Essay
Harvesting the Promising Fruits of Genomics: Applying
Genome Sequencing Technologies to Crop Breeding
Rajeev K. Varshney1,2*, Ryohei Terauchi3, Susan R. McCouch4
1InternationalCropsResearchInstitutefortheSemi-AridTropics(ICRISAT),Hyderabad,India,2TheUniversityofWesternAustralia(UWA),Crawley,WesternAustralia,
Australia,3IwateBiotechnologyResearchCenter,Kitakami,Iwate,Japan,4CornellUniversity,Ithaca,NewYork,UnitedStatesofAmerica
Abstract: Next generation se- further expanding farmland or damaging (Box1)inseveralcropspecies.Knowledge
theenvironment [3]. about the identity and map location of
quencing (NGS) technologies are
being used to generate whole To meet these challenges, scientists are agriculturally important genes and QTL
genome sequences for a wide developing new and more efficient breed- provides the basis for parental selection
range of crop species. When com- ing strategies that integrate genomic and marker-assisted selection (MAS) in
bined with precise phenotyping technologies and high throughput pheno- plant breeding. Alternatively, genotypic
methods, these technologies pro- typingtobetterutilizenaturalandinduced and phenotypic datasets on training pop-
vide a powerful and rapid tool for genetic variation. Rapid developments in ulations (TP; Box 1) can be used to
identifying the genetic basis of next generation sequencing (NGS) tech- develop models to predict the breeding
agriculturally important traits and nologiesoverthelastdecadehaveopened value of lines in an approach called
forpredictingthebreedingvalueof
upmanynewopportunitiestoexplorethe genomic selection (GS). We discuss both
individuals in a plant breeding
relationship between genotype and phe- approaches later inthisEssay.
population. Here we summarize
notype with greater resolution than ever NGS technologies have been available
currenttrendsandfutureprospects
before. As the cost of sequencing has foranumberofyearsandarewidelyused
for utilizing NGS-based technolo-
decreased, breeders have begun to utilize for de novo sequencing, whole genome
gies to develop crops with im-
NGS with increasing regularity to se- sequencing (WGS), whole genome re-
proved trait performance and in-
crease the efficiency of modern quence large populations of plants, in- sequencing (WGRS), genotyping by se-
plant breeding. It is our hope that creasing the resolution of gene and quencing (GBS) (Box 1), and transcrip-
the application of NGS technolo- quantitative trait locus (QTL) discovery tomeandepigeneticanalysis[4].Theyare
gies to plant breeding will help us and providing the basis for modeling also usedas thebasisfordeveloping fixed
tomeetthechallengeoffeedinga complexgenotype-phenotyperelationships SNP genotyping arrays that typically
growing worldpopulation. at thewhole-genome level. consist of a set of well-distributed genic
Specializedplantgeneticstocks,suchas and non-genic SNPs. NGS strategies are
bi-parental and multi-parent mapping now being improved by third generation
populations, mutant populations, and im- sequencing (TGS) technologies (Box 2).
This article is part of the PLOS mortalizedcollectionsofrecombinantlines TGS technologies can generate longer
Biology Collection ‘‘The Promise of (Figure 1), have been generated to facili- sequence reads in a shorter time and at
tate mapping and gene function analysis even lower costs per instrument run.
PlantTranslational Research.’’
via association studies andQTL mapping Collectively, NGStechnologies have been
Introduction
Citation:VarshneyRK,TerauchiR,McCouchSR(2014)HarvestingthePromisingFruitsofGenomics:Applying
In2012,theworldpopulationexceeded Genome Sequencing Technologies to Crop Breeding. PLoS Biol 12(6): e1001883. doi:10.1371/journal.pbio.
7 billion people and is expected to 1001883
continue growing. To feed this growing AcademicEditor:JefferyL.Dangl,TheUniversityofNorthCarolinaatChapelHill,UnitedStatesofAmerica
population and meet rising expectations PublishedJune10,2014
regarding food quality, food production
Copyright: (cid:2) 2014Varshneyetal.Thisisanopen-accessarticledistributedunderthetermsoftheCreative
must increase by an estimated 70% by CommonsAttributionLicense,whichpermitsunrestricteduse,distribution,andreproductioninanymedium,
2050 [1]. Recent abrupt climatic changes providedtheoriginalauthorandsourcearecredited.
