Mapping genes for yield and quality in barley ; a practical test of QTL analysis by Ji-ung Jeung A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Plant Sciences Montana State University © Copyright by Ji-ung Jeung (2000) Abstract: Genes modifying traits affecting agronomic performance and grain quality have been characterized using the techniques of quantitative trait locus (QTL) analysis. These have been most generally attempted in model plant and animal populations which are most often characterized by parents which are genetically quite different. Plant breeders improve crops most efficiently using parents which are more closely related, but which differ for a few important genes. In this project, three populations derived from a single relatively narrow plant breeding cross were developed and evaluated to determine whether QTL analysis could be productively utilized within the context of a practically-oriented plant improvement program. There are several problems associated with narrow parental divergence and genomewide genetic analysis. Generating a representative linlcage map is challenging and may indeed be impossible. Measuring and interpreting the actions of genes with relatively small effects, especially linked genes, is problematic as well. One of the three populations was used to generate a linkage map comprising 50 anchor markers and 191 AFLP markers. The map covered all seven barley chromosomes with an average distance between markers of 12cM. QTL analysis was based on results gathered from three years’ replicated field experiments and initially the map constructed in the ‘Fiber-2' mapping population. A substantial proportion of the variance for grain yield, βglucan content, flowering date and plant height was accounted for by a few, relatively well-marked genes. Composite interval mapping provided a clearer picture of the impacts of these genes on phenotype than did simple interval mapping, and strongly supported the contention that the n and Ik2 genes both impacted grain glucan content. A search for digenic interactions suggested that the waxy gene acted as a controller of gene expression for flowering date and plant height, a result not previously noted.  MAPPING GENES FOR YIELD AND QUALITY IN BARLEY; A PRACTICAL TEST OF QTL ANALYSIS ) by Ji-ung Jeurig A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Plant Sciences MONTANA STATE UNIVERSITY-B OZEMAN Bozeman, Montana March 2000 APPROVAL of a thesis submitted by Ji-ung Jeung This thesis has been read by each member of the thesis committee and has been found to be satisfactory regarding content, English usage, format, citations, bibliographic style, and consistency, and is ready for submission to the College of Graduate Studies. Thomas K. Blake (Signature) Date Norman F. Weeden Approved for the Department of Plant Sciences Date Approved for the College of Graduate Studies Bruce R. McLeod (Signature) 3 - 3 / Date Ill STATEMENT OF PERMISSION TO USE In presenting this thesis in partial fulfillment of the requirement for a doctoral degree at Montana State University-Bozeman, I agree that the Library shall make it available to borrowers under rules of the Library. I further agree that copying of this thesis is allowable only for scholarly purpose, consistent with “fair use” as prescribed in the U.S. Copyright Law. Requests for extensive copying or reproduction of this thesis should be referred to University Microfilms International, 300 North Zeeb Road, Ann Arbor, Michigan 48106, to whom I have granted “the exclusive right to reproduce and distribute my dissertation in and from microform along with the non-exclusive right to reproduce and distribute my abstract in any format in whole or in part.” Date iv ACKNOWLEDGMENTS I sincerely appreciate the assistance and guide of my major advisor, Dr. Tom Blake, who encouraged me to finish my study. I would also thank other committee members; Dr Michael Giroux, Dr. Richard Stout, Dr. Adam Rickman, and Dr. Luther Talbert for their help and advice on this study. I also wish to acknowledge Rural Development Administration Republic of Korea for funding this research and the Montana Agriculture Experiment Station, especially Pat Hensleigh, for helping me to conduct the field trials. I would express sincere thanks to the lab coworkers for their help in working on my study and overcoming the problems I met; Vladimer Kanazin, Deven See, and especially Elope Talbert. I am especially grateful to Jihye Lee, my wife, for her patience, support, and prayers. TABLE OF CONTENTS Page LIST OF TABLES................................................................................................................... viii LIST OF FIGURES..................................................................................................................... x ABSTRACT............................................................................................................................ xii I. INTRODUCTION ................................................................................................................ I Waxy - Hulless Barley for Human F o o d ......................................................................I Gene Sources of Waxy-Hulless B a r le y ....................................................................... 2 Genetic and Physiological Effects of wx, n and Ik2 .................................................. 3 Barley Endosperm and the Waxy Gene ........................................................ 3 p-D-Glucans ......................................................................................................5 Awn L en g th ........................................................................................................7 Hulless S eed ........................................................................................................ 8 Genetic Analysis to Investigate the Genetic Effects of wx, Ik2, and n on Glucan Content and Yield Components ............................................................ 9 Isogenic Line Approaches................................................................................ 9 Random Inbred Line Approaches ................................................................. 11 Limitations of Previous S tudies.................................................................................. 12 Isogenic Line Based Genetical Approaches..................................................12 Random Inbred Line Approaches . ..................................................... , 1 2 Current Status and Considerations.............................................................................. 13 2. LINKAGE MAP CONSTRUCTION IN A REAL BREEDING POPULATION; MTaz90-123 x MTH6860756 ......................................................................................... 15 In troduction ................................................................................................................... 15 Materials and M ethods..................................................................................................18 Population D evelopment................................................................................ 18 Morphological and Protein Markers .............................................................18 PCR Amplification and Polymorphism Detection of STS and SSR Markers ................................................................................ 19 AFLP Reactions and Polymorphism Detection........................................... 20 Genotyping of Progeny Lines ....................................................................... 22 Linkage Map Construction ............................................................................22 TABLE OF CONTENTS-continued Page Results ............................................................................................................................25 STS and SSR M arkers .................................................................................... 25 AFLP M arkers ................................................................................................. 30 Segregation Test of All Informative Markers ............................................. 35 First Attempt to Construct ‘Fiber-21 Linlcage Map .....................................36 Increasing AFLP Marker Utility with Band Size Comparative A na ly sis ...............................................................37 Construction of ‘Fiber-2' Linkage Map Skeleton .......................................39 Pseudo-Iinlcage Separation..............................................................................42 Linleage Map Construction with All Available In form ation ..................... 42 Discussion ..................................................................................................................... 46 3. MAPPING QTL CONTROLLING IMPORTANT CHARACTERS IN THE FIBER-2 POPULATION.................................................................................. 49 In troduction ...................................................................................................................49 Materials and M ethods................................................................................................. 52 Plant M ateria ls................................................................................................. 52 Field Experiments ...........................................................................................52 Analysis of Agronomic T ra i t s ....................................................................... 54 Single Marker QTL Analysis............. ............................................................55 QTL Mapping ................................................................................................. 55 Estimation of Additive Effects of Detected QTLs.......................................56 R e su lts .................................................................... 58 Analysis of Agronomic T ra i ts ....................................................................... 58 Single Marker QTL Analysis of ‘Fiber-21 Mapping Lines ....................... 59 QTL Detection with Interval Mapping; SIM and C IM ..............................64 QTLs for Grain Y ie ld ..........................................................................68 QTLs for Kernel Weight ...................................................................74 QTLs for p-D-glucan con ten ts.......................................................... 78 QTLs for heading date and plant height........................................... 79 Discussion ..................................................................................................................... 86 TABLE OF CONTENTS-continued Page 4. EVALUATION OF GENETIC EFFECTS UNDERLYING MAJOR QTL IN THE FIBER-2 POPULATION................. 89 In troduction ............................................................................ 89 Materials and M ethods................................................................................................. 92 Population Merging and Data Preparation....................................................92 Statistical Analysis...........................................................................................93 R e su lts ............................................................................................................................94 Correlation among traits ................................................................................ 94 Estimation of Hull Weights and Evaluation of Their Attributions to Grain T ra its ...............................................................97 Main Effect Estimation of a Locus within a Specific Genetic Background.................................................................................. 101 Discussion ................................................................................................................... 107 LITERATURE CITED .......................................................................................................... HO APPEND IX ...................... 123 Table A -I......................................................................... 124 Table A-2....................................................................................................................... 134 Table A-3....................................................................................................................... 143 vii V lll LIST OF TABLES Table Page 1. Morphological and biochemical characters evaluated ............................................. 19 2. List o f informative STS markers................................................................................ 27 3. List o f informative SSR markers ..............................................................................29 4. . List of informative AFLP m a rk e rs ............................................................................. 32 5. Distribution of AFLP markers .................................................................................. 34 6. The construction of linlcage map skeleton with previously reported polymorphic markers and their comparative locations on ‘Fiber-2' linkage map .........................................................................................40 7. Description of agronomic traits measured and field trial conditions ................... 53 8. Descriptive statistics of agronomic traits m easu red ................................................60 9. ANOVA for agronomic traits of the ‘Fiber’ populations in each year t r ia ls ......................................................................................................62 10. ANOVA for agronomic traits measured in ‘Fiber-2' and ‘Fiber-31 populations over seasons ............................................................... 63 11. Empirical threshold values estimated for simple- and composite- interval m apping........................................................... 65 12. Summary of detected QTLs with simple interval m apping .....................................75 13. Summary of detected QTLs with composite interval m app ing ...............................81 14. Phenotypic correlation coefficients among traits measured based on means of ‘Fiber-21 mapping population ............................................... 94 15. Phenotypic correlation coefficients among ‘grain traits’ based on the means o f 4 inferred genotypes in the ‘Fiber-2' population ........................ 96 LIST OF TABLES-continued Table Page 16. Hull weight estimation and its contributions to kernel weight in the ‘Fiber-2' popu la tion .......................................................................................98 17. Surveying interactions between major QTLs by means of ‘main effect estimation of a locus within a specific genetic background’ . . . 104 18. Phenotypic correlation coefficients among three traits in the ‘Fiber (merged)’ population based on means of two inferred waxy genotypes................................................................................ 108 A -I. Segregation, %2 goodness-of-fit analysis and genotypes of 241 loci o f 59 mapping lines in the ‘Fiber-21 population..................................................124 A-2. Marker intervals of the ‘Fiber-2' linlcage map with the results of single marker QTL analysis on traits measured................................................ 134 A-3. The amount of maternal genome (MTaz990-123) in 59 lines o f the ‘Fiber-21 mapping population.................................................... 143 ix XLIST OF FIGURES Figure Page 1. Examples of amplification products and segregation patterns of STS-PCRs in the ‘Fiber-2' mapping population..............................................26 2. Examples of amplification products and segregation patterns of SSR-PCRs in the ‘Fiber-2' mapping population..............................................28 3. Detection and databasing of informative AFLPs. Amplified AFLPs, with the combination of Mse I and fluorescent labeled Pst I primer set, were detected using computer software Genographer............... 31 4. Increasing AFLP marker utility with comparative analysis. AFLP markers which fell into ‘orphan’ linlcage groups were evaluated for potential placement on the ‘Fiber-21 linlcage map through comparative analysis using the ‘ Steptoe x Morex’ population ......................38 5. Linlcage map of the ‘Fiber-2 (MTaz90-123 x MTH6860756)’ mapping population. Linlcage map containes 199 markers (3 morphological, 2 protein, 30 STS, 14 SSR, and 150 AFLP markers) and covers a distance of 2370 cM. ..................................................................... 45 6-A- The chromosomal locations of putative QTLs for simple interval mapping. Arrows on chromosome I and 2 show the putative locations of QTLs associated with each trait surveyed.........................................................66 6-B. Scans of a test statistic for simple interval m apping.............................................67 7. Distribution of background markers (cofactor) used in composite interval mapping. Background markers were selected with ‘Forward and Backward stepwise regression’ ....................................... 70 8-A. The chromosomal locations of detected putative QTLs for composite interval mapping. Arrows on chromosomes show the putative locations of QTLs associated with each trait surveyed............. 71 ' 8-B. Scans of a test statistic for composite interval m apping ................................ 72 Figure 9. 10. xi LIST OF FIGURES-continued Page Graphical approaches to evaluate the genetic effects of the n locus region and differentiate two closely linlced genes, n and Ik2 . . 100 Cumulative distributions of heading date (HD99 trial) for the ‘Fiber (merged)’ individuals with particular genotypes at the Ik2 and wx l o c i ......................................................................................... 101 X ll ABSTRACT Genes modifying traits affecting agronomic performance and grain quality have been characterized using the techniques of quantitative trait locus (QTL) analysis. These have been most generally attempted in model plant and animal populations which are most often characterized by parents which are genetically quite different. Plant breeders improve crops most efficiently using parents which are more closely related, but which differ for a few important genes. In this project, three populations derived from a single relatively narrow plant breeding cross were developed and evaluated to determine whether QTL analysis could be productively utilized within the context of a practically-oriented plant improvement program. There are several problems associated with narrow parental divergence and genome­ wide genetic analysis. Generating a representative linlcage map is challenging and may indeed be impossible. Measuring and interpreting the actions of genes with relatively small effects, especially linlced genes, is problematic as well. One of the three populations was used to generate a linleage map comprising 50 anchor markers and 191 AFLP markers. The map covered all seven barley chromosomes with an average distance between markers of 12cM. QTL analysis was based on results gathered from three years’ replicated field experiments and initially the map constructed in the ‘Fiber-2' mapping population. A substantial proportion o f the variance for grain yield, /Lglucan content, flowering date and plant height was accounted for by a few, relatively well-marked genes. Composite interval mapping provided a clearer picture of the impacts of these genes on phenotype than did simple interval mapping, and strongly supported the contention that the n and Ik2 genes both impacted grain glucan content. A search for digenic interactions suggested that the waxy gene acted as a controller o f gene expression for flowering date and plant height, a result not previously noted. CHAPTER I INTRODUCTION Waxy - Hulless Bariev for Human Food Barley (Hordeum vulgare L.) is primarily used in the malting and brewing industry and for animal feed. Only 2% of U.S. barley is used for food (Berglund et al, 1992). Barley is available in both hulled and hulless types. Hulless barleys have some advantages for food uses, because hulless barley does not require pearling prior to consumption. Hulless barley can be milled directly to obtain a meal, or it may be steamed or boiled and directly consumed (Bhatty, 1986). Where barley is a major part of the human diet, naked types are preferred. Hulless barley is now primarily used as food in subsistence farming mountain regions o f the world because people generally prefer and purchase rice and wheat as their economic conditions improve (Bhatty, 1986). Waxy barley has been reported to contain high levels of soluble fiber, especially P-D- glucan (Newman et al, 1990; Bengtsson et a/.,1990; Oscarsson et a l, 1997). Soluble fiber has been shown to reduce serum cholesterol levels in rats (Hecker et a l, 1998), chicks (Bengtsson et a l, 1990), and humans (Newman,1989). Besides the cholesterol-lowering 2effects, possible positive effects on mineral and vitamin bioavailability, blood glucose levels and colon cancer have been suggested (Aman et al, 1989). Due to the high water-holding capacity of soluble, fiber, gumminess occurs when high levels o f hulless barley are incorporated in food. There have been various attempts to determine the acceptability of a wide variety of food products in which waxy-hulless barely is incorporated (Berglund et al, 1992), and evaluate the use of a barley |3-D-glucan concentrate as a fiber-enriching food ingredient (Hecker et al, 1998). Gene Sources of Waxv-Hulless Bariev Two recessive genes - hulless(n) and waxy(wc) - have been utilized in barley to produce hulless, high fiber food barley. A gene reducing awn length(//c2) was also incorporated into many early hulless, waxy lines, initially as an easy-to-see morphological I marker. Awn length reduction could have a pleiotropic positive effect on grain fiber content by reducing late season photosynthesis and starch deposition. Hulless barleys are distributed widely in the world, and were independently selected by early agriculturalists in many subsistence farming environments. The relative frequencies of naked forms tended to decrease steadily towards the west from the east. Hulless types are infrequently found in Europe where hulless barley was used very little (Takahashi, 1955) The physiological importance of the awn in small grains has been pointed out (Grundbacher, 1963; Paluska, 1979). The discomfort of handling rough-awned varieties led breeders to produce high-yielding, smooth-awned varieties of barley (Hayes and Wilcox, 31922). Short awned and smooth awned varieties are common in southern Korea, Japan, and China (Takahashi, 1955). A series of barley isogenic lines was developed from Compana (Cl 543 8;2 row, feed barley), Betzes (Cl 6398; 2 row, malting barley), and Titan (Cl 7055; 6 row, feed barley) cultivars with varying combinations of the non-waxy (WxWx), waxy (wxm>x), hulled (NN), hulless (nn) and long awn (Lk2Lk2) and short awn (Ik2Ik2) genes by Dr. R. F. Eslick and Dr. E. A. Hockett (described in Fox, 1981; Xue et al., 1997). The original genetic sources were Waxy Oderbrucker for m>xwx, and Sermo' for nn and Ik2Ik2. Consequently, there are eight (23) different genotypes in each isogenic series (Fox, 1981). These three genes are all carried on barley chromosome I, with Ik2 and n in close proximity on the long arm (Kleinhofs, 1996). Genetic and Physiological Effects of hoc, n and Ik1 Bariev Endosperm and the Waxy Gene Waxy mutant types can be distinguished from normal types by iodine staining. Waxy mutant types stain red while normal types produce a deep blue color due to the interaction between iodine and unbranched amylose. i Although the main carbon source used in grain development is sucrose. There have been multiple reports supporting that sucrose supply during the development o f barley endosperm may not be limiting even under environmental conditions in which starch 4deposition is reduced (Chevalier and Lingel, 1983; Felker et al., 1984; MacLeod and Duffus, 1988). Normal barley endosperm contains about 25% amylose and 75% amylopectin. The starch of mature barley endosperm shows various amylose - amylopectin ratios from low (about 2% amylose, waxy) to high (about 40% amylose, high-amylose) depending on genotype (Oscarsson et al, 1997). In barley endosperm, starch is deposited into membrane-bound granules, and these granules are classified according their size and shape: type A (lenticular, 1 0 -48/un) and type B (spherical, I -10/un). The large A-granules contain about 90% of the starch by volume (Borem et al, 1999). The large A-granules increase in number and size during the early stages of grain development, then the number is constant and the size is increased in late stages. The small B-granules increase in number throughout the middle and late stages (McDonald et a/., 1991). In wheat, one model has been developed for the wheat A-type granule in which there is a core of very-low-amylose starch containing little lipid and an outer shell o f high-amylose starch with a comparatively high lipid content. This pattern appears to be followed in barley as well (McDonald et a/., 1991). There is now good evidence indicating that the waxy gene is the structural gene for the granule-bound, ADP-glucose-dependent, starch synthase I isoform, and that this enzyme is responsible for amylose synthesis (John, 1992). The barley wx locus was cloned by Rohde et al (1988). McDonald et al (1991) and Oscarsson et al. (1997) observed that the numbers o f A-type granules per endosperm was significantly higher in waxy barleys than in wild 5types, and suggested that the B-type starch granules of waxy genotypes represented a smaller proportion o f the total starch at maturity in waxy lines. Oliveria et al (1994) found that the surface area and volume of A- and B-type granules was generally greater for malting cultivars than feed cultivars. Borem et al. (1999) conducted QTL analysis with the Steptoe (feed cultivar) x Morex (malting cultivar) mapping population on the size, shapes and relative proportions of A- and B-type granules to estimate the relationship between starch granule characters and the quality of barley malting for brewing. Although they detected QTLs affecting the starch granule characters, and the alleles from Morex contributed to larger granules, those QTLs were not associated with malting quality. In an X-ray diffraction study, the amylose-lipid complex was most obvious in high- amylose starch genotypes. Waxy genotypes did not show an amylose-lipid complex (Czuchajowska et a l, 1998). Czuchajowska et a/.