],QTLseq [30]andIndel-seq[31].NGStechnologieshaveenabled modification and improvement of traditionally tricky, time-consuming bulked segregant analysis (BSA, [32]) intorapidandwhole-genomesequence-based high-reso-lution trait mapping [33]. Due to the availability of genomeassemblies,inexpensivehigh-throughputWGRS pipelines have become available, so that the use of sequence-based trait mapping approaches has become possibleinseveralcropspecies.Followingthisapproach, sequencing-basedtraitmappingcanbebroadlygrouped intotwoclasses:i)traitmappingthroughpooled sequenc-ing of populations, and ii) trait mapping through complete sequencing of populations. Several examples ofNGS-basedtraitmappinghavebeenreportedincrops [34].Thiskind of traitmappinghas severaladvantages overtraditional marker-basedmapping. Forinstance, in addition to taking much less time, these approaches identify genes or even quantitative trait nucleotides (QTN) for a given trait. In several cases, such QTNs havebeenconvertedintodiagnosticmarkers.Webelieve

that genes and markers identified by using these approacheswillhaveauniquelyhigh prediction/diagnos-ticpowerfor breedingapplications.

3rd

G:

Gene

function

identification

Usingarangeof functionalgenomicsand traitmapping approaches,alargenumberofcandidategeneswith associ-ated molecular markers for traits of interest have been identifiedinmanycrops.Forinstance,various-omics plat-formswereestablishedinthepastthathaveallowedthe functionalcharacterizationofabout2296genescontrolling major traits in rice [35,36]. However,in most crops, the great majorityofcandidategenes,identifiedthrough transcrip-tomicapproachesand/ormapping,arefarfrom confirma-tion.Moreover,themolecularmechanismsoftheir poten-tialagronomicvaluesneedtobeunderstoodindetail. Systems Biology is an emerging holistic approach that proposes full understanding of biological systems by combining -omics approaches such as genomics, tran-scriptomics,epigenomics,proteomics,andmetabolomics, togetherwithmodelingandhigh-performance computa-tionalanalysis[37].Inbrief,systemsbiologyisthestudy ofanorganismand/ortrait,viewedasanintegratedand interactingnetwork of genes,proteins, andbiochemical reactions,includingtheinputsfromvariousinternaland externalenvironments.Onegoalofsystemsbiologyisto discover emergent properties derived from molecular interactions that will further our understanding of the entiretyofprocessesthatoccurinabiologicalsystem.In furtheranceofthisgoal,geneexpressionatlases[38–42], epigenome maps [43–45], proteome maps [46–48] and metabolomemaps[49–51]havebeendevelopedinsome cropspecies.Availabilityoftheseresourceswill acceler-atetheuseofsystemsbiologyapproachestounderstand the molecular mechanism of complex traits such as droughttolerance [52]or heterosis [53].Once traitsare associatedwithparticularpathways,andsuperior alleles identified, then breeders can employ a deeper under-standingof plant biology to predictparentaland allelic combinations that will uncover improved agronomic traits.

4th

G:

Genomic

breeding

(GB)

Genomic breeding involves approaches that use multi-omicsdata,knowledgeresources,genesandtechnologies generated by genomics research for breeding the gen-omes to enhance crop breeding programmes. [35]. Althoughsome methodsof GB suchas marker-assisted selection (MAS), marker-assisted backcrossing(MABC) and marker-assisted recurrent selection (MARS) have been usedfor breeding in several crops,it is important tohaveGBmethodologieswell-integratedintomostorall cropbreedingprograms.Inadditiontoabove-mentioned GBmethodologies,somenewapproachessuchasforward breeding (FB), haplotype-based breeding (HBB) and genomic selection (GS), coupled with speed breeding

(SB),havealsobeensuggestedforenhancingthe preci-sion, efficiencyandrateofacquiredgeneticgainincrop breeding [34].While diagnosticmarkersassociatedwith genes and major effect QTL are required for MAS, MABCand FB,superior haplotypesatagivenlocusfor a target trait need to be identified for HBB. The GS approach,incontrast,doesnotneedmarkersspecifically associatedwithatraitbecausebreedinglinesareselected forcrossingandadvancinggenerationsbasedon genomic-estimatedbreedingvaluescalculatedfromgenome-wide markerdata.

