evaluating diversity array technology (dart) for the nsw ...evaluating diversity array technology...

Evaluating Diversity Array Technology (DArT) for the NSW Rice Breeding Program

A report for the Rural Industries Research and Development Corporation by Russell Reinke June 2006 RIRDC Publication No 06/055 RIRDC Project No DAN-204A

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© 2006 Rural Industries Research and Development Corporation. All rights reserved. ISBN 1 74151 318 9 ISSN 1440-6845 Evaluating Diversity Array Technology (DArT) for the NSW Rice Breeding Program Publication No. 06/055 Project No. DAN204A The information contained in this publication is intended for general use to assist public knowledge and discussion and to help improve the development of sustainable industries. The information should not be relied upon for the purpose of a particular matter. Specialist and/or appropriate legal advice should be obtained before any action or decision is taken on the basis of any material in this document. The Commonwealth of Australia, Rural Industries Research and Development Corporation, the authors or contributors do not assume liability of any kind whatsoever resulting from any person's use or reliance upon the content of this document. This publication is copyright. However, RIRDC encourages wide dissemination of its research, providing the Corporation is clearly acknowledged. For any other enquiries concerning reproduction, contact the Publications Manager on phone 02 6272 3186. Researcher Contact Details Russell Reinke Yanco Agricultural institute Private Mail Bag NSW 2703 Phone: 02 6951 2516 Fax: 02 6955 7580 Email: [email protected]

In submitting this report, the researcher has agreed to RIRDC publishing this material in its edited form. RIRDC Contact Details Rural Industries Research and Development Corporation Level 2, 15 National Circuit BARTON ACT 2600 PO Box 4776 KINGSTON ACT 2604 Phone: 02 6272 4819 Fax: 02 6272 5877 Email: [email protected]. Web: http://www.rirdc.gov.au Published in June 2006 Printed on environmentally friendly paper by Canprint

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Foreword The rice breeding program focuses mainly on improving grain yield and quality, both of which are polygenic or complex traits, conditioned by many interacting genes. Although important, the need for marker tests for simply inherited traits is relatively small. For example, many of the genes for disease resistance are relatively simply inherited and often require simple molecular marker tests, however the absence of significant rice pests and diseases in NSW means that these tests are a lower priority. While the industry is of central importance to the Riverina Region of NSW, it is both geographically compact, and small by world standards. There is limited funding for research, and the amount of resources directed at genetic improvement must be commensurate with the overall size of the industry. Consequently there is a need to use new technology that will complement the existing program and improve the development of new varieties, where the emphasis is on traits with complex inheritance such as yield and quality. This study aimed to examine one such new technique, Diversity Array Technology (DArT), with a view to establishing its advantages and disadvantages, and the extent to which it should be integrated into the existing rice improvement program. There were three phases to this research project. The first involved constructing a DArT reference panel, a micro-array of 6,144 DNA fragments that varied between the 254 rice varieties from which the central pool of DNA was derived. Secondly relationships between all current NSW rice varieties and breeding lines were examined. We then chose a series of populations derived from crosses in which the native African species Oryza glaberrima was used as a parent. Finally, a sub-set of lines derived from a cross between a NSW cultivar and an inter-specific cross (between the widely cultivated Oryza sativa species and the native African species Oryza glaberrima) were carefully phenotyped for seedling vigour under controlled environment conditions. A series of high and low-vigour lines from this experiment were genotyped using DArT, and of the 61 DNA fragments or DArT markers which varied among these lines, five markers were significantly associated with improved seedling vigour. The project has highlighted the capability of DArT to provide detailed genetic fingerprints of varieties, to distinguish closely related lines, and to establish relationships between lines providing useful information for choosing parental combinations for breeding. Each DArT analysis can provide up to 600 markers widely distributed across the genome and thus in diverse crosses enables selection of lines with small DNA segments of the diverse parent inserted into a locally-adapted line. Future association studies relating agronomic and grain quality data will determine if DArT markers can replace existing molecular marker tests such as for fragrance. DArT analysis will play an increasing role in the rice breeding program because the cost per marker is low, its highly automated system has high throughput capacity, and the data generated will have increasing value with continuing advances in bioinformatics. This report is an addition to RIRDC’s diverse range of over 1500 research publications. It forms part of our Rice R&D sub-program which aims to improve the profitability and sustainability of the Australian rice industry. Most of our publications are available for viewing, downloading or purchasing online through our website: • downloads at www.rirdc.gov.au/fullreports/index.html • purchases at www.rirdc.gov.au/eshop Peter O’Brien Managing Director Rural Industries Research and Development Corporation

http://www.rirdc.gov.au/fullreports/index.html

http://www.rirdc.gov.au/eshop

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Acknowledgments My thanks are due to co-operators at the University of Queensland for evaluating seedling vigour within a number of populations, and included Prof Shu Fukai, Dr Thusitha Gunawardena and Dr Tim Farrell. Ben Ovendon (Honours student, Charles Sturt University) also provided significant input, through detailed seedling vigour phenotyping of a sub-set of lines from one of the populations, and through extensive discussion of the benefits and disadvantages of whole-genome profiling. Thanks to Dr Andrzej Kilian for his collaboration on this project, for providing the technology and for the hours spent discussing and educating as to the meaning and implications of using whole-genome analysis. The work of Sujin Patarapuwadol was critical to this research for development and constant refinement of the reference panel. Thanks to Sujin for the regular interaction, explanations and provision of information relating to the underlying concepts of DArT.

Abbreviations DArT: Diversity Array Technology QTL: Quantitative Trait Loci

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Contents Foreword ............................................................................................................................................... iii Acknowledgments................................................................................................................................. iv Abbreviations........................................................................................................................................ iv Executive Summary ........................................................................................................................... viii Executive Summary ........................................................................................................................... viii

New technology to increase efficiency of variety development................................................... viii Background .................................................................................................................................. viii Aims and objectives ....................................................................................................................... ix Methods used.................................................................................................................................. ix

The DArT reference panel.......................................................................................................... ix Assessing diversity of varieties included in the reference panel ................................................. x Seedling vigour in a range of populations................................................................................... x Detailed measurement of seedling vigour and DArT analysis .................................................... x

Results/Key findings ...................................................................................................................... xi DArT reference panel development ........................................................................................... xi DArT fingerprinting ................................................................................................................... xi DArT Markers and seedling vigour........................................................................................... xii

Implications .................................................................................................................................. xiii Recommendations ........................................................................................................................ xiv

Introduction ........................................................................................................................................... 1 Technology for complex traits ............................................................................................................ 1

DArT applications ........................................................................................................................... 1 Objectives ............................................................................................................................................... 2 Methodology .......................................................................................................................................... 2 Results .................................................................................................................................................... 3

Development of DArT Reference Panel.............................................................................................. 3 Microarray-based markers............................................................................................................... 3 What is DArT? ................................................................................................................................ 3 Relationships between varieties used for the reference array.......................................................... 5

Dendrograms ............................................................................................................................... 5 Principal Coordinate Analysis..................................................................................................... 6

Phenotyping populations for seedling vigour...................................................................................... 9 Introduction ..................................................................................................................................... 9 Materials and Methods .................................................................................................................... 9 Results ........................................................................................................................................... 10

Detailed phenotyping under controlled-environment conditions ...................................................... 12 Introduction ................................................................................................................................... 12 Materials and Methods .................................................................................................................. 12

Trial Design............................................................................................................................... 13 Trial Location and Environment ............................................................................................... 13 Environmental Variables Controlled in the Trials..................................................................... 13 Measurements............................................................................................................................ 14 Data Analysis ............................................................................................................................ 15

Results ........................................................................................................................................... 15 Correlations between Component Traits ................................................................................... 15 Repeatability between Trials ..................................................................................................... 17 Variation and Distribution of Vigour Traits.............................................................................. 18

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Discussion ..................................................................................................................................... 20 Relationships between Seedling Vigour and Vigour Traits ...................................................... 20 Leaf Width and Seedling Vigour............................................................................................... 21 Population Variation for Vigour................................................................................................ 21 Effects of the Controlled Growth Environment on Phenotype ................................................. 22

Leaf width, a significant component of seedling vigour ............................................................... 23 Genetic Analysis of Seedling Vigour using DArT............................................................................ 23

Introduction ................................................................................................................................... 23 Materials and Methods .................................................................................................................. 24

Line selection ............................................................................................................................ 24 DNA extraction ......................................................................................................................... 25 DArT Analysis .......................................................................................................................... 26

Results ........................................................................................................................................... 26 DArT Markers and Genetic Diversity among Lines ................................................................. 26 Marker-Trait Association .......................................................................................................... 29

Discussion ..................................................................................................................................... 29 Features of the Diversity Analysis ............................................................................................ 29 Clones Associated with the High or Low Vigour Groups......................................................... 30 Future Directions for DArT Analysis of Vigour ....................................................................... 31

Conclusions ................................................................................................................................... 32 Discussion............................................................................................................................................. 32 Implications.......................................................................................................................................... 33 Recommendations ............................................................................................................................... 34 Appendices ........................................................................................................................................... 36

Appendix 1. ....................................................................................................................................... 36 References ............................................................................................................................................ 41

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List of Tables Table 1. Three Oryza glaberrima inter-specific crosses from West Africa and four Australian adapted

Oryza sativa cultivars selected for hybridising.............................................................................. 9 Table 2. F2-derived F3 populations tested for early seedling vigour under low temperature conditions...... 9 Table 3. Minimum, maximum and mean air temperature from sowing to 35 days after sowing at Redland

Bay in 2002.................................................................................................................................. 10 Table 4. The population mean (standard deviation), population range and Australian and WAB parent for

dry weight (mg per plant), leaf number, height to the highest ligule in cm (height 1), height to the tip of the longest leaf in cm (height 2) for each population in sowing 1 and 2. .................... 11

Table 5. Correlation coefficients for trial 1. Best linear unbiased predictors (adjusted values) are shown in bold, while arithmetic means for raw data are not bold. Correlations significant at p = 0.01 are highlighted................................................................................................................................... 16

Table 6. Correlation coefficients for trial 2. Best linear unbiased predictors (adjusted values) are shown in bold, while arithmetic means for raw data are not bold. Correlations significant at p = 0.01 are highlighted................................................................................................................................... 16

Table 7. Repeatability of measurements between trials 1 and 2, as indicated by correlations between best linear unbiased predictor trait values from each trial................................................................... 17

Table 8. DArT marker scoretable showing Fisher’s exact test p-values for each polymorphic DArT clone. Clones significantly (p>0.01) associated with high or low vigour are shown in bold................. 28

List of Figures Figure 1. Schematic of Diversity Array Technology. A subset of genomic DNA is selected and cloned into

a vector for printing as an array. DNA for hybridisation follows the same process, only instead of being cloned it is labelled and hybridised to the array (Lezar et al. 2004)…………………………………………………………………………………3

Figure 2. DArT comparison of rice varieties Millin and IR20, showing DNA common to both varieties (yellow spots), DNA present only in Millin (red spots) or IR20 (green spots). ............................ 4

Figure 3. Dendrogram showing relationships between cultivars used in development of the DArT reference panel. Cultivars include Oryza sativa and related species. ............................................ 6

Figure 4. Dendrogram showing relationships based on DArT analysis within Oryza sativa, including NSW commercial varieties and advanced breeding lines........................................................................ 7

Figure 5. Principal coordinate (PCO) analysis results for varieties included in the DArT reference panel development. PCO axis 1 and PCO axis 2 are shown. Symbols represent different varietal groups. ........................................................................................................................................... 8

Figure 6. Principal coordinate (PCO) analysis results for varieties included in the DArT reference panel development. PCO axis 1 and PCO axis 3 are shown. Symbols represent different varietal groups. ........................................................................................................................................... 8

Figure 1. Rice seedlings in the second phenotyping trial under controlled-environment conditions. Destructive vigour measurements were conducted at this stage.................................................. 14

Figure 8. Coefficient of variation (standard deviation divided by the mean) for vigour component traits in trials 1 and 2. ............................................................................................................................... 18

Figure 10. Best linear unbiased predictors (±SE) for the sum of leaf 2 and leaf 3 widths of high and low vigour groups and the population parents.................................................................................... 24

Figure 11. Best linear unbiased predictors (±SE) for the leaf area of high and low vigour groups and the population parents........................................................................................................................ 25

Figure 12. Principle coordinate analysis of high and low vigour lines, and parent varieties. ....................... 27 Figure 13. Frequency of dry weight, leaf number, height to ligule and height to leaf tip for two sowing times

for the YC00001 population (n=46). The parental means are also shown. ................................. 37 Figure 14. Frequency of dry weight, leaf number, height to ligule and height to leaf tip for two sowing times



for the YC000011 population (n=23). The parental means are also shown. ............................... 39 Figure 17. Frequency of dry weight, leaf number, height to ligule and height to leaf tip for two sowing times

for the YC000017 population (n=84). The parental means are also shown. ............................... 40

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Executive Summary New technology to increase efficiency of variety development A significant challenge facing the NSW Rice Breeding Program is to deliver new varieties, with enhanced yield, quality and production efficiency in a timely and cost-effective manner. Consequently there is a need to use new technology that will complement the existing program and improve the efficiency and timeliness of development of new varieties. There have been rapid advances in DNA technology over the past 10 years, and rice has been pivotal in this expansion. Because of its relatively small genome size and relative ease of transformation, rice has featured significantly in gene discovery research. It was also the first of the crop species to have the entire genome sequenced. Initially the Japanese rice cultivar Nipponbare (from the japonica sub-species) was the first to be sequenced, followed by the indica variety 93-11. Against this background there is a need for the NSW Rice Breeding Program to capture the benefits of these advances as they become warranted. The challenge is using the information and new biotechnology techniques in a breeding program where the emphasis is on traits with complex inheritance such as yield and quality. This study aimed to examine one such new technique, Diversity Array Technology (DArT), with a view to establishing its advantages and disadvantages, and the extent to which it should be integrated into the existing rice improvement program Outcomes from this project are relevant to the RIRDC rice research committee and to the rice improvement program. Background The NSW rice industry is of central importance to the Riverina Region of NSW, it is both geographically compact, and small by world standards. There is limited funding for research, and the amount of resources directed at genetic improvement must be commensurate with the overall size of the industry. The genome of rice has been sequenced, many genes have been located on the rice genome map, and molecular markers for a wide range of traits have been developed. Although an important adjunct to the NSW Rice Breeding Program, the need for marker tests for simply inherited traits is relatively small. Micro-satellite markers for starch structure have been integrated as a routine test into the breeding program on F4 breeding lines, and a marker for fragrance is used on specific populations at this stage. Although many of the genes for disease resistance are relatively simply inherited and require simple molecular marker tests, the absence of significant rice pests and diseases in NSW means that these tests are a lower priority and the number of individual marker tests needed is relatively low. The NSW rice breeding program focuses mainly on improving grain yield and quality, both of which are polygenic or complex traits, conditioned by many interacting genes. Rice biotechnology research has focused on regions of the genome conditioning complex traits (quantitative trait loci or QTL), but these are not widely applicable, effective or affordable in breeding programs. Often QTL are only relevant to the population in which they have been developed, not to the broader germplasm base used in most breeding programs. Any association between a trait observed in the breeding population and the regions of the genome which vary (QTL) are highly dependent on the accuracy with which both measurements are taken. Measurement of phenotype is problematic, as the environment in which the plants are grown can have a significant impact on the expression of the trait. If the measurement of phenotype is flawed, then the association of genotype and phenotype is similarly flawed and the associations will have little relevance or application to practical plant breeding programs. Given the worldwide expansion in rice biotechnology, and the need for the NSW Rice Breeding Program to focus on more complex traits, there is a need to test and use relevant technology to assess its value and balance the efficiency and affordability.

