Improving Crop Resistance to Abiotic Stress (TUTEJA:PLANT STRESS OMICS O-BK) || Sunflower: Improving Crop Productivity and Abiotic Stress Tolerance

Section IIID Oil Crops Including Brassicas

Improving Crop Resistance to Abiotic Stress, First Edition.Edited by Narendra Tuteja, Sarvajeet Singh Gill, Antonio F. Tiburcio, and Renu Tuteja� 2012 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2012 by Wiley-VCH Verlag GmbH & Co. KGaA.

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47Sunflower: Improving Crop Productivityand Abiotic Stress ToleranceCarlos A. Sala, Mariano Bulos, Emiliano Altieri, and María Laura Ramos

Conventional breeding has been successful in constantly raising the sunflower(Helianthus annuus var.macrocarpus) yield potential and its stability. This improve-ment has been possible through both the direct manipulation of several genescontrolling resistance to fungal diseases, pests, and parasitic weeds and the indirectselection of quantitative trait loci that control heritable variability of the traits andphysiologicalmechanisms that determine biomass production and its partitioning.However, this approach may now be insufficient, since genetic progress has beenslower in recent decades, and it is necessary to provide improvements at a rapidpace due to the redistribution of sunflower production toward marginal areas, dueto the rapid changing cultural practices such as no-till planting or weed manage-ment, and due to the increases in the frequency and severity of abiotic constraintsbecause of global climate change. Research in the last decades led to three mainapproaches to change the objectives and the current tools for sunflower breeding.First of all, plant physiology provided new tools and models to understand thecomplex network of yield- and stress-related traits in order to identify target traitsuseful to improve selection efficiency. Second, molecular genetics has led to thediscovery of a large number of loci affecting yield under potential and stressconditions or the expression of stress tolerance-related traits. Third, molecularbiology has provided genes that are useful either as candidate sequences to dissectQTL or for transgenic approaches. In this chapter, we reviewed and discussedmolecular breeding strategies to improve sunflower yield potential and its toleranceto abiotic stresses and xenobiotics, emphasizing the requirement to face this taskthrough an integrated multidisciplinary approach based on plant genetics andgenomics, physiology, and modeling.

47.1Introduction

Sunflower (Helianthus annuus L. var.macrocarpus Ckll.) is grown all over the worldwith three main purposes: beauty (ornamental sunflower), direct consumption ofthe seeds (confectionary sunflower), and oil production (oilseed sunflower). By far,

Improving Crop Resistance to Abiotic Stress, First Edition.Edited by Narendra Tuteja, Sarvajeet Singh Gill, Antonio F. Tiburcio, and Renu Tuteja� 2012 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2012 by Wiley-VCH Verlag GmbH & Co. KGaA.

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the last of them is the most important objective in terms of acreage andproduction [1] and is the subject of this chapter. Sunflower oil has been traditionallyviewed as a healthy vegetable oil and it is considered a premium oil for salad,cooking, and margarine production [2] and is also being evaluated as a source ofbiodiesel [3].

With a cultivated acreage of over 22million ha and an annual production of around9 million ton, sunflower is grown on every continent, but its production is mainlyconcentrated in theRussianFederation,Ukraine, India, andArgentina. Sunflower oilis the fourth most important vegetable oil in world trade after soy, palm, and canolaoils. Unlike soybean, sunflower is primarily an oil crop, with high proteinmeal beinga by-product. The world production of sunflower pellets is also important, as it is theprincipal grinding subproduct. Argentina is the leading exporter and the EuropeanUnion is the greatest importing block [4].

Morphological, geographical, molecular, and archeological evidence indicates thatsunflower domestication took place in eastern North America [5–7], where it wasused as a source of food, pigment, and medicine by the Native American Indians [8].A substantial genetic bottleneck occurred during domestication, both at the nucle-ar [9] and at the plasmon levels [7]. In fact, the cultivated sunflower gene pool hasretained only 40–50% of the nucleotide diversity that can be found in wild sunflowerpopulations [10].

The transformation of sunflower into a major oilseed crop, however, took placeonly in the second half of the twentieth century due to two major breedingachievements: the drastic increase in oil percentage in sunflower achenes achievedin the former Soviet Union from 1920 to 1960 [11] and the development of acytoplasmic male sterility system [12] combined with fertility restoration by nucleargenes [13] that enabled the commercial production of hybrid seed [14, 15]. Eventhough domestication and breeding create population bottlenecks and erodedgenetic diversity in sunflower [6, 9, 10], diverse and complex parentage andmigrationhave apparently partially counteracted the effects of domestication and other diver-sity-reducing processes in modern oilseed sunflower inbred lines [16]. Significantnucleotide diversity was discovered across inbred lines despite the effects of geneticdrift and the winnowing of unfavorable alleles through intense selection andinbreeding in single-cross hybrid sunflower breeding programs. Surprisingly, nucle-otide diversity was estimated to be 1.7-fold greater in elite inbred lines than primitiveand early open-pollinated (OP) cultivars. In fact, nucleotide diversity in sunflower isonly slightly lower than maize, two- to fivefold greater than other domesticatedgrasses, eight- to tenfold greater than soybean, and several-fold greater than otherautogamous plant species [17].

Crop performance is the end result of the action of thousands of genes and theirinteractions with environmental conditions and cultural practices. Conventionalbreeding has been very successful in constantly raising the sunflower yield potentialand its stability. This improvement has been possible through the direct manipu-lation of several genes controlling resistance to fungal diseases, pests, and parasiticweeds, and the indirect selection of quantitative trait loci (QTL) that control heritable

1206j 47 Sunflower: Improving Crop Productivity and Abiotic Stress Tolerance

variability in the traits and physiological mechanisms that determine biomassproduction and its partitioning. This last approach came into being with little orno knowledge of the factors governing the genetic variability exploited by breeders forcrop improvement. However, this approach may now be insufficient since it isnecessary to provide improvements at a rapid pace due to the redistribution ofsunflower production towardmarginal areas and due to the increase in the frequencyand severity of abiotic constraints because of global climate change. Cold stress,drought, and salinity will probably become more prevalent in certain areas, whilethere will be an increased demand for agricultural products and reduced availabilityof agricultural land and natural resources such as water and fertilizers. Finally,breeding also needs to exploit positive interactions with rapid changing culturalpractices such as no-till planting or weed management.

We review and discuss published results about molecular breeding strategies toimprove sunflower yield potential and its tolerance to abiotic stresses and xenobio-tics, emphasizing the requirement to face this task through a multidisciplinaryapproach based on plant genetics and genomics, physiology, and modeling.

47.2Breeding Achievements

Scientific sunflower breeding was started in 1910–1912 at Krasnodar by VasiliiStepanovich Pustovoit, an academic, based on the varieties locally developed duringthe nineteenth century [18]. The efforts of breeders were initially devotedmainly togenetically control parasitic weeds (broomrape, Orobanche cumana) and insects(sunflower moth, Homeosoma electellum), but the development of varieties withhigh oil content by Pustovoit became amilestone in the evolution of sunflower as anoil crop throughout the world. The local varieties cultivated in Russia in 1913contained only 30–33% of oil in dry seeds. This percentage increased up to 43% in1935, 46% in 1953, and 51% in 1958, when the variety �Peredovik�was released [18].This spectacular increase in oil content of the achenes did not cause any decline inthe seed yield of the varieties released. The open pollinated Russian cultivarPeredovik, with high oil content, introduced during the 1960s in the Westerncountries (the United States, Canada, Western Europe, and Argentina), was thebasis of the first sustained commercial production of oilseed sunflower in thesecountries [19].

The discovery of cytoplasmic male sterility, with its inherent advantages, pro-vided a highly efficient method for commercial production of hybrid seed and wasthe second milestone in the development of sunflower. The first stable source ofcytoplasmic male sterility was discovered by Patrice Leclercq in 1968 from aninterspecific cross involving H. petiolaris and H. annuus [12]. Subsequent identi-fication of genes for fertility restoration in wild species [13] and in certain obsoletesunflower cultivars [20] allowed an efficient and economical production of hybridseed. The development of the first sunflower hybrids based on cytoplasmic male

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sterility in the early 1970s intensified the interest of seed companies in the crop,which led to a considerable increase in sunflower production in many countries.When comparing sunflower yields in the countries that grew open-pollinatedvarieties before the introduction of hybrids, seed yields increases of about 20%were estimated [19].

Information regarding breeding achievements with respect to seed and oil yieldsafter the initial introduction of hybrids is scarce. The main exceptions are the resultsreported for Argentina [21–25]. Taking into account that approximately 1.9million haof sunflower is grown inArgentina between latitudes 26�S (Chaco province) and 39�S(southern Buenos Aires province), that this area includes a wide range of environ-mental conditions (subtropical and temperate climates and different types of soils)and management practices, breeding achievements for sunflower in Argentina area representative example of other regions in the world and will be considered inthis chapter.

The relative contributions of plant breeding and crop management to yieldimprovement over time in a given cropping region can be separated (e.g.,[26, 27]). Genetic gains can be estimated by comparing a historic set of cultivarswith uniformmanagement practice or the trial data collected by breeding programs.This gain in relative terms is subtracted from the total gain in farmers� fields and theresidual is assumed to be due to changes in management practices [28].

Using this approach and a set of sunflower cultivars released inArgentina between1930 and 1995, L�opez Pereira et al. [21] found that both grain and oil yields werepositively associated with the year of cultivar release and that there was a cleardiscontinuity in yield trends with a marked step around 1970, when the first hybridswere released. On average, hybrids outyielded open-pollinated cultivars by 23% forgrain and 36% for oil. No improvement in yield potential, however, was apparentduring long periods before and after this turning point (Figure 47.1). These authorshypothesized that the historic requirement for disease tolerance and grain quality,together with a rather narrow genetic base, has imposed restrictions on the improve-ment in yield potential. In further studies, focusing on the genetic gain by selection inthe period between 1983 and 1998, Sadras et al. [24] found a positive associationbetween oil yield and year of commercial release, whichwas related to both resistanceto fungal diseases (specially verticillium wilt, caused by Verticillium dahliae, [29, 30])and response to intraspecific competition.

