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DATE DUE DATE DUE DATE DUE 11/00 atom-4059.14 GLOBAL PATTERNS OF WHEAT GENETIC DIVERSITY WITH EMPHASIS ON IMPROVED GERMPLASM FROM CHINA By Samuel Philip Hazen A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Plant Breeding and Genetics - Crop and Soil Sciences 2000 ABSTRACT GLOBAL PATTERN OF WHEAT GENETIC DIVERSITY WITH EMPHASIS ON IMPROVED GERMPLASM FROM CHINA By Samuel Philip Hazen Chinese wheat (T riticum aestivum L.) landraces have been shown to be extremely diverse and unique relative to several SW Asian landraces and improved winter wheat germplasm pools. The genetic diversity within improved Chinese germplasm, which has proven to be a valuable resource in plant improvement worldwide, is heretofore unknown. The utilization, monitoring, and maintenance of wheat genetic diversity are imperative for the continuation of genetic improvement in plant breeding and avoiding genetic vulnerability to environmental stress. The tools available to measure genetic identity and subsequent interpretation of those values remain ill defined. The objectives of this research were to characterize the genetic structure of wheat germplasm worldwide, with special emphasis on improved Chinese germplasm. The amplified fragment length polymorphism (AF LP) marker system was employed to measure genetic relationship among accessions and was characterized by placing one hundred forty markers to the consensus wheat genetic linkage map. AF LP loci in wheat are distributed throughout the genome, generally have only one detectable sequence variant, and exhibit monogenic dominant Mendelian inheritance. AF LP bands that map to individual loci in the mapping population will frequently be polymorphic in other crosses or germplasm. nc \Vl Polymorphism, manifested as DNA sequence variation and revealed using both restriction fragment length polymorphism and AF LP markers, are primarily monomorphic across a comprehensive sample of wheat germplasm. The three ancestral genomes of wheat consistently varied in genetic distance regardless of comparative material. Previously concealed patterns of genetic distance were revealed using markers with known genome position. This study and others suggest that geography is an adequate classifier of genetic diversity in wheat. However, only a small proportion of the total variation differentiates these pools. China contains a relatively diverse pool of improved wheat germplasm, but the diversity characteristic of Chinese landrace pools is not present in the improved Chinese cultivars. The average level of genetic diversity within Chinese and EUS pools is comparable, but the two pools are quite distinct. Map- based analysis of genetic diversity revealed a gradient of diversity across the three genomes. It appears that annotated molecular markers are more useful than random molecular markers in elucidating patterns of diversity. Dedicated to my loving parents Daniel and Karen Hazen iv Hon Slim ACKNOWLEDGMENTS I would like to thank Dr. Rick Ward for his vital support and for serving as my major professor. The guidance of Drs. Mitch McGrath, Andy Jarocz, and Jim Hancock are greatly appreciated. Special thanks to Dr. Jim Hancock for being a friend, colleague and mentor. I appreciate the collaboration and assistance of Lieceng Zhu, Phillipe Leroy, Hong—Sik Kim, Guoshiun Tang, Dean Lehmann, Michael Retholtz,, Heather Borden, Lee Siler, Rhonda Bafus, Jenny Nosakowski, Keenan Amundson, and Ragaranga Gopalachar. My friends and colleagues, especially Heather Richardson, Danny Rozen, and Judy Kolkman have made life interesting and fun, thank you! LlST 0 LlST C LIST C on) SE 7A Ix.) Ix) Ix) ()1 (J --‘ .9) OJ 0) (II I; TABLE OF CONTENTS LIST OF TABLES ........................................................................................................... viii LIST OF FIGURES ............................................................................................................. x LIST OF APPENDICES .................................................................................................... xi 1 REVIEW: GENETIC DIVERSITY 1N HEXAPLOID WHEAT ................................ 3 1.1 INTRODUCTION ............................................................................................... 3 1.2 Evolution & domestication .................................................................................. 3 1.3 Change associated with the development of modern cultivars ............................ 5 1.4 Modern similarity studies .................................................................................... 6 1.4.1 Coefficient of parentage .............................................................................. 6 1.4.2 Biochemical and molecular markers ........................................................... 7 1.4.3 Comparison of the methods ....................................................................... 11 1.5 Calculation of genetic relationship .................................................................... 13 1.6 Genetic distance as a predictor of genetic variance and heterosis ..................... 15 1.7 Germplasm resources in use and storage ........................................................... 16 1.8 Alien species contribution ................................................................................. 17 1.9 Biotechnology and genetic engineering ............................................................. 18 1 . 10 CONCLUSIONS ............................................................................................... 20 1. 1 1 REFERENCES .................................................................................................. 26 2 GENETIC DIVERSITY OF WINTER WHEAT IN SHAANXI PROVINCE, CHINA BASED ON RFLP AND SSR MARKERS ......................................................... 34 2. 1 INTRODUCTION ............................................................................................. 34 2.2 MATERIALS AND METHODS ...................................................................... 37 2.3 RESULTS .......................................................................................................... 42 2.3.1 Molecular marker characteristics ............................................................... 42 2.3.2 Genetic distance and structure of the Shaanxi Germplasm ....................... 43 2.3.3 Genetic Distance and Structure of the Combined Shaanxi and World Germplasm ................................................................................................................. 46 2.4 DISCUSSIONS .................................................................................................. 50 2.5 REFERENCES .................................................................................................. 55 3 AFLP IN TRITICUMAESTICUM L. ........................................................................ 59 3. 1 INTRODUCTION ............................................................................................. 59 3.2 MATERIALS AND METHODS ...................................................................... 61 3.2.1 AFLP procedure ......................................................................................... 61 3.2.2 Polyacrylamide gel electrophoresis ........................................................... 62 3.2.3 Linkage analysis ........................................................................................ 62 3.2.4 Genetic distance and marker/trait association analysis ............................. 63 3.3 RESULTS .......................................................................................................... 69 3.4 DISCUSSION .................................................................................................... 83 3.5 REFERENCES .................................................................................................. 89 vi WI £44.44 API 4 GENOME SPECIFIC GENETIC DIVERSITY OF CHINESE AND EASTERN US WHEAT GERMPLASM ................................................................................................... 93 4. I INTRODUCTION ............................................................................................. 93 4.2 MATERIALS AND METHODS ...................................................................... 95 4.3 RESULTS ........................................................................................................ 100 4.4 DISCUSSION .................................................................................................. 112 4.5 REFERENCES ................................................................................................ 1 15 APPENDICES ................................................................................................................. l 17 TABLE FD TABLE Hl IABLI TABI TABl IAE TA LIST OF TABLES TABLE l-l VARIABLES FOR PAIRWISE COMPARISONS AT EACH DNA FINGERPRINT FRAGMENT STATE AND EQUATIONS USED TO CALCULATE SIMILARIT Y BETWEEN TWO GENOTYPES. ............................ 14 TABLE 1-2 SUMMARY OF LITERATURE MEASURING GENETIC DIVERSITY IN HEXAPLOID W HEAT ............................................................................................ 22 TABLE 2-1. NAME, PARENTAGE, AND DECADE OF PRIMARY CULTIVATION OF THE 24 T. AES T I VUM ACCESSIONS DEVELOPED IN SHAANXI, CHINA. ................................................................................................................................... 40 TABLE 2-2. ANALYSIS OF MOLECULAR (RFLP) VARIANCE OF THE 22 WHEAT GERMPLASM POOLS CLASSIFIED BASED ON GEOGRAPHIC AND BREEDING PROGRAM ORIGIN. .......................................................................... 50 TABLE 2-3 THE TEST OF SIGNIFICANCE FOR THE PAIRWISE GENETIC DIVERSITY OF GERMPLASM POOLS. VALUES REFLECT THE PROPORTION OF PERMUTATED POOLS LEADING TO A (DST VALUE LARGER OR EQUAL TO TRUE PAIRWISE DIFFERENCE. .............................. 52 TABLE 3-1 NAME, ORIGIN, AND PEDIGREE OF 46 WHEAT ACCESSIONS USED IN THE GERMPLASM SURVEY. ACCESSIONS ARE ANNOTATED WITH DESCRIPTORS OF GROWTH HABIT, LANDRACE OR IMPROVED CULTIVAR, AND PRESENCE OR ABSENCE OF A lBL/lRS WHEAT RYE TRANSLOCATION CARRIER OR NOT ................................................................ 65 TABLE 3-2 THE NUMBER AND GENOMIC DISTRIBUTION OF AFLP BANDS GENERATED FROM EACH OF THE 10 PRIMER PAIRS IN THE CROSS OF OPATA 85 AND W7984 AND A GERMPLASM PANEL COMPOSED OF THIRTY-SEVEN CULTIVARS AND SEVEN LANDRACES FROM 15 COUNTRIES. ............................................................................................................ 68 TABLE 3-3. GENOME DISTRIBUTION OF 136 AFLP LOCI PLACED ON THE WHEAT LINKAGE MAP CREATED FROM THE CROSS OF OPATA 85 AND W7984 ........................................................................................................................ 71 TABLE 34 LINEAR ORDER OF AF LP LOCI ON GENETIC LINKAGE MAP OF OPATA 85/W 7984 CROSS. AFLP LOCI ARE IN BOLD PRINT. OTHER LOCI WERE PREVIOUSLY RECORDED (SEE MATERIAL AND METHODS). EACH CHROMOSOME BEGINS WITH THE SHORT ARM OF THE CHROMOSOME. MAP DISTANCE LISTED AS INT. DENOTES ASSIGNMENT TO AN INTERVAL BETWEEN TWO PLACED MARKERS. THE CENTIMORGAN DISTANCE FOR A LOCUS REFERS TO THE viii IABL TABI DISTANCE TO THE ABOVE LOCUS NOT PLACED IN AN INTERVAL. MAP DISTANCE IS LISTED AS CENTIMORGAN S FROM THE PRECEDING LOCUS. THE FREQUENCY OF THE BAND REPRESENTING A GIVEN AFLP LOCUS ACROSS THE GERMPLASM ASSAY IS LISTED WITH BAND PARENT SOURCE. DISEQUILIBRIUM AND SEGREGATION DISTORTION (SD) WHERE TESTED USING 12 ANALYSIS AT P<0.05. CODOMINANTE MARKERS ON CHROMOSOME IB AND 3A ARE UNDERLINED. ND- NO DATA. ....................................................................................................................... 74 TABLE 4-1 NAME, GEOGRAPHICAL ORIGIN, GROWTH HABIT, AND GERMPLASM SET DESIGNATION OF 125 WHEAT ACCESSIONS. ............... 96 TABLE 4-2 THE FREQUENCY AND GENOME LOCATION OF POLYMORPHIC AND UNIQUE BANDS FOUND IN EACH OF THE GERMPLASM SETS. ...... 102 TABLE 4-3 AVERAGE ABSOLUTE BAND FREQUENCY DIFFERENCES BETWEEN SETS OF WHEAT ACCESSIONS USING MAPPED AND UNMAPPED AFLP LOCI. BANDS THAT WERE MONOMORPI-HCALLY PRESENT OR ABSENT FROM EITHER POOL IN A COMPARISON WERE OMITTED ............................................................................................................... 102 TABLE 4-4. AVERAGE GENETIC DISTANCE (l- JACCARD SIMILARITY COEFFICIENT) WITHIN AND BETWEEN SETS OF WHEAT ACCESSIONS USING MAPPED AND UNMAPPED AF LP LOCI. ............................................. 104 TABLE 4-5. ANALYSIS OF MOLECULAR VARIANCE OF THE BUS AND CHINA GERMPLASM POOLS. .......................................................................................... 112 ix FlGl FlGI FIGI FIGI FIGI FIGL LIST OF FIGURES FIGURE 2-1 CLUSTER ANALYSIS OF 24 WHEAT ACCESSIONS FROM SHAANXI PROVINCE, CHINA, AND CHINESE SPRING USING GENETIC DISTANCE (RFLP AND MICROSATELLITE DATA). BRACKETS SUBJECTIVELY IDENTIFY GROUPING OF GENETICALLY SIMILAR ACCESSIONS WITH A CLUSTER DESIGNATION AT GD = 0.065. .......................................................... 45 FIGURE 2-2 CLUSTER ANALYSIS OF MEAN GENETIC DIVERSITY BETWEEN 22 WHEAT GERMPLASM POOLS CLASSIFIED BY GEOGRAPHIC OR PLANT BREEDING PROGRAM ORIGIN. BRACKETS SUBJECTIVELY [IDENTIFY NATURAL GROUPING OF GENETICALLY SIMILAR ACCESSIONS WITH A CLUSTER CUTOFF OF GD = 0.056 AND SUBCLUSTER DESIGNATION AT GD = 0.058. . ................................................. 49 FIGURE 3-1. HISTOGRAM OF AFLP ALLELES ORIGINATING FROM EACH PARENT OF THE MAPPING POPULATION (OPATA 85 AND W7984) AS WELL AS ALL POLYMORPHIC LOCI DETECTED IN THE GERMPLASM PANEL. ..................................................................................................................... 72 FIGURE 3-2 GENETIC DISTANCE DENDROGRAM BASED ON AFLP OF 46 WHEAT ACCESSIONS CALCULATED USING JACCARD‘S COEFFICIENT. BRACKETS SUBJECTIVELY IDENTIFY GROUPING OF GENETICALLY SIMILAR ACCESSIONS. ........................................................................................ 82 FIGURE 4-1. GENOME SPECIFIC GENETIC DISTANCE ESTIMATES BETWEEN 17 DIVERSE ACCESSIONS OR ACCESSION GROUPS AND OPATA 85, W7984, AND KARL 92. EACH BAR REPRESENTS LEVEL OF GENETIC DISTANCE BETWEEN TWO ACCESSIONS OR ACCESSION GROUPS. ...... 105 FIGURE 4-2. CLUSTER ANALYSIS OF THE GENETIC DISTANCE 123 ACCESSIONS OF WHEAT ESTIMATED USING AFLP .................................... 108 FIGURE 4-3 PRINCIPAL COORDINATE ANALYSIS OF 124 ACCESSIONS CODED FOR ORIGIN IN THE LEGEND ............................................................................ 111 APPl 0Rl( CHlT CAL APP ANI ESTI C OE APP LIST OF APPENDICES APPENDIX A. GENETIC DISTANCE ESTIMATES FOR 24 ACCESSIONS ORIGINATING FROM SHAANXI PROVINCE CHINA AND ABBONDANZA AND CHINESE SPRING BASED ON RFLP AND SSR. ALL ESTIMATES WERE CALCULATED USING THE JACCARD COEFFICIENT ................................. 118 APPENDIX B. RFLP-BASED MEAN GENETIC DISTANCE ESTIMATES WITHIN AND BETWEEN 22 GERMPLASM POOLS OF COMMON WHEAT. ALL ESTIMATES WERE CALCULATED USING THE JACCARD COEFFICIENT .................................................................................... 119 APPENDIX C. AF LP PROTOCOL ............................................................. 120 xi AF A.\ CH CH CH CH CI.\ CLI CO} ELIE GD l\\'\ \11 PCC PEC PCR PIC RAP RFL; ii iii SSR SIS - UPC: LIST OF ABBREVIATIONS AF LP - Amplified fragment length polymorphism AMOVA - Analysis of molecular variance CHN_SW - Sichuan White wheat (China) CHN_TW - Tibetan Weedrace (China) CHN_XR - Xinjiang Rice wheat (China) CHN_YH - Yunnan Hulled wheat (China) CIMMYT - International Center for the Improvement of Maize and Wheat CLINK — Complete linkage clustering methods COP - Coefficient of parentage EUS - Eastern US sofi winter wheat GD - Genetic distance IWWSN - International winter wheat screening nursery MI — Marker index PCO - Principal coordinate analysis PEC — Probe enzyme combination PCR - Polymerace chain reaction PIC - Polymorphic information content RAPD - Random amplified polymorphic DNA RFLP - Restriction fragment length polymorphism SSR - Simple sequence repeat STS —— Sequence tagged site UPGMA - Unweighted pair group method using arithmetic averaging xii L'S_ L'S_ LTS_ L'S_ US_ER - Eastern US. soft red winter wheat US_EW - Eastern US. soft white winter wheat US_GP - U.S. Great Plains US_W — Western US. soft white winter wheat xiii CL TC an th: m: di\ col acc yiel mat amt dep‘ "an: illch eVOli INTRODUCTION Wheat (T riticum aestivum L.) is cultivated from as far north as the Arctic Circle in Norway, Finland, and Russia to the southern regions of Argentina, with a total area of cultivation that easily exceeds that of any other crop by more than 80 million hectares. Total production (z594 million metric tons/year 1997 to 2000) is equal to that of maize and is 200 million metric tons/year more than any other crop (U SDA-FAS, 2000). Thus, the vulnerability of wheat to external stress puts many human lives at direct risk of malnutrition and starvation. The security of this food supply relies directly on the genetic diversity and variation found in farmers’ fields, plant breeding programs, and germplasm collections. Genetic variation is the essential resource for plant improvement. The accumulation of small effect additive genes is responsible for the gradual increase in yield potential of wheat (Evans and Fischer 1999). Hybridization of diverse parental material is likely to produce a population whose performance has a normal distribution around the mean parental performance. The range of this distribution is partially dependent on the magnitude of the differences of the two parents. The likelihood of a transgressive segregant arising that is superior to the parent material is a function of genetic variation. An elevated quantity of genetic diversity in a breeding program increases the probability of gene complexes occurring through hybridization to meet evolving selection criteria. “Genetic vulnerability” became a permanent element of the agriculturist’s lexicon after the disastrous Southern Corn Leaf Blight epidemic in the US. during the early 1970’s. That episode, catalyzed by dominance of a single, unique cytoplasmic genotype, SIIZI‘; Shci repez anah parti< tooli amon the E; dramatically illustrated the food security risk presented by genetic uniformity. High levels of crop diversity across space provide a buffer against threatening changes in plant pathogen populations. The effective life span of a resistance gene is also lengthened when avirulent pathovars reproduce, thereby minimizing the relative frequency of virulent pathovars. This concept has become extremely important in developing strategies for the deployment of transgenic Bt corn and cotton (McGaughey et a1. 1998; Shelton et a1. 