PLEASE SCROLL DOWN FOR ARTICLE This article was downloaded by: [University of Jordan] On: 25 August 2010 Access details: Access Details: [subscription number 907784632] Publisher Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37- 41 Mortimer Street, London W1T 3JH, UK Journal of Crop Improvement Publication details, including instructions for authors and subscription information: http://www.informaworld.com/smpp/title~content=t792303981 Identifying QTL Controlling Kernel Color in Barley Muhanad W. Akash a a University of Jordan, Amman, Jordan Online publication date: 29 July 2010 To cite this Article Akash, Muhanad W.(2010) 'Identifying QTL Controlling Kernel Color in Barley', Journal of Crop Improvement, 24: 3, 219 — 227 To link to this Article: DOI: 10.1080/15427521003719109 URL: http://dx.doi.org/10.1080/15427521003719109 Full terms and conditions of use: http://www.informaworld.com/terms-and-conditions-of-access.pdf This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
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PLEASE SCROLL DOWN FOR ARTICLE
This article was downloaded by: [University of Jordan]On: 25 August 2010Access details: Access Details: [subscription number 907784632]Publisher Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Journal of Crop ImprovementPublication details, including instructions for authors and subscription information:http://www.informaworld.com/smpp/title~content=t792303981
Identifying QTL Controlling Kernel Color in BarleyMuhanad W. Akasha
a University of Jordan, Amman, Jordan
Online publication date: 29 July 2010
To cite this Article Akash, Muhanad W.(2010) 'Identifying QTL Controlling Kernel Color in Barley', Journal of CropImprovement, 24: 3, 219 — 227To link to this Article: DOI: 10.1080/15427521003719109URL: http://dx.doi.org/10.1080/15427521003719109
Full terms and conditions of use: http://www.informaworld.com/terms-and-conditions-of-access.pdf
This article may be used for research, teaching and private study purposes. Any substantial orsystematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply ordistribution in any form to anyone is expressly forbidden.
The publisher does not give any warranty express or implied or make any representation that the contentswill be complete or accurate or up to date. The accuracy of any instructions, formulae and drug dosesshould be independently verified with primary sources. The publisher shall not be liable for any loss,actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directlyor indirectly in connection with or arising out of the use of this material.
MUHANAD W. AKASHUniversity of Jordan, Amman, Jordan
Quantitative trait loci (QTL) analysis for kernel color in bar-ley (Hordeum vulgare L.) was performed using genotypic datafrom an AFLP-based linkage map and phenotypic data from 167recombinant inbred lines (RILs) derived from a cross between ablack-kernel and drought-tolerant barley parent (Tadmor) and alight-kernel and drought-susceptible parent (Er/Apm). A geneticlinkage map was constructed using 27 (out of 34) amplified frag-ment length polymorphism (AFLP) markers distributed across fivelinkage groups. A major QTL was detected using composite intervalmapping (CIM) with 4.1 LOD score and logistic regression analysiswith Pr > χ2 = 0.0079. As this linked QTL explained 77.1 of thephenotypic variation for kernel color in barley, it will provide thebasis for gene cloning and marker-assisted selection.
Traditional and molecular plant breeding efforts have been largely successfulin modifying crop quantitative and qualitative traits to meet the needs of bothproducers and consumers. However, future improvements will depend uponthe concerted application of traditional plant breeding and molecular-genetictools to increase yield. With the availability of various molecular markers aswell as saturated genetic maps, it is now possible to identify quantitativetrait loci (QTL). Genetic mapping is an efficient method to scan the genome
The authors acknowledge the financial support of the Deanship of Academic Researchat The University of Jordan, grant no. 1017.
Address correspondence to Muhanad W. Akash at The University of Jordan, Faculty ofAgriculture, Department of Horticulture and Crop Science, Amman 11942, Jordan. E-mail:[email protected]
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for putative QTL. The determination of the locations of QTL should increaseselection efficiency through the use of marker-assisted selection (MAS), andopen the door for their future genetic manipulation and possible transferamong different plant species (Akash 2003).
