Detection and validation of novel QTL for shoot and roottraits in barley (Hordeum vulgare L.)

Md. Arifuzzaman • Mohammed A. Sayed •

Shumaila Muzammil • Klaus Pillen •

Henrik Schumann • Ali Ahmad Naz • Jens Leon

Received: 2 December 2013 / Accepted: 22 May 2014

� Springer Science+Business Media Dordrecht 2014

Abstract Shoot and root attributes are essential for

plant performance in agriculture. Here, we report

detection and validation of quantitative trait loci

(QTL) for shoot and root traits in 301 BC2DH lines

achieved by crossing cultivar Scarlett and wild barley

accession ISR42-8. Phenotypic evaluations were

made for six traits across 3 years under control and

drought conditions. QTL analysis was performed

using 371 DNA markers genotyped by different

protocols, such as sequence repeats, diversity array

technology as well as gene-specific markers. Marker

by trait analysis revealed 33 QTL of which 15 and 18

QTL showed trait-improving effects of the exotic and

elite alleles, respectively. Two major QTL for plant

height (PH) were found on chromosome 2H

(QPh.S42.2H) and 3H (QPh.S42.3H.b). The strongest

QTL QSdw.S42.5H for increasing shoot dry weight

was associated with an exotic allele on chromosome

5H. QTL QTkw.S42.1H underlie a novel exotic allele

that improved thousand kernel weight. Seven QTL

were associated with root dry weight of which at four

loci introgression of exotic alleles enhanced traits

values. The strongest QTL QRdw.S42.7H was linked

to a gene-specific marker VrnH3 on chromosome 7H.

At QRl.S42.5H, the exotic allele accounted for a 9 %

increase in root length. In addition, 18 epistatic

interactions were linked to PH, shoot and root dry

weights. QTL validation was performed with 53

introgression lines (ILs) carrying ISR42-8 introgres-

sions in the Scarlett background. Nine novel QTL

alleles of exotic origin were validated in the isogenic

background. These QTL-bearing ILs provide valuable

genetic resources for plant breeding and positional

cloning of the underlying genes.

Keywords QTL analysis � QTL validation � Shoot

traits � Root traits �Wild barley

Key message

Wild barley bears unique alleles for agronomic and

adaptive traits. Here, we report the detection, valida-

tion and introgression of valuable exotic QTL alleles

in the cultivated background for their utility in basic

and applied research.

Introduction

Barley (Hordeum vulgare ssp. vulgare L.) is one of the

seven internationally grown cereal grains, currently

Md. Arifuzzaman � M. A. Sayed � S. Muzammil �H. Schumann � A. A. Naz (&) � J. Leon

Crop Genetics and Biotechnology Unit, Institute of Crop

Science and Resource Conservation, University of Bonn,

Katzenburgweg 5, 53115 Bonn, Germany

e-mail: a.naz@uni-bonn.de

K. Pillen

Plant Breeding, Institute of Agricultural and Nutritional

Sciences, Martin-Luther-University Halle-Wittenberg,

Betty-Heimann-Str. 3, 06120 Halle, Germany

123

Mol Breeding

DOI 10.1007/s11032-014-0122-3

ranking fourth in production behind maize, rice and

wheat. Its production was approximately 129.9 million

metric tons in 2012 while cultivated on 51.05 million

hectares worldwide (Anonymous 2012). Drought

stress is by far the biggest bottleneck in its cultivation

that affects around 45 % of the world agricultural

lands continuously or frequently and causes dramatic

yield losses up to 50 % (Bot et al. 2000; Jenks and

Hasegawa 2005). Therefore, development of the

improved barley cultivars for drought/rainfed envi-

ronments is highly demanding to reduce the gaps in

food production and consumption.

Barley plant develops multiple shoots called tillers

above ground and a network of fibrous roots below

ground. This root system comprises of seminal and

adventitious roots that develop from the embryo’s

radical and from the base of each tiller, respectively. A

direct positive correlation has been reported between

the shoot and root growth in barley (Naz et al. 2012).

Under drought, limited shoot growth is a rule rather

than exception and generally plants reduce number

and area of leaves to cut down their water budget

(Schuppler et al. 1998). Roots are the first organs that

sense water deficit conditions and pass this signal to

shoot via chemical or hydraulic signals transduction

which leads to early drought adaptive changes, such as

growth retardation and carbon assimilation, synthesis

of growth hormone abscisic acid, stomatal closure.

(Davies et al. 2002; Schachtman and Goodger 2008).

Unlike shoot, roots can grow even under drought

conditions, and therefore, its growth, density, prolif-

eration and size are the key determinants of drought

stress tolerance in plants. The possession of a deep and

thick root system allows access to water deep in the

soil, which was considered important in determining

drought resistance in upland rice (Kavar et al. 2007). It

has been reported that root–shoot ratio and root

penetration ability were correlated with drought

tolerance (Price et al. 1997; Li et al. 2005). In this

scenario, the wild progenitors of barley revealed an

inherent ability to develop an extensive root system

that may be decisive in their adaptation to rainfed

conditions of the Middle East. This unique variation

may have potential advantages to establish an optimal

root system and sustainability against drought.

Wild barley has often been considered a promising

resource for the improvement of agronomic and

quality traits as well as drought stress tolerance.