Consideringthebreedingobjectives,anyof above-men-tioned GBapproachescanbechosen for crop improve-ment. For example,if breeders need to selectparental lines or introgress some major effect QTL for a target trait,MASandMABCapproachescanbeused.MABCis useful tointrogress afew loci(<10)for improvingelite varieties. This approach has been extensively used to developalargenumberofbreedinglinesforcommercial release in public and privatesectors. The FBapproach will bethebestoptionwhenearlygenerationsof segre-gating populations (e.g. F2 generations) are used to advance plants carrying the target QTL/gene. The MARSapproachisusefultointrogressfrom10to40loci through intercrossing eliteelite parents to develop superior lineswithanoptimumcombinationofsuperior alleles[34].

Recentre-sequencingofgermplasmcollectionsinafew crops hasfacilitated identification of asmall numberof strongmarker-traitassociationsandhaplotypesfortarget traits[54,55].‘Haplotypeassembly’wasproposedasone new approach for developing improved crops through assembling superior haplotypes of the targeted traits [25]. ‘Superior haplotypes’, in which the phenotypic performanceofthegroupofindividualssharinga haplo-type(‘specifichaplotypegroup’),canbeidentified.The identifiedsuperiorhaplotypesthencanbeutilizedinthe breeding programthroughhaplotype-assistedbreeding. GS is an approach using genome-wide selectionwith a large number of markers [56]. GS works upon defined ‘genomic estimated breeding values’ (GEBVs) that are calculatedfromthegenotypicandphenotypicdatasetofa ‘trainingpopulation.’Thisapproachhasahigheraccuracy of prediction of elite genetic materials in the initial generations and permits shorter breeding cycles. GS, reviewedbyCrossaetal.[57],hasbeenextensivelyused inseveralcrops.Veryrecently,Watsonetal.[58] intro-duced theconceptof ‘speed breeding’bygiving plants lightfor22hoursanddarkforonly2hours.Speed breed-ing shortens generation times, and thus has been pro-posed orisnowbeingusedformanycrops[59].Infact, speed breeding has also been suggested to be coupled withGSinaprocesscalledSpeedGS,forrapid develop-ment of new breeding lines [1]. GS combined with

superiorhaplotypes(Haplo-GS)isanothernewand prom-isingapproachfortherapiddevelopmentofnewbreeding lines.

5th

G:

Gene

editing

(GE)

GE has emergedas apowerful approach for improving plantperformanceandthedevelopmentofvariousabiotic andbioticstresstolerancelines.Withtherecentdiscovery of Cas9 guideRNA and availability of functional geno-micsdatacoupledwithadvancesinbioinformatics pipe-lines,targetsarebeingidentifiedandsubjectedtoediting. A large number of genes with significant phenotypic effectshavebeenclonedandfunctionallycharacterized. Asaresult,GEhasbeenusedtogenerateusefultraitsin such crops as rice, maize, wheat, sugarcane, soybean, potato, sorghum, orange, cucumber, tomato, flax, and cassava,fortraitslikeherbicideresistance,drought toler-ance,thermo-sensitivegenicmalesterility,disease resis-tanceandalteredproductquality,includingsomeinthe process of commercial release [60]. For instance, Oliva etal.[61]editedpromotersofSWEET11,SWEET13and

SWEET14 at effector-binding elements recognized by

thepathogenXanthomonasoryzapvoryzae,acausalagent for rice bacterial blight. These experiments generated riceplantsthatarebroadlyresistanttothepathogen.To enhance the durability and management of resistance, Eom etal. [62] developedakit to tracethe disease, its virulence and resistance alleles.However, the steward-ship ofgene-edited linesin combinationwithan appro-priatedeploymentstrategyisessentialtomeet environ-mentalhealthandsafetystandards.Thereremainsalack of clarity as to the GMO or non-GMO status of such germplasmin manycountries [63].Itisanticipatedthat legislation anda better-educated publicwill soonallow thebenefitsofthisresearchtoreachthefarming commu-nity[64].

ItisalsoimportanttomentionthattheGEapproachisnot onlyusefultocreatenovelalleles,itcanalsobeusedfor the promotion of superior alleles [65] and removal of deleterious effectalleles [27] identified through large-scale sequencing efforts. Furthermore, ithas been sug-gested that a reverse domestication approach could be pursuedfor newcropsorcurrentcropsbyeditinggenes relatedtodomesticationtraitsinwildspecies.Thiscould providecrop diversificationandmakeavailablesuperior lines withenhanced stressresistances.As thisapproach may require several cycles of editing and line fixation, ‘ExpressEdit’ approaches thatcombine speed breeding withGE havebeensuggested[1].

