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Genome-Wide Association Study of Grain Polyphenol Concentrations in Global Sorghum [ Sorghum bicolor (L.) Moench] Germplasm

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Identifying natural variation of health-promoting compounds in staple crops and characterizing its genetic basis can help improve human nutrition through crop biofortification. Some varieties of sorghum, a staple cereal crop grown worldwide, have high concentrations of proanthocyanidins and 3-deoxyanthocyanidins, polyphenols with antioxidant and anti-inflammatory properties. We quantified total phenols, proanthocyanidins, and 3-deoxyanthocyanidins in a global sorghum diversity panel (n = 381) using near-infrared spectroscopy (NIRS), and characterized the patterns of variation with respect to geographic origin and botanical race. A genome-wide association study (GWAS) with 404,628 SNP markers identified novel quantitative trait loci for sorghum polyphenols, some of which colocalized with homologs of flavonoid pathway genes from other crops, including an ortholog of maize (Zea mays) Pr1 and a homolog of Arabidopsis (Arabidopsis thaliana) TT16. This survey of grain polyphenol variation in sorghum germplasm and catalog of flavonoid pathway loci may be useful to guide future enhancement of cereal polyphenols.
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Genome-Wide Association Study of Grain Polyphenol
Concentrations in Global Sorghum [Sorghum bicolor (L.) Moench]
Germplasm
Davina H. Rhodes,*
,
Leo Homann, Jr.,
William L. Rooney,
Punna Ramu,
§,
Georey P. Morris,
and Stephen Kresovich
Department of Biological Sciences, University of South Carolina, Columbia, South Carolina 29208, United States
Department of Soil & Crop Sciences, Texas A&M University, College Station, Texas 77843, United States
§
International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502 324, Andhra Pradesh, India
Department of Agronomy, Kansas State University, Manhattan, Kansas 66506, United States
Department of Genetics and Biochemistry, Clemson University, Clemson, South Carolina 29634, United States
*
SSupporting Information
ABSTRACT: Identifying natural variation of health-promoting compounds in staple crops and characterizing its genetic basis
can help improve human nutrition through crop biofortication. Some varieties of sorghum, a staple cereal crop grown
worldwide, have high concentrations of proanthocyanidins and 3-deoxyanthocyanidins, polyphenols with antioxidant and anti-
inammatory properties. We quantied total phenols, proanthocyanidins, and 3-deoxyanthocyanidins in a global sorghum
diversity panel (n= 381) using near-infrared spectroscopy (NIRS), and characterized the patterns of variation with respect to
geographic origin and botanical race. A genome-wide association study (GWAS) with 404,628 SNP markers identied novel
quantitative trait loci for sorghum polyphenols, some of which colocalized with homologues of avonoid pathway genes from
other plants, including an orthologue of maize (Zea mays)Pr1 and a homologue of Arabidopsis (Arabidopsis thaliana)TT16. This
survey of grain polyphenol variation in sorghum germplasm and catalog of avonoid pathway loci may be useful to guide future
enhancement of cereal polyphenols.
KEYWORDS: Sorghum bicolor, cereal, grain, polyphenol, avonoid, proanthocyanidin, 3-deoxyanthocyanidin, condensed tannins,
GWAS, QTL
INTRODUCTION
Polyphenols are a large diverse group of phytochemicals that
include phenolic acids, stilbenes, lignans, isoavonoids, and
avonoids.
1
All avonoids share a common C6C3C6
backbone structure but dier in their oxidation level,
glycosylation, acylation, and hydroxyl and methyl substitutions,
allowing for an enormous variety of structure and function.
2
In
plants, avonoid secondary metabolites are involved in growth,
pigmentation, pollination, and defense against pathogens,
predators, and physical factors.
3
In humans, dietary avonoids
are thought to act as antioxidants and signaling molecules, and
their consumption is correlated with lower incidence of
cardiovascular disease, cancer, type II diabetes, neurodegener-
ative disease, and other chronic illnesses.
4
Most plant-based
foods contain avonoids, making them some of the most
ubiquitous polyphenols in the human diet. Polymerization of
avonoids yields complex compounds including proanthocya-
nidins, avonoid polymers predominantly composed of avan-
3-ols, which are abundant in food plants. Proanthocyanidins
contribute to the astringency and bitterness found in foods such
as wine, cocoa, beans, and fruits, but they are not present in
most commonly consumed vegetables and cereals.
5
They are
also often considered antinutrients due to their nutrient binding
capacity, especially to proteins and iron.
6
In the past decade,
however, potential health protective eects of proanthocyani-
dins have been studied extensively, with particular focus on
their contributions to observed health benets of grape and
cranberry.
7
Sorghum is one of the worlds major cereal crops and a
dietary staple for more than 500 million people in sub-Saharan
Africa and Asia.
8
In the Unites States, it is primarily used as
animal feed, but it is becoming more popular in food products
due to a rise in demand for specialty grains, especially those
that are gluten-free.
912
Domesticated sorghum has been
classied into ve major races (bicolor, guinea, caudatum,
kar, and durra) and 10 intermediate races (all combinations of
the major races), based on morphological dierences.
13
Two of
the major polyphenol compounds in sorghum grain are
proanthocyanidin and 3-deoxyanthocyanidin. Consumption of
these two polyphenols has been correlated with several health
benets including protection against oxidative damage,
inammation, obesity, and diabetes.
14
Proanthocyanidins are
constitutively expressed, while 3-deoxyanthocyanidins are
phytoalexins, expressed only in response to fungal infec-
tion.
15,16
Sorghum grain is the only known dietary source of 3-
Received: April 7, 2014
Revised: September 30, 2014
Accepted: October 1, 2014
Article
pubs.acs.org/JAFC
© XXXX American Chemical Society Adx.doi.org/10.1021/jf503651t |J. Agric. Food Chem. XXXX, XXX, XXXXXX
deoxyanthocyanidins, which otherwise have only been found in
the owers of sinningia (Sinningia cardinalis), the silk tissues of
maize (Zea mays), and the stalks of sugar cane (Saccharum
sp.).
1719
In sorghum grains, polyphenol compounds can be found in
the pericarp (outer seed coat) and the testa (inner layer of
tissue between the pericarp and the endosperm). A number of
classical loci identied by their eects on grain color and testa
presence control the presence or absence of polyphenol
compounds in sorghum.
20
Genotypes with dominant alleles
at the B1 and B2 loci have proanthocyanidins in the testa.
Genotypes with a dominant allele at the spreader (S) locus, as
well as dominant alleles at the B1 and B2 loci, have
proanthocyanidins in both the pericarp and the testa, often,
but not always, resulting in a brown appearance to the grain.
The base pericarp color is red, yellow, or white, and these
colors are controlled by the R and Y loci. The S locus and
additional loci, such as intensier (I) and mesocarp thickness
(Z), modify the base pericarp color, resulting in a range of
colors from brilliant white to black with various shades of red,
yellow, pink, orange, and brown among sorghum genotypes
(see Figure 1). Using mutants for seed color traits, the
biochemical and regulatory pathways underlying avonoids and
avonoid products have been almost completely elucidated in
Arabidopsis and maize, and extensively studied in other species
(Table S1 in the Supporting Information).
21
Therefore,
homology can be used as a guide to discover genes involved
in the sorghum avonoid pathway. The gene underlying the B2
locus was recently cloned and designated Tannin1, along with
two nonfunctional alleles of Tannin1,tan1-a and tan1-b.
22
Tannin1 encodes a WD40 protein homologous to the
Arabidopsis proanthocyanidin regulator transparent testa glabra1
(TTG1). The gene underlying the Y locus has also been cloned
and designated Yellow seed1.Yellow seed1 encodes a MYB
protein, orthologous to the maize 3-deoxyanthocyanidin
regulator P1, that is needed for accumulation of 3-
deoxyanthocyanidins in the sorghum pericarp.
23
The R locus
has been mapped to chromosome 3 between 57 and 59 Mb,
and the Z locus has been mapped to chromosome 2 between
56 and 57 Mb,
24
but the underlying genes have not been
identied.
