GENETICS OF REMONTANCY IN OCTOPLOID STRAWBERRY (Fragaria × ananassa) By Sonali Mookerjee A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILSOPHY Plant Breeding, Genetics, and Biotechnology-Horticulture 2012 ABSTRACT GENETICS OF REMONTANCY IN OCTOPLOID STRAWBERRY (Fragaria × ananassa) By Sonali Mookerjee Flower initiation in strawberry genotypes is primarily determined by two environmental factors: photoperiod and temperature. Commercially grown strawberries are generally classified as remontant (repeat flowering) or short day types based on their photoperiod requirement for flower initiation. However, both types will flower in any photoperiod when temperatures are sufficiently cool and flower initiation is inhibited beyond a temperature threshold. The currently available remontant genotypes do not perform well in the extreme heat of midwestern summers. Therefore it is necessary to develop remontant cultivars tailored to the midwestern environmental growing conditions by incorporating heat tolerance and/or other sources of remontancy. This research was designed to identify the regions of the genome that regulate heat tolerance and remontancy in a population developed from ‘Honeoye’ × ‘Tribute’, where ‘Tribute’ is a remontant parent and as a result the progeny segregated for remontancy. A SSR-based linkage map was generated and the QTL associated with remontancy and duration of flowering were identified using phenotypic data collected in multiple environmental conditions (MI, MN, MD, OR, CA) and multiple years (2005, 2006, 2011). In addition, the same population was grown under different temperatures in the greenhouse to observe segregation of heat tolerance in the progeny. Flowering phenotypic data collected from the different temperature environments were used to identify QTL associated with heat tolerance. The ‘Honeoye’ × ‘Tribute’ linkage map consisted of 34 linkage groups (LG) and heat tolerance QTL were identified on 8 linkage groups. Five of the heat tolerance QTL co- located with remontancy QTL indicating that the commonly observed photoperiodic response in the field may actually be due to differences in heat tolerance. Remontancy QTL from all 5 field locations overlapped at 8 chromosomal locations. QTL associated with remontancy in the cooler western states (CA and OR) co- located in three LG regions and QTL for remontancy in the warmer states (MI, MN, MD) co- located in two LG regions. Duration of flowering QTL co- located with several remontancy QTL indicating that our way of phenotypic categorization of remontant vs non-remontant trait was able to identify regions of the genome that determine extended flowering season. Duration of flowering QTL co- located with heat tolerance QTL suggesting that the ability of a plant to have an extended flowering season is dependent on its ability to tolerate extreme summer temperatures. Five markers associated with the heat tolerance trait were identified and several progeny that were both heat tolerant and remontant were identified. These markers associated with heat tolerance should be validated on a larger panel before their use in marker-assisted breeding. However, the most heat tolerant, remontant progeny may be used in further crosses to develop cultivars better suited to the hot, midwestern climate. Copyright by SONALI MOOKERJEE 2012 ACKNOWLEDGEMENTS I would like to thank my Major Professors Dr Jim Hancock and Dr Steve van Nocker for accepting me into their research program, for their guidance and training in research methods, and for providing me with financial assistance. I am especially grateful to Dr Jim Hancock for accepting me as his ‘Breeder’s Trainee’, for his continued encouragement and patience, for helping me gain experience in field and lab-based plant breeding, and for making research so much fun! I am thankful to the members of my Dissertation Committee: Dr Amy Iezzoni, Dr Rob Last, and Dr Doug Schemske for their suggestions and advice during this research. I would also like to thank Dr Amy Iezzoni for critically reviewing my dissertation chapters and providing valuable suggestions on data analysis methods. I would like to thank Dr Cholani Weebadde for her helpful suggestions and for laying the foundation for this project through her PhD research; and Dr Suneth Sooriyapathirana for training me in the lab and helping me plan my research. I appreciate the help from Dr Dechun Wang with linkage mapping and QTL analysis, and I am grateful to Dr Ryan Warner for letting me use his greenhouse space for my experiments. I am thankful to Dr Chad Finn and Megan Mathey (Oregon State University) for growing my plants at their location and collecting the OR data for me. I also thank the members of the RosBREED community for the training in data collection and analysis. v Thanks to Pete Callow for help in the lab, greenhouse, and field, and for maintaining an entertaining environment in the lab. Thanks also to Audrey Sebolt for being a constant help in the lab and for having answers to my numerous questions. Thanks to Kate (Zhongnan Zhang), Wezi, and Desmi for being there to discuss all my data analysis questions. Special thanks to Kate for help with statistical analysis. I thank Anne Boone, Mike Olrich, Dave Freville, and Dave Francis for keeping my plants alive and disease- free in the greenhouse and field. I appreciate the help of Hancock and van Nocker lab members. Thanks to Dan Svoboda for help with lab and greenhouse work. Thanks to Brad, Stu, Lance, Chris, and Mike for helping me keep all the plants watered in the greenhouses. I am thankful to Lorri Busick, Rita House, Joyce Lockwood, Sherry Mulvaney, and Joan Schneider for providing administrative support and for helping me keep all the paperwork in place. Thanks also to the friends and family members who have helped me keep my sanity during the past several years vi TABLE OF CONTENTS LIST OF TABLES ......................................................................................................................... ix LIST OF FIGURES ....................................................................................................................... xi CHAPTER 1: INTRODUCTION AND LITERATURE REVIEW ............................................... 1 1.1 Introduction and objectives ................................................................................................... 2 1.2 Ploidy in the genus Fragaria ................................................................................................ 4 1.3 Genomic model of F. × ananassa ......................................................................................... 7 1.4 Flowering types in strawberry .............................................................................................. 9 1.5 Photoperiod and temperature control of remontancy.......................................................... 13 1.6 Genetic control of flower remontancy (often described as day-neutral or everbearing) .... 14 1.7 Genetics of remontancy in diploid Fragaria ...................................................................... 24 1.8 Marker-assisted breeding for remontancy........................................................................... 25 1.9 Genetics of flowering in Arabidopsis and how it relates to strawberry.............................. 26 1.10 Conclusions and thesis introduction ................................................................................. 30 References ................................................................................................................................. 32 CHAPTER 2: EFFECT OF TEMPERATURE ON FLOWER AND RUNNER NUMBER IN A STRAWBERRY POPULATION SEGREGATING FOR REMONTANCY .............................. 37 Abstract ..................................................................................................................................... 38 2.1 Introduction ......................................................................................................................... 38 2.2 Material and Methods ......................................................................................................... 42 2.2.1 Selection of the segregating population ....................................................................... 42 2.2.2 Growth conditions........................................................................................................ 42 2.2.3 Phenotypic observations .............................................................................................. 47 2.2.4 Data collection and analysis......................................................................................... 47 2.3 Results and discussion ........................................................................................................ 48 2.3.1 Flower formation: Segregation for heat tolerance in the greenhouse .......................... 48 2.3.2 Runner formation in the greenhouse ............................................................................ 53 2.3.3 Remontancy in the field ............................................................................................... 55 2.4 Overall conclusions............................................................................................................. 55 Appendix 2.1 ............................................................................................................................. 58 Appendix 2.2 ............................................................................................................................. 60 References ................................................................................................................................. 63 CHAPTER 3: IDENTIFICATION OF QTL ASSOCIATED WITH HEAT TOLERANCE AND REMONTANCY .......................................................................................................................... 64 Abstract ..................................................................................................................................... 65 vii 3.1 Introduction ......................................................................................................................... 65 3.2 Material and Methods ......................................................................................................... 72 3.2.1 Mapping population ..................................................................................................... 72 3.2.2 DNA extraction ............................................................................................................ 72 3.2.3 Genotyping................................................................................................................... 73 3.2.4 Linkage map................................................................................................................. 74 3.2.5 Phenotypic evaluation .................................................................................................. 76 3.2.6 Distribution graphs....................................................................................................... 77 3.2.7 QTL identification........................................................................................................ 77 3.3 Results and discussion ........................................................................................................ 79 3.3.1 Linkage map................................................................................................................. 79 3.3.2 QTL identification........................................................................................................ 99 3.3.3 Phenotypic distribution of markers associated with heat tolerance/sensitivity ......... 109 3.3.4 Overall conclusions.................................................................................................... 115 Appendix 3.1 ........................................................................................................................... 117 Appendix 3.2 ........................................................................................................................... 129 Appendix 3.3 ........................................................................................................................... 137 Appendix 3.4 ........................................................................................................................... 150 Appendix 3.5 ......................................................................................................................... 1526 Appendix 3.6 ........................................................................................................................... 164 Appendix 3.7 ........................................................................................................................... 168 Appendix 3.8 ........................................................................................................................... 171 References ............................................................................................................................... 174 CHAPTER 4: CONCLUSIONS AND FUTURE RESEARCH ................................................. 180 References ............................................................................................................................... 189 viii LIST OF TABLES Table 1.1 Ploidy levels in Fragaria species along with their genomic models as proposed by Rousseau-Gueutin et al. (2009) and their geographical origin………………………………….6 Table 1.2 Summary of reports on inheritance of remontancy in published literature…………...23 Table 2.1 Average Daily Light Integral measured as mol m-2 d-1 in the greenhouses at Michigan State University, East Lansing…………………………………………………………………...45 Table 2.2 Air temperature at MI (Benton Harbor) and OR (Corvallis) field locations………….46 Table 2.3 ANOVA analyses showing significant effect of temperature, genotype, and temperature × genotype interaction on the number of flowers, number of inflorescences, and number of runners in ‘Honeoye’ × ‘Tribute’ progeny and the parents growing at 17°C, 20°C, and 23°C in a greenhouse in East Lansing, MI………………………………………………………49 Table 3.1 Average minimum and maximum temperatures at the field locations (MI-Benton Harbor, MN-St Paul, MD-Beltsville, OR-Corvallis, CA-Watsonville) in the different years of study (2005, 2006, 2011)………………………………………………………………………...78 Table 3.2 QTL regions associated with remontancy, weeks of flowering, and flower number at different temperatures (17, 20 and 23 °C) in the ‘Honeoye’ × ‘Tribute’ population…………..102 Table 3.3 Alleles associated with ‘Total flowers at 23°C’ QTL and the phenotypic observations associated with them…………………………………………………………………………....111 Table 3.4 Genotype of the parents and associated phenotypic observations for the alleles ARSFL8_301, ChFaM098_225, ChFaM040_315, EMFn117_157, and EMFn170_208……...112 Table 3.5 SSR loci used for genotyping the mapping population with their source, primer sequences, and putative functions of associated ESTs………………………………………....117 Table 3.6 Segregation type and Chi square (X2) values of the markers in the ‘Honeoye’ × ‘Tribute’ SSR map……………………………………………………………………………...129 Table 3.7 Multiplex segregation ratios of SSR markers with segregation distortion………………………………...…………………………………………….……….151 Table 3.8 QTL regions associated with remontancy (rem) in MI, OR, CA, MN, and MD in 2005, 2006, and 2011 in ‘Honeoye’ × ‘Tribute’ population………………………….……………164 ix Table 3.9 QTL regions associated with weeks of flowering in MI, OR, and CA in 2005, 2006, and 2011 in ‘Honeoye’ × ‘Tribute’ population………………………………………..………..168 Table 3.10 QTL regions associated with flowering at 17°C, 20°C, and 23°C in ‘Honeoye’ × ‘Tribute’ population. in ‘Honeoye’ × ‘Tribute’ population……………………………………171 x LIST OF FIGURES Figure 1.1 Bloom patterns in Short day, Day neutral, Long Day, Everbearing, and Remontant strawberry……………………………………………………………………………………..…12 Figure 1.2 Diagrammatic representation of regulation of flowering time in Arabidopsis……….29 Figure 2.1a-c Distribution of progeny with different numbers of flowers in the ‘Honeoye’ × ‘Tribute’ population. (a) Distribution of total flowers at 17°C, (b) Distribution of total flowers at 20°C, (c) Distribution of total flowers at 23°C…………………………………………………..50 Figure 2.2a-b Distribution of total flower numbers at 17°C minus total flower numbers at 23°C (y-axis) in the Honeoye (Hon) x Tribute (Tri) progeny and the parents. (a) Distribution of total flowers at 17°C minus total flower numbers at 23°C in progeny that had more flowers at 17°C than at 23°C (heat sensitive), (b) Distribution of total flowers at 17°C minus total flower numbers at 23°C in progeny that had fewer flowers at 17°C than at 23°C (heat tolerant). Remontant/Non-remontant phenotypes from the field observations at MI and OR are included with the genotype names on the x-axis…………………………………………………………..51 Figure 2.3a-c Distribution of progeny with different numbers of runners in the ‘Honeoye’ (H) × ‘Tribute’ (T) population grown in a greenhouse at 17°C, 20°C, and 23°C. (a) Distribution of total runners at 17°C, (b) Distribution of total runners at 20°C, (c) Distribution of total runners at 23°C……………………………………………………………………………………………...54 Figure 2.4 Total flowers (y-axis) at 17°C, 20°C, and 23°C in the ‘Honeoye’ × ‘Tribute’ progeny (HT1-54) and the parents. Remontant/Non-remontant phenotypes from the field observations at Benton Harbor, MI and Corvallis, OR are included with the genotype names on the x-axis (a) Total flowers at 17°C, 20°C, and 23°C in the heat tolerant progeny, (b) Total flowers at 17°C, 20°C, and 23°C in the heat sensitive progeny…………………………...……………………....58 Table 2.5. Total runners (y-axis) at 17°C, 20°C, and 23°C in the ‘Honeoye’ × ‘Tribute’ progeny (HT1-54) and the parents. Remontant/Non-remontant phenotype from the field observations at Benton Harbor,MI and Corvallis, OR are included with the genotype names on the x-axis. (a) Total runners at 17°C, 20°C, and 23°C in the heat tolerant progeny, (b) Total runners at 17°C, 20°C, and 23°C in the heat sensitive progeny……………………………….…………………60 Figure 3.1 Consensus ‘Honeoye’ × ‘Tribute’ linkage map and the QTL associated with remontancy, weeks of flowering and heat-tolerant/sensitive floral responses…………………..81 Figure 3.2a- f Distribution of weeks of flowering in ‘Honeoye’ x ‘Tribute’ progeny with different flowering durations. (a) Weeks of flowering in MI-2005, (b) Weeks of flowering in MI-2006, (c) Weeks of flowering in OR-2005, (d) Weeks of flowering in CA-2005, (e) Weeks of flowering in MI-2011, (f) Weeks of flowering in OR-2011………………………………………………104 xi Figure 3.3a-e Phenotypic distributions associated with presence of the alleles located in regions with significant QTL for flower formation at 23°C. (a) Phenotype associated with ARSFL8_301, (b) phenotype associated with ChFaM098_225, (c) phenotype associated with ChFaM040_315, (d) phenotype associated with EMFn117_157, and (e) phenotype associated with EMFn170_208…………………………………………………………………………….113 Figure 3.4 The male and female parent maps. Distances on linakge groups are in cM. The ‘Honeoye’ map had 103 markers in 23 linkage groups and the ‘Tribute’ map had 78 markers in 22 linkage groups……………………………………………………………………………….137 Figure 3.5 Colinearity in octoploid map………………………………………………………156 xii CHAPTER 1 INTRODUCTION AND LITERATURE REVIEW 1 1.1 Introduction and objectives Strawberries are among the most commercially important small fruit crops in the world. Most of the commercial production is in the Northern Hemisphere, although the environmental conditions in the southern hemisphere are also suitable for growing strawberries (Hummer and Hancock, 2009). United States is the leading producer of strawberries, producing 1,270,690 metric Tons in 2009 (http://faostat.fao.org). The total value of fresh and processed strawberries produced in US in 2009 was $158,665,000 (USDA Economics, Statistics, and Market Information System). California is the largest producer of strawberries and produced 2,485.6 million pounds of strawberries in 2009 (USDA Economics, Statistics, and Market Information System). Michigan produced 4.6 million pounds of strawberries the same year. The commercial strawberry, Fragaria × ananassa, is an octoploid derived from hybridization of F. virginiana and F. chiloensis. Flower initiation in strawberries is affected by photoperiod and temperature. A wide range of cultivars have been developed that have been categorized from short-day to remontant based on their photoperiodic requirement for flowering (Durner et al., 1984; Hancock, 1999). Over 60% of the commercially grown cultivars in California are remontant, while most grown in the eastern US are short-day (Hancock, 1999). Floral initiation in remontant cultivars is not affected by photoperiod. They produce crops about 60 days after planting, regardless of season, and they can have several crops during the growing season. On the other hand, short day cultivars initiate flowers in the shorter days of winter and as a result they bear fruits only at the beginning of spring. In the Californian growing regions, where there is an extended growing season, short day cultivars are grown from Jan-Apr and remontant types are grown from Apr-Oct (Hancock, 2 1999). Many growers in the midwestern and eastern US would prefer remontant cultivars because they produce multiple harvests. However, flower initiation in the currently available remontant cultivars is generally inhibited by the extremely hot temperatures during summer in the midwestern and eastern US. Therefore, the available remontant cultivars do not perform well in these conditions and it will be necessary to incorporate new sources of remontancy or develop heat tolerant remontant cultivars that are better suited to the extreme temperatures. Genetic control of remontancy has been debated and several hypotheses have been proposed that range from single to multiple gene control. Weebadde et al. (2008) identified several QTL determining remontancy in F. × ananassa (‘Honeoye’ x ‘Tribute’) in a multi- location study and proposed that heat tolerance QTL (Quantitative Trait Loci) may be acting along with photoperiod perception QTL in determining flower initiation. Bradford et al. (2010) determined that temperature plays a crucial role in determining whether the plant initiates flowers under short or long days. Both these studies concluded that in order to have a better understanding of the regulation of remontancy, it is important to identify the loci regulating temperature tolerance/sensitivity in the genome. In addition, since there are several sources of remontancy that are available for breeding, it is important to determine whether these sources share the same QTL or whether additional remontancy loci may be available that can be pooled together to develop new heat tolerant remontant cultivars for midwestern and eastern US climates. The specific objectives of this project were to: Quantify the effect of temperature on flower and runner production in a population segregating for remontancy (‘Honeoye’ x ‘Tribute’). 