Mapping quantitative trait loci for yield-related traitsin Chinese cabbage (Brassica rapa L. ssp. pekinensis)

Yang Liu • Yun Zhang • Jiying Xing •

Zhiyong Liu • Hui Feng

Received: 13 December 2012 / Accepted: 23 April 2013 / Published online: 28 April 2013

� Springer Science+Business Media Dordrecht 2013

Abstract Chinese cabbage (Brassica rapa L. ssp.

pekinensis) is one of the most important vegetables in

China. However, the inheritance of yield-related traits

in Chinese cabbage is poorly understood to date. To

map quantitative trait loci (QTL) for yield-related

traits in Chinese cabbage, a genetic linkage map was

constructed with 192 doubled haploid (DH) lines. The

genetic map was constructed based on 190 sequence-

related amplified polymorphisms and 43 simple

sequence repeats. QTL mapping was conducted for

11 yield-related traits in 170 DH lines derived from a

cross between two diverse Chinese cabbage lines,

‘WZ’ and ‘FT’, under different environmental condi-

tions. A total of 46 main QTL (M-QTL) and 7 epistatic

QTL (E-QTL) were identified. The phenotypic vari-

ation explained by each M-QTL and E-QTL ranged

from 4.85 to 25.06 % and 1.85 to 13.29 %, respec-

tively. The QTL-by-environment interactions were

detected using the QTLNetwork 2.0 program in joint

analyses of multi-environment phenotypic values. The

phenotypic variation explained by each QTL and by

QTL 9 environment interaction was 1.14–4.24 %

and 0.00–1.26 %, respectively. Our results provide a

better understanding of the genetic factors controlling

leaf and head-related traits in Chinese cabbage.

Keywords Simple sequence repeat (SSR) �Quantitative trait loci (QTL) mapping � Inheritance �Yield-related traits � QTLNetwork 2.0 � Chinese

cabbage

Introduction

The genus Brassica of the Brassicaceae family

contains many species of which three diploids (Bras-

sica oleracea L., B. nigra and B. rapa) and three

amphidiploids (B. juncea, B. napus and B. carinata),

derived from the natural hybridisation of the three

diploids, are cultivated as vegetables, condiments, and

fodder crops as well as for the production of vegetable

oils (Nagaharu 1935). Heading Chinese cabbage

(Brassica rapa ssp. pekinensis) is one of the most

important vegetables in eastern Asia. In China, it is

ranked first in annual vegetable production. The

inheritance of yield-related traits in Chinese cabbage

is poorly understood because only a handful of studies

on these traits have been conducted to date (Ge et al.

2011; Lou et al. 2007).

Recent advances in molecular genetics have made

it possible to use marker-assisted selection (MAS) to

improve the performance of traits in plant breeding.

Further, with the advent of different types of molec-

ular markers, several genetic maps have been con-

structed, which have helped to study genome

organisation and evolution in relation to other Bras-

sica species and to Arabidopsis thaliana, the closest

Y. Liu � Y. Zhang � J. Xing � Z. Liu � H. Feng (&)

Department of Horticulture, Shenyang Agricultural

University, Shenyang 110866, China

e-mail: fenghuiaaa@263.net

123

Euphytica (2013) 193:221–234

DOI 10.1007/s10681-013-0931-1

relative model plant of Brassicaceae family (Choi

et al. 2007; Kim et al. 2009; Snowdon and Friedt 2004;

Suwabe et al. 2006; Wu et al. 2012). The development

of genetic maps in Brassica using different molecular

markers, and the mapping of populations derived from

different parental crosses, has helped to map quanti-

tative trait loci (QTL) for root morphology (Lu et al.

2008), downy mildew resistance (Yu et al. 2009; Li

et al. 2011), flowering time, leaf morphology (Li et al.

2009; Lou et al. 2011), hairiness and seed coat colour

(Zhang et al. 2009).

To identify the genomic regions suitable for

marker-assisted breeding strategies to increase the

yield of Chinese cabbage, the most crucial step in a

QTL mapping project is to establish accurate pheno-

typing methods. In the present study, 11 traits were

identified to evaluate yield in Chinese cabbage. The

yield of Chinese cabbage is complex and influenced by

both genetic and environmental factors. Epistasis, or

interaction between non-allelic genes, and interactions

between QTL and their environments contribute to the

genetic control of quantitative traits (Li et al. 1997;

Kubo and Yoshimura 2005; Malmberg et al. 2005;

Zeng 2005; Carlborg et al. 2006; Shen et al., 2006;

Wurschum et al. 2011). However, it is difficult to

identify epistatic QTL (E-QTL) or QTL 9 environ-

ment interaction effects. Recently, an efficient map-

ping program, QTLNetwork 2.0 (MCIM), was used to

identify main QTL (M-QTL), E-QTL and the interac-

tion between QTL and the environment in several crop

species. Therefore, using QTLNetwork approaches, it

is necessary to identify E-QTL and the interaction

between QTL and different environment conditions, in

addition to simple M-QTL, to understand the yields of

Chinese cabbage.