2000). This dissertation has four objectives: 1) provide an overview of the current understanding of wheat genetic diversity and the methods and techniques used to measure it, 2) use AF LP (amplified fragment length polymorphism), SSR (simple sequence repeats), and RF LP (restriction fragment length polymorphism) molecular markers to analyze the distribution of genetic diversity in winter wheat germplasm pools, particularly one from Shaanxi Province, China, 3) assess the utility of AF LP markers as a tool for diversity studies in wheat, and 4) determine the map-based genetic relationships among a large collection of wheat germplasm consisting of accessions from China and the Eastern US soft winter wheat market class and the China. Kled: (197: liett pockr patter dnfi. andli CHAPTER 1 1 REVIEW: GENETIC DIVERSITY IN HEXAPLOID WHEAT 1.1 INTRODUCTION N.I. Vavilov (1926) believed the center of origin of wheat to be a broad Mediterranean area where there was a particularly high level of variability. Harlan (1975) later described a smaller center of origin in Turkey, Syria, Iraq and western Iran. He felt the distribution of genetic diversity of wheat was oligocentric with several pockets of variation in the Mediterranean, Asia Minor, Northwest India, and China. The pattern of genetic diversity in wheat is a product of founder effect, migration, genetic drift, and selection. To understand this distribution, we must begin with the evolution and history of cultivated wheat. 1.2 Evolution & domestication Prior to the innovation of agriculture, humans gathered wild diploid and tetraploid wheat for food. For reviews of the early history of wheat see F eldman et a1. (1995), Harlan (1992), Hancock (1992), Zohary and Hopf (1988), and Sauer (1993). The first records of collection of the diploid species, T. dicoccoides (2n=4x=28) dates as far back as 19, 000 to 30,000 years ago in Israel and Syria (Feldman et al. 1995). Through collection of wheat, our ancestors unconsciously selected the non-free threshing rachis phenotype, which first appeared as semi-brittle T. monococcum (2n=2x=14) about 10,000 years ago (Zohary and Hopf 1988). Several other domesticated species were selected over time by humans, including tetraploid (T. timophevi and T. turgidum), and hexaploid (T. aestivum) forms. The hexaploid arose soon after the domestication of diploid and tetraploid types, and was the product of the fusion of the AB genome of tetraploid T. turgidum var. dicoccum (2n = 4x = 28) with the D genome of diploid Aegilops tauschii ssp. strangulata (2n = 2x = 14). This speciation event occurred nearly 9,000 years ago in Transcaucasia and Southwest Caspian Iran (Dvorak et al. 1998; F eldman et al. 1995). The size of the founding population is believed to have been very small and genetically similar, since only a small portion of the allelic variation found in Ae. tauschii is found in common wheat (Dvorak et a1. 1998; Talbert et al. 1998; Kam-Morgan et a1. 1989; Nishikawa et a1. 1992). There are no known wild populations of T. aestivum, although hexaploid forms occur regularly where Ae. tauschii is sympatric with cultivated tetraploid wheat. The incorporation of three distinct diploid genomes created an adaptively plastic genome that could be cultivated in many parts of the world. The initial migration of hexaploid wheat began near the Caspian Sea nearly 10,000 years ago as part of a complex of cultivated species. This complex originated in the Near East and consisted of einkom (T. monococcum), emmer (T. turdgidum), and bread (T. aestivum) wheat, along with barley, pea, lentil, chickpea, bitter vetch, and flax (Harlan 1992). Wheat had moved south and east to several areas by 8000 BP including Mesopotamia, western Iran, central Asia, Iraq, Anatolia, and Crete (F eldman 1995). By the fifth millennium BC, wheat cultivation was interspersed throughout the Nile basin and western Mediterranean landscape with. Northern migration brought wheat into central and Western Europe. Late in the third millennium BC, wheat cultivation appeared in the Indus valley and China. Spanish colonization of the western hemisphere introduced wheat to the New World through Mexico in 1529 (Hancock 1992). phenot numbc pests. a han'cst other at modi tic are char dwarf p adaptati landrace dramatic 1990; C cultivan 1.3 Change associJated with the development of modern cultivars Selection in modern plant breeding has resulted in striking changes in plant phenotype, such as increased number of fertile florets per spikelet, increased size and number of spikes per plant, defensive characteristics such as resistance to disease and pests, as well as higher N use efficiency (Harlan 1992). Changes in physiology and harvest index have also been accomplished indirectly through selection for high yield and other agronomic traits (Raj aram 1999; Reynolds et al. 1999). The three key modifications were made by Norman Borlaug and other Green Revolution scientists that are characteristic of nearly all cultivated wheat today: 1) the short stature of genetically dwarf plants which inhibited lodging caused by increased grain mass per spike, 2) broad adaptation, and 3) selection against photoperiod sensitivity. The Green Revolution era is also characterized by the rapid replacement of local landraces with modern inbred varieties. This process is believed to have caused a dramatic erosion in the genetic diversity found in farmers’ fields (Fowler and Mooney 1990; Cooper et a1. 1992). The use of pure line varieties in conjunction with the cultivation of few numbers of genotypes, created a scenario of low genetic diversity and high genetic vulnerability. Plant breeding efforts are focused on rapid cultivar development. As a result, little unadapted and diverse germplasm is used, and related material is often inbred, erodeing genetic variation. The ability to improve upon existing wheat cultivars, as well as maintain an appropriate genetic buffer against variable environmental conditions, is a genuine food security issue. This makes it imperative that we know how to evaluate neti "Q ("J unders and pli hetero ti interact genes c variatio mEHSUIl Kim 19 genetic diversity. The following sections are intended as a review of the efforts made to understand wheat genetic diversity and the future prospects in this field. 1.4 Modern similarig studies The exploitation and measurement of heritability, which is a fimction of genetic and phenotypic variation is what diversity measures attempt to predict. However, heterogeneity of selective environments and the resulting genotype by environment interactions often render these measurements of genetic diversity ineffective. In addition, genes controlling visible differences in phenotype are a poor sample of the total genomic variation, and have been shown to only weakly correlate with genetic relationship measured using either molecular markers or parentage (van Beuningen and Busch 1997a; Kim 1995). 1.4.1 Coefficient of parentage Pedigree information has been used to estimate genetic relationship as the coefficient of parentage (COP). Coefficient of parentage estimates the probability of any one accession being identical by descent at any given locus to another accession at the same locus (Cox et al. 1986). Estimates range form zero to one, with higher values indicating greater relatedness. Trends in genetic diversity can readily be portrayed using this method, but several significant shortfalls exist. Chief among the problems is the enforced assumption that each of the ancestral landraces possesses a unique allele not identical by decent with any other landraces at the locus of interest. ICC C31" Stu ICII isr far. Par ten ini SEQ Cul inf} lim Pedigree analysis is attractive in that the only required data are pedigrees. This resource is cumulative and is only limited by the quality and quantity of historical records. The first attempts to quantify genetic similarity among wheat accessions were carried out using COP (Cox et al. 1986) and it has remained the dominant method to date. Studies have addressed the structure of germplasm pools and have attempted to measure temporal trends in crop genetic diversity (Table 1). Various patterns have been observed from sporadic to gradual changes, or slight trends of lower or higher COP values. There is no clear pattern in how much modern plant breeding has eroded genetic diversity in fanners’ fields. Trends of both increased and decreased diversity have been reported. Parentage analysis has also been used to describe the relationship between spatial and temporal groups of accessions (Table 1). In general, coefficient of parentage has been effective in assessing overall pattern of genetic similarity of a group of accessions, but is generally not well suited for determining degree of relatedness among individuals (Barrett et al. 1998). These results imply the need to measure genetic similarity in every germplasm pool. 1.4.; Biochemical and molecular markers A number of molecular markers have been used to directly measure genetic diversity in wheat. The first to be employed were seed storage proteins; however, no change in seed storage protein diversity has been observed from the 19305 to the 19905 in European cultivars (Cooke and Law 1998). Although seed storage proteins provide important information on milling and baking properties these selectable traits represent only a limited number of loci and therefore genome coverage (Cox et al. 1985). vari incl (ILA and uidi pres: li0\\ relau spekr aeszn ‘nithl Ofdix relati‘ butre POIyrr EX Se(IUei amplil Spfing leeI-St. aesr Spfin (JG Genetic relationship can be more effectively estimated by measuring DNA sequence variation at selectively neutral loci. A number of different markers have been employed including RAPD, RFLP, AF LP, and SSR. Random amplified polymorphic DNA (RAPD) markers have mixed reports of effectiveness in hexaploid wheat (Table 1; Devos and Gale 1992; He et al. 1992; Dweikat et al. 1993). Repeatability is sometimes an issue with this system (Jones et al. 1997) and non-homoeologous, non-dose specific and presence/absence nature of the amplicon devalues their utility (Devos and Gale 1992). However, two recent studies successfully employed RAPD to measure the genetic relationship among common spring and winter wheat, spring and winter T. aestivum. ssp. spelta, and T. aestivum ssp. tibetanum (Sun et al. 1998) and T. aestivum ssp. spelta and T. aestivum ssp. macha (Cao et al. 1998). Classification based on geography was consistent with grouping of sixty-nine spelt and thirty-two macha wheat accessions and high levels of diversity were found in both groups. RAPD markers remain one of the simplest and relatively inexpensive molecular marker techniques to measure DNA sequence variation, but remain nagged with questionable repeatability and relatively low levels of polymorphism. A similar PCR (polymerace chain reaction) based technology has been exploited, sequence-tagged-sites (STS), where specific low-copy-number sequences are targeted for amplification (Talbert et a1. 1994). Chen et al. (1994) assayed twenty-five hard red spring cultivars from Montana, North Dakota, US Northern Great Plains states and ten diverse accessions consisting of T. aestivum ssp. sphaerococcum, T. aestivum ssp. spelta, T. aestivum, and T. aestivum ssp. compactum for STS polymorphism. The hard red spring germplasm pool was found to be relatively narrow contrary to the parentage Sex disc 199 for em; HUU' CXIT RFI Ollie al. 1 P0 l y analysis of North American hard red spring wheat by Mercado et al. (1996) and van Beuningen and Busch (1997b). The contrary conclusions made in these studies speak of the need for interpretive values of COP and genetic similarity estimates. Microsatellites, simple sequence length polymorphism, or simple sequence repeat (SSR) loci have been proven to be highly polymorphic and repeatable (R6der et al. 1995). Several hundred microsatellite loci have been characterized and will continue to be discovered and fruitfiilly utilized (Donini et al. 1998; Roder et al. 1998; Bryan et al. 1999; Song et al. 1998). Results suggest that relatively few microsatellites are required for the estimation of genetic similarity and the differentiation of closely related wheat accessions. SSR markers were able to measure differences between highly related genotypes with relatively few loci (Plaschke et a1. 1995). SSR loci have been effectively employed to evaluate germplasm in many other crop species. The arrival of an increased number of freely available and characterized SSR markers may prove to provide an extremely useful tool for understanding genetic diversity in wheat. Probably the most extensively utilized molecular marker in wheat have been RFLP (Table 1). Siedler et al. (1994) first analyzed genetic diversity within European bread and spelt wheat breeding lines and cultivars using RFLP. Winter and spring grth habit and different subspecies were effectively separated by cluster analysis. The RF LP markers were unevenly distributed across the three genomes as seen in several other studies (Chao et al. 1989; Liu and Tsunewaki 1991; Nelson et al. 1995a; Nelson et al. 1995b; Roder et a1. 1998; Van Deynze et a1. 1995). The highest percentage of polymorphic loci originated from the B genome (58%) and fewest from the A (21%) and D (21%) genomes. In a similar study, Kim and Ward (1997) used RFLP to analyze the gene cuhb appe 0981 annl date nast “ink genn techr pone POW: UOCU facfli (fisco Only iDDEr genn has“ dfitel “The genetic relationship among Eastern US soft red winter and Eastern US soft white winter cultivars. The soft white pool included almost no unique bands or banding patterns and appeared to be a genetically narrow group of accessions with a mean genetic similarity of 0.988; whereas the soft red cultivars were extremely diverse with a mean genetic similarity of 0.959. Kim and Ward (2000) conducted the largest germplasm survey to date using 30 cDNA and genomic DNA probes on 21 germplasm pools. The most genetic diversity was present among the Turkish landraces. The Eastern US soft red pool was the most diverse among improved germplasm pools. These results indicate that while few diagnostic markers can be found, RFLP variation differentiates pools of wheat germplasm. Only recently has AF LP been available to study wheat diversity, but the technique looks very promising. Various features of AF LP technology render it more powerful than the aforementioned marker systems (Vos et al. 1995; Breyne et al. 1997; Powell et al. 1996). These properties allow the visualization of a large number of bands (loci) for each primer pair/enzyme combination. Acrylamide gel electrophoresis in turn facilitates a very high degree of sensitivity. In a study of AF LP loci in T. aestivum, it was discovered that AF LP fragments are distributed throughout the genome, generally have only one detectable sequence variant and exhibit monogenic dominant Mendelian inheritance (Chapter 3). In addition, frequencies of polymorphic bands in a diverse germplasm panel are in the range that enables informative cluster analyses and map- based diversity, along with association analysis studies. AF LP was found to effectively differentiate cultivars based on membership in market class in the Pacific Northwest, which is defined by growth habit, kernel hardness, and kernel color (Barrett and Kidwell, 10 199 stru rnol rnos Bry dete 199' rep: strai P01) and; frag: con] All 1 teChi IOci . COst have 10 de 0V6]- 1998). Significant quantities of variation exist at each level of wheat population structure, but the majority of the variation exists within market class. 1.4.3 Comparison of the methods The nature of the sequence variation detected as polymorphism varies among molecular marker systems. RFLPs can be caused by mutation in restriction sites, but most appear to be the result of insertion/deletion events GVIiller and Tanksley 1990; Bryan et al. 1999; Nelson et al. 1995a; Chao et al. 1989; McCouch et al. 1988). RAPDs detect polymorphism caused by single base changes in priming sites (Williams et al. 1990). SSR systems measure variation in number of di-, tri-, and tetra-nucleotide tandem repeats. Length polymorphism of tandem repeats is predominantly caused by slipped strand mispairing, but also by unequal crossing over (Levinson and Gutman 1987). Polymorphism in AF LP appears to be primarily caused by differences in restriction site and/or priming sequences (Chapter 3). These systems generate one or more DNA fragments whose genomic termini are defined either by specific restriction sites (RF LP), complementation to user-designed PCR primers (RAPD, SSR, STS), or both (AF LP). All four systems have varying degrees of cost of development and employment and technical difficulty (Ma and Lapitan 1998; Powell et al. 1996; Breyne et al. 1997). SSR loci offer the greatest amount of information per locus, but are limited in abundance and cost of development. RFLP are technically difficult, require large quantities of DNA, and have low level of polymorphism in wheat. While on the other hand, the ability of RFLP to detect orthologous loci across species is invaluable. Although RFLP has an advantage over RAPD and AF LP, in that it detects insertion/deletion events in addition to single 11 nucleotide changes, all systems possess similar single locus properties in wheat. The ability to detect polymorphism is often measured using the polymorphism information content (PIC) and average expected heterozygosity (Hay). PIC is calculated using the following formula (Anderson et al. 1993): PIC, = 1 - 2a,? where Pij is the frequency among the assayed accessions of the jth pattern of the marker (primer pair, PEC (probe enzyme combination), etc..) i. Expected heterozygosity is calculated as the sum of squares of allele frequencies: Hn = 1 - 2P,2 where Pi is the frequency among the assayed accessions of the ith allele. The mean of each marker group or class can be calculated as the: Hav = ZHn/n where n is the number of marker loci assayed. PIC measures differences in pattern frequencies, whereas Haw reflects the mean of single locus frequency. The utility of a marker system is a product of ability to measure polymorphism and the number of loci available for examination. Marker index (MI) is an estimate of overall marker utility and is calculated as the product of effective multiplex ratio (E, number of polymorphic loci in a germplasm set) and Hay: M1 = EHaV In a comparative analysis of molecular marker systems in soybean, potato, and barley, similar levels of H“, were discovered for RFLP, RAPD, and AF LP while Haw for SSR loci was considerably higher (Powell et a1. 1996; Milboume et al. 1997; Russell et al. 1997). PIC for RFLP, SSR, and AF LP data were similar in winter wheat (Bohn et al. 12 1999) and n et al. I ofthe measu Chfly usun; 1994) Kim a] usnng, 1999). similar 1999)‘ 1997), ofbina reCord-E eqUalio ”unchn 1945)‘. 1999), while AF LP MI was higher than these markers in soybean, potato, wheat, barley, and maize inbred lines (Russell et al. 1997; Bohn et al. 1999; Pejic et al. 1998; Milbourne et al. 1997; Powell et al. 1996). The polymorphisms measured using any set of molecular markers represent very few of the total number of sequence variants between any two genotypes. As a result, measurements of correlations between marker systems have generally proven to be low. Only a weak correlation was found between COP and genetic similarity estimates made using RFLP in oat (Moser and Lee 1994; Odonoughue et al. 1994), barley (Graner et al. 1994), durum wheat (Autrique et a1. 1996), and bread wheat (Barbosa Neto et al. 1996; Kim and Ward 1997; Bohn et al. 1999). Other estimates of genetic similarity in wheat using AF LP (Barrett et al. 1998; Bohn et al. 1999), SSR (R'o’der et al. 1995; Bohn et al. 1999), and gliadin protein profile (Cox et al. 1985) also had little correlation with COP, similar to estimates using different molecular marker techniques in wheat (Bohn et al. 1999), soybean (Powell et al. 1996), maize (Pejic et al. 1998), potato (Milbourne et al. 1997), and barley (Russell et al. 1997). 