3 Create a genetic linkage map of octoploid strawberry Fragaria x ananassa using SSR markers. Identify QTL linked to heat tolerance, remontancy, and duration of flowering in the ‘Honeoye’ x ‘Tribute’ population. 1.2 Ploidy in the genus Fragaria Commercial strawberry belongs to the genus Fragaria in the family Rosaceae and sub- family Potentilloideae (Hummer and Hancock, 2009). The genus includes 24 species that range in ploidy from diploid to decaploid (Staudt 1989, 2009; Hummer et al., 2009) (Table 1.1). All of the species, except F. chiloensis are native to the northern hemisphere (Hancock et al., 1991; Hancock, 1999; Potter et al., 2000). Fragaria vesca is the most common diploid species and is the most widely distributed in the world (Staudt, 1989; Hancock, 1999). F. vesca is native to regions in Europe, Asia, and North and South America. Other diploid species include F. viridis Duch. (native to Europe and Western Asia), F. daltonica (Sikkim, Himalayas), F. nilgerrensis Schlecht (south Asia, Sikkim, China), F. nubicola Lindl. ex Lacaita (Central Asia, Himalayas), F. gracilisa Lozinsk (North China), F. pentaphylla Lozinsk (North China), F. mandshurica Staudt (Siberia, Mongolia, Manchuria, Korea), F. innumae Makino (central and northern Japan), F. yezoensis Hara. (North Japan), and F. nipponica Lindl. (Japan) (Staudt, 1989; Hancock et al., 1991; Hancock, 1999). All known tetraploids are native to regions in Eurasia: F. orientalis Lozinsk (Siberia, Mongolia, Manchuria), F. corymbosa (North China), and F. moupinensis (French.) Card. (China) (Staudt, 1989; Hancock et al., 1991; Hancock, 1999). Only one hexaploid species has been described, F. moshchata Duch., and it is native to north and central Europe (Staudt, 1989; Hancock et al., 1991; Hancock, 1999). Octoploid species include Fragaria chiloensis (L.) Duch. (native to North and South America), and F. virginiana Duch. 4 (native to Central and North America) (Staudt, 1989; Hancock, 1999). F. iturupensis (native to Iturup Island, Japan) was initially classified as an octoploid (Staudt, 1989; Hancock, 1999, Staudt 2009), but subsequent flow cytometry analysis revealed that it is a decaploid (Hummer et al., 2009). Fragaria × ananassa is the cultivated strawberry and is grown in many regions of the world. It was formed by hybridization between Chilean F. chiloensis and North American F. virginiana that were growing in proximity in Europe (Hancock, 1999). Natural hybrids of F. chiloensis and F. virginiana have also been found in the western parts of North America by Nuttal and described as F. × ananassa nm cuneifolia (Staudt, 1989; Hancock 1991). 5 Table 1.1 Ploidy levels in Fragaria species along with their genomic models as proposed by Rousseau-Gueutin et al. (2009) and their geographical origin. Species Ploidy (2n=) F. vesca F. viridis Duch. F. daltonica F. nilgerrensis Schlecht F. nubicola Lindl. ex Lacaita F. gracilisa Lozinsk F. pentaphylla Lozinsk F. mandshurica Staudt F. innumae Makino F. yezoensis Hara. F. nipponica Lindl. F. orientalis Lozinsk F. corymbosa F. moupinensis(French.) Card. F. moshchata Duch., Fragaria chiloensis (L.) Duch. F. virginiana Duch. F. iturupensis 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 4x 4x 4x 6x 8x 8x 10x Genome model (Rousseau-Gueutin et al., 2009) Y1 Y2 X1 X2 X1 X1 Y1 Z X1 X1 Y1Y1Y1Y1 Y1Y1Y2Y2Y2Y2 or Y1Y1Y1Y1Y2Y2 Y1Y1Y1Y1ZZZZ Y1Y1Y1Y1ZZZZ 6 Location Europe, Asia, North and South America Europe and Western Asia Sikkim, Himalayas South Asia, Sikkim, China Central Asia, Himalayas North China North China Siberia, Mongolia, Manchuria, Korea North and Central Japan North Japan Japan Siberia, Mongolia, Manchuria North China China North and central Europe North and South America Central and North America Iturup Island (Japan) 1.3 Genomic model of F. × ananassa Cultivated strawberry, Fragaria × ananassa, is an allo-octoploid with chromosome number 2n=8x=56. Cytogenetic studies have proposed three genomic models for the cultivated strawberry: AABBBBCC (Federova, 1946 in Hancock, 1999), AAA’A’BBBB (Senanayake and Bringhurst, 1967), and AAA’A’BBB’B’ (Bringhurst 1990). In the original model, the A genome was thought to be contributed by F. orientalis, B genome by F. nipponica, and C genome by F. vesca. However, later models concluded that the A genome came from F. vesca or F. viridis, while the origin of the B genome was unknown. Hancock (1999) suggested that F. vesca, F. viridis, or F. nubicola may have contributed the A and A’ genome. He also suggested that the B genome may have originated from F. innumae because it has glaucous leaves that is seen in some octoploids and can be crossed with F. × ananassa. Several other studies have also concluded that F. vesca is one of the diploid ancestors of F. × ananassa. Bringhurst and Khan (1963) identified naturally occurring hybrids of F. chiloensis and F. vesca from the coastal regions of California. Staudt (2009) discussed the origin of octoploid F. virginiana and F. chiloensis based on his studies of ploidy levels, stolon branching, pollen morphology, and sex expression in 24 species of Fragaria. He proposed that F. daltonica may be an ancestor of F. chiloensis based on similarities in the leaf morphology and fruit color of the two species. In addition, Staudt (2009) proposed F. innumae and F. chinensis may be progenitors of F. virginiana from eastern and western N. America. In recent years, molecular phylogenetic approaches have been used to determine the interrelationship among the various species of Fragaria. Harrison et al. (1993) studied the relationship between 30 Fragaria accessions from 9 species using RFLP (Restriction Fragment 7 Length Polymorphism) markers on the chloroplast genome. Their study concluded that F. innumae was ancestral to the other species because it had one mutation that was common with Potentilla fruticosa which was the outgroup in this analysis. Potter et al. (2000) studied variations in the non-coding regions of chloroplast and nuclear DNA in 43 Fragaria accessions from 14 species that included representatives of naturally occurring ploidy levels. They concluded that F. vesca and F. nubicola were the most closely related to the octoploids F. virginiana and F. chiloensis. They also concluded that F. virginiana and F. chiloensis originated from a common octoploid ancestor. Some accessions of F. virginiana (F. virginiana subsp. platypetala) were more closely related to accessions of F. chiloensis than to F. virginiana. Similar observations were made in an earlier study by Harrison et al. (1997) when they used RAPD (Random Amplified Polymorphic DNA) markers to identify variation between octoploid F. virginiana and F. chiloensis and observed that F. virginiana ssp. platypetala had closer similarity to F. chiloensis than with other subspecies of F. virginiana. In the most recent study (Rousseau-Gueutin et al., 2009), the sequences of two nuclear genes (GBSSI-2 and DHAR) were compared from diploid, tetraploid, hexaploid, and octoploid Fragaria and it was concluded that the diploid Fragaria species can be divided into three main clades: X, Y, and Z (Table 1.1). The tetraploids originated from diploids of clades X and Y. The three octoploid species were shown to have allopolyploid constitution and originated from the Y and Z lineages. Their study demonstrated that the commercial octoploid strawberry that originated from F. virginiana and F. chiloensis consists of the Y and Z genomes. The Y genome represents F. vesca and the Z genome was contributed by F. innumae, making it the other likely diploid progenitor of F. x ananassa. Although strawberry is an octoploid, at least two studies (Lerceteau-Kohler et al., 2003 and Rousseau-Gueutin et al., 2008) concluded that the genome is 8 largely diploidized based on segregation patterns of AFLP (Amplified Fragment Length Polymorphism) and SSR (Simple Sequence Repeat) markers in a mapping population. 1.4 Flowe ring types in strawbe rry Flower initiation in strawberries occurs in response to environmental conditions and strawberry genotypes have been typically classified based on their flowering response to photoperiod as short day, long day, day-neutral and everbearing types (Figure 1.1). Among these, what has been called short day genotypes is the most clearly defined group. They initiate flowers in the late summer through winter and then flower only once in early spring (Hancock et al., 1991; Taylor, 2002; Stewart and Folta, 2010). The time of flowering in short day types varies with their specific chilling requirements (Hancock et al., 1991). The other flowering types are not so clearly defined (Taylor 2002). The strawberry genotypes that are thought to have no specific photoperiod requirement have been referred to as day-neutrals. The day-neutrals have several cycles of flowers from early spring through late summer until the plant goes dormant. These genotypes are perhaps more appropriately described as remontant because of their repeated cycles of flowering (Bradford et al., 2010). The day-neutral or remontant types are often confused with what have been called everbearing and long day types (Bringhurst et al., 1989; Taylor, 2002; Hancock et al., 1991). The term everbearing has been given to those genotypes that have an extended flowering season. These genotypes are variously described as being photoperiod sensitive or insensitive. The long day types are thought to flower in response to the long (>14 hr) photoperiods in summer. These genotypes may also have an extended flowering season from mid to late summer and are sometimes also categorized as everbearing (Hancock et al., 1991; Stewart and Folta, 2010). In this dissertation, the term remontant will be used to refer 9 to any octoploid genotype that has extended or multiple cropping seasons and flowers under both long and short days. Although there are multiple flowering responses in the octoploid strawberries, the diploid strawberry has clearly defined flowering types: short day type F. vesca vesca and repeat flowering type F. vesca semperflorens (Taylor 2002). Taylor (2002) summarized the processes involved in flower formation in strawberry in three stages: induction, when environmental and growth conditions create a stimulus to flower, and the plant transitions to the reproductive stage; initiation, when the floral primordia are differentiated following physiological and morphological changes caused by the stimulus; and differentiation, when the floral primordia develop into flowers. Photoperiodic response in strawberry depends on three factors, temperature, genotype, and chilling (the minimum duration of cold temperature required for a plant to break dormancy) (Stewart and Folta, 2010). Based on their flowering habits, growers in midwestern and north-eastern US would prefer remontant genotypes to extend the fruiting season and get maximum yield in the short growing season. However, as Dale et al. (2002) highlighted, several factors impede breeding for remontant cultivars adapted to the northern regions of North America. Among them are variations in segregation ratios when remontant and non-remontant genotypes are crossed. This is a clear reference to the fact that the remontant trait is a multi gene trait. Dale et al. (2002) also pointed out that the extreme high temperatures during summer and the short growing season affects expression of the trait and as a result the phenotypic ratios observed may not be accurate. In addition, fruit quality in the remontant genotypes is affected by the environment and this makes it necessary to make selections in the environment where the genotypes will be grown. They also pointed out that those cultivars that have been bred in other environments often yield 10 soft, small, dark red fruits when grown in the northern regions. Another major difficulty in breeding for a remontant cultivar is that remontant genotypes have few or no runners. Since strawberry cultivars are propagated clonally, it becomes necessary to use micropropagation or crown separation, both of which are labor- intensive and expensive. 11 Figure 1.1 Bloom patterns in Short day, Day neutral, Long Day, Everbearing, and Remontant strawberry. Short day genotypes initiate flowers in the short days of winter and flower during early spring/summer. Long day genotypes initiate flowers in the long days of summer and flower from mid to late summer. Day neutral types are photoperiod insensitive and flower in repeated cycles from early to late summer until the plant goes dormant. Everbearing is a term given to genotypes that have an extended flowering season and has been applied to both long day and day neutral types. In this research, the term ‘remontant’ is used for any genotype that has multiple flowering cycles, because temperature plays an important role in regulating flowering, not just photoperiod. Jan Feb Mar Apr May Jun Short day Long Day Day Neutral Everbearer-Type 1 Everbearer-Type 2 Remontant 12 Jul Aug Sep Oct Nov Dec 1.5 Photoperiod and temperature control of remontancy While most octoploid strawberry cultivars have been traditionally classified as short-day and day-neutral, several studies have demonstrated that temperature plays a critical role in the photoperiodic control of flowering in strawberry (Darrow, 1936; Serçe and Hancock, 2005a; Bradford et al. 2010). Darrow (1936) compared floral responses of strawberry cultivars growing in different photoperiods (13.5-16 hr) and different temperatures (~13°C to 21°C) and concluded that short days promote flowering and inhibit runner formation. Long days have the opposite effect on flower and runner formation. Variations in temperature affected flower and runner formation. Runner formation was favored by higher temperatures. Flower formation was optimal at 21°C, and at the lowest temperature (13.8°C) flower formation was inhibited. Durner et al. (1984) compared the effect of photoperiod and diurnal variations in temperatures. They studied what they classified as short day, day-neutral, and everbearing genotypes under three light treatments: 9 hr, 16 hr, and 9 hr with night interruption, and four temperature treatments: day/night temperatures: 18/14°C, 22/18°C, 26/22°C, and 30/26°C. When grown under long days with night interruption, short day plants did not form any runners. The everbearers were unaffected by the night interval, but flowered under long photoperiods. The day-neutrals flowered in all photoperiods. Short day plants had the most flowers at low temperatures 18/14°C and at 22/18°C with night interruption. Day- neutrals flowered in all photoperiods when temperatures were 18/14°C. Higher temperatures, 26/22°C and 30/26°C inhibited flowering in day-neutrals. Short day plants produced runners in all temperature conditions, but there were more runners at higher temperatures. In everbearers, 22/18°C was the most favorable for runner formation. Day-neutrals produced the most runners at 26/22°C. 13 Bradford et al. (2010) did a similar study where they grew what they classified as remontant ‘Tribute’, short day ‘Honeoye’, and remontant F. virginiana RH30 under short and long photoperiods and different temperatures from 14-29°C. ‘Tribute’ and RH30 had previously been classified as day- neutral. They were able to identify specific ‘permissive’ temperature (14-17°C) at which the genotypes flowered at similar rates under both short and long photoperiods. The genotypes had a temperature threshold beyond which flowering was photoperiod dependent; and flowering was inhibited above 23-26°C. Very few runners were formed under short days. Under long days there were increasing numbers of runners with increasing temperatures. All the above studies clearly showed that temperature and photoperiod interact to determine the flowering and vegetative response of genotypes. At lower temperatures, flowers are initiated in all photoperiods, regardless of whether a plant is categorized as photoperiod sensitive. Photoperiod- mediated flowering response is only observed above a particular threshold temperature. Both Durner et al. (1984) and Bradford et al. (2010) observed that flowers are inhibited above extremely high temperatures, while runner formation is favored at higher temperatures and under long photoperiod. Because temperature has such a strong influence on flowering and runnering in the octoploid strawberry, the term remontant is much more appropriate than day-neutral to describe genotypes that have extended or multiple cropping seasons. 1.6 Genetic control of flowe r re montancy (often described as day-neutral or everbearing) Three major sources of remontancy are known among strawberry cultivars: 1: Seedling of ‘Gloede’ which is the source of remontancy in European cultivars; 2. Clonal mutation in ‘Bismarck’; 3: F. virginiana glauca from Utah which is the source of remontancy in Californian 14 cultivars (Bringhurst et al., 1989; Ahmadi et al., 1990; Sakin et al., 1997). Hancock (1999) reported that the first remontant cultivar in Europe was ‘Climax’, but it did not perform as an everbearer in warm climates. In addition, he reported that European remontant types may have derived this trait from F. vesca. Sakin et al. (1997) also identified several sources of remontancy among F. virginiana accessions collected from the Rocky Mountains, although these sources have not yet been incorporated into commercial remontant cultivars. Inheritance of remontancy is a complicated debate with studies reporting remontancy as a single gene, two genes, or multiple gene trait (Table 1.2). The differences in opinion possibly arise because of variations in classifying remontant/short day types (Bringhurst et al., 1989; Serçe and Hancock, 2003), and variations in test environments that are caused by ambient temperature, chilling requirement, inconsistent cultural systems, and earliness of fruiting (Bringhurst et al., 1989). Hancock (2003) compared four methods of evaluating day-neutrality. Serçe and They categorized genotypes as day-neutral based on whether they flowered within 100 days of germination in the greenhouse, whether two year old seedlings growing in the greenhouse and in the field flowered under short and long photoperiods, and whether the seedlings flowered in the summer that they were planted in the field. They concluded that when the seedlings were observed in the greenhouse for the second year, their assessments on day-neutrality were highly correlated with field observations. They also concluded that scoring day-neutrals based on whether they flower within 100 days of germination is the least reliable method. Another potential source of conflicting results in inheritance studies occurs when progeny from multiple crosses are pooled together to determine segregation ratios, instead of analyzing each cross separately (Powers et al., 1954; Bringhurst et al., 1989). 