The development of leaf heads takes place via a

complex interaction among environmental, genetic

and physiological factors. The QTL mapping

approach is highly effective for understanding and

locating the genetic loci governing complex traits in

the genome. Although several QTL mapping and

genetic studies have been reported for B. rapa (Ge

et al. 2011; Lou et al. 2007), no detailed genetic

analysis or QTL mapping of yield-related traits has

been conducted for this species. Therefore, in this

study, an attempt was made to analyze yield-related

traits in Chinese cabbage using molecular markers so

that the genetics and evolutionary processes of these

traits could be understood.

Materials and methods

Plant materials and DNA isolation

The 192 line double haploid (DH) mapping population

was derived by crossing two diverse Chinese cabbage

lines, ‘WZ’ and ‘FT’ (Fig. 1). The maternal parent

‘FT’ (f. depressa Li) is a microspore culture-derived

DH line with a small and tight leaf head, nearly round

outer leaves, white petioles, white inner leaves, and a

growth period of 50 days. The paternal parent ‘WZ’ (f.

cylindrica Li) is an inbred line with a large and loose

leaf head, oblong outer leaves, pale green petioles,

yellow inner leaves, and a growth period of 80 days.

The yield-related traits of the two parents were

different (Table 2). From 192 DH lines of the mapping

population, the seeds of the 170 DH lines were

collected for QTL analysis.

Genomic DNA was isolated from fresh leaves of

the parents and 192 DH individuals following the

procedure described by Murray and Thompson (1980)

with minor modifications. The concentration of DNA

was adjusted to 50 ng/lL for PCR amplification.

Growth conditions and trait analysis

The seeds of parental lines ‘WZ’, ‘FT’, F1 and 170 DH

lines were sown in multi-cell trays (5 cm 9 5 cm 9

5 cm) in July 2011 and the seedlings were transplanted

to the field in August 2011 at an experimental farm at

Shenyang Agricultural University (characterised by a

semi-humid continental climate) and at an experimen-

tal farm at Dengshahe in Dalian (with a temperate

monsoon climate). In 2011, the average temperature

was 7.8 �C, the annual rainfall was 479.7 mm, the

mean relative humidity was 67.8 %, and the average

amount of sunlight was 7.1 h in Shenyang. The

average temperature was 10.6 �C, the annual rainfall

was 902.6 mm, the mean relative humidity was

63.1 %, and the average amount of sunlight was

7.0 h in Dalian. The agrotype was brunisolic soil in

Shenyang and sandy soil in Dalian. Three replicates of

12 plants per family were grown in rows, with 0.5 m

spacing between plants in a randomised design. Nine

plants per line were chosen randomly from the middle

of the row for data collection, and 11 phenotypic traits,

namely head length (HL), head width (HW), head

length/head width ratio (HR), gross weight (GW),

head weight (HWT), head weight/gross weight ratio

222 Euphytica (2013) 193:221–234

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(HGR), number of non-wrapper leaves (NNL), num-

ber of head-forming leaves (NHL), number of all

leaves (NAL), head petiole weight (HPW) and head

non-petiole leaves weight (HNLW), were recorded in

October 2011 following the international descriptors

given for Brassica by the international Bureau of Plant

Genetic Resources (Table 1).

SSR amplification and analysis

We used 393 previously developed simple sequence

repeat (SSR) primer pairs as potential anchor markers

to facilitate cross-referencing with other mapping

studies (Cheng et al. 2009; Choi et al. 2007; Iniguez-

Luy et al. 2008; Kim et al. 2009; Lowe et al. 2004;

Piquemal et al. 2005; Suwabe et al. 2002, 2006;

Uzunova and Ecke 1999). To screen for polymor-

phisms between two parents, we selected 393 markers

that were well distributed on the 10 linkage groups

(LGs) of the A genome to identify the markers linked

to the chromosomes. The polymorphic SSR markers

with clear bands were used for mapping. PCR

amplification was carried out in a total volume of

10 lL containing 25 ng template DNA, 0.8 lL

Fig. 1 Morphological

characteristics of the

paternal parent ‘WZ’ (a) and

the maternal parent ‘FT’ (b)

Table 1 Summary of yield-related traits and their measurements

Trait Measurement

Head length Length of head measured at the longest point at the time of harvest (cm)

Head width Width of the head measured at the widest point (cm)

Head length/head width ratio The ratio of head length and head width

Gross weight Gross weight of plant at the time of harvest (kg)

Head weight Weight of head at the time of harvest period (kg)

Head weight/gross weight ratio The ratio of head weight and gross weight

Number of non-wrapper leaves Extant number of external leaves of leaf head at the time of harvest

Number of head-forming leaves Extant number of leaves of leaf head at the time of harvest period ([2 cm)

Number of all leaves The sum of number of non-wrapper leaves and number of head-forming leaves

Head petiole weight Weight of head petiole at the time of harvest (kg)

Head non-petiole leaves weight Weight of head non-petiole leaves at the time of harvest period (kg)

Euphytica (2013) 193:221–234 223

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2.5 mM dNTP, 1.0 lL 109 buffer (containing Mg2?),

1 lL 0.5 lM primer, and 1 U Taq polymerase. The

PCR amplification was performed on a Bio-Rad

iCycler thermocycler as follows: PCR was initiated

with 95 �C for 5 min; followed by 30 cycles of 95 �C

for 30 s, 56 �C for 30 s, and 72 �C for 1 min; and

ending with an extension at 72 �C for 5 min. The PCR

products were separated on a 6 % denaturing poly-

acrylamide gel, and were stained with AgNO3.

