Phenotypic and Genetic Diversity Studies on Selected Rice (Oryza Sativa L.) Genotypes Based on Amylose Content and Gelatinization Temperature

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PHENOTYPIC AND GENETIC DIVERSITY STUDIES ON SELECTED RICE (Oryza sativa L.) GENOTYPES BASED ON AMYLOSE CONTENT AND

GELATINIZATION TEMPERATURE

Mawia Amos Musyoki (B.Tech) I56/25517/2013

A Thesis Submitted in Partial Fulfillment of the Requirements for the Award of the Degree of Master of Science (Biotechnology) in the School of Pure and

Applied Sciences of Kenyatta University

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DECLARATION

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DEDICATION

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ACKNOWLEDGEMENT

I would like to begin by acknowledging the Almighty God for His grace, care and protection that He has extended in my life since the day I began this academic journey. Despite the many challenges I faced, He made me lie down in green pastures and surely goodness and mercies followed me all the time.

I would like to thank my supervisor Dr. Mathew Piero Ngugi for his invaluable guidance, sympathetic attitude and friendly way of supervision throughout my work.

Next, heartfelt and sincere thanks go to Dr. Geoffrey Muriira, my other supervisor for organizing and allowing me to undertake this project at Kenya Bureau of Standards. His help, thoughts and suggestions have been invaluable throughout my candidature.

I owe my deep sense of reverence to my mother Zipporah Ngina, brothers James Kioko and Daniel Mwendwa, uncle Paul Ndavi, cousins Lizzy Ndinda and Kithuka, for their unconditional daily prayers, love, and financial support. They have been a source of inspiration and encouragement for me especially when things were getting tough! Mom, I will forever cherish your prayers.

I am also grateful to the following individuals whose assistance at certain intervals enabled me to smoothly carry out my work: Shadrack Njagi (Mwas), my long-term friend who inspired me to undertake a postgraduate course, filled the application form on my behalf and encouraged me to soldier on during times of darkness! Man, we have come from far and we are going far. The journey to prosperity has started. Also, thanks go to Dorothy Nyamai, Antony Ngari and Jonathan Mulwa for providing me with accommodation when I had no place to sleep, Rose Chemtai, Moureen Langat, Mary Nyawira, Matheri and Makori who assisted me in bench work especially during DNA extraction, Daniel Agyrifo and Susan Rwigi for their kind assistance in data analysis.

Thanks also to my great course mate and rice project colleague Festus Wambua (King’ang’i kya

ndalani) whom we started the bench work almost at the same time. Without his contribution and sense of humour while in the lab, the project would not be where it is today. I would also wish to thank the entire staff in the Department of Biochemistry and Biotechnology who encouraged me throughout the work.

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TABLE OF CONTENTS

DECLARATION... ii

DEDICATION... ii

ACKNOWLEDGEMENT ... iv

TABLE OF CONTENTS ... v

LIST OF FIGURES ... viii

LIST OF TABLES ... ix

LIST OF APPENDICES ... x

ABBREVIATIONS AND ACRONYMS ... xi

ABSTRACT ... xii

CHAPTER ONE ... 1

INTRODUCTION... 1

1.1 Background information ... 1

1.2 Problem statement ... 4

1.3 Research questions ... 5

1.4 Objectives ... 5

1.4.1 General objective ... 5

1.4.2 Specific objectives ... 6

1.5 Justification and significance of the study ... 6

CHAPTER TWO ... 8

LITERATURE REVIEW ... 8

2.1 Rice (Oryza sativa L.) ... 8

2.1.1 Taxonomy ... 8

2.1.2 Genome evolution... 8

2.1.3 Geographical origin, diversity and cultivation ... 9

2.1.4 World production and trade of rice ... 12

2.2 Rice starch content and grain quality ... 13

2.2.1 Genetic control of amylose content in rice ... 15

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2.4 The glutinous rice ... 17

2.5 Rice gelatinization temperature ... 18

2.5.1 Genetic controls of rice gelatinization temperature ... 18

2.6 Assessment of genetic diversity in rice ... 20

2.7 Use of molecular markers in assessment of genetic diversity ... 21

2.8 Isozyme analysis ... 21

2.9 Restriction Fragment Length Polymorphism (RFLPs) ... 22

2.10 Polymerase Chain Reaction (PCR) analysis ... 22

2.10.1 Amplified Fragment Length Polymorphism (AFLP) ... 23

2.10.2 Random Amplified Polymorphic DNA (RAPD) analysis ... 24

2.10.3 Simple Sequence Repeats (SSR) ... 24

2.10.4 Inter Simple Sequence Repeat (lSSR) Analysis ... 25

2.11 Genotyping by sequencing ... 26

CHAPTER THREE ... 28

MATERIALS AND METHODS ... 28

3.1 Plant materials ... 28

3.2 Determination of phenotypic diversity ... 28

3.2.1 Measurement of grains and kernel traits ... 28

3.3 Determination of genetic diversity ... 29

3.3.1 Total genomic DNA extraction ... 29

3.3.2 Analysis of DNA quality ... 30

3.3.3 Analysis of DNA purity based on optical density ... 31

3.3.4 DNA dilutions... 31

3.4 Primers selection for genetic diversity ... 32

3.5 Primers selection for waxy and alk genes ... 33

3.6 PCR amplification ... 33

3.7 PCR product resolution ... 34

3.8 Data management and analysis ... 34

CHAPTER FOUR ... 36

RESULTS ... 36

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4.2 Determination of phenotypic diversity ... 41

4.3 Principal component analysis (PCA) ... 42

4.4 Assessment of polymorphism from SSR profiles ... 44

4.5 Pairwise genetic dissimilarity ... 47

4.6 Clustering of rice genotypes ... 50

4.7 Principle coordinate analysis (PCoA) ... 51

4.7 Analysis of molecular variance (AMOVA) ... 52

4.8 Amplification of waxy and alk genes ... 53

CHAPTER FIVE ... 55

DISCUSSION, CONCLUSIONS, RECOMMENDATIONS AND SUGGESTIONS FOR FURTHER STUDIES ... 55

5.1 Discussion ... 55

5.2 Conclusions ... 65

5.3 Recommendations ... 65

5.4 Suggestions for further research ... 66

REFERENCES ... 67

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LIST OF FIGURES

Figure 4.1: Dendrogram from mean values of grain and kernel traits among 13 rice genotypes. 42

Figure 4. 2: Clustering of 13 rice genotypes based on 7 grain and kernel traits ... 44

Figure 4.3: SSR RM 141 showing polymorphic pattern of 13 rice genotypes. The letters represent:

M-100bp molecular ladder, A-R 2793, B-BS 217, C-BS 370, D-BW 196, E-ITA 310, F, Saro 5, G-IR 64, H-Kilombero, I-Red Afaa, J-Kahogo, K-Supa, L-IR54, M-Wahiwahi.47

Figure 4.4: Unweighted Neighbor Joining tree showing the genetic relationships among the 13 rice

genotypes based on amylose content and gelatinization temperature using 8 microsatellite

markers ... 51

Figure 4.5: Principle coordinate analysis of the 13 rice genotypes based on amylose content and

gelatinization temperature using 8 SSR markers. ... 52

Figure 4.6: Gel photo showing amplification of waxy gene in some of rice genotypes studied. The letters represent M-molecular ladder, A-R 2793, B-BS217, C-BS 370, D-BW 196, E-ITA 310, F, Saro 5, G-IR 64, H-Kilombero, I-Red Afaa, J-Kahogo, K-Supa, L-IR 54, and

