Towards markers assisted selection

Review

Towards marker-assisted selection in pulses: a review

J itendra KUMAR, Arbind K. CHOUDHARY, Ramesh K. SOLANKI and Aditya PRATAP

Division of Crop Improvement, Indian Institute of Pulses Research, Kanpur 208024, India, E-mail: [email protected]

With 2 figures and 2 tables

Received July 24, 2010/Accepted December 29, 2010Communicated by R. Varshney

Abstract

Pulses are important sources of proteins in vegetarian diet. However,

genetic improvement in production and productivity of pulse crops has

been very slow owing to several constraints. The present view of

researchers is that the effectiveness and efficiency of conventional

breeding can be significantly improved by using molecular markers.

Nowadays, molecular markers are routinely utilized worldwide in all

major crops as a component of breeding. The pace of development of

molecular markers and other genomic sources has been accelerated in

chickpea, pigeon pea and some other pulses, and marker–trait

associations have been established for a number of important

agronomic traits. The efforts are underway to use high-throughput

genotyping platforms besides developing more genomic resources in

other pulses. So far, progress in the use of marker-assisted selection as

a part of pulse breeding programmes has been very slow and limited to

few pulse crops such as chickpea and common bean. In this article, we

have reviewed the progress made, limitations encountered and future

possibilities for the application of marker-assisted selection in the

genetic improvement of pulse crops.

Key words: legumes — molecular markers — mapping —molecular breeding

Pulses are leguminous crops harvested almost exclusively fordry grains. The Food and Agriculture Organization (FAO)recognizes 10 primary and five minor pulses, which are

cultivated worldwide over 105 countries. From the standpointof production, dry beans (19.7 mt), field pea (10.4 mt),chickpea (9.7 mt), cowpea (5.7 mt), lentil (3.6 mt) and pigeon

pea (3.5 mt) are the most important (FAO, 2010). During thelast five decades, the global pulse production has witnessedonly an average marginal annual production increase of 0.77%

with a wide fluctuation from 40.78 to 55.85 million tons (FAO,2010). India, which is the largest producer and consumer ofpulses in the world, recorded a lower than average annualproductivity growth rate of only 0.24% (AICRP on MUL-

LaRP, 2009–2010). Slow pulse production growth (from 12.70million tones in 1960–1961 to 14.6 million tones in 2008–2009)leading to higher price of pulses, rising population and change

in food habits (i.e., use of alternative protein-rich foodproducts such as fish, meet and eggs) have substantiallyreduced the per capita consumption of pulses, especially in

predominantly vegetarian countries (Vepa 2003). It reducedconsumption from 63.0 g/day in 1961 to 35.3 g in 2009, andit is expected to decline up to 27.3 g in 2010 (ASSOCHAM

and Agri Watch; http://www.commodityonline.com/news/Indias-Green-Revolution-that-bypassed-pulses-15163-3-1.html).

In the future, this reduction trend is expected to continuebecause of the diversion of cultivated land to other uses, rising

temperature, frequent drought, diversification of food optionsand mainly declining productivity.Despite the systematic and continuous breeding efforts

through conventional methods, substantial genetic gain inpulses production and productivity could not be achieved. Forexample, in India alone, more than 550 improved varieties ofpulses have been released but production increased only

marginally from 12.70 million tones in 1960–1961 to 14.6million tones in 2008–2009 and productivity increased from539 kg/ha in 1960–1961 to 659 kg/ha in 2008–2009 (AICRP on

MULLaRP, 2009–2010). The major yield constraint in pulsesis high genotype · environment (G · E) interactions on theexpression of important quantitative traits leading to slow gain

in genetic improvement and yield stability of pulses (Kumarand Ali 2006), besides severe losses caused by susceptibility ofpulses to biotic and abiotic stresses. These issues require an

immediate attention, and overall, a paradigm shift is needed inthe breeding strategies to strengthen our traditional cropimprovement programmes. One way is to utilize genomicstools in conventional breeding programmes involving mole-

cular marker technology in selection of desirable genotypes orgrowing of transgenic crops. The use of transgenic crops isespecially required for those traits that are not easy to improve

genetically through conventional approaches because of thelack of satisfactory sources of desirable gene(s) in crossablegene pools. However, the ongoing debate on bio-safety and

ethical issues involving use of transgenic crops for commercialcultivation suggests that molecular marker–aided conventionalmethods of breeding may be the main short-term option for

increasing productivity. For this purpose, available high-yielding diverse lines need to be used as base material forincorporating some useful attributes/genes from otherwiseunadapted cultivars, lines or wild relatives. The present view of

researchers is that the efficiency and effectiveness of conven-tional breeding can be significantly improved by using molec-ular markers. Nowadays, deployment of molecular markers is

not a dream to a conventional plant breeder as it is routinelyused worldwide in all major cereal crops as a component ofbreeding because of the availability of a large amount of basic

genetic and genomic resources (Gupta et al. 2010). Thissuccess of marker-assisted breeding in a major cereal crop(i.e. wheat) has been discussed recently (Gupta et al. 2010). Inthe past few years, major emphasis has also been given to

develop similar kind of genomic resources for improving

Plant Breeding 130, 297—313 (2011) doi:10.1111/j.1439-0523.2011.01851.x� 2011 Blackwell Verlag GmbH

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productivity of pulse crops (Varshney et al. 2009, 2010a, Satoet al. 2010). Use of molecular marker technology can give realoutput in terms of high-yielding genotypes in pulses becausehigh phenotypic instability for important traits makes them

difficult for improvement through conventional breedingmethods. The progress made in using marker-assisted selection(MAS) in pulses has been highlighted in a few recent reviews

emphasizing on mapping genes controlling agronomicallyimportant traits and molecular breeding of pulses in general(Liu et al. 2007, Varshney et al. 2010b) and faba bean in

particular (Torres et al. 2010). In the present review, we havediscussed why molecular marker technology, in spite ofestablished marker–trait association, has not hitherto been apart of conventional pulse breeding programmes to bring real

changes in the genetic improvement in pulse crops in compar-ison with cereals. We have also discussed future possibilities todeploy molecular markers successfully as a component of

conventional breeding programmes.

Major Constraints in Genetic Improvement

During the past decades, concerted efforts were made toimprove the genetic potential of genotypes for increasing the

yield of pulses through conventional breeding approaches. Butthe pace of genetic gain has been very low translating in slowgrowth in production and productivity (Ali et al. 2003, Ali and

Kumar 2005, Kumar and Ali 2006). The major challengesmodern plant breeders face are discussed below briefly.

Genotype and environment interactions

The quantitative traits of economical importance in pulsesdisplay phenotypic instability resulting in variation in yield

potential owing to the influence of environments (70–80%)and genotype · environment (G · E) interactions (17–27%)(Kumar and Ali 2006). The contribution of the genotypic

effects is only 1.5–7% (Imrie and Shanmugasundaram 1987,Malhotra and Singh 1991, Singh and Kumar 1994, Kumaret al. 1996). Therefore, the role of the environment is of major

concern because of the slowdown in the progress in geneticimprovement and yield stability in pulses (Kumar and Ali2006).

