Abstract
Genotype × environment interactions (GEIs) should play an important
role in the selection of suitable germplasm in breeding programmes. We
here assessed GEI effects on pearl millet (Pennisetum glaucum L.)
genotypes, selected to possess a high concentration of slowly
digestible starch (SDS) and resistant starch (RS) in their grains.
Entries were grown in a randomized complete block design with three
replications at locations in Bawku-Ghana, Sadore-Niger, Bamako-Mali,
Konni-Nigeria, and Gampella-Burkina Faso across West Africa. Harvested
grains from these locations were metabolomically profiled using flow
injection ionization-high-resolution mass spectrometry (FIE-HRMS). A
total of 3144 mass features (m/z) (1560 negative ion mode and 1584
positive ion mode) were detected, of which, 475 m/z were linked to
metabolites be involved in starch, antioxidant and lipid biosynthesis,
and vitamin metabolism. Combined ANOVA revealed that the GEI was
significantly evident for 54 health-benefiting metabolites, many
associated with sugar, especially galactose, metabolism. Additive main
effects and multiplicative interaction (AMMI) analysis examined
genotype variation and GEI effects, which, when combined with principal
component analysis (PCA), found that m/z 171.14864 (positive
ionisation, propenyl heptanoate) accounted for 89% of the GEI variation
along PC1. The AMMI-based stability parameter (ASTAB), modified AMMI
stability value (MASV), and modified AMMI stability index (MASI) were
then applied to identify stable and high-performing genotypes for all
the health-benefiting metabolites. Similarly, the
best-linear-unbiased-prediction (BLUP)-based stability estimation was
also performed using the harmonic mean of genotypic values (HMGV),
relative performance of genotypic values (RPGV), and harmonic mean of
relative performance of genotypic values (HMRPGV), to identify genotype
rankings across multiple environments. The multi-trait stability index
(MTSI) was calculated and found that the genotypes G1 (ICMH-177111) and
G24 (ICMX-207137) were the most stable and were the best mean
performers across 52 health-benefiting metabolic traits. These findings
demonstrate the potential of G × E assessments on the delivery of
health-benefiting metabolite-rich grains in future varieties and
hybrids of pearl millet.
Keywords: pearl millet, slowly digestible starch, resistant starch,
nutritional traits, genotype by environment interaction, grain
metabolites
1. Introduction
Pearl millet (Pennisetum glaucum L.) is an important staple cereal crop
that is grown on more than 26 million hectares to produce more than 29
tons of grains worldwide. It is mostly grown in the arid and semi-arid
tropical regions of Africa (17 million ha) and Asia (10 million ha),
where drought, heat, and low-fertility soils can lead to losses in crop
production [[32]1,[33]2,[34]3]. Pearl millet is a climate-resilient and
nutrient-rich cereal grain possessing essential nutrients such as
starch, antioxidants, folate, and essential amino acids, which can
enrich the diets of the populations living in emerging economies
[[35]4,[36]5,[37]6]. Pearl millet also has non-glutinous and non-acid
forming properties and is easy to digest. Moreover, pearl millet grains
are rich in nutritionally important minerals such as iron, calcium,
zinc, magnesium, phosphorous, and potassium. Pearl millet grains are
also a good source of dietary fibre and several vitamins (β-carotene,
niacin, vitamin B6, and folic acid). Millets are also rich in
polyphenols, tannins, and phytosterols and are a good source of
antioxidants.
Recently, pearl millet entries possessing a high concentration of
slowly digestible starch (SDS), resistant starch (RS), antioxidants,
and vitamins were identified by Yadav et al. [[38]7] in the world
collection of pearl millet germplasm set known as the Pearl Millet
Inbred Germplasm Association Panel (PMiGAP). The current study was
planned to gain an understanding of the metabolites available in these
genotypes and how their concentrations are affected by changes in
environmental conditions. Multivariate statistical analysis of
metabolites can provide a comprehensive overview of the relationships
between metabolite components in pearl millet when grown under
different environmental conditions. Crucially, such a screening
strategy could be used to identify superior and stable pearl millet
genotypes with enhanced nutritional components across environments.
In the present study, we investigated metabolite diversity in eleven
pearl millet hybrids known to possess high concentrations of SDS and RS
in their grains. The study focused on comparative analysis of
health-benefiting metabolites in the harvested grains of these entries
after growing them at five different environmental locations across
West Africa, but under unified agronomical practices. Hybrids were
chosen for their high RS and SDS properties known to contribute to the
low glycaemic index (GI) of pearl millet.