(1998) also observed that waxy barley contained significantly more high-molecular-weight-amylopectin and low-molecular-weight- amylopectin but less intermediate-molecular-weight-amylopectin than did nonwaxy and high-amylose barley. p-D-GIucans Over 96% of total grain cellulose is present in the barley hull with very little found in the endosperm cell walls. Branched chain (f-(l ->-3; I ->4)-D-glucans (|3-D-glucans) are major components of barley and oat endosperm cell walls. Linear polymers with |3-(l->3) and |3-(l->4) linlcages are also found (Izydorczyk et al, 1998). The amount of p-D-glucan \ 6in barley seed is generally 3-7%, which is mostly present in the endosperm cell walls; the starch endosperm cell walls contain around 70% (3-D-glncan and 20% arabinoxylan, whereas the aleurone cell walls contain about 26% JB-D-glucan and 67% arabinoxylan (Ballance and Manners, 1978; Aman et al, 1989). The |3-D-glucan deposition occurs relatively late in grain development (Aman et al, 1989). There are two methods used to measure (l-D-glucan content of barley grain. [3-D- glucan may be extracted in either a highly acidic or basic solution and content estimated by measurement of viscosity of the extract. The results vary according to the conditions of extraction (Fleming and Kawakami, 1977). [3-D-glucan may be more directly measured with a streamlined enzymatic method (McCleary and Mugford, 1997). Following extraction and hydrolysis, glucose (i.e., degraded (3-D-glucan) content is measured optically by addition of a glucose oxidase reagent. The (3-D-glucans of barley have long been a problem for poultry feed manufacturers and the malting and brewing industries. In the former case, barley (3-D-glucans contributes to the small intestinal viscosity of chicks and the effect was related to altered lipid metabolism as well as reduced lipid and protein digestibility. In the latter case, the high viscosity of barley |3-D-glucan solutions can cause significant brewing problems associated with increased filtration times and reduced brewing yield (Bamforth and Barclay, 1993). Improved malting varieties have lower levels of (3-D-glucans and a higher proportion of C- hordein (Ellis et al., 1997). The malting and brewing industries have been interested in (3-D- glucan levels in barley varieties and in the potential of grain to produce rapidly (3-D-glucan hydrolases during germination. Two barley |3-D-glucanase isoenzymes EI and EU have been 7reported as single or low copy genes. They have been localized on barley chromosomes, and cloned. EI is on barley chromosome 5 (Slakesld et al, 1990), and EU is on barley chromosome I (Loi et a l, 1988; Hoj et al., 1989). Awn Length Awns are apical extensions of the lemma. They contain three vascular bundles and are active photosynthetic organs in small grains (Grundbacher, 1963; Paluska, 1979). Eslick and Hockett (1967) reported that Ik2 (short awn) is located on the long arm of barley chromosome I . Awnless(Zk) is on the long arm of chromosome 2, and tightly linlced to v locus and both Ik2 and Ik are epistatic to the hooded gene k (Milan, 1964). Another short awn gene(//Cj), lies on the short arm of chromosome 4, and may affect the rachilla, causing it to form additional florets (Tsuchiya and Hall, 1978). Awn barbing is conditioned by r , which results in lack of teeth on the awn (Milan, 1964). It lies on the long arm of chromosome 7. In long-awned genotypes, the awns account up to 90% of CO2 exchange rate but only 30% of the spike’s dark respiration rate (Kjack and Witters, 1974). Therefore, awns have the additional advantage of being the youngest photosynthetic tissue on the plant and they remain photosynthetically active throughout the grain-filling period (Johnson et al. 1975). Weyhrich et al. (1994) found no pattern in yield superiority of either awn types (awned or awnletted). However, previous investigations suggest that awns were are favorable under semi-arid environments and stress conditions (Qualset et al, 1965). 8Hulless Seed Kernel characters are widely used for distinguishing one cultivar from another. ■ Cultivated barleys can be classified into those in which the lemma and the palea are tightly fused to the pericarp of the caryopsis - covered or hulled barley-, and those in which the chaff is easily separated from the grain - naked or hulless barley- (Briggs, 1978). Helbaek (1966) estimated that hulless barley also appeared 8,000 years ago when six-rowed landraces (vv) were derived from two-rowed races (V-) by early agriculturalists. The caryopsis character is controlled by one gene with two common alleles (N and n) (Milan, 1964) and the hulless phenotype can easily be moved into hulled barley strains by repeated backcrossing. The naked caryopsis gene (n) was mapped by Heun ei al. (1991) in Proctor x Nudinlca 2.9 cM down from RFLP probe CD0673 and cosegregating with RFLP probe BG143. This places it near the centromere and 3.5 cM from Amy2 locus in the Steptoe x Morex linlcage map (Kleinhofs, 1996). Gaines et al. (1985) showed that the cementing substance is produced by the undifferentiated pericarp only two days after flowering. Since the hulls of naked lines are removed easily during threshing, kernel weight and yield reductions of hulless types are proportional to at least the weight of hulls (8-16% of the kernel weight) (Briggs, 197 8), and the proportion of other seed components mainly cellulose, hemicellulose, lignin, and a small quantity o f protein also changes accordingly (McGuire and Hockett, 1981; Newman et a l, 1990). Murphy and Witcombe (1981, 1986a) reported that Nepal, with its huge range of hulless land races, is the most likely place for the origin of the hulless form. Recently, Hang 9et al. (1996) conducted a ‘ cluster analysis ’ on thirty six hulless barely accessions from North America, China, Turkey and Central Asia using RAPD markers to estimate the genetic similarities among accessions. The accessions from China, Turkey and Central Asiabelong to one main group that could be divided into three sub-clusters while most of the accessions from North America are closely related and belong to one well-defined cluster. Genetic Analysis to Investigate the Genetic Effects of wx, Ikn and n on GIucan Content and Yield Components Isogenic Line Approaches Atkins and Mangelsdorf (1942) proposed to use ‘Isogenic lines’ in studying of gene effect on traits of interest, because it provided an essential uniform genetic background thereby avoiding subsidiary effects caused by various treatments. To investigate the gene effects of three genes on traits of interest, a series of six barley isogenic lines was developed from Compana (Cl 5438; 2 row, feed barley), Betzes (Cl 6398; 2 row, malting barley), and Titan (Cl 7055; 6 row, feed barley) cultivars with varying combinations o f the non-waxy ( WxWx), waxy (wxwx), hulled (NN), hulless (nn) and long awn (Lk2Lk2) and short awn (Ik2Ik2) genes in a seven backcross breeding program by Eslick and Hockett. McGuire and Hockett (1981) observed that kernel weights were significantly reduced in the short-awn isotypes in comparison of short-awn (Ik2Ik2) versus long Scwn(Lk2Lk2) isotypes o f hulled and hulless barley. Awn length, however, had no obvious effect on grain 10 yield. McGuire and Hockett (1981) tried to estimate the influence o f different awn lengths (Lk2', long awn versus Ik2, short awn) and caryopsis types (IV; hulled versus hulless) on yield with isogenic four Betzes lines. They observed that genotypes with the hulled allele averaged about 10% greater yield than hulless genotypes, and the length of awn showed a linear relationship with heavier kernels, but had little effect on grain yield. Swanston (1995) used isogenic lines developed in the barley cultivar Compana with varying combinations of the genotypes of three genes (wx, n, Ik2). He reported that I) the waxy gene was associated with changes in endosperm structure that increased milling energy, 2) both wx, and Ik2 were associated with a significant reduction in grain size with additive effects, 3) a combination of wx and n did not show additive effects on P-D-glucan content, and 4) Ik2 showed similar effects to wx on grain size, milling energy and P-D-glucan content. Xue et al. (1997) used a series of isogenic lines developed in Compana and Betzes with varying combinations of the genotypes of three genes (wx, nn, Ik2). More than 20 endosperm-related characters were measured using a General Linear Model procedure for analysis of variance. The effects of cultivar background, hull type, starch type, hull type and the two-way and three-way interactions were tested. The results demonstrated I) the M gene reduced total dietary fiber by preventing the hulls from adhering to the kernel. 2) waxy barleys contained less starch, but more free sugars. 3) Increased total dietary fiber in waxy barleys was due to an increase in total and soluble P-D-glucans. 4) the short awn gene is associated with the increased viscosity, and with the reduced test weight. I 11 Random Inbred Line Approaches Powell et al. (1985) used random inbred lines produced by doubled haploidy (DH) and single seed descent (SSD) with three crosses (Golden Promise x Mazurka, Golden Promise x Ark Royal, and BH4/143 x Ark Royal). In the experimental analysis, they adopted an assumption that if important genes for any trait were linked, the bias of additive genetic variation of S SD derived families would be less than that of DH derived families, due to multiple round of meiosis reducing linlcage disequilibrium (Falconer and Mackay, 1996, p368). They observed that I) (3-D-glucan content was controlled by a simple additive genetic system, 2) linlcage disequilibrium did not appear to be an important genetic component of P-D-glucan content in barley, and 3) epistasis on p-D-glucan content was detected in the Golden Promise x Mazurka cross only. They also reported a negative genetic relationship between P-D-glucan content, thousand grain weight and plant height with significant linkage disequilibrium levels. Swanston (1997) also used random inbred lines (SSD method) produced from a Chalky Glenn (6 row) x Waxy Hector (2 row) cross. In his experiment, he produced waxy lines with acceptable grain size, by avoiding the use of dwarfing (Swanston, 1995). Interestingly, the waxy lines were divided into two groups (high milling energy vs. low milling energy), and one group characterized with low milling energy was shown to have very low P-D-glucan content. Han et al. (1995) mapped QTL for barley P-D-glucan content on barley chromosomes 2 and 5. 12 Limitations of Previous Studies Isogenic Line Based Genetical Approaches Theoretically, isogenic line based genetical approaches (McGuire and Hockett ,1981; Swanston, 1995; Xue et al, 1997; Czuchajowska et ah, 1998) demand uniform genetic background except the loci introduced into recurrent parents. Multiple steps of backcrossing were used to generate 8 isogenic lines with 2 different donors (Waxy Oderbrucker for wxwx, and Sermo for nn and Ik2Ik2, Fox, 1981). The eight resulting lines may show different degree o f fitness due to the linkage drag (Young and Tanksley, 1989). Therefore, the pattern of ‘background effect’ on target QTL by partial donor chromosome retention may differ in each isogenic line. Another disadvantage of this approach is that epistatic interactions between target QTL and other QTLs on other genome regions could not be tested (Han et al., 1997). Random Inbred Line Approaches The idea of characterizing the individual QTL responsible for variation of quantitative traits among random inbred lines goes back to the early 20's (Sax, 1923). Application of this idea, however, was limited by a lack of segregating markers in populations of interest. A few morphological, disease resistance, isozyme, and seed storage protein markers could be used (Shahal and Tsuchiay, 1990), and attempts were made to 13 generate stocks containing multiple morphological markers (Benito et a l, 1987). Until the advent of RPLP markers, marker genome coverage remained very low. Powell et al. (1985), using random inbred lines, reported that three to five simple additive genes were enough to explain the variation of |3-D-glucan contents among lines, Han et al. (1995) mapped the QTLs for barley [3-D-glucan content with the aid o f well distributed molecular markers. They showed that (3-D-glucan content was controlled by additive gene action, but their population did not segregate for wx, n, and Ik2. In summary, except for the use of isogenic lines, there has been no report in which all three recessive alleles were considered at the entire genome level with all possible expected genetic mechanisms. Current Status and Considerations Rossnagel etal. (1981) reported that nonwaxy-hulless bailey (WxWx, nn) yielded 88% of hulled barley on the average of data from 93 station years. Since the yield reductions of hulless types are proportional to at least the weight of hulls (8-16% of the kernel weight) (Briggs, 1978), the productivity differential between the two classes of barley appeared to be quite small. Meanwhile, many barley breeding programs have developed and released high (3-D- glucan barley varieties (Goering et al, 1973; Eslick et al., 1990; Blalce et a l, 1990). So far, the developed waxy- hulless varieties commonly showed lower grain yield, performing about 20% lower than non-waxy, hulled varieties (Blake, personal communication). 14 Gill et al. (1966) reported that hulless barley has been historically regarded as an inadequate yielder when compared to hulled barley, even when the weight of hulls was considered. Witcombe and Murphy (1986) tested hypotheses with reciprocal crossings between accessions of two groups and compared the important traits’ performances among parental, F1 and F2 plants. They could not find any evidence o f negative pleiotropic interactions. It therefore seems likely that the observations of Gill et al. (1966) may result from poorer germplasm in the hulless pool, rather than representing a direct effect of the hulless phenotype. Ifthis is the case, then understanding the magnitude of the effects o f the waxy and hulless characters may lead to the design of more efficient food barleys. If not, then determining the interactions of these genes with performance-related characters will help determine whether barley has potential as a food crop in industrialized nations. The primary objective of this thesis was to discover in greater detail the genetic basis for poor performance in waxy-hulless barley and try to find ways to remedy it. For this objective, a linkage map was generated in a population of recombinant inbred lines derived from a cross between MTaz90-123 (waxy hull-less, short-awned and high fiber content) and MTH6860756 (a high yielding, stripe rust resistant feed barley line). QTL mapping was applied to evaluate putative QTLs of important traits, and all possible conditional- and combined- dialleleic interactions between major QTLs on each trait were surveyed. Finally, hypotheses regarding the genetic control of grain yield and grain fiber content were tested. 15 CHAPTER 2 LINKAGE MAP CONSTRUCTION IN A REAL BREEDING POPULATION; MTaz90-123 x MTH6860756 Introduction The idea of using genetic markers to locate the individual QTL responsible for variation in quantitative traits goes back to the early 20's (Sax, 1923). The practical application of this idea, however, was limited by a lack of segregating markers in the populations o f interest. A small number of morphological, disease resistance, isozyme, and seed storage protein markers could be used (Shahal and Tsuchiay, 1990), and lines containing several morphological markers have been constructed. Unfortunately, these morphological recessive genes are generally deleterious, and most of the major QTL identified using these stocks map to these marker genes. In order to detect QTL using linkage disequilibrium analysis, genetic markers must clearly and inexpensively differentiate between parental alleles (Beckmann and Seller, 1983). Markers should be highly polymorphic, abundant and neutral both with respect to the quantitative trait of interest and to reproductive fitness (Falconer and Mackay, 1996 pp 356-378). 16 Most of the QTL mapping experiments done thus far utilized populations derived from crosses between parents selected to maximize genetic distance (e.g. Hayes and Jyambo, 1993). This may not always be desirable, and. may shed little light on realistic crop improvement projects. If the objective of the experiment is to assist breeding for the improvement o f specific traits, elite parents are more desirable. This is especially true for crops like barley which have a long history of breeding (Martin et al, 1991; Hayes et ah, 1997). If a real breeding population is used as a mapping population genetic distance betwen the parents may be low, making it difficult to find enough informative markers to obtain even marker spacing on chromosomes. Entire chromosome arms may share a common origin between parents, making a search for informative markers for these arms futile. QTL analysis in wide crosses plainly has greater potential to uncover more significant gene x phenotype interactions and will better support the detection of hard-to-detect nonadditive sources of genetic variance (Rasmusson and Fillips, 1997). Low copy restriction fragment length polymorphism (RFLP; Bostein et al, 1980) markers, detected using Southern analysis, have been applied to many crop species to generate linlcage maps. However, this technique can be cumbersome when applied to practically-oriented plant breeding programs due to the time consuming and expensive procedures. The polymerase chain reaction (PCR; Saiki et al, 1988) provides a convenient and cost-effective alternative to RFLP analysis. Olson et al (1989) proposed using sequence tagged sites (STSs) as chromosome landmarks, and several hundred barley RFLP clones 17 were sequenced and converted to STSs (Shin et a/., 1991; Tragoonrung et al, 1992; Blake et al., 1996; Sayed-Tabatabaei et al., 1998). Simple sequence repeat (SSR; Weber and May, 1989) or microsatellite loci contain tandem repeat sequences of DNA and vary in repeat size and number. SSR markers are ideal for genetic mapping because o f their high level of polymorphism, and their compatibility with PCR-based detection schemes (Liu eta/., 1996). Several primer pairs flanking barley SSRs have been developed and incorporated within the barley linkage map (http:Wwheat.pw.usda.gov). Amplified Fragment Length Polymorphism (AFLP; Vos et al, 1995) analysis has some advantages over other DNA marker techniques. These include the detection of a larger number of polymorphisms within very short period time, and reduced cost. These AFLPs may be anchored to each barley chromosome with barley STS and SSR markers. Consequently, the most expected practical problem in generating a linlcage map of a real breeding population - lack of polymorphisms with even distribution across the entire genome- may be ameliorated with AFLP markers. In this report, we describe the generation of a linlcage map with a real breeding population derived from a cross between waxy-hulless barley line (MTaz90-123; wxwx, nn, Ik2Ik2) and high yielding feed barley line (MTH6860756; WxWx, NN1 Lk2LkJ). A combination of morphological, protein, STS, and SSR markers were used as anchor markers. As a major tool for ‘gap filling’, AFLP markers were applied. This study will describe the practical difficulties underlying linlcage map construction with a real breeding populations, and suggest possible solutions. 18 Materials and Methods Population Development Three parallel recombinant inbred line populations were developed from the cross between Mtaz90-123 (maternal line) and MTH6860756. MTaz90-123 is the best-performing and well adapted waxy, hulless 2 row barley line with short awn (wxwx, nn, Ik2Ikj) in Montana. This line derived from the cross of ‘ Washonupana’ (wxwx, nn, Ik2Ik2) x MT83424 (Fox, 1981; Blake, personal communication). MTH6860756 (WxWx, NN, Lk2Lk2) is high yielding, 2 row feed barley. Three parallel recombinant inbred line (RlL) populations were utilized for QTL analysis. The were called the ‘Fiber-T (F4 derived, 61 lines), ‘Fiber-2'(F5-derived, 59 individuals) and ‘Fiber-3' (F5-derived, 96 individuals) populations. Seed was advanced from each individual F5 line until the F7 generation to generate sufficient seed for replicated year trials. The ‘Fiber-21 population was utilized for map construction. Morphological and Protein Markers Two morphological markers, n (hull type) and Ik2 (awn length), were screened during the field trials. Waxy endosperm (wx) was identified with iodine staining of cleaved mature seeds (Frykman and Bengtsson, 1992). Variation at the hordein storage protein loci (B and C hordein) were characterized by SDS-PAGE electrophoresis of mature seeds (Blake et a l , 1982). These markers are described in Table I . 