15 Single gene trait: Bringhurst et al. (1989) proposed that day-neutrality is a dominant trait controlled by a single gene. They crossed the heterozygous day-neutral cultivar ‘Selva’ with four short day genotypes: ‘Chandler’, ‘Douglas’, 83.91-3, and 83.91-31. They also made a set of day-neutral × day-neutral crosses by selfing ‘Selva’ and crossing with other day- neutrals CN-25, 83.91-27, and 83.94-9. They germinated the seeds in July, planted them in Sept, and recorded the yield at intervals of 6 weeks from Apr to Jul in the following year. They were able to identify day-neutral and short day progeny based on the fruit yield during early and late summer. In the day-neutral × day- neutral crosses (two heterozygous parents), they observed 3:1 segregation between day-neutral and short day types. In the day-neutral × short day progeny, they observed a 1:1 segregation as would be expected from a test cross. Thus they concluded that the trait is determined by a single major dominant gene. Ahmadi et al. (1990) also proposed that day- neutrality is controlled by one major dominant gene based on their segregation ratios from a diallel cross made with four short day and four dayneutral cultivars, and with interspecific crosses between F. × ananassa, F. vesca, F. viridis, F. virginiana, and F. chiloensis. They categorized their plants as day-neutral based on 4 selection criteria: flowering in short and long photoperiods, flower initiation in seedlings 3-5 months after germination, repeated flowering cycles in 2 year old plants, and segregation pattern of progeny derived by crossing with a short day parent. However, they used different methods to categorize progeny from different crosses making it impossible to compare the efficiency of each of the methods, and making it difficult to compare segregation patterns of progeny derived from different crosses. They generated short day × day-neutral populations of F. × ananassa over 4 years (total of 28,000 progeny) and evaluated the progeny based on whether or not they flowered in late summer in the second year. Almost half of the progeny derived from heterozygous day16 neutral x short day were day-neutrals (1:1 segregation) and 75% of the progeny from day-neutral x day-neutral crosses were day- neutral (3:1 segregation) indicating that the trait is controlled by a single major gene. Homozygous day-neutral genotypes derived by selfing ‘Fern’ and ‘Mrak’ crossed with short day octoploid genotypes resulted in progeny that were all day-neutral, again confirming that this trait is controlled by a single dominant gene. Selfed day-neutral octoploids crossed with diploid short day genotypes of F. vesca and F. viridis resulted in 50% day-neutral progeny, further confirming the single dominant gene hypothesis. Multi-gene trait: Darrow (1937) reported that in crosses between everbearing × everbearing cultivars in Canada, the progeny segregated into 88 everbearing and 66 June-bearing which fits the 9:7 ratio of two dominant complementary genes. Everbearing × short day crosses resulted in 257 everbearing and 788 June bearing in a 1:3 ratio, again confirming that the trait is controlled by two dominant genes. Unfortunately, he did not provide details on the criteria he used to categorize the progeny as everbearing. In an extensive study in New Jersey (Clark, 1937), 4000 progeny from 61 crosses were evaluated based on whether or not they had an extended bloom (everbearers) or whether they flowered only in early summer (short day). He observed that while most of the crosses indicated that the everbearing trait was inherited as a dominant single gene trait, there were three parents in which the trait was regulated differently. The genotype ‘New Jersey 1’ did not produce any everbearing progeny when it was selfed or crossed with another parent. ‘New Jersey 8’ also resulted in a very low percentage of everbearer progeny: 11% when selfed and ~8% when crossed with other parents. Another genotype ‘New Jersey 220’ was not an everbearer but produced 11.8% everbearing progeny when crossed with a non-everbearer (‘Dorsett’). They 17 speculated that either the everbearing trait in ‘New Jersey 220’ is controlled by a recessive trait, or both the parents have one copy of a complementary gene. The progeny from all the remaining everbearer x non-everbearer crosses were pooled together and consisted of 1104 everbearers and 705 non-everbearers, similar to a 9:7 ratio of two complementary genes. He concluded that the trait is controlled by dominant genes which interact. This study demonstrated that the everbearing trait was being differentially regulated in different parents. Although there was evidence of two complementary genes controlling the trait in most of the crosses, and possible recessive gene control in ‘New Jersey 220’, the authors did not make any conclusions about the genetic makeup of ‘New Jersey 1’ and ‘New Jersey 8’. Powers et al. (1954) proposed that the everbearing trait is controlled by at least three dominant and recessive genes. They crossed 10 genotypes (3 everbearers and 7 non-everbearers) in 45 combinations and observed segregation of the trait in the progeny. They categorized the progeny that flowered only in May and Jun as non-everbearers and those that flowered in Jul, Aug, and Sept as everbearers. It is however unclear whether the everbearers were also in bloom in early summer (May and Jun) which would make them truly photoinsensitive, or whether they were actually long day plants flowering only in late summer. Only one out of the three everbearers they tested resulted in segregation ratios that would fit single dominant gene model for everbearing trait. Selection ‘473’ (everbearer) crossed with non-everbearing selections ‘471’, ‘472’, 474’, ‘477’, ‘478’, and ‘4710’ resulted in 1:1 segregation in the progeny. Segregation ratios for the other two everbearers (Selections ‘475’ a nd 476’) when selfed or crossed with each other did not fit the 3:1 ratio that would be expected for single dominant gene control. Instead, 33.3%, 42.3%, and 35.6% non-everbearering progeny were obtained by selfing ‘475’, selfing 476’, and crossing 475 and 476. Powers et al. (1954) suggested that the everbearing trait may be 18 controlled by two dominant genes. Progeny ratios from crossing the everbearers with non bearers supported this observation because they mostly fit the 9:7 ratio indicating that the everbearing trait is regulated by two dominant genes. However they did observe deviations from the expected 9:7 ratio in three crosses with ‘475’ as a parent, and two crosses with ‘476’ as a parent, and concluded that this was a result of presence of modifying genes in the progeny. They proposed that the everbearing trait is determined by at least 6 pairs of genes (dominant and recessive) with cumulative effect. A study by Ourecky and Slate (1967) reported complementary gene action controlling the everbearing trait. In this study 25 combinations derived by crossing 9 non-everbearing and 4 everbearing genotypes were evaluated for the everbearing trait based on whether they flowered in late summer (Sept and Oct). They compared the segregation ratios of the progeny with octoploid segregation ratios for single dominant gene and multiple gene control. The expected ratio for single dominant gene control aaaaaaaA (everbearer) × aaaaaaaaa (noneverbearer) is 1:1. If there are two dominant genes controlling the trait, the ratio of progeny from aaaaaaAA × aaaaaaaa would be 11:3. The same ratios for everbearing × everbearing would be 3:1 for single dominant gene control: aaaaaaaA × aaaaaaaA, and 25:3 for two dominant genes control: aaaaaaAA × aaaaaaAA. The segregation ratios from their crosses mostly fit the two dominant genes model. However, they also observed that the progeny from some crosses had a higher percentage of everbearers and proposed that there may be additional loci determining everbearing trait. Barritt et al. (1982) evaluated 3944 progeny from 54 crosses (day-neutral × day- neutral and dayneutral × short day) by recording presence of flowers from mid-June to mid-Sept in two and 19 three year old progeny . They observed that the percentage of day-neutral progeny in day-neutral × day-neutral crosses ranged from 70-100%. The percentage day- neutral progeny in day-neutral × short day crosses ranged from 43-100%. This indicated that day- neutrality was not controlled by a single major gene. They also pointed out that the percentage day-neutral progeny depends on the length of the growing season and the percent progeny categorized as day- neutral depends to a large extent on whether they were early flowering. Progeny from one or both day-neutral parents had earlier bloom dates than progeny from one or both short day parents. They acknowledged that some late flowering day-neutral seedlings may have been misclassified if they flowered after their 1 Sept cut-off date Shaw (2003) selfed 10 day- neutral genotypes that were selections in the University of California breeding program, and that were expected to be heterozygous for the trait because they were produced by crossing day-neutral and short day genotypes. They selfed each day-neutral genotype and scored the progeny as day- neutral based on whether or not they flowered in May and Aug and found that the percent day-neutral progeny ranged from 41.8% to 84.8%, which is a significant deviation from the expected 75% if there was one major gene determin ing the remontancy trait. In addition, the pooled segregation ratio for all the crosses resulted in 70.9% day-neutrals and this was also a significant deviation from the expected 75%. Similarly, Serçe and Hancock (2005b) crossed several day- neutral and short day genotypes belonging to both F. x ananassa and F. virginiana and categorized a progeny as day-neutral if it flowered in early and late summer. When they combined the progeny from different crosses, they observed that 71% of progeny were day-neutral in the day-neutral x day-neutral crosses and this was a significant deviation from the expected 3:1 ratio. Most crosses involving only F. × 20 ananassa parents fit into the single gene model. However crosses involving F. × ananassa × F. virginiana parents had 88% day-neutral progeny. The F. virginiana × F. virginiana progeny pooled together had 48% day-neutrals. Overall, 30-87% of the progeny from day-neutral × short day cross, and 22-93% of progeny from day-neutral × day-neutral cross were day-neutral. Such segregation patterns led them to conclude that the day-neutral phenotype is under the control of multiple genes. They also obtained different proportions of day-neutral progeny when they crossed different day-neutral parents to the same short day parent. For example, ‘Tribute’ (dayneutral) × ‘Chandler’ (short day) resulted in 74% day- neutrals, and ‘Aromas’ (day-neutral) × Chandler resulted in 55% day-neutrals. Their results lead to the conclusion that the day-neutral trait is most likely controlled by multiple loci, and the proportion of day-neutral progeny from a cross depends on the dosage of day-neutrality alleles in the parents. In another extensive experiment involving 30 crosses generated from 45 parents, Shaw and Famula (2005) compared the day- neutral vs short day ratio after combining the progeny from all the crosses. They identified day- neutrals based on whether they were flowering in Aug and Sept. They compared the segregation ratios to fit into three genetic models: multi gene inheritance along with environmental effect, dominant major gene with other additive genes and environmental effects, and single major gene with partial dominance along with additive genes and environmental effects, and concluded that the third model most accurately represents the genetic control of the trait Weebadde et al. (2008) developed one of the first octoploid linkage maps using progeny from the cross between ‘Honeoye’ (short day) and ‘Tribute’ (remontant). This study was an important step towards identifying regions of the genome that control the day-neutral trait. Their study was 21 unique in that replicate populations from the cross were grown in 5 states (MI, OR, CA, MD, and MN) and hence the interaction with the environment could also be detected. In the eastern/midwestern states, almost 50% of the progeny were day-neutral. However, in the western states (CA and OR), 80% and 87% of the population were day- neutral. Clearly there was a strong interaction with the environment and based on previous studies on interaction of photoperiod and temperature, the authors explained that this was probably due to presence of a heat sensitivity locus in the genome that complicated flowering response. The eastern states had warmer summer temperatures in comparison to the western states. This environmental effect was also reflected in the fact that different QTL were identified using phenotypic data from different locations. There was one QTL on linkage group 28 that was common to all the eastern states (MI, MN, MD). MI had another QTL on the same linkage group. Only one QTL was identified for CA and three additional QTL were detected for MN. Identification of multiple genomic regions determining day- neutrality was a clear indication that day-neutrality in this population was a multigenic trait. The QTL identified in this study explained ~11-36% of the phenotypic variation. 22 Table 1.2 Summary of reports on inheritance of remontancy in published literature. Genetics 1 gene- dominant 2 dominant complimentary genes Multiple genes-dominant and recessive Multiple genes with cumulative effect-dominant and recessive 2 complementary genes + modifier genes 1 major gene (partial dominance) + additive genes + environmental effects Multi gene Multi gene: photoperiod loci + possible heat tolerance loci 23 Reference Bringhurst et al., 1989; Ahmadi et al., 1990 Darrow, 1937 Clark, 1937 Powers et al., 1954 Ourecky and Slate, 1967 Shaw and Famula (2005) Barritt et al.,1982; Shaw, 2003; Serçe and Hancock, 2005 Weebadde et al., 2008 1.7 Genetics of re montancy in diploid Fragaria Diploid F. vesca genotypes are typically short day, although some everbearing (‘Alpine’) types exist (Brown and Wareling, 1965; Hancock 1999). Genetic control of the everbearing trait was studied in a short day wild F. vesca accession, and in two everbearing F. vesca cultivars ‘Baron Solemacher’ and ‘Bush White’, by observing segregation of the trait in F2 and backcross populations (Brown and Wareling, 1965). It was concluded that in the diploid strawberry, the everbearing trait is controlled by a single recessive gene. In addition, runner formation is controlled by a dominant gene present in the seasonal flowering types, while the everbearing types have the recessive form of the gene. They suggested that additional additive or minor genes affect runner formation in the absence of the major runnering gene. In another study, Ahmadi et al. (1990) proposed that day-neutrality or photoinsensitivity in diploid Californian F. vesca is controlled by three major genes in which day-neutral is recessive to short day. They crossed day-neutral Alpine F. vesca from Europe with short day F. vesca from California. Seedlings were categorized as day- neutral based on whether or not they flowered 3-5 months after germination. All the F1 seedlings were day-neutral. The F2 populations segregated as 1:63, and the BC 1 population segregated 1:7 for day neutrality. This led them to the conclusion that the North American F. vesca have diverged significantly from the European F. vesca in the genetic control of photoperiod sensitivity. In addition, day-neutral Alpine F. vesca crossed with short day F. chiloensis resulted in all short day progeny, further confirming that the trait is under a recessive gene control in the diploid. 24 1.8 Marker-assisted breeding for re montancy Kaczmarska and Hortynski (2002) performed a preliminary bulk segregant analysis to identify the association between RAPD markers (Williams et al., 1990) and ‘photoinsensitive’ (dayneutral/remontant) trait in F. × ananassa. Although they identified one RAPD marker that they described as ‘likely’ associated with the trait, there were no follow-up reports confirming the marker-trait association. In an attempt to develop markers associated with flowering in diploid Fragaria, Cekic et al. (2001) identified an ISSR (Inter Simple Sequence Repeat) marker (Zietkiewicz et al., 1994) located at 2.2 cM from the SEASONAL FLOWERING LOCUS using a bulk segregant analysis on a backcross population of 168 plants developed from F. vesca (short day) x F. vesca (remontant). As discussed above, the genetics of remontancy in diploid strawberry is significantly different from that of octoploid strawberry because the trait is controlled by a single gene in the diploid, whereas it is a multi- gene trait in the octoploid. Therefore, markers developed for the single major diploid gene are not likely to be applicable in breeding for remontancy in commercial octoploid strawberry where the trait is under a more complex genetic control. Weebadde et al. (2008) identified QTL associated with remontancy in F. × ananassa using AFLPs, but the markers associated with the QTL were not validated and converted to transferable markers for wider application in breeding. Sugimoto et al. (2005) developed a population from octoploid ‘Ever Berry’ (everbearing) and ‘Toyonoka’ (short day) and used bulk segregant approach on 191 F 1 seedlings that segregated 1:1 for the everbearing trait. They identified two RAPD markers associated with the everbearing trait at 11.8 and 15.8 cM from the locus. However, they did not use the markers in marker-assisted breeding. There are reports that the private company, Driscoll Associates, has used markers in remontancy breeding; however, few details have been provided (Chen Niu, personal communication). 25 1.9 Genetics of flowe ring in Arabidopsis and how it relates to strawbe rry Much of the research on the genetic regulation of flowering has focused on the diploid model plant Arabidopsis. In this section the major events in the process of flower initiation in Arabidopsis are summarized based on several extensive reviews (Aukerman and Amasino, 1996; Putterill et al., 2004; Boss et al., 2004; Kobayashi and Weigel, 2004; Samach and Wigge, 2005; Wilkie et al., 2008; Albani and Coupland, 2010; Srikanth and Schmid, 2011). In Arabidopsis, flower formation is regulated by environmental (photoperiod, vernalization, ambient temperature) and endogenous (autonomous, hormonal, and developmental) signals. Figure 1.2 is reproduced from Albani and Coupland (2010) and presents the complex interaction among the major flowering pathways in the floral meristem. All the pathways are integrated at the gene SOC1 (SUPPRESSOR OF OVEREXPRESSION OF CONSTANS) through FT (FLOWERING LOCUS T) and FD (FLOWERING LOCUS D) (photoperiodic pathway), FLC (FLOWERING LOCUS C) and SVP (SHORT VEGETATIVE PHASE) (vernalization and autonomous pathway), miR156, SPL3 (SQUAMOSA PROMOTER BINDING PROTEIN LIKE 3), and SPL9 (developmental pathway), and gibberellin- mediated activation (hormonal pathway). SOC1 acts upstream of floral meristem identity genes LFY (LEAFY) and AP1 (APETALA1). Photoperiodic flower induction in Arabidopsis occurs in response to long photoperiods that initiates expression of FT in the leaves and upregulation of FD in the meristem, which interact to activate SOC1. FT is activated by a zinc finger protein called CO (CONSTANS). CO is regulated by the circadian clock and the level of expression of CO oscillates in a 24 hr cycle. The oscillation in expression of CO is affected by other circadian pathway genes, GI (GIGANTEA), FKF1 (FLAVIN-BINDING, KELCH REPEAT, F-BOX 1), and CDF1 (CYCLING 26 DOF FACTOR1). Three photoreceptor genes, CRY1 (CRYPTOCHROME 1), CRY2, and PHYB (PHYTOCHROME B) also maintain the expression of CO. FT is the floral signal that moves from the leaves, where the photoperiodic signal is received, to the shoot apical meristem, where floral differentiation occurs. FT interacts with FD and upregulates the floral meristem identity gene AP1. Although the mechanism by which plants measure and perceive ambient temperature is not understood, studies have measured the effects of ambient temperature on plants (Samach and Wigge, 2005). In Arabidopsis, lower temperatures delay flowering and the genes FVE and FCA have been identified as candidate genes that prevent flower initiation under low temperatures. FVE maintains FLC expression through a histone modifying complex. Certain photoreceptor genes also require specific temperatures to function. PHYB inhibits flowering under high temperatures (23°C). In addition, Arabidopsis genotypes that contain the FRI (FRIGIDA) gene require prolonged treatment in cold temperatures (vernalization) to flower. Presence of the FRI gene increases expression of the flowering inhibitor FLC. FLC is a MADS box protein that acts as a transcriptional regulator and inhibits expression of the SOC1 gene. The key difference between annual plants like Arabidopsis and perennial plants such as strawberry is that annuals undergo floral transition only once in their life cycle, while perennials go through repeated vegetative and reproductive phases over the years (Wilkie et al., 2008; Albani and Coupland, 2010). However, flower initiation in perennials may involve some of the same pathways that stimulate reproductive growth in annuals. There is evidence of photoperiodic and temperature regulation of flower initiation in strawberry (Section 1.5 above). Most strawberry genotypes have a chilling requirement before the dormant flower buds can 27 resume differentiation (Hancock et al., 1991), and this may be comparable to the vernalization pathway in annual plants like Arabidopsis. There has been little investigation on how the regulation of flowering in strawberry relates to that of Arabidopsis. Mouhu et al. (2009) identified homologs of 66 Arabidopsis flowering time genes from Fragaria vesca EST sequences. These homologs represent genes from all the major flowering time pathways suggesting that the same major flowering pathways may be present in diploid strawberry. However, when they compared expression patterns of some of these genes in everbearing short day F. vesca, they were unable to detect any differences, suggesting that they probably did not identify the homolog of the SEASONAL FLOWERING LOCUS (Cekic et al., 2001), the major gene responsible for the everbearing trait in diploid strawberry. Thus, little is known about the regulation of flowering in the diploid strawberry and there are no published studies on the octoploid to date. Previous studies have demonstrated that remontancy in diploid strawberry is a qualitative trait under the control of a major gene, whereas in octoploids there are multiple loci determining the remontant phenotype. In order to develop an understanding for genetic control of flower initiation in commercial F. × ananassa, studies will have to be done using the octoploid species. 