While the genetic controls of polyphenol presence/absence
have been well-studied using mutant lines and nonfunctional
polymorphisms, there has been little study of quantitative
natural variation in polyphenols.
25
Polyphenol nonfunctional
mutations were strongly selected during cereal domestication,
when bitter tasting and/or dark compounds were partly or
completely lost in most cereals, including wheat, rice, and
maize.
26
However, sorghum provides a valuable resource for
polyphenol diversity, as adaptation to dierent environments
has led to extensive phenotypic and genetic diversity in the
crop.
13,27
This diversity can be useful for biofortication and
crop improvement (e.g., desirable traits can be bred into
existing elite varieties), but quantitative phenotyping is needed
to identify alleles responsible for quantitative trait variation in
grain polyphenols (reviewed by Flint-Garcia
28
). The goals of
this study were to quantify the natural variation of two of the
major sorghum grain polyphenols (proanthocyanidins and 3-
deoxyanthocyanidins) and to identify single-nucleotide poly-
morphisms (SNPs) that are associated with low or high
polyphenol concentrations using genome-wide association
studies (GWAS). GWAS are used to map the genomic regions
underlying phenotypic variation (known as quantitative trait
loci) by scanning the genome for statistical associations
between genetic variation and phenotypic variation.
29
In
contrast to the biparental linkage mapping approach, GWAS
takes advantage of historical recombinations in a diverse panel
and linkage disequilibrium between causal variants and nearby
SNP markers. Although it has been used extensively to identify
putative genetic controls of human disease,
30
it is a relatively
new but promising tool in plant genomics.
27,31,32
Here we
present a survey of the quantitative natural variation of
polyphenols in a diverse worldwide panel of sorghum and a
catalog of avonoid-associated loci across the sorghum genome.
MATERIALS AND METHODS
Plant Materials. We investigated a total of 381 sorghum
accessions, comprising 308 accessions from the Sorghum Association
Panel (SAP)
33
and an additional 73 accessions selected based on
presence of a pigmented testa using the U.S. National Plant
Germplasm Systems Germplasm Resources Information Network
(GRIN).
34
The SAP includes accessions from all major cultivated races
and geographic centers of diversity in sub-Saharan Africa and Asia, as
well as important breeding lines from the United States. The 73
additional accessions were included to increase the proportion of
accessions with high proanthocyanidins.
Seeds were obtained through GRIN and planted in late April 2012
at Clemson University Pee Dee Research and Education Center in
Florence, SC. A 2-fold replicated complete randomized block design
was used. Panicles from each plot were collected at physiological
maturity (signied by a black layer at the base of the seed that
normally forms about 35 days after anthesis). Due to dierences in
maturity among these accessions, harvest occurred between September
and October. Once harvested, panicles were air-dried in a greenhouse
and then mechanically threshed, and any remaining glumes were
removed with a wheat head thresher (Precision Machine Company,
Lincoln, NE).
Phenotyping. Twenty grams of cleaned whole grain from one
replicate was scanned with a FOSS XDS spectrometer (FOSS North
America, Eden Prairie, MN, USA) at a wavelength range of 4002500
nm. To determine reproducibility, duplicates on a subset of 218
accessions available from replicate plots were also scanned. The NIR
reectance spectra were recorded using the ISIscan software (Version
3.10.05933) and converted to estimates of total phenol, proanthocya-
nidin, and 3-deoxyanthocyanidin concentrations. The spectrometer,
software, and calibration curves used in this study were recently
described.
35
Samples with unusual reectance were visually inspected,
and near-infrared spectroscopy (NIRS) was repeated. Seventeen
samples were removed from further analysis either because they
Figure 1. Natural variation in sorghum grain color. Three accessions
(with three seeds of each accession) of grain with the appearance of
(A) brown (PI597965, PI533927, PI35038), (B) white (PI533755,
PI533845, PI534028), (C) yellow (PI659691, PI656011, PI533776),
and (D) red (PI576418, PI534047, PI564165) pericarps. The outer
coat has been scraped oof some samples, revealing the presence or
absence of a pigmented testa.
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dx.doi.org/10.1021/jf503651t |J. Agric. Food Chem. XXXX, XXX, XXXXXXB
contained mixed grain (mixed size, shape, or color) or because their
readings were outside the range of the available NIRS calibration
curve. Total phenol, proanthocyanidin, and 3-deoxyanthocyanidin data
are expressed as mg gallic acid equivalents (GAE)/g, mg catechin
equivalents (CE)/g, and absorbance (abs)/mL/g, respectively. These
were the units used in creating the calibration curves, which measured
total phenols with the FolinCiocalteu method, 3-deoxyanthocyani-
dins with the colorimetric method of Fuleki and Francis, and
proanthocyanidins with the modied vanillin/HCl assay.
35
For the
purposes of this study, we use a cutoof greater than 10.00 mg CE/g
to dene proanthocyanidin-containing varieties and greater than 50.00
abs/mL/g to dene 3-deoxyanthocyanidin-containing varieties.
Visual appearance of grain was classied independently by two
people by visually scoring three seeds per accession as white, yellow,
red, or brown. Testa presence was identied with three seeds per
accession by cutting a thin layer othe pericarp and examining under a
dissecting microscope. The total grain weight of 100 seeds per
accession was recorded.
Genomic Analysis. Genotypes were available for the 324
accessions that were part of the SAP.
27
Genotyping-by-sequencing
(GBS) was performed for the 73 additional accessions by the Institute
for Genomic Diversity using the methods by Elshire et al.
36
Briey, we
provided seeds of the 73 additional accessions (the same seeds
obtained from GRIN that we used to grow our panel) to the Institute
for Genomic Diversity, where the following work was performed:
Seedlings were grown to obtain tissue, DNA was isolated using the
Qiagen DNeasy Plant kit, genomic DNA was digested individually
using ApeKI, 96X multiplexed GBS libraries were constructed, and
DNA sequencing was performed on the Illumina Genome Analyzer
IIx. To extract SNP genotypes from sequence data, the GBS pipeline
3.0 in the TASSEL software package
57
was used, with mapping to the
BTx623 sorghum reference genome.
37
Missing genotype calls were
imputed using the FastImputationBitFixedWindow plugin in TASSEL
4.0.
38
GWAS was carried out on 404,628 SNP markers, using the
statistical genetics package Genome Association and Prediction
Integrated Tool (GAPIT)
39
with both a general linear model
(GLM) and a mixed linear model (MLM) with kinship. In a previous
study we found that an MLM
40
with kinship (K), which controls for
relatedness among the accessions in the panel, performs well to
identify causative loci for sorghum polyphenols.
41
Bonferroni
correction (family wise P-value of 0.01, P<10
6) was used to
identify signicant associations. Pseudoheritability (proportion of
phenotypic variation explained by genotype) was estimated from the
kinship (K) model in GAPIT
42
as the R-squared of a model with no
SNP aects. A previously developed a priori candidate gene list was
used, and 35 additional candidate genes were added (see Supporting
Information).
41
RESULTS
Quantitative Variation in Grain Polyphenols. We rst
sought to determine the reliability of the NIRS estimates across
the diverse material in the panel. Phenotypic variation for grain
polyphenol concentrations was determined using a diverse
association panel with 381 accessions (Figure 2). The standard
deviation between the duplicates was similar across all
concentrations of polyphenols (r2= 0.06, P= 0.0001) and
proanthocyanidins (r2= 0.01, P= 0.12), with an average
dierence of 47% and 4%, respectively. However, the standard
deviation between the 3-deoxyanthocyanidin duplicates be-
comes much larger for samples with higher 3-deoxyanthocya-
nidin concentrations (r2= 0.32, P=10
17), with an average
dierence of 72% (Figure 2C). To determine if the NIRS
measurements of proanthocyanidin concentration were con-
cordant with the known distribution of testa and tan1-a
nonfunctional allele,
22
we plotted proanthocyanidin concen-
tration of accessions with or without a pigmented testa (Figure
S1A in the Supporting Information), and accessions with the
wild-type Tannin1 allele or the tan1-a allele (Figure S1B in the
Supporting Information). As expected, the absence of a testa
and presence of tan1-a were primarily found in accessions
containing less than 10 mg CE/g of proanthocyanidins. The
mean proanthocyanidin concentrations in accessions with a
pigmented testa were signicantly higher than in accessions
without a pigmented testa (18.17 versus 1.45 mg CE/g; P=
1017), and the mean proanthocyanidin concentrations in
accessions with the wild-type Tannin1 were signicantly higher
than in accessions with tan1-a (12.28 versus 0.86 mg CE/g; P=
1011).
