Assessment of Genetic Diversity and Relationships Among Diverse Rice Genotypes using Heading Date/QTLs Linked SSR Markers

 

Ashraf M. Elmoghazy1*, Ola A. Galal2, Salma K. Kelany2 and Said A. Dora2

1Rice Research and Training Center, Field Crops Research Institute, Agricultural Research Center, 33717, Kafr Elsheikh, Egypt

2Genetics Department, Faculty of Agriculture, Kafrelsheikh University, 33516 Kafr Elsheikh, Egypt

*For correspondence: drashrafmoghazy@gmail.com

Received 19 December 2022; Accepted 23 February 2023; Published 13 April 2023

 

Abstract

 

Genetic diversity is the main source for plant breeder to develop new elite genotypes. The objective of this investigation is to study phenotypic and molecular genetic diversity among 150 rice (Oryza sativa L.) genotypes for their heading dates, due to the importance of this trait in avoiding some climate changes and minimize the water consumption. Results revealed a wide range of differences for heading dates ranged from 70 to 129 days. Genotypes were classified into four groups; very early, early, intermediate and late. Four SSR markers linked to heading date trait/QTLs were used to study 20 selected genotypes. Seventeen polymorphic alleles with molecular sizes ranged from 108 to 304 bp were amplified. Two out of the four primers (RM510 and RM585) produced the PCR expected product size (122 and 233 bp, respectively) in addition to other unexpected alleles. The RM585 primer generated an unexpected additional band with a molecular size of 257 bp. This band appeared only in five very early and early-dated genotypes and was completely absent from the intermediate and late genotypes. This primer also recorded the highest PIC value (0.765). RM7601 also produced an additional unexpected band with molecular size of 116 bp. This fragment only appeared in most very early and early heading date genotypes, not in intermediate and late genotypes. For the cluster analysis based on the SSR markers, the 20 rice genotypes were divided into two main clusters, which were respectively divided into three and two groups that matched in heading date. © 2023 Friends Science Publishers

 

Keywords: Rice; Heading date; Genetic variability; SSR markers

 


Introduction

 

Rice (Oryza sativa L., 2n = 24) is the most important staple food crop for more than 3.5 billion people (Xu et al. 2016; Saleh et al. 2020). It is cultivated in Egypt over an area of about 660 thousand hectares, with an annual production of about 4.6 million tons of paddy, with average productivity of 10 tons per hectare (EAS 2018; Elmoghazy and Elshenawy 2018; FAO 2018). Genetic variability is the basis of plant breeding as the success of any crop improvement program depends on the magnitude of genetic variability (Ganapathi et al. 2014; Sarker et al. 2015). The creation of variability in rice germplasm is one of the most effective methods that provides a wide range of genotypes that can be selected to develop new varieties with a desired combination of traits (Pandey et al. 2009; Sakran et al. 2022). Moreover, it accelerates the detection of promising genotypes without the need to evaluate all possible cross combinations in breeding programs (Palanga et al. 2016). Selection can be effectively practiced only in the presence of variability of desired traits. The development of early maturing rice genotypes is of great importance; it could be used to avoid certain climate changes such as high temperature during fertilization which represent a very serious problem affecting grain yield. The short duration rice varieties save time for planting other winter crops, save irrigation water, which represents the main constraint of rice cultivation and save efforts and expenses of rice cultivation. Heading date is determined by both genetic factors and environmental conditions (Andres and Coupland 2012). Cultivars with an appropriate heading date will be conductive to high grain yield by fully utilizing the light and temperature resources in their growing regions (Zhang et al. 2015). In rice, flowering time is regulated by the complex genetic mechanisms involving hundreds of quantitative trait loci; QTLs (Hori et al. 2016; Matsubara and Yano 2018; Liu et al. 2021).

DNA-based molecular markers have been used extensively for studying genetic diversity (Yadav et al. 2013; Sarif et al. 2020). Compared with agro-morphological markers, molecular markers are not influenced by environmental factors and are generally more sensitive to differences among genotypes at the DNA level, thus increasing their detection efficiency and fast (Ming et al. 2010). Currently, simple sequence repeats (SSRs) are the molecular tools used for diversity evaluation and detecting relationships among different crop species, populations, or individual rice accessions (Pachauri et al. 2013; Hoque et al. 2021). According to Allhgolipour et al. (2014), SSR markers are suitable for evaluating genetic diversity among closely related rice accessions. A study of Wang et al. (2014) has identified that microsatellite loci may be used to detect genetic variation and genetic relationships within rice through genome study as well as allelic diversity analysis.

The present investigation aimed to assess the genetic variability and heritability for heading date trait of 150 different rice genotypes under Egyptian conditions. Moreover, study of genetic diversity and phylogenetic relationships; among 20 selected genotypes, based on SSR markers related to heading date trait/QTLs.

 

Materials and Methods

 

The present study was conducted at Genetics Department, Faculty of Agriculture, Kafr Elsheikh University, Egypt and Rice Research and Training Center (RRTC), Sakha, (31°05'36.4"N 30°55'45.6"E and 4m elevation) Kafr Elsheikh, Egypt during the two summer seasons; 2017 and 2018. The experimental soil is silty clay and the temperature ranged from 22 to 38 (for maximum) and 15 to 28 (for minimum) during the growing season.

 

Plant material and experimental design

 

One hundred fifty rice (Oryza sativa L.) genotypes; six local genotypes (Giza 177, Giza 178, Sakha 102, Giza 171, NABATAT ASMAR and Egyptian Yasmin) and 144 exotic genotypes obtained from Egyptian Rice Gene Bank (ERGB) of RRTC, were used in this study. Names and origins of the 150 studied rice genotypes are listed in Table 1. All the 150 genotypes were grown in a randomized complete block design (RCBD) in three replications; each consisting of one row/genotype. Each row was 5 m long and contained 25 seedlings with 20×20 cm spacing among rows and hills. All standard crop management were applied as recommended by RRTC (RRTC 2013) and by Elmoghazy and Elshenawy (2018).

