Mapping QTLs and association of differentially expressed gene transcripts for multiple agronomic traits under different nitrogen levels in sorghum
Gelli et al. BMC Plant Biology
Mapping QTLs and association of differentially expressed gene transcripts for multiple agronomic traits under different nitrogen levels in sorghum
Malleswari Gelli 0
Sharon E. Mitchell 3 4
Kan Liu 1 3 4
Thomas E. Clemente 0 1
Donald P. Weeks 1 2
Chi Zhang 1 3 4
David R. Holding 0 1
Ismail M. Dweikat 0
0 Department of Agronomy and Horticulture, University of Nebraska , Lincoln, NE 68583 , USA
1 Center for Plant Science Innovation, University of Nebraska , Lincoln, NE 68588 , USA
2 Department of Biochemistry, University of Nebraska , Lincoln, NE 68588 , USA
3 Institute of Genomic Diversity, Cornell University , Ithaca, NY 14853 , USA
4 School of Biological Sciences, University of Nebraska , Lincoln, NE 68588 , USA
Background: Sorghum is an important C4 crop which relies on applied Nitrogen fertilizers (N) for optimal yields, of which substantial amounts are lost into the atmosphere. Understanding the genetic variation of sorghum in response to limited nitrogen supply is important for elucidating the underlying genetic mechanisms of nitrogen utilization. Results: A bi-parental mapping population consisting of 131 recombinant inbred lines (RILs) was used to map quantitative trait loci (QTLs) influencing different agronomic traits evaluated under normal N (100 kg.ha−1 fertilizer) and low N (0 kg.ha−1 fertilizer) conditions. A linkage map spanning 1614 cM was developed using 642 polymorphic single nucleotide polymorphisms (SNPs) detected in the population using Genotyping-By-Sequencing (GBS) technology. Composite interval mapping detected a total of 38 QTLs for 11 agronomic traits tested under different nitrogen levels. The phenotypic variation explained by individual QTL ranged from 6.2 to 50.8 %. Illumina RNA sequencing data generated on seedling root tissues revealed 726 differentially expressed gene (DEG) transcripts between parents, of which 108 were mapped close to the QTL regions. Conclusions: Co-localized regions affecting multiple traits were detected on chromosomes 1, 5, 6, 7 and 9. These potentially pleiotropic regions were coincident with the genomic regions of cloned QTLs, including genes associated with flowering time, Ma3 on chromosome 1 and Ma1 on chromosome 6, gene associated with plant height, Dw2 on chromosome 6. In these regions, RNA sequencing data showed differential expression of transcripts related to nitrogen metabolism (Ferredoxin-nitrate reductase), glycolysis (Phosphofructo-2-kinase), seed storage proteins, plant hormone metabolism and membrane transport. The differentially expressed transcripts underlying the pleiotropic QTL regions could be potential targets for improving sorghum performance under limited N fertilizer through marker assisted selection.
Sorghum; Agronomic traits; Differentially expressed gene transcripts; Genotyping-by-sequencing; Nitrogen fertilizer; QTL mapping; Illumina RNA-seq
Sorghum (Sorghum bicolor (L.) Moench) is the fifth most
cultivated cereal crop worldwide (http://www.fao.org/3/
a-ax443e.pdf ) and also an important source of fodder,
fiber and biofuel [
]. Sorghum performs C4
photosynthesis like maize and sugarcane, and uses Nitrogen, CO2
and water more efficiently than maize and most C3
]. Sorghum is an important model for genome
analysis among the C4 grasses because its genome is
relatively small (~818 Mbp) [
], and the cultivated
species is diploid (2n = 20). Due to its deep root system,
sorghum is drought tolerant and is preferentially grown in
water-limited environments [
]. Despite being a C4 crop,
sorghum still relies on applied fertilizer to achieve
maximal yields. Nitrogen (N) is the macronutrient which is
often limiting sorghum production. N is the most
abundantly absorbed mineral nutrient by plant roots [
75 % of the leaf N is allocated to the chloroplasts [
nitrogen is an essential part of many biomolecules, it
comprises 1.5 to 2 % of plant dry matter and 16 % of the
total plant protein [
N fertilizer application is expected to rise
approximately three-fold in the next 40 years [
]. In general,
plants absorb less than half of the applied fertilizer [
Both phosphorus and potassium are immobile nutrients
in the soil and are generally not vulnerable for leaching.
However, nitrogen is a mobile nutrient and when
present in excess, it is released in to the atmosphere
through volatilization or lost through leaching and
ground water runoff, of which both have adverse
environmental effects [
]. Excess N fertilizer application is a
major economic cost to farmers, and also leads to
acidification of soils [
]. Because of their potential positive
effects on improving economic returns and limiting
global climate change, lowering fertilizer input and
breeding plants with better nitrogen use efficiency (NUE) are
two major goals of research in plant nutrition [
]. As a
function of multiple interacting genetic and
environmental factors, the molecular basis of NUE is complex. NUE
is defined as the grain yield [
] or fresh/dry matter
] per unit of available N in the soil. Uptake of N
from the soil involves a variety of transporters, and a
number of enzymes for assimilation and transfer of the
absorbed N into amino acids and other compounds [
However, little is known about how these processes are
regulated especially under different N conditions.
