QTL mapping for yield and yield contributing traits in twomapping populations of bread wheat

N. Kumar Æ P. L. Kulwal Æ H. S. Balyan ÆP. K. Gupta

Received: 16 June 2006 / Accepted: 27 September 2006 / Published online: 25 October 2006� Springer Science+Business Media B.V. 2006

Abstract In bread wheat, single-locus and two-

locus QTL analyses were conducted for seven

yield and yield contributing traits using two

different mapping populations (P I and P II).

Single-locus QTL analyses involved composite

interval mapping (CIM) for individual traits and

multiple-trait composite interval mapping

(MCIM) for correlated yield traits to detect the

pleiotropic QTLs. Two-locus analyses were con-

ducted to detect main effect QTLs (M-QTLs),

epistatic QTLs (E-QTLs) and QTL · environ-

ment interactions (QE and QQE). Only a solitary

QTL for spikelets per spike was common between

the above two populations. HomoeoQTLs were

also detected, suggesting the presence of triplicate

QTLs in bread wheat. Relatively fewer QTLs

were detected in P I than in P II. This may be

partly due to low density of marker loci on P I

framework map (173) than in P II (521) and

partly due to more divergent parents used for

developing P II. Six QTLs were important which

were pleiotropic/coincident involving more than

one trait and were also consistent over environ-

ments. These QTLs could be utilized efficiently

for marker assisted selection (MAS).

Keywords Bread wheat � Pleiotropic QTL �Single-locus analysis � Two-locus analysis

Introduction

In bread wheat, a variety of complex traits have

been subjected to QTL analysis (for review see,

Gupta et al. 1999; Li and Gill 2004; Jahoor et al.

2004; Tuberosa and Salvi 2004). However, in

majority of these cases, an individual study

involved a single mapping population, and only

single-locus analysis was conducted. Conse-

quently, in each such study, only a limited number

of main effect QTLs (M-QTLs) could be detected,

and epistatic QTL · QTL (Q · Q = QQ) and

QTL · environment (Q · E = QE; Q · Q ·E = QQE) interactions could not be examined. A

dissection of these interactions is needed to better

understand the genetic control of a trait (Holland

2001; Mackay 2001; Carlborg and Haley 2004;

Erickson 2005). Two-locus analysis for the genetic

dissection of a few complex traits was also

attempted in bread wheat (Kulwal et al. 2004,

2005), oats (Holland et al. 2002), rice (Yu et al.

N. Kumar � P. L. Kulwal � H. S. Balyan �P. K. Gupta (&)Molecular Biology Laboratory, Department ofGenetics & Plant Breeding, Ch. Charan SinghUniversity, Meerut 250 004, Indiae-mail: pkgupta36@vsnl.com

P. L. KulwalDepartment of Agricultural Botany, BiotechnologyCenter, Dr. Panjabrao Deshmukh AgriculturalUniversity, Akola 444 104 Maharashtra (MS), India

Mol Breeding (2007) 19:163–177

DOI 10.1007/s11032-006-9056-8

123

1997; Li et al. 1997, 2003; Cao et al. 2001; Xing

et al. 2002; Zhuang et al. 2002; Gao et al. 2004;

Mei et al. 2003, 2005), maize (Yan et al. 2006), and

several other plant groups including cotton (Shen

et al. 2006), and the model plant system Arabid-

opsis (Juenger et al. 2005a, b). The results of these

latter studies reinforced the realization that while

conducting QTL analysis for an individual com-

plex trait, more than one mapping populations

should be used and single-locus as well as two-locus

analyses should be utilized.

In bread wheat, yield and yield contributing

traits were analyzed in only a few QTL studies

(Shah et al. 1999; Araki et al. 1999; Kato et al.

2000; Borner et al. 2002; Campbell et al. 2003;

Groos et al. 2003; Huang et al. 2003, 2004;

Quarrie et al. 2005; Marza et al. 2006; Nar-

asimhamoorthy et al. 2006). This is despite the

fact that these traits are economically the most

important and are complex in nature, exhibiting

low heritability (Grafius et al. 1978; Khalifa

et al. 1988). This warrants genetic dissection of

these traits using more than one population, and

employing more than one methods of QTL

analysis. Genetic relationship among yield traits

using simple correlations should also be worked

out and multitrait composite interval mapping

(MCIM) needs to be conducted for correlated

traits. In the present study, genetic dissection of

yield and yield contributing traits was attempted

to identify M-QTLs using single locus QTL

analysis following composite interval mapping

(CIM), to identify pleiotropic QTLs using

MCIM and to assess QQ and QE/QQE interac-

tions through two locus QTL analyses; two

different mapping populations were used, one

derived from two Indian bread wheat genotypes,

and the other the International Triticeae

Mapping Initiative population (ITMIpop).

Material and methods

Experimental populations and their

evaluation

Following two mapping populations were used: (i)

population I (P I) comprised a set of 100

recombinant inbred lines (RILs) derived from

the cross WL711 · PH132; and (ii) population II

(P II) was ITMIpop (for details, see Van Deynze

et al. 1995) comprising 110 RILs that were initially

derived from the cross, Opata85 · W7984. Both

these populations were used by us in an earlier

study also (Kulwal et al. 2005). The parents along

with the RILs were evaluated at three different

locations, Meerut, Pantnagar and Ludhiana, which

are the three major wheat-growing areas of

Northern India. The three locations provided six

environments for P I (environment I = Meerut

2000–2001, II = Pantnagar 2000–2001, III = Lu-

dhiana 2000–2001, IV = Meerut = 2001–2002,

V = Pantnagar 2001–2002, VI = Ludhiana 2001–

2002) and four environments for P II (environment

I = Meerut 2000–2001; II = Meerut 2001–2002;

III = Pantnagar 2001–2002; IV = Ludhiana 2001–

2002). In an individual replication, the parents and

each RIL were evaluated each in a plot of two rows

of 1 m each. The row-to-row distance was kept at

23 cm. All recommended agronomic practices

were followed. Data on seven yield and yield

contributing traits {tillers per plant (TPP), biolog-

ical yield (BY), grain yield (GY), harvest index

(HI), spike length (SL), spikelets per spike (SPS)

and grains per spike (GPS)} were recorded on all

the RILs and the parents using 10–15 plants (five

random plants from each replication) for each

individual genotype. Frequencies distributions of

means were tested for normal distribution using

MS Excel.