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Aims and objectives The aims and objectives of this project were to: • Examine diversity array technology (DArT) to build an understanding of the technology • Construct a DArT reference panel necessary for ongoing DArT analysis of varieties and breeding

lines • Assess the potential of the technology for integration into the NSW Rice Breeding Program. Methods used There were three phases to this research project. The first involved constructing a DArT reference panel, which is the array of DNA fragments shown to vary among a broad cross-section of rice varieties. Secondly relationships between all current NSW rice varieties and breeding lines were examined. The final phase involved an endeavour to relate phenotype to genotype, using the quantitatively inherited trait seedling vigour. A series of populations derived from crosses between NSW varieties and a variety derived from an inter-specific cross between the native African species Oryza glaberrima and Oryza sativa, were characterised for seedling vigour. A sub-set of lines derived from one of these populations were carefully phenotyped for seedling vigour under controlled environment conditions. A series of high and low-vigour lines from this experiment were genotyped using DArT, and the DArT markers which varied among these lines were examined against the seedling vigour values. The DArT reference panel The varieties selected for the central pool of DNA included approximately 50 commonly used in the hybridisation program, representatives of the indica, japonica and tropical japonica sub-species, and a range of other Oryza species which have the potential to be used in the breeding program to contribute useful traits in future. These included 21 Oryza rufipogon accessions from Nepal, China, India, Laos and Malaysia, Oryza barthii, Oryza meridionalis, Oryza glaberrima and a range of Oryza sativa including representatives from the japonica, indica and tropical japonica sub-groups. Briefly, DNA was extracted from all of these varieties and broken into fragments of varying sizes using specific enzymes. To reduce the number of fragments to a manageable size, a complexity reduction process was carried out. This involved selecting only those fragments which contained a specific small sequence (a miniature inverted transposable element or MITE) known to be widely and evenly distributed throughout the genome, and which is often associated with functional regions of DNA (ie. previously identified genes). The selected fragments had fluorescent dye attached and were arranged in an array on a glass slide. DNA extracted from each of the reference varieties in turn was fragmented, dyed, and washed over (hybridised to) the array to determine which of the fragments varied among the varieties constituting the broad sample of Oryza DNA. An automated system was used to measure variation in the array by examining the colour of each fragment on the array. Many of the fragments are common to all varieties, and are of no use in determining genetic differences. Hence, those fragments showing differences between varieties (polymorphism) were retained for the final reference array comprising 6,144 points. A total of 254 rice varieties were used in construction of the reference panel. Thereafter, by using different coloured dyes attached to fragments derived from different varieties, it is possible to carry out a pair-wise comparison showing where varieties have the same sequence and where they differ, for each of the fragments on the array. This is particularly useful when examining breeding lines from a bi-parental cross, as at each point where the parents are polymorphic, markers in the progeny show from which parent the DNA was derived at that position in the genome.

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Assessing diversity of varieties included in the reference panel Results from the initial DArT analysis were examined in two ways. First, from the pattern of DArT markers for each variety the degree to which each line varies from the others can be measured in terms of a dissimilarity value. All the pair-wise comparisons result in a 254 × 254 dissimilarity matrix. A way of presenting these values is in a dendrogram which shows visually the grouping of varieties according to their measured genetic differences and similarities. The grouping of varieties was also analysed by principal coordinate analysis (PCO). It is a variant of principal components analysis (PCA) which is a technique used to simplify a dataset. In essence PCA is a linear transformation that chooses a new coordinate system for the data set such that the greatest variance in a single dimension of the data set comes to lie on the first axis (then called the first principal component), and the second greatest variance on the second axis, etc. In contrast however, instead of the finding the coordinates maximising variance, PCO finds dimensions that maximise similarities among the data points. This provided an alternative method of visualising the grouping of varieties, and the information is highly relevant to selection of parents for future hybridisations. Seedling vigour in a range of populations Five populations were developed by hybridising four NSW cultivars with three inter-specific crosses between the West African Oryza glaberrima × Oryza sativa (Table 1 and Table 2, see page 9). The resulting F2 seed was sown at Leeton Farm in the 2001/02 season as a space-planted population to minimize the competitive effect between plants. Single panicles that flowered in less than 120 days and less than 1m tall at maturity were selected. These F2-derived F3 (F2:3) seeds were direct sown during 2002 at Redland Bay in Queensland. The seeds were sown at two dates, 4 September and 17 October. These sowing dates were selected because the temperatures at this time were similar to the temperatures experienced during the establishment of rice in south eastern Australia. Measurements on individual seedlings were made 35 days after sowing for both sowing dates (9 October and 22 November, respectively). There were 233 entries including parental lines and standard varieties. Lines were randomized within each population in each of three replications. Each plot was 1 m in length and comprised of 10 seedlings at 10 cm spacing. Due to establishment variability three uniform plants were selected for each plot for detailed measurements. The measurements included leaf number, height from the shoot base to the highest ligule (height 1), height from shoot base to the tip of the longest leaf (height 2) and total dry matter. Analysis was conducted using restricted maximum likelihood (REML) methods, and revealed significant spatial effects associated with columns and rows within each population. Detailed measurement of seedling vigour and DArT analysis A controlled-environment experiment was carried out to measure seedling vigour and its components. The experiment was conducted twice, under slightly different environmental conditions to provide some assessment of the interaction between genotype and environment. A total of 69 semi-dwarf lines selected from an F3:5 population of Quest × WAB-450-I-B-P-160-HB (WAB450). Quest is an Australian semi-dwarf medium grain semi-dwarf variety adapted to the temperate Australian climate. WAB450 is a tall West African inter-specific variety bred from the tropical japonica WAB56-104 and an O. glaberrima accession from Ivory Coast, CG14. WAB450 is estimated to retain about 8-10% of the O. glaberrima genome (Ndjiondjop et al. 2003). Trials were conducted with a randomised block design, with both phenotyping trials conducted on the same layout.

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Both parents where included in the trial, as were 4 control varieties: 2 high-vigour varieties (HSC55 and the tall variety Hungarian No. 1) as well as the most popular Australian cultivar (Amaroo) and the most widely grown rice world-wide, IR64 (Peng et al. 2000). Only 9 replications of 1 individual each were used for each parent and control variety, giving a total experiment size of 882 individual plants. Unblemished seeds were selected from 2-3 panicles within each line and each seed weighed to four decimal places. The seedling trays were kept at full soil moisture capacity by thorough misting each day. To approximate field conditions at sowing temperatures were set to a maximum of 22°C and minimum of 13°C. As seeds germinated, elongation measurements of the growing shoot were taken, from the tip of the emerging leaf to the soil level. For phenotyping trial 1 measurements were taken almost daily from 7 days after sowing (DAS) until at least 3 elongation measurements were obtained. These measurements were averaged to obtain a mean elongation rate. Seeds not having germinated by 19 DAS were treated as non-germinating. Individual dates of emergence were extrapolated from the data by subtracting the average elongation rate from the first shoot length measurement for the seedling. Between 22-24 DAS, key measurements were obtained by destructive sampling. The widths and lengths of individual leaves were measured, and the plants cut off at 5mm above soil level and dried in a 30-38°C oven for 3 weeks. The remaining dry matter was then weighed to 4 decimal places to obtain an aboveground biomass measurement (dry weight). Analysis of the components of seedling vigour indicated that leaf area and in turn, leaf width were the best and most repeatable representatives of vigour. Therefore, lines were ranked for their leaf area in both trials, and the widths of leaf 2 and 3 in both trials. By calculating the sum of these 4 rankings, an overall rank for each line was determined, as a representative index for vigour in the population. A total of 20 lines were selected to undergo DArT analysis, the two parents WAB450 and Quest and 9 lines from the high and low vigour extremes. The 9 lines with highest ranking (excluding parents and standard varieties) were selected as the 9 high-vigour lines, and the 9 lowest ranking lines were selected for the 9 low-vigour lines. These high and low-vigour lines were genotyped using DArT, and of the 61 DNA fragments or DArT markers which varied among these lines, four markers were significantly associated with improved seedling vigour. Results/Key findings DArT reference panel development The reference panel constructed during this project used DNA extracted from a total of 254 rice varieties, including 50 varieties commonly used in the breeding program. The reference panel was constructed using a broad range of varieties and species constituting the “meta-genome”, and the scope and diversity of these varieties has resulted in a reference panel that should remain relevant and useful to the breeding program for a considerable time. The reference panel should only need to be updated if the program has substantial change in the varieties used for hybridisation, or focuses on a species which has little representation on the panel. Significant expansion in the use of Oryza glaberrima per se. (rather than the inter-specific cross involving O. glaberrima and O. sativa) may require future changes to the reference panel to ensure that such germplasm is well-represented. DArT fingerprinting The project has highlighted the capability of DArT to provide detailed genetic fingerprints of varieties, to distinguish closely related lines, and to establish relationships between lines providing useful information for choosing parental combinations for breeding.

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Development of the reference panel demonstrated the capacity of the technique to differentiate all of the closely related lines within the suite of existing and prospective varieties from the NSW Rice Breeding Program.. Each DArT analysis can provide up to 600 markers widely distributed across the genome and thus in diverse crosses enables selection of lines with small DNA segments of the diverse parent inserted into a locally-adapted line. Future association studies relating agronomic and grain quality data will determine if DArT markers can replace existing molecular marker tests such as for fragrance. DArT analysis will play an increasing role in the rice breeding program because the cost per marker is low, its highly automated system has high throughput capacity, and the data generated will have increasing value with continuing advances in bioinformatics.

DArT Markers and seedling vigour Among the 20 lines genotyped (high and low vigour lines, and the parents) a total of 61 DArT markers varied resulting in 1189 individual data points. The number of DArT markers reflected about 1% polymorphism out of the 6,144 clone array. The hybridisation pattern for each line can be seen in Table 8 on page 28. A ‘1’ in the table indicates that the genomic DNA fragments from the individual line matched the DNA fragment on the array. A ‘0’ indicates that the DNA fragment from the line was different to that on the array and no hybridisation was detected. This way, alleles for each DArT marker in the parent varieties can be traced to the progeny lines to identify which alleles in the progeny were derived from which parent. DArT markers significantly associated with the high and low vigour groups were indicated by the probability value for Fisher’s Exact Test on the right hand column of Table 8 (page 28), and these results indicate that only 5 of the 61 markers varied significantly between the high and low vigour groups. Three of the significant DArT markers for vigour had identical marker distributions and are probably linked, although they may represent independent loci that influence the expression of vigour. It is interesting to note that for these 3 clones, the high vigour allele originated from Quest, the supposedly low vigour parent. The occurrence of high vigour alleles from the low vigour parents has been reported in other vigour studies (Redoña & Mackill, 1996b and Zhang et al.2005b). This further emphasises that vigour is a genetically complex character and underscores the importance of genetic context, where successful expression of this character requires the interaction of genetic components dispersed across the genome. Principle coordinates analysis, or metric multidimensional scaling, of the 61 DArT markers was performed, and a scatter plot of the first two axes (modelling a cumulative 91.68% of the variation observed) is presented in Figure 13 on page 27. This figure showed WAB450 and Quest well separated, although the progeny lines appeared more similar to Quest than WAB450. Several lines clustered with Quest, while the WAB450 parent was well separated from other lines. High and low vigour lines were not distinctly separated, however all high vigour lines fell below zero for axis 2, and 6 of 9 low vigour lines above. The high and low vigour lines did not separate into different clusters, although some smaller cluster emerged. For example, the high vigour lines 18:55, 18:59, 18:122, 19:287 and 19:289 as well as the low vigour line 18:136 clustered relatively close together. Given that most of the 61 DArT markers were not significantly associated with seedling vigour it is not surprising that the clustering did not clearly separate the high and low vigour groups. Developing meaningful marker-trait associations is not a simple exercise, even with the relatively large numbers of markers generated with microarray technology. With complex traits such as vigour there is often no clear-cut phenotype, and the vigour groups genotyped represent a collection of lines with generally high or generally low vigour. This, in turn, reduces the ability to search for clear-cut genotypic differences.

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Each DArT marker is sequence ready, so the next step in the process of identifying markers related to vigour would be to sequence the clone and search the published rice genome sequence to find the position within the genome. The gives the opportunity to relate previously identified genes, located close to the DArT marker, to the trait of interest, and may provide clues as to the underlying mechanism which influences or controls the trait. Further, populations from this specific cross and other related crosses can be grown under field conditions to confirm the marker-trait association. Implications The use of DArT analysis in this project has shown that the technology can provide detailed analysis of the suite of parental lines used in the Rice Breeding Program, showing the degree of genetic differences or similarities between all varieties. Such information is useful for future selection of parents, allowing the use of measurable genetic diversity rather than an estimate of diversity based on phenotypic differences. DArT is able to distinguish between closely related varieties and breeding lines emanating from the NSW Rice Breeding Program, demonstrating its capability to provide genetic fingerprinting in the case of uncertainty in seed identification, and as a means of quality assurance in the production of pure seed of existing commercial varieties and new breeding lines approaching release. In diverse and inter-specific crosses DArT analysis is able to provide a fast and accurate means of determining the extent of introgression of the genome of the diverse parent. Capturing useful variation from such crosses inevitably requires multiple backcrossing to end up with a suitable genetic background for the NSW rice growing environment. Hence there is a need to ensure that at each hybridisation to the recurrent parent is a real cross, and not an inadvertent self-pollination. DArT analysis provides this information as well as an indication of the extent of the genome transferred. If DArT analysis can be modified to include points on the array of known sequence that can test for existing well-defined molecular markers, then some of the resources devoted to existing simple markers can be used for DArT analysis, increasing the efficiency of the molecular marker part of the breeding program. The feasibility of this has not yet been canvassed in detail with Diversity Arrays Pty Ltd. Development of usable QTL’s for complex traits such as seedling vigour and cold tolerance rests on the capacity to phenotype accurately and repeatably. In future studies DArT markers associated with useful traits should be sequenced and located on the rice genome to provide useful information as to the possible mechanism and genetic control of such traits. However, the increased emphasis on DArT and other biotechnological systems should not be at the expense of phenotyping capacity within the NSW Rice Breeding Program, since both disciplines are critical to future success. There is a need for the rice improvement program to continue to develop the use of biotechnology to achieve greater efficiency, enhanced market responsiveness and address present and looming sustainability issues. As molecular markers for specific traits are added, greater efficiency is needed in the marker testing and selection program. DArT has the potential to encompass these needs through a staged introduction, drawing on the experience of other programs, while maintaining and improving existing phenotyping capacity.

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Recommendations

1. In the short term the NSW rice improvement program will use DArT judiciously for specific populations – such as the Oryza rufipogon backcrossing program to develop a series of introgression lines with small amounts of the O. rufipogon genome inserted into a genetic background suited to NSW conditions

2. DArT can also be used to monitor genetic integrity of pure seed lines, to build a genetic fingerprint of all current varieties and breeding lines, and as a foundation for quality control in pure seed maintenance.

3. The possibility of transferring existing markers such as fragrance, gelatinisation temperature and the range of variants in granule-bound starch synthase, to DArT panels should be explored.

4. If possible a small specific project aimed at an intractable problem such as developing a quantum increase in cold tolerance should be developed as a targeted use of DArT. A possible link to a Chinese program hybridising cold tolerant Oryza rufipogon with Oryza sativa could be developed.

5. Careful attention needs to be paid to the systems required to store and manipulate the quantities of data that DArT generates, and to make meaningful conclusions. DArT analyses have been successfully loaded into the rice implementation of the International Crop Information System. The capacity of this system to efficiently store DArT analyses for high numbers of lines is not known.