Usingmeta-analysis ofmultienvironment trials [31], de la Vega et al. [32] quantifiedincreases in oil yield and determined the contributions of change in both biotic stressresistance and yielding ability in favorable environments for sunflower hybridvarieties released during the period 1995–2005. Genetic gains came about due toboth an increase in the number of hybridswith resistance to themajor biotic stress (V.dahliae) and a genetic gain in oil yield of 14.4 kg ha�1 yr�1 in these resistant hybrids. Itis likely that at least part of the slowdown observed in grain yield gains in the nationaldata during 1995–2005 was a result of a breeding process that, for that period,increased oil yield mostly through an increase in grain oil concentration [32] and,also, because of the presence of large and regional genotype� environment (G�E)interactions [25].


47.3Identification of Key Traits Useful for Increasing Yield Potential and Abiotic StressTolerance

Conceptually, the environmental effect on a genotype depends on three mainelements: soil, cultural practices, and weather. Soil and cultural practices areusually persistent from year to year and can be regarded as fixed. The weatherelement ismore complex because it has a persistent part represented by the generalclimatic zone and an unpredictable part represented by time variation (year to year).Once the environmental effect has been conceptually subdivided into predictableand unpredictable components, a similar subdivision can be made for the G�Einteraction [33, 34]. Understanding of the underlying physiology of the genotype-specific responses to predictable and unpredictable environmental variation wouldimprove the efficiency of selection within a complex target population of environ-

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Figure 47.1 Comparison of sunflower, maize,and soybean grain yields per hectare inArgentina over the past 40 years. Two mainaspects permit to explain the bilinealrelationship between mean sunflower yieldsand year, as shown in the box: (a) national dataaccounts only for mean grain yield and not oilcontent that was a main breeding objective

during the past two decades; (b) the explosivegrowth of soybean in Argentina, whichincreased from 6.0 million ha planted area in1994 to more than 15 million ha in 2010, haspushed sunflower production to moremarginal areas [32] (data obtained from[298]).

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ments [25, 35, 36]. On the other hand, it permits to identify key traits as targets forselection to cope with the predictable and unpredictable elements of the environ-ment in a given region, namely, (i) attributes that can be selected to achieveadaptation to the target environment by emphasizing predictable interactions(e.g., phenology and photoperiodic response; duration of grain filling, a traitassociated with canopy stay green; and salt and cold tolerance) and (ii) attributesthat allow the unpredictable G�E interactions to be accommodated, improvingyield stability in a target production environment (e.g., osmotic adjustment, leafexpansion, etc.).

Sunflower is a glycophyte species moderately tolerant to salinity and isconsidered in the same tolerance category as soybean [37]; therefore, it can begrown successfully on most agricultural soils [38]. However, if the competencewith other crops requires the expansion of the sunflower crop to regions withsaline soils, apparently there exists enough genotypic variability and medium-to-high heritabilities for salt tolerance in sunflower, in order to develop salt-tolerantsunflower hybrids by conventional breeding methods [39–41]. In addition,genetic resources from wild species [42–46] have been exploited to developsalt-tolerant lines [47, 48].

Higher yield stability in regions with marked interyear variability in rainfall, acharacteristic of drought-prone areas, involves the identification, testing, and breed-ing for particular attributes. Identification of such attributes can be assessed bycomparing a range of genotypes over several years, under both irrigated and rainfedconditions that produced terminal droughts. This approach was followed by Ferereset al. [49] and Gim�enez and Fereres [50] in an extensive comparative analysis ofdrought tolerance in sunflower. They showed that yield under drought was closelyassociated with harvest index and found intraspecific variability in root depth, whichin turn was linked to cultivar maturity type. They also identified variability for bothsensitivity of leaf conductance to leaf water potential and for osmotic adjustment(OA). Furthermore, they found no association between yield potential and suscep-tibility to drought indicating that the development of high-yield, drought-tolerantcultivar is possible [51].

As was pointed out by Connor and Hall [51], evidence suggests that it shouldbe possible to breed for tolerance to some categories of stress in sunflowerwithout loss of yield potential because there are firm indications of intraspecificvariability in traits that confer tolerance (see Ref. [52] for a review). Thelikelihood of success will be increased if work is geared toward the identifi-cation of key attributes based on a good understanding of the causal relation-ships between the presence of a trait and the physiology of yield loss mini-mization under stress [53]. Further progress will depend on the introduction inhigh-yield genotypes of traits able to improve stress tolerance without detri-mental effects on yield potential, thus reducing the gap between yield potentialand yield in stress-prone environments. This goal can be achieved through theidentification of stress tolerance-related traits and the subsequent manipulationof the corresponding genes using marker-assisted selection (MAS) and/or genetransformation.


47.4Linkage Mapping

47.4.1Genetic Linkage Maps and other Genomic Resources

Over the past decades, several genetic linkage maps differing in length and densitywere developed for cultivated sunflower (a paleopolyploid, [54]; with 2n¼ 2x¼ 17chromosomes) or for crosses between cultivated andwild sunflowers (see Refs [15, 55]for reviews). Thesemaps are based on differentmolecularmarkers such as restrictionfragment length polymorphism (RFLP) and/or random amplification of polymorphicDNA (RAPD) markers for the first reported maps [56–63]. RAPDs have been usedprimarily for taggingphenotypic loci in sunflower, for example, rust (Puccinia helianthiSchw.) and broomrape-resistance genes [64, 65]. Later on, the addition of AmplifiedFragment Lenght Polymporphism (AFLPs) [66–68] and direct amplification of lengthpolymorphism markers (DALPs) [69] allowed further saturation of genetic linkagemaps. The distribution of DALPs and AFLPs revealed that both markers taggeddifferent regions to enable covering most of the sunflower genome.

Another multipoint marker developed for sunflower and used for mappingpurposes are the so-called target region amplification polymorphisms (TRAPs),using EST database information to generate polymorphic markers around targetedcandidate gene sequences [70]. TheTRAP techniquehas been employed in sunflowerto construct a linkage map [71], to define the sunflower linkage group (LG) endsthrough the use of TRAP markers based on Arabidopsis-type telomere repeatsequences [72], to map several traits (e.g., ms9, [73]; a gene for downy mildewresistance, [74]; the chlorophyll-deficientmutation yl, [75]; the fertility factorRf1, [76]),and to assess germplasm relationships.

Two of the RFLP maps have been used as tools for mapping phenotypic andquantitative trait loci [77–85]; however, the widespread use of RFLP markers andmaps in sunflower has been restricted by lack of public RFLP probes, a consequentlack of a dense public RFLP map, and low-throughput nature of RFLP markers. Thedifficulties posed by the historic lack of public, single-copy DNA markers were onlyslightly offset by the emergence of facile, universal DNA markers, such as RAPDs,AFLPs, and TRAPs. While RAPD, AFLP, and TRAP markers have a multitude ofuses, they are dominant, multicopy, and often nonspecific in nature and, as a whole,unsatisfactory for establishing a genome-wide framework of DNA markers foranchoring and cross-referencing genetic linkage maps. Single-copy, codominantDNA markers are preferred for such purposes and until 2002 were lacking insunflower [86].

The concomitant development of a large number of simple sequence repeat (SSR)markers and the automation of mapping procedures [87–89] eliminated the long-standing bottleneck caused by the scarcity of single-copy DNAmarkers in the publicdomain and supplied the critical mass of DNA markers needed to create a publicreference map, unify independently developed molecular genetic linkage maps, andestablish an universal LG nomenclature. Tang et al. [88] constructed the first genetic

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linkage map for sunflower on the basis of SSR markers and the first dense publicgenetic linkagemap on the basis of single- or low-copyDNAmarkers. Since the threeRFLP maps of sunflower lacked common, public domain DNA markers, QTL andother traitmapping results could not be universally exploited, compared, or validated,this resource created the basis for rapidly, efficiently, and fully integrating first-generation genetic linkage maps developed by using RFLP markers. Yu et al. [90]integrated and cross-referenced the Tang et al. [88] SSR map with the RFLP maps ofBerry et al. [58] and Jan et al. [59] using the Gedil et al. [67] RFLP map as a bridge.Insertion–deletion (INDEL) markers were also developed from RFLP markers bysequencing the cDNA clones, aligning sunflower cDNA and Arabidopsis genomicDNA sequences, predicting from such an alignment intron sites in sunflower genes,and designing flanking primers to amplify the introns and flanking coding regionsspanned by the primer pairs. The density and utility of the molecular genetic linkagemap of cultivated sunflower was increased by adding unmapped SSR markersdeveloped by European researchers (Cartisol, CRTx SSRs), together with thosealready developed by North American (ORSx SSR markers) and South Americanresearchers (INTA Argentina, Hax markers; [87]). These efforts contributed to theavailability of more than 2000 SSRmarkers for mapping purposes in sunflower [55].All the reported linkagemaps can be easily viewed and compared using a useful web-based application: CMap [91].

Most of the SSRmarkers used in sunflowermapping are neutral (usually located inintergenic genomic regions), as they were developed from genomic libraries usingmicrosatellite motives as hybridization probes [87–89]. In recent years, due to therapid increase in sequence information, sunflower transcript assemblies were builtand mined to identify SSRs and INDELs for marker development, comparativemapping, and other genomics applications in sunflower. In fact, more than 320 000expressed sequence tags (ESTs) have been generated and are available for sunflowerand related species of the genus Helianthus [92]. The information can be accessedthrough the Compositae DataBase [93], the GenBank dbEST division [92], theCompositae Genome Project [94], and the Gene index accessible through theGenBank UniGene division [95].

