New insights into the genetic basis of natural chilling...

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New insights into the genetic basis of natural chilling and cold shock

tolerance in rice by genome-wide association analysis

Yan Lv, Zilong Guo, Xiaokai Li, Haiyan Ye, Xianghua Li, Lizhong Xiong*

National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene

Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China

*Corresponding author: [email protected]

Brief summary statement

By association mapping study of 529 rice accessions, we revealed a distinct genetic basis for

natural chilling and cold shock stress tolerance at the seedling stage, and we also found that

the cold adaptability of rice is associated with the subpopulation and latitudinal distribution.

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/pce.12635

mailto:[email protected]


ABSTRACT

In order to understand cold adaptability and explore additional genetic resources for the cold

tolerance improvement of rice, we investigated the genetic variation of 529 rice accessions

under natural chilling and cold shock stress conditions at the seedling stage using

genome-wide association studies, a total of 132 loci were identified. Among them, 12 loci

were common for both chilling and cold shock tolerance, suggesting that rice has a distinct

and overlapping genetic response and adaptation to the two stresses. Haplotype analysis of a

known gene OsMYB2, which is involved in cold tolerance, revealed indica-japonica

differentiation and latitude tendency for the haplotypes of this gene. By checking the

subpopulation and geographical distribution of accessions with tolerance or sensitivity under

these two stress conditions, we found that the chilling tolerance group, which mainly

consisted of japonica accessions, has a wider latitudinal distribution than the chilling

sensitivity group. We conclude that the genetic basis of natural chilling stress tolerance in rice

is distinct from that of cold shock stress frequently used for low temperature treatment in the

laboratory, and the cold adaptability of rice is associated with the subpopulation and

latitudinal distribution.

Key-words: rice; chilling tolerance; cold shock tolerance; GWAS; subpopulation distribution;

geographical distribution.


INTRODUCTION

Low temperature is one of the major abiotic stresses limiting plant growth, productivity,

and quality. It has been generally accepted that the breeding of cereal crops should be focused

on the improvement of stress tolerance and photosynthesis by increasing the use efficiencies

of water and nutrients, adjusting to local temperature and precipitation (Sang, 2011). With

global environmental worsening and abnormal climate changes, it is urgent to reveal the

genetic and molecular mechanisms of plant responses to low temperature stress and to search

for useful genetic resources for improving low-temperature tolerance. As one of the major

crops, rice is widely grown in tropical, subtropical, and temporal regions, and temperature is

one of the major environmental factors limiting its geographic distribution. The optimal

temperature for rice growth is 25-30°C (Kim et al., 2014). Previous studies on low

temperature stress in rice mainly concentrated on chilling stress (temperature around 10°C)

which was frequently used to distinguish it from freezing stress (temperature around 0°C),

however there is no clear definition of chilling or cold/freezing stress, and the treatment

temperatures were often different for the same term in many reports (Cheng et al., 2007, Guo

et al., 2006, Ma et al., 2015, Wang et al., 2013, Wang et al., 2014, Yang et al., 2012). To

date, no report has compared different low-temperature stresses such as natural chilling stress

with the acute freezing stress in rice. Recent studies have revealed some mechanisms and

signaling networks involved in the cold stress response in rice (Knight & Knight, 2012, Ma et

al., 2015, Wang et al., 2014, Yang et al., 2012). Zhao et al focused on moderate cold stress

(8°C) under natural environmental conditions using a cold tolerant variety for genome-wide

expression profiling, and they proposed a series of cold response mechanisms: the induction


of OsDREB2A, glutathione peroxidase (GPX), and glutathione S-transferase (GST) serving

as the main reactive oxygen species (ROS) scavenger, and the ABA signaling pathway plays

a dominant role (Zhao et al., 2014b). In addition, this study suggested that the cold stress

response of rice varies with the specific temperature imposed and the rice genotypes utilized

(Zhao et al., 2014b).

Rice has two major subspecies, indica and japonica. The eco-geographical

differentiation of the two subspecies has been reported (Londo et al., 2006, Yu et al., 2003). It

has been generally accepted that japonica rice had higher potential in cold adaptability than

indica rice (Cheng et al., 2007, de Los Reyes et al., 2013, Lu et al., 2014, Ma et al., 2015,

Morsy et al., 2005, Pan et al., 2015), but the genetic evidence for this is very limited.

Dissection of the genetic and molecular basis of cold response and adaptation is the

foundation for the improvement of low-temperature tolerance. Among the previous studies,

QTL mapping based on the molecular marker and linkage maps has always been a common

and classical approach for the genetic study (Fujino et al., 2004, Koseki et al., 2010, Liu et al.,

2013, Yang et al., 2013b). Liu et al combined whole-genome expression profiling analysis of

two parents of a genetic population and QTL mapping of rice under cold stress conditions at

the early seedling stage, and they identified a candidate gene LOC_Os07g22494, which was

further confirmed by genetic transformation approach (Liu et al., 2013). Besides the studies

at the seedling stage, QTL associated with cold tolerance at the germination stage (qLTG3-1,

qLTG11, qLTG9), booting stage (qCTB7, qLTB3), fertilization stage (qCTF7, qCTF8,

qCTF12), and reproductive stage (qPSST-3, qPSST-7, qPSST-9) have also been reported

(Fujino & Sekiguchi, 2011, Fujino et al., 2008, Iwata & Fujino, 2010, Li et al., 2013, Shinada


et al., 2013, Shirasawa et al., 2012, Suh et al., 2010, Zhou et al., 2010).

Recently, genome-wide association study (GWAS), representing one of the newly

developed genetic approaches, has been adopted to investigate the genetic architectures of

various important agronomic traits in rice. Huang firstly performed GWAS for 14 agronomic

traits and identified a number of loci potentially important for rice grain yield and

improvement, which strongly suggested GWAS based on second-generation sequencing

could be a powerful supplement for the traditional linkage mapping (Huang et al., 2010).