M-Wahiwahi. ... 54

Figure 4.7: Gel photo showing amplification of alk gene across 13 rice genotypes studied. The letters represent M-molecular ladder, A-R 2793, B-BS217, C-BS 370, D-BW 196, E-ITA 310, F,

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LIST OF TABLES

Table 2.1: Species complexes of the genus Oryza ... 10

Table 2.2: Known haplotypes of rice based on mutations in the Waxy gene... 16

Table 3.1: Names, origin and category of the rice genotypes ... 29

Table 3.2: List of SSR markers used in the study ... 32

Table 3.3: List of primer pair for waxy and alk gene amplification. ... 33

Table 4.1: Analysis of variance among the 13 rice genotypes ………40

Table 4.2: Principal Component Analysis among the rice genotypes ……….43

Table 4.3: Genetic information of polymorphic SSR markers……….47

Table 4.4: SC. Cord coefficients of dissimilarity ………49

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LIST OF APPENDICES

Appendix 1: Analysis of grain and kernel traits among the rice genotypes ... 76

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ABBREVIATIONS AND ACRONYMS

AC Amylose Content

ADP Adenosine Diphosphate

AFLP Amplified Fragment length Polymorphism AMOVA Analysis of Molecular Variance

ASV Alkali Spreading Value

Bp Base pair

cDNA Complimentary DNA

CTAB Cetyltrimethylammonium bromide DNA Deoxyribonucleic Acid

DSC Differential Scanning Calorimetry EDTA Ethylenediaminetetraacetic acid

FAO Food Agricultural Organization

FAOSTAT Food Agricultural Organization Statistics GBBS Granule Bound Starch Synthase

GT Gelatinization Temperature

IRRI International Rice Research Institute ISSR Inter Simple Sequence Repeats MAB Marker Assisted Breeding

MIAD Mwea Irrigation Agricultural Development Centre

nm Nanometer

PCoA Principal Coordinate Analysis PCA Principal Component Analysis PVP Polyvinyl Pyrrolidone

RAPD Random Amplified Polymorphic DNA RFLP Restriction Fragment Length Polymorphism RPM Rotation Per Minute

RVA Rapid Visco Analyzer

SNP Single Nucleotide Polymorphism

SS Starch Synthase

SSR Simple Sequence Repeats,

TBE Tris-Borate-EDTA

TE Tris-EDTA

TRIPS Trade Related Intellectual Property Rights WTO World Trade Organization

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ABSTRACT

Rice cooking and eating qualities are mostly determined by the grain’s starch content which is composed of amylose and amylopectin. Amylose content is regarded as the most important indicator in classifying rice varieties in terms of starch content. Waxy gene located on chromosome 6 codes for Granule Bound Starch Synthase (GBBS) enzyme which is involved in biocatalysis of amylose synthesis in rice endosperm. In addition, amylopectin content influences gelatinization temperature in rice. Alk gene located on chromosome 6 codes for soluble Starch Synthase II (SSII) enzyme involved biocatalysis of amylopectin. Accurate evaluation of these two traits is difficult and has hindered development of better varieties with good eating and cooking qualities by rice breeders both in Kenya and Tanzania. The aim of the study was to carry out phenotypic characterization and genetic diversity studies on selected rice (Oryza sativa L.) genotypes from Kenya and Tanzania based on amylose content and gelatinization temperature using microsatellite markers. Measurement of 7 grain and kernel traits were determined among 13 rice genotypes studied. Genetic diversity based on amylose content and gelatinization temperature was studied using 8 microsatellite (SSR) markers tightly linked to major QTLs controlling these two traits. Furthermore, presence or absence of waxy and alk genes was determined using markers tightly linked to these two genes. Minitab 15.0 and Power marker version 3.25 softwares were used to analyze the data. A dendrogram was constructed from data set of mean values of grain and kernel traits and showed two super clusters; I and II. The number of alleles per locus ranged from 2 to 4 with an average of 2.75 alleles across 8 loci. The polymorphic information content (PIC) values ranged from 0.2920 (RM 202) to 0.6841 (RM 141) in all 8 loci with an average of 0.4697. Pair-wise genetic dissimilarity coefficients ranged from 0.9003 to 0.2201 with an average of 0.5627. Maximum genetic similarity was observed between R 2793 and BS 17, Supa and IR 64, R 2793 and ITA 310,Saro 5 and ITA 310, Saro 5 and R2794. Minimum similarity of was observed between Wahiwahi and BW 196, IR 64 and BW 196. The dendogram based on cluster analysis by microsatellite polymorphism grouped 13 rice genotypes into 2 groups effectively differentiating Kenyan and Tanzanian rice genotypes based on amylose content and gelatinization temperature. The

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CHAPTER ONE INTRODUCTION 1.1 Background information

Rice (Oryza spices) is a monocotyledonous plant belonging to the family Granineae and subfamily Oryzoidea. It is cultivated under diverse eco-geographical conditions

in various tropical and subtropical countries. It has a relatively small genome size

which makes it an ideal model plant for study of grass genetics and genome

organization (Caesse et al., 1994). Due to its importance as a food crop, rice is planted on approximately 11% of the Earth’s cultivated land area (Khush, 2005). It is

the crop with the third highest production globally after sugarcane and maize

(FAOSTAT, 2012). Oryza sativa and Oryza glaberrima are the only two cultivated species of rice while the other species are wild. Oryza sativa is commonly grown in Asia, North and South America, Europe and Africa. O. glaberrima is highly grown in West African but due to higher yields of O.sativa and O. glaberrima-sativa

varieties; itis being replaced in most parts of Africa (Linares, 2002).

Rice domestication has led to development of a large number of Oryza sativa

cultivars. Around 100 000 different rice genotypes are held at the international rice

gene bank of which most are Oryza sativa varieties. Rice diversity based on morphology and quality traits such as grain size, shape, colour, aroma and starch

content has been increased due to different socio-cultural traditions and practices

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namely: Indica (tropical and subtropical distribution) Japanica (grown in Indonesia)

and Japonica (temperate distribution). Indica and Japonica are the two most widely

grown and researched ecotypes (Takahashi, 1984).

Rice genome has a wider variation when compared to other plant varieties (Ashfaq et al., 2012). The genus oryza has 12 chromosomes in its genome. Oryza sativa, O. glaberrima and 14 wild species are diploid (2n=24) with 24 chromosomes and relatively small genome size (of 430 million base pairs). Other 8 wild species are

tetraploids (2n=48) with 48 chromosomes. Approximately, 50% of the rice genome

is composed of repetitive sequences (Chang, 2003).

Rice cooking and eating qualities are mostly determined by the grain’s starch content

which is composed of two polysaccharides: amylose and amylopectin. Amylose

content (A.C) is regarded as the most important indicator in classifying rice varieties

in terms of starch content. Waxy (Wx) gene located on the short arm of chromosome 6 is the major gene responsible for biosynthesis of amylose in rice. The gene codes

for Granule Bound Starch Synthase (GBBS) enzyme which is involved in

biocatalysis of amylose synthesis in rice endosperm (Umemoto et al., 2002).