Tanksley and Nelson (1996) devised advanced

backcross-quantitative trait loci (AB-QTL) strategy

that allows a targeted transfer of the favorable exotic

alleles into elite breeding material. Through this

approach, specific exotic alleles derived from the

exotic donor are tagged with molecular markers and

tested for association with agronomic traits. In paral-

lel, these QTL alleles can be transferred into near

isogenic lines (NILs) by means of marker-assisted

breeding. Therefore, unlike the conventional QTL

mapping methods, AB-QTL analysis can accelerate

the process of marker-based breeding because the end

products of analysis are close to NILs carrying

favorable alleles. Several studies have been conducted

in barley to introgress the exotic alleles into cultivated

barley for agronomic performance, quality, drought

tolerance and disease resistance. Pillen et al. (2004)

identified favorable QTL effects linked to the wild

barley accession ISR101-23 for quantitative traits,

such as heading, plant height (PH), harvest index,

number of kernels per ear, thousand grain weight,

yield Similarly, Talame et al. (2004) identified favor-

able QTL alleles for agronomic traits from the wild

germplasm under drought environment. Two different

mapping population, recombinant chromosome sub-

stitution lines and double-haploid lines derived from

the cross between cultivar Haruna Nijo (H. vulgare

ssp. vulgare L.) and exotic accession H602 (H.

vulgare ssp. spontaneum), resulted in the identification

of 18 and 24 QTL for nine agronomic traits, respec-

tively. Von Korff et al. (2006) identified 86 putative

QTL for nine shoot traits using a double-haploid

population S42 achieved by crossing spring barley

cultivar Scarlett (H. vulgare ssp. vulgare) and exotic

wild accession ISR42-8 (H. vulgare ssp. spontaneum).

More recently, Schmalenbach et al. (2008) developed

an introgression line (IL) library encompassing the

wild barley (ISR42-8) introgressions in the Scarlett

background and validated effects of the exotic QTL

alleles for agronomic performance and malting quality

(Schmalenbach et al. 2009; Schmalenbach and Pillen

2009). Naz et al. (2012) selected a candidate IL S42IL-

176 for root trait variations among this library and

validated effects of the exotic QTL alleles in the

Scarlett background under control and drought

conditions.

The present study was aimed to: (1) conduct a

genome-wide analyses of QTL associated with shoot

and root traits using 301 BC2DH lines of a cross

between cultivar Scarlett and wild barley accession

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ISR42-8 under control and drought conditions, (2)

dissect the role of digenic epistatic interaction in the

expression of the soot and root traits and (3) validate

QTL effects of the exotic alleles in a set of ILs carrying

ISR42-8 introgressions in the Scarlett background.

Materials and methods

Plant material

A double-haploid mapping population, designated as

S42 comprising of 301 BC2DH lines, was used for

QTL analysis. This population was generated from a

cross between a German spring barley cultivar Scarlett

(H. vulgare ssp. vulgare) and a wild barley accession

ISR42-8 (H. vulgare ssp. spontaneum). The details of

this material can be found in Sayed et al. (2012). From

these 301 BC2DH lines, a library of 53 barley ILs was

established using successive backcrossing with Scar-

lett and marker-assisted selection. The details of these

barley ILs are given in Schmalenbach et al. (2008).

Evaluation of phenotypic traits

Phenotypic evaluations of the S42 population were

made across years 2007, 2008 and 2009 in plastic

foliar tunnels at the Institute of Crop Science and

Resource Conservation, University of Bonn, Ger-

many. The experimental setup and cultural conditions

were according to Sayed et al. (2012).

A validation experiment was arranged in a split-plot

design with 12 replications (shoot traits) and three

replications (root traits) where the treatments (drought

and control) were assigned to the sub-plots in which

lines were assigned randomly. For this, four seeds of

individual BC2DH were sown in plastic pots

(22 9 22 9 26 cm) containing a mixture of top soil,

silica sand, milled lava and peat dust (Terrasoil�,

Cordel and Sohn, Salm, Germany). Water supply was

done with a drip irrigation system (Netafilm, Adelaide,

Australia) by watering pots three times per day. Echo2

sensors (Decagon Dev., Pullman WA, USA) were

used to determine the volumetric moisture content

(VMC) digitally with the frequency domain technique.

The drought stress treatment was carried out after

30 days of vegetative growth at plant development

stage BBCH 29-31 (Lancashire et al. 1991) by

eliminating the water supply completely. The plants

were kept under stress for 26 days till the VMC

reached the maximum drought stress threshold near

wilting point (VMC near to 0 %), whereas the control

block was kept under the continuous supply of

irrigation. Six shoot and root traits were evaluated

which are presented together with their breeding goals

in Table 1.

Genotyping of the S42 population

A total of 301 BC2DH lines were genotyped with 371

DNA markers using simple sequence repeats (SSRs),

diversity array technology (DArT) and gene-specific

marker systems according to Sayed et al. (2012). The

ILs were genotyped using Illumina 1536-SNP array by

Schmalenbach et al. (2011).

Statistical analysis

Statistical analyses were done using the software

package SAS Enterprise 9.2 (SAS Institute 2008). The

Pearson’s correlation coefficient (r) was calculated

with CORR procedure implemented in the SAS

program. Least squares means (Lsmeans) of the

investigated traits were calculated among BC2DH

lines across years for each treatment separately.

QTL analysis is to a great extent a model selection.

Broman and Speed (2002) and Bauer et al. (2009)

compared the forward selection strategy in restricted

maximum likelihood (REML) analysis with Bayesian

techniques and found that the forward selection

strategy is very effective to detect QTL associated

with quantitative traits. Therefore, we employed a

multiple-locus QTL model in which cofactors were

selected by the forward selection strategy and included

iteratively into the mixed model of the PROC MIXED

procedure in SAS (2008). In each of the iterative

rounds of the forward selection procedure, the most

informative marker was added as a fixed factor (QTL)

into the model. Then, all remaining markers were

reanalyzed again with the same model in which the

newly found QTL is now included. The results of the

current iteration were used as starting point of the next

round in the forward selection process. In our multi-

ple-locus QTL model, the iterations were continued

until no more additional QTL could be detected. The

critical threshold for including a marker (QTL) as a

cofactor in the model was the probability of false

discovery rate (PFDR) of P [ 0.05.

