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RESEARCH ARTICLE

Identification of Simple Sequence Repeat and Retrotransposon-Based Molecular Markers

Linked to Morphological Characters in Oily Sunflower (Helianthus annuus L.) under

natural and Water-limited states

ALI SOLEIMANI GEZELJEH1, REZA DARVISHZADEH2‡, ASA EBRAHIMI3 and MOHAMMAD

REZA BIHAMTA4

1PhD Student in Plant Breeding, Faculty of Agriculture and Natural Resources, Science and

Research Branch, Islamic Azad University, Tehran. 2Professor, Department of Plant Breeding

and Biotechnology, Urmia University, Urmia, Iran. 3Assistant professor, Faculty of Agriculture

and Natural Resources, Science and Research Branch, Islamic Azad University, Tehran.

4Professor, Faculty of Agriculture, Tehran University, Karaj, Iran

‡Corresponding author’s E-mail: r.darvishzadeh@urmia.ac.ir, Fax: +98 44 32779558.

Abstract

Sunflower is an important source of edible oil. Drought is known as an important factor

limiting the growth and productivity of field crops in most parts of the world. Agricultural

biotechnology mainly aims at developing crops with higher tolerance to the challenging

environmental conditions, such as drought. This study examined a number of morphological

characters, along with Relative Water Content (RWC) in 100 inbred sunflower lines. A 1010

simple lattice design with two replications was employed to measure the mentioned parameters

under natural and water-limited states during 2 successive years. In molecular trial, 30 SSR

primer pairs, as well as 14 IRAP and 14 REMAP primer combinations were used for DNA

fingerprinting of the lines. Most of the examined characters had lower average values under

water-limited than natural states. Maximum and minimum reductions were observed in the cases

of yield and oil percentage, respectively. The broad-sense heritabilities for all the examined

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characters were 0.20-0.73 and 0.10-0.34 under natural and water-limited states, respectively. In

the studied samples, 8.97% of the 435 possible locus pairs of the SSRs represented significant

Linkage Disequilibrium (LD) levels. In the association analysis using SSR markers, 22 and 21

markers were identified (P≤0.05) for the studied characters under natural and water-limited

states, respectively. The corresponding values were 50 and 37 using retrotransposon-based

molecular markers. Some detected markers were communal between the characters under water-

limited and natural states. This was in line with the phenotypic correlations detected between the

characters. Communal markers facilitate the simultaneous selection of several characters and can

thus improve the efficacy of selection based on markers in the plant-breeding activities.

Keywords: Linkage Disequilibrium (LD), microsatellite marker, oily sunflower, retrotransposon-

based molecular marker, water-stressed states

Abbreviation: Inter-Retrotransposon Amplified Polymorphism (IRAP), Marker-Assisted

Selection (MAS), Mixed Linear Model (MLM), Quantitative Trait Loci (QTL), Retrotransposon-

Microsatellite Amplified Polymorphism (REMAP), Simple Sequence Repeat (SSR).

Introduction

Seventy-two species of sunflower (Helianthus annuus L.) as an annual plant has been

identified in the Asteraceae family. Except for 3 species found in South America, all Helianthus

species are from North America (Hu et al., 2010). Sunflower is a diploid plant with the base

chromosome No. 17 and a genome size estimated around 2871 to 3189 Mbp (Bazin et al., 2011;

Rengel et al., 2012; Fernandez et al., 2012). The plant is mainly planted in temperate parts both

for nutriment and ornamental purposes. Sunflower, canola, cotton, and soybean are the major

plants used for edible vegetable oil production (Hu et al., 2010). Sunflower oil has a great

potential to be utilized as biodiesel fuel (Thirumarimurugan et al., 2012). As estimated by FAO,

an area of almost 25.6×106 ha is globally cultivated. From this area nearly 44.8×106 t of

sunflower seed are harvested. Plant growth and development are influenced by drought as a

major factor of environmental stress (Harb et al., 2010). Increased water insufficiency and

climate change maximize the worsening impacts of drought. Thus, an understanding of drought

stress and its undesirable effects on plant growth is of special importance for sustainable

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agriculture. Agricultural biotechnology mainly attempts to develop more tolerant crops in the

challenging environmental conditions like drought, oxidative stress, extreme temperature, and

salinity (Pua and Davey, 2007). Global food production can be significantly enhanced via the

improvement of abiotic stress tolerance of cultivars.

Over the past 20 years, DNA-based molecular markers, including microsatellite or SSRs

and retrotransposon-based markers, have been widely applied in various activities, such as QTL

identification in different animal and plant systems (Abdi et al., 2012; Darvishzadeh, 2012;

Najafzadeh et al., 2016). QTL identification clarifies the genetic control of characters and

facilitates the development of MAS breeding activities (Kole, 2003; Collard et al., 2005; Collard

and Mackill, 2008; Xu, 2013). MAS is a powerful alternative technique for the selection of

complex characters with small heritability (Lande and Thompson, 1990; Knapp, 1998) and a

valuable breeding tool for stress tolerance (Abdel-Tawab et al., 2003).