Next we investigated the range of total phenol, proantho-
cyanidin, and 3-deoxyanthocyanidin concentrations and their
covariation with each other and grain weight (Figure 3).
Overall, proanthocyanidins were detected in 55% of the
samples, while only 13% contained 3-deoxyanthocyanidins,
and only 6% contained both polyphenols. The mean total
polyphenol concentration was 7.00 mg (GAE)/g, the mean
proanthocyanidin concentration was 7.73 mg CE/g, and the
mean 3-deoxyanthocyanidin concentration was 27.40 abs/mL/
g (Table 1 and Figure 3). Pearsons correlations were calculated
between total phenols, proanthocyanidins, and 3-deoxyantho-
cyanidins. There was no signicant correlation between
proanthocyanidins and 3-deoxyanthocyanidins (0.02, P=
0.7), consistent with independent genetic control. In contrast,
Figure 2. Phenotypic variation of grain polyphenol concentrations in
381 sorghum varieties. Samples are ordered on the x-axis according to
their mean value for the accession. The observed value for each
replicate is given on the y-axis, with the higher value of the duplicates
in red and the lower value of the duplicates in blue. (A) Total
polyphenols, (B) proanthocyanidins (PAs), and (C) 3-deoxyantho-
cyanidins (3-DAs).
Journal of Agricultural and Food Chemistry Article
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there were a strong positive correlation between total phenols
and proanthocyanidins (0.95, P<10
15) and a weak positive
correlation between total phenols and 3-deoxyanthocyanidins
(0.12, P= 0.02). Variance in proanthocyanidins accounted for
90% of all the variance in total phenols (Figure 3). Since the
seed coat (pericarp and testa) contains most of the polyphenols
in the grain, and the ratio of seed coat (surface area) to
endosperm is generally greater in smaller grains, we wondered
if dierences in grain size might be underlying variation in
polyphenol concentrations. In other words, are high grain
polyphenol concentrations limited to small-grain varieties,
which have a high proportion of seed coat to endosperm?
No signicant correlation was found between grain weight and
either proanthocyanidins (0.02, P= 0.7) or 3-deoxyantho-
cyanidins (0.02, P= 0.7), and a small negative correlation was
found between grain weight and total polyphenols (0.10, P=
0.04). Pseudoheritability was 81.7% for proanthocyanidins and
66.5% for 3-deoxyanthocyanidins.
Population Structuring of Polyphenol Concentra-
tions. To determine the distribution of polyphenol traits
with respect to global genetic diversity, we conducted a
principal component analysis and highlighted the variation in
polyphenol concentration (Figures S2A and S2B in the
Supporting Information), as well as morphological races
(Figure S2C in the Supporting Information). At least some
high proanthocyanidin accessions were found in most
subpopulations, whereas high 3-deoxyanthocyanidin accessions
were more restricted (Table 2). Bicolor (21.18 mg CE/g) and
guinea-caudatum (17.89 mg CE/g) had the highest mean
concentration of proanthocyanidins. Caudatum had moderate
concentrations (13.20 mg CE/g), and the other botanical races
and intermediate groups showed an average less than 10.00 mg
CE/g. The highest mean concentrations of 3-deoxyanthocya-
nidins were found in bicolor-durra (36.95 abs/mL/g) and
guinea (35.63 abs/mL/g) accessions (Table 2). We also
determined the mean concentrations by country to better
understand the geographic patterns for sorghum polyphenols
(Table 3). Accessions from Uganda (19.03 mg CE/g) had the
highest mean proanthocyanidin concentrations, accessions
from South Africa (12.23 mg CE/g) and Sudan (10.33 mg
CE/g) had moderate concentrations, while accessions from the
other countries showed an average less than 10.00 mg CE/g.
The highest mean concentrations of 3-deoxyanthocyanidins
were found in accessions from Nigeria (36.39 abs/mL/g) and
Ethiopia (32.87 abs/mL/g).
Genome-Wide Association Studies. To investigate the
genetic basis of natural variation in sorghum grain polyphenols,
we conducted GWAS using 404,628 SNP markers. We were
able to obtain genotype data for 373 out of the 381 phenotyped
accessions. As a data quality check, we rst collapsed the
quantitative proanthocyanidin data to qualitative (presence or
absence) data, and were able to repeat ndings from previous
GWAS and linkage studies (Figures S3 and S4 and Tables S2
and S3 in the Supporting Information). Next, to identify novel
alleles associated with quantitative variation of proanthocyani-
Figure 3. Relationship within and between grain polyphenol traits in a
global sorghum germplasm collection. The center diagonal presents
histograms of the mean concentrations of each trait (n= 381). The
lower corner contains scatter plots with regression lines showing the
relationships between the traits. The upper corner shows Pearsons
correlations between the traits. Units are mg GAE/g for total phenols,
mg CE/g for proanthocyanidins, and abs/mL/g for 3-deoxyanthocya-
nidins.
Table 1. Polyphenol Concentrations in 373 Sorghum
Varieties
constituent mean range SD
total phenols (mg GAE/g) 7.00 ND37.46 5.92
proanthocyanidins (mg CE/g) 7.73 ND78.51 15.45
3-deoxyanthocyanidins (abs/mL/g) 27.40 ND149.21 24.05
Table 2. Polyphenol Concentrations by Race
total phenols (mg GAE/g) PA (mg CE/g) 3-DA (abs/mL/g)
race
a
nmean range mean range mean range
bicolor 15 13.68 ±6.69 0.7424.49 21.18 ±17.68 ND50.16 26.91 ±33.65 ND102.96
bicolor-durra 19 6.59 ±4.28 ND13.38 3.89 ±12.06 ND23.35 36.95 ±28.24 1.30113.42
caudatum 86 9.08 ±5.86 ND27.32 13.20 ±14.15 ND52.83 28.22 ±21.06 ND110.73
caudatum-kar 20 6.27 ±5.41 ND15.68 7.00 ±15.13 ND31.98 26.65 ±16.87 6.7058.25
durra 15 2.17 ±3.61 ND11.68 ND ND17.64 22.17 ±21.33 ND71.10
guinea 11 1.95 ±5.25 ND15.44 ND ND33.45 35.63 ±36.88 0.93135.34
guinea-caudatum 15 10.01 ±3.13 2.5415.87 17.89 ±9.76 ND34.92 19.72 ±15.69 0.4060.10
kar 29 6.02 ±4.05 1.3214.71 6.50 ±10.20 ND28.72 17.59 ±20.65 ND94.49
a
If a race contained a small sample size (less than 10 accessions), it was not included in this analysis. PA, proanthocyanidins; 3-DA, 3-
deoxyanthocyanidins; ND, not detected (absorbance was less than 0.001).
Journal of Agricultural and Food Chemistry Article
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dins, we conducted a GWAS on the 373 accessions (Figure 4;
Table S4 in the Supporting Information). A GLM identied
3,272 signicant SNPs (Figure 4A), while the MLM identied
24 signicant SNPs after accounting for population structure
(Figure 4B). The genomic locations of the association peaks
were generally similar between methods. A peak on
chromosome 4 at 61 Mb colocalized with Tannin1
(Sb04g031730), as well as three a priori candidate genes in
the region: a putative Zm1 homologue (Sb04g031110), a
putative TTG1 homologue (Sb04g030840), and a putative
TT16 homologue (Sb04g031750) (Figure 4C). The GLM
identied a peak at 58.6 Mb on chromosome 7 (S7_58603858;
P<10
15), which was not present in the MLM.
