ORIGINAL RESEARCH
published: 26 October 2017
doi: 10.3389/fpls.2017.01819
Trait and Marker Associations in
Oryza nivara and O. rufipogon
Derived Rice Lines under Two
Different Heat Stress Conditions
V. Vishnu Prasanth, M. Suchandranath Babu † , Ramana K. Basava † ,
V. G. N. Tripura Venkata, Satendra K. Mangrauthia, S. R. Voleti and Sarla Neelamraju*
Indian Institute of Rice Research (ICAR), Rajendranagar, Hyderabad, India
Edited by:
Sara Amâncio,
Universidade de Lisboa, Portugal
Reviewed by:
Mirza Hasanuzzaman,
Sher-e-Bangla Agricultural University,
Bangladesh
Baorong Lu,
Fudan University, China
*Correspondence:
Sarla Neelamraju
sarla_neelamraju@yahoo.com
†
These authors have contributed
equally to this work.
Specialty section:
This article was submitted to
Plant Abiotic Stress,
a section of the journal
Frontiers in Plant Science
Received: 27 April 2017
Accepted: 06 October 2017
Published: 26 October 2017
Citation:
Prasanth VV, Babu MS, Basava RK,
Tripura Venkata VGN,
Mangrauthia SK, Voleti SR and
Neelamraju S (2017) Trait and Marker
Associations in Oryza nivara and O.
rufipogon Derived Rice Lines under
Two Different Heat Stress Conditions.
Front. Plant Sci. 8:1819.
doi: 10.3389/fpls.2017.01819
Wild species and derived introgression lines (ILs) are a good source of genes for improving
complex traits such as heat tolerance. The effect of heat stress on 18 yield traits was
studied in four treatments in two seasons, under field conditions by subjecting 37 ILs
and recurrent parents Swarna and KMR3, N22 mutants, and wild type and 2 improved
rice cultivars to heat stress using polycover house method in wet season and late sowing
method in dry season. Normal grown unstressed plants were controls. Both correlation
and path coefficient analysis showed that the major contributing traits for high yield
per plant (YPP) under heat stress conditions were tiller number, secondary branches
in panicle, filled grain number, and percent spikelet fertility. Three ILs, K-377-24, K-16-3,
and S-148 which gave the highest YPP of 12.30–32.52 g under heat stress in both
the seasons were considered the most heat tolerant. In contrast, K-363-12, S-75, and
Vandana which gave the least YPP of 5.36–10.84 g were considered heat susceptible.
These lines are a good genetic resource for basic and applied studies on heat tolerance
in rice. Genotyping using 49 SSR markers and single marker analysis (SMA) revealed
613 significant marker- trait associations in all four treatments. Significantly, nine markers
(RM243, RM517, RM225, RM518, RM525, RM195, RM282, RM489, and RM570) on
chromosomes 1, 2, 3, 4, 6, and 8 showed association with six traits (flag leaf spad,
flag leaf thickness, vegetative leaf temperature, plant height, panicle number, and tiller
number) under heat stress conditions in both wet and dry seasons. Genes such as heat
shock protein binding DnaJ, Hsp70, and temperature-induced lipocalin-2 OsTIL-2 close
to these markers are candidates for expression studies and evaluation for use in marker
assisted selection for heat tolerance.
Keywords: heat tolerance, wild rice, introgression lines (ILs)
INTRODUCTION
The global mean air temperatures are expected to rise by 2–4.8◦ C in the next few decades as a result
of global warming (IPCC, 2013). Rice is an important cereal crop which provides food security
for more than 60 per cent of the world population. Most of the rice growing areas of tropical and
subtropical regions have temperature close to the threshold temperature of 33◦ C required for rice
(Teixeira et al., 2013). High temperatures both during day and at night cause considerable loss in
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Prasanth et al.
Heat Tolerance in Rice ILs
exhibited by plants, an in depth field based screening is
needed.
The wild progenitor species Oryza nivara and Oryza rufipogon
which cross easily with cultivated rice comprise an easily
exploitable and diverse gene pool for rice improvement.
Introgression lines (ILs) developed by crossing elite rice lines
with these two wild species showed lines with high yield and
tolerance to salinity and drought (Swamy et al., 2014; Sreenu
et al., 2015; Pushpalatha et al., 2016; Haritha et al., 2017).
Screening of these lines for heat tolerance would help in the
identification or development of new heat tolerant genotypes.
The objective of the present study was to (1) screen elite ×
wild ILs for heat tolerance under field conditions based on
morphological and yield related traits, (2) analyse direct and
indirect effect of these traits on grain yield and, (3) identify
markers and candidate genes linked to traits under heat stress
rice yield (Peng et al., 2004; Jagadish et al., 2010a,b; Kadam et al.,
2014). Temperature above 35◦ C for more than 60 min during
anthesis results in high sterility among panicles in rice (Jagadish
et al., 2007). High temperature stress during flowering causes
abnormal anther dehiscence (Matsui and Omasa, 2002), poor
pollen germination (Jagadish et al., 2010b), and reduced grain
filling duration (Cao et al., 2016). All these lead to increased
spikelet sterility and yield loss. Varieties differ in their response
to high temperature. Moroberekan, a heat-sensitive genotype
showed 82% spikelet sterility but Nagina22 (N22), a highly
tolerant genotype showed only 29% spikelet sterility at 38◦ C
(Jagadish et al., 2010b). The extent of reduction in rice yield
due to heat also depends on several other factors such as the
duration and intensity of heat stress, the growth stage at which
heat episode occurs, vapor pressure deficit, and the water status
of plants.
Heat tolerance is a complex trait which involves various
morphological, physiological, biochemical, and molecular
changes in plants. There are several reports on the effects of
high temperature and response of rice (Sailaja et al., 2015; Shi
et al., 2015; Ye et al., 2015a,b; Brito et al., 2016; Cao et al.,
2016; Tanamachi et al., 2016; Wu et al., 2016; Zhang et al.,
2016). Majority of these studies considered spikelet fertility
(SF) as major criterion for assessment of heat tolerance. Several
quantitative trait loci (QTLs) have also been mapped for heat
tolerance at booting, flowering and grain filling stages in rice
(Ishimaru et al., 2016). In general, QTLs were mapped for
SF under heat stress. Among all the QTLs, only one QTL
(qHTSF4.1) has been validated in different genetic backgrounds
and also fine mapped (Ye et al., 2015a,b). In our previous study,
using association mapping analysis we validated nine markers
for five traits [SF, yield per plant (YPP), Heat Stability Index
(HSI) of SF and HSI of YPP] under heat stress (Prasanth et al.,
2016). Evidence was provided that SF alone is not the best
criterion to assess heat tolerance in rice and yield should also
be included. Rice lines with low SF but high YPP and vice
versa were identified under two different heat stress conditions
(Prasanth et al., 2016). Hence, a thorough understanding on the
contribution of various morphological and yield related traits
toward heat tolerance and association of the markers with those
traits is required for efficient selection and development of heat
tolerant rice lines.