19 Table I . Morphological and biochemical characters evaluated. Locus Genotype ChrorQosomea Fib-2MTaz90-123 MTH6860756 S x M WX waxy endosperm WXWX BScfKc I I n naked caryopsis n n NN I I Ik2 short lawn Ik2Ik2 Lk2Lk2 I I Hor I C hordeins 5 5 Hor 2 B hordeins 5 5 a Chromosomal locations of morphological and biochemical markers integrated into Steptoe x Morex (S x M; Kleinhofs, 1996) linkage map and evaluated locations with ‘Fiber-2'(Fib-2) mapping population. PCR Amplification and Polymorphism Detection of STS and SSR Markers Approximately 120 STS primer sets (Blake et al, 1996; Sayed-Tabatabaei et a l, 1998) and 44 SSRprimer sets (Becker and Heun, 1995; Liu et al, 1996) were evaluated for informativeness in the Fiber-2 population. Out of 15 the Becker and HemTs SSR primer sets, 9 primer sets, with different primer sequences assay the same target SSR loci as do 9 of Liu et al. ’s primer sets (see Table 3). All STS- and SSR-PCR amplifications were conducted in 30pl volumes of IX PCR buffer(Promega, Madison, WC) [5OmM KC1,1 OmM Tris-HCl (pH 9.0), 0.1% TritonX -100, 1.5 mM MgCl2], 0.1 mM each dNTP, 0.3pM of each primer, 0.5 unit of Taq polymerase (Promega), and 5Ong of genomic DNA template. Typically, STS-PCR amplification conditions were: 5min at 94°C, followed by 35 cycles of 30 sec DNA denaturation at 94 °C, 30 sec annealing at 50°C, and I min extension at 72°C, and final 5 min. incubation at 72°C. When a large PCR product (> 1.0 kb) was expected (especially ‘MWG’ group; Sayed- Tabatabaei et al., 1998), Imin and 2 min were used for annealing and extension respectively. For the SSR-PCR, after passing 5min at 94°C, a ‘touchdown’ PCR consisted of 18 cycles o f 94 °C for I min denaturing and 72 °C for I min extension. Annealing (30 sec) 20 temperatures were progressively decreased by 0.5 °C every second cycle from 64 °C to 55 °C. The PCR reaction continued for 30 additional cycles of I min denaturation at 94°C, Imin annealing at 55°C, and I min extension at 720C, and final 5 min incubation at 12°C. PCR was performed in a GeneAmp™ RCR System 9600 (Perkin Elmer, Norwalk, CT) thermal cycler. The polymorphisms of STS and S SR markers were detected with 6% polyarcylamide gels in 0.5X TBE buffer with 2.0 hour electrophoresis at 250 V, followed by ethidium bromide staining. In the case of large PCR products (usually MWGs), a 1.8 % agarose gel was also used. Initially monomorphic STS-PCR products between MTaz90-123 and MTH6860756 were subsequently digested with several endonucleases. Two units of each restriction endonucleases were added to IOpl aliquots of PCR products. Ifthe size difference of SSR-PCR products was small, a 4% denaturing polyarcylamide gel (SM urea, IX TBE buffer) were used at 1,500 V. Those SSR PCR products were visualized using a DNA silver-nitrate staining method (Bassam et al, 1991). AFLP Reactions and Polymorphism Detection The protocol adopted for the generation of AFLP markers followed Vos et a/. (1995) with minor modifications. Genomic DNA (IOOng) , in a 20pi reaction volume, was restricted with 4 unit of Pst I and Mse I (New England Biolabs, Beverly, MA) at 37 °C for 2 hours. After digestion, without heat inactivation of the restriction enzymes, IOpl of adaptor ligation cocktail was added and the mixture incubated at 15 °C I h, 23 °C I h, and 37°C 30 min with programed thermal cycler. The ligation cocktail contains I unit of T4 2 1 DNA ligase (New England Biolabs), 4mM ATP, 3 pmol of the Pst I adaptor (5'-CTC GTA GAC TGC GTA CAT GCA; CAT CTG ACG CAT GT-5'), and 30 pmol of Mse I adaptor (5'-GAC GAT GAG TCC TGA G; TAC TCA GGA CTC AT-5'). Pre-amplification was conducted with 3 Ong each of Pst I and Mse I single-nucleotide selective primers(G and A for Pst I, C for Mse I), 5pi of 1:5 diluted ligated DNA, IX PCR buffer, 0.2mM dNTPs, 3 unit of Taq polymerase in 50pl final reaction volume. Pre­ amplification PCR-cycle profiles were performed as described by Vos et al. (1995). I pi of 1:5 diluted pre-amplified DNA was selectively amplified using 3 Ong of each of the Mse I primers and fluorescent labeled Pst I primers with three selective nucleotides (see Table 5), I unit of Taq polymerase, IX PCR buffer and 0.2mM of dNTPs using the PCR-profile described by Vos et al. (1995) with a 10 min final extension at 72°C. All PCR reactions were performed using a GeneAmp™ RCR System 9600 (Perkin Elmer). Fluorescent AFLPs were detected using an ABI 377™ automated DNA sequencer (Perkin Elmer) with 4% denaturing polyacrylamide gels (SM urea, IX TBE buffer). For the analysis of complex AFLP fingerprint patterns, the software package Genographer (Benham et a l, 1999) was used. This program reads in lanes from an AB I377 and reconstructs them into a gel image which is straightened and sized. Bins can be defined easily and viewed as thumbnails, so that the presence and absence of a band can be scored quickly and easily. AFLPs were named based on the primer combinations used for specific amplifications, the band size, and the band quality. The last capital letters among AFLP marker names represented the relative band quality; S for very strong and clear, A for clear, B for weak but countable. o 22 Genotvping of Progeny Lines In constructing the ‘Fiber-2 linlcage map’, the major marker generation tool was AFLP analysis. It is very difficult to score AFLPs co-dominantly without special considerations (Liu, 1998,pp77-78; Castiglionieta/., 1999). Therefore, highly homozygous RI lines in all loci (Fn, n-°°) are desirable to permit the simple scoring o f presence or absence o f bands. To mimic F7 generation RI lines, young leaves of a single plant were selected for the genomic DNA extraction in each progeny line using a CTAB method (Murray and Thompson, 1980) with minor modifications. Some heterozygous genotypes were detected using the STS markers at very low frequency. Those allele states were treated as a ‘missing data’ in that marker class. The morphological markers were scored with all three genotypes in the F5 individual lines during the field trials. The heterozygous (heterogeneous) genotypic RI line data points were also treated as missing data during linlcage map construction. Linkage Map Construction Linlcage analysis of the 59 RI lines was performed using several software packages with the following purposes. To perform a segregation test, and determine the marker order roughly; MAPL (Ukai, 1999) worked well. We used Map Manager XP 0.9 (Manly, 1999) as our primary linlcage analysis tool. For pseudo-linkage group separation, and marker interval estimation with Kosambi mapping function we used Mapmaker 3.0 (Lander et al, 1987). 23 Some linlcage groups were defined as ‘orphan’ groups if they contained no anchor markers. Those groups were evaluated for potential placement on the map through comparative analysis using the randomly selected 56 ‘Steptoe x Morex’ mapping lines. The basic idea of comparative mapping, assuming co-linear genetic maps, was adopted into the ‘Fiber-21 linlcage map construction. First, the comparative chromosomal locations o f STS and SSR anchor markers were surveyed very carefully across various barley mapping populations to infer the genome coverage of anchor markers in our ‘Fiber-2' m a p p i n g p o p u l a t i o n (W eb s i t e ; h p p t :/ / g e n om e . Co rne l l , e d u / c g i - bin/WebAce/webace?db=graingenes). The barley ‘Consensus 2' linlcage map (Qi et oh, 1996) was primarily used, because this linkage map were essentially generated using data from 4 different genetic maps (Proctor x Nudinka, Igri x Franlca, Steptoe x Morex, and Harrington x TR306) and integrating them into one linlcage map containing almost 900 markers. SSR markers were integrated into the Steptoe x Morex linlcage map by Saghai- Maroof et «/.(1996). The expected positions of STS- and SSR-markers spread over the genome and mapping in agreement with barley reference maps were initially identified to build a ‘linkage skeleton’. The markers in each pseudo-linkage group were isolated and used in preparing a ‘raw file’ to run in Mapmaker. Threshold levels was changed with the‘default’ command from LOD 10 to LOD 3 step by step to separate markers into two groups without generating conflicts with the anchor markers in the ‘linlcage skeleton’. All markers were grouped by two-point linkage analysis using series of minimum LOD thresholds from 6.0 to 2.5 with the ‘default’ option of Mapmaker 3.0 (the higher 24 number o f IinIcage groups at high threshold level tended to be reduce with lower threshold). During these grouping procedures, the ‘linkage map skeleton’ was used to track the anchor markers. If there were no conflicting anchor markers in ‘joined’ groups, with reduced threshold levels, the joined linkage groups were treated a new linlcage group. All markers were sorted by assigned chromosomes, and the marker orders were roughly determined by the automated marker ordering procedure in MAPL, and re-tested and confirmed with Map Manager, which offers very convenient editing tools. The remaining markers were placed to the most probable map position with the ‘Report’ command in Map Manager. This approach was only utilized to connect linlcage fragments to build specific chromosomes. 25 Results STS and SSR Markers Ofthe Approximately 120 STS primer sets and 44 SSR primer sets evaluated in this population, 31 STSs and 14 SSRs were informative. In the ‘Steptoe x Morex’ population, approximately 70% of these markers were informative (Table 2). This difference in genetic distance between the parents (36% vs 70%) demonstrates the limited genetic variance within this population when compared to the model ‘ Steptoe x Morex5 population. Some primer sets amplified multiple bands. In the cases of ABG20.11 and ABG55, BTA002, and MWG938 primer sets amplified two polymorphic bands, which were located on the ‘Fiber-21 to well-distinguished loci by linlcage analysis (Table A-2; Figure 5). Single monomorphic fragments generated by 11 primer sets were digested with several endonucleases to reveal polymorphisms. This general type of amplification and segregation pattern were commonly observed across all STS markers (Figure I). Out of 27 primer sets, 4 showed length polymorphism (ABC156.1, ABC 170, ABG054, and MWG620) and 12 showed a presence or absence polymorphism (ABC155, ABC483, ABG20.il, ABG064, ABG380, ABG452, BCD402, BTA002, MWG058, MWG938, M STl09, and WTAl) between the parents. The remaining 11 produced monomorphic fragments which contained internal restriction site polymorphisms. Endonucleases were applied to reveal the polymorphism for ABC253, ABC303, ABG55, ABG058, ABG070, ABG609, ABG618, cMWG694, MWG549, MWG2249, andMWG2261 (Table 2). 26 A ABC 483, Ch 7 — : i # , * * *" *»#*# 4 # 450bp, Monomorphic -<-250bp, A M ABG 618 / H inf I, Ch 4 B ■340bp, A -300bp, C C MWG938a ^ 1 .5K bp ,A 580bp, C MWG938b Figure I . Examples of amplification products and segregation patterns of STS-PCRs in the ‘Fiber-2' mapping population. Arrows indicate the segregating loci between MTaz90-123 (A) and MTH6860756(C) with the estimated molecular weights. Letter ‘M’ indicates DNA size markers, and other lanes represent a set of progeny lines from MTaz90-123 and MTH6860756 cross. Panel A. Primer set ABC483 produced a polymorphic band(250bp) derived from only MTaz90-123 allele. Meanwhile, monomorphic band(450bp) was also produced. Panel B. Primer set ABG618 amplified both parental alleles(550bp), and restriction enzyme (FIinf I) was applied to reveal the polymorphism between two parental alleles (340bp and 300bp were scored as co-dominantly). Panel C. Primer set MWG938 amplified two different loci MWG938aand MWG938b (l.Skbp for MTaz90-123 and 5 8 Obp for MTH6860756, respectively). Both two loci were mapped into chromosome 5. These general types of amplification and segregation pattern were commonly observed across all STS markers. The PCR products were analyzed using polyacrylamide gel electrophoresis (PAGF; panel A and panel B) and agarose gel electrophoresis (panel C; mostly for primer sets o f ‘MWG’ group). 27 Table 2. List of informative STS markers. Primer Band size (bp)b Restriction Other bands (bp)d Chromosome0 name" A C enzymes0 STS RFLP Fib-2 ABC155 - 350 7 7 7 ABC156.1 130 HO 3 3 5 ABC170 100 120 2 6 2 ABC253 500 Hind III I I I ABC303 330 HhaI 4 4 4 ABC483 250 - 450 7 7 7 ABG20.11f - 700 1100 + 340+240 I 450 " 7 ABG054 160 180 4 4 4 740 Rsa I 5 ABG55f 5 5 500 DdeI 5 ABG058 1000 Rsa I 2 2 2 ABG064 - 130 500 + 300 + 170 7 7 7 ABG070 450 BsrI 3 3 3 ABG380 - 320 370 + 350 - I I ABG452 - 330 550 5 5 5 ABG609 500 RsaI 220 + 120 2 2 ,3 2 ABG618 550 HinfI 4 4 4 BCD402 190 - - 4 4 230 4 BTA002f 1100 4 4 1300 - 4 CMWG694 1200 RsaI 2 2 2 MWG058 950 - 4 4 4 1500 5 MWG938f 5 5 - 580 5 MWG549 700 Msp I 3,6,7 3 3 MWG620 850 600 6 6 6 MWG2249 550 Ava II 7 7 7 MWG2261 500 RsaI 5 5 5 MSTl 09 - 1700 700 6 6 6 WTAl - 500 460 + 350 - - I Total 3 1 loci with 27 STS primer sets a Sequences o f the STS primers have been published by Blake e ta l. (1996) and Sayed-Tabatabaei e t al. (1998) except WTAl and MSTl09 (Blake, personal communication). b Polymorphic (A=MTaz90-123, C=MTH6860756), and monomorphic. c Endonucleases were used to reveal the polymorphism between two parents. d Non-informative amplified PCR products as common bands between two parents. e Blake e t al. (1996), Mano e t al. (1999) and Grain Gene (http://wheat.pw.usda.gov/ ; for ABG380 and BCD402). Column ‘Fib-2' indicates the evaluated locations on the ‘Fiber-21 linlcage map. f Two loci were detected as polymorphic with one primer set, upper rows and lower rows were named ‘a’ and ‘b’ in the end o f primer name respectively (see Table A -l). 28 B M-HVDHN9, Ch 7 ■ « " r M-HVDHN9b - 130 bp, C M-HVDHNOa 108 bp, A 110 bp, C Figure 2. Examples of amplification products and segregation patterns of SSR-PCRs in the ‘Fiber-21 mapping population. Arrows indicate the segregating loci between MTazOO- 123(A) and MTH6860756(C) with the estimated molecular weights. Lanes represent a set of progeny lines from MTazOO-123 and MTH6860756 cross. Panel A. Primer set HVM60 flanking (AG)n,(GA)n region, amplified both parental alleles co-dominantly. Panel B. Primer set M-HVDHNO flanking (GT)n region, amplified two different loci (130bp for MTH6860756 allele as a dominant marker, and I IObp and 108bp band pairs as co-dominant marker, respectively). Although these two loci, M-HVDHNOa and M-HVDFINO-b, were not separated with the ‘Fiber-2’ mapping population having 50 lines, it may be possible with increased population size. The PCR products were analyzed using 12% polyarcylamide gels (not in this figure) and 6M urea 4% polyarcrylamide gels (panel A and panel B) following gel staining with ethidium bromide staining and silver-staining, respectively. Table 3. List of informative SSR markers. Size (bp)b Basic information o f SSRs Chromosomed Primer name3 ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- A C Source Repeat Size(bp) Reference0 S xM Fib-2 HVM3 210 216 Rubisco activase 188 Liu e t al. 4 4 M-HVWAXYG (HVM4) 290 310 Starch synthase (AT), 205 Becker and Heun I I M-HVPRPIB (HVM5) 230 210 Pathogenesis-related protein (GT),, (AT)i« 167 Becker and Heun I I HVM6 170 168 Old ZZ Library with (GA)10 probe (GA), 175 Liu e t al. 7 I HVM36 106 HO Old ZZ Library with (GA)10 probe (GA),, 114 Liu e t al. 2 2 HVM054 160 140 Old ZZ Library with (GA)10 probe (GA),, 159 Liu e t al. 2 2 HVMO 60 120 112 Old ZZ Library with (GA)10 probe (AG),,, (GA),, 115 Liu e t al. 3 3 HVM68 200 206 Old ZZ Library with (GA)10 probe (GA)* 204 Liu e t al. 4 4 M-HVCMAe (HVCMA) 120 140 160 a-amylase inhibitor (AT), 141 Becker and Heun I I I M-HVDHN9* 3 (HVDHN9) 108 HO 130 mRNA for dehydrin-9 (GT); 128 Becker and Heun 7 7 - 7 M-HVCSG (HVCSG) 196 200 Chalcone synthase gene (GT),G„ 196 Becker and Heun 2 2 HVGNIRE 130 128 Nitrate reductase CTG^G,, 136 Becker and Heun 6 Total 14 loci with 12 SSR primer sets 3 Sequences of the SSR primers have been published by Becker and Heun (1995) and Liu e t al. (1996). The primers names in parentheses indicate the alternative Liu e t al. ’s primer sets flanking the same SSR. b Polymorphic bands (A=MTaz90-123, CA=MTH6860756). 0 Liu e t al. (1996) and Becker and Heun (1995). d The determined chromosomal locations of SSRs on Steptoe x Morex (S x M; Liu e t a l , 1996), and Fiber-2 (Fib-2) mapping population. 3 Two loci were detected as polymorphic with one SSR primer set. Upper rows and lower rows were named ‘a’ and ‘b’ in the end o f primer name respectively (see Table A -1). 30 Forty four SSR primer sets were tested and 12 identified polymorphisms at 14 segregating loci in the ‘Fiber-2' population (Table 3). All the primers were initially evaluated using the ‘touchdown’ PCR methodology. Primer sets which generated a smear o f PCR products, produced unexpected fragments with multiple lengths, or amplified no products were not used to test the ‘Fiber-2' progeny lines. Twelve out o f 14 SSR primer sets produced length polymorphism between two parental alleles due to the different copy number o f repetitive sequences (panel A in Figure 2). Minor bands were considered to be artifacts (Liu et al., 1996). M-HVCMA and M-F1VDHN9 amplified major products in two distinct size ranges. In both case, one allele was scored as a dominant allele (panel B in Figure 2). AFLP Markers Genographer was used to score informative AFLPs and estimate fragment sizes (Figure 3). Some times, the ABI377 automated sequencer failed to detect the DNA size standards utilized by Genographer for lane-to-lane standardization. Nalced eye scoring was used to score those AFLP gels. The number of detected informative AFLPs per one primer combination varied from 2 (M-ACG + P-ACG) to 17 (M-CTG + P-GGA), and 10.6 polymorphic AFLPs were detected on average (Table 5). Sometimes, scoring of some AFLPs across lanes, due to background and weak band signal, was not clear. 31 A M-CAC + P-GCT (FL1401S ~ FL1415S) C 20 B 21 B 22 B 23 A1 4 A 25 B 26 A 27 A 28 B 29 A 30 B 31 A 32 A 33 A 34 B M Ml M.lAk Figure 3. Detection and databasing of informative AFLPs. Amplified AFLPs, with the combination of Mse I and fluorescent labeled Pst I primer set, were detected using ABI377 automated sequencer. Computer software Genographer (Benham et al., 1999) collected lanes from ABI377 and displays a sized and straightened gel image reconstructed from lanes (panel A). Bins were set for the informative AFLPs (two horizontal lanes in panel A and panel B). The bins were automatically scored(panel C). Scoring were exported as a text file to use other mapping programs, such as MapMaker. Panel A. Partial reconstructed gel image of the amplification products of M-CAC + P-GCT primer combination for secondary amplification. One AFLP (FL1412S, 444bp, MTaz90-123 allele) was selected. Panel B. Magnified image. Panel C. The peaks representing amplified band strength were automatically scored after setting the thumbnail (horizontal lane). The progeny lines from #10 to #32 are in this figure. 32 Table 4. List of informative AFLP markers. Name" Primer set'1 Size(bp)c Alleled Chromo­some" Name" Primer set1’ Size(bp)c AlleIed Chromo­ some" FLOlOlA CAG+ ATT 132 A 6 FLUIDS CTG+ ATT 366 C 7 FLOI 02A CAG+ ATT 166 A 6 FL l11 IS CTG+ ATT 403 A 5 FL0103B CAG+ ATT 199 A FL1112S CTG+ ATT 650 A 3 FL0104B CAG+ ATT 217 A FL1201S CTA+ GGA 98 A FL0105B CAG+ ATT 244 A FL1202B CTA + GGA 280 A I FLOI 00A CAG+ ATT ■ 397 A FL1203B CTA + GGA 283 C FL0107B CAG+ ATT 456 A FL1204S CTA + GGA 298 C I FL0201B CAG+ GCA 62 A FL1205S CTA+ GGA 300 a ' I FL0202S CAG+GCA 93 A 7 FL1206A CTA + GGA 322 A 5 FL0203S CAG+ GCA 113 A I FL1207B CTA+ GGA 330 A FL0204S CAG+ GCA 121 A FL1208A CTA + GGA 452 C 5 FL0205B CAG+ GCA 394 A 2 FL1209S CTA+ GGA 490 C 4 FL0206B CAG+GCA 550 A 5 FL1301S CTG+GCA 60 A FL0207S CAG+GCA 570 C I FL1302A CTG+GCA 139 A I FL0301S CAC + GCA 55 C 6 FL1303S CTG+ GCA 172 C 6 FL0302B CAC + GCA 65 C FL1304S CTG+GCA 178 A 6 FL0303A CAC + GCA 75 A 2 FL1305S CTG+GCA 271 A I FL0304S CAC + GCA 90 C I FL1306B CTG+GCA 276 A I FL0305A CAC + GCA 190 C 3 FL1307B CTG+GCA 304 A FL0306A CAC + GCA 194 C 3 FL1308S CTG+GCA 384 C I FL0307B . CAC + GCA 270 A 6 FL1309B CTG+ GCA 515 C I FL0308S CAC + GCA 275 C I FL1310S CTG+ GCA 580 C 4 FL0309A CAC + GCA 310 ■ C FL1311S CTG+GCA 600 A 4 FL0310A CAC + GCA 350 A 4 FL1312A CTG+ GCA 680 C 6 FL0311S CAC + GCA 460 A 3 FL1401S CAC + GCT 79 A 7 FL0312S CAC + GCA 650 C 7 FL1402B CAC + GCT 93 C FL0401A CTA + GCT 60 C 5 FL1403B CAC + GCT 98 C FL0402S CTA + GCT 158 C 6 FL1404S CAC + GCT 170 A 2 FL0403S CTA + GCT 170 A 4 FL1405S CAC + GCT 175 C 2 FL0404S CTA+ GCT ■ 177 A 2 FL1406A CAC + GCT 197 A FL0405S CTA+ GCT 181 C 7 FL1407B CAC + GCT 199 C FL0406A CTA+ GCT 192 A 7 FL140 SB CAC + GCT 310 A 2 FL0407A CTA+GCT 194 C 7 FL1409B CAC + GCT 334 A 6 FL0408B CTA+ GCT 196 C 6 FL1410B CAC + GCT 337 C 2 FL0409A CTA+ GCT 200 C 4 FL1411B CAC + GCT 417 C 7 FL0410S CTA+ GCT 234 C 3 FL1412S CAC + GCT 444 A 3 FL0411S CTA+ GCT 254 A 4 FL1413S CAC + GCT 449 C 3 FL0412A CTA+ GCT 304 A 6 FL1414S CAC + GCT 494 A 4 FL0413A CTA+ GCT 368 C I FL1415S CAC + GCT 600 A 7 FL0414S CTA+ GCT 490 C 5 FL1501S CTC + GGA 145 C 2 FL0415S CTA+ GCT 510 C 5 FL1502B CTC + GGA 152 A FL0416S CTA+ GCT 515 A 5 FL1503A CTC + GGA 170 C I FL0501S CTT + GGA 76 A 6 FL1504B CTC + GGA 171 C FL0502B CTT + GGA 84 C 3 FL1505S CTC + GGA 176 C I FL0503A CTT + GGA 131 A 2 FL1506A CTC + GGA 205 A I FL0504S CTT + GGA 139 A I FL1507A CTC + GGA 241 C 2 FL0505B CTT+ GGA 160 C FL1508S CTC + GGA 343 C 6 FL0506A CTT + GGA 189 A 7 FL1509S CTC + GGA 348 A 6 FL0507B CTT+ GGA 419 A FL1510B CTC + GGA 362 A 6 FL0508A CTT+ GGA 500 C I FL l51IB CTC + GGA 366 C I FL0601B CTC + GCA ' 58 A 5 FL1512B CTC + GGA 369 C 5 33 Table 4. (continued) Name” Primer set1' Size(bp)c Alleled Uhromo-somec Name” Primer setb Size(bp)c Alleled Uiromo- somec FL0602A CTC + GCA 83 A 3 FL1513A CTC + GGA 411 C I FL0603S CTC + GCA 102 C 2 FL1514B CTC + GGA 433 A FL0604B CTC + GCA HO A FL1515A CTC + GGA 437 C I FL0605A CTC + GCA 147 A 4 FL1601S CTC + GCT 70 A 4 FL0606A CTC + GCA 210 C 5 FL1602A CTC + GCT 172 A 2 FL0607B CTC + GCA 249 A 2 FLl 603A CTC + GCT 173 C 2 FL0608A CTC + GCA 270 C 2 FL1604B CTC + GCT 201 A 3 FL0609S CTC + GCA 400 C 4 FL1605B CTC + GCT 282 C 5 FL0701S CAG+ ACG 220 C 4 FL1606B CTC + GCT 365 A FL0702A CAG+ ACG 331 C 2 FL1607B CTC + GCT 347 A 7 FL0801S CTT + GCT 161 A I FL1608A CTC + GCT 424 A 7 FL0802S CTT+ GCT 207 C 5 FL1609B CTC + GCT 460 A 5 FL0803S CTT+ GCT 453 A 7 FLl 61OB CTC + GCT 465 C FL0804B CTT + GCT 490 A FL1611B CTC + GCT 525 A FL0805A CTT + GCT 495 A 5 FL1612B CTC + GCT 530 A 3 FL0806S CTT + GCT 700 C 3 FL1613S CTC + GCT 570 A 7 FL0901B CTA+ ATC 61 A FL l614S CTC + GCT 580 A 3 FL0902B CTA+ ATC 130 C 5 FL1701S CTG+GGA 67 A 5 FL0903B CTA+ ATC ■ 161 A FL1702B CTG+ GGA 87 A 7 FL0904S CTA+ ATC 166 A I FL1703S CTG+ GGA 96 C 5 FL0905B CTA+ ATC 266 A FL1704B CTG+ GGA 178 A FL0906S CTA+ ATC ' 327 C 2 FL1705S CTG+ GGA 218 A 4 FL0907B CTA+ ATC 405 C FL1706S CTG+GGA 240 C 5 FL0908S CTA + ATC 434 A 2 FL1707S CTG+GGA 303 A 7 FL0909A CTA+ ATC 460 C I FL1708S CTG+GGA 304 C 7 FL0910B CTA+ ATC 470 A I FL1709A CTG+GGA 306 C 6 FLlOOlB CTG+ GCT 73 A FLl 71OA CTG+GGA 383 C FL1002B CTG+ GCT 111 A FL1711A CTG+GGA 388 C I FL1003S CTG+GCT 123 A I FL1712S CTG+GGA 422 A 7 FL1004S CTG+GCT 155 C I FL1713A CTG+GGA 435 A 3 FL1005A CTG+ GCT 180 A 7 FL1714B CTG+GGA 453 A FL1006B CTG+GCT 310 C 7 FL1715B CTG+GGA 520 A 4 FL1007B CTG+ GCT 315 C FL1716S CTG+ GGA 570 C 6 FL1008B CTG+ GCT 400 A FL1717A CTG+GGA 630 A 3 FL1009B CTG+ GCT 450 A I FL1801A CAC + GAG 56 C 7 FLlOlOB CTG+ GCT 500 C 3 FL1802A CAC + GAG 94 A 7 ■ FL llO lS CTG + ATT 74 A 6 FL1803A CAC + GAG 150 C 3 FL1102B CTG+ ATT 224 A 3 FL1804S CAC + GAG 252 C 6 FLl 103 A CTG+ ATT 225 A FL1805S CAC + GAG 316 C 4 FL1104S CTG+ ATT 245 C 2 FL1806S CAC + GAG 324 A 3 FL1105S CTG + ATT 272 C I FL1807S CAC + GAG 350 A 3 FL1106S CTG+ ATT 308 A 2 FL1808S CAC+ GAG 359 C 3 FL1107S CTG+ ATT 309 C 2 FL1809S CAC + GAG 364 C 6 FL1108B CTG+ ATT 335 C 2 FL1810A CAC + GAG 414 C FL1109S CTG+ ATT 352 A I Total 191 AFLPs a The last capital letters indicate the relative marker quality; S for very strong, A for clear, and B for weak AFLPs. b Three 3' selective nucleotides. (Mse I + Pst I). c Rough size estimation was applied for the bands over than 500bp. d MTaz90-123 allele (A) and MTH6860756 allele (C). e Chromosomal location of AFLP markers on ‘Fiber-2' linkage map (blank=not determined). Table 5. Distribution o f AFLP markers. All AFLP markers Markers on the 'Fiber-2' linkage map Gel 3' selective nucleotides Marker qualitya Number Ratio(%) . Marker qualitya Covered -chromosome (N o jbMse I PstI S + A B Total S + A B I CGA ATT 3 4 7 2 28.6 2 0 I 2 CAG GCA 4 3 7 5 71.4 3 2 4 3 CAC GCA 10 2 12 10 83.3 9 I 6 4 CTA GCT 15 I 16 16 100.0 15 I 7 5 CTT GGA 5 3 8 6 75.0 5 I 5 6 CTC GCA 6 3 9 8 88.9 6 2 4 7 CAG ACG 2 0 2 2 100.0 2 0 2 8 CTT GCT 5 I 6 5 83.3 5 0 4 9 CTA ATC 4 6 10 6 60.0 4 2 3 10 CTG GCT 3 7 10 6 60.0 3 3 3 11 CTG ATT 10 2 12 11 91.7 9 2 6 12 CTA GGA 6 3 9 6 ' 66.7 5 I 3 13 CTG GCA 9 3 12 10 83.3 8 2 3 14 CAC GCT 8 7 15 11 73.3 8 3 5 15 CTC GGA 9 6 15 12 80.0 8 4 4 16 CTC GCT 6 8 14 11 78.6 7 4 5 17 CTG GGA 13 4 17 14 82.4 13 I 6 18 CAC GAG 10 0 10 9 90.0 9 0 4 Total (band number) 128 63 191 Total (%) 150 (78.5%) 121(80.7%) 29(19.3%) Mean (band number) 7.1 3.5 10.6 Marker usage ratio (%) 94.5% 46.0% a All AFLP markers were classified with strong and clear bands (S + A) and weak bands (B) when AFLPs were scored. b The number o f chromosomes covered by AFLP markers. 