Conclusions

and

prospects

Although components of the described 5Gs are being usedinpublicandprivatecropimprovementprogramsin severaldevelopedcountries,comprehensive5G integra-tionislacking,especiallyindevelopingcountries. How-ever,wearehopefulthatrecentadvancesinsequencing,

phenotypinganddatasciencewillaccelerateutilizationof the 5G strategy in coordinated crop improvement pro-grams worldwide. In this context, capacity building of youngscientistsindevelopingcountriesisrequiredin5G breedingto handle,analyzeandinterprettheenormous data sets from sequencing, genotyping, phenotyping, -omics and systems biology studies pursued across large-scalegermplasmcollections.Inparticular,training on breeder-friendly pipelines, analytical and decision support tools and databases related to identification of variants and haplotype, diversity analysis, sequencing-based trait mapping, identification of GE targets and implementationofGBmethodologieswillbevery help-ful.Insummary,acomprehensivelyapplied5Gbreeding canenhancetheprecision,efficiencyandeffectivenessof breeding programs to develop climate-resilient, high-yieldingandnutritious varietieswhile deliveringahigh rateofgeneticgaininanybreedingprogram,includingin developingcountrieswherethesegainsaremostneeded.

Conflict

of

interest

statement

Nothingdeclared.

Acknowledgements

Wearegratefultocolleaguesforexchangingideasanddiscussionsrelatedto thecontentsofthisarticle.Oursincereapologiestotheauthorswhosework wasnotmentionedhereduetolimitedspace.RKVthankstheScience& Engineering Research Board (SERB) of the Department of Science & Technology(DST),GovernmentofIndiaforprovidingtheJCBose NationalFellowship(SB/S9/Z-13/2019)andalsotheBillandMelindaGates Foundation and CGIAR Research Program on Grain Legumes and Dryland Cereals(GLDC)forpartialfundingsupport.ICRISATisamemberofthe CGIAR.

References

and

recommended

reading

Papersofparticularinterest,publishedwithintheperiodofreview, have been highlighted as:

of special interest of outstanding interest

1. FAO:Agriculture and Climate Change – Challenges and Opportunities at the Global and Local Level – Collaboration on Climate-Smart Agriculture.FoodandAgricultureOrganization (FAO)oftheUnitedNations;2019.Rome52pp.

2. ZhaoC,LiuB,PiaoS,WangX,LobellDB,HuangY,HuangM, YaoY,BassuS,CiaisPetal.:Temperatureincreasereduces globalyieldsofmajorcropsinfourindependentestimates. ProcNatlAcadSciUSA2017,114:9326-9331.

3. AtlinGN,CairnsJE,DasB:Rapidbreedingandvarietal replacementarecriticaltoadaptationofcroppingsystemsin thedevelopingworldtoclimatechange.GlobFoodSecur2017, 12:31-37.

4.

HickeyBertioliSCM,LT,HafeezTesterAmberM,GaoN,C,RobinsonGodwinH,ID,JacksonHayesBJ,SA,WulffLeal-BBH: Breedingcropstofeed10billion.Nat Biotechnol 2019,37 :744-754.

The paper discusses how various tools and technologies, including speed breeding and express gene editing, can be utilized to dramatically enhance crop improvement.

5. ZhangX,ZhangS,ZhaoQ,MingR,TangH:Assemblyof allele-aware,chromosomal-scaleautopolyploidgenomesbasedon Hi-Cdata.Nat Plants 2019,5:833-845.

6. JohannesF,SchmitzRJ:Spontaneousepimutationsinplants. NewPhytol2019,221:1253-1259.

7. RasheedA,HaoY,XiaX,KhanA,XuY,VarshneyRK,HeZ:Crop breedingchipsandgenotypingplatforms:progress, challenges,andperspectives.MolPlant2017,10:1047-1064.

8. KimSA,ChoCS,KimSR,BullSB,YooYJ:Anewhaplotypeblock detectionmethodfordensegenomesequencingdatabased onintervalgraphmodelingofclustersofhighlycorrelated SNPs.Bioinformatics 2018,34:388-397.

9. Voss-FelsKP,StahlA,WittkopB,LichthardtC,NaglerS,RoseT, ChenTW,ZetzscheH,SeddigS,BaigMMet al.:Breeding improveswheatproductivityundercontrastingagrochemical inputlevels.NatPlants2019,17:1.