Most eukaryotes display SSRs in the genomes. Two, 3, or 4 nucleotides are mostly

included in their constant units. The SSR technology utilizes the primers designed for the

flanking regions to amplify DNA segment repeats. The results of Polymerase Chain Reaction

(PCR) can be recreated in laboratories throughout the world (Roder et al., 1998; Halton, 2001;

Gupta et al., 2002; Agrama and Tuinstra, 2003). SSRs are multi-allelic and have a co-dominant

behavior. Owing to these features, along with their random distributions in the genome, SSRs can

be used not only for a genetic variability evaluation, but also for QTL identification (Langridge et

al., 2001; Snowdon and Fried, 2004). Retrotransposons are described based on their

multiplication manner in the genome of eukaryotes. In many crop plants, retrotransposons with

Long Terminal Repeat (LTR) are present in 40-70% of the total DNA (Pearce et al., 1996;

Sanmiguel et al., 1996; Shirasu et al., 2000). To produce retrotransposon-based molecular

markers, two primers complement to retrotransposon and neighboring genome parts should be

used. Since no DNA digestion is required for REMAP and IRAP markers, these molecular

markers are employed to evaluate the genetic variability and retrotransposon integration in the

genomes of various plants, such as Triticum aestivum (Abdollahi Mandoulakani et al., 2012) and

Helianthus annuus (Vukich et al., 2009; Basirnia et al., 2016).

Kiani et al. (2007a, b, 2008) were the first to analyze the QTL of the characters related to

drought tolerance via a saturated genetic linkage map. They identified QTLs for water status

characters and osmotic adjustment under water-limited and natural states using a Recombinant

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Inbred Line (RIL) population developed by hybridization between PAC2 and RHA266 lines. The

detected QTLs explained 6-29% of the phenotypic variance of the characters. The Logarithm of

Odds (LOD) scores ranged between 3.05 and 9.86 (Kiani et al., 2007a). In a study conducted on

oily sunflower in water-limited states, Abdi et al. (2013) identified 7 and 2 SSR markers related

to chlorophyll concentration and RWC, respectively. The corresponding numbers were 4 and 1 in

the same study carried out by Herve et al. (2001). These markers explained 53% and 9.8% of

phenotypic variations of characters, respectively. Hence, this research aimed to identify SSR and

IRAP+REMAP markers linked to morphological characters in Helianthus annuus in natural and

water-limited states during 2 consecutive years. Introduction and identification of specific DNA

markers associated with drought tolerance-related characters will assist sunflower breeders in the

selection of drought tolerant genotypes and thus accelerate and facilitate breeding activities under

drought conditions.

Materials and method

Plant material and phenotyping

A 1010 simple lattice design with two replications was used to evaluate 100 inbred

sunflower lines supplied by different research centers in Iran, Europe, and the US

(Supplementary 1) under natural and water-limited states in field conditions. The evaluations

were made over 2 consecutive years. Each plot consisted of one 5-meter-long line. The space

between lines and plants were considered 60×50 cm, respectively. The natural and water-limited

experiments were done at a distance of 5 m. Field operations were carried out based on common

methods. To prevent the damage caused by sparrows during the seed-filling stage, the plant heads

were covered with white envelopes. The experiment was conducted in 2012 and repeated in 2013

at a farm in Ghezeljeh village (38°11′50″ north, 44°45′55″ east) in Salmas, Iran. The area is 1382

m above sea level and has got a cold and temperate climate (classified as Dsa in the Köppen-

Geiger system). The average annual rainfall is 388 mm. Most rainfall occurs in the winter and

there is little rain in the summer (http://en.climate-data.org/location/1784/). The average annual

temperature is 10.5°C.

The natural and water-limited treatments received an identical irrigation from the

sunflower planting onset until their complete establishments (8-leaf (V8) stage) (Pourtaghi et al.,

2011; Abdi et al., 2012). Then, the plots received different irrigations based on their groups.

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Water stress was induced by increasing the intervals between the irrigations. In fact, the natural

and water-limited treatments were irrigated when 60 and 180 mm evaporation were recorded in

Class 'A pan', respectively (Pourtaghi et al., 2011; Abdi et al., 2012). At the stage of maturity, 5

plants per plot were sampled and a number of morphological characters, i.e., stem height (SH,

cm), shoot diameter (SD, cm), number of leaf per plant (NL), length of leaf (LL, cm), length of

petiole (PL, cm), width of leaf (LW, cm), head or capitulum diameter (HD, cm), grain or seed

yield/plant (Yield, gm), days from planting to flowering (DF, day), and days from planting to

maturity (DM, day) were measured.