 

Heading date trait

 

The heading date trait was scored according to the International Rice Research Institute (IRRI) Standard Evaluation System (IRRI 2014). The 150 rice genotypes were classified into four groups as follows: Genotypes with heading date ranged from 70 to 92 days are very early, from 93 to 110 days are early, from 111 to 120 days are intermediate and from 121 to 130 days are late.

Molecular analysis

 

Based on heading date scoring, 20 genotypes (five genotypes/group) were selected for molecular characterization using four SSR molecular markers. Genomic DNA was isolated from 100 mg healthy leaves (three weeks old) of the 20 selected rice genotypes using CTAB method (Murray and Thompson 1980).

 

SSR markers and PCR amplification

 

Four SSR DNA markers (introduced from Eurofins Genomics Co., Germany); related to heading date trait/QTLs, were screened on DNA templates. All primer sequences were directly downloaded from Gramene database (www.gramene.com). Details of primer sequences, chromosomal locations, repeat motifs and expected product sizes are given in Table 2. The PCR amplification reaction was performed in 10 μL reaction mixture, containing 1 μL of DNA template (15 ng/μL), 1 μL of each of the forward and reverse primers (10 nmol/μL), 5 μL of 2X PCR master mix (amaROnePCRTM - GeneDireX, Inc) and 2 μL of double distilled water (ddH2O).

A thermocycler (TECHNE TC-412) was used to carry out the PCR amplification programme as follows: an initial denaturation step at 94°C for 5 min, followed by 35 cycles of denaturation at 94°C for 1 min, primer annealing at 50–55oC for 30 sec and primer extension at 72°C for 1 min. By the end of the 35th cycle, a final extension step at 72°C for 5 min was performed. The PCR amplified products were stored at -20°C until use. The PCR amplified fragments were separated by electrophoresis on 3% agarose gel stained with ethidium bromide, visualized under a UV transillumination and then photographed using Biometra gel documentation unit (BioDoc, Biometra, Germany). Molecular size of the separated fragments was determined against a known DNA ladder (HyperLadderTM 100 bp, Bioline) using Gel Analyzer 10 Program.

 

Data analysis

 

Data of heading date trait was subjected to analysis of variance (ANOVA) for randomized complete block design (RCBD), by using the statistical analysis software program MSTAT-C; version 2.10 (MSTAT-C 1991). Based on the combined analysis of the two studied seasons, the genotypic (GV) and phenotypic (PV) variances, genotypic (GCV%) and phenotypic (PCV%) coefficient of variations and heritability in broad sense () were calculated according to Falconer (1981). Least significant difference (LSD) method was used to compare means at 5% level of probability. For molecular analysis, data of SSR markers were introduced as binary values 1 and 0 for the presence and absence of an amplified band, respectively. Number of alleles was determining and polymorphic information content of the locus i (PICi) was calculated according to Roldan-Ruiz et al.

Table 1: The 150 studied rice genotypes, their country of origins and the mean of combined phenotypic data for heading date trait over the two seasons, 2017 and 2018 under Egyptian conditions. These genotypes were obtained from Egyptian Rice Gene Bank, located at RRTC, Sakha, Egypt. Each genotype was carefully examined and purified

 

No.

Genotype

Origin

Heading Date (days)

1

E B Gopher

Texas, United States

105

2

PR 325

United States

108

3

OwariMochi

Japan

120

4

WC 3777

Francisco Morazán

114

5

Tehran

Iran

71

6

Lang ShweiKeng

China

104

7

BG 79

Guyana

70

8

D. Sancho

Lisboa, Portugal

72

9

Barbado

Lisboa, Portugal

70

10

Sanakevelle

Liberia

124

11

MARATELLI

Italy

73

12

Chin

Panama

115

13

Italica Carolina

Lubelskie, Poland

70

14

Italica M1

Lubelskie, Poland

70

15

Alvario

Portugal

75

16

TAICHUNG 33

Taiwan

114

17

Romanica

Pest, Hungary

70

18

Rexora

Mozambique

124

19

IR 334-17-1-3-1

IRRI Philippines

124

20

Amber 33

Iraq

119

21

Giza 177

Egypt

93

22

Giza 178

Egypt

100

23

Blue Rose Sela

Argentina

113

24

Sakha 102

Egypt

101

25

Bungara

Rwanda

115

26

KhaoHao

Laos

120

27

J.P. 5

Pakistan

108

28

Nauta

Loreto, Peru

112

29

P 761-40-2-1

Colombia

113

30

NEANG MEAS

Cambodia

114

31

Fa Yiu Tsai

Hong Kong

116

32

JambaramVermelho

Guinea-Bissau

116

33

Kathmandu Valley No.1

Nepal

76

34

Daudzai

Pakistan

129

35

Thangone

Laos

111

36

PrataoPrecoce

Brazil

109

37

Precocinho

Brazil

109

38

Imbolo II

Congo

110

39

Onu B

Congo

115

40

Shima

Iraq

116

41

Giza 171

Egypt

113

42

P 1289

Turkey

112

43

CH 242-32

Biobío, Chile

109

44

HalwaGose Red

Iraq

113

45

MIYANG

China

107

46

Zira

Kenya

113

47

MEDUSA

Lombardia, Italy

119

48

HD14

Australia

112

49

Gidej

Azerbaijan

115

50

Grassy

Haiti

113

51

B459A1-49-1-2-1

Texas, United States

112

52

Dular

Dhaka, Bangladesh

115

53

Cesariot

Occitanie, France

113

54

CIGALON

France

113

55

IR 2061-214-2-3

IRRI Philippines

120

56

Precoz de Machiques

Aragua, Venezuela

71

57

Sesilla

Bulgaria

122

58

WC 3398

Nayarit, Mexico

108

59

B441B-24-4-5-1

Indonesia

123

60

Quinimpol

Philippines

119

Table 1: Continue

Table 1: Continue

 