Most of the studies on heat tolerance in rice are based on
exposing the plants to high temperature in green houses or
temperature controlled growth chambers. There are very few
studies on effects of heat stress on rice under near—natural
field conditions. In field, the temperature varies dynamically as
do the other environmental factors such as CO2 concentration,
relative humidity, vapor pressure deficit (VPD), light intensity,
wind velocity, and so the response to temperature is often
confounded (Poorter et al., 2016). Reduction in grain yield
due to heat stress is comparatively less in controlled chambers
than in natural field conditions (Hall, 2011). Also, the impact
of long duration stress upon the plants is quite different
than short duration stress. In long duration stress, plants
can acclimatize to high temperature stress beginning from
vegetative stage itself. To include the variability in acclimation
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MATERIALS AND METHODS
A set of 48 stable lines consisting of 16 Swarna × O. nivara
introgression lines (Swarna ILs), 18 KMR3 × O. rufipogon
introgression lines (KMR3 ILs), 4 EMS induced mutants
of Nagina 22 (N22) (NH219, NH363, NH686, NH787) and
seven wild rice/landraces/improved varieties viz., O. rufipogon
(WR120), O. nivara (IRGC 81848), IR64, Vandana, BPT5204,
Azucena and Nipponbare were used in the present study. Swarna
is a popular mega rice cultivar, KMR3 is a restorer line of the
popular 3-line rice hybrid KRH2 and N22 is a well-known heat
tolerant aus variety. The details of all the lines are given in
Supplementary Table 1.
Phenotyping
The experiments were conducted at IIRR field (latitude and
longitude: 17◦ 22′ 31′′ N and 78◦ 28′ 27′′ E) during wet season
2012 (July to December) using poly cover house method and
dry season 2013 (January to May) using late sown method
(flowering coinciding with high temperature) for heat stress
following Prasanth et al. (2016). The respective controls were
normal conditions without poly cover in 2012 and sown in
normal sowing time in 2013. In wet season 2012, temperature
in control conditions was ≤33, ≥24.5◦ C, and mean was
30◦ C during day time. The RH in control conditions was
≥30, ≤97%, and mean was 87%. The temperature inside
poly cover house was ≥30.2, ≤48◦ C, and mean was 44.3◦ C
during day time. In dry season, the temperature during
day time in normal sowing method was ≤38.9, ≥37.6◦ C,
and mean was 35.7◦ C while in late sown method it was
≤42.33, ≥40.0◦ C, with mean of 37.6◦ C. The RH observed
in both normal and late sown methods was not significantly
different.
The following 18 morphological and yield traits were
measured in both treatments in both seasons.
(1) Vegetative leaf spad value (VLS) (2) Flag leaf spad value
(FLS) (3) Vegetative leaf thickness (VLTH) (4) Flag leaf thickness
(FLTH) (5) Vegetative leaf temperature (VLT) (6) Flag leaf
temperature (FLT) (7) Time for 50% flowering (FT) (8) Plant
height (PH) (9) Tiller number per plant (TN) (10) Panicle
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Prasanth et al.
Heat Tolerance in Rice ILs
lines flowered earlier (by ∼5 days) under heat stress when
compared with flowering in control but in dry season, the average
flowering time of all genotypes was more (by ∼5 days) under
heat stress than in control. The trait mean values under heat
stress in case of three traits, VLS, VLTH, and FLT reduced
significantly during wet season but increased significantly during
dry season.
The effects of season (wet and dry), temperature (control
vs. heat stress), genotypes (36 ILs), and their interactions
on 18 traits are presented in Table 1. There were significant
variations among genotypes and also in interactions of genotype
× temperature × season for all traits. All traits except VLTH
showed highly significant differences with respect to season.
There were significant differences among treatments for all traits
except FLS, VLTH, FL, PB, and BM. The effects of genotype
× temperature and genotype × season were significant for
all traits except FT and YPP. SF, and YPP showed significant
differences with all seven sources of variances except genotype
× temperature and genotype × season did not show significant
differences in YPP.
Based on absolute values of all 18 traits (Supplementary Table
3), the top 11 genotypes with high YPP (>12.21 g) in WH 2012
flowered early (96–03 days) except Swarna IL S-65 (112 days).
These early flowering high yielding genotypes exhibited high
values for PH, TN, PN, PL, SB, FGN, %SF, and BM and low
values for VLS and FLTH. Out of top 10 genotypes showing
high YPP, five genotypes (KMR3, K-40, K-50, K-16-3, and S-65)
also showed high BM (>27 g) whereas four genotypes (K-198, K103, K-377-24, and K-137) had high SF % (>76%). These results
indicate the importance of both BM and %SF in contributing
to high YPP. However, K-13-5 which showed high values for
both %SF (86.76%) and BM (27.06 g) showed less YPP (10.85 g).
In DH, the top 11 genotypes, exhibiting high YPP (>20.48 g)
flowered late and flowering time ranged from 113 to 144 days.
These genotypes showed high VLS, TN, FGN, and %SF and they
had low VLT. The SF of these lines ranged from 88 to 91%.
Out of all 48 lines, Swarna IL S-248 (released as DRR Dhan
40) showed high values for three important traits, YPP, BM and
%SF. KMR3 IL K-13-7 showed high YPP and BM and four
ILs (K-377-24, K-458, S-250, and S-148) showed high YPP and
%SF.
These results indicate that the higher yielding ILs had in
general either higher BM or %SF or both but the reverse is
not true. S-24 which had high %SF (88.6%) and BM (26.38 g)
showed less YPP (17.02 g). Tiller number and FGN were the
common traits that contributed to higher yield in both WH
and DH treatments. Three ILs (K-377-24, K-16-3, and S-148)
were considered to be heat tolerant as they were in the list
of top 11 genotypes with high YPP in both WH and DH
conditions. On the contrary K-363-12, S-75, and Vandana were
considered as heat susceptible as they were in the list of 11
genotypes with least YPP in both WH and DH conditions.
Genotypes with more YPP in WH showed low VLS in WH
but genotypes with more YPP in DH showed high VLS in
DH. It may be noted that the leaf temperature was low
during the vegetative phase in DH and reproductive phase in
WH.
number per plant (PN) (11) Panicle length (PL) (12) Primary
branches per panicle (PB) (13) Secondary branches per panicle
(SB) (14) Filled grain number per panicle (FGN) (15) % Spikelet
fertility (%SF) (16) Total grain number (TGN) (17) Biomass per
plant (BM) (18) grain yield per plant (YPP).
Genotyping
Genomic DNA was isolated from fresh leaves of all the 48 lines
using CTAB method and genotyped using 49 SSR markers which
are previously reported to be linked or close to QTLs for spikelet
fertility or pollen fertility under heat stress (Prasanth et al., 2016).
The details of all these markers are available at http://www.
gramene.org/markers/microsat.
Statistical Analysis
Descriptive statistics, Pearson correlation analysis among 18
agronomic and yield traits were performed individually for
control and heat stress conditions (cover house/late sown)
in wet season 2012 and dry season 2013 using Statistix 8.1.
Percent increase or decrease in the trait values under high
temperature conditions was also calculated. Path coefficient
analysis using R programming version 3.2.3 using package
agricolae was performed to identify the direct and indirect
effects of component traits on grain yield. Single marker analysis
(SMA) was performed by one way Anova using MINITAB V14.0
(Minitab Inc., USA) to find out the association of each marker
with the traits.