2. Materials and Methods
2.1. Experimental Material, Field Trials, and Data Collection
Eleven pearl millet genotypes were used in this study, viz.
ICMX-207076, ICMX-207094, ICMX-207136, ICMX-207137, ICMX-207171,
ICMX-207181, ICMX-207183, ICMX-207190, ICMH-177111, ICMA177002, and
ICMA177003 ([39]Supplementary Table S1). The field trials were
conducted with these genotypes at five locations, Bawku-Ghana,
Sandore-Niger, Bamako-Mali, Kano-Nigeria, and Gampella-Burkina Faso in
West Africa. Geographic co-ordinates (latitude, longitude, and
altitude), type of soil, and weather data during the cropping season
(total rainfall, minimum and maximum temperature) for each location are
given in [40]Supplementary Table S2. The experiment used 8 hybrids
along with the parental genotypes that were sown under field conditions
in a randomized complete block design (RCBD) with three replications
during the 2018 rainy season. Each genotype was sown in 2-row plots of
3 m in length, with a 0.75 m distance between the rows and 0.20 m
in-between plants. Test plots were thinned at 12 days after seedling
emergence (DAE) to one plant per hill with a plant population of 30 per
plot. A basal dose of fertilizer, 100 kg/ha (NPK), was applied to the
fields at the land preparation stage. Micro-dosing of the crop with
urea at 2 g per hill was carried out at 30 DAE.
2.2. Metabolite Fingerprinting
Metabolite extraction was performed from frozen milled grain samples of
eleven accessions of pearl millet. Three biological replicates were
used to minimize any measurement errors. Pearl millet grains in an
amount of 50 mg ± 1 mg were flash frozen in liquid N[2] and homogenized
using a ball mill. The samples were placed on ice, and 1 mL of
extraction solution (chloro-form/methanol/water, 1:2.5:1, v/v/v) was
added followed by incubation at 4 °C for 15 min with constant stirring.
The aqueous supernatants were collected after centrifugation at 21,000×
g for 5 min at 4 °C and returned to the ice. Then, 100 µL of the
extracted samples was analysed using flow infusion electrospray
ionization high-resolution mass spectrometry (FIE-HRMS). The four
independent replicates were considered for each sample, and metabolite
fingerprinting was performed by FIE-HRMS using a Q Exactive Plus Hybrid
Quadrupole Orbitrap Mass Analyser with an Accela UHPLC system (Thermo
Fisher Scientific, Bremen, Germany). The sample was injected into the
capillary column in a randomized order, and the m/z (mass ion) values
were recorded in both positive and negative ionization modes as
described by Skalska et al. [[41]8] and Yadav et al. [[42]9]. The peak
area of each metabolite was analysed using the MetaboWorkflows R
package version 0.9.5
([43]https://jasenfinch.github.io/metaboWorkflows, accessed on 18–25
March 2022) for spectral processing, data pre-treatment, and quality
control. Statistical analyses for metabolites’ diversity were performed
using MetaboAnalystR ([44]http://www.metaboanalyst.ca, accessed on
18–25 March 2022), as described in Yadav et al. [[45]9]. Briefly,
metabolite m/z values were normalized as a percentage of total ion
count, and values were transformed to log[10] and Pareto scaling.
Significant metabolites were identified by cross-validated p-values
(adjusted using Bonferroni correction to reduce false positives), based
on one-way analysis of variance (ANOVA), setting the significance at p
< 0.05. The functional pathway of each untargeted metabolite was
identified by applying the functional analysis module of MetaboAnalyst
5.0 using the reference library: Oryza sativa. The selection of
metabolites contributing to health-benefiting traits was as described
by Yadav et al. [[46]9].
2.3. Statistical Analysis for Phenotypic Variance and Heritability
The phenotypic variance was analysed using a mixed linear model
implemented in the LME4 R-package that uses REML to estimate the
variance components. ANOVAs were performed on replicated data observed
in different environments (five locations). The analysis was performed
for individual environment, as well as combined across environments, in
a random complete block design (RCBD). In combined analysis across
environments, the genotype interactions with the environments were
considered as random effects [[47]10,[48]11,[49]12].