61

WC 3395

Jamaica

117

62

Sadri Type

Iraq

119

63

Criollo

Mexico

119

64

PuangNigern

Thailand

120

65

Daido

Mongolia

73

66

Natapasume

Taiwan

119

67

Rz No. 111

Congo

114

68

Apure

Aragua, Venezuela

119

69

WC 2810

Pohnpei, Micronesia

120

70

Ao Chiu 2 Hao

Sichuan Sheng, China

119

71

AgulhaBranco

El Salvador

117

72

MataoLizo

El Salvador

117

73

Secano do Brazil

El Salvador

115

74

ItalicaAlef

Former, Soviet Union

73

75

NABATAT ASMAR

Giza, Egypt

121

76

LOMELLO

Italy

73

77

I KUNG PAO 5-3-4

Taiwan

115

78

AP 439

Venezuela

115

79

Aguja

Bolivia

115

80

Cola de Burro

Bolivia

112

81

Mojito Colorado

Bolivia

116

82

Campino

Portugal

114

83

Laat

Suriname

114

84

MONTICELLI

Lazio, Italy

76

85

STIRPE 82 CHIAPPELLI

Piemonte, Italy

76

86

KamBauNgan

Hong Kong

76

87

IguapeCateto

São Paulo, Brazil

115

88

MUTSU HIKARI

Aomori, Japan

76

89

JUMA 1

Dominican Republic

123

90

KhaoKhao

Thailand

121

91

NAYIMA 45

Iraq

117

92

IARI 7449

Assam, India

113

93

Agbede

Nigeria

116

94

Sika

Cameroon

123

95

KhaoPhoi

Laos

113

96

KhaoLuang

Laos

115

97

NIQUEN

Biobío, Chile

73

98

IR 1614-168-2-2

Philippines

122

99

Basmati Sufaid

Punjab, Pakistan

109

100

Chak 48

Punjab, Pakistan

117

101

B35

Punjab, Pakistan

122

102

DhanSufaid

Punjab, Pakistan

118

103

Jhona

Punjab, India

112

104

P 79

Colombia

110

105

Ratua Red Nehri

Punjab, Pakistan

118

106

GPNO 19314

Brazil

118

107

Choei-ine

Japan

72

108

GPNO 16379

Pohnpei, Micronesia

72

109

Chao Hay b

Laos

109

110

Glutinous

Hong Kong

111

111

TJ

Guyana

74

112

Ambalalava 1283

Madagascar

74

113

PD 46

Sri Lanka

74

114

IacaEscuro

Guinea

119

115

PATNAI 6

Yangon, Myanmar

119

116

BIRIBRA

Ghana

76

117

WW 3/200

Netherlands

115

118

Subdesvauxii Vase

Portugal

115

119

E. Yasmine

Egypt

120

120

HON CHIM

Hong Kong

76

121

MAKALIOKA 752

Madagascar

116

122

COLOMBIA 1

Colombia

112

123

Batatais

Brazil

115

124

R 29/1

Congo

115

125

R 98

Congo

116

126

R 98/1

Congo

110

127

R 99/3

Congo

110

Table 1: Continue

 


 

 

Fig. 1: Profiles of DNA amplified fragments generated by RM223, RM510, RM585 and RM7601 markers for the 20 rice genotypes selected based on heading dates. M: 100 bp DNA ladder. Genotypes 1-5 (very early): BG 79, Barbado, Italica Carolina, Italica M1 and Romanica. Genotypes 6-10 (early): Giza 177, Giza 178, Sakha 102, Lang ShweiKeng and E B Gopher. Genotypes 11-15 (intermediate): KhaoHao, IR 2061-214-2-3, PuangNigern, WC 2810 and Mack Khoune. Genotypes 16-20 (late): GPNO 22236, Sanakevelle, Rexora, IR 334-17-1-3-1 and Daudzai

 

 Table 1: Continue

 

128

Nema

Iraq

118

131

GPNO 22236

Kankan, Guinea

123

132

IM 16

Nigeria

111

133

ECIA76-S89-1

Cuba

111

134

TAKAO 11

Taiwan

117

135

WC 1006

Iraq

117

136

Rubra

Former, Soviet Union

116

137

LATE CALORO

Australia

122

138

TrionfoFassone

Piemonte, Italy

114

139

YabaniMontakhab 57

Odisha, India

116

140

Precosur

Entre Ríos, Argentina

111

141

IR 773A1-36-2-1-3

Philippines

121

142

IARI 5753B

Assam, India

115

143

Mack Khoune

Laos

120

144

H64-9-1

Argentina

119

145

Mabla

Punjab, Pakistan

110

146

GPNO 29157

Jiangsu Sheng, China

119

147

Hae Zo

Korea, South

119

148

Vary TarvaOsla

Portugal

75

149

CHONTALPA 437

Mexico

115

150

Sathi Basmati

Punjab, Pakistan

122

 

Average

 

107

 

Max

 

129

 

Min

 

70

 

Stander Deviation

 

17.02

 

Stander Error

 

1.39

 

 

(2000) as follows: PICi = 2fi(1-fi); Where fi represents the frequency of the present bands and (1-fi) is the frequency of the absent bands. The PIC value of each primer was calculated using the average PIC values for all primer.