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We used the following mixed hierarchical model as

starting point to estimate single-locus QTL effects first:

Xijklm ¼ lþMi þ Lj Mið Þ þ Tk þ Lj � Tk þMi � Tk

þ Yl þ Tk � Yl þ Bm Tk � Ylð Þ þ eijklm ð1Þ

Here, the total phenotypic value Xijklm was the sum of

general mean l, fixed effect Mi of the ith marker,

random effect Lj(Mi) of the jth individual DH line

nested in the ith marker, fixed effect Tk of the kth

treatment, random interaction effect Lj 9 Tk of the jth

DH line and the kth treatment, fixed interaction effect

Mi 9 Tk of the ith marker genotype and the kth

treatment, fixed effect Yl of the lth year, fixed interaction

effect Tk 9 Yl of the kth treatment and lth year, random

effect Bm(Tk 9 Yl) of mth block nested in treatment and

years and residue eijklm. The P values from F tests were

adjusted genome wide across all single-marker tests

using the PFDR. The traits have been measured with

several replications; hence, we used all measurements

(not mean values) in the mixed model analysis.

The term Lj(Mi) represents the genetic background

and as being a random factor, it was used as an error

term for the marker in the statistical tests. The final

model, which can be considered as a conditional model,

was computed after the forward selection procedure.

The model which was used is described below in detail.

Xijklm ¼ lþ RQTLþMi þ Lj Mið Þ þ Tk

þ Lj � Tk þMi � Tk þ Yl þ Tk � Yl

þ Bm Tk � Ylð Þ þ eijklm ð2Þ

Here,P

QTL represents the sum of detected QTL for

main as well as QTL by marker interaction from the

forward selection approach. The contribution of a QTL

to trait genotypic variance was estimated by the R2

coefficient according to von Korff et al. (2006). The

relative performance of an exotic allele RP[Hsp] was

calculated by the formula (RP[Hsp] = [Hsp] - [Hv]/

[Hv] 9 100). [Hsp] and [Hv] denote the Lsmeans of the

homozygous exotic and elite genotypes, respectively.

The digenic epistatic interactions between all DArT

and SSR marker pairs were tested with SAS procedure

MIXED (SAS ver. 9.2, SAS Institute, 2008) using the

following mixed hierarchical model:

Xijklmn ¼ lþ RQTLþM1i þM2j þM1i �M2j

þ Lk M1i �M2j

� �þ Tlþ Lj � Tk þ Ym

þ Tl � Ym þ Bn Tl � Ymð Þ þ eijklm ð3Þ

Here, M1i and M2j are the fixed effects of the ith

marker and jth marker (M2), M1i 9 M2j is the fixed

interaction effect of the ith M1 genotype with jth M2

genotype and Lk(M1i 9 M2j) is the random effect of the

kth BC2DH line nested in the ith M1 and jth M2 marker

genotype interaction. The term Lk(M1i 9 M2j) represents

the genetic background and as being a random factor, it

was used as an error term for the marker in the statistical

tests. Epistatic effects were accepted based on PFDR

(P B 0.05) and has been calculated by PROC MULT-

TEST procedure in SAS (Benjamini and Yekutieli 2005).

Results

Phenotypic characterization of shoot and root traits

Mean comparison of studied traits among S42, Scarlett

and ISR42-8 is presented in Table 2. S42 revealed a

Table 1 List of shoot and root traits evaluated in the S42 population across 3 years

Trait Abbreviation Unit Measurement Breeding

goala

Plant height PH cm Height of the main tiller was measured at harvest maturity

from soil surface to the tip of spike excluding awns

-

Shoot dry weight SDW g The above ground shoots were dried in an oven and weighed ?

Thousand kernel weight TKW g/plant 1,000 kernels were weighed ?

Root length RL cm After washing, fresh root was laid straight and measured

with a ruler

?

Root dry weight RDW g Roots were dried in an oven and weighed ?

Root–shoot ratio RSR A ratio between root dry weight and shoot dry weight ?

a The breeding goals for the evaluated traits were defined according to the barley breeding programs, where (?) and (-) indicates the

desirable increase and decrease, respectively

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significant variation in PH which ranged from 41.0 to

126.0 cm under control conditions, whereas the aver-

age PH in Scarlett and ISR42-8 was 67.3 and

101.7 cm, respectively. S42, Scarlett and ISR42-8

showed a slight reduction in PH under drought stress

conditions. ISR42-8 produced the higher shoot dry

weight (SDW = 5.7 g) than Scarlett (4.3 g) under

control conditions, whereas almost no difference was

found under drought conditions. S42 showed a

remarkable variation for SDW that ranged from 1.4

to 9.1 g and from 1.1 to 9.6 g under control and

drought conditions, respectively. For thousand kernel

weight (TKW), S42 showed higher mean value than

Scarlett under control conditions but almost no

difference was found under drought conditions.

ISR42-8 exhibited lower TKW compared to Scarlett

and S42 in all cases. Higher mean values for root

length (RL) were observed in ISR42-8 under control

(43.7 cm) and drought (37.0 cm) conditions. A sig-

nificant variation of RL was observed for the S42

population under control and drought conditions

where the highest RL (54.0 cm) was measured in the

drought block. A higher mean value of RDW was

found in ISR42-8 (7.8 g) than in the S42 population

and Scarlett under control conditions. However, the

S42 population revealed the highest RDW 12.7 and

10.8 g under control and drought conditions, respec-

tively. Similarly, a higher mean value of the root–

shoot ratio (RSR) was observed in ISR42-8 as

compared to S42 and Scarlett.