Molecular markers for QTL analysis in P I and

P II

Genotyping and mapping data for P I on a set of 173

SSR marker loci were available from an earlier

study (Prasad et al. 2003). Similar data for P II

(ITMIpop) on a set of 521 mapped molecular

markers was retrieved from the GrainGenes data-

base: http://wheat.pw.usda.gov/ggpages/map_sum-

mary.html.

Pearson’s correlation coefficient analysis

Correlation coefficients among the seven yield

contributing traits (pooled data of each trait)

were obtained using MS Excel.

164 Mol Breeding (2007) 19:163–177

123

QTL analyses for yield and yield contributing

traits

The QTLs in both the mapping populations were

identified by single-locus QTL analysis through CIM

using QTL Cartographer version 2.5 (Basten et al.

1994; Wang et al. 2004). For each trait, the analysis

was carried out for the data on individual environ-

ments as well as using the pooled data averaged over

all environments. The threshold LOD scores were

calculated using 1,000 permutations (Churchill and

Doerge 1994; Doerge and Churchill 1996).

Multiple-trait analysis involving MCIM was

conducted using the module JZmapqtl available

in QTL Cartographer with the objective to detect

pleiotropic QTLs. A LOD score of 3.0 was used for

detecting putative QTLs. A QTL was considered

consistent if the QTL was detected in at least four

of the seven sets of data (six environments + the

pooled data) in P I and in three of the five sets of

data (four environments + the pooled data) in P

II. Confidence intervals (CI) were obtained by

marking positions ±1LOD from the peak. QTLs in

the adjacent intervals and/or with overlapping

confidence intervals were treated as one QTL.

Two-locus analysis was conducted using QTLNet-

work version 2.0 http://ibi.zju.edu.cn/software/qtl-

network/, where P = 0.05 was used as the

threshold for detecting putative M-QTLs or epi-

static QTLs (E-QTLs).

Results

Performance of the RILs and correlations

among yield traits

In both the mapping populations, yield and yield

contributing traits showed a good fit to normal

distribution and the RILs transgressed both parents

in all the environments except for all traits grain

yield and harvest index in P I (data not shown). The

values of simple correlations among seven yield and

yield contributing traits obtained from each of the

two populations using data pooled over environ-

ments are presented in Table 1.

QTLs resolved by single-locus analysis

Composite interval mapping (CIM)

Following single-locus composite interval mapping,

25 QTLs in P I and 50 QTLs in P II were detected

above the threshold LOD scores (see Tables 2 and

3 for details). The number of QTLs for an individ-

ual trait ranged from two (BY and SL) to eight

(GY) in P I (Table 2), and from five (HI) to ten

(GPS) in P II (Table 3). In P I, a QTL for SPS,

which was consistent in four of the seven environ-

ments, was coincident with QTLs for two other

traits (BY and HI); it is possible that these three

QTLs for three traits represent one pleiotropic

QTL (Table 2; also see Table 4). Another QTL for

SL (on 2BL) was consistent over all the environ-

ments (Table 2). Similarly in P II, six sets of QTLs

were detected, each set having more than one

coincident QTLs (Table 3), four of these six sets of

QTLs represent, each a pleiotropic QTL as deter-

mined through MCIM and joint MCIM (Table 4).

Also in P II, six QTLs were consistent, which

included one QTL each for TPP, GY, HI, SPS, SL

and GPS; some of these consistent QTLs also

figured among the sets of coincident QTLs, sug-

gesting that they are definitive and reliable QTLs.

In the two mapping populations, the pheno-

typic variation explained (PVE) by individual

QTLs varied in an individual environment and

showed positive relationship with the LOD scores

Table 1 Simple correlations among seven yield contributing traits in P I and P II

Correlations BY GY HI SL SPS GPS

TPP 0.41**, 0.70** 0.24*, 0.45** –0.20*, –0.05 –0.03, –0.03 –0.03, 0.04 –0.13, 0.09BY 0.75**, 0.62** 0.36**, –0.08 0.64**, 0.33** 0.78**, 0.33** 0.66**, 0.40**GY 0.88**, 0.70** 0.80**, –0.05 0.85**, –0.14 0.78**, 0.64**HI 0.85**, –0.35** 0.86**, –0.45** 0.82**, 0.44**SL 0.94**, 0.57** 0.91**, 0.17SPS 0.95**, 0.17

* Significant at P < 0.05. ** Significant at P < 0.01. For each entry, the first figure belongs to P I and the second to P II

Mol Breeding (2007) 19:163–177 165

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gw

m276-X

ksu

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(10;

III)

QB

y.c

csu

-7A

.1137.2

13.0

6–0.4

111.4

0G

rain

yie

ld1A

LX

bcd

265-X

ksu

H9

(13;

I,II

)Q

Gy.c

csu

-1A

.279.4

1/7

6.4

13.8

7/4

.37

–1.7

4/–

1.6

211.1

7/1

0.5

62A

SX

cdo

57-X

ksu

D18

(4;

P)

QG

y.c

csu

-2A

.112.9

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6–0.9

69.9

12D

SX

gw

m261-X

cdo

1379

(6;

II,

III,

IV,

P)

QG

y.c

csu

-2D

.136.1

1/4

2.1

1/4

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16.8

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0.0

6/8

.11

/9.0

0–2.4

8/–

2.0

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1.8

526.2

8/4

1.1

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0/3

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44B

LX

fbb

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(8;

I,P

)Q

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.140.9

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6/3

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1.1

616.3

44B

LX

fbb

67-X

fba177

(9;

II)