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Introduction Technology for complex traits The NSW rice breeding program necessarily deals with complex traits, such as grain yield and grain quality, conditioned by many genes of small effect. Although simply inherited traits are important, (for example semi-dwarf plant height, grain quality parameters such as amylose content, and key genes for disease resistance), the greatest challenge facing the program is to deliver new varieties, with enhanced yield, quality and production efficiency in a timely and cost-effective manner. Classical plant breeding programs produce these outcomes by generating significant numbers of breeding lines with suitable genetic variation, selecting individuals which combine the traits of interest, and then simultaneously inbreeding and testing those lines across the spectrum of NSW rice growing environments to ascertain their performance relative to the best commercial varieties. The rice breeding program at Yanco follows a conventional procedure for inbreeding species, crossing selected parental lines and selecting useful recombinants combining appropriate traits from each parent. Despite continued efforts to use new and diverse rice varieties in the crossing program, most breeding lines which perform well under NSW conditions are of similar genetic background. Hence varieties released for commercial production are relatively closely related and the full suite of NSW commercial varieties have a relatively narrow genetic base. It is a generally accepted maxim that breeding programs must increase diversity in their parental material in order to generate new genetic combinations in the progeny and maximise the opportunity for enhanced productivity, enhanced grain quality or greater stress tolerance. Bi-parental crosses with diverse germplasm yields progeny which contain half their genes contributed from each parent, and in subsequent generations of inbreeding the performance of early generation segregants is usually very poor, conditioned by the number of genes received from the non-adapted or diverse parental line. Hence the need to backcross repeatedly, to ensure that relatively small sections of genome are transferred into varieties with genetic backgrounds that are adapted to NSW conditions. The challenge is to select appropriate individuals in early generations for future backcrossing. The performance of individuals carrying useful alleles for traits of interest may be masked by interactions with other sections of genome from the non-adapted parent. For example, genes for enhanced seedling vigour under NSW conditions may not be expressed in progeny sensitive to the relatively low temperatures of the NSW rice area. For simply inherited traits conditioned by few genes it is relatively straightforward to select for the character of interest in each generation, regardless of the overall performance of the segregants. However, for more complex traits there may well be a range of genes of small effect which together are necessary to effect a significant change in particular traits such as seedling vigour. The advantage of whole-genome profiling is that it theoretically allows the network of genes conditioning the trait to be visualised and selected within the progeny from a diverse cross, while maintaining the genetic background necessary for acceptable performance in a particular environment. DArT applications For quantitative trait analysis, DArT has many potential applications. So far, DArT marker patterns have been principally applied to the assessment of genetic variability in a group of organisms, such the assessment of cassava diversity by Xia et al. (2005), and barley diversity by Wenzl et al. (2004). As these studies illustrate, the most accurate diversity analysis require proportional amounts of clones from all individuals tested to be present on the array. If alleles from a genotype are under-represented on an array, then DArT will indicate potentially greater differences from the population average. DArT is especially suited to QTL mapping (Wittenburg et al. 2005), and can be used to construct medium-density linkage maps relatively quickly.

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Wenzl et al. (2004) gives an example of such a map, showing how the standard techniques of map construction using linkage disequilibrium can be applied using DArT markers. DArT markers can be used to track phenotypic traits in breeding like other molecular markers, and the high throughput and low cost nature of the technology makes DArT more affordable for marker assisted selection. Multiple loci can be involved in the selection process, but using an array means all loci are dealt with simultaneously. Such markers can then be tracked though an introgression or crossing program, and used to supplement phenotyping to reduce potential miss-identification of a trait due to environmental effects (Lande & Thompson 1990), as per any other marker-aided selection tool. Even though DArT can be applied in the absence of sequence information, individual DArT markers are sequence-ready and can be used in the development of probe-based markers for further research (Kilian 2004). One shortcoming of DArT is the number of positions on a DArT array that are consistently non-polymorphic, i.e. non-marker clones. This has been recognised since the inception of this technology (Jaccoud et al. 2001), and recent studies detail how polymorphic markers can be identified in an initial discovery array process, then re-arrayed for genotypic applications as polymorphism-enriched arrays (Wenzl et al. 2004, Xia et al. 2005). This project initially involved the construction of a diversity array (DArT) reference panel, which is an array of DNA fragments varying among all of the varieties contributing DNA to the broad sample of rice germplasm (the meta-genome in DArT parlance). This allowed development of dendrograms showing relationships between all current NSW rice varieties and breeding lines. A series of populations developed from diverse crosses using parents containing small amounts of the Oryza glaberrima genome, were phenotyped for seedling vigour under field conditions, and a smaller set of lines from one cross under controlled-environment conditions. The high and low-vigour extremes of this population were genotyped to look for DArT markers associated with seedling vigour. Possibilities for integrating DArT into the NSW Rice Breeding Program are discussed.

Objectives The objectives of this project were to examine diversity array technology (DArT) to build an understanding of the technology, construct a DArT reference panel necessary for ongoing DArT analysis of varieties and breeding lines, and assess the potential of the technology for integration into the NSW Rice Breeding Program.

Methodology At the outset, Diversity Array Technology was suggested as being well suited to analysis within populations from extremely diverse crosses, given that divergent parents are likely to have the greatest number of polymorphic markers or sections of DNA that vary. For this reason, populations investigated in this project focused on crosses with a series of West African breeding lines developed from inter-specific crosses between Oryza glaberrima and Oryza sativa. This project had three principal components common to all projects seeking to relate phenotype to genotype, the first was detailing the response of individual plants within populations developed for the purposes of improving a specific trait or traits, the second was conducting the DNA analysis to accurately characterise the genotype, and the third was relating the two to find meaningful linkages that may prove useful in gene discovery and for future selection within and among breeding lines. The methodology encompassed four project phases; • The development of the reference panel for DArT analysis, • The testing of a range of populations for seedling vigour,

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• The detailed testing under controlled-environment conditions for vigour and its component traits.

• Relating genotype to phenotype A series of populations were developed and analysed for seedling vigour under varying temperature regimes. A sub-set of selections from one of the populations was subject to further detailed tested for seedling vigour under controlled-environment conditions. A total of 18 breeding lines from the distributional extremes for seeding vigour (9 lines with the greatest vigour as measured by leaf area development and the 9 least vigorous) were subject to DArT analysis, and associations between DArT markers and component traits for vigour were examined.

Results Development of DArT Reference Panel Microarray-based markers Diversity array technology is one of a range of new microarray based molecular markers in the early stages of use. Unlike oligonucleotide arrays, the printed diversity arrays do not require prior genome sequence knowledge, instead using a subset of genetic information from a pool of genomes representing genetic diversity in a species or genus, for example, a range of cultivars, breeding germplasm and wild relatives (Jaccoud et al. 2001). Individuals can be genotyped by hybridisation to the array, with the genetic variation between tested genotypes evident in the presence or absence of hybridisation to array elements. The key attraction of microarray technology platform is the promise of high throughput capability and this is clearly evident with DArT. Studies such as Wenzl et al. (2004) and Xia et al. (2005) report simultaneous analysis of hundreds of markers at once, with the added advantage of much lower cost per marker than other technologies like SNPs and microsatellites (Huttner et al. 2005). The pattern of hybridisation for a genotype provides a unique genetic fingerprint that is especially useful for quantitative trait analysis. What is DArT? The DArT reference panel is a series of DNA fragments arrayed on a slide. The DNA fragments are derived from extracting the genomic DNA from a broad range of varieties including those that feature as parental lines in the current breeding program, current advanced breeding lines, and diverse varieties such as related species that may be used to expand the genetic base of the program. This broad pool of DNA is referred to as the meta-genome, and is a representation of the genetic variation that exists within the gene pool of the species. Cutting the DNA into small sections results in many more fragments than could physically be arrayed on a slide. There is a need to reduce the numbers involved and array only those fragments which differ among the varieties contributing to the reference array. Thus the diversity array process begins with complexity reduction of the reference genomes (Figure 1), so that genetic diversity can be scaled down to a workable representative sample. Using a rare cutter restriction enzyme such as PstI,

Figure 2: Schematic of Diversity Array Technology. A subset of genomic DNA is selected and cloned into a vector for printing as an array. DNA for hybridisation follows the same process, only instead of being cloned it is labelled and hybridised to the array (Lezar et al. 2004).

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pooled DNA from a large number of accessions are digested, and then ligated to adapters for the restriction enzyme ends. The second step of complexity reduction involves selective PCR amplification of a subset of these fragments, by using primers with selective bases attached to their 3’ end (Jaccoud et al. 2001). Since genome structure and organization can vary between species the complexity reduction procedure usually needs to be optimised to obtain the highest polymorphism levels possible (Wittenburg et al. 2005). This can involve different combinations of frequent cutter restriction enzyme and rare cutter in the complexity reduction process, such as those used by Wenzl et al. (2004), or utilisation of genome features such as miniature inverted transposable elements or other repeat motifs as primer sequences for selective amplification (Kilian 2004). In this project the selective amplification of fragments used a specific class of miniature inverted transposable elements (MITEs) as the identifying feature of fragments to be amplified. MITE’s are widely and relatively uniformly dispersed throughout the genome, and are often associated with regions of functional DNA. The amplified fragments are then cloned into a transformation vector, so they can be multiplied and preserved in transformed bacteria. Vectors including the clones are isolated, then amplified by PCR and printed as an array (Figure 1). For the individuals to be genotyped using DArT, the complexity reduction step is performed the same way as array fragments however in the last step of amplification, fragments are labelled using the exo-Klenow fragment of DNA polymerase to incorporate uracil bases with fluorescent tags (Jaccoud et al. 2001). These fragments are hybridised to the array along with the empty vector plasmids (which are labelled a different colour) to provide a reference level of hybridisation. With the hybridisation image, a relative hybridisation intensity for each array spot can be calculated, which is converted into a 0/1 scoring table for the polymorphic fragments between the comparator genotypes. A single genotype therefore has a constellation of markers on the reference array where fragments hybridise to the array. Further, two genotypes can be compared, with the colour combinations revealing whether fragments are common to each of the varieties or present in one or the other (Figure 3).

Figure 3. DArT comparison of rice varieties Millin and IR20, showing DNA common to

both varieties (yellow spots), DNA present only in Millin (red spots) or IR20 (green spots).

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Relationships between varieties used for the reference array The MITE-DArT method was in development of the reference array, using a total of 254 varieties chosen to encompass much of the genetic diversity available within the Oryza genus. Varieties used included germplasm widely utilised in the NSW rice breeding program in recent hybridisation series, a range of 20 Oryza rufipogon types and 7 Oryza glaberrima types, and Oryza nivara, O. meridionalis, O. officinalis and O. barthii. The remaining Oryza sativa entries were derived from a broad geographical area, and encompassed indica, japonica and tropical japonica sub-groups. In preliminary tests, two different libraries were generated based on Stowaway-IX and Gaijin MITE families, and tested on two genetically distant cultivars (93-11 and Nipponbare). From over 300 DArT-Stowaway markers and 200 DArT-Gaijin markers identified, 96 markers were selected for DNA sequencing. A very low level of redundancy was observed and the loci tagged by MITE-DArT markers were evenly distributed through out the rice genome when mapped onto the Nipponbare Sequence Map. Library expansion was carried out using Gaijin family and the array of approximately 6,144 clones was used for genetic diversity analysis of 96 rice accessions including O. sativa, O.rufipogon, O.nivara,O. glaberrima and O.officinalis. A total of 697 markers were detected, resulting in the polymorphism frequency of over 10%.Genetic mapping was performed based on the Gaijin-based array. Dendrograms Dendrograms showing the relationships between the 96 varieties are shown in Figure 4 and Figure 5. In general, varieties known to be related grouped together on the dendrogram. At the top of Figure 4 for example YRM67 groups alongside Illabong, Tucar, Sara and Tevero, which are all Arborio-style rices. YRM67 is derived from an Illabong cross, and while its grain quality is more like a standard medium-grain, it clearly retains genetic similarity to the Illabong parent. Advanced medium-grain breeding lines (YRM prefix) developed at Yanco group closely with the commercial medium-grain Amaroo, on which many are based. Similarly the long-grain lines (YRL prefix) produced at Yanco largely group together, grouping closely with irradiated Pelde and the commercial NSW long-grain Langi. There are a number of unusual relationships however, with the old Japanese variety Somewake grouping with Basmati’s from the Indian sub-continent, and close to Milagrossa, an aromatic Filipino variety noted for having small grains. Both would seem markedly different from Somewake, a clear japonica type. Similarly Doongara and Kyeema group closely, and while both are commercially produced NSW long-grain varieties, they have vastly different grain quality attributes and their genetic backgrounds based on pedigree are quite different. Figure 5 shows the same information however the varieties are restricted to Oryza sativa only. It is unusual to see the Californian variety Calmochi 202 grouping close to Illabong and YRM67, as the former has waxy grain in which all the starch is present as amylopectin, while the latter have normal translucent grains with around 19% amylose content. Based on pedigree, Calmochi would be more likely to group with the Californian medium-grain M7 and the NSW cultivar Amaroo. These unexpected groupings highlight the difference between grouping varieties based on phenotype (grain quality or growth habit etc.) and measuring the underlying genotype. The latter is often more useful in determining useful hybrid combinations as there is little point in crossing two varieties that are essentially genetically identical to try and uncover new and useful genetic recombination.

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CALMOCHI-2YRM67TUCARIllabongSARATEVEROSPALCHIKLIMANCARNAROLILABELLEDoongaraKyeemaT53GUNDILKUNINGWAB450-160MOREBEREKANIrr.-PELDEYRL125LangiYRL123YRL118YRL113YRM65YRM66ParagonQuestYRM54YRM64AMAROOM7CALROSEMILLINJarrahOpusKOSHIHIKARINipponbareTAINAN IKU 487Taipei-309BOMBA IIIKamenooKWAN CHU CHI 15 HAOMitriO.rufipogon chinaH7O.rufipogon (malaysia)Khao Dawk Mali 105O.rufipogon(0328)O.rufipogon (chinaH6)93-11LEUANG RAWNKalukanthaJOJUTLASERATUS MALUMSERI RAJABASMATI 6129BASMATI 370MILAGROSSASOMEWAKEDV85O.glaberrima(3168)O.glaberima(3187)O.nivaraO.glaberima(3039)O.glaberrima(3117)IG20CG10O.rufipogon(India)O.rufipogon(chinaH4)O.rufipogon(0329)O.rufipogon(0330)O.rufipogon(0321)O.rufipogon(0331)O.rufipogon(0320)WAB450--38O.rufipogon(Nepal)O.rufipogon(0322)

CALMOCHI-2YRM67TUCARIllabongSARATEVEROSPALCHIKLIMANCARNAROLILABELLEDoongaraKyeemaT53GUNDILKUNINGWAB450-160MOREBEREKANIrr.-PELDEYRL125LangiYRL123YRL118YRL113YRM65YRM66ParagonQuestYRM54YRM64AMAROOM7CALROSEMILLINJarrahOpusKOSHIHIKARINipponbareTAINAN IKU 487Taipei-309BOMBA IIIKamenooKWAN CHU CHI 15 HAOMitriO.rufipogon chinaH7O.rufipogon (malaysia)Khao Dawk Mali 105O.rufipogon(0328)O.rufipogon (chinaH6)93-11LEUANG RAWNKalukanthaJOJUTLASERATUS MALUMSERI RAJABASMATI 6129BASMATI 370MILAGROSSASOMEWAKEDV85O.glaberrima(3168)O.glaberima(3187)O.nivaraO.glaberima(3039)O.glaberrima(3117)IG20CG10O.rufipogon(India)O.rufipogon(chinaH4)O.rufipogon(0329)O.rufipogon(0330)O.rufipogon(0321)O.rufipogon(0331)O.rufipogon(0320)WAB450--38O.rufipogon(Nepal)O.rufipogon(0322)

Figure 4. Dendrogram showing relationships between cultivars used in development of the DArT reference panel. Cultivars include Oryza sativa and related species.

Principal Coordinate Analysis Principal coordinate analysis (PCO) is a variant of principal components analysis (PCA) which is a technique used to simplify a dataset. In essence PCA is a linear transformation that chooses a new coordinate system for the data set such that the greatest variance in a single dimension of the data set comes to lie on the first axis (then called the first principal component), and the second greatest variance on the second axis, etc. Instead of the finding the coordinates maximising variance, PCO finds dimensions that maximise similarities among the data points.

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MILAGROSSABASMATI 6129 BASMATI 370SOMEWAKESERI RAJAKhao Dawk Mali 105MitriKWAN CHU CHI 15 HAOKamenooJOJUTLAKalukantha93-11LEUANG RAWNSERATUS MALUMDV85GUNDIL-KUNINGMOREBEREKANLangiYRL123Irr--PELDEYRL125YRL118YRL113MILLINJarrahTAINAN-IKU 487KOSHIHIKARIOpusNipponbareTaipei-309YRM65ParagonYRM66YRM54QuestYRM64M7AMAROOCALROSEBOMBA IIITEVEROSARATUCARIllabongCALMOCHI202YRM67LIMANSPALCHIKCARNAROLIT-53DoongaraKyeemaLABELLE

MILAGROSSABASMATI 6129 BASMATI 370SOMEWAKESERI RAJAKhao Dawk Mali 105MitriKWAN CHU CHI 15 HAOKamenooJOJUTLAKalukantha93-11LEUANG RAWNSERATUS MALUMDV85GUNDIL-KUNINGMOREBEREKANLangiYRL123Irr--PELDEYRL125YRL118YRL113MILLINJarrahTAINAN-IKU 487KOSHIHIKARIOpusNipponbareTaipei-309YRM65ParagonYRM66YRM54QuestYRM64M7AMAROOCALROSEBOMBA IIITEVEROSARATUCARIllabongCALMOCHI202YRM67LIMANSPALCHIKCARNAROLIT-53DoongaraKyeemaLABELLE

Figure 5. Dendrogram showing relationships based on DArT analysis within Oryza sativa, including NSW commercial varieties and advanced breeding lines.