Sequence-related amplified polymorphism

(SRAP) amplification and analysis

A total of 234 SRAP makers derived from other crops

with previously developed SRAP primer pairs (Li and

Quiros 2001; Lin et al. 2003; Ferriol et al. 2003) were

screened for polymorphisms between the two parental

lines. The polymorphic SRAP markers with clear

bands were used for mapping. PCR amplification was

carried out in a total volume of 10 lL containing

12.5 ng template DNA, 0.8 lL 2.5 mM dNTP, 1.0 lL

109 buffer (containing Mg2?), 1 lL 0.5 lM primer,

and 1 U Taq polymerase. The PCR amplification was

performed on a Bio-Rad iCycler thermocycler as

follows: PCR was initiated with 95 �C for 5 min;

followed by 8 cycles of 95 �C for 30 s, 35 �C for 30 s,

and 72 �C for 1 min; followed by 35 cycles of 95 for

30 s, 56 �C for 30 s, and 72 �C for 1 min; and ending

with an extension at 72 �C for 5 min. The PCR

products were separated on a 6 % denaturing poly-

acrylamide gel, and were stained with AgNO3. SRAP

markers were scored on the basis of the presence or

absence of the band and were named using the primer

combination followed by a number in descending

molecular-weight order.

Segregation analysis and map construction

The segregation of each marker and linkage analysis

were performed using JoinMap version 3.0 (http://

www.kyazma.nl/) (Van Ooijen and Voorrips 2002).

LOD scores 2.0–15.0 were used to assign the markers

into LGs and Kosambi’s (1943) mapping function was

used to convert the recombination value into the map

distance. The threshold for goodness-of-fit was set to

B5.0, with a recombination frequency of \0.4 and a

minimum logarithm of odds scores of 2.0. The genetic

map was drawn using Mapchart 2.2 (Voorrips 2002).

Statistical analysis and QTL mapping

Correlation coefficient analysis between traits was

conducted using SPSS 16.0, while QTL mapping was

performed using the mix composite interval mapping

(MCIM) function provided in the freely available

software QTLNetwork 2.0 (Yang et al. 2007). The

M-QTL and E-QTL were identified by QTLNetwork

ver. 2.0 software using single environment phenotypic

values. In addition, QTLNetwork 2.0 was also used to

identify QTL 9 environment interactions for yield-

related traits in joint analyses of multienvironment

phenotypic values. The LOD thresholds for each trait

of QTL were determined by a 1,000 permutation test at

95 % confidence level. The proportion of observed

phenotypic variance explained by each M-QTL or

E-QTL and the corresponding additive effects were

also estimated. The QTL names given were abbrevi-

ations of the QTL followed by the trait name and its

respective LG number. An ‘a’ or ‘b’ was added if more

than one QTL for the same trait was detected in the

same LG. The first number, 1 or 2, represents the

environmental conditions at Shenyang or Dalian,

respectively. For details on the statistical methods

used for QTL mapping, and the calculation or

interpretation of genetic effects within the given

population structures, see Radoev et al. (2008).

Results

Linkage map

A total of 393 SSR primers were screened for

polymorphisms between the parental lines. Of these,

103 displayed a polymorphism, and of these, 43 SSRs

that could be easily scored using silver stained gels

were chosen for genotyping the mapping population.

Furthermore, the SSR primer pairs were used as

potential anchor markers to facilitate cross-referenc-

ing with other mapping studies. Out of the 234 SRAP

primer combinations tested on the parental lines, 43

combinations, each of which generated more than 4

polymorphic markers, were selected for genotyping

the DH mapping population. After removing ambig-

uous markers, a total of 233 polymorphic bands were

used for mapping. The total map length was

1063.8 cM with an average interval of 4.6 cM

between adjacent loci (Fig. 2). The 10 LGs were

224 Euphytica (2013) 193:221–234

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designated A1–A10 based on multiple anchor markers

located on each LG. The marker order in our map was

in perfect agreement with previously published link-

age maps (Cheng et al. 2009; Kim et al. 2009; Lowe

et al. 2004; Piquemal et al. 2005; Suwabe et al. 2006).