QG

y.c

csu

-4B

.256.3

14.2

1–1.9

316.1

9/1

5.4

16D

LX

bcd

1319-X

ksu

D27

(8;

II)

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y.c

csu

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(6;

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i.cc

su-2

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1/4

0.1

1/4

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112.7

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7–5.1

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bcd

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(82;

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(8;

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gw

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(6;

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l.cc

su-1

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70.4

514.4

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LX

ksu

G34-X

bcd

1562

(16;

III)

QS

l.cc

su-1

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13.3

70.5

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LX

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h-X

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(15;

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bcd

102-X

gw

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(4;

III,

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6.7

22D

SX

gw

m261-X

cdo

1379

(6;

II)

QS

l.cc

su-2

D.2

46.1

18.7

20.9

430.1

54A

LX

mw

g549-X

bcd

1670

(7;

II)

QS

l.cc

su-4

A.2

75.1

14.5

2–0.6

011.6

94A

LX

fba231-X

cdo

545

(12;

III,

P)

QS

l.cc

su-4

A.3

99.5

1/1

01.5

14.6

5/4

.13

–0.6

4/–

0.3

915.5

1/1

0.4

65A

LX

bcd

1235.1

-Xb

cd183

(10;

I,IV

,P

)Q

Sl.

ccsu

-5A

.197.9

1/9

3.9

1/9

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14.5

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0.6

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16.3

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0.6

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(9;

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522.2

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ele

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er

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cdo

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(6;

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ps.

ccsu

-2D

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8.5

1/5

2.5

110.0

5/5

.27

/3.9

42.0

4/1

.19/0

.95

29.6

2/1

9.1

5/1

3.4

64A

LX

gw

m637-X

mw

g549

(6;

II,

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0.5

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2/5

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4D

SX

mw

g634-X

bcd

265

(1;

IV)

QS

ps.

ccsu

-4D

.108.0

12.7

30.9

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LX

mw

g522-X

mw

g624

(8;

III)

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ps.

ccsu

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LX

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Sp

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1.5

4/0

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24.0

4/5

.78

Mol Breeding (2007) 19:163–177 167

123

(Tables 2 and 3). More QTLs were detected on

the A genome (13 in P I and 20 in P II) than on

either B or D genome; on individual chromo-

somes, more QTLs were detected on the long

arms than on the short arms.

Multiple-trait composite interval mapping

(MCIM)

Single-locus MCIM was also conducted using

each of the two populations (see ESM Tables 1

and 2). All QTLs detected through MCIM also

figured in joint MCIM. In P I, as many as 16 QTL

for five traits were detected using MCIM, and 86

QTLs were detected using joint MCIM. Similarly,

in P II 25 QTLs were detected using MCIM and

92 QTLs were detected using joint MCIM. Two

QTLs in P I and four QTLs in P II were

pleiotropic (Table 4; Fig. 1). The number of

QTLs detected for individual traits ranged from

one to four in P I and from one to six in P II (one

QTL each for GY in P I and GPS in P II; four

QTLs each for TPP, BY and HI in P I and six

QTLs for TPP in P II) (ESM Tables 1 and 2). The

LOD scores for an individual QTL ranged from

3.0 to 17.9 in two populations.

QTLs resolved by two-locus analysis

A total of 38 QTLs in P I and 37 QTLs in P II

were detected using two-locus analyses (Tables 5

and 6). Of these QTLs, eleven QTLs in P I and 18

QTLs in P II were M-QTLs; the remaining QTLs

were mainly E-QTLs except three QTLs in P I

(one QTL involved in QE and two QTLs

involved in QQE interactions) and only two

QTLs in P II (involved in QQE interactions; see

later). In P I, M-QTLs were available for only

four traits (leaving out HI, SPS and GPS), but in

P II, they were available for all the seven traits

(for details, see Table 5). For individual traits,

number of M-QTLs ranged from one (TPP and

BY) to seven (SL) in P I, and from one (BY and

HI) to four (GY and SPS) in P II (Table 5). Four

of the 11 M-QTLs in P I and 17 of the 18 M-QTLs

in P II that were detected through two-locus

analysis, were also detected through single-locus

CIM either in the same and/or adjacent marker

intervals (Tables 2, 3 and 5).Ta

ble

3co

nti

nu

ed

Ch

rom

oso

me

arm

aM

ark

er

inte

rvals

bQ

TL

sP

osi

tio

n(c

M)

LO

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AP

VE

(%)

6A

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ksu

G48-X

ksu

H4

(2;

III,

P)

QS

ps.

ccsu

-6A

.127.7

1/1

7.7

12.6

7/3

.74

–0.7

2/–

0.7

07.8

0/9

.03

Gra

ins

per

spik

e1A

LX

bcd

265-X

ksu

H9

(13;

III,

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QG

ps.

ccsu

-1A

.187.4

1/8

5.4

12.0

5/3

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–2.8

3/–

2.5

06.4

7/1

2.1

51B

LX

bcd

304-X

ksu

G34

(15;

III)

QG

ps.

ccsu

-1B

.1102.5

13.2

73.5

29.1

32B

SX

rz444-X

gw

m410

(4;

II)

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ps.

ccsu

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.114.9

13.6

7–3.4

311.5

92B

SX

ksu

F11-X

wg996

(10;

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.244.6

13.4

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010.3

22D

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gw

m296-X

gw

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(5;

III)

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ps.