28 Figure 1.2 Diagrammatic representation of regulation of flowering time in Arabidopsis. (Reprinted from Albani and Coupland (2010) Current topics in Developmental Biology 91: 323-348 with permission from Elsevier). 29 1.10 Conclusions and thesis introduction Developing heat tolerant remontant cultivars is critical for the midwestern market to extend the harvest season. However, we have little information on the regulation of flowering in strawberry and few molecular markers have been developed for marker-assisted breeding for remontancy in strawberry. We can perform marker-assisted breeding for remontancy without specific knowledge of the genes regulating the characteristic, but we will need tightly associated markers to use this strategy. To have maximum utility, it is important for the marker associated with the trait to be transferable across populations. This study was designed to identify regions of the octoploid strawberry genome that are associated with remontancy and heat tolerance, and identify marker-trait association that would help develop marker-assisted breeding in strawberry. SSRs were used as an efficient low cost transferable marker. The population was selected so that phenotypic data from multiple locations and years can be used to validate the QTL. In addition, the study was also designed to observe the effect of temperature in a population segregating for remontancy and identify genotypes that are heat tolerant by growing the replicates of the sa me population under different temperature conditions. 30 REFERENCES 31 REFERENCES Ahmadi, H., Bringhurst, R.S., Voth, V. (1990) Modes of inheritance of photoperiodism in Fragaria. Journal of the American Society of Horticultural Science 115(1): 146-152 Albani, M.C., Coupland, G. (2010) Comparative analysis of flowering in annual and perennial Plants. 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Molecular Phylogenetics and Evolution 51: 515-530 34 Sakin, M., Hancock, J.F., Luby, J.J. (1997) Identifying new sources of genes that determine cyclic flowering in Rocky Mountain populations of Fragaria virginiana ssp. glauca Staudt. Journal of American Society of Horticultural Science 122(2): 205-210 Samach, A., Wigge, P.A. (2005) Ambient temperature perception in plants. Current Opinion in Plant Biology 8: 483-486 Senanayake, Y.D.A., Bringhurst, R.S. (1967) Origin of Fragaria polyploids I. Cytological analysis. American Journal of Botany 54(2) 221-228 Serçe, S., Hancock, J.F. (2003) Assessment of day- neutrality scoring methods in strawberry families grown in greenhouse and field environments. Turkish Jounral of Agriculture and Forestry 27:191-198 Serçe, S., Hancock, J.F. (2005a) The temperature and photoperiod regulation of flowering in Fragaria chiloensis, F. virginiana, and F. ananassa genotypes. Scientia Horticulturae 103: 167177 Serçe, S., Hancock, J.F. 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(2010) A review of photoperiodic flowering research in strawberry (Fragaria spp.). Critical Reviews in Plant Sciences 29(1): 1-13 Sugimoto, T., Tamaki, K., Matsumoto, J., Yamamoto, Shiwaku, K., Watanabe, K. (2005) Detection of RAPD markers linked to the everbearing gene in Japanese strawberry. Plant Breeding 124: 498-501 Taylor, D.R. (2002) The physiology of flowering in strawberry. Acta Horticulturae 567: 245251 USDA Economics, Statistics and Market Information System, Albert R. Mann Library, Cornell University. D-9 Table-D9.xls Strawberries- fresh and processed: Production, price, value, U.S., 1980 to date. http://usda.mannlib.cornell.edu/MannUsda/viewStaticPage.do?url=http://usda01.library.cornell.e du/usda/ers/./89022/2010/../2009/index.html (accessed on Nov 7, 2011) USDA Economics, Statistics and Market Information System, Albert R. Mann Library, Cornell University. http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do?documentID=1381 table04.xls U.S. strawberry harvested acreage, yield per acre, and production, 13 States, 19702009 (accessed on Nov 7, 2011) Weebadde, C.K., Wang, D., Finn, C.E., Lewers, K.S., Luby, J.J., Bushakra, J., Sjulins, T.M., Hancock, J.F. (2008) Using a linkage mapping approach to identify QTL for day- neutrality in octoploid strawberry. Plant Breeding 127: 94-101 Wilkie, J.D., Sedgley, M., Olesen, T. (2008) Regulation of floral initiation in horticultural trees. Journal of Experimental Botany 59(12): 3215-3228 Williams, J.G.K., Kubelik, A.R., Livak, K.J., Rafalski, J.A., Tingey, S.V. (1990) DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Research 18(22) 6531-6535 Zietkiewicz, E., Rafalski, A, Labuda, D. (1994) Genome Fingerprinting by Simple Sequence Repeat (SSR)-Anchored Polymerase Chain Reaction Amplification. Genomics 20(2) 176-183 36 CHAPTER 2 EFFECT OF TEMPERATURE ON FLOWER AND RUNNER NUMBER IN A STRAWBERRY POPULATION SEGREGATING FOR REMONTANCY 37 Abstract Flower initiation in strawberry has been typically classified by photoperiod sensitivity; however, several recent studies indicate that temperature also plays a major role in determining flower initiation. In this study, a population developed from the non-remontant (short day) ‘Honeoye’ and remontant ‘Tribute’ were grown in three temperature conditions (17, 20, and 23°C) under a long photoperiod in the greenhouse and the differences in flower and runner formation among the progeny was compared. In addition, the same population was grown under field conditions in Michigan and Oregon and remontant vs non-remontant phenotype in the progeny was compared with the extent of heat tolerance (numbers of flowers at 17 vs 23 °C) observed in the greenhouse. Level of heat tolerance among the progeny was a continuous distribution. All the progeny that produced more flowers at 23°C than at 17°C in the greenhouse were remontant in MI and most were also remontant in OR. Flower initiation in both the parents was reduced at 23°C, but ‘Tribute’ performed better than ‘Honeoye’ at the higher temperature. Most remontant progeny had few runners, even at higher temperatures, although one remontant progeny had a high level of heat tolerance, and produced >20 runners. The results indicate that temperature tolerance plays a critical role in flower initiation. The most heat tolerant genotypes identified in this study will be useful in breeding new cultivars better adapted to hot mid-western climates. 2.1 Introduction Strawberry genotypes have been traditionally classified according to their flowering response to photoperiod as short day (June-bearers), day- neutral, long-day, and everbearing. Everbearing genotypes are actually long day because they initiate flowers under long photoperiods (Durner et al., 1984), although day-neutrals are also sometimes referred to as everbearers because of their 38 repeated cycles of flowering. Runner production is reported to be photoperiod-dependent, and is favored by long day photoperiods (Darrow, 1936; Bradford et al., 2010). The classification of strawberry genotypes based on their photoperiodic response is complicated by the effect of temperature (Darrow, 1936; Durner et al., 1984; Serçe and Hancock, 2005; Weebadde et al., 2008; Sonsteby and Heide, 2008; Bradford et al., 2010). What have been classified as short-day genotypes will flower under long days when temperatures are cool, and genotypes that have been called day-neutral will not flower under hot conditions. For this reason, day-neutrals are more accurately described as remontant and short-day types as nonremontant. Darrow (1936) studied the effect of photoperiod (13.5 hr, 14hr, 16 hr), along with different temperatures, 70°F (~21°C), 60°F (~15.5°C), and 55°F (~13.8°C) on flower and runner formation in several strawberry cultivars. His observations showed that while flowering is a photoperiodic response, differences in temperatures affected the number of flowers/runners that were produced. In another extensive study, Durner et al. (1984), studied the flowering response of what they classified as short day, day-neutral , and everbearing genotypes grown under different photoperiod (9 hr, 16 hr, and 9 hr with night interruption-NI), and different temperatures (day/night temperatures: 18/14°C, 22/18°C, 26/22°C, and 30/26°C). Their short day types produced more flowers at 18/14°C and night interruption was more effective in producing flowers at 22/18°C. In their day-neutrals, there was no effect of photoperiod at 18/14 °C. However, at 22/18°C and 26/22°C, there were more flowers under night interruption. Higher temperatures 26/22°C and 30/26°C inhibited flowering in all photoperiods. On the other hand, 39 runner production was favored by warmer temperature and long days or NI. Their short day plants produced more runners at all temperatures under NI and SD, although maximum runner production was at 26/22°C with night interruption and SD with 30/26°C. Their day- neutrals produced the most runners at 26/22°C with NI. Serçe and Hancock (2005) studied what they classified as day-neutral genotypes of F. × ananassa (‘Aromas’, ‘Fort Laramie’, ‘Ogallala’, ‘Tribute’, ‘Quinalt’) and F. virginiana (Brighton-3, LH30-4, LH39-15, LH40-4, LH50-4, RH23, RH30, RH43, RH45, Frederick-9) under different temperatures regimes (18°C, 22°C, 26°C, and 30°C) in growth chambers with 12 hr photoperiod and identified ‘Fort Laramie’ as the most heat tolerant, day-neutral genotype. Weebadde et al. (2008) identified QTL associated with what they classified as day-neutrals by evaluating the progeny from a ‘Honeoye’ × ‘Tribute’ cross at 5 field locations across North America (MI, OR, CA, MN, MD). They found that 80 and 87% of the progeny flowered in both the spring and summer in the cooler western states OR and CA where the average maximum temperatures in mid- late summer were 26°C and 21°C. In the warmer mid-western and eastern states, MI, MN, and MD, average maximum temperatures in mid- late summer were 29°C, 28°C, and 30°C. The same set of progeny growing in these warmer states had 49%, 50%, and 48% repeat flowerers in MI, MN, and MD respectively. The authors speculated that a heat tolerance QTL exists along with photoperiod response QTL and flower initiation depends on both photoperiod sensitivity and heat tolerance. All of these studies demonstrated that flower initiation is affected by both temperature and photoperiod, and that flower initiation is inhibited at higher temperatures. To further study the interaction between temperature and photoperiod, Bradford et al. (2010) observed the effect of 8 40 hr and 16 hr photoperiod, along with temperatures 14°C, 17°C, 20°C, 23°C, 26°C, and 29°C on ‘Honeoye’, ‘Tribute’, and a wild F. virginiana selection, RH30. Their study clearly showed that these genotypes were photoperiod sensitive only above a particular threshold temperature. Although ‘Honeoye’ is classified as a short day genotype, it had more flowers under long days when temperatures were 14°C and 17°C. There were fewer flowers at 20°C and 23°C, and flowering was inhibited at 26°C. ‘Honeoye’ had more flowers under short days when temperature was 20-26°C, although flowering was inhibited above 23°C. This indicates that ‘Honeoye’ performed as a short day plant when temperature was above 20°C. Similarly, RH30 was photosensitive above 23°C. RH30 was also more heat tolerant because it had some flowers at 29°C. ‘Tribute’, which had previously been classified as day-neutral, had more flowers under long days than under short days. However, flower formation decreased above 26°C. Runner production in strawberries is also affected by both photoperiod and temperature. In the study of Serçe and Hancock (2005), what they classified as day- neutral genotypes ‘Aromas’, ‘Tribute’, Frederick 9, and ‘Fort Laramie’ did not form any runners, even under long days. Other genotypes such as CFRA0368 and ‘Quinalt’ formed runners only under short days, while LH50-4 and RH30 formed more runners under long days than under short days. Genotypes also varied in their runner production abilities at different temperatures. While some genotypes (LH 40-4, RH 23, and RH 45) did not form any runners at all, others (LH50-4) formed runners at all temperatures. In the study by Bradford et al. (2010), ‘Honeoye’ produced no runners and ‘Tribute’ produced only a few runners under short days. However, the number of runners increased with increasing temperatures under long days. ‘Honeoye’ had the maximum number of runners at 26°C and ‘Tribute’ had the most runners at 23°C. 41 While the effect of temperature on flower and runner production has been well studied on individual strawberry genotypes, there has been little work on the genetics of these traits. All the previous work on the environmental regulation of remontancy has been conducted in the field, where both temperature and photoperiod were variable. In this study, the permissive and inhibitive temperatures identified by Bradford et al. (2010) were maintained in a greenhouse to determine the heat tolerance of segregating individuals from the same cross used by Weebadde et al. (2008), non-remontant ‘Honeoye’ × remontant ‘Tribute’. Runners from these progeny were also grown in the field in Michigan and Oregon to determine how a genotype’s heat tolerance was related to field performance. 2.2 Material and Methods 2.2.1 Selection of the segregating population A segregating population of 54 individuals from a F1 cross between ‘Honeoye’ and ‘Tribute’ was used for the greenhouse study. This population was also planted in the field in MI and OR for phenotypic evaluation of plant, flower, and fruit traits under different environmental conditions. In addition, these genotypes were included in the development of an SSR linkage map of octoploid strawberry that was used to identify QTL associated with remontancy and heat tolerance (Mookerjee et al., in review). 2.2.2 Growth conditions Seedlings from the ‘Honeoye’ × ‘Tribute’ cross were propagated by rooting runners under mist in 10 cm pots containing BACCTO High Porosity Professiona l Potting Mix (Michigan Peat 42 Company, Houston, TX). After 4 weeks, when the roots of the runners were firmly established, the runners were disconnected from the parent plant. Greenhouse: The rooted runners were transferred to 3.8 L pots filled with the same potting mix. The experiment was set up as a single block with two treatments: temperature and genotype. Three replicates of each genotype were grown in temperature controlled greenhouses at 17 °C, 20°C, and 23°C, and under 16 hr photoperiod using supplemental lights (400W high-pressure sodium lamps- P.L. Light Systems Inc. Beamsville, ON, Canada). Average Daily Light Integrals in the greenhouses are listed in Table 2.1. The plants were watered using distilled water injected with 125 ppm constant feed (14.7% N, 3.2% P 2 O5 , 14.1% K2O, 7.6% Ca, 1.4% Mg, 0.117% Fe, 0.059% Mn, 0.059% Zn, 0.117% Cu, 0.035% B, and 0.012% Mo). Predator mites (SPIDEX, Koppert Biological Systems, Howell, MI 48843), Volck (petroleum oil), Sulphur, Terragurard (triflumizole), and Floramite (bifenazate) were used for pest and disease control when necessary. The plants were grown in greenhouses under long day photoperiods to ensure that the differences in flowering responses were an effect of temperature and not due to inductive photoperiod. The selection of the three temperature levels was based on the study by Bradford et al. (2010) where they observed that 17°C was conducive to flower formation under all photoperiods, 20°C was the critical temperature beyond which flowering was photoperiod dependent, and 23°C was the lowest temperature at which flower formation was inhibited. Field: The same set of plants propagated through runners was planted in the field with two replicates in MI (Southwest Michigan Research and Extension Center) on Aug 12, 2010 and in OR (Corvallis) on Aug 26, 2010. The plants were set at a spacing of 0.9 m by 0.9 m between 43 plants at both locations. These plants were contained in a larger planting that included 960 genotypes for phenotypic assessment of commercial traits. Air temperature in MI was warmer than in OR. Table 2.2 lists the number of days in Apr, May, Jun, and Jul 2011 that had temperatures >20°C and >30°C and the mean air temperatures at the two locations. 44 Table 2.1 Average Daily Light Integral measured as mol m-2 d-1 in the greenhouses at Michigan State University, East Lansing. 17°C 20°C 23°C Nov 2010 10.66 9.51 13.29 Dec 2010 Jan 2010 8.54 9.38 7.13 6.05 11.22 12.09 Feb 2010 14.63 12.35 14.02 Mar 2010 14.6 16.75 16.59 45 Table 2.2 Air temperature at MI (Benton Harbor) and OR (Corvallis) field locations Apr May Jun Jul Avg min temp (Apr-May) Avg max. temp (Apr-May) Avg min. temp (Jun-Aug) Avg max. temp (Jun-Aug) Number of days >20°C MI OR 1 2 12 5 26 23 29 29 7°C 6°C 16°C 15°C 17°C 12°C 27°C 23°C 46 Number of days >30°C MI OR 0 0 1 0 4 0 12 5 2.2.3 Phenotypic observations Greenhouse: The plants were maintained under treatment conditions for 45 days before data were collected. This was to ensure that all phenotypic observations were a result of the treatment conditions, and not an effect of prior growing conditions. The total number of flowers, inflorescences, and runners were counted every week from Dec 1, 2010 to Mar 30, 2011. All open flowers and runners were removed after counting every week. All dead leaves present at the time of data collection were removed from the plant. Field: Presence/absence of flowers was recorded every week from May 2011 through Aug 15, 2011. Progeny that flowered both in the spring and in the long days of summer after Jul 23 were categorized as remontant. Progeny that only flowered early in the season were categorized as non-remontant. 2.2.4 Data collection and analysis Greenhouse air temperature was monitored and recorded every 10 s using thermocouples connected to a CR10 data logger (Campbell Scientific, Logan, UT). Graphs were plotted using MSExcel. ANOVA analysis was done with R 2.1.2.2. 47 2.3 Results and discussion 2.3.1 Flowe r formation: Segregation for heat tole rance in the greenhouse There was a significant effect of temperature, genotype, and genotype × temperature on total number of flowers (α=0.01) and on number of inflorescences (α=0.01) (Table 2.3). Figure 2.1a-c shows the distribution of total number of flowers among the 54 progeny at 17°C, 20°C, and 23°C. In general, the distributions were skewed towards individuals with lower flower production. Figure 2.2a-b shows the distribution of number of flowers at 17°C minus the number of flowers at 23°C, as a representation of the extent of heat tolerance among the progeny. The total flowers in all the progeny and the parents under the three treatment conditions are shown in Appendix 2.1. The level of heat tolerance varied continuously among the progeny (Figure 2.2a-b, Appendix 2.1). Some progeny, such as, HT5 and HT18, were highly heat tolerant and performed much better at 23°C than at 17°C. Others such as HT31 and HT37 were weakly heat tolerant and had only a few more flowers at 23°C than at 17°C. Still others such as HT2 and HT42 had flower numbers that were only slightly lower at 23°C than at 17°C, and some such as HT8 and HT25 were greatly affected by the higher temperature. These variations in total flower number were more strongly regulated by differences in inflorescence number than by flowers per inflorescence, as the total number of flowers per inflorescence was relatively constant across treatments. Both ‘Honeoye’ and ‘Tribute’ had fewer flowers at 23°C than 17°C and therefore were heat sensitive, although ‘Tribute’ performed better than ‘Honeoye’ at 23 °C. Bradford et al (2010) also found ‘Tribute’ to be more heat tolerant than ‘Honeoye’ and RH30 (a wild selection of F. virginiana) and that inflorescence numbers per plant varied more widely between treatments than flowers per inflorescence. 