Then the same group conducted GWAS on flowering time and grain yield traits by increasing

the population to 950 accessions, and they pointed out that the larger sample could increase

the power to detect variants associated with the traits of interest (Huang et al., 2012). Besides

these, association mapping was conducted for stigma and spikelet characteristics (Yan et al.,

2009), aluminum tolerance (Famoso et al., 2011), grain concentrations of arsenic, copper,

molybdenum, and zinc (Norton et al., 2014), root traits (Courtois et al., 2013), sheath blight

resistance (Jia et al., 2012), grain color, phenolic content, flavonoid content and antioxidant

capacity (Shao et al., 2011), grain quality traits (de Oliveira Borba et al., 2010), grain

metabolites (Lou et al., 2011), harvest index (Li et al., 2012), and silica concentration

in rice hulls (Bryant et al., 2011). Meanwhile, the association analysis approach has also been

used to investigate the genetic variation of candidate genes for important traits in rice (Lu et

al., 2012, Tian et al., 2009, Wu et al., 2013, Yan et al., 2013, Zhao et al., 2011). Despite the

wide application of GWAS in the genetic dissection of agronomic traits in crops, very few

studies have used this approach to investigate low temperature tolerance (Huang et al., 2013,

Strigens et al., 2013). Recently, Pan et al. conducted association mapping for rice cold


tolerance at the germination and booting stages with 174 Chinese rice accessions and 273

SSR markers, and 51 QTLs for cold tolerance were detected (Pan et al., 2015), but the

genetic basis of natural chilling stress was not addressed.

In this study, a panel containing 529 accessions was used to conduct association analysis

of rice tolerance to natural chilling and cold shock stresses with an aim to reveal the genetic

difference of rice in response to the two stresses. Further haplotype analysis of a candidate

gene and the geographical distribution of the chosen accessions revealed indica-japonica

differentiation and latitudinal tendencies of the cold adaptability, which provided valuable

reference for elucidating the genetic basis and differentiation of low temperature tolerance in

rice.

MATERIALS AND METHODS

Materials

A total of 529 rice accessions including 202 from the China Core Collection and 327

from the World Core Collection were used for the association analysis (Supporting

Information Table 1). This panel of rice accessions is essentially the same as the panel of 533

accessions as previously described (Yang et al., 2014b) except three accessions (C126, W196,

and W232) with severe heterozygosity and one (W190) with a low mapping rate (10%)

omitted.

Low-temperature treatment

After germination for 7 days, the seedlings with uniform growth were transplanted to 10

cm x 10 cm pots each containing 9 seedlings. Each accession was planted in 3 pots as three


biological repeats, and the layout of the pots followed a completely randomized block design.

In this study, we designed two treatments to mimic the natural chilling (temperature

gradually declined to a range of 5-12°C) and cold shock (constant temperature at 4°C)

stresses respectively. For the natural chilling stress treatment, the seedlings were grown in a

greenhouse for 3 weeks, at which time the plants went into the 4-leaf-stage, the natural

chilling treatment was carried out in the greenhouse in winter (Wuhan, China) with the

heating and light turned off and the natural low-temperature fluctuating between 5-12°C

depending on the outside temperature. The temperature in the greenhouse was recorded every

half hour by a weather station (Spectrum Technologies, Inc. WatchDog 2000), and a portion

of the record is provided in Supporting Information Fig. S1. For the cold shock treatment, the

4-leaf-stage rice plants was performed in a growth chamber set at a constant 4°C with 14

hours/10 hours of light/darkness.

Determination of electrolyte leakage

The electrolyte leakage (EL) measurement was performed as previously described (Guo

et al., 2006) with minor modifications. Two fully expanded leaves from two plants were cut

into segments of similar sizes, and immersed in 8 ml of double distilled water in a 10 ml test

tube for 24 h at 25°C with continual shaking at a speed of 100 rpm. The initial conductivity

(R1) was measured with a conductivity meter (Model DDS-IIA, Shanghai Leici Instrument

Inc., Shanghai, China). Then, the test tubes were placed in boiling water for 20 min and

cooled naturally to room temperature, and the conductivity (R2) was determined again. The

relative EL was calculated as the ratio of R1 to R2. A total of 7 traits including EL under

normal condition (ELN), EL after natural chilling stress treatment for 3 days (ELC1) and 7


days (ELC2), EL after recovery for 7 days (ELR), EL before cold shock treatment (4°C)

(ELSN), and EL after cold shock treatment for 1 day (ELSC1) and 3 days (ELSC2) were

investigated in this study. Five ratio traits (ELR1, ELR2, ELR3, ELSR1, and ELSR2) were

calculated as the ratios of the EL values under stress conditions to the EL under normal

conditions (see Table 1 for definitions of these parameters or traits).

Other phenotypic data collection

According to the extent of leaf rolling, the survival rate, and the chlorosis conditions, we

divided the accessions into 5 resistance levels (score 1-5, respectively), which is another

feasible method to investigate the cold response phenotype. Score 1 (the most resistant)

indicated that the seedlings had normal leaf color with no damage, while score 5 (the least

resistant) indicated that all of the seedlings were wilting or dead. The resistance level (score)

under natural chilling stress (RLC) and after recovery (RLR) were collected, and the average

resistance level of three repeats was used for further analysis. The survival rate (SRC) as a

commonly used criteria for the chilling tolerance of rice was collected after the natural

chilling stress treatment, and the ratio of fresh vs dry biomass after natural chilling stress

(BMR) was used as another criteria to evaluate the natural chilling tolerance.

Microarray Analysis

GO analysis was performed in a MAS 3.0 molecule annotation system

(http://bioinfo.capitalbio.com/mas3/) and the microarray dataset was collected from our

group based on Affymetrix GeneChip Rice Genome Array (Supporting Information Table 2).

The chip data has been submitted to NCBI GEO database (GSE71680). The chip contained

57381 probes, among these, 1347 probes representing 1260 transcripts from indica, and

http://bioinfo.capitalbio.com/mas3/


54168 probes representing 48564 transcripts from japonica. It should be noted that 9311,

MH63, ZS97, TRAT109 in the chip dada corresponds to C145, C148, C147, C153 in our

panel and C087, C063, C070, C079 corresponds to 4 species Lixingeng, Geng87-304,

Youmangzaogeng and Muguanuo-1 in our panel. Chip data listed the signal ratio of stress

condition (cold shock stress at 4°C for 6 hours and 24 hours) to normal condition. Gene

cluster analysis was conducted using Gene Cluster 3.0 and Java Tree View.