In order to reduce the eects of known Tannin1 nonfunc-
tional alleles and identify additional quantitative loci, samples
with the tan1-a and tan1-b alleles were removed and a GWAS
was conducted on the remaining samples (Figure 5 and Table
S5 in the Supporting Information). The GLM identied 2,641
signicant SNPs (Figure 5A). The association peak on
chromosome 7 was again identied in the GLM and not in
the MLM (Figure 5B). Additionally, there was a peak on
chromosome 2 around 8 Mb (S2_8258226; P<10
11)
identied in the GLM, near a putative TT8 homologue
(Sb02g006390). Both the GLM and the MLM identied a peak
on chromosome 4, again around 61 Mb, and another peak on
chromosome 4 between 53 Mb and 55 Mb, close to an F3H
Pr1 coorthologue.
To further map loci controlling quantitative proanthocyani-
din variation, we ran a GWAS only on samples that contained
proanthocyanidins (greater than 10.00 mg CE/g) and/or had a
visible pigmented testa (Figure 6 and Table S6 in the
Supporting Information). With this subset, there were 676
signicant SNPs identied in the GLM, but association peaks
were more diuse (Figure 6A). The most signicant SNP was
on chromosome 6 (S6_56992521, P<3×1010) near a TT16
a priori candidate (Sb06g028420). The MLM identied two
signicant SNPs, with a peak on chromosome 4, again around
61 Mb, and another peak on chromosome 4 between 53 Mb
and 55 Mb (Figure 6B). Both the GLM and the MLM
identied signicant SNPs around 61.1 Mb on chromosome 1,
which is near yellow seed1.
Next, a GWAS was conducted to identify genetic controls of
3-deoxyanthocyanidin variation among the 373 accessions
(Figure 7 and Table S7 in the Supporting Information). The
GLM identied 233 signicant SNPs, with distinct association
peaks on chromosomes 3 and 4 (Figure 7A). The peak on
chromosome 3 was between 71 and 72 Mb and colocalized
with a gene (Sb03g045170) homologous to both TT18 (ANS)
and TT6 (F3H). The peak on chromosome 4 was between 53
Mb and 55 Mb, close to TT1 and TT2 homologues, and an
F3HPr1 coorthologue. While there was not a distinct peak on
chromosome 1, the strongest association signal in the GWAS
was found in a diuse peak on chromosome 1 around 55 Mb (P
<10
9). The closest a priori candidates were putative TTG2
(Sb01g032120) and TT2 (Sb01g032770) homologues. There
were no distinct peaks or signicant associations identied in
the MLM (Figure 7B).
Grain Color. Since grain color is commonly used as a visual
marker for sorghum polyphenol content, we used our data set
to better understand both the correlation between visually
scored grain color and polyphenol concentration, and the
potential shared genetic basis for these traits. Based on visual
assessment of grain appearance, we designated 142 white, 35
yellow, 48 red, and 152 brown grain accessions. An analysis of
variance (ANOVA) showed signicant variation among the
grain color groups, so we conducted a post hoc Tukey test.
Grain classied as red contained signicantly more 3-
deoxyanthocyanidins than brown (P<10
5) or white grain
(P<10
5) accessions, but no signicant dierence was found
between red and yellow accessions (Figure 8A and Table 4).
Brown grain accessions contained signicantly more proantho-
cyanidins than accessions with red (P= 0.0001), white (P=
0.001), or yellow (P= 0.001) grain (Figure 8B and Table 4).
This was expected as most of the sorghums with testa layers
were classied as brown (57%). We also compared
proanthocyanidin concentrations between grain color in
proanthocyanidin-containing (greater than 10.00 mg CE/g or
presence of pigmented testa) accessions. Brown grain color
classes contained signicantly more proanthocyanidins than
nonbrown (brown n= 120, nonbrown n= 85, P<10
13).
However, when brown grain color classes were compared to
each color class individually, they only contained signicantly
more proanthocyanidins than white color classes (P<10
4).
Red and lemon-yellow grain color classes also contained
signicantly more proanthocyanidins than white in the
proanthocyanidin-containing accessions (P= 0.001 and P=
0.02).
To identify genes associated with brown grain, we conducted
a presence/absence (brown versus nonbrown) GWAS on all
373 of the accessions (Figures S5A and S5B and Table S8 in
the Supporting Information) and another presence/absence
(brown versus nonbrown) GWAS on the 203 proanthocyani-
din-containing accessions (Figures S5C and S5D and Table S9
in the Supporting Information). A distinct association peak on
chromosome 8 at 52.9 Mb was observed in both GWAS. The
nearest a priori candidate was a putative TT12 homologue
within 400 Kb (Sb08g021640). The GWAS conducted on all
373 accessions identied a peak on chromosome 3 at 63.6 Mb,
Table 3. Polyphenol Concentrations by Geographic Origin
total phenols (mg GAE/g) PA (mg CE/g) 3-DA (abs/mL/g)
country
a
nmean range mean range mean range
Uganda 44 10.99 ±5.17 1.1727.32 19.03 ±12.02 ND52.83 27.37 ±20.8 1.30110.73
South Africa 31 9.11 ±5.21 1.1120.63 12.23 ±12.37 ND43.75 13.52 ±14.1 ND38.82
Sudan 31 7.50 ±3.34 ND14.67 10.33 ±8.93 ND25.26 27.15 ±15.1 4.1360.10
Nigeria 21 5.0 ±6.46 ND24.49 1.21 ±21.36 ND50.16 36.39 ±35.8 ND135.34
Ethiopia 29 5.71 ±5.43 ND15.94 1.53 ±13.13 ND23.53 32.87 ±21.1 ND77.59
India 21 3.90 ±5.09 ND16.98 ND ND32.13 28.74 ±28.7 ND113.42
USA 71 5.09 ±5.25 ND29.93 3.6 ±12.55 ND63.80 27.50 ±24.2 ND95.20
a
If a country contained a small sample size (less than 10 accessions), it was not included in this analysis. PA, proanthocyanidins; 3-DA, 3-
deoxyanthocyanidins; ND, not detected (absorbance was less than 0.001).
Journal of Agricultural and Food Chemistry Article
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within 100 Kb of another putative TT12 homologue
(Sb03g035610), and also a peak on chromosome 6
(S6_56992521, P<3×1010) near a TT16 a priori candidate
(Sb06g028420) (Figures S4A and S4B in the Supporting
Information). The GWAS conducted on the proanthocyanidin-
containing accessions identied a peak on chromosome 2
around 69.6 Mb, very near another TT12 homologue
(Sb02g034720) (Figures S5C and S5D in the Supporting
Information). This peak was also identied in the GWAS
conducted on all 373 accessions, but was more diuse. There
were no peaks on chromosome 4 around Tannin1 or on
chromosome 2 around the Z locus.
To identify genes associated with red grain, we conducted a
presence/absence (red versus nonred) GWAS on all of the
samples (Figure S6 and Table S10 in the Supporting
Information). Two association peaks on chromosome 4 were
identied by both the GLM and MLM, in the same region as
the peak in the 3-deoxyanthocyanidin GWAS. The rst peak, at
54.5 Mb, colocalized with a priori candidate Sb04g024710, the
Figure 4. GWAS for proanthocyanidin concentration in sorghum
grain. Manhattan plot of association results from (A) a GLM analysis,
(B) an MLM analysis, and (C) a closeup of the peak on chromosome
4 showing Tannin1 and other candidate genes in the region, using
404,628 SNP markers and 373 accessions. Axes: the log10 p-values (y
axis) plotted against the position on each chromosome (xaxis). Each
circle represents a SNP. The dashed horizontal line represents the
genome-wide signicance threshold as determined by Bonferroni
correction. Regions with log10 p-values above the threshold are
candidates. The vertical lines indicate the location of Tannin1 and a
priori candidate genes in the Tannin1 region (61 Mb).
