Heat stress is becoming a major constraint for wheat production worldwide due to global warming. As per IPCC (2013) temperature will rise approximately by 0.3-0.6°C per decade over the next century. The increase in temperature during 1980 to 2008 resulted in 5.5% reduction in wheat production (Lobell et al. 2011). In South Asia with increase in one degree rise in temperature during the grain-filling period, there is 6 to 10 % reduction in wheat yield (Mondal et al. 2013; 2016; Asseng et al. 2015) and it is expected to rise in upcoming scenarios. Eastern Indo-Gangetic plains (IGP) of India, which has predominantly rice-wheat cropping system, is largely affected by terminal heat stress due to late sowing of wheat (Joshi et al. 2007). The genotypes differ with respect to their response to heat stress. Substantial genetic variations among wheat genotypes under heat stress for kernel number per spike (Shpiler and Blum 1990), temperature depression during grain filling (Ayeneh et al. 2002), thousand-grain weight (Hede et al. 1999), chlorophyll a fluorescence (Sharma et al. 2012), physiological traits (Saxena et al. 2016), high yield and early maturity (Mondel et al. 2016), yield and component traits (Okechukwu et al. 2016) and various morpho-physiological traits (Fischer 1985; Sareen et al. 2012; 2014; Fu et al. 2015; Lopes et al. 2015) has been reported. Evaluation of genetic diversity in wheat using molecular markers particularly SSRs or microsatellite received a great attention from wheat researchers probably due to higher level of polymorphism and their abundance throughout the genome. Incorporating molecular markers and traits associated with heat tolerance and their association may boost up the breeding efforts in improving heat tolerance through identification of potential parent for strategic crossing among parents and early selections. The present investigation was conducted to characterize genetic variability present in the germplasm accessions housed in National Gene Bank based on various morpho-physiological traits and SSR markers which will help to identify genotypes for the development of heat tolerant high-yielding cultivars.
Materials and methods
Plant material: Four hundred and ninety-six wheat germplasm accessions were evaluated for their performance under very late sown conditions by sowing during first fortnight of wheat during 2013-14 at Hisar and Delhi and during 2014-15 at Karnal, Delhi and Hisar using augumented design with repeated checks. Based on their performance, 47 accessions; 32 T. aestivum and 15 T. durum accessions were selected for evaluation for diversity in heat tolerance. These accessions included 37 indigenous (30 T. aestivum and 7 T. durum) and ten exotic (2 T. aestivum and 8 T. durum) which were utilized in present investigation to study genetic diversity among the selected germplasm based on morpho-physiological traits and microsatellite markers (SSRs).
Phenotyping: The field trials were conducted during 2015-16 at three locations IIWBR (Indian Institute of Wheat and Barley Research, Karnal, IIWBR Regional Station, Hisar and NBPGR (National Bureau of Plant Genetic Resources), New Delhi. Staggered sowing was done to impose heat stress. Sowing was done at two dates i.e. mid November and mid December. The experiment was conducted in lattice design with 3 replications keeping plot size 3.6m-2. The recommended cultivation practices for growing wheat under irrigated conditions were followed. The crop was protected from disease (rust) by spraying tilt. To calculate heat stress difference in temperature under timely and late sown conditions was recorded for post heading period.
Phenotypic data comprising of phenological, physiological, grain yield and its components were recorded. Phenological and grain yield components were recorded as per methodology given in Garg et al. (2012). Among physiological traits chlorophyll fluorescence (CFL) was measured using fluorometer (Model OS 30P, Opti-Sciences, Hudson, NH, USA). The data was recorded from three plants in the middle rows of the plot. The measurements were taken on the abaxial surface of fully expanded flag leaf at GS65 (DAA), 7 days after anthesis, GS70 (7DAA), 15 days after anthesis GS 75 (15DAA) and 21 days after anthesis GS 80 (21DAA) by adopting 20 min dark adaptation period. Three readings per genotype in each replication and condition were recorded.
To determine cell membrane stability three flag leaf of each genotype were taken from middle rows of the plot when genotype attended the stage of GS70 (7DAA). These samples were rinsed with distilled water and cut into 2.0 cm pieces after removing midrib and divided equally in five test tube containing 10 ml double distilled water for each genotype. After that, samples were incubated at 4oC for 24 h. After incubation, vials were brought to 25oC, and conductance (C1) was measured with a conductivity meter. After taking the first readings, each tube containing leaf samples was then subjected to heat stress for 1 h in a water bath at 55oC. Then samples were again incubated for 24 h at 4oC, thereafter samples were brought to 25oC and second conductance (C2) was measured with a conductivity meter. After taking the second readings, samples were autoclaved at 121psi for 15 min and again incubated for 24 h at 4oC then samples were again brought to 25oC to measure final conductance (C3).