       To determine the genetic relationships among the 20 selected rice genotypes, a dendrogram was constructed based on Jaccard's similarity coefficient (Jaccard 1901) using the Unweighted Pair-Group Method with Arithmetic mean (UPGMA) analysis (Nei 1973).

 

Results

 

Analysis of variance for heading date trait

 

Based on the combined data, analysis of variance represented in Table 3 showed highly significant differences among the 150 studied rice genotypes and between 2017 and 2018 seasons for heading date trait. Highly significant differences were also found for the interaction between genotypes and years. Significance of genotype mean squares revealed that there are genetic differences among genotypes in case of heading date trait. The existence of genetic variability is the key component of breeding programs for broadening the gene pool to develop high yielding varieties (Aditya and Bhartiya 2013; North 2013).

 

Mean performance and heading date scoring

 

The mean performance of heading date trait was scored for the 150 studied genotypes (Table 1). A wide range of differences was observed among genotypes with mean performance values ranged from 70 days (BG79, Barbado, Italica Carolina, Italica M1 and Romanica genotypes) to 128 days (Daudzai genotype). Results of heading date scoring indicated that the majority of studied genotypes heading date was belonging to the intermediate group including 87 genotypes of the 150 studied genotypes, while the minority was to late group of heading date by 16 genotypes. Mean performance of heading date for the 20 rice genotypes (five genotypes/group) which were selected for molecular characterization on the basis of their heading date estimates are listed in Table 4.

 

Molecular characterization

 

Polymerase chain reactions for RM223, RM510, RM585 and RM7601 SSR markers; which were reported to be linked to heading date trait/QTLs, were carried out with DNA templates of the 20 selected genotypes. Genotypic screening of the 20 genotypes with the four SSR markers are presented in Fig. 1 and Table 5.

Table 2: Forward (F) and reverse (R) primer sequences, chromosomal locations (CL), repeat motifs, annealing temperature and expected PCR product sizes of the used four SSR markers

 

Primer

F/R Primer 5'→3'

CL

Repeat motif

Annealing temperature (ºC)

Expected product size (bp)

References

RM223

F -GAGTGAGCTTGGGCTGAAAC

8

(CT)25

55

165

Khatab et al. (2016)

R- GAAGGCAAGTCTTGGCACTG

RM510

F- AACCGGATTAGTTTCTCGCC

6

(GA)15

55

122

Khatab et al. (2016)

R- TGAGGACGACGAGCAGATTC

RM585

F- CAGTCTTGCTCCGTTTGTTG

6

(TC)45

55

233

Khatab et al. (2016)

R- CTGTGACTGACTTGGTCATAGG

RM7601

F -GCCTCGCTGTCGCTAATATC

7

(TGGA)7

50

133

Fatimah et al. (2014)

R-CAGCCTCTCCTTGTGTTGTG

-groups, IIa and IIb, which contain the most late genotypes

 

All SSR fragments were determined and a high level of polymorphism (100%) was observed suggesting a high level of diversity among the used genotypes. The PIC (Polymorphic Information Content) values ranged from 0.418 (RM510) to 0.765 (RM585) with an average of 0.559 per marker (Table 6). Accordingly, primer RM585 was the most polymorphic primer while it produced a total of six alleles with different molecular sizes and also recorded the highest PIC value (0.765).

 

Table 3: Mean squares of heading date trait based on the combined data of the two studied seasons; 2017 and 2018

 

Source of variation

Degree of freedom

Mean square

Years

1

327.610**

Rep/Year

4

0.262

Genotypes

149

1738.428**

Genotypes ×Years

149

21.760**

Error

596

6.125

** Significant differences at 1% level of probability

 

Table 4: Mean, genotypic and phenotypic variances, genotypic and phenotypic coefficients of variation, and heritability in broad sense for heading date trait for the 150 studied genotypes

 

Parameter

Heading date

Mean (days)

107.22

Genotypic variance (GV)

288.72

Phenotypic variance (PV)

294.82

Genotypic coefficient of variation (GCV %)

15.85

Phenotypic coefficient of variation (PCV %)

16.01

Heritability (%)

97.93

 

Table 5: Mean performance of heading date trait for the 20 selected rice genotypes

 

S. No.

Genotype

Heading date (days)

Group

1

BG 79

70

Vary early

2

Barbado

70

Vary early

3

Italica Carolina

70

Vary early

4

Italica M1

70

Vary early

5

Romanica

70

Vary early

6

Giza 177

93

Early

7

Giza 178

99

Early

8

Sakha 102

100

Early

9

Lang ShweiKeng

103

Early

10

E B Gopher

105

Early

11

KhaoHao

119

Intermediate

12

IR 2061-214-2-3

119

Intermediate

13

PuangNigern

119

Intermediate

14

WC 2810

119

Intermediate

15

Mack Khoune

120

Intermediate

16

GPNO 22236

123

Late

17

Sanakevelle

123

Late

18

Rexora

124

Late

19

IR 334-17-1-3-1

124

Late

20

Daudzai

129

Late

 

 

A total of 17 fragments with an average of 4.25 alleles/marker were amplified in the present study. Alleles molecular size varied from 108 to 304 bp among the 20 genotypes. Multiple PCR amplicons ranged from 3 for RM510 and RM7601 to 6 for RM585 were yielded. The amplified number of alleles were varied among genotypes from 3 to 5 alleles. The three SSR primers; RM223, RM510 and RM585, produced PCR bands in all the studied genotypes, while RM7601 primer did not amplify any bands in Khao Hao (intermediate genotype) and IR 334-17-1-3-1 (late genotype) (Fig. 1 and Table 6).