[2] make stable food production even Funding:RKVthankstheAustraliaIndo-StrategicResearchFund(AISRF)andDepartmentofBiotechnology,
more difficult and put pressure on fragile GovernmentofIndiaforsponsoringresearchatICRISATonthetopicsmentionedinthearticle.RTthanksthe
ProgrammeforPromotionofBasicandAppliedResearchesforInnovationsinBio-orientedIndustry,Japan,
environments. There is, therefore, an
Grant-in-aid for MEXT (Scientific Research on Innovative Areas 23113009) and JSPS KAKENHI (Grant
urgent need to accelerate crop breeding No.24248004). SMc thanks the National Science Foundation Plant Genome Research Program (Grant
improvements and to implement new #1026555)andtheGlobalCropDiversityTrust.ThisstudyhasbeenundertakenasapartofCGIARResearch
ProgramonGrainLegumes.ICRISATisamemberoftheCGIARconsortium.Thefundershadnoroleinstudy
management strategies that together can
design,datacollectionandanalysis,decisiontopublish,orpreparationofthemanuscript.
achievesustainableyieldincreaseswithout
CompetingInterests:Theauthorshavedeclaredthatnocompetinginterestsexist.
Abbreviations: GEBV, genomic estimated breeding value; GS, genomic selection; GWAS, genome-wide
Essaysarticulateaspecificperspectiveonatopicof associationstudies;MAS,marker-assistedselection;NGS,nextgenerationsequencing;QTL,quantitativetrait
broadinteresttoscientists. locus;TGS,thirdgenerationsequencing;WGRS,wholegenomere-sequencing.
*E-mail:r.k.varshney@cgiar.org
PLOSBiology | www.plosbiology.org 1 June2014 | Volume 12 | Issue 6 | e1001883
Figure 1. Role of NGS in genomics-assisted breeding. NGS occupies a critical position in a genomics-assisted breeding pipeline; it helps
improvethespeedandprecisionoftraitmappingtoidentifygenesandQTLsthatarethetargetsofMAS,anditunderliestheabilitytocalculate
GEBVsbasedongenome-widepredictionthatpredictthebreedingvalueofindividualsinabreedingpopulationusingGS.
doi:10.1371/journal.pbio.1001883.g001
usedtosequencea rangeofplant species’ large-scale modeling in both basic plant model is generally tested using cross-
genomes, mapping populations, and genetics and applied plant breeding, is validation on subsets of the training
breeding lines. Their use has helped to helping tobridgethegap. population. Once validated, the model
broaden the plant research agenda over can be applied to a breeding population
the last decade, and to shift from a focus Genomics-Assisted Breeding where GEBVs are calculated for all lines
on only one or two model species to a for which genotypic information is avail-
much wider range of plant species today. Here we discuss two main types of able,andtheirphenotypicperformanceis
NGS technologies have also directly im- genomics-assisted breeding [5]: (1) predicted solely on the basis of that
pacted the kind of plant science research MAS and (2) GS. MAS, which includes genotypic information.
that is undertaken in both the basic and marker-assisted back-crossing (MABC; The advantage of genomics-assisted
translational research arenas. Box 1), uses molecular markers that map breeding is that genotypic data obtained
Understanding the complex relation- withinspecificgenesorQTLsknowntobe from a seed or seedling can be used to
ship between genotypic and phenotypic associatedwithtargettraitsorphenotypes predict the phenotypic performance of
variation lies at the heart of the study of to select individuals that carry favorable mature individuals without the need for
genetics and is also critically important to alleles for traits of interest (and/or to extensivephenotypicevaluationoveryears
applicationsinplantbreeding.Yetthereis discard those that do not). GS, on the and environments. The use of genomics-
a considerable gap between the informa- otherhand,usesallavailablemarkerdata assisted breeding, in both MAS and GS,
tion that is available on model species for a population as predictors of breeding allows for more selection cycles and
about the genes and QTLs that underlie value. Specifically, GS integrates marker greater genetic gain per unit of time.