During the grain filling stage 5 plants per genotype per replication were selected. The

measurement of chlorophyll concentrations was made in the middle of the youngest fully

expanded leaves. For the measurement of chlorophyll at the mentioned locations, a chlorophyll

meter (SPAD-502, Minolta, Japan) was applied. Then, the obtained values were averaged before

entering them into the analysis. The measurements were performed for both natural and water-

limited states.

To determine the RWCs of the plants under natural and water-limited states, selection and

sampling of leaves were followed as chlorophyll measurement stage. The samples were used to

prepare discs of leaf. After measuring their fresh weights (Wf), the discs of leaf were immersed

in 20 ml of deionized water in glass vials. The vials were maintained in the dark condition at

about 25ºC for 4 h. They were then blotted and their turgid weights (Wt) were determined. To

measure the dry weights (Wd) of the discs of leaf, they were oven-dried at 80ºC for 24 h. The

following equation was used to calculate RWC (Abdi et al., 2012):

RWC (%) =[(Wf-Wd)/(Wt-Wd)]×100

Data and association analysis

For each character, the simple statistics, including mean, standard error, and coefficient of

variation, were computed. PROC CORR in SAS software was utilized to evaluate the phenotypic

correlations between the characters both in the water-limited and natural states. The required SSR

data were extracted from a study performed by Sahranavard et al. (2015). Briefly, the molecular

profile of the sunflower lines was prepared by using 30 informative SSR primer pairs

(Supplementary 2) via touchdown PCR. For uniform covering the sunflower genome, SSR

markers were selected considering the clarity of bands on gels and their locations on linkage

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groups (Tang et al. 2002; Kiani et al. 2007a). Then, the PCR products were stained by ethidium

bromide with 1.0 µg/ml concentration and resolved on 3 percent (w/v) gel of agarose. Scoring of

the SSR data as co-dominant markers was followed to provide a differentiation of heterozygotes

and homozygotes for each locus. The data of retrotransposon-based molecular markers, including

IRAP and REMAP data, were extracted from the studies performed by Basirnia et al. (2016).

Briefly, after being mixed with amount of 5 µL formamide dye containing 98 percent formamide,

10 mmol/L of ethylenediaminetetraacetic acid (EDTA), 0.05 percent bromophenol blue, and 0.05

percent xylene cyanol, the reaction products were resolved in 1.8 percent (w/v) ultra-pure gel of

agarose in a 0.5X TBE solution (Tris/Borate/EDTA) at 65 V for 4 h. To indicate the presence or

absence of the fragments amplified from IRAP and REMAP markers at each position, the scores

of 1 and 0 were independently used, respectively. A model-based Bayesian approach was applied

for the analysis population structure by using a software package of structure (Pritchard et al.,

2000). The 2.3.4 version of software was used. The experiments were carried out in 5 replicates.

1-10 sub-populations (K) were considered during all the tests and replications of 100,000 of

Markov Chain Monte Carlo (MCMC) and burn in time were regarded. The admixture and

correlated frequencies of the alleles were put in a model. To specify the most likely value of K,

the data Log probability [LnP(D)] (Rosenberg et al., 2002) and ad hoc statistic ΔK of Evanno

(ΔK) were employed, the latter of which was based on LnP(D) variation rate of 2 successive K

values (Evanno et al., 2005; Robitzch Sierra, 2013). An inference of the estimates of ancestry of

the individuals [Q-matrix] in the picked subpopulations was followed (Pritchard et al., 2000).

Determination of kinship coefficients and LD, as well as cluster analysis, was conducted using

TASSEL version 2.1. An association analysis was performed, along with the ancestry [Q values]

and kinship [K-matrix] coefficients as covariates in the function of the MLM to determine the

marker-character association. To conduct the association analysis, TASSEL version 2.1 was

utilized.

Results and discussion

Most of the studied characters of the sunflower lines had a continuous frequency

distribution under both natural and water-limited states. Therefore, the evaluated morphological

characters must have been controlled by a polygenic system (Figures 1 and 2). The minimum,

maximum, mean, standard deviation, coefficient of variation, and broad-sense heritability

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estimates of the studied characters under the mentioned conditions are summarized in Table 1.

Under water-limited states, most of the tested characters showed lower average values (Figure 3).

Maximum reductions were observed in the grain yield, length of petiole, head diameter, and

width of leaf. Minimum reductions were seen in the oil percentage followed by the number of

leaves and days from planting to maturity (Figure 3). Although chlorophyll index was higher

under water-limited than natural states, the difference between the two values was not significant

(Figure 3). Under natural states, the highest and lowest genetic coefficients of variation belonged

to the yield and RWC, respectively. The highest and lowest genetic coefficients of variation

under water-limited states belonged to the number of leaf and grain yield, respectively. In

breeding materials, the characteristics with higher variations are more likely to be improved

through selection (Kaloo and Bergh, 1993). The broad-sense heritability estimates of the studied

characters were 0.20-0.73 and 0.10-0.34 under natural and water-limited states, respectively.