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change at the 5’ splice site of intron1. Apart from genetic factors, amylose content in

rice grain is also regulated by environmental factors such as temperature (Larkin and

Park, 1999).

Gelatinization temperature (GT) refers to the critical cooking temperature at which

water is absorbed and the endosperm starch granule swell irreversibly with

simultaneous loss of crystallinity and birefringence (Waters et al., 2006). A major gene (alk gene) codes for soluble Starch Synthase II (SSII) enzyme involved biocatalysis of amylopectin controls rice gelatinization temperature. The gene is

located in chromosome 6 of the rice genome (Umemoto et al., 2002). This temperature at which rice starch gelatinizes is an important component of rice

cooking quality (Fitzgerald et al., 2009). The texture and gelatinization temperature of rice is directly influenced by the properties of rice starch. Using physicochemical

methods such as Alkali Spreading Value (ASV) for measuring rice gelatinization

temperature, different rice varieties have been grouped into three classes, that is, high

(>74 °C), intermediate (70 – 74 °C) and low (<70 °C) (Juliano, 2003).

Molecular markers such as isozyme, Restriction Fragment Length Polymorphism,

Amplified Fragment length Polymorphism, Random Amplified Polymorphic DNA,

Simple Sequence Repeats, Inter Simple Sequence Repeats and Single Nucleotide

Polymorphism have a number of applications in the study of plant genetics. Each of

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application. In rice, SSR markers have been effectively utilized for many purposes

including: genetic diversity and relatedness (Pervaiz et al., 2010), QTL mapping (Guo et al., 2010), marker assisted selection (Thomson, 2009) and rice domestication (Sweeney and McCouch, 2007). These markers are commonly used when studying

rice genetic diversity because they are highly informative, mostly mono locus,

co-dominant and affordable (Chambers and Avoy, 2000). It is, therefore, against this

background that phenotypic characterization and genetic diversity studies on these

selected rice (Oryza sativa) genotypes from Kenya and Tanzania was studied.

1.2 Problem statement

There is a wide range of rice varieties grown both in Kenya and Tanzania. These rice

cultivars are either local landraces or improved varieties and they express different

levels of amylose and amylopectin that influences amylose content and gelatinization

temperature in rice respectively. Since these two traits are key determinant in

cooking and eating qualities of rice, unscrupulous traders often blend rice grains

which have good cooking and eating quality traits with grains which have poor

cooking and eating quality traits based on amylose content and gelatinization

temperature to make more profit from their trade. This causes a negative impact on

rice trade and consumption resulting from the low quality blended rice. Accurate

evaluation of these two traits is difficult and has hindered development of better

varieties with good eating and cooking qualities by rice breeders both in Kenya and

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amylose content and gelatinization temperature in rice are often inaccurate and time

consuming. However, genetic diversity analysis on these selected rice genotypes

from Kenya and Tanzania based on amylose content and gelatinization temperature

using microsatellite markers has not yet been studied.

1.3 Research questions

i. What are the phenotypic differences among the selected rice genotypes from

Kenya and Tanzania based on grain and kernel traits?

ii. What are the heterozygosity levels for the selected rice genotypes from

Kenya and Tanzania based on amylose content and gelatinization

temperature?

iii. What is the genetic relatedness among the selected rice genotypes from

Kenya and Tanzania based on amylose content and gelatinization

temperature?

1.4 Objectives

1.4.1 General objective

To carry out phenotypic and genetic diversity studies on selected rice (Oryza sativa

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1.4.2 Specific objectives

i. To determine phenotypic diversity among selected rice genotypes from

Kenya and Tanzania based on grain and kernel traits.

ii. To determine the level of genetic diversity for the selected rice genotypes

from Kenya and Tanzania based on amylose content and gelatinization

temperature using microsatellite markers.

iii. To determine genetic relatedness among selected rice genotypes from Kenya

and Tanzania based on amylose content and gelatinization temperature using

microsatellite markers.

1.5 Justification and significance of the study

Assessment of diversity in plants is an integral part of plant breeding programs

because it helps plant breeders to develop new crop varieties with desirable qualities.

Rice crop improvement in terms of amylose content and gelatinization temperature is

important since these two traits are key measures of rice eating, cooking and

processing quality. Therefore, the study of diversity based on these two traits using

microsatellite markers tightly linked to genes or Quantitative Trait Loci (QTLs)

controlling these two traits is very necessary for rice improvement strategies. Thus,

this study aimed at assessing diversity based on amylose content and gelatinization

temperature among selected rice genotypes from Kenya and Tanzania using

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The study findings provided useful information on rice diversity based on amylose

content and gelatinization temperature. These results could be used by rice breeders

in rice improvement strategies on the traits to introduce in the new rice varieties

either through marker assisted breeding (MAB) or genetic engineering to promote

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CHAPTER TWO LITERATURE REVIEW 2.1 Rice (Oryza sativa L.)

2.1.1 Taxonomy

Rice (Oryza sativa L.) is a plant belonging to the kingdom plantae, division magnoliophyta, class liliopsida, order poales, family gramineae, genus Oryza and species sativa. The genus Oryza consists of 25 species, of which 23 are wild species and two; O. sativa and O. glaberrima are domesticated (Brar and Khush, 2003). The species sativa is made up of three subspecies namely: Indica, Japonica and Japanica.

2.1.2 Genome evolution

Rice genome has vast variation more than thousand varieties have been produced

around the world (Ashfaq et al., 2012). The basic chromosome number of the genus

Oryza is 12. O. sativa, O. glaberrima and 14 wild species are diploid (2n=24) with 24 chromosomes and relatively small genome size (430 million base pairs).

Approximately, 50% of the genome is composed of repetitive sequences (Chang,

2003). Other 8 wild species are tetraploids (2n=48) with 48 chromosomes. Genome

analysis done on the basis of chromosome pairing behavior and fertility in

interspecific hybrids and degree of sexual compatibility has made possible to classify

species in the genius Oryza into nine distinct genomes. The genus Oryza has also been classified into four complexes which reflect genetic diversity of genus (Khush,

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are pantropical as shown in Table 2.1. Of these four complexes, O. Sativa and O. Officinalis complexes are the best studied. The Sativa complex comprises the cultivated species O. sativa and O. glaberrima and their wild ancestors’ viz., perennial rhizomatous O.longistaminata, O.barthii (formerly O. breviligulata) and

O. rufipogon, O. nivara and O. sativa f. spontanea. The species of Sativa complex constitute the primary gene pool of rice while the species belonging to Officinalis

complex constitute the secondary gene pool. The species belonging to Meyeriana,

Ridleyi complexes and O. schlechteri constitute the tertiary gene pool (Khush, 1997).

2.1.3 Geographical origin, diversity and cultivation

The centers of origin and diversity of O. sativa and O. glaberrima have been traced using archaeological evidences, geographical distribution and genetic diversity. River

valleys of Yangtze and Mekon are the primary centers of origin of O. sativa. On the other hand, Niger River delta in Africa is the centre of origin of O. glaberrima

(Huang et al., 2012). The foothills of the Himalayas, northern parts of Myanmar and Yunnan Province of China are some of the centresof diversity for Asian rice species.