The broad-sense heritability was calculated for each health-benefiting
metabolite in an individual environment as:
[MATH:
H2=σ2gσ2g+σ2ε/ηRep :MATH]
, where σ^2[g] and σ^2[ε] are the genotype and error variance
components, respectively, and
[MATH: η :MATH]
Rep denotes for replicates. The combined heritability analyses were
calculated as:
[MATH:
H2=σ2gσ2g+σ2ge/ηLoc+σ2ε/(ηLoc × ηRep) :MATH]
in multiple environment factors. Here, σ^2[ge] is the GEI variance
component and
[MATH: η :MATH]
Loc is the number of environments [[50]13,[51]14].
The REML model was applied for observation of the best linear unbiased
predictors (BLUPs) for each genotype, thus influencing the effect of
the adjacent rows [[52]15].
2.4. Additive Main Effects and Multiplicative Interaction Model
The additive main effects and multiplicative interaction (AMMI) model
was implemented in the metan r package [[53]15], which is the most
advanced model for additive main effects and multiplicative analysis.
This involves fitting an additive model (ANOVA) for general means, G
and E means, and a multiplicative model (i.e., PCA) for the residuals
of an additive model or a genotype, environment, and their interactions
[[54]16]. Patterson and Williams [[55]17] implemented a model to
observe the response variable for the ith genotype in the jth
environment (yij) calculated as:
[MATH: yij=μ+αi+
τj+∑k=1pλk
aik tjk+
ρij+εij :MATH]
, where λ[k] is the singular value for the kth interaction principal
component axis (IPCA); aik is the ith element of the kth eigenvector;
t[jk] is the jth element of the kth eigenvector. A residual ρij
remains, if not all p IPCA are used, where p ≤ min (g − 1; e − 1). An
additive main effects model along with a multiplicative model (i.e.,
PCA) explained the genotype environment interaction effect of each
genotype and partitioning of their interaction effects in response to
individual environments.
2.5. Genotype plus Genotype-by-Environment Model on Multi-Environment Factor
GGE modelling strategies are based on multi-environment trial data that
include GEI variation as a combination of the genotypic main effect and
the GEI as a sum of the multiplicative response (i.e., PCA). GGE
biplots provide graphical depictions that allow the evaluation of
genotype performance in different environments. This allows the
association between specific environments and between genotypes to be
explained. GGE biplots were generated using Genstat (18th edition)
[[56]18] by selecting the convex hull around genotype scores option to
mark the convex hull around the genotype scores, the sectors to divide
the biplot into segments, the mega environment to draw an ellipse round
those environments that share the same sector, and linking the
environment based on its scores with the origin.
2.6. Estimation of AMMI- and BLUP-Based Stability
Several AMMI-based stability methods were implemented in the Metan r
package by [[57]15]. The AMMI-based stability parameter
[MATH: (ASTAB=∑<
/mo>n=0N′λn
γin2) :MATH]
by Rao and Prabhakaran [[58]19], Annicchiarico’s D parameter
[MATH:
Da=∑n=0N′(λn γin
)2 :MATH]
by Annicchiarico [[59]20], Zhang’s D parameter (
[MATH:
Dz=∑n=0N′λin2
:MATH]
) by Zhang et al. [[60]21], the AMMI stability index (
[MATH: ASI=[PC12×<
/mo>θ12]+[PC22×<
/mo>θ22] :MATH]
) by Jambhulkar et al. [[61]22], the weighted average of absolute
scores
[MATH: (WAAS<
/mi>i=∑k=1N|IPCAik ×θk/∑k=<
mn>1pθk) :MATH]
by Olivoto et al. [[62]23], the sums of the averages of the squared
eigenvector values
[MATH: (EV=
∑n=1N′γi
n2N′<
/mo>) :MATH]
by Zobel [[63]24], the stability measure based on the fit AMMI model
[MATH: (FA=
∑n=1N′λn2γn2) :MATH]
by Raju [[64]25], the modified AMMI stability index
[MATH: (MASI=
∑n=1
N′(PCn2×θn2) :MATH]
by Ajay et al. [[65]26], and Spearman’s rank correlations among all 13
stability values were derived.
The best-linear-unbiased-prediction (BLUP) based stability estimation
method was implemented in the Metan r package for the selection of
stable genotypes with the best performance in a mixed effects model
structure. Colombari et al. [[66]27] (2013) demonstrated the use of
three BLUP-based indexes for the selection of genotypes for performance
and stability. The first methods involve the prediction of the harmonic
mean of genotypic values
[MATH: (HMGV<
/mi>i=E∑j=1E <
/mo>1Gvij <
/mo>),
:MATH]
the second RPGV index
[MATH: (RPGV<
/mi>i=1E∑j=1E<
/munderover>Gvij
/μj ), :MATH]
and the HMRPGV index
[MATH: (HMRPGVi=E∑j=1E<
/mi>1Gvij/μj) :MATH]
. E represents the number of environments; Gv[ij] is the genotypic
value (BLUP) for the ith genotype in the jth environment.
