Javed Iqbal Wattoo 2013

IDENTIFICATION OF GENES AND QTLS CONTROLLING THE AMYLOSE CONTENTS IN RICE (ORYZA SATIVA L.) USING GENOME BASED APPROACHES

Submitted in partial fulfillment of PhD By

Javed Iqbal Wattoo 2013

Department of Biotechnology (NIBGE) Pakistan Institute of Engineering and Applied Sciences Nilore-45650, Islamabad Pakistan

Dedicated To My Beloved

Father and Mother

ACKNOWLEDGEMENTS

Allah Almighty, the eternal of this universe. The most Beneficent, Merciful, Gracious and Compassionate whose bounteous blessing gave me potential, thoughts, talented teachers, helping friends and opportunity to make this humble effort and enabled me to pursue and perceive higher ideas of life.

All praises and respects to Holy Prophet Muhammad (Peace be upon him) whose blessing and exaltations flourished my thoughts and thrived my modest all glory to almighty efforts in the form of this write-up.

The work presented in this manuscript was accomplished under the inspiring guidance, generous assistance and obligated supervision of Dr. Muhammad Arif, Principal Scientist,

National Institute for Biotechnology & Genetic Engineering (NIBGE). He had given the author guidance and advice with great patience. His criticism and suggestions as well as his editorial corrections had been of much value during the writing of thesis.

I would like to thank to Dr. Shahid Mansoor, Head Agricultural Biotechnology Division (ABD), National

Institute for Biotechnology & Genetic Engineering (NIBGE) and Dr. Sohail Hameed, Director NIBGE for providing all the available facilities to conduct my research at NIBGE.

Sincere gratitude to Dr. Melissa A. Fitzgerald, Head of the Grain Quality, Nutrition and Post Harvest center at IRRI- The International Rice Research Institute. Melissa allowed me to experience the different aspects of rice grain quality in her lab. Also, my warmest thanks for taking the time to help me understand the importance and significance of rice and why grain quality is such an important consideration in rice farming.

I am greatly indebted to the Higher Education Commission (HEC) for providing funds for my study.

I offer my profound obligations to all my friends and lab fellows, for their encouragement, help and moral support throughout the study period.

Javed Iqbal Wattoo

TABLE OF CONTENTS Table of contents …………………………………………………………. I List of figures.……………………………………………………………. V List of tables .……………………………………………………………. VI Abbreviations .……………………………………………………………. VII Thesis abstract .…………………………………………………………… IX

CHAPTER 1 GENERAL INTRODUCTION 01 1.1 Biological information.……………………………………………….. 01 1.2 Global importance.……………………………………………………. 01 1.3 Rice status in Pakistan .………………………………………………. 02 1.4 Rice in genomics era .………………………………………………… 02 1.5 Molecular markers .…………………………………………………… 02 1.6 Single Nucleotide Polymorphism (SNPs) .…………………………… 03 1.7 QTL mapping in rice .………………………………………………… 04 1.8 Association mapping in rice .………………………………………… 05 1.9 Importance of rice grain quality .…………………………………….. 05 1.10 Pasting properties and protein content .…………………………….. 06

CHAPTER 2 IDENTIFICATION OF QUANTITATIVE TRAIT LOCI FOR 08 AMYLOSE CONTENT, PROTEIN CONTENT AND PASTING PROPERTIES IN RICE USING SINGLE NUCLEOTIDE POLYMORPHISMS (SNPs) MARKERS Abstract .………………………………………………………………….. 08 Introduction .……………………………………………………………… 10 Review of Literature .…………………………………………………….. 11 2.2.1 Amylose content .…………………………………………………… 11 2.2.1.1 Amylose content and rice grain quality .…………………………. 11 2.2.1.2 Inheritance of amylose content .………………………………….. 12

2.2.1.3 QTL mapping for amylose content .……………………………… 13

2.2.1.4 Allelic diversities at waxy locus .………………………………… 14 2.2.1.5 Sequence variation in the waxy gene .…………………………… 15 2.2.2 Pasting properties .…………………………………………………. 15 2.2.3 Correlation among pasting properties .…………………………….. 19 2.2.4 Protein content .……………………………………………………. 20 2.3 Materials and Methods .……………………………………………… 21 2.3.1 Genotypes and experimental design .………………………………. 21 2.4 Grain quality assays .…………………………………………………. 22 2.4.1 Dehulling, Milling and Grinding .………………………………….. 22 2.4.2 Estimation of amylose content .……………………………………. 22 2.4.2 Estimation of Pasting properties.……………………………………. 23 2.4.4 Estimation of protein content .………………………………………. 23 2.5 Statistical analysis .……………………………………………………. 25 2.6 Linkage analysis .……………………………………………………… 25 2.6.1 DNA extraction .…………………………………………………….. 25 2.7 SNPs genotyping .……………………………………………………... 26 2.7.1 DNA activation and Hybridization .………………………………… 26 2.7.2 Oligonucleotides designing and ligation .…………………………… 26 2.7.3 Polymerase chain reaction (PCR) cycle .……………………………. 27 2.74 Hybridization to Array-metrix .……………………………………… 27 2.75 Image array metrix .………………………………………………….. 27 2.8 QTL mapping .………………………………………………………… 27 2.9 Results and Discussions .……………………………………………… 29 2.9.1 Statistical analysis of phenotypic data .……………………………... 29 2.9.1.1 Amylose content .…………………………………………………. 29 2.9.1.2 Correlation among pasting properties .…………………………… 30 2.9.1.3 Protein content .…………………………………………………… 32 2.9.2 QTL mapping .……………………………………………………… 38 2.9.3 QTLs for amylose content .…………………………………………. 38 2.9.4 QTLs for protein content .…………………………………………… 38 2.9.5 QTLs for pasting properties .………………………………………... 38

2.9.5.1 Peak viscosity (PV) .……………………………………………… 38 2.9.5.2 Break down (BD) .………………………………………………… 39 2.9.5.3 Final viscosity (FV) .……………………………………………… 39 2.9.5.4 Set-back (SB) .……………………………………………………. 39 2.9.5.5 Peak time (PT) .…………………………………………………… 39 2.9.5.6 Pasting temperature (PsT) .………………………………………... 39 2.9.5.7 Starch retrogradation (LO) .……………………………………….. 40 2.9.6 Discussion .………………………………………………………….. 45 2.9.6.1 Amylose content .…………………………………………………. 45 2.9.6.2 Protein content .…………………………………………………… 46 2.9.6.3 Pasting properties .………………………………………………… 47 2.9.7 Summary .…………………………………………………………… 51 2.9.8 Practical implications of the study .………………………………… 52

CHAPTER 3 GENETIC STRUCTURE AND ASSOCIATION MAPPING FOR 53 STARCH CHROMATOGRAPHY IN RICE (ORYZA SATIVA L.) USING SNPs MARKERS Abstract .………………………………………………………………….. 53 3.1 Introduction .………………………………………………………….. 54 3.1.1 Inheritance of grain quality .………………………………………… 54 3.1.2 Single Nucleotide Polymorphism (SNPs) .………………………….. 55 3.1.3 Association mapping for complex traits .……………………………. 55 3.1.4 Size Exclusion Chromatography (SEC) .……………………………. 56 3.2 Materials and Methods .……………………………………………….. 56 3.2.1 Selection of plant materials and filed experiments.…………………. 56 3.2.2 DNA extraction.……………………………………………………... 57 3.2.3 Size exclusion chromatography using HPLC .……………………… 58

3.2.4 Estimation of gel consistency (GC) .………………………………... 59 3.2.5 SNPs Genotyping .…………………………………………………... 59 3.2.5.1 DNA activation and hybridization .……………………………….. 59

3.2.5.2 Oligonucleotides designing and ligation .…………………………. 59 3.2.5.3 Polymerase chain reaction (PCR) cycle .………………………….. 60 3.2.5.4 Hybridization to array-metrix .……………………………………. 60 3.2.5.5 Image array-metrix .………………………………………………. 60 3.3 Association mapping .………………………………………………… 61 3.3.1 Estimation of population structure .…………………………………. 61 3.3.2 Estimation of linkage disequilibrium .………………………………. 61 3.4 Results and Discussions .…………………………………………….... 63 3.4.1 Correlation among starch chains .…………………………………… 63 3.4.2 Population structure .………………………………………………… 67 3.4.3 Genome wide association scans .……………………………………. 70 3.5 Discussion .……………………………………………………………. 75 3.1 Summary .……………………………………………………………... 77

CHAPTER 4 ETYLE METHANE SULPHONATE (EMS) INDUCED 78 MUTAGENIC ATTEMPTS TO CREATE GENETIC VARIABILITY IN BASMATI RICE (ORYZA SATIVA L.) Abstract .…………………………………………………………………... 78 4.1 Introduction .…………………………………………………………... 79 4.1.1 Alkylating Agents .………………………………………………….. 80 4.2 Materials and Methods .………………………………………………. 80 4.2.1 Genotypes .………………………………………………………….. 80 4.2.2 Mutagenesis and Field Experiments .……………………………….. 81 4.3 Results and Discussions .……………………………………………… 82 4.2 Summary .……………………………………………………………... 85 SUMMARY OF THESIS .………………………………………………. 86 REFERENCES .………………………………………………………….. 90

LIST OF FIGURES

Figure 2.1 Brief sketch of Chapter-2……………………………………… 24 Figure 2.2 Flow sheet image for Illumina BeadXpress…………………… 28 Figure 2.3 Statistical distribution of amylose content in mapping 29 population ………………………………………………………………… Figure 2.4 Cluster image of mapping population based on RVA 31 properties Figure 2.5 Frequency distribution of protein content in mapping 32 population …………………………………………………………………. Figure 2.6 Frequency distribution of final viscosity (FV) and breakdown 33 (BD) ………………………………………………………………………. Figure 2.7 Frequency distribution of lift off (LO) and pasting temperature 34 (PsT) ……………………………………………………………………… Figure 2.8 Frequency distribution of peak viscosity (PV) and set back 35 (SB) ………………………………………………………………………. Figure 2.9 Frequency distribution of trough and peak time (PT) ………… 36 Figure 2.10 (A) Pasting profile of two parents IR-64 and IR-132 by RVA 41 Figure 2.10 (B) Parental survey image of both parents using 384 SNPs 41 markers ……………………………………………………………………. Figure 2.11 Distribution of 125 SNPs markers on 12 rice chromosomes… 42 Figure 2.12 The rice chromosomes associated with different grain quality 43 traits ………………………………………………………………………. Figure 3.1 Correlation of starch chain lengths components with AC and 64 GC ………………………………………………………………………… Figure 3.2 Distribution of gel consistency (GC) in diverse rice germplasm 65 Figure 3.3 Normalized SEC curves of 75 cultivars using HPLC ………… 66 Figure 3.4 Estimated population structure image based on 754 SNPs 68 markers ……………………………………………………………………. Figure 3.5 Bar plots of population structure on different K values ………. 69 Figure 3.6 General distribution of SNPs markers on 12 rice chromosomes 72

Figure 3.7 Linkage map of chromosomes with different QTLs ………….. 73 Figure 3.8 LD patterns of SNPs markers on chromosome 6 & 9 ………… 74 Figure 4.1 The germination % of Basmati-370 and Super Basmati in field 83 conditions …………………………………………………………………. Figure 4.2 Germination % of Basmati-370 and Super Basmati at different 84 EMS doses …………………………………………………………………

LIST OF TABLES

Table 2.1 Correlation among pasting properties………………………….. 30 Table 2.2 The QTLs associated with grain qulity traits…………………... 37 Table 3.1 List of rice cultivars used in the study …………………………. 62 Table 3.2 Correlation of Starch chains with grain quality traits …………. 63 Table 3.3 Genome wide association scans ……………………………….. 71 Table 4.1 An overview of mutagenic attempts made in rice …………….. 82 Table 4.2 Grain quality charactristics of Super-Basmati and Basmati-370. 82 Table 4.3 LSD showing the performance of both genotypes ……………. 83 Table 4.4 ANOVA table for comparisons of means of yield traits ……… 84

ABBREVIATIONS

ɥL microlitre ng nanogram AAC apparent amylose content ASO allele specific oligonucleotides AM-Long amylose long chains AM-short amylose short chains AP-Long amylopectin long chains AP-Short amylopectin short chains BD breakdown value on RVA curve CIM composite interval mapping CTAB cetyl trimethyl ammonium bromide DH double haploids DNA deoxyribonucleic acid EMS ethyl methane sulphonate FAO food and agriculture organization FV final viscosity value on RVA curve GC gel consistency GLM general linear model GT gelatinization temperature GBSS granule bound starch synthase HPLC high pressure liquid chromatography IRRI international rice research institute InDel insertion deletions IM interval mapping LO lift-off value on RVA curve LSO locus specific oligonucleotides LD linkage disequilibrium MAS marker assisted selection PsT pasting temperature value on RVA curve PV peak viscosity value on RVA curve

PCA principal component analysis PT peak time value on RVA curve PC protein content QTL quantitative traits loci RVA rapid visco analyzer RCBD randomized complete block design RILs recombinant inbred lines SB set-back value on RVA curve SSRG starch synthesis related genes SNP single nucleotide polymorphism SEC size exclusion chromatography SCARS sequence characterized amplified regions TILLING targeting induced local lesions in genome

THESIS ABSTARCT

Rice is the fundamental food for about half of the world’s population, supplying 20% of the calories consumed worldwide. In Pakistan, rice is second staple food after wheat and contributes more than two million tones to our food requirements. It shares 5.7 percent of the total value added in agriculture and 1.6 percent to GDP. QTL mapping is a marker facilitated genetic dissection of variation of complex phenotypes through proper experimental strategy and statistical analysis of segregating material. The detection of genes or QTLs for yield and quality traits is based on the principal of genetic recombination during meiosis. This allows the construction of linkage maps consisted of genetic markers for a specific population. In rice, association mapping is a viable alternative to QTL mapping. Based on linkage disequilibrium (LD), association mapping is powerful and high resolution mapping tool for complex traits. It has the potential to utilize the genetic diversity of the worldwide crop germplasm resources. Linkage disequilibrium (LD) refers to a reduced (non random) level of recombination of specific alleles at different loci controlling specific genetic variations in a population. Single nucleotide polymorphisms (SNPs) are most abundant source of genetic polymorphism between two individuals. SNPs have been extensively used to detect population structure and association mapping for yield and quality traits in rice. One of the major concerns in rice breeding is grain quality improvement. Grain quality in rice is second only to yield as a major breeding objective. The amylose content in rice is regarded as one of the most important determinant of cooking and eating quality. In breeding programme, new lines are selected based on amylose content as this indicator is associated with grain quality.

To identify the quantitative trait loci (QTLs) or genes for protein content, amylose content and pasting properties of rice, a segregating population was developed by crossing two parents IR-64 and IR-132. A QTL analysis was conducted using 125 SNPs markers distributed on all 12 rice chromosomes on a progeny of 213 plants. Many different genomic regions have been identified to influence the starch pasting properties on different linkage groups. A total of 24 main effect QTLs (M-QTLs) for different grain quality traits were identified and mapped on 7 different chromosomes (1, 4, 7, 8,9,10 &11).

The potential of genome wide association scans (GWAS) was explored to estimate the genetic structure and to map the genomic regions associated with starch chain length distribution. We used 754 genome wide single nucleotide polymorphisms (SNPs) based markers to study the patterns of linkage disequilibrium (LD) and structure of population among seventy-five diverse rice genotypes (indica, temperate japonica & tropical japonica). All the seventy-five accessions were divided into three major groups based on structure analysis (model based). The three groups represented three different geographic regions. For the 75 genotypes, the complex traits like amylose content, gelatinization temperature, amylose long chains, amylose short chains, amylopectin long chains, and amylopectin short chains were studied. The associations of SNPs markers with a phenotypic trait were disclosed by using the approach of GLM (general linear model). We examined variation both within and among three subgroups revealing significant heterogeneity. A total of 59 association signals were detected. From the results, we found that waxy locus not only affects amylose content and GC but also regulates starch branching patterns in rice. The study will help to provide a way to find out valuable genes and alleles associated with starch structure for grain quality improvement in rice.

Our mapping results have clear practical implications for the improvement of rice grain quality. The SNPs markers closely associated with the variation of all the studied phenotypic traits could greatly be used to replace the alleles linked with poor grain quality traits using marker-assisted selection. The possible applications of mapped QTLs include their utilization in screening of parents for introgression or pyramiding purpose.

CHAPTER 1

General Introduction to Rice

1.1 Biological Information

Rice (Oryza sativa L.) is a member of genus Oryza with 22 wild and 2 domesticated species. The cultivated species of rice include Oryza sativa and Oryza glaberrima. The cultivation of Oryza sativa is practiced all over the world while Oryza glaberrima is mostly domesticated in West Africa. Both species belong to the tribe Oryzeae and family Poaceae. Oryza sativa is self-pollinated diploid species with twelve pairs of chromosomes and a genome size of 410-450 Mb. The genomes of two species (indica & japonica) have been sequenced (Goff et al. 2002).

1.2 Global Importance

Rice is life for millions of heads around the globe, providing over 21% of caloric needs and 15% of per capital proteins. This unique grain helps to sustain millions of people around the globe (Mather et al. 2007; Zhang 2007; Tian et al. 2009). In Asia, Rice has shaped the economies of thousands of people. It provides about 76% of the caloric intake of the people of South East Asia. It has special importance in Asia where major percentage (about 90%) of the total world rice is produced and consumed as predominant staple food in its 17 countries (Zhang 2007). Rice farming is a major source of employment for thousands of families in Asia and life without rice is simply unthinkable for them. It is cultivated between the latitudes of 53 °N and 35 °S, and from elevations below sea level to above 2000M. It is grown under different production systems as 57% is cultivated on irrigated soils, rain- fed lowland soils share 25%, uplands hold 10%, 6% in deepwater, and 2% is cultivated on tidal wetlands. Because of its cultivation in diverse ecosystems, each rice variety has distinct characteristics, making one variety more selective over others in different parts of the world. It can be waxy (sticky), non-waxy, aromatic and with different colors and shapes (brown, red, purple, black & long, bold & cylindrical).

1.3 Rice Status in Pakistan

In Pakistan, rice is one of the major cash crops. After wheat, it is second staple crop and covers more than 2.5 million hectares of crop area. Pakistan holds a unique position around the world for production and export of long grain aromatic rice. Price of basmati rice is two to three times higher than non-basmati rice in international market. As an important export item of the country, it contributes 5.9% in agriculture sector while 1.3% in gross domestic product (GDP) (Economic survey of Pakistan, 2011). Rice industry is an important source of employment for thousands of heads in the country. Therefore, the progress in rice production would contribute to economic development that leads to national food security and poverty/hunger alleviation of the country.

1.4 Rice in Genomics Era

The complete genome sequencing of rice provides new prospects to develop genetic markers associated with qualitative and quantitative traits. The higher levels of synteny of rice genomic regions with other cereals (wheat, maize, barley & sorghum) make it a model plant to study the physiology and genetic evolution of cereals in detail (Goff et al. 2002). The availability of high density physical and genetic maps unravel gene location and function that paved new horizons for plant breeders to benefit the rice research to develop new rice varieties. Using the potential of genomic information, the ultimate target is to develop rice cultivars with enhanced yield, resistant to biotic and abiotic stresses and with improved grain quality.

1.5 Molecular Markers

A molecular marker is a sequence of DNA with known position in the genome. Molecular markers have become important tools for successful plant breeding. These markers have been successfully used in many plant species (rice, wheat, maize, cotton, barley & sorghum) for genetic mapping and marker-assisted selection (Collard et al. 2008; McCartney et al. 2004; Xu et al. 2004; Reif et al. 2005). The advanced markers systems being used for different crops are Sequence Characterized Amplified Regions

(SCARS), Insertions & Deletions (InDel) and Single Nucleotide Polymorphism (SNPs). An ideal molecular marker should be polymorphic, multi-allelic, co-dominant, independent to selection pressure, neutral and non-epistatic. The practical applications of molecular markers include marker-assisted selection, plant variety protection, varietal purity, assessment of genetic diversity and genetic mapping (Jena et al. 2008).

1.6 Single Nucleotide Polymorphism (SNPS)

SNPs are single nucleotide polymorphisms at a defined locus between two related species. These markers are most abundant source of genetic polymorphism between two individuals (Zhao et al. 2011). SNPs have been extensively used to detect population structure, genetic mapping and association mapping for yield and quality traits in rice (Caicedo et al. 2007; McNally et al. 2009; Ebana et al. 2010; Huang et al. 2010; Zhao et al. 2011). SNPs have potential advantage over SSR markers to greatly increase the precise speed and to reduce the cost of genotyping (McCouch et al. 2010). SNPs are the genotyping choice for geneticists to move forwards in genome wide association mapping techniques (Buckler et al. 2009; McMullen et al. 2009; Waugh et al. 2009; Moragues et al. 2010). SNPs genotyping offers powerful way to scan the genomic regions with high resolution. This approach is cost effective and reduces the time efforts of breeders. It is unique and different from conventional molecular markers systems like RAPD, AFLP, RFLP, SSR, and InDel. These conventional marker systems have uneven distribution on whole genome and require more time for genotyping. High degree of polymorphism, stability during evolution and low mutation rate make SNPs as markers of choice to study complex traits in plants (Gupta et al. 2001; Syvanen et al. 2001; Jorde et al. 2000). Based on the resolution, different SNPs assays have been developed and used in dissecting phenotype-genotype association in rice (Tung et al. 2010; McCouch et al. 2010; Huang et al. 2010).

1.7 QTL Mapping in Rice

QTL mapping is a genetic dissection of genomic regions associated with complex phenotypic traits using proper statistical strategy and analysis of segregating material (Tierney and Lamour, 2005). Mapping of chromosomal regions associated with the variation of a quantitative trait in segregating populations help to characterize the genetic basis of complex traits (Lu et al. 2010). Multiple loci control the genetic variation of complex traits (Bao et al. 2006). Genetic recombination is the basic principle for detection of genes or QTLs for yield and quality traits plants (Tanksley 1993). Construction of genetic/linkage maps with even distribution of molecular markers is one of the pre-requisite for QTL mapping. This mapping technique used different segregating populations like RILs (Recombinant Inbred Lines), F2, F3, BC1-5 (Backcross), and DH (Double haploids). However, RILs and double haploids are preferable because once they are developed; they can be permanently used for genetic mapping. The use of statistical methods such as SMA (single marker analysis), IM (interval mapping), and CIM (composite interval mapping) is a precise approach to detect the QTLs associated with a complex trait. However, in recent years, the validation of QTLs or fine mapping has become widely accepted. The validation of a QTL generally refers to the stability of QTL in different genetic backgrounds because a single segregating population provides only partial information. Therefore, genetic analysis of several populations with different genetic backgrounds is required to have a complete picture of QTL allelic diversity. The practical applications of mapped QTLs include their utilization in screening of parents for introgression or pyramiding purpose (Septiningsih et al. 2003). The other uses include selective genotyping, which is one of the most effective approaches and can be adapted for studying the yield and quality traits. Selective genotyping reduces the costs involved in genotyping the large populations without affecting the power to detect QTLs. Plant breeding, genomics and germplasm utilization are the major disciplines where QTL analysis can be used with most interests.

1.8 Association Mapping

Association mapping is an advanced mapping technique to scan the genomic regions associated with the variation of a quantitative trait (Mackay et al. 2007). It is an alternate to QTL mapping in rice. Also named as LD (linkage disequilibrium) mapping, it has great potential to map the genomic regions with high resolution and to access the genetic diversity of the crop germplasm (McCarthy et al. 2008). Association mapping detect a lower level of recombination of different haplotypes associated with a specific complex trait (Zhao et al. 2007). The significant level of linkage disequilibrium can be detected using different statistical tools and has been widely used to map a gene or QTL associated with desired phenotype. Association mapping is preferred over QTL mapping due to (a) availability of broader genetic variation (b) exploitation of major recombination events during meiosis to detect higher resolution (c) no need to develop a biparental population making the approach time saving and cost effective.