Candidate Gene Analysis
Candidate genes were identified in case of markers associated
with traits only under heat stress conditions in both wet and
dry seasons by obtaining physical position of the markers
from Gramene data base (http://www.gramene.org/). The stress
response putative candidate genes were identified in the genomic
region 1 Mb upstream and 1 Mb downstream of these marker
positions using RAPDB (http://rapdb.dna.affrc.go.jp/).
RESULTS
Phenotypic Performance
Descriptive statistics for 18 morphological and yield traits under
wet season normal (WN), wet season heat stress (WH) in 2012,
dry season normal (DN), dry season heat stress i.e., late sown
(DH) in 2013 are shown in Supplementary Table 2.
All traits followed normal distribution except FT in control
during wet season and FLT and %SF in control during dry
season. In wet season, the mean values increased significantly
under heat stress for VLS (by 7.09%), VLTH (by 30.8%), FLT
(by 28.26%) and PH (by 9.55%) but decreased significantly for
TN (by 1.34), FGN (by 37.7), %SF (by 23.45), and YPP (by
45.68%). In dry season, the mean values increased significantly
under heat stress for FLTH (by10.87%), VLT (by 5.54%), and
PL (by 4.04%) but reduced significantly for VLS (by 2.57%),
VLTH (by 20.4%), FLT (by 11.66%), and %SF (by 11.05%).
Though, YPP reduced under heat stress conditions during
both wet (by 45.68%) and dry (by 15%) seasons but reduction
was significant only during wet season. In wet season all
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October 2017 | Volume 8 | Article 1819
Pearson Correlation Analysis
***Significant at 0.001 level of probability; **Significant at 0.01 level of probability; *Significant at 0.05 level of probability. VLS, Vegetative leaf spad value; FLS, Flag leaf spad value; VLTH, Vegetative leaf thickness; FLTH, Flag leaf thickness;
VLT, Vegetative leaf temperature; FLT, Flag leaf temperature; FT, Time for 50% flowering; PH, Plant height; TN, Tiller number per plant; PN, Panicle number per plant; PL, Panicle length; PB, Primary branches per panicle; SB, Secondary
branches per panicle; FGN, Filled grain number per panicle; %SF, Percent spikelet fertility; TGN, Total grain number; BM, Biomass per plant; YPP: grain yield per plant.
3.12***
4.87***
3.76***
3.59***
3.34***
Season*Genotypes*
Temperature
35
4.92***
2.29***
8.25***
5.32***
6.32***
8.94***
2.19***
2.13***
2.6***
3.18***
1.55*
3.01***
3.26***
16.8***
1.08NS
8.74***
115.25***
55.97***
6.9***
4.01***
68***
26.5***
6.92***
5.04***
19.17***
4.17***
1.8NS
5.23***
13.08***
4.08***
6.81**
9.37**
3.75***
48.59***
43.98***
22.98***
1.03NS
7.16***
1906.27***
3.63***
7.44***
0.00NS 1062.25***
3.53***
415.04***
6.02***
1
46.05***
4.79***
73.10***
35
Season*Temperature
23.64***
5.66***
4.44***
285.71***
5.9***
224.85***
4.62***
2.19***
6.84**
3.29***
1.75**
16.16***
14.93***
1.63*
6.92***
272.06***
9.07***
91.52*** 1860.79***
4.65***
3.19***
10.89***
1
Temperature
Genotypes*
Temperature
Season*Genotypes
35
5.23***
2.1***
21.23***
16.81***
15.73***
3.51***
2.94NS 67.59***
3.36***
1.37NS
31.76***
29.29***
7.25***
17.36***
12.76*** 24.93***
0.05NS 61.35***
17.68***
233.11***
156.71***
10.07***
67.22***
46.02***
250.38***
5.59***
5.96***
275.47***
289.97*** 3061.45***
5.12*** 139.35***
0.04NS 256.41***
0.94NS 10.66***
11.26***
526.5***
8.42***
4.13***
3.21***
1.8NS
9.69***
1.71NS
225.15*** 2339.59***
1.56NS
191.46***
6.99***
35
1 355.45***
Season
Genotypes
FGN
%SF
SB
PL
(cm)
PN
TN
PH (cm)
FT
(days)
FLT
(◦ C)
VLT
(◦ C)
FLTH
(mm)
VLTH
(mm)
FLS
VLS
df
Source of variation
TABLE 1 | Analysis of variance of 18 agronomic and yield traits under normal conditions and polycover house/late sown conditions during wet season 2012 and dry season 2013.
Heat Tolerance in Rice ILs
PB
TGN
BM
(g)
YPP (g)
Prasanth et al.
Frontiers in Plant Science | www.frontiersin.org
Pearson correlation among 18 traits was computed separately in
two treatments and two seasons (Figure 1). During wet season,
YPP was correlated positively and significantly with BM, SB,
and PL in control but with BM, FGN, and %SF in heat stress.
During dry season, YPP was significantly correlated with PL,
FGN, and TGN in control but with FGN and %SF in heat stress.
Thus, in heat stress, YPP was correlated with both FGN and
%SF in both seasons. There was a significant negative correlation
between YPP and FLTH in WH but in dry season, this negative
correlation was observed only in control. YPP was positively
correlated with FGN in all treatments except in WN. It was also
significantly correlated with BM in both control and heat stress
during wet season but not in any treatment in dry season. It
is noteworthy that %SF was significantly correlated with YPP
and FGN only under heat stress but not in control during both
seasons.
Path Coefficient Analysis
As simple correlation does not provide the true contribution
of the traits to yield, these correlations were partitioned into
direct and indirect effects through path coefficient analysis. The
estimates of direct and indirect effects of 17 agronomic and yield
related traits on YPP by path coefficient analysis are provided
separately as two treatments and two seasons in Table 2. During
wet season control conditions, FLTH, TN, SB, and TGN were
highly correlated with YPP directly whereas FT, PB, FGN, and
TGN contributed to yield indirectly via SB and FGN and SB
via TGN. There was significant indirect effect of PL on YPP
via TN. PB and FGN were negatively correlated with yield
directly. Under wet season heat stress, FLT, PN, SB, and FGN
were highly and positively correlated directly to YPP whereas
FLS, PH, TN, and %SF were highly negatively correlated with
YPP. Even though, TN itself had negative and direct effect on
YPP, but 9 out of 17 traits contributed positively indirectly
via TN to YPP. These results on TN were in contrast with
results on PN. PN had highly positive and direct effect on YPP
but eight traits had negative effects on YPP via PN. During
dry season, under control conditions, TGN and BM had high
positive and direct effects whereas PB and %SF showed high
negative and direct effects on YPP. Eight traits showed high
positive indirect effects on YPP via TGN. Under late sown
conditions, PN, PL, and %SF showed high positive and direct
effects on YPP. Even though, TN showed high negative and
direct effect on YPP, it showed high positive indirect effect via
PN.
