Inheritance of cellulose, hemicellulose and lignin content in relation to seed oil and protein content in oilseed rape

Abstract

Oilseed rape is worldwide an important oil and protein crop. Its oil is valued because of its excellent quality. The oil extracted meal is marketed as a lower value by-product for feeding livestock. Recently, interest in vegetable proteins has increased to use the oilseed rape protein as an alternative vegetable source for human consumption. However, the use of the protein rich meal for food production is greatly limited by the presence of residual glucosinolate, phenolic acid esters and crude fibre contents which affect its techno-functional properties, taste and colour. Further reducing contents of glucosinolates, cellulose, hemicellulose and indigestible lignin, is expected to enhance protein content and quality. To this end, two half-sib DH populations were tested in replicated field experiments. Inheritance of individual seed fibre components in relation to each other and to oil, protein and glucosinolate content were investigated. The DH populations were genotyped with Brassica 15K SNP Illumina chip, QTL were mapped and candidate genes were identified using the high quality long read reference genome of Express 617. Novel QTL for fibre components were identified that co-located to each other, with QTL for oil, protein and glucosinolate content, and with opposite direction of additive effects. The parallel investigation of two half-sib DH populations gave insight into the direction of the additive effects which depended on the indvidual parents. The results provide additional understanding of genetic loci underlying the seed quality traits which may help achieving the breeding goals in oilseed rape.

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https://doi.org/10.1007/s10681-023-03264-4
RESEARCH
Inheritance ofcellulose, hemicellulose andlignin content
inrelation toseed oil andprotein content inoilseed rape
AbdusaheedOlabisiYusuf · ChristianMöllers
Received: 30 May 2023 / Accepted: 25 November 2023 / Published online: 14 December 2023
© The Author(s) 2023
Abstract Oilseed rape is worldwide an important
oil and protein crop. Its oil is valued because of its
excellent quality. The oil extracted meal is marketed
as a lower value by-product for feeding livestock.
Recently, interest in vegetable proteins has increased
to use the oilseed rape protein as an alternative veg-
etable source for human consumption. However, the
use of the protein rich meal for food production is
greatly limited by the presence of residual glucosi-
nolate, phenolic acid esters and crude fibre contents
which affect its techno-functional properties, taste and
colour. Further reducing contents of glucosinolates,
cellulose, hemicellulose and indigestible lignin, is
expected to enhance protein content and quality. To
this end, two half-sib DH populations were tested in
replicated field experiments. Inheritance of individual
seed fibre components in relation to each other and
to oil, protein and glucosinolate content were inves-
tigated. The DH populations were genotyped with
Brassica 15K SNP Illumina chip, QTL were mapped
and candidate genes were identified using the high
quality long read reference genome of Express 617.
Novel QTL for fibre components were identified that
co-located to each other, with QTL for oil, protein
and glucosinolate content, and with opposite direc-
tion of additive effects. The parallel investigation of
two half-sib DH populations gave insight into the
direction of the additive effects which depended on
the indvidual parents. The results provide additional
understanding of genetic loci underlying the seed
quality traits which may help achieving the breeding
goals in oilseed rape.
Keywords Hemicellulose· Cellulose· Lignin·
Glucosinolate
Introduction
Oilseed rape (Brassica napus L.) is one of the major
sources of vegetable oil in the world. The oil extracted
meal with about 40% protein serves as a good source
for feeding livestock. Recently, interest has increased
in European countries in using plant-based protein
for human consumption. Vegetable protein is more
environmentally friendly compared to animal-based
protein (So and Duncan 2021). However, the use of
the protein rich vegetable meal for food production
is greatly limited by the presence of residual glucosi-
nolate (GSL), phenolic acid esters and crude fibre
contents which affect its techno-functional properties,
taste and colour (Zum Felde etal. 2006; Wittkop etal.
2009; Hald etal. 2019). Their biosynthesis compete
with synthesis of oil and protein and can reduce their
Supplementary Information The online version
contains supplementary material available at https:// doi.
org/ 10. 1007/ s10681- 023- 03264-4.
A.O.Yusuf· C.Möllers(*)
Department ofCrop Sciences, Georg-August-University
Göttingen, Von-Siebold Str. 8, 37075Göttingen, Germany
e-mail: cmoelle2@gwdg.de
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value (Gacek et al. 2018, 2021). Hence, a genetic
reduction of the negatively associated constituents
is attempted to enhance seed protein content (SPC)
and quality. Oilseed rape protein content and quality
has been under intensive studies over the years and
a number of QTL for SPC on different chromosomes
has been identified in diverse bi-parental populations
(Schatzki etal 2014; Behnke etal. 2018; Chao etal.
2017; Gacek etal. 2021; Stolte etal. 2022). Schilbert
etal. (2022) identified 15 genomic regions on 7 chro-
mosomes associated with SPC in which many over-
lapped with regions associated with seed oil content
(OC).
Glucosinolate (GSL) content in modern canola
rapeseed has been reduced to 15 µmol per gram of
seed and less from the original level in traditional cul-
tivars with 60–100µmol per gram of seed (Nesi etal.
2008; Rahman etal. 2014). Because of their antinu-
tritive effects, breeding aims at a further reduction
of GSL content (Chao etal. 2022a). The genetic loci
involved in control of GSL have been broadly studied
in Brassica napus and major loci identified are mostly
on chromosome A04, A06, A09, C02, C07 and C09
(He et al. 2018; Liu et al. 2020; Chao et al. 2022a;
Gacek etal. 2021; Kittipol etal. 2019; Schilbert etal.
2022).
As an oil and protein crop, oilseed rape has a com-
paratively high crude fibre content. Crude fibre con-
sists of cellulose (CC), hemicellulose (HC) and lignin
(LC) content. Van Soest et al. (1991) developed a
method that allowed quantification of neutral deter-
gent fibre (NDF), acid detergent fibre (ADF) and acid
detergent lignin (ADL = LC). Subtraction of ADF
from NDF and ADL from ADF yields HC and CC,
respectively. Previous work reported QTL for lignin
content (LC) on different chromosomes and candi-
date genes (Liu etal. 2012, 2013; Stein etal. 2017;
Miao et al. 2019). Negative correlations between
fibre content and OC and SPC in oilseed rape have
been reported (Dimov etal. 2012; Behnke etal. 2018;
Miao et al. 2019). In a transcriptome- and genome-
wide association study, Zhang etal. (2022) identified
genes significantly associated with seed coat content
and negatively affecting OC during seed develop-
ment. In an attempt to further reducing fibre content
in oilseed rape, more detailed investigations of the
genes involved in the biosynthesis of LC, HC and CC,
and their individual effects on each other and on OC
and SPC is required. In a doubled haploid population
Miao et al. (2019) found that LC was significantly
positively correlated with CC, but negatively corre-
lated with HC. Furthermore, CC was positively cor-
related with HC. In addition, co-localized QTL for
individual fibre components and OC with opposite
additive effects were detected. Candidate genes were
identified based on the alignment of SNP marker
sequences with the ZS11 reference genome (Song
et al. 2020; Sun et al. 2017). The objective of this
project was to study the inheritance of individual
seed fibre components in relation to OC, SPC and
GSL content and to identify QTL in two half-sib DH
populations. Since one of the parental genotypes was
derived from a cross with Express 617, candidate
genes were identified based on the high quality long
read reference genome of this genotype (Lee et al.
2020).
Materials andmethods
Plant material
The study material consisted of two half-sib DH
populations. The first ASG population (hence-
forth referred to as population 1) consisted of 170
F1 derived doubled haploid (DH) lines from a cross
between the canola cultivar Adriana and the DH line
SGEDH13. Adriana is a German winter rapeseed line
cultivar (00 double low (canola) quality). SGEDH13
is a DH line derived from the cross between DH line
SGDH14 (Zhao et al. 2005) and inbred line 617 of
the German winter rapeseed cultivar Express (Behnke
et al. 2018). SGEDH13 is characterized by high oil
content, low GSL content and intermediate erucic
acid content caused by the presence of one fae1 gene
(Ecke etal. 1995). The second AZH DH population
(henceforth referred to as population 2) consisted
of 95 F1 derived doubled haploid lines derived by
microspore culture from a cross between Adriana and
Zheyou 50. Zheyou 50 is a canola quality semi-win-
ter cultivar from China. Both DH populations were
developed at the Division of Crop Plant Genetics,
Georg-August University, Göttingen, Germany.