Out of the four SSR markers, two primers (RM510 and RM585) produced the PCR expected product sizes, 122 and 233 bp, respectively, in addition to other unexpected alleles. On the other hand, the other two primers; RM223 and RM7601, had several unexpected alleles ranging from 3 alleles of molecular size 108, 116 and 125 bp for RM7601 to 5 alleles of molecular size ranging from 136 to 304 bp for RM223 but did not produce an allele of the expected size.

 

Cluster analysis

 

Based on molecular data of SSR markers linked to heading date trait/QTLs, Jaccard's similarity coefficients were calculated and a dendrogram was constructed using the UPGMA method to determine the genetic relationships among the 20 rice genotypes (Fig. 2). The dendrogram showed a clear separation of the 20 rice genotypes into two main clusters (I and II) at a genetic similarity of 9.0%. The first cluster (I) included 15 genotypes which were divided into three groups (Ia, Ib and Ic). Group Ia had two late heading date genotypes (Sanakevelle and IR 334-17-1-3-1). The seven genotypes in group Ib were divided into two sub-groups at a genetic similarity of 47.8%. The first one (Ib-1) contained four genotypes; with 100% genetic similarity, which were very early (Italica M1 and Romanica) and early (Giza 178 and Sakha 102) for heading date trait. However, the other three very early genotypes (BG 79, Barbado and Italica Carolina) were separated in the second sub-group Table 6: Genotypic screening of the 20 selected rice genotypes (based on heading dates) for the used four SSR markers; RM223, RM510, RM585 and RM7601. + and -, means presence and absence of respected allele, respectively. Some alleles were amplified only at very early and early heading genotypes like 304 bp of RM223 and 116 bp of RM 7601 while other alleles were found only at intermediate and late genotypes like 266 bp and 136 bp of RM223, 122 bp of RM510, 261 bp, 223 bp and 208 bp of RM585 and 125 bp of RM7601. R2 values were highly significant, which mean high association of the used markers to the trait

 

Primer

 

 

 

 

Expected size (bp)

 

 

 

Presented alleles (bp)

 

 

 

Range of size (bp)

 

 

 

Number of alleles

 

 

 

Genotypes

PIC value and R2

Very early genotypes

Early genotypes

Intermediate genotypes

Late genotypes

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

 

BG 79

Barbado

Italica Carolina

Italica M1

Romanica

Giza 177

Giza 178

Sakha 102

Lang ShweiKeng

E B Gopher

KhaoHao

IR 2061-214-2-3

PuangNigern

WC 2810

Mack Khoune

GPNO 22236

Sanakevelle

Rexora

IR 334-17-1-3-1

Daudzai

 

 

RM223

 

 

165

304

 

 

168

 

 

5

-

-

-

-

-

+

-

-

-

-

-

-

-

-

-

-

-

-

-

-

 

 

0.503

0.002

266

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

+

-

-

-

154

-

-

-

-

-

+

-

-

+

+

-

+

+

+

-

-

-

+

-

-

148

+

+

+

+

+

-

+

+

-

-

-

-

-

-

+

+

+

-

+

-

136

-

-

-

-

-

-

-

-

-

-

+

-

-

-

-

-

-

-

-

+

 

RM510

 

122

130

 

14

 

3

-

+

+

+

+

+

+

+

+

+

-

-

+

+

+

-

+

-

+

-

 

0.418

0.002

122

-

-

-

-

-

-

-

-

-

-

+

+

-

-

-

+

-

+

-

+

116

+

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

 

 

 

RM585

 

 

 

233

261

 

 

 

61

 

 

 

6

-

-

-

-

-

-

-

-

-

-

-

-

-

+

-

-

-

-

-

-

 

 

0.765

0.0003

257

-

-

-

+

+

+

+

+

-

-

-

-

-

-

-

-

-

-

-

-

233

+

+

+

-

-

-

-

-

+

+

-

-

+

-

-

-

-

-

-

-

223

-

-

-

-

-

-

-

-

-

-

-

-

-

+

+

-

-

-

-

-

208

-

-

-

-

-

-

-

-

-

-

-

+

-

-

-

+

+

-

+

+

200

-

+

-

-

-

-

-

-

-

-

+

-

-

-

-

-

-

+

-

-

 

RM7601

 

133

125

 

17

 

3

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

+

 

0.549

0.0004

116

+

+

+

+

+

-

+

+

+

-

-

-

-

-

-

-

-

-

-

-

108

-

-

-

-

-

+

-

-

-

+

-

+

+

+

+

+

+

+

-

-

 

 

(Ib-2). Group Ic contained six genotypes which differed in their heading dates, including three early genotypes (Giza 177, E B Gopher and Lang Shwei Keng) and three intermediate genotypes (Puang Nigern, WC 2810 and Mack Khoune). These six genotypes in group Ic were separated from the other nine genotypes (groups Ia and Ib) at similarity percentage of 26.2%. Cluster II included five genotypes; represented the intermediate and late heading date genotypes, which were classified into two groups; IIa and IIb, with 23.4% similarity. The first group (IIa) consisted of the two genotypes; KhaoHao (intermediate) and Daudzai (late), while the second group (IIb) included three rice genotypes; the intermediate genotype (IR 2061-214-2-3) and the two late genotypes (GPNO 22236 and Rexora).