The centre of diversity of O. glaberrima is believed to be the Inner delta of River Niger and some areas around Guinean coast. O. sativa is believed to have evolved from O. nivara while O. barthii is believed to be the progenitor of O. glaberrima

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O. sativa L. 24 Worldwide: originally South and Southeast Asia

O. nivara 24 South and Southeast Asia

O. rufipogon 24 South and Southeast Asia,South China

O. meridionalis 24 Tropical Australia

O. glumaepetula 24 Tropical America

O. glaberrima 24 Tropical West Africa

O. barthii 24 West Africa

O. longistaminata 24 Tropical Africa

II. Officinalis Complex

O. punctata 24 East Africa

O. rhizomatis 24 Sri Lanka

O. minuta 48 Philippines, New Guinea

O. malamphuzaensis 48 Kerala and Tamil Nadu

O. officinalis 24 South and Southeast Asia

O. alta 48 Central and South America

O. grandiglumis 48 South America

III. Meyeriana Complex

O. granulata 24 South and Southeast Asia

O. meyeriana 24 Southeast Asia

IV. Ridleyi Complex

O. longiglumis 48 Indonesia, New Guinea

O. ridleyi 48 Southeast Asia

V. Unclassified (belonging to no complex)

O. brachyantha 24 West and Central Africa

O. schlechteri 48 Indonesia, New Guinea

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A large number of O. sativa cultivars have been developed through centuries of rice domestication either through natural evolution, conventional breeding or marker

assisted breeding (MAB). Around 100 000 different rice genotypes are held at the

international rice gene bank in Philippine. Both phenotypic and genetic diversity of

Oryza sativa is very wide. Different rice varieties are distinguished based on many features such as growth habit, adaptation to different water regimes, shape, size and

colour of the grain among many others. Generally, grains of japonica subspecies are

shorter and wider. Grains of indica subspecies are softer and stickier when cooked

(Kasem, 2012). Based on starch content, different rice genotypes have different levels

of amylose and amylopection in their grain endosperm. In terms of amylose content,

there are five distinct classes, namely: high amylose (above 24%), intermediate amylose

(20–24%), low amylose (10–19%), very low amylose (3–9%) and waxy (0–2%),

(Suwannaporn et al., 2007). In terms of amylopectin content which influences rice gelatinization temperature, there are three different classes, namely: low (<70 °C),

intermediate (70 – 74 °C) and high (>74 °C) (Juliano, 2003).

Oryza sativa is the most widely grown in Asia, Europe, North and South America and Africa continent. On the other hand, O. glaberrima is mostly grown in West African. Desirable agronomic, eating and cooking quality traits associated with O.sativa and O. glaberrima-sativa hybrids has led to replacement of O.glaberrima in most parts of African countries (Linares, 2002). Based on ecological growth distribution, rice

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subtropical distribution) and Japanica (grown in indonasia). Indica cultivars are highly

grown in tropical and subtropical regions while japonica cultivars are highly grown in

temperate regions and can germinate and grow under low temperatures (15-20oC)

(McDonald, 1994).

2.1.4 World production and trade of rice

Fifty seven percent of (57%) of the world’s total population depends on rice as their

chief staple food. It is the crop with the third-highest worldwide production after

sugarcane and corn (Rutger et al., 1998). It is cultivated in about 162.3 million hectares in the world accounting for the total production of about 738.1 million tones. Rice

production has doubled in the last 40 years. The increased production has been as a

result of improved field yields and cultivation of hybrid varieties with better agronomic

traits (FAOSTAT, 2012).

China is the largest producer of rice followed by India, Indonesia, Bangladesh, Vietnam

and Thailand. These countries accounts for 95% of its production. As a result, many

rural populations depend on it as the major source of income. In Africa, Benin, Burkina

Faso, Cameroon and Chad are the greatest producers. However, rice production in

Africa has declined over the last 20 years due to shortcomings such as climatic changes

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locally. As a result, the huge deficit is met through imports especially from Asian

countries such as India and Thailand (FAO, 2013).

The international rice market is regarded as “thin” and “volatile”. This is because global

rice trade accounts for only 7% (35 MMT) of total production. Rice is mostly eaten in

the same country where it is produced and a shortfall in production results in volatility

in rice prices. In addition, many Asian countries have strict policies and restrictions on

rice import and exports in order to achieve domestic food security and to protect

producer prices and income, causing major distortions in trade. Thailand is the chief

exporter of rice followed by Vietnam, Pakistan, the United States, India, Italy, Uruguay,

China, the United Arab Emirates, Benin, Argentina, and Brazil, which account for more

than 90% of the global rice traded. Both China and India continue to view rice as a

strategic commodity for food security (FAO, 2013).

Many countries such as Saudi Arabia, Iraq, Cote d’Ivore, South Africa, Cameroon,

Mexico, the United States, and Brazil import rice from Asian countries. Many of the

world’s least developed countries import rice to meet their national food security needs

(Muthayya et al., 2014).

2.2 Rice starch content and grain quality

There are two classes of rice based on starch content, that is, waxy and non waxy rice.

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amylopectin. Glutinous or waxy rice in which endosperm starch lacks or has very little

amylose content consists mainly of amylopectin starch (Bertoft and Koch, 2000)

The ratio of amylose to amylopectin has a major effect on the physical properties of

starch. When cooked, the semi-crystalline structure of rice starch is disrupted thus

transforming the starch into a softer, edible, and gel-like material (Ramesh et al., 1999).

Various physical and chemical characteristics such as milling, cooking and nutritional

qualities are used to determine the quality of rice grain. Cooking quality is measured by

physicochemical characteristics such as: gelatinization temperature (GT), amylose

content (AC) and gel consistency (GC). Amylose content (AC) is used as the main

indicator in classifying rice varieties because it is associated with cooked rice

tenderness (Champagne et al., 2004).

Rice varieties with low-amylose such as japonica content tend to be soft and sticky

when cooked (Asghar et al., 2012). Indica varieties which have high amylose content tend to be dry, firm and fluffy when cooked. Rice varieties with intermediate amylose

content such as IR 64 variety are more preferable because they do not become sticky or

dry when cooked. Generally, the amylose content of milled rice is classified into five

classes: waxy (0–2%), very low amylose (3–9%), low amylose (10–19%), intermediate

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2.2.1 Genetic control of amylose content in rice

Amylose content in rice is controlled by an allelic series at one locus with major effects

and by one or more modifier genes with minor effects (Sano, 1984). Waxy (Wx) gene located on chromosome 6 is the major gene responsible for amylose synthesis in rice.

The gene code for Granule Bound Starch Synthase (GBBS) enzyme which is plays a

crucial role in biocatalysis of amylose synthesis in rice endosperm (Tanaka et al., 1995). In addition, other minor genes such as dull and amylose extender (ae) are reported to play an important role in modification of amylose in rice (Isshiki et al., 2000).

The different amylose classes as shown in Table 2.2 are due to single nucleotide

polymorphisms in the Wx gene. The high and low amylose content in rice controlled by

Wxa and Wxb alleles respectively. These functional alleles are caused by single base change at the 5’ splice site of intron 1 (Hirano et al., 1998). The G→T mutation in the splice site of intron 1 causes inefficient transcription because of alternate splicing

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Table 2.2: Known haplotypes of rice based on mutations in the Waxy gene

Post-transcriptional processing of GBSS transcripts plays a key role in determining

amylose content (Wang et al., 1995). The amylose and Wx protein content in rice endosperm are correlated with the ability of the variety to excise intron 1 from the

leader sequence of the Wx transcript. The low-level expression of Wxb results from G to-T mutation at 5’ splice site of the first intron. This mutation was shown the

inefficient splicing of intron 1 present in the untranslated leader region (Wang et al.,

1995).