10.

WangZhengW,T,FuentesMauleonRR,R,ZhangHuZ,ChebotarovFetal.:GenomicD,TaivariationS,WuZ,inLi3,010M, diverseaccessionsofAsiancultivatedrice.Nature2018, 557:43-01049.

An important initiative in rice whole-genome re-sequencing, with 3010accessionsoriginating from86 countries.Thisstudyopensthe pathforbigdatagenomicsincropplants.

11.

VarshneyZhaoY,WangRK,ShiX,RathoreC,ThudiAM,et Mariacal.:PearlC,milletWallacegenomeJ,QiP,sequenceZhangH, providesaresourcetoimproveagronomictraitsinarid environments.Nat Biotechnol 2017,35:969-976.

This paper presents the vision of using sequencing-based technologies for germplasm identification, trait mapping and breeding. This is the first genome sequence paper for pearl millet and includes re-sequencing of 994 pearl millet accessions, enabling insights into population structure, genetic diversity, and domestication.

12. VarshneyRK,ThudiM,RoorkiwalM,HeW,UpadhyayaHD, YangW,BajajP,CubryP,RathoreA,JianJ:Resequencingof 429chickpeaaccessionsfrom45countriesprovidesinsights intogenomediversity,domesticationandagronomictraits. NatGenet2019,51:857-864.

13. StephensZD,LeeSY,FaghriF,CampbellRH,ZhaiC,EfronMJ

etal.:Bigdata:astronomicalorgenomical?PLoSBiol2015,13:

e1002195http://dx.doi.org/10.1371/journal.pbio.1002195. 14. Smy´kal P, Nelson MN, Berger JD, von Wettberg EJB: Theimpact

ofgeneticchangesduringcropdomestication. Agronomy

2018, 8:119http://dx.doi.org/10.3390/agronomy8070119.

15.

JanzenintrogressionGM,WangincropsL,Hufford.NewPhytolMB:The2019,extent221:1279-1288.ofadaptivewild Thisisacomprehensivereviewpaperabouttheuseofwildrelativesin cropbreeding.

16.

JulianaCrespo-HerreraP,PolandL,J,ToledoHuerta-EspinoFH,GovindanJ,ShresthaV,MondalS,CrossaS,KumarJ, U etal.:Improvinggrainyield,stressresilienceandqualityof breadwheatusinglarge-scalegenomics.NatGenet2019, 51:1530-1539.

Thispresentsthevisionofhowlarge-scalesequence-basedgenotyping (>40000individuals)couldbeusefulfortraitmapping.

17. MilnerSG,JostM,TaketaS,Mazo´nER,HimmelbachA, OppermannM,WeiseS,Knu¨pfferH,BasterrecheaMet al.: Genebankgenomicshighlightsthediversityofaglobalbarley collection.Nat Genet 2019,51:319-326.

18. LangridgeP,WaughR:Harnessingthepotentialofgermplasm collections.NatGenet2019,51:200-201.

19.

HuangZhangX,ZetWeial.X,:Genome-wideSangT,ZhaoQ,associationFengQ,ZhaostudiesY,LiC,ofZhuC,LuT, 14agronomictraitsinricelandraces.NatGenet2010,42 :961-967.

Thisstudydemonstratesthefirstuseofwhole-genomeresequencingfor constructingthecomprehensivehaplotypemapforGWASofcomplex traitsinrice.ItisalsothefirstapplicationofGWASincrops.

20. JiaG,HuangX,ZhiH,ZhaoY,ZhaoQ,LiW,ChaiY,YangL,LiuK, LuH,ZhuC:Ahaplotypemapofgenomicvariationsand genome-wideassociationstudiesofagronomictraitsin foxtailmillet(_{Setariaitalica}).Nat Genet 2013,45:957. 21.

VarshneyRathoreA,RK,KimSaxenaD,KimRK,J,UpadhyayaAnSet al.:HD,Whole-genomeKhanAW,YuY,KimC, resequencingof292pigeonpeaaccessionsidentifies genomicregionsassociatedwithdomesticationand agronomictraits.NatGenet2017,49:1082-1088.

ThisisanearlypaperdemonstratingtheuseofNGS-basedtechnologies forassemblinggenomeinlegumecropsandidentified domestication-relatedgenesandmarker-traitassociations.