QTL analysis, based on high density linkage maps, is a
powerful tool for dissecting the genetic basis underlying
complex traits [
]. QTL mapping studies have been
conducted under different N conditions for NUE and
other agronomic traits in maize [
], Arabidopsis [
and rice [
]. QTLs associated with low-nitrogen
tolerance were detected in rice  and barley [
different traits, at the seedling stage. In barley, Mickelson
et al. [
] mapped a QTL for grain protein
concentration, which is homologous to a durum wheat grain
protein QTL mapped by Joppa et al. [
]. QTLs for NUE
and enzymes involved in nitrogen metabolism were
reported in wheat [
] and QTLs for glutamine synthetase
(GS) activity were co-localized with those for grain N
] and confirmed in another population [
]. In wheat,
Quraishi et al. [
] identified 11 major regions
controlling NUE, which co-localized with key developmental
genes such as Ppd (photoperiod sensitivity), Vrn
(vernalization) and Rht (reduced height). However, there
are no previous QTL mapping reports for agronomic
traits tested under different nitrogen levels in sorghum.
Significant genotypic differences for N utilization
efficiency have been documented in sorghum [
utilization of genotypes varied with different nitrogen
sources, nitrogen amounts and other environmental
conditions . Thus, there is good reason to believe
that improvements in N utilization efficiency in sorghum
can be achieved using genetic approaches.
Different kinds of DNA based low-throughput marker
systems such as restriction fragment length
polymorphism (RFLP), amplified fragment length polymorphism
(AFLP), and simple sequence repeat (SSR) markers have
been developed and used to investigate the variants and
quantitative trait loci (QTLs) controlling >150 traits in
sorghum. AFLPs, SSRs and RFLPs were used for
generating the dense linkage maps [
]. Diversity Array
Technology was evolved [
] as a cost effective
hybridizationbased alternative to the gel-based marker technologies,
which offers a multiplexed genotyping independent of
sequence information. DArT markers were developed
for sorghum and used for genotyping a diverse set of
sorghum lines and a bi-parental mapping population
]. With the availability of sorghum whole genome
], Mace et al. [
] generated a single, reference
consensus map by integrating six independent sorghum
genetic maps containing 2029 unique loci consists of
SSRs, AFLPs, and DArT markers. Using this as a
framework map, Mace and Jordon et al. [
] mapped 35 major
effect genes commonly observed in segregating mapping
populations onto a common reference map to enable
sorghum researchers link the information of QTLs and
select the major genes. Furthermore, Mace et al. [
projected 771 QTL relating to 161 unique traits from 44
studies onto the sorghum consensus map, which is
useful for development of efficient marker-assisted breeding
strategies. With the advent of high-throughput DNA
sequencing technologies, it became possible to re-sequence
genomes and detect single nucleotide polymorphisms
(SNPs) which can be used for rapid genotyping [
]. Zou et
] developed a linkage map based on SNPs generated
from whole-genome re-sequencing by the Illumina
Genome Analyzer IIx as described by Huang et al. [
used it for detecting QTLs for important agronomic traits
under contrasting photoperiods in sorghum. However, it
remains costly to employ whole-genome sequencing to
evaluate multiple individuals in mapping populations. Next
generation sequencing of a reduced representation genomic
library, where fewer sequence reads are needed to obtain
meaningful information compared to whole genome
sequencing, is a convenient approach for capturing
genetic variation. Genotyping-by-sequencing (GBS) is
an efficient strategy for constructing multiplexed
reduced representation library [
]. This technique has
successfully been applied to generate high-density
genetic maps and QTL mapping in several plant species
In this study, we used SNPs generated from GBS
technology to develop a linkage map and which then used to
map QTLs for different agronomic traits in RIL
population of sorghum. This process of QTL detection enabled
us to link variation at the trait level to the variation at
sequence level. However, a QTL may contain tens to
hundreds of genes, figuring out the genes responsible for
trait variation is a major challenge. With the
advancement of sequencing technology, transcriptome
comparisons were made between different sorghum genotypes at
different tissue levels and at different growing conditions
]. In addition, Morokoshi et al. [
] compiled all
these datasets and developed a transcriptome database
for sorghum which will be useful to researchers for
transcriptome comparisons. The desire to identify the
underlying genes responsible for trait variation in QTL regions
has been increasing and to this end, we used previously
generated high throughput Illumina-based RNA
sequencing data [
] to identify differentially expressed gene
transcripts in QTL regions. By further evaluation, the
resulting candidate genes could be potential targets for
improving N-stress tolerance and nitrogen utilization of
sorghum and related crops.
A mapping population derived from a cross between the
inbred lines CK60 and China17 was used in this study.
CK60, a public sorghum line, which is short,
photoperiod-sensitive, late-maturing U.S. sorghum line
and an inefficient N user. China17, a
photoperiodinsensitive Chinese sorghum line was provided by Dr.
Jerry Maranville (University of Nebraska, Lincoln, USA),
uses nitrogen more efficiently than CK60 and has higher
assimilation efficiency indices at both low and high soil
nitrogen levels [
]. China17 retains higher
phosphoenolpyruvate carboxylase (PEPcase) activity than CK60
when grown under low N conditions [
]. The seedlings
of China17 had greater root and shoot mass than CK60
under both low N and normal N conditions [
of the 131 RILs was derived from a single F2 plant
following a single seed descent method until the F7
The F7 RILs and the two parents (CK60 and China17)
were evaluated in an alpha lattice incomplete block
design under two N levels with two independent replicates
each for two years (2011 and 2012). The two N
treatments were low N (LN, 0 kg.ha−1 fertilizer) and normal
N (NN, 100 kg.ha−1 anhydrous ammonia fertilizer). The
preceding crops were soybean in the NN field and oats
or maize in the LN filed. The LN field had not received
nitrogen fertilizer since 1986. The soil testing was done
by collecting soil samples from 0 to 12 in. and 12–24 in.
randomly across the NN and LN fields and results were
described in Additional file 1. Single-row plots
measuring five meters long at 0.75 m row spacing were sown at
a density of 50 seeds for each RIL and parents. All
entries were planted on the same day in conventionally
tilled plots and maintained under rain fed conditions.