35 Those were scored as missing data. To control marker informativeness during mapping, all AFLP markers were named dependent on band strength and clarity (Table 4). This type of quality tagging on the AFLP marker names was very helpful in judging and handling problematic markers during segregation analysis and linkage map construction. A survey of different primer combinations, the number o f polymorphisms, the band sizes and the distribution o f AFLP markers across chromosomes is provided in Table 4. Segregation Test of All Informative Markers In total, 241 informative markers (3 morphological, 2 protein, 31 STSs, 14SSRs, and 191 AFLPs) were used in linkage map construction. Three morphological markers Qk2, n, and wx) were screened during the 1998 field trials and re-confirmed in 1999. The 59 ‘Fiber- 2' mapping lines were scored whether F5 derived lines contained both phenotypes to infer 3 possible genotypes in each individual lines. The segregation ratio was skewed at the wx locus, with 31 homozygous recessives, 20 homozygous dominants and 8 heterozygous (heterogeneous) lines (Table A-l). All 241 markers, including three morphological markers with considering missing « data in those lines having mixed phenotypes, were tested against expected segregation ratio at F33 (as ideal recombinant inbred line) generation (Table A-l). The majority of markers showed I : I segregation ratio for the two parental alleles (85% and 95% at the significance level P<0.05 and ? <0.01 level, respectively). Only two STS (ABC156.1 and ABG058) and 33 AFLP markers exhibited distorted segregation at the 0.05 level. Twenty-seven of these loci were skewed towards MTaz90-123 (the maternal parent) alleles and 8 towards 36 MTH6860756 alleles. About 45% of the markers that showed distorted segregation ratio belonged to AFLP marker class ‘B (weak band). Table A-I shows the result of segregation test and genotyping o f all 241 loci. Considering the information collected from 241 loci, the percentage of the MTaz90- 123 genome in the ‘Fiber-2' progenies was on average 48.7%, ranging from 34.9 (#25) to 65.8% (#46) (Table A-3). First Attempt to Construct ‘Fiber-2' Tdnkage Map The first attempt was carried out following ‘routine’ procedures. Markers were grouped using pairwise linlcage analysis to generate linlcage groups. Then chromosomes were assigned to linleage groups using anchor probes. Markers were then ordered within each linleage group and multipoint recombination fractions were estimated among adjacent loci. Mapping was carried out using Mapmaker and STS markers were used anchor probes to assign each barley chromosome. LOD 3.0 and a 50cM maximum distance were used thresholds in grouping procedures. As a result, 29 linkage groups were identified. Among o f them, 10 linkage groups with 42 AFLP markers were ‘orphan’ fragments in which there was no anchor markers. Two psuedo-linkage groups were detected. Chromosomes I and I were apparently linlced (wx and M-HVWAXYG as chromosome I , and ABC483 and HVM6 as chromosome 7 anchor markers, respectively). Also chromosomes 3 and 6 showed significant association 37 (MWG549 as chromosome 3 andMWG620 as chromosome 6 anchor markers, respectively). Over 45% of the AFLP markers remained outside the boundaries o f the anchored map. Increasing AFLP Marker Utility with Band Size Comparative Analysis The AFLP technique has been used to estimate genetic diversity in barley (Hayes et al, 1997; Palcniyat et al, 1997). It is possible to consider comparative analysis of AFLPs between two different mapping populations. We have a well-developed map in the Steptoe x Morex population and decided to attempt positional analysis o f ‘orphan’ AFLP fragments in the ‘Fiber-21 population by looking for homologous variation in the Steptoe x Morex population. If an AFLP polymorphism could be mapped with certainty in the Steptoe x Morex population, its homologue segregating in Fiber-2 should be at the same position. We looked for ‘orphan’ fragment AFLP products which segregated in both populations. The fragments had to be identically sized in both populations, and had to segregate in both. Consequently, if any ‘Fiber-2'AFLP could be localized, the ‘orphan’ fragment including the AFLP could be also potentially localized. Fifty six Steptoe x Morex lines were randomly selected from 150 mapping lines and used as DNA template in AFLP analysis with same primer combinations used in the ‘ Fiber- 2'. The detected Steptoe x Morex AFLPs were placed into the previously constructed linkage map with the ‘place’ command of Mapmalcer program. The band sizes of those assigned AFLPs were compared with AFLPs from the ‘Fiber-21. Using the primer combination (M- CAC + P-GAG), 20 informative AFLPs were detected, and 4 AFLPs had same band size between Steptoe x Morex and the ‘Fiber-2' population (Figure 4). 38 Steptoe x M orex mapping lines S M S/M 443, 5 S/M 407, 6 S/M 397,2 S/M 387, 5 S/M 382, 4 S/M 364, 6* S/M 358, 6 S/M 355, 3 S/M 338, 3 S/M 331,6 S/M 324, 3* S/M 305, I S/M 283, 2 S/M 264, 3 S/M 207, I S/M 150,3* S/M 96, I S/M 94, 7* S/M 91, 5 S/M 58, I Fiber-2 M-CAC + P-GAG Name bp Ch. FL1810A 414 FL1809S 364 * 6 FL1808S 359 3 FL1807S 350 3 FL1806S 324* 3 FL1805S 316 4 FL1804S 252 6 FL1803A 150 * 3 FL1802A 94 * 7 FL1801A 56 7 Figure 4. Increasing AFLP marker utility with comparative analysis. AFLP markers which fell into ‘orphan’ linkage groups were evaluated for potential placement on the ‘Fiber-2' linkage map through comparative analysis using the ‘Steptoe x Morex’(S/M) population. A primer combination of M-CAC + P-GAG was used to generate AFLPs with 56 S/M lines. Informative S/M AFLPs are reported with the band size and assigned chromosome number. ‘*’ indicate the same band sizes between S/M and ‘Fiber-2' AFLPs. 39 Two orphan groups were assigned to chromosomes using this approach (Figure 5; FL1809S, the end of chromosome 6 long arm and FL18 02A, the top o f chromosome 7 short arm, respectively). One AFLP (FL18 03A) was used to join two linlcage fragments into 1 one ’ chromosome 3. The successful outcome of this comparative analysis could be confirmed after building the Tinlcage skeleton’(see below). Still we had more linleage groups than chromosomes and we needed better information to line-up those fragments to build more complete maps for each of the seven chromosomes. Construction of ‘Fiber-21 Linkage Map Skeleton The ‘Barley Consensus 2' (Con2) linleage map was primarily used to determine the relative locations of anchor markers among barley chromosomes. If Con2 did not contain the marker, three other linleage maps were screened; Steptoe x Morex (SxM), Igri x Franlea (IxF), and Chebec x Herrington (CxH). For the SSR markers, the SSR-integrated SxM linleage map (Saghai-Maroof et al. 1996) was used. From the first observation in which anchor markers were not scaffolded across the ‘Fiber-2' chromosomes, 6 more STS markers were added to anchor the ‘anchor empty’ regions; ABC253 (Ch I, 294cM), ABG058 (Ch 2 , 16cM), cMWG694 (Ch 2, 140cM), and BCD402 (Ch4,342cM) (Figure 5 and Table A-2). As we added additional anchor markers, some ‘orphan’ AFLPs were rescued by additional anchor markers due to their linkage relationships with the anchors. Table 6 shows the distribution of anchor markers expressed with the percentage of chromosome length of each detected population. 40 Table 6. The construction of linkage map skeleton with previously reported polymorphic markers and their comparative locations on 'Fiber-2' linlcage map._________. Linlcage map skeleton Locations on 'Fiber-2' linkage map CH Marker Pop." Length(cM) Loc.(cM) %!» CH Length(cM) Loc.(cM) % WX S x M 170 20 11.8 I 325 30 9.2 M- HV WAX YYG Ix F 200 28 13.7 I 325 29 8.9 ABG380 Con2 152 32 20.9 I 325 78 24.0 I M-HVCMA S x M 170 68 40.0 I 325 140 43.1 n S x M 170 75 44.2 I 325 156 48.0 ik2 S x M 170 85 50.1 I 325 170 52.3 ABC253 Con2 152 120 78.7 I 325 283 87.1 M-HVPKPIB S xM 172 170 99.0 I 325 325 100.0 ABG058 Con2 157 12 7.5 2 361 15 4.2 HVM36 S x M 179 36 20.1 2 361 40 11.1 HVM054 S x M 179 161 90.1 2 361 268 74.2 ABG609 ' Con2 157 143 91.0 2 361 309 85.6 CMWG694 CXH 157 145 92.1 2 361 131 36.3 M-HVCSG S x M 179 170 95.0 2 361 316 87.5 ABG070 S x M 185 10 • 5.4 3 257 0 0.0 3 HVM060 S x M 185 93 50.3 3 257 112 43.6 MWG549 Con2 131 104 79.4 • 3 257 . 213 82.9 ABC303 Con2 134 49 36.8 4 350 73 20.9 MWG058 Con2 134 53 39.5 4 350 91 26.0 HVM3 Ix F 137 61 44.7 4 350 87 24.9 4 HVM68 S x M 177 90 53.0 4 350 115 32.9 ABG618 Con2 134 85 63.8 4 350 135 38.6 ABG054 Con2 134 97 .72.1 4 350 148 42.3 BCD402 Con2 134 124 92.6 4 350 • 331 94.6 MWG938 Con2 150 24 16.3 5 390 43 11.0 Hor 2 Con2 150 26 17.0 ■ 5 390 79 20.3 5 Hor I Con2 150 35 23.4 5 390 80 20.5 ABG452 Con2 150 74 49.1 5 390 248 63.6 ABG55 Con2 150 144 95.9 5 390 387 99.2 MWG620 Con2 141 7 4.9 6 313 12 3.8 ABClVOc Con2 141 99 70.5 2 361 56 15.5 ABC483 Con2 195 17 8.6 I 335 6 1.8 M-HVDHN9 S x M 202 112 55.5 I 335 104 31.0 Table 6. (continued) Linkage map skeleton 41 Locations on ‘Fiber-2' linlcage map CH Marker Pop." Length(cM) Loc.(cM) CH Length(cM) Loc.(cM) % ABC155 Con2 195 159 81.5 7 335 169 50.4 7 MWG2249 IxF 239 195 .81.6 7 335 319 95.2 HVM6 S x M 202 188 93.2 I 325 32 9.8 ABG20.il I 325 167 51.4 BTA02 S 4 R4 4 350 305 87.1 ABC156.1 S3 RB 5 390 84 21.5 I MWG2261 S 5 R5 5 390 98 25.1 I MST109 S 6 R6 6 313 5 1.6 I HVGNIRE 6 313 53 16.9 ABG064 S 6 R7 7 335 0 0.0 a The location of anchor markers were surveyed at a web site, http://genome.cornell.edu/cgi-bin/WebAce /webace?db=graingenes with ‘Locus’ search category. Barley consensus 2(Con2) linkage map was primarily used to determine the relative locations o f anchor markers among barley chromosomes. If Con2 did not contain the locus, three other linlcage maps were screened; Steptoe x Morex (SxM), Igri x Franka (IxF), and Chebec x Herrington (CxH). SSR markers were integrated into the Sx M linlcage map by Saghai-Maroof e t a/.(1996). b The locations o f each locus were expressed as the percentage of the linlcage map length. c Chromosomal location of the STS-PCR product was previously reported (see Table 2). d The numbers followed by S and R indicate chromosomes (see Table 2), where S and R stand for the wheat- barley additional line test (S) and the original location o f RFLP probes (R), respectively. In total, 3 6 anchors were incorporated into the ‘ linkage skeleton’ and 7 markers were remained as ‘un-localized’. However, 5 of these markers (BTA02, AB C15 6.1, MWG2261, M ST l09, and ABG064) still had very useful information- the potential chromosomal locations revealed by ‘wheat-barley additional line’ test, and the original location of KFLP probes (Table 2; Table 6), so that those markers could be tentatively assigned to chromosomes. In summary, the ‘linkage map skeleton’ included a relatively large number of reasonably well-distributed previously-mapped anchor markers. Furthermore, the assigned chromosome numbers and relative positions of anchor markers in each chromosome were also utilized. 42 Still large gaps remained on every chromosome except chromosome I. For example, chromosome 2 did not have anchor markers over 70% of its recombinational length (Table 6). The majority of these gaps were filled with AFLP markers. Pseudo-linkage Separation The pseudo-linkage of Ch3 -Ch6 included 26 markers which separated appropriately into two groups at LOD 5.5. The markers belonging to chromosome 3 were located on chromosome 3's long (m) arm, while those belonging to chromosome 6 marked the short arm. Among of the 14 partial chromosome 6 markers, including MWG620 as an anchor on ‘linkage skeleton’, MSTl 09 was also included. The potential chromosomal location o f MSTl 09 was determined as barley chromosome 6 (Blake et a l, 1996; Table 2). The pseudo-linkage of Chl-Ch? also resolved into two groups at LOD 5.0. Among o f 17 markers in C h l-Ch7 pseudo-linkage group, FTVMb (chromosome 7 anchor marker; genotyping data in Table A -1) could not be separated from chromosome I anchor markers (wx and M-HVWAXYG) even when a very high threshold (LOD 10) was applied, so that map distortion at HVM6 was allowed in the ‘Fiber-2' linkage map (Figure 5, top of the chromosome I). Linkage Map Construction with All Available Information Marker grouping was carried out using Mapmaker. All markers were grouped with a high threshold level, LOD 8 and a 5 OcM maximum distance, and confirmed that there were no conflicting anchor markers. Reduced threshold levels were applied step by step (LOD 0.5 43 intervals) to increase marker usage. If any group showed an anchor marker conflict, newly incoming markers, due to the reduced threshold, were not used to increase marker number in that group and were treated as ‘ Still-Orphan’. Threshold level was reduced down to LOD 2.5. During this procedure, 161 markers were assigned to groups having at least one anchor markers on the ‘linkage skeleton’. Those markers were imported into both MAPL and MapManager software. The marker orders in each group Were rapidly established with the automated ordering procedure in MAPL. The groups assigned to each chromosome were lined up on one chromosome depending on the information of ‘linkage map skeleton’ with MapManager. The orientation of each group were also determined with MapManager comparing two-point linlcage analysis data. The predetermined marker orders with MAPL were re-tested and confirmed. Using the ‘Distribute’ and ‘Report’ commands in MapManager, the remaining 60 markers were placed on the map (mostly those markers treated as ‘Still Orphan’). Chromosome 2, 3, and 4 still had disconnected two linlcage groups. These groups were connected with most likely AFLP markers (FL1104S for chromosome 2, and FL1808S for chromosome 3). Due to the gap between the short arm region of chromosome 4 and a small group tagged with anchor marker BCD402, three AFLP markers (FL0605A, FL1805A, and FL0411S) were used. During additional marker placement those markers classified as AFLP marker group ‘B; weak band but countable (Table 4, Table 5) were not added. The 199 markers (3 morphological, 2 protein, 30 STSs, 14 SSRs and 150 AFLPs) used for the ‘Fiber-2' linlcage map covered a distance of 2370 cM, corresponding to approximately 12 cM per marker. The percent of the genome within 20 cM to the nearest marker was 97 44 %(Figure 5). Chromosome I had the largest number of markers. Chromosome 5 displayed the longest genetic length. Forty two markers (ABG20.11b and 41 AFLPs) were not consistently ordered along the respective chromosomes by multi-point analysis and, therefore, were not included in the map. In total, 150 AFLP markers(78%) were used in the Tiber-2' linkage map. The best-characterized AFLPs, (class S and A) were almost completely used (95%), while only 46% of class B AFLPs were used (Table 5). The relative location on each chromosome of anchor markers showed good alinement between ‘linkage skeleton’ and Tiber-2' linkage map except cMWG694 tagged region. The map position of this marker was on the bottom of long arm in ‘ Chebec x Herrington’ population, the position in Tiber-2' linkage map was on short arm region (Table 6). Seven markers on the Tiber-2' linkage map showed segregation distortion (P<0.001). Two AFLPs were on chromosome 2, and the other 5 AFLPs were anchored by MWG938 and placed on the short arm of chromosome 5 (Figure 5). 45 CM - 0.0 Ch l 335.6 CM £ ■100 '150 ■200 ■250 ■300 • FL0308S FL1505S FL1308S FL1711A -FL0207S -M-HVWAXYG \-w x HVM6 • FL0904S ABG380 FL1202B FL1205S FL1511B FL1204S •FL0304S FL1009B FL0504S FL1302A FL1305S FL1306B M-HVCMAa • M-HVCMAb FL1105S n -WTAl LABG20.lla Ik2 '-FL1004S Ch.2 374.3 cM FL1507A t - t , F L 1 4 0 4 S • FL1405S LFL1003S FL1503A FL1515A FL0910B .FL0909A FL0203S -FL0508A ■ ABC253 FL0801S ■FL1109S " FL1513A FL1506A FL1309B M-HVPRPl B '350 FL1501S ABG058 HVM36 ■ FL0205B FL1106S -FL1107S CMWG694 ' FL0906S • FL0908S Ch.3 258.4 cM - -FL0603S \ : L - - FL0608A \' t -ABG070 -FLl 112S -FL1717A -FL1713A - FL0602A ‘ FL1614S ■ FL1806S - HVM060 ■ FL1807S " FL0410S FL0306A FL0502B •FL1612B • FL0311S ' r FL1413S FL1412S FL0806S MWG549 FL1102B FL1604B FL0305A FLlOlOB ■ FL0702A -HVM054 - FL0303A ABG609 M-HVCSG FL1108B FL1603A FL0607B FLI602A ChA 361.5 cM * FL0310A -FL1601S ■FL1209S ABC303 ■ FL0609S HV M3 MWG 058 FL1705S FL1414S HVM68 L ABG618 ABG054 FL1310S FL1311S FL0701S FL0605A FLI805A ■ BTA02a BTA02b - BCD402 -FL0409A ■ FL1715B Ch.5 390.0 cM -FL1609B - FL06Q1B - FL0902B - FL04Q1A - FL1701S • FL0805A -Hor 2 H orl -ABC156.1 ' MWG2261 • FL0802S •FL1605B •FL1512B FL0415S • FL0416S - FL0206B ■ FL1208A ABG452 FL1703S FL0414S • FL0606A ABGSSa ■ ABGSSb Ch.6 Ch 7 315.8 cM 330.8 cM ■ - ABG064 - - ABC483 - - FLI005A - - FL1802A - - FL180IA - - FL1712S - - FL0407A v FL0406A - - FL0803S - - FL0405S /-M-HVDHN9a '-M-HVDHN9b - -FLUIDS ‘ -FL1401S . . r FL0408B - - - FL1312A -FL1508S - — FL1411B - - FL1702B - " ABClSS • - —FL0301S - - — FL0501S ■FL1708S FL1707S - - -FLlSlOB - - -FLllO lS ■ -FL0506A - - FL1006B - - FL1613S FL1409B FL1809S FL0307B - -F L l 608A - - FL1415S - - MWG2249 - - FL0202S " -FL0312S — 400 (Kosambi mapping function) Figure 5. Linkage map of the ‘Fiber-2 (MTaz90-l 23 x MTH6860756)’ mapping population. Linkage map containes 199 markers (3 morphological, 2 protein, 30 STS, 14 SSR, and 150 AFLP markers) and covers a distance of 2370 cM. The location o f anchor markers used in ‘linkage map skeleton’ construction (Table 6) are indicated with bold letters. Underlined markers indicate the segregation distortion (P<0.01) (Table A-l). Map distance are centimorgans calculated using the Kosambi mapping function. Detail marker intervals are in Table A-2. 46 Discussion The morphological markers Qk2, n and wx) were showed 2 times higher heterozygosity levels than expected value (6.25%) in F5 generation. Three times higher heterozygous types than the expected level were reported by Burr and Burr (1991) in two maize RILs5 and Mano et a/. (1999) reported the level of heterozygosity in barley RILs was 3 times higher than expected level. The result of unintentional selection against plants with low fertility could be one reason of identified high level of heterozygosity (Paran et al., 1995). Another possible reason may be that the relatively high adaptability of heterozygous types to stresses may affect the frequency of genotypes (Mano et a l, 1999). During the development o f the RIL5 there was no fertility barrier between parents, meanwhile, some lines were lost during single seed descent (Blake, personal communication). Also, the population is small (n=59), the morphological markers lie on the same chromosome and this level o f deviation from expectation may merely be the result of inadequate sampling. Segregation ratios of observed genotypic frequencies may depart from the expected frequencies. Altered survival among some classes of gametes or zygotes could be one of the genetical reason (Harushima, et al., 1996). Genotype classification error could be one o f the reasons that segregation ratio distortion occurs. Segregation distortion affects linkage tests and the estimation of recombination fractions (Lorieux et al, 1995), and few software can treat the distorted marker classes with very limited range (Manly, 1999). 47 Segregation ratio was seriously skewed at the Waxy locus (20: 8: 31, for JVxJVx: JVxwx: wxwx at F5 generation) (Table A -1). Bezant et al (1997) reported that segregation distortion was detected on barley chromosome I, with the wx locus in the center o f the distorted region, and explained that Iinlcage of a gametophytic factor, Ga, to the wx locus in barley causing segregation distortion. Frylanan and Bengtsson (1992) also reported seriously distorted segregation ratio in waxy-heterozygous barley plants. However, the other markers around wx locus on ‘Fiber-21 linleage map did not show segregation distortion (Figure 5; Table A -l). Classification of AFLP markers by their band strength and clarity helped to control problems derived from artifact-based scoring of detected AFLPs (Table 5). Out of 63 class ‘B ’ AFLPs, only 29 (46%) were used, so that the percentage of class ‘B ’ AFLP markers was only 14% in ‘Fiber-21 linleage map (Table 6). Five distorted markers AFLP markers ( f <0.01) were mapped on chromosome 5 (Figure 5). The level of polymorphism is a reflection of the genetic distance between the two parents. In this population, SSR analysis showed 27% polymorphism (12/44), while 60% polymorphism was reported in the Steptoe x Morex cross (Liu et al., 1996). Comparative AFLP and STS analysis of the Steptoe x Morex vs. the ‘Fiber-2' populations showed a similar 2-fold higher polymorphism frequency in Steptoe x Morex. The relative level of polymorphisms in this cross was lower than in of ideal mapping populations, and resulted in gaps in the ‘Fiber-2' linkage map. At first, we expected that the AFLP technique would be a helpful to overcome two potential problems underlying linleage map generation of real breeding populations- I) 48 enough number of informative markers and 2) even marker spacing across chromosomes, and it is clear that enough number of markers were successfully developed with the technique (191 AFLP markers). It should be noticed that, under this study, just Pst I and Mse I digested-DNA templates were used to generated AFLP markers from recombinationally active chromosomal regions ( Finnegan et al, 1998; Castiglinoi et al, 1999). The ‘Fiber-21 linkage map showed that all mapped AFLP markers were randomly distributed across chromosomes and chromosome regions. The major difficulty in constructing the ‘Fiber-2' linlcage map was the failure of markers to resolve into seven linlcage groups due to the lack of polymorphisms at certain chromosomal areas. These regions are presumably recombinationally active, but monomorphic between the two parents. Consequently, without the aid o f anchor markers, the groups containing only AFLPs tended to be ‘orphan groups’. Therefore, there should be more effort made in developing and choosing anchor markers to generate linlcage maps for real breeding populations, in which genetically close elite lines are the starting point o f new breeding programs. \ 49 CHAPTER 3 MAPPING QTL CONTROLLING IMPORTANT CHARACTERS IN THE FIBER-2 POPULATION Introduction The objective of this chapter is to identify the markers linlced to QTLs controlling high fiber content (|3-D-glucan) and grain yield, including three other important traits- kernel weight, heading dates and plant height, within ‘ MTaz90-123 x MTH6860756' population and determine the genetic basis for poor agronomic performance in waxy-hulless barley lines. In this study, the ‘Fiber-21'linlcage map (chapter 2) was used. Hulless barley varieties suffer from poor yield (Goering et al, 1973; Eslick et al, 1990; Blake et a l, 1990). Three genes, wx, Ik2, and n, have been implicated as important contributors to hulless waxy barley [3-D-glucan content and also have been shown to contribute to grain yield reduction (McGuire and Hockett 1981; Swanston, 1995; XueetaZ., 1997; Czuchajowska et a l , 1998). These analyses unfortunately confound linlcage, linkage drag, background effects and small sample sizes to varying degrees. The availability of molecular marker-based linlcage maps makes it possible to dissect quantitative traits into discrete genetic factors. With a good map and mapping population, 50 all regions o f a genome can be assayed for gene content by interval analysis (Lander and Botstein, 1989; Haley and Knott, 1992). Having the ability to identify specific quantitative trait loci (QTL) may lead to more powerful means of investigating the number o f genes impacting a character, their primary effects and their interactions (Edward et al, 1987). The multilocus approaches that have been developed for marker or interval by phenotype interaction analysis are extensions of standard methods which were adopted to map single genes and their effects on phenotype (Kearsey, 1998). The ‘composite interval’ concept permits the use of estimation procedures to propose the presence of one or more QTL at different positions throughout the genome, then translate the data into a statistical model which can evaluate the plausibility of the hypothesis. The most common QTL analysis approaches evaluate intervals between pairs of markers for the presence of QTL (Simple interval mapping; Lander and Botstein, 1989; Haley and Knott, 1992). However, simple interval mapping is an insufficient approach for QTL mapping if multiple QTL of dramatically different effect simultaneously segregate in a population, or if QTL are poorly marked (Liu, 1998; pp 417-444). Three easily identified genes typically used in construction o f high fiber hulless barley varieties (waxy, Ik2, and n), are generally believed to have maj or effects on (3-D-glucan content and grain yield. All are located on barley chromosome I (Kleinhofs, 1996), and Ik2, and n are closely linlced to each other. Therefore, it is unreasonable to treat these genes as independent factors to estimate those gene effects with ‘linear model’ or ‘simple interval mapping’ procedures. 51 Jansen and Stam (1994) and Zeng (1994) proposed another method called composite interval mapping for dealing with multiple QTLs simultaneously to solve the non­ independence problem. This analysis method increases the resolution of QTL location by controlling residual noise in the model using markers other than the markers immediately flanking the segment. The problems associated with QTL analysis are numerous. If individual QTL effects are small, the number of informative meioses is limiting, or the complexity of the characters of interest is large, marker-assisted selection may not prove helpful. However, marker- assisted selection has proven useful in many instances (Larson et al, 1996; Spaner et a l, 1999). 