Rice varieties with high levels of amylose content accumulates significant amount of

completely processed GBSS mRNA, the mature 2.3 kb Wx mRNA, and have the sequence AGGTATA at the putative leader intron 5'-splice site. In contrast, rice

varieties with low amylose content accumulates both completely processed GBSS

mRNA, and a partially processed transcript containing the leader intron, a 3.3-kb

pre-mRNA, and have a single G-to-T mutation at the putative leader intron 5'-splice site that

Haplotype Intron 1 Exon 2 23-bp dupl’n

Exon 4 Exon 6 Phenotype

1 G - A A High AC

2 G - A C Intermediate

AC

3 G - G Low AC

4 T - Very low

AC

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converts this sequence to AGTTATA. This mutation causes the reduction of mature

GBSS mRNA, accumulation of GBSS transcripts that retain the leader intron, and

activation of cryptic splice sites (Wang et al., 1995).

2.3 Role of temperature in rice amylose content

Environmental factors such as temperature regulate rice amylose content especially

during seed development (Larkin and Park, 1999). Some rice varieties accumulate more

amylose when grain-filling proceeds under cooler temperature condition. The Wx locus regulates the increase in amylose content at cool temperature. Umemoto et al. (1995) found that the GBSS activity in endosperm was 3 to 3.5 folds higher at 15οC than 25οC.

In contrast, the levels of other enzymes involving in starch synthesis such as sucrose

synthesis and soluble starch synthase remained the same or were lower at 15οC.

2.4 The glutinous rice

Glutinous rice is commonly grown for in-house consumption especially in Thailand and

Japan. It has a unique starch quality that makes its demand too high especially to

modified food manufacturers for the production of foodstuffs such as rice crackers. It is

highly adapted to lowland rainfed fields where growing conditions are harsh and

diverse. A single base pair mutation in the Wxa allele encoding GBSS enzyme leads to a low amylose content accumulation in the endosperm of glutinous rice (Wang et al.,

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2.5 Rice gelatinization temperature

The cooking temperature at which water is absorbed and the endosperm starch granule

swell irreversibly with subsequent loss of crystalline structure is referred to as

gelatinization temperature (GT). Gelatinization temperature is an important component

of rice cooking quality (Cuevas et al., 2010). Rice grain with low gelatinization temperature takes shorter cooking times leading to significant potential savings in fuel

costs (Fitzgerald et al., 2009).

Three classes of GT are recognized in rice breeding programs: high (>74 °C),

intermediate (70 – 74 °C), and low (<70 °C) (Juliano, 2003; Waters et al., 2006). In these programs, physicochemical methods such as differential scanning calorimetry

(DSC) are commonly used to classify grains by GT: deducting 3 °C from the pasting

temperature derived from the Rapid Visco Analyser (RVA) (Normand and Marshall,

1989). However, these methods are associated with several limitations such as time

consuming and inaccuracy of the machine (Cuevas et al., 2010).

2.5.1 Genetic controls of rice gelatinization temperature

A link between the GT of rice starch and enzymes of starch bio-synthesis was made

when it was found that the major gene (alk gene) that controls rice starch GT via amylopectin structure, codes for soluble Starch Synthase IIa (SSIIa) enzyme. The gene

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(SNP), two of which are associated with GT class (Umemoto and Aoki, 2005;

Waters et al., 2006). Rice cultivars belonging to the high GT class has haplotype G/GC while rice cultivars with low GT class either have A/GC or G/TT at the key SNP

sites (Nakamura et al., 2002).

Fortunately, the structure and the mechanism leading to high and low GT have already

been discovered. Amylopectin, the other polymer of starch, is composed of linear

glucose units linked by α-1, 4 bonds, which are organised into clusters with α-1, 6

bonds at the branch points (Ball and Morell, 2003). At the ultra-structural level, GT is

affected by the debranched chain-length distributions of amylopectin within the

crystalline lamella; particularly, the proportions of chains with lengths (measured as X)

6 – 12 and 12 – 24 (Nakamura et al., 2002). Starch Synthase I (SSI) elongates chains of X6 – 12 (Fujita et al., 2006) while Starch SynthaseIIa (SSIIa) extends the lengths of these chains further to X12 – 24 (Konik-Rose et al., 2007). These differences in proportions of chains with X6 – 12 and X12 – 24 is as a result of mutations in the alk

gene (Umemoto et al., 2004).

Apart from high and low rice gelatinization temperature classes; we also have rice

varieties with intermediate GT (Juliano, 2003). Rice breeders tend to selects rice

varieties with intermediate GT during rice improvement although the genetic basis

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6, called alk2(t), is associated with amylopectin biosynthesis in (Shu et al., 2006). Other enzymes such as starch synthase (Butardo et al., 2011), could also be involved in changing the amylopectin structure leading to intermediate GT (Ryoo et al., 2007).

2.6 Assessment of genetic diversity in rice

Genetic diversity studies in plants are an integral part of plant breeding. Exploitation of

phenotypic and genetic diversity helps the plant breeder to develop new varieties with

improved traits. Diversity studies on Oryza sativa have attracted great interest. This is due to the fact that rice is a major world crop. For instance, exploitation of diversity in

indigenous landraces has led to identification of new superior genes which has been

used by rice breeders in improvement of rice germplasm (Oliveira et al., 2007; Thomson et al., 2007).

Sequencing of the Indica and japonica genomes have made rice a model crop and

provided breeders a quick approach to improve rice cultivars. Some researchers use

morphological characterization approaches for studying diversity although it is time

consuming. Biochemical markers are also used for diversity studies although they are

limited in number and affected by developmental stages of the plant (Winter and Kahl,

1995). As a result, molecular markers have been widely used in genetic diversity studies

due to their various merits associated with them such as cost effective, abundance in

nature and are not affected by different developmental stages in plants (McCouch et al.,

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2.7 Use of molecular markers in assessment of genetic diversity

There are numerous types of molecular markers available for use when carrying out

genetic diversity studies. Each of these markers have their own advantages and

disadvantages hence there is need to consider these carefully before their application.

Above all, a major consideration should be taken so that the marker technique used

should provide maximum information in a cost-effective manner and do not require

complex equipment when using them (Godwin et al., 1997)

Use of restriction fragment length polymorphism (RFLP) and polymerase chain

reaction (PCR) have recently replaced application of allozyme analyses in detection of

genetic polymorphism (Williams et al., 1990)

2.8 Isozyme analysis

Isozyme markers have been widely used in plant breeding programmes and

determination of genetic diversity in natural populations. Isozymes are usually allelic

or occur at independent loci and can be separated on polyacrylamide gels on the basis

of size and charge at a set PH (Lebot and Aradhya, 1991).

Isozymes have got several merits over other classes of simply inherited genetic

markers. These include the ability to assay many samples at a time and relatively low

cost (Avise, 1994). However, they are unable to detect diversity within non-coding

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genes (Dawson et al., 1995). Also, isozymes are usually affected by growth stage and conditions of the plant growth (Dawson et al., 1995).