Figure 5. GWAS for proanthocyanidin concentration in sorghum grain
with accessions containing tan1-a and tan1-b nonfunctional alleles
removed. Manhattan plot of association results from (A) a GLM
analysis and (B) an MLM analysis, using 404,628 SNP markers and
312 accessions. Axes: the log10 p-values (yaxis) plotted against the
position on each chromosome (xaxis). Each circle represents a SNP.
The dashed horizontal line represents the genome-wide signicance
threshold as determined by Bonferroni correction. Regions with
log10 p-values above the threshold are candidates. The red vertical
lines highlight the location of candidate genes. (TT8 on chrm. 2 and
TTG1,Zm1, and TT16 on chrm. 4)
Journal of Agricultural and Food Chemistry Article
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F3HPr1 coorthologue that was also in one of the 3-
deoxyanthocyanidin GWAS peaks. The second peak, at 55.9
Mb, was very close to a priori candidate Sb04g026480, a
putative MYB homologue. There was also a peak around 72 Mb
on chromosome 3, in the same region as the peak in the 3-
deoxyanthocyanidin GWAS, near a priori candidate
Sb03g044980, a putative TT19 homologue. A peak was
identied on chromosome 6 between 7 and 8 Mb, which was
not near any a priori genes, but was near a putative vacuolar
sorting protein gene (Sb06g003780). There were no peaks on
chromosome 3 around the R locus.
DISCUSSION
Genetic Controls of Sorghum Polyphenols. The genetic
controls of the avonoid pathway (Figure 9) have been well
studied in many economically important food plants, including
grape (Vitis vinifera), barley (Hordeum vulgare), maize (Zea
mays), rice (Oryza sativa), and wheat (Triticum spp.).
43
Much
of our understanding of avonoid genetics, including
biosynthetic enzymes, transporters, and regulatory proteins,
come from analysis of Transparent Testa (TT) mutants in
Arabidopsis.
44
Transcriptional regulation occurs through a
ternary complex made up of TT2,TT8, and TTG1, which
encode for MYB, bHLH, and WD40 proteins (MBW complex),
respectively.
44
This ternary complex is highly conserved among
plant species.
45
In the sorghum proanthocyanidin pathway, the
WD40 (Tannin1) component of the MBW complex has been
identied, as well as a likely candidate for the bHLH; several
studies have found a signicant linkage and association on
sorghum chromosome 2 around 8 Mb, near a putative bHLH
transcription factor orthologous to Arabidopsis TT8.
22,24,41,46,47
The MYB transcription factor that would complete the ternary
complex has not been found in sorghum. The Zm1 homologue
on chromosome 4 at 61.1 Mb (Sb04g031110, 66.8% similarity),
which was mapped in all of our proanthocyanidin GWAS, is a
possible candidate for the missing MYB. The maize Zm1 gene
is a MYB transcription factor, homologous to classical maize
grain pigmentation gene C1 that can induce transcription of
DFR, an essential structural enzyme in the avonoid pathway.
48
Figure 6. GWAS for proanthocyanidin concentration in proanthocya-
nidin-containing sorghum grain (greater than 10.00 mg CE/g or
pigmented testa). Manhattan plot of association results from (A) a
GLM analysis and (B) an MLM analysis, using 404,628 SNP markers
and 208 accessions. Axes: the log10 p-values (yaxis) plotted against
the position on each chromosome (xaxis). Each circle represents a
SNP. The dashed horizontal line represents the genome-wide
signicance threshold as determined by Bonferroni correction. Regions
with log10 p-values above the threshold are candidates. The red
vertical lines highlight the location of candidate genes (TT16,Tannin1
region, Pr1/TT7).
Figure 7. GWAS for 3-deoxyanthocyanidin concentration in sorghum
grain. Manhattan plot of association results from (A) a GLM analysis
and (B) an MLM analysis, using 404,628 SNP markers and 373
accessions. Axes: the log10 p-values (yaxis) plotted against the
position on each chromosome (xaxis). Each circle represents a SNP.
The dashed horizontal line represents the genome-wide signicance
threshold as determined by Bonferroni correction. Regions with
log10 p-values above the threshold are candidates. The red vertical
lines highlight the location of candidate genes (TT18/ANS,TT6/F3H,
Pr1/TT7).
Journal of Agricultural and Food Chemistry Article
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Another possible explanation for the signicant SNPs at this
location is an indirect association with an undescribed allele at
Tannin-1.
About two-thirds of the SAP accessions we studied were
convertedtropical accessions, meaning that alleles for
reduced height and early owering have been introgressed so
they can be grown in temperate regions.
49
Surprisingly, the
proanthocyanidin GWAS association peak on chromosome 7
(58.6 Kb) precisely colocalizes with dw3 (Sb07g023730), a
dwarng loci used in the conversion, in conjunction with dw1,
dw2, and dw4.
27
Smaller peaks on chromosomes 6 (39 Kb)
and 9 (57 Kb) were near the dw2 and dw1 loci. The
association peaks on chromosomes 6, 7, and 9 may be artifacts
arising from a lower mean proanthocyanidin concentration in
the converted lines (4.4 mg CE/g) which all shared the same
dw alleles, compared to the unconverted lines (11.0 mg CE/g).
Accordingly, when we conducted a proanthocyanidin GWAS
using only converted accessions to control for this spurious
phenotypic covariation between proanthocyanidin and height,
the peaks near dw1,dw2, and dw3 disappeared, while the
Tannin1 peak remained (Figure S7 in the Supporting
Information).
As a phytoalexin,
15,16
the eect of the environment may
make it more dicult to map the genetic basis of 3-
deoxyanthocyanidins than the genetic basis of proanthocyani-
dins. Although the GLM was able to identify signicant SNP
associations for 3-deoxyanthocyanidins, there were few peaks,
and the MLM did not identify any signicant associations.
Detection of alleles contributing to variance of 3-deoxyantho-
cyanidins may require a larger sample size, additional
replication, a biparental mapping population, or controlled
fungal inoculations to induce biosynthesis of polyphenol
compounds.
23
However, our results did provide a promising
candidate for followup. A Pr1 orthologue (Sb04g024750) lies
within a distinct peak on chromosome 4, about 400 Kb from
the top SNP identied in the 3-deoxyanthocyanidin GWAS
(S4_54975391; P<10
8), and 100 Kb from the top SNP in the
red grain GWAS (S4_54555458, P<10
13). Pr1 is a maize
F3H enzyme, homologous to TT7 in Arabidopsis. The F3H
enzyme is essential for production of 3-deoxyanthocyanidins, as
well as the red phlobaphene pigments visible in maize,
18
and
has been implicated in production of these compounds in
sorghum (Figure 9).
50
Overall, we observe a 1.6-fold dierence
in 3-deoxyanthocyanidin concentrations between accessions
carrying the high concentration alleles and low concentration
alleles for the top red grain-associated SNP (P= 0.001). F3H
is necessary for proanthocyanidin production as well, and,
indeed, signicant associations with SNPs in the 54 Mb
region on chromosome 4 were also identied in the GWAS
with tan1-a and tan1-b samples removed, as well as the GWAS
with only proanthocyanidin-containing samples.
Our study identied many peaks and SNPs signicantly
associated with proanthocyanidins and 3-deoxyanthocyanidins,
hence there appear to be many small eect genes controlling
natural variation of these traits. Consequently, a larger
association panel, or a targeted biparental mapping population,
Figure 8. Polyphenol dierences between grain colors. Mean
concentrations of (A) proanthocyanidins and (B) 3-deoxyanthocya-
nidins in accessions of each grain color. Color categories share the
same letter if they are not signicantly dierent from each other, based
on a post hoc Tukey HSD test (brown, n= 152; red, n= 48; white, n=
142; yellow, n= 35).
Table 4. Polyphenol Concentrations by Color
total phenols (mg GAE/g) PA (mg CE/g) 3-DA (abs/mL/g)
color nmean range mean range mean range
white 142 4.0 ±3.10 ND14.67 2.00 ±8.84 ND25.26 22.74 ±14.03 ND58.41
yellow 35 6.0 3 ±6.18 ND23.69 4.60 ±15.98 ND42.30 29.30 ±27.89 ND98.90
red 48 6.97 ±7.30 ND27.32 4.48 ±21.10 ND52.83 42.21 ±30.43 ND135.34
brown 152 10.01 ±6.01 ND37.46 14.74 ±15.63 ND78.51 26.46 ±26.64 ND149.21
Journal of Agricultural and Food Chemistry Article
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may be more eective in precisely identifying causal alleles.