Genome wide association study

A total of 529 accessions were collected to construct this association panel. For GWAS

of the 16 traits, we adopted a mixed-model approach using the factoral spectrally transformed

linear mixed model (FaST-LMM) program, with 4,358,600 SNPs across the entire rice

genome (minor allele frequency ≥ 0.05; the number of accessions with minor alleles ≥ 6). The

suggestive and significant P value thresholds of the entire population were respectively

1.21E-06 and 6.03E-08. The linkage disequilibrium (LD) statistic r2 was calculated by Plink

based on haplotype frequencies. More detailed information about the GWAS analysis was

referenced from the recent study (Yang et al., 2014b).

Subpopulation, geographical distribution and classification of rice accessions

The 529 accessions in our panel were divided into 4 subpopulations, including indica

subset (accounting for 56.77% of the collection), japonica subset (29.48%), aus type (8.70%),

and other subset (6.05%). The information on the subpopulation, and the degrees longitude

and latitude was referred to RiceVarMap (http://ricevarmap.ncpgr.cn/) (Zhao et al., 2014a).

Since our panel included 202 China Core Collection (CCC) accessions and 327 World Core

Collection (WCC) accessions, the selected accessions from different provinces of China and

http://ricevarmap.ncpgr.cn/


these were marked on the China Map, and the accessions with a geographical location (116.4,

39.9; China) were marked on the World Map.

The selection criteria for sensitivity or tolerance classification of the accession to the

two stresses were based on the indicative traits, except for the ELN and ELSN in chilling or

cold shock stress. The rice accessions selected for subpopulation or latitudinal distribution

analysis should be from the top or bottom 150 accessions according to the order of the values

for each trait used for evaluating the chilling or cold shock tolerance, and meanwhile the

accessions selected for each type should match the tolerant or sensitive criteria by 80% of the

traits under the same stress condition.

Statistical analysis

Differences in phenotypic and latitude values of accessions in the haplotypes or subgroups

were examined by one-way ANOVA and Duncan multiple comparison if ANOVA result is

significant (P＜0.05) (Lu et al., 2013). For the phenotypic values of the four haplotypes of

gene locus 07g44410, we used the Kruskal-Wallis test, which is a non-parametric test for one

factor ANOVA, and multiple comparison was examined if the test result was significant

different (P＜0.01). Differences in latitude values between specified groups such as

cold-tolerant and cold-sensitive groups were examined by Student's t-test. Statistical analysis

was run by IBM SPSS statistics 19.0.


RESULTS

Evaluation of the cold response of rice at the seedling stage

In previous studies of cold stress responses in rice, the artificial cold treatment is a cold

shock stress, and very few experiments mimic the natural chilling stress conditions. In this

study, we designed two treatments to mimic the chilling (temperature gradually declined to a

range of 5-12°C) and cold shock (constant temperature at 4°C) stresses respectively. The

temperature record of the natural chilling stress treatment is shown in Supporting Information

Fig. S1. Beside survival rate, a common criteria reflecting the final performance of plants

after stress recovery, electrolyte leakage (EL) in leaves was adopted as a major physiological

parameter in this study since EL can partially reflect the damage of green leaves during the

stress process but its genetic basis was seldom addressed. EL was measured to evaluate the

cold response of rice seedlings under natural chilling stress (ELN, ELC1, ELC2, ELR) and

cold shock stress (ELSN, ELSC1, and ELSC2) (Table 1 for definitions of these parameters or

traits). Meanwhile, other traits such as the fresh vs dry biomass ratio after natural chilling

stress treatment (BMR), resistance level (score) under natural chilling stress treatment (RLC),

resistance level (score) after recovery (RLR), and the survival rate after natural chilling stress

treatment (SRC) were applied in the evaluation of natural chilling stress tolerance since these

indices are more meaningful in rice breeding in natural chilling tolerance.

The results of seven electrolyte leakage indices (ELN, ELC1, ELC2, ELR, ELSN,

ELSC1, and ELSC2) indicated that with the prolonged time duration of stress treatment, large

variation was observed for EL and the relevant ratio traits including ELR1, ELR2, ELR3,

ELSR1, and ELSR2 (Table 1). The range of ELN and ELSN was not exactly the same, which


may be due to the slight differences of seedling growth state in the two experiments. Among

these traits, the variation range of ELC1 is similar to ELN, but the variations of ELC2 and

ELR were obviously increased, suggesting slight cell membrane damage at the early stage of

natural chilling stress treatment, but that the damage became serious and irreversible at the

later stage of the stress treatment. The situation was different in the cold shock stress

treatment, in which ELSN, ELSC1, and ELSC2 were increased since the onset of the stress

treatment (Table 1), indicating that the cell membrane damage was faster and more

significant.

The variation distributions of the cold response indices or traits are shown in Supporting

Information Fig. S2, which can be roughly classified into three categories: three traits (ELN,

ELC1, and ELSN) showed typical normal distribution; eight traits (BMR, RLR, ELR1, ELR2,

ELR3, ELSC1, ELSC2, ELSR1, and ELSR2) showed skewed distribution; while the other

four traits (RLC, SRC, ELC2, and ELR) showed bimodal distribution.

Correlations among the cold response traits

The correlation analysis among the cold response traits was performed, and the results

are presented in Table 2. Eleven traits under the natural chilling stress treatment and five

traits under the cold shock stress treatment had significant correlations with r which ranged

from 0.3 to 0.8 between each other. Significant correlation was observed between the green

leaf area or biomass-related traits (such as BMR, RLR, and RLC) and the EL traits (Table 2),

suggesting that the EL indices can largely reflect the damage of green leaves during cold

stress process. In addition, the ratio trait ELR2 was strongly correlated with the


corresponding trait under natural chilling stress conditions (ELC2; r ＞0.8), as well as ELR

and ELR3, ELR1 and ELC1, ELR2 and ELC2. However, the 11 traits and 5 traits under

different stress conditions had no significant correlations, indicating that the rice seedlings

exhibited different responses under these two types of low-temperature stress conditions at

the physiological or biochemical levels.