2.9 Restriction Fragment Length Polymorphism (RFLPs)

Restriction Fragment Length Polymorphism technique examines the variation in size

of specific DNA fragment following digestion with restriction enzyme. Numerous

studies have demonstrated the application of RFLP analysis genetic analysis within

species such as rice (Wang and Tanksley, 1989). Due to probe sequence homology,

these markers are suited to phylogenetic analysis between related species (Bhat et al.,

1997). They are co-dominant markers and hence can be good estimators of

heterozygosity (Godwin et al., 2001).

Despite the advantages associated with RFLP markers, they have considerable

limitations such as: relatively large quantities of pure genomic DNA are required and

the technique generally uses radioisotopes in the detection system (Chalmers et al.,

1995).

2.10 Polymerase Chain Reaction (PCR) analysis

Discovery of polymerase chain reaction (PCR) has revolutionized the analysis of

nucleotide sequence variability (Saiki et al., 1988). It provides a huge array of techniques for detecting polymorphism that do not require the use of radioactivity and

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Three widely used PCR based markers are Random Amplified Polymorphic DNA

(RAPD) (Williams et al., 1990), microsatellites (Tautz, 1989) and Amplified Fragment Length Polymorphism (AFLP) (Vos et al., 1995). Each marker technique has its own merits and demerits.

The major advantage of using PCR technique is the high speed with which results are

generated, ability to share information on primer sequence without the need to

exchange DNA and low amounts of genomic DNA required for PCR (Godwin et al.,

2001).

2.10.1 Amplified Fragment Length Polymorphism (AFLP)

This technique uses restriction fragment analysis but a PCR approach is used instead of

hybridization probe. Amplified Fragment Length Polymorphism markers are genomic

fragments detected after selective PCR amplification, which provide a number of

appealing features in the fingerprinting of genomes of different complexity (Vos et al.,

1995).

The advantages of using AFLP technique include production of a large number of

polymorphisms, no sequence information is required and results are reproducible

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steps involved to produce results. In addition, AFLP is mainly a dominant marker

(Wolfe and Liston, 1998).

2.10.2 Random Amplified Polymorphic DNA (RAPD) analysis

Random Amplified Polymorphic DNA markers have been used to study diversity

among many plants species (Duarte et al., 1999; Maciel et al., 2001). Random Amplified Polymorphic DNAs have also been employed in the construction of genetic

linkage maps in E. grandis and E. urophylla (Grattapaglia and Sedroff, 1994). However, there are some reliability problems and most of the markers generated are

dominant. There is also a lack of cross-transferability and it must be acknowledged that

in some cases, fragments that are of the same length may not necessarily be of the same

sequence. Some of these problems can be overcome by cloning and partially sequencing

the fragments, to turn these into sequence characterized amplified regions (Paran and

Michelmore, 1993), which are usually more robust than RAPDs.

2.10.3 Simple Sequence Repeats (SSR)

Simple Sequence Repeats are also known as microsatellites or variable number of

tandem repeat markers and they are easily available in any region of the genome

(McCouch et al., 1997). In rice, SSR markers have been effectively utilized in the study of genetic diversity and relatedness (Pervaiz et al., 2010), QTL mapping (Guo et al.,

2010), marker assisted selection (Thomson, 2009) and rice domestication (Sweeney and

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These markers are more popular in rice because they are highly informative, mostly

mono locus, co-dominant, easily analyzed and cost effective (Chambers and Avoy,

2000). Compared to RFLPs, microsatellite markers detect a significantly higher degree

of polymorphism in rice (Wu and Tanksley, 1993) and are especially suitable for

evaluating genetic diversity among closely related rice cultivars (Akigi et al., 1997).The greatest disadvantage of the SSRs is the initial cost in finding and sequencing the loci.

They also have limited use for phylogenetic analysis due to their high mutation rate

(Chambers and Avoy, 2000).

2.10.4 Inter Simple Sequence Repeat (lSSR) Analysis

Inter Simple Sequence Repeats are semi-arbitrary markers amplified by PCR in the

presence of one primer that is complementary to a target SSR. Inter Simple Sequence

Repeats are useful for detecting genetic polymorphism, and have been used to

fingerprint closely related individuals (Zietkiewicz et al., 1994) and for genetic diversity studies (Tsumura et al., 1996). Inter Simple Sequence Repeats markers have been successfully used for the assessment of genetic diversity in corn (Kantety et al.,

1995), for cultivar identification in potatoes (Charters et al., 1996) and for linkage to a specific gene (Akagi et al., 1996).

The ISSR technique does not require prior knowledge of the SSR target sequences, are

highly reproducible, does not require radiolabelling and show high levels of

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The main disadvantage of ISSR technique is lack of co-dominance and the consequent

resolution of effectively biallelic loci (band presence verses absence) (Tsumura et al.,

1996).

2.11 Genotyping by sequencing

Genotyping by sequencing, or next-generation genotyping, is a genetic screening

method for discovering novel plant and animal SNPs and performing genotyping

studies (Poland and Rife, 2012). For some applications, such as genotype screening and

genetic mapping, sequence-based genotyping provides a lower-cost alternative to arrays

for studying genetic variation. The application of next-generation sequencing (NGS)

technologies has led to remarkable advances in whole genome sequencing, which

provides ultra-throughput sequences to revolutionize plant genotyping and breeding. To

further broaden NGS usages to large crop genomes such as maize and wheat,

genotyping-by-sequencing (GBS) has been developed and applied in sequencing

multiplexed samples that combine molecular marker discovery and genotyping.

Genotyping-by-sequencing is a novel application of NGS protocols for discovering and

genotyping SNPs in crop genomes and populations. The GBS approach includes the

digestion of genomic DNA with restriction enzymes followed by the ligation of barcode

adapter, PCR amplification and sequencing of the amplified DNA pool on a single lane

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interpret GBS datasets. As an ultimate MAS tool and a cost-effective technique, GBS

has been successfully used in implementing genome-wide association study (GWAS),

genomic diversity study, genetic linkage analysis, molecular marker discovery and

genomic selection under a large scale of plant breeding programs (Huang and Han,

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CHAPTER THREE MATERIALS AND METHODS 3.1 Plant materials

A total of 13 rice genotypes comprising of local landraces and improved rice genotypes

were collected from Mwea Irrigation Agricultural Development Centre (MIAD) repository

in Mwea, Kenya and Kilimanjaro Agricultural Research Institute repository in Moshi,

Tanzania. The 13 rice genotypes studied were mostly farmer preferred genotypes due to

their high yield production. The name, country of origin and category of the rice genotype

chosen for the study are given in table 3.1. Genotype IR 64 was used as the check variety

since it is known to have both intermediate amylose content and gelatinization temperature

(Khush and Virk, 2005).