Multiple stresses

Pulses encounter multiple stresses including wilt, ascochyta

blight (lentil, chickpea), rust (pea and lentil), powdery mildew(pea), terminal drought, heat and salinity (chickpea, lentil,pigeon pea), and water-logging (pigeon pea) during their life

cycle. To ensure stable productivity, it is necessary to developvarieties resistant to more than one stress. However, geneticimprovement for a single biotic stress using single gene–basedresistance does not result in permanent gains in productivity

because of emergence of increasingly more virulent races/biotypes in nature (Ali et al. 2003). The introgression ofseveral genes to develop superior genotypes is expected to

perform better against incidence of multiple stresses and/ordiseases. Sometimes, negative linkage between the genescontrolling resistance to different stresses and agronomic and

quality parameters makes it problematic to combine them inthe background of high-yielding genotypes using conventionalmethods of breeding (Chaturvedi et al. 1998). Pyramiding

multiple genes is challenging and time-consuming; modern

biotechnological tools facilitate and guide the introgression ofmultiple resistant genes and also assist in minimizing linkagedrag (Shi et al. 2009, Li et al. 2010).

Dissection of abiotic stresses into its components

Several abiotic stresses such as terminal drought, water-

logging, extreme temperatures, salinity, alkalinity and acidityare encountered during the crop growth period. For improvingbreeding efficiency, there is need to identify the specific

physiological, biochemical and molecular characteristics thatmay improve yields under such stresses. So far, these stresseshave not been properly dissected into its different componentsat physiological, chemical and molecular genetics levels, and

only limited information is available in pulse crops (Stoddardet al. 2006), placing constraints in designing an appropriatebreeding methodology to facilitate the genetic improvements.

Therefore, detailed understanding of the mechanisms under-lying these traits is needed, which could lead to novel andsuperior mitigating strategies.

Limited screening methods for precise phenotyping of target

traits

Several traits of economic importance such as root length andbiomass, wilt resistance and so on are difficult to screenbecause of imprecise measurements or visual observations

owing to lack of a simple, easy, precise, repeatable and large-scale screening technique (Singh et al. 1997, Infantino et al.2006, Agbicodo et al. 2009). Thus, the lack of precise

phenotyping makes us incompetent to select for desirablegenotype having genetic potential for yield and quality traits.However, phenotypic screening for adaption to drought is now

available in chickpea and pigeon pea (Upadhyaya et al. 2010).This has led to the use of genomic and physiologicalapproaches for root trait breeding to improve drought

tolerance in chickpea (Varshney et al. 2010c).

Limited genetic diversity in the primary gene pool

For pulse crops, a large collection of genetic resources (bothcultivated and wild relatives) are conserved globally in genebanks. For utilization of these genetic resources, studies have

been conducted to perform analysis of genetic variabilityamong a large collection of germplasm accessions on the basisof phenotypic characters. For example, in chickpea and pigeon

pea, a range of variability for different qualitative andquantitative traits have been documented, but the blueprintof diversity varies in different regions (Upadhyaya et al. 2001).In lentil, 8550 accessions have been evaluated for 20 key traits

at ICRADA, and a �Lentil Germplasm Catalog� of 4000accessions was published to enhance their potential forutilization in lentil breeding programme (Erskine and

Witcombe 1984, Furman et al. 2009). Morphological charac-terizations of accessions belonging to cultivated species revealonly ample genetic variability for a trait rather than genetic

diversity, which is estimated by taking all economically keytraits together. For example, using 12 key descriptors tocharacterize 1770 lentil accessions of Indian origin confirmed

genetic variability ranging from 20% to 30% (Solanki et al.2010). Notwithstanding these, in pulses, only few genotypeswith high degree of relatedness have been repeatedly utilized asparents in crossing programmes for the development of new

298 J . Kum ar , A . K . Choud hary , R . K . Solank i et al.

cultivars, leading to narrowing down the genetic base ofcultivated germplasm of pulses (Kumar et al. 2004). Forexample, 41% (35) and 34% (16) of the released cultivars inchickpea and in pigeon pea, respectively, were developed in

India involving one or two genotypes as one of the ancestors intheir pedigree (Kumar et al. 2004). Moreover, pedigree anal-ysis of released varieties revealed that only the top three to ten

ancestors contributed 30–79% to the genetic base in lentil,urdbean, mungbean and field pea (Dixit and Katiyar 2004,2006, Katiyar et al. 2007, 2008). Thus, it is general perception

among the scientists that current genetic improvement meth-ods lead to narrowing down the cultivated gene pool becauseof limited use of diversity. Contrary to this, the trend of geneticdiversity estimated recently using molecular markers in differ-

ent crops including pea as the representative of pulse cropsdemonstrates that no gain or reduction in genetic diversity hasoccurred since the last five decades (van de Wouw et al. 2010).

In pulse crops, the level of genetic diversity has also beenstudied among a set of cultivated and wild accessions using themolecular markers that varies with crop species, type of

molecular marker system and genetic materials (Table 1). Therange of diversity has been found to be narrow among theaccessions of both the cultivated and wild relatives of pigeon

pea and lentil compared to other pulses. In pigeon pea, geneticdiversity has been studied at the molecular level throughdiversity arrays technology (DArT) among 96 accessions

representing nearly 20 species of Cajanus including thecultivated one. This study revealed that most of the diversitywas among the wild relatives of pigeon pea or between the wildand the cultivated species, but not among the cultivated

accessions. Such narrow genetic base is likely to represent aserious impediment to breeding progress in pigeon pea (Yanget al. 2006). DArT markers have been considered very useful

for MAS because of low cost (US$ 0.1 per data point), ultra-high-throughput and extensive genome-wide coverage (Guptaet al. 2010). Keeping these facts in view, efforts have been

made to generate more such markers in pigeon pea, which canbe useful in molecular breeding programmes of this crop(Varshney et al. 2010a). Amplified fragment length polymor-phism (AFLP) markers are very effective in detecting variation

with respect to other markers in Vigna (Seehalak et al. 2006).These markers have also been used in other pulse crops such aslentil, cowpea, pea, pigeon pea and broad bean (Table 1).

Contrasting views have been reported on the detection ofvariation by using these markers. For example, use of randomamplified polymorphic DNA (RAPD) and microsatellites

indicates a low level of variation in cowpea (Pasquet 2000,Li et al. 2001, Tosti and Negri 2002), but high level ofpolymorphism has been observed in lentil, soybean and

cowpea using AFLP technique (Maughan et al. 1996, Sharmaet al. 1996, Coulibaly et al. 2002). Although AFLP markersdetect sufficient polymorphism among genotypes, level of

Table 1: Genetic diversity analysis using molecular markers in pulse crops

CropType of genetic

materialNumber ofaccessions Markers

Geneticidentity/similarity

value1Coefficient

used References

Aduzki bean Germplasm accessions 75 SSR 0.07–0.85 J1 Ning et al. (2009)Braod bean Cultivated 79 AFLP 0.71–0.79 J Zeid et al. (2003)Chickpea Cultivated, wild 1, 8 RAPD 0.66–0.89 G Ahmad (1998)

Cultivated, wild 35, 1 SSR 0.14–0.63 J Sethy et al. (2006)Cowpea Cultivated 90 SSR 0.30–0.93 J Li et al. (2001)