Single Marker Analysis
Marker Trait Associations in Control and Heat Stress
Conditions during Wet Season 2012 and Dry Season
2013
Single marker analysis was performed with 49 selected SSR
markers and 18 agronomic and yield traits to find out significant
(p < 0.05) marker-trait associations. In all, 613 significant marker
trait associations were obtained, out of which 109 and 172 were
from control and heat stress in wet season and 174 and 158
were from control and heat stress in dry season respectively.
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Prasanth et al.
Heat Tolerance in Rice ILs
FIGURE 1 | Pearson correlation among 18 traits under normal conditions and polycover house /late sown conditions during wet season 2012 and dry season 2013.
(A) WN, Wet season normal; (B) WH, wet season heat stress; (C) DN, Dry season normal; (D) DH, Dry season heat stress.
were no common marker associations with %SF and YPP in wet
season.
There were only 12 marker-trait associations, common in all
four treatments (Figure 2). The list of significant marker-trait
associations in each treatment and in each season is given in
Supplementary Table 4. RM430 showed maximum number of
associations (27) followed by RM440 and RM405 (26 each)
and RM210 (25). The markers showing only one association
were RM108 (with SB in WN), RM148 (with PB in WH) and
RM349 (with VLS in DH). RM128 and RM406 showed two
associations each. RM128 was associated with FLS and VLTH
under WH and DH respectively and RM406 was associated with
FLT and PH only under WH. Irrespective of treatments, YPP
showed more number of associations (51) next only to PH (71)
whereas FGN (12) and FLT (13) showed least associations. Out
of 51 significant marker associations with YPP, four markers,
RM106, RM225, RM440, and RM518 were associated with YPP
in at least three treatments out of four. However, none were
associated with YPP only under heat stress in both seasons.
Likewise, there were 16 significant marker associations with
%SF, out of which 11 associations were shared with TN, and
TGN but only seven with YPP. All these seven common
associations with YPP (two in control and five in heat stress)
were observed only during dry season. It is noteworthy that there
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Marker-Trait Associations Only under Heat
Stress
The list of 11 common marker-trait associations found only
under heat stress and in both wet and dry seasons involving
nine markers and six traits is shown in Table 3. RM 243 was
associated with both FLS and VLT whereas RM517 was associated
only with FLS. RM518 and RM525 were also associated with
VLT. RM225 was associated with FLTH and RM185 and RM282
were associated with plant height. Both panicle number and
tiller number were associated with two common markers, RM489
and RM570. In addition to these common associations, there
were 67 unique significant marker-trait associations only in WH,
out of which more associations were with FLS (15) and VLT
(11). Similarly, there were 58 unique significant marker-trait
associations in DH, out of which more associations were with
VLTH (13). In WH, only four unique associations (with markers
RM 183, 332, 401, and 3735) were observed with YPP whereas
there was only one unique association with YPP (with RM210)
in DH.
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Trait/Treatment
VLS
FLS
VLTH (mm) FLTH (mm) VLT (◦C) FLT (◦C) FT (days) PH (cm)
TN
PN
PL (cm)
PB
SB
FGN
%SF
TGN
BM (g)
Total effect
Prasanth et al.
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TABLE 2 | The direct and indirect contribution of 17 agronomic traits to yield per plant (YPP) under normal conditions and polycover house/late sown conditions during wet season 2012 and dry season 2013.
WET SEASON 2012 NORMAL CONDITIONS
VLS
−0.033
0.086
0.023
0.104
−0.147
−0.026
−0.136
−0.149
0.083
0.004
−0.029
−0.028 −0.066
0.038
0.023 −0.017
0.126
−0.260
FLS
−0.010
0.276
−0.019
0.023
−0.067
0.130
−0.003
−0.058
0.120 −0.072
0.014
−0.196
0.186
0.000
0.053 −0.020 −0.042
0.169
VLTH (mm)
0.008
0.055
−0.093
−0.081
0.017
0.011
0.003
0.111
0.004
0.018
−0.063
0.199 −0.048 −0.006
FLTH (mm)
−0.006
0.011
0.013
0.575
−0.073
0.015
0.000
−0.067
0.138 −0.080
0.000
−0.259
0.093
VLT (◦ C)
−0.015
0.055
0.005
0.127
−0.333
−0.133
−0.162
0.111
0.442 −0.181
−0.012
FLT (◦ C)
0.424 −0.143
−0.032
0.169
0.033
−0.357
0.517 −0.380
0.258
0.001 −0.108
0.184
0.023
0.119
0.013 −0.067
0.000
0.015 −0.135
0.291
−0.055
−0.002 −0.097
0.003
−0.023
−0.120
−0.371
−0.184
0.082
FT (days)
0.014 −0.003
−0.001
0.000
0.170
0.215
0.317
−0.058
PH (cm)
0.010 −0.033
−0.021
−0.081
−0.077
−0.063
−0.038
0.481
0.083 −0.051
−0.479
0.095
0.019 −0.009
0.178
0.029 −0.031
0.003
0.230
0.070 −0.159
0.114 −0.064 −0.005
0.021
−0.119
0.280 −0.371
0.238 −0.182
0.069
−0.285
0.004
TN
−0.003
0.036
0.006
0.086
−0.160
−0.171
−0.165
0.043
0.922
−0.409
−0.011
0.196 −0.491
0.333 −0.229
0.004 −0.042
0.068
PN
0.000
0.047
0.001
0.109
−0.143
−0.126
−0.127
0.058
0.894
−0.422
0.000
0.154 −0.411
0.304 −0.211
0.004 −0.087
0.159
PL (cm)
0.012
0.047
−0.021
0.000
0.047
0.145
0.127
0.135
−0.120
0.000
0.083
−0.252
0.438 −0.333
0.188
−0.001
0.077
−0.008
0.213
0.033
0.148
0.162