Moving forward, sequence analysis and expression analysis of
the candidate genes are needed to identify causal poly-
morphisms and lay the groundwork for the use of polyphenol
genetic variation in crop improvement.
Crop Improvement for Sorghum Polyphenols. Eorts
to characterize polyphenols, with the goal of producing high
polyphenol specialty varieties, have been undertaken in several
grain crops, including purple wheat,
51
black rice,
52
multicolored
maize,
53
multicolored barley,
54
and black sorghum.
55
Our
diverse association panel contained a wide range of
proanthocyanidin and 3-deoxyanthocyanidin concentrations,
and this genetic variation may be useful in breeding programs
to produce high polyphenol specialty varieties. Bicolor
sorghums had the highest mean proanthocyanidin concen-
trations, but their grain weight is signicantly less (20% less)
than that of non-bicolor sorghums (P<10
9). Combined with
low yield potential, the small grain size makes it dicult to use
bicolor race sorghums in a grain sorghum breeding program,
but may still be of interest to breeders wanting to produce
specialty varieties. In addition to bicolor sorghums, caudatum
and guinea-caudatum sorghums also had high mean proantho-
cyanidin concentrations, and are promising sources for
increasing proanthocyanidin concentrations in sorghum. In
particular, among the caudatum and guinea-caudatum sor-
ghums, caudatum sorghums from tropical climates such as
Uganda had the highest mean proanthocyanidin concentra-
tions, so they may be good material for breeding high
polyphenol sorghums. While bicolor-durra and guinea sor-
ghums had the highest mean 3-deoxyanthocyanidin concen-
trations, the dierence among all the races was not signicant,
so it may be more important to simply identify unique
genotypes across the sorghum collection. Chemical analysis is
underway on the samples that were outside of the NIRS
calibration curves, and true biological outliers may open up new
avenues for future work on sorghum varieties with extreme
polyphenol concentrations.
Increasing 3-deoxyanthocyanidin production may be chal-
lenging, since, as phytoalexins, they are not constitutively
expressed, but rather synthesized by plants under pathogen
attack.
15,16
We note in our comparison of 3-deoxyanthocyani-
din concentrations from duplicate samples that the dierence
between duplicates becomes larger for accessions with higher 3-
deoxyanthocyanidin concentrations. One possibility is that
there is greater technical variation in the 3-deoxyanthocyanidin
NIRS estimates, but Dykes et al.
35
demonstrated the same
correlation coecient between the NIRS-predicted values and
the values in the validation set for proanthocyanidins (r= 0.81)
and 3-deoxyanthocyanidins (r= 0.82). Therefore, we would not
expect to see dierences in accuracy of the NIRS predictions
for proanthocyanidins and 3-deoxyanthocyanidins in our study.
As this was a eld study, another possibility is that uncontrolled
environmental variation may have contributed to the dierence
between the duplicate samples. Accessions with the genetic
capability to produce grain 3-deoxyanthocyanidins may be
producing low or high 3-deoxyanthocyanidin concentrations
depending on the exposure to inducing agents on a given
panicle. Controlled inoculation studies are needed to further
explore this possibility.
23
The spreader gene is a promising target for increasing grain
proanthocyanidin concentrations, and a previous report using a
small number of varieties has shown higher proanthocyanidin
Figure 9. Simplied scheme of avonoid biosynthetic pathway in sorghum grain with candidate genes noted. Enzyme abbreviations are in uppercase
letters, while gene abbreviations are in italics. Question marks depict unknown steps. Chalcone synthase (CHS), chalcone-avanone isomerase
(CHI), avanone 3-hydroxylase (F3H), avanone 3-hydroxylase (F3H), dihydroavonol-4-reductase (DFR), anthocyanidin synthase (ANS),
anthocyanidin reductase (ANR), leucoanthocyanidin reductase (LAR); MYB-bHLH-WD40 (MBW).
Journal of Agricultural and Food Chemistry Article
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concentrations in varieties with a functional spreader.
56
Given
that three peak SNP associations in the brown grain GWAS
were near putative MATE transporter TT12 homologues, we
propose that the spreader gene may be a MATE transporter. A
biparental mapping population segregating the spreader gene
would be needed to conrm this hypothesis. To get a sense of
the eect these loci may have on proanthocyanidin
concentrations, we compared concentrations of each allele in
proanthocyanidin-containing accessions. There was a 1.8-fold
(S3_63633634, P= 0.04), a 1.5-fold (S2_69656067, P=
0.0003), and a 1.7-fold (S8_52906014, P= 0.0002) dierence
between accessions carrying the high concentration alleles and
low concentration alleles. When the three polymorphisms are
considered together, accessions with all three high-alleles
(S2_69656067 = A, S3_63633634 = A, S8_52906014 =
G) have 1.7- to 2.7-fold higher proanthocyanidin concen-
trations (P=10
8), consistent with an additive eect more than
doubling the concentration of proanthocyanidins in sorghum
grain.
Appearance of grain color is predominantly due to
polyphenols, but can also be inuenced by endosperm color
and grain weathering. Taken in total, the color classes used for
our analysis represent general groups and are not denitive
descriptors of any specic trait. For example, it is possible to
have a sorghum classied as brown that does not have a testa
layer, as well to have a sorghum classied as white that has a
testa layer (see Figure 1). However, our results support the use
of visual categorization of grain color as a simple assessment of
polyphenol concentrations in crop improvement programs;
brown grain has signicantly higher proanthocyanidin concen-
trations than nonbrown, red grain has signicantly higher 3-
deoxyanthocyanidin concentrations than nonred, and white
grain has signicantly lower concentrations of these poly-
phenols than nonwhite. Additionally, the genetic architecture of
grain color reects, to an extent, that of the polyphenols with
which they are associated. For instance, the red grain GWAS
and the 3-deoxyanthocyanidin GWAS produced similar
association peaks on chromosome 4 (54 Mb), which may
map to the sorghum Pr1 orthologue, and chromosome 3 (72
Mb), which colocalizes with putative homologues of ANS,F3H,
and TT19. The brown grain GWAS and the proanthocyanidin-
containing GWAS produced similar association peaks on
chromosome 6 (57 Mb) near a priori candidate TT16,a
key regulatory protein in the proanthocyanidin branch of the
avonoid pathway. Overall, to increase sorghum proanthocya-
nidin and 3-deoxyanthocyanidin concentrations quantitatively,
there are many associated alleles available, but none of them
have large eect. This survey of grain polyphenol variation in
sorghum germplasm and catalog of avonoid pathway-
associated loci contributes toward the goal of producing
sorghum crops that will contribute to marker-assisted breeding
of sorghum crops that will benet human health.
ASSOCIATED CONTENT
*
SSupporting Information
Proanthocyanidin concentration in accessions with Tannin1
and tan1-a (Figure S1), population structure of polyphenols
(Figure S2), GWAS for proanthocyanidin presence/absence
(Figures S3, S4), GWAS for grain color (Figures S5, S6),
GWAS for converted lines (Figure S7), avonoid pathway-
related genes (Table S1), signicant SNPs identied in each
GWAS (Tables S2S10), and avonoid pathway a priori
candidate gene list. This material is available free of charge via
the Internet at http://pubs.acs.org.
AUTHOR INFORMATION
Corresponding Author
*Tel: 773-603-2897. Fax: 785-532-6094. E-mail: rhodesdh@
email.sc.edu.
Present Address
Institute for Genomic Diversity, Cornell University, Ithaca,
NY, 14853, USA.
Notes
The authors declare no competing nancial interest.
ACKNOWLEDGMENTS
We thank Zach Brenton for considerable help with sample
preparation, and Scott Bean, Prini Gadgil, Tom Herald, Amy
Murphy, and the reviewers for their thoughtful comments.