1.5 Calculation of genetic relationship Genetic fingerprints generated using molecular markers are generally in the form of binary code where presence of a band is recorded as one, and the absence of a band is recorded as zero. This code is then used to calculate genetic similarity. There are three equations that are most commonly used to calculate genetic similarity: 1) simple matching coefficient, 2) Jaccard coefficient (Jaccard 1901) and 3) Dice coefficient (Dice 1945), which is also known as Sorenson, Czekanowski, and Nei and Li’s (1979) 13 comp consn mesa marke- (co-pr exhg sequer thereft Tabk: equaUI The CO- Numera. beavecr C0ef U C It grealer l computation (Table 1-1). In the simple matching coefficient, co-absence of a fragment is considered to represent similarity, so the absence of that allele is assumed to be caused by the same sequence variant across genotypes, which only exist in the case of bi-allelic markers. In both the J accard and Dice methods, the total number of similar fragments (co-presence) between two genotypes is divided by the sum total number of fragments in each genotype, discounting co-absence of a fragment (item (I in Table 1-1). Multiple sequence variants could be responsible for the observed absence of a molecular marker; therefore, co-absence of a fragment is not considered a similar state. Table 1-1 Variables for pairwise comparisons at each DNA fingerprint fragment state and equations used to calculate similarity between two genotypes. Present Absent Present a b Absent c d Simple matching = (a + d) / (a + b + c + d) Jaccard = a/ (a+ b + c) Dice=2a/(2a+b+c) The co-presence state is weighted by doubling the value in both the denominator and numerator in the Dice coefficient. The Jaccard coefficient has a linear relationship between the proportion of co-present bands to levels of polymorphism, while in the Dice coefficient similarity has a parabolic relationship. This results in Dice values being greater than J accard; these differences decrease when there is a high or low proportion of monomorphically present fragments. Half of the studies reviewed within used Jaccard 14 andli subtn tree d comp centr: liouw cone dendi theti aSpr comr fr Equ Vana gene! pIOgc and [A “ere and S SOYbe and half used the Dice coefficient. Several authors reported genetic distance by subtracting genetic similarity from one. Cluster analysis is generally used to arrange the matrix of objects into a hierarchal tree display. Several clustering methods have been developed, including single linkage, complete linkage, unweighted pair — group method using arithmetic averages (UPGMA), centroid linkage, median linkage, Ward’s method, and flexible beta (Rohlf, 1997). However, all the studies reviewed conducted a cluster analysis using UPGMA. The correlation between the genetic similarity matrix and a matrix derived fi'om the dendrogram (cophenetic matrix) is then commonly used to evaluate the effectiveness of the dendrogram in displaying the true structure of the data set. Ordination analyses, such as principal coordinate analysis or multidimensional scaling, are commonly utilized in conjunction with cluster analysis. The total molecular variance calculated using bands frequencies has been partitioned into hierarchical components using analysis of molecular variance (Barrett and Kidwell 1998; Excoffier et al. 1992; Schneider et al. 1997). 1.6 Gepetic distance as a predictor of genetic variance and heterosis Molecular marker data and pedigree information has been used to predict the genetic variance of plant populations, with inconclusive results. Attempts to predict progeny performance using COP and STS-PCR (Burkharner 1998) or COP, RF LP, SSR, and AF LP (Bohn et al. 1999) were determined largely ineffective. These same problems were incurred when morphological traits were measured along with COP in oat (Souza and Sorrells 1991) and RFLP genetic distance measurements were combined with COP in soybean (Kisha et al. 1997). 15 perfl POPL true Vafia quan nucrc ‘Ihear Geneti CYIOgQ Small ( Heterosis is believed to be a product of dominant gene action (Falconer and Mackay 1996) and therefore maximal parental divergence should maximize heterozygosity. Part of the extensive and costly process of developing parental lines for hybrid crops is the evaluation of combining ability. Therefore, parentage and molecular marker data has been used to predict hybrid performance. As with the predictions of genetic variance, COP, STS-PCR (Martin et al. 1995) and RF LP, (Barbosa Neto et a1. 1996) have proven to be poor predictors of hybrid performance in wheat. Results have also been inconsistent in maize, with generally low correlation between parental divergence measurements and hybrid performance (Dudley et al. 1991; Melchinger et al. 1990). Discussions addressing this issue revolve around the precision of plant performance and genome diversity estimates. Error in assessing genetic variance of a population or relative hybrid performance will in turn reduce correlation. The same is true for estimates of parental genetic relationships where only small samples of sequence variants are measured. Random molecular markers and COP are apparently not quantifying loci important for heterosis, except in one study using RF LP and microsatellite data to predict grain yield in maize (Zhang et al. 1994). 1.7 GermpLasm resources in use and storage National, international, and university institutions maintain genetic resources of wheat and related species. For example, Kansas State University operates the Wheat Genetics Resource Center which curates nearly 5,000 wheat species accessions and cytogenetic stocks, The United State Department of Agriculture maintains the National Small Grains Collection that houses over 42,000 accessions of T riticum species, and the 16 lnten samp netm plant sun'e crossi very s from ( less fr 1 breedi own at and or genen. only d I31am ‘ Br impro' delem are oft funCllc International Maize and Wheat Improvement Center maintains over 130,000 Triticeae samples. These centers not only collect, store, and maintain accessions, but also actively network information, evaluate lines and disseminate accessions. Unfortunately, very little of these stored genetic resources are utilized in any active plant breeding program. Rej esus et al. (1996) conducted an informative worldwide survey of plant breeders to determine what types of materials were being included in crossing blocks. Nearly 40% of all crossing block entries are advanced lines from the very same breeding program. In hi gh-income countries such as the US, plant material from CIMMYT (International Center for The Improvement of Maize and Wheat) is used less frequently than released varieties and advanced material from within and among breeding programs. Lower income countries utilize CIMMYT material second to their own advanced lines. Worldwide, landraces and wild species are used in less than five and one percent of all crosses, respectively. Regardless, wheat breeder attitudes towards genetic diversity are that of cautious optimism (Duvick 1984; Brennan et al. 1999). The only departure from this attitude is the fear of restricted access to genetic resources due to Plant Variety Protection and plant patents (Price 1999). 1.8 Alien species contribution Breeders have exploited alien species as a source of valuable genes for the improvement of critical traits such as pest resistance and agronomic performance. To determine what germplasm may be of use to a breeder, taxonomical classification criteria are often useful. Sexual compatibility and genomic constitution define the practical function and accessibility of a particular species for wheat improvement. These genetic 17 ICSO‘ 1973 Split} (.4 e. . tetra; pc’II'I '1' Vvhen genor hyb I'lt crossc genus' 1994,) Se I0\Vh( gene a 1983‘). g€nes blOlecl Public l.mpfov resources are divided into a primary, secondary, and tertiary gene pool (Harlan et al. 1973). The primary pools for T. aestivum are the subspecies (ssp. macha, spelta, sphaerococcom, vavilovi, tibetanum, compacticum, and yunnanensis), progenitor species (Ae. tauschii and T. turgidum ssp. dicoccum) along with wild and cultivated forms of the tetraploid progenitor (ssp. dicoccoides, durum, turgidum, polonicum, carthlicum, parvicoccum). The secondary pool includes species that will produce fertile progeny when hybridized to T. aestivum. Theses species are often polyploids and have shared genomes with wheat such as Aegilops. Extreme measures are required to successfully hybridize wheat with tertiary gene pool members and recover fertile progeny; however, crosses between wheat and several hundred different species including those found in the genus’s Agropyron, Elymus, Leymus, Thiopyron, and Hordium are possible (Jiang et al. 1994) Several post hybridization barriers continue to impede advances in alien gene transfer to wheat. Contributions of alien gene transfer to hexaploid wheat consist mostly of single gene additions for disease resistance (for review see Jiang et al. 1994; Sharma and Gill 1983). The elimination of potentially deleterious alien chromatin linked to favorable genes is important in developing functional enhanced germplasm. Recent advances in biotechnology and molecular markers may aid is this process. 1.9 Biotechpology and genetic engineering Genetic engineering is a likely imperative to meet future food requirements. Public acceptance of genetically modified organisms is uncertain, but nutritional improvement of staple food crops via introduction and expression of foreign genes will 18 beth subst (fiver const genot ofax snnH. breed Mngh: finger incoqi needt biotecj increa; backer Cthn] imPier CHUCa: be difficult to reject (Ye et al. 2000). Although changes in plant performance will be substantial, genetic engineering will not import an appreciable quantity of genetic diversity with the addition of single or multiple loci gene constructs. New gene constructs will be deployed in single unadapted genotypes due to technique difficulty and genotype-dependent success rates of transformation (Loeb et al. 1999). The development of a valuable transgenic genotype with broad use as a parent could increase genetic similarity across germplasm pools. The broad use of a single transgenic genotype as a breeding parent may cause an increase in genetic similarity. Although similarity at a single locus is not sufficient to effect estimates of genetic similarity using multilocus fingerprints, it is remains cause for concern. Changes in crop vulnerability due to incorporation of single genes and broad based use of common transgenic parents will need to be considered. Conventional plant breeding activities are likely to benefit from advances in biotechnology. Many breeding and germplasm development programs will adopt an increased molecular marker component to applied plant breeding. The success of backcrossing will increase considerably with enhanced ability to eliminate unwanted chromatin from unadapted donor parents using molecular markers. The cost to implement these technologies is often high, but retention of the gene(s) of interest is critical to produce functional plant material in an expedited fashion. Marker assisted selection has been successful in tomato, rice, and maize (Bemacchi et al. 1998a; Bemacchi et al. 1998b; Xiao et al. 1998). Unfortunately, a functional and freely available and annotated set of markers for the purposes of marker-assisted selection does not exist today. It is critical that a group of such markers be developed that are evenly distributed 19 BCI'OS noth .‘ p—d prese Chara has bk breed and ft PGIIEI ofinc fiekk NUISI POSS know relat relat Stud app? and En]I across the genome and highly polymorphic so that each individual breeding program does not have to develop their own marker system. 1.10 CONCLUSIONS There is a great deal of available wheat genetic diversity in both cultivated and preserved germplasm worldwide. As reviewed, many studies have attempted to characterize genetic diversity in one or several germplasm pools. Of paramount concern has been the question of temporal change in genetic diversity. Has modern plant breeding eroded the quantity of genetic diversity in farmers’ fields, presenting a current and future risk to food security? The answer is unclear. There is no steady and clear pattern of increased similarity across time in developed or undeveloped countries. Peaks of increased genetic similarity have been followed by large growth of diversity in farmer fields. The largest observed increase in similarity is in the International Spring Wheat Nursery, but COP levels remain extremely low. The tools used to measure and monitor genetic similarity remain ill-defined and possibly ill-suited for the task. Only COP analyses offer a cumulative and comparative knowledge base. Although molecular markers may be helpful in establishing genetic relationship of baseline populations to overcome the flawed assumption of zero relatedness, problems in parentage analysis make molecular markers the superior tool in studying genetic relationship. Microsatellite and AF LP are most suited for this application. Microsatellites are highly polymorphic, co-dominant, and easily analyzed and scored. However, AF LP have an exceedingly high marker index and cost less to employ. 20 The future of exploiting wheat genetic diversity is promising. Microarrays may provide the technology necessary to efficiently and effectively measure genetic relationship in wheat. They offer the ability to screen a single genotype for allele states at more than one thousand loci in a single hybridization event (Wang et al. 1998). Advances in technology development have been rapid and our knowledge base will increase with the use of characterized loci in germplasm assays. The use of markers with known genome position provides the ability to perform detailed subgenome analysis (Zhu et al. 1999). Genetic distance estimates based on increasingly large numbers of molecular marker loci accompanied by phenotype evaluation will be productive in assessing linkage disequilibrium between those two factors. From these analyses we can not only assign putatively linked phenotype to marker loci, but we can also explore allelic richness in regions of special interest. 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AN 8:80: 8050: $30 6:80: 55:.» 000: 803:0: $02 .8:80: :33? 8:033 dew 2 _00_ Aqua ::0 38: 802500 :58.“ 82006008 AN 00pwm:0: 5:5 80:06:00 8:03.20 A — :0:0:wm8: 8:00:83 08:00 80:8 56:03: :0 :00: 08% < mod hmd 3.0 3.0 $6 $6 .med «32.6 ::0 *onwd .1de ::0 $36 $36 000 805 m8 3%: 0:2 60: S 0 $2 23 SEE 90:02 Ba 5:50 5:: .0 s 500 mflommmooom :33 : Ba 0.82 Ba 8:220 $2 33 memo: 80250: 850% :8moSm 32$ ::0 0:000 32 5:202 ASHE .00 :3: 8080000 =< 33:50 NNV Egan ::0 8::va .0 H 38:83“ 03 3:93 :50 88:5 000% :8 .0 H moa— AD 5000000.: d8 .0 H as ._0 “0 :0m 25 1.11 REFERENCES Anderson JA, Churchill GA, Autrique JE, Tanksley SD, Sorrells ME (1993) Optimizing parental selection for genetic-linkage maps. Genome 36: 181-186 Autrique E, Nachit MM, Monneveux P, Tanksley SD, Sorrells ME (1996) Genetic diversity in durum wheat based on RFLPs, morphophysiological traits, and coefficient of parentage. 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Mol Gen Genet 261:184-195 Zohary D, Hopf M (1988) Domestication of plants in the Old World. Clarendon Press, Oxford, 33 CHAPTER 2 2 GENETIC DIVERSITY OF WINTER WHEAT IN SHAANXI PROVINCE, CHINA BASED ON RFLP AND SSR MARKERS 2.1 INTRODUCTION China is home to a large reservoir of genetic diversity of wheat-related species as well as some of the most morphologically unique hexaploid wheat (Yang and Smale 1996; Harlan 1975; Ward et al. 1998; Yen et al. 1988). Hexaploid wheat entered China as early as the middle of the third millennium BC (Feldman 1995). Modern plant breeding in China began with pure line selections from local landraces. These landraces and subsequent selections were hybridized with foreign germplasm, primarily from Australia, Chile, Denmark, Italy, Korea, Romania, Russia, and USA (Yang and Smale 1996). Improvement of wheat in China has often emphasized introgression of alien species and local landraces into breeding material, a practice largely neglected outside of Asia (Rejesus et al. 1996). This practice of combining foreign elite germplasm and local landraces has created a genetic resource useful in environments outside of China. Genes for resistance to F usarium head scab and Barley Yellow Dwarf Virus were recently discovered in Chinese germplasm (VanGinkel et al. 1996; McGuire et al. 1995). The use of Chinese wheat germplasm is believed to have contributed novel yield genes to CIMMYT germplasm as well as genes for tolerance to heat and Karnal bunt, resistance to Septoria, lodging tolerance, fast grain fill, immunity to stripe rust, and bacterial resistance (Maarten Van Ginkel, personal communication, Raj aram 1999). It is therefore apparent that Chinese germplasm is a valuable genetic resource and it is therefore important to 34 understand the genetic relationship among Chinese germplasm and other germplasm pools. Much of the evidence that describes Chinese germplasm as diverse is derived from the analysis of landraces (Ward et a1. 1998; Yen et al. 1988; Kim and Ward 2000). The present work provides a full analysis of a set of accessions cultivated from the 19403 to the 19908 in Shaanxi province, China. These accessions were developed at the Northwest Agricultural University or Shaanxi Academy of Agricultural Science. Breeding at these institutes has traditionally selected high yielding, disease resistant semi- winter breeding material with good noodle quality. Wheat is the most important crop in Shaanxi province with an annual area of cultivation of nearly 1.7 million hectares (Yang and Smale 1996). The parentages of the first cultivars developed through conventional plant breeding in Shaanxi province are Chinese landraces and foreign introductions namely Quality, Villa Glori, and Danmai 1. Subsequent cultivar parentage consisted of the interrnating of initial cultivars as well as the incorporation of several other foreign plant introductions. Comprehensive characterization of crop germplasm is an essential component of an ideal global crop improvement system. Detailed knowledge of available genetic diversity informs decisions affecting the monitoring and management of genetic diversity in farmers’ fields, the design and operation of plant breeding programs, along with the design of national and international research systems. Efforts have been made to characterize the genetic diversity in wheat using morphology (vanBeuningen and Busch 1997a; Sharma et a1. 1998; Ritchie et al. 1987), COP (Barrett et a1. 1998; Cox et al. 1985; Cox et a1. 1986; Kim and Ward 1997; Murphy et al. 1986; Plaschke et al. 1995; Souza et 35 al. 1998; vanBeuningen and Busch 1997b; Mercado et al. 1996; Nightingale 1996), biochemical markers (Bakhella and Branlard 1997; Cooke and Law 1998; Cox et al. 1985; Gregova et a1. 