22. MaZ,HeS,WangX,SunJ,ZhangY,ZhangG,WuL,LiZ,LiuZ, SunGetal.:Resequencingacorecollectionofuplandcotton identifiesgenomicvariationandlociinfluencingfiberquality andyield.NatGenet2018,50:803-813.

23. LuK,WeiL,LiX,WangY,WuJ,LiuM,ZhangC,ChenZ,XiaoZ,

JianHetal.:Whole-genomeresequencingrevealsBrassica

napusoriginandgeneticlociinvolvedinitsimprovement.Nat Commun2019,10:1154 http://dx.doi.org/10.1038/s41467-019-09134-9.

24. LiangZ,DuanS,ShengJ,ZhuS,NiX,ShaoJ,LiuC,NickP,DuF,

FanPetal.:Whole-genomeresequencingof472Vitis

accessionsforgrapevinediversityanddemographichistory

analyses.NatCommun2019,10:1190http://dx.doi.org/10.1038/

s41467-019-09135-8. 25.

BevanGenomicMW,innovationUauyC,WulffforcropBBH,improvementZhouJ,Krasileva.NatureK,2017,ClarkMD: 543:346-354.

Thispioneeringpaperdiscussedhowgenomicscanprovidenew foun-dationsforcrop-breedingsystemsandproposedthe haplotype-assem-blyconcept.

26. RamuP,EsumaW,KawukiR,RabbiIY,EgesiC,BredesonJV, BartRS,VermaJ,BucklerES,LuF:Cassavahaplotypemap highlightsfixationofdeleteriousmutationsduringclonal propagation.NatGenet2017,49:959-963.

27.

HickeyJohnssonJM:M,RemovalGaynorRC,ofallelesJenkobyJ,GorjancgenomeG,editingdeKoning(RAGE)DJ,

againstdeleteriousload.GenetSelEvol2019,51:14http://dx.

doi.org/10.1186/s12711-019-0456-8.

Thispaperproposesthatdeleteriousalleleloadscanbeeliminatedby genomeediting.

28. LiuS,YehCT,TangHM,NettletonD,SchnablePS:Gene mappingviabulkedsegregantRNA-Seq(BSR-Seq).PLoSOne 2012,7:e36406.

29. AbeA,KosugiS,YoshidaK,NatsumeS,TakagiH,KanzakiH, MatsumuraH,YoshidaK,MitsuokaC,TamiruMetal.:Genome sequencingrevealsagronomicallyimportantlociinriceusing MutMap.NatBiotechnol2012,30:174-178.

30. TakagiH,AbeA,YoshidaK,KosugiS,NatsumeS,MitsuokaC, UemuraA,UtsushiH,TamiruM,TakunoSetal.:QTL-seq:rapid mappingofquantitativetraitlociinricebywholegenome resequencingofDNAfromtwobulkedpopulations.PlantJ 2013,74:174-183.

31. SinghVK,KhanAW,SaxenaRK,SinhaP,KaleSM,ParupalliS, KumarV,ChitikineniA,VechalapuS,SameerKumarCVetal.: Indel-seq:afast-forwardgeneticsapproachforidentification oftrait-associatedputativecandidategenomicregionsandits applicationinpigeonpea(Cajanuscajan).PlantBiotechnolJ 2017,15:906-914.

32. MichelmoreRW,ParanI,KesseliRV:Identificationofmarkers linkedtodiseaseresistancegenesbybulkedsegregant analysis:arapidmethodtodetectmarkersinspecific genomicregionsbyusingsegregatingpopulations.ProcNatl AcadSciUSA1991,88:9828-9832.

33.

SchlottererindividualsC,-miningToblergenome-wideR,KoflerR,NoltepolymorphismV:Sequencingdatapoolswithoutof bigfunding.NatRevGenet2014,15:749-763.

Pioneering paper describeshow a pool-basedsequencing approach couldbeusefulinrapidmarkeridentificationwithlessinputcost. 34. VarshneyRK,PandeyMK,BohraA,SinghVK,ThudiM,

SaxenaRK:Towardthesequence-basedbreedinginlegumes inthepost-genomesequencingera.TheorApplGenet2019, 132:797-816.

35. WingRA,PuruggananMD,ZhangQ:Thericegenome revolution:fromanancientgraintogreensuperrice.NatRev Genet2018,19:505517.

36. LiY,XiaoJ,ChenL,HuangX,ChengZ,HanB,ZhangQ,WuC: Ricefunctionalgenomicsresearch:pastdecadeandfuture. MolPlant2018,11:359-380.