Cultivated, wildand weedy,perennial

47, 52, 18 AFLP 0.89–0.92 N Coulibaly et al. (2002)

Landraces 38 RAPD 0.09–0.59 J Nkongolo (2003)Faba bean Cultivated 79 AFLP 0.55–0.90 J Zeid et al. (2003)

Landraces 20 ISSR, SSR 0.03–0.19 N Terzopoulos and Bebeli (2008)Lentil Landraces 11 ISSR 0.56–0.86 J Sonnante and Pignone (2007)

Cultivated 38 ISSR, AFLP 0.69–0.89 J Toklu et al. (2009)Cultivated 39 SSR 0.30–1.00 J1 Babayeva et al. (2009)

Cultivated, wild 13, 9 ISSR, RAPD 0.15–0.93 J Duran and Perez de la Vega (2004)Cultivated, wild 57, 52 SSR 0.03–0.93 N Hamwieh et al. (2009)

Lima bean Wild 11 SSR 0.20–0.93 N Martınez-Castillo et al. (2006)Mungbean Cultivated 32 RAPD 0.65–0.92 J Lakhanpaul et al. (2000)

Cultivated, wild 12, 11 RAPD 0.30–0.98 J Kaga et al. (1996)Cultivated, wild

and weedy415, 200 SSR 0.32–0.78 N Sangir et al. (2007)

Cultivated 27 AFLP 0.68–0.92 J Bhat et al. (2005)Pea Cultivated, wild 15, 5 STMS 0.53–0.99 S Ford et al. (2002)

Cultivated 21 RAPD, AFLP 0.80–0.94 NL Simioniuc et al. (2002)Cultivated, wild 31, 11 RAPD 0.49–0.98 J Samec and Nasinec (1996)

Pigeon pea Cultivated, wild 20, 2 AFLP 0.82–1.00 J Panguluri et al. (2006)Cultivated, wild 15, 9 SSR 0.34–0.98 J Odeny et al. (2007)Cultivated, wild 280, 43 DArT 0.67–1.00 NL Yang et al. (2006)

Rice bean Land races 10 RAPD, ISSR 0.56–0.78 J Muthusamy et al. (2008)Urdbean Cultivated 18 RAPD, ISSR 0.68–0.87 J Souframanien and Gopalkrishna

(2004)

1Genetic similarity value is presented as reported or it is calculated according to the formula (1 ) genetic dissimilarity), if coefficient value wasgiven in terms of genetic dissimilarity or genetic heterogeneity.N, Nei�s coefficient (Nei 1973); J, Jaccard�s coefficient (Jaccard 1908); S, Simple matching coefficient (Legendre and Legendre 1983); NL, Nei andLi�s coefficient (Nei and Li 1979); G, Gelfand�s coefficient (Angus et al. 1988); ISSR, inter-simple sequence repeat; STMS, sequence-taggedmicrosatellite site; DArT, diversity arrays technology; RAPD, random amplified polymorphic DNA; AFLP, amplified fragment lengthpolymorphism.

Towards marker-assisted selection in pulses 299

genetic diversity reported was still narrow in several pulsecrops (see Table 1). Like other crops, simple sequence repeat(SSR) markers have shown higher range of diversity thanobserved earlier in pulses (Table 1) and therefore their

development and use in each pulse crop will certainly boostthe efficiency of breeding programmes. However, difference inthe detection of DNA polymorphism by different marker

systems may be attributed to different mechanisms involved ingenerating the various molecular markers and low level ofdiversity may be attributed to the extensive use of dominant

markers (Powell et al. 1996). Therefore, more than one type ofmarker systems for diversity analysis must be encouraged asthese could estimate higher range of molecular diversityobserved earlier in several reports (Millan et al. 2010). In fact,

most of the studies (Table 1) have focused to characterizelimited accessions using molecular markers, although vastgermplasm collections are available to plant breeders in

various pulses (Duc et al. 2010, Glaszmann et al. 2010). Forassessing genetic diversity contained in the large germplasmcollections, molecular markers have clarified the structure of

genetic diversity in a broad range of crops and helped todevelop the accessible core reference sets. According toGlaszmann et al. (2010), a crop core reference set is a set of

genetic stocks that are representative of the genetic resourcesof the crop and are used by the scientific community asreference for an integrated characterization of its biologicaldiversity. Therefore, efforts have been made to develop

reference sets in pulses like chickpea (Upadhyaya et al. 2008)and similar efforts are underway in lentil and faba bean underGeneration Challenge Program (GCP). In pea, a Chinese core

collection has also been made using SSR markers after analysisof global diversity (Zong et al. 2009).The pod-boring insect (Helicoverpa armigera) is perhaps the

most persistent and solemn constraint to chickpea and pigeonpea productivity. In these crops, screening of more than 5000germplasm accessions revealed that there is no resistant source

against this insect pest in germplasm (Kumar et al. 2004).Although few resistance sources have been reported in the pastin cultivated gene pool, they show either inconsistency or lowlevels of resistance leading to their limited exploitation in

breeding programmes so far (Lateef 1992). Therefore, there isneed to identify stable genetic resistance sources in thecrossable gene pool for pod borers to facilitate conventional

genetic improvement programmes. Use of unexplored genes inexisting gene pools and use of the wild relatives as a richreservoir of resistance genes against abiotic and biotic stresses

merit special attention to broaden the genetic base of breedingpool (Clement et al. 2009).

Development and Use of Breeder-Friendly Markers

The success of deployment of the marker technology to deal

with the above-mentioned constraints depends on the avail-ability of suitable marker systems in pulses. During the lastthree decades, DNA-based marker systems such as restrictionfragment length polymorphisms (RFLPs), RAPD, SSRs or

microsatellites, AFLPs, single-nucleotide polymorphism(SNP), and DArT have become available. Among these markersystems, RAPD, RFLP and AFLP are commonly employed for

marker–trait association and diversity analysis in pulses, buttheir use for MAS is not preferred by conventional plantbreeders because of the poor reproducibility, difficulty in

handling, requirement of highly skilled person and use of

radioactive elements for generating these markers (Gupta et al.2010). Only PCR (polymerase chain reaction)-based SSR andSNP markers have been preferred by breeders because thesemarkers can easily be employed in genotyping of large

segregating populations in a cost-effective manner and withminimum infrastructure facilities. Also, high reproducibilityand user-friendliness make them preferential to traditional

plant breeders for MAS (Gupta et al. 2010). In many othercrops, these have been extensively utilized (Kumar et al. 2010),but their use is still limited in pulses like chickpea and pigeon

pea (Varshney et al. 2009, Saxena et al. 2010). Therefore, inrecent years, attention has been paid to develop more markersin pulses considered as orphan legumes (Hamwieh et al. 2009,Varshney et al. 2009). To reduce the cost of development of

these markers, close phylogenetic similarity has encouragedresearchers to transfer SSR markers from one pulse crop to theother (Datta et al. 2010, Reddy et al. 2010). However, the fast-

growing advances in genomic technology, especially in the next-generation DNA sequencing and high-throughput genotyping,have made available a large amount of SSR and SNP types of

markers in a cost-effective manner and in a short period of timein chickpea and pigeon pea (Varshney et al. 2009), mungbean(Tangphatsornruang et al. 2009) and cowpea (Muchero et al.