PCO analyses are shown in Figure 6 and Figure 7. If the coordinate axes are thought of as producing a three dimensional object, Figure 6 shows the two dimensional view from the front of the object while Figure 7 shows the view from above the object. The indica varieties group to the left in both figures with one variety clearly separating from the group along axis 2. It is interesting that there appear to be two groups of japonica varieties with representatives of the Yanco japonica’s in each group. Similarly the Oryza rufipogon varieties tend to separate into two groups along axis 2 (Figure 6) but this is not evident along axis 3 (Figure 7). This information is useful in determining genetic distances between groups of varieties when deciding useful hybrid combinations for crossing. While the dendrograms show individual variety information and degrees of genetic similarity, PCO analysis identifies groupings of cultivars based on the underlying genotype. For example, it is interesting to note the japonica types from Yanco appear to fall into two groups, and crosses between individuals from differing groups may well be more useful, providing greater genetic dissimilarity between parents, than crosses of individuals selected from within groups.

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Figure 6. Principal coordinate (PCO) analysis results for varieties included in the DArT reference panel development. PCO axis 1 and PCO axis 2 are shown. Symbols represent different varietal groups.

Figure 7. Principal coordinate (PCO) analysis results for varieties included in the DArT reference

panel development. PCO axis 1 and PCO axis 3 are shown. Symbols represent different varietal groups.

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Phenotyping populations for seedling vigour Introduction Native African rices (Oryza glaberrima) and lines from inter-specific crosses between Oryza sativa and Oryza glaberrima have been reported to have rapid early leaf area development, providing increased weed competitiveness during establishment. The lines developed from inter-specific crosses are unique in that they are reported to have long, floppy leaves during early growth but have shorter, more erect leaves at maturity resulting in canopy characteristics at maturity that are conducive to high yield (Jones et. al., 1997) The objective of this experiment was to investigate whether the rapid leaf area development and weed competitiveness of Oryza glaberrima can be captured in lines that are adapted to the NSW rice growing environment. Materials and Methods Five populations were developed by hybridising four NSW cultivars with three inter-specific crosses between the West African Oryza glaberrima × Oryza sativa (Table 1 and Table 2). The resulting F2 seed was sown at Leeton Farm in the 2001/02 season as a space-planted population to minimize the competitive effect between plants. Single panicles that flowered in less than 120 days and less than 1m tall at maturity were selected. These F2-derived F3 seeds were direct sown during 2002 at Redland Bay in Queensland. The seeds were sown at two dates, 4 September and 17 October. These sowing dates were selected because the temperatures at this time were similar to the temperatures experienced during the establishment of rice in south eastern Australia. Measurements on individual seedlings were made 35 days after sowing for both sowing dates (9 October and 22 November, respectively).

Table 1. Three Oryza glaberrima inter-specific crosses from West Africa and four Australian adapted Oryza sativa cultivars selected for hybridising.

Reference Species Pedigree WAB 1 O.glaberrima/O. sativa WAB 450-11-1-P31-1-HB WAB 3 O.glaberrima/O. sativa WAB 450-I-B-P-160-HB WAB 4 O.glaberrima/O. sativa WAB 450-I-B-P-38-HB YRM 49 Oryza sativa Amaroo/M201 YRL 113 Oryza sativa Bluebelle//M9/Pelde/3/YC 71048-262/Pelde YRM 63 Oryza sativa M201/Amaroo//Bogan YRM 64 Oryza sativa M201/Amaroo//Bogan

Table 2. F2-derived F3 populations tested for early seedling vigour under low temperature conditions.

Identification Cross Number of lines YC 00001 AYRM49/WAB3 52 YC 00006 YRL113/WAB1 24 YC 00007 BYRM63/WAB1 19 YC 00011 YRM64/WAB4 18 YC 00017 YRM64/WAB3 87 Total 200

A,B The NSW breeding lines YRM49 and YRM63 have been released for commercial production as varieties Quest and Paragon, respectively. Quest was named and released in 2003 and Paragon in 2002.

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There were 233 entries including parental lines and standard varieties. Lines were randomized within each population in each of three replications. Each plot was 1 m in length and comprised of 10 seedlings at 10 cm spacing. Due to establishment variability three uniform plants were selected for each plot for detailed measurements. The measurements included leaf number, height from the shoot base to the highest ligule (height 1), height from shoot base to the tip of the longest leaf (height 2) and total dry matter. Analysis was conducted using restricted maximum likelihood (REML) methods, and revealed significant spatial effects associated with columns and rows within each population. All models were fitted in ASREML as linear mixed models in the form;

yield ~ mean + entry + column + row

where entry, column and row were random effects. Table 3. Minimum, maximum and mean air temperature from sowing to 35 days after sowing at

Redland Bay in 2002.

Sowing date Air temperature (ºC) Minimum Maximum Mean

1 13.0 24.9 19.0 2 17.5 26.3 21.9

Results The distributions for dry weight, plant number, height to the highest ligule (height 1) and height to the tip of the longest leaf (height 2) show transgressive segregation for most populations in both sowings. The distributions for each population are shown in Appendix 1 (Figure 14 to Figure 18) and are summarised below in Table 4. The mean air temperature was 3ºC higher in sowing 2 than sowing 1 (Table 3), which contributed to increased dry weights, leaf number and height to the highest ligule and height to the leaf tip (Table 4). In general, the Australian and WAB parents performed similarly at low temperature (sowing 1) but as the temperature increased (sowing 2) in most cases the WAB parent had significantly greater biomass and height than the Australian parent, indicating better adaptation of the WAB inter-specific lines to warmer conditions. The crosses can be broadly categorised into two groups. Crosses YC 00001, YC 00007, YC 00011 and YC 00017 have medium-grain lines adapted to temperate conditions as their female parent. The latter three crosses (YC 00007, YC 00011 and YC 00017) have YRM63 and YRM64 as their female parent, and these are sister lines selected from the same cross and are genetically similar. The former cross (YC 00001) has YRM49 as the female parent, and the two progenitors of this line feature in the pedigrees of YRM63 and YRM64 (Table 1 and Table 2). YRM49 differs from YRM63 and YRM64 in having significantly shorter growth duration under NSW conditions. The cross YC 00006 has an Australian long-grain line as the female parent. In general medium-grain lines have better adaptation to cold conditions during establishment which may be due to their japonica background and to the greater seed size (~25mg) giving enhanced seed reserves for germination and establishment. In contrast, long-grain lines have smaller grain size (~19mg) and have a proportion of indica genetic background, conferring long slender grains and increased sensitivity to low temperature at all growth stages. It is interesting to note that despite the limited differences between parental lines in the first sowing (Table 4), the progeny were approximately normally distributed and showed transgressive segregation from the narrow parental range. This suggests that selection is possible for recombinants with superior vigour under low-temperature conditions.

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Table 4. The population mean (standard deviation), population range and Australian and WAB parent for dry weight (mg per plant), leaf number, height to the highest ligule in cm (height 1), height to the tip of the longest leaf in cm (height 2) for each population in sowing 1 and 2. Identification Population mean Population range Australian parent WAB Parent YC00001-sowing 1 Dry weight 25.0 (3.6) 16.6-32.7 24.4 25.3 Leaf number 2.14 (0.08) 2.00-2.36 2.23 2.07 Height 1 39 (3) 34-47 38 38 Height 2 127(8) 102-143 118 124 YC00001-sowing 2 Dry weight 107.9 (12.0) 81.4-136.5 99.9 105.0 Leaf number 3.5 (0.3) 2.9-4.0 3.4 4.1 Height 1 58.7 (5.1) 46.8-70.5 52.3 56.5 Height 2 190.2 (21.4) 148.8-246.0 165.5 186.8 YC00006-sowing 1 Dry weight 25.8 (1.0) 24.0-28.3 24.1 25.5 Leaf number 2.24(0.05) 2.15-2.32 2.24 2.21 Height 1 39.2 (1.6) 36.7-42.7 37.9 39.8 Height 2 130.1 (5.3) 118.7-140.1 125.3 126.9 YC00006-sowing 2 Dry weight 100.4 (16.2) 60.9-127.3 84.3 106.8 Leaf number 3.45 (0.02) 3.41-3.50 3.40 3.43 Height 1 55.0 (7.4) 42.5-71.2 45.6 57.7 Height 2 180.2 (15.3) 145.2-205.9 169.6 183.7 YC00007-sowing 1 Dry weight 30.9 (6.5) 23.5-44.5 33.9 28.4 Leaf number 2.34 (0.09) 4.08-2.48 2.41 2.43 Height 1 42.0 (3.8) 37.1-49.4 40.7 41.5 Height 2 125.4 (8.8) 107.8-140.8 120.9 124 YC00007-sowing 2 Dry weight 102.8 (16.9) 68.4-136.0 89.8 115.8 Leaf number 3.29 (0.10) 8.08-3.50 3.32 3.40 Height 1 52.1 (3.8) 49.5-62.3 48.8 55.7 Height 2 162.0 (11.1) 137.1-176.2 155.4 172.3 YC00011-sowing 1 Dry weight 32.4 (2.6) 27.1-36.6 37.8 27.6 Leaf number 2.33 (0.23) 1.89-2.67 2.56 2.33 Height 1 41.6 (0.7) 29.4-42.6 41.9 41.4 Height 2 125.2 (2.3) 119.0-128.5 125.5 124.5 YC00011-sowing 2 Dry weight 101.4 (16.3) 72.5-134.5 107.7 169.5 Leaf number 3.31 (0.25) 2.78-3.67 2.89 3.23 Height 1 51.6 (5.7) 42.0-63.4 52.2 60.5 Height 2 160.2 (21.0) 126.6-211.0 157.0 191.6 YC00017-sowing 1 Dry weight 33.5 (5.8) 15.6-43.6 37.3 34.2 Leaf number 2.29 (0.09) 2.14-2.97 2.35 2.27 Height 1 45.3 (3.9) 32.6-53.1 45.7 43.1 Height 2 137.0 (13.9) 78.4-159.0 133.5 136.6 YC00017-sowing 2 Dry weight 94.1 (19.4) 33.7-125.7 87.7 101.5 Leaf number 3.18 (0.25) 2.31-3.74 3.32 3.83 Height 1 54.9 (6.9) 33.1-66.8 50.7 61.0

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Height 2 171.4 (24.2) 97.8-208.0 149.9 191.6 This series of trials showed the extent of variation in relatively simple measurements of seedling vigour, under temperatures relevant to the NSW rice growing environment. However, vigour is known to be associated with seed size (larger seeds producing more vigorous seedlings) and early germination and emergence. A series of lines were selected from one population for more detailed vigour analysis under controlled conditions, to examine the effect of seed size and emergence, and to provide detailed phenotypic information to link with future DArT analysis. Detailed phenotyping under controlled-environment conditions Introduction While vigour is defined differently in different rice growing regions, the attributes of a vigorous variety remain universal. Vigour is needed for weed competitiveness, so rice plants can out-grow and then shade out weeds. This contributes to more sustainable weed control, reducing reliance on herbicides (Gibson et al. 2003). Dingkuhn et al. (2001) notes that faster early growth will contribute to yield, as high biomass accumulation early in the growing season provides the plant infrastructure for increased yield potential. Moreover, rapid biomass production is a necessary foundation to allow reduction in the duration of the vegetative phase in the development of high-yielding short season varieties (Khush 1987). In temperate areas, more vigorous seedling growth help plants withstand cold stress from the low temperatures often encountered at sowing time (Jones & Peterson 1976). Variation in crop establishment resulting from poor seedling vigour in temperate regions results in difficulty in coordinating crop management with specific growth dates which leads to reduced yield (Gravois & Helms 1994). To facilitate genetic improvement, a greater understanding of the genetic foundations of seedling vigour is required. However before genetic analysis can take place, a thorough effort must be made to quantify the trait in the population under study. There are many approaches to characterising phenotypic variation for complex traits, and all depend on the precise definition of the trait under study - often a difficult task with multifactorial complex traits such as seedling vigour. Hence, vigour component traits, or traits related to vigour are often used as indicator traits for an overall concept of seedling vigour. In this study, a number of component traits identified by Reinke (2000) were measured in a population from a cross between high and low vigour parents in two different controlled environments. The relationships between these component traits were dissected, and the most robust component trait identified as the best indirect measure of vigour. Through detailed phenotyping in two environments, the population was also characterised for overall variation in vigour, so that an estimate of environmental and experimental variation versus genotypic variation could be obtained. An appropriate indicator trait for seeding vigour was chosen, and the suitability of the population for further analysis was assessed. Materials and Methods Lines for this study were panicle rows selected from an F3:5 population of 166 Quest × WAB-450-I-B-P-160-HB (WAB450) lines. Quest is an Australian medium grain semi-dwarf variety adapted to the temperate Australian climate. WAB450 is a West African interspecific variety bred from the tropical japonica WAB56-104 and an O. glaberrima accession from Ivory Coast, CG14. WAB450 is estimated to retain about 8-10% of the O. glaberrima genome (Ndjiondjop et al. 2003). Selection was based on height, in order to select only semi-dwarf lines. A total of 69 lines were found to have panicle rows measured at less than 85cm tall, a phenotype indicating they possess the recessive

13

sd-1 semi-dwarfing gene. Only semi-dwarf lines were considered in this study in an attempt to avoid the known confounding effect on seedling vigour posed by variation in endogenous gibberellin (Pharis et al. 1992).

Trial Design Trials were conducted with a randomised block design, with both phenotyping trials conducted on the same layout. The experimental design was generated by the software program DiGGer (Coombes 2002) by Dr Peter Snell (Rice Breeder, Yanco Agricultural Institute). A total of 12 replications of 1 individual each were used from the 69 progeny lines. The number of individuals per line was set at 12 in order to ensure the standard error of the genotype mean was less than 1 (Reinke 2000). Both parents where included in the trial, as were 4 control varieties: 2 high-vigour varieties (HSC55 and the tall variety Hungarian No. 1) as well as the most popular Australian cultivar (Amaroo) and the most widely grown rice world-wide, IR64 (Peng et al. 2000). Only 9 replications of 1 individual each were used for each parent and control variety, giving a total experiment size of 882 individual plant positions.

Trial Location and Environment Both phenotyping trials were conducted in a controlled-environment growth room at Yanco Agricultural Institute. For the 69 progeny lines unblemished seeds off 2-3 panicles from each line were selected. Seeds were placed onto seedling trays containing Birganbigil clay loam soil (van Dijk 1961) and covered with a dusting of white sand to prevent surface crusting, and subsequent potential growth inhibition of germination. The seedling trays were kept at full soil moisture capacity by thorough misting each day. The trial was not conducted with the seedlings in water like the standard field situation, as anaerobic seedling growth is highly variable, depending to a large degree on the level of dissolved oxygen in the water which is also highly variable and difficult to control (Chapman & Peterson 1962, Lewin 1995). Although water temperature in early establishment also effects seedling growth, it is very closely related to air temperature fluctuations during this period. Therefore, the practical necessity of conducting this study aerobically to avoid these environmental effects resulted in seedlings experiencing similar conditions to those in the field.

Environmental Variables Controlled in the Trials The CSIRO 1962-2003 long term temperature averages for Griffith (NSW) indicate an average maximum temperature of 25°C and minimum of 10°C for late October, the period when the bulk of the Australian rice crop is planted. Temperature controls for the growth room used in this study operated on a square wave, so minimum and maximum temperatures for the square wave were calculated so that the number of degree hours above and below the midpoint of 17.5°C was similar to those experienced under a sine wave, which better approximates diurnal temperature fluctuations. Thus, the square wave was set to a maximum of 22°C and minimum of 13°C. Data loggers measured the temperature during the phenotyping trial 1 and recorded a maximum of 22.3°C and minimum of 13.3°C, with a midpoint of 17.8°C which was close to the desired range. For phenotyping trial 2, the mean temperature of phenotyping trial 1 was lowered by 1.3°C to 16.5°C, giving a maximum of 21°C and minimum of 12°C confirmed by data loggers. Light intensity for the phenotyping trials was measured with a quantum and light quality with a Red / Far Red Sensor. For phenotyping trial 1 the red: far red ratio (660nm: 730nm) (RFR) was an average of 8.8, compared to around 1 for sunlight.

