RESEARCH ARTICLE Open Access Wild Barley Introgression .

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Naz et al. BMC Genetics 2014, 7RESEARCH ARTICLEOpen AccessWild barley introgression lines revealed novel QTLalleles for root and related shoot traits in thecultivated barley (Hordeum vulgare L.)Ali Ahmad Naz1*†, Md Arifuzzaman1†, Shumaila Muzammil1, Klaus Pillen2 and Jens Léon1AbstractBackground: Root is the prime organ that sucks water and nutrients from deep layer of soil. Wild barley diversityexhibits remarkable variation in root system architecture that seems crucial in its adaptation to abiotic stresses likedrought. In the present study, we performed quantitative trait locus (QTL) mapping of root and related shoot traitsunder control and drought conditions using a population of wild barley introgression lines (ILs). This population(S42IL) comprising of genome-wide introgressions of wild barley accession ISR42-8 in the cultivar Scarlettbackground. Here, we aimed to detect novel QTL alleles for improved root and related shoot features and tointroduce them in modern cultivars.Results: The cultivar Scarlett and wild barley accession ISR42-8 revealed significant variation of root and relatedshoot traits. ISR42-8 showed a higher performance in root system attributes like root dry weight (RDW), root volume(RV), root length (RL) and tiller number per plant (TIL) than Scarlett. Whereas, Scarlett exhibited erect type growthhabit (GH) as compared to spreading growth habit in ISR42-8. The S42IL population revealed significant and widerange of variation for the investigated traits. Strong positive correlations were found among the root related traitswhereas GH revealed negative correlation with root and shoot traits. The trait-wise comparison of phenotypic datawith the ILs genetic map revealed six, eight, five, five and four QTL for RL, RDW, RV, TIL and GH, respectively. TheseQTL were linked to one or several traits simultaneously and localized to 15 regions across all chromosomes.Among these, beneficial QTL alleles of wild origin for RL, RDW, RV, TIL and GH, have been fixed in the cultivarScarlett background.Conclusions: The present study revealed 15 chromosomal regions where the exotic QTL alleles showed improvementfor root and related shoot traits. These data suggest that wild barley accession ISR42-8 bears alleles different from thoseof Scarlett. Hence, the utility of genome-wide wild barley introgression lines is desirable to test the performance ofindividual exotic alleles in the elite gene pool as well as to transfer them in the cultivated germplasm.Keywords: QTL analysis, Introgression lines, Root traits, Drought stress, Wild barleyBackgroundDrought is the most common abiotic stress that causesaround 70% yield losses in crops in conjunction with heatand salinity [1,2]. These losses are one of the reasons behind the sufferings of around one billion people living inchronic hunger world-wide [3]. The morphological traits* Correspondence: a.naz@uni-bonn.de†Equal contributors1Institute of Crop Science and Resource Conservation, Crop Genetics andBiotechnology Unit, University of Bonn, Katzenburgweg 5, Bonn 53115,GermanyFull list of author information is available at the end of the articlerelated to water-use efficiency appear to play a fundamental role in drought tolerance/avoidance in crop plants[4,5]. Therefore, dissecting the genetic basis of such traitscan offer potential leads for selection in plant breeding todevelop drought resilient cultivars that may help to bridgethe gap of food shortage in the world [6,7].Roots are the first plant organ that perceives water deficit signals and transduce them to shoot which in turncuts down its water budget via stomatal closure and thecessation of its development and growth [8]. Hence, a longperiod of drought leads to dramatic losses in shoot biomass and crop yield or under extreme drought scenario 2014 Naz et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly credited. The Creative Commons Public DomainDedication waiver ) applies to the data made available in this article,unless otherwise stated.