To create a transcript map for sunflower, Lai et al. [96] identified 605 ESTs thatdisplayed small INDEL or single-nucleotide polymorphism (SNP) variations in silico,had apparent tissue-specific expression patterns, and/or were ESTs with candidatefunctions in traits such as development, cell transport, metabolism, plant defense,and tolerance to abiotic stress. Primer pairs for 535 of the loci were designed from theESTs and screened for polymorphism in recombinant inbred lines (RIL). In total, 273of the loci amplified polymorphic products, of which 243 mapped to the 17 LGs ofsunflower. Comparisons with previously mapped QTL revealed some cases whereESTs with putatively related functions mapped near QTLs identified in other crossesfor salt tolerance and for domestication traits such as stem diameter, shattering,flowering time, and achene size. The generation of EST-SSR and SNP markerscomplemented existing SSR marker collections [97, 98] allowing the inclusion offunctionalmarkers in geneticmaps [96, 99]. Through direct sequencing of sunflowergenomic regions, belonging to a small group of inbreds and landraces, more than


1700 SNPs and 147 INDELs were obtained [10, 17, 100]. Recently, a restriction site-associated DNA sequencing technology for SNP discovery identifiedmore than 1400unique contigs with at least one high-quality SNP [101].

Sequencing more diverse EST libraries within cultivated sunflower or from wildsunflower species could enhance the number of polymorphisms discovered. It isquestionable, however, whether all these polymorphisms would also be segregatingin the available mapping populations. As shown in maize and its wild relativeteosinte [102], cultivated maize harbors much less polymorphism than teosinte. Aframework to integrate all the reported information concerning sunflower genomicsand to generate the basis for future research is the Sunflower Genome SequencingProject [103], which is still in progress.

47.4.2Recombinant Inbred Lines Used for Mapping Purposes

Populations of RILs have been used for developing reference linkage maps of thesunflower genome and for QTL mapping studies. Two of them were used andreported on several occasions and for this reason both of themwill be described herein some detail.

The population of RILs from the cross RHA266�PAC2 was developed by theINRA in France. RHA266 is a restorer, unbranched, oilseed sunflower line, releasedin 1971 by the USDA-ARS and Texas Agricultural Experiment Station [104]. It isbased on Peredovik germplasm crossed by a linewith rust resistance, 953-102-1-1-41.PAC2 is a line developed by INRA and was obtained from a cross between the USDAline HA61 and a wild H. petiolaris population [57].

The other population of RILs was developed by US researchers from the crossRHA280�RHA801 [88]. RHA280 is a restorer, unbranched, nonoilseed sunflow-er (confectionary) inbred line developed by selection from Sundak and released in1974 by the USDA. RHA801 is a restorer, branched, oilseed sunflower line,developed and released by USDA in 1981, and derived from a restorer populationwith a complex pedigree after one cycle of recurrent selection for improvedyield [105].

These populations represent interesting tools for mapping studies because theywere used as a framework for developing linkagemaps obtained with different kindsof molecular markers. However, their use as populations to identify yield or stress-related traits may be limited by the little differences between their parental lines formany of the features under study and because none of them is a high-yield moderninbred line, so the QTL obtained using these RILsmay now be ubiquitous in the elitegermplasm.

47.4.3QTL Mapping

Yield and tolerance to abiotic stress are complex traits regulated by a number offactors that can be studied as component traits. The development of different


genetic maps for sunflower allowed genetic dissection of the quantitative traitscontrolling a wide range of physiological characteristics related to oil yield andthe adaptive response of sunflower to abiotic stress. This is a prerequisite toallow cost-effective applications of genomics-based approaches to breedingprograms aimed at improving the sustainability and stability of yield underadverse conditions.

47.4.3.1 Oil YieldMost cultivated sunflower is grown as a source of vegetable oil. Thus, the principalgoal of sunflower breeding programs is to develop F1 hybrid cultivars with high oilyield. Sunflower oil yield per unit area is determined by the product of seed yield perunit area and oil percentage in the seed. Therefore, consideration of both compo-nents is important when breeding for high oil yield hybrids.

Seed oil concentration is a complex trait determined by the genotype and theenvironmental conditions. Search for seed oil concentration QTL using agenetic map of 205 loci defined by RFLP [81] and composite interval mappingresulted in the detection of eight QTL on seven LGs that accounted for 88% ofthe phenotypic variation for seed oil concentration across environments [79].Gene action was additive for four QTL and dominant or overdominant for theothers. Four of the eight QTL were detected in two or more environments, andthe parental effects were the same across generations and environments. Inanother study, six QTL for percentage of oil in the grain were detected using aset of 244 F3 families derived from another cross and a genetic map based on170 AFLP and SSR markers. The percentage of phenotypic variance explainedwas 90.4% [106]. On 220 F2 and 180 F3 progenies of different genetic origin,and using a genetic map based on RFLP and SSR [107], four QTL wereidentified in both F2 plants and F3 families on four LG, which explained68–70% of the phenotypic variance. Tang et al. [108] identified six QTL for seedoil content in a low-� high-oil (RHA280�RHA801) RIL mapping populationsegregating for apical branching (B), phytomelanin pigment (P), and hypoder-mal pigment (Hyp) loci. The seed oil concentrations of RHA280 and RHA801differed dramatically, from 254 to 481 g kg�1, respectively. Interestingly, three ofthe QTL were tightly linked to B, P, and Hyp. The same relationship betweenapical branching and seed oil content was observed in other mapping popula-tions [109, 110].

Hajduch et al. [111] using a proteomic approach reported 77 protein spotsdifferentially expressed in the high oil line RHA801 versus the low oil line RHA280.Identification of 44 of these proteins indicated that the two main processes affectinglow or high oil concentration in these lines were glycolysis and amino acidmetabolism suggesting that seed oil content is tightly linked to carbohydratemetabolism and protein synthesis in a complex manner. Although the number ofdifferences found by these authors should not be related only to seed oil content asthey stated, since RHA801 and RHA280 are not isolines but members of ratherdifferent gene pools, their results describe the proteomic differences betweenconfectionary and oilseed varieties.


47.4.3.2 Seed WeightSeed weight is an important property under complex genetic and environmentalcontrol, and associated with morphological and developmental characteristics suchas plant height or flowering dates. A set of 244 F3 families was screened with AFLPandmicrosatellitemarkers and a linkagemapwas constructed based on 170markers.One putative QTL for 1000-grain weight was detected explaining 5.4% of totalphenotypic variance [106]. Al-Chaarani et al. [68] reported three QTL for this traitand one of them was a major QTL that explained 37% of the phenotypic variance forthis trait.

Using a genetic map with 290 markers for a cross between two inbred sunflowerlines and 2 years of observations on F3 families, two QTL controlling seed weightwere detected. Phenotypic variation explained by both QTLwere 16.0 and 25.2 for F2an F3 populations, respectively. Some of the QTL controlling seed weight overlappedwith those controlling oil content. TheQTL on the same LGas the branching gene b1,also reported by Mestries et al. [109] for another cross, was almost certainly linked tocapitulum size. The second QTL for seed weight was close to the one for floweringdate. The seed weight character measured is not �yield� in the agricultural sense ofseed production per hectare of hybrid genotypes. It was seedweight per capitulumonselfed plants, with little involvement of heterosis [110].

In the RIL population from the cross RHA280�RHA801 mentioned before, fiveQTL controlling seed weight were reported explaining 72.8% of the phenotypicvariation. However, the contribution of the pleiotropic effect of the apical branchinggene B accounted again for the bulk (52.5%) of this variation.

47.4.3.3 Days to FloweringSunflower can be grownunder awide variety of climatic conditions so awide range oftotal crop durations are required around the world. In addition, knowledge of therelative lengths of the period from sowing to flowering, when potential seed numberis determined, and of the period fromflowering tomaturity, when seedfilling occurs,can be important in breeding for yield. Present-day sunflower varieties show widevariation in these characters.

Diversity of the production area together with the characteristic of the originalgermplasm base and the subsequent introduction of foreign germplasm determinedthe coexistence of a great variability in types of hybrids grown in Argentina.Historically, two major hybrid types of different genetic origin were grown in theArgentine sunflower production area from the 1970s to early 1990s: (i) intermediate-late to late maturity hybrids of white-striped seed, low grain oil concentration, andhigh relative grain yield, mostly developed from locally bred, open-pollinatedvarieties, and (ii) intermediate-early to early maturity hybrids of black seed, highgrain oil concentration, and low relative grain yield, largely derived from EasternEuropean and US germplasm. Breeding and selection by recombining both typesconverged to a third hybrid type that combined high grain yield and high grain oilconcentration with an intermediate maturity. The breeding process involved theselection of the maturities that tended to maximize yield potential and stability ineach of the three megaenvironments or subregions of sunflower production in


Argentina [25]. This process reduced the original range of phenological responseswithin subregions, but tended to concentrate on different maturity types amongmegaenvironments. The modern high-yield hybrids are late, intermediate, or earlycompared to the mean of the old ones in the northern, central, and southernsubregions of Argentina, respectively, which in turn reflects the positive, orthogonal,and negative associations between oil yield andmaturity in the three subregions [25].