plant phenotypes and the integration of data from a training population with While some phenotyping is still advanta-
this information into applied plant im- phenotypic and, when available, pedigree geous to validate performance prior to
provement. In part, this gap is due to the data collected on the same population to further crossing or variety release, and in
geneticists’ desire to simplify the genetic generate a prediction model. The model thecaseofGS,tomaintainorincreasethe
background (by using ‘‘wild-type’’ or outputs genomic estimated breeding val- accuracy of prediction models as the
‘‘reference’’ populations that do not cap- ues (GEBVs) forall genotyped individuals breeding population evolves, extensive
ture the complex genotype 6 genotype within a breeding population [6]. The multi-location field trials become unnec-
interactions in materials used by the GEBVsservesasapredictorofhowwella essary inevery generation.
breedingcommunity),andminimizeenvi- plantwillperformasaparentforcrossing Over the past several decades, as the
ronmental ‘‘noise’’ (by using carefully and generation advance in a breeding processofselectingplantsforbreedinghas
controlled environments that do not cap- pipeline, based on the similarity of its shifted from an almost complete reliance
turethecomplexityofreal-worldenviron- genomic profile to other plants in the TP on phenotyping to an increasing reliance
mental variation) to study the function of thatareknowntohaveperformedwellin on some level of genotyping-based meth-
genes at a mechanistic level. The use of the target environment(s). Before the ods, the number of markers used for
NGSforgenediscoveryindiversespecies prediction model can be applied to a selection has steadily increased. This has
and populations, and as a foundation for breeding population, the accuracy of the been made possible by NGS technologies
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contrast, the use of NGS technologies
Box 1. Glossary
providesgenome-widemarkercoverageat
Bulkedsegregantanalysis(BSA):Thisapproachidentifiesmolecularmarkers averylowcostperdatapoint,allowingus
associated with a trait of interest by genotyping DNA extracted from bulked to assess the inheritance of the entire
samples ofindividualsatthe trait’s phenotypic extremes. genome withnucleotide-level precision.
In the context of genomics-assisted
Genome-wide association studies (GWAS): These studies utilizecollections breeding applications, both MAS and GS
of diverse, unrelated lines that are genotyped and phenotyped for traits of
have benefited tremendously from NGS
interest,andstatisticalassociationsareestablishedbetweenDNApolymorphisms
technologies.Theresolutionofmostbasic
and trait variation to identify genomic regions where genes governing traits of
and translational studies is no longer
interest are located.
limited by our ability to genotype large
populations, but rather by the high cost
Genotyping-by-sequencing (GBS): A highly multiplexed genotyping system
involving DNA digestion with different enzymes and the construction of a and low throughput of phenotyping strat-
reduced representation library, which is sequenced using an NGS platform. It egies for traits of interest and in environ-
enablesthedetectionofthousandsofSNPsinlargepopulationsorcollectionsof mentsrelevanttoplantbreeding[7,8].As
lines that can be used for mapping, genetic diversity analysis, and evolutionary a result, breeders are looking for ways to
studies. leverage genotypic information, which is
relativelyfast,cheap,andeasytogenerate,
Marker-assisted back-crossing (MABC): In this form of marker-assisted to inform them about the phenotypic
selection, a genomic locus (gene or QTL) associated with a desired trait is potential of their materials. Both MAS
introducedintothegeneticbackgroundofanelitebreedinglinethroughseveral and GS are attempts to dothat, and they
generations ofbackcrossing.
each have different strengths and limita-
tions. The utility of each depends on the
Multi-parent advanced generation inter-cross (MAGIC): A type of multi-
geneticarchitectureandheritabilityofthe
parentpopulationdevelopedfromfourtoeightdiversefounderlines,generated
trait(s) involved, the diversity of genetic
to increase the precision and resolution of QTL mapping because of the larger
backgrounds managed in the breeding
number of alleles and recombination events compared to bi-parental mapping
populations. program, the number of generations that
abreedingpopulationisremovedfromthe
Nested association mapping (NAM): NAM combines advantages of linkage original mapping or training population,
and association mapping and eliminates disadvantages of both; it takes into andtheoverallorganizationandbioinfor-
consideration recent and historical recombination events, facilitating high matics capabilities of the breeding pro-
resolution mapping. gram.