Loci associated with low-temperature tolerance identified by GWAS

We selected 4,358,600 SNPs across the entire rice genome for GWAS for the cold

response traits described above. Any two leading SNPs within a 200 kb range were

considered as one association locus. The association analysis for the whole panel ultimately

identified 132 loci associated with natural chilling stress and cold shock stress with the

suggestive threshold value at 1.21E-06 (Supporting Information Table 3). We also performed

GWAS on indica and japonica subpopulations and a large number of peaks were also

detected (Supporting Information Table 4, 5). More detailed information for SNPs, physical

positions, P values are listed in Supporting Information Table 3 (the whole panel), Supporting

Information Table 4 (the indica panel), Supporting Information Table 5 (the japonica panel).

A quantile-quantile plot of all 16 traits is provided in Supporting Information Fig. S3, S4, S5.

Manhattan plots for the association analysis of these traits are displayed in Fig. 1 and

Supporting Information Fig. S6, S7, S8. Since FaST-LMM program used here could reduce

the effect of population structure (Yang et al., 2014a), and the quantile-quantile plot of all 16

traits for the whole panel showed satisfied effect in control of population structure

(Supporting Information Fig. S3), we focused on the GWAS results from the whole panel in


the following analyses.

For a better view of the comprehensive association results, all of the detected association

loci in the whole panel were marked on the 12 chromosomes according to their physical

distance on the rice genome (Fig. 2), and the loci for natural chilling stress (57 loci) and cold

shock stress (63 loci) are shown in blue and green respectively. The loci detected for the traits

under the two stress conditions (12 loci) are marked in red. From the graphic view, we

noticed that 132 of the loci are distributed widely in the rice genome, with chromosomes 2

and 10 exhibiting relatively fewer detected loci.

Among the 132 association loci, 39 were detected for two or three different traits, and 24

loci had a significant P value (threshold value at 6.03E-08) for at least one trait. Examples of

loci (L18, L27, L63, L79, and L104) which were detected for two traits are shown in Fig. 1, L

is short for locus. In addition, 18 of the 39 loci were associated with EL traits under the stress

condition and the relevant ratio traits as well, which were consistent with the correlation

results in Table 2. It was noted that six loci (L18, L39, L51, L79, L96, and L121) in Fig. 2

were detected for three traits which were correlated with each other, indicating that these loci

probably have important roles in cold tolerance.

Comparison of cold tolerance loci from GWAS and genetic mapping

There were many overlaps between the loci detected by GWAS in this study and the

reported QTL related to low-temperature stress tolerance. A total of 68 loci from this study

were located in or overlapped with the reported QTL (shown in grey in Fig. 2 with the

corresponding references in Supporting Information Table 3) in which half of them were


detected for the cold tolerance at the seedling stage, while the other QTL were detected at the

germination stage or the reproductive stage. Although no significant loci in this study were

found with overlapping to the reported QTL qCOLD1 with causal gene COLD1 characterized

recently (Ma et al., 2015), overlapping loci were found for the other two QTLs, qCOLD4

(overlapped with L59 and L60) and qCOLD2 (overlapped with L87 and L88), reported in

their study. Such a significant portion of overlapping further supported our GWAS results.

The co-localization results of the 68 loci suggested our GWAS on cold stress in rice was

feasible and efficient, while the other 64 loci without overlap to reported QTL may be

potential novel loci for chilling and/or cold shock tolerance in rice.

Cold-responsive genes within the significant association loci

By checking our whole genome expression profiling data, all of the cold

stress-responsive genes (potential candidates) within 200 kb (100 kb upstream and 100 kb

downstream of the leading SNPs) for the 132 loci were selected and listed in Supporting

Information Table 6. Five reported genes, OsMYB2 (Os03g20090), Ctb1 (Os04g52830),

OsRAN2 (Os05g49890), OsiSAP8 (Os06g41010), and OsLti6a (Os07g44180), have been

confirmed to participate in cold response (Chen et al., 2011, Kanneganti & Gupta, 2008, Ma

et al., 2015, Morsy et al., 2005, Saito et al., 2004, Yang et al., 2012), and these genes are

indicated with black arrows in the Manhattan plots (Fig. 1). Besides these five genes,

additional stress-related genes such as SRWD5, OsPR4a (Huang et al., 2008, Wang et al.,

2011), SLAC1, OsACO7, OsACS2, and CRR6 that have been reported to be involved in

stomatal conductance, ethylene biosynthesis and metabolism (Iwai et al., 2006, Kusumi et al.,


2012, Yamori et al., 2011), were also induced by cold stress according to the expression

profiling data (Supporting Information Fig. S9), suggesting that they may also be involved in

cold response.

We performed cluster analysis for the cold responsive genes located in the association

loci using the gene chip expression profiles from the seedlings of 8 rice accessions (3

cold-sensitive: C148, C147, C145; and 4 cold-tolerant: C087, C063, C070, C079; and one

intermediate type C153) treated with cold shock stress (4°C) for 6 hours and 24 hours

(Supporting Information Table 2). The analysis revealed two main categories of expression

patterns: 53 genes were down-regulated in at least 4 accessions by cold shock stress, and 49

genes were up-regulated in different levels (Supporting Information Fig. S9). The 5 reported

genes involved in cold stress also exhibited different expression patterns. Ctb1 and OsLti6a

were both weakly (less than two-fold) down-regulated in these 8 accessions after cold shock

treatment for 6 hours and 24 hours, while OsRAN2 was weakly up-regulated in the 24 hour

cold shock treatment. OsMYB2 was highly induced after stress in the rice accessions C148,

C153, C063, C070, and C079. The differential expression patterns of these genes suggested

that these rice accessions may have different mechanisms in response to the cold stress.

Haplotype analysis for the reported gene OsMYB2

Further haplotype analysis was focused on the reported gene OsMYB2 since it was

reported to participate in abiotic stress (including cold, salt, and drought) response at the

seedling stage (Yang et al., 2012), and the L33 containing OsMYB2 was overlapped with the

known QTL qLVG3 (Han et al., 2006). The SNP data was referred to RiceVarMap including


its intragenic region and 2 kb upstream (Zhao et al., 2014a). Four major haplotypes were

observed, with low frequency haplotypes (less than 5 accessions) being omitted (Fig. 3a). We

conducted multiple comparison tests of the traits with log transformation for equal variances,

and the results showed that Hap1 and Hap2 had lower ELSR2 values (corresponding to low

cold shock sensitivity) than Hap3 (corresponding to high cold shock sensitivity) (P＜0.05),

while Hap4 was an intermediate type. At the OsMYB2 locus, there was one synonymous SNP

(11325754) and three nonsynonymous SNPs (11325395, 11325497, and 11325747) in the

second exon, six SNPs in 5’ and 3’ untranslated regions, one SNP in the intron, and 35

substitutions in the 2 kb cis-regulatory region. We noticed that two nonsynonymous SNPs led

to changes in amino acids (one at 11325395 caused a C in Hap1 to a Y in the other 3

haplotypes, and the other at 11325747 caused a W in Hap1 to an R in the other 3 haplotypes),

which suggests that these SNPs may be associated with the gene function to a certain degree.