Le�on et al. [78] mapped QTL associated with growing degree days (GDD) toflowering and photoperiod (PP) response in an elite sunflower population derivedfrom a cross of two divergent lines representative of the two original germplasmpools: HA89 and ZENB8 [25]. HA89 exhibits an ambiphotoperiodic response (short-or long-day response depending on photoperiod); it shows its longest relativematurity when the photoperiod at emergence is about 11–13 h and a long-dayresponse at longer day lengths. Relative to ZENB8, HA89 line types require moregrowing degree days to flower when the photoperiod during emergence is equal to orless than 14 h. This type of response reflects that of group II hybrids, which arerelatively late in the northern subregion, where emergence and vegetative periodoccur under short photoperiods, and relatively early in the central and southernsubregions, where later planting and higher latitudes are associated with longerphotoperiods for the same crop phase. The line ZENB8 exhibits a day-neutral toshort-day response, and takes more days to flower than HA89 under photoperiodslonger than 14 h; this type of response being representative of that of group I hybrids.

Twohundred and thirty-five F2-generation plants and their F2 : 3 and F2 : 4 progeniesof a single-cross population derived from the crossHA89�ZENB8were evaluated insix environments (locations, years, and sowing dates) with photoperiods known toelicit a PP response between the inbred lines. Detection of QTLwas facilitated with agenetic linkage map of 205 RFLP loci and composite interval mapping. Six QTL inLGs A, B, F, I, J, and Lwere associated with GDD to flowering and accounted for 76%of the genotypic variation in the mean environment; however, QTL in LG A and Baccounted for 72% of the genetic variation and were highly dependent on PP. QTLmapping of the ratio of the GDD required by a progeny to flower at a PP of 12.1 and15.0 h, defined as the photoperiod response (PPR), suggested that alleles at QTL inLGAandBwere responsive to PP.QTL in LGFand J showedQTL�E interaction butthe LOD values were not associated with PP. QTL�E interactions for additive effectswere highly significant for LG A, B, and F. QTL�E interactions for QTL withdominant effects were significant for LG A, B, and J. The dominant effect of QTL inLG B increased in environments with a longer PP [78].

Given these two QTL (A and B) that are strongly associated with the photoperiodresponse that controlsGDD toflowering, Fonts et al. [112] examined the phenology ofnear-isogenic families bearing all combinations of alleles for both of them associatedwith photoperiodic response when growing in controlled environment chambersunder short and extended photoperiods. Plants were harvested at intervals, the apicesdissected out, and apex development from the start (apex transformation) to the endoffloral differentiation scored.Near-isogenic lines (NILs) exhibited significant effectsof photoperiod, QTL, QTL� photoperiod, and QTL�QTL interactions for thetiming of apex transformation and for the inverse of rate of development during


floral differentiation. The strong QTL A�QTL B interaction for both traits reflects amuch greater delay in development under both photoperiods when QTL A wasderived from HA89 and QTL B from ZENB8. Also, they found a three-factor(QTL A�QTL B�photoperiod) interaction, acting on the rate of developmentduring the floral differentiation process [112]. Interestingly, some of the combina-tions of alleles at both QTL should be representatives of the type III group of high-yield hybrids grown in Argentina. These and other alleles may be used to achieve abetter match between the edaphoclimatic supply and the physiological requirementsduring critical periods of the crop in different production areas of the world. Thisbetter adaptation to the predictable environmental variation should be complemen-ted with a better understanding of the physiological and genetic causes underlyinggenotypic differences for the duration of the grain filling period.

47.4.4Mapping QTL for Yield-Related Traits under Stress Conditions

Generally, QTL studies in sunflower have been performed under only one waterregime. Such studies do not lead to separation of constitutive QTL from adaptiveones. Sorting out constitutive from adaptive QTL effects is possible by evaluation ofthe same mapping population under different conditions. These studies permit toinvestigate the genetic basis of trait association by looking for colocation of corre-sponding QTLs for yield-related traits on the geneticmap under stress and nonstressconditions as exemplified below.

Using a set of sunflower RILs derived from the cross PAC2�RHA266, Ebra-himi et al. [113] determined the effects of water stress on several seed quality traitsincluding oil content in the seed, and mapped QTL controlling these traits undertwo different water treatments. Interestingly, in spite of that there were nosignificant differences for oil content in the seed between both parents in thefour environmental conditions, significant variation among RILs was observed.Genotype� environment interaction was detected only for the RILs under green-house but not under field conditions. Eighteen QTL for oil content were detectedunder well-watered conditions and eleven under stress conditions, but only threeof themwere common to both treatments, although the phenotypic correlation foroil content under both water regimes was significant. Both parental lines con-tributed to the expression of oil content. The most important QTL for oil contentwas found on LG 16 and explained 16% of the phenotypic variance. The positivealleles for this QTL come from RHA266. Four other QTL for this trait under bothwater treatments were found on LG 16, so this region appears to be important foroil content under water-stressed and well-watered conditions. Two QTL, in LG1and LG16, were also reported by Tang et al. [108] for oil content in sunflowerrecombinant inbred lines.

QTL controlling four chlorophyll fluorescence parameters were analyzed in thesame population of RILs under well-watered andwater-stressed conditions in 45 day-old plants at stage near flower bud formation. A large genetic variation andtransgressive segregation were observed for the traits studied under two water


treatments. Results showed that the progressive water stress did not cause long-termdownregulation of photosynthesis apparatus, but it reduced actual efficiency of PSIIelectron transport. QTL analysis showed that several putative genomic regions wereinvolved in the total variation of chlorophyllfluorescence parameters under twowatertreatments. Among the 26 QTL detected under well-watered conditions, 5 wereshown to be constitutive by QTL-by-water treatment (environment) interaction.Mostof the QTLwere specific for one condition, demonstrating that the genetic control ofthe expression of the traits related to photosynthesis differed under different waterconditions. In several cases, one QTLwas found to be associated withmore than onetrait. The results also showed overlapping QTL for some of the chlorophyll fluores-cence parameters and plant water status traits described above, mainly on LGs 7 and16 [114].

The same population of RILs was used to study agronomical traits undergreenhouse and field conditions, each with two water treatments. The differenceamong RILs was significant for all the traits studied under all conditions; and watertreatment�RIL interaction was also observed for most of the traits under both fieldand greenhouse conditions. Several QTL were identified for yield-related traits withthe percentage of phenotypic variance explained by QTL (R2) ranging from 4 to 40%.QTL for grain yield per plant under fourwater treatmentswere identified on differentLGs, among which two were specific to a single treatment. Three QTL for grain yieldper plant were overlapped with several QTL for some of the drought-adaptive traitsdescribed before [115].

Differential display analysis was used to compare overall differences in geneexpression between drought- or salinity-stressed and unstressed (control) plants ofsunflower. Five drought-regulated cDNAs and twelve salinity-regulated cDNAs werecloned and sequenced. Thirteen of these cDNAs were confirmed to be expresseddifferentially in response to drought or salinity stress by quantitative reversetranscriptase polymerase chain reaction (RT-PCR). Regulation of the expression ofthese 13 genes was analyzed in leaves of drought-stressed plants and in roots andshoots of drought- and salinity-stressed seedlings. Results showed that certain genesrespond to both stresses, while others are uniquely regulated either in terms of thestress stimulus or in terms of the plant tissue [116]. In this context, results of QTLanalysis for different traits under stress and nonstress conditions confirm thedifferential display results and highlight the existence of adaptive QTL (or genes)that are detected (expressed) only under specific environmental conditions ormodifyits expression with the level of an environmental factor, andQTL (genes) consistentlydetected (expressed) across most environments.

Selecting which QTL/traits follow with MAS is crucial. The improvement ofdrought tolerance should not be achieved with a parallel limitation of yield potential.Hence, drought-tolerance traits should be tested in both stressed and nonstressedenvironments before being introduced in an MAS breeding program. QTL fordrought-related traits coincident with QTL for yield potential should be consideredas priority targets for MAS. However, confirmation and validation of the reportedQTL in different genetic backgrounds should be performed prior to their utilizationin breeding.


47.4.5Mapping QTL for Stress-Related Traits

Genetic mapping with dense marker maps can be used to identify the number andgenetic positions of QTL associated with specific traits that confer yield advantagesunder stress conditions. In addition, this process can be used both to estimate effectsof the segregating QTL and their contributions to trait variation (individually and incombinedQTLmodels) and to obtain estimates of their stability across environmentsand across genetic backgrounds. Trait-based physiological approach has been usedsuccessfully in other crops to improve performance in drought-prone environ-ments [117] and it can be used in sunflower, integrated with an MAS approach, toimprove yield and yield stability efficiently.

Three target traits in sunflower that can confer yield advantages in stress-proneenvironments and for which QTL analyses were reported are osmotic adjustment,leaf expansion, and cold tolerance during initial stages of development, and will bereviewed below.

47.4.5.1 Osmotic AdjustmentOsmotic adjustment refers to the lowering of osmotic potential due to the netaccumulation of solutes in response to water deficits [118]. Since OA helps tomaintain higher leaf relative water content (RWC) at low leaf water potential (LWP),it is evident that OAhelps to sustain turgormaintenance and hence growth, while theplant is meeting transpirational demand by reducing its LWP [119, 120].

Chimenti and Hall [121] examined intraspecific variation in osmotic adjustment insunflower using a collection of 33 genotypes of different origin, which were exposed towater stress at the 8-leaf stage. Changes in osmotic adjustment with ontogenywere alsoevaluated in the pre- and postanthesis phases using seven genotypes drawn from thiscollection. Estimates of OAwere derived frommeasurements of leaf RWCand osmoticpotential during a period in which the soil was allowed to dry gradually. All genotypes atthe 8-leaf stage showed some degree of OA and significant differences in RWC werefound between extreme genotypes in all three ontogenetic phases. The value of OA as atrait that can contribute effectively to yieldmaintenanceunderdrought in sunflowerwaslater examined byChimenti et al. [122]. They screened a set of 25 inbred lines reputed todiffer for drought tolerance for OA expressed in the 8-leaf stage. They crossed theextreme lines (high and low OA) and selected four individuals (two high OA, two lowOA) from the F2 population derived from this cross. Crops of F3 families obtained byself-pollination of these individualsweregrownunder a rainout shelter and subjected toa 30 day drought ending at anthesis. High OA families expressed greater OA at fullturgor, estimated as the difference in osmotic potential between drought and controltreatments, than the lowOA families at the end of the drought period. Crops of highOAfamilies extracted more water from the profile during the stress period, had greatershoot biomass and harvest index at physiological maturity, and greater grain yield(�30%). There was no effect of OA on these variables in the irrigated controls [122].