Phenotyping of important agronomic traits
Three plants were randomly selected for each genotype
for phenotypic evaluation of eleven agronomic traits.
The measured phenotypes include leaf chlorophyll
content at three different stages of plant growth: before
flowering (vegetative stage, Chl1), during flowering
(Chl2) and at maturity (Chl3); plant height (PH, from
base of the plant to tip of the head, in centimeters); and
days to anthesis (AD, no. of days from planting to 50 %
anthesis). Stover moisture contents (MC1) and head
moisture contents (MC2) were calculated as the percent
difference between wet and dry weights. Total biomass
yield (BY, t.ha−1), grain yield (GY, t.ha−1), 1000 seed
weight in grams (Test weight, TW) and grain-to-stover
ratio (GS, %) were calculated and recorded from NN
and LN fields. Haussmann et al. [
] described that the
upper six leaves are a good source for measuring the
greenness of leaves since they are photosynthetically
active at anthesis and contribute nutrients to the grain
]. In this study, chlorophyll contents were measured
in the 3rd leaf from the top using a portable chlorophyll
meter model SPAD-502 (Minolta, Japan). In summary,
the phenotypes were classified into three groups,
chlorophyll contents (Chl1, Chl2, and Chl3), morphological
traits (PH, AD, MC1, and MC2), and yield-related traits
(BY, GY, TW and GS).
The statistical model adopted for the alpha lattice
incomplete block design in each N condition was Yijk = μ +
gi + rj + bk(j) + eij. Yijk is the response of ith genotype in kth
bock of jth replication, μ is the grand mean, gi is the
genotype or line effect, rj is the replication effect, bk(j) is
the random block k (k = 1…n) effect within replicate with
bk(j) ~ N(0, σ2b) and eij is the residual term with ~ N(0, σe2).
Analysis of variance (ANOVA) for eleven traits was
performed for each individual environment using the PROC
MIXED procedure [
] of SAS version 9.2 (SAS Institute,
2008) where the genotype was considered as fixed,
replications and blocks as random effects. The phenotypic data,
from both seasons (2011 and 2012), were pooled to obtain
single trait values for each family under NN and LN [
ANOVA was performed on pooled data by considering
that genotype effect is fixed and environments (years),
replication within environments, blocks within environments,
and genotype by environment (GxE) interaction effects
are random. Narrow-sense heritability with standard error
was estimated using the PROC MIXED procedure of SAS
version 9.2. For the heritability estimates, parental lines
data were excluded, and estimates followed a method
described by Holland et al. [
]. Pearson’s correlation
coefficients between traits were calculated for the least square
genotype means using the PROC CORR procedure of
SAS. The RIL trait data were subjected to normality test
using PROC UNIVARIATE to determine its suitability for
High-throughput Genotyping and Linkage map construction
Total genomic DNA of the RILs and their parents were
isolated from leaf tissues using a DNeasy Plant Mini Kit
(Qiagen). DNA (500 ng) from each sample was digested
with ApeKI (New England Bio-labs, Ipswich, MA), a
type II restriction endonuclease that recognizes a
degenerate 5 bp sequence (5’-GCWGC) and creates 5’
overhangs. Adapters with specific barcodes [
] were then
ligated to the overhanging sequences using T4 ligase. A
set of 96 DNA samples, each sample with a different
barcode adapter, were combined and purified (Quick
PCR Purification Kit; Qiagen, Valencia, CA) according to
the manufacturer’s instructions. DNA fragments
containing ligated adapters were amplified with primers
containing complementary sequences for each adapter.
PCR products were then purified and diluted for
]. Single-end, 100 bp reads were collected for
one 48- or 96-plex library per flow cell channel on a
Genome Analyzer IIx (GAIIx; Illumina, Inc., San Diego,
] at Cornell University, USA.
Raw reads obtained from GAIIx were filtered [
aligned to the sorghum reference genome version 1.4
]. The genotypes of the population were determined
based on the procedure described by Elshire et al. [
The biallelic SNP markers were checked for
polymorphism between the parents. Prior to map construction, all
polymorphic SNPs were checked by the chi-square (χ2)
test for the goodness of fit against a 1:1 segregation ratio
at the 0.05 probability level. SNPs with >70 % missing
data were removed from data set. A total of 668 SNPs
were selected and used for constructing linkage maps
using Mapmaker/EXP 3.0 along with IciMapping
(Inclusive composite interval mapping) V3.2 [
]. The genetic
distance (cM) was calculated using the Kosambi
The composite interval mapping method of
] was used for QTL detection. QTL analysis
was performed based on averaged mean values of each
trait across two NN and two LN environments
respectively. The walking speed chosen for all traits was 1 cM.
Cofactors were determined using the forward and
backward step-wise regression method with a probability in
and out of 0.1 and a window size of 10 cM. A
thousandpermutation test was applied to each data set to decide
the LOD (logarithm of odds) thresholds (P ≤ 0.05) to
determine significance of identified QTLs [
]. A 2-LOD
support interval was calculated for each QTL to obtain a
95 % confidence interval. Adjacent QTLs on the same
chromosome for the same trait were considered different
when the support intervals were non-overlapping. The
contribution rate (R2) was estimated as the percentage
of variance explained by each QTL in proportion to the
total phenotypic variance. The additive effect of a
putative QTL was estimated by half the difference between
two homozygous classes. QTLs were named according
to McCouch et al. [
] and alphabetical order was used
for QTLs on the same chromosome. QTLs with a
positive or negative additive effect for a trait imply that the
increase in the phenotypic value of the trait is
contributed by alleles from CK60 or China17.