37.

LavarennespringofsystemsJ,Guyomarc’hbiology-drivenS,SallaudbreedingC,Gantet.TrendsP,LucasPlantM:SciThe 2018,23:706-720.

Thisopinionpaperconnects systemsbiologyresearchwithbreeding programs.

38. NobutaK,VenuRC,LuC,Belo´ A,VemarajuK,KulkarniK,WangW, PillayM,GreenPJ,WangGL,MeyersBC:Anexpressionatlasof ricemRNAsandsmallRNAs.NatBiotechnol2007,25:473-477. 39. PazhamalaLT,PurohitS,SaxenaRK,GargV,KrishnamurthyL,

VerdierJ,VarshneyRK:Geneexpressionatlasofpigeonpea anditsapplicationtogaininsightsintogenesassociatedwith pollenfertilityimplicatedinseedformation.JExpBot2017, 68:2037-2054.

40. KudapaH,GargV,ChitikineniA,VarshneyRK:The RNA-Seq-basedhighresolutiongeneexpressionatlasofchickpea (_{Cicerarietinum}L.)revealsdynamicspatio-temporalchanges associatedwithgrowthanddevelopment.Plant Cell Environ 2018,41:2209-2225.

41. ShinozakiY,NicolasP,Fernandez-PozoN,MaQ,EvanichDJ, ShiY,XuY,ZhengY,SnyderSI,MartinLaetitiaBBetal.: High-resolutionspatiotemporaltranscriptomemappingoftomato fruitdevelopmentandripening.Nat Commun 2018,9:364.

42. HoopesGM,HamiltonJP,WoodJC,EstebanE,PashaA, VaillancourtB,ProvartNJ,BuellCR:Anupdatedgeneatlasfor maizerevealsorgan-specificandstress-inducedgenes.Plant J2019,97:1154-1167.

43. JunaidA,KumarH,RaoAR,PatilAN,SinghNK,GaikwadK: Unravellingtheepigenomicinteractionsbetweenparental inbredsresultinginanalteredhybridmethylomeinpigeonpea. DNARes2018,25:361-373.

44. LiZ,WangM,LinK,XieY,GuoJ,YeL,ZhuangY,TengW,RanX, TongYet al.:Thebreadwheatepigenomicmaprevealsdistinct chromatinarchitecturalandevolutionaryfeaturesof functionalgeneticelements.GenomeBiol2019,20:139. 45. PengY,XiongD,ZhaoL,OuyangW,WangS,SunJ,ZhangQ,

GuanP,XieL,LiW,LiG,YanJ,LiX:Chromatininteractionmaps revealgeneticregulationforquantitativetraitsinmaize.Nat Commun 2019,10:2632.

46. DuncanO,Tro¨schJ,FenskeR,TaylorNL,MillarAH:Resource: mappingtheTriticumaestivumproteome.PlantJ2017,89 :601-616.

47. BaruaP,LandeNV,SubbaP,GayenD,PintoS,Keshava PrasadTS,ChakrabortyS,ChakrabortyN: Dehydration-responsivenuclearproteomelandscapeofchickpea(Cicer arietinumL.)revealsphosphorylation-mediatedregulationof stressresponse.PlantCellEnviron2019,42:230-244. 48. JiangLG,LiB,LiuSX,WangHW,LiCP,SongSH,BeattyM,

Zastrow-HayesG,YangXH,QinFetal.:Characterizationof proteomevariationduringmodernmaizebreeding.MolCell Proteomics2019,18:263-276.

49. OkazakiY,SaitoK:Integratedmetabolomicsand phytochemicalgenomicsapproachesforstudiesonrice. Gigascience 2016,5:11.

50. ChenJ,WangJ,ChenW,SunW,PengM,YuanZ,ShenS,XieK, JinC,SunYetal.:Metabolomeanalysisofmulti-connected biparentalchromosomesegmentsubstitutionline

populations.Plant Physiol 2018,178:612-625.

51. ZhouS,KremlingKA,BandilloN,RichterA,ZhangYK,AhernKR, ArtyukhinAB,HuiJX,YounkinGC,SchroederFCetal.: Metabolome-scalegenome-wideassociationstudiesreveal chemicaldiversityandgeneticcontrolofmaizespecialized metabolites.PlantCell2019,31:937-955.