2009). This strongly suggests the rapid development of a hugeamount of polymorphic markers in several pulses in the nearfuture. However, it is not possible for plant breeders to use thesemarkers routinely in conventional plant breeding programmes

without the support of skilled personnel and good laboratoryfacilities.Over the last few years, functionally characterized genes,

ESTs and coding genome sequences have been made availableto build up molecular markers like SNP, SSR or COS(conserved orthologous set) (Varshney et al. 2005). These

markers are often called perfect or functional markers and aredeveloped from putative coding sequences having knownfunction and consequently have complete association with the

quantitative trait loci (QTL) or gene. Nowadays, functionalgenomic resources (ESTs and other gene sequences) areavailable in related model legumes as well as pulse crops likechickpea, pigeon pea, cowpea, common bean and soybean

(Choi et al. 2004, Young and Udvardi 2009, Kumar et al.2009a), which certainly will boost up development of perfectmarkers in pulses. For example, use of the sequences of

resistance gene analogues (RGAs) or resistance gene candidates(RGACs) has been transformed into molecular markersincluding RGA polymorphism (Mutlu et al. 2006), NBS

profiling (van der Linden et al. 2004), CAPS (Gutierrez et al.2006, Muchero et al. 2010a) and derived CAPS (dCAPS)(Palomino et al. 2009, Torres et al. 2010). The RGAs belongingto NBS gene families were recently isolated and characterized in

faba bean and chickpea (Palomino et al. 2006). The dCAPs andCAPs have been developed from 10 RGA classes and used toexplore the presence of SNPs to map them in the available

mapping populations of faba bean, pea and chickpeas (Palo-mino et al. 2009, Torres et al. 2010). Two genes (PvP5CS andP5CS) controlling D1-pyrroline-5-carboxylate synthase in com-

mon bean and several genes controlling flowering time in modellegume species were identified and cloned (Hecht et al. 2005,Chen et al. 2009, 2010). Several other structural genes involved

in phytic acid biosynthesis have also been characterized incommon bean (Fileppi et al. 2009). Nucleotide sequences ofthese genes are useful to provide opportunity to develop gene-based functional markers and finding out their association with


agronomically important traits through genetic mapping, asthese markers have been developed and mapped in commonbean (Hanai et al. 2010), cowpea (Muchero et al. 2009, 2010a)and chickpea (Nayak et al. 2010, Spurthi et al. 2010).

Marker–Trait Association

To use MAS in conventional breeding programmes, markerstightly linked to gene(s) controlling target trait must beidentified first. Therefore, efforts have been made to find out

the genetic distance between molecular markers and the genescontrolling qualitative and quantitative traits of interestthrough different approaches.

Simple vs. complex traits

Inheritance studies conducted in major pulses suggest thatgenetic variation for a trait of importance could be eitherqualitative or quantitative in nature. The traits like seedcotyledon colour, some disease resistance, seed coat colour,

flower colour and double poddedness have been reported tohave simple inheritance (Gill and Cubero 1993, Sandhu et al.1993, Ghatge 1994, Mathur 1998, Kumar et al. 2003, Singh

and Raina 2005, Sharma 2009, Barilli et al. 2009, Halila et al.2010). However, many traits contributing to yield, yieldcomponents (seed number, seed weight), quality and resistance

to biotic and abiotic stresses are quantitative in nature. In thepast years, classical biometrical techniques were used to studythe inheritance of such traits. But through biometricalapproaches, individual genes could not be identified and the

contribution of each individual gene to the trait could not beworked out. Sax (1923) for the first time tagged individual locuscontributing to a quantitative trait (seeds size) with the help of

monogenic trait (seed coat colour) in bean. But QTL analysisusing polygenic traits could not become popular because oflimited availability of morphological markers. After the advent

of DNA-based molecular markers (which are inherited as themajor genes), it became possible to use them routinely to detectand locate the positions of QTL controlling variation for

quantitative traits. The genetic dissection of complex polyge-netic traits through QTL analysis leads to the identification ofmolecular markers linked with genomic regions having major(main effect QTL or M-QTL) or interactive (epistatic effect

QTL or E-QTL) or interactive with environments (QE-QTL)on the expression of a trait of interest. In the case of pulseswhere G · E interactions for a quantitative trait are promi-

nent, QTLs can be categorized into constitutive and adaptiveQTLs. Constitutive QTLs show the stability of their effectsacross environmental conditions, whereas adaptive QTLs are

expressed in specific environmental conditions (Collins et al.2008). Thus, QTLs that control heritable variability of thetraits and have positive direct impact on target traits can bemanipulated precisely and can be pyramided towards the

development of improved plant types through MAS. Further-more, this may also result in greatest potential genetic gainseven for the traits having low heritability.

Genetic mapping of traits

Genetic linkage mapping, QTL mapping and associationmapping, and comparative genomics are commonly usedapproaches for genetic mapping in crop plants. In pulses,

several genes/QTL controlling the target traits (qualitative and

quantitative) have been mapped/tagged using molecular mark-ers (Table 2). Different mapping approaches used for marker–trait analysis in pulses are discussed in following text.

Genetic linkage mapping

For this purpose, a biparental mapping population developed

from parents having contrasting traits is required, which maybe an F2, BC (backcross) and DH (doubled haploid) popula-tion. A mapping population consisting of 50–200 individuals isgenotyped with a number of markers polymorphic between

parents (Kumar et al. 2009b). If the target trait is measured asdiscrete trait, marker–trait association can be established onthe basis of expected and observed recombination frequency

using chi-square test, and genetic distance between marker andtrait can be measured with or without preparing the geneticlinkage maps. However, for QTL mapping, construction of

molecular maps is essential. This kind of mapping can beperformed for all kind of traits (i.e., qualitative and quanti-tative traits), which are measured quantitatively. For this

purpose, genotypic data of a mapping population are used toprepare a molecular map using statistical software (e.g. MAP-

MAKER). Then, genotypic data and the phenotypic datarecorded on target traits over the environments are used to

establish marker–trait association using computer software(e.g. QTL CATOGRAPHER, JOINMAP) (see Kumar et al.2009b for details). Both framework and dense molecular maps

comprising more than one type of molecular markers havebeen constructed in the past years in chickpea (Winter et al.2000), cowpea (Kelly et al. 2003), lentil (Hamwieh et al. 2005)

and other pulse crops listed in a recent review (Varshney et al.2010b). However, more recently, such molecular maps havebeen developed in chickpea (Nayak et al. 2010) and commonbean (Hanai et al. 2010). Moreover, a consensus genetic map

consisting of 555 loci involving different markers has also beendeveloped in chickpea from 10 mapping populations (Millanet al. 2010). A genetic map was developed covering 1283.3 cM

and including 146 markers (144 RAPDs and two sequence-tagged sites (STS), and markers distributed in nine linkagegroups have been used to map QTL controlling partial

resistance to rust (Barilli et al. 2010). During the past years,considerable work has been carried out on molecular geneticmapping of oligogenic traits (Varshney et al. 2010b). However,

limited efforts have been made on genetic dissection ofquantitative traits (Table 2). These traits often encounter highlevel of G · E interaction leading to confounding performanceof the genotypes, thereby reducing the progress through

empirical selection (Kumar and Ali 2006). QTL analysisfacilitates to understand networks of genes controlling thephenotypic expression of traits and estimates the magnitude of

each individual QTL for a trait in specific environmentalcondition. Thus, major individual QTL accounting for arelatively larger phenotypic variation (R2 > 20%) in a partic-

ular environment (environment-responsive QTL) can be dis-sected from other poorly or negatively contributing genes/QTL(West et al. 2007, Ikeda et al. 2009). Therefore, QTL analysis

using phenotypic data across the environment helps us identify(i) the inconsistent QTL owing to their weak expression acrossthe environments and (ii) QTL interacting significantly withenvironment in the opposite direction to the QTL with main

effects (Liu et al. 2007). Thus, effects of each individual QTLcan be taken into account during their application in MAS forquantitative traits (Li et al. 2003).