 

Discussion

 

Understanding of genetic variability nature of a trait is very important for plant breeder to know the role of environment in the expression of this trait. Thus, genetic variability of heading date trait was determined on the basis of the results of combined analysis of the two seasons; 2017 and 2018 indicating real differences between the studied genotypes and wide genetic base to select the superior genotypes for crossing and selection. Similar findings were reported by (Elgamal 2019; Al-daej et al. 2023). The genetic parameters: such as variance components (GV and PV), coefficients of variation (GCV and PCV) has a narrow difference and the estimate of PV was slightly higher than GV. Regarding GCV and PCV parameters, the GCV value was close to PCV value, as well as high estimates of heritability in broad sense () indicating that, the heading date trait has less affected by environment. Heading date trait is under genetic control, selection would be successful based on phenotypic performance. These results agree with those of (Ahmadikhah 2010; Mallimar et al. 2015; Rashid et al. 2017; Gyawali et al. 2018).

From molecular genetics point of view, it was of great interest to notice that RM223 primer did not produce the expected size band of 165 bp in any of the used genotypes, but was able to produce two distinct unexpected bands with molecular sizes of 148 and 154 bp. The two unexpected bands were appeared in both early and late genotypes under study. Therefore, this primer does not play any role for heading date trait in the used rice genotypes.

For RM510 primer, it also generated an important unexpected band with molecular sizes of 130 bp, but it does not play any role for heading date trait while it was present in all the four heading date groups. On the other hand, this primer generated the expected size band of 122 bp which was appeared in five genotypes belonging to the intermediate (KhaoHao and IR 2061-214-2-3) and late (GPNO 22236, Rexora and Daudzai) genotypes. Therefore, this fragment could be considered as a good marker for the intermediate and late rice genotypes for heading date trait.

Primer RM585 was of great interest in this study, it was able to generate the expected size band of 233 bp in three very early genotypes (BG 79, Barbado and Italica Carolina), two early genotypes (Lang ShweiKeng and E B Gopher) and one intermediate genotype (Puang Nigern). On the other hand, this primer generated an additional unexpected band with molecular size of 257 bp which was appeared only in five genotypes belonging to the very early (Italica M1 and Romanica) and early (Giza 177, Giza 178 and Sakha 102) genotypes and did not appear in the intermediate and late rice genotypes. Also, another additional unexpected band with molecular size of 208 bp was amplified only in the intermediate (IR 2061-214-2-3) and late (GPNO 22236, Sanakevelle, IR 334-17-1-3-1 and Daudzai) genotypes for heading date trait. These results proved that this primer is considered very important for studying heading date earliness in rice. Moreover, this primer is considered the best one for evaluation of heading date trait in rice breeding programs (Khatab et al. 2016; Weerasinghe et al. 2022), while it was able to produce the highest number of polymorphic alleles (6 alleles) and also recorded the highest PIC value (0.765).

 

Fig. 2: The UPGMA dendrogram derived from four SSR markers; linked to heading date, showing genetic relationships among the selected 20 rice genotypes based on similarity indices. The studied rice genotypes were divided into two main groups I and II with genetic similarity of 9%. Group I contained 15 genotypes which divided into two sup-groups Ia and Ib, this group contain most very early, early and intermediate genotypes. The group II was divided also to two sup-groups, IIa and IIb, which contain the most-late genotypes

 

For RM7601 primer, it proved to be very important for heading date studies in the used rice genotypes. It failed to produce the expected size fragment of 133 bp, but was able to produce another additional unexpected band with molecular size of 116 bp. It was surprised that this fragment was appeared only in all the five genotypes of very early group (BG 79, Barbado, Italica Carolina, Italica M1 and Romanica) in addition to three early genotypes (Giza 178, Sakha 102 and Lang ShweiKeng), but was completely absent in the intermediate and late genotypes under study. This fragment may play an important role in the very early and early heading date genotypes under study. Producing more alleles than the expected; using SSR primers, was previously reported by (Galal et al. 2014; Aboulila and Galal 2019).

Finally, we recommend in further study that both unexpected DNA fragments with molecular sizes of 257 and 116 bp which were only generated in the very early and early genotypes by RM585 and RM7601 SSR primers, respectively, should be isolated, sequenced and compared with the heading date responsible genes in rice gene banks.

Interestingly, the intermediate and late genotypes were closely related and placed in the same cluster (cluster II) suggesting that these genotypes were grouped according to their gene pools. The same trend was observed for the other two groups of genotypes, which separated in group Ia, and also group Ib which included seven genotypes (early or very early) in two sub-groups. Thus, there was a close relationship among the seven genotypes which clustered in group Ib with a similarity percentage of 47.8%. These results were supported by (Dutta et al. 2011; Khatab et al. 2016; Galal and Aboulila 2018; Elgamal 2019). They showed that the phylogenetic analysis; based on SSR markers, grouped the genotypes belonging to the same gene pool in the same clusters. Thus, SSR markers were useful for studying the genetic diversity and defining the genetic relationships among rice genotypes. In this respect, SSR molecular markers have been extensively used for identifying and characterizing of gene(s) linked to important traits (Wang et al. 2012; Salah et al. 2021) and phylogenetic relationship and genetic diversity analysis among rice genotypes (Das et al. 2013; Babu et al. 2014; Filiz et al. 2018). This allows fast screening at an early stage of growth; independent of environmental conditions, that consequently speed up breeding (Tester and Langridge 2010; Weerasinghe et al. 2022). Thus, these markers have proven to be the choice for marker-assisted selection (MAS) in rice breeding programs.

 

Conclusion

 

Heading date is one of the serious aspects determining regional and seasonal adaptation for climate changes and has been a main target of selection in rice breeding programs. Some rice genotypes were used to study phenotypic and molecular diversity. High amount of diversity was detected among genotypes by both morphology and molecular markers. The ssr markers produced some alleles which were specific to heading date in most screened genotypes; those markers could be used as MAS for rice cultivar identification and associated with heading date.

