全 文 ：作物学报 ACTA AGRONOMICA SINICA 2014, 40(8): 13861391 http://zwxb.chinacrops.org/
ISSN 0496-3490; CODEN TSHPA9 E-mail: xbzw@chinajournal.net.cn
This research was supported by the National Natural Science Foundation of China (No. 31171584), the Natural Science Foundation of
Chongqing (No. cstc2011jjA8000g), and The Program of Introducing International Super Agricultural Science and Technology (948 Program)
(No.2011-G23).
* Corresponding author: LIU Lie-Zhao, E-mail: liezhao2003@126.com, Tel: 023-68250701
Received(收稿日期): 2014-02-15; Accepted(接受日期): 2014-04-16; Published online(网络出版日期): 2014-06-03.
URL: http://www.cnki.net/kcms/detail/11.1809.S.20140603.1550.003.html
DOI: 10.3724/SP.J.1006.2014.01386
Mapping Quantitative Traits Loci for Seed Glucosinolate Content in Brassica
napus Using High-density SNP Map
JIAN Hong-Ju, WEI Li-Juan, LI Jia-Na, XU Xin-Fu, CHEN Li, and LIU Lie-Zhao*
Chongqing Engineering Research Center for Rapeseed / College of Agronomy and Biotechnology, Southwest University, Chongqing 400716, China
Abstract: Seed glucosinolate plays important biological and economic roles in Brassica napus. In this study, we aimed at identi-
fying QTLs associated with seed glucosinolate content of B. napus using the composite interval mapping (CIM) method based on
the high density SNP genetic map. The total seed glucosinolate content was analyzed via near infrared spectroscopy (NIR) using
standard methods with three technical replicates. The QTLs associated with seed glucosinolate content were detected using the
SNP genetic map constructed in 2013, which contains 2795 SNP markers with the total map length of 1832.9 cM and an average
distance of 0.66 cM. Five QTLs for total seed glucosinolate content were identified on linkage groups A03, A09, and C02 in both
2011 and 2012, and their LOD threshold values ranged from 2.90 to 10.40. These QTLs explained 56.9% and 55.1% of pheno-
typic variance in 2011 and 2012, respectively. Another five minor QTLs were detected in either 2011 or 2012 with phenotypic
contributions between 4.1% and 7.9%, and their LOD threshold values were 2.53–3.83.
Keywords: Brassica napus; Single nucleotide polymorphism; Quantitative trait loci; Seed glucosinolate content
利用 SNP高密度遗传连锁图谱定位甘蓝型油菜种子硫苷含量的 QTL
荐红举 魏丽娟 李加纳 徐新福 谌 利 刘列钊*
西南大学农学与生物科技学院 / 重庆市油菜工程技术研究中心, 重庆 400716
摘 要: 种子硫苷在甘蓝型油菜中有着重要的生物学作用和经济价值。本文旨在通过复合区间作图法利用高密度
SNP 遗传连锁图谱定位种子硫苷的 QTL。用近红外扫描获得种子硫苷含量, 每株系扫描 3 次, 取平均值。所用的高
密度 SNP遗传图谱包含 2795个 SNP多态性标记位点, 图谱总长 1832.9 cM, 相邻标记间平均距离为 0.66 cM。定位
了 2年的种子硫苷含量 QTL, 其中有 5个 QTL在 2年内被重复检测到, 分别分布在 A03、A09和 C02染色体上, LOD
阈值在 2.90~10.40之间。这些 QTL在 2011和 2012年试验中分别解释了 56.9%和 55.1%的表型变异。另外有 5个 QTL
仅在其中一年被检测到, 这些 QTL能够解释 4.1%~7.9%的表型变异, QTL阈值在 2.53~3.83之间。
关键词: 甘蓝型油菜; 单核苷酸多态性; 数量性状位点; 种子硫苷含量
Rapeseed (Brassica napus) is one of the most im-
portant oilseed crops in the world, and serves as the third
largest source of plant oil. The residual meal, which is
used in livestock feed mixtures, contains 38%–44% of
high quality protein after oil extraction from rapeseed
seeds [1]. Beta-thioglucoside-N-hydroxysulfates commonly
known as glucosinolates were accumulated extensively in
rapeseed. Glucosinolate plays the important biological
and economic roles in plant defense and human nutri-
tion [2]. Glucosinolate and their hydrolysis products are
involved in different bioactivities. Degradation products
of glucosinolate are thought to inhibit carcinogenesis by
affecting cell cycle arrest and stimulating apoptosis [3-4].