Field experiments
DH lines of population 1 and the parents were tested
in three growing seasons (2015/16, 2016/17, and
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2017/18) in five field environments located in north-
western Germany and Poland. The DH population
2 was evaluated in four consecutive seasons in one
environment in north-western Germany. The field
experiments were conducted in small plots as a Ran-
domized Complete Block design without replication.
Each genotype was sown with 100 seeds in a row of
five meters length; distance between the rows was 75
to 90 cm. At maturity, open pollinated seeds were
bulk harvested from each genotype from the terminal
raceme and three upper most primary branches of ten
healthy plants. The harvested seeds were de-husked
and cleaned and stored at room temperature for seed
quality trait analysis using near-infrared reflectance
spectroscopy (NIRS).
Phenotyping using near infrared reflectance
spectroscopy (NIRS)
In order to measure the seed oil and quality traits
contents, about 3g of bulked harvested seed samples
for each genotype were scanned with NIRS mono-
chromatic as described in Behnke etal. (2018). The
seed oil, seed protein and GSL content measured
were expressed on basis of 91% dry matter content.
The fibre components of the Neutral detergent Fibre
(NDF), Acid Detergent Fibre (ADF) and Acid Deter-
gent Lignin (ADL = LC) in the defatted meal were
estimated using the calibration equation developed by
Dimov etal. (2012). The HC and CC contents were
calculated by subtracting ADF from NDF and LC
from ADF contents, respectively. The protein (PidM)
in the defatted meal was calculated from the estimated
OC, SPC using the following equation: %Protein in
the defatted meal (PidM) = %SPC/(100 − %seed oil
content) × 100.
Statistical analysis
Analysis of Variance (ANOVA) was calculated
for the data using Restricted maximum likelihood
(REML) using lme4 package (Bates etal. 2015) and
lmer test (Kuznetsova etal. 2017) in R (R core team
2022). Both the genotype and the environment were
considered as random factors using the following
simple linear model:
where Yij is the trait value of ith genotype in jth envi-
ronment and µ is the overall mean, gi is the effect of
the ith genotype (i = 1,2…), while ej is the effect of
j environment and geij is the interaction between ith
genotype and jth environment and the random error.
Broad sense heritability (H2) was calculated for each
trait using
where
σ2
g
and
σ2
ge
are variance components for the
genotype and random error and E is the number of
environments. The mean values across the environ-
ments were used to calculate the spearman rank cor-
relation coefficient using R 4.0.3 Package (R Core
Team 2022).
Linkage map construction and QTL mapping
Details and results on linkage map construction and
QTL mapping procedure for both DH populations
are provided in Yusuf etal. (2022). Mean phenotypic
data from the different field experiments were used
for QTL mapping.
SNP marker sequence alignments to reference
genomes and candidate gene identification
To identify the potential candidate genes of QTL,
the positions of the SNP markers on the genetic map
were aligned with their physical position by blasting
the sequence of each SNP against the Brassica napus
Express 617 reference genome (Lee etal. 2020). The
SNP sequences were provided by Isobel Parking
(Agriculture and Agri-Food Canada). The physical
position of each SNP locus was located by blasting
the sequence of each SNP against the high quality
Express 617 Brassica napus reference genome (Lee
et al. 2020). The position was recorded based on
genetic map data information, as well as on the best
matching and the lowest E-value. Arabidopsis thali-
ana related functional genes were annotated on A.
thaliana Araport 11 (TAIR; https:// www. arabi dopsis.
org/ index. jsp). The assignment of A. thaliana annota-
tion to the Brassica napus Express 617 gene models
Yij
=𝜇+
gi
+
ej
+
geij
H2=
σ
2
g
(
σ2
g+
σ2
ge
E)
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was based on Schilbert etal. (2021). The QTL inter-
val spanned over several Kbp and many potential
candidate genes were found within each QTL region
(Table Suppl.S3 and S4). The available literature was
scrutinized for candidate genes involved in biosynthe-
sis of cellulose, hemicellulose, lignin, oil and SPC,
and genes identified within QTL confidence intervals
were mentioned in the discussion.
Results
Phenotypic analysis
The genotypic and environmental variance compo-
nents were statistically significant for all traits studied
in both DH populations (Table1). The heritabilities
for seed quality traits in both populations ranged from
66% for GSL content to 95% for LC. Although paren-
tal lines of both populations had similar seed quality
characters, there was a large range and transgressive
segregation in both populations. For most traits,
including HC and CC, a normal frequency distribu-
tion was found in both populations (Suppl. Figs.S1
and S2). However, LC content showed a bimodal
distribution and a similar large variation in both
populations.
Seed quality correlations in the two half-sib
populations
In population 1, the three fibre fractions NDF, ADF
and LC were closely correlated to each other based on
their overlapping contents of HC and CC (Table 2).
However, NDF was more closely correlated with
LC followed by CC and was not correlated with HC.
OC was negatively correlated with LC and was more
strongly positive correlated with HC than with CC.
SPC was more strongly negative correlated with CC
than with HC, followed by LC. LC was negatively
correlated with HC and positively correlated with
CC. HC was weakly positive correlated with CC.
Table 1 Descriptive statistics for the quality traits and vari-
ance components in two doubled haploid populations for hemi-
cellulose (HC), cellulose (CC), seed oil content (OC), lignin
content (LC), seed protein content (SPC), protein in the defat-
ted meal (PidM), glucosinolate content (GSL), neutral deter-
gent fibre (NDF), acid detergent fibre (ADF)
POP population, SD standard deviation, CV coefficient of variation (%), P1 parent Adriana, P2 parent SGEDH13 in pop 1 and
Zheyou 50 in pop 2, G genotypic variance, E environmental variance, GE genotype by environment variance, H2 broad sense herit-
ability
**Significant at p ≤ 0.01
Traits POP Mean [%] Range SD CV P1 P2 G E GE H2 [%]
CC 1 15.5 13.9–16.9 0.6 3.54 15.6 15.6 0.24** 0.34** 0.24 82.9
CC 2 15.5 14.1–17.0 0.6 3.70 15.8 15.6 0.24** 0.44** 0.32 74.6
HC 1 3.77 1.64–5.39 0.7 18.3 4.16 4.73 0.36** 0.28** 0.44 80.1
HC 2 3.51 1.78–5.27 0.9 24.1 4.01 4.32 0.45** 0.37** 0.88 67.0
LC 1 12.5 9.79–15.8 1.6 12.6 12.5 10.5 2.31** 0.07** 0.59 95.1
LC 2 13.3 10.1–17.8 2.1 16.0 12.4 11.5 4.30** 0.10** 0.85 95.3
OC 1 44.7 39.1–47.1 1.0 2.33 44.4 46.1 0.96** 0.13** 0.38 92.7
OC 2 43.8 41.5–46.2 0.9 2.14 44.6 44.6 0.64** 0.25** 0.94 72.9
SPC 1 17.9 16.7–20.8 0.5 2.78 17.5 17.9 0.18** 0.08** 0.19 82.4
SPC 2 17.8 16.4–19.0 0.6 3.19 17.3 17.4 0.22** 0.30** 0.36 71.3
PidM 1 32.4 30.2–34.2 0.8 2.38 31.5 33.4 0.52** 0.15** 0.29 90.0
PidM 2 31.6 29.8–33.2 0.7 2.26 31.2 31.3 0.40** 0.50** 0.38 80.6
GSL 1 15.8 11.4–33.8 3.1 19.4 13.0 14.5 5.41** 1.89** 10.7 71.7
GSL 2 16.3 10.3–25.0 2.9 18.3 13.7 14.2 4.91** 36.4** 10.9 66.4
NDF 1 31.8 25.2–35.9 1.8 5.68 32.2 30.8 2.71** 1.00** 1.70 88.8
NDF 2 32.3 27.8–37.1 2.5 7.74 32.2 31.5 5.32** 1.84** 3.28 86.6
ADF 1 28.0 24.1–32.5 1.8 6.49 28.0 26.1 3.04** 0.68** 1.18 92.8
ADF 2 28.8 24.7–33.1 2.1 7.31 28.2 27.2 4.04** 0.79** 1.47 91.6
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Interestingly, GSL content was not correlated with
LC but was negatively correlated with HC and CC.