1.9 Importance of Grain Quality

The improvement of grain quality is one of the major concerns in rice breeding. It is second only to yield and an important component to increase its market worth (Fitzgerald et al. 2009). The people from different parts of the world have different preferences, for example the consumers from Middle East prefer long grain and aromatic rice while the European people like long grain but non aromatic rice. According to them, the presence of fragrance in rice is a sign of spoilage and contamination (Efferson 1985). Similarly, the people from South East Asia prefer medium grain rice with porridge. However, aromatic rices (Basmati & Jasmine) have supreme demand and rice consumers pay higher price for fragrant rice in local market. Developing cultivars with superior cooking and eating quality traits have been the focus of rice breeders around the world (Khush, 2005). Rice quality is influenced by factors under genetic control, environmental conditions, and processing techniques. Sometimes environmental factors under some circumstances have great impact on quality than inherited traits (Juliano 2003). Rice starch has been used in the food industry for numerous applications. The modification of starch to improve its functional properties is normally attained by physical, such as heat or moisture

18 treatments, or chemical means through etherification, cross-linking and grafting of starch (Fitzgerald et al. 2004). The process in which starch is heated in water is called pasting, which is the formation of a viscous material consisted of leached amylose and disintegrated starch granules.

1.1 0 Pasting Properties and Protein Content

Pasting properties reflect the cooking profile and eating properties of rice starch and are important indicators of final product quality (Champagne et al. 1999). The starch profile generated by Rapid Visco Analyzer (RVA) has become popular to study the viscosity properties of rice starch. This technique is preferred because it requires small sample and easy to operate. The factors that influence the cooking and eating quality traits of rice grain have been identified and genetic links have been established between the two (Ge et al. 2005). Amylose plays a key role in predicting rice grain quality and selection of cultivars in a breeding programme (Tian et al. 2009). However, amylose content does not always offer an absolute image of rice grain quality. Cultivars within one amylose content class may differ in textural and cooking properties. Recent research has shown that other components in the rice grain should be considered while explaining physical and chemical characteristics. To get a more complete picture of the eating quality of rice grains, amylose content must be only one of many tests in the evaluation process. These tests should ideally include other components in the grain such as proteins, because they are also known to contribute to quality differences within the same amylose class (Martin et al. 2002). Proteins are second highest components in rice grain after starch. Rice is also one of the protein sources for all the rice growing countries in Asia. Proteins affect the physiochemical properties of cooked rice. In general, the protein content and cooking quality of rice are negatively correlated. Rice protein is enriched with about 4% lysine. Rice protein is unique to have lysine when compared with other cereals. The transfer of useful traits from one species to other using conventional techniques is not successful due to low heritability (Ge et al. 2005). However, exploring the potential of genomic-based breeding techniques could lead to improve the nutrient content of rice. In the later stages of the breeding program, amylography is utilized to predict the textural properties of cooked rice. When rice is cooked, a series of actions occur before the raw grain is

19 transformed into a gel. The cooking process starts as soon as water is added and as heat is supplied to the grains of rice. Starch properties are used as a tool to classify different rice varieties. In future, rice quality will gain more importance because it is the only cheapest source of food for billions of people on the earth who demand high quality rice. However, the preferences of end users are highly variable in defining the rice quality (Bergman et al. 2004).

CHAPTER 2

IDENTIFICATION OF GENOMIC REGIONS ASSOCIATED WITH AMYLOSE CONTENT (AC), PROTEIN CONTENT (PC) AND PASTING PROPERTIES IN RICE USING SINGLE NUCLEOTIDE POLYMORPHISMS (SNPs) MARKERS

Abstract

Grain quality of rice is economically important trait and is mainly influenced by starch properties. Amylose content, pasting properties and protein content have been considered most important to predict the rice grain quality. The improvement of these biochemical traits is one of the major objectives in rice breeding based on quality improvement. To identify the quantitative trait loci (QTLs) or genes for protein content, amylose content and pasting properties of rice, a segregating population (F5) was developed by crossing two parents IR-64 and IR-132. A QTL analysis was conducted using 125 SNPs markers distributed on all 12 rice chromosomes on a progeny of 213 plants. A total of 24 main effect QTLs (M-QTLs) for different grain quality traits were identified and mapped on 7 different chromosomes (1, 4, 7, 8,9,10 &11).

21 two QTLs qFV1 and qFV10 on chromosome 1 and 10 respectively. Phenotypic variance of both QTLs was 14% and 12% respectively. The variation of setback viscosity was detected to link with two QTLs qSB10 and qSB11 on chromosomes 10 and 11 with phenotypic variance of 8% and 29% respectively. The inheritance of peak time was controlled by six QTLs, out which three were identified on chromosome 1, two on chromosome 8 and one on chromosome 9 respectively. The phenotypic variance of five QTLs was 18%, 22%, 36%, 12% and 14% respectively. Two novel alleles were detected with a significant effect on pasting temperature on chromosome 11 with phenotypic variance of 5% and 8% respectively. The genetic mapping further revealed that the inheritance of retrogradation was controlled by two alleles on chromosome 1 and 11 (qLO-1and qLO-11) with phenotypic variance of 12% and 14% respectively. The results clearly revealed that genes/alleles from IR-64 could be used to improve the pasting profile and biochemical components of high yielding cultivars. In summary, the alleles from IR-64 provided a small but statistically significant improvement for the components of grain quality based on starch properties and protein content.

2.1 Introduction

Grain quality of rice is a major concern of rice consumers worldwide. It is an important factor affecting its market value. In breeding programmes, new lines are selected based on amylose content and starch pasting profile, as these indicators are associated with grain quality (Fitzgerald et al. 2009). However sometimes amylose content does not always present a clear picture of grain quality. In cases where rice varieties have same amylose content but have different cooking and eating quality, protein content is known to be associated with these differences (Zhong et al. 2004). To get a more complete picture of the eating quality of rice grains, amylose content must be only one of many tests in the evaluation process. Proteins are second highest components in rice grain after starch. However, the role of proteins in determining the rheological properties is not completely understood (Chrastil, 1990). In the later stages of the breeding program, amylography is utilized to check on the pasting profile of cooked rice. A series of reactions occur in cooked rice before the raw grain is transformed into a gel. The cooking process starts as soon as water is added and as heat is supplied to the grains of rice (Fitzgerald et al. 2004). Pasting properties are used as a tool to classify different rice varieties (Juliano 2001). Pasting properties are also known as RVA properties because they are tested on a machine called RVA (Rapid Visco Analyzer). During RVA analysis, rice flour is subjected to a standard temperature to generate a pasting graph/curve. The pasting profile is used to evaluate different rice varieties based on grain quality differences. The varieties with different pasting profile have different cooking properties. This technique has become more popular with the availability of latest instruments, which give rapid results and require small samples. The overall objectives of the study were.

 To understand the grain quality differences between the two parents (IR-64 & IR- 132).  To see a correlation among pasting properties, amylose content and protein content.  To use the SNPs markers to construct rice linkage map.

 To map the rice genomic regions associated with the variation of grain quality traits like amylose content, protein content and pasting properties.  How much variation of the trait is caused by specific region?

2.2 Review of Literature

2.2.1 Amylose Content

2.2.1.1 Amylose Content and Rice Grain Quality

Amylose content is a major component of rice grain affecting its cooking and eating profile (Bao et al. 2001). It plays a key role in the selection of new lines in a breeding programme. The first possible correlation between amylose content with rice grain quality was explored by Sanjiva et al (1952). Nelson and palm (1995) reported the relation of amylose content with several modifying genes and environmental factors such as temperature. Webb, (1999) reported that varieties with high amylose content had high rate of volume expansion and absorb more water than the varieties with lower amylose content. He further revealed a straight relationship between amylose content and cohesiveness and grain texture. The level of amylose content in rice grain ranges from 5- 35% (Trop et al. 2003). The milled rice grain can be classified into five different categories based on different levels of amylose content as waxy rice (1-2%), non-waxy rice (>2%), low amylose rice (2-9%), intermediate amylose rice (20-25%), and high amylose rice (25-33%). The genus Oryza sativa has most of the rice species with intermediate amylose content (Resurreccion et al. 1994; Ayres et al. 1997; Frances et al. 1998). The amount of Amylose varies among the grains from the same panicle (Zhang et al. 2003). Cheng et al. (2007) studied the effect of panicle morphology on amylose content within the same panicle and found a strong correlation between amylose content and panicle morphology. Within the same rice grain, the inner and outer layers may have different levels of amylose content. The timing of crop harvest and days to grain storage do not have any effect on amylose content (Muraue et al. 1997). Aboubacar et al. (2002) observed different level of amylose content with different amylopectin structure within the same rice variety when he used to grow it in two different ecosystems. Lisle et al.

(2000) studied the importance of chalk as an important quality trait and found a negative correlation of amylose content with amylopectin and protein content. Ayers et al. (1999) reported a poor relationship between grain quality traits and their associated markers. Singh et al. (2005) found a negative correlation between amylose content and cooking time. It is also well known that the cooking properties of different rice cultivars having same amylose content can differ enormously (Bergman et al. 2001). Takeda et al. (1990) reported that differences in sensory and cooking properties of rice within same amylose class were due to the difference in the structure of amylose.

2.2.1.2 Inheritance of Amylose Content

Triploid endosperm and epistatic interactions along with cytoplasmic effects lead to a complex genetic inheritance of rice grain components associated with its quality (Kumar et al. 1986; Pooni et al. 1993). Kumar and Khush (1987) linked the inheritance of intermediate amylose content with single gene but they could not identify the complete allelic pattern of that particular gene at waxy locus. The results of Heda et al. (1999) revealed that the inheritance of high amylose was dominant while that of low amylose was recessive. The gene responsible for amylose synthesis is known as waxy gene (Wx). In rice endosperm, this gene directs an enzyme called GBSS (granule bound starch synthase). The studies of Sano (1984) revealed that GBSS controlled the quantitative variation of amylose content in rice endosperm. During 1986, he further reported the role of alleles of waxy gene (Wxa and Wxb) to dictate the different levels of amylose in indica and japonica rice varieties respectively. The region of chromosome 6 having waxy locus plays a significant role in controlling the quantitative variation of amylose content but it is not still completely understood whether the phenotypic expressions are caused by waxy alleles or its attached modifiers (Tan et al. 1999; Tian et al. 2005; Wang et al. 2007). However, some isoforms of GBSS (GBSSI, GBSSII) are also known to involve in starch biosynthesis of cereals (Nakamura et al. 2005). Dry et al. (1992) found amino acid similarity between two isoforms of granule bound starch synthase (GBSSI & GBSSII). Ainsworth et al. (1993) studied four cereals (rice, maize, barley & wheat) along with peas and potatoes. They observed a clear similarity in GBSSI among all the studied crops. Some Asians rice accessions show significant loss of diversity at waxy gene (Olsen et al.

2006). Tian et al. (2009) did an association analysis and revealed that the genes that control starch synthesis interact to form a fine network to control the variation of starch properties. Amylopectin has also been reported to control the synthesis of amylose content in rice (Wan et al. 1998).

2.2.1.3 QTL Mapping for Amylose Content

The studies of Pooni et al. (1992) revealed a significant correlation of cytoplasmic inheritance with amylose content. Tan et al. (1999) reported the waxy locus to be associated with the phenotypic variation of quality traits and pasting properties of rice grain. He et al. (1999) found a major QTL for amylose content at the waxy locus. Lanceras et al. (2000) found four QTLs for amylose content. A significant QTL was detected at the chromosome 6 (waxy locus) while minor QTLs were detected at chromosome 3, 4 & 7. Lee et al. (2000) reported three loci associated with amylose content on the linkage groups 1, 6 and 11respectively. They further revealed that waxy region on linkage group 6 was controlled much of the phenotypic variation of the. The results of Bao et al. (2003) indicated that two major genes (Wx gene & Alk gene) were responsible for most of starch properties in rice. Ge et al (2004) reported a single QTL near waxy locus on chromosome 6 to be associated with most of the studied grain quality traits. Bao et al. (1999) observed variation in gene expression under different environments and suggested that the performance and association of quantitative traits were affected by genotype × environment (G×E). Fan et al. (2005) confirmed that Wx locus also affected alkali spreading value. They further found the relationship of waxy locus with gel consistency (GC) along with amylose content and pasting properties. They detected Waxy locus as a major region to be associated with the variation of starch properties. The waxy region also contains some alleles that are associated with water absorption. The same region controls the inheritance of volume expansion in rice grain (Ge et al. 2005; Tian et al. 2005). Wang et al. (2007) studied 17 grain quality parameters by using recombinant inbred lines (RILs) and found that waxy locus also affected alkali spreading value (ASV). They also found the link of alkali locus (Alk) with gel consistency (GC) and viscosity profile of cooked rice. Wang et al. (1995) explained that high amylose rice varieties contained Wx protein during seed development. They further

26 revealed that waxy rice cultivars had no amylose. Wang et al. (1995) reported a positive correlation between waxy proteins and further observed that waxy proteins had negative correlation with amylose content. The results of Shi et al. (2010) revealed that protein content was associated with amylose content and amino acids content of rice.

2.2.1.4 Allelic Diversities at Waxy Locus

The variation in waxy gene haplotypes (Wxa and Wxb) has also been reported to contribute the phenotypic variation of high and intermediate amylose respectively in different Oryza species (Sano 1984; Sano et al. 1985; Wang et al. 1995). Mikami et al. 2008 studied near isogenic lines (NILs) and discovered five waxy alleles associated with different levels of amylose as low amylose (Wxa), intermediate amylose (Wxin), high amylose (Wxb), opaque rice (Wxop) and waxy rice (wx). They also concluded that allelic diversity was partially influenced by gene flow. They further observed synteny between the waxy gene of indica and japonica landrace and reported that waxy gene was introgressed from indica into japonica species. Garris et al. (2005) studied the aromatic rice and found the frequent distribution of Wxint allele associated with intermediate amylose. She found close association of Basmati or high quality rice with spp. japonica than to spp. indica. However, the effect of some modifier genes on Wxint allele was still not clear (Mikami et al. 2000; Dung et al. 2000). Mikami et al. (1999) discovered that

Wxop allele was responsible for opaque or chalky endosperm. Ayres et al. (1997) reported the presence of some unknown haplotypes at waxy locus to control the quantitative variation of amylose content. However, variation in temperature is another factor that affects the allelic variation at Wx locus (Asaoka et al. 1987). Wang et al. 1995 reported the effect of post transcriptional modifications in waxy gene to direct the quantitative variation of amylose content. Ayres et al. (1997) studied 85 US rice varieties (non- glutinous) and found seven different GBSS alleles explaining 82.9% variation of amylose content. They found that about 80% variation of amylose content was due to result of a single transition (G/T) in the leader intron. Isshiki et al. (2000) reported a relationship of trans-acting genes with the expression of Wx gene. The splicing of intron 1 as a result of single base substitution at the splice site of 5 prime directs the variation in the expression

27 of Wxb allele to lower amylose content (Bligh et al. 1998). Olsen et al. (2002) studied the evolution patterns of waxy rice and found that genetic variation among the alleles of waxy gene caused the evolution of waxy rice. But according to the study of Inukai et al. (2000); Bao et al. (2002) and Yamanaka et al. (2004), lack of splice donor mutations at waxy locus suggested that waxy genotype was not associated with mutation.

2.2.1.5 Sequence Variation in Waxy Gene

The interaction of modifier genes with known alleles affect the inheritance of amylose content and make it very complex especially in Asian rice (Isshiki et al. 2000). Larkin et al. (2003) studied a series of rice cultivars with different cooking and eating profiles. They cloned and sequenced GBSS cDNA to compare different rice genotypes. They found two single nucleotide polymorphisms (SNPs) between exon 6 and 10. They further revealed that these SNPs were the result of substitution mutation at exon 6 and 10. They further confirmed the association of these SNPs with amylose content and pasting properties by subsequent sequencing of these specific regions from additional rice cultivars. Chen et al. (2004) conducted an association study by using 164 rice accessions. They studied the effect of microsatellite marker and SNPs in exon 10 of the waxy gene on the variation of amylose content and pasting profile and revealed a strong correlation among them. Sirisoontaralak et al. (2005) studied the effect of Gamma radiations on physiochemical properties of rice and found positive correlation between intensity of Gamma radiation and pasting properties of rice. Dobo et al. (2010) investigated European and US rice germplasm to detect polymorphism in the GBSS gene. They also found an association of the exon mutation (tyrosine/serine) with different levels of GBSS.

2.2.2 Pasting Properties

As amylose content is generally a key factor for determining rice grain quality. But, sometimes varieties within same amylose content class show different cooking and eating quality. Several methods have been developed to address this issue. Gelatinization temperature (GT) is one of the most common secondary tests, which can be estimated either directly or by alkali spreading value (Bhattacharya et al. 1980). However,

28 sometimes rice varieties with same amylose contents and gelatinization temperatures can still have different cooking and eating properties (Ayres et al. 1997; McClung et al. 1998 and Larkin et al. 2003). To study a wide range of factors controlling the pasting profile of rice starch, advanced techniques are in use to rapidly estimate the viscosity of rice flour during a cooking cycle (Juliano et al. 1964 and Bao et al. 1999). The rice starch viscosity profile is a statistical curve created from rice flour when it is provided with predefined heat profile in Rapid Visco Analyzer (RVA). In a typical RVA experiment, an accurately weighed flour of rice is mixed with a set amount of water and allowed to gelatinize by gradual heating to 95 °C. The gelatinized sample is then held at 95 °C for a defined period and then gradually cooled to 50 °C with constant stirring. The typical parameters estimated are the pasting temperature (the temperature at which initial velocity going to increase or at which rice starts to cook), peak viscosity (PV) is the highest viscosity of rice starch at maximum temperature (95 °C), retrogradation is the difference between pasting temperature and peak viscosity, peak time (PT) is the total time required to reach at the peak, trough is minimum viscosity when the sample was allowed to cool, break down is (BD) is the difference between maximum and minimum viscosity, final viscosity (FV) is the viscosity at maximum temperature and set back (SB) is the difference between final viscosity and minimum viscosity (Trough). All these parameters play a defined role to predict a clear image of the cooking and eating profile of rice grain (Bao et al. 1999). It has long been used as a tool to select desirable varieties in a breeding programme. Juliano et al. 1985 reported that starch viscosity is one of the components responsible for secondary differences among the varieties having same amylose content.

Different terms are used to describe RVA parameters. Dengate et al. (1984) defined different parameters of RVA profile as

Peak viscosity (PV) is maximum viscosity when paste of rice starch was heated at 95 °C.

Pasting temperature (PT) is the temperature at which initial velocity going to increase. They also listed this temperature at which rice starts to cook.

Paste stability is the degree of stability of rice starch with varying levels of temperature (4-95 °C)

Setback (SB) is the difference between final viscosity and trough. It also explains the texture of rice grain.

Champagne et al. (1999) explained RVA parameters as.

Pasting temperature, the temperature at which initial velocity increased.

Peak viscosity is the maximum viscosity when temperature exceeded from 4-95 °C following heating and holding cycles.

Peak time is the time required to reach the peak.

Trough is the minimum viscosity at holding temperature (70 °C).

Final viscosity is the end viscosity. It was also called amylography cool paste viscosity.

Break down reveal the difference of maximum and minimum viscosity.

They reported the difference of final viscosity and minimum viscosity as “Set back” (SB) Limpisut et al. (2002) studied the pasting profile of rice and linked the pasting temperature (PT) with increase in the initial viscosity of starch. They defined the peak viscosity (PV) as the highest viscosity at maximum temperature (95 °C), they reported the difference of maximum viscosity and final viscosity as break down (BD), they further revealed the setback (SB) as the variation of final and peak viscosity. Varavinit et al. (2002) studied three different types of rice flour gels with different amylose contents (28%, 18% and 5%). They reported that waxy rice varieties had highest peak viscosity. They also reported that the rice with 28% amylose content had highest setback (SB) value while lowest set back value was observed in waxy rice. Varavinit et al. (2003) characterized the amylose of rice starch into three different classes (low, intermediate and high) based on retrogradation. They also reported a significant correlation between breakdown and peak viscosity. They further discovered that breakdown could not be used

30 to differentiate the rice varieties with amylose content levels. The findings of Bao et al. (1999) linked the peak viscosity as the viscosity of rice starch just after gelatinization; the end viscosity at holding temperature gave hot paste viscosity; the end viscosity of the test was reported as cool paste viscosity; the difference of peak viscosity and hot paste gave the setback. They reported the variation between hot paste and cool paste viscosity as consistency viscosity. Watanbe et al. (2002) studied the starch characteristics of interspecific progenies developed from two different species (Oryza glaberrima & Oryza sativa). They reported a more significant effect of amylose content on starch viscosity than protein content of rice grain. Allahgholipour et al. (2006) concluded that paste viscosity parameters like peak viscosity (PV), breakdown (BD) and set back (SB) are important to predict the cooking quality of rice cultivars within same amylose content class. They observed strong correlation between amylose content and RVA pasting properties. The secondary differences among the varieties having same amylose content are the paste viscosity profiles (Juliano et al. 1985). Yan et al. (2005) reported the importance of rice starch profile as an index in the development of rice varieties with improved grain quality. Yan et al. (2005) reported a significant correlation of good qulity rice genotypes with breakdown (BD), setback (SB) and final viscosity (FV). Zulueta et al. (2000) studied the different retrogradation levels of rice starch and reported it as setback viscosity (SB).

The study of viscosity parameters at genetic level has so far received little attention. Bao et al. (2000) identified 20 QTLs for six RVA parameters. They reported that the inheritance of five pasting parameters (PV, PsT, BD, CV, & SB) was controlled by waxy locus on linkage group 6. The only waxy locus controlled 19-63% of the total phenotypic variation of the starch traits. Chen et al. (2004) studied waxy gene haplotypes and their relation with starch pasting properties using diverse rice germplasm. They further reported the linkage of waxy gene with starch properties and discovered a single nucleotide polymorphism (SNP) within an exon of waxy gene (10). They suggested that the single nucleotide polymorphism difference between the waxy gene explains a significant level of amylose content and RVA traits between two related rice varieties. The findings of Larkin et al. (2003) also reported a significant effect of waxy gene on

RVA traits. They also found a correlation of high amylose varieties with peak viscosity (PV) and set back (SB). Gravois et al. (1997) reported the effect of modifier genes along with waxy locus to control the inheritance of RVA traits. Bao et al. (2004) studied the effect of genotype × environment on rice starch properties and reported that variation at genetic level affected the amylose content, peak time, set back, and retrogradation. They further reported the effect of phenotypic variation on trough and peak viscosity. Bao et al. (1999) studied the effect of cytoplasmic inheritance and seed dormancy and found its direct effect on the pasting profile of indica rice. Zhu et al. (1994) worked to study the triploid endosperm and proposed a model to explain the cytoplasmic and maternal inheritance. Mo (1995) introduced another mating design with a statistical method to test the genetic effects of endosperm and maternal genotype. Bao et al. (1999) identified two QTLs qPKV-2 and qPKV-12 on chromosome 2 and 12 controlling the peak viscosity. They also indentified two QTLs for peak viscosity and final viscosity on chromosome 6. They identified Five QTLs for breakdown on different linkage groups (1, 5, 6, 7 & 8). Five QTLs for consistency viscosities were also detected on chromosomes 5, 7 and11. For setback viscosity, they reported four QTLs (qSBV-1, qSBV-5-1, qSBV-5-2, and qSBV- 6) on chromosomes 1, 2, 5 & 6.