The CMS was calculated by using the formula: CMS = [1-(1-C2/C3)/(1-C1/C3)*100]
Heat susceptibility index (HSI) as measure of heat tolerance for each trait was calculated using the formula given by Fischer and Maurer (1978). SAS 9.3 software was used to analyze data.
Molecular data: Total genomic DNA of accessions was isolated from 30 days old seedlings after crushing in liquid nitrogen in microfuge tubes using modified CTAB extraction method. The methodology for DNA extraction and amplification followed in the present study is given in Sharma et al. (2016). A total of 137 SSR markers representing A, B and D genome with 62, 47 and 28, respectively reported in literature for association with various traits under heat stress condition were used to determine genetic diversity. Primer sequences were obtained from http://www.wheat.pw.usda.gov/ggpages/SSR.
Genetic Diversity and Structure analysis: Gel images were scored visually for presence (1) or absence (0) of the band. PowerMarker (Version, 3.25) was used to estimate the gene diversity, heterozygosity and polymorphic information index (PIC) (Liu and Muse 2005). Dissimilarity index was calculated by using NEI coefficient with bootstrap value of 1000 and DARwin6 software (Perrier and Jacquemoud-Collet 2006) was used to construct unweighted neighbor joining un-rooted tree. STRUCTURE 2.3.4 software was used to infer population structure (Evanno et al. 2005).
Results and discussion
Daily minimum and maximum temperature was recorded throughout the crop season. During the pre-heading period, the daily maximum temperature ranged from 9.90C to 28.10C, 13.20C to 28.40C and 13.90C to 27.80C at Karnal, Hisar and Delhi respectively under timely sown and from 9.90C to 30.10C, 13.20C to 29.80C and 13.90C to 33.00C at three locations under late sown conditions. Similarly, the minimum temperature ranged from 3.4 – 15.20C, 1.5 – 15.20C and 0.5 – 15.00C at Karnal, Hisar and Delhi respectively under timely sown and from 3.4 – 17.10C, 1.5 – 16.50C and 0.5 -17.50C at these locations under late sown. During post heading period, the daily maximum temperature ranged from 17.90C to 37.90C, 19.60C to 41.90C and 22.40C to 39.50C at Karnal, Hisar and Delhi respectively under timely sown and from 23.60C to 39.60C, 24.40C to 41.90C and 26.00C to 43.00C at three locations under late sown conditions. Similarly during this period minimum temperature ranged from 6.40C to 23.10C, 3.40C to 23.30C and 6.50C to 19.00C at Karnal, Hisar and Delhi respectively under timely sown and from 12.10C to 23.10C, 8.00C to 23.30C and 10.50C to 24.70C at three locations under late sown conditions. Mean minimum and maximum temperature was higher by 0.20C and 0.40C, 0.20C and0.00C and 0.50C and 1.60C during pre heading period and 3.00C and 3.60C, 1.60C and 1.90C and 3.10C and 3.80C indicating high temperature stress during grain filling duration (Fig. S1).
Phenotypic data: The pooled ANOVA revealed that genotypes differ significantly for all the traits except days to maturity, biomass and cell membrane stability. Centres as well as conditions were significantly different for all the traits studied. G x E interactions (genotype x conditions and centres x genotype x conditions) were also significant. Genotypes showed significant differences under normal (timely sown) and stress (late sown) conditions (Table 1). The data recorded on phenological, physiological, and agronomical and grain yield components revealed that grain filling duration ranged from 29 to 41 days under non stressed conditions and 26 to 32 days under stressed conditions with an average reduction of 14%. Grain yield suffered 9% reduction under stressed conditions. CFL1 ranged from 0.752 to 0.794 and 0.70 to 0.765 with mean value of 0.769 and 0.733 under non-stressed and stressed conditions respectively. Cell membrane stability had reduction of 23.7% under stressed conditions. Reduction in various traits ranged from 5 to 27%; biomass and cell membrane stability showed more sensitivity to heat stress. The coefficient of variance for cell membrane stability was higher as compared with other traits under both timely (non-stress) and late (stress) sown conditions. Genotypes were categorized as tolerant or susceptible using data on heat susceptibility index for grain yield. Genotypes with HSI value less than 1.0 were tolerant and those with more than 1.0 were susceptible. Further the tolerant genotypes were categorized into two groups; highly tolerant (HT) with HSI value less than 0.55 and moderately tolerant (MT) with values between 0.55 and 1.0. Similarly, susceptible genotypes were categorized into two groups; highly susceptible (HS) with HSI value more than 1.5 and moderately susceptible (MS) with values between 1.05 and 1.5. Heat Susceptibility Index for grain yield in the present study ranged from 0.14 to 2.45 (Table 2) and 8, 16, 12 and 13 genotypes were categorized as HT, MT, MS and HS respectively. Among indigenous accessions 5 were placed in HT category, 13 in MT, 10 in MS and 11 in HS category. Similarly, for exotic accessions 3, 3, 2 and 2 were placed in HT, MT, MS and HS category respectively. For other traits HSI ranged from 0.13 to 1.94 for grain filling duration, -0.01 to 1.63 for biomass, -2.26 to 4.69 for CFL1, -1.09 to 3.81 for CFL2 and-1.66 to 2.26 for cell membrane stability. Under stress conditions cell membrane stability, grain yield, biomass and thousand grain weight were higher in tolerant genotypes than susceptible genotypes.