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412.5

52D

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cdo

1008-X

cdo

36

(17;

III,

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ccsu

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.3144.7

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52.7

13.1

8/3

.78

–4.2

2/–

2.8

714.0

1/1

4.5

23B

LX

gw

m-3

76-X

gw

m285

(43;

I)Q

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s.cc

su-3

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210.4

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4–5.6

919.8

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LX

ab

c176-X

gb

xG

305

(25;

IV)

QG

ps.

ccsu

-3D

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14.0

04.8

314.4

23D

LX

ksu

d19-X

ksu

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(43;

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QG

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ccsu

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.2198.9

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811.7

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LX

gw

m276-X

ksu

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(10;

III,

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ccsu

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.1157.2

1/1

75.2

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2/4

.00

/4.3

3–5.3

6/–

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0/1

9.0

9/4

3.3

1

aC

hro

mo

som

ea

rms

carr

yin

gco

inci

de

nt

QT

La

resh

ow

nin

bo

ldb

Inp

are

nth

ese

s,a

rab

icn

um

era

lsa

reth

em

ark

er

inte

rva

ls,

foll

ow

ed

by

rom

an

nu

me

rals

ind

ica

tin

ge

nv

iro

nm

en

ts,

inw

hic

hth

eQ

TL

wa

sd

ete

cte

dc

LO

Dsc

ore

tha

te

xce

ed

sth

eth

resh

old

are

sho

wn

as

bo

ld(Q

TL

sb

elo

wa

LO

Dsc

ore

of

3.0

we

ren

ot

incl

ud

ed

,e

xce

pt

wh

ere

QT

Ls

we

rea

va

ila

ble

on

lya

tL

OD

>2

<3

).P

VE

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no

typ

icv

ari

ati

on

ex

pla

ine

d.

A,

Ad

dit

ive

eff

ect

168 Mol Breeding (2007) 19:163–177

123

Four M-QTLs in P I and 10 M-QTLs in P II

also exhibited QTL · environment interactions.

In P I, however, there was an additional QTL (for

GY), which had no main effect, but was involved

in QE interactions. In P I, QE interactions were

observed for only three traits (TPP, BY and GY)

and that too in only three of the six environments.

In P II, QE interactions were observed for six

traits (except GPS), but in all the four environ-

ments. Only a solitary M-QTL (for GY in P I)

was involved in epistatic QQ interactions

(Table 6). The other QTL involved in QQ and

QQE interactions were either E-QTLs or QTL

with no main or epistatic effects; however two

QTLs involved in these interactions were also

detected in CIM (one QTL for GY in P I, and one

for BY in P II). Epistatic interactions included 14

QQ interactions involving 27 QTLs in P I and 10

QQ interactions involving 19 QTLs in P II

(Table 6). In P I, for the traits BY and HI, no

QQ interactions was detected but in P II, QQ

interactions were observed for all the traits. QQE

Table 4 PleiotropicQTLs detected for yieldcontributing traits in P Iand P II

Traits Chromosome arm Marker interval forpleiotropicQTLs

Population

BY+HI+SPS 4AL Xgwm959-Xgwm832 P IBY+SL 5AS Xgwm154-Xgwm186 P IGY+HI+SPS 2DS Xgwm1379-Xbcd262 P IIGY+HI+SL+SPS 2DS Xgwm261-Xcdo1379 P IISL+SPS 5AL Xbcd1235.1-Xbcd183 P IIGY+TPP 6DL Xbcd1319-XksuD27 P II

Fig. 1 QTL Cartographer plot for chromosome 2DSobtained using MCIM and joint-MCIM involving sevenyield traits in PII; the plot shows a pleiotropic QTL on

2DS. Marker designations are given below the horizontalline; solid black rectangle indicates centromere; asterisks(*) indicate flanking markers of the QTL

Mol Breeding (2007) 19:163–177 169

123

interactions (six QTLs) were also observed and

included three QQE interactions (involving six

QTLs) in P I; out of six QTLs, four were E-QTLs

(for GY) and the remaining two QTLs (for TPP)

were only involved in QQE interactions. Simi-

larly, in P II four QQE interactions (eight QTLs)

were detected; out of eight QTLs, six were

E-QTLs involving three traits (BY, GY and HI)

and remaining two QTLs (for HI) were involved

in QQE interactions only. None of the QQs for

SL, SPS and GPS in P I and for TPP, SL, SPS and

GPS in P II exhibited interaction with the

environment (Table 6).

Common QTLs resolved in both the populations

Since mapped markers used for QTL analysis in

two populations differed, a comparison of marker

Table 5 QTLs with main effects and those involved in interaction with the environment for yield and yield contributingtraits in bread wheat detected in P I and P II following two-locus analysis

Trait/Marker intervala QTLs Chromosome arm andPosition (cM)