Detection of differentially expressed gene transcripts in the QTL intervals
In an earlier study [
], we detected several common
DEG transcripts between the transcriptomes of seven
sorghum genotypes (four low-N tolerant and three
lowN sensitive) using Illumina RNA sequencing.
Transcriptomes were prepared from root tissues of 3 week old
seedlings grown under N-stress from four N-stress
tolerant (China17, San Chi San, KS78 and high NUE bulk)
and three sensitive (CK60, BTx623 and low NUE bulk)
genotypes. In the present study, we used the RNA-seq
data generated earlier in order to check the differential
expression of gene transcripts between CK60 and
China17 in the QTL regions. Pair-wise comparison was
made between the transcriptomes of CK60 and China17
to detect DEG transcripts. The cutoff of log2-fold value >1
(2-fold absolute value) and adjusted P-value <0.001 (FDR)
were used for determining significant DEG transcripts.
Statistical analysis of phenotypic data
Mean values of 11 traits measured for parents (CK60, and
China17) and the RIL population under NN and LN
environments are given in Tables 1 and 2, respectively. The
mean chlorophyll content was higher at flowering than at
vegetative and mature stages under both N-conditions.
CK60 retained more chlorophyll at all stages compared to
China17 and the mean chlorophyll content of the RIL
population was lower under LN compared to NN
conditions. The plant height of CK60 was reduced by 23 cm,
while that of China17 remained the same under LN
compared to NN. Days to anthesis for the two parental lines
were also significantly affected by N-condition, and LN
delayed flowering in both parents. Compared to China17,
the flowering was delayed more in CK60 under both
Nlevels. The biomass yield of CK60 was lower than China17
in both N conditions. The grain yield was also significantly
different between the two parents; CK60 had lower grain
yield under the two N-conditions. The average values of
biomass and grain yield for the RILs were greatly reduced
from NN to LN conditions, respectively. Similarly, the test
weight of China17 was higher than CK60 under both
Nconditions. The grain/stover ratio of China17 was
decreased almost half, while no significant change was
observed for CK60 under LN compared to NN. In contrast,
the stover and head moisture contents of CK60 were
higher than China17 under both N-conditions. The
average of grain/stover ratio and stover moisture contents of
the RILs remained the same under both N conditions but
the average of head moisture content in the RIL
population was increased under LN conditions.
The narrow sense heritability (h2) was estimated for each
trait measured under both N conditions (Tables 1 and 2).
Under NN, the heritability estimates of the 11 traits ranged
from 39 to 71 %. Chlorophyll at the vegetative stage had
the highest h2 value followed by plant height and test
weight. Grain/stover ratio had the lowest heritability
estimate. Under LN, h2 values ranged from 32 to 80 %. Plant
height had the highest h2 values and grain/stover ratio had
the lowest h2 value. ANOVA showed significant phenotypic
variation for all the traits among RILs (Tables 1 and 2).
GxE interaction was mainly associated with differences in
magnitude of effects between years. Therefore, phenotypic
data from 2011 and 2012 seasons were averaged separately
for NN and LN conditions. GxE interactions were
significant for all the traits except chlorophyll at the vegetative
stage across two LN environments. Genotype variance was
greater than GxE interaction variance for all traits across
NN and LN environments (Tables 1 and 2).
Correlation of the traits
The focus of this work was evaluation of the genetic
control of traits under NN and LN conditions in
sorghum. Correlation coefficients based on the line
means among three chlorophyll contents, yield-related
traits and other morphological traits showed that most
of the traits tested under the contrasting N conditions
were significantly correlated (P < 0.05) (Table 3).
Interestingly, leaf chlorophyll contents measured at three
different stages of plant growth were negatively correlated
with most of the yield-related (biomass yield, grain yield
and test weight) and morphological traits (plant height,
days to anthesis and head moisture content) in both
Nconditions (Table 3). Under NN conditions, significant
positive correlations were observed between chlorophylls
and stover moisture content (P < 0.01). In addition, plant
height had significant positive correlation with biomass
and grain yield in both N conditions. Highest positive
correlation was observed between biomass and grain
yield in both NN and LN environments. Days to anthesis
was positively correlated with stover and head moisture
contents under both N conditions. Grain/stover ratio
was not significantly correlated with many traits, but it
had significant positive correlation with grain yield.
Linkage mapping and QTL analysis
Polymorphic SNP markers between CK60 and China17
were identified by the GBS pipeline. A linkage map was
developed with 642 polymorphic SNPs (Additional file 2)
with an average inter marker distance of 2.55 cM. The
resulting linkage map comprised of 10 linkage groups and
map spanning a total length of 1641 cM. Composite
interval mapping detected a total of 38 QTLs for 11
traits analyzed across NN and LN environments. No
significant QTLs were detected on chromosomes 2, 3, 4
and 10 (not shown in Fig. 1). The number of QTLs per
trait ranged from one to four, and is listed in Tables 4
and 5 and shown in Fig. 1. Across two NN conditions,
four QTLs for chlorophyll contents were detected
including one QTL each for chlorophyll at vegetative and
flowering stage, and two QTLs for chlorophyll at
maturity explaining phenotypic variation range from 7.1 to
50.8 % (Table 4). Six QTLs were identified for four
morphological traits including one major QTL for days
to anthesis on chromosome 1, for which the CK60
allele delayed flowering by 3.6 days. Two QTLs each for
stover and head moisture contents were detected under
NN conditions. For all these QTLs, the CK60 allele
contributed to increase the chlorophyll contents and
the moisture contents. In contrast, the China17 allele
contributed to an increase in the plant height by
39.8 cm for the QTL detected on chromosome 9.