Table 2: Molecular markers linked to desirable genes/QTL for marker-assisted selection in legumes

Crop Trait(s) QTL/genes Type of markersGenetic

distance (cM)1 References

Common bean Resistance to common bacterialblight

QTL RAPD, SCAR,STS, SSR,RFLP

4.0 Taran et al. (2001)

QTL RAPD 13.0 Jung et al. (1997)Resistance to bean commonmosaic virus

I SCAR 1.0–5.0 Melotto et al. (1996)

Resistance to anthracnose Are SCAR 12.0 Adam-Blondon et al. (1994)Resistance to white mould QTL RAPD, AFLP 14.0 Kolkman and Kelly (2003)Resistance to Fusarium wilt PvPR1, PvPR2 RAPD 28.4 Schneider et al. (2001)

Cowpea Resistance to Striga gesneriodes Rsg1 SCAR 4.8 Bouker et al. (2004)Resistance to Thrips tabaci andFrankliniella schultzei

QTL AFLP 22.8, 6.91 Muchero et al. (2010b)

Chickpea Resistance to Ascochyta blight QTLs RAPD 2.3 Millan et al. (2003),Cobos et al. (2005)

Ar19 RAPD 13.7 Rakshit et al. (2003)QTL SSR – Udupa and Baum (2003)QTLAR3 SSR 24.0 Iruela et al. (2007, 2009)QTL SSR – Anbessa et al. (2009)

Resistance to Fusarium wilt QTLFoc02,

QTLFoc5

SSR 10.7 Cobos et al. (2009)

foc1 SSR 6.0 Gowda et al. (2009)foc2 SSR 2.9 Gowda et al. (2009)foc3 SSR 0.9 Gowda et al. (2009)

Seed/pod Spp SSR 1.9 Radhika et al. (2007)Double podding Sfl SSR 3.1 Radhika et al. (2007)Plant growth habit prostrate SSR 14.5 Aryamanesh et al. (2010)

Hg/hg RAPD 10.0 Cobos et al. (2009)Resistance to Ascochyta blight QTL SSR 8.1 Aryamanesh et al. (2010)Days to flowering QTLDF3 SSR 4.0 Cobos et al. (2009)

2 QTL SSR 10.1

17.9

Aryamanesh et al. (2010)

Seed coat thickness QTLTt SSR 10.0 Cobos et al. (2009)Seed size QTLSW1 SSR 10.3 Cobos et al. (2009)

Pea Resistance to powdery mildew er RFLP 11.0 Dirlewanger et al. (1994)Resistance to pea seed bornemosaic virus

Sbm-1 STS 4.0 Frew et al. (2002)

Resistance to Fusarium wilt Fw RFLP 6.0 Dirlewanger et al. (1994)Resistance to pea common mosaicvirus

mo RFLP 15.0 Dirlewanger et al. (1994)

Sbm-1 cDNA-AFLP 0.7 Gao et al. (2004)Resistance to rust Up1 RAPD 13.4 Barilli et al. (2010).

Mungbean Resistance to powdery mildew QTLs RFLP 1.3 Humphry et al. (2003)RAPD, CAP,AFLP

Chen et al. (2007)

Blackgram Resistance to yellow mosaic virus Monogenic STS-RGA 0.0 Basak et al. (2004)Faba bean Resistance to broomrape Oc1, Oc2, Oc3 RAPD 19.3 Roman et al. (2002)

Resistance to rust Uvf-1 RAPD 0.0 cM Avila et al. (2003)Lentil Resistance to Ascochyta blight Ra/2 RAPD, SCAR 14.0

16.0Chowdhery et al. (2001)

Resistance to anthracnose LCt-2 AFLP, RAPD 4.1 Tullu et al. (2003)Resistance to Fusarium wilt fw RAPD 10.8 Eujayl et al. (1998)

AFLP 3.5 Hamwieh et al. (2005)SSR 8.0 Hamwieh et al. (2005)

Cold winter hardiness Frt RAPD 9.1 Eujayl et al. (1999)RAPD, SSR,AFLP

0.3–21.0 Kahraman et al. (2004)

Earliness and plant height QTLs RAPD, SSR,AFLP

17.0 Tullu et al. (2008)

Plant structure, growth habit andyield

QTLs RAPDs, ISSRs,AFLPs, SSRs

6.0–48.0 Fratini et al. (2007)

Blue Lupin Resistance to Diaporthe toxica Phr1 SCAR – Yang et al. (2002)Resistance to Colletotrichumgloeosporiodes

Lanr1 SCAR – Yang et al. (2004)

Lathyrus Resistance to Ascochyta blight QTL RAPD, STMS 18.3 Skiba et al. (2004)Pigeon pea Resistance to Fusarium wilt Gene RAPD – Kotresh et al. (2006)

Plant type 2 Genes RAPD 8.49.60

Dhanasekar et al. (2010)

1Values given in bold font are genetic distance interval between two adjacent markers harbouring the QTL.RAPD, random amplified polymorphic DNA; RFLP, restriction fragment length polymorphism; SSR, simple sequence repeat; STS, sequence-tagged site; AFLP, amplified fragment length polymorphism; ISSR, inter-simple sequence repeat; STMS, sequence-tagged microsatellite site.


Association mapping

In the recent years, emphasis has been given to make use ofassociation mapping based on linkage disequilibrium (LD)mapping for determining tight linkage between the molecular

marker and QTL controlling target traits (within 1–2 cMintervals or within the gene itself; Bachlava et al. 2009). This isbecause of the use of a natural population comprising the

individuals, which was developed after several rounds ofrecombination events occurring over the years. This leads to ahigh level of LD between the adjacent markers. If these

markers show the association with a target trait, it reflects tightmarker–trait association (Risch and Merikangas 1996, Nord-borg and Tavare 2002). In crop plants, candidate gene and

whole-genome scan or genome-wide association-mappingmethods are currently used. Originally, utility of candidategenes and genome scan methods were compared by Risch andMerikangas (1996). The detailed understanding of above

association-mapping approaches has been discussed recentlyin two reviews (Zhu et al. 2008, Rafalski 2010). For conduct-ing association mapping, several steps are needed (Fig. 1). (i) A

natural mapping population for association analysis must becomprised of diverse individuals that can be derived from wildrelatives, core collection of germplasm or subsets of breeding

germplasm and elite germplasm (Breseghello and Sorrells

2006). Use of release cultivars (elite germplasm) is likely toidentify superior alleles that have been captured by breedingpractices. Thus, marker(s) associated with these alleles can beutilized by MAS. Association mapping with diverse germ-

plasm or wild populations can identify new superior allelesthat were not captured by breeding practices and supportintrogression of these alleles into elite breeding germplasm.