1999; Metakovsky and Branlard 1998; Tahir et al. 1996), and DNA molecular markers (Plaschke et al. 1995; Bohn et al. 1999; Martin et al. 1995; Kim and Ward 1997; Chen et al. 1994; Cao et al. 1998; Sun et al. 1998; Siedler et al. 1994; Paull et al. 1998; Kim and Ward 2000). Despite the large amount of information gathered concerning wheat genetic resources, these efforts do not necessarily have a cumulative effect. These data are incongruous and relate specifically to germplasm within each specific study. For example, few inferences can be made between an analysis using COP and one using RFLP (restriction fragment length polymorphism). Data gathered using a different set of RF LP-PEC (probe-enzyme combination) or primer pairs within a PCR based marker system are also not cumulative. COP offers a cumulative field of knowledge within a species, but has disadvantages (Murphy et al. 1986). Chief among the problems with COP is the assumption that each of the ancestral landraces possessed a unique allele not identical by decent with any other landraces at the locus of interest. An implausible assumption that each parent contributes equally to progeny in spite of selection and genetic drifi contributes to inaccuracy of COP estimates. A rigid and consistent use of a molecular marker system can overcome these problems by directly measuring DNA sequence variation without the need for parentage information. Kim and Ward (2000) conducted a survey of 292 winter wheat accessions from 21 germplasm pools. In the work presented here, the same RFLP-PEC used by Kim and Ward (2000) and six SSR flanking primer pairs were applied to the analysis of hexaploid wheat from Shaanxi province, China. 36 A; MATERIALS AND METHODS Twenty-four accessions of T. aestivum from Shaanxi Province, China were characterized for RFLP and SSR variability (Table 2-1). With the exception of one landrace (Maza mai), all of these accessions are inbred line cultivars, most of which were developed at either the Northwest Agricultural University or the Shaanxi Academy of Agricultural Science. Chinese Spring was included as a common genotype for comparison to other data sets. Abbondanza, an Italian cultivar, is a parent of Shaanxi cultivars and was also cultivated in the province in the 19805 and is considered here to be part of the Shaanxi germplasm. All of the Shaanxi accessions in Table 2-1 have winter or facultative grth habit. Six previously described primer pairs (gwm 2, 6, 43, 155, 165, and 174) were used for SSR analysis (Roder et al. 1998). Fragments amplified by those primer pairs were previously associated with 8 distinct loci (Roder et a1. 1998). PCR amplicons were electrophoresed and stained as described in Chapter 3. The 30 probes used by Kim and Ward (2000) to screen 292 wheat accessions were used in combination with the HindIII restriction enzyme for RFLP analysis. The RF LP data for the Shaanxi accessions were analyzed separately and jointly with corresponding data for the 292 accessions classified into 21 geography-based germplasm pools studied by Kim and Ward (2000). The NTSYSpc version 2.02k software was used to generate genetic distance (GD) matrixes, create dendrograms and corresponding cophenetic matrixes, and calculate matrix correlations (Rohlf 1997). Genetic distance was calculated using Nei and Li’s (1979) computation: 37 (3ny = 1‘ [Zny/ (Nx + Ny H; where Nx and Ny are the number of bands for each genotype, and ny is the number of bands in common between the two genotypes. Dendrograrns were generated after cluster analysis with either the unweighted pair-group method using arithmetic averaging (UPGMA), the complete linkage (CLINK), or flexible clustering procedures. Cophenetic matrixes were computed from the tree matrixes that generated the dendrograms. The matrix correlation (cophenetic correlation) between the original marker-based GD matrix and the corresponding cophenetic matrix was calculated to test the goodness of fit of a tree matrix and its associated dendrogram to the orginal distance matrix. Cophenetic matrix correlation values were interpreted as follows: <0.7, very poor fit; 0.7 to 0.8 poor fit; 0.8 to 0.9, good fit; and 0.9 to 1.0, very good fit (Rohlf, 1997). PCO was also conducted. The number and relative frequency of haplotypes were determined for each probe or primer pair. PIC was calculated for each RFLP probe or primer pair as follows (Anderson et a1. 1993): PIC, = 1 - ZF,-2 where Pij is the relative frequency among the assayed lines of the jth haplotype of clone i. Cluster analysis of the mean pair-wise GD among accessions in different germplasm pools was used to analyze among-pool diversity patterns. Cluster analysis of the mean pair-wise GD among accessions in different germplasm pools was used to analyze among-pool diversity patterns. The analysis of molecular variance (AMOVA) procedure of ARLEQUIN version 1.1 (Schneider et al. 1997) was used to estimate and test the significance of within and among pool molecular covariances and (DST. Tests of 38 significance were based on ten thousand random permutations of the data for a given AMOVA. 39 Table 2-1. Name, parentage, and decade of primary cultivation of the 24 T. aestivum accessions developed in Shaanxi, China. Name Parentage Decade of primary cultivation Maza mai Landrace 19405 Bima 1 Maza mai/Quality 19505 Bima 4 Maza mai/Quality 19505 Xinong 6028 Jingyang 60 /Villa Glori 19505 Shaannong 9 Bima 5/ Xinong 6028 19605 Fengchan 3 Danmai 1/ Xinong 6028 19605 Aifeng 3 Xiannong 19705 39/58(18)2//Fengchan 3 Xiaoyan 4 Fengchan 3/Xiaoyan 759 19705 Xiaoyan 5 ST 2422-464/Xiaoyan 96 19705 Shaanhe 6 Bima 4/Early Premium 19705 (Zaoyangmai) Weimai 5 Kedong 51/p6402 19705 Weimai 4 Nongda 311/Fengchan 3 19705 Xiaoyan 6 ST 2422-464/Xiaoyan 96 19805 Xiannong 151 Mianyang 75-20/75392-4-3-4 19805 Shaan 7859 Predgomaja 2 (Shanqian mai) 19805 //Ama/Abbondanza/3/Xibulai/ /Fengchan 3/62(9)2-1 4O Qinmai 6 Qinmai 9 Abbondanza Xiaoyan 168 Shaan 167 Shaan 229 Shaan 213 Xinong 65 Xinong 85 Zhengzhou 1/ Predgomaja 2 (Shanqian mai) Zhaomai 2/741001 1-10 Autonomia/Fontarronco 1386/Xiaoyan 6 Shaan 7587/Tai 3429 Shaan 7853/80356 77-31/Xiaoyan 6 Xiannong 39/58(18)2//Fengchan 3 Sumai 3/Shaan 7859//78(6)9- 2/ 809(6)-5-6-1 19805 19805 19805 19905 19905 19905 19905 19905 19905 41 2.3 RESULTS 2.3.1 Molecflr marker characteristics The results of Kim and Ward (2000) are critically compared to the RF LP-based genetic diversity of the Shaanxi province accessions and will be presented throughout the following sections. The thirty RFLP probes generated a combined total of 122 (53.7%) monomorphic and 105 (46.3%) polymorphic bands when all 23 accessions of the Shaanxi pool were considered. The relative frequency of monomorphic bands within each of the other 21 germplasm pools ranged from 62 to 83%. All but one of the 227 bands reported here for the Shaanxi accessions were also found in one or more of the 292 accessions studied by Kim and Ward (2000). The eastern United Stated soft winter wheat pool (EUS) had nine unique bands, but more than half of the other 21 germplasm pools had none (Kim and Ward 2000). The single band unique to the Shaanxi pool is a product of the BCD1086 probe and has an apparent size of 3.0 kb. That band is present in F engchan 3 and its progeny Weimai 5. Cultivar Xinong 6028, a parent of Fengchan 3 does not posses this band. Therefore, Danmai 1 (not analyzed here) appears to be the donor. Two bands were absent in the Shaanxi pool but were common or fixed in the other 21 germplasm pools. One of those bands (a 3.0 kb band produced by probe WG181) was also rare in the Chinese landrace pools considered in Kim and Ward (2000) and fixed or nearly fixed in all other pools. The second band that was uniquely absent from the Shaanxi pool (a 8.3 kb band produced by the probe WG 822) was virtually fixed in all other pools. Four bands were fixed in the Shaanxi germplasm and absent or rare in Kim and Ward’s (2000) other non-Chinese germplasm pools. Two of the those four bands were completely absent from non-Chinese pools but fixed in the Shaanxi pool and 42 in the four Chinese landraces pools included in Kim and Ward’s (2000) study. Two other bands were fixed in the Shaanxi pool and absent or rare in all other pools. The only other accession that carried the BCD 1230 band was Ai lan mai, a tetraploid landrace from China. The average probe PIC value was 0.22 for the Shaanxi pool, while the range in other pools was 0.18 (CHN_YH (Yunnan Hulled wheat landrace from China)) to 0.38 (Turkey landrace pool). Among the advanced germplasm pools, the range in average probe PIC values was 0.19 (U S_EW (eastern US soft white winter wheat)) to 0.34 (US_ER (eastern US soft red winter wheat)) (Ward and Kim, 2000). Within the Shaanxi pool, fifteen (50%) of the probes generated a single haplotype (PIC=0.0) and one probe generated nine (PIC=0.843). The relative frequency of monomorphic probes in the other germplasm pools had a minimum of 0.133 for the Turkey pool, and a maximum of 0.633 for the CHIN_YH pool (Kim and Ward, 2000). The six SSR primer pairs generated a total of 28 polymorphic and 2 monomorphic bands over all Shaanxi accessions. Primer pairs produced between one and five bands with a mean of 4.7. The SSR primer pairs generated between one and nine distinct haplotypes with a mean of 6.8. The PIC values for SSR primer pairs ranged from 0.0 to 0.84 with a mean of 0.62. 2.3.2 Genetic distance and structure of the Shaanxi Germplasm Each of the Shaanxi accessions had a distinct haplotype relative to all of the 292 other accessions when all 30 RFLP probes were considered jointly. Pairwise RFLP- based GD for the Shaanxi accessions varied from 0.004 (Bima1,Bima 4) to 0.068 (Qinmai 9,Shaan 167), with a mean of 0.041. The corresponding SSR-based GD values 43 varied from 0.00 (Shaan 213,Xiaoyan 6) to 0.818 (Xiannong 151,Bima 4) with a mean of 0.443. The mean GD for the combined RFLP and SSR dataset was 0.076 with a range of 0.018 (Bima l/Bima 4) to 0.115 (Weimai 5/Shaan 213). The existence of accession based structure in the combined RF LP-SSR data for the Shaanxi accessions was investigated with one ordination (PCO) technique and three methods of hierarchical clustering (CLINK, flexible, and UPGMA). The separate cophenetic correlations for the CLINK, flexible, and CLINK analyses were very poor, poor, and good at 0.70, 0.78, and 0.81, respectively. The correlation between the cophenetic matrixes from the UPGMA and CLINK and UPGMA and flexible clustering were good and excellent at 0.83 and 0.94, respectively. All three analyses (PCO, UPGMA, Flexible, CLINK) suggested three major groups within the Shaanxi germplasm (data not shown for either the PCO, Flexible or CLINK analyses). The three postulated groups of related accessions are identified in the dendrogram derived from UPGMA cluster analysis (Figure. 2-1). The classification system represented in Figure. 2-1 was used in the AMOVA analysis of the RFLP-SSR data. Both among group covariance and the (DST parameter were significant, indicating that the deduced classification system reflects true structure among the Shaanxi accessions. 44 8:53 £88 _ _ .c 8.: 8.5 85 3.0 F - _ . p p p p h p p p . p _ p . _ . _ Eocfix J _ «is; 8955“ _ mug—£2 W mfisowcom _ 438x c @550 J @3285 £550 “553x 333x 325% ”Sioux 1_ _ 225nm _ essfix 3.5532 A 8.25% 1 ”8885A awcoczaanm ll .25 i as C mafia; _ $.52; 3.85% mfiamomofino V n: Y 2 .mood .I. Q0 3 cozmzwmmoc $320 a £3, 32388 8:86 3:850:qu mo wcfisew @352 >32qu .33 $335 A83 05—83228 use Aqua: 3ng ouoaow mama: wctam 80:an 98 £520 60:30.5 mgnm Bob £868on 30:? vm .«o mix—28 5620 TN onE 45 The large cluster (designated group 1 in Figure. 2-1) consists of cultivars with parentage that includes an introduction from either Italy (st2422-464 and Villa Glori) or Australia (Quality). The older breeding lines Bima 1, Bima 4, Shannong 9, Xinong 6028, Shaanhe 6, and the landrace Maza mai were subjectively divided into a subcluster (Figure 2 subcluster 1a). Six of the nine accessions in the second subcluster (1b) have parents that are thought to carry a 1BL/ IRS wheat rye translocation. The other three accessions in subcluster 1b have uncertain parentage (Xiaoyan 168 and Xiaoyan 5), and one has a pedigree that gives no indication that it would carry 1BL/ IRS translocation (Xiaoyan 6). Clusters 2 is composed of cultivars derived from crosses that included Fengchan 3 (Danmail/Jingyang 60/Villa Glori) as a parent. Cluster 3 consists of three accessions (Abbondanza, Shaan 167, and Weimai 5) that have unique parents not found in other accessions and may cluster due to their distance to other accessions rather than similarity to each other. Separate GD averages for accessions cultivated in the 19705 (n=6), 19805 (n=6), or 19905 (n=6) were calculated with the combined RLFP-SSR dataset. Diversity remained relatively constant in the first two decades (0.073 and 0.075, respectively) but decreased to 0.058 in the 19905. Results of an AMOVA analysis of the decade of cultivation grouping revealed no evidence of among-decade differentiation (data not shown). 2.3.3 Genetic Distance and Structure of the Combined Shaanxi and World Germplasm. The pool classification system used here and by Kim and Ward (2000) groups accessions according to institutional or geographic origin. AMOVA analysis of the 46 RFLP data for all 316 accessions showed significant within and among pool covariance. Although the majority (74%) of the total variation was within pools, this finding supports the validity of this classification system. Differentiation of these accessions along the lines of the pool classification system is further supported by the results of AMOVA for all possible pairs of pools (Table 2-2). In only 20 of the total of 231 pair-wise pool comparisons was there insufficient evidence to conclude that two pools were different (alpha=0.001). The between-pool covariance and (DST were both significant for all of the pair-wise AMOVA analyses involving the Shaanxi pool. Pool-related structure was not, however, evident in the dendrogram (not shown) created from UPGMA cluster analysis of the complete pairwise GD matrix for all 316 accessions. The cophenetic correlation was 0.61, indicating that any structure in the dendrogram was not evident in the GD matrix. Some interesting patterns were present in the dendrogram (data not shown). There was a diffuse distribution across the dendrogram with few pools forming distinct clusters except the landrace groups. Accessions within a geographic pool generally cluster together, but membership in any one cluster was rarely exclusive. Accessions cluster primarily adjacent to members of the same pool as do twenty-two of the Shaanxi accessions. The French cultivar Montjoie (Providence/Hybride de Joncquois//Florence/Aurore/3/Vi1morin23/Institut Agronomique//PLM/4/Bankut/Chanteclair//Etoile de Choisy), which has Australian parentage of Florence (a.k.a. Quality) and Aurore, is the only non-Shaanxi member of this cluster. The nearest cluster is comprised of eastern European germplasm ranging from Yugoslavian, Ukraine, Russia, and the US Great Plains (US_GP). The Shaanxi cluster is not associated with areas of the dendrogram dominated by the Sichuan, 47 Yunnan, Tibet, and Xinjiang Chinese landraces. Weimai 5 clusters distantly from other Shaanxi accessions. The genetic distance among all 22 pools was analyzed by UPGMA cluster analysis of the mean genetic distance among pools. The cophenetic correlation for the resulting dendrogram was very high at 0.90. Four clusters were subjectively identified in the dendrogram, the largest of which (cluster 2) includes 15 pools divided into three subclusters. The first subcluster (2a) consists of Argentina, Odessa, International Winter Wheat Screening Nursery (IWWSN), Romania, Russia, Ukraine, US_GP, Shaanxi, western US soft white winter wheat (U S_W), and Yugoslavia. The second (2b) and third (20) subcluster consists of Eastern US and Western European pools, respectively. The remaining pools are comprised of landrace material. The landraces from Turkey and the Xinjiang Rice wheat (CH_XR) cluster independently from all other pools. The three landrace groups, Sichuan white wheat (CH_SW), Tibetan weedrace (CH_TVW, and Yunnan hulled wheat (CH_YH), make up the last cluster. Cluster analysis and principal coordinate analysis (data not shown) suggest a subdivision in the data between primarily eastern European germplasm (Romania, Russia, Ukraine, Odessa, US_GP, and Turkey) and primarily western European, South American and US (Germany, France, Argentina, US_ER, US_EW, US_W, Yugoslavia, and Shaanxi). The six remaining landrace pools cluster distantly from these two groups. 48 .bomSZ mama—080m 38:5 8853 Becca—:85 I 29523 anon? .883 83? a8 .m.D 5883 I BImD $553 386 .m.D I mOImD anon? be? 223 «8 .md 858m I BmIm: €23 Ea; we e8 .m.: Beam I mmImD x828 so? 223 535% I BmIzmo x228 save; 33: 5:55 I Eflzmo x228 88803 533. I 3580 x228 383 BE 82?? I 6380 Heoz 3.5.9.5 £680 2 .o 86 word 85 36 — h p p b + L . p . p . r . b . p _ . Efizzo i BeIzmo W m BmIzmo 6355 AI v hoe—SH T m s L 58.56 0N4 v 1 852m _ Bid: 1 r Bud: pm ”Ed: 3d: .EIm: 4 £5.88; V N 1 I— mgam 85: 3mm W scanx 388 2m;— aéfiwa \ ¥ _ 5E W _ _ 59.5w? .wmod N Q0 a 593886 H88:08am can 036 n GO .80 $83 888.3 a £15 8863on 8:86 b—mouocow mo chEHw 353: @352 £350.33 mace—08m .Emto 8.8on $68.5 ESQ co oEQBwoow 3 panama—o Econ Ema—950w 30:3 mm 52.33 383% 885w 508 me 29:28 .835 NIN 858m 49 Table 2-2. Analysis of molecular (RFLP) variance of the 22 wheat germplasm pools classified based on geographic and breeding program origin. Source of variation df Sum of squares Variance component % Variation Among germplasm pools 21 946.0 2.63* 26.01 Within germplasm pools 294 2200.6 7.48* 73.99 Total 315 3146.6 10.11 * - Significant at P < 0.001 2.4 DISCUSSIONS The development of improved cultivated material involves generating and evaluating new recombinant genotypes. Understanding crop genetic diversity aids in the creation of improved plants and their subsequent dissemination. A great deal of effort has been expended to understand the population structure of domesticated plant species. Analyses are often isolated where different methods are used to measure distance among accessions. Investigations in hexaploid wheat relationship using COP and various biochemical and molecular marker systems or different markers within a system do not cumulatively add to the understanding of wheat genetic diversity as would be possible if evaluated using the same methods. In addition to understanding the genetic distance among accessions within a pool, all pools should be compared to currently available and characterized accessions. The relative value of a germplasm pool in different breeding scenarios is in part a function of the diversity within the pool and its differentiation from other pools. In this study, we incorporated the Shaanxi germplasm into a previous analysis of 21 wheat 50 germplasm pools using 30 RF LP-PEC. These same markers were later used to assay a newly available set of germplasm from Shaanxi province, China. While RFLP are often well characterized and offer comparisons across species (Gale and Devos 1998; Van Deynze et al. 1995) they are somewhat laborious and costly and have lower expected heterozygosity or PIC values, lower effective multiplex ratios, and lower marker index than microsatellite markers in several species (Bohn et al. 1999; Pejic et al. 1998; Powell et al. 1996). Fragments generated from these primers often are highly polymorphic, have well characterized chromosome position and easily recognized allelic variants. The availability of automated sizing of fluorescent labeled fragments and multiplexed gel electrophoresis (Diwan and Cregan 1997) present microsatellites as a prime candidate to characterize germplasm in the future. 