−0.082
−0.258
0.093
0.030
−0.701
0.928 −0.571
0.398 −0.001 −0.060
PB
0.014 −0.167
0.343*
0.182
SB
0.002
0.039
−0.014
0.040
0.040
0.104
0.124
0.005
−0.341
0.131
0.027
−0.490
1.326
−0.799
0.522
0.008 −0.105
0.397*
FGN
0.001
0.000
−0.005
−0.058
0.040
0.093
0.127
0.034
−0.323
0.135
0.029
−0.420
1.114 −0.951
0.568
0.031 −0.075
0.226
%SF
−0.001
−0.360
0.258
6
0.025
0.001
0.029
0.037
0.115
0.140
0.000
0.152
0.027
−0.476
1.180 −0.923
0.586
0.015 −0.072
TGN
0.008 −0.077
−0.024
−0.247
0.023
−0.019
0.003
0.096
0.046 −0.021
0.016
0.007
0.146 −0.409
0.117
0.073
−0.030
0.010
BM (g)
0.014
−0.003
−0.006
0.023
0.085
0.114
0.216
0.129 −0.122
0.046
−0.140
0.464 −0.238
0.141
0.007
−0.299
0.453**
0.039
WET SEASON 2012 POLYCOVER HOUSE CONDITIONS (HEAT STRESS)
VLS
−0.404 −0.123
−0.019
−0.099
−0.070
0.200
0.007
0.665
1.248 −1.401
−0.041
0.009
0.045
0.000 −0.017
0.094 −0.126
−0.213
FLS
−0.085
−0.585
0.078
−0.175
−0.138
0.094
0.092
0.588
−0.602
0.525
0.008
0.024
0.440
0.060 −0.077
0.222 −0.037
−0.240
VLTH (mm)
−0.016
0.094
−0.487
0.132
0.132
0.235
−0.070
−0.022
−0.043
0.131
0.008
FLTH (mm)
−0.085 −0.217
0.136
−0.473
−0.178
0.165
0.097
0.466
0.946 −0.875
0.030
VLT (◦ C)
−0.093 −0.263
0.210
−0.274
−0.306
0.094
0.105
0.421
FLT (◦ C)
−0.069 −0.047
−0.097
−0.066
−0.025
1.177
−0.057
0.044
FT (days)
−0.012 −0.217
1.334 −1.313
0.022
0.175
−0.036
−0.860
−0.019 −0.182 −0.157
0.364 −0.075 −0.071
0.043
0.258
−0.184
−0.129
−0.271
0.249
0.277
1.161 −1.007
−0.025
0.310
−0.010
0.199
0.116
−0.047
−0.062
−1.109
−0.258
0.438
0.085
−0.024 −0.061
TN
0.117 −0.082
−0.005
0.104
0.095
0.235
−0.067
−0.067
−4.302
4.246
0.033
PN
0.129 −0.070
−0.015
0.095
0.092
0.047
−0.057
−0.111
−4.173
4.377
0.052
PL (cm)
0.061 −0.018
−0.015
−0.052
−0.025
−0.153
−0.022
−0.344
−0.516
0.832
0.275
0.007
PB
−0.040 −0.158
0.107
−0.213
−0.150
−0.177
0.107
0.299
1.420 −1.269
0.022
SB
−0.012 −0.170
0.058
−0.114
−0.052
−0.129
0.015
0.044
0.989 −1.050
FGN
0.000 −0.047
0.102
0.047
−0.031
−0.271
0.057
−0.211
%SF
−0.032 −0.211
0.088
−0.156
−0.080
−0.153
0.047
0.067
0.032
0.111
0.073
0.128
0.009
−0.259
0.047
−0.277
BM (g)
0.206
0.088
−0.127
0.128
0.129
−0.224
−0.020
−0.676
0.038
0.091
0.064
0.316 −0.067
0.050
−0.377*
0.035 −0.104
−0.221
0.028
0.257 −0.047
−0.079
0.171 −0.041 −0.222 −0.020
−0.016
0.013 −0.292
0.151
0.309
−0.029 −0.349 −0.224
0.060
0.316
0.062
0.247
−0.026 −0.364 −0.238
0.058
0.339
0.081
0.267
0.045
0.179 −0.030 −0.222
0.091
0.199
0.088
0.667
0.224 −0.110 −0.070 −0.037
0.111
0.008
0.039
1.511
0.335 −0.193
1.291 −1.401
0.066
0.026
0.682
1.204 −1.182
0.039
0.045
1.364
1.161 −1.269
0.052
0.005 −0.030
1.444
0.102
−1.075
−0.013
0.182
0.142
0.023
0.030
0.115
0.745
−0.135 −0.935
0.039
0.377*
0.470
−0.215 −0.140
0.015
0.039
−1.169
0.037
0.372*
0.119 −0.013 −0.175
0.247
0.482**
0.596 −0.026
(Continued)
Heat Tolerance in Rice ILs
October 2017 | Volume 8 | Article 1819
0.136
0.243
0.175
0.075 −0.056
−0.013 −0.167 −0.171
PH (cm)
TGN
0.039
0.040
Trait/Treatment
VLS
FLS
VLTH (mm)
FLTH (mm)
VLT (◦C)
FLT (◦C)
FT (days)
PH (cm)
TN
PN
PL (cm)
PB
SB
FGN
%SF
TGN
BM (g)
Total effect
DRY SEASON 2013 NORMAL SOWN CONDITIONS
VLS
0.302
−0.163
0.001
−0.043
−0.004
0.043
0.003
0.023
0.028 −0.094
0.134
−0.160
−0.040
0.036
0.602
0.097 −0.214
0.116
FLS
0.191
−0.259
0.008
−0.021
−0.003
0.108
−0.001
0.033
0.003 −0.015
0.113
−0.127
−0.019
0.021
0.332
0.054 −0.285
−0.058
VLTH (mm)
0.006 −0.039
0.055
0.159
0.004
0.041
0.001
0.001
0.037
0.071
−0.028
0.007
0.003
0.037
0.016 −0.131
FLTH (mm)
0.030 −0.013
−0.020
−0.429
−0.003
−0.003
−0.022
−0.009
0.012 −0.047
0.054
−0.271
−0.027
0.032
0.504
0.081
−0.011
VLT (◦ C)
−0.057
0.047
0.010
0.056
0.019
−0.035
0.003
0.021
0.003 −0.007
−0.196
0.066
FLT (◦ C)
−0.048
0.103
−0.008
−0.004
0.003
−0.271
0.004
−0.027
−0.012
0.035
−0.017
−0.006
−0.009 −0.001 −0.061 −0.107
FT (days)
−0.006 −0.003
−0.001
−0.069
0.000
0.008
−0.140
−0.022
−0.002
0.007
0.075
−0.221
−0.032
0.027
PH (cm)
−0.082
0.101
−0.001
−0.043
−0.005
−0.084
−0.036
−0.086
0.002 −0.005
0.088
−0.138
−0.011
0.006
TN
−0.109
0.010
0.008
0.064
−0.001
−0.041
−0.004
0.003
−0.078
0.246
0.038
0.006
PN
−0.115
0.016
0.008
0.081
−0.001
−0.038
−0.004
0.002
−0.077
0.248
0.025
0.028
0.023 −0.023 −0.442 −0.166
PL (cm)
0.097 −0.070
0.009
−0.056
−0.009
0.011
−0.025
−0.018
−0.007
0.015
0.418
−0.315
−0.053
0.044
0.688
PB
0.088 −0.059
0.003
−0.210
−0.002
−0.003
−0.056
−0.021
0.001 −0.012
0.238
−0.553 −0.056
0.063
0.946
SB
0.145 −0.059
−0.004
−0.137
−0.005
−0.030
−0.054
−0.011
0.020 −0.070
0.267
−0.371
−0.083
0.068
1.045
0.077
0.020 −0.003 −0.074 −0.021 −0.036
0.192
−0.356*
−0.093
0.285
0.059
0.332 −0.113
0.202
0.095
0.074
0.027
0.374
0.220
0.021 −0.018 −0.369 −0.172
0.036
0.081
0.036
0.064
0.113
0.042
0.494**
0.070
0.077
0.103
0.075
0.143
0.421
FGN
0.130 −0.065
0.002
−0.163
−0.001
0.003
−0.045
−0.006
0.017 −0.070
0.221
−0.415
−0.067
0.084
1.204 −0.048
0.012
0.415**
%SF
0.148 −0.070
0.002
−0.176
−0.001
0.014
−0.038
−0.005
0.023 −0.089
0.234
−0.426
−0.070
0.082
1.229
0.006
0.378*
TGN
−0.054
0.026
−0.002
0.064
0.001
−0.054
−0.029
0.004
−0.025
0.077
−0.088
0.072
0.012
BM (g)
−0.109
0.124
−0.012
−0.056
−0.001
−0.130
−0.048
−0.054
−0.005
0.015
0.029
−0.072
−0.020
0.008 −0.147
0.002
0.012
0.064
−0.537 −0.012
0.011
Prasanth et al.