We further checked the latitude distribution of the haplotypes by a scatter diagram of the

latitude of origin of these accessions (Fig. 3b). The accessions in Hap1 were from higher

latitude regions compared to the accessions in the other three haplotypes (P＜0.05) (Fig. 3c).

We also checked the subpopulation and geographical distribution of all 412 accessions

in relation to the four haplotypes of OsMYB2 (Fig. 4). It was noticed that 97.75% of the

accessions in Hap1 group belong to japonica, while 89.03% of the accessions in Hap2 belong

to indica. Meanwhile, 77.27% of the accessions in Hap3 group belong to aus subgroup, and

67.81% and 23.29% of the accessions in Hap4 group belong to indica and japonica

respectively (Fig. 4b), which may partially explain why Hap4 exhibits an intermediate type in

terms of cold sensitivity. We also found that accessions in Hap1 in red distributed widely no


matter around the world or China (Fig. 4a,c). These results suggest that the rice accessions in

different haplotypes of OsMYB2 had indica-japonica differentiation and differential

latitudinal distribution tendency.

Indica-japonica differentiation and latitudinal distribution are associated with the cold

adaptability of rice

From the results of the OsMYB2 haplotype analysis above, we hypothesized that

indica-japonica differentiation and the latitudinal distribution of rice may generally be

associated with cold adaptability. To test this hypothesis, we further checked if there existed

such differences between the accessions in our panel based on the relative chilling tolerance

or cold shock sensitivity. We selected 114 chilling tolerant (CT), 143 chilling sensitive (CS),

123 cold shock insensitive (CSI), and 131 cold shock sensitive (CSS) accessions from the

panel according to their comprehensive performance under the two different stress conditions,

and checked their subspecies classification and latitudinal distribution (Supporting

Information Fig. S10, S11). The results showed that 82% of the CT accessions belong to

japonica rice while 76% of the CS accessions belong to indica rice. However, for the cold

shock stress tolerance, the indica-japonica subpopulation distribution for both CSI and CSS

had no obvious tendencies (Supporting Information Fig. S10b, S11b).

The geographic origins of these accessions were also marked on a world map and a map

of China (Supporting Information Fig. S10a, c, and Fig. 11a, c). For the accessions under

natural chilling stress condition, when compared with the CS accessions (marked in blue), the

CT accessions (red) were distributed in a much larger geographic region both in China and


the world (Supporting Information Fig. S10). However, the geographic distribution

differences between CSI and CSS accessions were not obvious. As latitude has a great

influence on temperature which limits rice growth, we also performed scatter diagram and a

statistical analysis for the latitudes of all of the chosen accessions (Fig. 5a, b, Supporting

Information Fig. S12a, b). The results showed that the latitudinal distribution of the CT group

is significantly higher than that of CS group (P=1.28E-03) (Fig. 5a, b), while the two groups

under cold shock stress condition (CSI and CSS) exhibited no differences in latitudinal

distribution (Supporting Information Fig. S12a, b). Among the four types (CT, CS, CSI, CSS),

there are 32 accessions classified as both CT and CSI (tolerant to both stresses) and 38

accessions classified as both CS and CSS (sensitive to both stresses) (Fig. 5c). The latitudes

of the CT and CSI accessions are also generally higher than that of the CS and CSS

accessions (P=8.27E-04) (Fig. 5d, e), while the latitudinal distributions of the overlapped 25

accessions between CT and CSS and the overlapped 32 accessions between CS and CSI

showed no obvious difference (Supporting Information Fig. S12c, d). The subpopulation and

the geographical distribution of the overlapped accessions between any two of the four types

(Supporting Information Fig. S13) showed no obvious trends probably because of the small

sample number. These results together indicate that indica-japonica differentiation and the

latitudinal distribution of rice is associated with cold adaptability, which is essentially

determined by the natural chilling tolerance rather than by cold shock tolerance.


DISCUSSION

Comparison of association and QTL mapping of cold tolerance

Association mapping was applied to a vast range of complex traits which are important

in the agronomy and breeding improvement of many fundamental crops (Hall et al., 2010,

Shao et al., 2011). According to our results, association mapping was more efficient to detect

the number of loci controlling complex traits such as chilling or cold tolerance compared to

the traditional QTL mapping. Previous studies identified quite a few QTLs which contributed

to chilling tolerance either at the seedling stage or the germination stage in rice, such as

qCTS2, qCTS7, qLTG-3, qLTG-11-1, and clr9, which are shown in Fig. 2 (Jiang et al. 2006;

Liu et al. 2013; Oh et al. 2004). However, these QTLs have large physical intervals, making

subsequent fine mapping difficult. When utilizing QTL mapping one always needs to

generate high quality mapping populations, which is costly and time consuming (Hall et al.,

2010, Holland, 2007, Paterson, 1995). Certainly there are successful examples utilizing a

QTL mapping strategy for fine mapping of low temperature tolerance such as qLTG-9 and

qLTG3-1 (Fujino et al., 2008, Fujino et al., 2004, Li et al., 2013, Ma et al., 2015), and even

for cloning of cold tolerance genes such as COLD1 (Ma et al., 2015).

It should be noted that association mapping may lead to false positive associations. In

this study, although most of the 16 traits used for the association analysis were correlated

with each other, 93 of the 132 loci were detected only for one trait, which hinted that some of

these loci (especially those with a high suggestive P-value) may be false associations. In

addition, one association locus in this study (and other studies in rice as well) was defined as

a 200 kb region containing 10 or more genes, which makes further confirmation of these loci


more difficult. For comparison, species such as maize exhibit a small linkage disequilibrium

(LD), which determines the resolution at 50-100 kb for association mapping, as the average

gene number is 1-2 genes within a 100 kb region of the maize genome (Huang et al., 2013).