3.2 Determination of phenotypic diversity 3.2.1 Measurement of grains and kernel traits

The following seven (7) phenotypic traits were measured in this study; grain length

(GL), grain breadth (GB), grain length/breadth (G-L/B), kernel length (KL), kernel

breadth (KB), kernel length/breadth (K-L/B) and 100 grain weight (100 GW). For each

of the rice genotype, 10 grains were randomly selected and their measurement taken

using a digital vernier caliper. Weight of 100 rice grains from each genotype was

determined using an electronic weighing balance (Mettler toledo), and average weight ±

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Table 3.1: Names, origin and category of the rice genotypes

3.3 Determination of genetic diversity 3.3.1 Total genomic DNA extraction

Genomic DNA was extracted from seed samples using a modified CTAB method

according to Doyle and Doyle (1990). The seeds were washed in 70% ethanol and

rinsed with distilled water. The sterilized seeds were ground into powder by the use of a

Variety Origin Category of rice

R 2793 Kenya Improved variety

BS 217 Kenya Improved variety

BS 370 Kenya Improved variety

BW 196 Kenya Improved variety

ITA 310 Kenya Improved variety

SARO 5 Tanzania Improved variety

IR 64 Philippine Improved variety

KILOBERO Tanzania Local land race

RED AFAA Tanzania Local land race

KAHOGO Tanzania Local land race

SUPA Tanzania Local land race

IR 54 Philippine Improved variety

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tissuelyser (QIAGEN) and the DNA extracted with 550 µl of CTAB extraction buffer

(2% CTAB), 100 mM Tris-HCL (PH 7.5), 1.4 M NaCI, 20 mM EDTA (PH 8.0) 0.2%

mercaptoethanol and 0.8g of polyvinyl pyrrolidone (PVP) preheated at 65°C. The

grinded seeds mixed with the buffer were then incubated for 30 minutes at 65°C with

intermittent shaking. Five hundred and fifty microlitres of chloroform-isoamyalcohol

(24:1) was then added to the tubes with the mixture in a fume hood and shaken gently.

The mixture was then centrifuged at 14000 rpm for 10 minutes at room temperature.

The supernatant (top layer) containing DNA was pipetted out into a 1.5ml tube to which

DNA was precipitated with 0.7 volume of ice cold isopropanol which was allowed to

take place overnight. The samples were centrifuged for 10 minutes at 13000 rpm at 4°C

so as to obtain the nucleic acid pellet. The solution was then poured out carefully so as

not to dislodge the pellet. Five hundred microlitres of 70% ethanol was added to the

pellet making sure the pellet was suspended fully. Centrifugation was then done for 10

minutes at 13000 rpm at 4°C and the ethanol poured out. This cleaning step was

repeated again including centrifugation and ethanol carefully poured out so as not to

dislodge the pellet. The pellet was then dried under vacuum before being resuspended in

100 µl TE buffer (100 mM TrisHCl pH 7.5,1 mM EDTA) and then stored at -20°C.

3.3.2 Analysis of DNA quality

The quality of the genomic DNA was determined in a 1% agarose gel in 100ml TBE

electrophoresis by running 10 µl of genomic DNA at a voltage of 75 for 45 minutes.

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31

DNA (Hosaka and Uno, 2011). The gel was pre-stained using ethidium bromide. Two

(2) µl of each sample mixed with 1µl of loading dye (bromophenol blue, xylene cyanol

and sucrose) was loaded into each well. Electrophoresis was carried out at 75 volts and

150 mA for 45 minutes. Gels were viewed under UV illumination and photographed

using GDS 8000 Gel Documentation System (UVP Inc., California, USA).

3.3.3 Analysis of DNA purity based on optical density

DNA purity for each sample solution was evaluated using a spectrophotometer which

employed the Thermo Scientific Nano drop 2000 system (Wilmington, USA). A 1: 100

dilution of DNA in IX TE buffer was made by mixing 5 µl of the DNA solution with

495 µl of TE buffer. The absorbance (optical density) of the DNA samples was

measured at 260 nm and the concentration determined assuming an equivalent of

50mgl-1 to one absorbance unit at 260 nm. In essence, A260/280 of 1.8-2.0 represents pure

DNA preparation, purity of the samples were obtained by taking their absorbance at 260

nm and 280 nm and comparing the resulting ratio with that of pure DNA preparation.

3.3.4 DNA dilutions

For PCR analysis, a dilution of genomic DNA was prepared by adding 20 µl of sterile

distilled water to the tube containing the DNA pellet. The diluted DNA was divided into

three portions. The first portion was used to carry out genetic diversity among the rice

genotypes using SSR markers. The second and the third portions were used to

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were closely linked to these genes. These dilutions were stored at -20°C and were only

thawed twice before discarding, so as to maintain the integrity of DNA.

3.4 Primers selection for genetic diversity

A set of 8 microsatellite markers shown in table 3.2 covering different genomic regions of

rice were selected from published research-based data for rice SSR markers as described

by (Panaud et al., 1996; McCouch et al., 2002).

Table 3.2: List of SSR markers used in the study

Chr*-chromosome on which marker is located, T**-annealing temperature.

Marker Motif Chr* T** Sequence

RM 125 (GCT)8 7 55 ATCAGCAGCCATGGCAGCGACC AGGGGATCATGTGCCGAAGGCC

RM 141 (CT)12 6 55 CACCACCACCACCACGCCTCTC

TCTTGGAGAGGAGGAGGCGCGG

RM 488 (GA)17 1 55 CAGCTAGGGTTTTGAGGCTG TAGCAACAACCAGCGTATGC

RM 225 (CT)18 55 TGCCCATATGGTCTGGATG GAAAGTGGATCAGGAAGGC

RM 202 (CT)30 11 55 CAGATTGGAGATGAAGTCCTCC CCAGCAAGCATGTCAATGTA

RM 434 (TC)12 9 55 GCCTCATCCCTCTAACCCTC CAAGAAAGATCAGTGCGTGG

RM 55 (GA)17 3 55 CCGTCGCCGTAGTAGAGAAG TCCCGGTTATTTTAAGGCG

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33

3.5 Primers selection for waxy and alk genes

The synthetic primer used for amplification of waxy gene was designed from the published nucleotide sequence of the waxy gene that is available from GenBank under accession number AF031162. This primer pair has been reported previously and

flanked the beginning of exon 1 and the beginning of intron 1 (Prathepha, 2003). The

primer used for amplification of alk gene was selected from published data of a list of PCR-based molecular markers developed by Ghao et al. (2003). The expected product size of each primer is shown in table 3.3 below. The amplification of waxy and alk

genes was to provide a preliminary data for determining presence or absence of these

two genes.

Table 3.3: List of primer pair for waxy and alk gene amplification.

3.6 PCR amplification

PCR reactions were carried out in Thermal cycler (Bio Rad Inc. USA) with the total

reaction volume of 25μl containing, 5μl of genomic DNA, 1X assay buffer, 200μM of

dNTPs, 2μM MgCl2, 0.2μM of forward and reverse primer and 1 unit of Taq DNA

polymerase (Fermentas Life Sciences). The PCR cycles were programmed as 95oC for 2

Primer sequence Product size Melting temperature

5’-ACCATTCCTTCAGTTCTTTGTCT-3' 5’-TAGCATGTATGAGACTACTTGTAA-3'

(For waxy gene)

250bp 600C

5′ -ATCGCGCCAACCCTCCTCTCAGC-3′ 5′ -ATTCCCTTTACGTCCTTCGGTGG-3′

(For alk gene)

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min, 94oC for 1 min, 55oC to 670C (depending on the primer used) for 1 min, 72oC for 2

min for 35 cycles and an additional temperature of 72oC for 10 min for final extension.