A AE

Tillers per plantXgwm1065-Xgwm332 (6; P I) QTp.ccsu-7A.3 7AL (159.5) 0.25 –0.46 (AE4)Xbcd1670-Xcdo475 (8; P II) QTp.ccsu-4A.1 4AL (79.1) 0.49 0.71 (AE2)Xbcd1319-XksuD27 (8; P II) QTp.ccsu-6D.1 6DL (120.9) –0.89 –Xfba248-Xbcd1066 (6; P II) QTp.ccsu-7A.2 7AS (98.3) –0.68 –0.46 (AE2)Biological yieldXgwm130-Xgwm1171 (1; P I) QBy.ccsu-7A.2 7AS (26.0) –2.29 –4.76 (AE4)Xcdo57-XksuD18 (5; P II) QBy.ccsu-2A.2. 2AS (14.0) –3.12 –3.17 (AE1); 2.11 (AE4)Grain yieldXgwm232-Xgdm111 (3; P I) QGy.ccsu-1D.2 1DL (48.7) – 0.70 (AE4)Xgwm938-Xgwm980 (7; P I) QGy.ccsu-3B.4 3BL (141.5) –0.31 0.34 (AE5)Xgwm130-Xgwm1171 (1; P I) QGy.ccsu-7A.3 7AS (25.0) –0.34 –1.81 (AE4)Xbcd265-XksuH9.1 (13; P II) QGy.ccsu-1A.2 1AL (80.4) –0.83 –0.58 (AE1)Xgwm261-Xcdo1379 (6; P II) QGy.ccsu-2D.1 2DS (35.1) –1.67 0.69 (AE3)Xfbb178-Xfbb67 (8; PII) QGy.ccsu-4B.1 4BL (49.8) –1.04 –0.74 (AE1)Xbcd1319-XksuD27 (8; P II) QGy.ccsu-6D.1 6DL (117.9) –0.64 –1.03 (AE2)Harvest indexXgwm261-Xcdo1379 (6; P II) QHi.ccsu-2D.2 2DS (39.1) –4.66 1.67 (AE1)Spike lengthXgwm357-Xwmc312 (6; P I) QSl.ccsu-1A.5 1AL (91.0) –0.24 –Xwmc453-Xgwm1256 (14; P I) QSl.ccsu-2A.6 2AL (132.6) 0.17 –Xgwm1249-Xwmc272 (7; P I) QSl.ccsu-2B.7 2BL (54.9) –0.29 –Xgwm186-Xgwm1236 (2; P I) QSl.ccsu-5A.2 5AL (37.0) –0.18 –Xgwm700-Xgwm182 (2; P I) QSl.ccsu-5D.4 5DL (15.4) –0.11 –Xwmc256-Xgwm570 (5; P I) QSl.ccsu-6A.1 6AL (64.7) –0.19 –Xgwm825-Xgwm768 (2; P I) QSl.ccsu-6B.3 6BS (21.3) 0.13 –Xbcd102-Xgwm296 (4; P II) QSl.ccsu-2D.1 2DS (19.1) 0.33 –0.20 (AE4)Xfba231-Xcdo545 (12; P II) QSl.ccsu-4A.3 4AL (101.5) –0.35 –Xbcd1235.1-Xbcd183 (10; P II) QSl.ccsu-5A.1 5AL (96.9) 0.52 –Spikelets per spikeXcdo1379-Xbcd262 (7; P II) QSps.ccsu-2D.2 2DS (52.5) 1.16 0.83 (AE2); –0.84 (AE4)Xbcd1670-Xcdo475 (8; P II) QSps.ccsu-4A.1 4AL (76.1) –0.64 –Xmwg522-Xmwg624 (8; P II) QSps.ccsu-5A.1 5AL (71.1) 0.47 –XksuG48-XksuH4 (2; P II) QSps.ccsu-6A.1 6AS (23.7) –0.84 –Grains per spikeXksuH9.1-Xcdo1160 (14; P II) QGps.ccsu-1A.1 1AL (103.8) –3.19 –Xgwm296-Xgwm261 (5; P II) QGps.ccsu-2D.2 2DS (25.9) –3.12 –

a In parentheses, arabic numerals are the marker intervals, followed by P I = Population I and P II = Population II. A,additive effect; AE1, AE2, AE3, AE4 and AE5, QTL · environment interaction effects for environments 1, 2, 3, 4 and 5,respectively

170 Mol Breeding (2007) 19:163–177

123

Ta

ble

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)X

gw

m819-X

gw

m976

(2;

PI)

QG

y.c

csu

-4D

.14D

L(7

9.2

)0.6

00.7

1(A

E4)

Xgw

m980-X

gw

m655

(8;

PI)

QG

y.c

csu

-3B

.43B

L(1

57.8

)X

gw

m397-X

gw

m959

(5;

PI)

QG

y.c

csu

-4A

.24A

L(5

7.0

)–0.3

0–0.6

7(A

AE

2);

–0.5

9(A

AE

3);

0.9

0(A

AE

4)

Xw

g241-X

ksu

E11.2

(19;

PII

)Q

Gy.c

csu

-1A

.31A

L(1

55.2

)X

fba166-X

fba332

(18;

PII

)Q

Gy.c

csu

-5B

.15B

L(9

0.8

)0.7

90.8

3(A

AE

1);

–0.5

4(A

AE

4)

Xw

g241-X

ksu

E11.2

(19;

PII

)Q

Gy.c

csu

-1A

.31A

L(1

55.2

)X

cdo

638-X

psr

903

(22;

PII

)Q

Gy.c

csu

-3A

.13A

L(9

9.1

)–0.8

5–

Harv

est

ind

ex

Xfb

a347-X

gw

m391

(67;

PII

)Q

Hi.

ccsu

-3A

.33A

L(3

54.9

)X

bcd

1140-X

gw

m499

(12;

PII

)Q

Hi.

ccsu

-5B

.15B

L(5

9.0

)1.4

51.2

7(A

AE

1)

Xm

wg549-X

bcd

1670

(7;

PII

)Q

Hi.

ccsu

-4A

.34A

L(7

5.1

)X

wg114-X

gw

m126

(15;

PII

)Q

Hi.

ccsu

-5A

.25A

L(1

59.2

)–

–1.9

3(A

AE

4)

Sp

ike

len

gth

Xw

mc4

38-X

gw

m136

(1;

PI)

QS

l.cc

su-1

A.5

1A

S(0

.00)

Xgw

m234-X

gw

m213

(2;

PI)

QS

l.cc

su-5

B.2

5B

S(5

4.8

)–0.2

2–

Xgw

m1130-X

gw

m1078

(1;

PI)

QS

l.cc

su-1

B.4

1B

S(0

.00)

Xw

mc5

01-X

wm

c273

(6;

PI)

QS

l.cc

su-7

B.3

7B

L(1

05.1

)0.2

7–

Xgw

m1050-X

gw

m926

(3;

PI)

QS

l.cc

su-1

B.5

1B

L(2

1.6

)X

gw

m493-X

gw

m1037

(1;

PI)

QS

l.cc

su-3

B.2

3B

S(1

0.0

)0.2

4–

Xgw

m1050-X

gw

m926

(3;

PI)

QS

l.cc

su-1

B.5

1B

L(2

1.6

)X

gw

m963-X

wm

c501

(5;

PI)

QS

l.cc

su-7

B.2

7B

L(8

2.6

)–0.2

1–

Xgw

m614-X

gw

m830

(1;

PI)

QS

l.cc

su-2

A.5

2A

S(8

.00)

Xgw

m415b

-Xgw

m1043

(7;

PI)

QS

l.cc

su-5

B.3

5B

L(1

48.3

)0.2

3–

Mol Breeding (2007) 19:163–177 171

123

intervals carrying common QTLs was not always

possible except in few cases. There were only

three chromosome arms which carried QTLs for

the same trait in both the populations (common

QTL was detected only through CIM/MCIM; no

common QTL was detected in two-locus analy-

sis). These three arms included the following: (i)

4AL carrying QTL for SPS; (ii) 5AL carrying

QTL for SL, and (iii) 7AL carrying QTL for GPS.