Quantitativetraitlocus(QTL):Agenomicregionencompassingoneormore
Gene and QTL Discovery
genesthataccountsforaportionofthevariationofacomplexquantitativetrait,
identifiedbyphenotypingandgenotypingasegregatingpopulationfollowedby The application of MAS in plant
statistical analysis. breedingispredicatedonpriorknowledge
about major-effect genes and QTLs that
Recombinant inbred line (RIL): An immortal mapping population consisting
serve as the targets of selection. NGS
of fixed (inbred) lines in which recombination events between chromosomes
technologies have proven useful in identi-
inherited from two inbred strains are preserved. RILs are generated by crossing
fying these loci in diverse populations. In
two divergent parentsfollowed byseveralgenerations ofinbreedingto achieve
the following section, we discuss various
homozygosity.
approaches to gene and QTL discovery
Sequence-based mapping (SbM): An approach requiring deep sequencing where the use of NGS enhances the
(56to86genomecoverage)oftwoDNApoolsderivedfromindividualsfromthe efficiency and resolution of the mapping
phenotypic extremes of a segregating population, to identify candidate genes process.
associated with aphenotype ofinterest.
Genome-Wide Association Studies
Training population (TP): A genotyped and phenotyped reference breeding
Genome-wide association studies
population used to develop a model to predict genomic-estimated estimate
(GWAS; Box 1) utilize association map-
breeding values forGenomicSelection (GS).
ping, also known as linkage disequilibri-
Wholegenomere-sequencing(WGRS):Astrategytosequenceanindividual um (LD) mapping, to map QTLs by
genomewhereshortsequencereadsgeneratedbyNGSarealignedtoareference taking advantage of historic LD to
genomeforthespecies,providinginformationonvariants,mutations,structural identify statistically significant pheno-
variations, copy number variation, and rearrangements between and among type-genotype associations (Figure 1).
individuals, basedon comparisonto the referencegenome. GWAS have been successfully performed
in several crop plants, including maize
[9–12],rice[13–15],wheat[16],soybean
[17], sorghum [18], and foxtail millet
that have augmented the speed, through- constrainedbythenumberofmarkersthat [19]. The use of NGS in the context of
put,andcosteffectivenessofgenome-wide could efficiently be assayed. As a result, GWAS makes it possible to genotype
genotyping. Previously, marker data were only markers in critical genomic regions largerpopulations of plantswith ahigher
expensive and laborious to generate, and were utilized to predict the presence or density of markers than was previously
marker-assisted breeding strategies were absenceofagriculturallyvaluabletraits.By possible, and this contributes directly to
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where the resistance trait was associated
Box 2. Innovations in Sequencing Technologies
with the presence of a nucleotide-binding
In addition to classical Sanger sequencing methodology, a range of sequencing site-leucine rich repeat (NBS-LRR) gene
technologieshavebecomeavailableinrecentyears.Thesetechnologiesarebeing in a gap, that is, a structurally variable
used to sequence the genomes of a number of crops. Here we provide a brief genomicregion,wheretheresistancegene
update on these technologies and their use in sequencing the genomes of key was not present in the reference genome
plant species. usedforWGRSalignment[37].Asimilar
approach, known as QTL-Seq, involves
Second/next generationsequencers (SGS/NGS)
WGRSonbulkedDNAsamplesfromthe
phenotypic extremes of a population of
NGStechnologieshaveenabledthewholegenomesequencing(WGS)ofseveral
recombinantinbredlines(RILs)(Box1)or
plant species and the re-sequencing of multiple genotypes [98,99]. Two NGS
F individuals derived from inter-varietal
approaches, de novo assembly and reference-based assembly, are employed for 2
crosses. The QTL-Seq strategy was used
assembling short sequence reads into longer contigs. The sequencing of more
plantgenomesisexpectedassequencingtechnologiesbecomecheaper[4,100]. to identify QTLs for seedling vigour and
partial resistance to blast disease in rice
Third generationsequencing (TGS) [36]. The QTLs were validated on the
basis of classical QTL mapping studies,
The TGS approaches increase sequencing rates, throughput, and read lengths, but thepopulation sizes used tomake the
ultimately decreasing sequencing costs and lowering the complexity of sample bulks (20–50 individuals each) were not
preparation. The current TGS technologies include: Ion Torrent’s (Life Technol- largeenoughtoprovidegene-levelresolu-
ogies)technology,asequencerthatusessemi-conductortechnologytocreatea tion.