Similarly, we detected eight significant QTLs for
yieldrelated traits. Of the eight detected, two QTLs are for
biomass yield, three for grain yield, one for test weight
and two for grain/stover ratio. For the two QTLs
detected for biomass yield, China17 allele increased the
TW −0.23** 0.02 −0.166 0.20* −0.1 −0.24** −0.21* 0.23** 0.31** 0.064
The numbers below the diagonal are correlation coefficients under normal N environments and numbers above the is diagonal are correlation coefficients under
low N environments. Chlorophyll contents at vegetative stage (Chl1), at anthesis (Chl2), and at maturity (Chl3); PH, plant height (cm); AD, days to anthesis; MC1, % stover
moisture content; MC2, % head moisture content; BY, biomass yield (t.ha−1); GY, grain yield (t.ha−1); TW, test weight (g); GS, grain/stover ratio (%).
***P < 0.0001; **P < 0.01; *P < 0.05
biomass yield by 1.8 t.ha−1. For grain yield, CK60 allele
increased grain yield by 0.5 t.ha−1 for the two QTLs on
chromosome 1 and China17 allele increased grain yield
for the other QTL on chromosome 9. CK60 allele
responsible for an increase in the test weight of seeds for
the major QTL detected on chromosome 5 for test
weight. In contrast, the China17 allele increased the
grain/stover ratio for two QTLs.
Under LN conditions, 20 QTLs were found to be
significant for 11 traits studied (Table 5, Fig. 1). We
detected four QTLs for chlorophyll content including two
each for chlorophyll at flowering and maturity. No
significant QTLs were detected for chlorophyll content at
the vegetative stage. For these QTLs, the China17 allele
increased the chlorophyll content at flowering for the
QTL on chromosome 1 and the CK60 alleles increased
the chlorophyll contents for the other QTLs. We
detected seven significant QTLs for morphological traits.
One major QTL explaining 13.2 % of the phenotypic
variation was associated with plant height with the allele
from China17 increasing plant height by 16.4 cm. Two
QTLs were detected for days to anthesis. The CK60
Chlorophyll contents at vegetative stage (Chl1), at anthesis (Chl2), and at maturity (Chl3); PH, plant height (cm)
AD, days to anthesis; MC1, % stover moisture content; MC2, % head moisture content; BY, biomass yield (t.ha−1)
GY, grain yield (t.ha−1); TW, test weight (g); GS, grain/stover ratio (%). a2.0-LOD drop support interval of the QTL; bAdditive effect: positive values of the additive
effect indicate that alleles from CK60 were in the direction of increasing the trait score and vice versa; c Percentage of phenotypic variation explained by the QTL.
The SNP underlined is the corresponding SNP of QTL
allele associated with the QTL on chromosome 1
delayed heading by 3.6 d, while the China17 allele,
associated with the QTL on chromosome 9, delayed heading
by 3 d. Two QTLs for stover moisture content and head
moisture content were identified with presence of the
CK60 alleles resulting in increasing the moisture
contents. Nine significant QTLs were found for yield-related
traits under LN conditions. Two QTLs were detected
for biomass yield, of which the China17 allele
contributed for increased biomass yield by 1.0 t.ha−1 for QTL
on chromosome 5, while the CK60 allele increased
biomass yield at other QTL. Four QTLs were identified for
grain yield, of which the CK60 allele increased the grain
yield for one QTL on chromosome 5 and China17 alleles
improved the grain yield for all other QTLs. One
significant QTL explaining 17.9 % of the phenotypic variation
was detected for test weight on chromosome 1 with the
China17 allele increasing test weight by 1.8 g. Two
QTLs were found for grain/stover ratio on
chromosomes 1 and 5. The China17 allele contributed to an
increase the grain/stover ratio for QTL on chromosome 1
while the CK60 allele was responsible for increasing the
grain/stover ratio at the other QTL on chromosome 5.
The additive effect of a single QTL could explain 7 to
20.3 % of the total phenotypic variation.
Differential expression of gene transcripts in the QTL regions
The previously generated Illumina RNA-sequencing data
] was used to determine the variations in transcript
abundance between nitrogen use inefficient (CK60)
and efficient (China17) genotypes of sorghum. False
discovery rate (FDR) ≤ 0.001 and the absolute value of
|log2 -Ratio| ≥ 1 were used as thresholds to judge the
significance of differences in transcript abundance of
the same gene between two genotypes. Pair-wise
comparison of the transcriptomes of CK60 and China17
seedling root tissues grown under N-stress revealed a
total of 726 DEGs detected using v1.4 sorghum
genome (Additional file 3). The sequences of all these
DEGs compared to v2.1 sorghum genome and
respective gene IDs were listed in Additional file 3. In
addition, compared the sequences of polymorphic
SNPs between CK60 and China17 to the sequences of
DEG transcripts, and differential expression levels
were listed in Additional file 2.