48 Table 2.3 ANOVA analyses showing significant effect of temperature, genotype, and temperature × genotype interaction on the number of flowers, number of inflorescences, and number of runners in ‘Honeoye’ × ‘Tribute’ progeny and the parents growing at 17°C, 20°C, and 23°C in a greenhouse in East Lansing, MI . Genotype (54 progeny + 2 parents) Temperature Genotype x Temperature df 55 2 110 Total flowers <0.01 <0.01 <0.01 49 Probability Total inflorescence <0.01 <0.01 <0.01 Total runners <0.001 <0.001 <0.001 Figure 2.1a-c Distribution of progeny with different numbers of flowers in the ‘Honeoye’ × ‘Tribute’ population. (a) Distribution of total flowers at 17°C, (b) Distribution of total flowers at 20°C, (c) Distribution of total flowers at 23°C. Number of flowers in the parents are indicated: 301-325 276-300 251-275 226-250 201-225 176-200 151-175 126-150 101-125 76-100 51-75 (a) 26-50 12 10 8 6 4 2 0 1-25 Frequency Honeoye: Shaded arrow, Tribute: Black arrow. Number of flowers at 17°C (b) 15 10 226-250 201-225 176-200 151-175 126-150 101-125 51-75 1-25 0 76-100 5 26-50 Frequency 20 Number of flowers at 20°C (c) 15 10 Total flowers at 23°C 50 301-325 276-300 251-275 226-250 201-225 176-200 151-175 126-150 101-125 76-100 51-75 26-50 0 1-25 5 <1 Frequency 20 Figure 2.2a-b Distribution of total flower numbers at 17°C minus total flower numbers at 23°C (y-axis) in the ‘Honeoye’ (Hon) × ‘Tribute’ (Tri) progeny and the parents. (a) Distribution of total flowers at 17°C minus total flower numbers at 23°C in progeny that had more flowers at 17°C than at 23°C (heat sensitive). The parents are included for reference. (b) Distribution of total flowers at 17°C minus total flower numbers at 23°C in progeny that had fewer flowers at 17°C than at 23°C (heat tolerant). Remontant/Nonremontant phenotypes from the field observations at MI and OR are included with the genotype names on the x-axis. The progeny that were remontant at one location and non-remontant at the other are labeled as RM(m)=Remontant in MI, NR(o)= Non-remontant in OR. (a) NR-HT-33 NR-HT-12 NR-HT-27 NR-HT-21 RM-HT-2 RM(o)-HT-16 NR-HT-14 NR(m)-RM(o)-HT-42 NR-HT-4 NR(m)-RM(o)-HT-51 NR-HT-46 RM-HT-19 NR-HT-34 RM(m)-NR(o)-HT-10 NR-HT-11 NR(m)-RM(o)-HT-30 RM-HT-39 NR(m)-RM(o)-HT-48 RM-HT-24 NR-HT-59 NR-HT-45 NR-HT-38 NR-HT-22 RM-Tri NR-HT-61 RM(o)-HT-25 NR-HT-49 NR-HT-15 NR-HT-28 RM(m)-NR(o)-HT-47 NR-HT-44 NR(m)-RM(o)-HT-40 NR-Hon RM-HT-3 RM(m)-NR(o)-HT-58 NR-HT-8 RM(m)-NR(o)-HT-1 180 160 140 120 100 80 60 40 20 0 51 -50 -150 RM-HT-20 RM-HT-13 RM-HT-18 RM-HT-5 52 NR-Hon RM-Tri RM-HT-37 RM(m)-NR(o)-HT-35 RM(m)-NR(o)-HT-36 RM-HT-31 RM-HT-50 RM-HT-9 RM-HT-17 RM-HT-41 RM-HT-43 RM-HT-29 RM-HT-23 RM-HT-32 RM-HT-6 150 RM(m)-NR(o)-HT-7 -100 RM-HT-26 Figure 2.2 (cont’d) (b) 100 50 0 2.3.2 Runner formation in the greenhouse There was a significant effect of temperature, genotype, and genotype × temperature on number of runners (α=0.001) (Table 2.3). Figure 2.3a-c shows the distribution of progeny and parents with different numbers of runners at 17°C, 20°C, and 23°C. The total number of runners in each progeny and the parents is displayed in Appendix 2.2. In general, there were fewer runners at lower temperatures than at 23°C. Most of the progeny with floral sensitivity to heat had more runners at 23°C than at the lower temperatures, similar to the phenotype of the parents. The progeny with the highest floral heat tolerance generally had very few or no runners at 23°C; however, one progeny (HT43) was heat tolerant and had >20 runners at 23°C. This genotype is of breeding and commercial interest, as it has the potential to produce crops in extreme heat and still produce runners for propagation. Serçe and Hancock (2005) also reported that the genotypes with the most floral heat tolerance had few runners, if any. Their most floral heat tolerant genotype, ‘Fort Laramie’, did not form any runners, even under long days. Bradford et al. (2010) observed that under long days, ‘Honeoye’ formed runners above 20°C, but runner formation was inhibited above 26 °C. ‘Tribute’ produced runners under long days above 23°C, although the number of runners was less than ‘Honeoye’ and RH30. In our experiment only three temperature conditions were tested, but ‘Tribute’ produced more runners than ‘Honeoye’ in all treatments. In the Bradford et al. (2010) study, although ‘Tribute’ had more runners than ‘Honeoye’ at 17°C, ‘Honeoye’ had more runners than ‘Tribute’ at 20°C and 23°C, and there was little difference in runner numbers between the two genotypes at 20°C and 23°C. 53 Figure 2.3a-c Distribution of progeny with different numbers of runners in the ‘Honeoye’ (H) × ‘Tribute’ (T) population grown in a greenhouse at 17°C, 20°C, and 23°C. (a) Distribution of total runners at 17°C, (b) Distribution of total runners at 20°C, (c) Distribution of total runners at 23°C. Numbers of runners in the parents are indicated: Honeoye: Shaded arrow, Tribute: Black arrow. Frequency 50 (a) 40 30 20 10 12-16 9-12 5-8 1-4 0 50 40 30 20 10 0 21-24 17-20 12-16 5-8 9-12 (b) 1-4 Frequency Total runners at 17°C Total runners at 20°C (c) 20 15 10 Total runners at 23°C 54 61-64 57-60 53-56 49-52 45-48 41-44 37-40 33-36 29-32 25-28 21-24 17-20 12-16 9-12 0 5-8 5 1-4 Frequency 25 2.3.3 Remontancy in the field Out of the 54 progeny planted in the field in MI, 52 survived and 28 (54%) were remontant. Among the progeny in OR, all survived and 28 (51%) were remontant. 21 progeny were remontant at both locations, while 5 progeny were remontant in OR and non-remontant in MI, and 7 progeny were remontant in MI and non-remontant in OR. All the field planted remontants in Michigan were heat tolerant in the greenhouse screen, while 75% of the remontants in Oregon were heat tolerant (Appendix 2.1). While ~50% of the progeny in MI and OR were remontant in this study, in Weebadde et al (2008), 80% of the progeny in OR and 50% in MI were remontant. It is unclear why the proportion of remontants was so much lower in OR in 2005 than 2011. A comparison of average maximum air temperatures in early and late summer in OR in 2005 vs. 2011 shows it was 3°C cooler during the latter study. However, the fruiting season in OR was 3 weeks later this year than in other years (Chad Finn pers comm.). Therefore it is possible that some of the remontant types were incorrectly identified as short day because they did not flower by the Aug 15 cutoff date. The population size used in this study was much smaller than that used in the Weebadde et al. (2008) study, making it also possible that the differences in the percent remontant progeny were an artifact of small population sizes. 2.4 Overall conclusions These data clearly demonstrate that the progeny of ‘Honeoye’ × ‘Tribute’ segregate for degree of floral heat tolerance, suggesting that it is a heritable trait. The degree of heat tolerance is likely quantitatively controlled as the difference between flowers produced at 23°C vs 17°C varied 55 continuously among genotypes. While neither of the parents was heat tolerant, a continuum in heat tolerance was observed among the progeny, with 19 of them having more flowers at 23°C than at 17°C. Overall, our experiment demonstrates that when photoperiodic conditions are non- inductive, flower formation is largely determined by a genotype’s level of heat tolerance. Phenotypic evaluations of these progeny under field conditions showed a clear relationship between remontancy and heat tolerance. All the progeny that had high levels of heat tolerance in the greenhouse experiment were remontant under field conditions in MI, and most were in OR. The most heat tolerant progeny will likely be useful in breeding new remontant cultivars for hot midwestern climates. The ability of a remontant genotype to form runners is also an important consideration for a clonally propagated crop. At least one pro geny in this experiment had the ability to form flowers under high temperatures and also produce runners. The results of this experiment show that ambient temperature is an important factor when classifying genotypes based on their photoperiod requirements. The importance of heat tolerance in the expression of remontancy may explain why the remontant cultivars bred for the milder temperatures of CA flower weakly in the more extreme summer temperatures of the Midwest. Our most heat tolerant, remontant genotypes could be used to breed cultivars better adapted to the Midwestern conditions. 56 APPENDIX 57 Appendix 2.1 Figure 2.4. Total flowers (y-axis) at 17°C, 20°C, and 23°C in the ‘Honeoye’ × ‘Tribute’ progeny (HT1-54) and the parents. Remontant/Non-remontant phenotypes from the field observations at Benton Harbor, MI and Corvallis, OR are included with the genotype names on the x-axis. The progeny that were remontant at one location and non-remontant at other are labeled as RM(m)=Remontant in MI, NR(o)= Non-remontant at OR. (a) Total flowers at 17°C, 20°C, and 23°C in the heat tolerant progeny. (b) Total flowers at 17°C, 20°C, and 23°C in the heat sensitive progeny. For interpretation of the references to color in this and all other figures, the reader is referred to the electronic version of this dissertation. RM-Tri NR-Hon RM-HT-50 RM-HT-43 RM(m)-NR(o)-HT-36 RM(m)-NR(o)-HT-35 RM-HT-32 RM-HT-31 RM-HT-29 RM-HT-23 RM-HT-20 RM-HT-18 RM-HT-17 RM-HT-13 RM-HT-9 RM(m)-NR(o)-HT-7 RM-HT-6 RM-HT-5 RM-HT-26 58 RM-HT-41 Total flowers-17C Total Flowers-20C Total flowers-23C (a) RM-HT-37 350 300 250 200 150 100 50 0 350 300 RM(m)-NR(o)-HT-1 RM-HT-2 RM-HT-3 NR-HT-4 NR-HT-8 RM(m)-NR(o)-HT-10 NR-HT-11 NR-HT-12 NR-HT-14 NR-HT-15 RM(o)-HT-16 RM-HT-19 NR-HT-21 NR-HT-22 RM-HT-24 RM(o)-HT-25 NR-HT-27 NR-HT-28 NR(m)-RM(o)-HT-30 NR-HT-33 NR-HT-34 NR-HT-38 RM-HT-39 NR(m)-RM(o)-HT-40 NR(m)-RM(o)-HT-42 NR-HT-44 NR-HT-45 NR-HT-46 RM(m)-NR(o)-HT-47 NR(m)-RM(o)-HT-48 NR-HT-49 NR(m)-RM(o)-HT-51 RM(m)-NR(o)-HT-58 NR-HT-59 NR-HT-61 NR-Hon RM-Tri Figure 2.4 (cont’d) (b) Total flowers-17C 250 Total Flowers-20C 200 Total flowers-23C 150 100 50 0 59 Appendix 2.2 Figure 2.5 Total runners (y-axis) at 17°C, 20°C, and 23°C in the ‘Honeoye’ × ‘Tribute’ progeny (HT1-54) and the parents. Remontant/Non-remontant phenotype from the field observations at Benton Harbor,MI and Corvallis, OR are included with the genotype names on the x-axis. The progeny that were remontant at one location and non-remontant at the other are labeled as RM(m)=Remontant in MI, NR(o)= Non-remontant at OR. (a) Total runners at 17°C, 20°C, and 23°C in the heat tolerant progeny. (b) Total runners at 17°C, 20°C, and 23°C in the heat sensitive progeny. 25 Total runners-17C Total runners-20C Total runners 23C (a) 20 15 10 5 60 RM-Tri NR-Hon RM-HT-50 RM-HT-43 RM-HT-41 RM-HT-37 RM(m)-NR(o)-HT-36 RM(m)-NR(o)-HT-35 RM-HT-32 RM-HT-31 RM-HT-29 RM-HT-26 RM-HT-23 RM-HT-20 RM-HT-18 RM-HT-17 RM-HT-13 RM-HT-9 RM(m)-NR(o)-HT-7 RM-HT-6 RM-HT-5 0 60 RM(m)-NR(o)-HT-1 RM-HT-2 RM-HT-3 NR-HT-4 NR-HT-8 RM(m)-NR(o)-HT-10 NR-HT-11 NR-HT-12 NR-HT-14 NR-HT-15 RM(o)-HT-16 RM-HT-19 NR-HT-21 NR-HT-22 RM-HT-24 RM(o)-HT-25 NR-HT-27 NR-HT-28 NR(m)-RM(o)-HT-30 NR-HT-33 NR-HT-34 NR-HT-38 RM-HT-39 NR(m)-RM(o)-HT-40 NR(m)-RM(o)-HT-42 NR-HT-44 NR-HT-45 NR-HT-46 RM(m)-NR(o)-HT-47 NR(m)-RM(o)-HT-48 NR-HT-49 NR(m)-RM(o)-HT-51 RM(m)-NR(o)-HT-58 NR-HT-59 NR-HT-61 NR-Hon RM-Tri Figure 2.5 (cont’d) 70 (b) 50 Total runners-17C Total runners-20C Total runners 23C 40 30 20 10 0 61 REFERENCES 62 REFERENCES Bradford, E., Hancock, J.F., Warner, R.M. (2010) Interactions of temperature and photoperiod determine expression of repeat flowering in strawberry. Journal of the American Society of Horticultural Science 135(2): 1-6 Darrow (1936) Inter-relation of temperature and photoperiodism in the production of fruit buds and runners in strawberry. Proceedings of the American Society of Horticultural Science 34: 360-363 Durner, E.F., Barden, J.A., Himelrick, D.G., Poling, E.B. (1984) Photoperiod and temperature effects on flower and runner development in day- neutral, Junebearing, and everbearing strawberries. Journal of the American Society of Horticultural Science 109(3): 396-400 Mookerjee et al. (in review) Identification of QTL associated with heat tolerance and remontancy in Fragaria × ananassa R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, http://www.Rproject.org. Serçe, S., Hancock, J.F. (2005) The temperature and photoperiod regulation of flowering and runnering in the strawberries, Fragaria chiloensis, F. virginina, and F. x ananassa. Scientia Horticulturae 103: 167-177 Sonsteby and Heide (2008) Temperature responses, flowering and fruit yield of the June-bearing strawberry cultivars ‘Florence’, ‘Frida’ and ‘Korona’. Scientia Horticulturae 119: 49-54 Weebadde, C.K., Wang, D., Finn, C.E., Lewers, K.S., Luby, J.J., Bushakra, J., Sjulins, T.M., Hancock, J.F. (2008) Using a linkage mapping approach to identify QTL for day- neutrality in octoploid strawberry. Plant Breeding 127: 94-101 63 CHAPTER 3 IDENTIFICATION OF QTL ASSOCIATED WITH HEAT TOLERANCE AND REMONTANCY 64 Abstract Flower formation in strawberry is typically considered to be determined by photoperiod, although several studies have demonstrated that temperature plays an important role in the process. Remontancy or repeat flowering is determined by multiple loci and it has been proposed that some of these loci may be determining floral heat tolerance. In this study, a segregating population developed from ‘Honeoye’ (non-remontant) × ‘Tribute’ (remontant) was used to develop a linkage map using SSR markers. Phenotypic observations were collected from replicated sets of this population growing at 5 locations (MI, OR, CA, MN, and MD) over two years (2005 and 2006). An additional population developed from the same parents and replicated sets were grown in the greenhouse under three temperature conditions (17, 20, and 23°C) to observe effect of temperature on flowering. This second population was also replicated and grown in the field at MI and OR and phenotypic observations were collected in 2011. Phenotypic data (remontant vs non-remontant, weeks of flowering, and total flowers at 17, 20, and 23°C) was used to identify QTL. Heat tolerance QTL were identified on 8 linkage groups and 5 of these overlapped with remontancy and weeks of flowering QTL. Co-location of heat tolerance QTL with remontancy and weeks of flowering QTL indicated that temperature tolerance is an important aspect of the repeat flowering trait of remontant genotypes. One allele was identified that resulted in significantly higher numbers of flowers at 23°C. 3.1 Introduction Genetic control of remontancy has been debated and several hypotheses have been proposed including single dominant gene (Bringhurst et al., 1989; Sugimoto et al., 2005), single major gene with modifier genes (Shaw and Famula, 2005 ), dominant complementary genes (Ourecky 65 and Slate, 1967), and multiple gene control (Shaw, 2003; Serçe and Hancock, 2005a; Weebade et al., 2008). It has been demonstrated that flower initiation in strawberry is determined by both photoperiod and temperature (Darrow, 1934; Durner et al., 1984; Serçe and Hancock, 2005b; Sonsteby and Heide, 2008; Bradford et al., 2010). However, previous studies exploring inheritance of remontancy have not considered the presence of heat tolerance loci. In the recent study by Weebadde et al. (2008), a multi- location (MI, OR, MD, CA, MN) study was conducted to identify QTL (Quantitative Trait Loci) associated with remontancy. Several QTL were identified, some of which were location specific. They proposed that there may be heat tolerance QTL in addition to photoperiod response QTL that determine flowering in strawberry. Development of heat tolerant remontant genotypes is critical for the midwestern market where the extreme summer temperatures inhibit flower initiation and adversely affect yield. To date there have been no studies to identify chromosomal regions that determine heat tolerant flowering response in cultivated strawberry. Identification of QTL affecting remontancy and heat tolerance will be the first step towards developing markers linked to these traits for markerassisted breeding. There have been several studies identifying genes and observing variations in gene expression associated with various developmental processes in strawberry, including flower formation, fruit development, fruit ripening, and disease responses (Aharoni and O’Connell, 2002; BlancoPortales et al., 2002; Rosin et al., 2003; Mehli et al., 2004; Fuente et al., 2006; Figueroa et al., 2009; Chatterjee et al., 2011), but there has been little effort to identify allelic variants and marker-trait associations. The first report on association between a molecular marker and a phenotypic trait was by Haymes et al. (1997) who used a bulk segregant analysis approach to 66 screen RAPD (Random Amplified Polymorphic DNA) markers for association with resistance to Phytophthora fragariae var. fragariae, the pathogen causing red stele disease in strawberry. They identified seven RAPD markers linked to red stele resistance in octoploid strawberry, although these markers have not been used for marker-assisted breeding in the public sector. Lerceteau-Kohler et al. (2004) used a population derived from ‘Capitola’ and ‘CF1116’ to identify QTL associated with fruit traits. They studied 34 traits and they were able to identify 37 QTL associated with 18 traits related to fruit maturity, color, and firmness, sugar and acid content. In a later report, (Lerceteau-Kohler et al., 2006), they identified 30 QTL associated with sugar and acid content in strawberry fruits. Since of these studies used an AFLP (Amplified Fragment Length Polymorphism) map, the markers would need to be converted to transferable markers to be applicable in breeding programs. Zorilla-Fonatesi et al. (2011) studied a population derived from F. × ananassa selection lines ‘232’ and ‘1392’ and identified 33 QTL associated with 14 (out of 17 studied) agronomic and fruit quality traits. Their linkage map included several genic SSRs and they were able to identify associations between gene function and QTL locations. For example, ARSFL-099 was derived from the Fa-Exp-2 gene and was associated with the QTL for fruit firmness. The Fa-Exp-2 gene is an expansin gene identified in strawberry that is associated with softening of the fruit cell-wall during ripening (Civello et al., 1999). Razavi et al. (2011) used an association mapping approach to identify marker trait associations by correlating 24 EST (Expressed Sequence Tag) markers and 369 AFLP marker profiles of 23 Fragaria genotypes (20 Fragaria × ananassa, 2 F. vesca, and 1 F. chiloensis) with phenotypic observations associated with water deficit. Although they were able to identify several marker-trait associations, they acknowledged that their sample 67 size was too small to be conclusive and these identified markers would need to be validated in larger sample sets. There have been few reports on QTL identification and marker-trait associations of flowering traits in strawberry. Three SCAR (Sequence Characterized Amplified Region) markers were developed from ISSRs (Inter Simple Sequence Repeat) associated with the SEASONAL FLOWERING LOCUS (SFL) in F. vesca (Albani et al., 2004). The SFL locus is associated with flowering pattern in diploid F. vesca. Genotypes that have the dominant allele of this locus are short day plants. Genotypes that have two recessive alleles of this locus are remontant (Brown and Wareing, 1965; Albani et al., 2004). In another study, Sugimoto et al. (2005) used a population derived from F. × ananassa ‘Ever berry’ and ‘Toyonoka’ to identify RAPD markers associated with everbearing trait. They identified 5 RAPD markers associated with the everbearing trait: OPE07-1 (11.8 cM) was the closest marker and 88.9 % everbearers had this marker. Other RAPD markers associated with the trait were: OPB05-1 (15.8 cM), OPG11-1 (13.9 cM), OPD20-2 (15.1 cM), and OPG09-1 (24.3 cM). However, none of these markers have been exploited in commercial marker-assisted breeding. Weebadde et al. (2008) developed an AFLP linkage map using a population segregating for remontancy and used this map to identify QTL based on phenotypic observations collected in 5 states across US with different environmental conditions. Proportion of remontant progeny in the population varied with the location. While in MI, MN, and MD approximately 50% progeny were remontant, the cooler western states (OR and CA) had >80% remontant progeny. This indicated that there is a strong environmental effect on the remontant trait. They identified 5 QTL using phenotypic observations from the eastern states, and 2 QTL for the western states. In 68 addition to providing conclusive evidence of remontancy being a multi- genic trait, their study demonstrated that environmental factors determine whether or not the genotype will be remontant. Construction of a genetic linkage map is the first step towards identification of QTL related to any phenotypic trait. Although mapping efforts with octoploid strawberry have been limited until recently, the diploid Fragaria provided the attractive alternative for developing reference maps for studying colinearity within Fragaria species (Rousseau-Gueutin et al., 2008; Sargent et al., 2008, 2011). The first linkage map for diploid strawberry was developed by Davis et al. (1997) using an F2 population of 80 individuals from F. vesca ‘Baron Solemacher’ × F. vesca ‘WC6’. However, the exclusive use of RAPD (Random Amplified Polymorphic DNA) markers in this map limited its transferability and use. The most complete SSR (Simple Sequence Repeat)-based diploid Fragaria linkage map was developed by Sargent et al. (2004, 2006, 2007, 2008, and 2011). They developed a linkage map using an F 2 population from F. vesca semperflorens 815 × F. bucharica (previously referred to as F. nubicola ((Hook.f.) Lindl. ex Lacaita). Their initial map (Sargent et al., 2004) was developed using a population of 94 seedlings and consisted of 68 SSRs, 1 SCAR (Seq uence Characterized Amplified Region), 3 morphological markers, and 6 gene-specific markers, covering 448 cM on 7 linkage groups. This map was later improved by addition of 109 SSRs (Sargent et al., 2006), 29 loci linked to functional genes (Sargent et al., 2007), and an additional 38 new SSRs and 4 markers linked to a fruit ripening gene from Prunus (Sargent et al., 2008). The following year, Sargent et al. (2009) added additional markers to bring the map coverage up to 568.8 cM. This included 348 published transferable genetic markers, including SSR, and gene-specific markers (173 SSRs, 31 gene-specific and STS markers, 40 RFLPs and 1 SCAR). Ruiz- Rojas et al. (2010) used T-DNA 69 insertional mutagenesis to develop mutants in F1 and then used the T-DNA flanking sequences to map 74 polymorphic loci on the same mapping population. After the availability of the diploid F. vesca genome sequence (Shulaev et al. 2011), Sargent et al. (2011) developed SSR markers for those regions of the genome that did not have adequate coverage in their earlier map. They developed an additional 152 SSR markers and placed these in addition to 42 published SSRs on the map. The F. vesca genome sequence was also divided into pseudochromosomes based on the map locations of SSR loci and their corresponding positions on the sequence scaffolds. The latest map has 444 SSR markers covering 442.8 cM. Until recently, octoploid Fragaria linkage maps were mostly developed using non-transferable AFLP (Amplified Fragment Length Polymorphism) markers (Lerceteau-Kohler et al. 2003; Weebadde et al., 2008) and as a result have limited use for identifying markers for markerassisted breeding. The first octoploid strawberry map was developed by Lerceteau-Kohler et al. (2003) using AFLP markers on a population of 119 individuals derived from ‘Capitola’ × ‘CF116’. The female map included 235 markers (out of 257) in 43 linkage groups covering 1604cM with marker density of 8.4cM. The male map included 283 markers (out of 293) in 43 linkage covering 1496 cM and an average marker density of 6.3cM. A second AFLP-based map was developed by Weebadde et al. (2008) on a population of 127 individuals derived from a F1 cross between ‘Honeoye’ × ‘Tribute’. They mapped 427 SDRF (Single Dose Restriction Fragments) (Wu et al., 1992) on 43 linkage groups as a consensus map. Although the average marker density was 0.3 markers/cM, the map consisted of 42 linkage groups (more than the expected number: 28) indicating that the more markers were needed to link the groups together. 