high-density array of micro-machined wells that carry out sequencing-by-
Inanotherexample,Xuandcolleagues
synthesis, although it still requires PCR amplification of the DNA template and
[38] re-sequenced 246 RILs of soybean
termination events, which limit read length to that of current NGS systems;
and evaluated the lines for root knot
Heliscope Single Molecule sequencer, which performs single molecule sequenc-
nematode (RKN, Meloidogyne incognita) re-
ing (SMS) [101,102], the read lengths are 32 nucleotides long and no PCR
sistancetoidentifythegene(s)underlyinga
amplification is required; Single-Molecule Real-Time (SMRT) sequencer performs
QTLforRKNresistance.RKNdiseaseis
sequencingbysynthesisandovercomesmanyoftheshortcomingsofNGS[103],
difficult to evaluate phenotypically, but
and produces maximum read lengths of 10,000bp, enabling de novo assembly,
however,therawreaderrorratescanbeover5%;OxfordNanoporesequencing can cause up to 90% loss of susceptible
technology, which employs nanopore sequencing technology and a portable soybean cultivars [39]. Identifying the
gene-sequencing device for use with GridION and MiniION single molecule genomic region(s) associated with RKN
sequencers, it offers 50–100kbread lengthat4% errorrate. resistance was useful for developing resis-
tant genotypes. Compared to previous
marker systems, NGS is very efficient for
increasedmappingresolution.Withlarger High Resolution Genetic Mapping map-based gene discovery because it
populations, more recombination break- and Candidate Gene Identification simultaneously performs SNP discovery,
pointsareidentified,definingtheposition NGS-based approaches, including SNPvalidation,andSNPgenotypingina
of candidate genes with higher precision. sequencing-based mapping (SbM; Box 1), mappingormutantpopulation.Thework
In parallel to developments in NGS can be used in combination with bulked by Xu and colleagues [38] illustrates how
technologies, specialized mapping popu- segregant analysis (BSA; Box 1) and NGScanalsohelpresolveissuesrelatedto
lations have also been developed that modifications thereof to help speed the genomeduplicationinacomplex,palaeo-
significantly enhance the power and identification of candidate genes [28]. In polyploid species like soybean.
efficiency of GWAS. Nested association BSA,DNAisextractedfromplantsatthe
mapping (NAM;Box 1) populations were extremesofthephenotypicdistributionfor TILLING/Eco-TILLING by Sequencing
first developed for maize as a way of a given trait, and samples from several Targeting-induced local lesions in ge-
taking advantage of both historic and plants at each of the extremes are pooled nomes (TILLING) is a reverse genetics
recent recombination events. This devel- togetherandusedtoidentifythegenomic approach for the rapid discovery and
opment was important to minimize the region(s) underlying thetrait[29]. mapping of induced causal mutation
density of markers required by GWAS NGS-based approaches that involve responsible fortraitsofinterest (Figure1).
while taking advantage of the high allele whole genome sequencing can improve Eco-TILLING is a method that uses
richness, high mapping resolution, and the power of BSA and are being widely TILLING techniques to identify natural
high statistical power of association map- usedinmanyplantspeciestoday[30–37]. mutations in individuals [40]. TILLING
ping [20,21]. The NAM approach is MutMapisamethodbasedonWGRSof populations have been developed for
similar in principle to the use of multi- pooledDNAsamplesfromthephenotypic several crop plants, such as rice [41,42],
parent advanced generation inter-cross extremes of a segregating population wheat [43,44], sorghum [45], oat [46],
(MAGIC; Box 1) populations, which are derived from a cross between a mutant Brassica [47], chickpea and pearl millet
used to shuffle the genetic background of interest and the progenitor wild type (http://www.icrisat.org/bt-gene-discovery.