51 .bomcs Z wanEom 88$? .883 3:038:85 I kafig 83:3 .853 833 «cm .m.D E883 I BImD ”mafia 380 .m.D I mOImD smog? use; 323 as. .md Beam I BmIm: use? be? we «a .m: Beam I MmIm: ”5:28 383 223 $555 I BmIzmu x258 323 Ban §§> I Efizmo x228 858:» 38¢ I 3525 A238 383 SE mares“ I 5380 N202 c c o o o o c c o o o COCO CCCCCC o OCC as m C. o as CO CCCC o O. 9 CCC O COCCCCCC C CCCOCCCCO C N 00 [\ CCC CCCCOC o o o o o o o o o o o o o o o o o o o o o c o o o o o o o o o o 390 o wmfio o c o o o o o o o o o o o o o o o o o o aood 0 bond o 3.6.6 wm_.o o o o o o o o o o o o aood o o o o o o o o o o o o o o c o mmod 0 $06 mafia o o o o o o owed o o o oood o o o c o o owed o o o mood o o o oood o o o a o o o :3 83 a: a: C: G: G: as: a: a: 2: 8: 6 a: E 6V 5 3 5 o c o o o o o o o o o o o o o o o o o o o o o o c o o o o o o 0 mod o o o o o o o o 3 E Eflmu 85 «$283, :3 3d: 83 am: a: mod: 3: BmIm: Ev mmIm: G: 0585 G: 83H :5 mpumo a: BmImo a: seam : : sage a: 635 6 $850 as $32 E 5: 6v bag—emu 5 858m CL «582 5 553%? E 55% 3 60:88.86 023.83 85 8 :33 co comb: 02.; $9 a 8 8:38 208 385.5% mo :oEomoa 05 88.88 83:5 Econ EmmEEeow mo 5836 onocow 08383 05 com 356$:me mo 68 2E. mIN 8an 52 The genetic base of Shaanxi germplasm appears to be formed from Chinese landraces, primarily Maza mai, landrace selection Jingyang 60, and a few important foreign introductions: Villa Glori, Quality, and Danmai 1. The cluster analysis indicates the importance of a few parents as described by Yang and Smale (1996). The early accessions are products of two-way and three-way crosses with Quality, Villa Glori, and Early Premium, Maza mai, and J inyang 60 a landrace selection. This material was then hybridized with Fengchan 3 or lines possessing the 1BL/ IRS wheat rye translocation. The most recently developed lines lack the historic introgression of foreign or unrelated plant introductions. This may be responsible for the large decrease in genetic diversity among Shaanxi accessions from the 19705 and 19803 to the 19905. Accessions classified based on primary decade of cultivation were also found to be significantly undifferentiated. The absence of novel germplasm introduction into breeding material has resulted in the development of increasingly similar cultivars. This trend warrants action to reverse the apparent trend of increasingly similar cultivars in farmers’ fields. Hexaploid wheat is believed to be founded on only a few polyploidization events (Talbert et a1. 1998; Dvorak et al. 1998). Rapid dissemination of the species across Europe and Asia soon after domestication as early as the third and fourth millennium BC has led to an oligocentric distribution of genetic variation (Harlan 1975; Feldman 1995). The total amount of genetic diversity in common wheat appears to be spread across geography and an unambiguous categorization of wheat genetic diversity measured using molecular markers has not been identified. The dendrogram of over 300 winter wheat accessions has diffuse and overlapping clusters of accessions based on geographic origin. The low level of polymorphism revealed by RFLP suggests an overall low level of 53 genetic variation in wheat (Kim and Ward 2000). The analysis of the mean genetic distance among pools of germplasm suggests many regions are significantly undifferentiated. Some germplasm pools such as hard red winter type pools in the former Soviet Union and the US_GP appear to be sub-samples of the same population. Most other pairwise comparisons of germplasm pools do not show a significant lack of differentiation. A vast majority of the total amount of variation was found within pools; therefore, pools appear to be differentiated based on few if any unique bands and primarily small differences in band relative frequency. Kim and Ward (2000) suggest the pool classification based on geographic and plant breeding program reflects the true structure of wheat genetic diversity. The AMOVA amplifies on this maxim. However, not all pools are differentiated and the proportion of the total amount of variation responsible for those that are differentiated is relatively small. The Chinese landrace pools were collectively the most diverse pool and had several unique bands and banding patterns (Kim and Ward 2000; Ward et a1. 1998). The evidence presented here suggests that improved Chinese germplasm does not have this same distinction from other germplasm pools. Shaanxi germplasm is typical of other gene pools in Kim and Ward (2000) in amount and type of within pool diversity. The Shaanxi pool is no more similar to the China landrace pools than other improved pools other than the few RFLP bands at a unique relative frequency in the Shaanxi and Chinese landrace pools. 54 L5 REFERENCES Anderson JA, Churchill GA, Autrique JE, Tanksley SD, Sorrells ME (1993) Optimizing parental selection for genetic-linkage maps. 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Genome 38:45-59 Van Beuningen LT, Busch RH (1997b) Genetic diversity among North American spring wheat cultivars .1. Analysis of the coefficient of parentage matrix. Crop Sci 37:570-579 Van Beuningen LT, Busch RH (1997a) Genetic diversity among north American spring wheat cultivars .3. Cluster analysis based on quantitative morphological traits. Crop Sci 37:981-988 VanGinkel M, VanderSchaar W, Yang ZP, Rajaram S (1996) Inheritance of resistance to scab in two wheat cultivars from Brazil and China. Plant Disease 80:863-867 Ward RW, Yang ZL, Kim HS, Yen C (1998) Comparative analyses of RF LP diversity in landraces of Triticum aestivum and collections of T. tauschii from China and southwest Asia. Theo Appl Genet 96:312-318 Yang C, Smale M (1996) Indicators of wheat genetic diversity and germplasm use in the People's Republic of China. NRG Paper 96-04 Mexico, D F : CINIMYT Yen C, Luo MC, Yang JL (1988) The origin of the Tibetan weedrace of hexaploid wheat, Chinese Spring, Cheng-guang-tou and other landraces of the white wheat complex from China. In: Miller TE, Koebner RMD (eds) Proc 7th Intern Wheat Genet Symp Cambridge, England, pp 175-179 58 CHAPTER 3 3 AFLP IN TRITICUMAESTICUM L. 3.1 INTRODUCTION Electrophoresis-based DNA marker systems vary in procedural complexity and cost of operation (Staub et al. 1996). Most of these systems generate one or more DNA fragments with genomic termini defined by restriction enzyme recognition sites (e. g., RFLP), complementation to PCR primers (e. g., RAPD, Microsatellite), or both (e. g., AFLP, STS; Williams et a1. 1990; V05 et al. 1995; Weber and May 1989; Talbert et a1. 1994; Staub et a1. 1996). Genotypes exhibit contrasting pools of fragments as a result of point mutations in the restriction or PCR priming sites or because the distance between the terminal sequences is altered by insertion/deletion events. In some cases, differences in methylation state in the terminal sequences can lead to polymorphism (Young et al. 1999) A marker system’s power to visualize fragments and resolve polymorphisms depends on how DNA fragments are generated and the sensitivity of the detection system to length differences among sequences. The applicability of any marker system to plant genetic research depends both on the marker system itself and on DNA sequence variation among germplasm. The interaction and therefore unpredictability of the joint properties of marker systems and germplasm dictates the need for empirical annotation and characterization of new markers. The AF LP system (Vos et a1. 1995) has been shown to be effective and reproducible (Jones et al. 1997) for the analysis of genetic linkage and gene mapping (Mackill et al. 1996; Voorrips et al. 1997), map based cloning 59 (Cnops et al. 1996), plant evolution (Heun et al. 1997), and biodiversity studies (Barrett and Kidwell 1998; Zhu et a1. 1998; Zhu et al. 1999). AF LP present an opportunity where generous amounts of polymorphism are generated per gel lane and per unit of resources expended. AF LP technology has been applied to wheat (T riticum aestivum L. em. Thell.) in localized situations (Barrett and Kidwell 1998; Parker et al. 1998; Singh et al. 1999; Shan et al. 1999; Hartl et al. 1999; Bai et al. 1999; Bohn et a1. 1999; Law et al. 1998; Ma and Lapitan 1998; Burkhamer et al. 1998), however, the marker system's properties in the species as a whole are unknown. The current wheat genetic linkage map was primarily created using a single common recombinant inbred line (RIL) population derived from a cross of Opata 85 and a synthetic allohexaploid created from a cross between Altar 84 (a T. dicoccoides var. durum cultivar) and Aegilops tauschii, accession CI = 18 WP1219 (PR88-89) (Nelson et al. 1995a). The utility and application of the AF LP system in wheat depends in part on the relative frequency of polymorphic markers in the species. Loci that are specific to a particular mapping population serve little function in germplasm analysis or other mapping populations due to their unlikely occurrence. Similarly, polymorphisms at extremely low frequencies within or across populations are inept in differentiating and classifying genotypes as well as measuring population structure. Other key performance measures include the physical and genetic distribution of detectable loci; the number and abundance of detectable alleles per locus; the power to distinguish between identity by state and identity by descent; and the genetic behavior of visualized polymorphisms. These properties are best analyzed by map-based diversity studies, which characterize sets of germplasm using loci localized on a genetic linkage 6O map. Here, we report the results of a species-level characterization of AF LP marker technology in bread wheat (T. aestivum) using a common RIL population and a diverse collection of germplasm. The results suggest that AF LP are applicable to various research problems in wheat genetics and breeding. J MATERIALS AND METHODS 3.2.1 AFLP procedure DNA extraction was performed using a modified CTAB extraction procedure (Murray and Thompsom 1980). Double digestion, adapter ligation, pre-amplification, and selective amplification was can'ied out as described by Barrett and Kidwell (1998) with the following modifications. Pre-amplification of 2 pl of restriction ligation product was combined with 25 ng of Msel and EcoRI or PstI adapter, 0.5mM dNTP, 1X PCR buffer (10mM Tris-HCl, 50mM KCl, and 0.1% Triton X-lOO), 0.5U Taq polymerase, 1.5 mM MgC12, total volume 20 ul and amplified with the following thermocycler profile (94°C 2 min — 26 cycles (94°C 1 min, 56°C 1 min, 72°C 1 min) — 72°C 5 min). The preamplification PCR product was added to 100 pl of sterile water. One microliter of dilute preamplification PCR product was added to 19 u] of selective amplification cocktail (25 ng EcoRI primer, 30 ng MseI primer, 0.4 mM dNTPs, 1X PCR buffer (10mM Tris-HCI, 50mM KCl, and 0.1% Triton X-100), 0.4 U Taq polymerase, 1.5 mM MgCl2) and amplified via PCR (94°C 2 min — 12 cycles with decreasing annealing temperature by O.7°C each step (94°C 30 sec, 65°C 30 sec, 72°C 1 min) — 23 cycles (94°C 30 sec, 56°C 30 sec, 72°C 1 min) — 72°C 2 min). Primer pairs used are listed in Table 2. Eight EcoRI/MseI +3 primer pairs were tested against the germplasm panel. The RILs 61 were analyzed with two additional PstI/MseI primer pairs. The complete protocol can be viewed at http//www.msue.msu.edu/msuwheat/ and appendix A. 3.2.2 Polyacrylamide gel electrophoresis The selective amplification product was combined with 8 ul of formamide loading buffer (98% formamide, 10mM EDTA pH 8.0, 1.0 mg/ml bromophenol blue, and 1.0 mg/ml xylene cyanol) and electrophoresed for 3hrs at 75W on a 6% polyacrylamide gel cast on a 38x50cm Sequi-Gen GT sequencing cell (BioRad Laboratories Inc., Hercules, CA, USA). A 10bp ladder (Gibco BRL) was used as a molecular weight marker in two lanes. Silver staining was conducted with a commercial kit (Promega) according to instructions except that both the fix/stop and developing solutions were partially frozen. Banding patterns were assessed by visual observation before the gels were removed from the glass plate then gels were transferred to and dried on chromatography paper. 3.2.3 Linkage analysis Identification of the genetic map positions and mode of inheritance of AF LP loci was achieved by analysis of segregation among 69 recombinant inbred lines (RIL) from the single seed descent derived population employed by Nelson et al. (1995a, b) and others (Marino et al. 1996) for the purpose of linkage mapping (Roder et al. 1998; Van Deynze et a1. 1995). The population’s parents are Opata 85 and W7984. W7984 is a synthetic allohexaploid created from a cross between Altar 84 (a cultivar of T. dicoccoides var. durum) and Aegilops tauschii, accession CI = 18 WPI 219 (PR88-89) (Nelson et al. 1995a). 62 AF LP data for the 69 recombinant inbred lines were combined with data previously published by others for 265 RFLP, microsatellite, and enzyme loci (http://wheat.pw.usda.gov/ggpages/mapshtml; Marino et a1. 1996; Van Deynze et al. 1995; Nelson et al. 1995a; Nelson et al. 1995b). Putative chromosome locations of AF LP loci were resolved using Mapmaker Macintosh V2.0 (Lincoln 1992) with the LODs option set at LODZ 3.0. Location within a chromosome was then tested using TRY and best order found using the RIPPLE command. The Kosarnbi function was use to transform recombination frequencies into centimorgans. 3.2.4 Genetic distance and marker/trait association analysis Characteristics of AF LP patterns in T. aestivum were derived from analysis of a germplasm panel composed of thirty-seven cultivars and seven landraces from 15 countries (Table 1). Neither Opata 85 nor W7984 were included in the analysis of the germplasm panel. Seed was obtained directly from breeders or from the USDA National Small Grains Collection, Aberdeen, Idaho. The NTSYSpc version 2.02k software was used to generate genetic distance (GD) matrixes, create dendrograms and corresponding cophenetic matrixes, and calculate matrix correlations (Rohlf 1997). Genetic distance was calculated as one minus the J accard coefficient (J accard 1901). Dendrograms were generated after cluster analysis with either the UPGMA or the CLINK procedures. PCO was also conducted. Cophenetic matrixes were computed from the tree matrixes that generated the dendrograms. The matrix correlation (cophenetic correlation) between the original marker-based GD matrix and the corresponding cophenetic matrix was calculated to test the goodness of fit of a tree matrix and its associated dendrogram to the original distance 63 matrix. Cophenetic matrix correlation values were interpreted as follows: <0.7, very poor fit; 0.7 to 0.8 poor fit; 0.8 to 0.9, good fit; and 0.9 to 1.0, very good fit (Rohlf, 1997). Chi-square analysis was used to test for non-random association of AF LP bands and the lBL/lRS wheat rye translocation. 64 Table 3-1 Name, origin, and pedigree of 46 wheat accessions used in the germplasm survey. Accessions are annotated with descriptors of growth habit, landrace or improved cultivar, and presence or absence of a lBL/ IRS wheat rye translocation carrier or not. Accession name Origin Pedigree Classification* Abbondanza Tuscany, Italy Autonomia/Fontarronco W Cv N Anza California, USA LermaRojo/lNorinlO/Brevor/4/ W Cv N ASA 97 (T. a. ssp. tibetanum) Augusta Bainong 3217 Bavaria Bradley Bugday (PI 341740) Casey Chinese Spring Dynasty Fan7 Florida 303 Tibet, China Michigan, USA Henan, China Michigan, USA Indiana, USA Maras, Turkey Ontario, Canada Sichuan, China Indiana, USA Sichuan, China Florida, USA Yaktana54//Norin10/Brevor/3/3 *Andes Landrace Genessee/RedcoatI/Yorkstar F uno/Niexiang 5//Xiannong39///64(4)43 sel./Yanda 24 (Exotic 4870//2*Asosan/3*Genesee)/(P d5517- A1//Suwon92/Brevor/5*Genese e)//W9021R C83980#1//Twain F1// HRW9641/Benni Landrace Fredrick/3/Yorkstar//Avon/Ross W /4/Augusta/3/Jen521489/Ross// 361 -20/Nudwarf Sel. Chengduguangtou BEl- 5/Logan//Arthur/3/NY5726ab- 3B-23/TN1403 IBO 1828/NP824///Wuyi maU/Chengduguangtou/Zhongn ong483/Zhongnong28B/IBO 1828//NP284/Funo Coker 65-20 /8/ (Norin 33-3 /6/(Fairfield /4/ Fultz / Hungarian”? /2/PI94587 durum /3/ Fultz/ Hungarian, Pd39153A1-11-1-1) /5/ (Trumbull*3 /2/Hope / Hussar, Pd3932A7-3- l -2) /3/Newsar, PD4946A4-18-2-lO-1) /7/ Hadden /9/ (Norin 10 / Brevor /4/ Anderson /3/ Coker 55-9, Chancellor”? /2/ T. timopheevi / Steinweidel), Vogel 5) 65 Lr Cv Cv Cv Cv Lr Cv Lr Cv Cv Cv N N N 22 Foster Freedom Glennson Glory Hazen Heine VII IL-85-31521-1 INW9531 Jackson Jinan 8 hao J inmai 16 hao Kavkaz Kooperatorka Lovrin 21 Lowell Luopo Rice (T. petropavlovskyi) Mason Mianyang 11 Nongda 183 Opata 85 Patterson PI 245583 Kentucky, USA Ohio, USA CIMMYT Ohio, USA Arkansas, USA Saxony, Germany Illinois, USA Indiana, USA Virginia, USA Shandong, China Shanxi, China Krasnodar, Russian Federation Odesa, Ukraine Romania Michigan, USA Xinjiang, China Indiana, USA Sichuan, China Beijing, China CIMMYT Indiana, USA Hindu Kush /8/ (Norin 33-3 /6/ (Fairfield /4/ Fultz/ Hungarian*2 /2/ P194587 durum /3/ Fultz /Hungarian, Pd39153A1-1 1-1-1) /5/ (Trumbull*3 /2/ Hope / Hussar,Pd3932A7-3-l-2) /3/ Newsar, Pd4946A4-18-2-10-1) /7/ Hadden /10/ Coker 797 Coker 65- W 20/Arthur/4/Chul*8CC//VA68- 22-7/Abe/3/VA72-54- 14/T yler// Suwon92/Arthur// Art hur/VA70-52 Hart/VA66-54- 10//Kavkaz/Pur6693///OH217 Kavkaz/Buho sib//Kalyansona/Bluebird Tyler/Pioneer 2550 Doublecrop/Beau Hybride a Courte Paille/Kronen 2 C1) ”C3 W W W McNair 1003/Caldwell W Auburn/Coker 84- W 27/3/OH256/Scotty/Clark Saluda/Coker 762 W Bima 4/Skorospelka W unknown W W Lutescens 314H147/Bezostaja 1 = Neuzucht/Bezostaja 4//Bezostaja 1 Se]. Krymka W Neuzucht/Bezostaja 1//Lovrin W 62 (Genesee/Winoka)/5/((Suwon W 92/Brevor/2/5*Genesee)/4/(Nor inl O/Brevor/2/Y orkwin/ 3/ 3 *Ge nesee))/6/((Talbot/CItr8487)/3/( Genesee*4/2/Norin 10/ Brevor)) Landrace Sp Cardinal/C78318//Coker 9323 W 70-85 85/F an 6 Sp Triumph/Yanda 1 81 7 W Bluej ay/Jupateco 73 Sp P69184BS-21-1-1-2- W 4*2/Caldwell Landrace W 66 CV Cv CV Cv Cv Cv Cv Cv Cv Cv Cv Cv Cv Cv Cv <222 22 222 '-< '-< <2: 2 22222 2 Mtns, Afghanistan PI 367181 Kabul, Landrace W Lr N Afghanistan PI 382070 Ilam, Iran Landrace W Lr N Roter Muri Zurich, unknown W Cv N Switzerland Seri 82 CIMMYT Kavkaz/Buho Sp Cv Y sib//Kalyansona/Bluebird TAM 107 Texas, USA TAM 105 *4/Amigo W Cv N Thatcher Minnesota, USA Marquis/Iumillo//Marquis/Kanr Sp Cv N ed Tokwe Zimbabwe Lee/ND74//F573/Mazoe Sp Cv N Tomahawk Colorado, USA Unrecorded bulk selection W Cv N Trio Nord, France Cappelle Desprez/3/Hybride de W Cv N Bersee/Gametl/YGA/Thatcher W7984 CIMMYT Opata 85//Altar 84/ Sp Sy N n Wakefield Virginia, USA Selection from one of four Sp Cv N populations Arthur // CI 13836 /8* Chancellor , VA 68-22 -7// CI 13836 /8* Chancellor , Doublecrop // Abe / VA 68-24 - 42 /3/ CI 13836/8* Chancellor , and Oasis / VA 68-24 - 42 // CI 13836 /8* Chancellor *Classification: grth habit — Sp — spring, W — winter; 1BL/1RS wheat rye translocation — Y — present, N — absent; Accession type— Cv — cultivar, Lr — landrace, Syn — synthetic. 67 Table 3-2 The number and genomic distribution of AF LP bands generated from each of the 10 primer pairs in the cross of Opata 85 and W7984 and a germplasm panel composed of thirty-seven cultivars and seven landraces fi'om 15 countries. Primer pair Number of polymorphic Number of bands Number of bands mapped chromosomes covered Opata 85 Germplasm Opata 85 W7984 and W7984 panel Eaac/Mcaa* 16 21 4 11 12 Eaac/Mctg 10 28 6 3 7 Eaac/Mctt 13 17 10 3 9 Eacg/Mctg 15 32 7 8 7 Eacg/Mctt 17 41 9 5 13 Eagg/Mcag 21 21 l 1 9 13 Eagg/Mctg 22 40 9 12 13 Eagg/Mctt 23 37 7 14 10 Paca/Mcac 9 nd 3 3 5 Paca/Mcgc 7 nd 0 6 6 Average 15.3 29.6 6.1 7.4 9.6 * The first and fifth letter of primer pair code represents the restriction enzyme used where E = EcoRl, M = Msel , and P = Pstl. The three letters following restriction enzyme code represent the three selective downstream nucleotides of each primer. 