Several barley mapping populations have been used by different sci-entists to identify QTL. For example, Qi, Stam, and Lindhout (1998), VanBerloo et al. (2001), and Marcel et al. (2007) employed an RIL population,constructed from a cross between L94 and Vada parents, to identify QTLfor agronomic traits using AFLP markers. Larson et al. (1996) and Han et al.(2004) used a backcross population (Steptoe and Morex parents) to identifyQTL for amylase activity, glucan content, and several agronomic traits usingrestriction fragment length polymorphism (RFLP) and sequence-tagged site(STS) markers. Steffenson, Hayes, and Kleinhofs (1996), Mano and Takeda(1997), Han et al. (1997), El Attari et al. (1998), Zhu et al. (1999), Kandemiret al. (2000), and Szira et al. (2008) used a doubled-haploid population,constructed from the same parents (Steptoe and Morex) to identify QTLfor several physiological, agronomic, and disease-related traits. Teulat et al.(1997, 1998, 2001b, 2002, 2003); Teulat, Borries, and This (2001); Diab andcolleagues (2004); Forster et al. (2004); Von Korff, Grando, and Del Greco(2008); and Szira et al. (2008) used an RIL population, constructed from across between Tadmor and Er/Apm parents, to find QTL for many traits,especially those endowed with drought-tolerance.
As drought is an economically important quantitative trait in theMediterranean climate, especially Jordan, and as most of barley varietiesgrown for commercial cultivation are light-kernel colored while black-kernelcolor in barley was reported to be tolerant to water stress (Yasseen &Al-Maamari, 1995), this study analyzed an RIL population derived from across between a black-kernel and drought-tolerant barley parent knownas Tadmor and a light-kernel and drought-susceptible parent known asEr/Apm. This RIL population was used to identify QTL linked to kernel colorusing an AFLP linkage map by means of logistic regression and compos-ite interval analysis using (SAS Institute 2002) and Cartographer software’s,respectively.
MATERIALS AND METHODS
Plant Materials and Trait Measurement
A barley population of 167 recombinant inbred lines (RILs), developedby ICARDA (Centre for Agricultural Research in Dry Areas) and CIMMYT(International Maize and wheat Improvement Center), was used. This popu-lation was developed from a cross between Tadmor and Er/Apm. Tadmor isa black-kernel barley variety adapted to drought conditions of the Middle-East (Grando 1989). Er/Apm is a whitish kernel barley variety (Acevedo
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Identifying QTL Controlling Kernel Color in Barley 221
Group 1 Group 2 Group 3
Group 4 Group 5
FIGURE 1 The five groups of different kernel color in barley.
1987). The 167 RILs were classified visually into five groups on the basis oftheir kernel color (Figure 1).
AFLP Analysis
Genomic DNA was extracted from young leaves of each RIL and their par-ents using the CTAB methods described by Rogowsky and colleagues (1991).The AFLP data were generated following procedure used by Vos and col-leagues (1995) with some modifications. Sample DNA was double digestedwith EcoRI (rare cutter with GAATTC restriction site) and MseI (frequentcutter with TTAA restriction site). Endonuclease enzymes and oligo adaptersspecific to enzyme recognition sites (Table 1) were ligated to the resultingfragments. This digestion-ligation step was performed through incubation for150 min at 37◦C with the presence of Ligase enzyme. The pre-amplificationstep was performed using primers that correspond to the adapter and restric-tion site sequences and that have one additional nucleotide at the 3’ endextending into the restriction fragments (Table 1). Three primer combina-tions comprising three nucleotide extensions (Table 1) were used to generateAFLP data. All three steps of AFLP procedure were carried out on GeneAmp®
PCR System 9700 (Applied Biosystems). The amplified fragments were sep-arated and detected using a 6% denaturing polyacrylamide gel and silverstaining method, respectively.
Map Construction and QTL Identification
Polymorphic AFLP bands were scored visually as present (1) or absent(0) and then transformed to A, B, C, D genotype codes following the
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222 M. W. Akash
TABLE 1 Adapters and Primers used for Pre-Amplification and SelectiveAmplification of AFLP Procedure
Oligo Sequence (5′-3′)
AdaptersEcoRI CTCGTAGACTGCGTACC
CATCTGACGCATGGTTAAMseI GACGATGAGTCCTGAG
TACTCAGGACTCAT
Pre-selective primersEcoRI -A GACTGCGTACCAATTCAMseI -C GATGAGTCCTGAGTAAC
Selective primer combinationsEcoRI -ACA / GACTGCGTACCAATTCACAMseI -CAA GATGAGTCCTGAGTAACAA
Mapmaker3.0 software convention (Lander et al. 1987). A LOD score of4 and a maximum recombination frequency of 0.34 were used to constructa genetic linkage map after all recombination frequencies were transformedto genetic distances using Haldane function (Haldane 1919).