Heritability is the ratio of genotypic variation to phenotypic one. Characters with higher

heritability can be more easily modified through selection. When heritability is high, phenotypic

value is a good estimator of genotypic value. Overall, the heritability values (except for oil

percentage) were lower under water-limited than natural states. In other words, since the

phenotypic variance tends to proportionally increase with mean yield, heritability is higher in

favorable environments for most characters (Allen et al., 1978).

Table 2 shows the phenotypic simple correlation coefficients between the studied

characters under natural and water-limited states. Under natural states, most characters had

significant positive correlations. However, RWC had significant negative correlations with both

days from planting to flowering and days from planting to maturity. Yield had significant

positive correlations with days from planting to maturity, length of leaf, width of leaf, head or

capitulum diameter, length of petiole, stem height, and shoot diameter. Moreover, chlorophyll

index showed a significant positive correlation (0.241) with days from planting to flowering.

Under water-limited states, the correlations between yield and head or capitulum

diameter, length of leaf, width of leaf, stem height, and shoot diameter were positive and

significant. Chlorophyll index represented a significant correlation with stem height. Meanwhile,

RWC had a significant negative correlation with days from planting to flowering, thus to be

regarded as an important indicator of plant water status. It is significantly influenced by plant

phenology and productivity characters under drought stress and has a strong association with

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delayed flowering under drought stress. Hence, delayed flowering under stress is a strong

indicator of drought susceptibility. Similarly, a negative correlation between days to fifty percent

of flowering and RWC in rice has been reported by Hanamaratti and Salimath (2012). Oil

percentage had no correlation with any of the studied characters under either natural or water-

limited states. The correlations between grain yield and morphological characters were affected

by water conditions. For instance, the correlation between yield and both days from planting to

maturity and length of petiole were only positive and significant under natural conditions, but not

under water-limited states. According to the conventional quantitative genetics, the correlations

between the characters can be determined by either the pleiotropic impacts of single genes or the

tight linkage of several genes affecting specific characters individually. Therefore, correlation

analysis can be helpful for the indirect selection of characters and improvement of seed yield via

other characteristics (Borojevic, 1990). However, the correlations obtained between the

characters did not provide sufficient information about the functional relations between the

diverse hierarchical components. A third variable or group of variables can be the cause of a high

or low correlation coefficient between two variables. The correlations between the two variables

of X and Y may be decomposed to direct effect of X on Y and the impacts of X on Y by other

independent variables via a path analysis.

In the collection under investigation (association panel), 8.97% of the possible SSR locus

pairs {[n(n-1)/2]=[30(30-1)/2]=435 pairs} displayed a significant Linkage Disequilibrium (LD)

levels (P<0.05) (Figure 4). LD presents the non-random relation of alleles at different genetic loci

on a single chromosome (Mackay and Powell, 2007). Association analysis cannot be performed

in the absence of LD. Moreover, the results of an association analysis are determined by LD

structure across a genome (Remington et al., 2001). The evaluated panel was divided into 5

subpopulations based on the SSR marker data (Figure 5). Following retrotransposon-based

marker assessments with 28 REMAP and IRAP primers (Supplementary 3), a total of 248 loci

were identified. Significant LD levels were observed in 6.84% of IRAP+REMAP pair of markers

{[n(n-1)/2]=[248(248-1)/2]=30628 pairs} (P<0.05). Accordingly, the studied panel was divided

into 2 subpopulations. Based on the data obtained from the genetic diversity and population

structure analysis, the studied association panel had a diverse genetic variation and was thus

suitable for the association analysis. In the association analysis with SSR markers, 22 and 21

markers were identified (P≤0.05) for the studied characters under natural and water-limited

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states, respectively (Table 3). In water-limited states, 4, 3, 2, 2, 1, 1, 2, 2, 1, 1, and 2 informative

SSR markers were identified for chlorophyll index, days from planting to flowering, days from

planting to maturity, head or capitulum diameter, length of leaf, number of leaf per plant, width

of leaf, oil percentage, stem height, RWC, and grain yield/plant, respectively (Table 3). The

corresponding numbers of the identified SSR markers under natural states were 1, 2, 2, 3, 1, 1, 1,

2, 2, 4, and 1, respectively. One SSR marker was identified for length of petiole and shoot

diameter under natural states (Table 3). In the association analysis with retrotransposon-based

molecular markers, 50 and 37 IRAP and REMAP markers were identified (P≤0.05), which were

significantly associated with the studied characters under natural and water-limited states,

respectively (Table 4). Some identified markers were common in both natural and water-limited

states. For instance, under both irrigation conditions, the SSR markers P304, P694, P718, and

P807 were associated with QTL controlling width of leaf, chlorophyll index, days from planting

to maturity, and days from planting to flowering, respectively. The genetic relationships between

any characters under different conditions were indicative of the presence of common QTL, which

further suggest improving a character in one condition results in offspring with improved

character in other states. These findings were congruent with the previous results demonstrating a

phenotypic correlation between the characters under varied conditions (Kiani et al., 2009; Abdi et

al., 2012). Therefore, it was concluded that at least partial controlling of some genetic characters

under natural and water-limited states is possible in sunflower via the same QTL. Nevertheless,

the common effects of QTL cannot be clearly related to the pleiotropic impacts of the same

genes, the physical linkage of different genes, or both of them.