However, the presence of degradation products is not
always beneficial. High intakes of glucosinolate and their
degradation products can cause problems of palatability
and are associated with goitrogenic, liver and kidney
abnormalities [5]. Besides, certain degradation products of
glucosinolate may be involved in growth regulation [6-8].
This particularly limits the use of the rich-protein meal as
a feed supplement for monogastric livestock. In order to
regulate and optimize the level of individual glucosi-
第 8期 荐红举等: 利用 SNP高密度遗传连锁图谱定位甘蓝型油菜种子硫苷含量的 QTL 1387
nolate, breeders are interested in tissue-specific im-
provement of crop nutritional quality and resistance to
diseases and insect pests.
In recent years, great achievements have been
achieved in molecular genetics and biochemistry of
glucosinolate biosynthesis in the model species
Arabidopsis [2]. More than 20 genes involved in glu-
cosinolate biosynthesis in Arabidopsis have been iden-
tified over the past three years. QTLs for total seed
glucosinolate content have been detected using differ-
ent oilseed rape populations. Uzunova et al. [9] first
reported four QTLs in 1995, which explained 61.7% of
seed glucosinolate content in the B. napus double
haploid (DH) population. Howell et al. [10] mapped
four QTLs using the B. napus population derived from
Victor × Tapidor and three QTLs using the B. napus
population derived from Bienvenu × Tapidor, which
explained more than 90% of seed glucosinolate content.
Zhao and Meng [12] detected three QTLs responsible for
seed glucosinolate content. Hasan et al. [1] found that four
genes involved in the biosynthesis of indole, aliphatic
and aromatic glucosinolate might be associated with
known quantitative traits loci for total seed glucosinolate
content in B. napus. However, the densities of maps used
in these studies were relatively low, which were con-
structed with traditional DNA markers, such as AFLP,
RFLP, and SSR.
For QTL analysis, constructing a high density link-
age map composed of DNA markers without large gaps
can detect the minor QTLs. In this study, we analyzed
QTLs for seed glucosinolate content in the B. napus re-
combinant inbred line (RIL) populations with the high
density SNP linkage map, which contains 9164 SNP
markers covering 1832.9 cM, with 1232 bins accounting
for 7648 of the markers [13]. We identified 10 QTLs asso-
ciated with seed glucosinolate content in oilseed rape
through genetic analysis of a RIL population of B. napus
over two years. The QTLs detected in this study would
provide more information on QTLs that are associated
for seed glucosinolate content. The results deposit can-
didate molecular markers closely linked to target
genes/QTLs, which might be used not only in rapeseed
breeding but also in map-based gene cloning.
1 Materials and Methods
1.1 Plant materials and mapping populations
The female parent GH06 was derived from the
selfing generations of yellow-seeded B. napus, with
73.34 μmol g–1 of glucosinolate content. The male parent,
Pl74, was derived from selfing generation of Youyan 2,
with 18.75 μmol g–1 of glucosinolate content. The re-
combinant inbred lines (RILs) were the ninth generation
by successively selfing from the GH06 P174 cross. In
this study, 172 RILs and their parents were planted in a
randomized complete block design with three replicates
in experimental field of Southwest University in 2011
and 2012. Each plot contained 20 plants in two rows,
with 0.5 m between rows and 0.2 m between plants.