2.7. Multi-Trait Stability Analysis
The multi-trait stability index (MTSI) by Olivoto and Lúcio [[67]15]
was implemented in the Metan r package to predict the stability and
mean performance across all the traits together. The MTSI was estimated
using the WAASBY index based on the superiority index based on the
mixed effects model
[MATH:
WAASBYi=(rGi×θY)+(rWi×θS)θY+θS<
/mrow> :MATH]
described by Olivoto et al. [[68]23].
3. Results
3.1. Metabolite Profiling
Eleven pearl millet genotypes were selected based on high RS and SDS
traits by Yadav et al. [[69]28]. The cultivars were grown in five
locations in West Africa (Bawku-Ghana, Sadore-Niger, Bamako-Mali,
Konni-Nigeria, Gampella-Burkina Faso), and metabolite content was
evaluated using flow infusion electrospray high-resolution mass
spectrometry (FIE-HRMS). A total of 3144 mass features (m/z) (1560
negative ion mode and 1584 positive ion mode) were assessed based on
the presence in at least three replicates of each genotype of pearl
millet. Metabolites were tentatively identified, and pathway enrichment
factor analysis using the mummichog module of Metaboanalyst
([70]https://shuzhao-li.github.io/mummichog.org, accessed on 18–25
March 2022) linked 475 mass features to metabolites linked with human
health benefits ([71]Supplementary Table S3).
Out of the 475 metabolites, 97 metabolites (65 negative ion and 32
positive ion modes) were targeted as being involved in “starch and
sucrose”, “galactose”, and “fructose and mannose” metabolism through
pathway enrichment analysis. Similarly, 115 mass features (63 negative
ions and 52 positive ion mode) were associated with antioxidant
biosynthesis pathways (such as anthocyanins, carotenoids, glutathione,
flavonoids, flavones, and flavonols) and 131 metabolites (73 negative
ions and 58 positive ion mode). Other important pathways were linked to
vitamin metabolism (ascorbate, biotin, riboflavin (vitamin B2)),
pyridoxine (vitamin B6), folate (vitamin B9), lipid metabolism (“fatty
acid degradation”, “fatty acid biosynthesis”, biosynthesis of
unsaturated fatty acids), nitrogen metabolism, inositol phosphate, and
zeatin biosynthesis ([72]Supplementary Table S3). Two-way ANOVA allowed
the ranking of 25 metabolites that showed the most variation across the
locations ([73]Figure 1A). Pathway enrichment and impact assessments
indicated the importance of galactose metabolism (p = 2.1874 × 10^−4,
FDR = 0.02078) ([74]Figure 1B). These preliminary assessments indicated
the existence of G × E effects that required further assessment.
Figure 1.
[75]Figure 1
[76]Open in a new tab
(A) Heatmap visualization of the mean performance of the 25
health-benefiting metabolites identified across five locations in pearl
millet lines. Each value represents the normalized (median-centred and
log[10]-transformed) mean of three biological replicates, with red and
blue colours denoting relatively high and low intensities
(Loc1-Bawku-Ghana, Loc2-Sadore-Niger, Loc3-Bamako-Mali,
Loc4-Konni-Nigeria, Loc5-Gampella-Burkina Faso). (B) Biochemical
pathway enrichment assessments of the metabolites shown in (A). The
circle sizes and deeper colours indicate increasingly significantly and
biochemical important pathways.
3.2. ANOVA for Phenotypic Variance
The 475 metabolites were subjected to a combined ANOVA analysis for
significance at p < 0.05 for both factors (genotypes (G) and
environments (E)) and GEI (genotype and environment interaction) to
define multi-factor responses in these genotypes in the five different
environmental conditions. Combined analyses of variance across the
environments suggested that genotypic, environmental, and interactions
were significant for 52 health-benefiting metabolites
([77]Supplementary Table S4). Of these, 13 metabolites (10 negative
ions and 3 positive ion mode) that were involved in starch and sucrose
metabolism showed significant (G), (E), and (GEI) differences.