However, whatever population is being utilized, the level ofgenetic diversity, extent of genome-wide LD, populationstructure and relatedness within the population must be taken

into consideration because these factors determine mappingresolution, marker density, statistical methods and mappingpower. (ii) It is obvious that larger germplasm collectionsprovide more power and precision. Therefore, for association

mapping, at least 100–500 individuals are needed. (iii) Thechoice of appropriate type of marker and their density isimportant for association mapping. Use of codominant SSRs

and SNPs is more informative (no allelic ambiguity) than otherdominant markers. Besides, DArT markers are also becomingpopular for analysing the whole-genome diversity, and hence,

their development has been focused in those pulse crops (i.e.pigeon pea) that have narrow genetic base (Varshney et al.2010a). In case of candidate gene association mapping, a

modest number of genetic markers ranging from 50 to 100 are

Fig. 1: The steps involved in association mapping of crop plants; solid black colour arrows indicate the steps involved in both association-mapping approaches, while grey and white colour arrows indicate the steps involved in genome-wide and candidate gene association mapping,respectively


sufficient for population structure analysis (Pritcharda et al.2000, Rafalski 2010).In genome-wide association analysis, density of markers has

to increase for scanning every region of genome and popula-

tion structure analysis has to be performed by selecting anumber of unlinked markers from each chromosome arms. (iv)After selecting the markers, population structure needs to be

estimated so that spurious (false positives) associations couldbe avoided. A set of unlinked markers covering the wholegenome are employed to broadly characterize the genetic

composition of individuals. The genotypic data obtained isused to estimate population structure (Q) and the relativekinship matrix (K) among the individuals (Lynch and Ritland1999, Pritcharda et al. 2000, Yu et al. 2006). Different methods

and softwares have been developed to correct the result ofpopulation structure, usually dividing the germplasm collec-tions into subgroups or adjusting the probability of the null

hypothesis (P value). Alternatively, use of a population havinggenetically diverse individuals is the best way to minimizepopulation structure. (v) For genome-wide association map-

ping, understanding of association between the marker locineeds to be taken into consideration before choosing theappropriate number of genetic markers for genome scan. It has

been shown that decay of LD is slower in inbred species andfaster in outbred species. (vi) The power of associationmapping depends upon the quality of the phenotypic dataand number of individuals. The field experimental design

should provide good control over the error variances causedby environments. The data recording should be accurate andprecise. (vii) Finally, information obtained on phenotypic

data, genotypic data of markers and population structure andkinship relatedness is used in software (e.g. TASSEL; Bradburyet al. 2007) to estimate the association between markers/

candidate genes and the target traits.In recent years, nested association mapping has been

reported. This approach, which has been used successfully in

maize, reduces the LD and avoids population structureanalysis. The nested population, which consists of diverseRILs of several high-resolution biparental progeny, is gener-ated and used in nested association mapping. Because these

biparental progeny have one parent in common, it leads tovery high resolution and power to detect associations includingepistatic interactions. As very high density genotypic data are

available on each biparental population, one can map imme-diately a new trait by taking the new phenotype measurements(Yu et al. 2008, McMullen et al. 2009).

In pulses, the use of association mapping is only restricted tobasic studies involving diversity and population structureanalysis (Huh and Huh 2001, Jin et al. 2008, Smykal et al.2008, Asfaw et al. 2009, Kwak and Gepts 2009, Vir et al. 2009,

Burle et al. 2010). However, the use of association mappinghas indeed a great scope in pulse crops owing to accessibility toa large collection of worldwide germplasm (Varshney et al.

2009). Under the generation challenge programme (GCP;http://www.generationcp.org), association mapping for impor-tant traits are being carried out in cassava and beans with the

goal of discovering more useful markers for a wider range ofgenotypes (Blair et al. 2007). Moreover, use of high-through-put next-generation sequencing and genotyping technologies

has markedly reduced the cost of development of molecularmarkers, leading to possibilities of genome-wide associationmapping for complex traits in the pulses (Hirschhorn and Daly2005, Syvanen 2005).

Comparative mapping

The well-known close phylogenetic relationships among dif-ferent pulse crops (Cannon et al. 2009) have the way fortransfer of molecular markers from one species to the other.

That is why molecular markers linked to a trait are employeddirectly in mapping the genes for the same trait in anotherrelated species. This approach is more fruitful to domesticated

traits because they are conserved over the evolutionary periodamong different species and are under the control of few geneshaving major phenotypic effects. At least 15 genes are known

to control such traits besides few major QTL (Weeden 2007).For instance, QTL analysis in common bean verified that threedifferent QTLs collectively explained 36% and 48% of the

variation for seed weight and length, respectively (Park et al.2000). The study carried out for seed size in cowpea [Vignaunguiculata (L.) Walp.] and mungbean [Vigna radiata (L.)Wilczek] revealed that a major gene discriminates wild

progenitor from cultivated descendant (Fatokun et al. 1992).The genes controlling different traits of domestication syn-drome are shown to be frequently clustered or linked within

the genome in several crops including pulses. As in commonbean, genes controlling growth habit and flowering time, seeddispersal and dormancy, and pod and seed size are clustered

together in only three genomic locations, respectively (Koin-ange et al. 1996). Therefore, clustering of genes may beapparent sometimes as a single gene with pleiotropic effects

(Johnson et al. 1996, Koinange et al. 1996, Xiong et al. 1999,Poncet et al. 2000), which is the sole explanation for clusteringof the domestication syndrome loci (Park et al. 2000). Traits ofdomestication syndrome are involved in adaptation and hence

favoured during selection, leading to their conservation over aperiod of time between the species. For example, comparativegenomics of a QTL for seed weight mapped in cowpea was

found apparently conserved in cowpea and mungbean on thesame locations (Fatokun et al. 1992). A comparative studyamong common bean, cowpea and mungbean suggested that a

seed weight QTL was associated with Phs locus. This locusencodes the most abundant seed storage protein (phaseolin) incommon bean, which has a putative orthologue in mungbean(Johnson et al. 1996). Interestingly, a similar candidate for

orthology was not identified in cowpea. Maughan et al. (1996)identified a different soybean seed weight locus with a potentialcounterpart in cowpea. Moreover, comparative genomics

using molecular markers linked with QTL for domesticatedtraits have facilitated to know whether two crops have same ordifferent pattern of genes/QTL. In case of pea, a different set of

genes were used to produce the same or similar phenotypicchanges when compared with common bean, which demon-strated that relatively few genes appear to have been modified

during the domestication of pea (Weeden 2007). However,similar gene pattern for trait(s) among different species canfacilitate the use of marker–trait association from one speciesto other related crop species in marker-assisted breeding or

cloning of the genes of agronomic importance.