The significant value of OA as a key trait contributing to drought tolerance insunflower prompted the analysis of QTL and the development of markers for this


trait. Jamaux et al. [123] identified both physiological and molecular markers of OAbased on two pools of genotypes differing for the trait. Two families of sunflower lineswith contrasting OA, T� (low desiccation rate) and Tþ (high desiccation rate),differed significantly in relative water loss (RWL) of excised leaves under watered anddry conditions. Since the T�/Tþ lines had contrasting OA, the RWL criterion couldbe considered amarker ofOA, at least in the T�/Tþ genetic background. The higherthe values of OA, the lower were the RWLvalues. Differential screening of two cDNAlibraries, one obtained from a T� and the other from a Tþ nonstressed sunflowerplant, led to the isolation of three constitutive clones, DRS12, DRS14, and DRS26.Although no DRS12- and DRS14-related protein was found in databases, the DRS26cDNA showed a high sequence homology and identity with amammalian amino acidtransporter, suggesting that the DRS26 polypeptide could be involved in vacuolartransport of osmolytes such as amino acids. RFLP analysis with the restrictionenzyme BamHI and the DRS26 cDNA probe differentiated the two families ofsunflower and suggested a role for DRS26/BamHI as a marker of T� genotypes.When bulk analysis with RAPDs was conducted, a primer was found that easilydifferentiated T� and Tþ individuals [123]. Using an integrated and high-densitylinkage map based on SSR and AFLPs, Poormohammad Kiani et al. [124] localized 8QTLs for OA on a population of 129 RILs. Four of them were colocated with QTL forother plant water status variables. Amajor QTL for OA on LG 5 accounted for 29% ofthe phenotypic variation. These exciting results indicate not only that OA is a key traitfor developing sunflower genotypeswith increased tolerance towater deficits but alsothat this complex and technically difficult-to-assess trait can be approached by MAS.However, more research is needed for QTL confirmation and validation before itspractical implementation in sunflower breeding programs.

47.4.5.2 Leaf ExpansionDuring the vegetative phase, and to maintain plant water status within tolerablelimits, the sunflower crop relies more on restricting interception of radiation andhence evaporative demand than it does on stomatal closure, due to its explorative rootsystem. The sensitivity of leaf expansion to water deficit has been demonstrated in anumber of studies. The results showed that crops subjected to water deficits beforeanthesis develop small leaf area index (LAI), but maintain activity per unit leafarea [125–129]. Even in crops that are visibly wilted, stomata do not close completelyand photosynthesis continues [130]. The effects of water stress on leaf expansion aremediated by changes in both cell number and cell size. The latter effect predominatesin leaves unfolding early during stress episodes and the former in later stages ofexposure to stress, consistent with the partial temporal separation of the processes ofcell division and expansion [131].

The extent of leaf growth reduction caused by water deficit is very important indetermining the adaptation of a certain crop variety to a climate scenario. In ascenario where long-term droughts are expected, a genotype that reduces its leafgrowth is more likely to reach maturity with a certain amount of available water. Onthe other hand, in a scenario where short-termwater deficits are expected, a genotypethatmaintains leaf growth is likely to havehigher yields [132]. It has been shown that a


genotype canmaintain its leaf area bymaintaining growth rate [132] or by increasingthe duration of leaf growth [133]. Moreover, an increased duration of growth couldhave the benefit of increasing the opportunity for recovery after rainfall [134]. Thenatural genetic variability for these traits could be used to develop crop varietiesadapted to specific scenarios. Despite this, breeding for these traits is not a commonapproach for obtaining drought resistance in crop species, probably because of a lackof well-characterized sources of genetic variability. In this sense, thework reported byPereyra-Irujo et al. [135] is an important first step to determine the feasibility of thisapproach in sunflower breeding. Eighteen nonbranching sunflower inbred lines,comprising most of the genetic variability of cultivated sunflower, were selected bythese authors to analyze the response of leaf growth towater deficit in order to identifyand quantitatively describe sources of genetic variability for this trait that could beused to develop sunflower varieties adapted to specific scenarios. Plants weresubjected to long-term, constant-level, water deficit treatments [136] and the responseto water deficit quantified by means of growth models at cell, leaf, and plant scale[137, 138]. Significant variation among lines was found for the response of leafexpansion rate and of leaf growth duration, with an equal contribution of theseresponses to the variability in the reduction of leaf area. Intrinsic genotypic responsesof rate and duration at a cellular scale were linked to genotypic differences in whole-plant leaf area profile to water deficit. The results reported suggested that geneticdifferences in leaf growth rate under water deficit could be determined by cell wallproperties, while increased duration of leaf growth is partly due to a prolonged phaseof epidermal cell division. This implies that rate and duration responses could be theresult of different physiological mechanisms and are therefore capable of beingcombined to increase the variability in leaf area response to water deficit insunflower [135]. Identifying the mechanisms underlying genetic differences in theresponse of leaf growth to water deficit and exploring the genetic base of the crop forthese mechanisms are of paramount importance as initial steps toward markerdissection of the relevant traits and their validation in different genetic backgroundsthat, ultimately, will allow the implementation of this novel strategy in sunflowerimprovement for drought tolerance.

47.4.5.3 Cold Tolerance during Germination and EmergenceThe anticipation of planting dates as a strategy tomaximize the growing season and toescape drought stress during flowering or grain filling has increased the importanceof low-temperature stress tolerance in sunflower during germination and earlygrowth to increase yield potential and its stability [139, 140].

Even though it has been reported that growth of sunflower seedlings wasinhibited to some degree when they were subjected to suboptimal tempera-tures [141], there exists genetic variability for cold tolerance during initial growthstages. Genotypic variability for emergence rate under field conditions duringearly planting and for germination rate at low temperatures under controlledconditions has been reported for a set of 13 commercial hybrids and 26 inbredlines [139]. Also, it has been shown that there exists variability in the cultivatedgene pool for the relative crop growth rates under low-temperature conditions by


screening a group of 21 inbred lines under field conditions [140]. More recently, astudy was conducted to identify physiological traits associated with cold tolerancein sunflower and to identify the genomic regions involved in their variation [142]. Apopulation of 98 RILs and their two parents were sown in the field under theconventional sowing date (control) and 1 or 2 months earlier (long-term low-temperature treatments). Several traits putatively associated with cold toleranceand acclimation mechanisms to stress conditions have been investigated at earlydevelopment stages. Significant differences were observed among the threesowing dates for all traits. Chlorophyll content and specific leaf area weregenetically associated with cold tolerance, which suggests that they could be usedas selection criteria in conventional breeding programs. QTL analyses show thatseveral putative genomic regions are involved in the variation in the physiologicaltraits studied under low-temperature conditions [142].

All these results indicated that there exist enough variability and phenotypic andmolecular tools to breed sunflower for cold tolerance during initial stages of growth.

47.4.6Traits that are Awaiting More Research

47.4.6.1 Stay GreenDelayed leaf senescence during the grain-filling phase of grain crops, or stay green(SG), may be functional, when the loss of canopy capacity for carbon fixation isdelayed or occurs at a slower rate, or is cosmetic, when maintenance of leafchlorophyll is combined with the disassembly of the photosynthetic apparatus[143, 144]. Functional SG has been recently demonstrated in sunflower and isconsidered a valuable trait in sunflower breeding since it may contribute positivelyto increases in yield potential through increments in biomass production or yieldstability under conditions of water shortage, late sowings, or high plant populationdensity [145]. Functional SG can also increase resistance to stalk breakage (stemlodging) by preventing (or minimizing) remobilization of carbohydrates from thestem during grain filling by maintaining crop photosynthesis [146]. Susceptibility tostem and root lodging in sunflower increases with crop population density suggest-ing that SG could be a valuable secondary trait in selection for higher andmore stableyields in this crop species [147, 148].

Studies on variability and inheritance of the stay-green trait in sunflower using twocrosses indicated that additive effects were the main source of genetic variation andthe authors concluded that selection for this trait could be made in early-generationsegregating populations [149]. However, the trait studied by them was stem color atmaturity and not delayed leaf senescence.

Identification of sources for the functional SG trait in sunflower by exploringthe genetic base of this crop will lead to a significant advance in breeding foryield potential and stability and greater adaptation to drought conditions. Also, itwill allow the discovery of stay-green drought tolerance QTL or genes to speedup their introduction into elite genotypes, as was the approach in othercrops [150–152].


47.4.6.2 Tolerance to Stem LodgingRoot and stem lodging, defined as the permanent displacement of the stem from itsvertical position, causes important yield losses in sunflower. The prostrate head oflodged plants is not retrieved during mechanical harvesting causing significantlosses. Also, lodging may contribute to fixing the upper limit to commercially viablecrop population density since yield is known to increase up to densities higher thanthose used at present [148]. Sunflower lodging has been observed to result fromfailure of the root anchorage system or from tensile failure of the stems. Thesusceptibility to lodging and its occurrence in stems or roots of crops exposed tohigh winds depend on complex interactions between the mechanical properties ofthe stems and the soil–root system that anchors the plants, the shape of the uppersections of the plant that capture wind gusts, and rain. The values of these variablesvary throughout crop development and can change with cultural practices, genotype,and soil properties. Stem lodging can occur in well-anchored crops when the forceapplied to the lower portion of the stem exceeds the stem failure moment. Rootlodging is usually associated with rain that weakens the anchorage (i.e., the soil–rootsystem) via a reduction in soil strength [147, 153]. Crops are particularly susceptible tostem lodging during grainfilling and at harvestmaturity, but the temporal and spatialunpredictability of lodging events under field conditions has hampered systematicresearch on this issue [148].