14

Photosynthetically active radiation (PAR) averaged 80 μmol m-2 sec-1. This compares to daylight at 1,000 – 2,000 μmol m-2 sec-1. Thus both the RFR ratio and PAR would be equivalent to very dark shade. Lighting in the growth room was changed for phenotyping trial 2, adjusting the RFR ratio to 1 and increasing the average PAR to 150 μmol m-2 sec-1, still very low compared to daylight PAR. Relative humidity for both phenotyping trials averaged at about 75-80% over the duration of the trials.

Measurements Before sowing, individual seeds were weighed in grams to 4 decimal places on a precision balance. As seeds germinated, elongation measurements of the growing shoot were taken, from the tip of the emerging leaf to the soil level. For phenotyping trial 1 measurements were taken almost daily from 7 days after sowing (DAS) until at least 3 elongation measurements were obtained. These measurements (sometimes more than 3) were averaged to obtain a mean elongation rate. Seeds not having germinated by 19 DAS were treated as non-germinating. Individual dates of emergence were extrapolated from the data by subtracting the average elongation rate from the first shoot length measurement for the seedling. Between 22-24 DAS, key measurements were obtained by destructive sampling.

Figure 8. Rice seedlings in the second phenotyping trial under controlled-environment conditions. Destructive vigour measurements were conducted at this stage.

15

The widths and lengths of individual leaves was measured, and the plants cut off at 5mm above soil level and dried in a 30-38°C oven for 3 weeks. The remaining dry matter was then weighed to 4 decimal places on a precision weigh balance to obtain an aboveground biomass measurement (dry weight). Thus data for the following traits were collected:

1. seed weight 2. leaf dimensions 3. rate of shoot elongation 4. date of emergence 5. leaf area and dry weight

For leaf dimensions, the leaf naming system of Hoshikawa (1973) was used, where the prophyll (rudimentary leaf) was counted as leaf 1, and the first true leaf (with blade and sheath) counted as leaf 2. Measurements for phenotyping trial 2 were the same as measurements for trial 1. Shoot elongation was measured daily from 9 DAS, until the appearance of the first ligule or if no ligule appeared by 24 DAS, measurements daily until that date. At 30-32 DAS the same destructive measurements as trial 1 were taken, and the plants dried in an 80°C oven for 3 days. The dry weight of plants was then weighed as per phenotyping trial 1. Destructive measurements were delayed by 8 days in order to finalise the slower elongation measurements and ensure that plant growth stages were comparable to trial 1.

Data Analysis Data sets from both phenotyping trials were analysed with the statistical software package ASReml (Gilmour et al. 2002). The experimental value for each line was statistically adjusted to obtain a best unbiased linear predictor (BLUP) of the trait for each line. Each BLUP was a function of the mean, the fixed effects of covariates, random effects of variety and if relevant, random effects of rows or columns in the experiment. Inclusion of a factor as a covariate in the generation of predicted values is a way of accounting for the effects of that factor on the trait in question. For example, seed weight is used as a covariate for all traits analysed because it is known to influence vigour (Reinke 2000), but enhanced vigour that is independent of seed weight is sought in the NSW breeding program. Using seed weight as a covariate highlights lines that are more vigorous than others for a given seed weight. Emergence date was used as a covariate for leaf area and dry weight, as well as leaf area components because early emergence may be due to environmental factors as well as genetic. These factors include the depths at which seeds are sown, or differences in soil moisture content which may influence water uptake and germination. Anything early to begin growth will likely be larger at the time of measurement, so using emergence as a covariate assesses vigour as if all plants emerged at the same time - again providing a more accurate measure of genetic potential for vigour. Elongation rate was also used as a covariate for leaf area and leaf area component BLUPs, as comparing entries at a common elongation rate intensifies selection for leaf area, and because elongation rate has a close (and undesirable) relationship with mature plant height (Reinke 2000).

Results

Correlations between Component Traits Analysis of correlation coefficients between vigour traits revealed associations between the different component traits. These correlations for both BLUPs and arithmetic means are presented in Table 5 and Table 6 for trials 1 and 2 respectively. For significance at the 1% level, correlation coefficients must be greater than 0.295. Significant correlation coefficients are highlighted in both tables.

16

Of the 5 vigour components (seed weight, elongation rate, emergence date, leaf area and dry weight) the only significant correlation in BLUP data to emerge in both trials was between elongation rate and dry weight, where in trial 1 a correlation of 0.62 was observed, rising to 0.72 in trial 2. At the higher temperature in trial 1, leaf area and dry weight had a correlation of 0.86, although this strong association was sharply reduced in trial 2 where the two vigour components had a correlation of 0.19. For trial 1, elongation rate was significantly correlated with emergence date and leaf area, while dry weight correlated with emergence date in trial 2.

Table 5. Correlation coefficients for trial 1. Best linear unbiased predictors (adjusted values) are shown in bold, while arithmetic means for raw data are not bold. Correlations significant at p = 0.01 are highlighted

Trial 1

Seed

Wei

ght

Elon

gatio

n R

ate

Emer

genc

e D

ate

Leaf

Are

a

Dry

Wei

ght

Tota

l Lea

f Le

ngth

Tota

l Lea

f W

idth

Leaf

2 W

idth

Leaf

3 W

idth

Leaf

2 &

3

Wid

th

Seed Weight 0.03 -0.05 0.09 0.13 -0.07 0.24 0.29 0.27 0.27 Elongation Rate 0.27 -0.42 0.50 0.62 0.73 0.11 0.21 0.13 0.21 Emergence Date -0.19 -0.48 0.06 0.04 0.00 0.15 0.19 0.08 0.11 Leaf Area 0.41 0.67 -0.33 0.86 0.73 0.68 0.72 0.73 0.76 Dry Weight 0.48 0.76 -0.36 0.90 0.75 0.65 0.63 0.59 0.65 Total Leaf Length 0.20 0.86 -0.43 0.78 0.79 0.32 0.28 0.15 0.24 Total Leaf Width 0.48 0.37 -0.22 0.78 0.76 0.46 0.66 0.67 0.71 Leaf 2 Width 0.46 0.37 -0.02 0.75 0.68 0.35 0.70 0.85 0.94 Leaf 3 Width 0.41 0.25 -0.08 0.75 0.63 0.23 0.71 0.86 0.95 Leaf 2 & 3 Width 0.43 0.36 -0.10 0.79 0.70 0.34 0.76 0.95 0.95

Table 6. Correlation coefficients for trial 2. Best linear unbiased predictors (adjusted values) are shown in bold, while arithmetic means for raw data are not bold. Correlations significant at p = 0.01 are highlighted

Trial 2

Seed

Wei

ght

Elon

gatio

n R

ate

Emer

genc

e D

ate

Leaf

Are

a

Dry

Wei

ght

Tota

l Lea

f Len

gth

Tota

l Lea

f Wid

th

Leaf

2 W

idth

Leaf

3 W

idth

Leaf

2 &

3 W

idth

Seed Weight 0.11 0.11 -0.01 0.04 0.11 0.21 0.30 0.14 0.23 Elongation Rate 0.24 -0.09 0.06 0.75 0.67 0.42 0.50 0.40 0.48 Emergence Date -0.05 -0.24 0.00 -0.52 0.03 0.06 0.05 0.07 0.09 Leaf Area 0.23 0.81 -0.52 0.19 0.13 0.51 0.39 0.54 0.55 Dry Weight 0.23 0.77 -0.57 0.89 0.65 0.43 0.43 0.39 0.44 Total Leaf Length 0.11 0.70 -0.45 0.64 0.70 0.54 0.48 0.51 0.54 Total Leaf Width 0.37 0.61 -0.44 0.84 0.78 0.48 0.77 0.85 0.89 Leaf 2 Width 0.40 0.60 -0.13 0.70 0.57 0.30 0.76 0.59 0.84 Leaf 3 Width 0.25 0.57 -0.34 0.80 0.71 0.41 0.83 0.59 0.92 Leaf 2 & 3 Width 0.34 0.62 -0.23 0.81 0.69 0.37 0.85 0.83 0.92

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The absence of correlations between BLUPs for seed weight and emergence date and other vigour characters in trial 1 is attributable to these factors being used as covariates for the other vigour characters, as can be seen in the significant raw value correlations between these traits. In addition, the raw values of seed weight reveal a significant correlation with both leaf area and dry weight, although these relationships are not observed in trial 2. The inclusion of components of leaf area (leaf length and width values) revealed additional correlations in phenotype BLUPs. Total leaf length was significantly correlated with elongation rate in both trials 1 and 2 (correlations of 0.73 and 0.67 respectively) and also with dry weight (correlations of 0.75 in trial 1 and 0.65 in trial 2). Leaf width components were also correlated with dry weight in both trials, although leaf width components were only significantly correlated with elongation rate in trial 2. Leaf width components were significantly correlated with leaf area in both trials, although correlations declined markedly between trials 1 and 2. Similarly, a strong correlation between total leaf length and leaf area in trial 1 (0.73) declined to an insignificant correlation of 0.13 in trial 2. Correlations between the arithmetic means also revealed further relationships. In particular seed weight does not have significant correlations with any traits except leaf width characters in trial 2. This relationship is also observed in trial 1. For characteristics other than seed weight, correlations were significant for almost all traits excepting leaf width components and emergence date in both trials. Repeatability between Trials Correlation of predicted values between trials 1 and 2 is a measure of repeatability, as presented in Table 7. This correlation provides an indication of environmental variation evident in the phenotypic values, and the precision of measurements. Because the genetic materials used were very similar (although not completely identical) variation in phenotype between the trials should be predominantly caused by the variation in environment and measurement error. For instance, the contribution of experimental error and a minor amount of genetic variation to repeatability can be seen in the correlation in seed weight between trials. As seed weight cannot be affected by trial environmental conditions, the correlation of less than 1.00 indicates that genetic materials and/or experimental measurements were not 100% identical between the trials. All correlations between trials are significant at the 1% level, however given the relatively minor environmental differences between the trials (1.3ºC decrease and a minor increase in light intensity from trial 1 to 2) the repeatability was relatively low. The exceptions in this data are the leaf width traits, which present as having the highest repeatability of traits measured within the phenotyping trials. Leaf 2 width measurements have a correlation of 0.73 between trials, and leaf 3 width a correlation of 0.55, and the sum of leaf 2 and 3 widths had a correlation of 0.69. Table 7. Repeatability of measurements between trials 1 and 2, as indicated by correlations

between best linear unbiased predictor trait values from each trial.

Seed Weight 0.91 Elongation Rate 0.49 Emergence Date 0.32 Leaf Area 0.51 Dry Weight 0.47 Total Leaf Length 0.53 Total Leaf Width 0.61 Leaf 2 Width 0.73 Leaf 3 Width 0.55 Sum of Leaf 2 & 3 Widths 0.69

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Variation and Distribution of Vigour Traits Variation can be measured in terms of the coefficient of variation (CV), a measure of spread in the population. Figure 9 shows this statistic for the predicted values of each trait, in both trials 1 and 2. Emergence date had the lowest CV, approaching 5%. Leaf area and leaf area components showed CVs of around 10%, although leaf area CV declined from 13.4% to 9.3% from trial 1 to trial 2. More dramatic changes were evident in elongation rate, where CV more than doubled (8.7% to 20.8%) from trial 1 to trial 2, and also dry weight (CV increased from 12.3% to 19.7% from trial 1 to trial 2). Population distributions did not appear to be normally distributed for most vigour trait predicted values. To test for normality, a D’Agostino test was performed using skewness and kurtosis values (Zar 1999). Seed weight and total leaf width were normally distributed in both trials, while emergence date values were normally distributed in trial 1 only. All other traits measured did not appear to be normally distributed according to this test. The frequency distributions for vigour traits also show this. Dry weight, leaf area, total leaf width and sum of leaf 2 and leaf 3 widths frequency distributions are presented in Figure 10, and it is clear that leaf area and dry weight do not display normal distributions. Total leaf width is normally distributed, while the sum of leaves 2 and 3 width shows what is evidently a near-normal distribution. The disparity between total leaf width, and sum of leaves 2 and 3 widths shows how increasing the amount of data (by including leaf 4 widths) frequency distributions can be made smoother and more stable. The separation of the WAB450 and Quest parents in sum of leaf 2 and 3 widths is not seen in total leaf width, probably because Quest plants tended to be at a more advanced growth stage than WAB450 plants, and therefore more Quest plants had a fully or partially expanded leaf 4 to contribute to leaf width measurements.

Coefficient of Variation

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

SeedWeight

ElongationRate

EmergenceDate

Leaf Area Dry Weight Total LeafLength

Total LeafWidth

Leaf 2 Width Leaf 3 Width Sum of Leaf2 and 3Widths

Trial 1Trial 2

Figure 9. Coefficient of variation (standard deviation divided by the mean) for vigour component traits in trials 1 and 2.

19

0

5

10

15

20

25

0.013 0.014 0.015 0.016 0.017 0.018 0.019 0.020 0.021 0.022

Trial 1 Dry Weight (grams)

Q, W

0

5

10

15

20

25

0.005 0.006 0.006 0.007 0.007 0.008 0.009 0.009 0.01 0.01

Trial 2 Dry Weight (grams)

Q

W

0

5

10

15

20

25

304.4

1

330.5

1

356.6

0

382.7

0

408.8

0

434.8

9

460.9

9

487.0

9

513.1

8

539.2

8

Trial 1 Leaf Area (mm2)

QW

0

5

10

15

20

25

159.7

5

169.3

6

178.9

7

188.5

9

198.2

0

207.8

1

217.4

2

227.0

3

236.6

4

246.2

5

Trial 2 Leaf Area (mm2)

Q

W

0

5

10

15

20

25

4.67 5.04 5.41 5.78 6.14 6.51 6.88 7.25 7.62 7.98

Trial 1 Sum of Leaf 2 and 3 Widths (mm)

Q

W

0

5

10

15

20

25

4.55 4.79 5.04 5.28 5.52 5.77 6.01 6.25 6.50 6.74

Trial 2 Sum of Leaf 2 and 3 Widths (mm)

Q W

0

5

10

15

20

25

6.12 6.66 7.20 7.74 8.28 8.82 9.36 9.90 10.44 10.98

Trial 1 Total Leaf Width (mm)

W, Q

0

5

10

15

20

25

4.71 5.06 5.41 5.75 6.10 6.45 6.79 7.14 7.48 7.83

Trial 2 Total Leaf Width (mm)

W, Q

Figure 10. Frequency distribution of best linear unbiased predictors for dry weight, leaf area, leaf 2 and 3 widths and total leaf widths in both trial 1 and trial 2. Mean parent value position are indicated as ‘Q’ for Quest and ‘W’ for WAB450. The vertical axis scale indicates number of lines in each category.