1.2 Glucosinolate content analysis
Selfing seeds in each line from three plants were
mixed as a sample. The total seed glucosinolate content
and its components were tested using near infrared spec-
troscopy (NIR) by means of WinISI II software of an
NIR System 6500. Each assay had three technical repli-
cates, and the mean value was considered as a measure-
ment.
1.3 SNP marker analysis
The Brassica 60 K SNP BeadChip Array was used
to genotype 172 lines and parental plants. This array,
which successfully assays 52 157 Infinium Type II SNP
loci in B. napus, was developed by an international con-
sortium using preferentially single-locus SNPs contrib-
uted from genomic and transcriptomic sequencing in
genetically diverse Brassica germplasm (Isobel Parkin,
Agriculture and AgriFood Canada, unpublished data).
Total genomic DNA was extracted from well-
expanded leaves of RIL and the two parental lines, fol-
lowing the methodology of DP321-03 DNA extraction
kits (Tiangen, Beijing, China). DNA sample preparation,
hybridisation to the BeadChip, washing, primer extension
and staining were performed according to the work flow
described in the Infinium HD Assay Ultra manual. Imag-
ing of the arrays was performed using an Illumina HiS-
CAN scanner after BeadChip washing and coating. Allele
calling for each locus was performed using the Ge-
nomeStudio genotyping software V.2011 (Illumina, Inc.).
SNP markers were named using SNP plus index numbers
assigned by GenomeStudio, followed by the chromosome
number.
1.4 Construction of genetic linkage map and QTL
analysis
A high-density genetic linkage map for the complex
allotetraploid species B. napus (oilseed rape) was con-
structed using the above-referenced RIL population. This
map was composed of genome-wide single nucleotide
polymorphism (SNP) markers assayed by the Brassica
60 K Infinium BeadChip Array. Composite interval map-
ping (CIM) was carried out using the software package
Windows QTL Cartographer 2.5 [14]. The LOD thresholds
for QTL significance were determined by a permutation
test (1000 replications) with a genome-wide significance
level P=0.01 to judge whether there exist QTLs. Running
result of software can show additive effects of QTLs and
phenotypic contribution rate. QTL nomenclature fol-
lowed the protocol of McCouch et al. [15]. Take “qGSL-
A01-11” as an example, “q” refers to QTL; “GSL” refers
to glucosinolate content, “A01” indicates the linkage
group, and the number “11” indicates the year of field
experiment (the first “1”, short form of 2011) and the
series number of the QTL (the second “1”). This number
1388 作 物 学 报 第 40卷
is in one-digit for some QTLs, which means the QTL was
identified in both years.
2 Results
2.1 Seed glucosinolate content variation in the
RIL population
The total glucosinolate content of the parents (GH06
and P174) were 73.34 and 18.75 μmol g–1, respectively,
with significant difference in B. napus that provided ideal
material for QTLs analysis. The content of the RIL
population was distributed from 12.00 to 117.75 μmol g–1
with an average of 63.9 μmol g–1 in 2011, and from 13.50
to 128.80 μmol g–1 with an average content was 70.80
μmol g–1 in 2012 (Table 1). As expected, the two years
total glucosinolate contents were significantly correlated
(P < 0.01, R2 = 0.997). The seed glucosinolate content
from 50 to 100 μmol g–1 accounted for 80.6% of the RIL
population (Fig. 1). The RIL population showed a con-
tinuous and bell-shaped distribution for glucosinolate
content in two years, suggesting the multi-genic control
of glucosinolate in B. napus. However, transgressive
segregations in the RIL population were observed, indi-
cating that glucosinolate synthesis in seeds was con-
trolled by some genes in the two parents.