Naz et al. BMC Genetics 2014, 7inability of crop plants to survive. An extensive rooting toaccess water from deeper soils layers is prevalent in landplants adapted to water deficit or rain-fed conditions. Avigorous root system (depth, orientation and branching)leads to a greater contact between roots and soil which inturn enhances the uptake of water and nutrients, favorablegas-exchange and carbon assimilation [9]. Moreover, rootcan suck water even from the drier layer of soil, thus bearthe ability to differentiate and grow under extreme droughtconditions. Therefore, an extensive rooting is desirabletrait in crops. Recently, Uga et al. [10] reported that positional cloning of QTL underlying DEEPER ROOTING 1(Dro1) gene in rice by using a cross between an uplanddeep rooting rice cultivar Kinandang Patong with a lowland shallow rooting cultivar IR64. They developed nearisogenic line (NIL) containing Dro1 in the IR64 background via marker assisted selection. Dro1-NIL demonstrated significant increase in shoot biomass, yield anddrought stress avoidance as compared to control genotypeIR64 suggesting that the alteration of root system architecture improves yield and drought avoidance in rice.Barley (Hordeum vulgare L.) root system is composed oftwo distinct components: (1) the seminal roots that originate from primordia in embryo, and (2) the nodal roots thatarise from basal nodes of the main shoot and tillers[11,12]. The seminal roots develop first and function untilthe nodal roots become established. Both of these rootseventually initiate lateral roots (secondary and tertiaryroots) on which water and nutrient absorbing root hairsare developed [13]. It has been reported that the numberof roots in a plant is closely related to the tiller number perplant [14,15]. For example, Anderson-Taylor and Marshall[16] reported seminal and nodal roots of spring barley (H.distichum L.) comprised of 40 and 60 percent, respectivelyof total roots and nodal root dry weight was correlatedwith primary tillers. Chloupek et al. [17] studied root system size (RSS) in barley (H. vulgare L.) under droughtconditions and observed significant positive correlationsbetween RSS and grain yield. Tiller number per plant is amajor determinant of yield in crops like barley. Therefore,proper dissection and understanding of root attributesfacilitating water uptake under drought can helpbreeders to elucidate essential traits for drought tolerance. Barley reveals great diversity in its root pattern,size and architecture. In general, wild barley accessionsshowed much higher variation due to its diverse ecological adaptation [18,19]. Tyagi et al. [20] evaluateddrought stress tolerance in wild barley accessions of different origins and reported the highest level of droughtstress tolerance in wild barley accessions collected fromIsrael and Jordan. It highlights the existence of valuablediversity within the wild barley gene pool. Therefore, itis necessary to dissect the genetic basis of this natural diversity to identify vital genes for improved root attributesPage 2 of 12and drought stress tolerance to introduce them in themodern cultivars [21].Tanksley and Nelson [22] devised advanced backcrossquantitative trait locus (AB-QTL) strategy that allows atargeted detection and transfer of the favorable exotic alleles into elite breeding material. Several AB-QTL studieswere performed in barley for morphological and agronomic traits, malting quality, disease resistance and tolerance to drought stress [23-26]. In a similar approach,Zamir [27] developed genome-wide introgression lines(ILs) libraries where marker-defined genomic regionstaken from wild species were introgressed onto the background of elite crop lines. This material allows a straightforward comparison of ILs with the elite recurrent parentto dissect the effects of wild introgressions in a near isogenic background. Such genetic background is essentialfor detection, validation and positional cloning of QTL.Till now, QTL detection studies in relation to root relateddrought stress using ILs or NILs were conducted in rice[28], wheat [29], maize [30], tomato [31] and chickpea[32] etc. In barley, Schmalenbach et al. [33] developed anintrogression line library S42IL encompassing the wildbarley (ISR42-8) introgressions in the background of cultivar Scarlett. Naz et al. [13] identified and validated QTLon chromosome 5H for root-related traits using two ILsamong this population. Hoffmann et al. [34] detected nitrogen deficiency QTL using 42 ILs of this populationgrowing in hydroponic system. However, identification ofgenome-wide QTL for root and related shoot traits usingcomplete set of the S42IL population under control anddrought stress conditions is still missing. Therefore, in thepresent study we aimed to execute genome-wide exploration of QTL related to root and shoot traits using a set of72 ILs of the S42IL population under control and droughtstress conditions. The final goal was to identify beneficialQTL alleles of wild origin for root and related shoot parameters and to utilize them for breeding as well as forpositional cloning of the underlying genes.MethodsPlant materialA wild barley introgression library consisting of 72 lineswas used in the present study. The introgression lines(ILs) were developed from an initial cross between theGerman spring cultivar Scarlett (Hordeum vulgare ssp.vulgare) and the wild barley accession ISR42-8 (H. vulgaressp. spontaneum) from Israel. The resultant F1 cross wasbackcrossed two times with recurrent parent Scarlett and301 BC2DH population was produced which is known asS42 population. From this population, 40 lines were selected through marker assisted selection and backcrosssedagain with Scarlett. After several rounds of selfing andmarker-assisted selection, BC3S6 population was produced. This population was designated as S42IL and the