Variation in phenotypic traits

As summarised in Table 2, the two parents showed

significant genetic differences in all of the traits

measured in our experiments. The paternal parent

‘WZ’ formed significantly larger, heavier, but looser

leaf heads than the maternal parent ‘FT’. The 11

phenotypic traits evaluated from the population of 170

DH lines derived from crossing the two parental lines

showed wide genetic variation (Table 2). A continu-

ous distribution was observed for all of the traits in the

DH population at both locations (Fig. 3).

The population of 170 DH lines exhibited a wide

range of variation for the yield parameters studied. For

HW, GW at Shenyang, HWT at Dalian, NNL, NHL,

HPW at Shenyang, and HNLW at Shenyang, some DH

lines formed heavier weight or more leaves than the

better parent, while some other DH lines developed

lighter weight or less leaves than the worse parent.

That is to say, they showed obvious transgressive

segregations in both directions. However, the other

traits showed no remarkable transgressive variation.

The correlation coefficient analyses showed that

most of the traits were significantly correlated with

each other (Table 3). HW showed significant correla-

tions with all other traits except NNL. HR was

relatively independent of HWT at Dalian, HGR at

Shenyang, and NHL and NAL at both locations, but

showed significant correlations with all other traits.

M-QTLs for yield-related traits

A total of 46 putative M-QTL for the 11 yield-related

traits assessed under Shenyang and Dalian conditions

were identified (Table 4; Fig. 2). The number of M-QTL

identified ranged from one for HW, to nine for HR under

Shenyang and Dalian conditions. The percentage of

phenotypic variation explained by individual M-QTL

was 4.85–25.06 %. The confidence interval covered by

individual M-QTL was 2.0–23.1 cM.

We detected four M-QTL each for GW (1GWTA9a,

1GWA9b, 1GWA10 and 2GWTA6), HGR (1HGRA3,

2HGRA1, 2HGRA3 and 2HGRA9) and NNL

(1NNLA10, 2NNLA1, 2NNLA7 and 2NNLA9); three

each for HWT (1HWTA9, 1HWTA10 and 2HWTA6),

NHL (1NHLA9, 2NHLA9 and 2NHLA10) and NAL

(1NALA10, 2NALA3 2NALA10); nine for HR (1HRA1,

1HRA5, 1HRA6, 1HRA8, 2HRA1, 2HRA4, 2HRA5,

2HRA6 and 2HRA9); seven for HL (1HLA1, 1HLA9,

1HLA10, 2HLA1, 2HLA2, 2HLA6 and 2HLA8); and

six for HPW (1HPWA9a, 1HPWA9b, 1HPWA10,

2HPWA4, 2HPWA6 and 2HPWA9). There was only

one M-QTL, 2HWA5, for controlling HW under

Dalian conditions, and no M-QTL for HW under

Shenyang conditions. As above, two M-QTL were

detected for HNLW (1HNLWA9 and 1HNLWA10)

under Shenyang conditions only. Of the M-QTL

detected for these traits, we only detected one

M-QTL position for the HR (1HRA5 and 2HRA5)

under both conditions. This M-QTL explained 12.01

and 12.31 % of the phenotypic variation in Shenyang

and Dalian, respectively.

Genetic analyses of individual M-QTL revealed

additive gene action. However, only 9 out of 46 M-

QTL identified for the 11 traits were of an additive

nature. Compared to the inheritabilities of the 11 traits,

the total phenotypic variance explained by the M-QTL

was different under Shenyang and Dalian conditions,

indicating there might be an E-QTL controlling the

yield-related traits.

E-QTL for yield-related traits

With the MCIM approach, epistatic interaction anal-

ysis was further conducted using single environment

phenotypic values under two different conditions. A

total of seven E-QTL were identified for yield-related

traits (Table 5).

There was one E-QTL controlling the HGR and the

NHL under Shenyang conditions, but none identified

for Dalian. There were one and two E-QTL controlling

the HR under Shenyang and Dalian conditions,

respectively. There was one E-QTL each controlling

the GW under Shenyang and Dalian conditions,

respectively. The variation explained by these

E-QTL was 1.85–13.29 % (Table 5).

QTL 9 environment interactions for yield-related

traits

To identify the interactions between QTL and the

environment in terms of Chinese cabbage yield, joint

Euphytica (2013) 193:221–234 225

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226 Euphytica (2013) 193:221–234

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analyses of multienvironment phenotypic values for

yield-related traits under Shenyang and Dalian condi-

tions were conducted using QTLNetwork. Five

E-QTL were identified (Table 6): one each for HL,

GW, HWT, NHL, and NAL. The phenotypic variance

explained by each QTL was 1.14–4.24 %, and that

explained by the QTL 9 environment interaction was

0.00–1.26 % (Table 6).