 

Acknowledgement

 

No Acknowledgement

 

Author Contributions

 

The authors were contributed equally in this research and preparation of the paper

 

Conflicts of Interest

 

No conflict of interest concerning this research

 

References

 

Aboulila AA, OA Galal (2019). Effect of silica nanoparticles and somaclonal variation on genome template stability in rice using RAPD and SSR markers. Egypt J Genet Cytol 48:1‒16

Aditya JP, A Bhartiya (2013). Genetic variability, correlation and path analysis for quantitative characters in rainfed upland rice of Uttarakhand Hills. J Rice Res 6:24‒34

Allhgolipour M, E Farshdfar, B Rabiei (2014). Molecular characterization and genetic diversity analysis of different rice cultivars by microsatellite markers. Genetica 46:187‒198

Al-daej MI, AA Rezk, MM El-Malky, TA Shalaby, M Ismail (2023). Comparative genetic diversity assessment and marker–trait association using two DNA marker systems in rice (Oryza sativa L.). Agronomy 13:329

Andres F, G Coupland (2012). The genetic basis of flowering responses to seasonal cues. Nat Rev Genet 13:627‒639

Ahmadikhah A (2010). Study on selection effect, genetic advance and genetic parameters in rice. Ann Biol Res 1:45‒51

Babu NN, KK Vinod, SG Krishnan, PK Bhowmick, T Vanaja, SL Krishnamurthy, M Nagarajan, NK Singh, KV Prabhu, AK Singh (2014). Marker based haplotype diversity of Saltol QTL in relation to seedling stage salinity tolerance in selected genotypes of rice. Ind J Genet Plant Breed 74:16‒25

Das B, S Sengupta, SK Parida, B Roy, M Ghosh, M Prasad, TK Ghose (2013). Genetic diversity and population structure of rice landraces from Eastern and North Eastern States of India. BMC Genet 14:71

Dutta S, G Kumawat, BP Singh, DK Gupta, S Singh, V Dogra, K Gaikwad, TR Sharma, RS Raje, TK Bandhopadhya, S Datta, MN Singh, F Bashasab, P Kulwal, KB Wanjari, RK Varshney, DR Cook, NK Singh (2011). Development of genic-SSR markers by deep transcriptome sequencing in pigeonpea [(Cajanus cajan L.) Millspaugh]. BMC Plant Biol 11:17

Economic Affairs Sector (2018). Final Report for Rice Productivity in Egypt. Economic Affairs Sector, Ministry of Agriculture and Land Reclamation, Egypt

Elgamal WH (2019). Field performance and molecular evaluation of new Promising Lines (PLs) for water deficit tolerance in rice (Oryza sativa L.). J Plant Prod 10:307316

Elmoghazy AM, MM Elshenawy (2018). Sustainable cultivation of rice in Egypt. In: Sustainability of Agricultural Environment in Egypt, Part I: Soil-water-food Nexus, Vol. 76, pp:119‒144. Negram AM, M Abu-Hashim (Eds). Springer International Publishing, Cham, Switzerland

Falconer DS (1981). Introduction to Quantitative Genetics, 2nd end. Lougman Green, London

FAO (2018). Food and Agriculture Organization. FAO Cereal Supply and Demand Brief. http://www.fao.org/worldfoodsituation/csdb/en/. 24 February, 2018

Fatimah F, J Prasetiyono, A Dadang, T Tasliah (2014). Improvement of early maturity in rice variety by marker assisted backcross breeding of Hd2 gene. Indo J Agric Sci 15:55‒64

Filiz E, ME Uras, II Ozyigit, U Sen, H Gungor (2018). Genetic diversity and phylogenetic analysis of Turkish rice varieties revealed by ISSR markers and chloroplast trnL-F region. Fres Environ Bull 27:8351‒8358

Galal OA, AA Aboulila (2018). Identification of SSR markers for drought stress induced by mannitol in three different Gramineae plant genera. Egypt J Genet Cytol 47:229‒252

Galal OA, MI Abo-Youssef, M Abdelaziz, AT Gharib, SA Dora (2014). Assessment of genetic purity of some hybrid rice parental lines using protein profile and fertility restorer gene linked markers. Intl J Biotechnol Res 2:75‒88

Ganapathi RK, MG Rasul, MAK Mian, U Sarker (2014). Genetic variability and character association of T-Aman rice (Oryza Sativa L.). Intl J Plant Biol Res 2:1013

Gyawali S, A Poudel, S Poudel (2018). Genetic variability and association analysis in different rice genotypes in mid hill of western Nepal. Acta Sci Agric 2:69‒76

Hori K, K Matsubara, M Yano (2016). Genetic control of flowering time in rice: Integration of Mendelian genetics and genomics. Theor Appl Genet 129:2241‒2252

Hoque MI, MM Islam, SN Begum, F Yasmine, MSR Khanom, MM Islam (2021). Genetic diversity analysis of rice (Oryzae sativa L.) landraces using SSR markers in Bangladesh. SAARC J Agric 19:13‒25

IRRI (2014). Standard Evaluation System for Rice (SES), 5th edn, pp:19‒20. Los Banos, Philippines, International Rice Research Institute

Jaccard P (1901). Étude comparative de la distribution floral edansune portion des Alpeset des Jura. Bull Soc Sci Nat 37:547‒579

Khatab IA, MA Farid, T Kumamaru (2016). Genetic diversity associated with heading date in some rice (Oryza sativa L.) genotypes using microsatellite markers. J Environ Agric Sci 6:58‒63