Naz et al. BMC Genetics 2014, 7details of its development and marker genotyping can befound in Schmalenbach et al. [33].Phenotypic evaluation of traitsThe experiment was arranged in a split-plot design withthree replications in 2012 and four replications in 2013.The treatments (control and drought) were assigned tothe sub-plots in which lines were assigned randomly. Forthis, four seeds of individual S42IL were sown in plasticpots (22 22 26 cm) containing a mixture of top soil, silica sand, milled lava and peat dust (Terrasoil , Cordel &Sohn, Salm, Germany). Water supply was done with a dripirrigation system (Netafilm, Adelaide, Australia) by watering pots three times per day. Echo2 sensors (DecagonDev., Pullman WA, USA) were used to determine thevolumetric moisture content (VMC) digitally with the frequency domain technique. The drought stress treatmentwas carried out 30 days after sowing (DAS) by eliminatingthe water supply completely at plant development stageBBCH 29–31 [35]. The plants were kept under stress for26 days till VMC reached the maximum drought stressthreshold near to wilting point (VMC near to 0%). Thecontrol block was kept under continuous supply of irrigation. The mean average temperature during the experimental period was 18.2 C in 2012 and 14.2 C in 2013. Therelative humidity in 2012 and 2013 was 59.5% and 64.1%,respectively.Five root and related shoot traits which were evaluatedas follows:Page 3 of 12of S42ILs was estimated with PROC GLM procedure asfollows:Y ijk ¼ μ þ Gi þ T j þ Y k þ Gi T j þ T j ðY k Þ þ εijkð1Þwhere μ is the general mean, Gi the fixed effect of i-thgenotype, Tj the fixed effect of j-th treatment, Yk the random effect of k-th year, Gi Tj the fixed interaction effectof the i-th genotype with j-th treatment, Tj(Yk) the random effect of the k-th year.The PROC VARCOMP in SAS was used to measurethe variance components of genotype (VG), genotype bytreatment (VG T), genotype by year (VG Y) and experimental error (VE). Coefficients of broad-sense heritability (h2) were performed for five studied traits across allthe treatments according to Holland et al. [38]:h2 ¼VGVG YVEVG þ VG Tt þ y þ tyrð2Þwhere t, y and r are the number of treatments (t 2),number of years (y 2) and the average number of replications (r 3.5), respectively.Least square means (Lsmeans) were calculated withGLM procedure considering all replications and years separately for both control and drought conditions. Geneticcorrelation coefficients (r) between traits were estimatedusing Lsmeans of 72 S42ILs with CORR procedure in SAS.QTL detectionRoot length (RL): After manual root washing, RL wasmeasured from the stem base to the root tip by spreadingthe complete root on a ruler in centimetres (cm).Root dry weight (RDW): Roots were dried in the ovenat 50 C for seven days and weighed in grams (g).Root volume (RV): Measured in cubic centimetres (cm3)by calculating the volume differences between before andafter immersing the total roots in a 500 ml measuringcylinder containing water.Tiller number per plant (TIL): Before harvesting, totalnumber of tillers were counted in each plant.Growth habit (GH): Plants were scored from 1 to 5considering spreading growth type (1) to erect growthtype (5).Genotypic dataFor QTL detection, the post-hoc Dunnet test was appliedto see the significant differences between the individualintrogression lines of the S42IL population and Scarlett[39]. The QTL detection was assumed if the individualintrogression lines revealed significant difference to Scarlett across both treatments. Later, putative QTL regionswere refined by comparing the common overlapping wildintrogressions among the ILs having QTL alleles of wildorigin as well as by comparing wild introgression acrosschromosomal regions having no QTL effect.The quantification of QTL effects was made by calculating the relative performance (RP) of particular S42ILintrogression line bearing the QTL in comparison tocontrol parent Scarlett by the following formula:RP ðS42ILÞ ¼Genetic map of the S42IL population was achieved using1536-SNP barley BOPA1 set according to Schmalenbachet al. [36].LsmeansðS42ILÞ LsmeansðScarlett Þ 100LsmeansðScarlett Þð3ÞResultsStatistical analysesVariance analysesStatistical analyses were carried out using the softwarepackage SAS Enterprise 9.2 [37]. The variance analysisThe variance analyses revealed highly significant variation among the genotypes for root length (RL), root dry