Table 1 Variation of glucosinolate content in the RIL population of B. napus
Year Max (μmol g–1) Min (μmol g–1) Average (μmol g–1) Standard deviation Coefficient of variation (%)
2011 117.5 12.0 63.9 21.4 33.5
2012 128.8 13.5 70.8 24.9 35.1
Fig. 1 Frequency distribution of seed glucosinolate content in the
RIL populations over two years
2.2 QTL analysis of glucosinolate contents in B.
napus seeds
The SNP linkage map contains 9164 SNP markers
covering 1832.9 cM, with 1232 bins accounting for 7648
of the markers. A subset of 2795 SNP markers, with an
average distance of 0.66 cM between adjacent markers,
was applied for QTL mapping of seed glucosinolate con-
tent. Five significant QTLs for the total glucosinolate
content in B. napus seed were detected on chromosome
A03, C02, and A09 by analysis of the RIL population
harvested in 2011 and 2012 (Fig. 2). LOD threshold val-
ues for significant QTLs in 2011 and in 2012 were de-
termined to be 3.01–10.43 by the permutation test (Table
2). These QTLs explained 56.9% and 55.1% of the total
phenotypic variance in 2011 and 2012, respectively. Lo-
cus qGSL-A09-2 detected in 2011 shared the highest con-
tribution value (26.2%), while the QTL qGSL-C02-1 de-
tected in 2011 explained the lowest phenotypic variance
(4.9%). Four of these QTLs had the positive additive
effect, indicating the female parent GH06 contributed to
a strong increase in seed glucosinolate content except for
the qGSL-C02-1. The qGSL-A09-2 and qGSL-A09-3 de-
tected in 2011 explained the total phenotypic variances of
26.2% and 11.1%, respectively, and in 2012 were 19.0%
and 16.5%, respectively.
Besides, five QTLs for total glucosinolate content
were identified in either 2011 or 2012, which were
mapped on chromosomes A01, A07, A09, C02, and
C06. A single QTL explained 4.1%–7.9% of the phe-
notypic variance and the LOD threshold value varied
from 2.18 to 3.93 (Table 2). Three QTLs (qGSL-A01-
11, qGSL-A07-12, and qGSL-C02-12) had negative
effect, showing the additive alleles from parent P174.
3 Discussion
In this study, we mapped QTLs of seed glucosi-
nolate content in B. napus, a major oilseed crop in the
world. Glucosinolate content is a complex trait and its
expression is controlled by a gene network in the family
Brassicaceae [16]. In previous studies, more than fifty
genes have been found to be involved in glucosinolate
biosynthesis, including transcription factors, core struc-
ture formation, secondary modification, and core-substrate
pathways [2-3]. In B. napus, however, the studies have
primarily laid emphasis on the genetics of total glucosi-
nolate contents [17-18]. Four QTLs on B. napus chromo-
somes N9, N12, N17, and N19 were detected independ-
ently in different studies, indicating that these QTLs rep-
resent major loci that influence seed glucosinolate con-
tent in different materials [9-12,19]. Uzunova et al. [9]
detected two major and two minor QTLs for glucosi-
nolate content, whereas Toroser et al.[17] detected two
major and three minor QTLs. The genetic maps of B.
napus developed by Parkin et al. [20] and Ferreira et al. [21]
have been fully aligned. The results showed that the ma-
jor QTLs GSL-1 and GSL-2, and the minor QTL GSL-4
第 8期 荐红举等: 利用 SNP高密度遗传连锁图谱定位甘蓝型油菜种子硫苷含量的 QTL 1389
Fig. 2 QTLs associated with total seed glucosinolate content detected in the RIL from a cross of GH06P174
The bar length on the right represents one LOD supporting interval for each QTL and only QTL region markers are displayed on the linkage map.