GSL content was in addition not correlated with OC
but was positively correlated with SPC (Table2). As
for population 2, all three fibre fractions were closely
correlated to each other as in population 1. In contrast
to the population 1, NDF was positively correlated
with HC and was not correlated with CC (Table3).
OC was again weak negatively correlated with LC
and positively correlated with HC and CC. In contrast
to the population 1, SPC was much stronger nega-
tively correlated with HC than with CC, followed by
LC. Furthermore, LC was in contrast positively cor-
related with HC and weak negatively correlated with
CC. HC was weak positively correlated with CC. As
for population 1, GSL content was not significantly
correlated with LC but was negatively correlated with
HC and CC. GSL content was negatively correlated
with OC and was positively correlated with SPC
(Table3).
QTL analysis and identification of candidate genes in
the two half-sib DH populations
The SNP positions on the genetic map (in cM) in each
linkage group were aligned with physical position
based on the reference genome. The genetic marker
position was predominant linearly correlated with the
physical marker position in all linkage groups in both
populations (Suppl. TablesS1 and S2; Suppl. Figs.S3
and S4). The alignment of the SNP marker sequences
to the Express 617 reference genome allowed the
comparison of their physical positions with those of
candidate genes. The main interest was to identify
Table 2 Correlations among seed quality traits in population
1 for hemicellulose (HC), cellulose (CC), lignin (LC), seed oil
content (OC), seed protein content (SPC), protein in the defat-
ted meal (PidM), glucosinolate content (GSL), neutral deter-
gent fibre (NDF) and acid detergent fibre (ADF)
*Significant at p ≤ 0.05
**Significant at p ≤ 0.01
Trait HC CC LC OC SPC PidM GSL NDF
CC 0.11
LC − 0.44** 0.35**
OC 0.48** 0.33** − 0.24*
SPC − 0.45** − 0.68** − 0.34** − 0.42**
PidM − 0.11 − 0.47** − 0.54** 0.31** 0.68**
GSL − 0.38** − 0.32** − 0.01 − 0.02 0.38** 0.43**
NDF − 0.02 0.60** 0.86** 0.04 − 0.65** − 0.68** − 0.23*
ADF − 0.35** 0.55** 0.96** − 0.12 − 0.46** − 0.59** − 0.10 0.93**
Table 3 Correlations among seed quality traits in population
2 for hemicellulose (HC), cellulose (CC), lignin (LC), seed oil
content (OC), seed protein content (SPC), protein in the defat-
ted meal (PidM), glucosinolate content (GSL), neutral deter-
gent fibre (NDF) and acid detergent fibre (ADF)
*Significant at p ≤ 0.05
**Significant at p ≤ 0.01
Trait HC CC LC OC SPC PidM GSL NDF
CC 0.22*
LC 0.25* − 0.17
OC 0.52** 0.55** − 0.23*
SPC − 0.78** − 0.52** − 0.28* − 0.74**
PidM − 0.71** − 0.33** − 0.57** − 0.30* 0.87**
GSL − 0.27* − 0.32** 0.15 − 0.22* 0.25* 0.20
NDF 0.60** 0.16 0.90** 0.11 − 0.62** − 0.80** − 0.03
ADF 0.31** 0.10 0.96** − 0.08 − 0.43** − 0.67** 0.07 0.95**
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co-locating QTL for individual fibre components with
the same or opposite direction of the additive effects
to each other and as well as to QTL for oil, protein
and GSL content. This finding could facilitate under-
standing connections between the different biosyn-
thetic pathways and identifying genes reducing fibre
and simultaneously enhancing oil and protein content.
Population 1 Transgressive segregation for oil
and protein content in the DH population is caused
by a number of different QTL for OC and SPC with
alleles from both parents either increasing oil or pro-
tein content. The majority of QTL alleles with nega-
tive additive effects increasing OC were derived from
SGEDH13. SGEDH13 contributed with the QTL
1Oil-3 on chromosome C03 the fae1 allele for erucic
acid biosynthesis leading to enhanced oil content
(Table 4). Candidate is the well-known 3-ketoacyl-
CoA synthase (KCS) gene (C03p062840.1; Table6).
This QTL 1Oil-3 allele did not lead to an enhanced
SPC. However, the QTL 1Oil-3 allele collocated with
QTL 1Pidm-4 and led to enhanced protein content in
the defatted meal, indicating that fibre content in the
meal is reduced by the erucic acid allele. However,
there was no significant QTL at the same position on
C03 with an opposite additive effect neither for NDF
nor for CC, HC or LC. The confidence interval of the
QTL 1Oil-1 overlapped with the QTL 1CC-1 with an
opposite additive effect, suggesting that an increase in
OC led to a reduction of CC or vice versa (Table4).
Candidate for QTL 1Oil-1 is the lysophosphatidyl
acyltransferase gene (LPAT 5; Table 6). However,
co-location of QTL 1Oil-1 with QTL 1ADF-1 and
1NDF-1 specifically confirmed the presence of a cel-
lulose biosynthesis gene as a causal factor. Candidates
for QTL 1CC-1 are two NAC domain transcription
factors (Table6). Candidates for QTL 1Oil-2 on A02
are the 3-ketoacyl-CoA synthase gene (KCS21) and
the MYB96 transcription factor gene (Table6). The
two QTL 1oilpro-1 and 1oilpro-2 both with a negative
additive effect increased contents of the sum of oil
and protein in the seed. However, this was only due
to their effects on OC on C03 and C05. QTL 1oil-4
and 1oil-pro-2 on C05 co-located with QTL 1LC-3
with an opposite additive effect, suggesting that a
reduction of LC led to an enhanced OC. There are a
number of candidates for QTL 1LC-3 which include
phenylalanine ammonia-lyase 4 (PAL4), laccase
(LAC7), cellulose synthase (CEV1), cinnamoyl-CoA
reductase (CCR1), MYB83 gene, SEC8 and a MYB5
gene (Table6, Suppl. Fig.S5). Notably, QTL 1LC-3
also co-located with QTL 1HC-3 with a negative
additive effect, indicating that a LC reduction leads
to an increase in HC. There was no corresponding
QTL effect on CC. The second QTL 1CC-2 on A07
was detected at a similar position as QTL 1LC-2 with
opposite additive effects, suggesting competing bio-
synthetic pathways. It also mapped at the same posi-
tion as 1SPC-2 with the same direction of the effect
(Table4, Suppl. Fig.S5). Candidates for QTL 1LC-2
are a cellulose synthase-like gene and a PAL2 gene
(Table 6). Furthermore, QTL 1CC-2 mapped with
overlapping confidence intervals with QTL 1SPC-
2, 1PidM-1 and 1LC-2 implying that a reduction of
CC led to an increase in SPC, PidM and LC. Candi-
date for QTL 1CC-2 is a cellulose synthase-like gene
(CSLA10; Table6). Otherwise, candidates for QTL
1SPC-2 are LEC1 and LEC2 genes. QTL 1PidM-3
was identified at a very similar position as QTL
1CC-3 with opposite additive effects. QTL 1HC-1
mapped at a very similar position as QTL 1SPC-3
on C01 with opposite additive effects. Candidate for
QTL 1HC-1 is a COBRA like protein gene. QTL
1HC-1 and 1HC-2 did not show co-locating positions
with QTL for LC and CC. QTL positions of NDF and
ADF confirmed individual QTL positions for HC, CC
and LC (Table4). There was no significant QTL for
GSL content detected in this population, indicating
that parental lines had identical or similar alleles at
relevant loci.