Table 2 Mean comparisons of the shoot and root traits among the S42 population, Scarlett and ISR42-8 under control and drought

conditions

Traita Mean Minimum Maximum Standard errorb

Control Drought Control Drought Control Drought Control Drought

PH

Scarlett 67.3 65.6 50.0 54.0 77.0 77.0 1.0 0.8

ISR42-8 101.7 98.0 84.0 95.0 121.0 103.0 10.7 2.5

S42 69.2 67.0 41.0 42.0 126.0 109.0 0.4 0.3

SDW

Scarlett 4.3 3.2 1.8 1.6 6.8 4.8 0.2 0.1

ISR42-8 5.7 3.1 3.4 2.4 7.4 4.3 1.2 0.6

S42 4.2 3.2 1.4 1.1 9.1 9.6 0.1 0.1

TKW

Scarlett 50.9 48.4 38.7 29.7 61 74.6 0.9 1.6

ISR42-8 30.3 28.3 20.5 20.4 44.3 40.7 7.2 6.3

S42 52.6 47.3 34.7 26.2 88.4 66.5 0.2 0.2

RL

Scarlett 21.5 24.1 9.0 9.0 35.0 42.0 1.1 1.4

ISR42-8 43.7 37.0 30.0 27.0 51.0 42.0 6.8 5.0

S42 20.5 23.0 5.0 5.0 43.0 54.0 0.2 0.3

RDW

Scarlett 2.2 2.3 0.7 0.6 5.6 5.5 0.2 0.2

ISR42-8 7.8 6.7 5.3 5.7 12.1 7.9 2.1 0.7

S42 2.1 2.0 0.3 0.1 12.7 10.8 0.1 0.4

RSR

Scarlett 4.3 6.2 2.0 1.7 7.9 11.5 0.3 0.5

ISR42-8 13.3 19.4 7.2 18.5 17.5 19.9 3.1 0.4

S42 4.5 5.6 1.0 0.4 31.8 29.3 0.1 0.1

a Trait abbreviations are given in Table 1b Standard error was calculated from the variability of genotype means

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Trait correlations

Correlation among studied traits has been listed in

Table 3. PH revealed significant but weak correlation

with SDW under control (0.25) and drought (0.21)

conditions. TKW showed highly significant and strong

correlations under drought but weak correlations

under control conditions with RL, RDW and RSR.

Strong positive correlations were detected between

RL and RDW under control (0.45) and drought (0.68)

conditions. RSR showed highly significant and

positive correlations with RL and RDW in both

control (0.29, 0.81) and drought (0.68, 0.93) condi-

tions, respectively.

QTL analysis

A total of 33 putative QTL were identified for shoot

and root traits of which 15 and 18 QTL alleles were

linked to favorable performance of the exotic and elite

alleles, respectively (Fig. 1; Table 4).

Detection of QTL for shoot traits

The QTL analysis revealed six putative QTL for PH

located on chromosomes 1H, 2H, 3H and 4H. Accord-

ing to relative performance of the exotic allele, three

QTL QPh.S42.2H, QPh.S42.4H.a and QPh.S42.4H.b

resulted in the reduction of PH ranging from 8 to 11 %.

The exotic QTL alleles at QPh.S42.1H, QPh.S42.3H.a

and QPh.S42.3H.b revealed an increase in PH ranging

from 2 to 28 %. The strongest QTL effect

(QPh.S42.3H.b) was linked to marker locus bPb-

9110 that explained huge proportion of the genetic

variance (R2 = 59.2 %) as the marker main effect. The

QTL for SDW were distributed on chromosomes 2H,

5H and 6H. Among these, at QSdw.S42.5H, the exotic

allele revealed an increase in SDW by 11 % and

explained 3.6 % of the genetic variance. Seven QTL

were found for TKW on chromosomes 1H, 3H, 4H, 6H

and 7H. At QTkw.S42.1H, the exotic allele showed a

positive increase in TKW up to 5 % by explaining

7.4 % of the genetic variation. Two minor QTL

QSdw.S42.2H.c and QTkw.S42.6H.b revealed signifi-

cant marker by treatment interactions. The exotic

alleles at these loci were inferior to the elite alleles.

Detection of QTL for root traits

Three QTL for RL located on chromosomes 2H, 3H and

5H. At QRl.S42.2H and QRr.S42.3H, performance of

the elite alleles was higher than the exotic alleles by 15

and 8 %, respectively. At QRl.S42.5H, the exotic allele

was superior to the elite allele by 9 % and explained

1.7 % of the genetic variance. Seven QTL for RDW

were mapped on chromosomes 1H, 2H, 3H, 4H, 5H and

7H. Among these, at QRdw.S42.1H.a, QRdw.S42.1H.b,

QRdw.S42.5H and QRdw.S42.7H, the exotic alleles

increased RDW which ranged from 21 to 42 % and

explained 6.5, 7.9, 4.2 and 6.9 % of the genetic

variance, respectively. QRdw.S42.2H, QRdw.S42.3H

and QRdw.S42.4H revealed the preeminence of elite

allele over the exotic allele which ranged from 13 to

34 %. For RSR, five QTL were detected on

Table 3 Pearson’s correlation coefficients (r) among the shoot and root traits under control and drought conditions

Traita Treatment PH SDW TKW RL RDW

SDW Control 0.25***

Drought 0.21***

TKW Control 0.11** 0.31***

Drought ns -0.22***

RL Control 0.24*** 0.28*** 0.28***

Drought -0.09** -0.06* 0.39***

RDW Control 0.12*** 0.38*** 0.28*** 0.45***

Drought -0.08* ns 0.40*** 0.68***

RSR Control ns -0.13*** 0.09* 0.29*** 0.81***

Drought -0.14*** -0.31*** 0.38*** 0.62*** 0.93***

*, **, *** Significant at \0.05, \0.01 and \0.001 levels of P value, respectivelya The phenotypic traits are defined in Table 1

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chromosomes 1H, 3H, 5H and 7H. The strongest exotic

QTL allele was detected at QRsr.S42.7H which

accounted for 37 % increase in RSR and 7 % of the

genetic variance. At other three QTL, QRsr.S42.1H.a,

QRsr.S42.1H.b and QRsr.S42.5H, performance of the

exotic alleles was higher than the elite alleles by 26, 20

and 23 %, respectively. At QRsr.S42.3H, the elite allele

was superior to the exotic alleles by 19 % and explained

7.4 % of the genetic variance.