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Discussion Relationships between Seedling Vigour and Vigour Traits Correlation coefficients between both BLUPs and raw data allow a comprehensive picture of vigour to be formed from the phenotyping measurements. The correlations between vigour traits in these phenotyping trials are consistent with those observed in other studies, and add weight to the overall concept of a vigorous seedling as having a large biomass and large leaf area. Large biomass is central to the concept of vigour reported in several studies (Reinke 2000, Richards & Lukacs 2002, Gibson et al. 2003), given the logical integration of almost all vigour traits into the end result of a bigger seedling. In this study, trial 1 (Table 5) shows the relationship between dry weight and leaf area, and the other vigour characters observed. Leaf area, dry weight and elongation rate BLUPs were all correlated, at highly significant levels. Conversely, data from trial 2 (at a lower midpoint temperature, Table 6) showed correlations between predicted values for leaf area and both dry weight and elongation rate as non-significant. This is surprising, especially given the result from trial 1 and the significant correlation of leaf width and length components with both dry weight and elongation rate in trial 2. Given the relatively low temperatures at which the trials were conducted, a small change in temperature had a dramatic effect on phenotype. However, temperature is the principle environmental determinant of leaf appearance in rice (Yin & Kropff 1996). Jones & Peterson (1976) found in laboratory tests for seedling height found that a constant 18ºC resulted in adequate growth, while reducing the temperature just 3ºC severely retarded seedling development. Apart from being linked with dry weight, leaf area is a key vigour character in its own right. The leaf area display of a seedling represents the radiation interception capacity of the seedling, and therefore an upper limit of future growth (Richards 2000). It is this enhanced growth capacity that achieves the desired aim of seedling vigour in being able to outgrow weeds and increase biomass accumulation rates for short season varieties. For this reason, and because dry weight is correlated with leaf area, leaf area display can be considered the unifying trait for seedling vigour in rice. Other workers have made similar conclusions, for example in wheat (Rebetzke & Richards 1999, Rebetzke et al. 2004) and in a range of temperate cereals (Lopez-Castaneda et al. 1996). In contrast, Jones & Peterson (1976), Peterson et al. (1978) and others, used shoot length as an integrating vigour trait. The main reason for this appears to be a correlation between field establishment and laboratory measurements of this trait (Peterson et al. 1978) and ease of measurement. Considering the significant correlations between dry weight, elongation rate and leaf length observed in this study, it is easy to see the justification of using shoot length as an indirect means of vigour selection. However the ‘slantboard’ laboratory test advocated by these authors to measure shoot length has been tried in the Australian breeding program with little success (Lewin pers. comm.). Response to selection was low, and technical issues with the slantboard testing apparatus produced poor results. Furthermore, selection for shoot length has been associated with increased plant height, an undesirable trait in rice improvement (Li & Rutger 1980, Redoña & Mackill 1996a). In wheat, longer leaf or shoot lengths have been associated with a longer time between the complete emergence of each leaf, or phyllochron interval (pers. comm. Dr G. Rebetzke, CSIRO Plant Industries). As in wheat, in rice this in turn relates to slower tillering, as tiller buds are initiated from the leaf axils of fully expanded leaves in a highly ordered process (Nemoto et al. 1995). Dingkuhn et al. (2001) argues that relative tillering rate is related to relative growth rate, because the relative growth rate of a plant affects the phyllochron interval. This in turn reflects early biomass accumulation and weed competitive ability. If this is the case, then selection for shoot length and seedling height may not achieve these core objectives of seedling vigour improvement.

21

Leaf Width and Seedling Vigour Leaf width results from these experiments showed this trait was closely related to leaf area, and therefore dry weight and other vigour characters. More importantly, this trait showed greater correlations between experiments than other seedling vigour components. In a study covering a range of temperate cereals, Lopez-Castaneda et al. (1996) investigated traits contributing to variation in leaf area. The authors identified embryo size and specific leaf area (SLA) as the major determinants of leaf area development, however they also note that the width of the first seedling leaf integrated both these characters in one simple measurement. Moreover, width of the first seedling leaf itself explained up to 80% of the total variation in leaf area. These findings were continued with the work of Richards & Lukacs (2002) in wheat, where results confirmed the association between the widths of seedling leaves 1 and 2 with both leaf area and dry weight. Further work done by Rebetzke et al. (2004) showed genetic increase in leaf 1 width was strongly correlated with early leaf area in a range of environments. Similarly, the study of rice seedling vigour reported here showed high and significant correlations between the first two true leaf widths and dry weight and leaf area (Table 5 and Table 6). Further, raw data showed similar, if not stronger correlations between these traits. The arithmetic means show significant correlations between seed weight and leaf width components, not surprising given the relationship between leaf width and embryo size, and embryo size and seed weight (Lopez-Castaneda et al., 1996).

Population Variation for Vigour Variation for vigour components in the population tended to be low (around 10% for most traits), and most trait values did not appear to be normally distributed. The coefficient of variation (CV) estimates show the spread of variation in each trait. The CV estimates are important in terms of ascertaining the breeding application of this population. Houle (1992) notes that a large CV can be an indication of variation in a large proportion of the genome for that trait, and therefore greater potential for genetic gain. Because CV estimates for vigour traits showed moderate variability, this germplasm has relatively limited applications for vigour improvement under the conditions tested. However elongation rate and dry weight CV estimates for trial 2 stand out from other traits. These large changes in elongation rate and dry weight CV estimates from trial 1 to trial 2 are probably linked, given the close correlation between elongation rate and dry weight discussed above. The increase in CV for dry weight between trials was probably due to the increase in variation in elongation rate. Interestingly, population variation for most vigour traits did not form a normal distribution. Seed weight was normally distributed, although seed weight values are not affected by the phenotyping trials. For the other vigour traits, only emergence date in trial 1 and total leaf width in both trials were normally distributed. The small population size of 69 lines probably had the most significant effect on the distribution, given smaller sample sizes display less tolerance to deviations in the data set. There are several factors that may be contributing to the overall low to moderate CV estimates and non-normal distributions. The first is the inclusion of only semi-dwarf lines from the semi-dwarf Quest and medium-tall WAB450 cross. Exclusion of ‘tall’ lines (those without the sd-1 semi-dwarf gene) had the positive effect of reducing phenotypic noise, allowing measurements of inherent vigour rather than the variation in vigour due to variation in gibberellin production. Unfortunately, genetic variation in the population generally would also have been reduced, especially around the sd-1 locus. Genetic variation would also have decreased with each generation of field cultivation the population underwent, as the progeny from this cross were selected for flowering date and general agronomic fitness in the F2 and F3 generations as they progressed through the breeding program. It can also be expected that the field environment imposed an additional degree of selection pressure. This left the population with reduced potential phenotypic variation.

22

The other factor involves the highly responsive nature of rice growth to changes in temperature. Performance of the WAB450 parent was most probably affected by the low temperature conditions used, considering this variety is germplasm optimised for performance in tropical West Africa. The midpoint mean temperatures of trials 1 and 2 (17.8°C and 16.5°C respectively) are well below the optimum temperature for rice seedling growth ascertained by Chapman & Peterson (1962) and Chaudhary & Ghildyal (1970) to be in the range of 25-30°C. They are also below the temperature ranges recorded by Dingkuhn et al. (1998) in a study documenting the high vigour of WAB450 lines. The sensitivity of the WAB450 parent to the temperatures used in the phenotyping trials is seen in frequency distribution histograms, where in the small drop in mean temperature between trial 1 and trial 2, WAB450 ranking for sum of leaf 2 and 3 width drops from being at the upper extreme to almost the centre of the distribution (Figure 10). In contrast, Quest maintained its position in the centre of the distribution for both trials. A similar rank change is evident in the dry weight histograms, in which Quest moved from the centre of the distribution to the positive end, however in this instance the rank shifting probably reflects the greater effect of temperature on the tropical germplasm in the whole population, including WAB450. However using a higher temperature environment, which would presumably increase variation, would not reflect temperature conditions during rice sowing in the NSW rice industry, in which case the variation observed would have little practical application to the NSW rice improvement program.

Effects of the Controlled Growth Environment on Phenotype The controlled environment growth room is very different to the environment experienced in the field. For example, light intensities (photon flux) in the growth room were about 6% of average daylight for trial 1, and about 12% for trial 2. Undoubtedly this affected seedling growth as low light intensity triggers an etiolation response in which the seedling maximises cell elongation and limits leaf development. Plants in the phenotyping trials were noticeably taller and less compact than rice seedlings found in field situations. In addition, the high red: far red light ratio in trial 1 may also have effected seedling growth, given phytochrome receptors of red and far red light have the ability to mediate seedling growth and development. However this mechanism may not be as important as the level of light generally as phytochromes interact with gibberellins and other plant hormones to influence growth and all lines in the phenotyping population had the mutant sd-1 gene, and therefore low levels of endogenous active gibberellins. The temperature environment used was also very different to field temperature conditions. Temperatures operated on a diurnal day/night square wave and remained constant for the duration of the trials, compared to a field environment where temperatures typically vary diurnally in response to a range of climatic factors and increase over the course of seedling development. It is difficult to suggest what impact this might have had on phenotypic variation, other than the controlled growth environment has given the ability to ascribe temperature effects to variation in the population and between trials. This would have been more difficult in the highly variable field environment. Another aspect of the phenotyping trials involved reducing environmental variables involved growing seedlings aerobically in moist soil, rather than submerged in water as is the case for most field-sown rice (McDonald et al. 1994). Apart from the operational difficulties of growing and measuring the submerged seedlings, anaerobic growth introduces variables such as dissolved oxygen content of the water, and growth of algae and water-mould which may compete with rice seedlings (Chapman & Peterson 1962). The effect of growing seedlings aerobically in the phenotyping trials is, however, countered by the similarity between air and water diurnal temperature fluctuations, as in the early stages of growth water depth in rice fields has been found to have little insulating effect. Linkage information for mapping studies is more accurate when genotype can be more clearly inferred from phenotype. The measures of repeatability generated in this trial provide an insight into the non-

23

genetic effects on phenotype, with higher repeatability indicating lower non-genotypic effects. Leaf width appeared to have the most repeatable phenotype (Table 7). While the relatively small population size used in this study rules out any substantial attempts at QTL mapping, it is clear that as the only trait (apart from seed weight) displaying normal distributions in both phenotyping trials, measures of leaf width were the most suitable of the traits analysed, to relate phenotype to genotype. Leaf width, a significant component of seedling vigour The variation for vigour in this population was best measured in terms of leaf width rather than leaf area or other components of vigour. This trait displayed high correlations with other key vigour traits, high heritability, and values for this trait were the most normally distributed of the traits measured in the controlled growth environment. Under the conditions of the phenotyping trials, leaf width appeared to be the major determinant of leaf area, which in both of these trials and other studies has been shown to encompass most of the desirable features of vigour. The most prominent of these include the ability to compete with weeds, and the ability to generate biomass at a faster rate for higher yields in short season varieties. Therefore leaf width could be used as an indirect measure of these characters. Using leaf width as a measure of vigour compared to other vigour component traits such as leaf area, dry weight, shoot length or shoot elongation rate has several advantages. Measurements are non-destructive, so sampled plants can be grown to maturity and seed recovered, and require no specialised or expensive measuring equipment. Variation for the population tested was relatively low, seen in the coefficient of variance of around 10% for most traits. This is attributed in part to the low temperatures used in these trials, as the high vigour parent WAB450 was a tropical variety better adapted to higher temperatures and the low vigour parent Quest was selected in a temperate environment. Other factors in the experiment could have adversely influenced the variation observed, including the population used (selected for flowering date, general agronomic characteristics and the semi-dwarfing trait), a relatively small sample size, and low light intensities in the trials. The controlled growth environment enabled the phenotyping trials to be repeated under environments differing only in temperature and light quality, which in turn meant a measure of non-genotypic effects on phenotype in the trials could be obtained. By limiting the variables phenotypic noise can also be limited in a controlled growth environment, although it remains to be seen if correlations between vigour traits obtained in this study can be validated in the field. Based on correlations, repeatability measures, and frequency distributions the vigour component traits of leaf width and leaf area were the most accurate phenotypic indicators of vigour in this population, and these traits are therefore the most appropriate basis for describing vigour in further analysis.

Genetic Analysis of Seedling Vigour using DArT

Introduction The underlying genetic components of complex traits like seedling vigour are of considerable importance to their understanding and improvement. While several studies have reported QTL for vigour components like shoot length and seedling height, there is little work done on the genetic basis of leaf area development in rice seedlings. This study provided an opportunity to identify some of these underlying components with molecular markers.

24

The recent development of microarray-based genotyping technologies has seen the advent of relatively affordable whole genome profiling, greatly increasing the number of markers available for genetic investigations of complex traits. One such technology is diversity arrays technology (DArT) with which hundreds of markers can be profiled in a single experiment (Wenzl et al. 2004). Although this technology is generally promoted for orphan crops where genome sequence and other elements of molecular breeding are not available (Huttner et al. 2005), DArT was first developed for use on rice owing to its status as a model crop for molecular biology (Jaccoud et al. 2001). In spite of this, DArT has not yet been applied to marker-trait associations in rice. This project was a small pilot study on genotyping using DArT, using the relatively large numbers of molecular markers generated by this technique to identify genetic differences between high and low vigour lines from the phenotyping population. Further, DArT provides the ability to characterise genetic diversity in the parental varieties used in the development of the vigour population.

Materials and Methods

Line selection Lines from the phenotyping trial population were ranked for their leaf area in both trials, and the widths of leaf 2 and 3 in both trials. By calculating the sum of these 4 rankings, an overall rank for each line was determined, as a representative index for vigour in the population. A total of 20 lines were selected to undergo DArT analysis, the two parents WAB450 and Quest and 9 lines from the high and low vigour extremes. The 9 lines with highest ranking (excluding parents and standard varieties) were selected as the 9 high-vigour lines, and the 9 lowest ranking lines were selected for the 9 low-vigour lines. The phenotypes of these groups are shown in Figure 11 for the sum of widths of leaf 2 and 3, and for leaf area in Figure 12. In both cases, the phenotypic differences are not large between the high and low vigour groups, however the distinction is much clearer in the sum of leaf 2 and 3 chart. This is a reflection of the greater variation for leaf width than for leaf area in the population under study.

Vigour Groups - Sum of Leaf 2 and 3 Widths

3.5

4

4.5

5

5.5

6

6.5

7

7.5

8

8.5

Quest

WAB450

18:55

19:14

6 18

:59

19:21

9

19:28

7

19:13

9

19:24

9

20:27

3

18:12

2

19:15

6 18

:35 19

:91

18:16

5

19:28

1

19:26

7

18:13

6

19:16

7

19:27

9

Sum

of L

eaf 2

and

3 L

eaf W

idth

s (m

m)

Trial 1Trial 2

Parents High Vigour Group Low Vigour Group

Figure 11. Best linear unbiased predictors (±SE) for the sum of leaf 2 and leaf 3 widths of high and

low vigour groups and the population parents.

25

Vigour Groups - Leaf Area

125.00

175.00

225.00

275.00

325.00

375.00

425.00

475.00

525.00

Quest

WAB450

18:55

19:14

6 18

:59

19:21

9

19:28

7

19:13

9

19:24

9

20:27

3

18:12

2

19:15

6 18

:35 19

:91

18:16

5

19:28

1

19:26

7

18:13

6

19:16

7

19:27

9

Leaf

Are

a (m

m2 )

Trial 1Trial 2

Parents High Vigour Group Low Vigour Group

Figure 12. Best linear unbiased predictors (±SE) for the leaf area of high and low vigour groups and

the population parents.

This method, a combination of distributional extremes selection and bulked segregant analysis, was used as operational restraints meant DArT analysis could only be performed on a limited number of lines. Lander & Botstein (1989) report that sampling from the distributional extremes results in more statistically useful information for QTL investigations than the same number of individuals selected from the total population. Bulked segregant analysis was chosen due to the high homozygosity of the lines (heterozygosity declines by approximately 50% with each inbreeding generation). Furthermore, pooled individuals for each line increase the probability of revealing polymorphism between the phenotype extremes (Michaelmore et al. 1991).

DNA extraction For bulk samples from each line, a 5cm length of leaf was taken from all surviving individuals in each line. Tissue samples were placed into Eppendorf tubes and frozen in liquid nitrogen. The frozen tissue samples were ground to powder and left in an -80°C freezer for one day to promote the fracture and disruption of cells to release cell contents. 1000μl of DNA extraction buffer (100mM Tris-Cl (tris(hydroxymethyl) aminomethane adjusted to pH 8.0 with HCl), 100mM sodium chloride, 10mM Na2-EDTA (disodium ethylenediamine tetra-acetic acid), 1.5% (v/v) SDS (sodium dodecyl sulfate) and 0.5% (v/v) sarcosine) was added to the tissue samples and the tubes mixed. Then 600μl of phenol: chloroform: iso-amyl alcohol (24:24:1) (equilibrated to pH 8.0 with Tris-Cl to facilitate DNA solubilization) was added. After mixing on a shaker for 10min, the tubes were centrifuged for 8min at 9,000rpm to separate the organic phase containing cell debris and denatured protein from the aqueous phase containing nucleic acids. This aqueous phase was transferred to sterile Eppendorf tubes. To remove residual phenol from the solution, 600μl of chloroform: iso-amyl alcohol (24:1) was added and the tubes mixed for 30min on a shaker. The tubes were then centrifuged for 5min at 9,000rpm to separate the aqueous phase from the phenol-containing organic phase. The aqueous phase was transferred to new sterile Eppendorf tubes. Then 75μl of 3M sodium acetate (pH 4.8) and 700μl iso-propanol was added, and the tubes placed in a -20°C freezer for 10min to precipitate DNA. Following this, the tubes were centrifuged for 15min at 9,000rpm and the supernatant discarded leaving the DNA pellet formed in each tube. A volume of 300μl of 70% ethanol was added to each tube, and the tubes inverted several times to wash any remaining salts from the DNA pellets.