Coefficient of correlation: Pooled pearson correlation coefficients for all trait combinations were determined (Table 3). Under non-stress conditions CFL1 as well as cell membrane stability had significant negative correlation with days to heading, anthesis and maturity and positive with grain filling duration. days to heading, anthesis and maturity had significant positive correlation with grain number/spike and grain weight/spike; both these traits had positive correlation with grain yield and grain yield had significant positive correlation with biomass. Biomass had significant positive correlation with HSI. Under stress conditions, CFL1 had significant negative correlation with days to heading, anthesis and maturity and cell membrane stability had positive correlation with days to anthesis and maturity. days to heading, anthesis and maturity had significant positive correlation with with grain number/spike, thousand grain weight, biomass and grain yield. Grain number and grain weight had positive correlation with grain yield and Grain yield with biomass. Under non-stress conditions, the highest significant correlation (0.84) was found between grain number and grain weight and under stress conditions also it hold true with coefficient of correlation of 0.82. HSI for grain yield showed significant correlation with biomass and grain yield under non stress conditions. Biomass also showed a positive correlation with cell membrane stability under stress conditions. In case of non-stressed conditions, biomass showed significant correlation with grain yield (0.37) and HSI (0.39).
Genetic diversity using SSRs markers: Genetic diversity in wheat (Tritucum aestivum) and (Triticum durum) genotypes was determined by 137 (representing A, B and D genome) and 109 (representing A and B genome) SSRs microsatellite markers and 73.7 and 71.6 per cent were polymorphic respectively. The details of their genetic diversity parameters obtained were presented in (Supplementary Table S1a & S1b). In Triticum aestivum 101 co-dominant SSRs markers amplified 308 alleles with an average of 3.02 per locus and range of 2 to 6. Highest number of alleles was detected with barc167. The polymorphic information content (PIC) which is determined by taking into account the number of alleles per locus and their relative frequencies, varied from 0.093 (wmc363) to 0.722 (barc167) with mean 0.464. The genetic diversity was ranged from 0.096 (wmc363) to 0.741(barc167) with average 0.521. Major allelic frequency of polymorphic markers ranged from 0.313 to 0.948 with mean of 0.553. The observed heterozygosity for markers used in present investigation ranged from 0.0 to 1.0 with an average of 0.449 and 79.2 per cent of the markers showed more than zero level of heterozygosity. While, in Triticum durum total 79 SSR microsatellites were polymorphic and amplified 234 alleles. Number of alleles per locus was ranged from 2 to 6 with an average of 2.92 per locus, detecting highest no of alleles from barc167 and wmc42. Major allelic frequency of polymorphic markers ranged from 0.318 to 0.944 with mean of 0.577. The observed heterozygosity was ranged from 0.0 to 1.0 with an average of 0.441 and 63.29 per cent of the markers showed more than zero level of heterozygosity. The polymorphic information content (PIC) varied from 0.099 (gwm319) to 0.738 (barc167) with mean 0.444. The genetic diversity was ranged from 0.099 (gwm319) to 0.733 (barc167) with average 0.488. Pagnotta et al. (2005) has reported 2-8 alleles per locus in his study with 39 Italian wheat accessions, whereas Salunkhe et al. (2013) reported 2 – 9 alleles per locus. The PIC values provide an estimate of the discriminating ability of any locus by considering the number of alleles per locus and their relative frequency (Anderson et al. 1993). The average PIC was 0.464 for aestivum genotypes and 0.444 for durum genotypes. This average PIC values were similar to reports by Pagnotta et al. (2005), but lower than the value of 0.62 reported by Teklu et al. (2006). The microsatellite markers used in the present study show different levels of gene diversity. Markers with PIC value more than 0.5 are considered as highly informative while those with PIC value between 0.25 and 0.5 are just informative marker, while those with less than 0.25 are slightly informative markers (Botstein et al. 1980). Therefore, PIC values of the markers have been studied extensively in diversity studies to distinguish different genotypes (Varshney et al. 2007). However, Prasad et al. (2000) did not conform the correlation between PIC value and the number of alleles. Considering polymorphism, the markers used in the present study are very informative; 50 of these had PIC value more than 0.5 for aestivum genotypes and 32 for durum genotypes. The markers gwm 448(PIC=0.586 and 0.584), gwm122 (PIC=0.533 and 0.461) and wmc296 (PIC=0.486 and 0.386) are linked with the QTL for heat tolerance in wheat (Bhusal 2016). Therefore, these markers could be used in marker assisted selection for heat tolerance in wheat.