ABBREVIATIONS USED
abs, absorbance; ANOVA, analysis of variance; CE, catechin
equivalents; GAE, gallic acid equivalent; GBS, genotyping-by-
sequencing; GAPIT, Genome Association and Prediction
Integrated Tool; GLM, general linear model; GRIN,
Germplasm Resources Information Network; GWAS, ge-
nome-wide association study; K, kinship; MLM, mixed linear
model; NIRS, near-infrared spectroscopy; SAP, Sorghum
Association Panel; SNP, single-nucleotide polymorphism
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... GWAS screens numerous genetic markers across the entire genome of unrelated individuals in a population to identify those associated with a trait of interest (Yang et al. 2015;Alqudah et al. 2020). GWAS on sorghum grain composition has identified QTLs for polyphenol (Rhodes et al. 2014), mineral traits (Shakoor et al. 2016) and starch, fat and protein (Boyles et al. 2017). Grain quality is an essential trait for sorghum breeding programmes, and there has been increasing interest in identifying genetic markers associated with grain quality traits through GWAS and candidate gene approaches (Rhodes et al. 2014;Boyles et al. 2017;Ayalew et al. 2022;Kamal et al. 2023). ...
... GWAS on sorghum grain composition has identified QTLs for polyphenol (Rhodes et al. 2014), mineral traits (Shakoor et al. 2016) and starch, fat and protein (Boyles et al. 2017). Grain quality is an essential trait for sorghum breeding programmes, and there has been increasing interest in identifying genetic markers associated with grain quality traits through GWAS and candidate gene approaches (Rhodes et al. 2014;Boyles et al. 2017;Ayalew et al. 2022;Kamal et al. 2023). The candidate gene approach, on the other hand, involves selecting genes known to be involved in the pathway or function of a trait of interest and screening for genetic variation within those genes (Alenezi et al. 2023). ...
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... Color data from visual classification of three seeds per genotype was sufficient to identify broad genomic intervals corresponding to tan1 and Y using data from the sorghum association panel, a widely used diversity panel typically consisting of 350-400 sorghum genotypes (Casa et al., 2008). Direct quantification of condensed tannins in the same sorghum association panel was able to localize the position of tan1 more precisely (Rhodes et al., 2014). An analysis conducted with a much larger population of 1,386 sorghum genotypes and human visual assessment of seed color identified two signals, one corresponding to the Y locus and the other which may correspond to the Z locus as the same genomic interval was associated with variation in both seed color and mesocarp thickness (Hu et al., 2019). ...
... F3'H and CHI catalyze reactions at metabolic junctions which can lead to different pigmentation (Falcone Ferreyra et al., 2012;Grotewold et al., 1994). The position of this chromosome 3 GWAS signal and associated candidate gene is somewhat consistent with the reported approximate localization of the uncloned sorghum locus R which acts downstream of y1 (Mace & Jordan, 2010;Rhodes et al., 2014). However, in the absence of strong statistical evidence supporting an epistatic interaction between this marker-trait association on chromosome 3 and y1 for seed color, as well as inconsistent location of signal with previously reported associations, the location does not yet represent strong evidence for having identified the location of R. ...
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Seed color is a complex phenotype linked to both the impact of grains on human health and consumer acceptance of new crop varieties. Today, seed color is often quantified via qualitative human assessment or biochemical assays for specific colored metabolites. Imaging‐based approaches have the potential to be more quantitative than human scoring while being lower cost than biochemical assays. We assessed the feasibility of employing image analysis tools trained on rice (Oryza sativa) or wheat (Triticum aestivum) seeds to quantify seed color in sorghum (Sorghum bicolor) using a dataset of >>>1,500 images. Quantitative measurements of seed color from images were substantially more consistent across biological replicates than human assessment. Genome‐wide association studies conducted using color phenotypes for 682 sorghum genotypes identified more signals near known seed color genes in sorghum with stronger support than manually scored seed color for the same experiment. Previously unreported genomic intervals linked to variation in seed color in our study co‐localized with a gene encoding an enzyme in the biosynthetic pathway leading to anthocyanins, tannins, and phlobaphenes—colored metabolites in sorghum seeds—and with the sorghum ortholog of a transcription factor shown to regulate several enzymes in the same pathway in rice. The cross‐species transferability of image analysis tools, without the retraining, may aid efforts to develop higher value and health‐promoting crop varieties in sorghum and other specialty and orphan grain crops.
... Due to the ability of sorghum phenolic extracts to prevent chronic human diseases such as obesity, cancer, and Alzheimer's Disease [18,19], studies have utilized techniques such as Genome-Wide Association Studies (GWAS) to explore quantitative trait locus and key genes involved in sorghum polyphenol synthesis [20]. Sorghum can activate or modify certain small molecule metabolites in response to biotic or abiotic stress, and metabolomics and other technical methods can provide important information on related metabolic pathways [21,22]. ...
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This study aims to utilize metabolomics to elucidate the key metabolites and metabolic pathways contributing to the quality differences among the grains of ‘hongyingzi’ (hyz) sorghum and four other varieties naming ‘jinuoliang’ (Jinl), ‘jinnuoliang’ (Jinnl), ‘lunuohong’ (lnh) and ‘liaoza 19’ (lz19). By analyzing the metabolomics data of the five sorghum varieties, we identified a total of 384 differentially accumulated metabolites (DAMs). Among these, flavonoids, phenolic acids and lipid compounds were the most abundant, exhibiting varying degrees of upregulation and downregulation. Compared to the four cultivars, the hyz sorghum differently exhibited 78, 40, 59 and 63 flavonoids; 29, 54, 30 and 30 phenolic acids; and 9, 27, 26 and 20 lipids, respectively. Multiple comparisons among the five sorghum varieties also identified 38 common DAMs, and the flavonoid pathway is particularly significant in KEGG enrichment. Additionally, as a high tannin content variety, correlation analysis revealed in sorghum that ellagic acid-4-O-glucoside plays a central role in tannin synthesis. These findings would highlight significant differences in the metabolomic profiles between hyz and the control varieties, providing valuable insights for the exploration of key genes involved in flavonoid metabolism and biosynthesis pathways in sorghum seeds.
... In sorghum, near-infrared spectroscopy was applied to quantify total phenolic content, procyanidins, and 3-deoxyanthocyanins in grain samples from 381 accessions. That study identified novel QTLs linked to polyphenol synthesis, with some homologous to flavonoid genes in Zea mays and Arabidopsis (Pr1 in maize and TT16 in Arabidopsis) (Rhodes et al., 2014). Visible light and fluorescence imaging have further facilitated genetic variation analyses of growth traits for crops like canola (Knoch et al., 2020) and Arabidopsis thaliana, for which GWASs identified 23 genes implicated in pathogen responses, including resistance to Botrytis infection and immunity to P. syringae effectors (Martel et al., 2020;Fordyce et al., 2018). ...
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In modern plant breeding, molecular markers have become indispensable tools, allowing the precise identification of genetic loci linked to key agronomic traits. These markers provide critical insight into the genetic architecture of crops, accelerating the selection of desirable traits for sustainable agriculture. This review focuses on the advancements in quantitative trait locus (QTL) mapping and genome-wide association studies (GWASs), highlighting their effective roles in identifying complex traits such as stress tolerance, yield, disease resistance, and nutrient efficiency. QTL mapping identifies the significant genetic regions linked to desired traits, while GWASs enhance precision using larger populations. The integration of high-throughput phenotyping has further improved the efficiency and accuracy of QTL research and GWASs, enabling precise trait analysis across diverse conditions. Additionally, next-generation sequencing, clustered regularly interspaced short palindromic repeats (CRISPR) technology, and transcriptomics have transformed these methods, offering profound insights into gene function and regulation. Single-cell RNA sequencing further enhances our understanding of plant responses at the cellular level, especially under environmental stress. Despite this progress, however, challenges persist in optimizing methods, refining training populations, and integrating these tools into breeding programs. Future studies must aim to enhance genetic prediction models, incorporate advanced molecular technologies, and refine functional markers to tackle the challenges of sustainable agriculture.