52 Materials and Methods Plant Materials Population development procedures are described in Chapter 2. The ‘Fiber-1' population contained 61 individuals, while the ‘Fiber-2' and ‘Fiber-3' populations contained 59 and 96 lines respectively. The linkage map was generated using the ‘Fiber-2' population (Chapter 2). Field Experiments Table 7 describes methods of trait measurement, experimental conditions, and agronomic traits measured of the three populations in each field trial. F5 (‘Fiber-2' and ‘Fiber-3') and F4 (‘Fiber-1') derived seed was advanced from each individual lines through two years (in 1996, 1997) to generate sufficient seeds for replicated year trials, and P-D- glucan content analyses were performed with the ‘Fiber-1' (in 1997) and ‘Fiber-2' (in 1996, 1997) populations. Populations were evaluated over two years in replicated field trials at the Arthur Post Research Farm near Bozeman in irrigated and dryland fields. Lines in each experiment were replicated as two randomized blocks. In 1998, each plot was 4-row (4m per row), seeded at a rate o f 40g/plot. In 1999, 2-row plots were utilized. Four agronomic traits, grain yield, p-D-glucan content, heading date and plant height, were measured following the methods described in Table 7. 53 Table 7. Description of agronomic traits measured and field trial conditions Agronomic traits Trait (Short name) Units Method of measurement Grain yield (YD) kg/ha Weight o f barley grain harvested per unit area (excluding hull weight in hulless lines). Kernel weight (KW) mg/seed Average weight of an individual barley kernel (excluding hull weight in hulless lines) from a sample 500 grains. (3-D-glucan (GU) % (3-D-glucan content was determined on duplicates o f air-dried ground grain according McCleary and Codd (1991) using a commercial kit (Megazyme International Ireland Ltd., Wicklow, Ireand). Heading date (HD) days Number of days until emergence 50% o f heads on main tillers. Plant height (PH) cm Average plant height measured from soil surface to the top of spike (excluding awns). Field trial conditions" Fiber-2 Fiber-3 Fiber-1 Year F5 59 lines F5 96 lines F4 61 lines F ieldb P lotc Ear.d Fieldb P lotc Ear.d Fieldb P lotc Ear.d 96' Irri. I HS Irri. I HS Irri I HS 97' Irri. 2 HS Irri. 2 HS Irri 2 HS 98' Dry 4 SC Irri 2 HS Dry 4 SC 99' Irri. 2 HS Irri. 2 HS Not evaluated List of traits measured in each year tested6 96' GU 97' GU GU 98' YD, HD, PH, KW YD, HD, PH YD, HD, PH 99' YD, HD, PH, GU, KW YD, HD, PH, GU a Seed was advanced from each individual lines during two years (96' and 97') to generate sufficient seed for replicated year trials in 98' and 99'. b Bozeman dry and Irrigated (Irri.) land fields. c Numbers o f row(s) in each plot. One row is approximately 1.5 m2. d Harvesting methods (Ear.). Sfraw was cut using hand sickles and grain was mechanically threshed (HS) or plots were harvested with a small plot combine (SC). e Grain yield (YD), Heading date (HD), Plant height (PH), (3-D-glucan content (GD), and Kernel weight (KW). 54 Due to seed limitations, one line in the ‘Fiber-2' population (line #8) was planted at half the normal seeding rate (0.5g/ft) for each plot in 1998, and this line was treated as missing data for grain yield in 1998. Straw was cut using hand sickles and grain was mechanically threshed (I and 2 row plots) or plots were harvested with a small plot combine (4 row plots, in 1998). Harvested grain was cleaned before weighing, and the hulls of hulless lines (ri) were excluded from the measurement. Ten grams of seeds were randomly chosen and ground and air-dried before assaying the (3-D-glucan content using a commercially available enzymatic method (McCleay and Codd, 1991 Megazyme International Ireland Ltd., Wicklow, Ireland). Kernel weight measurement were conducted separately in 1999 with the seeds produced in 1998 and 1999 for only ‘Fiber-21 population without replications to infer the genetic effects of two closely linlced genes, n and Ik2 (see below). Analysis of Agronomic Traits Primary statistical analyses were performed using SAS (S AS Institute, 1988; version 6.10). Analysis of variance was performed to detect differences between years, blocks, and recombinant inbred lines for each trait using the PROC ANOYA and GLM procedures (S AS Institute, 1988). For the QTL analysis, the ‘Fiber-21 field trial data was used. All measured traits were named depend on the trait measured and year tested (YD98, YD99 for grain yield, HD98, HD99 for heading date, PH98, PH99 for plant height, GU96, GU97, GU99 for P-D-glucan contents, and KW98, KW99 for kernel weight). 55 Single Marker OTL Analysis fSM(J) Single marker QTL analysis (Falconer and Mackay, 1996; pp361 -363) was conducted to screen the association between marker alleles and all scored traits, and establish a reference for simple- and composite- interval mapping. The LRmapqtl procedure in QTL Cartographer (Basten et a /.,1999) was used for 191 markers in the ‘Fiber-2' linkage map. This procedure does not report the R2 values (the amount of total phenotypic variance that is explained by the specific molecular marker of each markers). Therefore, PROC GLM was. used to estimated the R2 values for each marker for each trait surveyed. We included the 42 remaining ‘orphan’ markers in the PROC GLM procedure. OTL Mapping QTL mapping was conducted using two methods, simple interval mapping (SIM; Lander and Bostein, 1989 for maximum likelihood procedure; Haley and Knott, 1992 for multiple regression approach), and composite interval mapping (CIM; Jansen and Stam, 1994; Zeng, 1994). Martinez and Curnow (1994) mentioned that one of the advantages of multiple regression based interval mapping is that it is easy to generalize to use the information from more than one pair of markers at the same time, and this advantage (simultaneous use of three markers) reduces the impact of linlcage drag-based QTLs in neighboring regions and thus helps permit discrimination between the presence of one or two QTLs. We thought that this might help to distinguish among multiple effects mapping to barley chromosome I to 56 which Ik2 and n are belong. We utilized a multiple regression based QTL mapping program, PLABQTL (Utz and Melchinger, 1999) for this QTL analysis. Log-transformation of the phenotypic data was used for 3 surveyed traits (HD98, HD99, and GU99) to improve the normality. After conducting analysis, the reported results of three traits were re-transformed for easy comparison with other traits surveyed. For CIM, the background markers (cofactor) were selected by the ‘Forward and Backward stepwise regression’ (FB) search option of the SRmapqtl procedure o f QTL cartographer. ■ A threshold level(p<0.05) was used for the partial F1-Statistics [p(F-m), and X-F-out)]. Those pre-selected background markers were used to run composite interval mapping with PLABQTL. To control type-I error (false positive), empirical critical values were constructed by 1,000 permutation tests (Churchill and Doerge, 1994) in interval mapping for each trait surveyed. The permutation tests were run with PLABQTL on a PC with a I OOMhz CPU. A five percent type-I error level was applied as threshold in each analysis. All detected QTLs were named dependent on the trait surveyed, the interval mapping method used, and the chromosomal position. Estimation of Additive Effects of Detected QTLs Each detected QTL was reported by LCD score, percentage o f phenotypic variance (R2%), and additive value under the rejected null hypothesis (no QTL at the tested positon). The phenotypes of individuals in the population are estimated in a combined manner using QTLs and undetectable latent genetic components, such as weak QTLs and interactions. 57 Simultaneous multiple regression on all detected QTLs was conducted on each trait surveyed to obtain final estimates of the additive value. At this time, those QTLs having non­ significant partial R2 values (the relative importance among detected QTLs) were discarded manually, and we re-ran the multiple regressions on only those QTLs having significant partial R2 values with the SEQUENCE command in PLABQTL. Applying permutation test to establish threshold level for CIM analysis has been suggested by some researchers (Tinker and Mather, 1996). Therefore, if any minor QTLs displayed significant F-statistics with reasonable R2 values by SMQ, the closest markers to those minor QTLs were also considered as additional variables for multiple regression models. As a reference to those detected QTLs, the SMQ result (R2 value and F-statistics with the significance level) of the flanking markers closest to each QTL was also reported. 58 Results Analysis of Agronomic Traits T able 8 describes that the ranges of variation among recombinant inbred lines in each trait surveyed. |3-D-glucan contents showed unimodal distributions in each population. Population means ranged from 5.56 % to 6.14 %, while the parents showed about 4% and 8% (3-D-glucan content except in 1999. In this year, the MTaz90-123 mean was much lower than in 1996 and 1997, and progeny were skewed toward MTaz90-123 (in the ‘Fiber-21 population. Skewness was 0.66*). Heading date analysis showed population means which were skewed toward the maternal the line mean (in the ‘Fiber-2' population, Skewness were 1.06* and 0.69*, for HD98 and HD99, respectively). Skewness indicates that the effect of gene substitution might be non-linear for (3-D-glucan contents and heading date QTLs in the ‘Fiber-2' population. Weller (1992) mentioned the possibility of ‘higher-order’ QTL effects on traits exhibiting either the skewness or kurtosis. The 1999 nursery was seeded a month later than would normally be considered optimal. Still, the mean difference between the two parents for heading date was greater in 1999 than in 1998. The mean of kernel weight was also much greater in 1999 than 1998. None of the measured traits of the ‘Fiber-2' mapping population showed bimodal distributions, although heading date data showed some skewness. ANOVA detected significant differences among recombinant inbred lines for each trait in all years tested 59 (Table 9). Since year x line variation was large for all traits surveyed (Table 10) all trait data over years kept separate and separate QTL analyses were performed for each year. Single Marker OTL Analysis of ‘Fiber-2' Mapping Lines Table A-2 provides a ‘quick view’ of putative QTLs for each trait. Chromosomes I, 2 and 7 carried genes which had a strong effect on phenotype over locations and years. Grain yield, kernel weight, and (3-D-glucan content were affected by genes in the vicinity o f n and Ik2 on the long arm of chromosome I . (3-D-glucan content was strongly impacted by genes around wx on chromosome I and by a gene near ABC483 on chromosome 7, and wx also affected kernel weight. Plant height and heading date were impacted by genes on chromosomes I (ABG380) and 2 (ABC170). During testing the 42 orphan markers with PROC GLM procedure, FLl 81OA was identified as another marker heading date and plant height-related genes, and this marker explained over 20% phenotypic variation o f heading date and plant height (21%, 28% and 27% for HD98, HD99 and PH99, respectively). The most likely position o f FL l 81OA was estimated around ABG380 on chromosome I (3OcM apart with LOD 1.2), and displayed a smaller additive effect than ABG380. Meanwhile, regression models did not explain additional variation by adding FLl 81OA to the effect of ABG3 80. During interval mapping, therefore, FLl 81OA was not included in estimating genetic effects with multiple regressions on detected QTLs, since we could not determine a map location for it. Table 8. Descriptive statistics o f agronomic traits measured Population Traita Descriptive statistics b Parental line mean Mean Std. dev Coef. var(%) Skewness Kurtosis Min. Max. MTaz90-123 MTH6860 Fiber-2 YD98 5156.67 547.00 10.61 -0.03 -0.40 3756.5 6157.5 4251.23 5861.56 YD99 4159.93 . 549.63 13.21 0.45 0.06 3073.4 5624.7 4051.33 5182.35 KW98 42.49 3.961 9.32 -0.02 -0.31 33.0 51.2 37.84 44.42 KW99 47.24 4.04 8.55 -0.07 -1.26* 40.3 53.9 42.24 48.85 GU96 5.71 1.10 19.29 0.09 0.33 2.5 8.2 8.1 4.4 GU97 6.14 1.00 16.33 0.20 -0.64 4.4 8.3 8.0 4.2 GU99 5.56 1.08 19.36 0.66* 0.17 3.7 8.4 6.2 3.8 HD98 182.23 2.16 3.56 1.06** 0.40 179.0 188.5 182.0 184.0 HD99 189.54 2.86 5.78 0.69* -0.86 186.0 195.5 189.5 193.0 PH98 84.03 4.05 4.82 0.00 0.06 74 93.5 81.5 85.5 PH99 83.64 6.95 8.31 -0.23 -0.72 69.2 96.8 83.0 87.3 Fiber-3 YD98 3789.22 921.15 24.31 0.21 -0.55 1560.19 5898.08 3956.11 5397.54 YD99 4257.41 601.20 14.21 -0.09 -0.09 2641.23 5672.58 3948.63 5131.24 Table 8. (continued) Population Traita Descriptive statistics Parental line mean Mean Std. dev Coef. var(%) Skewnessb Kurtosisb . Min. Max. MTaz90-123MTH68607 Fiber-3 GU97 5.40 1.70 31.48 -0.43 1.31* 0.6 10.1 8.1 3.9 GU99 6.20 1.40 22.58 0.72* 0.53 4.0 10.6 7.5 4.4 HD98 178.63 2.61 4.45 1.07** 0.24 175.0 186.0 178.5 181.5 HD99 189.21 2.42 4.92 1.05** 0.45 186.0 196.0 189.0 193.0 PH98 75.44 4.13 5.47 0.40 0.08 66.5 85.5 71.5 77.0 PH99 82.17 7.22 8.79 0.40 -0.53 66.5 99.3 81.2 85.2 Fiber-1 YD98 5287.45 559.26 10.58 -0.20 -1.05 4003.53 6309.34 4441.45 5930.27 HD98 181.70 ' 1.40 2.27 1.04** 2.07 179.0 186.0 182.0 184.0 PH98 86.3 3.5 4.06 -0.33 -0.59 77.5 92.5 83.0 87.5 a Grain yield (YD; kg/ha), Kernel weight (KW; mg/seed), P-D-Glucan content (GD; %), Heading date (HD; days), and Plant height (PH; cm), with the year traits measured. b Standard deviation (Std. Dev), coefficient of variance (C.V. %), minimum (Min.), and maximum (Max.) Significant deviations from the normal distribution are indicated with * and ** at P< 0.05 and P< 0.01, respectively. 6 2 Table 9. ANOVA for agronomic traits of the ‘Fiber’ populations in each year trials. Trait(unit) Year Source Fiber-2 Fiber-3 Fiber-1 Between df MS df MS df MS Grain yield 1998 Replicates I 353.747 ** I 318.141 * I 0.286 Lines 57 59.834 ** 95 169.699 ** 60 63.873 ** Residual 57 9.524 95 55.687 60 15.262 Total 115 191 121 1999 Replicates I 20.706 I 525.595 ** Lines 58 60.427 ** 95 72 .288* Residual 58 19.844 95 51.245 Total 117 191 (3-D-Glucan 1996 Replicates I 0.001 Lines 58 2.430 ** Residual 58 0.041 Total 117 1997 Replicates I 1.096 ** I 0.119 Lines 58 2.430 ** ■ 94 5.071 ** Residual 58 2.017 94 0.068 Total 117 189 1999 Replicates I 0.194 ** I 0.418 ** Lines 58 2.313 ** 95 4.091 ** Residual 58 0.022 95 0.027 Total 117 191 Heading date 1998 Replicates I 0.000 I 318.141 * I 0.295 Lines 58 9.814 ** 95 169.699** 60 3.623 ** Residual 58 0.224 95 55.687 60 0.362 Total 117 191 121 1999 Replicates I 353.747 ** I 0.034 Lines 58 59.834 ** 95 16.402 ** Residual 58 9.524 95 0.620 Total 117 . 191 Plant height 1998 Replicates I 20.347 I 41.255 I 5.541 Lines 58 32 .800** 95 33.891 ** 60 24.376 * Residual 58 11.347 95 11.771 60 14.058 Total 117 191 121 1999 Replicates I 11.204 I 20.672 Lines 58 96.564 ** 95 102.453** Residual 58 7.317 95 9.043 Total 117 191 Kernel w tb 1998 Lines 58 15.69 (mg/seed) 1999 Lines 58 16.31 a One line (#8) was missing data. b Kernel weight (Kernel wt) was measured without replications. *, ** are levels o f significance atP< 0.05 and P< 0.01, respectively. 63 Table 10. ANOVA for agronomic traits measured in ‘Fiber-21 and ‘Fiber-3' populations over seasons. Trait Source of variance Fiber-2 Fiber-3 59 lines 96 lines Between df Mean Square R2(%)' df Mean Square R:(%)' Grain y ie ldb Replicates I 271.385** 1.9 I 830.786** 2.3 Lines 58 60.541** 23.7 95 160.324** 39.2 Years I 5811.299** 39.3 I 2104.596** 5.8 Lme x Year 57 59.719** 23.0 95 81.6630 24.5 Residual 116 15.490 12.1 191 53.254 28.2 Total 233 100.0 383 loo.d Kernel weightc Lines 58 29.086** 26.3 Years I 664.407** 67.7 Residual 58 2.908 6.0 Totla 117 100.0 P-D-Glucan Replicates I 0.701** 0.2 I 0.046 0.0 Lines 58 4.979** 67.3 95 4.377** 45.3 Years 2 15.171** 7.1 I 43.560** 4.7 Line x Year 116 0.891** 24.1 94 4.782** 49.0 Residual 176 0.031 1.3 190 0.050 1.0 Total 353 100.0 381 100.0 Heading date Replicates I 0.017 0.0 I 4.594** 0.0 Lines 58 24.979** 13.0 95 24.652** 21.6 Years I 9584.814** 85.9 I 8381.344** 77.0 Line x Year 58 1.236** 0.6 95 0.796** 0.7 Residual 117 0.419 0.4 191 0.395 0.7 Total 235 100.0 383 100.0 Plant height Replicates I 30.874 0.4 I 60.1670 0.3 Lines 58 76.600** 51.5 95 113.333** 56.3 Years I 8.695 0.1 I 4143.305** 21.7 Line x Year 58 52.765** 35.4 95 23.011** 56.3 Residual 117 9.258 12.6 191 10.362 10.3 Total . 235 100.0 383 100.0 a The ratios o f the sum of squares of each variance component were expressed with the percent of total sum of square. b One line (#8) was missing data in 1998. c Kernel weights were measured without replications in each year trial. *, ** are levels o f significance at P< 0.05 and P< 0.01, respectively. 64 OTL Detection with Interval Mapping; SIM and CIM To control Type-I error during interval mapping, permutation test were used, and 5% Type-I level was applied to all analysis. Table 11 shows the estimated empirical threshold values for both interval mappings of each trait tested. Figure 7 shows the background markers chosen with stepwise multiple regression for composite interval mapping. Figure 6-A (SIM) and Figure 8-A (CIM) offer a view of the chromosomal locations o f detected QTLs. Scans of test statistics of interval mappings are given in Figure 6-B (SIM) and Figure 8-B (CIM). To compare the chromosomal locations of putative QTLs, map locations o f five markers (SIM; Figure 6-B), and 10 chromosome segments (CIM; Figure 8- B) were used as ‘reference points’ across traits measured and years tested. The numerical summaries of interval mapping results are organized in Table 12 (SIM) and Table 13 (CIM). No interval mapping methods completely account for all heritable variance (‘QTL summary’ column), due to extreme individuals (including experimental outliers) and non-perfect algorithms (Kuittinen et ah, 1997). Furthermore, some minor CIM QTLs provided uncertain allelic effects, especially where SMQ showed very low significance without clear repulsion linkage as in the cases of the long arm of chromosome 7 in YD98 and HD98 trials. Therefore, to avoid wrong interpretation of interval mapping data, two additional columns are included; ‘MR on putative QTLs’ and ‘SMQ of the closet marker’ column in Table 12 and Table 13. Table 11. Empirical threshold values estimated for simple- and composite- interval mapping Genome-wise type-I error values for LOD scores Simple interval mapping (SIM) Type-I error level YD98 KW98 KW99 GU96 GU97 GU99 HD98 HD99 PH99 30% 2.15 2.21 3.10 2.15 2.23 2.21 2.18 2.21 2 2 6 25% 2.29 2.29 3.27 2 2 6 2.32 2.32 2 2 9 2.33 2.38 10% 2.79 2.78 3.91 2.68 2.89 2.80 2.75 2.83 2.85 5% 3.15 3.06 4.50 2.99 3 2 8 3.24 3.06 3.19 3.22 1% 3.75 3.97 5.47 3.62 3.88 3.77 3.73 3.95 4.07 Composite interval mapping (CIM)b Type-I error level YD98 KW98 KW99 GU96 GU97 GU99 HD98 HD99 PH99 30% 2.76 3.10 4.50 2.96 3.02 2.87 2.76 2.43 2.62 25% 2.92 3.27 4.72 3.08 3.13 3.05 2.88 2.52 2.75 10% 3.46 3.91 5.71 3.74 3.76 3.6 3.50 3.02 3.32 5% 3.84 4.50 6.24 4.25 4.11 4.12 3.85 3.38 3.82 1% 4.96 5.47 7.72 5.14 5.32 5.14 4.64 4.21 4.72 a The statistical genome-wise type-I error rate was controlled by means o f 1000 permutation tests (Churchill and Doerge3 1994) for each trait surveyed. b Background markers were selected by ‘Forward and Backward stepwise regression’(PB) search option of SRmapqtl procedure in QTL Cartographer (version 1.13; Basten e t a l , 1999). Threshold level 0.05 was used for the partial F statistics [^(F-in), andJc(F-Out)]. 6 6 GU96-SQ1 GU97-SQ1 GU99-SQ1 C h l 335.6 cM - FL0308S FL1505S FL1308S ' Iv-F L H llA - »L FL0207S M-IlVW AXYG Wx HVM6 - - FL0904S HD98-SQ1 HD99-SQ1 X w ABG380 ✓ ^ _ /“ FL1202B - — FL1205S • -FL1511B I r- FL1204S X-FL0304S YD98-SQ1 KW98-SQ1 KW99-SQ1 YD98-SQ2 KW98-SQ2 YD98-SQ3 KW98-SQ3 KW99-SQ2 - - FL1009B - - FL0504S . r FL1302A - - FL1305S - - FL1306B ) — » [i M-HVCMAa M-HVCMAb F L l105S WTAl ABG20.11a //C2 •FL1004S '-FL1003S L FL0413A - - F L 1503A : -F L lS lS A FL0910B t FL0909A FL0203S FL0508A ABC2S3 FL0801S FL1109S FL1513A — FLI506A - - FL1309B ' “ M -HVPRPl B Ch.2 374.3 cM50 cM - - FL1507A : - FL1404S - y - FL1405S ■ V -FL1501S u ABG058 - - HVM36 - — FL0205BHD98-SQ2 HD99-SQ2 PH99-SQ1 ABC170 = - FL1106S X-FL I107S - - FL1104S - - CMWG694 - - FL0906S Grain yield (YD) Kernel weight (KW) - -FL0503A - -FL0603S P-D-glucan (GU) - - FL0608A Heading Date (HD) Plant Height (PH) ■ - - FL0702A - -FL1410B QTL position ; -H VM 054 ^ FL0303A ------- FL1408B • ---- ABG609 ------ M-HVCSG - - FL1108B • - - FL1603A ------- FL0607B - - FLI602A - - FL0404S Figure 6-A. The chromosomal locations of putative QTLs for simple interval mapping. Arrows on chromosome I and 2 show the putative locations of QTLs associated with each trait surveyed. The detected QTLs are reported with the QTL name used in Table 12. The location of anchor markers used in ‘linkage map skeleton’ construction (Table 6) are indicated with bold letters, and underlined markers indicate the segregation distortion (P O .01) (Table A-l). Detail marker intervals are in Table A-2. 67 Ik1 n FL1003 S YD98 Ch I 0 ]% KW98 Ch I 6 I£ 2; KW99 Ch I •» SQlj \sQ2 - GU96 \SQ1 Ch I ) L A J GU97 4 Ch I SQl GU99 to C h l 8 Isqi A -HH---1 HHN IHH Ulft n I N--IHt-H-W ABG380 ABG380 HD98 8 Ch I „ HD99 C hl PH99 Ch I Not significant, but same peak pattern +riH—I—H+ HD98 Ch 2 HD99 Ch 2 PH99 Ch 2 ABC 170 Figure 6-B. Scans of a test statistic for simple interval mapping. Scans are showed for nine traits as indicated. The grids in the small bars locating top and bottom of chromosomes show the marker locations (see Figure 6-A), and dotted lines across traits indicate the marker positions linked to detected QTLs, which are reported in Table 12. Horizontal dashed lines, in each panel, show thresholds for testing SIM at 5% genome-wise-type I error (Table 11). 68 OTLs for Grain Yield SIM on grain yield exhibited broad, trimodal peaks, with subpeaks near markers n (YD98SQ1), 6 cM distal from Ik2 (YD98SQ2), andnear FL0413A (YD98SQ3) (Figure 6-B; YD98 Chi). From the scan alone, it was not clear whether there were one, two, or three QTLs affected grain yield. The position near n explained more of the observed variation (R2, 43.5% at n) than the other two positions, and regression models including two other possible QTL positions showed that inclusion o f additional loci in the model did not improve the model (Table 12; R2 % and Part. R2 % of YD98). This suggested the presence of a single QTL near n with 387kg/ha as its estimated additive value attributed by MTH6860756 allele. It was possible, however, that there was more than one locus affecting grain yield near Ik2 and FL0413A. CIM with 11 background markers (Figure 7), produced surprising results. First, one QTL peak distal to the n locus was identified (Figure 8-B; segment C). YD98Q1 was flanked by FLl 105S and n with 3cM interval with 376kg/ha as the estimated additive value (Table 13). The difference for the peak near the n locus between SIM and CIM could be explained by increased QTL resolution with CIM method. Meanwhile, Ik2 still displayed a significant impact on grain yield (LCD 2.6) (Figure 8-B; segment C). Second, three QTLs were identified on chromosome 7 with very high estimated additive values. Two QTLs, YD98Q3 and YD98Q4 (Figure 8-B; segment I), were tightly linkedto each other(14cM) in repulsion phase on chromosome 7 (Table 13; QTL summary). Each QTL displayed 350kg/ha and 560kg/ha additive values by MTaz90-123 allele and MTH6860756 allele, respectively. Liu (1998, pp417-458) mentioned that when QTLs are linked in the repulsion phase, scans of test statistics underestimate their true effect. Only 69 conditional analysis can resolve the problem of repulsion linlcage phase for linked QTLs. SMQ results o f these distal markers FL1802A and FL0407A (Table 13; R2% in SMQ), support L iu’s contention (0.1% and 0.7% R2 values on yield, respectively). Furthermore, YD98Q4 showed a much higher estimated additive value (560kg/ha) than YD98Q1 near the n locus (376kg/ha). In both cases, favorable alleles were from MTH6860756. Another QTL on chromosome 7, YD98Q5 (Figure 8-B; segment J), showed 313kg/ha as its estimated additive value attributed by MTaz90-123 allele. This QTL was also identified with SQM (Table A-2). One more QTL, flanked by ABC 170 andFLl 106S, was detected on chromosome 2. This QTL, YD98Q2 (Figure 8-B;segment E), showed 193kg/ha as its additive value (Table 13; QTL summary). Simultaneous multiple regression was applied to all 5 putative QTLs to estimate their additive values in combined manner under the assumption of second parent (MTH6860756) always carries the favorable alleles; YD98fkg/ha! = 5115+ 446 fY D 980n+ 173(,YD9802')-278fYD9803t + 385tYD98Q4f - 184(YD9805~). This model explained 74.4% of phenotypic variation (Table 13; MR on putative QTLs). YD98Q1 near n locus showed the biggest additive value, otherwise, the other QTLs were greatly reduced except YD98Q2. Perhaps the repulsion phase linlcage between YD98Q3 and YD98Q4 interfered with the liner estimation. J - 2... 