Therefore, reducing the false positives as much as possible is important for the subsequent

verification of GWAS results and the cloning of causal genes. Besides a low P-value, the loci

with pleiotropic effects or overlapping with known QTL for similar traits can be considered

as positives with high confidence. In this study, 39 loci were detected for two or three traits,

such as L18, L27, L63, L79, and L104 which are indicated in Fig. 1, and 68 loci were

co-localized with known QTL, and these loci may be considered with high priority for further

validation. For example, the L18 in Fig. 2 with the most significant P value was co-located

with the reported QTL qSCT1a/qSCT1B (Kim et al., 2014), which was also detected at the

seedling stage under chilling stress conditions, even though the treatment temperature was

different. For preliminary validation of the association results, we selected a few candidate

genes (Supporting Information Fig. 9, Supporting Information Table 6) for haplotype analysis.

Besides the OsMYB2 that had been reported in cold stress response, LOC_Os07g44410

which encodes a WD-40 protein and was induced by cold stress in the 8 accessions, is

another example. This gene is located in L77 (P = 5.30E-08) which was detected for the trait

ELR and overlapped with the reported QTL qLVG7-2 (Han et al., 2006) . The mean values of

ELR of the accessions in Hap1 and Hap3 of this gene were significantly smaller than that of

Hap2 and Hap4, and the four haplotypes also showed indica-japonica distribution tendency

(Supporting Information Fig. S14a) and difference in latitude distribution (Supporting

Information Fig. S14b, c), which is similar to the haplotype analysis results of OsMYB2.


Therefore, combining the whole genome expression profiling data with haplotype analysis

may narrow down the number of candidate genes.

Rice has low LD decay which made it easy to produce the false positives (Han & Huang,

2013). Actually, LD can be affected by genetic drift, natural selection and population

stratification, and the last one was considered as the major factor (Cardon & Palmer, 2003).

Researchers have explored analytical methods to reduce false positives including structured

association (SA), and genomic control (GC) for population structure (Yu et al., 2006).

Recently, GWAS was successfully adopted to dissect the genetic basis of metabolites by

integrating genomic data and comprehensive metabolic profiles in rice, which allows for the

large-scale identification of candidate genes, the elucidation of metabolic pathways, and for

notably improving the resolution of association mapping (Chen et al., 2014). The

combination of association studies of traits and metabolic changes may be adopted for

dissecting the genetic architecture of complex traits such as chilling or cold tolerance in

future studies.

Distinct genetic basis of natural chilling and cold shock tolerance

The low temperatures action on rice under natural conditions mainly includes two types:

1) natural chilling stress during which the temperature gradually declines to an unfavorable

range for rice growth; 2) cold shock stress caused by the rapid (even overnight) decline of the

temperature nearly to the freezing point, which occasionally occurs in temporal regions at the

rice seedling stage. In most studies of cold stress responses in rice, the artificial cold

treatment is very close to the cold shock, and very few experiments mimic natural chilling

stress conditions. Plants have evolved diverse mechanisms in response to chilling and


freezing temperatures (Zhu et al., 2007). Although some studies on chilling or cold shock

responses have been reported (Chaikam & Karlson, 2008, Chawade et al., 2013, de Los

Reyes et al., 2013, Mao & Chen, 2012, Yang et al., 2013a), the differences between the

genetic basis of chilling and cold tolerance were seldom addressed. To the best of our

knowledge, this study was the first attempt to address the difference in genetic basis of

chilling and cold shock stress tolerance. We found that the performance of various traits

under chilling and cold shock stress conditions exhibited no correlations (Table 2), suggesting

that rice may have evolved different mechanisms to cope with chilling and cold shock

stresses. Taking EL for example, which were the main traits analyzed here, it is generally

accepted that the cell membrane is one of the first targets of abiotic stresses, especially when

different low temperature stress occurs, and the maintenance of their integrity and stability

under stress conditions is a major component of resistance (Morsy et al., 2005, Thomashow,

1999, Whitlow et al., 1992). We observed that most of the loci were unique for chilling or

cold shock stress, except for 12 loci (Fig. 2, Supporting Information Table 3) which were

simultaneously detected for some of the traits under the two stress conditions. Half of the 12

common loci were detected for EL traits, suggesting the genetic basis of EL change under

chilling and cold shock stress tolerance may be partially common. These common loci for

both chilling and cold shock tolerance may provide referential information to unveil the

common molecular mechanisms in response to low temperature stresses.

Another notable result is that 68 of 132 loci were co-localized with known QTL for low

temperature tolerance at different stages of rice development (Fig. 2). Most of the QTL were

detected in rice at the seedling stage, while quite a few were detected in rice at the germination


and reproductive stages, indicating the genetic basis of low temperature tolerance at different

growth periods may have an overlap. Such overlap can be also reflected by the reported QTL

listed in Fig. 2. qGR2, qLTG5-2, and qLTG5-1 which were located at the germination stage

exhibited overlap with qCSH2, qCTS2, qCTSS2B, qCTS5, and qSV-5 detected at the seedling

stage, and this phenomenon was also found in chromosomes 6, 7, 9, 10, and 11. However, an

association study in maize showed that none of the 43 identified SNPs were simultaneously

detected for chilling tolerance at the seedling stage and the germination stage, and the

correlation analysis of these traits also suggested that the genetic basis of chilling tolerance at

the germination and the seedling stages were different (Huang et al., 2013).