Liu C, Y Tu, S Liao, X Fu, X Lian, Y He, W Xie, G Wang (2021). Genome-wide association study of flowering time reveals complex genetic heterogeneity and epistatic interactions in rice. Gene 770:1–29

Mallimar M, P Surendra, NG Hanamaratti, M Jogi, TN Sathisha, R Hundekar (2015). Genetic variability for yield and yield attributing traits in F3 generation of rice (Oryza sativa L.). Res Environ Life Sci 9:24‒28

Matsubara K, M Yano (2018). Genetic and molecular dissection of flowering time control in rice. In: Rice Genomics, Genetics and Breeding, pp:177‒190. Sasaki T, M Ashikari (Eds). Springer, Singapore

Ming H, X Fang-min, C Li-yun, Z Xiang-qian, L Jojee, D Madonna (2010). Comparative analysis of genetic diversity and structure in rice using ILP and SSR markers. Rice Sci 17:257‒268

MSTAT-C (1991). A Software Program for the Design, Management and Analysis of Agronomic Research Experiments. Michigan State University, East Lansing, Michigan, USA

Murray AA, WF Thompson (1980). Rapid isolation of high molecular weight plant DNA. Nucl Acid Res 8:4321‒4325

Nei M (1973). Analysis of gene diversity in subdivided populations. Proceed. Natl Acad Sci USA 70:3321‒3323

North S (2013). Estimation of genetic variability and correlation for grain yield components in rice (Oryza sativa L.). Glob J Plant Ecophysiol 3:1‒6

Pachauri V, N Taneja, P Vikram, NK Singh, S Singh (2013). Molecular and morphological characterization of Indian farmers rice varieties (Oryza sativa L.). Aust J Crop Sci 7:923‒932

Palanga KK, K Traore, K Bimpong, M Jamshed, MAP Mkulama (2016). Genetic diversity studies on selected rice varieties grown in Africa based on aroma, cooking and eating quality. Afr J Biotechnol 15:1136‒1146

Pandey P, PJ Anurag, DK Tiwari, SK Yadav, B Kumar (2009). Genetic variability, diversity and association of quantitative traits with grain yield in rice (Oryza sativa L.). J Bio-Sci 17:77‒82

Rashid MM, M Nuruzzaman, L Hassan, SN Begum (2017). Genetic variability analysis for various yield-attributing traits in rice genotypes. J Bang Agric Univ 15:15‒19

Roldan-Ruiz IJD, EV Bockstaele, A Depicker, A Depicker, MD Loose (2000). AFLP markers reveal high polymorphic rates in Ryegrasses (Lolium spp.). Mol Breed 6:125‒134

RRTC (2013). Rice Research and Training Centre. National Rice Research Program: Final Results of 2012 Growing Season. Sakha, Kafr El-Sheikh, Egypt

Sakran RM, MI Ghazy, M Rehan, AS Alsohim, E Mansour (2022). Molecular genetic diversity and combining ability for some physiological and agronomic traits in rice under well-watered and water-deficit conditions. Plants 11:702

Salah S, OA Galal, E Ramadan, G El-Fadly (2021). Genetic and molecular evaluation of rice (Oryza Sativa L.) genotypes for salinity tolerance at seedling stage. Fres Environ Bull 30:12204‒12214

Saleh MM, KF Salem, AB Elabd (2020). Definition of selection criterion using correlation and path coefficient analysis in rice (Oryza sativa L.) genotypes. Bull Nat Res Cent 44:1‒6

Sarif HM, MY Rafii, A Ramli, Y Oladosu, HM Musa, HA Rahim, ZM Zuki, SC Chukwu (2020). Genetic diversity and variability among pigmented rice germplasm using molecular marker and morphological traits. Biotechnol Equip 34:747‒762

Sarker U, MT Islam, MG Rabbani, O Shinya (2015). Variability, heritability and genetic association in green amaranth (Amaranthus tricolor). Span J Agric Res 13:1‒8


Tester M, P Langridge (2010). Breeding technologies to increase crop production in a changing world. Science 327:818‒822

Wang CH, XM Zheng, Q Xu, XP Yuan, L Huang, HF Zhou, XH Wei, S Ge (2014). Genetic diversity and classification of (Oryza sativa L.) with emphasis on Chinese rice germplasm. Heredity 112:489‒496

Wang Z, J Cheng, Z Chen, J Huang, Y Bao, J Wang, H Zhang (2012). Identification of QTLs with main, epistatic and QTL × environment interaction effects for salt tolerance in rice seedlings under different salinity conditions. Theor Appl Genet 125:807‒815

Weerasinghe WDP, NT Prathapasinghe, GDA Priyantha (2022). Evaluation of morphological variations in exotic rice (Oryza Sativa L.) genetic materials under Sri Lankan field conditions. New Countryside 1:16‒22

Xu Q, XP Yuan, S Wang, Y Feng, HY Yu, YP Wang, YL Yang, XH Wei, XM Li (2016). The genetic diversity and structure of Indica rice in China as detected by single nucleotide polymorphism analysis. BMC Genet 17:53

Yadav S, A Singh, MR Singh, N Goel, KK Vinod, T Mohapatra, AK Singh (2013). Assessment of genetic diversity in Indian rice germplasm (Oryza sativa L.): use of random versus trait-linked microsatellite markers. J Genet 92:5545‒5571

Zhang J, X Zhou, W Yan, Z Zhang, L Lu, Z Han, H Zhao, H Liu, P Song, Y Hu, G Shen, Q He, S Guo, G Gao, G Wang, Y Xing (2015). Combinations of the Ghd7, Ghd8 and Hd1 genes largely define the ecogeographical adaptation and yield potential of cultivated rice. New Phytol 208:1056‒1066