Epistatic interactions

Altogether, 18 digenic epistatic interactions were

identified for PH, SDW and RDW (Table 5). For

PH, eight significant associations were found where

four interactions resulted in decreasing and four in

increasing PH. The strongest epistatic interaction was

observed between the homozygous exotic alleles at

marker loci HvGI (3H) and bPb-1366 (1H) which

reduced PH from 72.6 (Hv/Hv) to 54.0 cm (Hsp/Hsp).

Similarly, the presence of the homozygous exotic

alleles (Hsp/Hsp) at marker loci bPb-4515 (1H) and

bPb-5480 (4H), Bmag 149 (1H) and HvFT2 (3H),

bPb-0229 (2H) and Bmag7 (7H) accounted a decrease

in PH to 59.6, 63.1, 69.0 cm as compared to the

homozygous Hv alleles which averaged 72 cm. Oppo-

sitely, the strongest interaction for increasing PH was

found between marker locus bPb-4219 (7H) and

MGB396 (4H), where BC2DH lines carrying the

homozygous Hsp alleles were on average 86.8 cm as

compared to the homozygous Hv allele (average

73.2 cm). The combination of the homozygous Hsp

alleles at markers bPb-4515 (1H) and GBM1007 (1H),

bPb-4219 (7H) and TACMD (4H), bPb-5339 (1H) and

MGB396 (4H) also resulted in an increased PH to 84.7,

83.3, 78.7 cm, respectively when compared to the

association of respective Hv alleles (average 73 cm).

For SDW, interactions between the Hsp alleles at

marker loci bPb-4209 (3H) and bPb-5201 (1H), bPb-

0443 (6H) and bPb-3605 (1H), bPb-7899 (1H) and

Fig. 1 QTL map of six shoot and root traits detected in the S42 population. QTL regions are marked with black bars. The details of

these QTL are presented in Table 4

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Table 4 Summary of QTL for six shoot and root traits in the S42 population

Traita QTLb Chrc Markerd Typee Posf Rangeg SSR binh

PH QPh.S42.1H 1H bPb-3605 DArT 62.23 60.21–62.9 –

QPh.S42.2H 2H GMS3 SSR 81 70.02–97 8–10

QPh.S42.3H.a 3H GBM1043 SSR 100.7 100.7–115.5 9–10

QPh.S42.3H.b 3H bPb-9110 DArT 118.72 118.72 –

QPh.S42.4H.a 4H EBmac635 SSR 131 123.24–138 13

QPh.S42.4H.b 4H HDAMYB SSR 146 140.2–146 13

SDW QSdw.S42.2H.a 2H PpdH1 SSR 41.1 38.97–66 4–7

QSdw.S42.2H.b 2H GMS3 SSR 81 70.02–81 7–9

QSdw.S42.2H.c 2H bPb-8143 DArT 98.21 98.21–108.71 –

QSdw.S42.5H 5H bPb-0071 DArT 126.77 126–138 –

QSdw.S42.6H 6H EBmac624 SSR 68.1 63.75–74.34 6

TKW QTkw.S42.1H 1H HvFT3 SSR 115 94.90–116 3

QTkw.S42.3H 3H bPb-4209 DArT 111.7 111.7 –

QTkw.S42.4H.a 4H EBmac635 SSR 131 123.24–140 13

QTkw.S42.4H.b 4H HVM67 SSR 141.1 141–141.1 13

QTkw.S42.6H.a 6H HvNAM1 SSR 63 50.06–63.75 6

QTkw.S42.6H.b 6H bPb-6721 DArT 72.7 68–74.34 –

QTkw.S42.7H 7H BMS64 SSR 100.3 87.5–107 8–10

RL QRl.S42.2H 2H PpdH1 SSR 41.1 41.1 5

QRl.S42.3H 3H bPb-9110 DArT 118.72 111.68–118.72 –

QRl.S42.5H 5H VrnH1 SSR 125.1 94.95–126.76 10–12

RDW QRdw.S42.1H.a 1H GBM1042 SSR 39 38.5–63.2 4–6

QRdw.S42.1H.b 1H bPb-2240 DArT 123.09 115–123.09 –

QRdw.S42.2H 2H bPb-4261 DArT 44.79 38.97–66 –

QRdw.S42.3H 3H bPb-9110 DArT 118.72 111.68–145.40 –

QRdw.S42.4H 4H EBmac635 SSR 131 123.24–132 13

QRdw.S42.5H 5H bPb-0071 DArT 126.77 125.1–126.77 –

QRdw.S42.7H 7H VrnH3 SSR 42.5 40–42.5 3

RSR QRsr.S42.1H.a 1H GBM1042 SSR 39 38.5–63.2 4–6

QRsr.S42.1H.b 1H bPb-2240 DArT 123.09 106.21–123.09 –

QRsr.S42.3H 3H bPb-9110 DArT 118.72 111.68–145.40 –

QRsr.S42.5H 5H bPb-0071 DArT 126.77 125.1–126.77 –

QRsr.S42.7H 7H VrnH3 SSR 42.5 40–42.5 3

Traita Effecti F valuej P-FDRk R2 (%)l [Hsp]m RP [Hsp]n(%) Addo

PH M 14.0 \0.05 0.2 69.1 2 2.32

M 42.7 \0.001 13.0 62.6 -11 -2.60

M 12.5 \0.05 7.9 72.9 9 1.44

M 533.3 \0.001 59.2 82.6 28 8.22

M 15.3 \0.05 5.9 63.7 -8 -1.42

M 11.6 \0.05 7.0 64.2 -8 -1.23

SDW M 51.8 \0.001 14.9 2.9 -22 -0.36

M 14.9 \0.05 5.8 3.5 -7 -0.10

M 9 T 18.4 \0.01 4.7 3.5 -6 -0.12

M 27.2 \0.001 3.6 4.1 11 0.27

Mol Breeding

123

bPb-2993 (3H) showed an increase in SDW up to 4.4,

4.3 and 4.6 g as compared to their homozygous Hv

counterparts that resulted in an average SDW 3.4 g.