26

The tubes were centrifuged for 5min at 9,000rpm to re-form the DNA pellets, after which the supernatant was discarded and tubes then allowed to dry at room temperature for 5hrs. To the dry DNA, 20μl of 5mg/ml type-A RNAse in TE buffer (10mM Tris-Cl and 1mM Na2-EDTA) was added and allowed to incubate at room temperature for 12hrs to degrade RNA. DNA was then suspended in 600μl of TE buffer and stored at 4°C.

DArT Analysis DNA samples were sent to DArT Pty. Limited (Canberra) for diversity arrays analysis. DNA quality and concentration were assessed by spectrophotometer analysis, and 100ng of each sample used in the genotyping process. Genotyping was carried out using a polymorphism enriched rice array consisting of clones from a pool of 254 accessions, including NSW rice cultivars, international cultivars, and rice wild relatives. Both parents were included in the varietal mix. Each clone on the array is a fragment of genomic DNA approximately 100-600bp long inserted into a cloning vector. A total of 6,144 clones were used in the array, each replicated twice. Genotyping was conducted according to the methods of Jaccoud et al. (2001), excepting that array clones were produced using a technique utilising miniature inverted transposable elements (MITEs). These repeat motifs were used as flanking primer sequence for clone amplification (pers. comm. S. Patarapuwadol, formerly Diversity Arrays Pty. Limited). MITEs are the most abundant from of transposable element in rice (comprising about 6% of the genome), and tend to be distributed in and around expressed regions (Jiang et al. 2004). This makes these repeats ideally suited to marker generation for DArT. This technique is known as MITE-DArT to distinguish it from the AFLP-like fragment generation process described in Jaccoud et al. (2001). Sample DNA was subjected to the same MITE-DArT process as array clones, only after adaptor ligation, clones were fluorescently labelled. In the final stage of PCR amplification Cy-3-labelled uracil bases were incorporated into the replicated clones using the exo-Klenow fragment of DNA polymerase. Hybridisation was conducted according to the procedure of Jaccoud et al. (2001) with the resulting arrays scanned with a confocal laser scanner (Affymetrix, Santa Clara, California). Using the software package DArTSOFT (DArT Pty. Limited, Canberra) hybridisation intensities were calculated for each array element (as detailed in Wenzl et al. 2004) and converted into a 0/1 scoretable. Cells not containing a number in this scoretable indicate missing values, where the hybridisation pattern for that clone was inconclusive.

Results

DArT Markers and Genetic Diversity among Lines A total of 61 DArT clones, or markers, were identified as polymorphic among the 20 lines genotyped representing 1189 individual data points. These clones translate to about 1% polymorphism out of the 6,144 clone array. The hybridisation pattern for each line can be seen in Table 8. A ‘1’ in the table indicates hybridisation of the genomic DNA fragments to the arrayed clone, indicating sequence homology between clone and the tested genotype. A ‘0’ indicates that no hybridisation was detected and therefore the sequence at the locus represented by the arrayed clone is different to that of the tested genotype. This way, alleles for each clone locus in the parent varieties can be traced to the progeny lines to identify which alleles in the progeny are derived from which parent. Principle coordinates analysis, or metric multidimensional scaling, of the 61 DArT markers was performed using the FORTRAN program PCO (Anderson 2003), and a scatter plot of the first two axis (modelling a cumulative 91.68% of the variation observed) is presented in Figure 13. Using principle coordinates analysis preserves the geometric distances between points so that they represent the actual genetic dissimilarity evident from the marker data (Mohammadi & Prasanna 2003).

27

Thus the distances for the graph represent genetic distances between genotypes. This graph shows WAB450 and Quest well separated, although the progeny lines appear more similar to Quest than WAB450. Several lines cluster with Quest, while the WAB450 parent is well separated from other lines. High and low vigour lines do appear to be separated, with all high vigour lines falling below zero for axis 2, and 6 of 9 low vigour lines above. The high and low vigour lines did not separate into different clusters, however the high vigour lines 18:55, 18:59, 18:122, 19:287 and 19:289 as well as the low vigour line 18:136 clustered relatively close together. It is interesting to note that despite lines 18:55 and 18:59 being genetically identical (there is no marker polymorphism between these lines in Table 3.1) these lines do not perfectly overlay one another in the principle components analysis (Figure 13). Given their pedigree, it is possible these two lines were selected from the same F3 plant, so it is not unexpected that these lines are so similar. It is possible that the principle coordinate analysis takes into consideration the missing values for 2 markers in line 18:55.

Principle Coordinates Analysis (PCO) of DArT Markers

WAB450

19:13920:273

19:146

19:21919:24919:287 18:55

18:12218:13618:59

19:91

18:3519:167

18:165

19:267

19:156

19:281

19:279

Quest

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

Coordinate 1 (70.85% of Variation)

Coo

rdin

ate

2 ( 2

0.83

% o

f Var

iatio

n)

High VigourLow VigourQuestWAB450

Figure 13. Principle coordinate analysis of high and low vigour lines, and parent varieties.

28

Table 8. DArT marker scoretable showing Fisher’s exact test p-values for each polymorphic DArT clone. Clones significantly (p>0.01) associated with high or low vigour are shown in bold.

Parents High Vigour Lines Low Vigour Lines

Clo

ne

ID

WA

B

Que

st

18:5

5

19:1

46

18:5

9

19:2

19

19:2

87

19:1

39

19:2

49

20:2

73

18:1

22

19:1

56

18:3

5

19:9

1

18:1

65

19:2

81

19:2

67

18:1

36

19:1

67

19:2

79

p- valu

e

227806 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 0 0.2%202006 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0.7%

70541 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1 1 0.9%229333 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1 1 0.9%202073 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1 1 0.9%

71219 0 1 1 0 1 1 0 0 1 0 1 1 1 1 1 1 1 1 7.7%159984 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 0 8.2%159471 0 1 0 1 0 1 0 1 0 1 0 1 1 1 1 1 1 0 1 1 13.1%160041 1 0 1 0 1 1 0 1 1 1 0 0 0 0 1 0 1 0 1 15.3%202481 0 1 0 0 0 1 0 1 0 1 0 1 1 1 1 0 1 0 1 15.3%

70638 0 1 0 0 0 1 0 1 0 0 0 1 1 1 1 0 1 0 1 0 15.3%70937 0 1 0 0 0 0 0 1 0 1 1 1 1 1 1 0 1 1 1 0 15.3%

227648 1 0 1 1 1 1 0 1 1 1 0 0 0 1 0 1 0 0 0 1 15.3%159208 0 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 20.6%

70427 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 20.6%139521 1 0 1 0 1 1 1 1 1 1 0 0 1 1 1 0 1 0 1 29.4%211268 0 1 0 1 0 0 0 1 0 0 1 1 1 1 0 1 0 0 0 33.5%159818 1 0 0 1 0 1 1 0 1 1 1 0 0 0 0 1 0 1 0 1 34.7%160050 1 0 0 1 0 1 1 0 1 1 1 0 0 0 0 1 0 1 0 1 34.7%211060 0 1 0 0 0 1 0 1 0 1 0 1 1 1 1 0 1 0 1 0 34.7%227462 0 1 0 0 0 1 0 1 0 1 0 1 1 1 1 0 1 0 1 0 34.7%202459 0 1 0 0 0 1 0 1 0 1 0 1 1 1 0 1 0 1 0 34.7%139527 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 47.1%159388 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 47.1%160299 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 47.1%

71367 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 47.1%227402 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 47.1%210967 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 57.6%

70566 0 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 57.6%70770 0 1 0 1 0 0 0 1 0 0 1 0 1 1 0 1 0 0 0 62.0%70944 1 0 1 0 1 0 1 1 1 1 0 0 1 1 1 0 1 0 1 62.0%

229372 0 1 0 1 0 0 0 1 0 0 1 0 1 1 0 1 0 0 0 62.0%159378 0 1 0 1 0 0 0 1 0 1 0 1 1 1 1 0 1 0 0 0 63.7%160243 0 1 0 1 0 0 0 1 0 1 0 1 1 1 1 0 1 0 0 0 63.7%159258 0 1 1 0 1 0 1 0 0 0 1 1 0 1 0 1 0 1 0 63.7%

71302 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 1 0 1 0 63.7%229025 0 1 0 1 0 0 0 1 0 1 0 1 0 1 1 0 1 0 0 63.7%202052 1 0 1 0 1 0 1 0 0 1 0 1 1 0 1 0 1 1 1 63.7%202578 1 0 1 1 1 0 0 1 0 0 0 0 0 1 1 0 0 0 1 63.7%159202 0 1 0 1 0 0 0 1 0 1 0 1 0 1 1 0 1 0 0 0 100.0%159291 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 100.0%159714 1 0 1 0 1 0 1 0 1 1 1 0 1 1 0 1 0 1 1 1 100.0%159810 0 1 0 1 0 0 0 1 0 1 0 1 0 1 1 0 1 0 0 0 100.0%159911 0 1 1 1 1 1 0 1 0 1 0 1 0 0 1 1 1 0 0 1 100.0%160317 0 1 1 0 1 1 0 1 0 1 0 1 1 1 1 0 1 0 1 0 100.0%160345 0 1 0 1 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 100.0%

70385 1 0 1 0 1 0 1 0 1 1 1 0 1 1 0 1 0 1 1 1 100.0%70644 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 100.0%70726 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 100.0%71034 0 1 0 1 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 100.0%71415 1 0 1 0 1 1 1 0 1 1 1 0 1 0 1 1 0 1 1 1 100.0%

211503 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 100.0%227341 1 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1 0 1 1 1 100.0%227429 0 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 100.0%229171 0 1 0 1 0 0 0 1 0 1 0 1 0 1 1 0 1 0 0 0 100.0%202012 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 100.0%202037 0 1 0 1 0 0 0 1 0 1 0 1 0 1 1 0 1 0 0 0 100.0%202405 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 100.0%202453 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 100.0%202555 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 100.0%202660 0 1 0 1 0 0 0 1 0 1 0 1 0 1 1 0 1 0 0 0 100.0%

29

Marker-Trait Association In order to identify clones that were associated with either high or low vigour groups, Fisher’s exact test was performed on the 61 clones. Because of the small number of lines a large sample approximation was inappropriate, therefore the Chi-square test could not be used. The resulting p-value is the exact probability that DArT marker alleles assort by chance between the high and low vigour groups. Fisher’s exact test has no formal test statistic, so the 1% level of significance was used to identify highly significant deviations from chance marker assortment. The group of 5 clones significant at this level are highlighted in the p-value column of Table 3.1 (clones 227806, 202006, 70541, 229333 and 202073).

Discussion

Features of the Diversity Analysis The DArT analysis shows the parents of the vigour population are generally very similar. These two varieties show roughly 1% polymorphism in the 6,144 clone array, and while the polymorphism indicated by the DArT array is only a representation of actual diversity this is particularly low compared to other DArT diversity analyses in rice, barley and cassava (Jaccoud et al. 2001, Wenzl et al. 2004, Xia et al. 2005). The phenotype of the parental lines are quite different; WAB450 is a tropically adapted upland cultivar (Jones et al. 1997), very different from Quest which is a high-yielding cultivar bred in the temperate irrigated NSW rice growing environment. Yet the lack of DArT polymorphisms suggests that they are not very different genetically. This phenomenon emphasises the lack of diversity in cultivated rice and highlights the usefulness of molecular marker diversity analysis in the selection of breeding parents, a process detailed by Xu et al. (2004). While each variety was developed independently in very different parts of the world both share a japonica background, meaning these findings are in line with the research of Zhang et al. (1992) and Ni et al. (2002) who found that temperate and tropical japonicas were closely related and had lower genetic diversity than the indica subspecies. The genetic relatedness of the parents helps explain the low phenotypic variation observed in the phenotyping trials. Apart from any environmental factors the genetic variation in the population may also have been limiting phenotypic variation giving the low diversity between the parents. Out of the 61 markers, 4 showed no polymorphism between parents, only among the progeny lines tested. Of the 57 markers polymorphic between parent lines, 74% of Quest alleles were hybridised fragments (the ‘1’ allele), the balance being with WAB450. This bias in favour of Quest could occur either because WAB450 alleles are under-represented on the array, or because Quest alleles are over-represented. Varieties used in the construction of the array were dominated by varieties from the NSW breeding program, so genotypes including and similar to Quest probably form a significant proportion of the arrayed clones. Conversely, even though WAB450 was included in array construction, WAB450 or related varieties do not form a significant part of the breeding program, and although some O. glaberrima clones from the wild relatives used in the array construction may share sequence with WAB450, the small proportion of the O. glaberrima genome in WAB450 makes this unlikely. To increase the resolution power of DArT for this kind of germplasm DArT clones from WAB450 or similar varieties would have to be added to the array. A similar procedure for barley accessions has been discussed by Wenzl et al. (2004). The 4 clones homozygous between parents yet still showing polymorphism between the progeny lines are most probably due to a small amount of heterozygosity in the parent varieties. The parent DNA used in the genotyping did not come from the exact parents used in the phenotyping population cross, instead plants were grown from seed stored in the NSW rice breeding program seed bank so there is the possibility that actual and tested parental genotypes differed slightly.

30

The heterogeneity can originate from at least two sources in this analysis. Residual heterozygosity can be retained from the initial cross, as continuous selfing will not totally eliminate heterozygosity from every locus. Otherwise heterogeneity can develop through genetic drift as varieties evolve. The detection of this varietal heterozygosity demonstrates the sensitivity of DArT, something also noted by Wenzel et al. (2004). The extremely large number of loci tested (number of clones on the DArT array) relative to other marker technologies is the basis of this sensitivity.

Clones Associated with the High or Low Vigour Groups The process of marker-trait association revealed just 5 clones significantly associated with either the high or low vigour groupings. Because of the small number of lines tested it is difficult to check for possible spurious associations through false positives of Fisher’s exact test. One popular method, the Bonferroni correction, involves multiplying the p-value by the number of hypotheses (clones) to obtain a p-value below which associations will be genuine (Newton-Cheh & Hirshhorn 2005). However the small number of samples in this study means the critical p-value for this test would be 0.0008, a highly conservative value that rejects all significant p-values from this study. Alternatively, permutation testing can be conducted by generating a distribution of the best p-value expected in the entire experiment for situations where there is no marker-trait association (Newton-Cheh & Hirshhorn 2005). This p-value can then be used as the significance level for marker-trait association testing. In this study however, the number of lines genotyped was too small to enable meaningful false-positive testing, an essential step in the marker-trait association process in large-scale studies. Of the 5 clones identified as associating with either the high or low vigour groups, the first two most significant DArT markers (clones 227806 and 202006) show no polymorphism between parents. There is no ready explanation for this phenomenon, and it is most likely that the situation is due to chance and the small number of lines tested. The other markers (clones 70541, 229333 and 202073) have identical marker distributions. In all likelihood, this indicates that these 3 markers are linked, although it is possible that they represent independent loci that together influence the expression of vigour. While the zero alleles do not provide any indication of sequence homology at a marker locus, it is reasonable to suggest in this case that there are 2 alleles for this clone in the lines tested, the ‘0’ allele from Quest and the ‘1’ allele from WAB450. It is interesting to note that for these 3 clones, the high vigour allele (‘0’) originated from Quest, the supposedly low vigour parent. If these clones are indeed associated with vigour traits then this reinforces the results of the phenotyping trials where Quest proved the superior genotype out of the two parents under the conditions tested. The occurrence of high vigour alleles from the low vigour parents has been widely documented in other vigour studies including Redoña & Mackill (1996b) and Zhang et al. (2005b). This phenomenon reinforces the consensus depiction by various workers of vigour being a particularly genetically complex character. In addition, the importance of genetic context is emphasised, demonstrating how the successful expression of this character requires the interaction of various genetic components across the genome. The phenotypic value of an allele for a complex trait belongs to the interaction between the allele and its genetic background rather than the allele itself (Wade, 2001). The difficulty in making marker-trait associations with such a complex trait even with the relatively large numbers of markers generated with microarray technology is highlighted in this study. Because there is no clear-cut phenotype for vigour, the vigour groups genotyped represent a collection of lines with generally high or generally low vigour (Figure 11 and Figure 12). This greatly inhibits the ability to search for a clear-cut difference in genotype between the high and low vigour groups.