2.2.3 Correlation among Pasting Properties

Juliano et al. (1980) found a positive correlation between peak viscosity and stickiness of cooked rice. They also observed a significant positive correlation between consistency viscosities and setback. They also found a negative correlation between hardness and stickiness. A positive correlation was observed between amylose content and water absorption and grain size expansion while cooking. Juliano et al. (1980) also reported a negative correlation of gel consistency (GC) with apparent amylose content. Limpisut et al. (2002) reported some RVA traits (Peak viscosity, setback, and pasting temperature) as significant predictable to detect the textural profile of cooked rice grain. Tan et al. (2002) observed a correlation between final viscosity and breakdown. They also reported a negative correlation between protein content and peak viscosity. Varavinit et al. (2003) linked the high level of amylose content with increased values of breakdown and peak viscosity. Glaszmann (1987) studied some rice varieties to explain the pasting profile.

They revealed a positive correlation of amylose content with hard gel consistency. They also revealed a negative correlation between amylose content and stickiness of cooked rice. Ramesh et al. (2000) reported a strong correlation between the amount of long-B chains and texture of cooked rice. Juliano et al. (1971) found that lower level of apparent amylose gave rise to high cohesiveness and glossiness of rice grain. The studies of Tan et al. (2000) revealed that amylose content determined the gel consistency and swelling volume. Bao et al. (2000) linked the setback, amylose content and peak viscosity with positive correlation. They also observed a negative correlation between setback and adhesiveness.

2.2.4 Protein Content

Rice is the cheapest source of dietary proteins for the people who consume it as a staple food. Protein and lipids are major components of rice grain after amylose and amylopectin (Ring, 1995). When compared with other cereals, rice protein had more balanced profile of amino acids (Juliano, 1985). Rice protein content (RPC) can also affect the physiochemical properties of rice (Hamaker et al. 1990; Juliano et al. 1993). Rice proteins are difficult to separate because they are insoluble in water (Juliano, 1984). Rice protein content varies from 4.9% to 16.5% depending upon the species (Lin et al. 1993). Therefore, improvement of rice protein content has increasingly become an important objective in a breeding programme.

Very few reports about the inheritance of protein content are available because of the additive effects and environmental factors. Lim et al. (1999) reported a negative correlation of protein content with paste viscosity and a positive correlation with pasting temperature. The results of Perez et al. (1996) revealed that protein content was affected by degree of milling, nitrogen fertilizer and growth duration. Song et al. (1988) studied the indica and japonica rice varieties with special reference to protein content and reported that high amylose content (indica rice) gave 2% more protein than lower amylose in japonica rice. Protein content of rice is quantitative in nature and controlled by complex inheritance (Kaul 1983; Kambayshi et al. 1984; Sood et al. 1986; Gupta et al. 1988; Shenoy et al. 1991; Shi et al. 1996). Many scientists have reported that waxy locus

33 is associated with protein content (Aluko et al. 2004; Hu et al. 2004). The findings of Tan et al. (2001) linked the waxy locus (Wx) to control the inheritance of protein content and color of rice flour. They also reported the effect of minor alleles on waxy locus. They found two QTLs for protein content, one with interval of C952Wx on chromosome 6 with significant phenotypic variation (18%), other were detected on chromosome 7 with interval of R1245-RM-234.They reported a unique allele in waxy gene linked with color of rice grain along with protein content and also suggested the waxy gene as a major locus associated with protein and color of rice grain. The modifications in the sequence of waxy gene (Wx) could be helpful to improve the rice protein content (Wang et al. 1995). Hu et al. (2004) found five QTLs for rice protein content with 74% of the total phenotypic variation. Among five QTLs, they found a major QTL (qRPC-5) on chromosome 5 with a phenotypic variation of 35%. They found another main QTL (qRPC-7) on linkage group 7 with total phenotypic variation of 23%. The remaining three QTLs were mapped on chromosome 1, 4 and 6 respectively with relatively small additive effects.

2.3 Materials and Methods

2.3.1 Genotypes and Experimental Design

An F5 population comprised of 213 lines derived from two Indica varieties, IR-64 and IR-132 was used in the present study. The F4 population and parents were planted under three different replications in randomized complete block design (RCBD) at the experimental station of International Rice Research Institute (IRRI) in dry season of 2011. Fertilizer was applied following the recommended rate (35.9 kg/ha of nitrogen- phosphorous-potassium (0-30-0), at the planting time, 59.6 kg/ha of urea was applied at tillering stage). Before planting the nursery to the field, herbicide was sprayed at the rate of 836 ml/ha. At harvest, all the lines in three replications were hand cut and threshed manually. The seeds were cleaned and allowed to dry at 12% moisture. The seeds of all the three replications were mixed to make biological replicates.

2.4 Grain Quality Assays

2.4.1 Dehulling, Milling and Grinding

A 20 g paddy of each line was weighed separately for dehulling, milling and grinding. Dehulling of paddy rice samples was practiced using laboratory sheller model no 3712. The dehulled samples were taken in to the hoppers following two passes for partially filled grains. The samples were polished or milled by using test tube milling. The milled rice packets were subjected to ground one by one using a 0.42 mm sieve using a cyclone sample mill model no 3010-018 UDY mill (UDY, Fort Collins, Colorado, USA).

2.4.2 Estimation of Apparent Amylose Content

The Apparent amylose content of the rice grains was estimated following the method given by Perez and Juliano (1978). (1) The grinded rice samples were weighed separately (100 mg) and added to 100 ml volumetric flask. (2) Each sample was mixed with ethanol (95%, 1 ml) and sodium hydroxide (1M, 9 ml). (3) The samples were allowed to gelatinize by keeping in boiling water bath for 10 minutes. (4) The gelatinized samples were allowed to cool for one hour and then mixed with distilled water with vigorous shaking. (5) Different standards were set against each of the sample using diverse rice varieties with varying levels of amylose contents (low, intermediate, high, very high). (6) A starch solution of 5 ml was mixed with iodine (0.2 g) and 2.0 g potassium in a volumetric flask and the volume was adjusted up to the mark using distilled water. (7) The samples were allowed to stand for 30 minutes after vigorous shaking. (8) The absorbance of starch solution containing iodine was detected at 620 nm using an auto analyzer 3 with software AACE version 5.24 (Bran and Luebbe). The starch contents were filled inside the sample cups of auto analyzer and allowed to run following a predefined protocol. The resulting curves were compared with standards curves, which were generated using the rice varieties with different amylose classes to estimate the amylose content of a sample at 620 nm. The results of amylose content were presented as percentage of milled rice.

2.4.3 Estimation of Pasting Properties

The paddy rice samples were dehulled, polished and grinded following the protocol mentioned earlier. The complete pasting profile of rice starch was estimated using an instrument called Rapid Visco Analyzer (RVA) with standard protocols defined by AACC (American Association of Cereal Chemists). The RVA instrument with following specifications (Thermocline Windows control and analysis software, Version 1.2, Newport Scientific, Sydney, Australia) was used. A rice flour of 3 g was weighed and added into a metallic cylinder with 25 ml of water (Juliano 1996). The initial temperature was raised from 4 °C to 50 °C for 1 min. The temperature was allowed to rise up to the 95 °C with gradual increase of 12 °C per min. The samples were cooled by lowering the temperature to 50 °C with a gradual decrease of 12 °C per min. A total time of 13 minutes was required to generate a viscosity profile of each sample. The pasting parameters obtained from rapid Visco units were taken as pasting temperature (PsT), starch retrogradation, peak viscosity (PV), trough, breakdown (BD), setback (SB), final viscosity (FV) and peak time. All the parameters were estimated using the rapid visco units (RVU). One rapid Visco unit was expressed as 1RVU=10cp.

2.4.4 Estimation of Protein Content

A classical kjeldahl method was used to predict the rice grain protein. A 2.5 g of rice flour was weighed and added to a sample tube. Digestion was performed using strong acid (sulfuric acid, 25 ml) with 2 kjeldahl tablets. Then the samples were allowed to steam distillate and were titrated back using sodium hydroxide to predict the rice grain protein. The values of nitrogen content of each sample were multiplied with a constant factor (5.95) to determine the protein content. Each sample was repeated three times and protein content was averaged over replications.

Figure-2.1 Brief sketch of all the steps from phenotyping to QTL mapping

2.5 STATISTICAL ANALYSIS

The phenotypic data was subjected to statistical analysis to check the significance of data by using windows operated statistical software R-crop stat.

2.6 Linkage Analysis

2.6.1 DNA Extraction

The green leaves were lyophilized at 45 °C for 24 hours. About 50 mg of dried leaf sample was cut into very small pieces with the help of scissor and then transferred in to a 2ml tube. The tubes containing two metallic beads inside were kept in liquid nitrogen and then vortexed in order to pulverize each sample completely. The contents of the pulverized samples were mixed with CTAB buffer (900 μl) and allowed to vortex again to eliminate the air bubbles. The tubes containing the samples were kept in water bath (65 °C) for one hour. After one hour, the samples were kept at room temperature for half an hour and then mixed with chloroform (600 μl) for precipitation. The tubes were shaken for five minutes with chloroform and then allowed to centrifuge for 10 min at 12,000rpm. The supernatant containing the DNA was taken carefully with the help of pipette and transferred into a duplicate tube of the same size. The contents of the tube were mixed with 1ml of chilled absolute isopropanol for DNA precipitation. The tubes were shaken carefully and allowed to keep at -20 °C for one hour. After one hour, the tubes were centrifuged at 13000 rpm for 12 minutes to get the DNA pellet. After centrifugation, the supernatant was removed by inverting the tubes carefully to protect the DNA pellet. Then the pellet was washed using 100% ethanol and gently shaken the tube. The tubes containing the 100% ethanol were kept at room temperature for 30-35 minutes and then allowed to refrigerate at -20 °C for the upcoming working day. In the next working day, the tubes were taken out from refrigerator and allowed to centrifuge at 15,000 rpm for 10 minutes. After centrifugation, the ethanol was carefully removed from each tube in order to save the pellet. The DNA pellet was mixed with TE buffer to make final dilutions after immediate drying at 30-40 °C. The samples were kept overnight for complete mixing of DNA pellet with buffer. For any reaction, the tubes were removed and vortexed for

38 complete mixing and then centrifuged shortly to remove the small liquid from top or walls of the tube. The contents of the tubes were shifted to new labeled tubes and allowed to store at 4 °C.

2.7 SNPs GENOTYPING

Single nucleotide polymorphism (SNPs) genotyping was done using Illumina BeadXpress with the help of golden gate assays. First of all, a parent survey was conducted using Indica-Indica 384 SNP chip. The 125 polymorphic markers out of 384 were surveyed on 210 plants of mapping population and results were used for linkage analysis to find the genetic basis of amylose content, pasting properties and protein content. The steps involved during the genotyping of population using SNPs markers are described below.

2.7.1 DNA Activation and Hybridization

The activation of DNA is very important and robust step which requires very small quantity of DNA (250 ng) with a concentration of 50 ng/μl. A total of 160 pg of DNA per SNP genotype call was used. The components used in hybridization step were paramagnetic particles with oligonucleotides and hybridization buffer. The genomic DNA already bounded to paramagnetic particles hybridized with oligonucleotides during hybridization process which occur before the amplification of DNA started in order to avoid any contamination. Continuous washing of the samples was required to remove the impurities (non-hybridized oligonucleotides) (Fig 2.2).

2.7.2 Oligonucleotides Designing and Ligation

Three different oligonucleotides were designed. Two for each of the SNPs locus called Allele-Specific Oligonucleotides (ASOs) and the third oligo was called Locus Specific Oligo (LSO) which hybridized many bases downstream to the position of SNPs locus. All the oligonucleotides contained the sites for universal PCR primers and complementary regions with DNA of interest. The Locus Specific Oligo had the capacity to target a specific bead type based on sequence identity.

2.7.3 Polymerase Chain Reaction (PCR) Cycle

Dye labeled universal PCR primers were used to carry out the PCR cycle. The primers were named as P1, P2 and P3. The unique address sequences of the primers were used to hybridize their specific beads after they gone through down streaming.

2.7.4 Hybridization to Array-metrix

The products were then hybridized on Bead Chip called “Array Metrix” and kept in a solution to separate the products in a solution. The whole technology is called “Illumina Golden Gate Genotyping Assay”.

2.7.5 Image Array-metrix

2.8 QTL Mapping

A rice linkage map using 125 SNPs markers evenly distributed over 12 rice chromosomes was used for QTL mapping. QTLs for amylose content, protein content and pasting properties were identified using both non regression and regression mapping approaches SMA & CIM (single marker analysis and composite interval mapping) using a mapping software, QTL cartographer 2.5 (Basten et al. 2002). The factors of interval mapping were estimated by following both of the regression procedure (forward and reverse). A window of 10 cM with likelihood ratio (computed after every 2 cM) was used was used. The permutation rate (1000) was used to set a threshold P value of P < 0.05 s for QTL detection. The likelihood ratio test was used to determine the phenotypic variance (R2) and LOD values and to set a hypothesis with dominance and additive effects (H3: H0). A LOD score ≥ 2.5 was used to detect the significance of a QTL.

Figure-2.2 Flow sheet image for SNPs genotyping using oligonucleotides specific assays on Ill umina BeadXpress (Golden Gate Assay)

2.9 Results and Discussions

2.9.1 Statistical Analysis of Phenotypic Data

2.9.1.1 Amylose Content

The two parents showed significant differences in amylose content although they belong to the same amylose content class (intermediate). The range of amylose content in mapping population was 19-25% (Figure 2.3). The parent IR-64 showed 19.5% amylose content while IR-132 showed 24.5% amylose content.

4440

4035 3530 3025

2520 P1 2015 IR-64 P1 P2 IR-64 IR-132 No of plantsNoof 1510 P2 IR-132 plantsNoof 105 50 0 18 18.718 18. 19.05 1919.9 19.9 20.5 20.5 21.1 21 21.8 21.8 22.4 22.4 23 23.0 23 2324.5 24.2 24 24.9 24.2 25.5 24.9 26 25.5 26 18 18.7 19.0 19.9 20.5 21.1 21.8 22.4 23 23.0 24 24.2 24.9 25.5 26 Amylose content % Amylose content %

Figure-2.3 Statistical distribution using histogram showing the distribution of amylose content in mapping population along with both parents IR-64 and IR-132. The X- axis showing the range of % age amylose content and Y-axis is showing the possible number of grouping in mapping population based on amylose content.

2.9.1.2 Correlation among Pasting Properties

A correlation (pair-wise) analysis was conducted to predict the relationship among pasting parameters of rice starch. The results are summarized in Table-2.1. Out of 45 pair-wise combinations of 10 parameters studied, a significant correlation between 11 pair-wise combinations was observed at 0.05 levels. Significant positive correlations were found between break down (BD) and peak viscosity (PV) (0.92), retrogradation (LO) and final viscosity (FV) (0.86), set back (SB) and lift off (LO) (0.66), set back (SB) and pasting temperature (PsT) (0.52), terminal viscosity (TV) and final viscosity (FV) (0.65) and terminal viscosity (TV) and peak viscosity (PV) (0.76). Negative correlations were observed among peak time (PT) and break down (BD) (-0.51), protein content (PC) and break down (BD) (-0.54), protein content (PC) and final viscosity (FV) (-0.52), set back (SB) and break down (BD) (-0.74) and set back (SB) and peak viscosity (PV) (- 0.63). The remaining 34 of the pair-wise correlations between two parameters did not reach the significant level (Table 2.1). The results showed that most of the important pasting parameters were interdependent to each other. The results were matched with Wang et al. (2007).

Table-2.1 correlation among starch pasting properties among each other and with amylose content (AC) and protein content (PC) * Significant at 0.05 level

Pasting Peak AAC BD FV LO temp temp Protein PV SB TV

AAC 1

BD 0.39 1

FV 0.00 0.24 1

LO -0.15 0.02 0.86* 1 Pasting temp -0.36 -0.29 0.38 0.44 1.00

Peak time -0.32 -0.51* 0.27 0.12 0.31 1.00

Protein -0.38 -0.54* -0.52* 0.12 0.12 0.29 1.00

PV 0.38 0.92* 0.46 0.09 -0.18 -0.23 -0.42 1.00

SB -0.39 -0.74* 0.40 0.66* 0.52* 0.47 0.42 -0.63* 1.00

TV 0.22 0.44 0.65* 0.16 0.07 0.32 -0.23 0.76* -0.22 1

132

- IR

132. -

64 and 64 IR -

- IR

Cluster diagram showing the division of mapping population samples based on pasting properties. There ar pasting on There based showing population Clusterproperties. division the samples diagram mapping of

2.4

two distinct groups corresponding to the both of parents IR parents of both the to distinct two corresponding groups Figure

2.9.1.3 Protein Content

Both parents (IR-64 & IR-132) and population showed significant differences for the protein content. The range of protein content in the population was 6.7-10%. The protein content of IR-64 was 9.5% and of IR-132 was 7%. The bimodal frequency distribution of protein content along with few plants as transgressive segregates showed the polygenic inheritance of protein content (Figure 2.5).

Figure-2.5 Frequency distribution using histogram showing the range of Protein content % age in mapping population along with both parents IR-64 (P1) and IR- 132 (P2).

Figure-2.6 Frequency distribution using histogram showing the range of final viscosity (FV) and break down (BD) in mapping population along with both parents IR-64 (P461 ) and IR-132 (P2).

Figure-2.7 statistical distributions of starch retrogradation (LO) and pasting temperature47 (PsT) of mapping population along with their parents IR-64 and IR-132.

Figure-2.8 statistical distributions of peak viscosity (PV) and set back (SB) of mapping48 population along with their parents IR-64 and IR-132

Figure-2.9 statistical distributions of trough and peak time (PT) of mapping population49 along with their parents IR-64 and IR-132 2.9.2 QTL Mapping

A total of 24 highly significant QTLs were detected (main effect-QTLs) for all the studied grain quality traits. These QTLs were mapped on 7 different linkage groups (1, 4, 7, 8,9,10 and11). The number of all the loci with location and all the statistical information are presented in Figure 2.12 & Table 2.2.

Table-2.2 Location and biometrical parameters of QTLs for amylose content (AC), protein content (PC) and pasting properties (RVA properties). The positive additive values represent the allelic contribution from parent 1 (IR-64) while the negative additive values show the contribution of parent 2 (IR-132)

Trait QTL Linkage Distance Marker Interval LOD Additiv R2

Group (cM) e

Amylose content qAC4 4 14 id4007105-id4002562 3.1 10.20 18 (AC) qAC4 4 48 id4011774-id4006135 5.9 14.60 14 qAC11 11 8.7 id118862-id11001469 6.5 -6.82 9 qPC1a 1 35 id1004348-id1008684 5.5 -7.75 24 Protein content (PC) qPC1b 1 14 id1022408-id1023892 6.8 -10.5 14 qPC8 8 35 id8003309-id8001854 5.8 -12.1 27 qPC10 10 18 id1000260-id1000553 6.5 17.5 10 qPC11 11 10 id1009687-id1101150 12 11.5 39 qPV7 7 8.0 id7000519-id7002859 6.5 14.4 10 Peak viscosity (PV) qPV9 9 47 id9002721-id9003720 5.5 50.5 12 qPV11 11 60 id1004398-id1100481 8.5 5.50 6 qBD1 1 16 id1023892-id1028304 3.2 4.55 8 Break down (BD) qBD4 4 80 id4002562-id4006135 5.5 -8.35 5 qFV1 1 12 id1014853-id1020828 5.5 22.2 14 Final viscosity (FV) qFV10 10 5.0 id1000162-id1000125 7.5 32.5 12 qSB10 10 60 id1000524-id1006963 3.0 -19.3 8 Set back (SB) qSB11 11 10 id1009687-id1011505 3.0 -22.4 29 qPT1a 1 57 id1000711-id1000727 4.5 24.2 18 Peak time (PT) qPT1 b 1 20 id1004348-id1008684 3.5 22.3 22 qPT1c 1 68 id1010526-id1010609 3.2 31.1 36 qPT8a 8 15 id8001477-id8002194 4.0 20.1 12 qPT8 b 8 10 id8001223-id8001477 5.0 15.2 14 qPT9 9 55 id9003720-id9004168 6.9 23.7 8 qPsT11a 11 5.0 id11000133-id110515 5.5 10.1 5 Pasting temp (PsT) qPsT11b 11 45 id1007488-id1100784 4.5 15.2 8 qLO11 11 80 id1004240-id1001464 5.0 27.1 12 Retro (Lift off) qLO1 1 18 id1014853-id1020828 6.0 21.6 14

2.9.3 QTLs for Amylose Content

The waxy locus on chromosome 6 has previously been reported to be associated with the phenotypic variation of amylose content. However, no locus for amylose content on chromosome 6 was detected. Three QTLs were detected for amylose content out of which two on chromosome 4 (qAM-4a & qAM-4b) and one on chromosome 11 (qAC-11). The QTLs detected on chromosome 4 explained a total of 32% phenotypic variance with a LOD score of 3.1 and 5.9 respectively (Table 2.2; Figure 2.12). Li et al. 2004 also reported a QTL associated with amylose content on chromosome 4 in rice.

2.9.4 QTLs for Protein Content

For protein content, five QTLs were detected, of which two on chromosomes 1 explaining a total of 38% of phenotypic variation (Table 2.2; Figure 2.12). Three QTLs were detected on each of chromosome 8, 10 and 11 with 27%, 10%, and 39% of phenotypic variance respectively (Table 2.2). Li et al. (2004) also reported a QTL for protein content on chromosome 8. They used back cross population using (Oryza sativa L.) and African rice (Oryza glaberrima). However there have no previous reports of other QTLs for protein content for other chromosomes. Two QTLs were detected on chromosome 1 where IR-132 alleles were seem to be associated to decrease in protein content with total phenotypic variation of 38%. It is of interest that among the both parents which were used to develop mapping population, IR-132 showed a decreased level for protein content when compared with IR-64.

2.9.5 QTLs for Pasting Properties

2.9.5.1 Peak Viscosity (PV)

The inheritance of peak viscosity was found to be controlled by three loci qPV-7, qPV-9 and qPV-11 on chromosomes 7, 9 and 11 explaining 10%, 12% and 6% phenotypic variance (R2) and LOD=6.5, LOD=5.5 and LOD=8.2 respectively (Table 2.2; Figure 2.12).

2.9.5.2 Break Down (BD) Two QTLs qBD-1 and qBD-4 were detected to be associated with the quantitative variation of break down on chromosome 1 and 4 showing 8% and 5% phenotypic variance and LOD=3.2 and LOD=5.5 respectively (Table 2.2;

Figure 2.12).

2.9.5.3 Final Viscosity (FV)

Two QTLs qFV-1 and qFV-10 were found to be associated with final viscosity on chromosomes 1 and 10 respectively. Phenotypic variance of both QTLs was 14% and 12% with LOD=5.5 and LOD=7.5 respectively (Table 2.2; Figure 2.12).

2.9.5.4 Set Back (SB)

Two QTLs qSB-10 and qSB-11 were detected for set back on chromosomes 10 and 11 with phenotypic variance of 8% and 29% with LOD=3.0 and LOD=3.0 respectively (Table 2.2; Figure 2.12).

2.9.5.5 Peak Time (PT)

Six QTLs were detected for peak time, out of which three on chromosome 1, two on chromosome 8 and one on chromosome 9 respectively. The phenotypic variance of six QTLs was 18%, 22%, 36%, 12%, 13% and 14% with LOD=4.5, LOD=3.5, LOD=3.2, LOD=4.0, LOD=5.0 and LOD=6.9 respectively (Table 2.2; Figure 2.12).