Frontiers in Plant Science | www.frontiersin.org
TABLE 2 | Continued
0.128
7
0.594
0.297
0.104 −0.004
0.253
DRY SEASON 2013 LATE SOWN CONDITIONS (HEAT STRESS)
VLS
0.125
0.014
−0.026
0.011
−0.003
−0.002
0.001
0.027
−0.288
0.356
−0.092
0.016
FLS
0.045
0.040
0.030
0.033
−0.003
0.013
−0.005
0.025
−0.039
0.073
−0.074
−0.005
−0.002
0.018
0.010 −0.005 −0.005
−0.021
0.008
0.156
−0.002
−0.010
0.001
−0.022
0.022
0.118 −0.106
0.069
−0.033
−0.037
0.074
0.043 −0.233
0.002
0.006
−0.003
0.006
0.003 −0.010
VLTH (mm)
FLTH (mm)
VLT (◦ C)
FLT (◦ C)
0.006
−0.002
0.219
0.004
−0.005
−0.001
0.005
0.068 −0.073
−0.051
−0.003
−0.012 −0.004
−0.058
0.033
0.026
−0.008
0.007
−0.042
0.028 −0.046
0.051
0.005
0.004 −0.007
0.024 −0.012 −0.014
0.136
−0.010
0.003
0.168
0.005 −0.089 −0.011 −0.089 −0.002
−0.211
0.003 −0.031
−0.002
0.013
0.003
−0.075
0.014
0.031
0.000
0.020
−0.023
0.007
0.006 −0.070 −0.001
−0.117
FT (days)
−0.001
0.004
0.065
0.004
−0.003
0.020
−0.052
−0.009
−0.130
0.139
0.055
−0.041
−0.018
0.083
0.028 −0.159
0.004
−0.029
PH (cm)
−0.030 −0.009
−0.030
−0.009
0.010
0.020
−0.004
−0.114
0.102 −0.092
0.267
−0.035
−0.020
0.080
0.013 −0.040
0.001
0.114
0.020
−0.564
0.646
−0.064
0.013
0.023
0.004
0.187
−0.553
0.660
−0.041
0.009
0.021
0.079 −0.059
0.461
−0.053
−0.030
TN
PN
PL (cm)
0.064
0.003
−0.033
−0.026
−0.001
0.000
−0.012
0.067
0.004
−0.025
−0.024
−0.002
−0.002
−0.011
0.016
−0.025 −0.006
0.023
−0.024
0.003
0.004
−0.006
−0.066
0.000 −0.017
0.114
0.021 −0.014
0.058
0.119
0.004
0.272
0.022 −0.164
0.000
0.200
−0.026
0.003
0.070
0.009
−0.002
0.007
−0.029
−0.053
0.096 −0.079
0.332
−0.074 −0.041
0.163
0.049 −0.199
0.005
0.204
−0.050
0.001
0.098
0.011
−0.002
0.004
−0.016
−0.039
0.220 −0.231
0.235
−0.052
−0.059
0.160
0.060 −0.258
0.001
0.044
FGN
−0.005
0.002
0.037
0.004
−0.008
0.008
−0.014
−0.030
0.000
0.046
0.088
−0.039
−0.031
0.307
0.047
0.119
0.009
0.537***
%SF
−0.025
0.006
0.097
0.011
−0.004
−0.007
−0.021
−0.022
0.141 −0.132
0.147
−0.052
−0.051
0.209
0.069
−0.253
0.004
0.068
0.026
0.000
−0.073
−0.004
−0.005
0.011
0.017
0.009
−0.130
0.158
−0.152
0.030
0.031
0.074 −0.035
0.497
0.003
0.514**
−0.027 −0.009
0.019
0.031
−0.003
0.004
−0.011
−0.007
−0.118
0.125
0.009
−0.019
−0.004
0.135
0.074
0.020
0.238
TGN
BM (g)
0.013
VLS, Vegetative leaf spad value; FLS, Flag leaf spad value; VLTH, Vegetative leaf thickness; FLTH, Flag leaf thickness; VLT, Vegetative leaf temperature; FLT, Flag leaf temperature; FT, Time for 50% flowering; PH, Plant height; TN, Tiller
number per plant; PN, Panicle number per plant; PL, Panicle length; PB, Primary branches per panicle; SB, Secondary branches per panicle; FGN, Filled grain number per panicle; %SF, Percent spikelet fertility; TGN, Total grain number;
BM, Biomass per plant; YPP, grain yield per plant. The values in bold are direct contribution of traits to YPP. ***Significant at 0.001 level of probability; **Significant at 0.01 level of probability; *Significant at 0.05 level of probability.
Heat Tolerance in Rice ILs
October 2017 | Volume 8 | Article 1819
PB
SB
Prasanth et al.
Heat Tolerance in Rice ILs
Candidate Genes
In all, 45 candidate genes which are known to respond to various
stress conditions were identified within the region, from 1 Mb
upstream to 1 Mb downstream of the nine markers significantly
associated with six traits (flag leaf spad, flag leaf thickness,
vegetative leaf temperature, plant height, panicle number and
tiller number) under heat stress conditions in both wet and dry
seasons (Supplementary Table 5). Out of these 45, nine are stress
response genes, six are defense responsive genes, five are heat
shock binding protein genes and four are related to oxidative
stress response.
Correlation of Yield Per Plant (YPP) with
Other Traits
FIGURE 2 | Venn diagram showing total 613 unique and shared significant
marker-trait associations among normal and heat stress conditions during wet
season 2012 and dry season 2013.
Table 4 gives the list of traits correlated significantly with YPP
based on Pearson correlation and list of traits sharing marker
associations with YPP based on SMA. During wet season, YPP
was correlated positively and significantly with BM, SB, and PL
in control but it was correlated with BM, FGN and %SF in heat
stress. SMA results showed four markers (RM88, RM106, RM274,
and RM3586) associated with YPP in WN, out of which two
were shared by BM (RM106 and RM274) and nine other traits
shared only one common marker (FLTH, FLT with RM88, PB,
PL, FLS with RM106, FT, PH, TN, PN with RM274). In WH,
out of 17 markers associated with YPP, both BM and PH showed
14 common markers each and FLTH and FLS had 13 and 10
markers common, respectively. BM was strongly associated with
YPP in wet season in both control and heat stress based on
both correlation and SMA analysis. During dry season, YPP was
correlated significantly with PL, FGN, and TGN in control and
with FGN and %SF in heat stress. According to SMA, in DN,
out of 21 YPP associated markers, FLTH and PH showed 16
and 9 common markers, respectively. TGN and BM also showed
seven and six common markers with YPP. In DH, out of nine
markers associated with YPP, seven, six and five markers were
common with VLTH, TGN and %SF respectively. These results
indicate that based on both correlation and SMA analysis, FGN
was consistently associated with YPP under both treatments in
dry season whereas %SF was associated with YPP only under heat
stress in dry season. There was a significant negative correlation
between YPP and FLTH in WH and DN conditions.