However, positions of QTLs for SL (5AL) and

GPS (7AL) differed in P I and P II, suggesting

that different QTL on these arms were perhaps

detected in the two populations, unless the

populations differed for interchanges/inversions

on these chromosome arms. Thus, it seems that

only a solitary QTL for SPS (4AL) may be

common in the two populations used. The above

region on 4AL perhaps represents a gene-rich

region (GRR), because QTLs for several traits

are clustered in this region, which covers a

segment from 42.4 cM to 89.1 cM in P I, and

from 33.8 cM to 87.1 cM in P II.

Genetic variance configuration

During the present study, desirable alleles for the

different QTLs were distributed in both the

parents in each of the two populations. In P I,

among the two sets of important QTLs control-

ling four traits (BY, HI, SPS and SL), positive

QTL alleles for BY and SPS were carried by

PH132 and those for HI and SL were carried by

WL711. Similarly in P II, desirable QTL alleles

for GY, HI, TPP and GPS were carried by

Opata85 and those for SL and SPS were carried

by W7984.

Discussion

The results of the present study reconfirm that the

genetics of yield and yield components in bread

wheat is complex in nature, and is controlled by a

large number of major and minor QTLs. These

QTLs may have only main effects and/or may be

involved in epistatic (QQ) or environmental (QE,

QQE) interactions. The magnitude and directions

of the additive effects of individual QTLs may alsoTa

ble

6co

nti

nu

ed

Tra

it/M

ark

er

inte

rvala

QT

Ls

Ch

rom

oso

me

arm

an

dP

osi

tio

n(c

M)

Mark

er

inte

rvala

QT

Ls

Ch

rom

oso

me

arm

an

dP

osi

tio

n(c

M)

AA

AA

E

Xgw

m1264-X

gw

m1204

(9;

PI)

QS

l.cc

su-2

D.6

2D

L(1

09.7

)X

gw

m601-X

gw

m929

(1;

PI)

QS

l.cc

su-4

A.4

4A

S(0

.00)

–0.1

7–

Xgw

m1204-X

gw

m539

(10;

PI)

QS

l.cc

su-2

D.7

2D

L(1

25.6

)X

gw

m929-X

gw

m1093

(2;

PI)

QS

l.cc

su-4

A.5

4A

S(9

.00)

0.1

8–

Xgw

m513-X

gw

m375

(6;

PI)

QS

l.cc

su-4

B.1

4B

L(4

0.9

)X

gw

m745-X

gw

m577

(8;

PI)

QS

l.cc

su-7

B.6

7B

L(1

31.5

)–0.2

1–

Xk

suH

4-X

cdo

270

(3;

PII

)Q

Sl.

ccsu

-6A

.36A

S(3

7.4

)X

cdo

836-X

ksu

D27

(13;

PII

)Q

Sl.

ccsu

-6A

.46A

L(1

26.9

)0.3

6–

Sp

ikele

tsp

er

spik

eX

gw

m122-X

gw

m1045

(11;

PI)

QS

ps.

ccsu

-2A

.22A

S(9

6.7

)X

gw

m295-X

gw

m676

(3;

PI)

QS

ps.

ccsu

-7A

.37D

L(6

6.1

)–0.3

2–

Xw

mc2

89-X

gw

m720

(1;

PI)

QS

ps.

ccsu

-3A

.13A

S(3

2.0

)X

gw

m130-X

gw

m1171

(1;

PI)

QS

ps.

ccsu

-7A

.27A

S(0

.0)

–0.6

8–

Xgw

m131.2

-Xcd

o583

(35;

PII

)Q

Sp

s.cc

su-3

B.2

3B

L(1

59.9

)X

cdo

346.1

-Xb

cd1670.1

(15;

PII

)Q

Sp

s.cc

su-5

D.1

5D

L(1

45.6

)–0.5

5–

Gra

ins

per

spik

eX

gw

m513-X

gw

m375

(6;

PI)

QG

ps.

ccsu

-4B

.54B

L(4

0.9

)X

gw

m913-X

gw

m1065

(5;

PI)

QG

ps.

ccsu

-7A

.47A

L(1

37.5

)1.5

9–

Xk

suE

18.1

-Xcd

o580

(7;

PII

)Q

Gp

s.cc

su-1

A.3

1A

S(3

4.8

)X

gw

m403-X

bcd

442

(2;

PII

)Q

Gp

s.cc

su-5

A.3

5A

S(7

7.2

)3.2

1–

aIn

pa

ren

the

ses,

ara

bic

nu

me

rals

are

the

ma

rke

rin

terv

als

,fo

llo

we

db

yP

I=

Po

pu

lati

on

Ia

nd

PII

=P

op

ula

tio

nII

.A

Aa

dd

itiv

ee

ffe

ct;

AA

E1

,A

AE

2,

AA

E3

,A

AE

4,

AA

E5

an

dA

AE

6e

pis

tasi

sa

sso

cia

ted

wit

he

nv

iro

nm

en

ts1

,2

,3

,4

,5

an

d6

,re

spe

ctiv

ely

172 Mol Breeding (2007) 19:163–177

123

vary due to genetic background of different geno-

types and due to epistatic-by-environment inter-

actions. However, it was intriguing to find that most

major QTLs, which explained large proportion of

phenotypic variation and/or detected at high LOD

scores were those, which were not only consistent

over environments, but were also pleiotropic, and/

or coincident with QTLs for other traits.

In majority of QTL studies in bread wheat

conducted in the recent past, no interactions

(QE, QQ and QQE) were examined (Shah et al.

1999; Araki et al. 1999; Kato et al. 2000; Borner

et al. 2002; Campbell et al. 2003; Groos et al.

2003; Huang et al. 2003, 2004; Quarrie et al.