2.9.5.6 Pasting Temperature (PsT)

Two QTLs were found to be associated with the quantitative variation of pasting temperature on chromosome 11 with phenotypic variance of 5% and 8% with LOD=7.0 and LOD=5.5 respectively (Table 2.2; Figure 2.12).

2.9.5.7 Starch Retrogradation (LIFT-OFF)

Two loci for retrogradation were detected on chromosome 1 and 11 (qLO-1and qLO-11) with phenotypic variance of 12% and 14% and LOD=4.5 and LOD=5 respectively. On chromosome 1 and 11, IR-64 alleles were associated with increase in starch retrogradation 58% (Table 2.2; Figure 2.12).

Figure-2.10 (A) Pasting profile of two parents IR-64 and IR-132 tested by Rapid Visco Analyzer (RVA). (B) Parental survey done by IIIumina BeadXpress showing the single nucleotide polymorphism between the genomes of two parents. The brown color showing the polymorphic regions while yellow color showing monomorphic regions and red color represent missing SNPs between two parents.

Figure-2.11 Distribution of 125 SNPs markers on 12 rice chromosomes. A total of 384 markers were surveyed, out of which 125 were found polymorphic between two parents IR-64 and IR-132. These 125 were then surveyed on mapping population.

Figure-2.12 Rice chromosomes associated with amylose content, protein content and pasting properties represented with different symbols. The scale at left side of chromosome is showing the centi-Morgans (cM) distance. The major and minor QTLs positions for different traits are shown on right side of chromosomes in bold style where AC= amylose content, PC= protein content, PV= peak viscosity, BD= break down, FV= final viscosity, SB= set back, PT= peak time, PsT= pasting temperature and LO= lift off.

Figure 2.12 Continued……

2.9.6 Discussion

Rice endosperm is a triploid tissue resulting from the fusion of one sperm nucleus with two nuclei of the egg. The triploid nature of rice endosperm results in variation of its components both at genetic and phenotypic level (Aluko et al. 2004). Properties of starch mainly affect the cooking and eating behavior of rice as starch constitutes 90% of the dry weight of rice grain. Traits of rice grain quality have been the subject of interest for many scientists (He et al. 1999; Tan et al. 1999; Bao et al. 2000a; Lanceras et al. 2000; Septiningsih et al. 2003; Aluko et al. 2004). The objectives of present study were to make a comprehensive investigation about the genetic basis of amylose content, pasting properties and protein content. A segregating population (F5) was developed by crossing two parents IR-64 and IR-132. A QTL analysis was conducted using 125 SNPs markers distributed on all 12 rice chromosomes on a progeny of 213 plants. Both major and minor QTLs were detected for all the studied traits. A total of 24 main effect QTLs (M-QTLs) for different grain quality traits were identified and mapped on 7 different chromosomes (1, 4, 7, 8,9,10 &11). This study could be helpful to explore the potential of SNPs markers and to find the accurate location of different genes/QTLs associated with amylose content, viscosity parameters and protein content of rice grain. The possible applications of mapped QTLs include their utilization in screening of parents for introgression or pyramiding purpose.

2.9.6.1 Amylose Content

Inheritance of amylose content is mainly controlled by waxy locus on chromosome 6 and has been previously reported by many researchers (He et al. 1999; Tan et al. 1999; Bao et al. 2000a; Lanceras et al. 2000; Septiningsih et al. 2003; Aluko et al. 2004). However, the effect of waxy locus on other characteristics of starch is not completely understood. Tan et al. (1999) also linked the waxy locus to control the quantitative variation of gel consistency (GC) and alkali spreading value (ASV). The frequency distribution of amylose content indicated three distinct groups in population as IR-64 (P1), IR-132 (P2) and transgressive segregates (Figure 2.8). IR-64 group contained greater number of plants (78) than IR-132 (60). Some transgressive segregates were also observed (70) in the

58 population indicating the presence of modifying genes for amylose content. The uni- model distribution of amylose content (Figure 2.3) may be the result of a major gene along with its modifying alleles. Based on the uni-model frequency distribution of amylose content, the results of McKenzie et al. (1983) revealed that the inheritance of amylose content was controlled by a major gene. However they also reported the modifying gene action to be associated with the inheritance of amylose content. Three QTLs were detected for amylose content. Two QTLs were detected on chromosome 4 (qAC-4a & qAC-4b) and one (qAC-11) on chromosome 11 (Table 2.2; Figure 2.12). The alleles from parent 1 (IR-64) were found to be associated to increase the amylose content by 10 & 15% on chromosome 4 (Table 2.2). Similarly, the decreased effect of amylose content was found to be shared by the alleles from parent 2 (IR-132). Li et al. (2004) also reported a QTL associated with amylose content on chromosome 4. However, no effect of waxy locus (Wx) was detected to control the inheritance of amylose content because both of the parents (IR-64 & IR-132) belong to the same intermediate amylose class. The parent 1 (IR-64) could serve as one of the preferable parents to introgress the desirable grain quality traits into other rice varieties (with poor quality traits) using marker assisted selection techniques. We also surveyed gene specific markers for amylose content like RM-190 especially linked with the variation of amylose content in rice but it could not result polymorphism between IR-64 and IR-132 which showed the lack of segregation of waxy allele in the mapping population.

2.9.6.2 Protein Content

Rice protein is main source of dietary protein for the people who consume it as a staple food. Only few reports are available about the genetic basis of protein content because it is highly affected by environmental factors such as growth duration and nitrogen dose (Perez et al. 1996). The genetic complexity of protein content has led to the collapse in breeding efforts to improve the grain protein. Therefore, a detailed investigation about the genetic basis of rice protein would lead to improve the trait which in turn will improve the protein intake of rice consumers worldwide. Although differences in protein content between two parents (IR-64 & IR-132) were small, yet it revealed a wide range of variation along with transgressive segregates confirming its polygenic inheritance (Figure

2.5). Five QTLs were detected to be linked with the inheritance of protein content. Two were mapped on chromosome 1 (qPC-1a, qPC-1b) explaining a phenotypic variance of 24% and 14% and with LOD=5.5 and LOD=6.8 respectively (Table 2.2; Figure 2.12). At these loci, IR-132 alleles decreased the protein content by -7.7% & -10.5% respectively (Table 2.2). One locus (qPC-8) was detected on chromosome 8 explaining 27% phenotypic variance with LOD=5.8. IR-132 alleles decreased the protein content by -12.1 at this locus. Two QTLs (qPC-10 & qPC-11) were detected on chromosome 10 and 11 with phenotypic variance of 10% and 39% and LOD=6.5 & LOD=12 respectively (Table 2.2). IR-64 alleles increased the protein content by 17.5 & 11.5 respectively at both loci. These results showed that different haplotypes (Alleles of different genes) from different linkage groups interact with each other to control inheritance of protein content. We detected a major region on chromosome 11 explaining 39% of phenotypic variance and LOD=12. This region may contain major gene for protein content. A QTL for peak viscosity (PV) was also detected in the same locus which confirmed the allelic interaction of protein content with pasting properties (Tan et al. 2001). However, the allelic modifications at different loci may also improve the rice protein (Wang et al. 1995).

2.9.6.3 Pasting Properties

Final Viscosity (FV) & Break-Down (BD)

The frequency distribution of final viscosity and breakdown resulted a bell shaped curve along with transgressive segregates, confirming polygenic inheritance of both traits (Table 2.2; Figure 2.12). We detected two QTLs (qFV-1 & qFV-10) for final viscosity on chromosomes 1 and 10 with phenotypic variance of 14% & 12% and LOD=5.5 & 7.5 respectively. At both loci, IR-64 alleles increased the final viscosity by 22% & 32% respectively (Table 2.2). On chromosome 1, final viscosity was found to share the same locus with lift off (LO) or starch retrogradation and protein content (Table 2.2) which confirmed the allelic interaction of starch properties with each other and with protein content. Our results matched with Wang et al. (2007) who also reported the QTLs for final viscosity on chromosome 10.

For break down, two QTLs (qBD-1, qBD-4) were detected on chromosome 1 &4 with phenotypic variance of 8% and 5% and LOD=3.2 and 5.5 respectively. At chromosome 1, IR-64 alleles increased the break down by 4.5% while on chromosome 4, IR-132 alleles decrease the break down by -8.3 (Table 2.2). The break down locus was found in close association with amylose content on chromosome 4 which confirmed the role of this locus to control the starch pasting properties (Li et al. 2004).

Retrogradation (LIFT-OFF) & Pasting Temperature (PsT)

The frequency distribution of retrogradation and pasting temperature showed a bell shaped curve along with transgressive segregates, confirming polygenic inheritance of both traits (Table 2.2; Figure 2.12). Two QTLs were detected for retrogradation on chromosomes 1 & 11 with phenotypic variance of 14% & 12% and LOD=5 & 4.5 respectively (Table 2.2). The locus of retrogradation was linked with final viscosity. A strong positive correlation (0.86) between these two traits was also observed (Table 2.1) which showed that these two traits play a significant role to describe the pasting properties of rice. Two QTLs (qPsT-11a, qPsT-11b) were detected for pasting temperature on chromosome 11 with phenotypic variance of 5% & 8% and LOD=7.0 & LOD=5.5 respectively. IR-64 alleles increased the pasting temperature by 10% & 15% for both loci respectively (Table 2.2).The alleles at this locus may interact with other alleles with different chromosomes to control the inheritance of pasting properties. Bao et al. (2000a) reported some minor genes to on different chromosomes to control the inheritance of different viscosity parameters.

Peak Viscosity (PV) & Set Back (SB)

The uni-model phenotypic distribution with transgressive segregates of both traits showed polygenic inheritance (Table 2.2 & Figure 2.8). Three QTLs were detected for peak viscosity (qPV-7, qPV-9, and qPV-11) on chromosomes 7, 9 & 11 with phenotypic variation of 10%, 12% & 6% and LOD=6.5, LOD=5.5 & 8.5 respectively (Table- 2.2; Figure 2.8). Peak viscosity was significantly correlated with break down (Table 2.2) but we did not find any co- localization of these two traits in QTL mapping (Figure 2.12).

Wang et al. (2007) reported a QTL for peak viscosity on chromosome 7. A significant QTL for peak viscosity (PV) was detected on the chromosome 7.

Trough and Peak Time (PT)

The bell shaped frequency distribution curves of both traits confirmed the polygenic inheritance (Figure 2.9). Six QTLs were detected for peak time (qPT-1a, qPT-1b, qPT- 1c, qPT-8a, qPT-8b & qPT-9), three were detected on chromosome 1, two were detected on chromosome 8 and one on chromosome 9 respectively. All these six QTLs explained a phenotypic variance 29%, 18%, 22%, 36%, 12% & 14% with LOD=4.5, LOD=3.5, LOD=3.2, LOD=3.0, LOD=2.5 & LOD=3.0 (Table 2.2; Figure 2.12). No QTL was detected for trough. The protein locus on chromosome 1 was found in close association with the locus of peak time although significant correlation was not found between these two traits (Table 2.1). The co-localization of both traits confirmed the role of this locus to control the inheritance of protein content and peak time (Bao et al. 2000). Wang et al. (2007) studied the pasting properties of rice using RILs (recombinant inbred lines) developed from Zhenshan-97 and Delong-208 and reported that the inheritance of pasting properties was mostly governed by the genes on chromosome 1, 6, 7, 9 and 10. We also detected about 15 QTLs on these chromosomes except the chromosome 6. Gravois and Webb (1997) reported the waxy locus (Wx) to be associated with the quantitative variation of different viscosity parameters like peak viscosity, break down and pasting temperature. Break down viscosities (BD) and setback viscosities are found to be significantly correlated with overall grain quality of rice (Shu et al. 1998; Larkin and Park 2003). The rice genotypes with high breakdown viscosity and low setback viscosity have been reported to have good eating quality (Shu et al. 1998).

Eighteen genes (starch synthesis related genes, SSRG) are involved in different steps of starch synthesis in rice (Tian et al. 2009). The genetic effect of waxy locus along with its modifier genes to control the phenotypic variation of starch related properties is still not clear. Any knowledge about the genetic basis of grain quality traits will be helpful for rice breeders. The major and minor QTLs detected for grain quality traits in the present study could be used to accelerate the breeding efforts to develop new rice varieties with

62 higher yield and improved quality. The small-scale manipulation in starch synthesis related genes (SSRG) cannot attain the grain quality improvement in rice. Moreover, the interactions among SSRGs are not clear and we need more advanced populations (NILs, RILs and transgenic lines) to disclose the functions of SSRGs in relation to starch properties.

Therefore on the basis of the current study, the following questions should be addressed in the future. Firstly, if the QTLs on chromosome 1, 4 and 11 contain starch synthesis related genes (SSRGs), participating in starch biosynthesis, the mechanisms by which they affect the starch and thereby the eating and cooking quality, need to be established. Secondly, the other QTLs with large effects, such as the loci on chromosome 1, 8 and 9, might encode the other enzymes participating in starch biosynthesis, which need to be identified. Lastly, because some of the viscosity parameters are interrelated with each other, the molecular and biochemical mechanism for the correlation of these traits need to be studied. Understanding the genetic mechanism of cooking and eating profile of rice starch will be improved by addressing these questions. Generally, few parameters that were significantly correlated with each other shared identical QTLs, but it was not true in some cases indicating that correlation could not be derived from genetic linkage. However the QTLs for most of the RVA properties are quite different from previous studies indicating that the genetic behavior of RVA profile is complex. For instance, FV (Final Viscosity) was significantly correlated with most of the RVA parameters, but we identified only two QTLs for FV with chromosomes 1 and 10. Similarly, Peak Viscosity was significantly correlated with BD, but no common QTL was found for them. Thus comprehensive understanding about the genetic mechanism behind the pasting properties, amylose content and protein content can aid in screening early in breeding programme and also help to identify the genes related with starch biosynthesis.

2.9.7 Summary

A segregating rice population comprising of 213 plants was developed from two parents IR-64 (a famous rice cultivar) and an upland rice cultivar IR-132, a high yielding rice variety with poor grain quality traits. A rice linkage map was constructed using 125 SNPs markers. The population was evaluated against eight RVA parameters, protein content and amylose content. We identified both major and minor QTLs for all the said traits. A correlation (pair-wise) analysis was conducted to predict the relationship among pasting parameters of rice starch (Table-2.1). Out of 45 pair-wise combinations of 10 parameters studied, we found significant correlation between 11 pair-wise combinations at 0.05 levels. Significant positive correlations were found between break down (BD) and peak viscosity (PV) (0.92), retrogradation (LO) and final viscosity (FV) (0.86), set back (SB) and lift off (LO) (0.66), set back (SB) and pasting temperature (PsT) (0.52), terminal viscosity (TV) and final viscosity (FV) (0.65) and terminal viscosity (TV) and peak viscosity (PV) (0.76). We observed negative correlations among peak time (PT) and break down (BD) (-0.51), protein content (PC) and break down (BD) (-0.54), protein content (PC) and final viscosity (FV) (-0.52), set back (SB) and break down (BD) (-0.74) and set back (SB) and peak viscosity (PV) (-0.63). The remaining 34 of the pair-wise correlations between two parameters did not reach the significant level (Table 2.1). The results showed that most of the important pasting parameters were interdependent to each other.

We detected both major and minor QTLs for amylose content, protein content and pasting properties. For amylose content, three QTLs were detected out of which two on chromosome 4 (qAM-4a & qAM-4b) and one on chromosome 11 (qAC-11). The locus on chromosome 4 explained a total of 32% phenotypic variance with a LOD score of 3.1 and 5.9 respectively (Table 2.2; Figure 2.12). For protein content, five QTLs were detected, of which two on chromosomes 1 explaining a total of 38% of phenotypic variation (Table 2.2; Figure 2.12). Three QTLs were detected on each of chromosome 8, 10 and 11 with 27%, 10%, and 39% of phenotypic variance respectively (Table 2.2). Three QTLs were detected for peak viscosity (qPV-7, qPV-9 and qPV-11) on chromosomes 7, 9 and 11 explaining 10%, 12% and 6% phenotypic variance (R2) respectively. Two QTLs qBD-1

64 and qBD-4 were detected to be associated with the quantitative variation of break down on chromosome 1 and 4 showing 8% and 5% phenotypic variance (R2) respectively (Table 2.2; Figure 2.12). Two QTLs qFV-1 and qFV-10 were detected for final viscosity on chromosomes 1 and 10 respectively. Phenotypic variance of both QTLs was 14% and 12% respectively. For set back, two QTLs qSB-10 and qSB-11 were detected on chromosomes 10 and 11 with phenotypic variance (R2) of 8% and 29% respectively. Six QTLs were detected for peak time, out of which three on chromosome 1, two on chromosome 8 and one on chromosome 9 respectively. The phenotypic variance (R2) of six QTLs was 18%, 22%, 36%, 12%, 13% and 14% respectively. Two QTLs were detected for pasting temperature on chromosome 11 explaining 5% and 8% phenotypic variance (R2) respectively. For retrogradation, two QTLs were detected on chromosome 1 and 11 (qLO-1and qLO-11) with phenotypic variance of 12% and 14% respectively (Table 2.2; Figure 2.12). In summary, the alleles from IR-64 provided a small but statistically significant improvement for the components of grain quality based on starch properties and protein content. The alleles from IR-64 parent could be used to improve the pasting profile and biochemical components of high yielding rice cultivars.

2.9.8 Implications of Study

Our findings have clear objectives to implement in rice breeding programme to improve the grain quality. The close availability of SNPs markers with all the mapped traits could be used to replace the alleles of poor grain quality with the desired one using the techniques of marker-assisted selection. The possible applications of mapped QTLs include their utilization in screening of parents for introgression or pyramiding purpose. The other uses include selective genotyping which is one of the most effective approaches and can be adapted for studying the grain quality traits. Selective genotyping reduces the costs involved in genotyping the large populations without affecting the power to detect QTLs. The progress in QTL mapping for pasting properties will also help to identify the genes related with starch biosynthesis.

CAHAPTER 3

GENETIC STRUCTURE AND ASSOCIATION MAPPING OF STARCH CHROMATOGRAPHY IN RICE (ORYZA SATIVA L.) USING SINGLE NUCLEOTIDE POLYMORPHISMS (SNPs) MARKERS

Abstract

Grain quality of rice is an important component to predict its market worth. Chemical characteristics of rice grain are considered more important because of its consumption as a whole grain. Rice starch has many applications in food industry due to its excellent characteristics and neutral taste. Starch structure defines cooking and eating properties of rice. The developments in mapping techniques have provided useful tools for dissecting and investigating the molecular basis of complex traits. The triploid endosperm of rice grain along with maternal inheritance and strong epistatic effects leads to complex inheritance. Genome wide association scans (GWAS) is a method of choice for identifying the genomic regions associated with the quantitative variation of complex phenotypic traits. In this study, 754 genome wide single nucleotide polymorphisms (SNPs) based markers were applied to study the patterns of linkage disequilibrium (LD) and structure of population among seventy-five diverse rice genotypes (indica, temperate japonica & tropical japonica). All the seventy-five accessions were divided into three major groups based on structure analysis (model based). The three groups represented three different geographic regions. For the 75 genotypes, the complex traits like amylose content, gelatinization temperature, amylose long chains, amylose short chains, amylopectin long chains, and amylopectin short chains were studied. The associations of SNPs markers with a phenotypic trait were disclosed by using the approach of GLM (general linear model). We examined variation both within and among three subgroups revealing significant heterogeneity. A total of 59 association signals were detected. Most of the SNPs were found in the regions where loci for different grain quality traits have already been reported. From the results, we found that waxy locus not only affects amylose content and GC but also regulates starch branching patterns in rice. The study will help to provide a way to find out valuable genes and alleles associated with starch structure for grain quality improvement in rice.

3.1 Introduction

Millions of people around the world depend on rice as a staple food. Its grain quality is primary focus of consumers and has considered very important during breeding programme (Tian et al. 2009). Traits of grain quality predict its market worth and share a vital role in the selection of new cultivars in a breeding programme (Fitzgerald et al. 2008; Ge et al. 2005). Rice starch shares about 90% of its weight. Amylose & amylopectin constitute the rice starch. Starch is an integrator of overall metabolic response in plants (Sulpice et al. 2009). Cooking and eating quality traits have long been selected by plant breeders in different geographic regions of the world (Tian et al. 2009; Fitzgerald et al. 2009).

3.1.1 Inheritance of Grain Quality

The complex inheritance of grain quality traits in cereals have been reported by different scientists (He et al. 1999; Pooni et al. 1993). The quantitative variation of starch in rice endosperm has been associated mainly with the waxy locus (Wx) on linkage group six (Aluko et al. 2004; Fan et al. 2005; Tian et al. 2005; Septiningsih et al. 2003; Ge et al. 2005). The waxy gene directs the synthesis of GBSS (granule bound starch synthase) which controls the synthesis of amylose of rice starch whereas amylopectin is the product of starch synthase and starch de-branching enzyme (Smith et al. 1997). The cooking and eating profile of rice grain is mainly determined by three grain quality parameters (Amylose content (AC), gelatinization temperature (GT) & gel consistency (GC) (Juliano 1985; Cagampang et al. 1973; Little et al. 1958). Many studies reported the effect of Wx region to govern the quantitative variation of amylose & gel consistency (Bao et al. 2000a; Lanceras et al. 2000; Tian et al. 2005; Fan et al. 2005). However the inheritance patterns of waxy locus to explain the starch properties are less clear which may lead to the failure of breeding efforts. Therefore elucidation of genetic basis of starch structure with detailed and comprehensive understanding of chemical basis of the target trait would be of great help to develop elite rice varieties with improved grain quality.

3.1.2 Single Nucleotide Polymorphisms (SNPs)

SNPs are frequent way of genetic polymorphism differentiating two individuals on single base pair at defined locus. SNPs have potential advantage over SSR to greatly increase the precise speed and to reduce the cost of genotyping (McCouch et al. 2010). These markers have routinely been applied in genetic mapping and linkage studies in different crops (Buckler et al. 2009; McMullen et al. 2009; Waugh et al. 2009; Moragues et al. 2010). High degree of polymorphism, stability during evolution and low mutation rate make SNPs as markers of choice to study complex traits in plants (Gupta et al. 2001; Syvanen et al. 2001; Jorde et al. 2000). Based on the resolution, SNPs assays with different number of markers capacity (384-1million) have been designed to associate the genotype and phenotype in rice (Tung et al. 2010; McCouch et al. 2010; Huang et al. 2010).

3.1.3 Association Mapping for Complex Traits

Genome wide association studies (GWAS) is an advanced mapping technique to associate the plant functional genomics with phenomics (Remington et al. 2001; Stich et al. 2005). It also leads to new ways to highlight novel genes in different plants. The availability of reference genome and self-fertilization makes rice suitable for GWAS studies (IRGSP, 2005). GWAS has been preferred to conventional genetic mapping or QTL mapping (Jannink et al. 2002). In rice, once the pure lines (homozygous lines) established and confirmed genetically, the genetic information can be used again & again in different phenotypes and environments (Zhao et al. 2011). Understanding the linkage disequilibrium (LD) patterns of target population along with detailed structure of population is mandatory association mapping analysis because they can reduce the chances of error between a marker and trait of interest among inbreeding species (Goldstein et al. 2009; Flint et al. 2005; Yu et al. 2006; Agrama et al. 2008).