3.7 PCR product resolution

The PCR products were resolved by electrophoresis on 1.0% agarose gels in 0.5X TBE

buffer pre-stained with 10 µl of ethidium bromide then electrophorized at 100V for 1

hour. Ten (10) µl of each of the PCR product was mixed with 2 µl of loading dye and the

mixture loaded and run on the gel at a constant voltage of 100V for a period of 1h to 2

hours. The PCR products were visualized under UV light and then gel photos taken and

saved in a computer. The gels were then visualized under UV trans-illuminator and

photographs were taken using GDS 8000 Gel Documentation System (UVP Inc.,

California, USA). The PCR products were sized against l00bp DNA ladder (Life

sciences-USA). Clearly resolved, unambiguous bands were scored visually for their

presence or absence with each primer. The scores were obtained in the form of matrix

with ‘1’ and ‘0’, indicating the presence and absence of bands in each variety

respectively.

3.8 Data management and analysis

To determine phenotypic relatedness based on grain and kernel traits of the 13 rice

genotypes, the data was analyzed statistically for the difference in means for the 7 grain

and kernel traits measurement through ANOVA followed by Tukey’s post hoc to

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35

phenotypic relatedness among the 13 rice genotypes. To assess the underlying source of

variation in morphology based on the 7 grain and kernel traits among the 13 rice

genotypes, Principal component analysis (PCA) was carried out. All analyses were done

using software Minitab 15.0. (State College Pennsylvania-USA).

The different alleles amplified using the 8 SSR markers among the 13 rice genotypes

were identified on the basis of their size using 100bp DNA ladder (Life sciences-USA).

Genetic diversity of each rice genotype was analyzed on the basis of four statistical

parameters: major alleles, allele number, gene diversity and polymorphism information

content (PIC), which measures the genetic diversity (Botstein et al., 1980). Genetic

distance was calculated using ‘’C.S Cord 1967’’ distance (Cavalli-Sforza and Edwards,

1967) followed by phylogeny reconstruction using unweighted Neighbor Joining as

implemented in DARwin version 6.0.12 software.

To visualize the relationship between the sample populations among the 13 rice

genotypes, principle coordinate analysis (PCoA) was conducted using GenALEx 6.5

software (Peakall and Smouse, 2009). It was chosen to complement the UPGMA cluster

analysis.

To reveal the partition and variation within and among the populations, analysis of

molecular variance (AMOVA) was carried out using GenALEx 6.5 software (Peakall

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CHAPTER FOUR RESULTS 4.1 Measurement of grain and kernel traits

The measurement of the grain and kernel traits for the 13 rice genotypes and their mean

values are shown in Table 4.1. From the table, it can be seen that Supa, a local landrace rice genotype from Tanzania had the highest value of the grain length (10.48). This value

was significantly different from the grain length values recorded in BS 217, BS 370, ITA 310 and IR 64 (P<0.05; Table 4.1), but not significantly different from grain length values recorded in R 2793, BW 196, Saro 5, Kilombero, Red Afaa, Kahogo, IR 54 and

Wahiwahi (P>0.05; Table 4.1). On the other hand, ITA 310, an improved rice genotype from Kenya had the lowest value of the grain length (9.00). This value was significantly

different from the grain length values recorded in Kilombero, Supa and Wahiwahi

(P<0.05; Table 4.1), but not significantly different from those values reported in R 2793, BS 217, BS 370, BW 196, Saro 5, IR 64, Red Afaa, Kahogo and IR 54 (P>0.05; Table 4.1).

In terms of grain breadth, Supa, a local landrace rice genotype from Tanzania had the highest value of the grain breadth (2.14). This value was significantly different from the

grain breadth values recorded in R 2793, BS 217, BS 370, ITA 310, Saro 5 and IR 64, (P<0.05; Table 4.1), but not significantly different from grain breadth values recorded in

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37

grain breadth (1.79). This value was significantly different from the grain breadth values

recorded in R 2793, BW 196, IR 64, Kilombero, Red Afaa, Kahogo, Supa, IR 54 and

Wahiwahi (P<0.05; Table 4.1), but insignificantly different from those values reported in BS 217, BS 370, and Saro 5 (P>0.05; Table 4.1).

Furthermore, it was observed that Wahiwahi, a local landrace rice genotype from Tanzania reported the highest value of grain length/breadth ratio (5.26). This value was

significantly different from the grain length/breadth ratio values recorded in IR 64, Red Afaa and Kahogo (P<0.05; Table 4.1), but insignificantly different from rest of the seven genotypes (P>0.05; Table 4.1). On the other hand, lowest value of grain length/breadth

ratio (4.60) was observed in IR 64, an improved rice genotype from Philippine. This value was significantly different from grain length/breadth ratio values reported in

Wahiwahi (P<0.05; Table 4.1), but insignificantly different from grain length/ breadth ratio values recorded in rest of the eleven genotypes (P>0.05; Table 4.1).

The Supa genotype had the highest value of the kernel length (7.78). This value was significantly different from the kernel length values recorded in R 2793, BS 217, BS 370, BW 196, ITA 310, Saro 5, IR 64, Red Afaa and Kahogo (P<0.05; Table 4.1), but insignificantly different from rest of the three rice genotypes (P>0.05; Table 4.1). On the

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Wahiwahi (P<0.05; Table 4.1), but insignificantly different from rest of the nine genotypes (P>0.05; Table 4.1).

In terms of kernel breadth, Supa, a local landrace rice genotype from Tanzania had the highest value of the kernel breadth (1.90). This value was significantly different from

thekernel breadth values recorded in R 2793, BS 217, BS 370, ITA 310, Saro 5, Kilombero and Wahiwahi (P<0.05; Table 4.1), but insignificantly different from rest of the seven rice genotypes (P>0.05; Table 4.1).On the other hand, ITA 310, an improved genotype from Kenya recorded the lowest value of kernel breadth (1.60). This value was

significantly different from the kernel breadth values recorded in BW 196, IR 64, Red Afaa, Kahogo, Supa and IR 54 (P<0.05; Table 4.1), but insignificantly different from rest of the six genotypes (P>0.05; Table 4.1).

In terms of kernel length/breadth ratio, Kilombero, a local landrace rice genotype from Tanzania had the highest value of the kernel length/breadth ratio (4.45). This value was

significantly different from the kernel length/breadth ratio values recorded in R 2793, BW 196, IR 64, Red Afaa, Kahogo and IR 54 (P<0.05; Table 4.1), but insignificantly different from rest of the six rice genotypes (P>0.05; Table 4.1). On the other hand, BW 196, an improved genotype from Kenya had the lowest value of the kernel length/breadth ratio (3.68). This value was significantly different from the kernel length/breadth ratio

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(P<0.05; Table 4.1), but insignificantly different from rest of the five rice genotypes

(P>0.05; Table 4.1).

In terms of grain weight, Supa had the highest value of the grain weight (2.91). This value was significantly different from the grain weight values recorded in BS 370, BW 196, ITA 310, IR 64 and Kilombero (P<0.05; Table 4.1), but insignificantly different from rest of the seven rice genotypes (P>0.05; Table 4.1). On the other hand, ITA 310, an improved genotype from Kenya recorded the lowest value of grain weight (1.67). This

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