Sposaro et al. [154] have successfully adapted previous models for lodging incereals to the sunflower crop, and this model has been shown to perform well underfield conditions [148, 154]. It provides a systemic framework that can handle themultiple determinants of lodging and can serve to establish the relative importance ofseveral plant variables critical to the determination of root and stem lodgingsusceptibility, providing useful guidelines for conventional breeding and for thedissection of this complex trait by molecular markers. The demonstration of theexistence of genotypic differences in tolerance to root [147] and stem lodging [148]should encourage the exploration of the sunflower genetic base in order to identifysources of tolerance at high crop population densities in sunflower.

47.4.6.3 Reduced HeightProgress in improving the standability of conventional height sunflower has beenslow [1]. Present hybrids, when protected from lodging and disease, show increase inyield potential with crop population densities almost three times the commercialdensity of 5 plantsm�2 [155]; it seems very likely that propensity to lodge at high croppopulation densities also plays a part in reducing realizable yield potential in thiscrop [148]. Therefore, reduced height germplasm has the potential to increase bothstem strength and yield potential of the sunflower crop.

Reduced height controlled by recessive genes in lines with a reduced number ofleaves has been reported on several occasions [156–161]. However, none of them hasbeen used to improve yield as yet because of the excessively severe phenotypes ofthese mutants. On the other hand, three sources of reduced height (�DDR�,�Donsky,� and �Donskoi 47�) with an equal or similar number of leaves as conven-tional-height sunflowers were reported [162, 163]. DDR and Donsky were used to


develop several restorer and maintainer lines [164–166]. The inheritance of reducedplant height in the sunflower line Dw89, which traces back to �Donsky,� wasdetermined to be controlled by alleles at two loci, designated dw1 and dw2 [166].Reduced height in Donskoi 47, on the other hand, is controlled by a single dominantgene, Dw [163]. The inheritance of dwarfism in the source DDR has not beendetermined. The utilization of traits for reduced plant height to maximize yieldpotential and reducing stem lodging in other crops [167, 168] is a strategy thatdeserves to be fully explored in sunflower.

47.5Tolerance to Herbicides

Weeds compete with sunflower for moisture and nutrients, and depend on speciesfor light and space. Weed competition causes substantial yield losses in sunflower,ranging from 20 to 70% [169–173]. The amount of yield reduction varies dependingon the weed species, weed density, time of weed and crop emergence, climaticconditions, and type of soil. Competition can occur from early germinating weeds,such as species of winter annuals that germinate early in the spring and develop acompetitive advantage if they emerge before the sunflower. Competition can be aserious problem under drought conditions, since several weeds have tolerance tolimited moisture (e.g., Kochia scoparia), and even under cool temperature conditionsfollowing planting since sunflower emergence and initial growth are reduced, butthese variables remain unaffected for many weed species [174].

Herbicides are the most desirable method for weed control; however, the avail-ability of selective herbicides for the sunflower crop is quite limited and due to thehigh cost of herbicide registration, new molecules of herbicides are unlikely to bespecifically developed for weed control in sunflower. For this reason, gene discoveryand trait development for herbicide resistance in sunflower is one of the mostimportant issues in raising the productivity and the competitive ability of this crop inthe near future.

47.5.1Nontarget-Site Herbicide Resistance

Herbicides can cause several injury problems to the sunflower crop (see, for example,Blamey et al. [174], Table 12–13, pp. 642–644). As a matter of fact, sunflowergenotypes varied widely in their response to soil-applied and to postemergenceherbicides [175]. For diclofop {2-[4-(2,4-dichlorophenoxy)phenoxy] propanoic acid),for example, genotypic response can range from susceptibility to tolerance and thetolerance level also varies according to growth stage of the plants, herbicide rate, andenvironmental conditions, such as temperature and relative humidity [176]. Naturalvariation in tolerance was recently investigated by screening 97 inbred lines with acombination of the herbicide imazamox and the insecticide Malathion, an inhibitorof cytochrome P450 monooxygenases (P450s). One tolerant line, named TolP450-1,


was selected and characterized in the field and in the greenhouse to evaluate itsresponse to the herbicides imazamox, prosulfuron, and atrazine at different plantdevelopment stages (germination, VE, and V3) with and without Malathion. For allherbicides and all development stages analyzed, TolP450-1 showed significantlyhigher tolerance compared to the susceptible line RHA266, and in all cases thetolerance was reversed by Malathion [177]. The P450 gene family in plants encodesthe most versatile class of enzymes involved in the metabolic detoxification ofxenobiotics and in nontarget-site herbicide resistance in plants [178]. One of thefirst P450 genes identified for herbicide resistance, CYP76B1, was cloned from asunflower relative, the Jerusalem artichoke, H. tuberosus [179, 180]. CYP76B1metabolizes with high efficiency a wide range of xenobiotics, including alkoxycou-marins, alkoxyresorufins, and several herbicides of the class of phenylureas [181].These observations indicate that there exists natural variation for P450s genes in thecultivated and wild sunflower germplasm to be used in developing new traits fornontarget herbicide tolerance for the sunflower crop.

47.5.2Target-Site Herbicide Resistance

Imidazolinone and sulfonylurea herbicides have been demonstrated to have a broadspectrum of weed control activity, flexibility in timing of application, low usage rates,and low mammalian toxicity [182, 183]. These herbicides inhibit the enzymaticactivity of acetohydroxyacid synthase (AHAS, EC 4.1.3.18, also known as acetolactatesynthase, ALS [184, 185]), the first enzyme in the pathway for the synthesis of thebranched chain amino acids valine, leucine, and isoleucine [186]. The same enzymehas been shown to be the site of action for the triazolopyrimidines [187], pyrimi-dyloxybenzoates [188], and sulfonylaminocarbonyl-triazolinones [189].

Imidazolinone- and/or sulfonilurea-tolerant plants with altered AHAS genes andenzymes have been discovered in many species, which permitted the developmentand commercialization of several herbicide-tolerant crops [183]. Resistance in mostof these cases is due to a formof theAHAS large subunit enzyme (AHASL) that is lesssensitive to herbicide inhibition and is conferred by a single, partially dominantnuclear gene [183, 190]. This reduction inherbicide binding is caused bymutations atkey sites in the genes coding for the catalytic subunit of AHAS. Several authors havereviewed known mutations of the AHAS genes that confer tolerance to AHAS-inhibiting herbicides in plants [191, 192].

On the basis of molecular studies, Kolkman et al. [193] identified and characterizedthree genes coding for the AHAS catalytic subunits in sunflower (Ahasl1, Ahasl2, andAhasl3).Ahasl1 is amultiallelic locus and the onlymember of this small familywhere allthe induced andnaturalmutations for herbicide resistance have beendescribed thus farin sunflower.Ahasl1-1 (also knownas Imr1orArpur [193, 194]) harbors aC-to-Tmutationin codon 205 (Arabidopsis thaliana nomenclature) that confers a moderate resistance toimidazolinones, Ahasl1-2 (also known as Arkan) shows a C-to-Tmutation in codon 197conferring high levels of sulfonylurea resistance [193], and Ahasl1-3 presents a G-to-Amutation in codon 122 that confers high levels of imidazolinone resistance [195].

47.5 Tolerance to Herbicides j 1225

All these alleles are being used for the production of sunflower hybrids resistant toherbicides. The first commercial herbicide tolerance trait in sunflowers is known as�Imisun� and its development started in 1996, when imidazolinone-tolerant wildsunflowers were discovered in a field in Kansas, USA. Subsequent crossing of theseplantswith cultivated sunflower lines gave rise to imidazolinone-tolerant populationsand lines [196] that were released as donor materials for developing hybrid varietiescommercially launched in the United States, Argentina, and Turkey in 2004. Theinheritance of Imisun is additively controlled by two genes, where one of them is thepartially dominant allele Ahasl1-1 and the other (Imr2) is a modifier or enhancerfactor [194, 197]. Synergistic effects of imidazolinones andMalathion on tolerance inImisun genotypes suggest that Imr2 is a member of the P450 gene family (Bulos andSala, unpublished). To produce Imisun sunflower hybrids that express commercialtolerance levels to imidazolinone herbicides, both components need to be homozy-gous in the final variety. The second imidazolinone tolerance trait in sunflowers,known as CLPlus, is controlled by the expression of the partially dominant nuclearallele Ahasl1-3 that was developed by seed mutagenesis and selection with imaza-pyr [198]. Biochemical studies together with the results of several years of evaluationunder field conditions in many countries permit to conclude that CLPlus provides abetter level of tolerance to imidazolinones than Imisun [199].Owing to the high levelsof tolerance, only onehomozygous component, namely,Ahasl1-3, or the combinationof both Ahasl1-1 and Ahasl1-3 alleles in the final hybrid variety, is required to achievecommercial tolerance levels [199, 200].

Sulfonylurea-tolerant sunflowerswere developed fromwild sunflower populationsdiscovered in the United States [201]. The tolerance allele Ahasl1-2 was introgressedinto cultivated sunflower by forward crossing and selection with the herbicidetribenuron, and gave rise to the trait known as �Sures� [202]. The same type oftolerance was obtained by EMS mutagenesis [203] and was developed and commer-cialized under the name �ExpressSun� [204].

Introgression of genes for herbicide resistance into high-yield sunflower germ-plasm is being facilitated by MAS with diagnostic markers for each one of theresistance alleles [193, 205]. Selection of cultivated germplasm, wild Helianthusspecies, and mutagenized libraries will allow the discovery of new sources ofherbicide resistance (e.g., [206]), especially other modes of action apart from theinhibition of AHAS in order to complement the current technologies.