70 In recent years, at least three SSR-based octoploid linkage maps have been developed. ZorillaFonatessi et al. (2011) developed a linkage map using a population of 95 F1 individuals from selection lines ‘232’ and ‘1392’ of F. × ananassa. This map has 338 markers (from 146 SSR primers) on 37 linkage groups covering 1259.8cM with average marker spacing 4.3 cM. Rousseau-Gueutin et al. (2008) added 306 markers from 79 SSR primers to the AFLP map developed by Lerceteau-Kohler et al. (2003). The resulting female map covered 2582 cM on 28 linkage groups. The male map covered 2185 cM on 26 linkage groups. As with the diploid, Sargent et al. (2009, 2012 submitted) most recently constructed the most comprehensive octoploid linkage map with transferable SSR markers. They used an F 1 population of 188 seedlings developed from ‘Redgauntlet’ × ‘Hapil’. The initial map was developed using 71 SSRs, 5 gene specific primers, 11 RAPDs and 10 AFLP primers combination (Sargent et al., 2009). The female (‘Redgauntlet’) map consisted of 32 linkage groups with 170 loci covering 1675.3 cM. The male (‘Hapil’) map consisted of 37 linkage groups with 182 loci covering 1440.7 cM. Sargent et al (2012 submitted) added another 331 SSR loci to this map resulting in a map covering 2140.3 cM in 28 linkage groups and with 550 marker loci (491 SSRs from 241 primer pairs). In addition to the cultivated strawberry, linkage map has also been developed for the native octoploid F. virgininana (Spigler et al., 2008). The F. virginiana map consists of 42 linkage groups with 210 SSR markers (from 100 primers) markers and covers 2373 cM. The availability of several SSR-based linkage maps makes comparison of QTL across different populations possible. Weebadde et al., (2008) identified 8 QTL linked to remontancy using a ‘Honeoye’ × ‘Tribute’ population. However, markers associated with these QTL cannot be compared across maps developed with other parents because the AFLP markers on the map were not developed into SCAR markers. This mapping population is an excellent resource to study 71 remontancy and other commercially important traits associated with remontant cultivars. In this study, the ‘Honeoye’ × ‘Tribute’ population was used to develop a transferable SSR-based linkage map and this map was used to identify QTL associated with heat tolerance and remontancy using phenotypic data collected in the greenhouse (Chapter 2), and in the field (Chapter 2 and Weebadde et al., 2008). These different data sets provided several routes to verify QTL associated with remontancy (Lander and Kruglyak, 1995; Collard et al., 2005, Pelgas et al., 2011). Since remontant genotypes are characterized by flowering in early and late summer, and an extended flowering season, bo th the traits were used to identify QTL in this study. 3.2 Material and Methods 3.2.1 Mapping population An F1 population of 174 progeny of ‘Honeoye’ × ‘Tribute’ was used to build the linkage map. ‘Tribute’ is a remontant cultivar released in 1981 by Draper et al. (1981) in Maryland. ‘Honeoye’ is a short day cultivar released in 1979 in New York (Ourecky, 1979). This population was selected because it segregates for remontancy and had earlier been used by Weebadde et al. (2008) to identify QTL determining remontancy. Out of the 174 individuals in our mapping population, 112 had been phenotyped by Weebadde et al. (2008) and 62 were used in their AFLP map. The additional 62 progeny were generated from a cross made in 2009. 3.2.2 DNA extraction Young leaf samples from the parents and progeny were collected from greenhouse grown plants and placed on ice while transporting to the lab. The leaves were stored at 4 °C for a maximum of 2 days until DNA extraction. When DNA was not extracted immediately, the leaves were stored 72 at -80°C. The leaf samples were ground to a fine powder with liquid nitrogen and DNA was extracted using the DNeasy Plant mini kit (Qiagen) following the manufacturer’s protocol. DNA was eluted in a volume of 200 L. DNA quality was determined by running 4 L of the eluted DNA on a 0.8% (w/v) agarose (Invitrogen, Carlbad, California 92008) gel containing 0.5 mg/mL ethidium bromide, in 1x TBE buffer (90mM Trizma Base (Sigma-Aldrich Corp, St Louis, MO) + 90mM Boric acid (J.T. Baker, Phillipsburg NJ 08865) + 2mM EDTA (Invitrogen, Carlbad, California 92008), pH 8.0). DNA samples were loaded along with a loading dye (30% glycerol, 0.25% Bromophenol blue). The gels were run at 100V for 20 min and visualized using the ChemiDocT M XRS+ system (Biorad, Hercules, CA 94547). The DNA was diluted 1:4 with Nuclease free water (Promega Corporation, Madison, WI 53711) for PCR amplification. 3.2.3 Genotyping Selection of SSRs: SSR loci developed from F. × ananassa, F. vesca, F. nubicola, and F. viridis, in previously published literature (Sargent et al., 2003; Cipriani and Testolin, 2004; Lewers et al., 2005; Bassil et al., 2006; Bassil et al., 2006b; Sargent et al., 2006; Gil- Ariza et al., 2006; Njuguna 2010; Zorrilla-Fontanesi et al., 2011) were screened for polymorphism in a subset of 54 progeny and the parents. 157 SSR markers were screened and 99 were selected based on polymorphism and distinct scorable bands on the 6% polyacrylamide gel. These 99 SSR primer sets were used for genotyping the population. These included 70 (out of 115 tested) derived from F. × ananassa, 20 (out of 26 tested) from F. nubicola, 7 (out of 13 tested) from F. vesca, and 2 (out of 3 tested) from F. viridis. Approximately 60 per cent of the SSR markers mapped were EST derived (Lewers et al., 2005; Gil- Ariza et al., 2006; Bassil et al., 2006; Bassil et al., 2006b; Zorrilla-Fontanesi et al., 2011) and therefore can potentially be associated with a gene function. Appendix 3.1 lists the SSR markers used to genotype the population, along with 73 their primer sequences and the putative functions associated with the EST-SSRs based on BLAST searches reported in the original literature. DNA amplification: DNA amplification was performed in 20 L reactions containing 1 x GoTaq® Green Master Mix (Promega Corporation, Madison, WI 53711), 0.5 mM of forward and reverse primer, and 1 L of diluted DNA template. Amplifications were done in C1000TM Thermal Cycler (Biorad, Hercules, CA 94547) using the following PCR cycle: Initial denaturation: 95°C for 2 min; 34 cycles of 1 min at 95°C, 60 s at annealing temperature (=Tm + 2°C), 1.5 min at 72°C; and a final extension step of 10 min at 72°C, hold at 15°C. Polyacryamide Gel Electrophoresis: PCR amplified products were separated using a 6% denaturing polyacrylamide gel (15 mL of 40% Acrylamide/Bis Solution (BioRad, Hercules, CA 94547), 10 mL 10 x TBE buffer, 42 g Ultra Pure Urea (Invitrogen, Carlbad, California 92008), 500 L 10% APS, 100 mL TEMED (BioRad, Hercules, CA 94547). The PCR amplicons were denatured (95°C for 5 min, hold at 4°C) and loaded on to 38 cm x 50 cm Sequi-Gen GT system (BioRad, Hercules, CA 94547) that was preheated for 20-30 min. The gels were run at 80W for 3.5 hrs. Silver staining (Bassam et al., 1991) was used to visualize the products and the fragment sizes were estimated by comparing with 10 and 50 bp ladders (Invitrogen, Carlbad, California 92008). 3.2.4 Linkage map The Single Dose Restriction Fragment (SDRF) (Wu et al., 1992) approach was used for scoring the markers. In this approach, each segregating fragment was treated as an individual allele and the genotypes are scored for presence/absence of the allele. Each gel was scored twice to 74 minimize errors. Markers present in both the parents that segregate in a 3:1 ratio were coded as dominant markers. Markers present in only one parent that segregate in a 1:1 ratio were coded as codominant markers. Both the dominant and codominant markers were used to develop the linkage map using Joinmap 3.0 (Stam 1993). The linkage map was developed using a minimum threshold LOD score value of 3.0, maximum recombination frequency of 0.3, and Kosambi mapping function. The linkage groups created by Joinmap were visualized using MapChart 2.2 (Voorips, 2002). The SSR markers were color coded to reflect whether they were present in ‘Honeoye’, ‘Tribute’, or both (Figure 3.1). Marker names included the name of the SSR locus as in the original publication, followed by the band size in bp. When SSR primers resulted in duplicated bands, the marker names included sizes of all the cosegregating bands. Markers with segregation distortion were indicated with an asterisk (*). Identification of homeologs: The linkage groups identified in the ‘Honeoye’ × ‘Tribute” population were compared with the diploid (Sargent et al., 2011) and octoploid (Sargent et al., 2012) maps to identify their homeologous groups. In addition, locations of the SSR markers were identified on the F. vesca pseudochromosomes (Shulaev et al, 2011; Sargent et al., 2011) using the BLASTn function in the PFR strawberry genome server www.strawberrygenome.org. The locations on the diploid pseudochromosomes was considered conclusive only when >20 bp of both the forward and the reverse primer sequence had a complete match with only one pseudochromosome. This criterion was used to ensure BLASTn was not picking up random similarities with the short oligonucleotide sequences. The locations of the markers on the diploid map were indicated on the octoploid linkage groups in parenthesis (Figure 3.1). 75 3.2.5 Phenotypic evaluation Greenhouse: 54 progeny were propagated and grown in the greenhouse under three temperature conditions: 17°C, 20°C and 23°C under a 16 hr photoperiod as described in Section 2.2.2 (Chapter 2). Number of flowers for each of the 54 progeny in the greenhouse was counted every week from Dec 2010 to Mar 2011 as described in 2.2.3 (Chapter 2). Field observations : 62 progeny from ‘Honeoye’ × ‘Tribute’ cross made in 2009, including the 54 that were used in the greenhouse study, were planted in the field at MI and OR in Aug 2010 as described in Section 2.2.2 (Chapter 2). In addition, phenotypic data for a set of 112 progeny that were collected by Weebadde et al. (2008) from 5 locations in MI, MN, MD, OR, CA was used for QTL validation. The growth conditions for these plants are described in Weebadde et al. (2008). The average minimum and maximum temperatures in early and late summer at all the field locations and in all the years of study are listed in Table 3.1. Remontant vs non-remontant phenotype was recorded for each of the 62 progeny planted at MI and OR. Presence/absence of flowers was recorded every week from May, 1 2011 to Aug 15, 2011. Progeny that flowered in the spring and then again after July 22 were categorized as remontant. In these genotypes, flower initiation had occurred both in short days (early summer flowering), and long days (late summer flowering). The genotypes that only flowered in the spring were considered non-remontant. 76 The phenotypic data collected by Weebadde et al. (2008) was also used to identify QTL. Information on whether the genotypes were remontant or non-remontant was available from OR, MN, MD, and CA in 2005, and from MI in 2005 and 2006. Weeks of flowering data was also available in 2005 from May 6-Jul 6 for MI, May 2-Aug 25 for CA, and Mar 10-Aug 25 for OR. In 2006, weeks of flowering data was collected in MI from Apr 27-Aug 2. The differences in data collection dates reflect the differences in growing seasons. Weeks of flowering information for 2005 was not available for MN and MD. 3.2.6 Distribution graphs Phenotypic distribution histograms were prepared using MSExcel. 3.2.7 QTL identification MapQTL5 (Van Ooijen, 2004) was used for QTL identification using the MQM or Composite Interval Mapping approach. The population was derived from two heterozygous parents and was coded as CP to include the three types of marker data: 1: codominant markers segregating in ‘Honeoye’, 2: codominant markers segregating in ‘Tribute’, 3: Dominant markers present in both parents. Markers identified as significant by the Kruskal-Wallis test were used as cofactors. The significant LOD score at p ≤ 0.05 was determined from 1000 permutations with the dataset. Significant QTL regions along with the linkage groups were visualized using MapChart 2.2 (Voorips, 2002). Only those regions that had a variance of >10% are reported. The location of the highest peak in the QTL region is reported in the Appendix 3.6, 3.7, and 3.8, and the entire range of QTL region is represented in Figure 3.1 for comparison of all QTL locations. Regions with significant LOD scores that were separated by less than 10 cM were considered to be the same QTL. 77 Table 3.1 Average minimum and maximum temperatures at the field locations (MI-Benton Harbor, MN-St Paul, MD-Beltsville, OR-Corvallis, CA-Watsonville) in the different years of study (2005, 2006, 2011). Location Year MI MN MD OR CA 2005 2006 2011 2005 2005 2005 2011 2005 Avg min temp (Apr-May) 6 6 7 3 2 6 6 7 Avg max. temp (Apr-May) 18 17 16 17 20 18 15 19 Avg min. temp (June-Aug) 17 15 17 18 14 10 12 11 Avg max. temp (June-Aug) 29 26 27 28 30 26 23 21 49.2 45 50 48 80 50 87.3 Percent remontant plants 50 78 3.3 Results and discussion 3.3.1 Linkage map The 99 SSR primer pairs resulted in the amplification of 258 segregating markers and ~556 monomorphic markers. Out of the 258 segregating markers, 77 were present only in ‘Tribute’, 115 were present only in ‘Honeoye’, and 66 were present in both parents. The segregation type and Chi square values of the markers are listed in Appendix 3.2. Initially, markers heterozygous in either of the parents were used to develop separate male and female maps. However, these maps had very limited coverage. The ‘Honeoye’ map had 103 markers in 23 linkage groups, and the ‘Tribute’ map had 78 markers in 22 linkage groups (Appendix 3.3). Only 7 male and female groups had common markers. Therefore, a consensus linkage map with all the markers was developed. The consensus map includes 130 (out of 258 markers) in 34 linkage groups and covers 1028 cM with an average marker density of 1 marker per 7.9 cM (Figure 3.1). This map length has slightly less coverage than the F. x ananassa SSR map developed by Zorilla-Fonatesi et al. (2011): 1259.8 cM on 37 linkage groups. Their map was also denser with an average of 4.3 cM between markers. The map coverage is approximately half of the map developed by Sargent et al.(2012 submitted): 2140.3 cM in 28 linkage groups, and the F. virginiana SSR map developed by Spigler et al.(2008): 2373 cM in 42 linkage groups. Segregation distortion was observed for 34% of the markers (Appendix 3.4). Out the markers that showed segregation distortion, 40 were in ‘Honeoye’, 24 were in ‘Tribute’, and 24 were in both the parents. Segregation distortion has been observed in the diploid and octoploid Fragaria maps developed to date. Sargent et al. (2004) observed segregation distortion in 54% of the markers they used to assemble the diploid map. They attributed it to the fact that they were 79 using progeny from an interspecific cross (F. vesca semperflorens x F. bucharica), and that one of the parents was self incompatible. Nier et al. (2006) observed significant segregation distortion on 3 linkage groups while mapping 33 SSRs using a backcross population (Fragaria vesca × (F. vesca × [F. vesca × F. viridis]). Ruiz-Roja et al (2010) observed segregation distortion in 42% of the markers developed through T-DNA insertional mutagenesis. Twenty eight percent of the markers in the octoploid F. x ananassa map developed by Sargent et al. (2009) had segregation distortion. In the case of the other octoploid species, 30% of the markers in the F. virginiana map (Spigler et al., 2008) showed distortion. Sargent et al. (2009) observed that the markers with distorted ratios were evenly distributed across the linkage groups. In the present ‘Honeoye’ × ‘Tribute’ map, the distorted markers were distributed in 22 groups. Removal of the markers with distorted segregation did not change the groups formed by the markers. Spigler et al. (2008) speculated that the distortions are a result of PCR amplification errors rather than a biological phenomenon. However, Ruiz- Roja et al (2010) discussed the possibility of gametophytic selection, non- homologous recombinations, transposons, and the structure of mapping population contributing to the distorted segregation. Similar speculations were made by Zorilla-Fonatessi et al. (2011) who explained that segregation distortions may be the result of genes that lead to low survival of some genotypes. 