2„3_5_6 9 J J J ' ' J_ 5 .. 5 - J J J - I i ' ' 2 - J 7- 27 J - 2J- ii .6 12 " SJ 1_2_Z - Chl 335.6 CM FL0308S ■FLI50SS FLI308S -FLm lA FL02075 _m -hvw axvg f-ABG 380 -FL1202B ' -FL1205S - -FL1511B -J -FL1204S M-HVCMAa • M-HVCMAb -ABGMIla 1-FU004S ^-FLI003S - FL0413A FL0909A FL0203S FL0508A FLII09S ‘-FL1513A M-HV PRFlB 6 i i— n s— CMWG694 -FL0906S -FL0908S 2— 5/ 6— 2— 8— Ch.3 258.4 cM 4 - M-HVCSG ■FL1614S FL1806S ■ HVM060 Ch.4 361.5 cM 5---- FL1310S L-FLI3US 2 ---- FL0701S Ch.5 393.0 cM I - MWG938b 2--- - Z- 2,4- 6- 6 7-- - - ■EL0601B ■ FL0902B ■ FLMOlA ■ FL1701S ■FL0805A ■ MWG226I FL0802S f FL0415S FL0206B Ch.6 315.8 cM -FL0402S “ MST109 - MWGCM kFL!804S I FLI716S -FL1709A -HVGNIRE 9 — r FL0408B 2--- FL0501S 2.4 FL0307B Ch.7 3 J _ 24— " 1— : 30.8 cM i " ' ' 1 .. -FLI7I2S Selected Background markers (B.C) r M-HVDHN9a ID Trait No. ofZ '-M-HVDHN9b B.C. J - - 1 YD98 11 J— ■ 2 KW98 15 I— - 3 KW99 28 J— ■ 4 GU96 13 I— : - FL1707S 5 GU97 13 I— -FL0506A LJ---- ■ 6 GU99 13 7 HD98 12 8 HD99 4 21---- -FL1608A 3 ■ - MWG2249 9 PH99 8 F igure 7. Distribution of background markers (cofactor) used in composite interval mapping. Background markers were selected with ‘Forward and Backward stepwise regression (FB)’ option o f Srmapqtl procedure in QTL cartographer (version 1.13; Basten et a l, 1999). Threshold level 0.05 was used for the partial Fstatistics [p(F-in), andp(F-out)]. Loci marked with various trait IDs indicate selected background markers for each trait measured. C h l 335.6 cM KW98-ql KW99-01 GU97-Q1 GUSfcQl: GUSfcQJ FL0308S /-FL150SS -FL1308S -FLm iA ■FL0207S _M-HVWAXYC HDSfcQll HDSfcql HVM36 ■ FL0205B ■ CMVVG694 ‘ FL0906S ' FL0908S Ch.3 258.4 CM KW98-Q4-> GUSfcQA HD98-Q4 GUSfcQi GU99-Q4 HDS8Q-5 > t Qtm=QZ- -ABG670 ‘ FL1112S -FL1717A -FL1713A -FL0602A FL1806S ■ HVM 069 • FL1807S • FL0410S • FL0502B /-FL16I2B '/-FL0311S TFLMUS r FL1412S FL0806S MWGS49 [ -HVM054 FL0303A ABG609 M-HVCSG ’ FL1108B Ch.4 361.5 cM ABC303 r FL0609S " HVM3 MWG058 FL1705S FLMMS HVM68 L GU97-Q4 — - GU96-06 ^ FL0310A FL1310S FL1311S FL0701S FL0605A BTA02* BTA02b r FL0409A FL1715B ChS 393.0 cM — FLI609B -FLMOlB - EL0902B “ FL040IA - MWG938a — FL1701S ~ ELflSQSA -Hor 2 • Hor I •ABC1S6.1 MWG2261 GU96-ql > J QU91zQ5: GU9PrQ4—> FL0802S — FL1605B “ FLI512B r FL041SS - FL0416S FL0206B FL1208A ABG4S2 FL1703S •FL0606A - ABGSSa ABGSSb Ch.6 315.8cM GUSfcQI - > -FL0402S MST109 MVVG620 ■ P-FL1804S k FLI716S ^FL I709A L FL1303S - FL1304S HVGNIRE KW99-_Q4 FL1S09S r FL0408B FL1312A FLlSIOB FLllOIS FL1809S FL0307B Ch.7 330.8 cM YD98-Q3—^ YD98-Q4 - C KW99-Q5 m % -Q 6 u G U 9 9 - Q 7 ' m98^ql-> YD98^ Q5r FLlOOSA FLI802A FLI801A FL1712S FL0407A ■FL0406A FL0803S FL040SS M-HVDHN9a M-HVDHN9b FLUIDS FL1401S FLMlIB FL1702B ABClSS FL1607B f- FL1708S FL1707S FL1006B FL1613S FL1608A FLMlSS MWG2249 FL0202S FL0312S Figure 8-A. The chromosomal locations of detected putative QTLs for composite interval mapping. Arrows on chromosomes show the putative locations of QTLs associated with each trait surveyed. The detected QTLs are reported with the QTL name used in Table 13. Detail marker intervals are in Table A-2. 72 Ch 3 Trait YD98 KW98 KW99 GU96 GU97 GU99 HD98 HD99 PH99 Ch I C h2 A B C D I I I I Ql K n i> ( ql q2 Q2 q3 i x J L I \ J Q i Q2 -s< »— Q ‘ Q \ 2 |Qi . P 0 3 ¥ j Q l (32 I ........... Q l________Q2 F I G I : c )2 : IQ3: J : Q3 ; Q: (Cl. .yTNX.S.rm.n-Av ________ _ Us • A ‘n A ---------................... Q3 • Q i ' r s .....wdBkww Q1 Q2 ______________fL d _____________I - > * Q4 ,\.nA ./2 r .c^ : ' K * Q4 A Q5 A .............^ .n ; * U Q 4Q 5 *T W v “ IQ4 Q5 :K Z L - A - = ■ ,A,T l , ,4 Segment A(Chl-SOcM ) w x B (Chi - 85cM) ABG380 C (Chi - 170cM) «, lk2 D (Chi - 305cM) FL1109S E (Ch2 - 60cM) ABC170 F (Ch2 - 280cM) HVM054 G (Ch3 - 11 OcM) HVM060 H (Ch5 - 290cM) FLl703S I (Ch7 - 30cM) FL1802S, FLI407A J (Ch7 - 228cM) FLI506A Figure 8-B . Scans of a test statistic for composite interval mapping (continued). 73 Trait Ch4 Ch5 Ch6 H I Ch7 J I YD98 * * S i Q4 .Q5Q3 I A . _ .... • _ _ o w ' I ^ a N L ,. KW98 KW99 W . ...... .. » S Q4 i Q5 4 ESt * ' »— ........................................ ' ....... r Z \ v . . . /,\^\ .* ,c. GU96 Q6 qi • Q7 A ___ ___________________________/ ..... ‘.J V A — , I *\L . , a ,Ci ................. GU97 Q4 ________ /L ^ Q5 ^ /I • ....... ... J 11 GU99 ° i I........ i; J l ...... HD98 ; ( i Q6ql - . m , , L w X A m w , HD99 ‘ ...... .. . .,C ............................................... .............. PH99 U /V _ I' W rr^rrT rrrrr^N t.fT <77> i..4)-|3-D-glucan in barley and oats during kernel development and storage. Journal o f Cereal Science 10:45-50. Atkins51. M.,. and Mangelsdorf. P. C. 1942. The isolation of isogenic lines as a means of measuring the effects of awns and other characters in small grains. Agronomy Journal 34:667-668. Ballance5 G. M., and Manners5 D. J. 1978. Structural analysis and enzymic solubilization o f barley endosperm cell-walls. Carbohydrate Research 61:107-118. Bamforth5 C. W.5 and Barclay A. H. P. 1993. Malting technology and the uses of malt. In: MacGregor, A. W., Bhatty5 R. S. (eds) Barley: chemistry and technology. American Association o f Cereal Chemistry5 St. Paul, MN5 pp297-354. Bassam5 B. J., Caetano-Anolles5 G., and Gresshoff5 P. M. 1991. Fast and sensitive silver staining of DNA in polyacrylamide gels. Analytical Biochemistry 196: 80. Basten5 C. J., Weir5 B. S., and Zeng. Z. -B. 1999. QTL Cartographer; A reference manual and tutorial for QTL mapping. North Carolina State University. Becker, J., and Heun5 M. 1995. Barley microsatellites: allele variation and mapping. Plant Molecular Biology 27:835-845. Becmann5 J. S., and Soller5 M. 1983 . Restriction fragment length polymorphisms in genetic provement: methodologies, mapping and costs. Theoretical and Applied Genetics 67:35-43. Bencham5 J., Jeung5 J. U., Jasieniulc5 M., Kanazin5 V., and Blake5 K. 1999. Genographer: a graphical tool for automated fluorescent AFLP and microsatellite analysis. Journal of Agricultural Genomics 4: p. n/a. Bengtsson5 S., Aman5 P., Graham5 H. 1990. Chemical studies on mixed-linked (3-glucans in hullless barley cultivars giving different hypocholesterolaemic responses in chickens. Journal of the Science of Food and Agriculture 52:435-445. Benito5 M. C., Sanchez, M., Shin5 J. S., and Blake5 T. 1988. A map of barley chromosome 2 using isozyme and morphological markers. Biochemical Genetics 26: 387-394. Berglund5 P. T., Fastnaught5 C. E., and Holm5 E. T. 1992. Food uses of waxy hull-less barley. Cereal Food World 37:707,710-714. 112 Berloo, R. V., and Stam3 P. 1998. Marker-assisted selection in autogamous RIL populations: simulation study. Theoretical and Applied Genetics 96:147 - 154. Bezant3 L3 Laurie3 D., Pratchett, N., Chojecki3 L3 and Kearsey3 M. 1996. Marker regression mapping of QTL controlling flowering time and plant height in a spring barley (Hordeum vulgare L.)cross. Heredity 77:64-73. Bhatty3 R. S. 1986. The potential of hull-less barley-A review. Cereal Chemistry 63(2):97-103. Blake3 T. K., Ullrich, S. E., and Nilan3 R. A. 1982. Purification and characterization of barley D-hordein. Cereal Chemistry 61 (2): 120-123. Blalce3 T. K., Hensleigh3 P., Eslick3 R. F., Stalllcnecht3 G., Jackson, G., Eckhoff3 J., Carlson, G., Kushnale3 G., and Stewart3 V. 1990. Registration o f ‘shonlein’ barley. Crop Science 30:1355. Blalee3 T. K., Kadyrzhanova3 D., Shepherd3 K. W., Islam, A. K. M. R., Langridge3 P. L., McDonald, C. L., Erpelding3 J., Larson3 S., Blake3 N. K., and Talbert, L. E. 1996. STS-PCR markers appropriate for wheat-barley introgression. Theoretical and Applied Genetics 93:826-832. Borem3 A., Mather3 D. E., Rasmusson3 D. C., Fulcher, R. G., and Hayes3 P. M. 1999. Mapping quantitative trait loci for starch granule traits in barley. Journal of Cereal Science 29:153-160. Botstein3 D., White3 R. L., Skolnick3 M., and Davis3 R. W. 1980. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. American Journal o f Human Genetics 32:314-331. Briggs3 D. E. 1978. Barley. Chapman and Hall3 London. pp612. Burr3 B., and Burr F. A. 1991. Recombinant inbreds for molecular mapping in maize. Trends in Genetics 7: 55-60. Castiglioni3 P., Ajmone-Marsan3 P., Wijk3 R. V., and Motto3 M. 1999. AFLP marker in a molecular linlcage map of maize: co-dominant scoring and linkage group distribution. Theoretical and Applied Genetics 99:425-431. Chase3 K., Adler3 F. R., and Lark3 K. G. 1997. Epistat: a computer program for identifying and testing interactions between pairs of quantitative trait loci. Theoretical and Applied Genetics 94:724-730. 113 Chevalier, P., and Lingel, S.E. 1983. Sugar metabolism in developing kernels o f wheat and barley. Crop Science 23:272-277. Cheverrud, J. M., and Routman, E. J. 1995. Epistasis and its contributions to genetic variance components. Genetics 139:1455-1461. Churchil, G. A., and Doerge, R. W. 1994. Empirical threshold values for quantitative trait mapping. Genetics 138:963-971. Czuchajowska, Z., Klamczynski, A., Paszczynska, B., and Baik, B. K. 1998. Structure and functionality o f barley starches. Cereal Chemistry 75:747-754. Edwards, M. D., Stuber, C. W., and Wendel, J. F. 1987. Molecular-marker-facilitated investigations of quantitative-trait loci in maize. I. Numbers, genomic distribution and types of gene action. Genetics 116:113-125. Ellis, R. P., Swanston, J. S., Rubiot, A., Rerez-Vendrell, A. M., Romagosa, I., and Molina- Cano, J. L. 1997. The development of p-glucanase and degradation o f (Tglucan in Barley grown in Scotland and Spain. Journal of Cereal Science 26:75-82. Eslick, R. F., and Elockett, E. A. 1967. Allelism for awn length, Ik2, in barley (Hordeum species). Crop Science 7:266-267. Eslick, R. F., Blake, T. K., Stallknecht, G., Jackson, G., Eckhoff, J., Carlson, G., Kushnak, G., and Stewart, V. 1990. Registration of wanubet, a hulless, waxy barley germplasm. Crop Science 30:1371. Falconer, D. S., and Mackay, T. F. C. 1996. Introduction to Quantitative genetics. 4th edition. Longman. UK. Felker, F. C., Peterson, D. M., and Nelson, 0 . E. 1984. [14C]-sucrose uptake and labeling o f starch in developing grains of normal and segl barley. Plant Physiology 74:43-46. Finnegan, E. J„ Genger, R. K., Peacock, W. J., and Dennis, E. S. 1998. DNA methylation in plants. Annual review of Plant Physiology and Plant Molecular Biology 49:223-247 Fleming, M., and Kawakami, K. 1977. Studies of the fine structure of (TD-glucans of barleys extracted at different temperatures. Carbohydrate Research 57:15-23. 114 F ox, G. J. 1981. The effect of the waxy endosperm, short awn, and hulless seed genes upon biochemical and physiological seed characteristics important in barley (Hordeum vulare L.) utilization. Pb. D thesis. Montana State University. Frykman, L., and Bengtsson, B. 0 . 1992. The segregation ratio in waxy-heterozygous barley plants. Genetical Research 60: 159-164. Gaines, R. L., Bechtel, D. B., and Pomeranz, Y. 1985. A macroscopic study on the development of a layer in barley that causes hull-caryopsis adherence. Cereal Science 62:35. Gill, D. R., Oldfield, J. E., and England, D. C. 1966. Comparative value of hulless barley, regular barley, corn and wheat for growing pigs. Journal o f Animal Science 25:34-36. Goering, K. J., Eslick, R., and HeHaas, B. W. 1973. Barley starch. V. A comparison o f the properties of waxy Compana barley starch with the starches o f its parents. Cereal Chemistry 50:322-328. Goffmet, B. and Mangin, B. 1998. Comparing methods to detect more than one QTL on a chromosome. Theoretical and Applied Genetics 96:628-633. Greenberg, D. C. 1977. A diallel cross analysis of gum content in barley {Hordeum vulare). Theoretical and Applied Genetics 50:41-46. Grundbacher, F. J. 1963. The physiological function of the cereal awn. Botanical Review. 29:366-381. Haley, C. S., and Knott, S. A. 1992. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315-324. Han, F., Ullrich, S. E., Chirat, S., Menteur, S., Jestin, L., Sarrafi, A., Hayes, P. M., Jones, B. L., Blake, T. K., Wesenberg, D. M., Kleinhofs, A., and Kalian, A. 1995. Mapping o f P-glucan content and (3-glucanase activity loci in barley grain and malt. Theoretical and Applied Genetics 91: 921-927. Han, F., Ullrich, S. E., Kleinhofs, A., Jones, B. L., Hayes, P. M., and Wesenberg, D. M. 1997. Fine structure mapping of the barley chromosome-1 centromere region containing making-quality QTLs. Theoretical and Applied Genetics 95:903-910. Hang A., Hoffman, D. L., and Burton, C. S. 1996. DNA variation and genetic relationships among hulless barley accessions as detected by Random Amplified Polymorphic DNA (RAPD). Journal of Genetics and Breeding 50:337-341. 115 Hamshima, Y., Kurata, N., Yano, M., Nagamura, Y., Sasalci, T., Minobe, Y., andNalcagahra, M. 1996. Detection of segregation distortions in an indica-japonica rice cross using a high-resolution molecular map. Theoretical and Applied Genetics 92:145-150. Hayes, H. K., and Wilcox, A. N. 1922. The physiological value of smooth-awned barleys. Agronomy Journal 14:113-118. Hayes, P. M., and Jyambo, 0 . 1993. Summary of QTL effects in the Steptoe x Morex population. Barley Genetics Newsletter 23:98-143. Hayes, P. M., Liu, B. H., Knapp, S. J., Chen, F., Jones, B., Blake, T., Franckowiak, J., Rasmusson, D., Sorrells, M., Ullrich, S. E., Wesenberg, D., andKleinhofs, A. 1993. Quantitative trait locus effects and environmental interaction in a sample of North American barley germ plasm. Theoretical and Applied Genetics 87:392-401. Hayes, P. M., Cjirono, H., Witsenbore, M., Kuiper, M., Zabeau, M., Sato, K., Kleinhofs, A., Kudrna, D., !Lilian, A., Saghai-Maroof, M., and Hoffman, D. 1997. Characterizing and exploiting genetic diversity and quantitative traits in barley (Hordeum vulgare) using AFLP markers. J. Quantitative Trait Loci. Vol. 3. http://probe.nalusda/ gov:800/otherodcs/jqtl. Hecker, K. D., Meier, M. L., Newman, R. K., and Newman, C. W. 1998. Barley [Lglucan is effective as a hypocholesterolaemic ingredient in foods. Journal of the Science of Food and Agriculture 77:179-183. Helbaek, H. 1966. Commentary on the phylogenesis of Triticum and Hordeum. Economic Botany 20:350-360. Helm, J. H., Salmon, D. F., Dyson, D. H., and Stewart, W. M. 1992. Registration of cultivars. Crop Science 32:278. Helm, J. H., Cortez, M. J., Salmon, D. F., Jedel, P. E., and Stewart, W. M. 1996a. Registration o f ‘Falcon’ barley. Crop Science 36:807. Helm, J. H., Cortez, M. J., Salmon, D. F., Jedel, P. E., and Stewart, W. M. 1996b. Registration o f ‘Phoenix’ barley. Crop Science 36:807-808. Heun, M., Kennedy, A. E., Anderson, J. A., Lapitan, N. L. V., Sorrells, M. E., and Tanksley, S. D. 1991. Construction of a restriction fragment length polymorphism map for bailey (Hordeum vulgare). Genome 34:437-447. 116 H0j, P. B., Hartman, D. J., Morrice, N. A., Doan, D. N. P., and Fincher, G. B. 1989. Purification of (l-3)-(3-glucan endohydrolase isoenzyme II from germinated barley and determination if its primary structure from a cDNA clone. Plant Molecular Biology 13:31-42. Hockett, E. A. 1981. Registration of hulless and hulless short-awned spring barley germplasm. Crop Science 21:146-147. Izydorczyk, M. S., Biliaderis, C. G., Macri, L. I , and MacGregor, W. 1998. Fractionating o f Oat (1-3), (l-4)-(3-D-glucans and characterization of the fractions. Journal of Cereal Science 27:321-325. Jansen, R. C., and Stam, P. 1994. High resolution mapping of quantitative traits into multiple loci via interval mapping. Genetics 136:1447-1455'. Jansen, R. C. 1994. Controlling the type I and type II errors in mapping quantitative trait loci. Genetics 138:871-881. John, P. 1992. Biosynthesis of the major corp products. John wiley and sons. pp51-54. Johnson, R. R., Willmer, C. M., and Moss, D. N. 1975. Role of awns in photosynthesis, respiration, and transpiration of barley spikes. Crop Science 15:217-221. Kearsey, M. J. 1998. The principles of QTL analysis (a minimal mathematics approach). Journal of Experimental Botany 49:1619-1623. Kjack, J. L., and Witters, R. E. 1974. Physiological activity of awns in isolines of Atlas barley. Crop Science 14:243-248. Kleinhofs, A., Kilian, A., Saghai Maroof, M. A., Biyashev, R. M., Hayes, P., Chen, F. Q., Lapitan, N., Fenwick, A., Blake, T. K., Kanazin, V., Ananiev, E., Dahleen, L., Kundma, D., Bollinger, J., Knapp, S. J., Liu, B., Sorrells, M., Heun, M., Franckowiak, J. D., Hoffman, D., Skadsen, F., and Steffenson, B. J. 1993. A molecular, isozyme, and morphological map of the barley genome. Theoretical and Applied Genetics 86: 705-712. Kleinhofs, A. 1996. Integrating barley RFLP and classical marker maps. Barley Genetics Newsletter 27:105-112. Knapp, S. J., and Bridges, W. C. 1990. Using molecular markers to estimate quantitative trait locus parameters: power and genetic variance for unreplicated and replicated progeny. Genetics 126:769-777. 117 Kuittinen, H., Sillanpaa, M. J., and Savolainen, 0 . 1997. Genetic basis o f adaptation: flowering time in Arabidopsis thaliana. Theoretical and Applied Genetics 95:573-583. Lander, E. S., Green, P., Abrahamson, J., Barlow, A., Daly, M. I , Lincoln, S.E., and Newburg, L. 1987. Mapmaker: an interactive computer package for constructing primary genetic linlcage maps of experimental and natural populations. Genomics I: 174-181. Lander, E. S., and Botstein, D. 1989. Mapping Mendelian factors underlying quantitative traits using RPLP linlcage maps. Genetics 121:185-199. Lark, K. G., Chase, K., Adler, F., Mansur, L. M., and Orf, J. H. 1995. Interaction between quantitative trait loci in soybean in which trait variation at one locus is conditional upon a specific allele at another. Proceedings of the National Academy of Sciences USA 92:4656-4660. Larson, S. R., Kadyrzhanova, D., McDonald, D., Sorrells, M., and Blake, T. K. 1996. Evaluation o f barley chromosome-3 yield QTLs in a backcross F2 population using STS-PCR. Theoretical and Applied Genetics 93:618-625. Liu, Z. W., Biyashev, R. M., and Saghai Maroof, M. A. 1996. Development of simple sequence repeat DNA markers and their integration into a barley linlcage map. Theoretical and Applied Genetics 93: 869-876. Liu, B. H. 1998. Statistical genomics; linkage, mapping and QTL analysis. CRC Press, Boca Raton, FL. Loi, L., Ahluwalia, B., and Fincher, G. B. 1988. Chromosomal location of genes encoding barley (1-3, I -4)-|3-glucan 4-glucanohydrolases. Plant Physiology 87:300-302. Lorieux, M., Perrier, X, Goffmet, B., Lanaud, C., and Gonzalez-de-Leon, D. 1995. Maximum-likelihood models for mapping genetic markers showing segregation distortion. II. F2 populations. Theoretical and Applied Genetics 90:81-89. MacLeod, L. C., and Duffus, C. M. 1988. Reduced starch content and sucrose synthase activity in developing endosperm of barley plants grown at elevated temperatures. Australian Journal of Plant Physiology 15:367-375. Mangin, BI, Thoquet, P., Olivier, J., and Grimsley, N. H. 1999. Temporal and multiple quantitative trait loci analysis of resistance to bacterial wilt tomato permit the resolution of linlced loci. Genetics 151:1165-1172. 118 Manly, K. F. 1999. Map Manager XP. Ver 0.9 and User Manual, http ://mcbio .med.buffalo .edu/mmXP .html. Mano, Y., Sayed-Tabatabaei, B. E., Graner, A., Blake, T., Takaiwa, F., Oka, S., and Komatsuda, T. 1999. Map construction o f sequence-tagged sites (STSs) in barley {Hordeum vulgare L.). Theoretical and Applied Genetics 98:937-946. Martin, J. M., Blake, T. K., and Hockett, E. A. 1991. Diversity among North American spring barley cultivars based upon coefficients of parentage. Crop Science 31:1131-1137. Martinez, O., and Curnow, R. N. 1994. Missing markers when estimating quantitative trait loci using regression mapping. Heredity 73:198-206. McCleary, B . V., and Codd, R. 1991. Measurement of (l-3)(l-4)-(3-D-glucan in barley and oats: a streamlined enzymic procedure. Journal of the Science of Food and Agriculture 55: 303-312. McCleary, B. V., and Mugford, D. C. 1997. Determination of (3-glucan in barley and oats by streamlined enzymatic method: Summary of collaborative study. Journal of AOAC international 80:580-583. McDonald, A. M. L., Stark, J. R., Morrison, W. R., and Ellis, R. P. 1991. The composition of starch granules from developing barley genotypes. Journal of Cereal Science 13:93-112. McGuire, C. F., and Hockett, E. A. 1981. Effect of awn length and naked caryopsis on malting quality of ‘Betez’ barley. Crop Science 21:18-21. Murphy, P. J., and Witcombe, J. R. 1981. Variation in Himalayan barley and the concept o f centres of diversity. In: Asher, M. J. C., Ellis, R. P., Hayter, A. M., and Whitehouse, R. N. H. (eds) Barley genetics, vol. IV. Editorial sub-committee, 4th Int. Barley Genet. Symp., Edinburg, PP26-36. Murphy, P. J., and Witcombe, J. R. 1986a. Covered and naked barleys from the Himalaya I .Evidence of multivariate differences between the two types. Theoretical and Applied Genetics 71:730-735. Murphy, P. J., and Witcombe, J. R. 1986b. Covered and naked barleys from the Himalaya 2. Why do they differ from each other so extensively? Theoretical and Applied Genetics 71:736-741. 119 Murray, M. G., and Thompson, W. F. 1980. The isolation of high molecular weight plant DNA. Nucleic Acids Research 8: 4321-4325. Newman, R. K., Newman, C. W., and Graham, H. 1989. The hypochoesterolemic function o f barley p-glucans. Cereal Foods World 34:883-886. Newman, C. W., Newman, R. K., and Graham, H. 1990. Influence of the waxy starch gene on the carbohydrate composition of covered and hulless barleys. Cereal Foods World 35:835. Nilan, R. A. 1964. The cytology and genetics of barley. Washington State University Press, Pullman, WA. Oliveria, A. B., Rasmusson, D. C., and Fulcher, R. G. 1994. Genetic aspects of starch granule traits in barley. Crop Science 34:1176-1180. Olson, J. M., Hood, L., Cantor, C., and Botstein, D. 1989. A common language for physical mapping of the human genome. Science 243:1434-1435. Oscarsson, M., Parkkonen, T., Autio, K., and Aman, P. 1997. Composition and microstructure of waxy, normal and high amylose barley samples. Journal of Cereal Science 26:259-264. Pakniyat, H., Powell, W., Baird, E., Handley, L. L., Robinson, D., Scrimgeour, C. M., Nevo, E., Hackett, C. A., Caligari, P. D. S., and Forster, B. P. 1997. AFLP variation in wild barley (Hordeum spontaneum c. Koch) with reference to salt tolerance and associated ecogeography. Genome 40:332-341. Paluska, M. M. 1979. The anatomy of a thick stem-wall barley line. Barley Newsletter 2 2 :8 0 -8 1 . Paran I., Goldman, I., Tanksely, S. D., and Zamir D. 1995. Recombinant inbred lines for genetic mapping in tomato. Theoretical and Applied Genetics 90: 542-350. Powell, W., Caligari, P. D. S., Swanston, J. S., and Jinks, J. L. 1985. Genetical investigation into p-glucan content in barley. Theoretical and Applied Genetics 71:461-466. Qualset, C. O., Schaller, C. W., and Williams, J. C. 1965. Performance of isogenic lines of barley as influenced by awn length, linkage blocks, and environment. Crop Science 5 :4 8 9 _ 4 9 4 . 120 Qi, X., Stam, P., and Lindhout, P. 1996. Comparison and integration of four barley genetic maps. Genome 39:379-394. Rasmusson, D. C., and Phillips R. L. 1997. Plant breeding progress and genetic diversity from de novo variation and elevated epistasis. Crop Science 37:303-310. Rohde, W., Becker, D., and Salamini, F. 1988. Structural analysis o f the waxy locus from Hordeum vulgare. Nucleic Acids Research 16:7185-7186. Rossnagel, B. G., Harvey, B. L., and Bhatty, R. S. 1985. Tapper hulless barley. Canadian Journal of Plant Science 65:453-454. Saghai Maroof, M. A., Biyashev, R. M., Yang, G. P., Zhang, Q., and Allard, R. W. 1994. Extraordinarily polymorphic microsatellite DNA in barley: Species diversity, chromosomal locations, and population dynamics. Proceedings of the National Academy of Sciences USA 91:5466-5470. Sailri, R. K., Scarf, S., Faloona, F., Mullis, K. B., Horn, G. T., Erlich, H. A., and Arnheim, N. 1988. Enzymatic amplification of P-globin genomic sequences and restriction site analysis for diagnosis of sickle cell anemia. Science 230:1350-1354. SAS institute. 