among a set of diverse parental lines and line. Abe and colleagues [30] utilized this htm), andused to identifyuseful alleles.To
increaserecombination,andconsequently strategy to identify causal SNPs in a gene identify rare mutations in rice and wheat,
the mapping resolution of QTL. Both (OsCAO1)forthepalegreenleafmutantin Tsai and colleagues [48] developed a new
typesofpopulationhavebeensuccessfully rice, and results were validated transgeni- approach called ‘‘TILLING-by-Sequenc-
developed and used to identify QTLs for cally. In a related study, MutMap-Gap, ing,’’ in which target genes were amplified
a number of traits in diverse crop species was used to identify a major gene from pooled templates representing 768
[22–27]. responsible forblast resistance, Pii,inrice individuals per experiment and then
PLOSBiology | www.plosbiology.org 4 June2014 | Volume 12 | Issue 6 | e1001883
sequencedusingNGStechnology,leadingto information in a timely way. The density Overcoming Linkage Drag
the discovery of novel rare mutants. Eco- of SNPs on an array is typically less than Breeders using MAS to introgress a
TILLING, has also been used to identify the SNPs assayed by NGS, but the favorable QTL allele from a wild or
novel variants of flowering related genes in selection of array-based SNPs can be unadapted donor parent into an elite,
sugarbeetaccessions[49],variantsforafatty optimizedforparticularbreeding applica- adaptedlineoftenencountertheproblemof
aciddesaturasegeneinanolive(Oleaeuropaea) tions. The reliability, turn-around-time, linkage drag. The transfer of a large QTL
collection[50],areducedheight(Rht)-1gene easeofinformationretrieval,aswellasthe region from a donor plant into a divergent
inwheat[51],adroughttolerancetranscrip- costofagenotypingassayandrelationship breeding line may introduce undesirable
tionfactorinrice[52],andgenesassociated to the service provider(s) are critical to a phenotypic effects owing to the presence of
with salinity stress tolerance in rice [53]. In breeder where decisions about which linkedgenesintheintrogressedQTLregion.
thefuture, weenvision that the useof Eco- individuals to advance to the next gener- Theselinkedgenesoftenhavenothingtodo
TILLINGandrelatedapproacheswillenrich ation rely on timely access to genotypic with the target trait but can make the new
the gene pools of many crop species by information. line unacceptable. NGS is vital for quickly
identifying useful variants that have only SNP-genotyping arrays, constructed identifying the individuals that carry critical
rarely been used in modern crop improve- from NGS datasets, have been developed recombination breakpoints that break the
mentprograms. and used to augment breeding efficiency linkagedrag.Inoneexample,NGSwasused
in several crops, including maize (60 K toidentifytherecombinantstobreaklinkage
Marker-Assisted Selection as a SNPs [69]), rice (44 K SNPs [14]; 6 K between a favorable allele conferring rice
Breeding Practice SNPs [70]; 384 SNPs [71]), chickpea blastdiseaseresistanceandadeleteriousgene
(2,068 SNPs [72]; 96 SNPs [73]), pigeon- affecting grain quality [87] and in another
Theoldestandmostwidelyusedtypeof pea(1,616SNPs[74];48SNPs[73]),and betweenafavorablealleleconferringdrought
genomics-assisted breeding is MAS.Identi-
groundnut (96SNPs [75]). toleranceinriceandanunfavorableallelefor
fyingageneorgenomicregion(QTL)thatis tallplantstature[66].Becausethelandraces
responsible for a trait of interest isonly an
Use of MAS in Interspecific thatservedasthebreedingdonorscarriedthe
initial step in using MAS in a crop
favorable and the unfavorable alleles in
Populations
improvement program. Once found, the
coupling, it took a concentrated effort and
nextstepistointrogresstheidentifiedgene MAS and marker-assisted back-cross-
deepsequencingwithinthetargetregionona
or genomic region(s) into an adapted crop ing (MABC) have been valuable for
large segregating population to identify a
line(s)usingmarkerstoidentifytheoffspring harnessing agriculturally valuable genes
recombinantindividualinwhichthelinkage
thatcarrythemostfavorablecombinationof and QTLs from wild or unadapted
hadbeenbroken.Insuchcases,ifthecausal
alleles. Both genotyping arrays and NGS genetic resources, particularly where the