Under water-limited states, some markers were communal between several characters

(Table 3). For instance, P733 was identified for stem height, RWC, and grain yield. Communal

markers can facilitate the simultaneous selection of several characters and thus improve MAS

efficiency in plant-breeding activities.

Identifying QTLs associated with sunflower characters under drought stress has been

dealt with in several studies conducted by Kiani et al. (2007a, 2008, 2009), Haddadi et al. (2010,

2011a, b), Abdi et al. (2012), and Jannatdoust et al. (2015a, b). Kiani et al. (2007a, 2008, and

2009) carried out the first QTL analysis on the characters relevant to drought tolerance. In their

analysis, three to eight QTLs were identified for the characters of water status by using a

saturated genetic linkage map. Six to 29% of the phenotypic variance of the characters could be

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explained by the QTLs. From 24 QTLs identified under natural conditions, 5 (twenty one

percent) QTLs were also identified in water-limited states, thus representing stable QTLs. For

osmotic adjustment 8 QTLs were identified, and 4 of them showed overlapping with QTLs

identified for water status characters (Kiani et al., 2007a). Also, the main characters related to

yield such as sowing-to-flowering days, grain yield per plant, total dry matter, leaf area at

flowering and its duration, leaf number per plant, plant height, and head weight were examined in

the field and greenhouse under natural and water-limited states. Based on the character and

growth conditions, two to seven QTLs were detected, most of which were communal among

growth and developmental conditions. Meanwhile, interesting co-locations were seen between

some identified QTLs for the relevant agro-morphological characters and them controlling grain

yield per plant. In a study on RILs, Abdi et al. (2012) identified 64 QTLs for different agro-

morphological characters under 2 irrigation conditions. They used overlapping support intervals

and detected some co-localized QTLs on different linkage groups for the studied characters. In an

attempt to determine the markers associated with 22 agro-morphological and 9 seed-related

characters, i.e., oil and protein yield, oil and protein percentage, grain length/diameter/weight,

dehulled kernel weight, and percentage of dehulled kernel weight to the whole grain in 48

confectionery sunflower landraces under natural, mild, and severe drought stress conditions,

Jannatdoust et al. (2015a, b) used 12 retrotransposons-based molecular markers with the MLM.

They identified 117 loci significantly (P<0.01) associated with the assessed agro-morphological

characters under different environmental conditions (Jannatdoust et al., 2015a). According to the

MLM, 2, 5, and 11 loci were significantly (P<0.01) associated with seed-related characters under

natural, mild, and severe drought stress conditions, respectively (Jannatdoust et al., 2015b).

Under severe drought conditions, some markers were communal between oil and protein yields

and grain length.

A genetic complexity is attributed to many agricultural characters, which are affected by

genetic and environmental factors, as well as their interactions. Identification of molecular

markers facilitates understanding the genetic structure of characters. Thus, MAS strategies can be

easily developed via this advanced genetic understanding of complex characters. Adaptation of

genotypes to diverse environments is controlled via varied QTLs that can be usefully pyramided

through MAS. Furthermore, overlapping QTLs can be assessed to genetically determine character

associations, which are helpful for indirectly selecting the yields through yield-related characters.

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Nonetheless, before MAS application as a practical strategy, the identified QTLs must be

corroborated in the same way as any other quantitative characters are verified.

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Table 1: Genetic parameters for the measured characters in the studied germplasm of oily sunflower under natural and water-limited

states over 2 years

Condition Character Mean SE StDev Minimum Q1 Median Q3 Maximum h2 Vg Ve Vg×e Vph CVg CVph

Natural Ch 38.621 0.328 3.277 30.18 36.608 38.49 40.438 50.03 0.20±0.05 5.24 20.96 0.00 26.20 5.93 13.25