Population 2 The half-sib DH population 2 shared
with population 1 the QTL 2Oil-1 for oil content
on A01 (Table5). In both populations, the flanking
markers were located between 20 and 28 Mbp with
the same LPAT5 candidate gene (Table 7). How-
ever, in population 1, the SGEDH13 allele increased
the OC whereas the Adriana allele increased the
OC in population 2. The increase in OC in popula-
tion 2 was accompanied by lower protein content
at QTL 2SPC-1 and by an increase in CC at QTL
2CC-1. For the second QTL 2Oil-2 on A04, the
Zheyou 50 allele led to an increase in OC and LC
(QTL 2LC-1; Table5; Suppl. Fig. S6). On the other
hand, the effect of QTL 2Oil-2 is accompanied by a
decrease in SPC at QTL 2SPC-2 and 2PidM-1. Can-
didate is an acetyl CoA synthetase (ACS; Table 7;
Suppl. Fig. S6). With almost 20% the largest frac-
tion of variance for oil content is explained by QTL
2Oil-4 on C02 with the Adriana allele increasing
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trait value. Candidate is a 3-ketoacyl-CoA synthase
gene (KCS19; Table7). Confidence interval of QTL
2Oil-3 on A05 overlapped with QTL 2LC-2 with
opposite direction of additive effects, suggesting
that an increase in oil content led to a reduction in
lignin content. Candidate is a glycerol-3-phosphate
acyltransferase gene (GPAT6). The QTL 2Oil-4 and
2Oil-3 were not identified in population 1. Population
2 shared the QTL 2Oil-5 on C05 with population 1.
In both populations, the SGEDH13 and the Zheyou
Table 4 QTL mapped for oil content (OC), seed protein (SPC), protein in the defatted meal (PidM), NDF, ADF, lignin content (LC),
hemicellulose content (HC) and cellulose content (CC) in population 1 (Adriana X SGEDH13)
a QTL confidence interval at p ≤ 0.01
b Negative sign indicates alleles from SGEDH13 increase trait values
c R2 percentage of the phenotypic variation explained by a QTL
d TR2 percentage of the phenotypic variation explained by all the QTL for that trait
Trait QTL name LG Peak (cM) CIa (cM) bAdditive effect LOD cR2 dTR2P-value
OC 1Oil-1 A01 106.6 94–106 − 0.23 3.40 4.50 53.2 0.000104
1Oil-2 A02 9.00 5–14 0.22 3.10 4.10 0.000217
1Oil-3 C03 23.8 22–25 − 0.67 22.5 39.4 < 2e−16
1Oil-4 C05 68.0 60–73 − 0.31 5.90 8.20 2.72E−07
SPC 1SPC-1 A02 39.2 32–47 0.13 3.39 7.27 24.9 9.62E−05
1SPC-2 A07 6.10 0–15 0.15 4.24 9.18 1.31E−05
1SPC-3 C01 10.0 0–16 − 0.15 3.76 8.09 4.04E−05
PidM 1PidM-1 A07 12.0 1–20 0.21 5.05 6.33 47.1 2.43E−06
1PidM-2 A10 14.6 6–18 − 0.15 3.09 3.77 2.26E−04
1PidM-3 C01 56.8 50–67 − 0.19 4.84 6.05 3.96E−06
1PidM-4 C03 26.2 21–31 − 0.36 14.7 21.1 9.99E−16
1PidM-5 C05 64.0 56–71 − 0.27 7.32 9.47 1.39E−08
OC + SPC 1oilpro-1 C03 23.3 22–24 − 0.65 33.6 51.2 65.9 < 2e−16
1oilpro-2 C05 66.0 61–69 − 0.36 12.5 13.8 6.68E−11
NDF 1NDF-1 A01 93.4 86–106 0.40 3.23 4.73 48.6 1.48E−04
1NDF-2 C01 23.0 16–32 0.58 6.59 10.1 6.05E−08
1NDF-3 C04 80.0 76–88 0.41 2.98 4.35 2.69E−04
1NDF-4 C05 68.6 64–69 1.05 18.2 32.9 2.00E−16
ADF 1ADF-1 A01 93.4 85–101 0.38 5.12 4.14 72.4 2.22E−06
1ADF-2 A04 38.0 30–53 − 0.25 2.38 1.85 0.00124
1ADF-3 A07 2.40 0–14 − 0.37 4.69 3.76 5.95E−06
1ADF-4 A10 14.6 6–19.5 0.37 4.73 3.80 5.38E−06
1ADF-5 C01 69.0 60–80 0.34 4.28 3.41 1.53E−05
1ADF-6 C05 68.0 65–69 1.35 37.9 49.8 2.00E−16
LC 1LC-1 A04 38.0 30–43 0.22 2.80 2.00 74.5 0.000447
1LC-2 A07 2.40 0–20 0.24 2.90 2.10 0.00031
1LC-3 A10 15.0 8–19 − 0.24 5.20 3.90 9.18E−06
1LC-4 C05 68.6 66–70 1.28 44.9 61.3 2.00E−16
HC 1HC-1 C01 8.50 0–22 0.18 3.29 6.35 32.2 0.000124
1HC-2 C04 75.1 69–88 0.21 3.90 7.61 2.87E−05
1HC-3 C05 68.6 63–69 − 0.31 9.37 19.7 9.05E−11
CC 1CC-1 A01 96.0 86–106 0.19 5.71 11.1 33.8 4.53E−07
1CC-2 A07 7.00 0–15 − 0.14 3.54 6.71 7.10E−05
1CC-3 C01 69.0 57–80 0.17 5.23 10.1 1.38E−06
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50 alleles were increasing the OC. Likewise, in both
populations these QTL co-located with a QTL for
LC content with an opposite direction of the effect
and with the Adriana allele increasing trait values
(1LC-3 and 2LC-5). There are a number of candidate
genes for this QTL, e.g. the PAL4 gene and the LAC7
gene (cf. population 1 and Table7). Epistatic inter-
actions were found between QTL 2Oil-2, 2Oil-3 and
Table 5 QTL mapped for oil content (OC), seed protein (SPC), protein in the defatted meal (PidM), NDF, ADF, lignin content (LC),
hemicellulose content (HC) and cellulose content (CC) in population 2 (Adriana X Zheyou 50)
a QTL confidence interval at p ≤ 0.01
b Negative sign indicates alleles from Zheyou 50 increasing trait values
c R2 percentage of the phenotypic variation explained by a QTL
d TR2 percentage of the phenotypic variation explained by all the QTL for that trait
Trait QTL name LG Peak (cM) CIa (cM) bAdditive effect LOD cR2 dTR2 P-value
OC 2Oil-1 A01 18.3 8–32 0.32 5.90 11.7 65.2 6.58e−07
2Oil-2 A04 25.0 13–27 − 0.23 5.88 11.6 4.21e−06
2Oil-3 A05 31.0 26–42 0.20 6.84 13.9 5.55e−07
2Oil-4 C02 2.10 0–6 0.06 9.24 19.9 1.62e−08
2Oil-5 C05 31.0 25–41 − 0.42 8.27 17.4 3.96e−09
A04:C02 A04:C02 0.23 3.00 5.51 0.00039
A05:C02 A05:C02 0.32 5.57 10.9 1.36e−06
SPC 2SPC-1 A01 23.4 15–33 − 0.16 2.32 7.17 40.4 0.001433
2SPC-2 A04 24.0 21–27 0.31 7.84 27.9 4.57e−09
2SPC-3 C05 80.0 75–85 − 0.18 3.03 9.55 0.000266
PidM 2PidM-1 A04 23.0 19–26 0.33 6.27 20.1 44.2 1.34e−07
2PidM-2 C05 66.0 62–81 − 0.48 9.58 33.4 7.12e−11
GSL 2GSL-1 A02 101 97–109 1.63 6.80 26.1 33.9 4.03E−08
2GSL-2 A09 73.0 65–80 − 0.80 3.16 7.02 0.00251
NDF 2NDF-1 A01 23.4 18–31 0.58 3.71 5.38 73.0 6.02E−05
2NDF-2 A04 23.0 18–27 − 1.20 11.6 20.6 1.41E−12
2NDF-3 C05 33.0 26–39 1.17 7.01 11.1 3.49E−08
2NDF-4 C05 79.3 75–85 0.99 5.53 8.39 9.71E−07
ADF 2ADF-1 A01 28.0 20–34 1.04 4.29 5.60 76.1 2.23e−05
2ADF-2 A01 39.0 29–58 − 0.72 2.07 2.55 0.00325
2ADF-3 A04 23.0 17–30 − 0.68 6.55 9.06 1.62e−07
2ADF-4 A09 39.0 31–48 − 0.30 1.20 1.45 0.02497
2ADF-5 C05 33.0 31–38 1.48 19.4 37.8 < 2e−16
2ADF-6 C06 50.4 40–50 0.21 2.26 2.81 0.00849
LC 2LC-1 A04 23.0 21–26 − 0.48 4.02 4.26 67.3 3.27e−05