gene(s) and/or functional polymorphism(s)
approaches have been successfully used to phenotype of a wild accession offers little
for the favorable and/or the deleterious
introgress target loci into elite varieties to ornoinsightaboutitspotentialvalueasa
trait(s) are known, the breeder can use that
improveperformance[54]. breeding parent [76,77]. Prior to the
informationtoguidetheselectionofindivid-
Simply inherited traits commonly tar- adventofDNAmarkers,itwasextremely uals that carry key recombination events to
geted for MAS include disease and insect cumbersome and inefficient to try to minimizetheeffectoflinkagedrag.Oncea
resistance, abiotic stress tolerance, and select for recombinant offspring from recombinant individual is identified, it be-
grain quality. MAS is particularly valu- interspecific populations that carried the comes immediately useful as a donor in
able for introgressing recessive alleles, favorable wild allele(s) of interest because breeding and may serve to introduce new
pyramiding genes with overlapping phe- many unfavorable alleles that were also geneticvariationintoabreedingpipeline.In
notypic effects, for traits that are not inherited from the wild donor typically thecaseofFukuokaandcolleagues[87],the
expressed until late in plant development masked the favorable phenotype. Geno- gene conferring blast disease resistance had
andtraitsthataredifficultorexpensiveto mics-assisted breeding has dramatically notbeenusedinbreedingbecauseprevious
phenotype [55]. MAS is used for both shiftedthewaybreeders areableto work attemptstointrogresstheresistancehadbeen
inbred and hybrid variety development with unadapted genetic resources. Exam- plaguedbythepoorgrainqualitytrait.Thus,
[56–63]. In some crops, traits that are ples can be found in wheat [78–80], NGScanbeextremelyhelpfultoidentifythe
expected to have complex inheritance tomato [81], rice [13,59,64,82,83], maize recombinants in breaking linkage drag and
have been associated with major-effect [57,84], barley [58], pigeon pea (http:// liberatingnewformsofgeneticvariationfor
QTLs that can be immediately targeted goo.gl/zrdICo),chickpea[85],andfoxtail useinbreeding.
forMAS.Examplesinclude‘‘grainyield’’ millet [19].
and ‘‘yield under drought’’ in rice [64– NGS technology is vitally important as
Genomic Selection as a
66], biomass accumulation in Triticale a tool for characterizing plant genetic
Breeding Practice
[67], and drought tolerance in chickpea resources globally. The vast majority of
[68]. accessionsfoundintheworld’sgenebanks As we have already discussed, GS does
arecurrentlypoorlycharacterizedandasa not depend on prior knowledge about a
Choice of Genotyping Platform result, rarely used. An international effort few, large-effect genes or QTL, and was
Fixed SNP genotyping arrays may be is underway to take advantage of the low not feasible prior to the development of
preferredtoNGStechnologiesiftheycan cost and high throughput of NGS, in genotyping technologies that provided
achieve higherthroughputat alowercost combination with appropriate databasing high throughput, low-cost, genome-wide
per sample, or if they are specially of information, large-scale phenotyping, markercoverage.GSwasoriginallydevel-
designed to target high value functional and population development, to help oped for use in livestock breeding [6,88],
alleles for traits of interest if a breeding characterize gene bank materials and and is currently being applied to a wide
programlackstheinformaticssupportthat providearationalbasisfortheirutilization range of crops [89–95]. The efficiency
would be required to decipher NGS [86]. withwhichsuperiorlinescanbepredicted
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through GS depends upon the genetic crop production in the face of climate phenotyping methodologies that will un-
relationship between the training popula- change and increasing human demand. doubtedly involve digital image capture,
tion and the breeding population, the remote sensing, and many new forms of
numberofgenerationsthatseparatethem, Perspectives informationandcommunicationtechnolo-
thetypeandnumberofmarkersused,the gies. To cope with the deluge of data
The development of improved breed-