Out of 726 DGE transcripts observed between CK60
and China17 (Additional file 3), 108 DEGs were located
in the vicinity of the QTL confidence intervals on
chromosome 1, 6, 7, 8, and 9 (Additional file 3) and
some of those were listed in Table 6. The QTL interval
Chlorophyll contents at vegetative stage (Chl1), at anthesis (Chl2), and at maturity (Chl3); PH, plant height (cm); AD, days to anthesis; MC1, % stover moisture
content; MC2, % head moisture content; BY, biomass yield (t.ha−1); GY, grain yield (t.ha−1); TW, test weight (g); GS, grain/stover ratio (%). a2.0-LOD drop support
interval of the QTL; bAdditive effect: positive values of the additive effect indicate that alleles from Ck60 were in the direction of increasing the trait score and vice
versa; c Percentage of phenotypic variation explained by the QTL. The SNP underlined is the corresponding SNP of QTL
on chromosome 1 has 40 DEGs and chromosome 9 has
28 DEGs. Gene transcripts related to nitrogen
metabolism (Ferredoxin-nitrate reductase), glycolysis
(Phosphofructo-2-kinase), seed storage proteins, plant hormone
metabolism (Gibberellin receptor GID1L2, Auxin
response factor 2) were differentially expressed between
CK60 and China17. The majority of these gene
transcripts were expressed higher in CK60 than China17
under N-stress conditions in the seedling stage. For
example, transcripts of Frigida, Auxin response factor 2
and translation elongation factor expressed six-fold
higher in CK60 than China17. In contrast, magnesium
transporter6, HSP21 and senescence associated protein
were expressed higher in China17. A ferredoxin-nitrite
reductase gene transcript which had higher expression
in China17, coincided with the pleiotropic QTL region
on chromosome 9.
Trait variation in the mapping population under different
The RILs showed transgressive segregation for all the
traits measured and in most cases, the mean value of the
traits was intermediate between the parental lines, CK60
and China17 (Tables 1 and 2), suggesting a polygenic
inheritance of the traits. Transgressive segregation can be
caused by both parental lines contributing favorable or
unfavorable alleles for a particular trait and is common
in inbred populations [
]. In both N conditions, the
genetic variance was greater than genotype by environment
interaction variance for all the traits (Tables 1 and 2). This
finding is in agreement with earlier studies [
]. The more
marked contribution of genetic variance to trait
determination suggests the opportunity for more robust detection of
QTLs that govern nitrogen use efficiency [
]. Here, for
both parental lines and RILs marked reductions were
observed in mean values for chlorophyll contents measured at
three different stages, plant height, biomass and grain yield
traits grown under LN compared to NN. In maize, a 38 %
reduction in grain yield was observed in plants grown
under low-N compared to high-N conditions [
decrease was caused by a significant reduction in kernel
number, but has little effect on kernel size. Kernel number is
very susceptible to N-stress because ovules are susceptible
to abortion soon after fertilization [
], a possible result of
limitation in supply of photosynthetic products [
inosine-uridine preferring nucleoside hydrolase family
Major facilitator superfamily protein
ethylene-responsive transcription factor
qGY-9, qChl2-9a, qBY-9, qChl3-9,
qGY-9, qChl2-9a, qBY-9, qChl3-9,
qGY-9, qChl2-9a, qBY-9, qChl3-9,
qGY-9, qChl2-9a, qBY-9, qChl3-9,
Chr, chromosome number; log2 ratio; number of folds the gene transcript is differentially expressed in RNA-seq. Log2 ratio >0 indicates, positive values indicates
gene transcript expressed high in CK60. ns, indicate the transcript is not differentially expressed between CK60 and china17
Comparison of QTL regions under contrasting N environments
In this study, a total of 38 QTLs were identified using a
SNP based genetic map in the RIL mapping population
tested under two different nitrogen levels. However,
almost half of these QTLs were detected under one N
level, indicating that these traits were controlled by
different genes under different N conditions. Major QTLs
detected across two normal and two low-N
environments were considered as consistent across
environments. However, five QTLs for four morphological traits
were detected consistently under both N conditions.
These included, one QTL each for chlorophyll at
maturity, day to anthesis and stover moisture content and two
QTLs for head moisture content. For all these QTLs,
the CK60 alleles increased chlorophyll content, delayed
flowering, and increased stover and head moisture
contents under NN and LN. This indicates that these
traits shared a similar genetic basis under different N
Co-localization of QTLs between traits and associated differentially expressed gene transcripts
Co-localization may suggest pleiotropy whereby a
genomic region contains genes that affect a number of traits
]. In this study, co-localized QTLs affecting different
traits were detected on chromosomes 1, 5, 6, 7, and 9
(Fig. 1). For example, the support intervals of ten QTLs
explaining 8.1 to 20.3 % of phenotypic variation for eight
traits were overlapping in the distal end of chromosome 1.
Of the ten QTLs detected, two QTLs are for grain
moisture content, one QTL each for test weight, chlorophyll
content at anthesis, stover moisture content and grain/
stover ratio detected under LN conditions, biomass
yield under NN and for days to anthesis detected under
NN and LN conditions. An additive effect from CK60
increased days to anthesis (delayed flowering), stover
and head moisture content and grain yield. These traits
were highly correlated (Table 3) and the correlations
resulted in co-localization. Within this co-localized
region, QTLs for green leaf area at maturity [
], days to
] fresh panicle weight, plant height [
], and panicle architecture [
] were reported earlier.
Stay green QTLs and the Ma3 gene encoding
phytochrome B, which is involved in photoperiod sensitivity
], were also reported in this region.
In this co-localized region containing ten QTLs,
RNAseq detected 19 differentially expressed gene transcripts
between CK60 and China17, of which only six DEGs
had higher expression in China17 (Table 6). Some of
these DEGs including SPX domain-3, Frigida, late
embryogenesis abundant protein 1 (LEA) were expressed
higher in CK60, and lysine histidine transporter 1
(LHT1) had higher expression in China17. An SPX
domain gene-3 was reported to be up-regulated and plays
an important role in plant adaptation to phosphate
]. This region containing a major QTL for days
to anthesis, was detected under both N conditions
explaining 16 % of phenotypic variation. The CK60 allele
contributed to flowering delay by three days. This region
contained the flowering time gene transcript, Frigida,
Which showed more abundant expression in CK60. It
was reported earlier that ethylene insensitive 3-Like 1
(EIL-1), key regulator of ethylene biosynthesis, underlies
the QTL cluster for days to anthesis, and green leaf area
at maturity [
]. However, this gene is not differentially
expressed in the root tissues of young seedlings in our
RNA-seq analysis (not listed in Table 6). Together, these
data suggest that high expression levels of the Frigida
gene may contribute to the delayed flowering in CK60,
but this is not the only gene influencing this phenotype.