68 Li RESULTS Each of the eight EcoRI/Msel primer pairs generated between 84 and 154 bands. In total, 995 bands were identified. Seven hundred and sixty-four (76.8%) bands were monomorphic among the 44 T. aestivum accessions in the germplasm panel. Most (99.1%) of the monomorphic bands in germplasm panel were also present in the synthetic wheat W7984. In other words, the majority of monomorphic bands in the T. aestivum germplasm panel were also present in the T. diccocoides var. durum and/or Ae. tauschii accessions used to construct W7984. All but 15 of the bands from W7984 were found in one or more of the T. aestivum accessions. Two hundred thirty one (23.2%) bands were polymorphic among the germplasm panel (i.e., absent in at least one accession). Every primer pair generated polymorphic bands (range: 17-41) among T. aestivum accessions, however 43.2% of these were uninforrnative in the mapping population because neither parent had the band (66 bands), or because both parents carried the band (34 bands). Ten primer pairs (eight EcoRI/Msel and two PstI/Msel ) generated 1098 distinct AF LP bands between the parents of the mapping population (Table 2). One hundred and fifty-three (14.0% of the total) bands were used for segregation analysis. RIL data for all but 8 of the polymorphic bands conformed to a 1:1 ratio (or=0.05). One hundred forty of the 153 polymorphic bands were assigned to a chromosome using a presence/absence dominance model. Co-dominance was ruled out for all but four bands, Eaac/Mctg.130 and Eaac/Mctg.l39, Eagg/Mcag.205 and Eagg/Mcag.207. Those four bands appeared to represent two pairs of co-dominant alleles from two loci although the possibility of repulsion phase linkage could not be eliminated. We therefore mapped a total of one hundred thirty eight AF LP loci (136 dominant and two co-dominant). AF LP loci mapped 69 to all genomes [A (32.6%), B (46.4%), and D (21.0%)] and all chromosomes except for 4D (Table 3). Information on mapped loci including genetic linkage map position, Opata 85 and W7984 genotype for each AF LP and frequency in the germplasm panel is compiled in Table 4. All but seven pairs of loci showed mutual recombination. Linkage mapping indicated that loci were not concentrated in the centromere or telemoere regions. In all but three instances, more than three AF LP mapped to a single chromosome interval not populated by markers from other studies (see chromosomes 1BS, 3BL, and 7A8 in table 1-4). The average frequency of mapped bands within the germplasm panel was lower for bands originating from W7984 (0.29) versus Opata 85 (0.61). The distribution of the frequencies of mapped and unmapped polymorphic bands in the combined set of 46 accessions was fairly uniform (Figure. 3-1). Most fragments (87.7%) were not rare alleles and were present in more than 10% of the germplasm panel. Few bands were unique to any one accession, with the exception of the synthetic hexaploid having 14 unique bands. Many of the W7984 mapped loci were found at a low frequency in the germplasm panel with 44.6% of the bands present in fewer than 20% of the accessions. Approximately half (50.9%) of the mapped fragments originating from Opata 85 where present in 30 to 80% of the germplasm panel accessions. Six mapped bands were monomorphic across T. aestivum and absent in the synthetic. These bands in part account for the 28.1% of the Opata 85 bands that have a frequency above 0.9. 70 Table 3-3. Genome distribution of 136 AF LP loci placed on the wheat linkage map created fi'om the cross of Opata 85 and W7984. Genome Chromosome A B D Total Average nmnber per group chromosome group 1 4 12 1 17 5.7 2 5 10 6 21 7.0 3 3 15 7 25 8.3 4 8 4 0 12 4.0 5 6 9 4 19 6.3 6 8 6 7 21 7.0 7 l l 8 4 23 7.7 Total 45 64 29 138 % 32.6 46.4 21.0 Average 6.4 9.1 4.1 6.7 number per genome 71 Figure 3-1. Histogram of AF LP alleles originating from each parent of the mapping population (Opata 85 and W7984) as well as all polymorphic loci detected in the germplasm panel. 0.3 _-*_--,, 0.25 - § 0.2 - 2 '5 c... O __S_ 0.15 - t.’ O O. O a: 0.1 - 0.05 - OI 0.2 AF LP fragment frequency in germplasm 0.3 0.4 0.5 0.6 0.7 0.8 0.9 l DAII loci observed in germplasm El Mapped W7985 loci I Mapped Opata 85 loci Chi square tests of the random association of band states (e. g. presence or absence) and germplasm characteristics (Table 1) were significant in several cases. Seven umnapped AF LP bands probably amplified from loci on the rye arm of the lBL/ IRS chromosome since five of the six accessions carrying those bands are known to carry that translocation and one with an unknown status. Two more bands, one mapped and one unmapped, were found in all of the 1BL/ IRS translocation-bearing lines plus an additional accession. Five loci mapped to chromosome arm lBL deviated from expected equilibrium frequencies; however, the bands only appeared in accessions without the lBL/ IRS translocation. Those bands are therefore amplification products from wheat chromatin. 72 Ignoring monomorphic bands, average pairwise genetic distance (GD) for all 46 accessions was 0.507 (J accard coefficient), and almost all genotype/primer pair combinations generated a unique overall banding pattern. The PI3671 81/INW 953 pair had a higher GD than all of the T. aestivum pairs. The highest pair-wise GD (0.793) was observed for Opata 85/W 7984. Foster and INW9531 had the lowest GD of any pair at 0.083. Cluster analysis of the GD matrix of all accessions (including the mapping parents) produced a dendrogram subjectively interpreted to have four clusters of T. aestivum accessions, and one singleton for W7984 (Figure 3-2). The patterns evident in the Figure 3-2 were deemed significant since the correlation between the GD matrix and the cophenetic matrix derived from the dendrogram was good at 0.85 (Rohlf 1997). The first cluster is comprised of twelve accessions, six of which are landraces. The second and largest cluster contains 24 accessions including 13 eastern US. soft winter cultivars, three Chinese and two US Great Plains varieties of recent origin. Cluster three and four includes Kooperatorka plus four of six cultivars in this study known to carry the Kavkaz lBL/ 1RS wheat-rye translocation. The fifth cluster contains four eastern US soft winter cultivars including one bearing the Kavkaz 1BL/ IRS wheat-rye translocation. 73 Table 3-4 Linear order of AF LP loci on genetic linkage map of Opata 85/W 7984 cross. AF LP loci are in bold print. Other loci were previously recorded (see material and methods). Each chromosome begins with the short arm of the chromosome. Map distance listed as int. denotes assignment to an interval between two placed markers. The centimorgan distance for a locus refers to the distance to the above locus not placed in an interval. Map distance is listed as centimorgans from the preceding locus. The frequency of the band representing a given AF LP locus across the germplasm assay is listed with band parent source. Disequilibrium and segregation distortion (SD) where tested using x2 analysis at P<0.05. Codominante markers on chromosome 18 and 3A are underlined. nd- no data. Chr cM Locus Frequency Parent 1A Pcac/Mcac.204 nd W7984 11.1 stuD14 41.5 Xr2244 6.1 Xabg373 4.5 Eacg/Mctt.186 0.07 W7984 4.0 Eagg/Mcag.297 0.59 W7984 5.3 Xcdo473 centromere 39.8 stqu4 int. Eacg/Mctt.86 0.05 Opata 85 38.8 meg632 1B int. Eagg/Mcag.88 0.53 Opata 85 stuD14 int. Eaac/Mcaa.103 nd W7984 15.2 meg938 Int. Eacg/Mctg.238 0.00 W7984 Int. Eacg/Mctg.240 0.00 W7984 Int. Paca/Mcac.221 nd W7984 18.8 Xcdoll73 4.2 Eaac/Mctg.130 0.09 W7984 0.0 Eaac/Mctg.139 0.77 Opata 85 5.1 Eagg/Mctt.86 nd Opata 85 5.9 Eaac/Mcth72 0.68 Opata 85 6.5 Eagg/Mctg.218 0.00 W7984 0.0 Eagg/Mctg.219 0.00 W7984 6.2 Eacg/Mctg.185 0.45 Opata 85 centromere 4.2 Xcdo637 17.2 Eagg/Mctg.l47 nd Opata 85 7.1 Xcmwg733 putative 1BL/1RS translocation loci Eaac/Mcaa.250 0.13 neither 74 1D 2A 28 Eaac/Mctg.239 0.1 1 neither Eagg/Mctg.261 0.16 neither Eacg/Mctt.284 0.16 neither Eacg.Mctt.124 0.15 neither Eacg/Mctg.120 0.16 neither Ewan” 0.15 neither Xabc156 centromere 28.7 meg967 int. Eaac/Mctt.124 1.00 Opata 85 13.8 beb194a 7.3 Xcdo312 Paca/Mcgc.200 nd W7984 Xcdo456 14.4 Xbcd348 5.5 Eagg/Mctg.188 0.21 W7984 0 Eaac/Mcaa.139 0.09 W7984 9.1 stuD18 44.2 sz395 centromere int. Eagg/Mcag.335 nd W7984 int. Eaac/Mcaa.231 0.68 W7984 8.7 Xbcd543 Xbcd348 59.3 bea272 int. Eaac/Mctt.105 0.00 Opata 85 8.1 sz444 16.2 Xcdo405 int. Eacg/Mctt.293 0.38 Opata 85 int. Eagg/Mcag.123 0.07 W7984 11.4 Xbcd260 centromere 8.9 Xbcd1119 11.7 Xbcd1779 int. Eaac/Mctt.95 0.68 Opata 85 int. Eaac/Mctt.227 0.91 Opata 85 int. Eagg/Mctg.173 0.63 Opata 85 11.4 Xbcd1095 20.9 beb113 int. Eagg/Mctg.352 0.14 W7984 int. Eagg/Mctg.353 0.14 W7984 12.7 meg660 31.8 Xbcd1231 int. Eaac/Mctt.331 0.32 Opata 85 75 2D 3A 3B int. Paca/Mcgc.156 Nd W7984 Eagg/Mctg.77 10.9 Eagg/Mctt.166 0.0 Eagg/Mctt.167 6.2 beb189 37.0 Xcmwg682 21.3 Eagg/McagJS9SD 5.2 Eaac/Mcaa.86 10.7 Xbcd102 9.1 bea400 centromere 124.5 Xcd036 9.8 Eacg/Mctt.129 1.00 0.00 0.00 0.51 0.66 0.00 Opata 85 W7984 W7984 Opata 85 Opata 85 W7984 Xcdo638 4.7 Xpsr903 9.6 Eagg/Mctt.310 centromere 13.0 Xtarn33 23.3 meg30 int. Eaac/McanSSSD int. Ea Mca .205 int. EaggZMcag.207 34.1 beb293 0.79 nd 0.56 0.33 Opata 85 Opata 85 Opata 85 Opata 85 stuG53 int. Eagg/Mcag.158 int. Eagg/McthOS 12.1 Xcdo460 int. Paca/Mcgc.99 11.8 Xcdoll64 28.9 Xpsr903 centromere 6.5 Eacg/Mctt.258 5.3 Eagg/Mctt.312 3.6 Eagg/Mctg.239 8.3 XATPasel 28.2 Xbcd1418 int. Eagg/Mctt.273 12.0 meg69 6.2 beb378 27.8 Xbcd131 int. Eagg/Mctt.98 23.3 bea360 0.35 0.23 nd 0.72 0.60 0.40 0.24 0.91 76 W7984 W7984 W7984 Opata 85 W7984 W7984 W7984 W7984 3D 4A int. Eaac/Mctt.207SD 0.98 Opata 85 int Eacg/Mctg.328 0.07 W7984 int Eagg/Mcag.3258D 0.20 Opata 85 int. Eacg/Mctg.332 0.94 W7984 int Eagg/Mcag.118 0.09 Opata 85 19.6 ng131 5.7 bea235 int. Eagg/Mcag.l77 0.94 Opata 85 int. Eaac/Mctg.457 0.63 Opata 85 8.6 Xtarn63 beb370 int. Eagg/Mctg.314 0.88 Opata 85 22.0 bea91 7.7 bea241 8.2 Xcdo407 int. Eagg/Mctg.l68 0.00 W7984 int. Eagg/Mcag.112 nd Opata 85 int. Eaac/Mctt.l88SD 0.98 Opata 85 centromere 20.7 Xabc176 int. Eaac/Mctt.190 0.37 W7984 int. Eaac/Mcaa.l688D 0.00 W7984 6.9 thb237 28.1 beb316 int. Eacg/Mctg.208 0.30 W7984 int. Eagg/Mcag.7l 0.09 W7984 int. Eaac/Mcaa.118 nd W7984 bebl centromere 35.6 Xwg622 27.6 meg549 3.1 Xbcd1670 2.5 Eacg/Mctg.87 0.10 W7984 8.7 Xbcd130 8.4 Xcd0545 int. Eacg/Mctt.114 0.86 Opata 85 12.1 beb114 int. Eacg/Mctg.130 0.36 W7984 int. Eaac/Mcaa.159 0.09 Opata 85 beb1 14 int. Eacg/Mctg.345 0.19 Opata 85 int. Eaac/Mctt.308 0.00 W7984 77 4B 5A 5B 5D int. int. 1 1.6 28.3 3.2 5.3 Eacngctt.213 Xbcd402 Eaachcaa.23OSD Xcdo795 W beb182 Eagg/Mctg.175 Eagg/Mctg.21 1 0.98 0.00 0.93 0.73 Opata 85 W7984 Opata 85 Opata 85 int. 23.2 int. int. 12.0 8.3 10.5 int. int. 15.0 68.0 int. Xbcd1871 Eagg/Mctt.189 W Xbcdl 355 Paca/Mcgc.152 Eacg/Mctt.325 Xcd01090 meg624 Xbcd1235 Eagg/Mctt.24l Eagg/Mctg.284 Xbcdl 83 meg21 12 Eaac/Mctg.263 0.05 nd 0.02 nd 0.90 0.00 W7984 W7984 W7984 W7984 Opata 85 W7984 14.7 4.7 4.9 4.3 NO 6.4 int. int. 5.6 15.2 33.2 4.2 3.1 17.4 int. Xbcd873 Eaac/Mcaa.320 bea367 Eacg/Mctt.297 bea393 Eagg/Mctt.163 Eagg/Mctt.164 Eagg/Mctt.288 Xpsr929 were Eacg/Mctt.247 Eagg/Mctt.354 meg56 1 Xabg47 3 Xbcdl 030 Eagg/Mctg.21 0 Xcdo 504 XcdoS 84 Eaac/Mctg.l32 0.73 0.41 0.74 0.74 0.81 0.77 0.41 0.02 0.32 W7984 Opata 85 W7984 W7984 W7984 Opata 85 W7984 W7984 Opata 85 9.4 8.4 thal 0 tha393 78 6A 68 6D int. int. int. 10.8 26.1 32.2 40.3 9.7 9.6 Paca/Mcac.230 Eagg/Mctg.86 Eacg/Mctg.205 9W bea137 XcdoS7 meg900 Xcdol 508 Eagg/Mctt140 Xbcdl 103 nd 0.00 0.10 nd W7984 W7984 W7984 W7984 int. 4.6 int. 22.5 13.4 int. 1 1.0 int. 13.1 12.0 int. 7.8 44.3 int. int. 13 .4 6.3 int. Eaac/Mcaa.200 Xpsr167 Eacg/Mctg.300 stuG48 bea152 Paca/Mcac.180 beb145 Eagngcag.310 Xcd0270 Xcd029 W Eacg/Mctg.91 Xbcdl 860 beal 1 1 Eagngctt.254 Eagg/Mctt.255 stuD27 meg57 3 Eaac/Mctt.204 0.27 0.60 nd 0.69 1.00 0.40 0.40 0.77 W7984 Opata 85 Opata 85 W7984 Opata 85 Opata 85 Opata 85 W7984 7.5 23.6 int. int. 6.6 1.8 13.0 3.2 32.8 7.1 7.0 Eagg/Mcag.238 Xpsr167 sz995 Eacg/Mctt.150 Eagg/Mcag.154 bea152 Eaac/Mctg.178 Eaac/Mcaa.210 bea344 W beb169 Eagg/Mctg.330 beb3 7 7 0.10 0.86 0.37 nd 0.00 1.00 W7984 W7984 Opata 85 W7984 W7984 W7984 Xbcd342 79 7A 7B 7.9 Eaac/Mctg.187 1.00 Opata 85 12.4 stuG48 42.1 bea336 int. Eagg/Mctg.130 1.00 Opata 85 centromere 2.6 Xbcd1716 int. Eacg/Mctt.219 0.00 W7984 Eaac/Mcaa.157 nd Opata 85 int. Eaac/Mcaa.156 nd W7984 18.1 beb231.2 14.9 bea81 31.2 Xbcd1510 6.4 Eaac/Mctt.200 0.55 Opata 85 15 stuD27 7.4 meg2053 Eagg/McagJ41 0.67 Opata 85 int. Eagg/Mcag.90 nd W7984 int. Eagg/Mctt.379 0.60 Opata 85 int. Eacg/Mctg.263 0.55 Opata 85 int. Eacg/Mctg.261 0.55 Opata 85 Xcd0545 69.6 Xabc158 10.2 bea248 10.1 Eaac/Mctg.145 0.33 Opata 85 4.6 bea340 13.3 Xbcd1066 centromere 9.1 bea234 int. Eacg/Mctt.202 0.66 Opata 85 30.0 bea69 int. Eagg/Mctg.286 0.51 Opata 85 int. Eagg/Mctg.l44 0.24 W7984 17.0 bea350 17.8 beb145 14.6 Eacg/Mcth69 0.55 Opata 85 4.2 Eagg/Mcag.380 0.69 W7984 Eagg/Mcag.330 0.33 Opata 85 17.3 stuH9 int. Eagg/Mctt.405 0.58 W7984 int. Paca/Mcac.68 nd W7984 bea42 int. Eaac/Mctt.430 0.61 Opata 85 21.7 beb150 int. Eaac/Mcaa.205 0.23 W7984 80 int. Eagg/Mctt.330 1.00 Opata 85 int. Eagg/Mctt.89 0.98 Opata 85 14.8 Xabc455 centromere 7.3 Xwg514 3.7 Eagg/Mctt.84 nd W7984 7.4 bea305 59.6 beb189 int. Eacg/Mctt.265 0.20 Opata 85 9.2 Xcdo414 bea377 11.2 Eaac/Mctt.348 nd Opata 85 5.9 Paca/Mcgc.131 nd W7984 centromere 3.1 Xbcd707 16.2 Xcdo775 int. Eacg/Mctt.127 nd W7984 33.2 meg975 10.5 bea204 int. Paca/Mcac.105 nd Opata 85 81 3535 2.250 ~60 0nd 9.6 VNd mod _ . _ _ _ P L L _ _ _ _ . p _ _ . _ . . _ ~ . a . .2.->2 . 53w _ :moBZ— Egon...— V .ADSED : t sic—2&8! conga—O Scum m . ~535— ~ _ Nurse; :32?th no _ .322. 02$ _ 5.3:. 2:55 2289.2 WHO“. 32 83... .5332. :>o=._u: 1| 8:2 [ EELS—a3 Onommflm .52 38¢ W N 2:. sm=m=< comet—i Ea: boa «.593 I 32.10 .330 :23 5:82: filT‘l/ 752 Tana: ; _ now: m:th~3=EU nuns—53¢. _ 3:52.32 28m 5 _ 29555 :30 .mcofiuoooa 5:85 330.58% mo wfiqsew &u:o£ b03833 Boxqflm .EomoEoOo £5.83. min: 35:23 82388 28:3 3 we as”? no woman magnet 85%? 0:050 Tm 059m 82 3.4 DISCUSSION A DNA sequence is amplified by the AF LP procedure only if it is flanked by pr0perly oriented recognition sites for the two restriction enzyme, if those sites are between 60 and 500 nucleotides apart. A further criterion is that the sequences of nucleotides immediately internal to the restriction sites complement those of the selective nucleotides of the PCR primers used in the final amplification. Here, the AF LP protocol employed a six-base cutting and a four-base cutting enzyme plus three selective nucleotides internal to each restriction site. Locus amplification was therefore determined by the existence of a specific 16 nucleotide sequence (9+7) interrupted with an additional non-selective 60-500 nucleotide sequence. These data demonstrate that AF LP patterns in wheat are highly reproducible. Two lines of logic contribute to that conclusion. First, the random occurrence of fragments randomly exhibiting expected Mendelian inheritance is unlikely and therefore would not occur if repeatability was poor. Second, the large abundance of bands monomorphic throughout the entire germplasm panel (7 6.8%) could only occur if the procedure was highly repeatable. Reproducibility also has been good in other plant species and across laboratories (Jones et al. 1997). A genomic locus can only be detected by the AF LP procedure if there is at least one sequence variant that enables amplification. Polymorphism at AF LP detectable loci can arise from one of three causes. First, polymorphism may reflect point mutations in the critical nucleotides recognized by the restriction enzymes or the adjacent selective nucleotides. Changes in methylation status alone also can induce polymorphism when methylation sensitive restriction enzymes are used. In these two instances, 83 polymorphism will manifest, as a change in state from band presence to band absence unless another properly oriented second critical sequence exists within approximately 500 bp. Genetic behavior of presence/absence polymorphism will mimic that of pairs of gene alleles exhibiting dominant/recessive gene action. The third cause of AF LP polymorphism is insertion/deletion-driven change in the length of the nucleotide sequence between the critical nucleotide termini of the locus. In that case, polymorphism may manifest as a series of amplified fragment lengths, unless the number of nucleotides between the termini is greater than the maximum permitted by the PCR protocol in use. Genetic behavior in the case of true fragment length polymorphism would be co- dominance. In both presence/absence and true fragment length polymorphism, epistatic dominance can arise from co-migrating fiagments amplified from multiple loci. That problem could be expected to be a particular problem in polyploids if divergence between parental genomes is minimal. Evidence presented here indicates that the great majority of the AF LP loci found in the Opata 85/W 7984 RIL p0pulation conform to the presence/absence dominance model, and are therefore likely to be caused by point mutations in the 16 critical nucleotides. Most loci conformed to allele frequency patterns expected from single locus segregation, as opposed to multi-locus epistatic interactions expected from co-migration of fragments from homoeologous loci. Apparently the three genomes of T. aestivum have diverged to an extent that it is now rare for identical bands to be generated from homoeologous loci. Predominance of the presence/ absence type of polymorphism with AF LP procedures also is apparent in several other plant species (Becker et al. 1995; Boivin et al. 84 1999; Cho et a1. 