80 Figure 3.1 Consensus ‘Honeoye’ × ‘Tribute’ linkage map and the QTL associated with remontancy (rem), weeks of flowering (wks), and heat-tolerant/sensitive floral responses (FL23C, FL20C, FL17C). Map distances are in cM. Only the groups with significant QTL regions are shown. The significant LOD score at p ≤ 0.05 was determined from 1000 permutations with the dataset. The markers are color coded to indicate whether they segregate in Honeoye (red), in ‘Tribute’ (green), or both (blue). Locations of the markers in the diploid map are indicated in parenthesis. Markers with segregation distortion are indicated with asterisk (*). HT1 ARSFL8_301 FL23C wksCA05 FL20C FL17C wksCA05 wksOR11 wksOR05 wksMI11 wksCA05 wksOR11 wksMI06 remCA05 81 remCA05 ChFaM040_300* EMFvi104_80 remOR11 49.2 52.1 remOR11 ARSFL98_205(III) remOR05 44.8 remMI11 ChFaM129_500/190 ChFaM080_219* EMFn170_240(III) ChFaM040_315* ChFaM040_95 remMI11 30.7 31.6 36.6 37.9 38.4 remMI11 ChFaM098_225*(III) UDF004_136(III) remMI11 13.3 15.5 remOR05 remOR05 0.0 Figure 3.1 (cont’d) HT2 FL17C 82 FL17C ChFaM072_350(I) FL17C 68.1 FL17C ChFaM072_385*(I) wksCA05 61.7 wksCA05 ChFaM061_220(I) wksOR11 54.9 wksOR11 UFFa16H07_256(I) wksMI11 42.4 remMN05 UFFa16H07_262(I) remOR11 36.8 remOR11 remOR11 ChFam032_205 remMI05 27.0 remMI11 EMFn230_225 wksMI06 remMI05 17.1 FL20C EMFn152_148*(I) remOR11 0.0 Figure 3.1 (cont’d) HT3 HT4 22.5 24.8 ChFaM101_153(II) ChFaM063_90 29.3 ChFaM063_111/131 83 wksCA05 ChFaM101_139(II) remOR11 FL20C EMFn121_243(II) ChFaM101_137(II) 17.3 FL17C wksCA05 wksOR11 wksMI05 9.7 10.7 FL20C FL23C ChFaM063_106 wksOR11 wksOR11 FL20C wksCA05 wksOR11 wksMI06 0.0 FL20C HT4 0.0 ChFaM06 9.7 10.7 EMFn121 ChFaM10 17.3 FL17C wksCA05 CHFaM067_161 wksCA05 39.7 wksOR11 ChFaM104_197* UaFv9404_390(II) UAFv8216_101(II) wksOR11 29.7 30.9 32.9 wksMI05 ChFaM004_230* ChFaM004_132 remMI11 22.3 23.4 wksMI06 EMFn134_214*(II) 12.4 remOR11 remMI11 remMI06 ChFaM088_106(II) 0.0 ChFaM10 22.5 24.8 ChFaM10 ChFaM06 29.3 ChFaM06 Figure 3.1 (cont’d) HT5 FL20C wksCA05 wksCA05 wksMI11 84 wksMI11 EMFn184_245(V) wksMI06 34.8 remMN05 ChFaM017_250 remMD05 27.4 remCA05 SF5G02_228 remOR11 22.7 remOR05 EMFn184_260(V) remMI11 12.6 remOR11 ChFaM017_147 remOR05 8.6 remMI06 ChFaM017_92* remMI05 0.0 wksCA05 wksOR11 HT6 wksCA05 wksOR11 wksOR05 EMFn160_160(II) remMN05 50.8 wksMI11 EMFn160_161(II) wksMI06 EMFn134_160(II) 44.4 remMN05 ChFaM088_300(II) 37.8 remMD05 remMD05 32.3 remCA05 UAFv8216_161(II) remOR11 23.6 remMI11 UAFv8216_182/185(II) remMI11 remMI05 17.5 remOR05 ChFaM103_450*(II) 0.0 Figure 3.1 (cont’d) HT7 FL17C FL23C wksCA05 wksMI11 wksMI06 remMN05 remMD05 wksCA05 FL20C wksCA05 wksOR11 wksMI06 remMN05 remMD05 remCA05 remOR11 remOR05 FL20C wksCA05 wksOR11 32.6 43.4 ChFaM111_189 59.7 85 remCA05 remOR05 remMI11 EMFn117_157(VI) ChFaM111_176* remMI11 remMI11 17.2 EMFvi104_130 remMI06 EMFn117_188(VI) remMI05 remMI05 13.1 remMI05 EMFn198_169 0.0 Figure 3.1 (cont’d) HT8 0.0 20.0 HT9 EMFn235_215(II) 0.0 ChFaM101_180(II) 44.0 ChFaM063_141/107/104/86 27.2 29.4 ChFaM046_135(V) ChFaM046_157(V) 47.2 SF5G02_250 EMFn121_249(II) 48.4 ChFaM046_152(V) 86 remOR05 SFGRP7_134/141/147(II) Uffa11A11_206/260 22.1 wksCA05 32.9 35.3 ChFaM022_305-225* ChFaM022_305-225* FL20C HT10 FL17C wksCA05 wksOR11 SF5G02_250 wksMI11 ChFaM046_135(V) ChFaM046_157(V) 47.2 wksMI11 27.2 29.4 wksMI06 ChFaM046_152(V) remOR11 22.1 remMN05 remMD05 0.0 remOR05 /104/86 HT9 wksCA05 47(II) Figure 3.1 (cont’d) HT11 ChFaM151_228/113 87 0.0 FL17C FL17C 32.0 wksCA05 ChFaM076_101 wksMI11 25.4 remOR11 ChFaM003_400* remMI11 15.9 wksCA05 UFFa16H07_270(I) wksMI06 6.7 remOR11 ChFaM003_495* remMD05 0.0 SFGRP7_125*(II) 24.1 SF6E02_119 35.1 EMFn235_219(II) 40.6 ChFaM103_215(II) Figure 3.1 (cont’d) HT11 wksCA05 wksMI06 0.0 FL17C FL17C wksCA05 wksMI11 _228/113 remOR11 _101 remMI11 _400* remMD05 7_270(I) remOR11 _495* SFGRP7_125*(II) 24.1 SF6E02_119 35.1 EMFn235_219(II) 40.6 ChFaM103_215(II) 52.4 ChFaM103_190(II) 88 Figure 3.1 (cont’d) HT12 HT13 UFFa03B05_214/175(II)* ChFaM111_210 11.3 ChFaM111_195 UaFv8936_410(V) UAFv8216_239/107(II) 43.7 7.5 EMFn134_192(II) 22.2 ChFaM078_140(V) T12 HT13 0.0 ChFaM078_140(V) 7.5 ChFaM111_210 11.3 0.0 ChFaM111_195 wksCA05 wksCA05 EMFn134_185*(II) HT14 EMFn134_192(II) wksCA05 UAFv8216_239/107(II) 41.4 UaFv8936_410(V) 89 wksMI06 UFFa03B05_214/175(II)* SF5C08_325* 5.8 EMFn181_170(V) 17.5 UFFa20D02_109 wksMI06 16.2 0.0 41.4 wksCA05 EMFn134_185*(II) 0.0 Figure 3.1 (cont’d) HT14 ChFaM078_140(V) wksCA05 0.0 ChFaM111_210 ChFaM111_195 wksCA05 5.8 EMFn181_170(V) 17.5 UFFa20D02_109 wksMI06 UaFv8936_410(V) SF5C08_325* HT15 remMI06 FAC006_225 ChFaM147_219* remMI05 90 ChFaM147_210* 31.0 34.1 UFFa04G04_158*(IV) ChFaM161_190 0.0 wksCA05 13.4 15.9 EMFn225_250(VI) wksOR11 0.0 HT16 FL17C HT17 HT18 12.9 ChFaM095_320* ChFaM018_450* ChFaM018_460* 32.5 ChFaM031_138/190 91 wksCA05 22.5 EMFn123_200(VI) 0.0 wksCA05 ChFaM095_400 remOR11 0.0 30.5 FL20C FL17C wksOR11 remMI05 remMN05 FAC006_225 ChFaM147_219* remMD05 31.0 34.1 remOR11 ChFaM147_210* remMI06 0.0 wksCA05 4_158*(IV) _190 HT16 wksOR11 250(VI) Figure 3.1 (cont’d) Figure 3.1 (cont’d) ChFaM018_450* 22.5 ChFaM018_460* 32.5 ChFaM031_138/190 remOR11 0.0 wksCA05 ChFaM095_400 HT18 ChFaM095_320* wksCA05 EMFn123_200(VI) HT19 0.0 HT20 ChFaM040_143* 18.1 ChFaM ChFaM FL23C FL17C wksOR11 92 wksOR05 ChFaM080_220 remMN05 remMD05 35.2 remCA05 UDF004_143(III) remMI05 28.2 EMFn1 34.0 remOR05 0.0 Figure 3.1 (cont’d) HT20 wksMI11 0.0 EMFn170_208(III) ChFaM098_226*(III) FL17C 34.0 FL23C wksCA05 ChFaM094_120 FL23C FL17C wksOR11 wksOR05 remMN05 remMD05 18.1 HT21 HT22 ChFam039_196* wksCA05 24.2 SF5C08_410 93 EMFn213_298(VII) EMFn213_320(VII) 15.3 ARSFL19_295 remOR05 7.2 0.0 2.6 remMI11 EMFn128_157*(I) FL20C 0.0 Figure 3.1 (cont’d) HT22 HT24 0.0 UFFa03B05_210(II) 4.5 HT25 ChFaM103_124(II) 3 EMFn230_220* ChFaM095_170 SF6E02_149 HT24 0.0 wksCA05 16.8 0.0 11.7 HT25 0.0 EMFn230_220* 11.7 ChFaM095_170 30.6 EMFn117_155/127(VI) EMFn117_155/127(VI) 0.0 EMFn170_215*(III) ChFaM103_124(II) wksCA05 SF6E02_149 wksCA05 94 13.5 wksCA05 30.6 UFFa03B05_210(II) FL23C wksOR11 wksMI11 HT23 08_410 remCA05 ARSFL19_295 15.3 remOR11 remOR05 wksCA05 FL20C m039_196* wksCA05 EMFn213_298(VII) EMFn213_320(VII) 0.0 2.6 remMI11 128_157*(I) ChFaM056_112 13.5 EMFn ChFaM Figure 3.1 (cont’d) T24 HT25 EMFn230_220* EMFn170_215*(III) 0.0 ChFaM095_170 wksCA05 EMFn117_155/127(VI) wksCA05 ChFaM056_112 13.5 HT26 HT27 5.8 EMFvi136_157(IV) wksCA05 ChFam011_117* wksMI06 0.0 HT2 0.0 ChFaM151_223 12.8 Uffa20H10_260 0.0 15.3 26 HT27 0.0 ChFaM151_223 12.8 Uffa20H10_260 0.0 15.3 95 EMFn202_192-215(III) wksCA05 wksCA05 EMFvi136_157(IV) wksMI06 ChFam011_117* HT28 ChFaM098_165(III) Figure 3.1 (cont’d) HT27 HT28 ChFaM151_223 8 Uffa20H10_260 HT29 HT30 3.9 Uffa20H10_247 HT30 FL23C FL20C FL17C wksCA05 96 remCA05 Uffa20H10_247 remOR11 3.9 remOR05 ChFaM076_139 remMI11 0.0 remOR11 ChFaM076_139 remOR05 ChFaM107_192 0.0 remMI11 5.9 wksCA05 EMFn128_162/161(I) wksMI11 0.0 wksCA05 M107_192 ChFaM098_165(III) 15.3 wksMI11 128_162/161(I) EMFn202_192-215(III) 0.0 wksCA05 0 Figure 3.1 (cont’d) HT31 0.0 ChFaM086_440/225/198(I) EMFn115-140(I) 0.0 HT32 0.0 EMFn115-140(I) wksCA05 wksMI06 remMN05 remMD05 97 remCA05 ChFaM081_110(I) remOR05 remMI06 remMI05 wksCA05 14.6 remOR05 ChFaM093_240 ChFaM081_110(I) 14.6 remMI06 remMI05 wksCA05 25.3 440/225/198(I) 240 HT32 HT33 HT34 FL23C FL20C FL17C wksOR11 wksMI11 remMN05 remMD05 98 remCA05 hFaM085_155 remOR11 EMFvi136_159*(IV) remOR05 7.8 remMI11 ChFam011_129* remMI06 0.0 remMN05 HT34 remMD05 ChFaM085_155 remCA05 remOR11 remOR05 EMFvi136_159*(IV) remMI06 ChFam011_129* remMI11 EMFn198_189* remMI05 27.5 0.0 7.8 0.0 remMI05 MFn198_189* Figure 3.1 (cont’d) 3.3.2 QTL identification To identify the most robust QTL regulating remontancy, fifteen phenotypic data sets were available that were collected on the same segregating population (‘Honeoye’ × ‘Tribute’) at multiple field locations and in different years. Remontancy was evaluated in two ways, as flowering in both spring and summer and by duration of flowering. The co- localization of floral heat tolerance QTL with remontancy QTL was also documented using data from a controlled greenhouse study. Table 3.2 summarizes the QTL identified from all the datasets on the ‘Honeoye’ x ‘Tribute’ linkage groups. QTL identification with 2005-2006 data from CA, MD, MI, MN and OR Phenotypic data collected from 5 locations in 2005 and 2006 (Weebadde et al., 2008) and the SSR linkage map was used to identify QTL associated with remontancy and weeks of flowering. The remontancy QTL identified along with nearest marker and percent variance explained are listed in Appendix 3.6. Phenotypic distributions of weeks of flowering were continuous (Figure 3.2a-f), although the distribution was skewed to longer durations in CA, indicating a longer fruiting season at this location. The weeks of flowering QTL identified along with the nearest marker and percent variance are listed in Appendix 3.7. The significant QTL regions are also represented on Figure 3.1 along with the linkage groups and all other QTL identified. Several region-specific remontancy QTL were identified (Figure 3.1; Appendix 3.6 ). These included two QTL each for MI (HT2, HT3), MD (HT5, HT10), and OR (HT6, HT9), and one for MN (HT5). Three regions specific to the western states (OR and CA) were identified in groups HT1, HT22, and HT30 and two regions associated with the three eastern states (MI, MN, MD) were identified in groups HT16 and HT19. Five regions were identified where QTL for all 5 99 states collocated: HT5, HT6, HT7, HT32 and HT34. One region was identified with QTL for MN and MD (HT9) and one region for MI and MN (HT2). Variances of the QTL identified using 2005/2006 data ranged from 10.1% to 69%. These observations were similar to those of Weebadde et al. (2008) who also identified regionspecific QTL and some QTL common to multiple datasets. They identified one QTL common to the eastern states MN, MI, and MD, in addition to three QTL specific to MN, and one QTL each specific to MI and CA, and none for MD. However, our study found many more QTL using the same phenotypic data. It is possible that we identified more QTL because the population size (112 progeny) used in our study was larger than the population (65 progeny) used by Weebadde et al., (2008) for QTL identification. It is also possible that we observed more QTL because we had fewer markers and as a result our linkage groups were more fragmented. Many of our QTL were placed at the end of linkage groups, which might have been joined with more markers. Although Weebadde et al. (2008) did not find any QTL common to all locations, we did identify regions where QTL for remontancy at all 5 locations overlapped, and, there were two regions specific for western states that were not identified in the earlier study. Unfortunately, the earlier map was developed using AFLP markers and it is not possible to directly compare those QTL with the ones identified here. Most ‘weeks of flowering’ QTL overlapped with the remontancy QTL (HT1, HT2, HT3, HT5, HT6, HT7, HT9, HT10, HT16, HT19, HT22, HT30, HT32, and HT34) (Figure 3.1; Appendix 3.6 and 3.7). This suggests that our method of categorizing remontant vs non-remontants was able to identify regions of the genome that determine extended flowering season that is typical of remontant genotypes. Several regions were identified specific to weeks of flowering in CA on 100 groups HT8, HT12, HT13, HT18, HT21, HT24, HT28, HT30, and HT31. Two regions, HT16 and HT19, had regions specific for weeks of flowering in OR. QTL for weeks of flowering in all three states (MI, OR, and CA) collocated on groups HT1, HT2, HT3, HT7, HT9, HT22. This suggests that there is significant environmental effect on determining the duration of flowering in genotypes, but a few QTL are represented across locations. Only one QTL was identified for weeks of flowering at MI in 2005. This is likely because the data was collected only until early July, whereas in the other states the data was collected until Aug. Variances for the QTL identified ranged from 15.6-36.4% for MI 2006, 28.4-61.1% in OR2005, and 23-54.5% in CA2005 (Appendix 3.7). 101 Table 3.2 QTL regions associated with remontancy, weeks of flowering, and flower number at different temperatures (17, 20 and 23 °C) in the ‘Honeoye’ × ‘Tribute’ population. . The place and time of the phenotypic observations are listed by the initials of the state and the year of collection. LG = linkage group. ‘x’ indicates presence of QTL on the linkage group (HT1- HT34). LG Remontancy QTL Weeks of flowering QTL Temp QTL MI05 MI06 MI11 OR05 OR11 MN05 MD05 CA05 MI05 MI06 MI11 OR05 OR11 CA05 23 20 17 HT1 x x x x x x x x x x x HT2 x x x x x x x x x x HT3 x x x x x x x x x HT4 x x x x HT5 x x x x x x x x x x x x x HT6 x x x x x x x x x x x HT7 x x x x x x x x x x x x x x x HT8 x HT9 x x x x x x x x x HT10 x x x x x x x HT12 x HT13 x x HT15 x x HT16 x x x x x x x x HT18 x x HT19 x x x x x x x x x HT20 x x x x HT21 x x HT22 x x x x x x x x HT24 x HT26 x x 102 Table 3.2 (cont’d) LG HT28 HT29 HT30 HT32 HT34 Remontancy QTL Duration of flowering QTL Temp QTL MI05 MI06 MI11 OR05 OR11 MN05 MD05 CA05 MI05 MI06 MI11 OR05 OR11 CA05 23 20 17 x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x 103 Figure 3.2a-f Distribution of weeks of flowering in ‘Honeoye’ x ‘Tribute’ progeny with different flowering durations. Weeks of flowering in the parents are indicated: ‘Honeoye’: shaded arrow, ‘Tribute’: dark arrow. (a) Weeks of flowering in MI-2005, (b) Weeks of flowering in MI-2006, (c) Weeks of flowering in OR-2005, (d) Weeks of flowering in CA-2005, (e) Weeks of flowering in MI-2011, (f) Weeks of flowering in OR-2011 60 (a) Frequency 50 40 30 20 10 9-10 7-8 5-6 3-4 0-2 0 Number of weeks of flowering in MI-2005 60 (b) 40 30 20 10 Number of weeks of flowering in MI-2006 104 11-12 9-10 7-8 5-6 3-4 0 0-2 Frequency 50 Figure 3.2 (cont’d) 60 Frequency 50 (c) 40 30 20 10 23-24 21-22 19-20 17-18 15-16 13-14 11-12 9-10 7-8 5-6 3-4 0-2 0 Number of weeks of flowering in OR-2005 60 (d) 40 30 20 10 Number of weeks of flowering in CA-2005 105 17-18 15-16 13-14 11-12 9-10 7-8 5-6 3-4 0 0-2 Frequency 50 Figure 3.2 (cont’d) 60 Frequency 50 (e) 40 30 20 10 9-10 7-8 5-6 3-4 0-2 0 Number of weeks of flowering in MI-2011 60 (f) 40 30 20 10 Number of weeks of flowering in OR-2011 106 13-14 11-12 9-10 7-8 5-6 3-4 0 0-2 Frequency 50 QTL identification with 2011 data from MI and OR Field observations on the remontant/non-remontant flowering habit of the ‘Honeoye’ × ‘Tribute’ population planted in 2010 was used to further validate the QTL from MI and OR. Half of the progeny in both MI and OR in 2011 were remontant. This was unexpected based on observations by Weebadde et al. (2008) that 80% of the progeny in OR were remontant in 2005 and the 2011 air temperature in late summer in OR was 3°C cooler than in 2005. However, it was an unusually late growing season in OR in 2011 and it is possible that some very late flowering remontants were missed. The significant QTL regions are listed in Appendix 3.6, and shown in Figure 3.1 for comparison with other QTL. Significant QTL regions associated with remontancy in MI (2011) were identified on 11 linkage groups (Appendix 3.6). Variances of these regions ranged from 39.7 to 51.3%. Significant QTL regions associated with remontancy in OR (2011) were identified on 13 linkage groups (Appendix 3.6) with variances from 45.4 to 69.7%. QTL for remontancy in MI in 2011 were identified in groups HT1, HT3, HT5, HT6, HT7, HT10, HT22, HT30, and HT34 (Figure 3.1; Appendix 3.6). Among these, the regions on HT3 overlapped with the QTL for MI 2006, the region on HT5 overlapped with the QTL identified for MI 2005, and the QTL for all three years overlapped on groups HT7 and HT34. QTL for remontancy in OR (2011) were identified in HT1, HT2, HT3, HT4, HT6, HT7, HT9, HT10, HT16, HT18, HT22, HT30, HT34. The regions in groups HT1, HT5, HT6, HT7, HT9, HT22, HT30, and HT34 had QTL for both years of study (2005, 2011) indicating that these QTL are among the most robust. The MI (2011) QTL in group HT1 overlapped with QTL from the western states. Some regions were identified that were specific for a particular location and year 107 such as MI (2011) on HT5 and OR (2011) on HT18. MI (2011) QTL on group HT5, HT6, HT7, and HT34 overlapped with the QTL identified using 2005 and 2006 data, as well as QTL from all 5 states, suggesting these QTL are also very robust. Weeks of flowering QTL for MI 2011 were identified on groups HT1, HT2, HT5, HT6, HT7, HT9, HT10, HT20, HT22, HT29, and HT34 with variances ranging from 15.8-59.6% (Figure 3.2, Appendix 3.7). Weeks of flowering QTL for OR 2011 was identified in groups HT1, HT2, HT3, HT4, HT5, HT7, HT9, HT15, HT16, HT19, HT22, and HT34) (Figure 3.1; Appendix 3.7). As with the remontancy QTL, weeks of flowering QTL for OR were identified in many more groups with 2011 data than with 2005 data. Weeks of flowering QTL for OR in 2005 were identified in only 2 groups and both of these overlapped with the 2011 QTL indicating that these are stable regions. Weeks of flowering QTL in MI for 2006 and 2011 collocated on groups HT5, HT6, HT7, and HT9. QTL associated with heat tolerant floral response in a controlled greenhous e study Significant QTL regions were associated with flower numbers at 17°C, 20°C, and 23°C (Figure 3.1; Appendix 3.8). Ten linkage groups had QTL associated with flower numbers at 17°C (Figure 3.1, Appendix 3.8). Some of these QTL regions (example on group HT2) were spread out over a wide range (Figure 3.1), likely due to inadequate marker coverage. The variance explained by the QTL ranged from 14.5% to 47.3%, indicating that both major and minor QTL were present. QTL associated with flower number at 20°C were identified in 9 linkage groups and their variance ranged from 32-44% (Appendix 3.8). QTL for flower number at 17°C and 20°C collocated on three linkage groups (HT1, HT2, and HT16). These regions are probably associated with the regulation of flower formation at lower temperatures. 108 QTL associated with flower number at 23°C were identified on 8 linkage groups with variances from 12.2 to 50.3% (Figure 3.1; Appendix 3.8). QTL for flower number at all three temperatures collocated on one linkage group (HT30 and HT34). These regions are probably associated with the general process of flower formation, instead of ambient temperature perception. QTL for flowering at 17°C and 23°C collocate on two groups (HT19 and HT20). QTL for flower numbers at 17°C and 20°C (low temperature QTL) collocated with remontancy QTL from MI, OR, and CA in group HT1, with MI, OR, MN in group HT2, with all remontancy QTL in group HT5, HT7, with MI, OR, MD, and MN in group 16. All three temperature QTL overlapped with QTL from MI, OR, and CA in group HT30, and with QTL from all locations in group HT34. Interestingly there were two regions (HT19 and HT20) where QTL for flower numbers at 17°C and 23°C overlapped. In addition, all heat tolerant progeny had higher flower numbers at 17°C. It is possible that QTL in these regions were related to the increased flower numbers in the heat tolerant progeny. Weeks of flowering QTL for MI, OR, and CA collocated with the heat tolerance QTL on group HT1, HT2, HT4, HT7, HT19, HT20, HT22, HT30, and HT34 (Figure 3.1), suggesting that heat tolerance most likely plays an important role in determining whether a genotype continues flowering over several weeks. 3.3.3 Phenotypic distribution of markers associated with heat tole rance/sensitivity The phenotypes of the individuals carrying those alleles that were present in the QTL regions associated with flowering at high temperature (23 °C) were compared to examine whether these markers can be exploited for marker-assisted selection for temperature tolerant genotypes. Means of total number of flowers at 17°C, 20°C, and 23°C in progeny with and without the allele 109 were compared (Figure 3.3 and Table 3.3). The marker profiles of the parents along with their phenotypes are listed in Table 3.4. Only those alleles that resulted in higher flower numbers are shown in Table 3.3 and Figure 3.3. Three alleles in group HT1 (ARSFL8_301, ChFaM098_225, ChFaM040_315), and one allele each in group HT7 (EMFn117_157), and in group HT20 (EMFn170_208) were associated with higher flower numbers at 23 °C, although the difference was significant for only one of these, HT20 (EMFn170_208). This allele would be a good candidate for marker assisted breeding. While the effects of the other alleles (Figure 3.3) were non-significant, they remain potential candidates for marker assisted breeding as the overall levels of variance were quite high due to high numbers of non- flowering genotypes. These alleles need to be further validated using a larger panel. The alleles on groups HT1 and HT7 were associated with several of the QTL associated with remontancy and weeks of flowering. HT7 is particularly interesting because the remontancy QTL from all years and 5 states, and the weeks of flowering QTL from 3 states collocate with the heat tolerance QTL on this group. The QTL associated with the allele EMFn170_208 partially overlapped with the total flowers at 17 °C allele, but did not collocate with any of the other remontancy QTL. However, this region is associated with weeks of flowering at MI and CA. 110 Table 3.3 Alleles associated with ‘Total flowers at 23°C’ QTL and the phenotypic observations associated with them. Presence and absence of the allele are indicated by ‘p’ and ‘a’. Mean flower numbers at 17°C, 20°C, 23°C in the progeny with and without the allele are listed. Allele ARSFL8_301-a (28) Group ChFaM098_225-a (31) ChFaM098_225-p (23) ChFaM040_315-a (31) HT1 ChFaM040_315-p (23) 104.88 109.54 83.48 82.24 85.7 115.62 101.62 76.42 85.65 111.91 HT1 88.35 101.24 ARSFL8_301-p (26) 23°C 88.63 100.95 HT1 17°C 110.7 Temperature 20°C 80.79 95.22 111.03 EMFn117_157-a (33) EMFn117_157-p (21) HT7 97.67 119.1 75.41 98.6 88.72 108.62 EMFn170_208-p (35) HT20 120.4 96.3 115.8 79.6 62.5 60.8 EMFn170_208-a (19) 111 Table 3.4 Genotype of the parents and associated phenotypic observations for the alleles ARSFL8_301, ChFaM098_225, ChFaM040_315, EMFn117_157, and EMFn170_208. Total flower numbers are the mean of three replicates. Allele ‘Honeoye’ ‘Tribute’ HT1 ARSFL8_301 H A HT1 ChFaM098_225 H A HT1 ChFaM040_315 H A HT7 EMFn117_157 A H HT20 EMFn170_208 A H Total flowers-17°C 104.67 108.67 Total flowers-20°C 58.33 45.67 Total flowers-23°C 11.33 48.00 Group 112 Figure 3.3a-e Phenotypic distributions associated with presence of the alleles located in regions with significant QTL for flower formation at 23 °C. Blue bar represents presence of the allele; brown bar represents absence of the allele. (a) Phenotype associated with ARSFL8_301, (b) phenotype associated with ChFaM098_225, (c) phenotype associated with ChFaM040_315, (d) phenotype associated with EMFn117_157, and (e) phenotype associated with EMFn170_208 140 (a) ARSFL8_301-P 120 ARSFL8_301-A 100 80 60 40 20 0 17C 20C 23C 160 140 (b) ChFaM098_225-P ChFaM098_225-A 120 100 80 60 40 20 0 17C 20C 113 23C Figure 3.3 (cont’d) 160 140 (c) ChFaM040_315-P ChFaM040_315-A 120 100 80 60 40 20 0 17C 160 140 (d) 20C 23C EMFn117_157-P EMFn117_157-A 120 100 80 60 40 20 0 17C 160 140 (e) 20C 23C EMFn170_208-P EMFn170_208-A 120 100 80 60 40 20 0 17C 20C 114 23C 3.3.4 Overall conclusions In many instances, remontancy QTL from several datasets co- located on the linkage groups, indicating they are quite robust. Many of the remontancy QTL also co-located with heat tolerance QTL. The QTL region on HT7 is perhaps the most interesting region, because remontancy QTL from all 5 states overlapped in this region, as well as the QTL identified using multiple years’ data from MI and OR. In addition, QTL for weeks of flowering also co- located on HT7. This region is also associated with heat tolerant floral response (QTL for flowers at 23°C). A comparison of the mean flower numbers at 23 °C associated with presence and absence of this allele (EMFn117_157) suggests that this allele contributes to higher flower numbers. Several remontancy QTL regions from multiple data sets were spread over wide ranges with few markers (Figure 3.1) making it difficult to associate a specific marker to the trait. Adding markers to the regions of interest identified in this study will probably narrow down the QTL regions. The availability of the diploid F. vesca genome sequence (Schulaev et al., 2011) provides an excellent resource for identifying and developing markers associated with the regions of interest. Regardless, this study was able to identify QTL associated with ambient temperature perception, remontancy, and weeks of flowering using data from controlled and natural environment in multiple years and locations. At least one allele (EMFn170_208) was identified whose presence has resulted in significantly higher flower numbers at 23°C compared to 17°C. This QTL was specific to heat tolerant floral response, and also collocated with weeks of flowering QTL. could prove to be useful in marker assisted breeding of remontant cultivars. 