DF 74.533 0.357 3.573 64.25 71.875 74.125 76.75 84.75 0.33±0.06 7.97 14.54 2.05 24.56 3.79 6.65

DM 107.05 0.523 5.23 82.88 104.5 106.25 109.25 132.75 0.32±0.06 17.73 38.54 0.00 56.27 3.93 7.01

HD 13.3 0.222 2.223 7.84 11.935 13.385 14.71 19.22 0.02±0.09 0.14 1.47 8.55 10.16 2.84 23.97

LL 16.602 0.218 2.178 11.95 15.21 16.355 18.095 21.95 0.29±0.07 2.60 4.75 1.70 9.05 9.71 18.12

NL 17.033 0.271 2.706 9.55 15.45 16.775 18.338 24.55 0.23±0.07 3.32 8.27 2.75 14.34 10.70 22.23

LW 14.286 0.23 2.303 9.93 12.585 14 15.773 21.35 0.20±0.07 2.35 6.80 2.38 11.53 10.73 23.77

OIL 47.347 0.353 3.535 38.9 44.94 48.005 50.005 53.41 0.73±0.05 10.64 0.48 3.45 14.57 6.89 8.06

SH 108.57 1.52 15.19 84.9 96.38 107.75 117.71 149.6 0.42±0.06 140.27 150.85 42.37 333.49 10.91 16.82

PL 9.293 0.134 1.34 5.88 8.335 9.155 10.245 13.48 0.22±0.07 0.82 2.14 0.80 3.76 9.74 20.87

RWC 71.275 0.484 4.842 60.45 68.275 71.195 74.428 87.49 0.0±0.00 0.00 95.32 1.12 96.44 0.00 13.78

SD 4.7813 0.0719 0.7186 3.18 4.35 4.64 5.145 8.35 0.27±0.07 0.27 0.54 0.19 1.00 10.87 20.91

YIELD 41.79 1.75 17.5 13.14 27.6 39.56 51.68 97.27 0.22±0.06 151.05 517.07 27.74 695.86 29.41 63.12

WL Ch 40.256 0.324 3.24 31.85 37.958 40.7 42.705 46.55 0.16±0.05 4.58 23.49 0.00 28.07 5.32 13.16

DF 69.71 0.311 3.115 61 67.75 69.75 72.188 79 0.32±0.06 6.16 12.44 0.66 19.26 3.56 6.30

DM 102.58 0.459 4.59 77.63 100.25 102.75 104.75 121 0.16±0.07 8.19 33.66 8.97 50.82 2.79 6.95

HD 10.524 0.156 1.557 7.23 9.365 10.625 11.54 14.56 0.04±0.07 0.21 3.36 1.92 5.49 4.35 22.26

LL 13.879 0.169 1.691 9.85 12.658 13.675 15.025 18.9 0.23±0.07 1.22 2.70 1.41 5.33 7.96 16.63

NL 16.028 0.279 2.787 8.7 14.075 15.825 17.238 23.45 0.18±0.08 2.67 6.89 5.64 15.20 10.19 24.32

LW 11.49 0.17 1.702 8.2 10.285 11.24 12.68 16.95 0.17±0.07 0.96 3.19 1.35 5.50 8.53 20.41

OIL 45.73 0.372 3.722 36.88 43.305 45.82 48.728 52.96 0.76±0.04 11.99 3.09 3.09 18.17 7.57 9.32

SH 99.73 1.18 11.84 70.8 90.55 100.18 109.64 124.65 0.34±0.08 67.83 67.32 66.82 201.97 8.26 14.25

PL 6.6411 0.0919 0.9188 5.2 5.9925 6.5 7.145 10.4 0.23±0.07 0.35 0.89 0.32 1.56 8.91 18.81

RWC 62.325 0.587 5.874 49.64 58.615 62.445 66.313 77.62 0.10±0.06 8.88 77.05 7.43 93.36 4.78 15.50

SD 4.1294 0.0484 0.4842 3.13 3.785 4.2 4.3725 5.8 0.14±0.07 0.08 0.33 0.14 0.55 6.85 17.96

YIELD 21.624 0.775 7.749 8.12 15.043 21.685 27.633 43.13 0.00±0.00 0.00 105.06 57.60 162.66 0.00 58.98

19

WL: water-limited states; Ch: chlorophyll; DF: days form planting to flowering; DM: days form planting to maturity; HD: head or

capitulum diameter; LL: length of leaf; NL: number leaf per plant; LW: width of leaf; SH: stem height; PL: length of petiole; RWC:

relative water content; SD: shoot diameter; Yield: grain or seed yield/plant; SE: standard error; StDev: standard deviation; Min:

minimum value; Q1: Quartile 1; Q2: Quartile 2, which is also the median; Q3: Quartile 3; Max: maximum value; h2: broad sense

heritability; Vg: genotypic variance; Ve: environmental variance; Vg×e: genotype by environment interaction variance; Vph:

phenotypic variance; CVg: coefficient of genetic variation; CVph: coefficient of phenotypic variation