1988. User Guide: Statistics, SAS institute, Cary, North Carolina. Sax, K. 1923. The association of size differences with seed-coat pattern and pigmentation mphaseeolus vularis. Genetics 8:552-560. Sayed-Tabatabaei, B. E., Komatsuda, T., Takaiwa, F., Graner, A. 1998. DNA sequencing and primer designing for RFLP clones evenly distributed in the barley genome. Barely Genetics Newsletter 28:15-18. Sdialler, C. W., Qualset, C. O., and Rutger, J. N. 1972. Isogenic analysis of the effects of the awn on productivity of barley. Crop Science 12:531-535. Shahla, A., and Tsuchiya, T. 1990. Genetic analysis in six telotrisomic lines in barley (Hordeum vulgare L.). Journal of Heredity 81:127-130. Shin, J. S., Chao, S., Corpuz, L., and Blake, T. K. 1990. A partial map of the barley genome incorporating restriction fragment length polymorphism, polymerase chain reaction, isozyme, and morphological marker lod. Genome 33:803-810. 121 Slalceski5 N., Baulcombe5 D. C., Devos5 K. M., AMuwalia5 B., Doan5 D. N. P.5 and Fincher, G. B. 1990. Structure and tissue-specific regulation of genes encoding barley (1-3, l-4)-|3-glucan endohydrolases. Molecular and General Genetics 224:437-449. Spaner5 D., Rossnagel5 B. G., Legge5 W. G., ScoleS5 G. L5 Eckstein, P. E., Penner5 G. A., Tinlcer5 N. A., Briggs5 K. G., Falk5 D. E., Afele5 J. C., Hayes5 P. M., and Mather5 D. E. 1999. Verification of a quantitative trait locus affecting agronomic traits in two- row barley. Crop Science 39:248-252. Swanston5 J. S. 1995. Effects on barley grain size, texture and modification during malting associated with three genes on chromosome I . Journal o f Cereal Science 22:157- 161. Swanston5 J. S. 1997. Waxy starch barley genotypes with reduced P-glucan contents. Cereal Chemistry 74(4):452-455. Talcahashi5 R. 1955. The origin and evolution of cultivated barley. Advances in Genetics 7:227-266. Tinlcer5 N. A., and Mather5 D.E. 1996. Methods for QTL analysis with progeny replicated in m u l t i p l e e n v i r o nm e n t s . J. Q u a n t i t a t i v e T r a i t L o c i (http ://probe.nalusda.gov: 8000/otherdocs/j qtl/1). Tinleer5 N. A., Mather5 D. E., Rossnagel5 B. G., Kasha5 K. J., Kleinhofs5 A., Hayes5 P. M., Falk5 D. E., Ferguson5 T., Shugar5 L. P., Legge5 W. G., Irvine5 R. B., Choo5 T. M., Briggs5 K. G., Ullrich, S. E., Franckowiak5 J. D., Blalee5 T. K., Graf5 R. J., Dofing5 S. M., Saghai5 M. A., Scoles5 G. J., Hoffinan5 D., Dahleen5 L. S., Kilian5 A., Chen5 F. , Biyashev5 R. M., Kudrna5 D. A., and Steffenson5 B. J. 1996. Regions of the genome that affect agronomic performance in two-row barley. Crop Science 36:1053-1062. Tragoonrung5 S., Kanazin5 V., Hayes5 P. M., andBlake5 T. K. 1992. Sequence-tagged-site- facilitated PCR for barley genome mapping. Theoretical and Applied Genetics 84:1002-1008. Tsuchiya5 T., and Hall5 L. B. 1978. Telotrisomic analysis of four mutant genes in barley. Barley Genetics Newsletter 8:104-107. Ukai5 Y. 1999. MAPL; programs for construction of linkage map and QTL analysis, http ://peach. ab. a.u-tokyo. ac.jp/~ulcai/ Utz5. H. F. and Melchinger5 A. E. 1999. PLABQTL; a computer program to map QTL Version L l5 University of Hohenheim. 122 Vos, P., Rogers, R., Sleeker, M., Reijans, M., Lee, T. V. D., Hornes, M., Frijters, A., Pot, J., Peleman, J., Kuiper, M., and Zabeau, M. 1995. AFLP: a new technique for DNA fingerprinting. Nucleic Acids Research 23:4407-4414. Wade, M. J. 1992. Sewall Wright: Gene interaction and the shifting balance theory. Oxf. Surv. EvoL Biol. 8:33-62. Weber, J. L., and May, P. E. 1989. Abundant class of human DNA polymorphisms which can be typed using the polymerase chain reaction. American Journal of Human Genetics 44:388-396. Weller, J. I. 1992. Statistical methodologies for mapping and analysis o f quantitative trait loci. In: Beckmann, J. S., and Osborn, T. C. (eds) Plant genomics: methods for genetic and physical mapping. Kluwer Academic Publishers, Dordrecht, Netherlands. ppl81-207. Weyhrich, R. A., Carver, B. F., and Smith, E. L. 1994. Effects of awn suppression on grain yield and agronomic traits in hard red winter wheat. Crop Science 34:965-969. Witcombe, J. R., and Murphy, P. J. 1986. Covered and naked barleys from the Himalaya 2.Why do they differ from each other so extensively? Theoretical and Applied Genetics 71:736-741. Xue, Q., Wang, L., Newmant5R. K., Newmant, C. W., and Graham, H. 1997. Influence of the hulless, waxy starch and short-awn genes on the composition of barleys. Journal of Cereal Science 26:251-257. Young, D. N., and Tanksley, S. D. 1989. Restriction fragment length polymorphism maps and the concept of graphical genotypes. Theoretical and Applied Genetics 77 : 95- 101. Zeng,Z .-B . 1994. Precision mapping of quantitative trait loci. Genetics 136:1457-1468. Zhu, H., Briceno, G., Dovel, R., Hayes, P. M., Liu, B. H., Liu, C. T., and Ullrich, S. E. 1999. Molecular breeding for grain yield in barley: an evaluating of QTL effects in a spring barley cross. Theoretical and Applied Genetics 98:772-779. 123 APPENDIX Table A-I. Segregation, x2 goodness-of-fit analysis and genotypes of 241 loci of 59 mapping lines in the ‘Fiber-21 population. Segregation testb____________ Lines of the 'Fiber-2' population (A; MTaz90-123, C; MTH6860756, Missing data I Marker name Cha A C To Tt2 I - 10 11-20 2 1 - 30 3 1 - 4 0 4 1 - 5 0 51 - 59 M2 I 23 28 51 0.49 CCCAACAC -A CAAACCAAAC AACC -CAA -C - ACAC— ACCA AAACCCACC- C-ACCC-CC n I 22 28 50 0.72 CCCAACAC-A CAACCCAAAC AAC -ACAC -C -CACCACCCA AC AC AC AC— C -A A C -C C - W X I 31 20 51 2.37 A— A C A -A -A AAAAAAAAAA ACCCACCAAA -CACCACACC AC -A AC -CCA -CAAAACCC H o rl 5 36 23 59 2.86 CAAACACCCA ACAAACAACA AAAAACCAAC AAACCCAAAA CAAACCAAAC ACCCACAAC Hor2 5 37 22 59 3.81 CAAACACCCA ACAAACAACA AAAAAC CAAC AAACCCAAAA CAAACCAAAC AC CCAAAAC ABC 155 7 28 31 59 0.15 ACCCAACCCC CCACCAACAC AAAAACCCCC CAACCACCCC AAAAACAACA ACCACCAAA ABC156.1 5 38 20 58 5.59 * -AAACACACA ACAAACAACA AAAAACAAAC AAACCCAAAA CAAACCAACC ACCCACAAA ABC 170 2 30 23 53 0.92 CAACAACC-C CA-AAAAACA CCACAAC-AA A— ACCC -CC AAAACAAACC CACAACACA ABC253 I 32 27 59 0.42 CCCAAACCCA CAC C C CAAAA AAC CAAAACA CCAAAACCAA AACCCCACCA CAC CAAAAA ABC303 4 31 26 57 0.44 -CACCAAC -C ACCAAACACC CCAAAAAAAC ACCAAAAAAA CCCCCAACCA CAACCAACA ABC483 7 29 25 54 0.30 A -C -C A C A -A -CCAACCAAA ACCC -CCCAA CAACCAAACC AC CAAACAAA ACAAAACCC ABG20.11a I 26 33 59 0.83 C C CAACAC CA CAAAC CAAAC AACCCCAACC C CAC CAAC CA AAACCCACAC CAAACCCCC ABG20.11b 31 28 59 0.15 ACCACACAAC AAAACAC CAA ACCCACCCAA AAACCACACC ACCACAACAA CCAAAACCC ABG054 4 ■ 28 29 57 0.02 ACAA -C CC -C ACCCACCACC AAAAAC CACA AACACAAAAA CCCCACCACA ACCACACAC ABG55a 5 33 26 59 0.83 ACAC CACAC C CCAAAAAAAC AAC CAAACAA ACCCCCAAAA ACACAAACCA AAAACCCCC ABG55b 5 34 25 59 1.37 ACACAACAC C CCAAAAAAAA AAC CAAACAA ACCCCCAAAA ACACAAACCC AAAACCCCC ABG058 2 34 17 51 5.67 * ------ A -C A C -A ACAAAACAAC AAAAAAACAA ACAAC-AACC ACCC-ACAAC CAAAACA-A ABG064 7 27 30 57 0.16 — CCCCCAAC AC CAC C CAAA ■ ACCCACCCAA CAACCAAAAC ACCAAAC CAA ACCAAACCC ABG070 3 33 22 55 2.20 -ACC -CAAAC AAACCCACCA AACACAAACA CCCAAAAA-A CAAA-CCCAC ACAAAAAAC ABG380 I 30 29 59 0.02 AC CAAAAACA ACCCCCAAAA AACCACAACC CCCCCCACCA ACCAACCCAA CAAACACAA ABG452 5 23 33 56 1.79 CAAA -ACC -A CCACCAAACA CCAAACCCCC CCACCAACCA CACA-CCCCC CACACACCA ABG609 2 23 31 54 1.19 -A A A -A A C -A ACCCACCCCC AACCAACCCC CACACCAACC CCAA -CCCA - CACACAACC ABG618 4 27 29 56 0.07 ACAACCCCCC ACCCAACACC CAA-CAACAC AACACAAAAA CCCC-CAACA A-CAAACCC BCD402 4 25 32 57 0.86 -CACCCCCAA CCCACCCAA- CAACACCCCC C CACAACAAA AAAAAACCCC CCAACCACA BTA02a 4 29 30 59 0.02 CACACCCACA CCAACCAACA CCCCCCAACC CAAAAACACC AAACAAACAC CCAAACACA BTA 02b 4 V 1 ?r7 59 0.42 CCACCCACAA CCAACCAACA CAC CACAAC C CAAAAACAAA AAAAAACCAC CCAACCACA Table A-1. (continued) Marker name Cha Segregation testb Lines o f the 'Fiber-2' population (A; MTaz90-123, C; MTH6860756, -; Missing data I A C To Y2 I - 10 11-20 2 1 - 3 0 3 1 - 4 0 4 1 - 50 51 - 59 CMWG694 2 28 25 53 0.17 — C C C A C -C CAAACACCAA ACAAACCAAA CAAA-CACCA CCAAA-CACA CCAACACCA MWG058 4 30 28 58 0.07 ACAACAA-AC AACAACCACC CCAACACACC AC CAAAAAAA CCCCCCACCA CACACAAAC MWG938a 5 34 25 59 1.37 CCAAAACCCA AAAAC C CACA AACAAACAAC CAACACAACA ' CCCACAAAAC CAACAACAC MWG938b 5 24 34 58 1.72 CCCCCCCACC ACACCACCCC CCAAAACACC CACCACAACC AACCCCCAA- CCAAAAAAA MWG549 3 27 32 59 0.42 ACAACACACC CACCACCCCC AACAACAAAA ACCAACCCCA CCCCACCCCC AAAAAAACC MWG620 6 29 29 58 0.00 CACCAACACC CACAAAACAA - CAAAAAAAAC AACCCCCCAC CCCCCCAAAA CCCCAC-AA MWG2249 7 30 26 56 0.29 — ACAACAAC CAAAACAAAC ACCCAACCCA CAACCCCCC- AAACACAACA CCCCAAAAA MWG2261 5 34 23 57 2.12 CAAACACC-C C CAAACAACA AAAAAC CAAC AAAAACAACA CCCA-AAACC AACCCCAAA MST109 6 30 29 59 0.02 CACCAACACC CACAAAACAA ACAAAAAAAC ACCCCCCAAC CCAC CAAAAA CCCCACCCA WTAl I 24 35 59 2.05 CCCAACACCA CAAACCAAAA CACCACACCC CCACCACCCA ACACACACCC CAAACCCCC HVM3 4 29 28 57 0.02 ACAACAACAC AACAACCACC C CAACACAC C ACAAA-AACA CC-CCAACCA CCCACAAAC M-HVWAXY I 35 23 58 2.48 AAAACACAAA AAAAAAAAAA ACCCACCAAA ACACCACACC ACCAACCCCA ACCAAACC- M-HVPRPIB I 29 29 58 0.00 CCCCAACCCA CAACAAACAA ACACACCCCC CAAAAACCAA ACCAAAACCC CCC-CAAAA HVM6 I 34 22 56 2.57 ■ ACAACACACA AAAAAAAAAA ACCCACCAAA ACACCACACC AC-AACCCCA ACCAAAA -- HVM36 2 36 23 59 2.86 AAACAAC CAA CCAAAAAACC CACAAAACAA AAAACCAACC AAAAC CAAC C CACACCACA HVM054 2 27 31 58 0.28 -AACACACAC CACACCCCCA AACCCAACCA CCCAACACCC CCAAACACAA CCAAAAACC HVM060 3 30 28 58 0.07 -C C CAACACA ACACAAAC C C CCCAACCACC CAACACCAAC CAACCCAAAA CCAAAAAAA HVM68 4 31 21 52 1.92 ACCA -CACAC AACAAACA— C CAACACACA A-CAAAAAAA CCCC -CA C -A AAACAACA- M-HVCMAa I 27 32 59 0.42 CCCAAAACCA ACAAC CAAAC CAACCCAAAC ACACCCCCCA ACACACACAC CCCACACCA M-HVCMAb I 28 31 59 0.15 CCCAAAACCA AAAACCAAAC CAACCCAAAC ACACCCCCCA ACACACACAC CCCACACCA M-HVDHN9a 7 25 32 57 0.86 CCCCCCCC -C ACCACACCAC AACCACCACC CAAAACCCCC AA-ACAACAC AAAAACACA M-HVDHN9b 7 31 28 59 0.15 AAAAC CAACA CAACACAACA CCAACAACAA ACCCCAAAAA CCACACCACA CCCCCACAC M-HVCSG 2 23 35 58 2.48 -AAAACACAA CCCCCCCCCA CACCAACCCC CACACCCACC CCAAACCCAC CACAAAACC HVGNIRE 6 26 32 58 0.62 CACCAACA-C CACAAAACAC AC C AC AAAAC AACACCCCCC CCACCCAACA ACCCACCAC FLO10IA 6 40 19 59 7.47 * AAAACAAAAC AAAACAAAAC CAAAAAAAAA CAAAAAAACC CAACCCAACA AACCCCCCA FL0102A 6 21 38 59 4.90 * CAACCACCAC CACACCACCC ACCAACACAC CCCCCCCACC CAACCCAACA AACCCCCCA Table A-1. (continued) Segregation test Marker name Chd A C To f 1 - 10 11 - 20 2 1 - 3 0 3 1 - 4 0 4 1 - 50 51 - 59 FL0103B 31 28 59 0.15 ACAACAACCC AAACCCAAAC AAAAAAAAAA CCACAACCCC CAACCCCACC AACCACACC FL0104B 40 16 56 10.29 ** CAAA-AAAAA AAACCAAAA- AAAAAAAAAA AC CAAAAAC C CAAAC CAACA AA-CCCCCA FL0105B 21 35 56 3.50 ACCC-CAACC AACACCCCA- AAACACCACC CCAAACCAAC CAACCCCACC CA-CCCCCC FL0106A 36 22 58 3.38 ACCA-AAAAA AAACCCCAAC CAAAACAAAA AC CAAACAAC AAACCCAACA ACACCACCA FL0107B 27 32 59 0.42 AAAAC CACCA AAAAACACAC AACAC C CAC C CACCACCCAC ACACACCACC ACCCCCAAC FL0201B 40 19 59 7.47 * AAACAACCAC CAACAACCCA ACAAAAACCA CACAAAAAAC CACAAAAAAA AAACAAAAC FL0202S 7 24 35 59 2.05 ACACAACCCC CAAAACACAC ACCCCACCCA CCACCCCCAC ACACACCCCA CCCCAAAAA FL0203S I 34 25 59 1.37 AC CACAAAAC CACCCAAAAA AACCAAAACC CACACAACAA AAACCCCCAA CACCACAAA FL0204S 37 22 59 3.81 ACAACAACAA CACAAACAAA AACCCAAAAA AACAAACAAA AAAACCCCAC ACACCCCCA FL0205B 2 ■ 37 21 58 4.41 * AAACAAC C CA CAAAAAAACC CAAAA-CAAA AACAC CAAC C AAAAC CAACA CACAACACA FL0206B 5 23 33 56 1.79 CACAAACCCC AC C CAAAACA ACCAA-ACCC CCCCC-AACC CCCAAC-ACC CACAAACCC FL0207S I 34 23 57 2.12 AAAACACAAA AACAAAAAAA ACCCA-CCAA ACACC-CACC AC CAAAACAA CCCAAACCC FL0301S 6 20 39 59 6.12 * CCAACACACC CCCCCCCCCC CCCCCCAAAA ACACAAC CAC CCACCCAACC ACACCCCAA FL0302B 30 29 59 0.02 AAACAC CACA ACAAACCCCC C CAAAAC CAC AACCAAAACC CCAAAAAACC ACAACCCCC FL0303A 2 26 33 59 0.83 AAACACAAAC CACCCCCCCA AACCCAACCA CCCAACCCCC CCAAACACAA CCCAAAACC FL0304S I 27 32 59 0.42 AC CAAAAACA ACCCCCAAAA CCCCAAAAAC CCCCCCACCA AC CAACCCAA CCCACCCAA FL0305A 3 29 30 59 0.02 AC CAAC CACC AACCACCCCC CCCAACAAAA ACCAAACACA CCACCCCACC AAAAAAACA FL0306A 3 28 31 59 0.15 CCAAAACACC CACAAAC CAC a cCaccaaca ACCAACCCCA CCCCCCCACC AAAAAAACA FL0307B 6 31 28 59 0.15 CAACCCCACA AAACAAC CAC CACAACACCA CAAAAACACC CAACAACAAC ACACACCCA FL0308S I 23 36 59 2.86 AAACCCCAAC CCCCACCCCA CCCCACCCAA ACACCACACC AC CAAAC CAA CCCAAACCC FL0309A 28 28 56 0.00 AAAACCC-AA C CAAACAACA CCACC-CCAA AC CAC CAAAA CCCCAA-ACC CACCAACAC FL0310A 4 29 28 57 0.02 CAAACCCCAA AAC CAAACAC ACCAC-AACC ACCACACCAC CAAAAC-CCA AAACCAACC FL0311S 3 31 28 59 0.15 ACAAAACAC C CAC CAC C CAC AACAAAACAA ACGAACCCCA CCCCACAACC AAAAAAACC FL0312S 7 24 34 58 1.72 ACACAACAC C CAAAACACAC CCCCCCCCCA CAACCCCCCC ACACAC-ACA CCCCAAAAA FL0401A 5 41 18 59 8.97 ** ACAAAACACA AAAAACCAAA AACAAACAAC CAAACAAAAA CCAACCAAAC AAACAACAC FL0402S 6 25 34 59 1.37 CAC CAACAC C CACAAAACAC ACAAAACAAC ACCCCCCCAC CCACCACCAA CCCCACCCA to O n Table A-L (continued) Segregation testb____________ Lines of the 'Fiber-2' population (A; MTaz90-123, C; MTH6860756, Missing data ) .Markername Cha A C To Y2 1 - 10 11-20 2 1 - 30 3 1 - 4 0 4 1 - 50 51 - 59 FL0403S 4 33 26 59 0.83 AAAACCCAAA ACCCAACCCC CCCACAAAAC CAAAAAAAAA CAACCCCCCA AAAACAACC FL.0404S '2 39 20 59 6.12 * CAACACAAAA AAACAACCAA AACAAACCAC CACAAACAAC CAAAAAACAA CACAACAAC FL0405S I 27 32 59 0.42 CCACCCACAC ACCACACAAC AACCACCAAC AAAAACCCCC ACAACAC CAC CACAACCCA FL0406A 7 33 25 58 1.10 ACCACACAAA ACAAAACAAA ACCCCACCAA CAACCCCAAC AACAAA-AAA CCCAAACCC FL0407A 7 32 26 58 0.62 ACAACACAAA ACCAAACAAA ACCCCACCAA CAACCCCAAC ACCAAA-AAA CCCAAACCC FL0408B 6 30 27 57 0.16 CAAAAACAC C AAAAAAACAC AC CACAACAA AACCC-ACCC CCCCCC-ACA CAACAACAC FL0409A 4 27 30 57 0.16 ACAC C CACAA CCCCCCAACA CACCAACACC CAACA-CAAA AACAAA-CAC ACCCCCACA FL0410S 3 29 30 59 0.02 ACACAACACA ACACACACCC CCCAAAAACC CACCAC CAAC CACCCCAACA CCCAAAAAA FL0411S 4 34 25 59 1.37 CCCCCCCAAA CAAACAAC CA ACACAACAAC CAAAC CAACA AAAACACAAA ACAAACACC FL0412A 6 27 31 58 0.28 CAC CAAAC C C CACAAAACAC ACAAACAAAC AACCCCACCC CCACCC-ACA ACACACCAC FL0413A I 32 26 58 0.62 C C CAACACAA CACAC CAAAC AAACCAAAAC CCACCCACAA ACAAAC-AAA CCACCCAAA FL0414S 5 28 31 59 0.15 CCCACCCCCA CCACACAACC CAAAACCAAC ACCCACCACC AACAC CAAAA ACAAACAAC FL0415S 5 24 34 58 1.72 CACAAACACC ACCCACAACA CCCAACAACC CCCCCAAACC CCCAAC-ACC CACAAACCC FL0416S 5 25 33 58 1.10 CACAAACACC AC C CAAAACA CCCAACAACC CCCCCAAACC CCCAAC-ACC CACAAACCC FL0501S 6 31 28 59 0.15 AAAC CACAC C C C CAACAAAC CCACCAAAAA AAACAAC CAA AAACCCAACC ACCCCACCA FL0502B 3 32 27 59 0.42 ACAAAACACC CAACAAACAA AC CACAAAAA ACCAACCCCA CCACCCCACC AAAAAACCC FL0503A 2 30 29 59 0.02 ACCCAACAAC CACACCC CAA ACAACAAC CA CACCAAACCA ACAAC CAACA CACACCACA FL0504S I 32 27 59 0.42 CCCAAAACCA ACCCCCAAAA CACCAAAAAC ACAC CAACAA ACACACACCA CACACACAA FL0505B 29 30 59 0.02 CAAAACCACA CAAAAAAACC CCACACCCAC ACCCCCAAAA CCCACCAACC CACAAAACC FL0506A I 30 29 59 0.02 ACCCAACCCA CCACCACCAA ACCAAAAAAC AAAAACACCC AACACCAACA CCCAACCCA FL0507B 35 24 59 2.05 CACAACCCAA AC CACAAAAA CACAACCAAA AAAACCAACA CACAAACCCC AACAAAACC FL0508A I 30 27 57 0.16 CCCACAAAAC * CACCCCAAAA AACCA-AACA CACACACCAA AACCCC-CAA CACCACAAA FL0601B 5 43 16 59 12.36 ** AC CAAACAC C AACAACCAAA AACAAAAACC CAAAAAAAAA CAAACAAAAC AAAAAACAA FL0602A 3 31 28 59 0.15 CAC CAAC CAC ACCCCCACCA AACAAACACA C C CACAAC CA AAAAAACAC C CAAAAACAC FL0603S 2 26 33 59 0.83 AC C CAACAAC CACACCCCAA ACCAAACCCA CACCACACCC CCAACCAACA CACACCACA FL0604B 17 42 59 10.59 ** CCACCCACCC CCCCCACAAA CCCCCCACCC CCCCCCAAAC CCCCACCAAC CAACAACCC Table A-1. (continued) Marker name Cha Segregation testb Lines o f the 'Fiber-2' population ("A; MTaz90-123, C; MTH6860756, Missing data I A C To Y2 I -1 0 11 - 20 2 1 - 30 3 1 - 4 0 4 1 - 5 0 51 - 59 FL0605A 4 34 24 58 1.72 CCCACCCCAA CAAACAACAC ACACC-AAAA CAAAAC CACA ACAAACAAAC ACACAAACA FL0606A 5 32 26 58 0.62 CCCACCACCA CCACACAAA - CAAAAACAAC ACCCACCACC AAAACCAAAA ACAAACAAC FL0607B 2 26 32 58 0.62 CCCCACACAA AAACCACCC- ACCAACCCCC CCCACCAACC CAAAAACCAA CACAAAACC FL0608A 2 24 33 57 1.42 ACACAAAAAC CAAAACACA- ACCCC -CCCC CACACCACCC CCAACCACCC CACAACACC FL0609S 4 30 28 58 0.07 ACAACAACAC AACAACCAC- C CAACACAAC ACCAAAAACA CCCCCAACCA CCCACAAAC FL0701S 4 29 29 58 0.00 ACCACCCACC CCCAACCACC CAAAAC C CAA AAAACCACCA CCAAACAC-A ACACACAAA FL0702A 2 28 30 58 0.07 ACAAC CAAAC CACAACCACC CACCCAAAAC CCAAAACCAC CCAAACAC-A CACCACCAC FL0801S I 27 32 59 0.42 ACCAAACCCC CACCCAACAA AACCACAACA CCACCACCAA AACCCCACCA CACCCAAAC FL0802S 5 25 34 59 1.37 CCCCACCACC ACAAACAC C C CCAAAACCCC CCCCCAAACC CACCAAAAAA CCCAAAACC FL0803S 7 39 18 57 7.74 * ACAAAACAAA ACAAAACAAA ACCCAACCAA AAAACACACC ACCAAA-AAA CACAAA-CA FL0804B 30 27 57 0.16 C CAACACAC C CACCAACCAC AC CACAACAA AAAC CAAAC C CCCAAC-ACC c a a a a a - a a FL0805A 5 43 14 57 14.75 ** AAAACAACAA ACAAACAACA AAAAACCAAC AAACCAAAAA AAAACA-AAC AAACAC-AA FL0806S 3 26 31 57 0.44 AC AAAACAC C CACCACCCCC AACAACACAA ACCAACCCCA CCCCAC-ACC AAAACA-CC FL0901B 41 17 58 9.93 ** CAAAAAAAAC CAAACCAAAA AAAAAAAACA AAACCAACCA -AACCAAAAA CACACCACA FL0902B 5 41 17 58 9.93 ** ACAAAAAACC AACAAC CAAA AAC CAAAAC C AAAAAAAAAA -CAACCAAAC AAACAACAC FL0903B 37 21 58 4.41 * AAAAAAACCC CCAACCCCAA AC CAAAAACA AAAAAAAACA -AAACAAACC CCAAAACCC FL0904S I 39 20 59 6.12 * ACAAAAAAAA ACCCACAAAA ACCCAAAAAC CCACCAACCA ACAAACAAAA AACACACAA FL0905B 35 23 58 2.48 ACCAAACCAA AAAACAACAC CAAC C C CAAA CCC ACAAAAA -ACACCCCAA CCAAAAAAA FL0906S 2 31 27 58 0.28 ACCAACCACC CAAACAC CAA CCAAAACAAA CAAAC CAC CA -CAACAAACA CCCACACCA FL0907B 30 28 58 0.07 CAACAAAACC AAAACCCACA ACCAAAACAA ACACCACCCA -AACCCAAAA CCCCCCACC FL0908S 2 39 19 58 6.90 ** AC AAAAAACC CAAACAACAA ACAAAAAAAA CAAAAAACCA -CAACAAACA CCCACACCA FL0909A I 37 21 58 4.41 * ACAACAAAAC CACC CAAAAA AACCAAAACC CAAACAACAA -AACCCAAAA CACCACAAA FL0910B I 33 25 58 1.10 C CAACAACAC CCCCCAAAAA AACCAAAACC CAAACAACAA -ACCCCAAAA CACCACAAA FLlOOlB 33 26 59 0.83 CAACAC CAAA AACAC CACAA CCACCCAACC CACAAAAAAC CAAACAACCA CCCAACAAA FL1002B 30 29 59 0.02 CACCAAAACC CCCAAACCCA ACCCAAAAAA AACCCAAAAC CCAAACAACA AACCCCCCC FL1003S I 28 31 59 0.15 CCCAACACCA CAACCCAAAC CCCCACACAC ACACAACCCA AAACACAACA CCCACCAAA Table A-1. (continued) Marker name Cha Segregation testb Lines o f the 'Fiber-2' population (A; MTaz90-123, C; MTH6860756, Missing data I A C To Y2 I -10 11 - 20 2 1 - 30 3 1 - 4 0 4 1 - 5 0 5 1 - 5 9 FL1004S I 27 32 59 0.42 CCCAACAACA CAACCCAAAC AACCACACAC ACACCACCAA AAACCAACCA CCCACCCCC FLI005A 7 35 24 59 2.05 ACAACACAAA ACAAAACAAA ACCCACCCAA AAACCCCACC ACCAAAAAAA CCCAAACCA FL1006B 7 33 26 59 0.83 AACCCACCAA CCACCCACAA CAAAACAAAA AAAAACACCC AAAACAAACA CCCCACCCA FLI007B 39 20 59 6.12 * CAACCAAACC AAACAAC CCA ACAAAAAC CA CACCAACAAA AACAAACAAA AAAAAACCA FL1008B 38 21 59 4.90 * AACAAAAACC AC CAACAC CA ACACCAACCA AAAAAAAAAC AACAAACCAC CCAAACAAA FL1009B I 32 27 59 0.42 CCCAAAAACA ACCCCCAAAA CACCAAAAAC ACAACCACCA ACCCACACCA CACACAAAA FLlOlOB 3 32 27 59 0.42 AC CAAC CACA AACCCCCCCC CCCAAAAAAA ACCCACCCAA AAAAACAACC AAAAAAACC FLl lOlS 6 36 23 59 2.86 ACCCCACACC CAAAAAAAAC ACAAC CAAAA AAACAAACAA AAACCAAACC ACACCCCAA FL1102B 3 23 36 59 2.86 CCCAACCACC AACCCCCCCC C C CAACAACA ACCAACCCCA CCCCACACCC AAAAAAACC F L l103 A 27 32 59 0.42 ACAAACAACC AACAAACAC C ACCCCACCCC CCCCCCCAAA CCACACACAC AAAACAACC FL1104S 2 28 31 59 0.15 CACAAAC CAA CAAACACCCA CCC CAAACAA CAACACCCCA CCACCAAACA CACACCCCA FL1105S I 22 37 59 3.81 C C CAACAC CA CAACCCAAAC AACCACACAC ACACCACCCA ' ACACACCCCC CCCACCCCC FL1106S 2 34 25 59 1.37 CAAAACCCCA CAAAAAACCC C CACAACAAA AACAC CACAA AACACAAACC CACAACACA FL1107S 2 32 27 59 0.42 CAAAACCCCA CAAAAAACCC CCACAACCAA ACCACCACAA AACACAAACC CACAACACA FL1108B 2 28 31 59 0.15 CCAAACACAA ACACCACCCA CACAAACCCC CCAACCAACC C CAAAAC CAC CACAAAACC FL1109S I 26 33 59 0.83 CCCAAACCCC CACCCAACAA AACCACAACA CCAACACCAA AACCCCCCCA CACCCAAAC FLl l lOS 7 20 39 59 6.12 * CCCCCACCAC ACCCCACCAC CACCACAACC CAACCCCCCC ACAACCCCAC ACAACCACA F L l l l l S 5 26 33 59 0.83 ACCCCCACCA CCACAAAACC AACCAAACAA' ACCACCCACA ACCCACACCC AAACCCCAC FL1112S 3 34 25 59 1.37 CACCAAAAAC ACACCCACCA AACAAAAACA CCCAAAACAA CCAAACCCAC CCAAAAAAC FL1201S 22 35 57 2.96 ACACAACAC C CACCC-ACCA CCCCACCACC C-CCACCCAA CCAACAACCA ACACCACAC FL1202B I 31 26 57 0.44 AC CAAAAACA ACCCA-AAAA ACCCACAAAC C-A C CCACCA ACCAACCCAA CACACACAA FL1203B 28 29 57 0.02 CAACCCCCAC CAAAC-CCCC CAAACACCCA A-AAAACAAC CAACCAAACC ACACAAACC FL1204S I 29 28 57 0.02 ACCAAAAAAA ACCCC-AAAA CCCCACAAAC C-CCCCACCA ACCAACACAA CACACCCAA FL1205S I 33 24 57 1.42 ACCAAAAAAA ACCCA-AAAA ACCCACAAAC C-CCACACCA AC CAACACAA CACACACAA FLI206A 5 34 23 57 2.12 CAAACCCCCA CCACA-ACCC CAAAAACAAC A-ACACAACC AAAAACCAAA ACAAAAAAC FL1207B 33 23 56 1.79 CAACAACAAC ACCAA-ACCA CCACAACACA C-CCACAAAC CCAACA-AAA CAACAAAAA Table A-1. (continued) Marker name Cha Segregation testb Lines of the 'Fiber-2' population (A; MTaz90-123, C; MTH6860756, Missing data) A C To T2 I - 10 11 - 20 2 1 - 3 0 3 1 - 4 0 4 1 - 50 51 - 59 FL1208A 5 25 32 57 0.86 CACAAACCCC ACACA-ACCA CCCAAAACCC C-CACAAACC CCAAACCACC CACAAACCC FL1209S 4 28 29 57 0.02 ACAACAACAC ACCAA -CCCA CCACCCAAAC C-CACAAACA ACAACAAC CA CACCCAACC FL1301S 31 28 59 0.15 CCCAAAACAC ACACACCACC AC CAAAACAA AACCCCAACA AACCACCAAA CACACACCA FLI302A I 27 32 59 0.42 CCCACAACCA ACCCCCAAAA CACCCAAACC ACACCCACCA ACAAACACCA CCCACACAA FL1303S 6 26 33 59 0,83 CACCAACACC CACAAAACAC ACAACAAAAC AACCCCACCC CCAACCCACC CCACACCAC FL1304S 6 29 30 59 0.02 CACCAACACC CACAAAACAC ACAACACAAC AACCCCACCC CCAAAAAACA CCACACCAC FL1305S I 29 28 57 0.02 CCCAAAACCA ACCCCCAAAA AACCCAAACC ACACCCACCA ACAAAC-CCA CACA-ACAA FL1306B I 31 27 58 0.28 CCCAAAACCA ACCACCAAAC AAACCAAACC ACACCCACCA ACAAACACAA c a c a - a c c a FL1307B 23 34 57 2.12 CCACACACCC CCCACCCAAC CCCCCAAACC ACACCCACCA ACAAAC-CAA c a c a - a c c a FL1308S I 32 26 58 0.62 AAAACACAAA AACAAAAAAA CCCCACCCAA AAAC CACAC C ACCCAACCAA c c c a - a c c c FL1309B I 26 30 56 0.29 CCCCAACCCA CAACAAACAA CCAC-ACCCC CAAAAACCAA AACCAA-CCC c c c c - a a a c FL1310S 4 29 28 57 0.02 ACAACCCACC CCCCACCACC CAAAACCAAA AACACAAC CA CCAAAC-ACA ACAC-CAAA FL1311S 4 29 28 57 0.02 ACAACCCACC CCCCACCACC CAAAACCAAA AACACAAC CA CCAAAC-ACA ACAC-CAAA FL1312A 6 28 29 57 0.02 CACAAACAC C AAC CAAACAC AC CACAACAA AACCCCACCC CCAACC-ACA CAAC-ACAC FL1401S 7 25 33 58 1.10 ACCCCACCCC ACCCCACCAC AACAACAAC C CAACACCCCC AAAACC-CAA ACAACCACA FL1402B 32 26 58 0.62 CCACCCAAAA ACAAAC CACA ACAACAC CAC AAAAAAAACC CCCACC-CAC AAAACCACA FL1403B 25 33 58 1.10 CCCAACCCCA ACCCACCCCA CCAACACACC AAACCCAAAC CCCCAA-CAC CAAACCAAA FL1404S 2 39 19 58 6.90 ** CACACCACAA ACAAAACAAC AAAAAAACAA AAAACAAACC AACCAC-AAC CACAAAACA FL1405S 2 37 21 58 4.41 * CACACCACAA ACAAAACAAC AAAACAACAA AAAACAAACC AACCAC-AAC CACAACACA FL1406A 37 21 58 4.41 * CAAACAAAAA ACCAACCCAA ACAACCACCA CAAC CAACAA AACACA-AAC ' CAAAAAACA FL1407B 21 37 58 4.41 * CCCAAAAAAC CACACACCAA CCCCCACCCA CCACCCCCAC CCCAAC-AAC CCCCACACC FL1408B 2 • 23 35 58 2.48 CAAAACACCC ACCCCCCCCA CCCCAAACCC CCCACCCAAA CCCAAC-CAA CACAAAACC FL1409B 6 30 28 58 0.07 CAAC C C CACA AAAAAACAAC CACCCCAACA CAAAAAAACC CCCCAA-ACC AAACCACCC FL1410B 2 28 30 58 0.07 AC C CAAACAC CACACCCACA AACCCAAAAA' CCCAACCCCC CCAAAC-CAA CCCAAAACA FL1411B 7 33 25 58 1.10 ACACCACCCC CCACCAACAC ACCAAAAACA CAACAACCAC AAAAAC-AAC AAAACCAAA FL1412S 3 28 30 58 0.07 ACAAAACAC C CACCACCCCC AACAACACAA ACCAACCCCA CCCCAC-ACC AAAAAAACC Table A-L (continued) Marker name; Cha Searegation testb Lines o f the 'Fiber-2' population CA; MTaz90-123, C; MTH6860756, Missina data I A C To X2 I -10 11 - 20 2 1 - 3 0 3 1 - 4 0 4 1 - 50 51 - 59 FL1413S 3 27 31 58 0.28 CCAAAACACC CACCACCCCC AACAACACAA ACCAACCCCA CCCCAC-ACC AAAAAAACC FL1414S 4 25 33 58 1.10 CCAACAACCC AACCACCACC CCAACCCACC ACCAAAAAAA CCCCCC-CCA ACCACAAAC FL1415S 7 30 28 58 0.07 ACACAAAAAC CAAAACACAC ACCCCACACA CAACCCCCAC AAAAAC-ACA CCCCCCAAA FL1501S 2 36 23 59 2.86 CCAACCACAA ACAAAACAAC AAAACAACAA AAAACAAACC AAC CAC CAAC CCCAACACA FL1502B 36 23 59 2.86 CAAACAACCC CCACAACAAA ACACCCACAC AAAACCAACA ACCAAACACA AAAACAAAA FL1503A I 28 31 59 0.15 AC CAACACAA CCCACCAAAC AAC C CAAAAC ACAACCACAA ACAACCACCC CCCCCCAAC FL1504B 38 21 59 4.90 * CAAACAAACC AACACCAAAC AACAAAACAC AAACAACACA ACAACAAAC C CAACAAACA FL1505S I 32 27 59 0.42 AAAACACAAA ACCAAAAAAA ACCCACCCAA ACACCACACC ACCCAACCAC CCAAAACCC FL1506A I 30 29 59 0.02 CCCCAACCCA CAAC CAACAA ACACAACCCC CAAAAACCAA AACCAACACA CCCCCAAAA FL1507A 2 40 19 59 7.47 * CACACCACAA ACAAAACAAC AAAAAAACAA AAAAAACACC ACCCACAAAA CACAAAACA FL1508S 6 27 32 59 0.42 CCAAAACACC CACCAAACCC ACCACAACAA AACCCCACCC CCAACCAACA CCACAAC AC FL1509S 6 39 20 59 6.12 * AAAAAACAAC CAAAAAACAC ACCACAAAAA AACACCACCA C CAAC CAACA AAAAAACAC FL1510B 6 27 32 59 0.42 ACCCCACACC CACACAAAAC CCACCCAAAA AACCAACCAA AACCCCCACA ACCCCCCAA ■FL1511B I 32 27 59 0.42 AAAAAAAAAA ACCCACAAAA ACCCACAAAC CCCCCCACCA ACCCACACAA CACACCCAA FL1512B 5 24 35 59 2.05 CACCACCACC ACACCCACAC CCAAAACCCC CCCCCAACCC CCAAAACACA CACAAACAC FL1513A I 26 33 59 0.83 CCCAAACCCC CACCCAACAA AAC CAC AACA CCAACACCAA AACCCCCCCA CACCCAAAC FL1514B 30 29 59 0.02 CAACACACAC ACAACAC C CA AAACACCCAC CAAACAAAAC CCCCCACCAA CCAAAAACC FL1515A I 28 31 59 0.15 AC CAACACAA CCCCCCAAAC AACCCAAAAC ACAACCACAA ACAACCACCA CCCCCCAAC FL1601S 4 30 29 59 0.02 AAAACCCCAA AAACAAACAC AC CACAAAC C CCCACACCAC CAAACCCCCA AAACCAACC FL1602A 2 32 27 59 0.42 CCCAACACCA AAACAAC CAC AACAAACCCC CACACCAACC CAAAAAACAA CACAAAACC FLI603A 2 29 30 59 0.02 CCCAACACAC AAACCACCCA AACAAACCCC CCCACCAACC CCAAAAACAA CACAAAACC FL1604B 3 29 30 59 0.02 AACAACCACC AACACCAACC CCCAACAAAA ACCAACCCCA CCCCACCACC AAAAAAACC FL1605B 5 24 35 59 2.05 CCCCACCACC ACACACACCC CCACAACCCC CCCCCAACCC CAAAAAAACA CACAAACAC FL1606B 33 26 59 0.83 CCCAAAAAAA CCACACCCAA AAACACAC CA CAAACAACCA AAAAACCCAA ACACCACCC FL1607B 7 26 33 59 0.83 ACCCAACACA CCACCCACAC CCCAACACAC AAAACCCCCC CAAAACAACA CCCCAAACC FL1608A 7 36 22 58 3.38 AAACAACAAA CAAAACACAA AACCCCCACA CAACCCCCAC AAAAAC-AAA ACAACCAAA Table A-1. (continued) Marker name Cha Segregation testb Lines o f the 'Fiber-2' population CA; MTaz90-123, C; MTH6860756, Missing data) A C To Y2 I -10 11 - 20 2 1 - 3 0 31 - 4 0 . 