Seven significant QTL epistatic pairs were detected

for RDW. The strongest interaction was detected

between marker locus bPb-9746 (3H) and S5307 (1H)

where the combination Hsp/Hsp alleles resulted in a

mean RDW 4.7 g. As the Hsp alleles were substituted

Table 4 continued

Traita Effecti F valuej P-FDRk R2 (%)l [Hsp]m RP [Hsp]n(%) Addo

M 28.8 \0.001 6.5 3.4 -9 -0.14

TKW M 19.6 \0.01 7.4 51.8 5 0.92

M 25.6 \0.01 5.9 48.2 -4 -0.74

M 56.5 \0.01 1.4 48.2 1 0.04

M 15.0 \0.01 9.1 49.3 -2 -1.46

M 39.9 \0.01 1.6 47.9 -5 -0.60

M 9 T 11.8 \0.01 1.9 49.3 -2 -0.68

M 17.9 \0.01 9.6 47.4 -6 -1.30

RL M 17.6 \0.01 6.1 18.6 -15 -1.83

M 18.1 \0.01 5.5 20.4 -8 -0.95

M 13.3 \0.05 1.7 23.6 9 1.35

RDW M 16.1 \0.01 6.5 2.5 29 0.21

M 26.1 \0.001 7.9 2.3 21 0.20

M 24.4 \0.001 6.5 1.4 -34 -0.35

M 31.6 \0.001 7.9 1.7 -21 -0.21

M 11.3 \0.01 3.4 1.8 -13 -0.14

M 30.6 \0.001 4.2 2.6 29 0.36

M 35.7 \0.001 6.9 2.8 42 0.43

RSR M 21.2 \0.001 6.3 6.1 26 0.55

M 29.1 \0.001 8.3 5.7 20 0.42

M 17.6 \0.01 7.4 4.2 -19 -0.42

M 12.9 \0.05 3.2 6.1 23 0.57

M 28.1 \0.001 6.6 6.8 37 0.97

a Trait abbreviations are given in Table 1b Description of quantitative trait locusc Chromosomed Linked DNA markere Marker typesf Marker position in CentiMorgang Positions of DNA markers from the first to the last significant marker in a QTL regionh SSR markers were assigned to bins according to Kleinhofs and Graner (2001)i Effects showing by the QTL, marker main (M) and marker by treatment (M 9 T) interaction effectsj F value of the given marker locusk Probability of false discovery rate at \0.05, \0.01 and \0.001 levelsl Genetic variance explained by M or M 9 T. For calculation, see von Korff et al. (2006)m Least square means of homozygous exotic allele [Hsp]n Relative performance of the homozygous exotic allele, RP[Hsp]; ‘‘–’’ indicates a reduction in trait value due to the introgression of

exotic alleleo The additive effect is half the difference between phenotypic means of the homozygous elite and exotic marker genotypes

Mol Breeding

123

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ble

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sp/H

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spk

PH

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3b

Pb-1

36

61

H9

5.0

84

0.6

\0

.01

2.8

72

.67

2.2

76

.75

4.0

bP

b-4

51

51

H1

06

.22

bP

b-5

48

04

H7

2.2

11

7.0

\0

.05

1.1

73

.07

5.2

71

.65

9.6

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51

51

H1

06

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GB

M1

00

71

H1

91

3.5

\0

.05

1.3

74

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2.6

72

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4.7

bP

b-4

21

97

H7

3.8

9M

GB

39

64

H9

51

4.5

\0

.05

1.4

73

.27

2.9

72

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6.8

bP

b-4

21

97

H7

3.8

9T

AC

MD

4H

12

51

4.3

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1.3

73

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72

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3.3

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92

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77

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Mol Breeding

123

with Hv, RDW decreased to 2.3 g. Likewise, the

presence of Hsp alleles at marker loci bbPb-8779 (2H)

and Bmag357 (5H), bPb-4577 (2H) and Bmac32 (1H),

bPb-1318 (1H) and MGB338 (5H), bPb-3732 (7H)

and Ebmac603 (7H), bPb-1681 (3H) and Bmac40 (6H)

showed an increase of RDW up to 4.4, 4.3, 3.8, 3.6 and

3.4 g, respectively in comparison with combinations

of the respective Hv alleles that averaged 2.3 g. The

marker loci bPb-0353 (3H) and HVM67 (4H)

accounted a decrease in RDW from 2.4 to 1.8 g due

to the preeminence of homozygous Hv alleles in

comparison with their Hsp counterparts.

Validation of QTL

Nine QTL effects of the exotic origin were verified using

a library of 53 ILs (ILs, Fig. 2). For this, trait values of

individual ILs were compared to Scarlett as a control

using Student’s t test. The IL S42IL-109 revealed

significant decrease in PH as compared to Scarlett under

control and drought conditions. This IL carried Hsp

introgression from 63.96 to 110.84 cM on chromosome

2H which validated localization of the QTL

QPh.S42IL.2H. The strongest PH QTL QPh.S42IL.3H.b

was validated in multiple ILs, S42IL-121, S42IL-137,

S42IL-140 and S42IL-148. These lines carry overlap-

ping Hsp introgressions on chromosome 3H that shared

a common region from 185.12 to 211.25 cM. The IL

S42IL-133 exhibited significant increase in RDW and

RSR under both control and drought conditions. It bears

a unique introgression on chromosome 7H from 17.32 to

44.83 cM which confirmed localization of the QTL

QRdw.S42IL.7H and QRsr.S42IL.7H. Similarly, the IL

S42IL102 revealed superior performance in RDW and

RSR while bearing QRdw.S42IL.1H.a and

QRsr.S42IL.1H.a. The IL S42IL-125 showed significant

increase in multiple root traits, such as RL, RDW and

RSR. The comparison of putative QTL positions with

the Hsp introgression in the IL S42IL-125 revealed that

this IL harbor the QTL QRl.S42IL.5H, QRdw.S42IL.5H

and QRsr.S42IL.5H in the region between 104.73 and

154.37 cM on chromosome 5H.