Similarly another DEG transcript, LEA had two-fold
higher expression in CK60 under N-stress condition.
Transgenic expression of a barley LEA protein in rice
resulted in increased growth rate of transgenic plants than
non-transformed plants under stress conditions [
Thus, LEA proteins play an important role in protection
of plants under stress, a potential tool for genetic
improvement towards stress tolerance. In contrast, a DEG
transcript encoding high affinity amino acid transporter,
lysine histidine transporter (LHT1), was massively
expressed in China17 compared CK60 (Table 6). It was
reported that being expressed in the root, LHT1 is
responsible for uptake of amino acids from soil into root
], and distributes from roots to shoots through
] for further metabolism especially under
Nstress conditions. The amino acid uptake, and thus
nitrogen use efficiency could be higher with increased
LHT1 expression under limited inorganic N supply.
A QTL for grain yield is located on distal end of
chromosome 1. In this region QTLs for kernel weight
], maturity [
], number of kernels/panicle and
panicle length [
] and panicle architecture [
reported earlier. In this region, our RNA seq data detected
20 DEG transcripts including caleosin-related (Ca+2
binding) protein, a MADS-box transcription factor,
polyamine oxidase 1 were expressed higher in CK60. Gene
transcripts for magnesium transporter 6, a heat shock
protein (HSP21) and senescence associated protein were
more abundant in China17 (Table 6). Polyamines (PAs)
and ethylene are endogenous plant growth regulators
mediating many physiological processes such as growth,
senescence, and responses to environmental stresses
]. High levels of PAs were reported to be associated
with higher kernel set and better seed development in
] and increased grain-filling rates in rice [
On chromosome 5, QTLs for biomass yield detected
under LN and test weight under NN are co-localized
(Fig. 1). For these QTLs, the positive allele from China17
increased biomass yield by 1.0 t.ha−1 under LN
conditions. In this co-localized region, QTLs for stay green
], fresh panicle weight and plant height  were
detected earlier. In this region, RNA seq didn’t detect
any significant DEG transcripts between Ck60 and
On chromosome 6, co-localization was observed
between major QTLs for plant height and grain yield
under LN conditions. For these QTLs, the positive allele
from China17 increased plant height by 16.4 cm as well
as grain yield. In this region, QTLs for culm height and
kernel weight [
], maturity and total dry matter [
panicle architecture [
] and a major photoperiod
sensitivity locus, Ma1 [
] were reported earlier. Also, a
major QTL for plant height, QPhe-sbi06-1, conditioned
by the Dw2 gene was detected earlier by , and
showed pleiotropic effects on panicle length, yield, and
seed weight [
]. Transcriptome comparison showed
that a Dw2 transcript encoding a multidrug
resistanceassociated protein 9 homolog showed higher expression
levels in CK60, which may be involved in regulating
plant height under N-stress in the seedlings (Table 6). In
addition, RNA-seq found several differentially abundant
gene transcripts in this co-localized region, including
auxin response factor 2, seed storage 2S albumin,
aluminum activated malate transporter, copper
transporter and phosphofructokinase 2, all of which were
expressed higher in CK60 and HSP70 was expressed
higher in China17. Phosphofructo-2-kinase is the
principle enzyme regulating the entry of metabolites into
] through conversion of
fructose-6phosphate to fructose-1,6-bisphosphate. This results in
an increase of hexose phosphate, supplying more energy
and substrates that are necessary for strong seedling
development. It would be of interest to see whether
differential expression of these transcripts holds true with the
adult tissues and use them in marker assisted selection
to regulate the pleotropic regions under LN conditions.
On chromosome 7, QTLs for biomass yield,
chlorophyll content at vegetative and maturity were
colocalized. For these QTLs, the positive allele from
China17 increased biomass yield by 1.0 t.ha−1 under LN
conditions. In this region, QTLs for fresh total biomass
yield and dry total biomass yield was reported by Murray
et al. [
]. In this co-localized region, a major plant
height gene, Dw3 (Sb07g0232730), is located. Dw3
encodes a phosphoglycoprotein auxin efflux carrier
orthologous to PGP1 in Arabidopsis [
]. QTL for panicle
], total biomass yield t.ha−1  and
plant height [
] were reported earlier. In this region,
RNA seq detected 12 DEG’s between CK60 and China
17 (Table 6). Glutamate decarboxylase, gibberellin
receptor GID1L2 and ethylene responsive transcription factor
ERF114 were expressed higher in CK60 and ribosomal
protein L1p/L10e was abundant in China17. Glutamate
decarboxylase (GAD1) was reported to be expressed in
roots and catalyze the synthesis of γ-aminobutyric acid
(GABA) under heat stress, disruption of GAD1 gene
prevented accumulation of GABA in roots in response
to heat stress [
A co-localized region at the distal end of the
chromosome 9 contains QTLs for chlorophyll at flowering and
days to anthesis across two LN and chlorophyll at
maturity, plant height, biomass and grain yield traits across
two NN. This clustering of QTLs is supported by the
negative correlation observed between the chlorophyll
contents at flowering and maturity, morphological and
yield-related traits. In this region, alleles from China17
increased plant height, biomass and grain yield but
caused negative effects on chlorophyll content at
flowering and maturity. QTLs for stay green [
], total seed
weight , plant height [
], maturity [
reported previously in this region. Moreover, a QTL
interval for plant height (Sb-HT9.1) was fine mapped to
~100 kb region through association mapping , Dw3
and Sb-HT9.1 were consistently identified as two of the
most important plant height loci in crosses between tall
and dwarf sorghum [
]. Our RNA-seq data showed
that this region contains 28 DEG transcripts including
those encoding ferredoxin-nitrite reductase (FNR),
chloroplast localized serine/threonine-protein kinase,
and a SufE/NifU family protein. FNR gene transcripts
were highly expressed in China17 root tissues compared
to CK60. In general nitrate is absorbed from soil, reduced
to nitrite and then to ammonia by FNR in the plastids of
root cells. The ammonia produced is incorporated into
amino acids via the glutamine synthetase-glutamate
synthase (GS-GOGAT) pathway. This region of chromosome
9 harbors the highly expressed gene encoding
NADHGOGAT and a glutamine-rich protein. However, these
genes are not differentially expressed between the root
tissues of CK60 and China17 according to RNA-seq data.