2LC-2 A05 45.0 41–48 − 0.56 5.82 6.44 5.84e−07
2LC-3 A09 60.4 54–66 − 0.54 5.36 5.87 1.63e−06
2LC-4 C03 121 0–195 0.26 1.45 1.44 0.0127
2LC-5 C05 36.0 31–39 1.96 30.0 65.4 < 2e−16
HC 2HC-1 A04 32.0 29–47 − 0.47 6.93 26.8 33.8 2.28e−07
2HC-2 C05 61.0 54–89 0.42 4.90 17.9 2.04e−05
CC 2CC-1 A01 11.6 0–82 0.17 2.54 7.62 42.5 0.000904
2CC-2 A02 103.1 85–112 − 0.17 2.29 6.82 0.001632
2CC-3 A10 56.2 52–68 0.14 1.86 5.47 0.004554
2CC-4 C01 93.0 84–99 − 0.24 4.05 12.6 2.73e−05
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Table 6 QTL and candidate genes based on Express 617 for population 1 (Adriana X SGEDH13)
QTL Chr Flanking SNPs Physical position
(bp)
Candidate gene
(Express 617)
Physical position
(bp)
Functional annota-
tion araport11
Express 617 candidate gene
1Oil-1 A01 Bn-A01-p26426004 28,704,088 A01p031950.1 26,077,758 AT3G18850 BnaC01g33800D; LPAT5;
lysophosphatidyl acyltrans-
ferase 5
Bn-A01-p27954294 29,819,772
1Oil-2 A02 Bn-A02-p24971607 28,645,783 A02p035450.1 27,530,583 AT5G49070 BnaA02g30560D; KCS21;
3-ketoacyl-CoA synthase 21
Bn-A02-p25615285 29,351,916 A02p039110.2 30,032,315 AT5G62470 BnaA02g33410D; MYB96;
Myb domain protein 96
1Oil-3 C03 Bn-scaff_15794_3-p154541 55,932,529 C03p062840.1 56,197,015 AT4G34520 BnaC03g65980D; KCS18;3-
ketoacyl-CoA synthase 18
Bn-A08-p13019549 56,641,328 C03p062850.1 56,213,052 AT4G34510 BnaC03g66040D; KCS17;3-
ketoacyl-CoA synthase 17
1Oil-4 C05 Bn-scaff_21369_1-p1212906 39,180,456 C05p040780.1 See QTL 1LC-3
Bn-scaff_20219_1-p50816 42,540,453 C05p041260.1
1SPC-1 A02 Bn-A02-p7853194 7,161,946 A02p015980.1 9,109,411 AT1G66160 BnaA02g12360D; CMPG1;
CYS, MET, PRO, and GLY
protein 1
Bn-A02-p13123802 12,690,164 A02p020660.1 12,902,711 AT1G18570 BnaA02g16690D; MYB51;
myb domain protein 51
1SPC-2 A07 Bn-A07-p187342 206,199 A07p008230.1 9,868,724 AT1G28300 BnaA07g08500D; LEC2;
AP2/B3-like transcriptional
factor family protein
Bn-A07-p10554944 13,777,886 A07p011110.1 11,946,345 AT1G21970 BnaA07g10770D; LEC1;
Histone superfamily protein
A07p010610.1 11,598,637 AT1G22710 BnaA07g10320D; SUC2;
Sucrose-proton symporter 2
1SPC-3 C01 Bn-scaff_19168_1-p109723 38,651,892
Bn-A01-p27774666 41,762,974
1PidM- 1 A07 Bn-A07-p187342 206,199 See 1SPC-2
Bn-A07-p11512981 15,833,275
1PidM- 2 A10 Bn-A10-p7278102 10,939,905 A10p015840.1 14,165,784 AT5G20900 BnaA10g14660D; JAZ12;
jasmonate-zim-domain
protein 12
Bn-A10-p11059267 15,008,230 A10p011370.1 11,199,820 AT5G56750 BnaA10g10880D; NDL1;
N-MYC downregulated-
like 1
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Table 6 (Continued)
QTL Chr Flanking SNPs Physical position
(bp)
Candidate gene
(Express 617)
Physical position
(bp)
Functional annota-
tion araport11
Express 617 candidate gene
1PidM- 3 C01 Bn-scaff_20210_1-p166012 10,020,034
Bn-scaff_15803_1-p729620 17,384,422
1PidM- 4 C03 Bn-scaff_15794_3-p154541 55,932,529 See QTL 1Oil-3
Bn-A08-p13019549 56,641,328
1PidM- 5 C05 Bn-scaff_18826_1-p232573 36,047,584 C05p043140.1 42,591,158 AT3G07450 BnaC05g44510D; Bifunct.
Inhibit./lipid-transfer prot./
seed storage 2S albumin
Bn-scaff_20219_1-p50816 42,540,453 See QTL 1LC-3
1oilpro1 C03 Bn-scaff_15794_3-p154541 55,932,529 See QTL 1Oil-3
Bn-A08-p13019549 56,641,328
1oilpro2 C05 Bn-scaff_21369_1-p1212906 39,180,456 See QTL 1Oil-4 and 1LC-3
Bn-scaff_20219_1-p50816 42,540,453
1NDF-1 A01 Bn-A01-p18306617 26,704,088 See QTL 1CC-1
Bn-A01-p27954294 29,819,772
1NDF-2 C01 Bn-scaff_17217_1-p4769 36,966,399 See QTL 1ADF-5 and 1HC-1
Bn-scaff_19168_1-p109723 38,651,892
1NDF-3 C04 Bn-scaff_27914_1-p74694 30,664,328 See QTL 1HC-2
Bn-scaff_21956_1-p232420 44,985,085
1NDF-4 C05 Bn-scaff_21369_1-p1212906 39,180,456 See QTL 1LC-3, 1Oil-4,
1PidM-5, 1ADF-6, 1Oil-4,
1HC-3
Bn-scaff_17441_3-p37334 42,001,429
1ADF-1 A01 Bn-A01-p18306617 26,704,088 See QTL 1NDF-1 and 1CC-1
Bn-A01-p27954294 29,819,772
1ADF-2 A04 Bn-A04-p14315336 16,932,263 See QTL 1LC-1
Bn-A04-p16627363 19,433,149
1ADF-3 A07 Bn-A07-p187342 206,199 See QTL 1LC-2
Bn-A07-p10554944 13,777,886
1ADF-4 A10 Bn-A10-p7644362 11,362,272 See QTL 1PidM-2
Bn-A10-p11059267 15,008,230
1ADF-5 C01 Bn-scaff_17217_1-p4769 36,966,399 See QTL 1HC-1
Bn-scaff_19168_1-p109723 38,651,892
1ADF-6 C05 Bn-scaff_21369_1-p1212906 39,180,456 See QTL 1LC-3, 1Oil-4,
1NDF-4, 1ADF-6, 1HC-3
Bn-scaff_17441_3-p37334 42,001,429
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Table 6 (Continued)
QTL Chr Flanking SNPs Physical position
(bp)
Candidate gene
(Express 617)
Physical position
(bp)
Functional annota-
tion araport11
Express 617 candidate gene
1LC-1 A04 Bn-A04-p14410667 17,053,407 A04p018830.1 16,245,744 AT2G30490 BnaA04g17570D; C4H;
cinnamate-4-hydroxylase
Bn-A04-p16368852 19,181,647 A04p023010.1 18,786,242 AT2G37040 BnaA04g21230D; PAL1; phe-
nylalanine ammonialyase 1
1LC-2 A07 Bn-A07-p187342 206,199 A07p009890.1 11,020,886 AT1G24070 BnaA07g09050D; CSLA10;
cellulose synthase-like A10
Bn-A07-p10554944 13,777,886 A07p016620.1 15,797,092 AT3G53260 BnaA07g16060D; PAL2;
phenylalanine ammonia-
lyase 2
1LC-3 A10 Bn-A10-p7644362 11,362,272 See QTL 1PidM-2
Bn-A10-p11059267 15,008,230
1LC-4 C05 Bn-scaff_21369_1-p1212906 39,180,456 C05p038330.1 39,547,901 AT3G13540 BnaA05g37010D; MYB5;
myb domain protein 5
Bn-scaff_17441_3-p37334 42,001,429 C05p040780.1 41,326,930 AT5G05170 BnaA05g28060D; CEV1;
Cellulose synthase family
protein
C05p041220.1 4,15,62,723 AT3G10380 BnaC05g42720D; SEC8;
subunit of exocyst complex
8
C05p041260.1 41,588,681 AT3G10340 BnaC05g42780D; PAL4; phe-
nylalanine ammonia-lyase 4
C05p041720.1 41,852,113 AT3G09780 BnaC05g43230D; CCR1;
CRINKLY4 related 1
C05p042120.1 42,094,161 AT3G09220 BnaCnng24340D; LAC7;
laccase 7
C05p042650.1 42,319,659 AT3G08500 BnaC05g44010D; MYB83;
myb domain protein 83
1HC-1 C01 Bn-scaff_19168_1-p109723 38,651,892 C01p037480.1 42,680,552 AT3G16860 BnaC01g44070D; COBL8;
COBRA-like protein 8
precursor
Bn-A01-p27774666 41,762,974
1HC-2 C04 Bn-scaff_27914_1-p74694 30,664,328 C04p034430.1 40,652,520 AT5G04230 BnaC04g33780D; PAL3;