Fig. 2 Chromosomal map of the selected introgression lines showing the validation of nine exotic QTL alleles. The black regions

showed the location of wild introgressions according to Schmalenbach et al. (2011). Asterisk indicates the missing data

Mol Breeding

123

Discussion

Trait variation

The S42 population demonstrated significant variation

in the shoot and root traits among 301 BC2DH lines

possibly because it was achieved by crossing two

contrasting parents Scarlett and ISR42-8. Both parents

showed characteristic differences in development of

their shoots and roots under control and drought

conditions (Naz et al. 2012). These features made

them ideal candidates to establish a mapping popula-

tion for genetic analysis of the shoot and root

parameters. It has been reported that resolution of

the QTL mapping depends primarily on phenotypic

difference between the respective parents (Tanksley

1993). Although the wild barley accession ISR42-8

contain detrimental alleles for agronomic traits but

BC2DH lines showed transgression effects for PH,

SDW, RL, RDW and RSR under control and drought

conditions indicating presence of the valuable alleles

for these traits in the wild gene pool. Interestingly,

most shoot traits were influenced by water stress

except RL, RDW and RSR which increased among

individual BC2DH lines under drought conditions. In

this scenario, it is tempting to speculate that intro-

gression of the wild alleles in these BC2DH lines led to

influence the drought adaptive traits, such as RL,

RDW and RSR positively which seems crucial for

drought stress tolerance in barley. The shoot traits

showed significant correlation under control condition

but very weak and negative correlations under drought

conditions possibly due to strong and variable reduc-

tion in the trait values under the extreme conditions of

drought. But TKW showed weak correlation with root

traits under control conditions as compared to drought

putatively due to higher and variable expression of

trait value under control conditions as the recurrent

parent Scarlett is adapted to well water environment.

The BC2DH lines carry major proportion of their

genome from the Scarlett. This expression may be

impaired under drought conditions that lead to stron-

ger correlation of drought adaptive root traits with

TKW. However, root traits were strongly correlated

under both conditions indicating again a root-related

drought adaptation among the S42 population. These

results are in line with Naz et al. (2012) who found

significant positive correlation among root traits, such

as RDW, RL and root volume (RV). Yadav et al.

(1997) who studied root morphology and distribution

in rice also found a strong correlation between RL and

RDW.

QTL localization

PH is a quantitative trait which is controlled by many

genes, including dwarfing, semi dwarfing and many

additional loci (Yu et al. 2010). PH genes have been

mapped to short arm of chromosome 2H (Wang et al.

2010) and long arm of chromosome 4H (Hackett

et al.1992). In the present study, we mapped two QTL

QPh.S42.2H and QPh.S42.4H.b on chromosomes 2H

and 4H which accounted for 11 and 8 % reduction in

PH, respectively. In addition, the QTL QPh.S42.3H.a

and QPh.S42.3H.b were identified on chromosome

3H which associated with increase in PH. Pillen

et al. (2004) reported a QTL on chromosome 3H

which may agree with the chromosomal position

of QPh.S42.3H.a. The exotic allele at QTL

QPh.S42.3H.b showed the strongest increase in Ph

by explaining 59.2 % of the genetic variance. Von

Korff et al. (2006) used a genetic map based on 98 SSR

markers and found a major QTL for PH linked to

marker locus HV13GEIII which explained 19.9 % of

the genetic variance. As we utilized a high-resolution

genetic map based on 371 genetic markers, this QTL

showed linkage to a newly incorporated marker bPb-

9110 which possibly refined the position of this QTL

on chromosome 3H. PH was also influenced by a

number of digenic epistatic interactions where at four

loci the allelic combination of Hsp/Hsp resulted in an

increased PH. Similar to von Korff et al. (2010), these

marker does not appear significant in single-marker

analysis, however, in the combination posed a putative

additive response to enhance PH. The donor of these

exotic alleles, ISR42-8 is taller than the Scarlett which

supports the cumulative response of PH genes from

wild barley. However, individually these genes unable

to contribute significantly because of the prevalence of

respective PH reducing Scarlett alleles in the BC2DH

lines which harbor a major proportion of the Scarlett

genome. This phenomenon partially explains the

presence of transgression effects in BC2DH lines for

PH which may be a concert of PH genes from wild

barley in the presence of useful PH enhancers from

Scarlett.

The strongest QTL for SDW was linked to a gene-

specific marker locus PpdH1 where the preeminence

Mol Breeding

123

of elite allele favored trait performance. Wang et al.

(2010) found that the cultivar Scarlett harbors a

recessive late-flowering Ppd-H1 allele suggesting a

delayed flowering and an excessive vegetative growth

which increases SDW. A single exotic QTL allele was

associated with increased SDW at QSdw.S42.5H

indicating a unique exotic allele putatively linked

with the natural adaptation of ISR42-8 in the rainfed

conditions. Morgan et al. (1993) found that the above

ground dry matter production is an important criterion

to judge drought tolerance in crop breeding. However,

at QTL QSdw.S42.2H.c exotic alleles revealed treat-

ment interaction effect while decreasing trait value

that may explain a putative linkage drag of the exotic

allele on SDW. It may hold true for the drought

inducible exotic QTL alleles for the TKW on

chromosome 6H. Further, three digenic epistatic

interactions were responsible for SDW variation in

the BC2DH lines by additive effect of the homozygous

Hsp alleles. These interaction effects may also explain

transgression effects for SDW in the BC2DH lines.