Relationship of cold adaption, subpopulation and latitudinal distribution in rice

Tropical species are generally sensitive to cold stress conditions, so the distribution of

plants is partly determined by the sensitivity to low temperatures (Morsy et al., 2005). There

have been reports discussing the cold response difference between indica and japonica rice,

and one study concluded that the initial event was oxidative stress induced by chilling, which

partly explains the differential sensitivities of indica and japonica rice to chilling stress

(Cheng et al., 2007). Another group reported that three japonica cultivars exhibited higher

vigor than the two indica cultivars at the germination and the seedling stages under cold

stress conditions (Morsy et al., 2005). A recent report on the differentiation of COLD1 also

suggested that cultivars with COLD1jap

genotype showed stronger cold tolerance than

COLD1Ind

cultivars (Ma et al., 2015). However, there were few reports focusing on

indica-japonica differentiation of cold tolerance based on a large sample of natural

germplasms. A recent study using a mini core collection of 174 Chinese rice accessions


suggested that japonica rice had a stronger tolerance than indica rice at the germination stage

and the booting stage (Pan et al., 2015). Such difference was also observed in this study. We

observed that 94 japonica accessions displayed a stronger cold tolerance than 109 indica

accessions (Supporting Information Fig. S10). Even though there are some japonica and

indica accessions in our panel which displayed an intermediate type of cold tolerance, our

results in general indicated that japonica rice exhibited stronger cold tolerance capabilities

than indica rice.

From the haplotype analysis, we also found that the latitudinal distribution of rice

germplasms as well as the OsMYB2 haplotypes was associated with cold tolerance. We

checked the geographical distribution of 529 accessions which were divided into 4 subsets

according to their origins (Supporting Information Fig. S15). Although indica subset comes

from lower latitude regions including several major rice growing belts while the japonica

subset distributed more widely, the latitudinal distribution was not significantly different

between indica and japonica rice. Therefore, we propose that the geographical differences

between the CT and CS accessions were likely associated with the differences in cold

tolerance (Fig. 5, Supporting Information Fig. S10).

During the long-term evolution, rice may have evolved with adaptability to different

environments and latitudes. To further prove the association between chilling tolerance and

latitude, we compared the latitudinal distribution of the accessions with similar phenotypic

performance under the two stress conditions: i.e. overlapped accessions between the CT with

the CSI groups, or between the CS and the CSS groups (Fig. 5c,d,e), and the subpopulation

and the geographical distribution of such accessions were summarized in Supporting


Information Fig. S13. Most of the overlapping accessions between CT and CSI groups

(71.88%) are japonica rice with a much wider latitudinal distribution. On the other hand,

almost all (94.72%) of the 38 overlapping accessions between CS and CSS groups are aus

and indica rice with narrow latitudinal distributions (Fig. 5d). Therefore, it can be generally

predicted that japonica rice distributed in higher latitude regions will exhibit stronger

adaptability under cold stress conditions.

In conclusion, we employed a genome wide association strategy with 529 accessions for

rice cold tolerance at the seedling stage, and 68 of all loci from the whole panel were located

or overlapped with reported QTL. We found that the two types of cold stresses (chilling and

cold shock) had a distinct genetic basis. The rice accessions have indica-japonica

differentiation and differential latitudinal distribution with cold adaptability, which is also the

case for the haplotypes of the reported OsMYB2 gene. The loci detected in this study may

provide valuable information for the ecological adaptability of rice, and the accessions

classified as CT or CS would be potential genetic resources for rice improvement.

ACKNOWLEDGEMENTS

This work was supported by grants from the National Program on High Technology

Development (2014AA10603, 2012AA10A303) and the National Natural Science

Foundation (31271316).


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Tab

le 1

. P

erfo

rman

ces

of

the

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rait

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this

ass

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n s

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y.

Ab

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Mea

n

SD

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0.8

74

-4.9

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71

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rat

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nat

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s

RL

C

1.0

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93

1.4

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nce

level

(sc

ore

) u

nd

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atura

l chil

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g s

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ter

reco

ver

y

SR

C

0.0

00

-1.0

00

0.3

54

0.3

75

1.0

61

Surv

ival

rat

e aft

er n

atu

ral

chil

ling s

tres

s

EL

N

0.0

53

-0.2

01

0.0

93

0.0

17

0.1

80

Ele

ctro

lyte

lea

kage

und

er n

orm

al c

ond

itio

n

EL

C1

0

.06

2-0

.19

0

0.1

11

0.0

20

0.1

82

Ele

ctro

lyte

lea

kage

afte

r nat

ura

l ch

illi

ng s

tres

s fo

r 3

day

s

EL

C2

0

.08

3-1

.13

0

0.3

12

0.3

31

1.0

61

Ele

ctro

lyte

lea

kage

afte

r nat

ura

l ch

illi

ng s

tres

s fo

r 7

day

s

EL

R

0.0

59

-1.1

59

0.6

30

0.4

27

0.6

79

Ele

ctro

lyte

lea

kage

afte

r re

cover

y f

or

7 d

ays

EL

R1

0

.65

9-2

.14

7

1.2

11

0.2

44

0.2

02

Rat

io o

f el

ectr

oly

te l

eakage

un

der

mil

d n

atura

l chil

lin

g s

tres

s to

no

rmal

cond

itio

n

EL

R2

0

.84

6-1

5.0

97

3.4

26

3.6

39

1.0

62

Rat

io o

f el

ectr

oly

te l

eakage

un

der

sev

ere

nat

ura

l chil

lin

g s

tress

to

no

rmal

co

nd

itio

n

EL

R3

0

.75

2-1

9.2

59

6.9

52

4.9

45

0.7

11

Rat

io o

f el

ectr

oly

te l

eakage

un

der

str

ess

reco

ver

y t

o n

orm

al c

ond

itio

n

EL

SN

0

.07

4-0

.29

0

0.1

43

0.0

38

0.2

67

Ele

ctro

lyte

lea

kage

bef

ore

clo

d (

4°C

) sh

ock

(no

rmal

cond

itio

n)

EL

SC

1

0.0

73

-0.4

28

0.1

63

0.0

39

0.2

41

Ele

ctro

lyte

lea

kage

afte

r co

ld s

ho

ck f

or

1 d

ays

EL

SC

2

0.0

80

-0.7

39

0.1

92

0.0

68

0.3

56

Ele

ctro

lyte

lea

kage

afte

r co

ld s

ho

ck f

or

3 d

ays

EL

SR

1

0.5

59

-3.7

30

1.1

96

0.4

02

0.3

36

Rat

io o

f el

ectr

oly

te l

eakage

un

der

1-d

ay c

old

sho

ck s

tres

s to

no

rmal

co

nd

itio

n

EL

SR

2

0.5

46

-8.3

56

1.4

03

0.6

69

0.4

77

Rat

io o

f el

ectr

oly

te l

eakage

un

der

3-d

ay c

old

sho

ck s

tres

s to

no

rmal

co

nd

itio

n


Tab

le 2

. C

orr

elat

ion c

oef

fici

ents

of

pai

red t

rait

s of

all

trai

ts i

nves

tigat

ed.