1998; Keim et al. 1997; Lu et al. 1998; Maheswaran et al. 1997; Menéndez et al. 1997; Qi et al. 1998; Schondelmaier et al. 1996; Wang et al. 1997; Waugh et al. 1997). In contrast, RFLP studies point to insertion/deletion events as major contributors to polymorphism (Miller and Tanksley 1990), however, this (McCouch et al. 1988) is not, necessarily a contradiction to these results. The termini of fragments visualized by RF LP protocols are much further apart (5-20 kb) than the termini of AF LP fragments (60 to 500 bp). The larger fragment size with RF LP increases the likelihood of detection of insertion and/or deletions. Also, the lengths of RFLP fragments are usually defined by the presence of a particular 12 nucleotide sequence (2x the 6bp recognition site of the single restriction enzyme), rather than the 16 nucleotides required in the AF LP procedure used here. Hypothetically, rates of polymorphism-inducing mutations could be so high, or so low that all allelic forms are either very abundant, or very rare. That situation would diminish the value of markers since distinct crosses would either be relatively uninformative (low rate of mutation), or each would generate genetic maps with relatively unique sets of loci (high rates of mutation). Likewise, either very low or very high rates of polymorphism-inducing mutation would decrease the value of a marker system in measuring genetic diversity. The preponderance of monomorphism in the T. aestivum germplasm panel clearly indicates that sequence divergence is relatively small in this species. However, the fact that polymorphic bands in the germplasm panel were generally found at intermediate frequencies indicates that mutation rates are sufficiently high to enable discrimination among most genotypes. The intermediate level of abundance for polymorphic bands leads us to expect overlap in the AF LP loci mapped in 85 distinct crosses. The intermediate level of band frequencies on a species level was also true for bands mapped to loci in this study. That was true whether the bands originated from the T. aestivum parent (Opata 85), or the synthetic parent (W7984). We therefore conclude that a genetic map of AF LP loci from this highly annotated and RIL population will be a valuable tool for applications in other T. aestivum germplasm. Across population utility of AF LP maps also was reported in rice (Zhu et al. 1998) and barley (Ellis et al. 1997) and potato (Milbourne et al. 1997). The synthetic parent W7984 was constructed by A. Mujeeb-Kazi at CIMMYT from species (T. dicoccoides var. durum and Ae. tauschii) assumed to represent descendents of the same ancestral species that hybridized to form T. aestivum. The fact that 99.1% of the monomorphic bands in the T. aestivum germplasm panel also were present in W7984 suggests that the genomes of its parental accessions are only slightly diverged from the A, B, and D genomes found in T. aestivum. The distribution of mapped, unique bands originating from W7984 was not dramatically skewed towards any of the genomes with 2, 6 and 6 unique bands from the A, B, and D genomes respectively. Mapped bands monomorphic in T. aestivum and absent in the synthetic are more abundant in the D genome with 1, 1, and 4 bands from the A, B, and D genomes respectively. That suggests that W7984's two parental accessions are not dramatically different from each other in the amount each have diverged from their genomic counterparts in T. aestivum. Further germplasm surveys are required to establish the level of conservation of AF LP loci between the AB chromosome sets in T. aestivum and T. dicoccoides var. durum and between the D genomes in T. aestivum and Ae. tauschii. An issue that can be addressed by surveys of the germplasm of the ancestral species is 86 quantification of the relative contributions of diversity that arose since the domestication of T. aestivum versus that which was introduced directly from T. diccocoides var. durum and Ae. tauschii. Allelic variation shared by both T. aestivum and the ancestral species is most likely the product of speciation and introgression, while variation specific to T. aestivum is most likely the product of mutation. The accessions in the germplasm panel were chosen to minimize pairwise genetic similarities, although there is a disproportionate sampling from China and the eastern U.S.A. Nonetheless, the properties of the distance matrix and associated dendrogram (Figure 2) provide evidence of significant and complex structure in the germplasm panel. The across-germplasm applicability of AF LP loci mapped in the RIL population used here enables map-based analysis of genetic diversity. Band states can be viewed as characters in association analyses either with a priori groupings based on shared characteristics. In either event, non-random distribution of marker-locus character states across classification groups is suggestive that those loci are linked to chromosome segments preferentially associated with particular classes(Beer et al. 1995;Paull et al. 1998). The quantity of data available in this study is at the lower limits of sufficiency for this type of analysis, but nonetheless we did identify AF LP loci that were significantly associated with groups based on carriers of the 1BL/ IRS wheat rye translocation. 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Theo Appl Genet 961602-611 Zhu JH, Stephenson P, Laurie DA, Li W, Tang D, Gale MD (1999) Towards rice genome scanning by map-based AF LP fingerprinting. Mol Gen Genet 261:184-195 92 4 GENOME SPECIFIC GENETIC DIVERSITY OF CHINESE AND EASTERN US WHEAT GERMPLASM 4.1 INTRODUCTION Wheat landraces endemic to China are some of the most morphologically unique and genetically distinct accessions of T riticum aestivum L. (Ward et al. 1998; Yang and Smale 1996; Kim and Ward 2000). When four exotic Chinese landrace pools were analyzed with RF LPs they accounted for a greater amount of diversity than that found in landraces from Afghanistan, Iran, Turkey (Ward et al. 1998), and 14 improved winter wheat germplasm pools (Kim and Ward 2000). Extreme differences in band frequencies rather than unique bands were largely responsible for the distant relationship between the Chinese landraces and all other pools. Early plant breeding efforts in China emphasized the utilization of foreign germplasm, primarily from Australia, Chile, Denmark, Italy, Korea, Romania, Russia, and USA (Yang and Smale 1996) along with introgression of alien species and local landraces into breeding material, a practice largely neglected outside of Asia (Rejesus et a1. 1996).Presumably, improved Chinese cultivars should also be genetically distinct fiom other improved germplasm pools, if the core of the germplasm is built on Chinese landraces. In an analysis of accessions developed and grown in Shaanxi province China between 19408 and 19908 the overall genetic diversity estimates indicated that the group is actually no more related to the Chinese landraces than other improved germplasm pools (Chapter 2). This may have occurred because Shaanxi cultivar parentage is dominated by a small number of foreign plant introductions, Danish 1, Villa Glori, 93 Quality, and Predgomaja 2, and landraces, namely Maza mai, Hong mai, and Yanda 1817. It is not known whether the Shaanxi germplasm represents diversity patterns in other improved Chinese lines as cultivars have not been examined in other wheat growing regions. To that end, I assembled a set of cultivars grown from the 19505 to the 19805 from eleven wheat-growing provinces in China. Those accessions were analyzed in the context of the most diverse improved pool, the Eastern United States soft winter wheat cultivars (EUS) (Kim and Ward 2000), and a diverse collection designed to approximate wheat genetic diversity worldwide. To measure genetic diversity I used AFLPs, which I recently demonstrated can be used for map-infonned diversity studies (see Chapter 3). Various features of AF LP technology render it more powerfirl than other molecular marker systems (V 05 et al. 1995; Breyne et al. 1997; Powell et al. 1996). In a study of AFLP loci in T. aestivum it was discovered that AF LP fragments are distributed throughout the genome, generally have only one detectable sequence variant and exhibit monogenic dominant Mendelian inheritance (Chapter 3). In addition, frequencies of polymorphic bands in a diverse germplasm panel are in the range that enables informative cluster analyses and map- based diversity. The objective of this research was to evaluate the genetic diversity of improved Chinese cultivars relative to a diverse collection of accessions using annotated AF LP markers. Of particular interest was whether levels of diversity differed across the A, B, and D genomes. 94 4.2 MATERIALS AND METHODS One hundred twenty five accessions of T. aestivum from the eastern USA (42) and China (38) were genetically fingerprinted using the AF LP system (V 08 et al. 1995). The Chinese accessions, kindly provided by Prof. Li Li-Hui, Institute of Crop Germplasm Resources, Chinese Academy of Agricultural Science were cultivated primarily during the 19508 to 19808, have both spring and winter growth habit, and were developed at research institutes in eleven different provinces in China. The EUS accessions were cultivated primarily during the 19808 and 19908 and were developed by 19 different breeding programs. The remaining forty-four accessions designated as the ‘diverse set’ originated in twenty different countries (Table 4-1). Twenty-nine accessions have a spring grth habit and 98 have a winter growth habit. Canadian cultivars, TW92405, Casey, and Karena, which were developed in Ontario Canada, were considered members of the EUS germplasm pool. Five of the Chinese accessions were classified as part of the diverse set as they are not improved cultivars: ASA 362 (Luopo Rice; T. petropavlovskyi), ASA 97 (T. a. ssp. tibetanum), ASA 336 (T. a. ssp. yunannensis), Maza mai, and Chinese Spring (Ward et a1. 1998). The parents of the recombinant inbred line population, Opata 85 and W7984, used to characterize the AF LP (Chapter 3) and therefore collectively possess all mapped loci in this study were not considered part of a germplasm set so as to not distort analysis. 95 Table 4-1 Name, geographical origin, growth habit, and germplasm set designation of 125 wheat accessions. GermplaSUName Origin Growth Set Country Locality/Institute Habit“ Sumai 173 China Anhui w Beijing 6 hao China Beijing w CA 8686 China Beijing w Fengkang 4 hao China Beijing w J inghong 9 hao China Beijing 8 Nongda 183 China Beijing w Nongda 311 China Beijing w Xiezuo 2 hao China Beijing 8 Zhong 8338 China Beijing 8 Qingqxuan 27 China Gansu w Zhongliang 11 China Gansu w Hanxuan 2 hao China Hebei w Hengshui 6404 China Hebei w Jimai l hao China Hebei w Jimai 14 China Hebei w J imai 6 hao China Hebei w Shijiazhuang 52 China Hebei w Shijiazhuang 54 China Hebei w E; Xiangyang 2 hao China Hebei w 5 Gang 107 China Heilongjiang s Kehan 8 hao China Heilongjiang s Bainong 3217 China Henan w Baiquan 565 China Henan w Zhengzhou 742 China Henan S Siyang 117 China Jiangsu w Qiannong 4 hao China Shaanxi w Xiannong 68 China Shaanxi w Jinan 2 hao China Shandong w Jinan 8 hao China Shandong w J inanai 6 hao China Shandong w Luteng 1 hao China Shandong w Jinmai 11 hao China Shanxi w Jinmai l6 hao China Shanxi w Taifu 23 China Shanxi w Chuanmai 17 China Sichuan 8 Fan 7 China Sichuan s Mianyang 11 China Sichuan s Shuwan 8 hao China Sichuan s 96 Eastern United States Casey Karena TW92405 Coker 9835 Coker 9904 Hazen J aypee Florida 303 Florida 304 Patterson Pioneer 2510 Pioneer 2580 Shelby Shiloh Foster Catoctin Augusta Bavaria Lowell Rarnrod Caledonia Geneva NYBatavia Freedom Glory Hopewell Clemson 201 Coker 9766 FF R 555W Jackson Pocahontos Wakefield Pioneer 2737w LA87167-D8-10-2-B 'LA8949-AX3-4-1-B Canada Canada Canada USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA Ontario Ontario Ontario Arkansas Arkansas Arkansas Arkansas Florida Florida Georgia Illinois Illinois Indiana Indiana Indiana Indiana Indiana Indiana Indiana Indiana Indiana Indiana Kentucky Louisiana Louisiana Maryland Michigan Michigan Michigan Michigan New York New York New York Ohio Ohio Ohio South Carolina South Carolina Virginia Virginia Virginia Virginia 2Sfifififififiééfiéifiééfiiéfififiéfiééfifififiéééééiéfiéééfi 97 D «P1245583lr PI 367181lr Klein Atlas Quality Pelsart Olympic Triumph Orofen Rulofen Maza mai Chinese Spring lr tibetanum)lr ASA 362 (Luopo Rice; T. petropavlovskyi)lr yunnanerzsz's)lr Trio Heine VII Bastard 11 PI 382070 Funo Villa Glori Abbondanza Glennson Seri 82 Lovrin 21 Kavkaz Suwon 86 Probus Roter Muri Kooperatorka Anza Tomahawk arl92 hatcher Early Premium Windstar ASA 97 (T. a. ssp. ASA 336 (T. a. ssp. Bolulu (PI341629)" Bugday (P1341740)Ir Afghanistan Afghanistan Argentina Australia Australia Australia Australia Chile Chile China China China China China France Germany Germany Iran Italy Italy Italy Mexico Mexico Romania Russia South Korea Switzerland Switzerland Turkey Turkey Ukraine USA USA USA USA USA USA 98 Hindu Kush Mtns. Kabul Buenos Aires New South Wales Queensland Victoria Wesleys Sanfiago Santiago Shanxi Sichuan Tibet Xinjiang Yunnan Nord Saxony Saxony-Anhalt Ilam Emilia-Romaga Latium Tuscany CIMMYT CIMMYT Calarasi Krasnodar Kyonggi Vaud Zurich Bilecik Maras Odesa California Colorado Kansas Minnesota Missouri Nebraska mmfimmmmmmfififi €€M€€M€€€€€€€€mW€€m€MQ€ Merit USA North Dakota 8 TAM 107 USA Texas w Madsen USA Washington w Stephens USA Washington w Brkulj a 4 Yugoslavia Serbia w Tokwe Zimbabwe Harare 8 .1 g Opata 85 Mexico CIMMYT s E E: W7984 Mexico CIMMYT s Lr - landrace; * growth habit information collected from the Germplasm Resource Information Network and personal communication with breeders and CAAS. ** International Center for The Improvement of Maize and Wheat 99 DNA preparation, the AF LP analysis, and data collection were carried out as in Chapter 3. The NTSYSpc version 2.02k software was used to generate genetic distance (GD) matrixes, create dendrograms and corresponding cophenetic matrixes, and calculate matrix correlations (Rohlf 1997). Genetic distance was calculated using the J acaard coefficient. Dendrograms were generated with the unweighted pair-group method using arithmetic averaging (UPGMA), complete linkage, or flexible clustering methods. Intragenomic genetic diversity was analyzed using all AF LP bands and bands previously mapped to specific genomic regions. Principal coordinate analysis (PCO) was also conducted. The matrix correlation (cophenetic correlation) between the original marker-based GD matrix and cophenetic matrix derived from a dendrogram was calculated to test the goodness of fit of a cluster analysis to its originating dataset. Cophenetic matrix correlation values were interpreted as follows: <0.70, very poor fit; 0.7 to 0.8 poor fit; 0.8 to 0.9, good fit; and 0.9 to 1.0, very good fit (Rohlf 1997). The analysis of molecular variance (AMOVA) procedure of ARLEQUIN version 1.1 (Schneider et al. 1997) was used to estimate and test the significance of within and among pool molecular covariances and (DST. Tests of significance were based on ten thousand random permutations of the data for a given AMOVA. 4.3 RESULTS A total of 299 polymorphic bands were scored when DNA samples from the combined set of 123 accessions and the mapping parents (Opata 85 and W7984) were amplified with the eight AF LP primer pairs. One hundred fourteen of these polymorphic 100 bands were previously assigned chromosome map positions in an associated study (Chapter 3). All three genomes were represented by the mapped AF LP loci, but the distribution was skewed, with 56 loci in the B genome, and only 38 and 26 in the A and D genomes, respectively. Analysis of the data for these mapped bands revealed differences in the genetic diversity characteristics of the A, B, and D genomes when the three germplasm sets were considered together or separately (Tables 4-2, 4-3, and 4-4). The frequencies of polymorphic and unique bands varied across genome and germplasm set (Table 4-2). More polymorphic bands were present in the EUS pool than the diverse or China pool. Most (70.9%) of the unique bands were unmapped and none of the seven mapped unique bands was mapped to the B genome. The China pool had two bands not found in either of the other two germplasm sets. Thirteen unique bands were found in the EUS pool. These pool-specific bands occurred in fewer than 11% of their pool’s accessions. Some of the bands found in the diverse collection were absent from the China pool (22 bands), the EUS pool (19 bands), or both (9 bands). There were also differences between the germplasm sets in the relative frequencies of individual bands whose within-pool relative frequencies were neither 0.0 nor 1.0 (Table 4-3). The largest average absolute band frequency differences were evident in loci mapping to the B genome. The average difference in band frequency between the EUS and China pool for each genome was larger than that for either pool versus the diverse set. 101 Table 4-2 The frequency and genome location of polymorphic and unique bands found in each of the germplasm sets. Pool Polymorphic bands Unique bands not found in other pools Genome Genome A B D Uknown A B D Uknown Diverse 35 49 16 167 2 0 2 5 EUS 31 48 16 170 0 0 2 11 China 33 48 14 156 1 0 0 1 Combined 36 49 1 8 1 83 NA NA NA NA Table 4-3 Average absolute band freque ncy differences between sets of wheat accessions using mapped and unmapped AF LP loci. Bands that were monomorphically present or absent from either pool in a comparison were omitted Comparison Genome All loci“ A B D Mapped Unmapped & mapped combined China vs EUS 0.136 0.175 0.138 0.156 0.172 China vs diverse 0.120 0.108 0.062 0.106 0.120 EUS vs Diverse 0.129 0.130 0.112 0.127 0.124 *lncluding mapped and unmapped loci 102 Average genetic distance values (based on the J accard coefficient) were consistently greatest in the A genome and lowest in the D genome (Table 4-4). The relative magnitudes of GD for the A, B, and D genome varied with germplasm set. For example instance, the EUS germplasm pool was more diverse than the China pool by 0.041 when all polymorphic loci were considered, or by 0.009 when GD calculations were based only on loci that map to the B genome. Even grater differences were evident if GD was based either on loci from the A (AGD = 0.085) or the D (AGD = 0.120) genomes. Correlations among the three genome-specific genetic distance matrixes for all 123 accessions were also low (rAB = 0.125, T131) = 0.083, rAD = 0.174). Considerable variation was observed across accessions for genome specific AF LP bands. These are depicted in Figure 4-1, as the differences in GD between the various accessions and Opata 85, Karl 92, and W7984 (Figure 4-1). Overall, GD was slightly lower in the B than A genome, and very little diversity was observed in the D genome. The average GD of accessions using all loci was intermediate to Opata 85 and Karl 92 and high to W7984. The average GD within the A genome was slightly greater than estimates using all bands. While many of the accessions had similar patterns of variability across genomes, a few had distinct patterns. Cultivars Caledonia and Coker 9766 were quite distinct from the standards, while Baiquan 565 and ASA 362 were very similar to Opata 85. Bolulu and ASA 336 had an A genome fingerprint quite similar to W7984. Common wheat shared few bands with W7984. Sumai 173 shared all D genome bands with Opata 85, but shared no D genome bands with W7984. Coker 9766 and Zhongliang 11 had very distinct D genomes from all the common wheat accessions. 