phenyl alanine ammonia-
lyase 3
Bn-scaff_21956_1-p232420 44,985,085 C04p025700.1 30,335,686 AT3G53260 BnaCnng52250D; PAL2; phe-
nylalanine ammonia-lyase 2
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2Oil-4, respectively (Table 5). On chromosome C05
there was in addition QTL 2HC-2 at 61cM with the
Zheyou 50 allele decreasing HC and increasing SPC
and protein content in the defatted meal (QTL 2SPC-2
and 2PidM-2). These three QTL mapped in addition
at the same position as QTL 2NDF-4 specifying the
hemicellulose effect of this QTL. Candidates are a
number of NAC domain transcriptional regulator
genes (Table 7). The major GSL QTL 2GSL-1 on
A02 explained 26% of the phenotypic variance with
the Adriana allele increasing trait value. This QTL
mapped at the same position as QTL 2CC-2 with an
opposite effect, indicating that a reduction in GSL
content led to an increase in CC or vice versa. Candi-
dates for GSL content are GTR2, TGG1, TGG2, the
MBY28 and MYB34 transcription factor genes. Can-
didates for 2CC-2 are transparent testa genes TT4 and
TT10 (Table7; Suppl. Fig. S6). On the contrary to
this, an increase of LC with QTL 2LC-3 appears to
result in an increase of GSL content at QTL 2GSL-2
on A09. As for population 1 most of the QTL for
composite traits NDF and ADF confirmed individual
QTL positions for HC, CC and LC.
Discussion
The main objective of this study was to map QTL
for the three seed fibre components HC, CC and
LC and to determine individual interactions among
them and with OC, SPC and GSL content. There
are only few studies addressing this detailed ques-
tion. Miao et al. (2019) identified in the KN DH
population between 21 and 35 QTL for LC, HC, and
CC. They found a significant positive correlation
between LC and CC and a negative correlation to
HC and OC. In population 1 also a positive corre-
lation between LC and CC was found, whereas in
the population 2 a weak negative correlation was
found (Table2). In an earlier work, Liu etal. (2012)
also reported a somewhat lower positive correlation
between LC and CC. Furthermore, in population 1
a negative correlation between LC and HC and OC
was identified, whereas in the population 2 a posi-
tive correlation to HC and a negative correlation
to OC was detected. As in the KN DH population
a slightly positive correlation between CC and HC
content was found for both populations. In con-
trast to the results of Miao etal. (2019) a positive
Table 6 (Continued)
QTL Chr Flanking SNPs Physical position
(bp)
Candidate gene
(Express 617)
Physical position
(bp)
Functional annota-
tion araport11
Express 617 candidate gene
1HC-3 C05 Bn-scaff_21369_1-p1212906 39,180,456 See QTL 1LC-3
Bn-scaff_17441_3-p37334 42,001,429
1CC-1 A01 Bn-A01-p18306617 26,704,088 A01p032330.1 26,469,580 AT3G18400 BnaCnng63190D; NAC058;
NAC domain containing
protein 58
Bn-A01-p27954294 29,819,772 A01p034370.2 28,355,575 AT3G12910 BnaA01g30220D; NAC (No
Apical Meristem) domain
transcript. regulator
1CC-2 A07 Bn-A07-p187342 206,199 A07p009890.1 11,020,886 AT1G24070 BnaA07g09050D; CSLA10;
cellulose synthase-like A10
Bn-A07-p10554944 13,777,886
1CC-3 C01 Bn-scaff_17731_1-p590773 6,407,584
Bn-scaff_19193_1-p875889 8,040,678
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Table 7 QTL and candidate genes based on Express 617 for population 2 (Adriana x Zheyou 50)
QTL Chr Flanking SNPs Physical position (bp) Candidate gene (Express
617)
Physical position (bp) Functional annota-
tion araport11
Express candidate gene
1Oil-1 A01 Bn-A01-p18230629 20,247,938 A01p031950.1 26,077,758 AT3G18850 BnaC01g33800D; LPAT5; lysophos-
phatidyl acyltransferase 5
Bn-A01-p26426004 28,704,088 See QTL 2CC-1
2Oil-2 A04 Bn-A04-p8481222 9,621,091 A04p012950.1 11,892,091 AT5G36880 BnaA04g12330D; ACS; acetyl-CoA
synthetase
Bn-A04-p9296314 11,834,759
2Oil-3 A05 Bn-A05-p2645307 2,928,767 A05p007180.1 3,981,157 AT2G38110 BnaA05g06580D; GPAT6; glycerol-
3-phosphate acyltransferase 6
Bn-A05-p3414540 3,721,186
2Oil-4 C02 Bn-scaff_17752_1-p76499 1,136,711 C02p001490.1 1,317,027 AT5G04530 BnaC02g02670D; KCS19; 3-ketoacyl-
CoA synthase 19
Bn-scaff_22970_1-p335296 1,577,208
2Oil-5 C05 Bn-A05-p22111286 41,608,831 See QTL, 1Oil-4, 1NDF-4, 1ADF-6,
1HC-3, 2NDF-3, 2ADF-5, 2LC-4
Bn-scaff_17441_3-p156691 42,138,047
2SPC-1 A01 Bn-A01-p20990218 9,735,795 A01p021570.1 14,651,829 AT3G51810 BnaA01g19290D; EM1; Stress induced
protein
Bn-A01-p10026020 22,435,826
2SPC-2 A04 Bn-A04-p7373248 9,621,091
Bn-A04-p9296314 11,834,759
2SPC-3 C05 Bn-scaff_16770_1-p107862 34,645,234 See QTL 2NDF-4, 2PidM-2, 2HC-2
Bn-scaff_18826_1-p76018 36,181,332
2PidM-1 A04 Bn-A04-p7373248 9,621,091 See QTL 2Oil-2, 2SPC-2, 2ADF-3,
2LC-1
Bn-A04-p9296314 11,834,759
2PidM-2 C05 Bn-scaff_16770_1-p107862 34,645,234 See QTL 2NDF-4, 2HC-2
Bn-scaff_18826_1-p76018 36,181,332
2GSL-1 A02 Bn-A02-p24345550 28,231,787 A02p036570.1 28,280,336 AT5G26000 BnaCnng53320D; TGG1; thioglucoside
glucohydrolase 1
Bn-A02-p25615285 29,351,916 A02p036570.3 29,450,406 AT5G25980 BnaA09g26590D; TGG2; glucoside
glucohydrolase 2
A02p038170.1 29,440,826 AT5G60890 BnaAnng06640D; MYB34; myb
domain protein 34
A02p038110.1 29,371,538 AT5G23010 BnaA02g33040D; MAM1; methylthio-
alkylmalate synthase 1
A02p038540.1 29,647,030 AT5G61420 BnaC09g05300D; MYB28; myb domain
protein 28
A02p038990.2 29,973,314 AT5G62680 BnaC02g42260D; GTR2; Major facilita-
tor superfamily protein
See QTL 2CC_2
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Table 7 (Continued)
QTL Chr Flanking SNPs Physical position (bp) Candidate gene (Express
617)
Physical position (bp) Functional annota-
tion araport11
Express candidate gene
2GSL-2 A09 Bn-A09-p24854950 30,201,881 A09p036110.2 32,059,393 AT3G53260 BnaA09g33560D; PAL2; Phenylalanine
ammonia-lyase 2
Bn-A09-p28988381 34,547,984
2NDF-1 A01 Bn-A01-p10945930 10,392,745 See QTL 2ADF-1
Bn-A01-p11306490 10,739,014