accuracy of the phenotyping, and the generatedfromNGSandmoreautomated
inglinesforcommercialcropcultivation
heritability of the trait(s) [90,91,96]. phenotyping platforms, we need efficient
hastraditionallybeena timeconsuming
Where there is significant population dataanalysisanddecisionsupporttoolsto
andexpensivetask.Withthedeployment
structure within breeding populations of helpbreedersutilizethatdatainrealtime
of genomics-assisted breeding, the gen-
wheat and maize, pedigree information toselectsuperiorlinesforcrossing.Wealso
eration of such lines is destined to
has been found to account for a large need a massive reorganization of the way
become easier and faster, if also more
proportion of the prediction accuracy youngplantscientistsaretrained[97],the
expensiveintheshortterm.Tomeetthe
[89]. Genome-wide marker information waybreedingprogramsareorganized,and
demands of the human population and
canincreasethepredictionaccuracyofthe dataareshared.Wemustintegratetraining
increasing volatility of the climate, we
models, particularly where the trait is across scientific fields, including genetics,
must accelerate the pace of our current
governed by many genes of small effect plant breeding, computer science, mathe-
breeding practices and apply genomics-
thatarewidelydistributedthroughoutthe matics, engineering, biometrics and bioin-
basedselectionapproaches.
genome. However, in many inbreeding formatics, and to evolve new forms of
Selection based on NGS allows marker
species and wherever a trait of interest is communicationandprofessionalorganiza-
discovery, marker validation, and geno-
governed by a few genes of large effect, tion,sothatgenomics-assistedbreedingcan
typingitselftooccursimultaneously,aswe
specificinformationaboutSNPswithinor achieveitspotential.
have discussed (Figure 1). The trend for
near the target genes can enhance model Finally, we need to provide suitable
sequence-based genotyping to replace the
accuracy and thevalueof GS. cultivars to farmers in a timely manner.
use of fixed marker arrays seems realistic,
WhileNGS-basedapproachesarehelping
particularly as the cost of sequencing
Combining Marker-Assisted and improve the efficiency of breeding crops
continuestofall,andisalreadyhappening
Genomic Selection adapted to specific environments, we
for diploid crops with relatively small
simultaneously need to provide farmers
Despite the obvious differences be- genome sizes (#1 GB), such as rice
with information about the availability of
tween these two approaches to geno- (389 Mb), chickpea (738 Mb), sorghum
new varieties about crop management
mics-assisted breeding, there is much to (818 Mb), and pigeonpea (833 Mb). For
systems and marketing opportunities.
begainedbycombiningthestrengthsof polyploids and crops with larger genomes
It is critically important that the efforts
both approaches in the future. As (e.g., bread wheat, a hexaploid with a
of the plant breeding community be
information becomes increasingly avail- 17Gb genome), fixed SNP arrays will
fullyintegratedintotheentirevaluechain
able about which genes and alleles continue to be useful, particularly where
so they can be rapidly and effectively
contribute to phenotypic variation in theyassaygene-specificorgenome-specific
deployed in farmers’ fields, and so the
important breeding populations, greater markers that facilitate accurate mapping.
fruits of genomics can ultimately reach
weight can be given to specific poly- Nonetheless,itislikelythatNGS-generat-
the people they are intended to
morphismsthatmapwithinorverynear eddata,includingthemanyformsofGBS,
benefit.
to major-effect genes in GS models, willbecome thewayof thefuture.
which otherwise do a good job of Currently,phenotypingisamajoroper-
Acknowledgments
tracking genes of small effect. Thus ationalbottleneckthatlimitsthepowerand
bothapproachesarecriticalastheplant resolutionofmanykindsofgeneticanalysis.
RKV thanks Vikas K. Singh, Himabindu
breeding community seeks to enhance We recognize the urgent need for high- Kudapa,andRachitSaxenafortheirinvaluable
the productivity and sustainability of throughput, cost-effective, and precise helpwhilepreparingthemanuscript.
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