Naz et al. BMC Genetics 2014, 7Page 4 of 12weight (RDW), root volume (RV), tiller number perplant (TIL) and growth habit (GH, Table 1). The treatment effect was significant for RDW, RV and TIL,whereas years presented highly significant variations formost traits except GH. The effects for genotype by treatment and treatment by year interactions were significantfor RL, RDW, RV and TIL. Non-significant differenceswere observed between replications across years for alltraits except TIL. The highest heritability (h2) was foundfor GH (0.99) whereas RL, RDW, RV, TIL revealed 0.72,0.56, 0.64, 0.76 h2, respectively.Phenotypic characterizationTrait-wise means comparison among Scarlett, ISR42-8and the S42IL population is presented in Table 2.S42ILs, Scarlett and ISR42-8 revealed significant variation for most of the traits under control and droughtconditions. Under control conditions, the maximum RLTable 1 Variance analyses of five investigated traits among 33 common S42ILs and parents across the years 2012 and2013 under control and drought treatmentsTraitaSOVbDFcMSdF valueP valueeh2fRLGenotype34497.611.1 0.0010.72Treatment1147.83.3nsGenotype Treatment34110.52.5 0.001RDWRVTILGHaYear11628.436.2 0.001Treatment Year1348.57.7 0.01Replication (Year)584.01.9nsGenotype3426.515.2 0.001Treatment183.147.6 0.001Genotype Treatment347.74.4 0.001Year1120.268.8 0.001Treatment Year117.09.7 0.01Replication (Year)51.20.7nsGenotype341999.515.8 0.001Treatment16556.451.8 0.001Genotype Treatment34452.43.6 0.001Year117003.4134.4 0.001Treatment Year12321.918.4 0.001Replication (Year)567.40.5nsGenotype3422.712.5 0.001Treatment1664.1365.3 0.001Genotype Treatment344.42.4 0.001Year116.69.1 0.01Treatment Year1136.074.7 0.001Replication (Year)525.113.8 0.001Genotype3415.3536.4 0.001Treatment10.10.7nsGenotype Treatment340.10.4nsYear10.10.7nsTreatment Year10.10.2nsReplication (Year)50.11.3nsTrait: RL Root length, RDW Root dry weight, RV Root volume, TIL Tiller number per plant, GH Growth habit.bSources of variation.cDegrees of freedom.dMean sum of square.eP-value indicates the level of significance at 0.05, 0.01 and 0.001; ns: not significant.fHeritability.0.560.640.760.99

Naz et al. BMC Genetics 2014, 7Page 5 of 12Table 2 Mean comparison of root and related shoot traits among the S42IL population, Scarlett and ISR42-8 undercontrol and drought stress rolDroughtControlDroughtS42IL46.4b 0.546.0b 0.525.018.082.081.0ISR42-864.9a 4.156.0a 2.252.048.084.064.0Scarlettc42.4 1.137.4c 1.337.034.045.044.0S42IL4.6b 0.14.1b 0.11.72.011.58.8a6.9a 1.07.04.527.110.714.9 2.7ccScarlett3.4 0.23.1 0.12.72.83.83.4S42IL43.9b 0.838.3b 0.510.014.0120.080.0aaISR42-8130.3 18.967.3 10.580.043.0210.0115.0Scarlett34.3c 2.332.3c 0.925.030.040.035.0S42IL10.1b 0.17.8b 0.26.53.820.013.311.3a Meanb SEca16.5 0.4cbScarlett8.7 0.46.9 0.76.84.89.89.3S42IL4.5b 0.14.5b 0.22255ISR42-81a 0.01a 0.011115c 0.05555Scarlettac5 0.03Trait: RL Root length (cm), RDW Root dry weight (g), RV Root volume (cm ), TIL Tiller number per plant, GH Growth habit.The means of S42ILs, ISR42-8 and Scarlett were calculated as an average of the phenotypic data for each trait across 2012 and 2013 for each treatmentseparately. Statistical differences across means are indicated with letters a, b and c.cStandard error.b84.0 cm was observed in ISR42-8 whereas the S42IL population accounted for maximum RL (81.0 cm) which washigher than ISR42-8 (64.0 cm) and Scarlett (44.0 cm) underdrought stress conditions. ISR42-8 revealed maximumRDW under both control (27.1 g) and drought (10.7 g) conditions. The S42IL population accounted for remarkablevariation in RDW that ranged from 1.7 to 11.5 g undercontrol and 2.0 to 8.8 g under drought stress conditions.RV showed similar trend of variation like RDW. TIL washigher in ISR42-8 in both control and drought conditionscompared to Scarlett. However, the S42IL population revealed maximum TIL 20.0 under control conditions. Underdrought, the S42IL population revealed maximum TIL 13.3which was almost similar to TIL (14.0) in ISR42-8. For GH,we used a scale from 1 (spreading GH) to 5 (erect typeGH). ISR42-8 showed spreading GH (1) under control anddrought stress conditions where Scarlett was found as erecttype (score 5). S42ILs revealed a range of GH (score 2 to 5)under both control and drought conditions.Genetic correlationsThe correlation coefficients among the traits are presentedin Table 3. Under control conditions, RL revealed highlysignificant and positive correlations with RDW (0.65) andRV (0.68). A moderate positive correlation of RL was foundwith TIL (0.40) and a negative correlation with GH ( 0.35).The strongest positive correlation was detected betweenRDW and RV (0.93). RV was moderately correlated withTIL (0.51) and GH ( 0.46). TIL revealed highly significantand negative correlation with GH ( 0.68). Under droughtconditions, RL exhibited stro

Wild barley introgression lines revealed novel QTL . QTL were linked to one or several traits simultaneously and localized to 15 regions across all chromosomes. Among these, beneficial QTL alleles of wild origin for RL, RDW, RV, TIL and GH, have been fixed in the cultivar . develop drought resilient culti

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