TABLE 3 | Marker trait associations observed only under heat stress conditions in
both seasons (under polycover house during wet season 2012 and late sown
conditions during dry season 2013).
Trait
FLS
Chromosome
Polycover
house (F value)
Late sown (F
value)
RM243
1
5.62*
5.15*
RM517
3
4.94*
5.18*
FLTH
RM225
6
10.97**
6.60*
VLT
RM243
1
4.57*
6.93*
RM518
4
4.43*
4.74*
RM525
2
18.16***
7.13*
PH
RM195
8
4.58*
5.66*
RM282
3
8.17**
6.91*
TN
RM489
3
10.55**
5.29*
RM570
3
10.55**
5.29*
PN
RM489
3
8.61**
6.22*
RM570
3
8.61**
6.22*
***Significant at 0.001 level of probability; **Significant at 0.01 level of probability;
*Significant at 0.05 level of probability. FLS, Flag leaf spad value; FLTH, Flag leaf thickness;
VLT, Vegetative leaf temperature; PH, Plant height; TN, Tiller number per plant; PN, Panicle
number per plant.
beginning itself and had time to acclimatize slowly to heat unlike
in polycover house where plants were covered at only flowering
stage in wet season to give heat stress. Correspondingly, the
percent reduction in trait values in DH was lesser than in WH
when compared with their respective control conditions.
The present study showed that the mean %SF, BM, and YPP of
all genotypes deceased under heat stress conditions. These results
are in concurrence with earlier reports. High temperature at
heading stage significantly reduced anther dehiscence and pollen
fertility rate, leading to reduction in the number of pollen on
stigma and subsequent reduction in spikelet fertility and yield
in rice (Ahmad et al., 2010). High night temperature (32◦ C) led
to increase in spikelet sterility (by 61% compared to control) in
rice which resulted from decreased pollen germination (36%)
(Mohammed and Tarpley, 2009). Grain yield decreased by 2–6%
DISCUSSION
Changes in phenology in response to heat stress can reflect
the interactions between stress environment and plants. In the
present study, in heat stress conditions, tiller number, filled grain
number, % spikelet fertility and grain YPP decreased whereas
plant height increased significantly in both seasons. The effects
of heat stress were not consistent in the two seasons for other
traits such as flowering time, leaf spad, leaf temperature, biomass.
In case of VLS, VLTH, and FLT the trait mean values were
reduced significantly under WH but increased significantly under
DH. The reason for these contrasting results in the two seasons
might partly be due to more humidity in polycover house. Also
during DH, plants were exposed to heat gradually from the
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Marker
8
October 2017 | Volume 8 | Article 1819
Prasanth et al.
Heat Tolerance in Rice ILs
TABLE 4 | Correlation between grain yield and other agronomic and yield traits based on Pearson correlation and single marker analysis (SMA).
Season
Treatment
Wet season
2012
Normal conditions
BM (0.453**)
SB (0.397*)
PL (0.343*)
4
BM (2)
Polycover house conditions
(heat stress)
BM (0.482**)
FGN (0.377*)
% SF (0.372*) FLTH (-0.377*)
17
BM (14)
Normal sown conditions
PL (0.494**)
FGN (0.415**) TGN (0.378*)
21
FLTH (16) PH (9)
Late sown conditions (heat
stress)
FGN (0.537***) %SF (0.514**)
9
VLTH (7)
Dry season
2013
Pearson correlation
Number of markers
associated with YPP
FLTH (-0.356*)
SMA
PH (14)
FLTH (13) FLS (10)
TGN (7)
UFGN (7) TGN (6)
BM (6)
%SF (5)
The value in parenthesis are r2 value for Pearson correlation and no of markers associated in SMA. ***Significant at 0.001 level of probability; **Significant at 0.01 level of probability;
*Significant at 0.05 level of probability. FLS, Flag leaf spad value; VLTH, Vegetative leaf thickness; FLTH, Flag leaf thickness; PH, Plant height; PL, Panicle length; SB, Secondary branches
per panicle; FGN, Filled grain number per panicle; UFGN, Unfilled grain number per panicle; %SF, Percent spikelet fertility; TGN, Total grain number; BM, Biomass per plant; YPP, grain
yield per plant.
with increase in temperature by 1◦ C (Wu et al., 2016). High
temperature reduces plant growth by affecting the shoot net
assimilation rates and thus the total dry weight of the plant
(Wahid et al., 2007). However, Hatfield and Prueger (2015)
reported that even though grain yield was reduced due to high
temperature but leaf area and vegetative biomass were not
significantly affected. Oh-e et al. (2007) and Poli et al. (2013)
reported that plant height was more in high temperature than in
ambient temperature condition. The current study also showed
increase in plant height with increase in temperature. Increase
in plant height increases transpiration cooling effect and helps to
avoid high temperature in mungbean and wheat (Kumar et al.,
2011; Hasanuzzaman et al., 2013).
The performance of each genotype for 18 traits
(Supplementary Table 3) indicates that different genotypes
have different mechanisms to cope with heat stress as all top 10
high grain yielding genotypes did not show similar trait values
such as biomass, % spikelet fertility, spad values, leaf temperature
and leaf thickness under heat stress conditions. Tiller number
and FGN were the common traits that contributed to more
yield in both WH and DH treatments. Even in the same season
(for example in WH), few high yielding genotypes (eg., K-16-3
and KMR3) maintained low VLT and high VLS and BM to
achieve high YPP but other genotypes (e.g., K-103 and K-198)
maintained low VLTH and BM and high %SF to achieve high
YPP.
Heat tolerant rice genotypes that had relatively high
grain yield under high temperature stress maintained lower
leaf temperature and higher spad values than heat sensitive
genotypes. Heat tolerant genotypes often have the ability to
reduce leaf temperature leading to reduced transpiration rate and
thus retain normal physiological functions of the leaves under
heat stress. Leaf spad values increased under high temperature,
and led to delay in grain filling phase (Cao et al., 2008; Jumiatun
et al., 2016). Jumiatun et al. (2016) also reported that different
rice genotypes with better grain yield under heat stress, show
different responses as adaptation to high temperature. For
example, IR 64 showed the lowest leaf temperature, but Menthik
Wangi and Jatiluhur showed well-exerted panicles perhaps to
lower panicle temperature but the panicle temperature was not
mentioned in this report. Plants which can produce many leaves
Frontiers in Plant Science | www.frontiersin.org
around panicles can withstand high temperature because anther
dehiscence is not adversely affected and benefits panicles due to
transpiration cooling effect of leaves (Shah et al., 2011).