31

In future research using similar distributional extremes sampling, efforts should be directed to emphasising the phenotypic difference between the vigour groups, and more importantly, conducting analysis based on the potential network of components involved in vigour expression. This would mean not limiting analysis to markers either presence or absence of vigour, for example vigour loci could be masked by epistatic and context effects in low vigour genotypes, while maintaining a meaningful relationship with the trait in other genetic contexts. Such analysis would necessarily involve identifying loci for high vigour which should be increased, and loci for low vigour that should be decreased.

Future Directions for DArT Analysis of Vigour Each DArT marker is sequence ready, so the next step in the process of identifying markers related to vigour would be to sequence the clone and perform a BLAST search using the published rice genome sequence (Nagamura et al. 2003). This would reveal the position of the clone in the rice genome and enable further searches of known genes and reported QTL to be performed, potentially identifying a function for the genetic region near the DArT marker. Alternatively, DArT markers can be tested in seedling populations grown under field conditions to confirm the marker-trait association. This would enable such markers to join a future suite of DArT markers potentially covering traits such as grain quality and disease resistance. For each line, such markers could be profiled in one step with DArT genotyping. The development of such marker profiles could form the basis of marker-aided selection using DArT in rice breeding. The marker-trait associations discussed here must be seen in the context of the small number of lines analysed and the structure of the population these lines were drawn from. Because of these factors, the clones identified are of limited use to germplasm improvement. Any future genetic studies of vigour would benefit from the selection of vigour parents based on genetic diversity rather than apparent phenotypic diversity to greatly enhance the number of markers generated (Kilian 2004), a move also likely to greatly enhance phenotypic variation between the progeny. As well, the use of a greater number of lines in the genotyping process would improve the resolution of marker-trait associations. One other refinement to improve the quality of the marker-trait discovery process would be the development of a dedicated mapping population. Most QTL mapping is performed on either primary segregating populations like F2 or backcross populations, or advanced segregating populations like doubled haploid lines or recombinant inbred lines (RILs). F2 populations maximise linkage disequilibrium to enhance the detection of the magnitude of QTL effects, while backcross populations are more useful in detecting the presence of QTL (Kearsey & Luo 2004). On the other hand, doubled haploid and RIL populations provide for extra rounds of recombination to increase the density of crossovers, which in turn can break tight linkages and increase the resolution of QTL mapping exercises (Lander & Botstein 1989). Further, fixed lines can be evaluated several times, such as over different environments or years (Asins, 2002). In this study, the population used was intermediate to these categories, as a population of F3-derived F5 breeding lines. The generation of plants used does not make a great difference to the experimental results over against what might have been obtained with F2 or RILs. However, an important common property of dedicated mapping populations is that they are essentially unselected at all loci. This population had undergone some selection, based on flowering date and plant stature, as well as for general agronomic performance. This would have reduced genetic diversity and distorted linkage disequilibrium (and therefore mapping results) around the sd-1 locus and other loci needed for agronomic adaptation. The time and resources needed to develop an ideal mapping population are substantial in plant improvement terms (Kearsey & Luo 2004), factors predicating the use of a vigour population already progressing through the NSW breeding program in this study.

32

However the importance of population genetic structure for mapping applications cannot be understated, and future continuation of this work would substantially benefit from the establishment of a dedicated mapping population.

Conclusions This study reveals the lack of genetic diversity between the parent varieties of the vigour population, a situation that helps explain the relatively low phenotypic variation observed in both phenotyping trials. Given parental selection for the vigour population was based on phenotypic attributes, this study reflects the results of other genetic studies of complex traits where phenotypic appearance is not necessarily an accurate indication of genetic potential. The proportion of marker alleles hybridised to the array was strongly biased in favour of the Quest parent, probably because WAB450 alleles were under-represented on the diversity array. This did not appear to affect the sensitivity of the DArT genotyping process however, with 4 of the 61 markers non-polymorphic between parents. This indicates some residual heterozygosity in these varieties, as the parental DNA tested was not from the exact parental genotype used in the original population cross. The 5 clones identified as distinguishing between the high and low vigour groups with statistical significance represent potential markers for early leaf area development. With further confirmation, these clones could represent markers that differentiate between high and low vigour germplasm, and could in the future form part of an integrated marker assisted selection system using DArT. This study has identified a number of areas to be addressed in future DArT analyses of vigour, probably the most important being the genetic structure and size of the population tested. As in any QTL study, a large segregating population will give best results with DArT marker analysis, and the parents of the population should be as genetically diverse as possible to maximise marker number and also population genotypic and phenotypic variation.

Discussion Crop breeding and improvement programs have not changed markedly over the past 40-50 years. Yet there have been enormous changes in the understanding of genetics, from the basic building blocks of DNA sequence information to understanding the complex chain of events required for a particular sequence to produce a measurable change in phenotype. Similarly information technology has mirrored the advances in biotechnology, and an effective synthesis of these disciplines provides opportunities to further understand and manipulate the system from the gene level through to crop performance in the field. The NSW Rice Breeding Program has incorporated some routine molecular marker selection systems in variety development. The use of individual molecular markers is a step forward, but is relevant only to relatively simply inherited traits – or those where phenotyping is complex and it is more efficient to use the marker. In standard crossing there is little point in selecting semi-dwarf using a marker for example. DNA technology has changed rapidly and the next paradigm is to allow representation of the entire genome – in the case of DArT through generation of an array of only those fragments which exhibit polymorphism. It is at best only part of the genome however, and the sequence and function of the individual polymorphisms are unknown. In contrast, micro-arrays are constructed using known sequences but are limited by the extent to which the genes or sequence information is associated with known function.

33

The current era in biotechnology is that of functional genomics – ascertaining the relationship between the DNA structure and sequence, with the phenotype. While an exhaustive analysis is useful scientifically, the need for breeding programs focused on practical outcomes in response to industry demands, is how to use new methods of visualising the genotype to more rapidly develop new varieties. Hence there is a need to use associations between the measure of genotype (DarT or a similar system) and the phenotype. Meaningful associations require accurate and repeatable phenotyping to develop the associations before they can be used as a regular part of the evaluation program. The two components are of equal and fundamental importance. In embracing whole-genome technology the questions of resource allocation are pivotal. It would be unwise to limit the part of the program that evaluates lines in order to expand the genotyping function. Arguably, plant breeding in general is yet to capture the full effect of these advances in knowledge to enhance complex traits like yield and environmental adaptation. The NSW Rice Breeding Program is at the point of deciding how best to allocate resources between the fundamental requirements of the program – crossing, population development, effective selection pressure and accurate phenotypic evaluation, and the integration of simple markers and information from whole-genome analysis. A coordinated approach is necessary so that necessary resources for phenotyping capacity are not plundered to enable establishment of the genotyping capacity. Then there is the issue of the total resources devoted to varietal development, and there is little scope to increase these unless a clear and relatively near-term return is foreseeable.

The Sorghum Breeding Program based at Hermitage Research Station, Warwick QLD is embarking on a significant project to integrate DArT into the mainstream breeding. A major focus of the breeding program is the diversification of the gene pool utilised by the breeding program using sorghum lines from different parts of the world, including wild grassy sorghums. Using DArT will in their words “allow fundamental changes to be made to genetic improvement programs by changing the focus from the paradigm of identifying superior varieties to a focus on identifying superior combinations of genetic regions and packaging these regions into varieties. Beyond this, it can change the focus from the breeding paradigm of only developing superior varieties to a crop improvement paradigm of developing superior combinations of genetic regions and management systems to optimise resource capture and sustainability in particular cropping environments.”

The progress of integrating DArT into the Sorghum Breeding Program should be monitored closely to provide guidance on its future use for rice variety development. Monitoring issues such as bioinformatics, data storage and manipulation, analysis and most importantly, application of the technology should provide excellent case studies as to how to most effectively use the technology. The sorghum project will develop a novel approach to integrating enhanced marker technologies (DArTs), advanced QTL detection methods, and physiology and modelling to understand and evaluate gene-to-phenotype linkages. Sorghum will be used as the model crop and crop improvement system. It remains to be seen if the project will generate an enhanced rate of improvement in sorghum yield and a prototype integrated crop improvement system suitable for other crops.

Implications The use of DArT analysis in this project has shown that the technology can provide detailed analysis of the suite of parental lines used in the Rice Breeding Program, showing the degree of genetic differences or similarities between all varieties. Such information is useful for future selection of parents, allowing the use of measurable genetic diversity rather than an estimate of diversity based on phenotypic differences.

34

DArT is able to distinguish between closely related varieties and breeding lines emanating from the NSW Rice Breeding Program, demonstrating its capability to provide genetic fingerprinting in the case of uncertainty in seed identification, and as a means of quality assurance in the production of pure seed of existing commercial varieties and new breeding lines approaching release. In diverse and inter-specific crosses DArT analysis is able to provide a fast and inexpensive means of determining the extent of introgression of the genome of the diverse parent. Capturing useful variation from such crosses inevitably requires multiple backcrossing to end up with a suitable genetic background for the NSW rice growing environment. Hence there is a need to ensure that at each hybridisation to the recurrent parent is a real cross, and not an inadvertent self-pollination. DArT analysis provides this information as well as an indication of the extent of the genome transferred. If DArT analysis can be modified to include points on the array of known sequence that can test for existing well-defined molecular markers, then some of the resources devoted to existing simple markers can be used for DArT analysis, increasing the efficiency of the molecular marker part of the breeding program. The feasibility of this has not yet been canvassed in detail with Diversity Arrays Pty Ltd. Development of usable QTL’s for complex traits such as cold tolerance, rests on the capacity to phenotype accurately and repeatably. In future studies DArT markers associated with useful traits should be sequenced and located on the rice genome to provide useful information as to the possible mechanism and genetic control of such traits. Emphasis on DArT and other biotechnological systems should not be at the expense of phenotyping capacity within the NSW Rice Breeding Program At the same time there is a need for the rice improvement program to continue to develop the use of biotechnology to achieve greater efficiency, enhanced market responsiveness and address present and looming sustainability issues. As molecular markers for specific traits are added, greater efficiency is needed in the marker testing and selection program. DArT has the potential to encompass these needs through a staged introduction, drawing on the experience of other programs, while maintaining and improving phenotyping capacity.

Recommendations

1. The NSW rice improvement program will use DArT judiciously for specific populations – such as the Oryza rufipogon backcrossing program to develop a series of introgression lines with small amounts of the O. rufipogon genome inserted into a genetic background suited to NSW conditions. Possibilities exist to capture disease resistance, cold tolerance, variation in grain quality and other useful traits.

2. DArT can also be used to monitor genetic integrity of pure seed lines, to build a genetic

fingerprint of all current varieties and breeding lines, and as a foundation for quality control in pure seed maintenance.

3. The possibility of transferring existing markers such as fragrance, gelatinisation temperature

and the range of variants in granule-bound starch synthase, to DArT panels should be explored.

4. If possible a small specific project aimed at an intractable problem such as developing a

quantum increase in cold tolerance should be developed as a targeted use of DArT. A possible link to a Chinese program hybridising cold tolerant Oryza rufipogon with Oryza sativa could be developed.

35

5. Careful attention needs to be paid to the systems required to store and manipulate the quantities of data that DArT generates, and to make meaningful conclusions. DArT analyses have been successfully loaded into the rice implementation of the International Crop Information System. The capacity of this system to efficiently store DArT analyses for high numbers of lines is not known.

36

Appendices Appendix 1. Distribution of seedling vigour traits of five populations across 2 sowing dates.

Dry weight (mg per plant)16.6 18.4 20.2 22.0 23.7 25.5 27.3 29.1 30.9 32.6

Freq

uenc

y

0

2

4

6

8

10

12

14

16

18

Dry weight (mg per plant)81 87 94 100 106 112 118 124 130 137

0

2

4

6

8

10

12

14

16

18Dry weight- sowing 1 Dry weight- sowing 2

Leaf number2.00 2.04 2.08 2.12 2.16 2.20 2.24 2.28 2.32 2.36

Freq

uenc

y

0

2

4

6

8

10

12

14

16

18

Leaf number2.94 3.07 3.20 3.33 3.46 3.59 3.72 3.85 3.98 4.11

0

2

4

6

8

10

12

14

16

18Leaf Number- sowing 1 Leaf number- sowing 2

a b

c d

Height to ligule (mm)33.6 35.0 36.5 37.9 39.4 40.8 42.3 43.7 45.2 46.6

Freq

uenc

y

0

2

4

6

8

10

12

14

16

18


0

2

4

6

8

10

12

14

16

18Height to ligule- sowing 1 Height to ligule- sowing 2

Height to leaf tip (mm)102 106 111 116 120 125 129 134 138 143

Freq

uenc

y

0

2

4

6

8

10

12

14

16

18


0

2

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18Height to leaf tip- sowing 1 Height to leaf tip- sowing 2

e f

g h

YRM49

WAB3

WAB3 YRM49

WAB3

YRM49

YRM49WAB3

YRM49WAB3

YRM49

WAB3

YRM49YRM49

WAB3

WAB3

37

Figure 14. Frequency of dry weight, leaf number, height to ligule and height to leaf tip for two sowing times for the YC00001 population (n=46). The parental means are also shown.


Freq

uenc

y

0

2

4

6

8

10


0

2

4

6

8


Leaf number2.15 2.17 2.19 2.21 2.23 2.25 2.27 2.29 2.31 2.32

Freq

uenc

y

0

2

4

6

8

10

Leaf number3.40

3.41

3.42

3.43

3.44

3.45

3.46

3.47

3.48

3.49

3.50

0

2

4

6

8


a b

c d


Freq

uenc

y

0

2

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0

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8



Freq

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y

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e f

g h

YRL113

YRL113

YRL113

YRL113

WAB1

WAB1

WAB1

WAB1

YRL113

WAB1

YRL113

WAB1

YRL113

WAB1WAB1

YRL113

Figure 15. Frequency of dry weight, leaf number, height to ligule and height to leaf tip for two

sowing times for the YC00006 population (n=28). The parental means are also shown.

38


Freq

uenc

y

0

2

4

6

8

10


0

2

4

6

8


Leaf number2.08 2.13 2.17 2.21 2.26 2.30 2.35 2.39 2.43 2.48

Freq

uenc

y

0

2

4

6

8

10

Leaf number3.08 3.13 3.17 3.22 3.26 3.31 3.36 3.40 3.45 3.49

0

2

4

6

8


a b

c d


Freq

uenc

y

0

2

4

6

8

10


0

2

4

6

8



Freq

uenc

y

0

2

4

6

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10


0

2

4

6

8


e f

g h

YRM63

WAB1

WAB1

YRM63

WAB1

YRM63

WAB1

YRM63

WAB1

YRM63

WAB1

YRM63

YRM63

WAB1

YRM63

WAB1



39


Freq

uenc

y

0

2

4

6

8

10


0

2

4

6

8


Leaf number1.89 1.98 2.06 2.15 2.23 2.32 2.41 2.49 2.58 2.66

Freq

uenc

y

0

2

4

6

8

10

Leaf number2.78 2.88 2.98 3.07 3.17 3.27 3.37 3.47 3.57 3.67

0

2

4

6

8


a b

c d


Freq

uenc

y

0

2

4

6

8

10


0

2

4

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Freq

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e f

g h

YRM49

WAB3YRM64WAB4

YRM64

WAB4 YRM64

WAB4

YRM64WAB4

YRM64

WAB4

YRM64

WAB4

YRM64WAB4



40


Freq

uenc

y

0

5

10

15

20

25

30


0

5

10

15

20

25


Leaf number2.14 2.23 2.32 2.42 2.51 2.60 2.70 2.79 2.88 2.98

Freq

uenc

y

0

20

40

60

80

Leaf number2.31 2.48 2.65 2.82 2.99 3.15 3.32 3.49 3.66 3.83

0

5

10

15

20

25


a b

c d


Freq

uenc

y

0

5

10

15

20

25

30


0

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10

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20

25



Freq

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y

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5

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15

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25

30


0

5

10

15

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25


e f

g h

YRM64WAB3

WAB3

YRM64

WAB3

YRM64WAB3

YRM64

WAB3

YRM64

YRM64

WAB3

YRM64

WAB3

WAB3YRM64



41

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evaluating diversity array technology (dart) for the nsw ...evaluating diversity array technology...

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