B

MR

R

LC

R

LR

S

RC

E

LN

E

LC

1

EL

C2

E

LR

E

LR

1

EL

R2

E

LR

3

EL

SN

E

LS

C1

E

LS

C2

E

LS

R1

E

LS

R2

BM

R

1.0

00

RL

C

0.5

21

1.0

00

RL

R

0.5

86

0.6

63

1.0

00

SR

C

0.5

65

0.6

71

0.6

77

1.0

00

EL

N

0.6

12

0.6

90

0.7

87

0.7

03

1.0

00

EL

C1

0

.56

7

0.6

28

0.7

53

0.6

56

0.7

80

1.0

00

EL

C2

0

.41

6

0.4

71

0.5

46

0.5

14

0.5

71

0.6

18

1.0

00

EL

R

0.4

72

0.6

23

0.6

07

0.6

44

0.6

90

0.6

43

0.5

04

1.0

00

EL

R1

0

.34

8

0.3

79

0.4

62

0.4

12

0.5

25

0.5

27

0.4

00

0.4

28

1.0

00

EL

R2

0

.40

3

0.4

71

0.5

14

0.4

81

0.6

70

0.5

84

0.9

49

0.5

02

0.4

18

1.0

00

EL

R3

0

.46

6

0.6

04

0.5

79

0.6

13

0.7

59

0.6

14

0.4

73

0.9

59

0.4

36

0.5

47

1.0

00

EL

SN

0

.22

9

0.2

22

0.2

59

0.2

18

0.2

71

0.2

56

0.2

02

0.1

94

0.1

30

0.1

86

0.1

82

1

.00

0

EL

SC

1

0.0

73

0.0

73

0.1

25

0.0

90

0.1

14

0.1

21

0.1

04

0.0

76

0.0

65

0.0

87

0.0

66

0

.29

1

1.0

00

EL

SC

2

0.1

55

0.1

83

0.2

07

0.1

91

0.2

02

0.2

21

0.1

47

0.1

61

0.1

18

0.1

28

0.1

45

0

.43

2

0.4

25

1.0

00

EL

SR

1

0.0

79

0.0

60

0.1

13

0.0

77

0.1

02

0.1

09

0.0

99

0.0

62

0.0

48

0.0

81

0.0

52

0

.39

9

0.9

70

0.4

09

1.0

00

EL

SR

2

0.1

50

0.1

52

0.1

78

0.1

61

0.1

74

0.1

91

0.1

28

0.1

31

0.0

83

0.1

08

0.1

15

0

.60

4

0.4

04

0.9

31

0.4

65

1.0

00

(Lig

ht

gre

y b

ack

gro

und m

eans

corr

elat

ion, 0.3＜

r＜0.8

; dar

k g

rey b

ack

gro

und

mea

ns

stro

ng c

orr

elat

ion

, 0.8≤

r)



Figure 1. Manhattan plots for six traits: (a): ELR2, (b): ELC2, (c): RLC, (d): ELSR2, (e): ELSC1, (f):

SRC. The negative log10 transformed p-values of genome-wide scan are plotted against the marker

position in the genome. Dotted line: P=1e-06. The positions of 5 reported genes were indicated with

black arrow. Examples of some loci were indicated; L27, L104, L63 in red were detected by EL

related traits under the two cold stress conditions while locus L18 and L79 in blue were identified by

two traits under natural chilling stress condition. L is short for locus.


Figure 2. Distribution of 132 loci on 12 chromosomes according to physical distance. The loci and its

associated traits were marked on the right of chromosomes with the relative position of each locus

(200 kb) shown by its own front physical position; the overlapping known QTL were shown in grey

column on the left while the markers of QTL were shown on the right. The 57 loci in blue were

identified under natural chilling stress condition, the 63 loci in green were identified under cold shock

stress condition, and the 12 loci in red were detected under both stress conditions.


Figure 3. Haplotype analysis of OsMYB2. (a) Haplotypes in 412 accessions (haplotypes with

less than 5 accessions was omitted) according to SNP data from RiceVarMap based on

MSU6.1 annotation. The region contained coding region and 2 kb upstream of the gene.

Letters on the right of the average are ranked by Duncan test at P＜0.05, different letters

indicate significant difference. (b) Scatter diagram for the latitudes (ascending sorted) of the

origins of accessions of the four haplotypes at OsMYB2 locus. (c) Comparison of latitude

distribution between accessions of the four haplotypes. Letters above the bars are ranked by

Duncan test at P<0.05; different letters indicate significant difference.


Figure 4. Geographic and subpopulation distribution of accessions of the four OsMYB2

haplotypes. Geographic distribution of accessions in the four haplotypes on world map (a)

and map of China (c), the solid circle in different colors (red, light blue, dark blue, green)

represents accession numbers of the four haplotypes (Hap1−4 respectively), except for 5

accessions in Hap1, 16 accessions in Hap2 and 14 accessions in Hap4 with unknown

geographic location. (b) The subpopulation distribution of 89 accessions in Hap1, 155

accessions in Hap2, 22 accessions in Hap3 and 146 accessions in Hap4.


Figure 5. Latitudinal distributions of CT, CS accessions and the overlapped accessions under the two

stress conditions. (a) Scatter diagram for the latitudinal distribution of 108 CT accessions (red) and

131 CS accessions (blue), sorted by ascending latitude. (b) Comparison of latitude distribution

between CT and CS accessions. (c) Scatter diagram for the latitude distribution of 32 overlapped

accessions between CT and CSI (red) and 38 overlapped accessions between CS and CSS (blue). (e)

Comparison of latitude distribution between accessions overlapped by CT, CSI and CS, CSS. CT=

chilling tolerant accessions; CS= chilling sensitive accessions; CSS= cold shock sensitive accessions;

CSI= cold shock insensitive accessions. Differences in of latitude distribution between accessions

were examined by Student's t-test. *, P＜0.05,**, P＜0.01.

Date post:	15-Oct-2020
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New insights into the genetic basis of natural chilling...

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