Population structure analysis: Genetic diversity and relatedness of the genotypes within the population was worked out using STRUCTURE. There were two major subgroups (Fig 1). The likely number of assumed sub populations was identified using the higher ΔK value. Using the Evano table output, the K=2 was observed as best showing high ΔK value of 391.56 in the assumed K (Fig 1). The fixation index values (Fst) in sub populations were estimated 0.336 and 0.191 for sub-population 1 (SP1) and sub-population 2 (SP2), respectively. Average distances (expected heterozygosity) between individuals in the same sub populations were 0.284 (SP1) and 0.323 (SP2). The SP1 contains 16 genotypes (9 pure and 7 admixture), accommodating 12 tolerant and 4 susceptible genotypes (Fig 2). Out of 33 genotypes in SP2, 15 were pure and 18 were admixture comprising 12 tolerant and 21 susceptible under heat stress condition.
Cluster analysis based on phenotypic data and molecular markers: Using susceptibility index data for clustering, the genotypes were differentiated into tolerant and susceptible class (Fig. 3). There were three major clusters. Cluster I was comprised of 16 genotypes; cluster II of 20 genotypes and cluster III had 8 genotypes. The remaining 5 genotypes formed two clusters with 3 and 2 genotypes. The minimum distance between the clusters was 0.36 and maximum 1.71. The clustering was also done with data collected for other traits as shown in online Supplementary Figs S2–S3. Principal component analysis was done to determine the relationship among the wheat genotypes and the first and second components accounted for 37.9 and 26.9% of the variation between genotypes, giving a cumulative variation of 64.8%. The dendrogram, derived from SSR using unweighted neighbor joining (Fig 4), indicates genotypes were divided into two large groups. Group one contains 31 aestivum genotypes, most of the indigenous lines (IC lines) including checks. This group had predominantly heat susceptible genotypes. It was again regrouped into four sub groups with 8, 10, 10 and 3 genotypes, respectively. Second group contains 18 genotypes which was sub grouped into two groups with 16 genotypes (15 durum and 1 aestivum) in one subgroups and 2 aestivum genotypes in second sub group. This group had predominantly heat tolerant genotypes. Genotypes were showing genetic differences among them based on euclidian distance matrix ranged from 0.410 to 0.782 with maximum difference between HD2967 and EC464033 (0.782). Although these groups were comprised of predominantly tolerant or susceptible genotypes, both these groups had tolerant genotypes. This means that genotypes identified as tolerant were diverse in nature indicating that heat tolerance in these genotypes was genetically different. Phenotypic clustering based on heat susceptibility index differentiated tolerant and susceptible genotypes except for IC335832, IC 539221, EC 574737, EC 464033, IC 531180, IC 335787, IC 542518 and IC 531319 which were moderately tolerant / susceptible and were placed in one group. Likewise clustering based on physiological and phenological traits was done. With physiological traits one major cluster with 39 genotypes; 22 tolerant and 17 susceptible was obtained. Similarly, with phenological traits two major clusters with 23 (12 tolerant and 11 susceptible) and 19 (10 tolerant and 9 susceptible) were obtained. The placement of genotypes in various clusters was different using molecular markers or physiological traits. This indicates that genotypes differ not only in genetic basis for heat tolerance but also physiologically. The genetic distances between SSR and phenotypic data were weekly but significantly correlated (r=0.12; P>0.05) (Fig. 5). The weak correlation is due to higher genetic distance for phenotypic traits because of environmental interactions than for SSR markers (Dodig et al. 2010). It is not necessary that SSR markers and phenotypic traits will result into similar clustering pattern as phenotypic traits separate the genotypes according to single major gene for heat tolerance (Dodig et al. 2010).
The diverse genotypes identified for heat tolerance in the present study can be used for introgression of heat tolerance related traits into agronomically adapted genotypes. Genotypes with tolerance to CMS under heat stress are IC 252840, EC445382, EC445177, IC 536013, EC 276823, IC 535726 and IC 252637. Similarly, genotypes identified for Fv/Fm are EC 464033, EC445177, EC445481, IC 401988 and IC 290335. Utilization of these diverse alleles/genes will lead to of genetic enhancement and development of new cultivars (Abouzied et al. 2013).