2NDF-2 A04 Bn-A04-p7373248 9,621,091 See QTL 2Oil-2, 2SPC-2, 2PidM-1,
2ADF-3, 2LC-1
Bn-A04-p9296314 11,834,759
2NDF-3 C05 Bn-scaff_17441_1-p958418 41,704,005 See QTL 1Oil-4, 1NDF-4, 1ADF-6,
1HC-3, 2ADF-5, 2LC-4
Bn-scaff_20219_1-p143569 42,594,324
2NDF-4 C05 Bn-scaff_16770_1-p107862 34,645,234 See QTL 2HC-2
Bn-scaff_18826_1-p76018 36,181,332
2ADF-1 A01 Bn-A01-p10026020 9,735,795 See QTL 2NDF-2
Bn-A01-p11306490 10,739,014
2ADF-2 A01 Bn-A01-p6825673 6,945,733
Bn-A01-p10026020 9,735,795
2ADF-3 A04 Bn-A04-p7373248 9,621,091 See QTL 2Oil-2, 2SPC-2, 2PidM-1,
2LC-1
Bn-A04-p9296314 11,834,759
2ADF-4 A09 Bn-A09-p4905766 5,816,951
Bn-A09-p5476558 6,284,902
2ADF-5 C05 Bn-scaff_17441_1-p958418 41,704,005 See QTL 1Oil-4, 1NDF-4, 1ADF-6,
1HC-3, 2NDF-3, 2LC-4
Bn-scaff_20219_1-p143569 42,594,324
2LC-1 A04 Bn-A04-p7373248 9,621,091 A04p012590.1 11,661,554 AT5G04230 BnaA04g11940D; PAL3; phenylalanine
ammonia-lyase 3
Bn-A04-p9296314 11,834,759
2LC-2 A05 Bn-A05-p3414540 3,721,186 See QTL 2Oil-3
Bn-A05-p4093306 4,394,132
2LC-3 A09 Bn-A09-p11110730 25,154,269
Bn-A09-p23928417 28,902,308
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Table 7 (Continued)
QTL Chr Flanking SNPs Physical position (bp) Candidate gene (Express
617)
Physical position (bp) Functional annota-
tion araport11
Express candidate gene
2LC-5 C05 Bn-scaff_17441_1-p958418 41,704,005 C05p038330.1 39,547,901 AT3G13540 BnaA05g37010D; MYB5; Myb domain
protein 5
Bn-scaff_20219_1-p143569 42,594,324 C05p040780.1 41,326,930 AT5G05170 BnaA05g28060D; CEV1; Cellulose
synthase family protein
C05p041220.1 41,562,723 AT3G10380 BnaC05g42720D; SEC8; Subunit of
exocyst complex 8
C05p041260.1 41,588,681 AT3G10340 BnaC05g42780D; PAL4; Phenylalanine
ammonia-lyase 4
C05p041720.1 41,852,113 AT3G09780 BnaC05g43230D; CCR1; CRINKLY4
related 1
C05p042120.1 42,094,161 AT3G09220 BnaCnng24340D; LAC7; Laccase 7
C05p042650.1 42,319,659 AT3G08500 BnaC05g44010D; MYB83; Myb
domain protein 83
2HC-1 A04 Bn-A04-p9510750 12,105,311 A04p018820.1 16,240,675 AT2G30490 BnaA04g17560D; C4H; Cinnamate-
4-hydroxylase
Bn-A04-p13119516 15,827,404
2HC-2 C05 Bn-scaff_16770_1-p107862 34,645,234 C05p035490.1 35,754,463 AT3G15510 BnaC05g38130D; NAC2; NAC domain
containing protein 2
Bn-scaff_18826_1-p76018 36,181,332 C05p035510.1 35,779,863 AT3G15500 BnaC05g38150D; NAC3; NAC domain
containing protein 3
C05p035510.1 35,779,981 AT3G15500 BnaC05g38150D; NAC3; NAC domain
containing protein 3
C05p035900.1 36,067,006 AT3G15170 CUC1; NAC (No Apical Meristem)
domain transcript. regulator superfam.
protein
2CC-1 A01 Bn-A01-p20990218 22,435,826 A01p029500.1 23,785,201 AT3G18400 BnaA01g26760D; NAC058; NAC
domain containing protein 58
Bn-A01-p26426004 28,704,088 A01p032330.1 26,469,580 AT3G18400 BnaCnng63190D; NAC058; NAC
domain containing protein 58
A01p034370.2 28,355,575 AT3G12910 BnaA01g30220D; NAC (No Apical
Meristem) domain transcriptional
regulator
2CC-2 A02 Bn-A02-p23792703 27,449,577 A02p035210.1 27,390,029 AT5G13930 BnaA02g30320D; TT4; Chalcone and
stilbene synthase family protein
Bn-A02-p24971607 28,645,783 A02p034830.1 27,137,641 AT5G48100 BnaAnng08030D; TT10; Laccase/
Diphenol oxidase family protein
2CC-3 A10 Bn-A10-p10149557 14,104,587
Bn-A10-p13008713 15,747,237
2CC-4 C01 Bn-scaff_15712_3-p88356 41,758,234
Bn-A01-p23642494 43,461,421
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correlation for both CC and HC to OC was found
for population 1 and 2. All three fibre components
were negatively correlated with SPC in both of the
present DH populations. Liu et al. (2012) found a
positive correlation between HC and SPC but nega-
tive correlation between CC and SPC. GSL content
was negatively correlated with HC and CC. In this
study, in both populations there was a positive cor-
relation between the GSL content and the SPC as
was reported earlier for other populations (Schatzki
etal. 2014; Gacek etal. 2021). Correlations of GSL
to SPC were not reported by Miao etal. (2019).
QTL mapping in the KN DH population (Miao
etal. 2019) allowed the identification of 13 co-local-
ized QTL with pleiotropic effects on at least two of
the above mentioned four traits. These pleiotropic
unique QTL for seed fibre components and OC were
located on chromosomes A08, A09, A10, C03, C05
and C06. Interestingly, the QTL flanking markers on
these chromosomes were either not located on the
same chromosome as in the present two populations
or they mapped at a very large distance based on the
Express 617 reference genome. Obviously, different
co-locating QTL with opposite additive effects for
LC, CC, HC and OC were identified in the differ-
ent populations. The comparative analysis of the two
half-sib DH populations including the semi-winter
Chinese cultivar Zheyou 50 allows direct comparison
of QTL positions and of the direction of their allelic
effects. In an updated analysis of the same KN DH
population of Miao etal. (2019), Chao etal. (2022b)
reported that a major QTL for seed colour on A09
led to a reduction in LC and CC, and pleiotropic to
an increase in OC. Chao etal. (2022b) reported three
candidate genes for LC (JAZ1, GH3, LOX3); they
mapped however far away from the QTL 2LC-3 in
population 2. Liu etal. (2012; 2013) mapped a major
QTL for LC on A09 which collocated with seed
colour. In this study, only a minor QTL for LC was
mapped on A09.
In both of the present DH populations there was a
close negative correlation between SPC and OC. This
is in line with previous earlier results (Zum Felde
etal. 2006; Liu etal. 2012; Chao etal. 2017; Gacek
etal. 2021; Schilbert etal. 2022). Chao etal. (2017)
mapped in the same above mentioned KN DH popu-
lation the fae1 gene as a QTL for OC on C03 as in the
present population 1 (cf. Table6). Furthermore, Chao
etal. (2017) reported QTL for OC on C05 in the same
region (39–43 Mbp) as in the present two popula-
tions. However, co-location of QTL for fibre compo-
nents were not investigated in the work of Chao etal.