52. MiaoZ,HanZ,ZhangT,ChenS,MaC:Asystemsapproachtoa spatio-temporalunderstandingofthedroughtstress responseinmaize.SciRep2017,7:6590.

53. LiD,HuangZ,SongS,XinY,MaoD,LvQ,ZhouM,TianD,TangM, WuQet al.:Integratedanalysisofphenome,genome,and transcriptomeofhybridriceuncoveredmultiple heterosis-relatedlociforyieldincrease.Proc Natl Acad Sci U S A 2016, 113:E6026-E6035.

54. AbbaiR,SinghVK,NachimuthuVV,SinhaP,SelvarajR, VipparlaAK,SinghAK,SinghUM,VarshneyRK,KumarA: Haplotypeanalysisofkeygenesgoverninggrainyieldand qualitytraitsacross3KRGpanelrevealsscopeforthe developmentoftailor-madericewithenhancedgeneticgains. PlantBiotechnolJ2019,17:1612-1622.

55. KurohaT,NagaiK,GamuyaoR,WangDR,FurutaT,NakamoriM, KitaokaT,AdachiK,MinamiAet al.:Ethylene-gibberellin signalingunderliesadaptationofricetoperiodicflooding. Science 2018,361:181-186.

56. MeuwissenTHE,HayesBJ,GoddardME:Predictionoftotal geneticvalueusinggenome-widedensemarkermaps. Genetics 2001,157:1819-1829.

57.

CrossaJarquı´n D,J,Pe´rez-Rodrı´guezdeLosCamposG,P,Burguen˜oCuevasJ,J,Montesinos-Lo´pezGonza´lezCamachoO,JM, Pe´rez-ElizaldeS,BeyeneYetal.:Genomicselectioninplant breeding:methods,models,andperspectives.TrendsPlantSci 2017,22:961-975.

Thisisanoutstandingpaperthatdiscusseshowgenomicselectioncan beutilizedinplantbreeding.

58.

ReyWatsonMD,A,HattaGhoshMA,S,HinchliffeWilliamsA,MJ,SteedCuddyA,ReynoldsWS,SimmondsDetal.:SpeedJ, breedingisapowerfultooltoacceleratecropresearchand breeding.NatPlants2018,4:23-29.

Thisisthegame-changermanuscriptforhowrapidcyclicbreedingcould be usefulforrapiddeliveryofimprovedvarieties.

59. GhoshS,WatsonA,Gonzalez-NavarroOE,Ramirez-GonzalezRH, YanesL,Mendoza-Sua´rezM,SimmondsJ,WellsR,RaynerT,

GreenP:Speedbreedingingrowthchambersand

glasshousesforcropbreedingandmodelplantresearch.Nat Protoc2018,12:2944-2963.

60. ZhangY,MasselK,GodwinID,GaoC:Applicationsand

potentialofgenomeeditingincropimprovement.GenomeBiol

2018,1:13http://dx.doi.org/10.1186/s13059-019-1622-6.

61. Oliva R, Ji C, Atienza-Grande G, Huguet JC, Perez-Quintero A, Li T, Eom JS, Li C, Nguyen H, Liu B et al.: Broad-spectrumresistance

tobacterialblightinriceusinggenome-editing. Nat Biotechnol

2019, 37:1344-1350 http://dx.doi.org/10.1038/s41587-019-0267-z.

62. EomJS,LuoD,Atienza-GrandeG,YangJ,JiC,LuuVT, Huguet-TapiaJC,CharSN,LiuB,NguyenHetal.:Diagnostickitforrice

blightresistance.NatBiotechnol2019,37:1372-1379http://dx.

doi.org/10.1038/s41587-019-0268-y.

63. SchulmanAH,Oksman-CaldenteyKM,TeeriTH:Europeancourt

ofjusticedeliversnojusticetoEuropeongenome-edited

crops.PlantBiotechnolJ2020,18:8-10http://dx.doi.org/10.1111/

pbi.13200.

64. Varshney RK, Godwin I, Mohapatra T, Jones JDG, McCouch SR: A

SWEETsolutiontoriceblight. Nat Biotechnol 2019, 37

:1280-1282http://dx.doi.org/10.1038/s41587-019-0302-0.

65. JenkoJ,GorjancG,ClevelandMA,VarshneyRK,WhitelawCB, WoolliamsJA,HickeyJM:Potentialofpromotionofallelesby genomeeditingtoimprovequantitativetraitsinlivestock breedingprograms.Genet Sel Evol 2015,2:47-55.