Clustering of QTL

Of the 46 QTL detected in 10 LGs, several were found to

map to the same region of a few LGs. The QTL regions

of A1, A3, A5, A6, A9 and A10 showed multiple QTL

for three or more traits (Fig. 2). The lower part of A1

(66.6–111.2 cM) showed mapping of QTL for HL

(1HLA1, 2HLA1), HR (1HRA1, 2HRA1), HGR

(2HGRA1) and NNL (2NNLA1). However, increasing

alleles from 1HLA1, 2HLA1, 1HRA1, 2HRA1 and

2HGRA1 were from the parental line ‘WZ’ and

2NNLA1 was from the parental line ‘FT’. The middle

portion of A9 (32.0–88.1 cM) showed overlapping QTL

for nine traits, i.e., HL (1HLA9), HR (2HRA9), GW

(1GWA9a, 1GWA9b), HWT (2HWTA9), HGR

(2HGRA9), NNL (2NNLA9), NHL (1NHLA9,

Table 2 Mean and range of phenotypic traits in DH populations and parental lines used for QTL mapping in B. rapa

Trait Location WZ FT DH lines

Mean Mean Mean Range

HL(cm) SY 41.48 13.53 24.86 16.77–35.61

DL 32.52 13.72 25.20 18.33–34.88

HW(cm) SY 13.43 10.82 11.53 7.93–16.95

DL 11.34 9.22 11.29 8.20–15.63

HR SY 3.09 1.25 2.18 1.42–3.76

DL 2.87 1.49 2.26 1.56–3.81

GW(kg) SY 2.668 0.793 1.611 0.693–2.850

DL 2.516 0.323 1.613 0.763–2.859

HWT(kg) SY 1.531 0.559 0.825 0.278–1.654

DL 1.554 0.198 0.987 0.461–1.701

HGR SY 0.57 0.70 0.51 0.22–0.76

DL 0.62 0.61 0.61 0.37–0.73

NNL SY 12 7 11 7–15

DL 10 8 10 7–16

NHL SY 22 39 19 12–27

DL 23 13 21 13–31

NAL SY 35 46 30 22–41

DL 33 21 31 21–42

HPW(kg) SY 0.804 0.186 0.381 0.133–0.874

DL 0.850 0.062 0.432 0.198–0.800

HNLW(kg) SY 0.726 0.373 0.444 0.128–0.892

DL 0.704 0.136 0.555 0.219–0.999

HL head length, HW head width, HR head length/head width ratio, GW gross weight, HWT head weight, HGR head weight/gross

weight ratio, NNL number of non-wrapper leaves, NHL number of head-forming leaves, NAL number of all leaves, HPW head petiole

weight, HNLW head non-petiole leaves weight

Fig. 2 Brassica rapa genetic linkage map showing distribution

of unigene derived microsatellite markers and previously

developed SSR markers. The different rectangular bars on the

right of each linkage group indicate QTL for different yield-

related traits in Chinese cabbage. QTL names are abbreviations

of QTL and the trait followed by its respective linkage group

number. An alphabetical letter a or b was added if more than one

QTL for the same trait was detected in the same linkage group

b

Euphytica (2013) 193:221–234 227

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228 Euphytica (2013) 193:221–234

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2NHLA9), HPW (1HPWA9a, 1HPWA9b, 2HPWA9)

and HNLW (1HNLWA9). We also observed overlap-

ping QTL for GW (2GWA6), HWT (2HWTA6) and

HPW (2HPWA6) at the 4.3–9.2 cM region of A6. All of

the QTL for these traits obtained increasing alleles from

the parental line ‘WZ’ and these traits were significantly

positively correlated. Similarly, co-mapping of QTLs

for GW (1GWA10), HWT (1HWTA10), NNL

(1NNLA10), NAL (1NALA10), HPW (1HPWA10)

and HNLW (1HNLWA10) were observed on A10

(37.9–47.3 cM), which derived increasing additive

alleles from the parental line ‘WZ’.

Discussion

Recent advances in molecular genetics, particularly

the development of molecular markers, have proven

promising and have allowed for the easy identification

and manipulation of genetic loci governing qualitative

and quantitative traits in economically important crop

plants including vegetables (Snowdon and Friedt

2004). The present linkage map includes 190 SRAP

markers and 43 SSR markers grouped on 10 LGs.

Most of these LGs anchored to the corresponding

chromosome of the B. rapa reference map (Lowe et al.

2004; Kim et al. 2006; Suwabe et al. 2006; Choi et al.

2007) based on common SSR markers, except for A10.

Our linkage map covers a total genetic distance of

1063.8 cM. In addition, for three relatively small

chromosomes (Chr8, Chr4 and Chr10), the corre-

sponding LGs (A4, A6 and A10) were comparatively

Table 3 Correlation co-efficient analysis of eleven leaf and heading-related traits