MAS Targeting Individual Chromosome (via ReverseBreeding)

For a long time, it remained desirable in pulses to locate and

map the genes of individual chromosomes, but difficulties inthe development of trisomic, monosomic and nulli-tetrasomiclines did not make it possible so far. However, the concept of

reverse plant breeding developed recently can pave the way for


achieving similar goals in pulses also. Reverse breeding (RB),which is a novel breeding approach based on engineeredmeiosis, involves the identification of genotypes similar toparental types in segregating population (Dirks et al. 2009).

Engineering meiosis is all about controlling or more clearlyrestricting crossing over mechanism at a desired filial gener-ation (F1 or F2) to develop a need-based genotype by using

chemicals or post-transcriptional gene modifications. Thesuppression of crossing over in F1 generates the gametes fromthe chromosomes having no recombination. Thus, it offers an

opportunity to break the regular process of crossing over anddevelop desired variables because of shuffling and re-shufflingof whole chromosomes in all possible combinations. In pulses,this technology can be utilized to develop chromosome

substitution lines. These lines may be used for studying thegenetics, physical and molecular mapping of individual chro-mosomes. The substitution for each individual chromosome

can be developed with the help of recently developed molecularcytogenetic tools and doubled haploid technology. The tech-nique sounds good and effective, but it requires strong

background work to establish the protocol for the productionof DH lines in each and every pulse crop. Such protocol isalready available in chickpea (Grewal et al. 2009). It can be a

landmark technology in pulse improvement through breedingof single chromosome (Fig. 2).

In pulses, development of homozygous chromosome substi-tution lines provides novel tools for the study of gene

interactions caused by a specific individual chromosome andalso epistatic interactions between the background and genescontributed by the substitution chromosome. This has also

allowed the production of hybrids in which just one chromo-some is heterozygous. Thus, various heterozygous inbredfamilies (HIF) produced by crossing and backcrossing of

reverse breeding can be used to study gene interactions ofindividual chromosomes. The screening of populations thatsegregate for traits on single chromosome allows quick

identification of QTLs, when genotyping is combined withphenotypic data for the segregated traits (Dirks et al. 2009).This may also lead to the construction of chromosome-specificmaps. Thus, genetic stock of chromosome substitution lines

for each chromosome can be very helpful in physical mappingand studying genome at the functional level. It will also help tofix hybrid vigour of F2 gametes (because of a single process of

crossing over) and in advance state to develop the mappingpopulation (HIF) (Fig. 2, Dirks et al. 2009).

Marker-Assisted Selection: A Lesson from CerealGenomicsGeneral consideration for MAS

The general aspects of MAS, which merit special attentions,are discussed below briefly, although details have been

reported in several other reviews (Gupta et al. 2010). It is wellknown that MAS helps breeders to increase selection effi-ciency, precision and selection intensity and selection offavourable combinations of genes in early generation, resulting

in increased genetic gain. Besides, it is more cost-effective thanphenotypic selection, where phenotypic screening is difficult.However, before using a marker for the selection of desirable

plants in segregating generation, verification of the target QTLfor its magnitude of effects and accurate chromosomal locationare very important to realize the potential of MAS (Liu et al.

2003). Markers associated with a trait must be validated in

different genetic backgrounds (Kumar et al. 2010). MAS is not

useful for those traits where cost ratio of phenotypic selectionto score marker loci is less than one and heritability greaterthan 0.3 (Liu et al. 2007). For using MAS, optimal heritabilityof a trait should be 0.2, below which efficiency of MAS is

reduced (Moreau et al. 1998). Only QTL showing major andconsistent effect across the environments should be involved inMAS (Liu et al. 2007) and use of inconsistent QTL i.e.

detected in only one environment should be avoided for MAS(Liu et al. 2006). The use of MAS in combination withphenotypic selection has been shown to be more fruitful for

complex traits because phenotypic selection helps to retain theminor QTL and to select loci involved in epistatic interactions(Kumar et al. 2010).

Genetic improvement through MAS

The genomic resources developed in major cereal crops have

accelerated their use in marker-assisted selection breeding,

Fig. 2: Development of substitution lines using reverse breedingapproach and double haploid technology (modified from Dirks et al.2009)


leading to the availability of a number of improved cultivarsworldwide for farmers. For example, in wheat, more than 12cultivars developed through MAS have been released so far forcommercial cultivation (Gupta et al. 2010). The success of

MAS in cereals provides a lesson for orphan pulse crops.During the past few years, considerable genomic resourceshave been developed and utilized in marker–trait association

analysis in pulses (Sato et al. 2010). For example, to improvethe availability of pigeon pea genomic resources, the pigeonpea genomics initiative (PGI) programme was started in

November 2006 by the Indian Council of AgriculturalResearch (ICAR) and the Government of India, under theumbrella of Indo-US Agricultural Knowledge Initiative (AKI)programme. It resulted in the development of 25 different

mapping populations and molecular markers including SSR(>3000), SNP and DArT (>15 000 features) (Varshney et al.2010a). Similar progress in other pulses like chickpea, lentil,

common bean and faba bean has also been noted (Varshney2007, Torres et al. 2010, Chamarthi et al. 2011). This has led tothe use of MAS successfully in a few pulse crops. For example,

in common bean, introgression of major gene(s) using MAShas resulted in the development of several lines resistant to rust(Stavely 2000, Faleiro et al. 2001), anthracnose (Alzate-Marin

et al. 1999), BGYMV (Miklas 2002) and angular leaf spot (deOliveira et al. 2005). Also, two major QTLs controllingresistance to white mould in common bean (Ender et al.2007) and a major QTL associated with root rot resistant in

snap bean have been introgressed using MAS with realizedgain in the target traits (Navarro et al. 2009). The near-isogenic lines (NILs) of chickpea with resistance/susceptibility

to Fusarium oxysporum f. sp. ciceris (Foc) have also beendeveloped on the basis of selection made by closely linkedmarkers in RIL population (Castro et al. 2009). Moreover, in

common bean, two improved lines �USPT-ANT-1� (carryingCo-42 gene for resistance to all known North American racesof anthracnose) and �USDK-CBB-15� (carrying QTL for

bacterial blight resistance) have been registered in USA(Miklas et al. 2003, 2006). On the other hand, however,MAS could not be found very useful for some traits as nosignificant gain was observed for multiple branching in

common bean by introgression of a QTL through MAS(Fazio et al. 2003). For making MAS as a routine componentto conventional breeding, a one-stop shop having information,

analytical tools and related services for designing and effi-ciently conducting molecular-assisted breeding experimentshas been recently launched as the Molecular Breeding

Platform (MBP) at the Marriott Convention Center inHyderabad, India, on 17 February 2010. The aim of thisfacility is to increase breeding efficiency in developing coun-tries. MBP was jointly initiated by the GCP of the Consulta-

tive Group on International Agricultural Research (CGIAR),in collaboration with the Bill & Melinda Gates Foundationand the International Crops Research Institute for the

Semi-Arid Tropics (ICRISAT). The MBP will have threecomponents: the molecular breeding portal and helpdesk,information system and services component (http://www.icri-

sat.org/newsroom/news-releases/icrisat-pr-2010-media3.htm).