3.1.4 Size Exclusion Chromatography (SEC)

Size exclusion chromatography (SEC) is one of the advanced methods which can separate large molecules of starch in solution by size or their hydrodynamic volume. SEC provides both qualitative information on the molecular weight distribution of the starch chains and exact quantitative estimation of amylose and amylopectin. In classical methods, amylose is usually quantified by absorbance of amylose iodine complex (Shanthy et al. 1980). The amylopectin iodine complex may contribute to absorb readings at certain wavelengths and therefore can be a major source of error especially when it is not calibrated. Therefore SEC helps to give clear image of the starch molecular size to disclose the genetic inheritance of its components in detail.

The objectives of this study were find the genetic basis of starch chain lengths distribution, amylose content and gel consistency along with complete information about the population or genetic structure of all the accessions used in the study.

3.2 Materials and Methods

3.2.1 Selection of plant Materials and Field Experiments

Seventy five rice accessions belonging to different rice growing regions of Asia were selected for SNPs genotyping and phenotypic characterization. The field trials were conducted at NIBGE (National Institute for Biotechnology & Genetic Engineering). Twenty plants were planted in a single line with plant-plant distance of 10 inches and 12 inch distance between two rows. Recommended field and agronomic practices were applied. At the maturity, all the lines were separately harvested and seeds of each line were kept in separate envelops to avoid any mixing. Seeds of each line were transferred to International Rice Research Institute (IRRI) for phenotyping by signing standards material transfer agreement (SMTA).

3.2.2 DNA Extraction

The green leaves were lyophilized at 45 °C for 24 hours. About 50 mg of dried leaf sample was cut into very small pieces with the help of scissor and then transferred in to a 2 ml tube. The tubes containing two metallic beads inside were kept in liquid nitrogen and then vortexed in order to pulverize each sample completely. The contents of the pulverized samples were mixed with CTAB buffer (900 μl) and allowed to vortex again to eliminate the air bubbles. The tubes containing the samples were kept in water bath (65 °C) for one hour. After one hour, the samples were kept at room temperature for half an hour and then mixed with chloroform (600 μl) for precipitation. The tubes were shaken for five minutes with chloroform and then allowed to centrifuge for 10 min at 12,000 rpm. The supernatant containing the DNA was taken carefully with the help of pippet and transferred into a duplicate tube of the same size. The contents of the tube were mixed with 1ml of chilled absolute iso-propanol for DNA precipitation. The tubes were shaken carefully and allowed to keep at -20 °C for one hour. After one hour, the tubes were centrifuged at 13000 rpm for 12 minutes to get the DNA pellet. After centrifugation, the supernatant was removed by inverting the tubes carefully to protect the DNA pellet. Then the pellet was washed using 100% ethanol and gently shaken the tube. The tubes containing the 100% ethanol were kept at room temperature for 30-35 minutes and then allowed to refrigerate at -20 °C for the upcoming working day. In the next working day, the tubes were taken out from refrigerator and allowed to centrifuge at 15,000 rpm for 10 minutes. After centrifugation, the ethanol was carefully removed from each tube in order to save the pellet. The DNA pellet was mixed with TE buffer to make final dilutions after immediate drying at 30-40 °C. The samples were kept overnight for complete mixing of DNA pellet with buffer. For any reaction, the tubes were removed and vortexed for complete mixing and then centrifuged shortly to remove the small liquid from top or walls of the tube. The contents of the tubes were shifted to new labeled tubes and allowed to store at 4 °C.

3.2.3 Size Exclusion Chromatograph using High Pressure Liquid Chromatography (HPLC)

SEC of diverse rice accessions was done at rice grain quality lab, IRRI, Philippines. Paddy of each sample was dehulled (THU35A 250V50Hz Test Husker, Satake), milled (McGill No.2 Mill) and sub-samples were ground separately through a 0.40 mm screen by using a cyclone sample mill model no 3010-018 UDY mill (UDY, Fort Collins, Colorado, USA). Flour (50 mg) was solubilised by dispersing with ethanol (500 L), gelatinizing with sodium hydroxide (0.25 M, 2 ml), and heating for 10 min at 130 C°. The ethanol was allowed to evaporate and the weight of the solution was set to 4 g using double distilled water. An aliquot with 794 μL of this solution along with sodium acetate solution (0.05 M, pH 4, 206 μL) and glacial acetic acid (6 μL). Starch of the solution was de-branched with isoamylase (7 μl, 250 Uml-1) and the mixture incubated at 50 C° for 2 hours. For SEC, the de-branched mixture was boiled, then desalted (1 h) with mixed bed resin (Biorad, 1mg), and centrifuged (12 500 rpm, 10 min). The supernatant containing de-branched starch was collected for analysis. Chromatography of de-branched starch solution and the standards was performed on a Waters system consisting of an Alliance (2695) and differential refractive index detector (Waters 2410) with Waters software (Empower®) to direct the pump, and to obtain and process data. The fluent was ammonium acetate (0.05 M, pH 5.2) flowing at 0.5 ml min-1. Separation columns packed with a hydroxylated poly (methyl metha-crylate) based ultra-hydro-gel 250 (UH 250, from Waters), was used, and the column was held at 60 C°. The column was calibrated using universal calibration, with Mark-Houwink parameters for linear chains of starch (Castro, Ward, Gilbert & Fitzgerald, 2005). Volume of injection was 40 μl with 35 minutes of total run time. The standards were used to predict the elusion time of both starch components (Amylose & Amylopectin estimation). The linear component of starch was separated with the help of a calibration curve created by the UH 250.5 column using standards and universal calibration. The normalized detector response with starch degree of polymerization with all the accessions is presented in Fig.3.4.

3.2.4 Estimation of Gel Consistency (GC)

Gel consistency was estimated following the method of Cagampang et al. (1973) in triplicates. About 100 mg flour was taken in a culture tube (110 mm) and 0.2 ml of ethanol (95%) along with thyml blue (0.025%) was added to avoid clumping of rice starch. The contents were mixed with KOH (1 ml, 0.2 N) and shaken for homogenous mixing. The samples were allowed to boil for 8 minutes at 100 °C. After boiling, tubes were kept at room temperature for 10 minutes and then exposed to ice for 30 minutes. After one hour, the gel length was estimated by placing the tubes in horizontal position on a table surface. The length of gel provided an estimate of gel consistency. The longer distance was correlated with soft gel consistency.

3.2.5 SNPs Genotyping

Illumina BeadXpress was used to genotype the samples using SNPs markers (384 SNPs) by following the under given steps.

3.2.5.1 DNA Activation and Hybridization

3.2.5.2 Oligonucleotides Designing and Ligation

72 the oligonucleotides contained the sites for universal PCR primers and complementary regions with DNA of interest. The Locus Specific Oligo had the capacity to target a specific bead type based on sequence identity.

3.2.5.3 Polymerase Chain Reaction (PCR) Cycle

3.2.5.4 Hybridization to Array-metrix

3.2.5.5 Image Array-metrix

Fluorescent signals were detected using Bead Array Reader on Sentrix Array Metrix. The signals were then analyzed using the software Alchemy for calling, clustering and automated genotyping. The samples were genotyped by using two plates, Indica-Indica & Indica-Japonica. A total of 768 SNPs markers were surveyed on 75 accessions. The genotypic data of both plates (Indica-Indica & Indica-Japonica) was scored using genotyping software (Illumina Genome Studio Illumina Genome Studio). The status of SNPs alleles was predicted using ALCHEMY software which was designed to have accurate performance with small scale populations and pure lines (inbred lines) with less heterogeneity (Wright et al. 2010). The markers with lower frequency (less than 90%) were not selected. Out of 768, 754 markers were used for mapping analysis.

3.3 Association Mapping

3.3.1 Estimation of Population Structure

The selected 754 SNPs markers distributed on 12 linkage groups were used to estimate population structure of 75 varieties. A software (STRUCTURE 2.3.3) was used for structure analysis using predefined numbers of subpopulations (K) ranging from 1 to 10. Ten runs were completed for each K with 100,000 interactions following a burn-in period of 20,000 interactions to set the optimum number of subpopulations and interrelations among accessions. At K=3, 75 varieties were divided into three distinct subgroups. The population structure Q-Metrix was also estimated at K=3. Principal component analysis (PCA) was performed which divided the genotypes in to three subpopulations on the basis of phenotypic and genotypic data.

3.3.2 Estimation of Linkage Disequilibrium (LD)

Genome wide association analyses on the basis of linkage disequilibrium (LD) between pairs of locus were performed with accurate population structure using the software TASSEL (http://www. maizegenetics.net). The rate of linkage disequilibrium (LD) was estimated using R2 which determined the correlation between two phenotypic variables (Liu et al. 2004). GLM approach (general linear model) designed by Yu et al. (2006) was used to run all the association mapping tests.

Table-3.1 Rice cultivars used in the study with their country of origin

NO ACCESSION COUNTRY NO ACCESSION COUNTRY

1 IRBL12-M Philippines 41 DR-58 Pakistan 2 IRBLk-Ka Philippines 42 IR-64 Philippines 3 IRB LK S-F5 Philippines 43 PB-1 India 4 IRBLZ5-CA Philippines 44 Azucena Philippines 5 IR 79906-B-192-2-3 Philippines 45 SALUMPIKIT Philippines 6 PSBRC80 Philippines 46 IRBL11-Zh Philippines 7 IRBLt-K59 Philippines 47 IRBL19-A Philippines 8 IRBL9-W Philippines 48 IRBLkh-K3 Philippines 9 IRBLi-F5 Philippines 49 IRBLkm-Ts Philippines 10 IRBLz-Fu Philippines 50 IRBLsh-B Philippines 11 Brown Gora India 51 IRBLzt-T Philippines 12 IR 74371 -54-1-1 Philippines 52 KATARI BHOG Philippines 13 Vandana India 53 Supri Pakistan 14 IR 71525-19-1-1 Philippines 54 IR 78878-53-2-2-4 Philippines 15 IRBB 57 Philippines 55 BPI 76 India 16 Super fine Pakistan 56 IR43450 SKN-506-2-2 Philippines 17 IR55419-04 Philippines 57 IRB La-A Philippines 18 UPLRI7 India 58 NIAB-IR-9 Pakistan 19 IR 78908-263-2-2-3 Philippines 59 IR 74371-3-1-1 Philippines 20 IR 74371-46-1-1 Philippines 60 IR 77080-B-4-2-2 Philippines 21 IR 80021 -B-86-3-4 Philippines 61 Basmati-1121 India 22 IR 78875-131-B-1-4 Philippines 62 B-pak Pakistan 23 IR 78877-181 -B-1-2 Philippines 63 Shaheen Pakistan 24 Way Rarem Philippines 64 DR-82 Pakistan 25 KSK-133 Pakistan 65 IRBLa-C Philippines 26 KS-282 Pakistan 66 TKM-6 Thailand 27 IR-72 Philippines 67 B-198 Pakistan 28 DR-92 Pakistan 68 Basmati-2000 Pakistan 29 Sufaid 86 Pakistan 69 B-370 Pakistan 30 B.386 Pakistan 70 B-385 Pakistan 31 Supra Pakistan 71 98316 India 32 APO Philippines 72 99417 India 33 B 6144 F-MR-6-0-0 Philippines 73 99512 India 34 IR 72667-18-1-B-B-3 Philippines 74 Basmati-515 Pakistan 35 IR 74963-262-5-1-3-3 Philippines 75 Super Basmati Pakistan 36 IR 78875-131-B-1-1 Philippines 37 KCD-1 Philippines 38 IR 78878-53-2-2-2 Philippines 39 IR 64683-87-2-2-3-3 Philippines 40 IR-6 Philippines

3.4 Results and Discussions

3.4.1 Correlation among Pasting Properties Correlatins of amylose with its long and short chains were very strong. The amylose content (AC) ranged from 6-27%, gel consistency (GC) 30-78 mm, AM long 5-18%, AM short 3-12%, AP long 19-26% and AP short chains ranged from 50-62%. The 75 acccessions had a mean amylose content (AC) of 19.43%, a mean gel consistency (GC) of 55.26mm, a mean amylose long chains (AM long) 11.48, a mean amylose short chains (AM short) of 7.94, a mean amylopectin long chains (AP long) of 22.79 and a mean amylopectin short chains (AP short) of 57.96. The majority of indica varities and all aromatic cultivars showed intermediate amylose conten and soft gel consistency. All the japonica varities showed low amylose content and high gel consistency. A negative correlation between gel consistency and amylopectin long chains was observed (-0.186) (Fig.3.2, Table 3.2). Amylose long chains had negative correlation with both long and short chains of amylopectin (-0.525) & (-0.712). Amylose short chains had also negative correlation with amylopectin long and short chains. A strong positive correlation was observed between amylose long chains and amylose short chains (0.937). Amylose content had negative correlation with amylopectin long and short chains (-0.546), (- 0.803). Both long and shot chains of amylopectin showed negative correlation with amylose long and short chains. Gel consistency revealed positive correlation with amylose short chains but it was not significant.

Table-3.2 correlation among amylose content, gel consistency and starch chains

* Significant at the 0.01, **Significant at the 0.05, *** Significant at 0.001

Variables Amylose GC AM long AM short AP long AP short Amylose 1 0.067 0.937** 0.909** -0.546* -0.803** GC 0.067 1 0.057 0.079 -0.186 0.055 AM long 0.937*** 0.057 1 0.720** -0.525* -0.712** AM short 0.999*** 0.079 0.720** 1 -0.473** -0.778** AP long -0.546** -0.186 -0.525** -0.473* 1 0.043 AP short -0.803** 0.055 -0.712** -0.778** 0.043 1

Amylose GC AM short AM long AP short AP long 25 30 30 30 30 30 20 25 25 25 25 25 15 20 20 20 20 20

10 15 15 15 15 15 Amylose Amylose 5 10 10 10 10 10 0 5 5 5 5 5 0 20 30 80 130 0 10 20 0 10 15 25 50

15 90 90 90 90 90 10

70 70 70 70 70 GC GC 50 5 50 50 50 50

30 0 30 30 30 30 0 20 30 80 130 0 10 20 0 10 15 25 50

20 20 25 20 20 20 15 15 20 15 15 15 15 10 10 10 10 10

10 AMshort AMshort 5 5 5 5 5 5 0 0 0 0 0 0 0 20 30 80 130 0 10 20 0 10 15 25 50

14 14 14 20 14 14 12 12 12 12 12 15 10 10 10 10 10 8 8 8 10 8 8

6 6 6 6 6 AMlong AMlong 5 4 4 4 4 4 2 2 2 0 2 2 0 20 30 80 130 0 10 20 0 10 15 25 50

30 30 30 30 25 30 20 25 25 25 25 25 15 10

20 20 20 20 20

AP short AP short AP 5 15 15 15 15 0 15 0 20 30 80 130 0 10 20 0 10 15 25 50 75 75 75 75 75 20 70 70 70 70 70 15 65 65 65 65 65 10

60 60 60 60 60

AP longAP longAP 55 55 55 55 55 5 50 50 50 50 50 0 0 20 30 80 130 0 10 20 0 10 15 25 50 Amylose GC AM short AM long AP short AP long

Figure-3.1 Relationship of starch chains length components with amylose content (AC) and gel consistency (GC), the lines with right direction showing highly positive correlation while the lines with left direction showing negative correlation between two traits. There is not a significant correlation between two traits with straight line.

The are accessions

Medium Medium

83mm. -

Soft

Hard

Medium

Hard

Distribution of gel consistency (GC) among diverse germplasm.rice The of range gel consistency was

3.2 3.2

- Medium soft Medium

78 divided into five classed on the basis of GC as medium soft, hard, medium hard, soft and medium. and medium soft as hard, medium GC of classed basis the on hard, soft, into divided five Figure

High Intermediate Low Waxy D

B Detector (DR)Detector response

Degree of Polymerization (DP)

Figure-3.3 Normalized SEC curves of seventy five diverse rice accessions studied by using high performance liquid chromatography (HPLC) representing different classes of amylose; the curves are colored according to amylose class. Arrows represent different types of starch chains, A= amylose long chains, B= amylose short chains, C= amylopectin long chains and D= amylopectin short chains

3.4.2 Population Structure

All the SNPs markers were highly polymorphic and produced 1505 alleles across 12 chromosomes with all 75 rice accessions. An analysis of population structure using the programme STRUCTURE identified the most suitable grouping among accessions. There were some admixed accessions showing complex history of evolution. At K=2, the varieties were grouped into 2 main subgroups indica and japonica with varying degree of partial ancestry. The varieties were divided in to three subgroups or subpopulations at K=3 corresponding to the 46 indica landraces and two other subgroups of japonica varieties (Fig.3.3). Most of the accessions of the subgroups showed varying degree of admixtures (both indica and japonica). The Pop1 also included two aromatic cultivars of Pakistan, Basmati-198 and Basmati-386 showing 4-5% partial ancestry of indica background. In Pop1, among 46 indica accessions, 33 accessions showed 100% indica ancestry while rest of the 13 accessions showed varying degree of admixture (2%- 43%). Pop2 comprised of 8 accessions of tropical japonica. The admixture level among 5 accessions ranged from 5-90% (Fig.3.3) while rest of three accessions showed 100% tropical japonica ancestry. Pop3 comprised of temperate japonica accessions including most of the famous Basmati cultivars of Pakistan. All the basmati cultivars of Pakistan showed 100% temperate japonica blood except Basmati-385 and Basmati-2000 with 22- 24% mixing of indica and tropical japonica ancestry respectively. The accessions also showed differentiation among subgroups based on amylose and gel consistency. In Pop2, amylose ranged from 12-16% (low amylose) with medium-to-medium hard gel consistency (GC). In Pop3 which comprised of temperate japonica accessions, all members showed intermediate amylose content (AC) ranged from 20-25% with soft to medium hard gel consistency (GC). The degree of amylose content (AC) in Pop1 ranged from very low to intermediate (6-24%). While two indica accessions out of 46 showed high amylose content (> 25%). The results of simulation summary at K=3 (Fig.3.7) were used to detect marker trait association using TASSEL. Population structure on the basis of genotypic and phenotypic data is clearly shown in Fig.7. The Principal component analysis (PCA) summarized the genetic variation into three subgroups (Fig.3.6).

emperate emperate

elonging to to elonging and and

ropical japonica ropical t

ndica

Estimated population structure for 75 accessions at K=3 representing three major groups, major representing 75 three K=3 at population for Estimated accessions structure

. Each accession is represented by a separate vertical line. The height of each bar represents the probability of varieties b bar varieties of probability the represents height line. separate each of The vertical a by accession represented is Each . 3.4 - 81

ure

Fig japonica colors. to in plot different subgroups. broken is each of degree the upon admixture, Depending different

Figure-3.5 Bar plots of population structure estimates of 75 rice accessions at different K values. Each accession is represented by a separate vertical line. The height of each bar represents the probability of varieties belonging to different subgroups. Depending on the K value, each plot is broken in to different colors.

3.4.3 Genome Wide Association Scans

The complete results of GWSA or association mapping are presented in Table-3.3 and Fig.3.11. Both known associations (Amylose & Gel consistency) as well as candidate loci were identified in rice genome using general linear model (GLM) approach in TASSSEL programme. The P-values determine the association of QTLs with markers and R2 predicts the magnitude of QTL effects. A total of 59 association signals were detected for six traits with P < 5×10-4. Strong associations with P < 8.80×10-7 were also detected using the same approach. P values of QTLs for starch long and short chains in waxy locus ranged from 3.75×10-4 to 3.63×10-8. The inheritance of gel consistency was found to be controlled by three QTLs on three different linkage groups 4, 8 & 10 with P values 4.38×10-4, 8.91×10-4 & 3.78×10-4 respectively. Sabouri, 2009 also reported the same QTLs on chromosome 4 & 8 using SSR markers. For amylose content, three loci were identified on chromosome 6 in the waxy locus and one QTL on chromosome 4. The linkage disequilibrium (LD) patterns of different SNPs markers on chromosome 6 & 9 are described in fig.5. Twenty-two most significant associations were selected. Detailed information of all the most significant QTLs is summarized in the Table 1. Most of the association signals were detected with close association to already identified regions. The total phenotypic variance was represented by R2. (Table-3.3). The phenotypic variation of amylose content was 75% and gel consistency was 48%. The phenotypic variation of amylose short chains, amylopectin long chains and amylopectin short chains were 62%, 74% and 40% respectively. Six novel associations were also detected for amylopectin long chains, amylose short chains and gel consistency on chromosome 3, 5 and 10 respectively. For other traits like amylose long chains, amylose short chains, amylopectin long chains and amylopectin short chains, the phenotypic variation of single trait may be shared by multiple loci. The newly identified genomic regions (loci) could be used to understand the genetic architecture of these traits.

Table-3.3 Genome wide significant Associations (R2) of single nucleotide polymorphisms (SNPs) with amylose content (AC), gel consistency (GC), amylose long chains (AM Long), amylose short chains (AM short), amylopectin long chains (AP long) and amylopectin short chains (AP short).