Further, it would be important to check whether the
expression levels of NADH-GOGAT between China17 and
CK60 are changed in the shoots because most of the
nitrogen assimilation takes place in shoots rather than root
tissues. Transgenic over-expression of NADH-GOGAT in
rice resulted in an increase in grain weight, indicating that
NADH-GOGAT is indeed a key enzyme in nitrogen
utilization and grain filling in rice . In wheat, Quraishi
et al. [
] validated the NUE QTL on chromosome-3B, and
proposed that a GOGAT gene is conserved structurally and
functionally at orthologous positions in rice, sorghum and
maize genomes and that this gene likely contributes
significantly to NUE in wheat and other cereals. It will be of
interest to determine if breeding that allows for higher
expression of FNR and GOGAT can increase biomass and
grain yield by increasing nitrate assimilation and
QTLs detected for the different agronomic traits in the
same genomic regions were consistent with previous
QTL mapping studies conducted in diverse genetic and
environmental backgrounds in sorghum. RNA-seq
analyses detected differential expression of gene transcripts
in the pleiotropic QTLs related to nitrogen uptake and
metabolism and their expression levels were influenced
by the availability of nitrogen. These potential DEG
transcripts can possibly be used for improving sorghum
performance through marker-assisted selection (MAS)
strategies under N-stress conditions by further
validation in other mapping populations. The markers and
genes reported in this study will have applications in
QTL mapping studies, diversity studies, and
association mapping studies in sorghum and other members
of the Poaceae family collectively aimed at improving
Availability of supporting data
Supporting data are included as additional files
We deposited the RNA-seq data in Gene Expression
acc=GSE54705) and it was mentioned in Gelli et al. 2014,
BMC Genomics v15.
Additional file 1: Basic parameters showing soil properties at two
N levels across years. (xls 22.0 kb)
Additional file 2: Genetic distribution of SNPs discovered using
genotyping-by-sequencing (GBS) in CK60 x China17 population.
(xlsx 41.1 kb)
Additional file 3: The list of differentially expressed genes
identified between CK60 and China17 using RNA-seq. (xls 169 kb)
RILs: Recombinant inbred lines; QTLs: Quantitative trait loci; SNPs: Single
nucleotide polymorphisms; GBS: Genotyping-By-Sequencing;
DEG: Differentially expressed gene; NUE: Nitrogen use efficiency;
GS: Glutamine synthetase; Ppd: Photoperiod sensitivity; Vrn: Vernalization;
Rht: Reduced height; PEPcase: Phosphoenolpyruvate carboxylase; LN: Low
Nitrogen; NN: Normal Nitrogen; Chl1: Chlorophyll content at vegetative
stage; Chl2: Chlorophyll content at anthesis; Chl3: Chlorophyll content at
maturity; PH: Plant height (cm); AD: Days to anthesis (days); MC1: Stover
moisture content (%); MC2: Head moisture content (%); BY: Biomass yield
(t.ha−1); GY: Grain yield (t.ha−1); TW: Test weight (g); GS: Grain/stover ratio (%);
ANOVA: Analysis of variance; IciMapping: Inclusive composite interval
mapping; LOD: Logarithm of odds; h2: Narrow sense heritability; FDR: False
discovery rate; HSP: Heat shock protein; PAs: Polyamines; EIL: 1-ethylene
insensitive 3-Like-1; FNR: Ferredoxin-nitrite reductase; GOGAT: Glutamate
The authors declare that they have no competing interests.
MG designed the study, collected genotypic and phenotypic data, analyzed
data for linkage map, QTL analysis, designed and executed Illumina RNA
sequencing experiment, interpreted data, drafted and revised the
manuscript, SM performed GBS for SNP discovery, CZ and KL for
bioinformatics support; DH designed and supervised the RNA-seq study and
critically reviewed the manuscript; ID coordinated the project, developed the
RIL population and critically reviewed the manuscript; TC and DW are
Co-PI’s on the DOE grant and both contributed to the phenotyping of the
RIL population. All the authors read and approved the final manuscript.
This study was supported by Plant Feedstock Genomics for Bioenergy
#DESc0002259 and The United Sorghum Check off Program # R0002-10. We
thank Mei Chen and Jean Jack Reithoven of the University of Nebraska
Genomics Core Facility for RNA-sequencing and Dr. Yongchao Dou for assisting
with RNA-seq data analysis. We thank Tejindar Kumar Mall and Kanokwan for
assisting in field data collection and Anji Reddy Konda for extensive help in
experimental layout, field data collection, and critical review of the manuscript.
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