... Tannin has an antinutritional impact by decreasing the availability of proteins, carbohydrates, and minerals (De Morais Cardoso et al. 2016), despite its vital function in protecting grain against mold and bird predation. Sorghum polyphenols production has been linked to orthologues of maize Pr1 and arabidopsis transparent testa genes TT4, TT10, and TT16 (Rhodes et al. 2014;Rhodes et al. 2017a). Tannin1 (Tan1) encodes a WD40 repeat protein that regulates anthocyanin and pro-anthocyanin biosynthesis (Wu et al. 2012). ...
Chapter
This chapter offers a thorough examination of sorghum breeding in Asia, covering its history, distribution, and domestication. It addresses challenges in breeding, particularly cytoplasmic male sterility, and extensively explores the application of omics approaches such as transcriptomics, genomics, proteomics, metabolomics, and bioinformatics. Emphasis is placed on climate resilience, utilizing genomics to develop sorghum varieties adapted to biotic and abiotic stresses. We summarized biotic and abiotic stresses, along with biotechnological interventions to enhance important agronomic traits. This chapter also delves into modifying flowering, plant height, and the brown midrib structure’s implications for animal feed. Throughout, the significance of these tools and techniques in understanding sorghum genetics and aiding breeding programs is highlighted. In conclusion, we discussed addressing current challenges in sorghum breeding in Asia and advocating ongoing research and collaboration to ensure regional food security and sustainable agriculture. Overall, it provides a comprehensive, up-to-date account of sorghum breeding, showcasing genomics and biotechnology’s pivotal role in enhancing resilience and productivity.
... Genomic regions behind traits associated with abiotic and biotic stresses like tolerance to aluminium stress (Caniato et al. 2011;Hufnagel et al. 2018;Too et al. 2018), stalk rot resistance (Adeyanju et al. 2015), epicuticular wax (EW) formation (Elango et al. 2020b), environmental adaptative traits (Lasky et al. 2015), and agroclimatic adaptative traits (Morris et al. 2013) were successfully delineated in sorghum through GWAS. Apart from these, genomic regions for grain quality (Sukumaran et al. 2012), seed size (Zhang et al. 2015), and grain polyphenol concentrations (Rhodes et al. 2014) were also identified. Over the years, sorghum research community around the globe has developed various mapping panels; notable panels are the Sorghum Association Panel (SAP) (Casa et al. 2008), the minicore panel from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) (Upadhyaya et al. 2009), Bio-energy Association Panel (BAP) (Brenton et al. 2016), Queensland diversity panel (Mace et al. 2019), Nigerian diversity population (Maina et al. 2018), world core collection (Grenier et al. 2001), nested association mapping (NAM) panel (Bouchet et al. 2017), and CIRAD (the French agricultural research and international cooperation organization) landraces (Deu et al. 2006), of which most of the panels were sequenced and the high-density sequence reads are available for public use (Boyles et al. 2019). ...
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This chapter examines current trends in the U.S. grain industry.Many identity preservation (IP) grain systems have emerged recently,driven by a confluence of supply and demand factors. IP grainrequirements for specific production protocols, marketing channels, andquality assurance depend on whether the crops are trait-specific, non-GM(genetically modified), organic, or pharmaceutical. Cost structures varyaccording to the relative importance of segregation and risk management.High information management, greater market coordination, and frequentreliance on contracts characterize IP grains. IP grain markets are alsoinherently riskier, with volatile supply, inelastic demand, and fluctuatingprice premiums. Increasing grain differentiation is altering the marketingstructure of the U.S. grain industry and creating possible roles forgovernment policy, particularly in market facilitation, standard setting,and regulations affecting food safety and biosecurity.
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Sorghum, an African grass related to sugar cane and maize, is grown for food, feed, fibre and fuel. We present an initial analysis of the approximately 730-megabase Sorghum bicolor (L.) Moench genome, placing approximately 98% of genes in their chromosomal context using whole-genome shotgun sequence validated by genetic, physical and syntenic information. Genetic recombination is largely confined to about one-third of the sorghum genome with gene order and density similar to those of rice. Retrotransposon accumulation in recombinationally recalcitrant heterochromatin explains the approximately 75% larger genome size of sorghum compared with rice. Although gene and repetitive DNA distributions have been preserved since palaeopolyploidization approximately 70 million years ago, most duplicated gene sets lost one member before the sorghum-rice divergence. Concerted evolution makes one duplicated chromosomal segment appear to be only a few million years old. About 24% of genes are grass-specific and 7% are sorghum-specific. Recent gene and microRNA duplications may contribute to sorghum's drought tolerance.
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The TRANSPARENT TESTA8 ( TT8 ) locus is involved in the regulation of flavonoid biosynthesis in Arabidopsis. The tt8-3 allele was isolated from a T-DNA‐mutagenized Arabidopsis collection and found to be tagged by an integrative molecule, thus permitting the cloning and sequencing of the TT8 gene. TT8 identity was confirmed by complementation of tt8-3 and sequence analysis of an additional allele. The TT8 gene encodes a protein that displays a basic helix-loophelix at its C terminus and represents an Arabidopsis ortholog of the maize R transcription factors. The TT8 transcript is present in developing siliques and in young seedlings. The TT8 protein is required for normal expression of two flavonoid late biosynthetic genes, namely, DIHYDROFLAVONOL 4-REDUCTASE ( DFR ) and BANYULS ( BAN ), in Arabidopsis siliques. Interestingly, TRANSPARENT TESTA GLABRA1 ( TTG1 ) and TT2 genes also control the expression of DFR and BAN genes. Our results suggest that the TT8, TTG1, and TT2 proteins may interact to control flavonoid metabolism in the Arabidopsis seed coat.
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The reported high phenolic levels in sorghum have led an interest from sorghum breeding programs in developing and identifying germplasms with high phenolic levels, which require screening a large number of samples to find those with the highest levels. Since wet chemistry screening methods are slow, expensive, and destructive, the use of NIR calibration curves could be an alternative. The objectives of this project were to determine the variation range in total phenols, condensed tannins, and 3-deoxyanthocyanidin levels in a diverse set of sorghum genotypes and to assess the predictive value of NIR curves to estimate these compounds in sorghum. A calibration curve to estimate each compound was developed and validated with an independent validation set. Calibration curve correlations for total phenols, condensed tannins, and 3-deoxyanthocyanidins were r = 0.98, 0.97, and 0.99, respectively. Correlations between NIR-predicted values and reference values in the validation set were significant for total phenols (r = 0.93), condensed tannins (r = 0.81) and 3-deoxyanthocyanidin (r = 0.82). These indicated that sufficient variation for these compounds existed within sorghum and that NIR calibration curves could be used to rapidly and non-destructively predict total phenols, condensed tannins, and 3-deoxyanthocyanidins concentrations in whole grain sorghum.
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Black sorghums possess very high levels of the rare 3-deoxyanthocyanidins which can be used as natural food colorants with health benefits. However, these sorghum genotypes have undesirable agronomic properties (i.e. high height, low yield, increased weathering). Black sorghum hybrids with improved agronomic properties were developed and their phenolic profiles and antioxidant activity were compared with black sorghum lines. Black sorghum hybrids were significantly lighter in colour than the lines (P < 0.001). All hybrids had a pigmented testa which was indicated by the presence of condensed tannins, which significantly increased total phenol levels and antioxidant activity. The 3-deoxyanthocyanidin, flavan-4-ol, and flavone levels were significantly lower in the hybrids (P < 0.001) and were strongly correlated to pericarp colour (P < 0.001). Flavanone levels were not significantly different among the lines and hybrids (P > 0.05) and pericarp colour did not affect their levels (P > 0.05). Even though the 3-deoxyanthocyanidin levels were lower in black sorghum hybrids than in the lines, the presence of condensed tannins in the hybrids significantly increased their antioxidant activity. Since 3-deoxyanthocanidin levels were dependent on pericarp colour, hybrids with increased blackness intensity should be developed to increase the stable 3-deoxyanthocyanidins.