Trait Marker Chromosome Position F P R2 (CM) Amylose wd4001035 4 23.8803 12.85983 7.41E-04 0.157637 Amylose fd7 6 4.41190 17.18283 8.80E-07 0.255542 Amylose id6001535 6 5.02434 10.26897 1.25E-04 0.176305 Amylose id6002123 6 6.95655 10.72092 8.82E-05 0.182224 GC id4008092 4 61.9725 13.62675 4.38E-04 0.147966 GC id8000699 8 5.51947 7.801749 8.91E-04 0.169164 GC id1000553 10 47.2662 8.854591 3.78E-04 0.18546 AM long id6001397 6 4.66805 22.186908 3.63E-08 0.303704 AM long id6001535 6 5.02434 17.771433 5.95E-07 0.263812 AM long id6004481 6 17.5055 12.142829 3.10E-05 0.207114 AM long id5014669 6 51.2031 8.8778954 3.75E-04 0.163191 AM long id9005626 9 45.0080 8.4145534 5.37E-04 0.152147 AM long id9007180 9 54.1660 11.082434 6.70E-05 0.188657 AM short id5007205 5 44.84021 11.19975 6.49E-05 0.214412 AM short id5014669 5 72.9488 8.636963 4.49E-04 0.160463 AM short fd7 6 4.41190 15.82954 2.20E-06 0.252063 AP long fd7 6 4.41190 14.39463 5.96E-06 0.18546 AP long id6004481 6 17.5055 11.34005 5.62E-05 0.218519 AP long id6002123 6 6.95655 8.821593 3.88E-04 0.175693 AP long ud4000703 3 6.43201 9.449717 2.36E-04 0.185513 AP short id4004185 4 35.4698 21.0395 1.89E-05 0.152147 AP short id3001422 5 44.8402 19.3527 3.75E-05 0.252063

ch5 ch6 ch7 ch8 ch5 ch6 ch7 ch8 1 2 3 4 5 6 ch1 ch1ch1 ch1 ch2 ch2ch2ch2 ch3 ch3ch3ch3 ch4ch4ch4 ch4

0.6 fd13 qt-8 1.0 id8000140

qt-3 id1000259 id2010969 id3017266 id4001113 qt-8

1.2 0.5 1.0 qt-3 0.9 id1000259 id2010969 id3017266 qt-3 id4001113 id5000043 id7000070 1.2 1.21.2id1000259id10002590.5 0.50.5id2010969 id2010969 1.0 1.0 1.0id3017266 id3017266qt-3 0.9 0.9 id40011130.9 id40011130.2 0.6 fd13 0.9 1.0 id8000140 0.2 id5000043 1.6 id6004481 0.9 id70000705.5 id8004029 5.7 5.75.7id10010735.7id1001073id10010733.7id10010733.73.7wd20000063.7 wd2000006wd2000006wd2000006 3.1 3.1 3.13.1id3000913id3000913id3000913id3000913 2.6 2.62.6 id40117742.6 id4011774id4011774id40117742.9 id5000811 1.6 id6004481 5.5 id8004029 2.9 id5000811 4.4 id6003318 7.9 id8001029 10.0 10.010.0 id100334410.0id1003344id10033447.0id10033447.07.0id20119687.0 id2011968id2011968id2011968 6.4 6.4 6.46.4id3001422id3001422id3001422id3001422 6.1 6.16.1 id40010966.1 id4001096id4001096id4001096 4.4 id6003318 6.4 id7000448 7.9 id8001029 6.4 wd5001329 7.0 id6009470 id70004489.2 ud8001072 15.315.3id101417615.3id1014176id1014176id101417610.910.9id200229310.9 id2002293id2002293id2002293 9.1 9.19.1id3001992id3001992id3001992id3001992 8.28.2 wd40003478.2 wd4000347wd4000347wd40003476.4 wd5001329 7.0 id6009470 6.4 9.2 ud8001072 15.3 10.9 9.1 8.2 10.2 id6003088 9.2 id7002051 11.4 wd8001854 20.5 20.5id1006298id1006298id100629814.6 14.6 id2003067id2003067id2003067 13.713.7 id3007320id3007320id3007320 11.911.9 id4002032id4002032id400203212.0 id5013231 10.2 id6003088 9.2 id7002051 11.4 wd8001854 20.5 20.5id1006298 14.6 14.6id2003067 13.7 13.7id3007320 11.9 11.9id4002032 12.0 id5013231 id6013529 12.0 wd7001471 id8002025 25.9 id1007562 18.2 ud2000373 15.0 id3013669 13.8 id4002348 14.8qt-5 id5009997 14.2 12.0 wd700147114.6 25.9 id1007562 18.2 ud2000373 15.0 id3013669 13.8 id4002348 qt-5 14.2 id6013529 14.6 id8002025

qt-5 id1007562id1007562 ud2000373ud2000373 id3013669 id3013669 id4002348 id4002348 id5009997qt-5 25.9 25.9 18.2 18.2 15.0 15.0 13.8 13.8 14.8 qt-9 15.0 id7004442 32.2 id1008693 20.0 wd2000377 18.8 id3013308 16.2 id4010621 id6001397 qt-9 id8006789 id1008693 wd2000377 id3013308 id4010621 16.4 id5003303 17.5 id6001397 15.0 id700444218.9 id8006789qt-12 32.2 20.0 18.8 16.2 id5003303 17.5 18.9 qt-12 32.2 32.2 id1008693id1008684id100869320.0 20.0wd2000377id2004617wd200037718.8 18.8id3013308id3004399id301330816.2 16.2id4010621id4012434id401062116.4 36.5 id100868424.0 id2004617 21.0 id3004399 18.6 id4012434 18.6 id5012152 20.620.6 id6010434id6010434 19.1 19.1 ud8000441ud8000441 36.5 36.5id100868436.5id1018329id100868424.0 24.0id200461724.0 id2015361id2004617 21.0 21.021.0id3004399id3003215id300439918.6 18.6 18.6id4012434wd4001035id401243418.6 id5012152 40.3 25.1 22.1 qt-4 23.9 21.0 id5003785 22.1 id6012426id6012426 20.420.4 id7004429id700442921.7 id8002314id8002314 id1018329 id2015361 id3003215 wd4001035 qt-6 id5003785 22.1 21.7 id101832940.3 id201536125.1 22.1id3003215 qt-4 23.9 wd4001035 21.0 40.3 id101832925.1 id2015361 22.1 id3003215qt-4 23.9 wd4001035 40.3 25.1 22.1 qt-4 23.9

47.2 id1027099 28.6 id2005345 qt-1 24.6 id3005111 26.7 ud4000703

qt-6 qt-6 24.2 id6007312 23.0 id8002968 23.823.8 wd5000945wd5000945qt-6 24.2 id6007312 23.0 id8002968 id1027099 id2005345 qt-1 id3005111 ud4000703 47.2 id102709947.2 28.6 id200534528.6 qt-1 24.6 24.6id3005111 26.7 26.7 ud4000703 52.347.2 id1011513id1027099 30.528.6 id2005538id2005345 qt-1 27.9 24.6 id3014401id3005111 30.7 26.7 id4003793ud4000703 26.9 id5006470 26.426.4 id6006537id6006537 24.6 24.6 id8000699id8000699 52.3 56.752.3id101151352.3id1022408id101151330.5id101151332.630.5id200553830.5 id2005746id2005538id200553827.9 31.727.927.9id3014401id3000111id3014401id301440130.7 32.530.7 30.7id4003793wd4001906id4003793id400379326.9 id5006470 32.6 id5010661 28.628.6 id6007220id6007220 28.528.5 id7001628id7001628 56.7 60.556.7id102240856.7id1003559id102240832.6id102240837.432.6id200574632.6 ud2000761id2005746id200574631.7 33.231.731.7id3000111id3005216id3000111id300011132.5 35.532.5 32.5wd4001906id4004185wd4001906wd400190632.6 id5010661 29.9 29.9 id8003624id8003624 34.3 id5010992id5010992 30.330.3 ud6000539ud6000539 60.5 66.460.5id100355960.5ud1000711id100355937.4id100355940.737.4ud200076137.4 wd2001525ud2000761ud200076133.2 36.833.233.2id3005216id3010345id3005216id300521635.5 39.935.5 35.5id4004185id4004428id4004185id400418534.3 id5005882id5005882 32.632.6 id6011429id6011429 33.633.6 id7003748id700374833.7 33.7 id8003766id8003766 66.4 70.2ud100071166.4id1015417ud100071140.7ud100071142.2 wd200152540.7 id2002229wd2001525wd200152536.8 38.936.8id3010345id3007541id3010345id301034539.9 40.839.9 id4004428id4008092id4004428id400442835.835.8 66.4 40.7 36.8 39.9 id5006365 34.834.8 id6005608id6005608 35.635.6 ud7000964ud7000964 37.7 wd8003200wd8003200 39.9 id5006365 qt-7 37.7 39.9 qt-7 70.2 71.170.2id101541770.2id1018601id101541742.2id101541747.842.2id200222942.2 id2007273id2002229id200222938.9 41.138.938.9id3007541id3004633id3007541id300754140.8 43.140.8 40.8id4008092id4004914id4008092id4008092

qt-2 id6010766 71.1 73.5id1018601id101598447.8 50.2 id2007273id2011561 41.1 44.0 id3004633id3008386 43.1 46.9 id4004914id400540440.640.6 id5004668id5004668 36.436.4 id6010766 39.239.2 id7002392id700239239.3 39.3 id8004221id8004221 71.1 71.1 id1018601id101860147.847.8 id2007273id2007273 41.141.1 id3004633id3004633 43.1 43.1 id4004914id4004914 38.4 id6011379 id8003681 qt-2 42.7 id5005551 38.4 id6011379 41.6 41.6 id8003681 75.4 id1015541 54.4 ud2002015 qt-2 45.9 id3009433 49.1 id4005704 42.7 id5005551 73.5 id101598473.5 id101598450.2id1015984 id201156150.2 id2011561id201156144.0 qt-2 44.0id3008386id3008386id300838646.9 46.9 id4005404id4005404id4005404 43.9 id7002701 75.973.5 id1016790 57.150.2 ud2000793 47.1 44.0 id3017899 51.5 46.9 ud400155244.8 id5007205id5007205 41.041.0 id6003649id6003649 43.9 id7002701 43.2 43.2 id8005815id8005815 75.4 id101554175.4 54.4id1015541 ud200201554.4 ud200201545.9 45.9id3009433id3009433 49.1 49.1 id4005704id4005704 44.8 45.0 ud7001174 79.375.4 id1017859id1015541 59.954.4 id2009229ud2002015 51.6 45.9 id3004040id3009433 53.4 49.1 id4006867id400570447.0 id5007714id5007714 43.743.7 id6016490id6016490 45.0 ud7001174 75.9 id1016790 57.1 ud2000793 47.1 id3017899 51.5 ud4001552 47.0 qt-10 49.2 id8005235 qt-10 47.9 id7002427 80.575.9 75.9id1018311id1016790id101679062.657.157.1 id2016108ud2000793ud2000793 55.647.147.1 id3010055id3017899id3017899 57.151.5 51.5 id4005423ud4001552ud400155249.9 id5008218 45.0 id6009055 47.9 id7002427 49.2 id8005235 79.3 id1017859 59.9 id2009229 51.6 id3004040 53.4 id4006867 49.9 id5008218 45.0 id6009055 id7005665 51.2 id8003838 79.3 79.3id1018870id1017859id101785959.959.9 id2014684id2009229id2009229 51.651.6 id3003535id3004040id3004040 53.4 53.4 id4007698id4006867id400686751.3 id5005080 49.5 fd8 50.850.8 id7005665 51.2 id8003838 80.5 82.7id1018311 62.6 65.4 id2016108 55.6 57.6 id3010055 57.1 59.2 id4005423 51.3 id5005080 49.5 fd8 53.8 id8005966 80.5 id1018311 62.6 id2016108 55.6 id3010055 57.1 id4005423 51.2 fd17 53.2 id700324353.8 id8005966 qt-12 86.680.5 id1020384id1018311 68.162.6 id2001831id2016108 59.4 55.6 id3010557id3010055 64.1 57.1 id4008430id400542353.8 wd5000542 53.2 id7003243 qt-12 82.7 id1018870 65.4 id2014684 57.6 id3003535 59.2 id4007698 53.8 wd5000542 51.2 fd17 id7003591 55.5 id8006359 90.882.7 82.7id1021494id1018870id101887070.265.465.4 id2005554id2014684id2014684 62.957.657.6 fd10id3003535id3003535 66.159.2 59.2 id4008536id4007698id400769856.8 id5009967 53.8 id6001535 56.156.1 id7003591 55.5 id8006359 86.6 id1020384 68.1 id2001831 59.4 id3010557 64.1 id4008430 56.8 id5009967 53.8 id6001535 59.1 id8006485 93.286.6 86.6id1022407id1020384id102038473.868.168.1 id2007526id2001831id2001831 65.759.459.4 id3004190id3010557id3010557 69.664.1 64.1 id4008981id4008430id400843059.3 id5005872 56.4 id6015002 60.4 id700421059.1 id8006485 90.8 id1021494 70.2 id2005554 62.9 fd10 66.1 id4008536 59.3 id5005872 56.4 id6015002 60.4 id7004210 ud8001618 95.690.8 90.8id1023174id1021494id102149476.070.270.2 id2013007id2005554id2005554 69.562.962.9 id3011048fd10fd10 72.466.1 66.1 id4009413id4008536id400853660.2 id5010886 60.4 id6009447 62.4 id7005137 62.1 ud8001618 93.2 id1022407 73.8 id2007526 65.7 id3004190 69.6 id4008981 60.2 id5010886 60.4 id6009447 62.4 id7005137 62.1 id8007093 98.993.2 93.2id1019332id1022407id102240778.173.873.8 id2008501id2007526id2007526 72.665.765.7 id3013192id3004190id3004190 74.869.6 69.6 id4009823id4008981id400898163.2 id5011771 63.5 wd6001924 64.2 id7004645 65.1 95.6 id1023174 76.0 id2013007 69.5 id3011048 72.4 id4009413 63.2 id5011771 63.5 wd6001924 64.2 id7004645id700191265.1 67.1 id8007093id8001908 100.0 95.6id1024323id1023174id102317481.0 76.0 id2009319id2013007id2013007 75.169.5 id3017762id3011048id3011048 76.772.4 id4010200id4009413id400941365.0 id5012326 68.0 id6013446 qt-11 67.8 95.6 76.0 69.5 72.4 qt-8 id7001912 67.1 id8001908

id6013446 qt-11 67.8 98.9 104.4id1019332id102545578.1 84.9 id2008501id2005263 72.6 77.7 id3013192id3014361 74.8 79.4 id4009823id4008522 65.0 id5000015id5012326 69.268.0 id6010404 69.1 wd7000465 69.8 id8007764 98.9 98.9 id1019332id101933278.178.1 id2008501id2008501 72.672.6 id3013192id3013192 74.8 74.8 id4009823id400982368.4 qt-8 100.0 107.7 id1024323id102661381.0 87.3 id2009319id2000096 75.1 80.9 id3017762id3003491 76.7 81.3 id4010200id4002852 68.4 id5000015 69.2 id6010404fd7 69.172.3 wd7000465id700487069.8 id8007764 100.0 id1024323 81.0 id2009319 75.1 id3017762 76.7 id401020070.4 id5013798 71.3 104.4 111.7100.0id1025455id1010609id102432384.9 89.481.0id2005263id2006996id2009319 77.7 82.2 75.1id3014361id3005817id301776279.4 84.0 76.7id4008522id4011016id401020070.4 id5013798 71.3 fd7id6002123 72.374.6 id7004870id7005984 id1025455 id2005263 id3014361 id400852272.9 id5014669 73.5 107.7 104.4id1026613104.4 id102545587.3 84.9id200009684.9 id2005263 80.9 85.377.777.7id3003491id3016090id301436181.3 87.279.4 79.4id4002852id4011935id400852272.9 id5014669 77.473.5 id6002123id6016803 74.6 id7005984 111.7 107.7id1010609107.7 id102661389.4id102661387.3id200699687.3 id2000096id200009682.2 88.780.980.9id3005817id3002191id3003491id300349184.0 89.481.3 81.3id4011016id4000641id4002852id4002852 79.277.4 id6016803id6013038 111.7 111.7 id1010609id101060989.4ch989.4 id2006996id200699685.3ch991.7ch982.282.2id3016090ch10id3006941id3005817id300581787.2 ch1084.0 84.0id4011935ch10id4011016ch11id4011016 ch11 ch1179.2ch12id6013038 ch12 ch12 ch5 ch6 ch7 ch8 88.7 85.385.3id3002191id3016090id301609089.4 87.2 87.2id4000641id4011935id4011935 ch5 ch6 ch7 ch8 91.7 ch988.788.7id3006941id3002191id3002191 ch1089.4 89.4 id4000641id4000641 ch11 ch12 91.791.7 id3006941id3006941 7 8 9 10 11 12

fd13 qt-8 id8000140 0.6 qt-8 1.0 id12000232 id5000043 0.6 fd13 id7000070 1.91.0 id9000154id8000140 1.9 1.9 id9000154id9000154id9000154 ud10000620 id110115051.7 1.7id11011505id110115051.7 1.7 id12000232id120002321.7 1.7 id12000232 0.20.2 id5000043 0.90.9 id7000070 1.9 2.1 ud100006202.12.1 2.1ud100006201.7 ud10000620 1.7 id11011505 1.6 id6004481 5.5 id8004029 3.0 id9000339 qt-15 id11000343 3.5 id12000633

3.5qt-15 1.6 id6004481 5.5 id8004029 id9000339 qt-15 3.5 id120006333.5 id12000633 2.9 id5000811 3.0 id9000339 3.0 5.8 id10000644 id110003433.5 qt-15 id11000343 3.5 id12000633 2.9 id5000811 4.4 id6003318 7.9 id8001029 5.83.0 id9002563id9000339 3.5 3.5 5.6id11000343id12001043 4.4 id6003318 7.9 id8001029 5.8 id100006447.55.8 5.8id10000881id10000644id100006446.8 id11003556 id12001043 id12001043 6.4 wd5001329wd5001329 6.46.4 id7000448id7000448 5.8 id9002563 5.8 9.15.8 id9002563ud9000122id9002563 id11003556 id110035565.6 7.4 id120027285.6 5.6 id12001043 6.4 7.07.0 id6009470id6009470 9.2 ud8001072 7.5 id1000088110.07.5 id10002602id100008816.8 9.9 6.8id110015526.8 id11003556 9.29.2 id7002051id7002051 9.1 ud9000122 10.6 ud9000122id9001297 7.5 id10000881 7.4 11.7 id12002728id120021137.4 id12002728 12.012.0 id5013231id5013231 10.210.2 id6003088id6003088 11.4 wd8001854 9.1 9.1 ud9000122 12.9 id10003050 7.4 id12002728 12.0 wd7001471 14.7 10.0id9000661id1000260210.0 id100026029.9 id1100155215.7 9.9id11002690id1100155214.4id11001552id12002563 14.8 id5009997 14.214.2 id6013529id6013529 12.0 wd700147110.614.6 id9001297id8002025id800202510.6 id9001297 14.8 10.0id10001970 id10002602 9.9 14.8 id5009997 id6001397 qt-9 15.0 id7004442 id8006789 10.6 id9001297 18.6 id11000980 11.7 id1200211311.7 id1200211311.7 id12002113 id5003303 17.5 18.9 qt-12 12.9 id1000305017.7 id10002364 16.4 qt-9 12.9 id10003050 id6001397 15.0 id7004442 id8006789 18.9 id9003188 12.9 id1000305020.4 id11003145 19.0 id12003005 16.4 id5003303 17.5 id6010434 19.118.9 ud8000441 qt-12 15.7 id11002690 id11002690 id12002563 18.6 id5012152 20.6 14.7 id900066114.7 14.7 14.8id9000661id9000661id1000197023.9 id10001624id10001970 15.7 15.7 14.4 id1100269014.4 id1200256314.4 id12002563 id5012152 22.120.6 id6012426id6010434 20.4 id7004429 21.719.1 ud8000441id8002314 22.8 id9002551 14.8 14.8 id1000197022.9 id11002933 18.6 id5003785 qt-18 23.0 id12003717 21.0 25.6 id1000616118.6 id1100098024.0 18.6wd11000649id11000980 qt-13 id11000980 wd5000945 24.222.1 id6007312id6012426 20.4 id7004429 23.021.7 id8002314id8002968 25.5 17.7id9003003id1000236417.7 id10002364 18.6 23.821.0 id5003785 18.9 id900318818.9 id9003188 27.4 17.7id1000286720.4 id10002364id1100314527.0 id11004812 19.0 26.5 id12003005id12004099 id5006470 26.4 id6006537 24.6 id8000699 18.9 id9003188 20.4 20.4id11003145id1100314519.0 id1200300519.0 id12003005 26.923.8 wd5000945 24.2 id6007312 23.0 id8002968 28.7 23.9id9003030id1000162429.9 id10000174 29.9 id11005646 28.6 id6007220 28.5 id7001628 id9003720 23.9 id1000162422.9 id10001624qt-16 id11002933 31.3 id12005823 32.6 id5010661 26.4 id6006537 22.829.924.6 id9002551id8000699id8003624 31.1 32.6 23.9id10003334 30.2 22.9id1100426422.9id11002933id11002933 26.9 id5006470 22.8 id9002551 qt-18 23.0 id12003717 30.3 ud6000539 22.8 25.6 id9002551id10006161 id10005538 34.2 qt-18 wd1200197823.0 id12003717 34.3 id5010992 34.825.6 id1000616124.0 wd11000649 qt-18 23.0 id12003717 qt-13 25.6 id10006161 24.0 wd11000649 32.6 id5010661 28.6 id6007220 33.628.5 id7003748id7001628 id9003003 id9001614 qt-13 24.0 wd11000649id12005212 id6011429 25.533.7 id8003766 35.9 qt-13 id10001250 36.6 35.8 id5005882 32.6 29.9 id8003624 25.5 25.5 27.4id9003003id9003003id1000286736.1 id10002867 id1100481237.7 id11007488 26.5 id12004099 34.3 id5006365id5010992 34.830.3 id6005608ud6000539 35.6 ud7000964 37.7 wd8003200 38.9 id9006377 38.127.4 27.4id1000389127.0 id10002867 27.0 id1100481239.8 id1200554726.5 id12004099 id12004099 39.9 qt-7 39.4 id1100458427.0 id11004812 26.5 id700374828.7 id9003030 29.9 id10000174 41.3 wd12003207 40.635.8 id5004668id5005882 36.432.6 id6010766id6011429 39.233.6 id7002392 39.333.7 id8003766id800422128.7 28.742.8 id9003030id9000174id9003030 41.429.9 29.9id10004500id1000017429.9 id10000174id1100564641.5 29.9id11004845id11005646 id9003720 43.5 qt-16 ud10000989 29.9 31.3 43.9id11005646id12005823id12007988 id5005551 38.4 id6011379 ud700096431.141.6 id8003681 45.0 32.6id9005626id10003334 qt-16 id11009201 id12005823 42.7 id5006365 34.8 id6005608 35.6 37.7 wd800320031.1 id9003720 id1000333430.2 id1100426443.2 qt-16 31.3 31.3 id12005823 39.9 qt-7 id9003720 32.6 43.9 id7002701 31.1 qt-14 32.6id10005049 id10003334 30.2 id1100426446.2 id12006155 44.8 id5007205 41.0 id6003649 43.2 id8005815 47.6 id9007763id1000553845.2 qt-17 46.1 id1100803630.2 34.2 id11004264wd12001978 40.6 id5004668 36.4 id6010766 45.039.2 ud7001174id7002392 39.3 id8004221 34.8 47.334.8 id10007301id10005538 47.7 id1200633934.2 wd1200197834.2 wd12001978 47.0 id5007714 43.7 id6016490 50.0 id9006953 34.8 id1000553850.6 id11006537 38.4 id6011379 qt-10 47.9 id700242735.949.241.6 id9001614id8003681id8005235 36.1 id1000125049.7 wd10003790 36.6 49.2 id12005212id12000605 49.942.7 id5008218id5005551 45.0 id6009055 35.9 35.953.7 id9001614id9002721id9001614 36.1 id1000125037.7 id1100748852.9 id11007108 36.6 id1200521236.6 id12005212 43.9 id7005665id7002701 51.2 id8003838 51.2 36.1id10006340 id10001250 37.7 id1100748851.7 id12003803 51.344.8 id5005080id5007205 49.541.0 fd8id6003649 50.8 38.943.2 id9006377id8005815 54.2 38.1id9007180id10003891 54.7 id1100051537.7 39.8 id11007488id12005547 53.8 id800596638.9 id9006377 54.838.1 id10006243id1000389139.4 id11004584 52.7 wd1200399839.8 id1200554739.8 id12005547 wd5000542 51.2 fd17id6016490 53.245.0 id7003243ud7001174 qt-12 38.956.1 id9003471id9006377 38.1 id1000389156.5 39.4id11009687id11004584 53.847.0 id5007714 43.7 41.4 id1000450056.2 wd10001251 39.4 41.3 55.1id11004584wd12003207id12007216 qt-10 55.5 id8006359 53.8 id6001535 56.147.9 id7003591id700242742.849.2 id9000174id8005235 58.6 id9007784 41.4 id1000450041.5 id10004500id1100484558.2 id11009456 41.3 wd1200320741.3 wd12003207 56.849.9 id5009967id5008218 45.0 id6009055 42.8 42.8 id9000174id9000174 59.1 41.4id10007384 41.5 41.5id1100484543.9 57.7id11004845id12007988id12001996 id5005872 56.4 id6015002 60.4 id700421045.059.151.2 id9005626id8003838id8006485 43.5 ud10000989 id1100920160.3 dd11000336 43.9 id12007988 59.351.3 id5005080 49.5 fd8 50.8 id7005665 45.0 id9005626 43.5 43.5ud1000098943.2 ud10000989 id1100920162.1 id12008779 43.9 id12007988 60.4 id6009447 62.4 id7005137 62.1 ud8001618 45.0qt-14 id9005626id10005049 62.5 43.2id1100892943.2 46.2 id11009201id12006155 60.2 id5010886 53.8 id8005966 45.2 qt-17 65.1 id12001321

qt-12 53.2 id700324347.6 id9007763 qt-14 id11008036 46.2 id12006155 fd17 qt-14 id1000504946.1 46.2 id12006155 53.8 wd5000542 51.2 id8007093 45.2 id10005049qt-17 id11004215 63.2 id5011771 63.5 wd6001924 64.2 id7004645 65.155.5 id8006359 47.6 47.6 47.3id9007763id9007763id10007301 45.2 64.2 46.1 qt-17 46.1id1100803647.7 id11008036id12006339 53.8 id6001535 56.1 id7001912id700359150.067.1 id9006953id8001908 67.1 id11007840 69.2 id1201013047.7 id12006339 65.056.8 id5012326id5009967 68.0 id6013446 qt-11 67.8 id9006953 47.3 47.3id1000730150.6 id10007301id11006537 47.7 id12006339 qt-8 59.1 id8006485 50.0 50.0 49.7 id9006953wd10003790 70.4 id11004398id1100653749.2 id12000605 68.459.3 id5000015id5005872 69.256.4 id6010404id6015002 69.160.4 wd7000465id700421053.769.8 id9002721id8007764 wd10003790 id1100710850.6 50.6 id1100653749.2 id1200060549.2 id12000605 id7004870 53.7 id9002721 49.7 49.7 52.9 wd1000379073.6 id11006398 51.7 id12003803 70.460.2 id5013798id5010886 71.360.4 fd7id6009447 72.362.4 id700513754.262.1 id9007180ud8001618 53.7 51.2 id9002721id10006340 52.9 52.9id11007108id11007108 id6002123 74.6 id7005984 51.2 id1000634054.7 id10006340id1100051576.9 id11011652 51.7 id1200380351.7 id12003803 72.9 id5014669 73.563.5 wd6001924 64.2 id7004645 65.1 id8007093 54.2 54.2 54.8id9007180id9007180id10006243 51.2 id1100051552.7 wd12003998 63.2 id5011771 77.4 id6016803 56.1 id9003471 54.7 54.7 id1100051552.7 wd12003998 id7001912 67.1 id8001908 id9003471 54.8 54.8id1000624356.5 id10006243id11009687 55.1 id12007216 52.7 wd12003998 qt-11 67.8 56.1 65.0 id5012326 79.268.0 id6013038id6013446 58.6 id9007784 56.1 56.2 id9003471wd10001251 56.5 56.5id11009687id11009687 qt-8 id6010404 69.1 wd7000465 69.8 id8007764 56.2 56.2wd1000125158.2 wd10001251id11009456 id1200199655.1 id1200721655.1 id12007216 68.4 id5000015 69.2 58.6 58.6 59.1id9007784id9007784id10007384 58.2 58.2id1100945657.7 id11009456 id5013798 71.3 fd7 72.3 id7004870 59.1 59.1id1000738460.3 id10007384dd11000336 id1200877957.7 id1200199657.7 id12001996 70.4 60.3 60.3dd1100033662.1 dd11000336 72.9 id5014669 73.5 id6002123 74.6 id7005984 62.5 id11008929 65.1 id1200132162.1 id1200877962.1 id12008779 77.4 id6016803 64.2 id1100421562.5 62.5id11008929id1100892965.1 id1200132165.1 id12001321 79.2 id6013038 67.1 id1100784064.2 64.2id1100421569.2 id11004215id12010130 69.2 id1201013069.2 id12010130 70.4 id1100439867.1 67.1id11007840id11007840 73.6 id1100639870.4 70.4id11004398id11004398 76.9 id1101165273.6 73.6id11006398id11006398 76.9 76.9id11011652id11011652