Similarly, 13 mass features (7 negative ions and 6 positive ion mode)
associated with antioxidant biosynthesis pathways, 21 metabolites (10
negative ions and 11 positive ion mode) with vitamin metabolism, and 7
metabolites (3 negative ions and 4 positive ion mode) linked with lipid
biosynthesis showed that genotype, environment, and their interactions
were highly significant at p < 0.001.
The high broad-sense heritability observed for the 52 metabolites
indicated that the genotypic differences detected are primarily due to
genetic effects. The current analysis showed that m/z 164.04323
(negative ionization, tentatively identified as the vitamin 6
metabolite, 4-pyridoxolactone) was strongly heritable (>0.88) across
five environments, whereas broad-sense heritability for m/z 164.04323
was higher (>0.87) in an individual environment (Sadore, Niger).
However, when assessed on the basis of a pooled environment, a
partitioning of the GEI component lowered the heritability for both
traits across environments ([78]Supplementary Figure S1).
3.3. Additive Main Effects and Multiplicative Interaction Analysis
At the same time as the ANOVA based on an additive model, which
illustrates the effect of the source of variation, the AMMI model
emphasizes the pattern of genotypes (G) and/or environments (E) and
their interactions. AMMI-based statistical analysis for 54
health-benefiting metabolites showed significant variation within the
eleven genotypes across the five environments ([79]Supplementary Table
S5). AMMI-based combined ANOVA revealed that environment, genotype, and
GEI showed significant variation at 0.1% (p < 0.001) for 40
metabolites, whereas 9 metabolites showed non-significant genotype and
environment interactions ([80]Supplementary Table S5). The G × E, as
well as GEI variations were significant at p < 0.001 for most of the
metabolites. AMMI-based analysis revealed that GEI had major
contributions from the first principal components (PCs), which ranged
from 39.9 to 91.2% for health-benefiting metabolites. One m/z (positive
ionisation, 171.14864, tentatively identified as the fatty acid ester,
propenyl heptanoate) showed the maximum variance with the first PC
(which explained 91.2% of the total GEI), while the second, third, and
fourth PCs contributed 7.2, 1.0, and 0.7%, respectively
([81]Supplementary Table S5). Genotype contributed 18.38% to the total
variation of m/z p171.14864, whereas the environment contributed major
variations (33.07%), and GEI contributed 4.84%. Compared to other mass
features, PC1 for m/z 380.1561 (negative ionisation; tentatively
identified as cis-zeatin-9-N-glucoside) explained the least variance at
only 39.9% of the total GEI, while the second, third, and fourth PCs
contributed 26.9, 18.4, and 14.9%, respectively. Genotypic variance
explained 13.70% for m/z 380.1561; however, the environment and GEI
contributed about 15.78 and 2.34%, respectively.
AMMI1 biplots were generated to explain the interactive correlation
between genotypes and environments for the 54 metabolites
([82]Supplementary Figure S2a–e). The analyses showed distinct patterns
amongst genotypes interacting across the environments for m/z 171.04079
(negative ionisation), tentatively identified as luteone 7-glucoside,
involved in the antioxidant pathway, and m/z 539.13831 (negative
ionisation), tentatively identified as raffinose and involved in starch
metabolism. Similar environmental effects for m/z 285.04037 (negative
ionisation, tentatively identified as 7, 8, 3’,
4’-tetrahydroxyisoflavone) were observed except with E2 (Sadore-Niger),
which had a distinct effect on genotype adaptation ([83]Figure 2). For
m/z 285.04037, E2 (Sadore-Niger) and E4 (Konni-Nigeria) were the
furthest from the biplot origin, which indicated strong interaction
influences for genotype adaption. E1 (Bawku-Ghana), E3 (Bamako-Mali),
and E5 (Gampella-Burkina Faso) were closest to the biplot origin point
and had shorter vectors, indicating weak interaction with genotype
adaptation. The AMMI2 biplot described the performance of the entries
and their adaptation to a specific environmental condition ([84]Figure
2).
Figure 2.
[85]Figure 2
[86]Open in a new tab
Additive main effects and multiplicative interaction (AMMI) for
metabolites linked to antioxidant (n171.04079), starch (n539.13831),
and vitamin metabolism (n285.04037) in 11 pearl millet genotypes
evaluated in five environments. AMMI-based biplot generated using AMMI1
biplot (trait vs. Principal Component 1 (PC1)), AMMI2 biplot (PC1 vs.