Trait Location HL HW HR GW HWT HGR NNL NHL NAL HPW

HW SY 0.300**

DL 0.201**

HR SY 0.684** -0.475**

DL 0.693** -0.544**

GW SY 0.700** 0.338** 0.383**

DL 0.556** 0.338** 0.211**

HWT SY 0.679** 0.400** 0.313** 0.904**

DL 0.525** 0.425** 0.127 0.919**

HGR SY 0.095 0.263** -0.125 0.060 0.463**

DL -0.022 0.235** -0.176* -0.096 0.395**

NNL SY 0.174* -0.045 0.167* 0.371** 0.119 -0.494**

DL 0.139 -0.088 0.167* 0.393** 0.123 -0.639**

NHL SY 0.208** 0.282** -0.047 0.463** 0.545** 0.373** 0.178**

DL 0.130 0.243** -0.061 0.435** 0.425** 0.014 0.427**

NAL SY 0.250** 0.227** 0.026 0.549** 0.526** 0.143 0.539** 0.922**

DL 0.160* 0.152** 0.024 0.497** 0.384** -0.236** 0.708** 0.933**

HPW SY 0.676** 0.352** 0.352** 0.865** 0.928** 0.363** 0.138 0.556** 0.547**

DL 0.604** 0.202** 0.352** 0.862** 0.873** 0.124 0.213** 0.453** 0.442**

HNLW SY 0.577** 0.388** 0.223** 0.806** 0.921** 0.496** 0.081 0.450** 0.423** 0.710**

DL 0.356** 0.530** -0.087 0.787** 0.912** 0.383** 0.023 0.319** 0.261** 0.596**

HL head length, HW head width, HR head length/head width ratio, GW gross weight, HWT head weight, HGR head weight/gross

weight ratio, NNL number of non-wrapper leaves, NHL number of head-forming leaves, NAL number of all leaves, HPW head petiole

weight, HNLW head non-petiole leaves weight

* P \ 0.05; ** P \ 0.01

Fig. 3 Frequency distribution of yield-related traits averaged

over the number of sampled plants per plot for DH population

derived from a cross between two diverse Chinese cabbage

lines, ‘WZ’ and ‘FT’ under Shenyang and Dalian conditions

b

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Table 4 The M-QTLs for yield-related traits under Shenyang and Dalian conditions

Trait QTL namea Peak positionb Flanking markersb Confidence interval (cM) Ac R2 (%)d