Figure-3.6 General distributions of SNPs markers on 12 rice chromosomes

ch5 ch6 ch7 ch8 ch5 ch6 ch7 ch8 3 4 5 6 ch1 ch1 ch2 ch2 ch3 ch3 ch4 ch4

qt-8

0.6 fd13 qt-8 1.0 id8000140 1.2 id1000259 0.5 id2010969 1.0 id3017266 qt-3 0.9 id4001113 fd13 id8000140

qt-3 0.6 1.0 1.2 id1000259 0.5 id2010969 1.0 id3017266 0.9 id4001113 0.2 id5000043 0.2 id5000043 0.9 id7000070 0.9 id7000070 5.7 id1001073 3.7 wd2000006 3.1 id3000913 2.6 id4011774 1.6 id6004481 1.6 id6004481 q AM5.5-6 id8004029 5.5 id8004029 5.7 id1001073 3.7 wd2000006 3.1 id3000913 2.6 id4011774 2.9 id5000811 2.9 id5000811 10.0 10.0id1003344id10033447.0 7.0id2011968 id2011968 6.4 id30014226.4 id30014226.1 id40010966.1 id4001096 4.4 id6003318 4.4 id6003318 7.9 id8001029 7.9 id8001029 6.4 id7000448q AML-6 id7000448 15.3 15.3id1014176id101417610.9 10.9id2002293 id2002293 9.1 id30019929.1 id30019928.2 wd40003478.2 wd4000347 6.4 wd5001329 6.4 7.0wd5001329id6009470 7.0 id6009470 6.49.2 ud8001072 9.2 ud8001072 q AM-3 9.2 id7002051 9.2 id7002051 20.5 20.5id1006298id100629814.6 14.6id2003067 id200306713.7 13.7id3007320 id300732011.9 id400203211.9 id4002032 12.0 id5013231 12.0 10.2id5013231id6003088 10.2 id6003088 q AMS11.4 -6 wd8001854 11.4 wd8001854 12.0 wd7001471 wd7001471 id1007562 ud2000373 id3013669 id4002348 qt-5 25.9 25.9 id100756218.2 18.2 ud2000373 15.0 15.0 id301366913.8 13.8 id4002348 qt-5 14.8 id5009997 14.8 14.2id5009997id6013529 14.2 id6013529 12.014.6 id8002025 14.6 id8002025

qt-9 32.2 32.2id1008693id100869320.0 20.0wd2000377wd200037718.8 18.8id3013308 id301330816.2 id401062116.2 id4010621 id6001397 15.0id6001397id7004442qt-9 q APL15.0 -6 id7004442id8006789 id8006789 17.5 18.9 qt-12 16.4 id5003303 16.4 17.5id5003303 18.9 qt-12 36.5 36.5id1008684id100868424.0 24.0id2004617 id200461721.0 21.0id3004399 id300439918.6 id401243418.6 id4012434 id6010434 20.6 id6010434 ud8000441 19.1 ud8000441 id1018329 id2015361 id3003215 wd4001035 18.6 id5012152 18.6 20.6id5012152 19.1 40.3 id101832925.1 id201536122.1 id3003215qt-4 23.9 wd4001035 40.3 25.1 22.1 qt-4 23.9 q AM-4 id7004429 qt-6 21.0 id5003785id6012426 22.1 20.4id6012426id7004429 20.4 id8002314 21.7 id8002314 qt-6 21.0 id5003785 22.1 21.7 47.2 id1027099 28.6 id2005345 qt-1 24.6 id3005111 26.7 ud4000703 47.2 id1027099 28.6 id2005345 qt-1 24.6 id3005111 26.7 ud4000703 23.8 wd5000945id6007312 24.2 id6007312 id8002968 23.0 id8002968 52.3 id1011513id101151330.5 id2005538 id200553827.9 id3014401 id301440130.7 id4003793 id4003793 23.8 wd5000945 24.2 23.0 52.3 30.5 27.9 30.7 26.9 id5006470 26.4 id6006537 24.6 id8000699 56.7 56.7id1022408id102240832.6 32.6id2005746 id200574631.7 31.7id3000111 id300011132.5 wd400190632.5 wd4001906 26.9 id5006470 26.4 id6006537 24.6 id8000699 id1003559 ud2000761 id3005216 id4004185 32.6 id5010661 28.6 id6007220 28.5 id7001628 60.5 60.5 id100355937.4 37.4 ud2000761 33.2 33.2 id300521635.5 35.5 id4004185 32.6 id5010661 28.6 id6007220 28.5 id7001628q AML-6 29.9 id8003624 66.4 ud1000711 40.7 wd2001525 36.8 id3010345 39.9 id4004428 34.3 id5010992 30.3 ud6000539 29.9 id8003624 66.4 ud1000711 40.7 wd2001525 36.8 id3010345 39.9 id4004428 q APS-4 34.3 id5010992 30.3 ud6000539 70.2 id1015417 42.2 id2002229 38.9 id3007541 40.8 id4008092 35.8 id5005882 32.6 id6011429 q APL33.6 -6 id7003748 33.7 id8003766 id1015417 id2002229 id3007541 id4008092 32.6 id6011429 33.6 id7003748 33.7 id8003766 71.1 70.2id1018601 47.8 42.2id2007273 41.1 38.9id3004633 43.1 id400491440.8 35.8 id5005882 id5006365 34.8 id6005608 35.6 ud7000964 37.7 wd8003200 39.9 qt-7 ud7000964 71.1 id1018601 47.8 id2007273qt-2 41.1 id3004633 43.1 id4004914 id5006365 34.8 id6005608 35.6 37.7 wd8003200 73.5 id1015984 50.2 id2011561 44.0 id3008386 46.9 id4005404 39.9 40.6qt-7 id5004668 36.4 id6010766 39.2 id7002392 39.3 id8004221 73.5 id1015984 50.2 id2011561 qt-2 44.0 id3008386 46.9 id4005404 q AM-id60107665 75.4 id1015541 54.4 ud2002015 45.9 id3009433 49.1 id4005704 40.6 id5004668 42.7 36.4id5005551 38.4 39.2id6011379id7002392 39.3 id8004221 41.6 id8003681 75.4 id1015541 54.4 ud2002015 45.9 id3009433 49.1 id4005704 75.9 id1016790 57.1 ud2000793 47.1 id3017899 51.5 ud4001552 42.7 id5005551 44.8 38.4id5007205id6011379 41.0 id6003649 43.941.6 id7002701id8003681 43.2 id8005815 75.9id1017859id1016790 57.1id2009229 ud2000793 47.1id3004040 id3017899 id400686751.5 ud4001552 43.9 id7002701 79.3 59.9 51.6 53.4 44.8 id5007205 47.0 41.0id5007714id6003649 43.7 id6016490 45.043.2 ud7001174id8005815 80.5 79.3id1018311id101785962.6 59.9id2016108 id200922955.6 51.6id3010055 id300404057.1 id400542353.4 id4006867 45.0 ud7001174qt-10 49.2 id8005235 47.0 id5007714 49.9 43.7qid5008218 APS-id60164905 45.0 id6009055 47.9 id7002427 82.7 80.5id1018870id101831165.4 62.6id2014684 id201610857.6 55.6id3003535 id301005559.2 id400769857.1 id4005423 qt-10 id7002427 49.2 id8005235 51.2 id8003838 q GC-4 49.9 id5008218 51.3 45.0id5005080id6009055 49.5 47.9fd8 50.8 id7005665 86.6 82.7id1020384id101887068.1 65.4id2001831 id201468459.4 57.6id3010557 id300353564.1 id400843059.2 id4007698 id8003838 53.8 id8005966

51.2 id7003243 qt-12 51.3 id5005080 53.8 49.5wd5000542fd8 51.2 50.8fd17 id7005665 53.2 90.8 86.6id1021494id102038470.2 68.1id2005554 id2001831 62.9 59.4fd10 id301055766.1 id400853664.1 id4008430 56.153.8 id7003591id8005966 55.5 id8006359 53.8 53.2id6001535id7003243 qt-12 93.2 90.8id1022407id102149473.8 70.2id2007526 id200555465.7 62.9id3004190 fd10 69.6 id400898166.1 id4008536 53.8 wd5000542 56.8 51.2id5009967fd17 56.4 56.1id6015002id7003591 60.455.5 id7004210id8006359 59.1 id8006485 95.6 93.2id1023174id102240776.0 73.8id2013007 id200752669.5 65.7id3011048 id300419072.4 id400941369.6 id4008981 56.8 id5009967 59.3 53.8id5005872id6001535 id6009447 62.459.1 id7005137id8006485 62.1 ud8001618 98.9 95.6id1019332id102317478.1 76.0id2008501 id201300772.6 69.5id3013192 id301104874.8 id400982372.4 id4009413 59.3 id5005872 60.2 56.4id5010886id6015002 60.4 60.4 id7004210 wd6001924 64.2 id7004645 65.1 id8007093 100.0 98.9id1024323id101933281.0 78.1id2009319 id200850175.1 72.6id3017762 id301319276.7 id401020074.8 id4009823 id5010886 63.2 60.4id5011771id6009447 63.5 62.4 id7005137 62.1 ud8001618 id1025455 id2005263 id3014361 id4008522 60.2 id7001912 67.1 id8001908

104.4 84.9 77.7 79.4 id6013446 qt-11 67.8 100.0 id1024323 81.0 id2009319 75.1 id3017762 76.7 id4010200 65.0 id5012326 68.0 64.2 id7004645 65.1 id8007093 63.5 wd6001924qt-8 107.7 id1026613 87.3 id2000096 80.9 id3003491 81.3 id4002852 63.2 id5011771 q AMS-5 69.1 wd7000465 69.8 id8007764 104.4 id1025455 84.9 id2005263 77.7 id3014361 79.4 id4008522 68.4 id5000015 69.2 id6010404id7001912 67.1 id8001908

id6013446 qt-11 67.8 111.7 id1010609 89.4 id2006996 82.2 id3005817 84.0 id4011016 65.0 id5012326 68.0 fd7 72.3 id7004870 107.7 id1026613 87.3 id2000096 80.9 id3003491 81.3 id4002852 70.4qt-8 id5013798 71.3 id8007764 85.3 id3016090 87.2 id4011935 68.4 id5000015 69.2 id6010404 69.1 wd7000465 69.8 id7005984 111.7 id1010609 89.4 id2006996 82.2 id3005817 84.0 id4011016 72.9 id5014669 73.5 id6002123 74.6 88.7 id3002191 89.4 id4000641 70.4 id5013798 71.3 fd7 77.4 72.3id6016803id7004870 91.7 85.3id3006941 id3016090 87.2 id4011935 73.5 id6002123 74.6 id7005984 id3002191 id4000641 72.9 id5014669 79.2 id6013038 88.7 89.4 id6016803 91.7 id3006941 77.4 79.2 id6013038

ch9 ch9 ch10 ch10 ch11 ch11 ch12 ch12 ch5 ch6 ch7 ch8 8 9 10

0.6 fd13 qt-8 1.0 id8000140 1.9 id9000154 id11011505 1.7 id12000232 0.2 id5000043 0.9 id7000070 1.9 id9000154 2.1 ud100006202.1 ud100006201.7 id110115051.7 1.7 id12000232 1.6 id6004481 5.5 id8004029 3.0 id9000339 qt-15 3.5 id12000633 2.9 id5000811 3.0 id9000339 qt-15 id110003433.5 id110003433.5 id12000633 4.4 id6003318 7.9 id8001029 q GC-8 5.8 id100006443.5 id7000448 id9002563 5.8 id90025635.8 id10000644 5.6 id120010435.6 id12001043 6.4 wd5001329 7.0 id6009470 6.4 9.2 ud8001072 5.8 7.5 id100008816.8 id110035566.8 id11003556 9.2 id7002051 ud9000122 9.1 ud90001227.5 id10000881 7.4 id120027287.4 id12002728 12.0 id5013231 10.2 id6003088 11.4 wd8001854 9.1 10.0 id10002602 9.9 id11001552 12.0 wd7001471 10.6 10.0id9001297 id10002602 9.9 id11001552 14.8 id5009997 14.2 id6013529 14.6 id8002025 10.6 id9001297 12.9 id10003050 11.7 id1200211311.7 id12002113 id6001397 qt-9 15.0 id7004442 id8006789 12.9 id10003050 id11002690 16.4 id5003303 17.5 18.9 qt-12 14.7 id900066114.7 id9000661 14.8 id1000197015.7 id1100269015.7 14.4 id1200256314.4 id12002563 id6010434 ud8000441 14.8 id10001970 18.6 id5012152 20.6 19.1 id1000236418.6 id1100098018.6 id11000980 22.1 id6012426 20.4 id7004429 21.7 id8002314 17.7id9003188 id1000236417.7 21.0 id5003785 18.9 id900318818.9 20.4 id1100314520.4 id1100314519.0 id1200300519.0 id12003005 23.8 wd5000945 24.2 id6007312 23.0 id8002968 23.9 id1000162423.9 id10001624 22.9 id11002933 26.4 id6006537 24.6 id8000699 22.8 id9002551 22.9 id11002933 26.9 id5006470 22.8 id9002551 qt-18 23.0 id12003717 25.6 id1000616125.6 id10006161 24.0 wd11000649qt-18 23.0 id12003717 qt-13 24.0 wd11000649 28.6 id6007220 28.5 id7001628 qt-13 32.6 id5010661 29.9 id8003624 25.5 id900300325.5 id9003003 27.4 id10002867 id5010992 30.3 ud6000539 27.4 id10002867 27.0 id1100481227.0 id1100481226.5 id1200409926.5 id12004099 34.3 28.7 id9003030 29.9 id10000174 35.8 id5005882 32.6 id6011429 33.6 id7003748 33.7 id8003766 28.7 id9003030 29.9 id10000174 29.9 id1100564629.9 id11005646

qt-16 id12005823 id5006365 34.8 id6005608 35.6 ud7000964 37.7 wd8003200 31.1 id900372031.1 32.6id9003720 id1000333432.6 qt-16 id10003334 id1100426431.3 id1200582331.3 39.9 qt-7 30.2 id1100426430.2 40.6 id5004668 36.4 id6010766 39.2 id7002392 39.3 id8004221 34.8 id1000553834.8 id10005538 34.2 wd1200197834.2 wd12001978 id6011379 35.9 id9001614 36.6 id12005212 42.7 id5005551 38.4 41.6 id8003681 35.9 id9001614 36.1 id1000125036.1 id10001250 37.7 id1100748836.6 id12005212 id5007205 41.0 id6003649 43.9 id7002701 43.2 id8005815 37.7 id11007488 39.8 id12005547 44.8 38.9 id900637738.9 38.1id9006377 id1000389138.1 id10003891 39.4 id1100458439.8 id12005547 47.0 id5007714 43.7 id6016490 45.0 ud7001174 39.4 id11004584 41.3 wd12003207 qt-10 49.2 id8005235 41.4 id1000450041.4 id10004500 41.3 wd12003207 49.9 id5008218 45.0 id6009055 47.9 id7002427 42.8 id900017442.8 id9000174 41.5 id1100484541.5 id11004845 51.2 id8003838 43.5 ud10000989 43.9 id1200798843.9 id12007988 51.3 id5005080 49.5 fd8 50.8 id7005665 45.0 id900562645.0 43.5id9005626 ud10000989 43.2 id1100920143.2 id11009201

53.8 id8005966 qt-14 46.2 id12006155

qt-14 id7003243 qt-12 id10005049 id12006155 53.2 q AML-9 id1000504945.2 qt-17 46.2 53.8 wd5000542 51.2 fd17 47.6 45.2id9007763 qt-17 46.1 id11008036 55.5 id8006359 47.6 id9007763 46.1 id11008036 id12006339 56.8 id5009967 53.8 id6001535 56.1 id7003591 50.0 47.3id9006953 id1000730147.3 id10007301 q GC-10 47.7 id1200633947.7 59.1 id8006485 50.0 id9006953 50.6 id1100653750.6 id11006537 59.3 id5005872 56.4 id6015002 60.4 id7004210 49.7id9002721 wd1000379049.7 wd10003790 49.2 id1200060549.2 id12000605 ud8001618 53.7 id900272153.7 id1100710852.9 id11007108 60.2 id5010886 60.4 id6009447 62.4 id7005137 62.1 id1000634051.2 id1000634052.9 51.7 id1200380351.7 id12003803 id8007093 54.2 id900718054.2 51.2id9007180 54.7 id11000515 63.2 id5011771 63.5 wd6001924 64.2 id7004645 65.1 q AML-9 54.8 id1000624354.7 id11000515 52.7 wd1200399852.7 wd12003998 id7001912 67.1 id8001908 56.1 id900347156.1 54.8id9003471 id10006243 65.0 id5012326 68.0 id6013446 qt-11 67.8 56.5 id1100968756.5 id11009687 55.1 id12007216 qt-8 56.2id9007784 wd1000125156.2 wd10001251 55.1 id12007216 68.4 id5000015 69.2 id6010404 69.1 wd7000465 69.8 id8007764 58.6 id900778458.6 58.2 id1100945658.2 id11009456 59.1 id1000738459.1 id10007384 57.7 id1200199657.7 id12001996 70.4 id5013798 71.3 fd7 72.3 id7004870 dd1100033660.3 dd11000336 id7005984 60.3 62.1 id1200877962.1 id12008779 72.9 id5014669 73.5 id6002123 74.6 62.5 id11008929 77.4 id6016803 62.5 id11008929 65.1 id1200132165.1 id12001321 64.2 id1100421564.2 id11004215 79.2 id6013038 id12010130 67.1 id1100784067.1 id1100784069.2 id1201013069.2 70.4 id1100439870.4 id11004398

Figure-3.7 linkage map of chromosomes showing different QTLs Amylose73.6 id1100639873.6 id11006398

76.9 id1101165276.9 id11011652

Amylose long chains (AML)

associated with starch traits. Where AM (Amylose content), GC (Gel

Amylose short chains (AMS)

consistency), AMS (Amylose short chains), AML (Amylose long

Amylopectin long chains (APL)

chains), APS (Amylopectin short chains) and APL (Amylopectin long

Amylopectin short chains (APS)

chains). Different traits are represented with different symbols. Gel consistency (GC)

Figure-3.8 Linkage disequilibrium (LD) of different SNPs markers distributed on chromosomes 6 & 9. The frequency of linkage disequilibrium is associated with P value. At P<0.0001, there is strong LD between two markers and is represented with red color. The green color shows the LD between two markers at P<0.001. Similarly blue color is associated with lowest LD between two markers with P<0.01. At P>0.01, the white color represent no linkage between any two markers. R2 indicates the percentage of total variation explained. The values of R2 range from 0.00- 1.00 represented by different colors.

3.5 Discussion

Many scientists studied the genetic structure of rice (Ebana et al. 2010; McNally et al. 2009; Zhao et al 2011; Huang et al. 2010). But this study included diverse accessions on the basis of starch properties with large number of alleles (1508). The ancestral history of population, breeding system and different breeding ways by humans are some important factors that influence a population structure (Garris et al. 2005). An accurate knowledge about the ancestral background of cultivars facilitates the choice of using these cultivars as parents in rice breeding programme (Rosenberg et al. 2002). By using the STRUCTURE software at K=3, we obtained three subpopulations corresponding to indica, tropical japonica and temperate japonica. Temperate japonica comprised almost all the basmati accessions, which is in accordance with the results of Kovach et al. 2009. The temperate japonica and tropical japonica have close genetic relationship and lower genetic diversity than indica (Ni et al. 2002). In this study, many of the tropical japonica lines were collected from Philippines and most of the accessions from temperate japonica group were collected from Pakistan and India including aromatic rice accessions which formed a distinct subgroup in this and other studies (Ahuja et al. 1995; Jain et al. 2004). The two japonica groups (temperate and tropical) represent an adaptive spectrum of ancient subpopulations adapted to different environmental signals like temperature and day length. The indica group contains famous rice varieties like IR- 64 (a green revolution cultivar), IR-132, KS-282 and some bacterial blight resistant cultivars like IRBB-57. Some accessions revealed relation to more than one ancestral background. These admixed varieties are expected to have diverse history of evolution involving introgression between two varieties with different geographic regions (Mather et al. 2004). In other cases, these individuals are likely to be the result of modern breeding. The three different subgroups contributed varying levels of resolution which help to associate the genomic regions with phenotypic traits (Garris et al. 2003). Associations between SNPs markers and starch components were examined based on P-values using TASSEL. SNPs have potential advantage over simple sequence repeats (SSR) to increase the efficiency of linkage disequilibrium due to high heterozygosity and abundance across rice genome. The presence of multiple SNPs among the haplotypes also increases the power

88 to detect LD (Zollner et al. 2000). Amylose content has previously been reported to be governed by Wx gene on chromosome 6 & 4 (Zhou et al. 2003; Tan et al. 1999; Septiningsih et al. 2003; Mikami et al. 2008).