Table 2 Quantitative traits loci involved in the seed gluconsinolate content of RIL populations
2011 RILs 2012 RILs
QTL Chr. Marker interval
LOD Additive value R2 (%) LOD Additive value R2 (%)
qGSL-A03-1 A03 SNP5306A03–SNP5100A03 3.01 9.03 8.80 4.92 10.91 6.90
qGSL-A09-2 A09 SNP19550A09–SNP20943A09 10.43 18.39 26.20 7.01 18.12 19.00
qGSL-A09-3 A09 SNP20854A09–SNP20745A09 3.94 11.65 11.10 5.35 15.44 16.50
qGSL-C02-1 C02 SNP30935–SNP38136C02 3.23 –9.01 4.90 3.44 –6.24 5.90
qGSL-A01-11 A01 SNP2662A01–SNP2481A01 3.30 –5.81 5.20 — — —
qGSL-A07-12 A07 SNP16102A07–SNP16204A07 — — — 3.53 –5.07 4.10
qGSL-A09-11 A09 SNP21773A09–SNP20222A09 3.85 11.98 7.90 — — —
qGSL-C02-12 C02 SNP46003C02–SNP34478 — — — 3.93 –9.02 5.70
qGSL-C06-11 C06 SNP24582C06–SNP28486C06 3.18 5.06 5.40 — — —
A: additive effect; R2: phenotypic variation explained.
1390 作 物 学 报 第 40卷
in Toroser et al. correspond to GLN1, GLN2, and GLN4 [10].
respectively.
In our study, two major QTLs were located on A09
in both 2011 and 2012, which are consistent with the
previous results [9-10, 12]. The two major QTLs for seed
glucosinolate content on A09 were linked closely to the
SNP marker SNP21741A09 and SNP21712A09, respec-
tively. The two QTLs namely qGSL-A09-2 and qGSL-
A09-3 explained 26.2%, 11.1%, and 19.0%, 16.5% of
high phenotypic variance which in 2011, 2012, respec-
tively. Comparative mapping has showed that the
qGSL-A09-2 corresponded to GLN1 on N9 in Howell et
al. There were another four QTLs detected in both two
years namely qGSL-A03-1, qGSL-A03-2, qGSL-A09-1,
and qGSL-C02-1, which are distributed on A03, A03,
A09, and C02, respectively. The chromosome C02 is just
N12 described in previous studies [10], but the contribu-
tion is relatively small. In addition to the two major
QTLs for seed glucosinolate content, we also detected
three minor QTLs in 2011 and two minor QTLs in 2012.
These minor QTLs might be associated with several mi-
nor genes that associated with seed glucosinolate content
but were affected readily by environments.
Quantitative trait lous (QTL) mapping of seed glu-
cosinolates (GSL) content has been reported in Brassica
napus. The key loci responsible for low GSL content are
reported to be the transcription factors designated
MYB28 [22]. Scanning of the 600 kb regions flanking the
QTLs found two possible genes, i.e. BZO1p1 and SUR1
involved in GSL biosynthesis. The exact adjacent rela-
tionships are as follows: Bra004132 (benzoate–CoAligase,
physical region 17.01 Mb) located in qGSL-A07-12
closed to SNP1618 6A07 and Bra 036703 (S-alkylthioh-
ydroximatelyase/carbon-sulfur lyase/transaminase, physi-
cal region 5.53 Mb) located in qGSL-A09-3 region closed
to SNP21712A09 marker. However, the gene BZO1p1
catalyzes side chain modification and leads to formation
of different GSLs, but does not affect the GSL content [23-24].
Another gene SUR1 catalyzes core structure formation of
GSLs with production of GSLs intermediate metabolite
but not final GSLs [25]. The possible reason should be
studied in the future.
The bar length on the right represents one LOD
supporting interval for each QTL and only QTL region
markers are displayed on the linkage map.
4 Conclusion
Five QTLs detected in both two years are located on
A03, A09 and C02, respectively. Two QTLs distributed
on A09 explained phenotype variation 26.2% and
11.1% in 2011, respectively, 19.0% and 16.5% in 2012,
respectively. Furthermore, This overall control of glu-
cosinolate level could be primarily affected by two genes,
SUR1 and BZO1p1, contained in QTL qGSL-A09-3 and
QTL qGSL-A07-12, respectively.
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