HL 1HLA1 109.2 me11em11-06–me03em15-03 104.2–111.2 0.99 6.96

1HLA9 68.4 me03em09-15–me08em11-03 65.6–70.4 1.52 12.76

1HLA10 23.3 me13em09-01–me09em09-02 19.3–29.5 0.93 5.18

2HLA1 80.4 me03em09-01–cnu_m461a 66.6–88.4 0.84 8.18

2HLA2 77.2 me13em14-02–nia_m143a 62.3–85.2 1.11 9.11

2HLA6 27.8 me13em09-02–me11em11-07 21.8–33.1 1.30 13.94

2HLA8 74.9 me09em13-04–nia_m095a 69.9–77.1 1.27 10.97

HW 2HWA5 81.3 cnu_m344a–me04em12-04 67.2–90.3 -0.44 7.57

HR 1HRA1 110.2 me11em11-06–me03em15-03 104.2–111.2 0.10 8.32

1HRA5 72.1 me06em18-05–cnu_m344a 67.2–82.3 0.10 12.01

1HRA6 34.9 me02em13-01–me04em01-03 30.1–37.9 0.14 14.47

1HRA8 65.1 cnu_m537a–me09em13-04 59.7–68.1 0.12 10.60

2HRA1 85.4 me03em09-01–cnu_m461a 74.6–92.4 0.14 10.20

2HRA4 46.2 me11em02-06–me09em13-02 40.8–51.8 0.08 5.96

2HRA5 73.1 me06em18-05–cnu_m344a 68.2–78.3 0.13 12.31

2HRA6 31.1 me11em11-07–me02em13-01 24.8–36.9 0.09 9.50

2HRA9 42.6 me02em09-02–me04em12-01 39.6–46.6 0.08 8.07

GW 1GWA9a 61.0 me07em17-01–me08em11-05 60.0–63.0 0.08 14.23

1GWA9b 74.5 me08em11-03–nia_m003a 72.5–76.8 0.15 12.47

1GWA10 40.6 me03em15-04–me06em17-02 39.3–42.5 0.17 11.92

2GWA6 8.1 me03em16-01–nia_m049a 4.3–9.2 0.20 21.93

HWT 1HWTA9 75.5 me08em11-03–nia_m003a 72.5–76.8 0.13 14.84

1HWTA10 40.6 me03em15-04–me06em17-02 39.3–46.3 0.09 8.25

2HWTA6 8.1 me03em16-01–nia_m049a 5.3–9.2 0.02 19.72

HGR 1HGRA3 47.4 cnu_m079a–me08em15-06 42.9–51.6 -0.02 8.45

2HGRA1 76.0 me05em14-03–me06em18-06 70.6–87.4 0.02 7.42

2HGRA3 27.8 me05em14-05–me02em12-03 22.4–36.8 -0.02 9.80

2HGRA9 42.6 me02em09-02–me04em12-01 32.0–45.6 -0.02 8.25

NNL 1NNLA10 39.3 me02em05-02–me04em01-04 37.9–40.7 0.43 6.78

2NNLA1 87.4 me03em09-01–cnu_m461a 69.6–92.4 -0.45 6.55

2NNLA7 43.6 me06em08-02–nia_m043a 40.7–45.6 0.44 6.89

2NNLA9 36.1 me11em11-08–me02em09-02 32.0–42.6 0.60 12.09

NHL 1NHLA9 61.0 me07em17-01–me08em11-05 60.0–63.0 1.79 21.40

2NHLA9 57.4 nia_m027–me02em07-03 53.4–59.4 0.97 7.87

2NHLA10 2.0 me06em18-01–me13em07-01 0.0–5.5 1.30 10.17

NAL 1NALA10 39.8 me04em01-04–me02em05-01 37.9–47.3 1.16 7.94

2NALA3 119.0 me13em12-04–me07em02-06 115.2–126.4 1.03 4.85

2NALA10 3.0 me06em18-01–me13em07-01 0.0–7.5 1.78 12.04

HPW 1HPWA9a 61.0 me07em17-01–me08em11-05 60.0–62.0 0.04 20.73

1HPWA9b 75.5 me08em11-03–nia_m003a 72.5–76.8 0.06 21.80

1HPWA10 40.7 me06em17-02–me03em15-05 38.9–46.3 0.04 5.74

2HPWA4 13.4 me12em05-02–cnu_m256a 2.0–24.1 0.03 7.16

2HPWA6 7.1 me03em16-01–nia_m049a 5.3–9.2 0.06 25.06

2HPWA9 86.5 me05em01-01–me13em14-04 81.1–88.1 0.04 12.14

HNLW 1HNLWA9 76.8 nia_m003a–me05em01-03 72.5–80.8 0.04 5.98

230 Euphytica (2013) 193:221–234

123

shorter in the current map than in the three previously

published maps (Kim et al. 2009; Iniguez-Luy et al.

2008; Li et al. 2009). For three relatively large

chromosomes (Chr3, Chr2 and Chr1), the correspond-

ing LGs (A1, A3, and A9) were comparatively longer

in the current map than in the map of Li et al. (2009). A

high percentage of skewed markers (63.9 %) were

observed in our study. Skewed segregation ratios have

often been observed in populations of DH plants for

many other plant species (Graner et al. 1991), and are

most likely due to selection during microspore culture

(Foisset et al. 1996). The segregation of skewed

markers may generate errors in linkage analysis,

resulting in map stretching or a false order of markers

on LGs. However, simply deleting the markers

showing skewed segregation from the map results in

a loss of all of the skewed segments for QTL analysis.

We therefore decided to include markers with skewed

segregation in the map.

To reveal the genetic control of yield in Chinese

cabbage during the harvest period, we conducted QTL

identification for the yield-related traits mentioned

above, under different environment conditions. The

use of multiple yield-related traits in Chinese cabbage

not only facilitated the detection of QTL, but also

allowed the identification of QTL 9 environment

Table 5 The E-QTLs for yield-related traits under Shenyang and Dalian conditions

Condition Trait Loci(i) Loci(j) AAb R2 (%)c

Linkage

groupaMarker interval Linkage

groupaMarker interval

Shenyang HR A02 me02em07-02–me01em19-01 A07 me12em05-05–cnu_m553a 0.0903 5.37

GW A09 me07em17-01–me18em11-05 A09 me08em11-03–nia_m003a -0.1158 1.92

HGR A03 me05em14-05–me02em12-3 A09 me03em09-05–me08em11-03 0.0244 8.43

NHL A06 me12em05-01–nia_m029a A10 me13em09-01–me09em09-02 -1.4603 13.29

Dalian HR A01 me07em02-03–me05em14-03 A07 cnu_m049a–me02em07-05 -0.1433 12.08

A04 me11em02-06–me09em13-02 A06 me11em11-07–me02em13-01 -0.0661 1.85

GW A08 me19em13-01–me09em09-03 A09 me01em15-02–me05em14-04 -0.1431 10.79

HR head length/head width ratio, GW gross weight, HGR head weight/gross weight ratio, NHL number of head-forming leavesa Linkage on which the QTL was locatedb AA is the effect of additive by additive interaction between two points: its positive value indicates that two loci genotypes being the

same as those in parent ‘WZ’ (or ‘FT’) take the positive effects, while the two-loci recombinants take the negative effects. The case

of negative values is just the oppositec Variation explained by each pair of epistatic loci

Table 4 continued

Trait QTL namea Peak positionb Flanking markersb Confidence interval (cM) Ac R2 (%)d