4 1 - 5 0 51 - 59 FL1609B 5 33 25 58 1.10 ACAAAACCCA ACCAACCAAA AACAACCACC CAAACAAAAA CCCACA-AAC AAACCACCC FL1610B 29 29 58 0.00 ACACCCAAAC AACCCCCAAA AACAC CAAAC ACACCAAACC CAAACA-ACA ACCCCCACC FL1611B 39 18 57 7.74 * CACACAAACA ACC-AAAAAA ACACACCACA AAACCAAAAA AC CAAA-AAC CAAACAAAA FL1612B 3 30 27 57 0.16 ACAAAACAC C CAA -CCCCCC AACACCACAA ACCAACCCCA CAACAC-ACA AAAAAAACC FL1613S 7 38 21 59 4.90 * CACCCAACAA CAAC C CACAA AAAAACAAAA AAAAAAACCC AAAACAAACA CCCCACAAA FL1614S 3 30 28 58 0.07 ACAAAAC C CA AC CAACACCC CCCAACCAAC AACAC C CAAC CCAACC-AAA CCCAAAAAA FL1701S 5 39 19 58 6.90 ** CAAAAACCCA ACAAAC CACA AAAAACCAAC AAAAACAAAA -CAACAAAAC ACACACCAA FL1702B 7 30 28 58 0.07 ACCCAACCAC CAACAAACAC CCCCAACACA CAC C CAC CAC -AAAACAACA ACAACCAAA FLI703S 5 24 34 58 1.72 CAACACAC CA C CAAAAAC CA C CAAAACAC C CCACCCCCCA -ACCCCCCCC CACAAACCA FL1704B 19 39 58 6.90 ** AAAC C CAAC C CCCCCCCCAA AAAC CAAC C C C CAACAC CAC -CAACCCACC CCCCCCCCC FL1705S 4 28 30 58 0.07 ACAACAACAC AACAAC CAC C C CAACACAC C ACCAAAAAAA -CCCCCCCCA CCCACAAAC FL1706S 5 28 30 58 0.07 CCCCACCACC ACAC C CAC CA ACACAAAAAC CAACACAACC -CCAACAACC CCCAAAAAA FL1707S 7 27 31 58 0.28 ACCCAACACC C CAC CAC CAC CCCCCCAAAA AACAACCACC -AAAACAACA CACAAACCC FL1708S 7 25 33 58 1.10 ACCCAACACC CCACCACCAC CCCCCCAAAA AACACCCACC -AAAACCACA CACAAACCC FL1709A 6 28 30 58 0.07 CAC CAACAC C CCCAAAACAC ACCACAAAAC AACCCCACAA -AAACCCACA CCACACCAC FL1710A 17 41 58 9.93 ** CAAC C CACAC CCC CACAAAC CCCACACCCC CCCCACCCCC -CAACCCACA CACCCCACC FL1711A I 32 26 58 0.62 AAAACACAAA AACAAAAAAA CCCCACCCAA AAACCACACC -CCCAACCAA CCCAAACCC FL1712S 7 31 27 58 0.28 ACAACACAAA ACAAAACAAA ACCCCACCCA AAACCCCACC -CCCAACAAA CCCAAACCA FL1713A 3 28 29 57 0.02 CACCAAACAC ACCCCCACCA AACAACAACA CACACAACCA -CAAAA-CCC CCAAAACCC FL1714B 31 26 57 0.44 ACCACCACCA ACACACCAAA ACACACACCC AAACCAACCC -CCACC -AAC AAAAAAAAA FL1715B 4 25 32 57 0.86 CCCCCCACAA CCCACCAACA CAC CAACAC C CAACAACAAA - a c c a a - c a c ACCCCCACA FL1716S 6 28 29 57 0.02 CACCAACACC CACAAAACAC ACAAAAAAAC AACCCCCCAC -CAACC -AAA CCCCACCAC FL1717A 3 32 24 56 1.14 CACCAA-AAC ACAC C CAC CA AACAAAAACA CCCACAACAA -CAAAC -CAC CCAAAAAAC FLI80IA 7 31 28 59 0.15 ACAACACAAA ACCAAACCAA ACCCCCCCAA CAACCCCACC ACCAAAAAAA CCCAAACCA FL1802A 7 31 28 59 0.15 ACAACACAAA ACAAAACAAA ACCCCCCCAA CAACCCCACC AACAAACCAA CCCAAACCC FL1803A 3 27 32 59 0.42 ACACAC CACA CACCAACCAC AACACAAAAA CCCAACACCA CCCCCCAACC CCCAAAACC Table A-1. (continued) Segregation testb ________Lines o f the 'Fiber-2' population (A; MTaz90-123, C; MTH6860756, Missing data) Marker name Ch A C To Y2 I - 10 11-20 2 1 - 30 31 - 40 41 - 50 51 - 59 FL1804S 6 29 30 59 0.02 CACCAACACC CACAAAACAC ACAAAAAAAC AACCCCCCAC CCACCCAAAA CCCCACCAA FL1805S 4 31 27 58 0.28 CCCCCCAAAA CAAACAC CAC ACACCAA-AC CAACACAAAC CCAAACAAAC ACACAAACC FL1806S 3 27 30 57 0.16 AC C CAACACA ACACACACCC CCCAACC-CC c a a c c c Ca a c CAACAC-AAA CCCAAAAAA FL1807S 3 29 29 58 0.00 ACCCAACACA ACACAAACCC CCCAACC-CC CACCACAAAC CAACCCAAAA CCCAAAAAA FL1808S 3 29 29 58 0.00 AC CAAAACAC CCACCCACCA ACACACC-AC ACCCCCACAA AAAACCCACA CACAAAAAC FL1809S 6 27 30 57 0.16 CAACCCCACA AACAAACAAC CACACAA-CA CAAAAACAC C CCCCAC-AAC ACACCCCCC FL1810A 27 30 57 0.16 AC C C CAAAAC ACCCCCACAA AACACCA-CC AAACACCAAA CCCCAC-CAA ACAC CACAC Marker name Cha Segregation test1 Inferred genotypes of 3 markers at F5 generation _________________________ H; Heterogeneous (inferred by phenotypes’) A H C Y2 I - 10 11-20 2 1 - 30 31 - 40 41 - 50 51 - 59 Ik2 i 23 8 28 5.83 CCCAACACHA CAAACCAAAC AACCHCAAHC ACACHHACCA AAACCCACCH CHACCCHCC LU LU n i 24 9 26 8.24 * CCCAACACHA CAACCCAAAC AACHACACHC H CAC CAC CCA ACACACACHH CHAACHAAH W X i 31 8 20 7.57 * AHHACAHAHA AAAAAAAAAA ACCCACCAAA HCACCACACC ACHAACHCCA HCAAAACCC a Chromosomal locations of each locus on the ‘Fiber-21 linkage map. b * and ** are levels o f significance atp< 0.05 andp < 0.01, respectively. Table A-2. Marker intervals of the ‘Fiber-21 linkage map with the results of single marker QTL analysis on traits measured. ‘Fiber-2’ linkage map Single Marker QTL analysis on quantitative traits measureda Order Intervalb Locus YD98 KW98 KW99 GU96 GU97 GU99 HD98 HD99 PH99 CM(Kosambi) F-value F-value F-value F-value F-value F-value F-value F-value F-value Chl I 0.0 0.0 FL0308S 0.215 0.382 0.023 1.534 2.049 0.881 0.307 0.306 0.818 ' 2 14.5 14.5 FL1505S 2.026 1.034 3.781 8.371 ** 11.286 ** 18.959 **** 0.020 0.333 2.063 3 19.1 4.6 FL1308S 1.548 0.014 1.218 ■ 7.029 * 7.080 * 15.668 *** 0.004 0.339 1.422 4 22.8 3.7 FL0207S 1.523 1.093 3.265 6.189* 9.079 ** 19.895 **** 0.027 0.366 2.918 5 28.5 5.7 M-HVWAXYG 0.000 1.249 1.835 10.087 ** 14.842 *** 32.878 **** 0.307 0.000 2.124 6 30.0 ■ 1.5 WX 1.873 10.031 ** 12.601 *** 16.859 32.343 **** 54.924 **** 0.423 0.050 2.734 7 33.3 3.3 HVM6 0.524 0.724 1.811 19.029 **** 9 318 ** 27.344 0.589 0.552 1.608 8 69.9 36.6 FL0904S 1.845 0.646 4.453 * 0.001 1.546 1.202 8.208 ** 8.694 ** 1.674 9 84.4 14.5 ABG380 0.127 1.013 8.861 ** 2.261 7.766 ** 2.916 29.558 **** 25.768 14.666 *** 10 89.1 4.7 FL1202B 0.028 0.000 2.307 1.528 5.106* 1.887 15.165 12.354 *** 6.340 * 11 92.8 3.7 FL1205S 0.149 0.025 2.602 0.039 7.449 ** 0.986 12.409 *** 10.547 ** 5.880 * 12 97.5 4.7 FL1511B 0.283 0.452 4.933 * 0.136 2.257 2.702 9.391 ** 5.445 * 2.540 ' ' 13 102.2 4.7 FL1204S 0.037 0.026 3.165 0.096 2.651 1.034 10.065 ** 7.596 ** 5.685 * 14 105.9 3.7 FL0304S 0.099 0.224 3.079 ■ 0.035 2.475 1.224 15.329 *** 12.467 6.365 * 15 117.6 11.7 FL1009B 2.107 0.218 2.830 0.442 0.638 0.065 9.849 ** 7.916** 2.391 16 123.3 5.7 FL0504S 9.171 ** 1.131 5.932 * 0.129 0.676 0.261 12.390 11.458 ** 4.159 * 17 130.1 6.8 FL1302A 1.829 2.937 5.132* 0.641 5.323 * 0.889 13.465 9.721 ** 3.079 18 132.8 2.7 FL1305S 1.987 3.030 6.269 * 0.100 2.753 0.224 15.056 *** 11.097 ** 4.880 * 19 137.5 4.7 FL1306B 4.533 * 8.006 ** 11.278 ** 1.022 3.130 0.577 6.620 * 3.684 1.542 20 145.4 7.9 M-HVCMAa 9.276 ** 12.306 *** 20.477 **** 3.731 4.532 * 1.202 4.366 * 1.616 0.249 21 146.3 0.9 M-HVCMAb 10.587 ** 16.173 *** 25.231 4.114* 4.556 * 2.673 3.679 0.969 0.086 22 159.4 13.1 FL1105S 28.803 * 23.670 **** 59.221 **** 1.942 6.731 * 4.057 * 9.019** 4.349 * 0.494 23 162.3 2.9 n 27.330 **** 44.916**** 137.100 * 4.656 * 11.639** 7.107** 8.740 ** 3.893 0.673 24 166.8 4.5 WTAl 22.295 **** 22.515 **** 52.445 **** 4.498 * 11.825 ** 7.109 ** 5.960 * 3.204 0.473 25 174.7 7.9 ABG20.11a 16.689 **** 36.448 * 44.459 **** 2.288 5.774 * 5.429 * 3.054 1.191 0.975 134 Table A-2. (continued) ‘Fiber-2’ linkage map Single Marker QTL analysis on quantitative traits measured Order Intervalb Locus YD98 F-value KW98 F-value KW99 F-value GU96 F-value GU97 F-value GU99 F-value HD98 F-value HD99 F-value PH99 F-valueCM(Kosambi) :h i 26 178.4 3.7 Ik2 15.542 *** 47.857 **** 42.307 **** 0.529 9.099 ** 7.627 ** 2.910 1.424 0.382 27 190.2 11.8 FL1004S • 24.092 **** 12.227 *** 30.285 * 1.268 2.616 2.776 6.905 * 4.843 * 0.882 28 201.9 11.7 FL1003S 12.807 *** 17.199*** 17.717 * 0.678 3.976 1.689 3.679 2.708 0.044 29 228.0 26.1 FL0413A 10.041 ** 8.667 ** 9.037 ** 1.122 0.952 1.244 3.630 2.045 2.242 30 239.8 11.8 FL1503A 7.634 ** 3.708 5.486 * 1.327 0.209 0.090 2.567 3.037 1.457 31 241.6 1.8 FL1515A 6.214 * 2.781 4.547 * 1.598 0.226 0.066 5.650 * 5.999 * 3.558 32 270.1 28.5 FL0910B 0.171 0.751 0.392 5.737 * 1.885 0.801 0.880 0.869 2.833 33 273.8 3.7 FL0909A 1.184 0.097 0.082 2.484 1.076 0.041 1.239 1.762 2.898 34 277.5 3.7 FL0203S 0.148 0.463 0.084 3.119 1.116 0.130 4.434 * 4.795 * 7.306 ** 35 282.5 5.0 FL0508A 0.879 0.168 0.486 1.096 0.237 0.077 3.156 2.449 2.670 36 293.8 11.3 ABC253 1.294 0.358 0.597 0.442 0.128 0.686 2.325 2.216 1.016 37 302.9 9.1 FL0801S 1.928 0.021 1.698 0.494 0.240 0.554 7.660 ** 10.454 ** 5.388 * 38 305.6 2.7 FL1109S 1.048 0.132 0.258 0.014 0.051 ■ 0.001 6.448 * 6.659 * 4.939 * 39 325.1 19.5 FL1506A 0.030 0.330 0.457 0.180 0.022 0.741 0.010 0.146 0.943 40 330.3 5.2 FL1309B 0.156 0.865 0.576 0.451 0.244 0.843 0.252 0.188 0.152 41 335.6 5.3 M-HVPRPIB 0.007 0.085 0.002 0.206 0.874 0.039 0.031 0.002 0.074 :h2 I 0.0 0.0 FL1507A 1.090 0.501 0.520 2.509 0.854 0.048 0.036 0.556 1.791 2 4.1 4.1 FL1404S 1.737 1.040 0.734 1.015 0.207 0.158 0.894 2.717 2.310 3 6.1 2.0 FL1405S 1.435 1.375 0.476 0.014 0.607 0.111 1.354 3.824 3.100 4 9.1 3.0 FL1501S 1.165 0.696 0.168 0.064 0.664 0.029 1.018 2.749 1.616 5 16.2 7.1 ABG058 1.371 0.149 0.089 0.748 0.318 0.188 0.704 2.527 2.280 6 44.9 28.7 HVM36 0.029 ' 2.443 0.003 0.743 1.923 0.254 20.277 **** 20.507 **** 14.489 *** 7 51.7 6.8 FL0205B 0.067 1.378 0.911 3.508 1.761 0.266 28.808 31.010 **** 23.422 *** 8 60.4 8.7 ABC170 0.029 0.692 3.030 0.007 1.073 0.048 66.486 **** 80.571 **** 31.887 *** Table A-2. (continued) ______________ ‘Fiber-2' linkage map Single Marker QTL analysis on quantitative traits measureda Order Intervalb Locus YD98 KW98 KW99 GU96 GU97 GU99 HD98 HD99 PH99 CM(Kosambi) F-value F-value F-value J7-Value J7-Value J7-Value J7-Value J7-Value J7-Value :h2 9 70.4 10.0 FL1106S 1.742 0.160 4.253 * 0.471 3.460 1.047 15.091 20.984 **** 12.422 * 10 72.2 1.8 FL1107S 1.759 0.018 2.149 0.399 3.918 0.755 11.260 ** 16.840 * 14.631 11 106.4 34.2 FL1104S 0.248 0.397 0.002 0.003 0.048 0.259 0.593 1.688 1.952 12 140.1 33.7 CMWG694' 2.402 0.589 0.035 3.474 3.548 0.003 0.116 0.443 0.625 13 150.0 9.9 FL0906S 0.070 0.101 0.554 2.761 0.222 0.885 0.060 0.092 0.331 14 158.2 8.2 FL0908S • 0.000 0.192 0.036 1.130 0.739 0.563 0.267 0.377 0.076 15 178.7 20.5 FL0503A 0.199 0.301 0.041 0.182 0.188 0.869 0.234 0.085 0.262 16 184.4 5.7 FL0603S 1.190 0.254 0.014 0.158 0.000 0.308 1.409 0.139 0.296 17 201.3 16.9 FL0608A 0.032 1.145 1.176 0.022 0.214 0.016 1.782 1.336 0.792 18 245.6 44.3 FL0702A 1.064 0.045 0.319 0.769 0.002 0.214 2.260 2.071 0.181 19 270.2 24.6 FL1410B 2.572 4.440 * 1.621 1.078 0.641 0.017 0.072 0.136 0.004 20 279.4 9.2 HVM054 1.774 4.793 * 2.758 0.144 0.676 0.422 0.051 0.119 0.398 21 283.1 3.7 FL0303A 1.726 0.925 1.544 0.003 0.012 2.111 0.787 1.091 1.408 22 303.6 20.5 FL1408B 0.167 1.920 0.387 0.917 1.424 4.456 * 3.158 3.385 5.680 * 23 321.4 17.8 ABG609 0.128 0.169 0.002 0.076 0.394 0.898 0.569 0.715 4.014 * 24 329.1 7.7 M-HVCSG 0.014 0.545 0.021 0.049 0.785 3.848 0.035 0.102 3.780 25 338.3 9.2 FL1108B 0.302 0.842 0.202 0.301 1.859 5.263 * 0.051 0.011 5.602 * 26 345.1 6.8 FL1603A 0.423 0.002 0.318 0.175 0.417 3.142 0.606 0.227 4.759 * 27 351.2 6.1 FL0607B 2.858 0.475 0.359 0.000 0.827 1.330 0.000 0.058 3.073 28 359.8 8.6 FL1602A 0.000 0.242 0.611 0.596 0.560 1.534 0.544 0.081 2.645 29 374.3 14.5 FL0404S 1.005 1.193 0.083 2.757 4.199 * 2.817 0.023 0.001 2.687 3h3 I 0.0 0.0 ABG070 3.729 0.094 0.183 1.470 0.683 3.519 1.300 2.045 6.313 * 2 6.2 6.2 FL1112S 1.064 0.018 0.749 1.384 0.461 4.862 * 1.800 2.376 6.008 * 3 7.1 0.9 FL1717A 0.040 0.243 2.174 0.460 0.894 2.819 0.642 0.813 2.835 Table A-2. (continued) Tiber-2' linkage map Single Marker QTL analysis on quantitative traits measured Order Intervalb Locus YD98 KW98 KW99 GU96 GU97 GU99 HD98 HD99 PH99 CM(Kosambi) F-value F-value F-value F-value F-value F-value F-value F-value F-value 3h3 4 16.9 9.8 FL1713A 0.281 2.385 2.850 0.011 0.205 1.230 0.305 0.136 0.743 5 25.4 8.5 FL0602A 0.034 0.363 0.057 0.658 0.642 3.503 0.333 0.327 0.003 6 57.3 31.9 FL1808S 0.718 0.008 0.125 0.998 0.177 2.390 0.590 1.667 1.009 7 94.8 37.5 FL1614S 0.032 0.472 0.930 . 2.947 1.316 0.014 2.984 0.880 1.751 8 108.3 13.5 FL1806S 0.069 0.033 0.016 4.877 * 0.480 0.073 0.296 0.001 0.356 9 112.1 3.8 HVM060 0.913 0.023 0.195 5.607 * 0.004 0.080 2.167 0.575 0.004 10 114.8 2.7 FL1807S 2.778 0.756 1.132 2.071 0.862 0.502 1.141 0.101 0.266 11 121.6 6.8 FL0410S 2.240 1.722 2.408 0.826 2.211 0.834 0.851 0.012 0.025 12 155.8 34.2 FL1803A 0.635 0.009 0.136 0.455 1.300 0.840 0.008 0.011 0.609 13 173.6 17.8 FL0306A 1.635 0.075 0.993 0.345 0.240 0.050 0.033 0.143 4.479 14 183.9 10.3 FL0502B 0.044 0.000 0.028 0.087 0.040 0.755 0.008 0.015 2.650 15 197.9 14.0 FL1612B 0.042 2.780 3.416 3.096 0.004 0.546 0.314 0.421 2.358 16 206.3 8.4 FL0311S 0.219 0.017 0.554 0.825 0.195 0.703 0.352 0.313 2.452 17 209.3 3.0 FL1413S 0.134 0.107 0.650 0.959 0.170 0.494 0.003 0.023 4.481 18 210.3 1.0 FL1412S 0.019 0.047 0.661 1.842 0.051 0.265 0.063 0.087 3.484 19 211.3 1.0 FL0806S 0.162 0.059 0.791 1.812 0.003 0.308 0.211 0.381 2.318 20 215.4 4.1 MWG549 0.251 0.152 0.030 2.147 0.041 0.002 0.827 0.510 0.954 21 225.7 10.3 FL1102B 0.031 0.055 ■ 0.453 0.778 0.102 0.347 1.018 0.785 0.033 22 233.6 7.9 FL1604B 0.431 0.563 0.548 1.568 0.607 0.092 0.234 0.186 1.948 23 243.9 10.3 FL0305A 0.018 0.132 0.627 1.686 0.748 0.140 0.047 0.087 2.057 24 258.4 14.5 FLlOlOB 2.425 1.124 0.893 0.622 0.008 0.453 0.071 0.618 0.191 3h4 I 0.0 0.0 FL0310A 0.399 0.776 0.398 2.045 0.955 0.066 0.352 0.265 0.006 2 3.8 3.8 FL1601S 0.028 0.046 0.002 0.634 1.495 0.087 0.646 0.780 0.848 3 21.6 17.8 FL0403S 0.593 3.305 4.743 * 0.332 2.477 1.505 0.220 0.942 1.535 Table A-2. (continued) ‘Fiber-2' linkage map Single Marker QTL analysis on quantitative traits measured a Intervalb Locus YD98 KW98 KW99 GU96 GU97 GU99 HD98 HD99 PH99 cM(Kosambi) F-value F-value F-value F-value F-value F-value F-value F-value F-value '4 55.2 33.6 FL1209S 0.924 2.945 3.215 0.572 0.455 0.302 0.025 0.370 0.142 5 72.4 17.2 ABC303 2.474 2.977 7.234 ** 0.515 2.369 0.062 1.098 0.268 0.813 6 84.3 11.9 FL0609S 1.009 2.414 3.638 0.178 0.152 0.077 0.051 0.059 0.294 7 86.1 1.8 HVM3 0.000 0.369 1.488 0.029 0.020 0.139 . 0.051 0.023 0.185 8 89.8 3.7 MWG058 0.037 5.553 * 3.746 0.270 1.078 0.013 0.128 0.946 0.030 9 91.6 1.8 FL1705S 0.015 2.397 3.230 0.098 0.094 0.212 0.063 0.561 0.066 10 96.5 4.9 FL1414S 0.381 3.400 3.801 0.006 0.391 0.128 0.502 1.091 0.053 11 114.7 18.2 HVM68 1.285 4.088 * 2.981 1.137 0.004 0.003 0.857 1.070 0.941 12 136.8 22.1 ABG618 0.231 7.654 ** 3.449 0.105 4.265 * 2.571 0.392 0.556 0.170 13 149.7 12.9 ABG054 0.486 5.526 * 1.856 0.002 0.305 0.456 1.214 2.084 1.192 14 166.8 17.1 FL1311S 0.174 0.810 0.765 0.515 0.236 0.149 0.117 0.005 3.903 15 173.2 6.4 FL0701S 0.053 0.045 0.076 2.853 0.208 0.438 0.000 0.001 2.112 16 242.6 69.4 FL0605A 1.138 4.084 * 1.088 0.447 3.465 0.207 0.526 2.092 1.238 17 254.7 12.1 FL1805S 0.011 0.895 0.628 0.209 0.325 0.030 0.060 0.079 0.434 18 278.5 23.8 FL0411S 0.028 0.825 0.040 0.118 0.328 0.117 6.024 * 6.207 * 0.733 19 316.3 37.8 BTA02a 0.862 5.962*' 2J86 3.203 5.996 * 1.662 0.964 1.694 0.481 20 330.8 14.5 BTA02b 0.465 3.109 1.178 0.036 2.573 0.037 0.190 0.092 2.040 21 341.8 11.0 BCD402 1.426 1.632 0.594 0.637 2.638 1.292 0.022 0.049 0.846 22 357.7 15.9 FL0409A 0.069 0.136 0.570 1.656 0.861 0.011 0.024 0.157 6.120 * 23 361.5 3.8 FL1715B 0.117 0.002 0.070 1.282 0.768 0.008 0.032 0.060 3.691 I 0.0 0.0 FL1609B 0.004 1.331 0.618 1.110 0.067 1.257 0.001 0.418 1.065 2 14.9 14.9 FL0601B 0.898 0.820 0.758 0.268 0.362 0.526 0.225 1.437 1.182 3 22.8 7.9 FL0902B 0.542 0.264 0.029 0.003 0.529 0.461 0.920 3.289 1.152 4 30.7 7.9 FL0401A 0.630 0.757 0.013 1.642 0.401 1.685 0.028 0.102 0.060 Table A-2. (continued) ‘Fiber-2' linkage map Single Marker QTL analysis on quantitative traits measured Order Intervalb Locus YD98 KW98 KW99 GU96 GU97 GU99 HD98 HD99 PH99 CM(Kosambi) F-value F-value F-value F-value F-value F-value F-value F-value F-value ChS 5 42.4 11.7 MWG938a 0.002 0.016 0.023 0.473 0.228 0.571 0.858 0.921 0.041 6 57.8 15.4 FL1701S 0.343 0.196 1.819 0.000 0.044 0.145 4.894 * 4.984 * 1.407 7 69.5 11.7 FL0805A 0.202 0.046 0.277 0.859 0.267 1.068 2.922 1.768 0.262 8 80.5 11.0 Hor 2 0.070 0.353 . . 0.564 0.045 0.699 0.018 1.868 1.187 0.192 9 81.4 0.9 H o rl 0.117 0.193 0.552 0.100 0.922 0.120 2.672 1.995 0.410 10 85.1 3.7 ABC156.1 0.106 0.646 0.821 0.055 1.659 0.076 0.671 0.241 0.499 11 98.8 13.7 MWG2261 1.359 2.802 6.552 * 2.018 0.035 0.641 2.934 3.516 2.267 12 143.4 44.6 FL1706S 0.063 0.001 0.020 0.120 0.575 1.620 0.259 0.012 0.351 13 162.6 19.2 MWG938b 2.915 1.532 4.310* 0.728 0.208 1.850 2.434 1.315 0.151 . 14 183.1 20.5 FL0802S 0.018 0.005 0.002 0.597 1.073 0.977 1.485 0.854 0.343 15 192.2 9.1 FL1605B 0.833 0.138 0.020 0.001 1.195 2.555 0.028 0.000 0.165 16 197.9 5.7 FL1512B 0.274 • 0.764 0.003 0.333 1.661 3.976 0.821. 0.612 0.421 17 217.6 19.7 FL0415S 0.042 0.018 0.029 0.157 0.048 0.097 0.201 0.099 0.124 18 218.5 0.9 FL0416S 0.318 0.177 0.024 0.093 0.022 0.094 0.170 0.047 0.150 19 221.6 3.1 FL0206B 0.029 0.009 0.037 0.000 0.034 0.012 0.004 0.010 0.483 20 227.1 5.5 FL1208A 0.354 0.782 0.840 0.134 2.041 4.911 * 0.196 0.421 0.129 21 250.5 23.4 ABG452 0.028 0.068 0.125 0.111 0.028 0.027 0.049 0.146 0.132 22 264.8 14.3 FL1703S 0.348 0.056 0.276 0.198 0.158 0.410 1.485 2.528 0.611 23 315.8 51.0 FL1206A 0.110 0.220 0.179 0.575 0.439 0.301 ■ 0.909 1.570 0.315 24 326.6 10.8 FL0414S 0.316 1.456 0.269 2.598 2.575 2.233 1.274 1.452 2.568 25 330.3 3.7 FL0606A 1.167 2.770 0.859 0.993 2.250 0.674 0.136 0.160 0.665 26 372.5 42.2 F L l l l lS 1.925 0.713 3.346 1.287 1.895 1.858 0.421 0.030 1.600 27 390.3 17.8 ABG55a 3.690 0.426 2.485 2.314 2.158 1.818 0.418 0.056 1.304 28 393.0 2.7 ABG55b 3.028 0.331 2.323 0.701 1.002 0.403 0.020 0.002 0.712 Table A-2. (continued) ‘Fiber-21 linkage map Single Marker QTL analysis on quantitative traits measureda Order Intervalb Locus YD98 KW98 KW99 GU96 GU97 GU99 HD98 HD99 PH99 CM(Kosambi) F-value F-value F-value F-value F-value F-value F-value F-value F-value Ch6 I 0.0 0.0 FL0402S 0.106 0.010 0.168 0.661 2.218 0.003 1.638 2.686 1.894 2 4.6 4.6 MSTl 09 0.566 0.037 0.467 1.140 2.353 0.077 2.447 2.849 2.488 3 11.4 6.8 MWG620 0.823 1.791 1.328 1.829 6.248 * 0.737 1.433 0.959 . 1.042 4 15.1 3.7 FL1804S 0.004 0.010 0.004 0.972 3.652 0.210 1.647 1.819 3.210 5 17.0 1.9 FL1716S 0.305 0.106 0.195 0.800 1.097 0.021 1.738 1.705 3.207 6 25.9 8.9 FL1709A 0.018 0.464 0.002 2.702 0.967 0.455 0.153 0.085 1.367 7 31.6 5.7 FL1303S 0.099 0.452 0.003 1.696 1.834 0.055 2.423 3.002 5.593 * 8 36.2 4.6 FL1304S 0.001 0.705 0.063 0.784 0.089 0.071 2.152 3.172 6.368 * 9 45.3 9.1 FL0412A 0.152 2.268 1.067 0.166 0.043 0.510 0.425 0.775 4.105 * 10 53.5 8.2 HVGNIRE . 0.569 0.164 0.023 2.700 2.298 0.203 1.339 1.941 6.805 * 11 78.7 25.2 FLOI02A 0.615 2.287 0.583 0.341 1.491 0.045 2.235 2.812 4.225 * 12 109.8 31.1 FLOlOlA 0.092 0.007 0.313 0.014 0.320 0.001 0.004 0.127 0.114 13 147.6 37.8 FL1509S 0.033 0.051 0.058 0.051 0.389 0.207 0.076 0.536 5.989 * 14 158.0 10.4 FL0408B 0.869 0.004 0.565 0.034 0.905 0.569 0.068 0.333 5.656 * 15 162.7 4.7 FL1312A 0.334 0.107 0.369 _ 0.041 0.847 0.010 0.010 0.042 2.926 16 167.4 4.7 FL1508S 0.402 0.208 0.280 0.024 0.798 0.000 0.734 1.035 6.503 * 17 198.5 31.1 FL0301S 1.083 0.054 0.193 0.002 0.019 0.170 0.024 0.042 2.333 18 220.0 21.5 FL0501S 1.504 0.045 0.429 0.092 0.005 0.000 1.521 0.569 6.087 * 19 233.1 13.1 FL1510B 0.006 0.401 0.495 0.012 0.110 1:085 0.190 0.649 1.151 20 244.8 11.7 FLllO lS 0.618 0.898 0.087 0.162 0.481 1.314 1.282 0.962 5.697 * 21 295.8 51.0 FL1409B 0.053 0.187 0.492 1.948 1.350 2.182 0.023 0.540 0.428 22 304.4 8.6 FL1809S 0.056 0.005 0.030 1.037 0.228 2.279 0.524 1.075 1.255 23 315.8 11.4 FL0307B 1.475 0.000 0.135 0.607 0.298 0.001 0.662 1.976 0.373 Table A-2. (continued) __________ ‘Fiber-2' linkage map Single Marker QTL analysis on quantitative traits measureda Order Intervalb Locus YD98 KW98 KW99 GU96 GU97 GU99 HD98 HD99 PH99 cM(Kosambi) F-value Jr-Value Jr-VaIue Jr-Value Jr-Value Jr-VaIue Jr-Value Jr-VaIue Jr-Value :h7 I 0.0 0.0 ABG064 0.007 0.289 0.221 2.269 4.971 * 2.224 0.636 0.867 1.367 2 6.4 6.4 ABC483 0.002 0.370 0.760 12.970 *** 12.711 *** 13.062 *** 0.003 0.002 1.224 3 17.9 11.5 FL1005A 0.069 0.086 0.489 11.048 ** 10.285 ** 8.568 ** 0.297 0.767 2.763 4 23.6 5.7 FL1802A 0.069 0.021 0.507 7.947 ** 7.340 ** 5.768 * 0.174 0.005 0.003 5 29.3 5.7 FL1801A 0.301 0.106 0.002 6.278 * 5.113 * 3.748 0.012 0.090 0.777 6 36.1 6.8 FL1712S 0.001 0.395 0.147 4.776 * 3.646 3.220 0.060 0.391 0.808 7 41.9 5.8 FL0407A 0.429 0.906 0.164 3.583 3.290 3.326 0.010 0.038 0.188 8 44.6 2.7 FL0406A 0.252 0.033 0.010 4.478 * 4.010 2.157 0.000 0.040 0.070 9 55.3 10.7 FLO 8 03 S 0.262 0.241 0.027 5.145 * 1.220 2.488' 1.506 1.477 3.421 10 90.5 35.2 FL0405S 0.002 0.723 0.073 0.022 1.305 0.000 0.840 1.233 0.020 11 101.0 10.5 M-HVDHN9a 1.310 1.248 0.001 1.025 1.504 0.005 1.695 1.072 0.021 12 112.8 11.8 FLlllOS 0.369 1.479 0.623 0.517 0.522 0.102 0.001 0.004 1.234 13 119.8 7.0 FL1401S 3.441 3.254 0.993 3.813 2.246 0.144 0.376 1.618 2.360 14 136.6 16.8 FL1411B 0.075 0.408 0.022 0.137 0.001 0.803 0.630 0.060 0.000 15 151.9 15.3 FL1702B 0.399 1.583 0.171 0.173 0.039 0.384 1.218 0.443 0.977 16 166.5 14.6 ABC155 0.078 2.190 2.758 1.899 0.140 0.086 0.028 0.569 0.028 17 190.1 23.6 FL1607B 0.016 3.131 3.860 2.031 0.083 0.411 0.031 0.016 0.708 18 205.3 15.2 FL1708S 0.555 0.037 0.011 1.842 0.033 1.904 0.006 0.000 0.565 19 207.1 1.8 FL1707S 0.943 0.300 0.000 1.954 0.361 1.912 0.012 0.004 0.840 20 inn 20.6 FL0506A 4.788 * ' 1.895 0.013 0.509 0.325 - 0.343 1.433 0.870 0.835 21 242.2 14.5 FL1006B 2.497 1.108 0.112 0.015 0.142 0.824 2.430 3.461 1.274 22 249.0 6.8 FL1613S 0.896 2.603 0.186 0.690 0.047 0.169 1.504 1.956 0.960 ‘Fiber-2' linkage map Single Marker QTL analysis on quantitative traits measured a Table A-2. (continued) ______________________________________ Order Intervalb Locus YD98 KW98 KW99 GU96 GU97 GU99 HD98 HD99 PH99 CM(Kosambi) F-value F-value F-value F-value K-value F-value F-value F-value F-value Ch7 23 297.2 48.2 FL1608A 0.033 3.752 2.446 1.685 2.042 2.827 0.449 0.202 0.002 24 307.7 10.5 FL1415S 1.149 2.463 0.956 0.005 0.225 0.327 0.405 0.090 0.159 25 315.8 8.1 MWG2249 2.829 4.577 * 3.528 2.095 0.842 1.708 1.349 1.890 4.206 * 26 325.0 9.2 FL0202S 1.190 1.054 1.133 2.641 0.183 1.207 0.000 0.002 0.348 27 330.8 5.8 FL0312S 0.948 0.910 1.199 5.958 * 0.825 1.568 0.297 0.287 2.742 195 loci Pairs having identical genotypes in 59 mapping lines Map______ order Chl 3 with FL1711A Chl 38 • with FL1109S Ch4 14 with FL1310S Ch7 11 with M-HVHDN9b ■ Total 199 loci on the ‘Fiber-2' linkage map a Grain yield (YB), Kernel weight (KW), |3-D-glucan contents (GU), Heading date (HD) and Plant height (PH) with the year tested. *, **, *** and **** are levels o f significance at P< 0.05, 0.01, 0.001 and 0.0001, respectively. b Kosambi mapping function was used. Accumulative map size and marker intervals with cM. 142 143 Table A-3. The amount of maternal genome (MTaz90-123) in 59 lines o f the ‘Fiber-2' population. Line Total 241 loci ‘Fiber-2' linkage map 199 loci Chromosome I 43 loci M MTaz90-123 (%) M MTaz90-123 (%) M MTaz90-123 (%) I 12 52.0 12 50.3 0 44.2 2 6 61.3 6 64.6 I 16.3 3 2 49.4 2 52.8 I 25.6 4 I 40.4 I 40.2 0 90.7 5 9 42.2 6 39.7 0 69.8 6 0 36.5 0 34.4 0 74.4 7 2 57.3 2 62.2 I 65.1 8 3 45.4 2 46.6 0 46.5 9 13 57.0 14 58.0 3 39.5 10 0 52.3 0 49.7 0 81.4 11 I 49.6 I 50.5 0 48.8 12 0 49.8 0 49.7 0 58.1 13 I 50.8 I 50.0 0 34.9 14 2 47.3 I 47.4 0 34.9 15' 0 43.6 0 42.1 0 32.6 16 9 53.0 6 51.3 3 37.2 17 0 43.2 0 39.0 0 97.7 18 0 50.2 0 49.7 0 83.7 19 I 40.0 I 39.7 0 97.7 20 8 48.5 6 51.9 0 72.1 21 0 39.4 0 39 0 69.8 22 0 55.2 0 53.3 0 58.1 23 0 61.4 0 64.6 0 16.3 24 2 48.5 2 47.7 I 0.0 25 3 34.9 3 31.8 2 74.4 26 8 45.1 7 44.1 2 37.2 27 0 42.3 0 41.5 0 74.4 28 7 40.6 6 36.5 0 69.8 29 2 43.9 2 41.5 2 55.8 30 0 52.7 0 54.9 • 0 30.2 31 2 48.5 2 50.3 2 46.5 32 11 44.8 8 45.5 3 20.9 ' 33 I 45.8 I 45.9 0 83.7 34 0 51.9 0 49.7 0 30.2 35 2 61.1 2 61.1 I 14.0 36 7 52.6 7 56.4 2 60.5 37 0 46.1 0 47.2 0 46.5 38 I 50.0 I •54.1 ■ ■ 0 18.6 144 Table A-3. (continued) Line Total 241 loci ‘Fiber-2' linkage map 199 loci Chromosome I 43 loci M MTaz90-123 (%) M MTaz90-123 (%) M MTaz90-123 (%) 39 I 59.2 I 62.4 0 37.2 40 I 49.2 I 47.9 0 81.4 41 26 50.7 18 47.5 3 93.0 42 0 59.8 0 60.0 0 32.6 43 4 41.8 4 39.8 2 44.2 44 0 41.9 0 42.6 0 41.9 45 7 45.7 7 43.6 0 69.8 . 46 I 65.8 I 68.0 0 20.9 47 54 39.0 41 35.1 5 55.8 48 0 41.9 0 43.1 0 14.0 49 4 54.4 4 55.0 I 48.8 50 4 41.4 4 39.3 2 74.4 51 I 64.2 I 65.5 I 7.0 52 3 51.3 3 52.1 2 46.5 53 2 57.7 0 61.0 0 18.6 54 I 37.5 I 33.5 I 65.1 55 8 40.8 6 37.6 4 27.9 56 I 39.6 I 39.7 I 60.5 57 6 45.5 5 45.8 I 39.5 58 2 56.5 2 54.4 I 60.5 59 4 51.9 4 50.3 3 53.5 Mean (%) 48.7 48.9 50.0 Amount of the maternal alleles (MTaz90-123 %) in each individual line was expressed with the percentage o f total number o f loci in total informative markers, markers on Fiber-2 linkage map, and the markers on chromosome I . M indicates the number of missing markers.