47.6Candidate Gene Approach

A large number of abiotic stress-induced genes have been identified in a widerange of plant species although a molecular basis for plant tolerance to thesestresses remains far from being completely understood. Some examples ofcandidate genes obtained as a result of transcriptomic analysis and that plausiblyplay a relevant role in stress tolerance in sunflower have been reported, and theyare described below.


47.6.1Dehydrins

Late embryogenesis-abundant proteins are extremely hydrophilic proteins that werefirst identified in land plants. Intracellular accumulation is tightly correlated withacquisition of desiccation tolerance, and data support their capacity to stabilize otherproteins and membranes during drying, especially in the presence of sugars such astrehalose. Among the water stress-induced proteins so far identified, dehydrins, theD-11 subgroup of late-embryogenesis abundant proteins [207], are frequentlyobserved. Dehydrins are highly abundant in desiccation-tolerant seed embryos andaccumulate during periods of water deficit in vegetative tissues. These proteinsdisplay particular structural features such as the highly conserved Lys-rich domainpredicted to be involved in hydrophobic interaction leading to macromoleculestabilization [208, 209]. These drought-induced proteins lack a fixed three-dimen-sional structure. Their specific molecular action, as well as the reason for theirdisordered character, is as yet poorly understood. It has been speculated, however,that dehydrins are tuned to acquire a biologically active structure only under theconditions in which they normally function (i.e., upon dehydration) [210]. Very littleis known about dehydrin functions in planta. Studies have established correlationsbetween drought adaptation and dehydrin accumulation in several species. Positivecorrelations were also reported for species tolerant to stresses that have a dehydrativecomponent such as salt stress [211, 212] and freezing and cold stress [209, 213–215].

To investigate correlations between phenotypic adaptation to water limitation anddrought-induced gene expression, Cellier et al. [216] studied a model systemconsisting of a drought-tolerant line (R1) and a drought-sensitive line (S1) ofsunflower subjected to progressive drought. R1 tolerance is characterized by themaintenance of shoot cellular turgor. Drought-induced genes (HaElip1, HaDhn1,andHaDhn2) were previously identified in the tolerant line.HaDhn1- andHaDhn2-deduced proteins belong to the dehydrin family, andHaElip1 is a related homologueof early light-induced protein (ELIP) [217]. The accumulation of the correspondingtranscripts was compared as a function of soil and leaf water status in R1 and S1plants during progressive drought. In leaves of R1 plants, the accumulation ofHaDhn1 and HaDhn2 transcripts, but not HaElip1 transcripts, was correlated withthe drought-adaptive response. Drought-induced abscisic acid (ABA) concentrationwas not associated with the varietal difference in drought tolerance. Stomata of bothlines displayed similar sensitivity to ABA. ABA-induced accumulation of HaDhn2transcripts was higher in the tolerant than in the sensitive genotype. HaDhn1transcripts were similarly accumulated in the tolerant and in the sensitive plantsin response to ABA, suggesting that additional factors involved in drought regulationof HaDhn1 expression might exist in tolerant plants. In leaves of R1 plants, thesteady-state level of each transcript increased gradually as the water potentialdeclined. In the S1 plants, the fluctuations of the steady-state level of HaElip1transcripts were not correlated with the decreases in leaf water potential. Similarlevels ofHaElip1 transcripts were accumulated in S1 and R1 plants except in leaveswith water potentials between 20.9 and 21.2MPa. Steady-state levels ofHaDhn1 and

47.6 Candidate Gene Approach j 1227

HaDhn2 transcripts in S1 plants remained low and constant in leaves with a waterpotential of less than 20.6MPa. At an equivalent water potential, they accumulated ata higher level in R1 than in S1 leaves. At low leaf water potential, steady-state levels ofHaDhn1 and HaDhn2 transcripts were nine- and fivefold higher, respectively, inleaves of R1 compared to S1 plants [216]. Sequence analysis of the deduced dehydrin(Dhn1) proteins showed diversity in cultivated and wild sunflower accessions, thelatter being more variable [218, 219]. Despite these exciting findings, no other studyconcerning mapping or validation of these genes for their role in drought tolerancewas reported.

47.6.2Tanscription Factors

As was described in Section 47.4.5, a large array of genes are activated by stressconditions, meaning several proteins are produced to join the pathways thatsubsequently lead to synergistic enhancement of stress tolerance [220–228]. Thesegenes are classified into two groups: regulatory genes and functional genes. Theregulatory group includes genes encoding various transcription factors (TFs), whichcan regulate various stress-inducible genes cooperatively or separately and mayconstitute gene networks. The functional group contains genes encoding metaboliccomponents such as sugar, sugar alcohols, and amines, which play an important rolein stress tolerance. Gaining an understanding of the mechanisms that regulate theexpression of these genes is a fundamental issue in plant biology and will benecessary for the genetic improvement of plants for abiotic stress tolerance.

Stress tolerance seems to be controlled mostly at the transcriptional level [229],where the main players are proteins called transcription factors or trans-actingelements. Transcription factors are able to enhance or reduce the rate of transcriptionof their target genes. They specifically recognize and interact with DNA sequences(cis-acting elements) located in the regulatory regions of their targets. These TFs andcis-motifs function not only as molecular switches for gene expression but also asterminal points of signal transduction in the signaling processes. Typically, the TFscontain a distinct type ofDNAbinding domain and transcriptional regulation region,and are capable of activating or repressing transcription of multiple target genes[230, 231]. In plants, approximately 7% of the genome encodes for putative TFs [232].It has been estimated that Arabidopsis and rice have between 1300 and 1500transcription factor encoding genes [233, 234]. Some of them have been identifiedas stress responsive. Each of these stress-related transcription factor families exhibit adistinctive DNA binding domain [235].

Hahb-4 is a member of sunflower subfamily I of homeodomain-leucinezipper proteins, which constitutes a family of transcription factors found only inplants. Hahb-4 was found to be regulated at the transcriptional level by wateravailability and abscisic acid [236]. Transgenic A. thaliana plants that constitutivelyoverexpress Hahb-4 developed shorter stems and internodes, rounder leaves, andmore compact inflorescences than their nontransformed counterparts. Transgenicplants were more tolerant to water stress conditions, showing improved


development, a healthier appearance, and higher survival rates than wild-typeplants [237]. Indeed, either under normal or drought conditions, they produceapproximately the same seed weight per plant as wild-type plants under normalgrowth conditions. It has been proposed that Hahb-4 may participate in theregulation of the expression of genes involved in developmental responses of plantsto desiccation and that it is a component of ethylene signaling pathways inducing amarked delay in senescence [238]. Furthermore, transgenic plants expressing thisgene under the control of its own inducible promoter showed that the expression ofthis TF is regulated by external factors such as drought, extreme temperatures,osmotic stresses, and illumination conditions, and is specific to different tissues andorgans of the plant. Their role as transcription factors is related to developmentalevents in response to such environmental conditions, particularly those in whichabiotic factors generate stress [239–244]. Interestingly, this family of TFs was foundonly in sunflower and for this reason, it can be a useful candidate for obtainingtransgenics with enhanced resistance to abiotic stress in other crops (seeSection 47.8).

Another TF from sunflower isHAhb-10, amember of subfamily II homeodomain-leucine zipper proteins, whose expression is regulated by illumination conditions inphotosynthetic tissues, and their function in plant development is associated withthis environmental factor, particularly in the case of the shade avoidanceresponse [245–247]. Transformed plants of Arabidopsis overexpressing HAhb-10have a shorter time to maturity maintaining their seed yield [248], and also showedincreased tolerance to oxidative stress produced by paraquat [248, 249].

47.6.3Other Genes

AcDNAmicroarray containing about 800 clones coveringmajormetabolic and signaltransduction pathways allowed to identify many differentially expressed genes inleaves and embryos of drought-tolerant and -sensitive genotypes of sunflowersubjected to water-deficit under field conditions [250]. The majority of the cDNAclones differentially expressed under water stress was found to display opposite geneexpression profiles in a drought-tolerant genotype when compared to a drought-sensitive one. These dissimilarities suggest that the difference between tolerant andnontolerant plants ismainly associatedwith changes inmRNAexpression.However,phenotypic variation resides also in changes in allelic sequences that can affect theefficiency of the encoded proteins.Hence, sequence variability of stress-related genescan modulate the stress response within a species.

Differential expression of four water stress-associated genes, aquaporin, dehydrin,leafy cotyledon1-like protein, and fructose-1,6 bisphosphatase, was examined usingfour RILs and parental lines presenting contrasting responses to dehydration andrehydration [114]. Water stress revealed a high genetic variability for water status andgas exchange parameters compared to well-watered genotypes. QTL mappingshowed that RILs carrying different genomic regions for some QTL also presentedphysiologically different characteristics and gene expression patterns. The expres-

47.6 Candidate Gene Approach j 1229

sion level of aquaporin genes in leaves of four RILs and their parents was down-regulated by water stress and was associated with relative water content. Down-regulation was also associated with genomic regions having alleles with negativeeffects on plant water status. The level of dehydrin transcripts, on the other hand,increased in leaves of all studied RILs in response to water stress. Transcriptaccumulations of dehydrin and leafy cotyledon1-like genes, likely involved inprotective tolerance processes, were not correlated directly with plant water statusor QTL effects. Downregulation of fructose-1,6 bisphosphatase was observed underwater stress. Net photosynthesis rate and the fructose-1,6 bisphosphatase geneexpression levels were associated mainly after rehydration [114]. These resultsindicate that there exists an association between physiological response to waterstress and differential expression of water stress-related genes.