1HNLWA10 43.3 me02em09-03–me07em15-02 39.3–46.3 0.05 8.51

HL head length, HW head width, HR head length/head width ratio, GW gross weight, HWT head weight, HGR head weight/gross

weight ratio, NNL number of non-wrapper leaves, NHL number of head-forming leaves, NAL number of all leaves, HPW head petiole

weight, HNLW head non-petiole leaves weighta QTL names are abbreviations of the QTL followed by traits names and its respective linkage group number. An alphabetical letter a

or b was added if more than one QTL for the same trait were detected in the same linkage group. The first number 1 or 2 represents

under Shenyang or Dalian conditionsb Peak effect of the QTL, the closest flanking markers (LOD C 3.0)c A: additive value,. Additive effect: positive additivity indicates that the QTL allele originated from the parental line ‘WZ’ increases

the value of the trait; negative additivity means that the QTL allele originated from the parental line ‘FT’ increases the value of the

traitd Percentage of the phenotypic variance explained by each QTL

Euphytica (2013) 193:221–234 231

123

interactions (Wang et al. 2012). The QTL (M-QTL;

E-QTL) identified under a single environment were

distributed on all B. rapa LGs, with M-QTL mainly on

A1, A6, A9 and A10. There was one pair of M-QTL

detected under both Shenyang and Dalian conditions,

which was 1HRA5 and 2HRA5 for HR. This pair was

less affected by environment, so it was able to be

inherited stably (Ge et al. 2012). Because the yield-

related traits were highly correlated, many co-local-

ised M-QTL were detected. In Shenyang, the detection

of co-localised M-QTL occurred in four genomic

regions. The first one was for 1HLA1 and 1HRA1. The

second one was for 1GWA9a, 1NHLA9 and

1HPWA9a. The third one was for 1GWA9b,

1HWTA9 and 1HPWA9b. The fourth one was for

1GWA10 and 1HWTA10. In Dalian, the detection of

co-localised M-QTL occurred on four regions, which

were for 2HLA1, 2HRA1 and 2NNLA1, for 2HRA9

and 2HGRA9, for 2GWA6 and 2HWTA6 and for

2NHLA10 and 2NALA10. Such co-localisations have

also been found (Ge et al. 2011; Lou et al. 2007).

These co-localised M-QTL could be very useful for

the simultaneous improvement of more than one trait,

because the desirable alleles at these M-QTL were

contributed by a single parent. In summary, the

majority of the identified M-QTL did not reveal a

high phenotypic variance, indicating that the perfor-

mance of 11 traits may be affected by E-QTL, or the

interaction between QTL and the environment; there-

fore, E-QTL mapping or QTL 9 environment inter-

action analysis is needed.

Previous studies have demonstrated that epistatic

and QTL 9 environment interactions are prevalent in

quantitative trait inheritance (Doebley et al. 1995; Yu

et al. 1997). To further clarify the genetic control of

yield in Chinese cabbage, the E-QTL and

QTL 9 environment interactions were analysed in

the present study. Seven E-QTL were detected for

different yield-related traits in a single environment by

MCIM. Most E-QTL identified were for HR, followed

by GW, HGR and NHL. Compared to the M-QTL, the

phenotypic effects of E-QTL for HGR and HR were

larger (Tables 4, 5). These results suggest that epis-

tasis as a genetic factor was much more important than

M-QTL for HGR and HR in Chinese cabbage. The

joint analyses of multi-environment phenotypic values

can reveal QTL expressed under various environ-

ments. Five E-QTL were identified using multi-

environment phenotypic values in Shenyang andTa

ble

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232 Euphytica (2013) 193:221–234

123

Dalian conditions by QTLNetwork (Table 6). The

results indicated that QTL 9 environment interac-

tions are important components for yield of B. rapa,

although the degree of interaction was very low.

Yield-related traits were also reported in B. rapa

LGs A1, A6, A7 and A10 by Ge et al. (2011). They

also observed QTL clusters for yield-related traits in

A6. The detection of QTL for yield-related traits in

both studies confirmed that genetic loci present in A1,

A6, A9 and A10 played major roles in governing

yield-related traits irrespective of the genetic back-

ground they presented. These findings suggest that the

small regions of A9 and A10 LGs where many QTL

for different traits were detected, may represent QTL

hotspots, harbouring many important genes for those

traits, which could be useful for the identification of

candidate genes for these traits. However, in our map,

we have less number of markers mapped. Therefore, to

identify candidate genes for these traits in QTL

regions, additional markers are required so that a

high-density map can be developed for refining QTL

regions. The densely mapped markers in QTL regions

may in turn be used for fine mapping in the mapping of

large populations to determine whether the QTL

correspond to a single candidate gene with pleiotropic

effects or to several separate but closely linked

candidate genes, each controlling a single trait (Shib-

aike 1998). This suggests not only that yield-related

traits may be comparatively complex polygenic

inheritances in Chinese cabbage, but also that the

domestication of Chinese cabbage may have occurred

quite rapidly through the fixation of only a few

genomic regions.

In conclusion, of the 46 M-QTL identified for 11

yield-related traits, few of them, especially those in

A1, A6, A9, and A10 could be used for fine mapping to

allow for closely linked markers to be identified and

used for marker-assisted breeding to combine favour-

able alleles to improve leaf head yield.

Acknowledgments This research was financially supported

by a Grant from the National Natural Science Foundation of

China (No. 31071792, 31272157).

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