High temperature stress reduces yield indirectly through
affecting various yield components. In the present study, both
Pearson correlation and path coefficient analysis were performed
to measure direct and indirect effect of component characters
on YPP. In both seasons, in control, PL, TGN, and BM showed
significant positive correlation with YPP whereas in heat stress,
only %SF and FGN showed significant positive correlation. Yield
per plant (YPP) and FLTH were negatively correlated but not
consistent across treatments. In WN conditions, FTH, PH, TN,
SB, TGN showed significant direct effect on grain yield while
FGN showed significant direct negative effect. However, in WH,
FGN showed significant positive direct effect on yield as did
FLT, PN, and SB on yield. Based on both correlation and path
coefficient analysis results, the major traits that could be exploited
in breeding programmes for more grain yield are PL, BM, SB,
and FGN in control and %SF, FGN, TGN, SB, and TN in heat
stress. Mishra et al. (2015) and Reddy et al. (2013) also reported
that based on correlation and path-coefficient analysis on grain
and its related components in rice genotypes, biological yield per
hill, harvest index and number of spikelets per panicle were major
contributing characters to rice grain yield and could be depended
on for selection of genotypes to increase genetic yield potential
of rice. Greater yield can be attained by increasing total crop
biomass, as there is a possibility to increase the allocation of that
biomass toward grain production (Evans and Fischer, 1999; Peng
et al., 2004). Total crop biomass production mainly depends on
the balance between photosynthetic gains and respiratory losses,
which in turn are greatly influenced by temperature (Yoshida,
1981). Number of panicles and harvest index are good indictors
of indirect selection for grain yield due to their high direct effects
and significant correlation with grain YPP in rice under warm
conditions in Khuzestan (Moosavi et al., 2015).
Previously we reported association analysis with four traits
only viz., % spikelet fertility, grain YPP and their heat
susceptibility index (Prasanth et al., 2016). More agronomic traits
are considered in this paper. In all, 613 marker-trait associations
were observed using 48 selected SSR markers and 19 traits based
on SMA. Out of 613, more number of unique associations (67)
9
October 2017 | Volume 8 | Article 1819
Prasanth et al.
Heat Tolerance in Rice ILs
were observed in WH condition whereas more number (33)
of shared associations were between DN and DH conditions.
The common marker-trait associations observed under high
temperature conditions in both wet and dry season are given
importance as they were associated with traits only under heat
stress conditions. RM225 on chromosome 6 was associated with
flag leaf thickness. Xiao et al. (2011) reported two QTLs qPF4
and qPF6 between RM5687 and RM471 on chromosome 4
and between RM190 and RM225 on chromosome 6 affecting
pollen fertility in recombinant inbred lines derived from a cross
between a heat tolerant rice cultivar 996 and a sensitive cultivar
4628. Thus, RM225 is a common marker. In our study, both
panicle number and tiller number were associated with two
markers RM489 and RM570 on chromosome 3. Plant height
was associated with RM195 on chromosome 8 and RM282 on
chromosome 3 while flag leaf spad was associated with RM243
on chromosome 1 and RM517 on chromosome 3. These markers
were reported to be associated with spikelet fertility in previous
studies on heat stress (Cao et al., 2003; Zhang et al., 2008). RM570
is also reported to be linked with a gene Os03 g62910 involved
in heading date postponement and development of wider and
thicker leaves in rice (Yu et al., 2013). Thus, leaf thicknes and
width appear important traits in the context of heat tolerance.
RM282 was reported to be linked with spikelet number per
panicle under moderate and extreme drought stress conditions in
field (Mei et al., 2004). RM525 on chromosome 2 was associated
with VLT in the present study. Liu et al., 2015 also reported
that RM525 was associated with grain filling rate at two stages,
7 and 28 days after flowering during their studies of time-course
association mapping on grain filling rate in 96 rice genotypes.
Grain filling rate is influenced by temperature and we show that
the same region of RM525 is associated with leaf temperature.
In the present study, 45 candidate genes were identified close
to the nine markers significantly associated with six traits (flag
leaf spad, flag leaf thickness, vegetative leaf temperature, plant
height, panicle number and tiller number) under heat stress
conditions in both wet and dry seasons. These genes include
HSPs, OsTIL-2, Glyoxalase II, OsSPX1, TPR-1, and NAM. Hsp70
and DnaJ were reported to be highly up-regulated under heat
stress in panicles of heat tolerant rice cultivar 996 (Zhang et al.,
2012). The promoter region of OsTIL-2 on rice chromosome 8
has several heat shock elements (Charron et al., 2005). Chi et al.,
2009 reported that TILs (Temperature induced lipocalin) are
required for basal and acquired thermo-tolerance in Arabidopsis
and act against lipid peroxidation induced by high temperature.
Glyoxalase II is required for abiotic stress response including heat
stress in Arabidopsis (Devanathan et al., 2014). OsSPX1 is a gene
for semi male sterility whose down regulation causes sensitivity
to cold and other oxidative stresses in rice (Wang et al., 2013).
These genes are reported to be putatively functional under
heat stress conditions (http://rapdb.dna.affrc.go.jp/) and are
candidate genes for expression studies and evaluation for use in
marker assisted selection for heat tolerance.
CONCLUSION
Heat tolerance is a complex phenomenon which may be species
specific, tissue specific and even developmental stage specific.
Thus, heat tolerance should not be regarded as a single trait.
In the present study, correlation and path coefficient analysis
showed that the major traits that contribute to heat tolerance
in rice are TN, SB, TGN, FGN, and %SF. These traits can be
considered important while screening rice genotypes for heat
tolerance. This study also emphasized the importance of elite
× wild introgression lines in the development of heat stress
tolerant rice varieties. Three ILs (K-377-24, K-16-3, and S-148)
were identified as heat tolerant and another three lines (K363-12, S-75, and Vandana) were identified as heat susceptible
based on YPP in both WH and DH conditions. These selected
lines could be exploited in further breeding and genomic studies
on rice heat stress. Based on SMA, 12 significant marker-trait
associations which were common under heat stress conditions
in both wet and dry seasons are identified as high priority
regions for basic and applied studies on heat tolerance. Thus,
we identified markers and genes which may be useful for
marker assisted selection and also in functional genomics for
discovery of genes important in increasing heat tolerance of rice
crop.
AUTHOR CONTRIBUTIONS
The present study was done under the guidance of SN. SN
and VVP designed the experiment. VVP, MSB, and VGNTV
carried out experiment and collected the data. RKB did data
analysis and wrote manuscript. SN, SRV, and SKM corrected the
manuscript.
ACKNOWLEDGMENTS
This work was supported by National Innovations on Climate
Resilient Agriculture (NICRA), Indian Council of Agricultural
Research (ICAR), Ministry of Agriculture, Govt. of India [F. No.
Phy/NICRA/2011-2012] and rice lines used in this work were
generated in a previous project DBT No.BT/AB/FG −2 (PHII)
IA/2009 and (BT/PR-9264/AGR/02/406(04)/2007) funded by
Department of Biotechnology, Government of India to SN.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpls.2017.
01819/full#supplementary-material
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2017 Prasanth, Babu, Basava, Tripura Venkata, Mangrauthia, Voleti
and Neelamraju. This is an open-access article distributed under the terms of
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are credited and that the original publication in this journal is cited, in accordance
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