Sequence polymorphisms of eight unique genes putatively involved in droughtresponse in eight inbred lines of sunflower of different origin and phenotypiccharacters and showing different drought response in terms of leaf RWC wereanalyzed. The selected genes encode a dehydrin, a heat shock protein, a nonspecificlipid transfer protein, a z-carotene desaturase, a drought-responsive element bindingprotein, a NAC-domain transcription regulator, an auxin binding protein, and anABA-responsive C5 protein. A pairwise comparison between genetic distancescalculated on the eight genes and the difference in RWC showed a significantcorrelation in the first phases of drought stress [219].

These initial results concerning sequence variability for putative stress-relatedsequences encourage more research in this area, using a broader and diverse set oflines, coupled with association mapping and extensive phenotyping. The likelihoodof this approach for MAS will depend on the confirmation and validation of theselected candidate genes in modifying the stress response on different geneticbackgrounds.

47.7Marker-Assisted Selection

The fundamental advantages ofMASover conventional phenotypic selection that canbe exploited by breeders to accelerate the breeding process are as follows: (a) MASmay be simpler than phenotypic screening, which can save time, resources, andeffort; (b) selection can be carried out at the seedling stage; and (c) single plants can beselected [251, 252].

In previous sections, we described numerous studies on DNA markers linked togenes or QTL for different traits related to yield potential or tolerance to abioticstresses. In contrast, literature on practical application of thesemarkers in sunflowerbreeding programs remains very limited. Even though it was assumed that mostmarkers associated withQTL from preliminarymapping studies were directly usefulin MAS, it has become widely accepted that QTL confirmation, QTL validation, and/or fine (or high resolution) QTL mapping may be required. Although there areexamples of highly accurate preliminary QTL mapping data as determined by


subsequent QTL mapping research [253], ideally a confirmation step is requiredbecause QTL positions and effects can be inaccurate due to factors such as samplingbias [254]. QTL validation refers to the verification that a QTL is effective in differentgenetic backgrounds [255]. Additional marker-testing steps may involve identifyingmarkers within a 10 cM �window� spanning and flanking a QTL and convertingmarkers into a form that requires simpler methods of detection [251, 256].

Ideal markers for MAS are those based on gene mutations underlying the trait ofinterest. This kind of markers has been developed in sunflower for oil quality traits(e.g., [108]) and resistance to herbicides [193, 205]. For traits related to yield, yieldcomponents, or tolerance to abiotic stresses, the situation is more complex. Factorssuch as population structure and size, parental selection and genetic backgroundeffects, epistasis, inaccurate phenotyping, or QTL� environment interactions con-tribute to bias the estimation of QTL effects, thus reducing the likelihood ofsuccessful use of these QTL in MAS programs [15, 257, 258].

QTL validation in independent samples and in different genetic backgrounds andenvironments is, therefore, necessary before using them inMASbreeding programs.Amajor challenge that remains is to confirm thatQTL discovered in a givenmappingpopulation will improve yield potential or drought tolerance when introduced intohigh-yield elite genotypes. This is particularly difficult when the traits are governed by�context-dependent� gene effects (i.e., interaction with other genes and/or environ-ment). In these cases, the value of theQTL alleles can differ depending on the geneticstructure of the current germplasm set in the breeding program [259]. Also, adesirable QTL allele discovered in nonelite genetic material may not offer anyimprovement because the allele may already be ubiquitous in present varieties. Inaddition, the effects of the positive allelemay not be transferable to elite backgroundsdue to unfavorable epistatic interactions [260].

Taking into account all these considerations, there exist examples of successfulimplementation of MAS for yield-related traits in sunflower. QTL for oil content, forexample, have been validated across generations, environments, and mappingpopulations [77, 108] and some of theQTLunderlying differences between genotypeshave been associated with phenotypic markers (Hyp, hypodermal pigment [77, 80,108]), which allowed Le�on et al. [261] to establish combined marker and phenotypic-assisted selection for high oil content during the backcross process.

47.8Transgenic Breeding

Tissue culture and plant regeneration are key steps of the transformation process.Therefore, sunflower biotechnologists invest considerable effort in this area. Basedon the tissue type for regeneration, sunflower culture systems include somatic andzygotic embryos, hypocotyls, mature cotyledons, and protoplasts (see Ref. [262] forreview). The regeneration rate of whole sunflower plants is variable and depends ongenotypes used, explant type, and development stage, and culture media andconditions [263]. Genetic variation is closely associated with regeneration ability.

47.8 Transgenic Breeding j 1231

Organogenetic traits were mapped to QTL by Deglene et al. [264] and Flores Berrioset al. [265]. Some segments of the LG 1, 15, and 17 are likely to contain genesimportant for organogenesis, somatic embryogenesis, and protoplast division. TheQTL identified in these three LGs should be involved in cell division in early eventsassociated with cell differentiation [266].

Sunflower genetic transformation was achieved by Agrobacterium tumefaciens-mediated protocols [267–269] and by combining this technique with particle bom-bardment [270], sonication [271], wounding with glass beads [272], or enzymatictreatments [271, 273]. Most of the published protocols of sunflower transformationshowed a low efficiency [268, 270, 273–279]. Efficient transformation protocols withhigh reproducibility and high transformation frequency have been reported[280, 281] together with techniques to enhance the selection of transformedexplants [279]. The usefulness of these methodologies remains to be assessed. Onthe other hand, an optimized protocol for Agrobacterium-mediated transient trans-formation of sunflower leaf disks was developed with the aim of quickly over-expressing or silencing a given gene, enabling the study of several biochemicalprocesses and the characterization of sunflower regulatory sequences [282].

Most of the transgenic traits in sunflower under field trials at present are focusedon biotic stress andherbicide resistances. Cantamutto andPoverene [283]mentionedseveral examples of them, including tolerance to glyphosate and glufosinate ammo-nium and resistance to insects, fungal diseases, and broomrape, and quality traitssuch as enhanced protein quality andmodified stereate content. Theywere developedmainly by private companies, but some of them were obtained also from publicinstitutes [278, 284, 285]. Traits related to abiotic stresses in sunflower using thetransgenic approach remain to be developed.

Although transgenic sunflower varieties have already been obtained, and theyremain the subject of ongoing research in both the United States and Argentina,the interest in GM sunflower research has decreased in the twenty-first century,mainly because official control bureaus have imposed restrictions in the face ofecological concerns [283]. These concerns are related to the risks associated withthe gene flow from transgenic cultivars to the wild flora and are a matter of generalcontroversy not only in the United States but also in other parts of the world [286].Gene flow from domesticated, transgenic, or mutant sunflower plants to wildspecies is well documented [287–290]. This potential gene containment is dis-couraging the advancement and realization of transgenics� full development insunflower.

In spite of the low transformation efficiency and biosafety concerns related totransgenic sunflower, the use of sunflower genes to mitigate abiotic stresses in othercrops has been described. A xenobiotic-inducible cytochrome P450 gene, CYP76B1,obtained from H. tuberosus was constitutively expressed in tobacco and Arabidopsisconferring to the transformed plants an enhanced resistance to several herbicidesand xenobiotics. Beside increasedherbicide tolerance, expression ofCYP76B1has noother visible phenotype in the transgenic plants and can be a potential tool forphytoremediation of contaminated soils [291]. Sunflower transcription factors, likethose described in Section 47.6, have been proposed as candidates for genetic


transformation of different crops in order to enhance the tolerance not only to abioticstresses but also to modify the growth cycle and the tolerance to xenobiotics intransgenic plants [248, 249].

47.9Prospects

Improvements in sunflower yield potential and stability during the last decades havebeen slow and mainly based on the unconscious pyramiding of yield associatedcharacters with biotic and abiotic stress-related traits. Research has led to three mainapproaches to change the objectives and the current tools for sunflower breeding. Firstof all, plant physiology provided new tools and models to understand the complexnetwork of yield and stress-related traits in order to identify target traits useful forimproving selection efficiency. Second,molecular genetics has led to the discovery of alarge number of loci affecting yield under stress conditions or the expression of stresstolerance-related traits. Third, molecular biology has provided genes that are useful ascandidate sequences to dissect QTL or are useful for transgenic approaches.

Even though there exist some successful results of synergistic interactions amongthese three approaches, the great challenge in the near future is to formalize theintegration ofmolecular geneticswith physiology andbreeding in order to (a) identifythe most relevant traits as targets for research, (b) screen the genetic base of thesunflower crop to detect the most promising genotypes as putative sources for thesetraits, (c) establish and screen introgression libraries fromwild species ofHelianthusthat are reservoirs of potential useful genes for stress tolerance [292], and (d) dissectyield and other integrative traits that influence stress tolerance into heritable traits byusing phenotyping platforms with model-assisted methods [293–296]. Routinecloning of the genes underlying the QTL is still a long way off, but it will, ultimately,provide simple markers for an effective MAS.

To date most plant QTL have been cloned by the positional cloning approach,although alternative strategies based on candidate genes and linkage disequilib-rium may represent an interesting shortcut to QTL cloning [258, 297]. Cloninggenes for stress-related traits offers the opportunity to approach stress tolerance bymeans of reverse genetics. So far, the utilization of mutagenesis as a source of newtraits for increasing stress tolerance has been unaffordable. However, reversegenetics coupledwith a tilling strategy and an efficient phenotyping platform couldbe a virtual inexhaustible source of new useful allelic variants for increasing stresstolerance.

Ultimately, as was pointed out by Collins et al. [258], integration of QTLinformation into a breeding pipeline aimed at improving tolerance to abioticstresses will best be achieved within a multidisciplinary context able to providethe operational framework required to correctly link the stress-responsivemechan-isms of crops with the functional variation of the relevant networks at the cellularand molecular levels.

47.9 Prospects j 1233

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Improving Crop Resistance to Abiotic Stress (TUTEJA:PLANT STRESS OMICS O-BK) || Sunflower: Improving Crop Productivity and Abiotic Stress Tolerance