20

Table 2: Phenotypic simple correlation coefficients between the measured characters in the studied oily sunflower germplasm under

natural and water-limited states over 2 years

Ch DF DM HD LL NL LW OIL SH PL RWC SD

DF 0.241

DM 0.052 0.624

HD 0.114 0.274 0.273

LL -0.043 0.337 0.408 0.509

NL 0.066 0.175 0.222 0.098 0.245

LW 0.025 0.254 0.313 0.5 0.875 0.242

OIL 0.011 0.061 0.054 0.149 0.158 0.047 0.011

SH 0.064 0.328 0.34 0.348 0.54 0.431 0.529 0.101

PL -0.033 0.285 0.221 0.456 0.593 0.106 0.58 0.123 0.45

RWC -0.019 -0.304 -0.223 -0.043 -0.141 -0.022 -0.046 0.021 -0.116 -0.108

SD 0.023 0.206 0.221 0.51 0.703 0.25 0.742 -0.011 0.405 0.502 -0.038

YIELD 0.138 0.167 0.211 0.632 0.497 0.113 0.578 0.09 0.37 0.464 0.067 0.546

DF 0.171

DM 0.058 0.596

HD 0.012 0.028 0.015

LL 0.01 0.246 0.092 0.596

NL 0.128 0.233 0.136 0.002 0.325

LW 0.019 0.18 0.144 0.607 0.844 0.226

OIL -0.068 -0.04 0.003 0.046 0.052 0.15 -0.009

SH 0.198 0.286 0.171 0.363 0.469 0.438 0.412 0.054

PL 0.127 0.243 0.216 0.357 0.573 0.185 0.537 -0.055 0.396

RWC 0.004 -0.247 -0.183 -0.001 -0.123 -0.008 0.015 0.064 -0.029 -0.08

SD 0.126 0.159 0.138 0.654 0.74 0.237 0.743 0.078 0.394 0.484 0.048

YIELD -0.002 -0.139 -0.094 0.795 0.425 -0.134 0.536 0.07 0.289 0.135 0.136 0.56

The correlation coefficients were significant at P=0.05 with a value of ≥0.195.

Ch: chlorophyll; DF: days from planting to flowering; DM: days from planting to maturity; HD: head or capitulum diameter; LL:

length of leaf; NL: leaf number per plant; LW: width of leaf; OIL: Oil percentage; SH: stem height; PL: length of petiole; RWC:

relative water content; SD: shoot diameter; Yield: grain or seed yield/plant

21

Table 3: The SSR markers detected for the morphological characters in the investigated

germplasm of oily sunflower under natural and water-limited states over 2 years

Natural states Water-limited states

Character Marker

name F-Marker P-Marker Character

Marker

name F-Marker P-Marker

Ch P694 4.4576 0.0149 Ch P378 3.7324 0.0297

DF P694 6.3933 0.0027 P694 5.6214 0.0053

P807 3.4202 0.0377 P1209 2.2058 0.0427

DM P608 9.8217 0.0025 P1264 3.5297 0.0355

P718 5.5077 0.0218 DF P718 7.8744 0.0065

HD P307 3.1206 0.0198 P807 4.2482 0.0177

P488 6.0271 0.0167 P844 4.5461 0.0136

P844 3.7355 0.0283 DM P718 9.5094 0.0029

LL P844 4.0925 0.0205 P785 3.3587 0.0398

NL P733 2.5945 0.0334 HD P733 2.5227 0.0376

LW P304 2.698 0.0129 P844 3.8895 0.0246

OIL P304 2.5791 0.0169 LL P304 2.3089 0.0309

P1265 3.1336 0.049 NL P785 3.3324 0.0407

SH P844 5.5091 0.0058 LW P304 4.2678 0.0004

P1215 6.6239 0.012 P488 4.4451 0.0387

PL P844 3.1141 0.0500 OIL P304 3.0062 0.0065

RWC P378 3.3551 0.0416 P1265 3.1447 0.0485

P807 3.3633 0.0397 SH P733 2.4766 0.0407

P1064 3.218 0.0453 RWC P733 2.8859 0.0204

P1209 2.7264 0.014 YIELD P733 2.3992 0.0463

SD P844 5.36 0.0066 P807 3.9737 0.0227

YIELD P718 4.2593 0.0427

Ch: chlorophyll: DF: days from planting to flowering; DM: days from planting to maturity; HD:

head or capitulum diameter; LL: length of leaf; NL: number of leaf per plant; LW: width of leaf;

PH: setm height; PL: length of petiole; RWC: relative water content; SD: shoot diameter; Yield:

grain or seed yield/plant

22

Table 4: The retrotransposon markers detected for the morphological characters in the

investigated germplasm of oily sunflower under natural and water-limited states over 2 years