(2017) as in the present study. Therefore, it remains
unclear if the QTL for OC or the QTL for fibre com-
ponents are causal for the increase in OC. Schilbert
et al. (2022) in a mapping by sequencing approach
identified in different oilseed rape material chromo-
somes for seed quality traits, but none of regions
for SPC overlapped with regions for OC or SPC as
in the present DH populations. Regulation of seed
storage protein synthesis has been reviewed by Yang
etal. (2022a). Some of the candidate genes listed for
Arabidopsis were located within the flanking markers
of QTL 1SPC-2 and 2SPC-1 (Tables6 and 7). Some
of the key structural genes of fatty acid and triglyc-
eride biosynthesis listed by Yang etal. (2022a) were
identified within the oil QTL confidence intervals (cf.
Tables 6 and 7). This includes the acetyl-CoA syn-
thase (ACS), the lysophosphatidyl acyltransferase
(LPAAT), the glycerol-3-phosphate acyltransferase
(GPAT), and the 3-ketoacyl-CoA synthase (KCS)
genes in both populations. Except MYB96, none of
the other key regulators of seed oil accumulation (e.g.
LEC1, LEC2, ABI3, FUS3, LTL) and of the two epi-
genetic regulators (PICKLE, CLF) were found within
the oil QTL confidence intervals (Yang etal. 2022b).
The effect of the fatty acid elongase gene (fae1) in
population 1 confirms for a new population the earlier
observed positive effect of fae1 gene on the protein
content in the defatted meal (Behnke etal. 2018).
In a multi-omics study a negative correlation
between seed coat content and OC was found by
Zhang etal. (2022). In line with this, a negative cor-
relation between LC and OC was found in both DH
populations. In both DH populations co-locating QTL
positions were detected for LC and OC on C05. In
population 1 a reduction of LC led to an increase in
OC and SPC in defatted meal (PidM), whereas in the
population 2 only OC increased. Furthermore, in pop-
ulation 1 the reduction of LC led to an increase in HC,
whereas in the population 2 there was no co-locating
QTL for HC. Obviously, the effect of the QTL 1LC-3
and QTL 2LC-5 on C05 depends on the cross. Sur-
prisingly, both populations carried the same QTL on
C05. Furthermore, the same QTL on C05 was already
mentioned by Behnke etal. (2018) for a different pop-
ulation and the BnPAL4 gene on C05 was reported
as a likely candidate. Yusuf etal. (2022) speculated
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that the Chinese cultivar Zheyou 50 may be derived
from the same ancestor cross as SGEDH13. Genome
sequencing and read mapping of SGDH14 (Behnke
et al. 2018) against the Express 617 genome con-
firmed chromosomal structural rearrangement as
the cause for the reported major QTL for low lignin
content (Schilbert et al. 2023). This confirmed the
accurate position of the major QTL for low LC on
C05 (Behnke etal. 2018; Yusuf etal. 2022). Based
on the Express 617 reference genome, in addition to
the BnPAL4 gene, the CEV1, the CCR1, the SEC8
and the LAC7 were identified as candidate genes in
both populations. Phenylalanine Ammonia Lyase is
the key enzyme in phenylpropanoid pathway, which
leads to the biosynthesis of a wide array of second-
ary metabolites including phenolic acid esters and
lignin (Zhang et al. 2022). Members of the laccase
(LAC) gene family catalyzes lignification and rela-
tively high expressions have been found in seed coats
(Ping etal. 2019). Cinnamoyl-CoA reductase (CCR1)
and Cellulose synthase family genes (CEV1, CESA3)
are associated with the phenylpropanoid-lignin path-
ways and seed coat development (Miao etal. 2019).
SEC8 is involved in post-golgi trafficking of mucilage
components to the plasma membrane (Kulich et al.
2010) and was mentioned as candidate in the multi-
omics study of Zhang etal. (2022). Furthermore, the
transcription factor genes MYB83 and MYB5 are
known as regulator of phenylpropanoid metabolism
in plants (Liu etal. 2015; Wang etal. 2015) and of
mucilage differentiation (Xu etal. 2018), respectively.
All these genes were located between the flanking
SNP markers in both populations. However, also
individual QTL for CC and HC co-located with QTL
for OC. Overlapping QTL positions for OC and CC
were detected on chromosome A01, at which in popu-
lation 1 a reduction in CC led to an increase in OC
(QTL 1CC-1 and 1Oil-1), whereas in population 2 the
same QTL led to an increase in CC and OC. Zhang
et al. (2022) and Pedersen et al. (2022) provided a
comprehensive list of candidate genes involved in
the biosynthesis of HC, CC and LC. A COBRA like
protein gene (BnaC01g44070D) has been identified
nearby the flanking markers of QTL 1HC-1 on C01
(Ben-Tov etal. 2015). Among many others, phenyla-
lanine ammonium-lyase (PAL), cinnamate-4-hydrox-
ylase (C4H), cinnamoyl-CoA reductase (CCR1), lac-
case (LAC7), transparent testa genes TT4 and TT10,
NAC (No Apical Meristem) transcriptional regulator
genes were found as candidate genes between flank-
ing markers of QTL for individual seed fibre traits
(cf. Tables6 and 7). Transparent testa (TT) are key
enzymes in proanthocyanidin and lignin biosynthesis.
In population 2, the major QTL 2GSL-1 on A02
has not yet been reported by others. Candidate genes
for QTL 2GSL-1 on A02 are GTR2, MYB34, TGG1,
TGG2 and MYB28. All four genes were reported as
candidate genes (Seo and Kim 2017; Kittipol et al.
2019; Wei etal. 2017; Schilbert et al. 2022). QTL
2GSL-1 mapped with an opposite effect nearby QTL
2CL-2, suggesting competing biosynthetic path-
ways. Wei et al. (2017) found that GSL metabolic
processes affected lignin biosynthesis and Vanholme
et al. (2012) reported that transcripts involved in
GSL biosynthesis were more abundant in low lignin
mutants. Recently, Gacek etal. (2021) also reported
in oilseed rape negative correlations between GSL
and ADF and NDF contents, respectively. Additional
evidence on crosstalk of the glucosinolate pathway
with the phenylpropanoid pathway is provided by Yin
etal. (2022) and references given therein. A second
QTL for GSL content was located on A09. None of
the genomic intervals for GSL content identified by
Schilbert et al. (2022) in their mapping-by-sequenc-
ing study overlapped with the A09 GSL region identi-
fied in this study. This points to an additional minor
GSL locus on A09.
Conclusions
In two half-sib DH populations a large number of
novel diverse QTL for seed fibre components on dif-
ferent chromosomes were identified. The effect of a
major QTL for low LC on C05 on contents of CC,
HC, OC, SPC and GSL were determined. Some of
the fibre components related QTL co-located to each
other and with QTL for OC and SPC with opposite
direction of additive effects. This suggests that indi-
vidual QTL alleles for fibre components can be used
to further reduce overall fibre content and to increase
oil and protein content in oilseed rape. The parallel
investigation of two half-sib DH populations gave
insight into the direction of the additive effects which
depended on the indvidual parental lines of the two
crosses. This complicates breeding for improved seed
quality traits in oilseed rape.
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Acknowledgements The authors acknowledged KWS SAAT
SE & Co. KGaA, NPZ and Limagrain GmbH for performing
some of the field trials. We would like to thank Uwe Ammer-
mann, Dietrich Kaufmann and Rosemarie Clemens for their
technical support. AOY PhD Scholarship was funded by the
Islamic Development Bank (IsDB) no 600031095.
Authors contributions CM designed the experiment and
developed the DH populations. AOY and CM performed the
field experiments. AOY did the NIRS and Chromatography
analysis and analyszed the data. AOY wrote the initial draft
of the manuscript. CM revised the manuscript and all authors
agreed on the final manuscript.
Funding Open Access funding enabled and organized by
Projekt DEAL. Open Access funding enabled and organized by
Projekt DEAL.
Data availability The datasets of this study are available
from the corresponding author on resonbable requests.
Declarations
Conflict of interest The authors have no competeing interest.
Open Access This article is licensed under a Creative Com-
mons Attribution 4.0 International License, which permits
use, sharing, adaptation, distribution and reproduction in any
medium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Crea-
tive Commons licence, and indicate if changes were made. The
images or other third party material in this article are included
in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not
included in the article’s Creative Commons licence and your
intended use is not permitted by statutory regulation or exceeds
the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit
http://creativecommons.org/licenses/by/4.0/.
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