2005; Marza et al. 2006; Narasimhamoorthy

et al. 2006). We believe that QTL analysis

having no provision for detecting these interac-

tions would generally lead to a biased estimate

of main-effect QTLs, and consequently result in

considerable loss of response in marker assisted

selection (MAS). Knowledge about genetic var-

iance configuration and estimates of breeding

value are also important for achieving higher

response in MAS (Li et al. 2003). Another

important aspect of the present study is the use

of two populations, which permitted detection of

many more QTL. Surprisingly, only a solitary

QTL (on 4AL) was detected in both the popu-

lations, suggesting that for complete genetic

dissection of a trait, several diverse mapping

populations should be examined.

Comparison of P I and P II

Relatively fewer QTLs were detected in P I than in

P II. This may be partly because of low density of

marker loci on P I framework map (173) than in P

II (521) used in the present study and partly due to

more divergent parents used for developing P II

(ITMIpop). It may also be noted that in P I, D

genome carried the minimum number of QTLs,

perhaps due to relatively fewer markers mapped

on this genome as compared to the other two

genomes (Prasad et al. 2003). Similar situation was

available in another recent study (Quarrie et al.

2005). One may speculate that with increase in

density of markers on D genome, more QTL will

be detected on this genome in P I also.

Efficiency of two-locus analyses

When the results of two-locus QTL analysis were

compared with those of single-locus analysis

(including CIM and MCIM), it was interesting

to note that 17 of the 18 M-QTLs detected

following two-locus analysis in P II were also

detected following single-locus analysis (CIM and

in joint analysis of MCIM) either in the same and/

or adjacent intervals for all the traits. However, in

P I, only four of the 11 QTL detected in two locus

analyses were available in CIM. This also suggests

that many QTL detected through two locus

analysis escape detection in CIM and also that

this feature is more conspicuous in some popula-

tions than in others (perhaps also due to density

of the map that is used for QTL analysis).

It is also apparent in the present results that the

QTLs involved in QE interactions are mainly M-

QTLs, while QTLs involved in QQ/QQE inter-

actions are the QTLs, which have no main effects.

This suggests that many QTLs have no main

effects and exercise their effect through interac-

tions with other QTLs, which are either M-QTL

or E-QTLs. These QTLs will escape detection in

the analyses, where M-QTLs are first identified,

and interactions are examined only among these

M-QTLs. Also as expected, majority of QTLs

involved in QE interactions (five QE interactions

in P I and 10 in P II) are those, which were

detected only in one or two environments. The

so-called consistent QTL were stable and were

not involved in QE interaction. In addition to

other QQ interactions, there were three cases

(two in P I and one in P II), where same QTL is

involved in two epistatic interactions. In one of

these three interactions, a seemingly pleiotropic

QTL interacts with one QTL each for two

different traits (Fig. 2a, b, c).

Pleiotropic QTLs

Since the yield traits are correlated, MCIM and

joint MCIM were also conducted to detect

pleiotropic QTLs. Out of 86 QTLs detected

through MCIM and joint MCIM, some individual

QTLs that were available for more than one trait

in MCIM also figured in joint MCIM. Two such

pleiotropic QTLs in P I and four such QTLs in P

Mol Breeding (2007) 19:163–177 173

123

II were detected (Table 4 and Fig. 1). However,

additional sets of QTLs, which were coincident in

CIM were not detected by MCIM. This places

higher level of confidence in MCIM than in CIM

for detecting pleiotropic QTLs. This also sug-

gested that pleiotropy is the possible cause of the

correlations among yield traits. Molecular mech-

anism of pleiotropy has also been studied recently

suggesting that pleiotropy is largely due to con-

sequences of a single molecular function of the

gene product, rather than due to involvement of

the same gene product in several molecular

processes (Xionglei and Jianzhi 2006).

Two important genomic regions

In the present study following CIM, three

coincident M-QTLs for three traits (BY, HI

and SPS) were detected in the same marker

interval Xgwm959-Xgwm832 on chromosome

4AL in P I. Similarly, in P II consistent QTL

for five traits (GY, HI, SL, SPS and GPS) were

coincident in the interval Xgwm261-Xcdo1379

on 2DS. These coincident QTLs are largely

pleiotropic and consistent over environments. It

has been argued that the QTL(s) which are

detected in more than one environment or

detected by more than one method of QTL

analyses prove useful for marker assisted selec-

tion (Moncada et al. 2001). Therefore in P I,

the interval ‘Xgwm959-Xgwm832’ on 4AL asso-

ciated with QTLs for more than one trait is

important. Another important genomic region

that is associated with QTLs for more than one

trait was detected in P II and covers the

segment ‘Xgwm261-Xcdo1379-Xbcd262’ on

2DS. In earlier studies conducted by us, using

P II, this region on 2DS was also found

associated with growth traits (Kulwal et al.

2003), and two quality traits (grain protein

content and pre-harvest sprouting tolerance;

Kulwal et al. 2004, 2005). Borner et al. (2002)

also identified QTLs for 11 of the 20 agronomic

traits in the same region using the same

mapping population, thus confirming the signif-

icance of this region of chromosome 2DS.