The genetic basis of starch chain length distribution in rice has not been studied previously in detail. We detected 6 novel associations for amylose long chain, amylose short chains and gel consistency with linkage groups 3, 5 and 10 respectively. The waxy region (Wx) on linkage group 6 was found to have significant associations for short and long chains of amylose and amylopectin. This region of chromosome 6 was reported previously to be associated with starch synthesis and its properties (Mikami et al. 2008 & Septiningsih et al. 2003). However some unknown genes are also involved in the inheritance of starch content (He et al. 1999; Aluko et al. 2004). Eighteen genes (starch synthesis related genes, SSRGs) are involved in different steps of starch synthesis in rice (Tian et al. 2009). So it is still a complex mechanism to identify that the inheritance of starch components is controlled by waxy gene or its associated modifiers. Few genes in a pathway may result to diverse adaption and in some cases, breeders may select the new rice lines in different directions according to the preference of rice consumers for example grain shape (Wang et al. 2011), amylose content (Tian et al. 2009) and aroma (Kovach et al. 2009). Our results confirmed that GWAS could be used to dissect the phenotype to genotype association of complex traits in rice. Most of the haplotypes controlling amylose content and gel consistency had casual genes identified previously. However, replication of genotypes over different environments could be further helpful for fine mapping and reliability of the QTLs. Therefore, a comprehensive knowledge of phenotypes associated with deep genotype database will lead to a dominant approach for association mapping and discovery of alleles to achieve good quality in rice for coming years. The efficiency of linkage disequilibrium largely depends on statistical methods, no of available samples, mode of inheritance of trait, allelic heterogeneity and type of markers surveyed and recombination distance between marker and a trait (Chapman et al. 1998; Kaplan et al. 1997; Morton et al. 1998; Zollner et al. 2000). Due to this complexity, a single approach for genetic mapping cannot show optimal power under all these circumstances. However, a combined mapping approach using advanced populations

(RILs & double haploids) could be a powerful way for dissecting the genetic basis of complex traits. We compared our results with previously identified QTLs using Gramene web page (http://www.gramene.org/). Most of the QTLs detected in biparental populations, mutants and recombinant inbred lines (RILs) were present in our collection of genotypes and they could be detected using LD mapping.

3.6 Summary

Improvement of grain quality is a major concern of rice breeding worldwide. Starch structure is considered most important to define cooking and pasting profile in rice. We used 754 genome wide single nucleotide polymorphisms (SNPs) markers to determine the structure and patterns of LD among 75 diverse rice accessions. The 75 accessions were divided into three major groups corresponding to three different ecosystems using model based structure software. For the 75 genotypes, the complex traits like amylose content, gelatinization temperature, amylose long chains, amylose short chains, amylopectin long chains and amylopectin short chains were studied. We used GML (general linear model) to link the genomic regions with phenotypic traits. We evaluated variation both within and among three subgroups revealing significant heterogeneity. A total of 59 association signals were detected. We found some of the QTLs linked with SNPs in the genomic regions where QTLs have already been identified. From the results, we discovered that waxy locus not only affects amylose content and GC but also regulates starch branching patterns in rice. The hope that SNPs markers help to identify the genes that underlie complex traits with specific SNPs markers. This technique could be driving force to develop the SNPs genotyping technology at large scale. Moreover, advancement in SNPs genotyping will increase impact of knowledge to understand the relation of genomic region with the quantitative variation of phenotypic trait. The study will help to provide a way to find out valuable genes and alleles associated with starch structure for grain quality improvement in rice.

CHAPTER 4

MUTAGENIC EFFECTS OF DIFFERENT DOSES OF ETHYLE METHANE SULPHONATE (EMS) ON GERMINATION AND YIELD PARAMETERS OF BASMATI RICE

Abstract

Chemical mutagens have long been applied to generate genetic variability in crop plants for research on diverse scientific aspects (drought, salinity, genetic mapping). In rice, more than 500 varieties have been released through induced mutations. Ethyl methane sulphonate (EMS) is the chemical of enormous importance for scientists to induce mutations in different crops and animals. EMS normally induces G-C to A-T transitions. The present research was conducted to check the induced mutagenic effects of different concentrations of ethyl methane sulphonate (EMS) on germination and yield parameters of two basmati rice cultivars (Super basmati and Basmati 370). The seeds were subjected to different treatment levels of ethyl methane sulphonate (EMS). The treated and untreated plants were observed under different agronomic parameters. EMS was quite effective in inducing genetic variability in Basmati rice. The results revealed significant difference among all the traits studied. The efficiency of EMS was found to depend upon its concentration and it was higher at lower concentration in both genotypes. The study further revealed that the use of EMS is an effective approach for creating new rice germplasm.

Note: This chapter has been published as an article and is available online (http://www.academicjournals.org/jpbcs/abstracts/abstracts/abstracts2012/15%20 Apr/Wattoo%20et%20al.htm)

4.1 Introduction

The genetic diversity of rice offers a valuable way to understand the patterns of inheritance of different traits and to study how different genetic backgrounds alter these traits. Human selection and environmental factors have contributed to the genetic diversity in rice especially in Oryza sativa cultivars (Maclean et al. 2002). Basmati rices command higher price in international markets due to pleasant aroma, superfine long slender grain and extra-elongation with least breadth-wise swelling on cooking (Singh et al. 2000). The demand of aromatic rices increased significantly in the recent years as a result of food diversification and eating habits of the people. Pakistan has a unique repute in international market as a producer and exporter of aromatic rices. Rice industry is an important source of employment for thousands of heads in the country. Basmati rice is an important export item of Pakistan. Therefore, creating genetic diversity in Basmati rice to improve its productivity would contribute to national food security and economic development of the country.

The induced mutations have made an outstanding contribution for crop improvement worldwide (Ahloowalia et al. 2004). To date, 2,430 crop accessions have been developed and released following the techniques of induced mutagenesis (FAO/IAEA mutant variety database). Among all the released varieties, rice shares 501 varieties (Maluszynski et al. 2000). The induction of mutagenesis following chemical (EMS, nitrosoguandine) and physical mutagenesis (Irradiation mutagenesis) have previously been used in routine to create variation at genetic level in different crop species. Developing mutagenic plants using chemicals and physical mutagens is economical and so for a method of choice for many researchers around the world. The distribution of induced mutations is random throughout the genome of mutagenized species. Successful plant breeding depends on genetic variation in useful traits. Mutation breeding is a successful tool in obtaining new cultivars and broadening the genetic base of rice crop. Induced mutations have a long history in rice breeding. Most of the newly developed varieties were registered as mutants. Some mutant cultivars also served as a source of desirable alleles in cross breeding (Maluszynski et al. 2000). Mutations are fundamental source of heritable variation. Artificially induced mutations, by physical and chemical

92 mutagens, have greatly advanced the understanding of genetics of crop plants. Induced mutation has become a proven way for creating variation within a crop variety. It provides the possibility of inducing desired characters that either cannot be found in nature or have been lost during evolution (Ahloowalia et al. 2004; Maluszynski and et al. 2005). At present, model plant species are being used to induce mutations to study the functional genomics and physiology (Liu et al. 1999; Nadeau et al. 2000).

4.1.1 Alkylating Agents

Methyl Methane Sulfonate (MMS), mustard gas, Ethyl Methane Sulfonate (EMS) are important Alkylating agents that cause variation in DNA sequence. EMS is one of the most frequently used alkylating agent for chemical mutagenesis in plants due to its potency and ease with which it can be used. It can proficiently induce chemical modification of nucleotides, which results in various point mutations such as nonsense, missense and silent mutations (McCallum et al. 2000a, 2000b). EMS normally results transition mutations (G-C to A-T) (Rao, 1977; Koornneef et al. 1982). Silent mutations cannot generate any modification in phenotype and thus cannot be used for mutagenesis. An important advantage of using EMS as a source of mutagenesis is that a sufficient literature is available that confirms the utility of EMS to study the reverse genetics in a variety of plants and animals.

4.2 Materials and Methods

4.2.1 Genotypes

Most widely grown basmati rice cultivars of Pakistan, Super basmati and Basmati-370 were used in this study. The two cultivars have good grain yield potential but are susceptible to most insect pests. Super basmati is a premier variety of Pakistan with long cylindrical grain, famous for its aroma and nuanced flavor. Basmati 370 is another aromatic cultivar of the country with elite cooking characters. The grain quality characteristics of both varieties are described in Table-4.1.

4.2.2 Mutagenesis and Field Experiments

The experiment was conducted at the experiment station of NIBGE, Faisalabad. For current study, the 4000 dry seeds of each genotype were presoaked in distilled water for 16 hours at room temperature (24-28 °C). The presoaked seeds were divided into two equal parts and one part was taken as control and the second half were treated with different concentrations of aqueous EMS (Sigma, USA) to produce mutants. For EMS mutagenesis, seeds were put in falcon tubes containing different concentrations of EMS solutions (0.0, 0.5, 1.0, 1.5 and 2.0%), for 6 hours in the laboratory conditions with intermittent shaking to maintain uniformity. These doses were chosen because preceding germination assays marked that these doses may be suitable for mutation breeding. The mutagenized seeds were allowed to prewashed with water for six hours and then dried. The purpose of washing was to leach down the EMS residues in the seeds. The treated seeds were divided into three groups (Petri plates, tubs and field). In petri plates containing two layers of moist filter paper, seeds of both genotypes were sown in three replications against each concentration in lab conditions. The recording of data for germination % was started after two days of shifting the seeds into the petri plates. Then 100 seeds of each genotype against different concentrations of EMS were sown in the field to estimate the germination % in field conditions. Thirty days old nursery was transplanted to the field under randomized complete block design. The line to line and plant to plant distance was maintained 4.5 inches. No fertilizer was applied throughout the entire crop tenure. At maturity, data were recorded for the following parameters, plant height, no of unfilled grains/panicle, panicle length, productive tillers/plant, total no of grains/panicle, panicle fertility, and yield/plant. The methodology given by Steel et al. (1997) was used for statistical analysis to construct ANOVA to compare the differences of two genotypes at different doses of EMS. The germination % of both genotypes was recorded after six days of sowing the seeds in the field.

Table 4.1 An overview of mutagenic attempts made in rice

Mutagen Cultivar Nature of Method for Research Used Mutations Detection Group Ethyl methane IR-64 (indica) Point mutations TILLING IRRI sulfonate M202 (japonica) UC Davis (EMS) Nipponbare IPPE Diepoxybutane IR-64 Point mutations, PCR, IRRI (DEB) deletions TILLING N-methyl-N- Kinmaze Single-strand TILLING IGRKU, nitrosourea Taichung 65 DNA breaks Japan (MNU) Fast neutron IR-64,M202 Large deletions, PCR IRRI translocations USA Gamma IR-64 Large deletions, PCR IRRI Rays and point mutations X-rays Accelerated Nipponbare Double-strand breaks, CDMa RIKEN, carbon ions large structural frequency, Japan alterations PCR Sodium azide China-45 Data not available CDM TARI, Tainung- 67 frequency Taiwan

Table 4.2 Chemical composition, physiochemical and cooking characteristics of Super Basmati and Basmati- 370

Variety Shape Amylose content Gelatinisation temperature Gel Consistency Super Basmati Extra long cylender 24.80% 66° C 64 ± 0.2 Basmati- 370 Extra long cylender 23.50% 66° C 61± 0.3 Reasons why these varieties are highly priced Translucent appearance, uniform cylindrical shape, pleasant fragrance upon cooking, sweet taste, soft texture, freshness retains for more than 24 hours, excellent palatibility, tenderness >80%, flakiness > 80%, no stickness or cohesiveness, excellent separatibility, elongation index range 1.02 to 1.35 with 0-2% bursing upon cooking and 0-4% curviness.

4.3 Results and Discussions

The results from graph indicate that the germination % decreases with the increasing dose of EMS. After 6 days of sowing, Basmati-370 showed 96% germination at 0.0% EMS while at 0.5% EMS, it showed 87% germination. Similarly at the doses of 1.00% and 1.5%, basmati-370 showed 68% and 7% germination respectively. No germination was

95 noticed at 2.00% EMS for basmati-370. In case of Super basmati, germination % was 95% at 0.00% EMS and 88% at 0.5%. Similarly at the doses of 1.0% and 1.5%, Super basmati showed 60% and 16% germination % respectively. At the dose of 2.00%, 10% germination% was noticed only in super basmati.

Table 4.3 Least significant difference test (LSD) p< 0.05, where T1=0.0, T2=0.5, T3=1.0, T4=1.5 and T5=2.0%.Means that were significantly different based on LSD test are shown with different letters (a,b,c,d). The values in the same column with different lower case letters indicate significant differences at p < 0.05.

Height Tiller/plt Pn. Length NO of grains/pn Pn Fertility Yield/plant

B.370 T1 97.5714a 13.04762a 23.5714a 94.2857a 78.2146a 11.09524a B.370 T2 91.7619bc 6.85714a 23.4286a 94ab 78.6969a 7.2381a B.370 T3 90.375c 8.58333ab 23.0417b 81.8958a 80.3886a 9.91667b 95.9524ab 14.09524a 23.7619c 86.5714abc 77.9812a 7.19048a B.370 T4 B.370 T5 93.2222abc 11.33333b 24.3333ab 87.1481a 82.3094a 12.55556a S.B T1 145.452a 7.39683a 27.4286a 110.2222ab 91.8604a 13.63492ab ab c d c a d S.B T2 142 7.80952 26.1429 102.1905 87.3984 10.66667 S.B T3 138.571b 9.85714a 26.3333b 104.3333a 81.8901a 12.33333a

S.B T4 124.056d 6.22222b 26.1667a 93.1111d 69.0127b 6.88889a S.B T5 129.889c 7.77778d 26.2222a 114.6667a 87.0259a 7.11111b

Germination % of Basmati-370 in the field on different Germination % of Super Basmati in field on different levels of EMS levels of EMS 120 100 90 100 80 70 80 60 60 EMS % 50 EMS % 40 Germination % Germination % 40 30 20 20 10 0 0 0 0.5 1 1.5 2 0 0.5 1 1.5 2

Figure-4.1 The germination % of Basmati-370 and Super Basmati in field conditions

Germination % of Basmati-370 on different doses Germination % of S.B at different doses of EMS of EMS in Lab in Lab 120 120 100 100 80 80 60 EMS % 60 EMS % 40 Germination % 40 Germination % 20 20 0 0 0 0.5 1 1.5 2 0 0.5 1 1.5 2

Figure-4.2 Germination % of Basmati-370 and Super basmati at different doses of EMS in lab conditions

The results of lab and field conditions for both genotypes were significant. Basmati-370 showed 100% germination at control and 0.5% EMS. At EMS dose of 1.0% and 1.5%, it showed 94% and 55% germination% respectively. Figure 4.2 revealed that at the dose of 2.0%, 13% germination was recorded which is 13% more than filed conditions where no germination was observed. In case of super basmati, 100%, 100%, 80%, 30% and 3% germination recorded at EMS concentration of 0.00%, 0.5%, 1.0%, 1.5% and 2.0% respectively (Fig-4.1). The results from both cultivars revealed that germination % was decreased with increasing concentration of EMS.

Table 4.4 Analysis of variance (ANOVA) table for comparison of means, Where *p<0.5, **p<0.01,***p<0.001

Height Tiller/plt Pn. Length NO of grains/pn Pn Fertility Yield/plant

Replication 2 45 32.01* 0.49 334.5 13.66 21.596 Treatment 4 124*** 8.29 0.69 165.4 133.68* 24.981* Varieties 1 13367*** 66.19* 60.12*** 1950.0* 115.21 2.089 Treatment*variety 4 136*** 24.36 0.88 121.7 108.77* 19.645 Error 18

The results presented above (Table 4.3 and Table 4.4) clearly revealed that the performance of all the yield traits decreased with increasing dose of EMS. These mutants indicate that EMS can be used for the improvement of rice crop. Table-2 shows the analysis of variance for comparisons of means. These types of mutants were also reported earlier (Alcantara et al. 1996). The mechanism of a chemical mutagen is mainly influenced by pH and temperature during treatment especially in case of nitroso amides (Veleminsky et al. 1970). Some sterile mutants were also observed. Chemical mutagens work more efficiently at lower concentration. The higher concentration of a chemical mutagens leads to failure in induction of mutations due to injury and sterility (Kharkwal, 1998 and Cheema et al. 2003). Table-3 indicates the least significant (LSD) test of all the yield parameters studied at p<0.05. The variation in biological parameters viz., plant height and no of tillers may be due to lower auxin level (Gordon et al. 1955), chromosomal abrasions or due to decline of assimilation mechanism (Quastler et al. 1950). On the basis of lethality, the highest mutagenic efficiency was recorded at 2.0% EMS (highest dose).

4.4 Summary

The efficiency of EMS was higher at lower concentration. Results suggest that using a dose 0.5% - 1.0% of EMS for 6 hours can induce mutations in rice. The mutant plants generated in this study would be used for linkage and mapping studies of rice under different yield and grain quality parameters. These mutant plants could also be used as genetic markers. Thus mutation induction is a useful conventional breeding tool for developing superior cultivars. However with the development of novel approaches such as TILLING, a breeder could be able to identify useful mutations that might otherwise be ignored during the selection process.

THESIS SUMMARY

Grain quality of rice is a major concern of rice consumers worldwide and is economically important trait mainly influenced by starch properties. Components of grain quality are considered important in predicting its market worth. Amylose content of rice grain has key role in determining the cooking and eating quality of different rice varieties. In breeding programmes, new lines are selected based on amylose content and starch pasting profile, as these indicators are associated with grain quality. Developing cultivars with superior cooking and eating quality traits have been the focus of rice breeders around the world (Khush, 2005). Different factors (genetic control, triploid endosperm, environmental conditions, and processing techniques) influence the grain quality. Sometimes environmental factors under some circumstances have great impact on quality than inherited traits (Juliano 2003). Rice starch has been used in the food industry for numerous applications. The modification of starch to improve its functional properties is normally attained by physical, such as heat or moisture treatments, or chemical means through etherification, cross-linking and grafting of starch (Fitzgerald et al. 2004). The process in which starch is heated in water is called pasting, which is the formation of a viscous material consisted of leached amylose and disintegrated starch granules.

However sometimes amylose content does not always present a clear picture of grain quality. In cases where rice varieties have same amylose content but have different cooking and eating quality traits, protein content is known to be associated with these differences. To get a more complete picture of the eating quality of rice grains, amylose content must be only one of many tests in the evaluation process. Proteins are second highest components in rice grain after starch. However, the role of proteins in determining the rheological properties is not completely understood. In the later stages of the breeding program, amylography is utilized to check on the pasting profile of cooked rice. The pasting profile is used to evaluate different rice varieties based on grain quality differences. The varieties with different pasting profile have different cooking properties.

To identify the quantitative trait loci (QTLs) or genes for protein content, amylose content and pasting properties of rice, a segregating population was developed by

99 crossing two parents IR-64 and IR-132. A QTL analysis was conducted using 125 SNPs markers distributed on all 12 rice chromosomes on a progeny of 213 plants. Many different genomic regions have been identified to influence the starch pasting properties on different linkage groups. A total of 24 main effect QTLs (M-QTLs) for different grain quality traits were identified and mapped on 7 different chromosomes (1, 4, 7, 8,9,10 &11).

For amylose content, three QTLs were identified. Two QTL on chromosome 4 (qAM-4a, qAM4b) explaining 18% of total phenotypic variance (R2). A minor QTL on chromosome 11 (qAM-11) increased amylose by 12% from IR-64. For protein content, we mapped five QTLs, of which two on chromosomes 1 explain 38% of phenotypic variance. Three QTLs were identified on each of chromosome 8, 10 and 11 with 27%, 10%, and 39% of phenotypic variance (R2) respectively. Two QTLs linked with the phenotypic variation of protein content were detected on chromosome 1 showing the linkage of IR-132 alleles to lower the protein with total phenotypic variation of 38%. IR-132 parent showed lower protein content value than the IR-64. The mapping results showed that the inheritance of peak viscosity was controlled by three loci qPV7, qPV9 and qPV11 on chromosomes 7, 9 and 11 explaining 10%, 12% and 6% phenotypic variance respectively. Two loci qBD1 and qBD4 were detected for break down on chromosome 1 and 4 showing 8% and 5% phenotypic variance. The phenotypic variation of final viscosity was associated with two QTLs qFV1 and qFV10 on chromosome 1 and 10 respectively. Phenotypic variance of both QTLs was 14% and 12% respectively. The variation of setback viscosity was detected to link with two QTLs qSB10 and qSB11 on chromosomes 10 and 11 with phenotypic variance of 8% and 29% respectively. The inheritance of peak time was controlled by six QTLs, out which three were identified on chromosome 1, two on chromosome 8 and one on chromosome 9 respectively. The phenotypic variance of five QTLs was 18%, 22%, 36%, 12% and 14% respectively. Two novel alleles were detected with a significant effect on pasting temperature on chromosome 11 with phenotypic variance of 5% and 8% respectively. The genetic mapping further revealed that the inheritance of retrogradation was controlled by two alleles on chromosome 1 and 11 (qLO-1and qLO-11) with phenotypic variance of 12% and 14% respectively. The results

100 clearly revealed that genes/alleles from IR-64 could be used to improve the pasting profile and biochemical components of high yielding cultivars. In summary, the alleles from IR-64 provided a small but statistically significant improvement for the components of grain quality based on starch properties and protein content.

In another experiment, our objectives were to exploit the potential of genome wide association studies to estimate the genetic structure and to map the genomic regions associated with starch chain length distribution. We used 754 genome wide single nucleotide polymorphisms (SNPs) based markers to study the patterns of linkage disequilibrium (LD) and structure of population among seventy-five diverse rice genotypes (indica, temperate japonica & tropical japonica). All the seventy-five accessions were divided into three major groups based on structure analysis (model based). The three groups represented three different geographic regions. For the 75 genotypes, the complex traits like amylose content, gelatinization temperature, amylose long chains, amylose short chains, amylopectin long chains, and amylopectin short chains were studied. The associations of SNPs markers with a phenotypic trait were disclosed by using the approach of GLM (general linear model). We examined variation both within and among three subgroups revealing significant heterogeneity. A total of 59 association signals were detected. From the results, we found that waxy locus not only affects amylose content and GC but also regulates starch branching patterns in rice. The study will help to provide a way to find out valuable genes and alleles associated with starch structure for grain quality improvement in rice.

In another study we optimized the way of inducing mutations using chemical mutagen EMS (Ethyle methane sulphonate) in Basmati rice. Chemical mutagens have long been applied to generate genetic variability in crop plants for research on diverse scientific

101 aspects (drought, salinity, genetic mapping). In rice, more than 500 varieties have been released through induced mutations. Ethyl methane sulphonate (EMS) is the chemical of enormous importance for scientists to induce mutations in different crops and animals. EMS normally induces G-C to A-T transitions. The study was conducted to check the induced mutagenic effects of different concentrations of ethyl methane sulphonate (EMS) on germination and yield parameters of two basmati rice cultivars (Super basmati and Basmati 370). The seeds were subjected to different treatment levels of ethyl methane sulphonate (EMS). The treated and untreated plants were observed under different agronomic parameters. EMS was quite effective in inducing genetic variability in Basmati rice. The results revealed significant difference among all the traits studied. The efficiency of EMS was found to depend upon its concentration and it was higher at lower concentration in both genotypes. The study further revealed that the use of EMS is an effective approach for creating new rice germplasm.

102

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