Natural conditions

Character Marker

name

Marker

type

F-

Marker

P-

Marker Character

Marker

name

Marker

type

F-

Marker

P-

Marker

CH 64658 REMAP 7.3287 0.0083 LW 618183 REMAP 8.5043 0.0045

64654 IRAP 13.9501 0.0003 658268 REMAP 7.5716 0.0072

ufur17 IRAP 7.3005 0.0083 OIL cr9 IRAP 12.8421 0.0005

64a135 REMAP 7.2227 0.0086 cf8181 REMAP 8.6809 0.0042

64a136 REMAP 7.892 0.0061 SH 61652 IRAP 8.0959 0.0056

648406 REMAP 6.9684 0.0098 616510 IRAP 21.4884 0.000013

FD CR1 IRAP 7.8096 0.0064 646510 IRAP 22.1387 0.00001

Cr9 IRAP 17.076 0.00008 646511 IRAP 14.8282 0.0002

uf2 IRAP 9.2571 0.0031 cr7 IRAP 7.7436 0.0066

crur16 IRAP 8.6895 0.0041 ufur18 IRAP 12.2049 0.0007

648183 REMAP 7.0095 0.0096 648264 REMAP 7.2828 0.0083

648577 REMAP 9.6211 0.0026 PL 62653 IRAP 8.5038 0.0047

64a1311 REMAP 7.5147 0.0074 646510 IRAP 12.1214 0.0008

DM 63641 IRAP 9.0095 0.0035 cf8263 REMAP 7.0696 0.0093

64653 IRAP 10.9151 0.0014 RWC ur11 IRAP 12.2366 0.0007

646510 IRAP 11.7102 0.0009 618187 REMAP 11.4281 0.0011

cr9 IRAP 9.3475 0.003 cf8185 REMAP 8.2161 0.0053

uf2 IRAP 14.281 0.0002 SD 62655 IRAP 15.2046 0.0002

LL 616510 IRAP 17.0702 0.00008 658263 REMAP 10.4031 0.0018

646510 IRAP 9.4381 0.0029 cf8263 REMAP 9.7074 0.0025

6465511 IRAP 10.3023 0.0019 YELD 616510 IRAP 8.9608 0.0036

64a134 REMAP 7.1235 0.0091 62659 IRAP 8.088 0.0058

NL 618405 REMAP 7.4834 0.0075 648575 REMAP 7.0101 0.0096

cf8263 REMAP 7.1761 0.0088 64a139 REMAP 7.675 0.0068

LW 616510 IRAP 7.1231 0.0092 658268 REMAP 8.0507 0.0057

Water-limited conditions

CH 61651 IRAP 9.0579 0.0034 SH 61652 IRAP 8.0285 0.0058

64654 IRAP 8.7869 0.004 646510 IRAP 8.4979 0.0046

ufur17 IRAP 10.7149 0.0015 646511 IRAP 8.1777 0.0054

638267 REMAP 8.5196 0.0045 ufur16 IRAP 9.5426 0.0027

DF ufur13 IRAP 7.5727 0.0072 PL 63641 IRAP 7.8023 0.0064

ufur18 IRAP 7.1379 0.009 648263 REMAP 8.8098 0.0038

DM 646510 IRAP 7.5852 0.0072 RWC 654 REMAP 10.9693 0.0014

648263 REMAP 7.687 0.0068 618402 REMAP 7.235 0.0086

658265 REMAP 7.3166 0.0082 658268 REMAP 7.1073 0.0092

HD 62655 IRAP 8.9588 0.0038 648403 REMAP 7.8986 0.0061

cfcr1 IRAP 8.7202 0.0042 SD 646511 IRAP 7.6087 0.0071

LL uf14 IRAP 8.1886 0.0053 cfcr1 IRAP 7.5216 0.0077

658186 REMAP 8.9339 0.0036 658261 REMAP 7.0297 0.0095

NL cr4 IRAP 7.3442 0.0081 YELD ufur12 IRAP 7.4283 0.0077

cr11 IRAP 7.3183 0.0082 658183 REMAP 7.1784 0.0088

cryr17 IRAP 8.8926 0.0037 cf8269 REMAP 7.0333 0.0095

OIL cr7 IRAP 12.2748 0.00072

618572 REMAP 7.0571 0.0094

cf8181 REMAP 8.6463 0.0043

23

Ch: chlorophyll: DF: days from planting to flowering; DM: days from planting to maturity; HD:

head diameter; LL: length of leaf; LN: number of leaf per plant; LW: width of leaf; PH: stem

height; PL: length of petiole; RWC: relative water content; SD: shoot diameter; Yield: grain or

seed yield/plant

24

Fig. 1: The frequency distribution of the sunflower lines for morphological characters under

natural states

25

Fig. 2: The frequency distribution of the sunflower lines for agro-morphological characters under

water-limited states

26

27

Fig. 3: Percent reduction of the studied characters under water-limited compared to natural

conditions in the studied oily sunflower germplasm over 2 years.

Ch: chlorophyll; DF: days from planting to flowering; DM: days from planting to maturity; HD:

head diameter; LL: length of leaf; LN: number of leaf per plant; LW: width of leaf; PH: stem

height; PL: length of petiole; RWC: relative water content; SD: shoot diameter; Yield: grain or

seed yield/plant

Fig. 4: The LD plot produced by SSR marker pairs. The part upper of diagonal depicts D among

each pair of the markers. The lower part of diagonal displays the significance levels between each

pair of the markers.

28

Fig. 5: Genetic relatedness of the sunflower genotypes with SSR data as analyzed by the program

of structure. Numeric values on the y-axis show coefficient of membership (Q) and on the x-axis

demonstrate numbers of the individuals.