PC2), and AMMI2 biplot (PC1 vs. PC2) with the polygon option.
The AMMI2 biplot for the antioxidant metabolism metabolite (m/z
171.04079) showed that the genotype G24 (ICMX-207137) showed the best
performance in the E1 (Bawku-Ghana) environment. Genotype G33
(ICMX-207183) had the highest levels, as this showed good adaptation in
E5 (Gampella-Burkina Faso). In contrast, genotypes, namely G15
(ICMX-207076), G38 (ICMX-207190), and G1 (ICMH-177111), had higher m/z
285.04037 content, but negative adaptability across the environments
such as E2 (Sadore-Niger), E3 (Bamako-Mali), and E4 (Konni-Nigeria).
The AMMI2 biplot for the metabolite involved in starch metabolism (m/z
539.13831, raffinose) and genotypes such as G1 (ICMH-177111), G18
(ICMX-207094), G31 (ICMX-207171), and G32 (ICMX-207183) exhibited good
performance in E2 (Sadore-Niger) and E5 (Gampella-Burkina Faso).
However, with genotypes, G23 (ICMX-207136) and G3 (ICMX-207207) had
higher levels of m/z 539.13831, but had negative adaptabilities across
certain environments (E1-Bawku-Ghana, E3-Bamako-Mali,
E4-Konni-Nigeria). Similarly, for m/z 285.04037, genotypes such as G33
(ICMX-207183) had the highest degree of performance with high-ranking
adaptability in E2, but G38 (ICMX-207190) was associated with negative
adaptability in E3 (Bamako-Mali) and E5 (Gampella-Burkina Faso).
3.4. Genotype plus Genotype-by-Environment Biplots
Genotype-by-environment (GGE) biplot analysis of multiple traits was
used to compare multiple genotypes in multiple environments (five
environments) for the 54 health-benefiting metabolites
([87]Supplementary Figures S2 and S3). GGE-based biplots were generated
to connect environment scores with the origin and found favourable
environmental effects for m/z 171.04079 and m/z 285.04037. However,
contrastingly, unfavourable environmental effects were observed for the
starch-metabolism-related (m/z 539.13831) metabolite ([88]Figure 3a).
The GGE biplots also indicated that E2 (Sadore-Niger) and E3
(Bamako-Mali) showed a positive correlation for m/z 285.04037
metabolite levels, but a negative correlation with E1 (Bawku-Ghana) and
E2 (Sadore-Niger).
Figure 3.
[89]Figure 3
[90]Open in a new tab
Genotype-by-genotype × environment (GGE) biplot (a) to identify the
favourable environment for antioxidant-(n171.04079),
starch-(n539.13831), and vitamin-(n285.04037) linked metabolites and
(b) polygon view biplot for identification of stable genotypes for
antioxidant-(n171.04079), starch-(n539.13831), and vitamin-(n285.04037)
associated metabolites across the testing environments.
The GGE biplots also indicated the best-performing genotypes in each of
those environments by highlighting the convex hull around genotype
scores, sectors, and mega environments that share the same sector
([91]Supplementary Figure S4a,b). For example, the first two
interaction principal component axes (IPC) accounted for more than
88.51% of the G + GE sum of squares for m/z 285.04037. The biplot
showed four mega environments with E3 (Bamako-Mali) and E5
(Gampella-Burkina Faso) sharing a common mega environment and having a
similar effect on m/z n285.04037. Similarly, the first two IPCs
explained 75.28% (genotype–environment interaction) for the starch
metabolism (m/z 539.13831) and 95.27% of the GEI for m/z 285.04037. The
convex hull biplot exhibited two mega environments for m/z 539.13831 in
which E1 (Bawku-Ghana) and E3 (Bamako-Mali) share a common mega
environment and E2 (Sadore-Niger), E4 (Konni-Nigeria), and E5
(Gampella-Burkina Faso) are assigned together to have effects on the
starch metabolism (m/z 539.13831) metabolite ([92]Figure 3b). The
convex hull biplot exhibited two mega environments for m/z 285.04037 in
which E1 (Bawku-Ghana), E3 (Bamako-Mali), E4 (Konni-Nigeria), and E3
(Bamako-Mali) share a common mega environment and were different in E2
(Sadore-Niger).