A QTL controlling kernel color was identified through both single-marker analysis using logistic regression and interval-marker analysis usingcomposite-interval mapping. Logistic regression was performed with markergenotype as the independent variable using SAS software (SAS institute2002). Composite-interval mapping was carried out using WinQTLCart (Zeng1994; Zeng & Weir 1996) with the maximum number of background mark-ers set to five, a forward-backward regression method of selection, and adefault window size of 10 cM. As kernel color was scored as a categoricaltrait, 0trait option was used. After performing 1,000 permutations (Churchill& Doerge 1994), QTL with LOD scores of 3.0 or more were considered asputative QTL, and their R2 were used to estimate the explained phenotypicvariances.
RESULTS AND DISCUSSION
A total of 261 bands were generated using the three primer combinations(Table 1) with a mean of about 11 polymorphic and 76 monomorphic bandsper primer combination. The level of polymorphism was 12.5% for theEcoRI - ACA/MseI -CAA, 11.2% for the EcoRI -ACA/MseI -CTG, and 14.7% for
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Identifying QTL Controlling Kernel Color in Barley 223
FIGURE 2 Genetic linkage groups of barley (Hordium vulgare L.) comprised of 27 AFLPmarkers.
the EcoRI - ACT/MseI -CAT primer combinations. The number of polymor-phic bands generated per primer combination was similar to that reportedby Teulat, Borries, and This (2001), where 77 polymorphic bands weregenerated from seven AFLP primer combinations.
A total of 19 (out of 34) markers showed segregation distortionat P = 0.01. This distortion, the deviation of segregation ratio from theexpected Mendelian ratio, may occur due to the presence of lethal genesand/or fragment complexes (overlapping fragments consisting of identicallysized fragments) (Nikaido et al. 1999; Hansen et al. 1999) and has beenreported in a wide range of plant species (Jenczewski et al. 1997).
Twenty-seven markers were assigned to five linkage groups (Figure 2).Based on Haldane function, the five linkage groups ranged from 41.7 to264.2 cM in length, and each group carried 2 to 11 markers (Figure 2). Thesefive linkage groups cover a genetic distance of 747.8 cM. However, the cov-erage for the 34 markers is 987.8, assuming each unlinked locus and eachpair of the five linkage group ends accounts for 20 cM on average (Weng2000). Kesseli, Paran, and Michelmore (1994) and Keim and colleaguges(1997) suggested that increasing population size, and not the number ofmarkers, would most likely reduce the number of linkage groups by helpingidentify key recombinants and fill remaining gaps. However, with the addi-tion of more markers, the smaller linkage groups may converge or join withother linkage groups.
Loci were well distributed within linkage groups (lack of clustering).This has also been reported by Becker and colleagues (1995); however,the fact that AFLP markers characteristically cluster in heterochromatin-richcentromeric regions has been reported (Qi, Stam, & Lindhout 1998).
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FIGURE 3 LOD scan across the five linkage groups for the localization of QTL involved inthe variation of kernel color in barley. Numbers in brackets represent linkage group number,genetic distance in cM, and LOD value, respectively.
Using CIM, interval C03_192-C03_190, on linkage group 1, was detectedto be significantly linked with kernel color in barley (Figure 3). This singleinterval explained 77.1% of the phenotypic variation and showed a positiveadditive effect of 0.136. The significant linkage of this interval was confirmedwhen marker C003_192 was also identified by logistic regression to be sig-nificantly associated with kernel color trait at Pr > χ 2 = 0.0079. In Brassica,Sabharwal and colleagues (2004) identified two major QTL for seed coatcolor that collectively explained about 89% (69% + 20%) of the pheno-typic variation using AFLP primer combinations, whereas Liu and colleagues(2006) detected two QTL that collectively explained 76.9% (46% +30.9%) ofthe phenotypic variation using AFLP, SSR, RAPD, and SCAR markers.
Although this result is the first step in map-based cloning for kernelcolor gene in barley, a more saturated map, larger population size, and amore powerful statistical algorithm may have the potential to identify newQTL.
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