Therefore, the above two segments one (on

4AL) identified in P I, and the other (on 2DS)

identified in P II, may be used in future for

MAS and high-resolution mapping leading to

Table 7 Homoeoloci detected for yield contributing traitsin population II

Trait Chromosomearm

Flankingmarkers

Method

SL 1AS Xabc156-XksuE18

CIM/JMCIM

1BS Xabc156-XksuE18

MCIM/JMCIM

GY/HI 4BL Xfbb178-Xfbb67

CIM

4DL Xgwm165-Xfbb178

JMCIM

GY 4BL Xfbb67-Xfba177

CIM

4DL Xfbb226-Xfba177

JMCIM

5AL Xcdo1326-Xabg391

JMCIM

5BL Xcdo1326-Xcdo584

JMCIM

GY/SPS 6AL XksuD27-Xfbb70

CIM/Twolocus

GY/BY/TPP

6DL Xbcd1319-XksuD27

JMCIM/CIM

Xwg241

XksuE11.2

1AL

5BL

3AL

Xfba166

Xfba332

Xcdo638

Xpsr903

P II

(a)

Xwmc289

Xgwm720

3AS

7AS

Xgwm570

Xgwm130

Xgwm1171

TPP/SPS

TPP

SPS

6ALXwmc256

Xgwm1050

Xgwm926

1BL

3BS

7BL

Xgwm493

Xgwm1037

Xgwm963

Xwmc501

P I P I

(c)(b)

Fig. 2 QTL · QTL interactions each involving three QTLs detected through two locus analyses (a) for SL in P I; (b) fortwo different traits (TPP and SPS), in P I and (c) for GY in P II

174 Mol Breeding (2007) 19:163–177

123

map-based cloning of QTLs for agronomically

important traits.

Homoeo-QTLs

In the present study, four homoeologous groups

(1, 4, 5 and 6) were also found to carry QTLs for

the same trait on seemingly similar position on

two of three homoeologous chromosomes. As

many as 10 QTLs belong to this class in P II

(Table 7). No such homoeo-QTLs were detected

in P I. This shows that perhaps QTLs also exhibit

the pattern of the presence of triplicate loci, a

characteristic of bread wheat, although one would

only seldom detect all the three homoeo-QTLs

for a specific trait in the same population.

Comparison with earlier studies

The results of the present study can also be

compared with the earlier studies conducted by

cytogenetic approaches or by QTL analysis

(Table 8). The available reports together suggest

presence of QTLs on a number of chromosomes

using both the above approaches (Law 1967;

Joshi and Kumar 1967; Law and Worland 1972;

Sadananda 1977; Bhat and Goud 1979; Shnaider

and Dorokhova 1979; Kamat 1980; Sutka and

Rajki 1981; Hanchinal and Goud 1982; Hoogen-

doorn 1985; Goud and Sridevi 1988; Kato et al.

2000; Blanco et al. 2001; Borner et al. 2002;

Groos et al. 2003; Huang et al. 2003, 2004; Marza

et al. 2006; Quarrie et al. 2005). The results for

different yield traits in the present study using

both the mapping populations were in conformity

with the earlier results. In addition, new loci were

also identified, suggesting the heterogeneity in

the material used in different studies (Kato et al.

2000; Blanco et al. 2001; Groos et al. 2003;

Huang et al. 2003, 2004; Marza et al. 2006;

Quarrie et al. 2005) and the effect of environ-

ment on these traits. Using ITMI pop (P II) all

the QTLs detected for ear length by Borner et al.

(2002) were also detected for spike length in the

present study using the same mapping popula-

tion. It should however be noted that the analysis

in this earlier study was based on simple interval

mapping, which can not resolve the QTLs

involved in different types of interactions.

Acknowledgements National Agricultural TechnologyProject, Indian Council of Agricultural Research, NewDelhi and Department of Biotechnology, Government ofIndia supported this work. During the period of this study,N.K. and P.L.K. each held a Senior Research Fellowshipof the Council of Scientific and Industrial Research(CSIR), New Delhi and P.K.G. held positions of UGCEmeritus Fellow (2002–2003) and INSA Senior Scientist.Thanks are due to Professor Jun Zhu, Zhejiang University,Hangzhou, China for conducting two-locus QTL analysisand to G.B.P.U.A. & T., Pantnagar and P.A.U., Ludhiana,India for their help in conducting field trials.

Table 8 Comparison of chromosomes identified carryingQTLs in the present study and earlier studies for yieldcontributing traits in bread wheat (plus and minus signs

indicate presence and absence of QTL; in each case, firstsign represents QTL identified in the present study and thenext sign represents QTL identified in earlier studies)

Trait Chromosome carrying QTL

TPP 1A (+/+), 1B (+/+), 1D (+/–), 2A (–/+), 2B (–/+), 3A (+/–), 3B (+/+), 3D (+/+), 4A (+/+), 4B (–/+), 4D (–/+), 5A (–/+),5D (–/+), 6A (+/+), 6D (+/+), 7A (+/+), 7B (+/+)

BY 1A (+/–), 2A (+/–), 2B (+/–), 3B (+/–), 4A (+/–), 4B (+/–), 6D (+/–), 7A (+/–) 7B (–/+)GY 1A (+/+), 1B (–/+), 1D (+/–), 2A (+/+), 2B (–/+), 2D (+/+), 3A (+/+), 3B (+/+), 4A (+/+), 4B (+/+), 4D (+/+), 5A (–/+),

5B (+/+), 6A (–/+), 6B (–/+), 6D (+/–), 7A (+/+), 7B (–/+), 7D (–/+)HI 2B (+/–), 2D (+/–), 3A (+/–), 3B (+/–), 4A (+/–), 4B (+/–), 5A (+/–), 5B (+/–), 6A (+/–)SL 1A (+/–), 1B (+/+), 1D (+/–), 2A (+/–), 2B (+/+), 2D (+/+), 3B (+/+), 3D (+/–), 4A (+/+), 4B (+/+), 5A (+/+), 5B (+/+),

5D (+/+), 6A (+/+), 6B (+/+), 7A (–/+), 7B (+/–), 7D (–/+)SPS 1A (–/+), 1B (–/+), 2A (+/+), 2B (+/+), 2D (+/+), 3A (+/+), 3B (+/+), 3D (–/+), 4A (+/+), 4B (–/+), 5A (+/+), 5B (–/+),

5D (+/+), 6A (+/+), 7A (+/+), 7B (–/+), 7D (+/+)GPS 1A (+/–), 1B (+/–), 1D (–/+), 2A (+/–), 2B (+/+), 2D (+/+), 3B (+/+), 3D (+/–), 4A (–/+), 4B (+/+), 5A (+/+), 5B (–/+),

6A (–/+), 6D (–/+), 7A (+/–)

Mol Breeding (2007) 19:163–177 175

123

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