103 Table 4-4. Average genetic distance (1- J accard similarity coefficient) within and between sets of wheat accessions using mapped and unmapped AF LP loci. Accessions Genome* All loci“ A B D Mapped Unmapped & mapped combined All accessions 0.569 0.479 0.283 0.474 0.511 Diverse set 0.554 0.468 0.263 0.460 0.494 China 0.515 0.452 0.206 0.430 0.471 EUS 0.600 0.463 0.326 0.485 0.512 China vs Diverse 0.544 0.476 0.229 0.455 0.497 China vs EUS 0.571 0.501 0.306 0.488 0.530 EUS vs Diverse 0.604 0.485 0.334 0.496 0.529 # ofloci 38 56 20 114 299 *Loci are polymorphic in at least one of the accessions in table 1 and may be monomorphic within the EUS, China, or Diverse germplasm sets. MIncluding mapped and unmapped loci 104 Figure 4-1. Genome specific genetic distance estimates between 17 diverse accessions or accession groups and Opata 85, W7984, and Karl 92. Each bar represents level of genetic distance between two accessions or accession groups. Genome Allloci A B D 0'") N :3 N 81- 3 N a N <1- 9 o g 8 0‘ 0° 3 Ch § 3 O\ 00 a -— as a: 1: 0‘ ,6 —. as 7; ex :1. :3 “ 9- N h a. E l‘ 8.. as l‘ o 84 3 0 >4 3 o :4 3 o 84 3 All accession (124) I l I; L I: E] EUS HUI HUI UUT China HHH HHH HUI Diverse set HUI HHU HUI Hopewell HHi UUT Wakefield HUI UHH UUi Caledonia iii“ HUI UHT Coker 9766 H“ iii Baiquan 565 UII HHI HUT 105 HHH HHI HUI HUI 3 HHH ;I ll ‘ HHH HHI 11 1 11 l' 1 Hull HII HHI UUI nese Spring HHH HHI HHI HUT HHI HHI HUI UHI HHI HHI HHI HUT on hang ll HHH HHI HHI HHI °“ HHI IHH HHI HUT AAAAA HHH HHI HHI HUT AAAAAA HHI UHI HUI UHI AAAAAA HHH IIH HHH HHI llllll HUI HUI 0.1T UPGMA cluster analysis of the GD matrix based on all polymorphic bands for all accessions produced a dendrogram with several clusters, pairs and singletons (Figure 4- 2). The fit of the cluster analysis and the GD matrix was interpreted as poor, as the cophentic correlation was low at 0.69. Complete linkage and flexible clustering methods did not generate any dendrograms that improved the cophenetic correlation. However, in most dendrograms, accessions from the EUS and China, clustered in groups ranging from two to 14 accessions. The lower portion of the dendrograms also frequently formed several small clusters, pairs, and singletons. Many of these accessions carry the 1BL/ 1RS wheat rye translocation The PCO biplot depicted in Figure 4-3 illustrates a typical pattern of distinct grouping based on germplasm pool membership, although the proportion of variation explained by each principal coordinate axis was low at nearly 8 percent. The EUS accessions discretely occupy the top left portion of the biplot, while China accessions cluster in the top right section. In addition, the accessions that carry a 1BL/1RS wheat rye translocation cluster in the bottom left region of the biplot. 107 Figure 4-2. Cluster analysis of the genetic distance 123 accessions of wheat estimated using AFLP 108 g 0.598 mvd mmd NNd ood _ _ _ _ _ _ _ _ _ _ _ _ _ _ i. 93.... _ _ 882.8: an=< Ailing—053.55 i 5.32 H :- \. cho~m2m 982: n u! L: 38E. ”Fm—«.3350 :.>>3)J. H :— wQ- :w<‘_ Sm: =< 2.53. v” .= nan—II —r\ :3” .0351‘ I 0343”: «2:0— 320m _Vanm== >> .3qu: 2:. _ REESE scum .lito.0a:_> > 2:» «a sim . U _ C L...” 2.8 JC . .- ON :53: on GNDKC 2 _ 883m 109 Ed 8:88 39.60 mad NNd $06 _ _ _ _ _ _ _ _ _ _ _ _ uunmuoxou mX<éV¢Wmh~$ucc£ .u: r~ mm ”—6000 o l\ o M .— n. a :fi :5 a 1: 83 "' m a: 5 -~. 5.: NS? an: NN—_ :3__nw=.o.= ‘kaf‘? Om CDM" ‘l' .. a 3 $3.... .- n 2_ .aEc: mm “ma—52 :u..:w:A_u:um _ mama... HOW—m 110 Figure 4-3 Principal Coordinate analysis of 124 accessions coded for origin in the legend. O EUS 0 China 0 0 US Great Plains + W. Europe A Italy 0 9 Australia 0 0 I Middle East pnw US Pacific Northwest 0 C Chile ‘ o mums '0.32 I I I I I I l I l I I T f T I V 1 1 fl —0.32 -0.l8 .0 03 0.11 0.25 111 When the AMOVA procedure (Excoffier et al. 1992) was used to characterize differences between the EUS and China germplasm pools, the within- and among-pool covariance estimates were both significant (P<0.001) and accounted for 90.0 and 10.0% of the total variation, respectively (Table 4-3). The degree of pool subdivision appeared robust, as none of ten thousand permutations created a pairwise comparison with greater genetic diversity than the true China and EUS distance. Table 4-5. Analysis of molecular variance of the EUS and China germplasm pools. Source of (if Sum of Variance Percentage of Variation Squares components variation Among population 1 127.70 2.44 9.53* Within populations 84 1942.54 23.13 90.47* Total 85 2070.24 25.56 * - Significant at P < 0.001 4.4 DISCUSSION Common bread wheat evolved from the unification of three genomes in a small geographic region (Dvorak et al. 1998; Talbert et al. 1998). The three genomes appear to have quite different levels of molecular marker polymorphism. Using RFLP, SSR, and AF LP, more polymorphism has been detected in the B genome of T. aestivum than the A and D genome (Chao et a1. 1989; Liu and Tsunewaki 1991; Nelson et al. 1995a; Nelson 112 et al. 1995b; Roder et a1. 1998; Van Deynze et al. 1995). This suggests that the three genomes contribute differentially to the genetic relationships among individual accessions and germplasm pools. In this study, the average frequency of the A genome bands in all accessions observed within was lower than that for B and D genome bands. Average genome-specific genetic distance was highest for the A genome followed by the B and the very narrow D genome. The overall lack of wheat genetic diversity and highly monomorphic D genome supports the notion that T. aestivum was founded from a small number of individuals and that very little variation was introgressed from the D genome donor, Ae. tauchiz' (Dvorak et a1. 1998; Talbert et a1. 1998). The increased level of diversity in the A and B genome is either the product of multiple polyploidization events, or introgression though hybridization of tetraploids or diploids with T. aestivum. However, the direct introgression of the AB or D genome progenitor is unlikely due to infertility barriers (Sharma 1995). Perhaps the most plausible explanation for the differences in genetic diversity across the three genomes, is the formation of multiple polyploids with genetically distinct AB genome donors and genetically similar D genome donors. China has been identified as an area of particularly high genetic diversity in wheat (Ward et a1. 1998) and was believed to be a region of secondary domestication (Yen et al. 1983). When I analyzed a large collection of Chinese wheat accessions alongside a collection of geographically diverse accessions as well as one of the most diverse improved wheat germplasm pools in the world, EUS cultivars, I found that while the magnitude of the difference between these two pools is small, AMOVA and (DST imply that they are still differentiated. Likewise. cluster analysis and PCO of AF LP loci 113 suggest that China and EUS are distinct collections of germplasm. This study and others (see Chapter 1 for review) suggest that geography is an adequate classifier of genetic diversity in wheat. In summary, China contains a relatively diverse pool of improved wheat germplasm, but the diversity characteristic of Chinese landrace pools is not present in the improved Chinese cultivars. The average level of genetic diversity within Chinese and EUS pools is comparable, but the two pools are quite distinct. Map-based analysis of genetic diversity revealed a gradient of diversity across the three genomes. It appears that annotated molecular markers are more useful than random molecular markers in elucidating patterns of diversity. 114 4.5 REFERENCES Barrett BA, Kidwell KK (1998) AF LP-based genetic diversity assessment among wheat cultivars from the Pacific Northwest. Crop Sci 38:1261-1271 Breyne P, Boerjan W, Gerats T, VanMontagu M, VanGysel A (1997) Applications of AFLP(TM) in plant breeding, molecular biology and genetics. Bel J Bot 129:107-117 Chao S, Sharp PJ, Worland AJ, Warham EJ, Koebner RMD, Gale MD (1989) RF LP- based genetic maps of wheat homologous group-7 chromosomes. Theo Appl Genet 78:495-504 Dvorak J, Luo MC, Yang ZL, Zhang HB (1998) The structure of the Aegilops tauschii genepool and the evolution of hexaploid wheat. Theo Appl Genet 97:657-670 Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes - application to human mitochondrial- DNA restriction data. Genetics 131:479-491 Kim HS, Ward RW (2000) Patterns of RF LP-based genetic diversity in germplasm pools of common wheat with different geographical or breeding programs. Euphytica In Press Liu YG, Tsunewaki K (1991) Restriction-fragment-length-polymorphism (RF LP) analysis in wheat .2. linkage maps of the RFLP sites in common wheat. Jpn J Genet 66:617-633 Nelson JC, Sorrells ME, Van Deynze AE, Lu YH, Atkinson M, Bernard M, Leroy P, Faris JD, Anderson JA (1995b) Molecular mapping of wheat: major genes and rearrangements in homoeologous group 4, 5, and 7. Genetics 141 :721-731 Nelson J C, Van Deynze AE, Autrique E, Sorrells ME, Lu YH, Negre S, Bernard M, Leroy P (19953) Molecular mapping of wheat: homoeologous group 3. Genome 38:525- 533 Powell W, Morgante M, Andre C, Hanafey M, Vogel J, Tingey S, Rafalski A (1996) The comparison of RF LP, RAPD, AF LP and SSR (microsatellite) markers for germplasm analysis. Mol Breeding 2:225-238 Rejesus RM, Smale M, VanGinkel M (1996) Wheat breeders' perspectives on genetic diversity and germplasm use: Findings from an international survey. Plant Var Seeds 9:129-147 Roder MS, Korzun V, Wendehake K, Plaschke J, Tixier MH, Leroy P, Ganal MW (1998) A microsatellite map of wheat. Genetics 149:2007-2023 115 Rohlf FJ (1997) NTSYS-pc: numerical taxonomy and multivariate analysis system. version 2.00. Exeter software Setauket New York Schneider S, Kueffer J -M, Roessli D, Excoffier L. Arlequin ver. 1.1: A software for population genetic data analysis. Genetics and Biometry Laboratory, University of Geneva, Switzerland. 1997. Shanna HC (1995) How wide can a wide cross be? Euphytica 82: (1) 43-64 1995. Talbert LE, Smith LY, Blake MK (1998) More than one origin of hexaploid wheat is indicated by sequence comparison of low-copy DNA. Genome 41 :402-407 Van Deynze AE, Dubcovsky J, Gill KS, Nelson JC, Sorrells ME, Dvorak J, Gill BS, Lagudah ES, McCouch SR, Appels R (1995) Molecular-genetic maps for group 1 chromosomes of Triticeae species and their relation to chromosomes in rice and oat. Genome 38245-59 Vos P, Hogers R, Bleeker M, Reij ans M, van de Lee T, Homes M, Frijters A, Pot J, Peleman J, Kuiper M, Zabeuu M (1995) AF LP - a new technique for DNA- fingerprinting. Nuc Acids Res 23:4407-4414 Ward RW, Yang ZL, Kim HS, Yen C (1998) Comparative analyses of RFLP diversity in landraces of T riticum aestivum and collections of T. tauschii from China and southwest Asia. Theo Appl Genet 96:312-318 Yang C, Smale M (1996) Indicators of wheat genetic diversity and germplasm use in the People's Republic of China. NRG Paper 96-04 Mexico, D F : CIMMYT Yen C, Yang ZL, Liu XD, Li LR (1983) The distribution of Aegilops tauschii Cosson in China and with refemce to the origin of the Chinese common wheat. In: Sakamoto S (ed) Proc 6th Intern Wheat Genet Symp Kyoto, Japan, pp 55-58 116 APPENDICES 117 Goo .8.o m8o 8o.o Nooo coo 8oo o8.o 8..o o8.o :3 too o8.o moo goo 8o.o .8o w8o .8.o too o8.o o8.o o8.o wwoo oo..ooémfim. ooo.o .8o ooo.o ~8.o o8.o .8o 83 .8o goo Eoo fioo o8.o wvoo w8o o8.o o8.o o8.o .8.o o8.o 8o.o .8.o 8o.o 8o.o 8..o 8 82.2.43 ooo.o ....o.o ~..o.o ..8o 83 8o.o ...o.o .8o 83 .88 .8o o8.o m8.o Soo .8.o :oo o8.o o8.o 89o o8.o o8.o o8.o Koo a... «8.2.8. ooo.o 8o.o ”woo o8.o .8o .8o .88 o8.o ~..o.o ~..o.o o8.o o8.o o8.o .8.o o8.o 48o o8.o .8... fioo .8.o mwoo oo..o omm gunman. ooo.o w8o .8o 83 o8.o 8..o Nooo 8..o w8o .8.o o8.o .8o o8.o 8o.o ..8o 83 goo o8.o .8.o .8o 8..o 2.6.0:... ooo.o ~8o o8.o fioo ooo.o .8o ooo.o .8.o coo o8.o 8o.o o8.o .8.o .8.o too :oo .8.o ~8.o o8.o ooo.o 8 woosxsm. ooo.o Soo :oo oo..o o8.o .8.o ~8o o8.o o8.o o8.o o8.o .8.o o8.o .Noo o8.o 8o.o o8.o Koo Zoo m.~§..m.o.. ooo.o ooo.o Soo .88 too ooo.o .8o o8.o Nooo o8.o ooo.o Nooo :oo 8o.o 89o oo..o o8.o o...o .m. 888%.... ooo.o .8o too 83 «So o8.o ioo o8.o o8.o :oo wwoo :oo ooo.o :oo o8.o o8.o oo..o 3.8.03.2. ooo.o mooo o8.o 33 «So Boo .voo .8.o .8o 8..o 8..o ooo.o too 83 o8.o 2.8 88.53.... ooo.o o8.o Soo 8oo 8o.o Nooo o8.o :oo o8.o ~8.o ~8o 8o.o .8.o moo 2.8 on... 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N8... .3.... S... .m. o8.o .8.o o8.o .8.o bamboo... .8.o So... ooo.o 8.8.... .m. «co... .8.... «52.85. AN. 8.... 83.5%.... C. 3.. 2.. 3m. 3.. 3.. .2. 3.. 3.. 3.. 3.. 3.. .2. .o.. 3. 3. 3. 3. 3. 3. 3. .N. ... .8658”. 85 a... mafia .8...—:28 083 . 8.85.8 =< .8...... .8888 (.0 Boom 883.5% mm .892»... E... 55.3 88:58 8.88:. cameow :8... 889.3: .m x68mm< m .3sz93. 119 APPENDIX C AF LP protocol This protocol is an amalgamation of protocols used and provided by Vos et al., Brent Barrett (WSU), Greg Penner (Ag CA), A.S. Reddy (Texas A&M), Gibco BRL, and others as well as additions and changes made in Wheat lab. 1. Restriction Digestion of Genomic DNA OnePhorAll OP Pharrnacia Mse I ew En land Biolabs , Eco RI GibcoBRL r Pst I BSA comes w/ Mse ddeO Genomic DNA Total 1. Heat one oven to 700 and the other to 370. Distribute 40ul of cocktail in each labeled tube. Add 10p] of DNA to each tube. PP!" Vortex and briefly centrifuge. 5. Incubate @ 370 for 3 hours. Agitate every hour or so. I do a quick vortex so the sample stays in bottom of tube and centrifugation is not needed. 6. Inactivate enzyme @ 700 for 15 min. II. Adapter preparation Complete during or before digestion Eco RI Adapter (120 ligation recipe) Eco RI.1 oli 1 Eco RI.2 oli 1 OPA 120 [[ddeo 1| 107.6pl Mse I Adapter (120 ligation recipe)* Mse I.1 oli o 0.5 64.0 1 MseI.201i o 0.5 56.0 1 OPA 7.0 l *Mse I oligos may need to be speed vacuumed in order to increase concentration. Mix in thermocycler tubes and run file #44. 650C for 10 min. 37°C for 10 min. 25°C for 10 min. Store at -200C. III. Ligation of Adapters. Per rxn EcoRI ter 1 l Msel ter 1 1 T4 DNA 1i ase 10X buffer 1 1 T4 DNA 1i 3U/ l 0.33 l ‘ddH O 6.7 l 1. Add 10 ul of ligation mix to 50 ul of digested DNA. Vortex and briefly centrifuge. 2. Incubate at room temperature for 3 hrs. Agitate every hour or so as above. IV. Pre amplification Reactions Eco R1 + A oli 50m 1 Mse I + C oli 50m 1 dNTPs 5mM Gibco as 1 10X PCR buffer w/ T T l erase 5U/ l M l w/ T 121 lam; 1-31.9.1» . . 1. Template DNA from restriction/ligation 1 2p] Thermocycler file #36 94 2 min 94 w 1 min 5.6 1 min. , 26 cycles 72 1 min. ’72 i 5 min. . 4“ _ _\ hold 1. Transfer PCR product into new tubes with 100111 sterile ddeO. 2. Blot testing can test reaction success. Dot 2u1 of Ethidium bromide (2ug/ul) and 3ul of product on plexi-glass. Use 3 pl of cocktail as control. Visualize dots using UV box. V. Selective amplification Eco R1 + ANN oh 0 50h 1 Mse I + CNN oli 50n_ l dNTPs 5mM Gibco as 1 10X PCR buffer w/ T T l erase 5U/ l,Prom M 12 w/ T ddH O Dilute t late DNA from -selective PCR Thermocycler file #20 194‘" 12...... .911 WWW _- _ 395 12 cycles, 65 305 decrease ' . annealing 72 1 mm. temp by 0.7 m each 122 23 cycles 3. Test product using dot blot if necessary. 4. Combine 8u1 formamide-loading buffer and PCR product. VI. Gel electrophoresis 1. Acrylamide gel solution 42g Urea 10ml 10x TBE 15m140% acrylamide water up to 100mls 2. Combine urea, TBE, and approx. 25ml water in a beaker. Stir with heat until urea dissolves. 3. Transfer solution to IOOmI-graduated cylinder and add water up to 85ml. Transfer to vacuum flask. 4. Add acrylamide to flask and degas for approx. 10min. 5. Transfer solution to beaker and add 100p] TEMED and 500111 10% fresh APS. Draw solution into syringe. Keep tip submerged at all times. 6. Place tube on syringe and turn it upward. Push air out of tube and pinch end of tube. Insert into Caster base. 7. Glass preparation (all glass must be scrupulously clean!) 8. Wipe IPC unit with chem-wipe and ethanol. 9. Glass should be treated with Sigmacote about every 5 gels run or until top of gel sticks to long glass. Saturate a chem-wipe with Sigmacote and wipe vertically and 123 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. horizontally. Wait five minutes and wipe glass three times with ethanol. Change gloves. Wipe long glass with ethanol and chem-wipe. In an Eppindorf tube combine lml of 95%EtOH 0.05 %Acetic acid and 2p] of bind silane. Treat glass same as above description of Sigmacote. Use a great deal of pressure when wiping with EtOH. Change gloves. In the event of a contamination of either Si gmacote or Bind silane on the respective glass, soak in 10%NaOH. While horizontal, place spacers on IPC and long glass on top. Erect vertically and clamp side braces. Attach Caster base insert pegs and turn. Be sure to do this while vertical and that you can see the space in between glass plates through Caster base hole. Check to see if comb will easily insert between glass. If not, adjust. Lean clamps on top of tube racks. Inject gel solution. Insert combs and adjust unit to horizontal position. Allow gel to polymerize for at least 1 hour. VII. Gel loading 1. 99’!" Fill bottom tray with 1X TBE so about ‘/2 inch of the bottom of gel unit is submerged. Fill IPC until ‘/2 inch above short glass. Use needle and flush out well Run gel at 75W for 1hr. Flush well again and insert comb without piercing gel. Load 4.5ul sample. Run gel for 10min and then remove comb (good time to make fix/stop and developing solution). Run gel for total of 2hr and 50min. (Light blue dye should migrate 1 inch below bottom rib of IPC. Insert tube in IPC and drain buffer. Pull glass apart and wash IPC. 124 VHI Silver Staining From Promega Technical Manual and Sam and Suzanne Downey empirical knowledge. 1. Separate plates while keeping the gel attached to short glass. 2. Fix the gel: Place gel in tray, cover with cold fix/stop solution and agitate well for 20 minutes. Gel may be stored in fix/stop solution overnight. Save fix/stop solution and place back in freezer. 3. Wash the gel: Rinse the gel 3 times for 2-3 min. each in ddHZO using agitation. Lift gel from solution and allow to drain 10-20 seconds. 4. Stain the gel: Transfer the gel to staining solution and agitate well for 30 minutes. 5. Pour 1L of the developing solution into a tray. Transfer staining solution to beaker. Rinse tray and fill with ddHZO. 6. Rinse gel for 5-10 seconds ONLY. Transfer to developing solution. 7. Agitate in developing solution until bands begin to appear. Transfer gel to remaining chilled developing solution for 2-3 minutes. 8. Fix the gel: add 1L of Fix/stop solution directly to developing solution and agitate for 2-3 minutes 9. Rinse gel twice for two minutes each in ddeO. 10. Dry gel on glass Fix/stop solution Staining solution 200ml of glacial acidic acid 2g (1 packet) of silver nitrate (AgNO3) 1,800ml ultrapure water 3m] (1 vial) of 37% Formaldehyde Freeze for approx. 3 hours 2L ultrapure water 125 Developing solution 60g (1 packet) Sodium Carbonate (N azCO3) 2L ultrapure water **chill to 10°C. I place sol. In freezer for approx 4 hours and stir to break up ice prior to USC. Immediately before use add 3m] (1 vial) of 37% Formaldehyde 400m 1 aliquot Sodium Thiosulfate (discard remaining) IX. Gel scoring and scanning. 1. Scan gel in two sections without two options selected. Save as compressed tif and jpg- Score gel while still on glass and make notes on printout of scanned image. Keep original score sheet and gel printout in folder Record gel in record form with all pertinent details. X. Gel preservation Soak gel in 3% NaOH with gentle agitation for 30 to 60min, or until edge of comer of the gel starts coming loose. If gel does not come loose, tease a corner and pull gently. If it peels easily, gel is ready for transfer. Loosen edges with razor blade to facilitate transfer. Carefully transfer gel to 3.5% acetic acid and soak for 3 min without agitation. Rinse in ddHZO for 2 minutes without agitation. Drain excess water from gel and smooth a sheet of chromatography paper over gel. Very slowly pull edge or corner up while gel adheres to paper. Use a razor blade to persuade any lagging parts of the gel. Cover gel with plastic wrap and dry on gel dryer at 70° for 2 hrs. Oligo Fragments EcoRI Linker 1 CTC GTA GAC TGC GTA CC EcoRI Linker 2 AAT TGG TAC GCA GTC TAC EcoRI +A GAC TGC GTA CCA ATT CA Pst I Linker 1 CTC GTA GAC TGC GTA CAT GCA Pst I Linker 2 TGT ACG CAG TCT AC 126 Pst I +A GAC TGC GTA CAT GCA GAC A Mse I Linker 1 GAC GAT GAG TCC TGA G Mse I Linker 1 TAC TCA GGA CTC AT Mse I +C GAT GAG TCC TGA GTA AC 127 “11111111111111“