Marker-assisted gene pyramiding

Combining in a single genotype, the genes scattered in differentgenotypes (often called gene pyramiding), is not impossiblewhen individual gene controls different traits. However, it is

difficult through recombination breeding approach by select-ing desirable plants on the basis of phenotype. Breedersfrequently face complex choices in designing efficient crossesand selection strategies (Wang et al. 2007). This situation is

further complicated when different genes control the samephenotype, as selection of plants with multiple genes is difficultbased on phenotype alone due to epistasis (Mohler and

Singrun 2004) and inability of phenotypic screening techniquesto differentiate genes.The use of genetic resistance is the most effective and

economical way of controlling plant diseases. Therefore, genepyramiding is a pragmatic approach to achieve multiple anddurable resistance (Shi et al. 2009). However, identification ofa plant having multiple resistance genes for a disease like rust

becomes impossible because different resistance genes showsame disease reaction after screening under artificial conditionsagainst the target disease. Therefore, it is often cited that

genetic markers can be deployed as an alternative to identifyand select specific genes or combine multiple resistance genes(Brahm et al. 2000, Fjellstrom et al. 2004). The possible

breeding schemes that can be used for gene pyramidinginvolving MAS and required population size in each segregat-ing population have been discussed in several recent reviews

(Ishii and Yonezawa 2007a,b, Ye and Smith 2008a,b, 2009,Gupta et al. 2010). Marker-assisted pyramiding has beenemployed widely in cereal crops for combining multiplegenes/QTLs controlling both qualitative (Huang et al. 1997,

Hittalmani et al. 2000) and quantitative (Castro et al. 2003)disease resistance. In pulse crops, marker-assisted gene pyr-amiding has not been reported so far. However, in soybean, it

has been used to stack the genes controlling resistance tosoybean cyst nematode (SCN). Its use in this crop has beenshown to be cost-effective compared to greenhouse screening

and has increased the efficiency and speed of developing SCN-resistant cultivars (Concibido et al. 2004). Also, QTL/genescontrolling tolerance to Phytophthora root rot and resistance

to soybean mosaic virus have been pyramided in this crop (Shiet al. 2009, Li et al. 2010). The successful pyramiding of gene/QTL in soybean could have been possible only because of tightmarker–trait association. In pulses, well-established tight

association between markers and target traits has alreadybeen reported (Table 2). Thus, breeders can make similarefforts to use them for marker-assisted pyramiding in pulse

crops as well.

Why use of MAS is so uncommon in pulse breeding

programmes?

The present review indicates that involvement of MAS in pulseis very limited compared to cereals, although considerable

efforts have been made in developing genomic resources duringthe past decades (Sato et al. 2010). This may be ascribed toseveral reasons. First, it has been shown that pulse crops have

suffered to a great extent from poorly developed infrastructureincluding both knowledge and physical capacity for geneticand genomic analysis. Also, basic infrastructure facilities for

MAS are lacking for the conventional plant breeders. Sec-ondly, a dearth of sufficient genomic data in most of the pulsecrops except few ones such as chickpea, cowpea, common bean

and pigeon pea is the major constraint to establish closelinkage between molecular marker and trait of interest.Marker–trait association established so far in pulses haslinkage of such molecular markers that are not breeder-


friendly. This has limited their use in selection of desirableplant routinely in pulse breeding programmes. Therefore, toincrease the availability of genomic resources and data in keyspecies, highly polymorphic and reproducible markers such as

SSR and SNP in marker–trait association analysis is the majorissue for exploitation of MAS in breeding programmes.Thirdly, the interaction between biotechnologists and plant

breeders should be enhanced to use MAS more systematicallyand efficiently. Overcoming these barriers may facilitate theadoption of complex genomic data sets by crop improvement

specialists and development of improved cultivars involvingMAS. Fourthly, conventional plant breeders hesitate to useMAS in their routine breeding programmes. This limitationcan be surmounted by imparting training to the next gener-

ation of scientists to navigate both basic and applied plantscience and thus span the �gap� between genomics andbreeding. The final and last limitation relates to different

biological system of pulse crops having high plasticity andG · E interactions. Simultaneously, it also provides opportu-nity to improve the complex quantitative traits through MAS

because magnitude and direction of QTL effects differ indifferent environments (Mackay 2009). Therefore, in pulses,major emphasis has to be given on identification of appropri-

ate statistical analysis and there is need to study systematicgenetics from QTL to biology and omitting all of theintermediate aspects in the chain of causation from geneticperturbation to phenotype variation during association of

DNA sequence variation with variation in phenotypes.

Future Possibilities for Deploying MAS as a Componentof Pulse Breeding Programmes

During the last few years, considerable progress has been made

in the development of genomic resources in major pulses likecowpea, common bean, chickpea and pigeon pea. The time hascome for plant breeders to use these resources for improving

the pulses and making MAS an integral part of conventionalbreeding programmes. To this end, breeders need to developthe trait-specific mapping populations in these crops and map

the important gene/QTL so that they can fulfil the basicrequirements of MAS. The mapping of important traits willfacilitate identification of tightly linked markers for MAS.

Certainly, the latest technological developments in the areas

of DNA sequencing and genotyping are serving to redefine thescope of germplasm classification. Further, precise phenotyp-ing of germplasm accessions belonging to natural mapping

populations will facilitate marker–trait association, and alsofor mining rare alleles of genes controlling important traitsthrough TILLING approach.

The close phylogenetic relationship of pulse crops with themodel legume species Medicago truncatula and Lotus japonicus(Cannon et al. 2005) can facilitate the cross-species transfer-

ability of molecular markers from model to various pulseswhere progress of development of such genomic resources isstill slow. Therefore, research work in this direction canaccelerate mapping of important genes for target traits, and

tightly linked markers can be utilized through MAS.However, it will be better to pay attention to the develop-

ment of vast amount of genomic resources in every pulse. This

will help to develop the breeder-friendly molecular markerslike SSR and SNP and to improve our genetic understandingof complex traits, relations among traits, and between target

trait and environments. The high-throughput genotyping

platform equipped with new-generation sequencing technolo-gies is rapidly evolving. This will certainly provide cheap andlarge-scale screening of segregating individuals for selectingdesirable genotypes. It will make MAS breeding of pulse crops

more broad, useful and efficient as well as cost-effective infuture.The slow progress in the use of molecular technology in

pulses has been attributed to the limited efforts and resourcesdedicated to develop genomic resources. Breeders selectsimultaneously for many traits, which are quite often complex

in nature. In many species, the knowledge obtained on thebasis of marker–trait studies using mapping populationscannot be applied to the diverse germplasm the breeders use.There are genetic background effects, interactions, etc. Thus,

validation studies are always necessary. Also quite often, theeffect and percentage of the phenotypic variance explained isoverestimated in control experiments, and the values do not

hold when such breeding materials are used. Breeders usingMAS encounter such difficulties, so it is important to under-stand these limitations. MAS is a great tool but not a magic

tool. However, young generations of scientists including bothplant breeders and biotechnologists have potential to surpassthis barrier as most of them are aware of the potentiality of

MAS.

Acknowledgements

Thanks are due to the Head, Division of Crop Improvement, Indian

Institute of Pulses Research, Kanpur, for providing the facilities.

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