Although ISR42-8 was inferior in yield and at five

QTL, the exotic alleles showed deleterious effects on

TKW but the exotic alleles at marker locus HvFT3 on

chromosome 1H revealed a positive effect on TKW,

suggesting the presence of a valuable allele for yield in

the wild barley. Von Korff et al. (2006) found a

favorable exotic QTL allele for yield components on

the short arm of chromosome 2H under field condition.

A deep root system able to extract water from

depths and RL is one of the most consensual traits

contributing to drought avoidance (Courtois et al.

2000). Altogether, three QTL were detected for RL

where marker locus VrnH1 was linked to an exotic

QTL allele that increased RL by 9 %. Chen et al.

(2010) also found a QTL for RL in the corresponding

region on chromosome 5H. In wheat, Liu et al. (2012)

detected three QTL for RL under drought stress

conditions which were located on chromosomes 2D,

3A and 5B. A similar QTL Dro1 (Deeper rooting 1)

that controls deep rooting in rice under upland field

conditions was reported on chromosome 9. RDW is

another important parameter of root connected to

water-use efficiency and long-term drought (Songsri

et al. 2009). We identified seven QTL for RDW of

which four QTL QRdw.S42.1H.a, QRdw.S42.1H.b,

QRdw.S42.5H and QRdw.S42.7H had an increased

RDW by introgression of the exotic alleles. All of

these QTL would be useful for drought resistance

breeding in barley. Li et al. (2005) identified three

additive QTL for RDW in rice; however, no reports

were found yet on whole genome QTL mapping of

RDW in barley. Further, seven digenic epistatic

interactions were detected for RDW of which six

interaction effects acted positively and the BC2DH

lines carrying the homozygous Hsp alleles at these loci

were superior in RDW, suggesting a putative additive

effect of exotic alleles in determining this trait in

barley. These results are in line with our expectation

because the donor parent ISR42-8 was superior in

RDW as compared to Scarlett. In rice, Li et al. (2005)

detected three and four epistatic QTL for RDW and

RSR, respectively. Yamauchi and Aragones (1997)

found that the genotypes with high root–shoot ratios

were more resistant under drought conditions.

QTL validation

A total of 15 exotic QTL alleles were associated with

improval of shoot and root traits in the S42 population.

Among these, we validated nine exotic QTL alleles in a

set of ILs. Some exotic QTL alleles have not been

validated in the ILs possibly because of mixed

background of the S42 population. The role of multiple

introgressions in BC2DH lines is crucial due to additive

nature of the quantitative traits. This mixed back-

ground also provides a necessary platform for the

action of epistatic genes in a trait expression. We

validated two QTL for PH, QPh.S42IL.2H and

QPh.S42IL.3H.b. The QTL QPh.S42IL.2H was

responsible for decreasing PH in the IL S42IL-109

that possibly corresponds to a gibberellic acid

insensitive dwarfing gene ‘‘sdw3’’ which reduces PH

and located in the 67–92 cM region on chromosome

2H (Schmalenbach et al. 2009). The QTL QPh.

S42IL.3H.b linked to the strongest increase in PH

was validated in multiple ILs, S42IL-121, S42IL-137,

S42IL-140 and S42IL-148 sharing common overlap-

ping Hsp introgressions which help us to refine the

QTL position between 185.12 and 211.25 cM on

chromosome 3H. Although increasing PH is not a

desirable breeding trait but it is an important adaptive

trait for drought escape therefore, this major QTL

allele of exotic origin that explains 59.2 % of the

genetic variance may be a valuable novel allele for

plant adaptation. For root traits, we identified

two unique ILs, S42IL-133 (QRdw.S42IL.7H,

QRsr.S42IL.7H) and S42IL-102 (QRdw.S42IL.1Ha,

Mol Breeding

123

QRsr.S42IL.1H.a) possessing QTL for RDW and RSR

on chromosomes 7H and 1H, respectively. These QTL

may correspond to Hoffmann et al. (2012) who

reported QTL for shoot–root ratio in the IL S42IL-

133 and RDW in the IL S42IL-102 by studying the

juvenile plant development in a hydroponic system.

These data suggest stability of these QTL effects across

variable environmental conditions. Similarly, the IL

S42IL-125 harbored an Hsp introgression linked to

QTL QRl.S42.5H, QRdw.S42.5H and QRsr.S42.5H for

RL, RDW and RSR, respectively. We believe this

unique IL carried a major gene for root development

that has a broad effect on several root parameters. Naz

et al. (2012) found major QTL for RDW, RL and root

volume in the IL S42IL-176 which reside on chromo-

some 5H in the interval between 200.12 and

234.98 cM. However, this chromosomal region does

not correspond to the Hsp introgression in the IL

S42IL-125 suggesting the identification of a new

exotic allele for root trait variation on chromosome 5H.

Conclusion and future work

The present study describes the identification of 33 QTL

for the shoot and root traits of which 15 and 18 QTL

alleles were linked to favorable performance of the exotic

and elite alleles, respectively. Nine out of 15 exotic QTL

alleles were successfully validated in wild barley ILs.

These exotic QTL alleles are fixed in a near isogenic

background of Scarlett which provides an opportunity to

utilize this valuable genetic resource in plant breeding.

Furthermore, high-resolution mapping populations are

established from the QTL-bearing ILs for fine mapping

and positional cloning of underlying genes.

Acknowledgments We offer special thanks to Mrs. Anne

Reinders, Mrs. Karola Muller, Mrs. Iris Hermeling and Mrs.

Martina Ruland for their support during phenotyping and to Mrs.

Karin Woitol and Mr. Stephan Reinert for reading the

manuscript. Part of this work was funded by the German Plant

Genome Research Initiative (GABI) of the Federal Ministry of

Education and Research (BMBF, Project 0312278A).

Conflict of interest The authors declare that they have no

conflict of interest.

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