3.5. Estimation of AMMI-Based Stability Indices
The AMMI-based stability was performed to rank the genotypes for their
performance across the different environments. Various AMMI-based
statistical models and parameters were applied, namely ASI, ASV, ASTAB,
AVAMGE, DA, DZ, EV, FA, MASI, MASV, SIPC, and Za ([93]Supplementary
Table S6). The genotype G51 (ICMX-207192) was the most stable, and G31
(ICMX-207171) ranked second, when using each AMMI-based stability
assessment except ASV and MASV for m/z 285.04037. For the
starch-metabolism-related compound (m/z 539.13831), G15 (ICMX-207076)
was ranked 1st and G51 (ICMX-207192) 2nd for their performance across
the various environments. Similarly, the genotype G32 (ICMX-207181)
ranked 1st and G51 (ICMX-207192) 2nd and showed the highest stability
for the vitamin-metabolism-related metabolite m/z 593.15131
([94]Supplementary Table S6).
3.6. Estimation of Best-Linear-Unbiased-Prediction-Based Stability Indices
The BLUP-based analysis was performed with BLUP-based statistical
models to estimate highly stable genotypes across multiple
environmental conditions ([95]Supplementary Figure S5). The ranking of
the genotypes was as follows: G15 (ICMX-207076) 1st, G32 (ICMX-207181)
2nd, G1 (ICMH-177111) 3rd, and G18 (ICMX-207094) 4th as highly stable
genotypes for m/z 171.04079 according to HMGV, RPGV, and HMRPGV, but
not the WASSB method. Similarly, genotypes ranked as 1st G32
(ICMX-207181), 2nd G31 (ICMX-207171), 3rd G38 (ICMX-207190), and 4th
G23 (ICMX-207136) were identified as highly stable genotypes for starch
metabolism m/z 539.13831 by HMGV, RPGV, and HMRPGV, but not the WAASB
method. For the vitamin-associated metabolite (m/z 593.15131), G32
(ICMX-207181) ranked 1st, G31 (ICMX-207171) 2nd, G38 (ICMX-207171) 3rd,
and G23 (ICMX-207136) 4th using HMGV, RPGV, and HMRPGV stability
predictions ([96]Supplementary Table S7).
3.7. Best-Performing and Highly Stable Entries
The MTSI was performed by the WAASBY index for genotype stability and
mean performance across the 54 health-benefiting metabolites. This
suggested that the genotypes G1 (ICMH-177111) and G24 (ICMX-207137) had
the best stability and performance in the five environments. The
stability index score was lowest for G32 (ICMX-207181), showing poor
stability and mean performance in multiple environmental conditions
([97]Figure 4).
Figure 4.
[98]Figure 4
[99]Open in a new tab
Prediction of highly stable and best-performing genotypes (G1 =
ICMH-177111 and G24 = ICMX-207137) across the 52 metabolites in pearl
millet lines tested in five locations. (G3 = ICMX-207207, G15 =
ICMX-207076, G18 = ICMX-207094, G23 = ICMX-207136, G31 = ICMX-207171,
G32 = ICMX-207181, G33 = ICMX-207183, G38 = ICMX-207190, G51 =
ICMX-207192).
4. Discussion
Millets, particularly pearl millet, are important due to their critical
role in human nutrition. Substantial progress in nutritionally rich
crop production has been achieved through the exploitation of classical
genetics and the selection of crop varieties with improved
health-benefiting traits. However, the increasing worldwide population
and adverse climatic conditions have driven the need to protect and
augment health-benefiting traits in micronutrient-rich grains such as
pearl millet. Numerous studies have suggested that capturing naturally
occurring genetic variation could be important for nutritional values,
especially when considering genetic interactions in improving desired
attributes [[100]29]. Thus, this study was conducted with the aim to
identify the best-performing and most-stable genotypes in multiple
environments for various health-benefiting metabolites in pearl millet.
Previously, we used metabolomic-based quantifications of
health-benefiting nutritional metabolites as phenotypic traits in
conjunction with 76K SNP variants to conduct metabolic genome-wide
association analyses [[101]9]. This found significant SNPs associated
with health-benefiting nutritional metabolites at the −log p-value ≤
4.0. The study revealed 738 probable candidate genes, which had
significant roles in starch, antioxidant, vitamin, and lipid
metabolism. These genes encoded starch branching, α-amylase, β-amylase,
vitamin-K reductase, UDP-glucuronosyl, UDP-glucosyl transferase (UGTs),
L-ascorbate oxidase, and isoflavone 2’-monooxygenase. However,
environmental impacts on the levels of the associated metabolites were
not considered in that study and therefore was the aim of the current
study. To address G × E and GEI effects, we quantified
health-benefiting antioxidant, polyphenol, vitamin, and
starch-associated, especially dietary starch, metabolites in eleven
pearl millet lines at five growing locations in West Africa using
FIE-HRMS profiling.
The analysis of variance indicated the existence of homogenous error
variance for all the health-benefiting metabolites in each of the five
environments. Further, the combined ANOVA analysis showed that the mean
sum of squares due to genotype, environment, and G × E were significant
for 54 health-benefiting metabolites features involved in starch,
antioxidant, lipid biosynthesis, and vitamin metabolism
([102]Supplementary Table S2). Further, GEI effects were determined
using AMMI-based models to reveal if there were substantial genotypic
variations amongst the environments. Combined AMMI-based ANOVA
([103]Table S1) revealed that all components of variations—environment
(location), genotype, and GEI—were highly significant. Interestingly,
the percent sum of squares linked to environment (71.42%) was much
greater than genotypes (16.04%) and GEI (2.11%) for m/z 285.04037. This
signified that the environmental factor contributed sufficient
variations, which was explained by genotypes’ interaction with their
respective environment. Further, comparatively less variation in the
genotype was found due to other factors such as differences in rainfall
and temperature, and this led to different patterns of genotypic
performances. The large proportion of total variation linked to the
venvironment was also reported by Adugna et al. [[104]30], Molla et al.
[[105]31], Dagnachew et al. [[106]32], Birhanu et al. [[107]33], Lakew
et al. [[108]34], and Seyoum et al. [[109]35] in finger millet.
Significant GEIs were also observed by Tolessa et al. [[110]36] in pea
and Singamsetti et al. [[111]37] in maize.
Several other statistical models, namely ASI, ASV, ASTAB, AVAMGE, DA,
DZ, EV, FA, MASI, MASV, SIPC, and Za, were also applied to understand
the stability of genotypes. For all the genotypes, most of the
stability parameters showed consistent prediction for genotypic
ranking, except ZA and DA. Based on genotypic stability score
estimates, G1 (ICMH-177111), G24 (ICMX-207137), G18 (ICMX-207094), and
G38 (ICMX-207190) were identified as top-ranking in terms of stability
for most of the health-benefiting metabolites. Such AMMI-based
stability was also used by Anuradha et al. [[112]29] in finger millet
and identified the top ten of 60 genotypes for yield parameters.
Similarly, Cheloei et al. [[113]38] used similar approaches to report
stability indices in rice genotypes.
BLUP-based stability methods such as HMGV, RPGV, and HMRPGV further
represent robust statistical approaches for predicting stability
[[114]13,[115]29,[116]39]. These methods identify genotypic effects and
allow the ranking of genotypes concerning their performance [[117]40].
In our study, the BLUP-based models showed that the ranking of
genotypes had similar trends for all the genotypes with respect to each
metabolite’s genotypic stability score estimates. BLUP-based stability
prediction displayed that the genotypes G15 (ICMX-207076), G33
(ICMX-207183), G38 (ICMX-207190), and G23 (ICMX-207136) has top ranks
in BLUP-based stability prediction for most of the health-benefiting
metabolites. Similarly, the HMGV, RPGV, and HMRPGV models were applied
to predict the stable genotypes and ranking by Rosado et al. [[118]41]
in macaw palm and Jatropha by Alves et al. [[119]42].
The multi-trait stability index (MTSI) was calculated to identify the
best-performing genotypes across various environments for multiple
traits. In this study, have found that G1 (ICMH-177111) and G24
(ICMX-207137) are the most stable and best mean performers across the
52 health-benefiting metabolite traits. Our study had similar
conclusions to that of Szareski et al. [[120]43], which also reported
the stability indices to predict the favourable adaptation and
stability for multiple traits in wheat genotypes.
5. Conclusions
By combining metabolomics tools, we extensively characterized the
metabolite profiles of eleven pearl millet hybrids by growing them in
five distinct locations in West Africa. Multivariate statistical models
applied to the data dissected the genetic and environmental effects on
the accumulation of health-benefiting metabolites in these pearl millet
entries. The study identified the correlation of individual
environmental diversity with the metabolite diversity. The AMMI-based
models used in the study identified better-adapted hybrids for West
African regions. This study for the first time characterized G × E
interactions on health-benefiting metabolites’ accumulation in pearl
millet entries and identified hybrids that exhibit stable preferences