115 It APPENDIX 116 Appendix 3.1 Table 3.5 SSR loci used for genotyping the mapping population with their source, primer sequences, and putative functions of associated ESTs. SSR ARSFL19 ARSFL2 ARSFL31 ARSFL8 ARSFL98 Developed in F. x ananassa 'Earliglow' F. x ananassa 'Earliglow' F. x ananassa F. x ananassa 'Earliglow' F. x ananassa 'Elsanta' Putative function/BLAST hit (as reported in original publication) F-primer R-primer N.S. (no significant match) GCGAAACCGAAGAA GAACAAATGC GCGGCCCAAACGGACA AGA Lewers 2005 et al., N.S. GCGAAGCGAAGCGG TGATG GCGAACGTCGAGGAGC ATTCTCAT Lewers 2005 et al., Pectate lyase B CGACCCAGCGACTA CATTG ACTTTAACCGCCACCA ACTG Lewers 2005 et al., N.S. GCGGACCCAAGATG ACCTCACCC GCGTTAGCCGAGAATG TTCTACTG Lewers 2005 et al., Metallothionen- like protein CCCCTATTCGACAAC CAATG TGGCTACCAAAGAACA CGAA Lewers 2005 et al., Reference ChFam003 F. x ananassa Iron(III)- zinc(II) purple acid phosphatase CCTCCCCAACTGATT CTTCA TGCCATGGTTGTTCCTT CAC Zorrilla-Fontanesi et al., 2011 ChFaM004 F. x ananassa Anthocyanin regulatory C1 protein CCCAGCATATACTTT GCCGTA TCCTTTCTTCATCCCCT CCT Gil-Ariza et al., 2006 ChFaM010 F. x ananassa Unknown protein TATCGCCTGCAATTC ATCTG GCTGGCTCTGTGGAGT GAGT Zorrilla-Fontanesi et al., 2011 117 Table 3.5 (cont’d) SSR Developed in Putative function/BLAST hit (as reported in original publication) ChFam011 F. x ananassa N-acetylglucosaminyl transferase ChFaM017 F. x ananassa ChFaM018 F-primer R-primer Reference TCCTCTCCTTCTTTCC CGAGATCTCCCGAGAC CTTCA TGAG Zorrilla-Fontanesi et al., 2011 N.S. CTCACTCTCTGCGAA CTTGC CAACTCACCTTGCACC GATT Zorrilla-Fontanesi et al., 2011 F. x ananassa N.S. AGCCGCATCCCTCTT TTCTA CTAGGGATTGAGGACC GACA Zorrilla-Fontanesi et al., 2011 ChFaM022 F. x ananassa L-Asparaginase GGGCCACTCCTACTT CTTCA TTGGCCTTGAGAGCTTC GAT Zorrilla-Fontanesi et al., 2011 ChFaM030 F. x ananassa Thioredoxin H CCATGAAGCAGTGA AGTCCA AGAAAATCCCGAGAGC CTTT Zorrilla-Fontanesi et al., 2011 ChFaM031 F. x ananassa Avr9/Cf-9 elicited protein 146 GCTAGCAAAGCCCT AAGCAA ACGGTGGGCACACTTA AAGA Zorrilla-Fontanesi et al., 2011 ChFaM032 F. x ananassa Hydrophobic protein LTI6A GGTCCCTGCTTCTTC TTCTTT TTCAGCCCCATTTTCCA GTA Zorrilla-Fontanesi et al., 2011 ChFaM035 F. x ananassa N.S. AACCCACTTCCACAG AGGAAAAAGAGGGCTT GTGAC GGAG Zorrilla-Fontanesi et al., 2011 118 Table 3.5 (cont’d) SSR Developed in Putative function/BLAST hit (as reported in original publication) ChFaM039 F. x ananassa Mitochondrial carrier protein GTGGTTTTTGTTGGG CAAAG AACGGCTTATCATCCTG Zorrilla-Fontanesi CAT et al., 2011 ChFaM040 F. x ananassa 60S ribosomal protein L18a AGTGGTCATCAGCA CCATCA TAACCGGGAACGGTAC TCTG Zorrilla-Fontanesi et al., 2011 ChFaM044 F. x ananassa Dynamin- like protein CGCTGAGTCGTTCTA ATTTCA TTTTGTTGACGAGCGA GATG Zorrilla-Fontanesi et al., 2011 ChFaM046 F. x ananassa Lil3 protein CCATTTCCATGGCCT TGTTT GGCCTTGTTGGGTCTGA Zorrilla-Fontanesi GAG et al., 2011 ChFaM056 F. x ananassa Nuclear acid binding protein AAAACGTCGTCGTTC AGGAT CGTACTGCTGTTGCTGC TGT ChFaM060 F. x ananassa N.S. TGAGCTACCACCAA GAACCC AATACCCTTGGTACCCC Zorrilla-Fontanesi TCG et al., 2011 ChFaM061 F. x ananassa N.S. GTGCTCAAGAAACC CTTTCG GCGCTAGCAACAGTAA GGTG Zorrilla-Fontanesi et al., 2011 ChFaM063 F. x ananassa Cysteine proteinase RD19A GACGTCTCCGATCCG CTGGCTCGCGTACGAC TTGAT TTTC Zorrilla-Fontanesi et al., 2011 F-primer 119 R-primer Reference Zorrilla-Fontanesi et al., 2011 Table 3.5 (cont’d) SSR Developed in Putative function/BLAST hit (as reported in original publication) ChFaM065 F. x ananassa Unknown protein GACCGGGAGAGATA ACAGCA ATAGAAGCCAATGCGT GATG Zorrilla-Fontanesi et al., 2011 ChFaM067 F. x ananassa N.S. AGAACCAGCAAGAG CAGCAC CAGCTCTGTGTATGCCT GGA Zorrilla-Fontanesi et al., 2011 ChFaM072 F. x ananassa N.S. TGGCAGAAATTTCCA CTCCCCCAGAAGTCCA AAAGG GATT ChFaM076 F. x ananassa TINY transcription factor GCCTCCATGGAAAA CCTAAA CTCGGCAGCTCCACTAT Zorrilla-Fontanesi CTC et al., 2011 ChFaM077 F. x ananassa Plasma Membrane H+ ATPase GAAAGGGCTGGACA TGGATA ATGTTGTTATTTGGCCT GCT Zorrilla-Fontanesi et al., 2011 ChFaM078 F. x ananassa N.S. CAGCCTCATTGCAAA CTTACCGGTTTCGATGT TCTGA GGT Zorrilla-Fontanesi et al., 2011 ChFaM080 F. x ananassa Unknown protein TTCGGTGCCGGTAAA AAGTTCCACCACCATG GATAC CAAT Zorrilla-Fontanesi et al., 2011 ChFaM081 F. x ananassa Prenylcystein oxidase AACTGAGCTCTCGGC GAATACTCGCGGAGGA AAGTC AGTG Zorrilla-Fontanesi et al., 2011 F-primer R-primer Reference 120 Zorrilla-Fontanesi et al., 2011 Table 3.5 (cont’d) SSR Developed in Putative function/BLAST hit (as reported in original publication) ChFaM085 F. x ananassa Purple acid phosphatase AGATGGGTCATTTTC TGACGA GTAGTGCATGTCCGCC ACTT Zorrilla-Fontanesi et al., 2011 ChFaM086 F. x ananassa N.S. TTTGGAGCTCAATCC CATCTG ATTTGGCCAGCCTCCGT CT Zorrilla-Fontanesi et al., 2011 ChFaM088 F. x ananassa Unknown protein GGTGGCAAAACTCA TGGAGA GGGAAGCGAAGTTGAA GAGG Zorrilla-Fontanesi et al., 2011 ChFaM089 F. x ananassa 4-coumarate-CoA ligase GAAGGATGGTTGCA CACAGG GAGAGGTTGGGATGGG AGAT Zorrilla-Fontanesi et al., 2011 ChFaM092 F. x ananassa N.S. ACCCAAGTTCCCTTC GACTC ATGCGCTTTGCATAAC AGGT Zorrilla-Fontanesi et al., 2011 ChFaM093 F. x ananassa N.S. CGCCCTCAAATCCCT CTAAC GAAGTGAGTGTTCCGC TGCT Zorrilla-Fontanesi et al., 2011 ChFaM094 F. x ananassa tRNA isopentenylpyrophosph atase ATGGAGGGCGCTAC TGAAAA AATGGCGAGCTTGGAC TTTC Zorrilla-Fontanesi et al., 2011 ChFaM095 F. x ananassa Signal peptidase protein GCCAGAAGCAAAAA CCAGAA GGGAAGTTGAAATTGT CGGA Zorrilla-Fontanesi et al., 2011 F-primer 121 R-primer Reference Table 3.5 (cont’d) SSR Developed in Putative function/BLAST hit (as reported in original publication) ChFaM098 F. x ananassa Unknown protein GTGAGAGTCAGCCC ACCCTA GCGACGAGGATGAAGA AGAG Zorrilla-Fontanesi et al., 2011 ChFaM101 F. x ananassa 3-Methylcrotonyl-CoA Carboxilase GGAGTAAGCTGATC ACTCTGT ACTCCGAGGCTGTAAT CCCT Zorrilla-Fontanesi et al., 2011 ChFaM103 F. x ananassa Delta-7-sterol-C5(6)desaturase CATCTCTTCTCCTTT CCGATCT GAGCACAATGCGGTTG TAGA Zorrilla-Fontanesi et al., 2011 ChFaM104 F. x ananassa N.S. CAGTCATTTTTGGCT TCACC TTGGTCTGTTCCTTTCC TTG Zorrilla-Fontanesi et al., 2011 ChFaM107 F. x ananassa N.S. TGCCAAACAAACAA ATGTTGA CATATCGATGTCCTTCA TAGGG Zorrilla-Fontanesi et al., 2011 ChFam111 F. x ananassa N.S. GCCCAACCGAGTCTC CGGGCTTCAATTTGCTC TCTCT AAT Zorrilla-Fontanesi et al., 2011 ChFaM129 F. x ananassa N.S. AGATCAACATCGCCT TGCTCGTTGTCCATAAC CCAAC CTG Zorrilla-Fontanesi et al., 2011 ChFaM147 F. x ananassa NADH dehydrogenase ACGAGGGTCACCTG AGACTG Zorrilla-Fontanesi et al., 2011 F-primer R-primer Reference 122 CCAGGAGAAGGTACCG AAGG Table 3.5 (cont’d) SSR ChFaM148 ChFaM151 Developed in F. x ananassa F. x ananassa ChFaM161 F. x ananassa EMFn115 F. nubicola EMFn117 F. nubicola EMFn121 F. nubicola EMFn128 F. nubicola EMFn134 F. nubicola EMFn148 F. nubicola EMFn152 F. nubicola EMFn153 F. nubicola EMFn160 F. nubicola Putative function/BLAST hit (as reported in original publication) GalUR Unknown protein NAD-dependent glucuronic acid epimerase F-primer R-primer Reference CCCTCCATCAAAGCC AGTT ACCACCACCGTTTTC TCCTC CATTAGACCCCGACTT GTCA ACCACCGACTCGTCCTT CTT Zorrilla-Fontanesi et al., 2011 Zorrilla-Fontanesi et al., 2011 CGAGGCCTTGTCTTC TTTGT GCGGAGGTAGCTGTTG TAGC Zorrilla-Fontanesi et al., 2011 TGGAGATGATGGTC AAGACG ATCGGATCAACAAG CAAAGC GGTCCCTAAGTCCAT CATGC CATCAACATTCACAT GAATTTACC TGATTCTTTGAAAGG CTTTGG TTACCTGCACAGAA ACAACG GGGCCAAAATGAGT ATCTTGC CTCGAGCTCCCTTTC TATCG GCATCCTTGGGAAAT TAATGC GACAAGACCACGAAAA CACG ATGGATGAGGGGAGAA GAGG GAGTGGATGCAAACAT GAGC CGGCGGATCTAGTTTTG AGG AAAACAACCCCCTCTC ATCC CAACTTCCTCCTCACTC ACC TTAGAGCGAGGTGGTA ATGC TGGCCAAATGTTCTCAC TAGC TTGGGAAGGATCATAA AAACC Sargent 2006 Sargent 2006 Sargent 2006 Sargent 2006 Sargent 2006 Sargent 2006 Sargent 2006 Sargent 2006 Sargent 2006 123 et al., et al., et al., et al., et al., et al., et al., et al., et al., Table 3.5 (cont’d) SSR Developed in EMFn170 F. nubicola EMFn181 F. nubicola EMFn184 F. nubicola EMFn198 F. nubicola EMFn201 F. nubicola EMFn202 F. nubicola EMFn213 F. nubicola EMFn225 F. nubicola EMFn230 F. nubicola EMFn235 F. nubicola EMFvi104 F. viridis EMFvi136 F. viridis Putative function/BLAST hit (as reported in original publication) F-primer R-primer CAGTTTGCCCAACAA CAAGG CCAAATTCAAATTCC TCTTTCC GATGAGAATTGTTTG AGTGAAGG CCAAATTGTCCTTGA TGTCG CAGCTCAGAAAAGC TCACAGC CTCTCTCCCTCAACC TCTCG AGCGTGATTTTGCCT TTGTT AAGGAAAAATGCTC AAATACCC AATGACTACGACAA CGACAGTCT AGGAACAAGAGCTG GCAATG TGGAAACATTCTTAC ATAGCCAAA GAGCCTGCTACGCTT TTCTATG TTGATGGCAACAAATC ACG GCCGAAAAACTCAAAC TACCC TGACCAGCGGATTCAT AAGG CACCTGCTTCAAAGCA AACC TAGAACGCCAATCACA AACC TGGACCAATATCTCCCT TGC CACAGTAAAGAACAGG AGGGAGAT TACGTGCGACGTTAGA GTCC AGGGAAAATGCCCAAA TACC CTCAAGTATCAGGCCT CCAAG CAGACGAGTCCTTCAT GTGC CCTCTGATTCGATGATT TGCT 124 Reference Sargent 2006 Sargent 2006 Sargent 2006 Sargent 2006 Sargent 2006 Sargent 2006 Sargent 2006 Sargent 2006 Sargent 2006 Sargent 2006 Govan (2008) Sargent 2003 et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al. et al., Table 3.5 (cont’d) SSR Developed in Putative function/BLAST hit (as reported in original publication) FAC001 F. ananassa Cinnamyl alchohol dehydrogenase AAATCCTGTTCTTGC CAGTG TGGTGACGTATTGGGT GATG Lewers 2005 et al., FAC002 Chandler FaEG3 gene for end beta 1,4 glucanase TCATCCTCTTTCACC TCCACTT TCAAAAGACTTGGAAA TGTTGC Lewers 2005 et al., FAC006 F. ananassa Pectate Lyase ACTGGTGGAGGAGA GGACTGTA TGTGGAGCAGAGAGAA TTGAAG Lewers 2005 et al., GCAAAGTCGGAGAG AGATAGA CTGAAGAAGGTGTTGA GGAA Njuguna 2010 TCTCTTTCTCTTCTCT CACTCTC AAACATTCAACCAAAC AAA Njuguna 2010 CTTTTGCTGCTAGCT CTTTGTG TACGTACTCCACATCCC ATTTG Njuguna 2010 GAAGGAGCATAGAG TTGTGGAGA TGATCTCACTCTCGGTT TCAGA Njuguna 2010 SF4B12 SF5C08 SF5G02 SF6E02 F. x ananassa 'Strawberry Festival' F. x ananassa 'Strawberry Festival' F. x ananassa 'Strawberry Festival' F. x ananassa 'Strawberry Festival' F-primer 125 R-primer Reference Table 3.5 (cont’d) SSR Developed in SFGRP7 F. x ananassa 'Strawberry Festival' UaFv8150 F. vesca 'Yellow Wonder' UaFv8216 F. vesca 'Yellow Wonder' UaFv8936 F. vesca 'Yellow Wonder' Putative function/BLAST hit (as reported in original publication) F-primer R-primer Reference ATCTAGACGGCCGT AACATCAC :CCACTTCCATAGCTAC CACCTC Njuguna 2010 CCACCTCTCTCTCCA TTTCC AGCGGTGTGAAGACTT GAGG Bassil 2006b et al., GGTAATGCAGCACC AAATGA GGAAGCGAAGCAGTTA TGGA Bassil 2006b et al., A. thaliana expressed protein (gi:18378999) GTGACTTTGACGCTG ACC TGAGAGTGGTTCTGTTC CTC Bassil 2006b et al., UaFv9092 F. vesca 'Yellow Wonder' A. thaliana similar to dihydroflavonol reductase mRNA (gi|14596184) ACCACAATCCTCCGC CATT AGTCGTGCTTGATGTTG AG Bassil 2006b et al., UaFv9404 F. vesca 'Yellow Wonder' A. thaliana PHD finger protein — like AGTCGTGCATCATGG CATTAGTTGGCCACAC ATCAG ACCA Bassil 2006b et al., UDF-004 F. vesca GCTTGCATTTCAATA GCTGGA Cipriani and Testolin (2004) A. thaliana metallobeta-lactamase family protein mRNA (gi|42568732) Nicotiana tabacum mRNA for nucleic acid binding protein (nbp1 gene) (gi|15594034) 126 TTTACTGATGCAGGAG TAGAATGA Table 3.5 (cont’d) SSR Uffa01E03 Uffa01H05 Uffa02F02 Uffa03B05 Uffa04G04 Uffa11A11 Developed in F. x ananassa 'Strawberry Festival' F. x ananassa 'Strawberry Festival' F. x ananassa 'Strawberry Festival' F. x ananassa 'Strawberry Festival' F. x ananassa 'Strawberry Festival' F. x ananassa 'Strawberry Festival' Putative function/BLAST hit (as reported in original publication) F-primer R-primer Reference DegP protease ACCCCATCTTCTTCA AATCTCA GACAAGGCCAGAGCTA GAGAAG Bassil et al., 2006; Sargent et al., 2006 Similarity to pollen GGGAGCTTGCTAGCT AGATCCAAGTGTGGAA major allergen 2 protein AGATTTG GATGCT (Juniperus ashei) Bassil et al., 2006; Sargent et al., 2006 Similar to histone H13,(Lycopersicon pennellii) CTTTGCAGCTGAAGA CAGCAGCTGCCTTAGT ACTCTGA CTTAGT Bassil et al., 2006; Sargent et al., 2006 Response regulator 7 (ARR7) GGAATCCAAGTTAC AGGCTTCA AAGGAGCCTCTCCAAT AGCTTC Bassil et al., 2006; Sargent et al., 2006 Nucleotide sugar epimerase ACGAGGCCTTGTCTT CTTTGTA GCTCCAGCTTTATTGTC TTGCT Bassil et al., 2006; Sargent et al., 2006 EST-SSR- function not reported ACGAGGCTCCAATA GAGTTCTG CTGAGCAGAAGCCATA GTATCAC Bassil et al., 2006 127 Table 3.5 (cont’d) SSR Uffa14A11 Uffa16H07 Uffa18H04 Uffa20D02 Uffa20H10 Developed in F. x ananassa 'Strawberry Festival' F. x ananassa 'Strawberry Festival' F. x ananassa 'Strawberry Festival' F. x ananassa 'Strawberry Festival' F. x ananassa 'Strawberry Festival' Putative function/BLAST hit (as reported in original publication) F-primer R-primer Reference EST-SSR- function not reported ATGAAAGAAGTAGC CACTGAGC TACGAGAGATACTAGG CGTGCTA Bassil et al., 2006 EST-SSR- function not reported CTCTACCACCATTCA AAACCTC CACTGGAGACATCTAG CTCAAAC Bassil et al., 2006; Sargent et al., 2006 EST-SSR- function not reported CCTTCGTTACTCTAG TAGCTCCA GTGATGAAGACGATGA TGAGGT Bassil et al., 2006 EST-SSR- function not reported CTCCATCTCCACAAA TCCTCTC GGCTAGAGTGCATGAG ATGTAGT Bassil et al., 2006 EST-SSR- function not reported GATGTGCTAGGACTC TAAAAGACGAGGCCAT ATACTTGG CTGA Bassil et al., 2006 128 Appendix 3.2 Table 3.6 Segregation type and Chi square (X2) values of the markers in the ‘Honeoye’ × ‘Tribute’ SSR map. Segregation type : codominant marker segregating in ‘Honeoye’, : codominant marker segregating in ‘Tribute’, and : dominant marker present in both parents. Locus ARSFL19_295 ARSFL2_200-248 ARSFL2_203 ARSFL31_178-225 ARSFL8_301 ARSFL98_205 ChaM093_725 ChFaM003_400 ChFaM003_495 ChFaM004_132 ChFaM004_230 ChFaM010_290 ChFam011_117 ChFam011_129 ChFaM017_147 ChFaM017_211 ChFaM017_250 ChFaM017_525 ChFaM017_86 ChFaM017_92 ChFaM018_450 ChFaM018_460 ChFaM022_305-225 ChFaM030_205 ChFaM030_210 ChFaM030_230 ChFaM030_500 ChFaM031_138-190 ChFam032_205 ChFam032_210 Markers with segregation distortion are labeled with *. Segregation type 129 X2 2.1 1.7 0.1 0.5 0.3 0.1 4.9 3.8 18.4 0.3 3.7 1.3 2.7 11.7 1.7 2.8 2.6 22.4 8.5 3.1 6 3.1 3.1 1.8 8 7.6 2 0.1 0 2.8 Segregation Distortion * * * * * * * * * * * * * * * * Table 3.6 (cont’d) Locus ChFaM035_245 ChFam039_196 ChFam039_205 ChFaM040_100 ChFaM040_143 ChFaM040_155 ChFaM040_300 ChFaM040_315 ChFaM040_95 ChFaM044_129 ChFaM044_135 ChFaM044_240 ChFaM046_128 ChFaM046_135 ChFaM046_152 ChFaM046_157 ChFaM056_112 ChFaM056_130-220 ChFaM060_110 ChFaM060_140 ChFaM060_200-230 ChFaM061_220 ChFaM061_225 ChFaM063_106 ChFaM063_111 ChFaM063_131 ChFaM063_141-107 ChFaM063_90 ChFaM065_160 CHFaM067_161 CHFaM067_176 ChFaM072_350 ChFaM072_385 ChFaM076_101 ChFaM076_103 ChFaM076_139 ChFaM076_140 Segregation type 130 X2 0 3.3 10.6 7.9 13.7 22.4 113.8 12.8 0.3 0.1 8.2 0.7 13.1 1.4 0.4 0 1.3 0.5 0.1 12.2 0.6 0.8 2.6 0 1.5 7.9 0 0 1.1 0 0.4 0.1 3 2.1 2.4 0 2.6 Segregation Distortion * * * * * * * * * * * * - Table 3.6 (cont’d) Locus ChFaM077_220 ChFaM078_140 ChFaM080_219 ChFaM080_220 ChFaM081_110 ChFaM081_152-300 ChFaM085_155 ChFaM086_230 ChFaM086_360 ChFaM086_440-225-198 ChFaM088_106 ChFaM088_300 ChFaM089_390 ChFaM092_140 ChFaM093_200 ChFaM093_240 ChFaM093_750 ChFaM094_120 ChFaM095_135 ChFaM095_150 ChFaM095_170 ChFaM095_320 ChFaM095_400 ChFaM098_108 ChFaM098_165 ChFaM098_225 ChFaM098_226 ChFaM098_600 ChFaM101_136 ChFaM101_137 ChFaM101_139 ChFaM101_153 ChFaM101_180 ChFaM103_124 ChFaM103_190 ChFaM103_215 ChFaM103_450 Segregation type 131 X2 12.4 1.1 3 0.8 1.4 5.7 0 5.4 8.1 0.1 0.1 0.5 0.1 1.8 10.8 0.1 4.3 0 0 0.1 0.1 2.8 0.6 0.3 0 4 4.3 0.6 0.9 2.3 0 1.5 1.5 0.1 1.3 2.3 31.5 Segregation Distortion * * * * * * * * * * * Table 3.6 (cont’d) Locus ChFaM104_196 ChFaM104_197 ChFaM104_209 ChFaM104_220 ChFaM107_192 ChFaM111_150 ChFaM111_176 ChFaM111_189 ChFaM111_195 ChFaM111_210 ChFaM111_400 ChFaM111_650 ChFaM111_675 ChFaM129_189 ChFaM129_500-190 ChFaM147_210 ChFaM147_219 ChFaM147_275 ChFaM148_156 ChFaM148_161 ChFaM148_162 ChFaM151_210 ChFaM151_218 ChFaM151_223 ChFaM151_228 ChFaM151_325 ChFaM151_375 ChFaM151_475 ChFaM151_500 ChFaM161_190 ChFvM049_153 EMFn115-133 EMFn115-140 EMFn115-163 EMFn117_129 EMFn117_155-127 EMFn117_157 Segregation type 132 X2 38.3 5 1.1 3.8 1.3 2.9 4.2 1.1 0.3 0.7 0.3 1.1 1.6 2.4 0.1 14.7 17.8 5.2 11.4 43.5 2.4 6.3 6.4 0 2 12.3 56.2 19.9 16 1.9 9 0.1 0 0.5 1.9 1.7 0.8 Segregation Distortion * * * * * * * * * * * * * * * * * - Table 3.6 (cont’d) Locus EMFn117_188 EMFn121_243 EMFn121_249 EMFn123_154 EMFn123_163 EMFn123_200 EMFn128_146 EMFn128_155 EMFn128_157 EMFn128_162-161 EMFn134_160 EMFn134_185 EMFn134_192 EMFn134_214 EMFn148_189 EMFn152_148 EMFn160_160 EMFn160_161 EMFn170_190 EMFn170_208 EMFn170_215 EMFn170_240 EMFn181_155 EMFn181_170 EMFn181_221 EMFn181_240 EMFn184_239 EMFn184_245 EMFn184_260 EMFn198_161 EMFn198_169 EMFn198_189 EMFn201_201 EMFn201_240 EMFn202_192-215 EMFn202_205 EMFn213_298 Segregation type 133 X2 0.1 0.6 0.1 19.6 29.1 0.1 0.7 8.3 2.8 0.1 0 8.8 1.1 7.9 2.4 5.3 0.2 0.1 0.1 1.7 2.9 0.1 0.2 1.6 5.1 0.5 0.4 1.3 1.2 0.1 0.4 4.6 0.4 5.6 0.7 11.8 0.1 Segregation Distortion * * * * * * * * * * * * - Table 3.6 (cont’d) Locus EMFn213_310 EMFn213_320 EMFn225_250 EMFn225_259 EMFn230_220 EMFn230_225 EMFn235_210 EMFn235_214 EMFn235_215 EMFn235_219 EMFvi104_127 EMFvi104_130 EMFvi104_80 EMFvi136_157 EMFvi136_159 FAC001_182 FAC001_211 FAC001_258 FAC002_270 FAC006_225 SF4B12_600 SF5C08_325 SF5C08_410 SF5G02_228 SF5G02_250 SF5G02_260 SF5G02_273 SF5G02_275 SF6E02_119 SF6E02_149 SFGRP7_125 SFGRP7_134-141-147 UaFv8150_130 UAFv8216_101 UAFv8216_161 UAFv8216_182-185 UAFv8216_239-107 Segregation type 134 X2 2.4 0.1 0.4 3.5 9 0.4 1.9 0 2 0.2 4.5 0.3 0 0.1 11.1 0.1 0.2 0.9 1.3 0.2 0 8.1 2.4 0.2 1.1 1.5 0.2 1.3 1.9 0 3.9 1.1 1.1 0.1 0.9 24.3 2.4 Segregation Distortion * * * * * * * - Table 3.6 (cont’d) Locus UaFv8936_320 UaFv8936_410 UaFv9092_122 UaFv9094_390 UaFv9094_495 UaFv9094_700 UDF004_130 UDF004_136 UDF004_143 UDF004_150 UFFa01E03_135 UFFa01E03_165 UFFa01E03_175 UFFA01H05_250 UFFA01H05_251 UFFa02F02_106 UFFa02F02_209 UFFa02F02_500 UFFa02F02_505 UFFa02F02_510 UFFa03B05_210 UFFa03B05_214 UFFa04G04_158 UFFa04G04_161 UFFa04G04_162 Uffa11A11_206-260 Uffa11A11_225 Uffa14A11_105 Uffa14A11_114 UFFa16H07_250 UFFa16H07_256 UFFa16H07_262 UFFa16H07_270 UFfa18H04_120 UFfa18H04_121 UFfa18H04_143 UFFa20D02_109 Segregation type 135 X2 0.3 2.7 0.7 2.5 14.9 2.2 8.3 0 0.1 0.6 58.3 1.8 1.5 21.3 1.6 0.1 0.5 3 0.9 1.6 0.4 12.4 2.8 10.4 16.5 1.9 4.4 1.2 4.9 1.8 0 0.2 1.1 1.9 0.4 0.6 0.7 Segregation Distortion * * * * * * * * * * * - Table 3.6 (cont’d) Locus UFFa20D02_97 Uffa20H10_247 Segregation type X2 0.9 0.9 Segregation Distortion - Uffa20H10_260 0.2 - Uffa20H10_273 1.6 - 136 Appendix 3.3 Figure 3.4 The male and female parent maps. Distances on linakge groups are in cM. The ‘Honeoye’ map had 103 markers in 23 linkage groups and the ‘Tribute’ map had 78 markers in 22 linkage groups. Honeoye Map: H1 H1 0.0 ARSFL8_301 0.0 H2 H2 ARSFL8_301 ChFaM103_450 0.0 0.0 ChFaM103_450 ChFaM101_137 0.0 0.0 C 0. ChFaM101_139 7.2 C ChFaM098_225 ChFaM063_90 14.9 14.9 UDF004_136 UAFv8216_182-185 UAFv8216_182-185 17.5 17.5 ChFaM063_111 19.3 19.3 ChFaM063_131 22.5 22.5 C 17. 18. C 7.2 13.3 15.5 ChFaM098_225 13.3 UDF004_136 15.5 H3 H3 C 26. 30.7 31.6 36.6 37.9 38.4 ChFaM129_500-190ChFaM129_500-190 30.7 EMFn160_161 31.0 31.0 ChFaM080_219 ChFaM080_219 31.6 EMFn170_240 EMFn170_240 36.6 ChFaM040_315 ChFaM040_315 37.9 EMFn134_160 37.8 37.8 ChFaM040_95 ChFaM040_95 38.4 EMFn160_161 EMFn134_160 42. 44.8 ARSFL98_205 44.8 ARSFL98_205 49.2 52.1 ChFaM040_300 49.2 EMFvi104_80 52.1 ChFaM040_300 UAFv8216_161 50.8 50.8 EMFvi104_80EMFn160_160 52.4 52.4 137 UAFv8216_161 EMFn160_160 Figure 3.4 (cont’d) H2 H3 H3 H4 H4 ChFaM103_450 ChFaM101_137 ChFam039_205 0.0 0.0 03_450 ChFaM101_137 ChFam039_205 0.0 0.0 7.2 ChFaM101_139 7.2 ChFaM101_139 ChFaM063_90 14.9 EMFn128_155 17.3 ChFaM063_90 14.9 UAFv8216_182-185 EMFn128_155 EMFn128_157 17.3 18.3 ChFaM063_111 19.3 16_182-185 EMFn128_157 ChFaM063_111 18.3 19.3 ChFaM063_131 22.5 ChFaM063_131 22.5 ChFam039_196 26.1 ChFam039_196 26.1 EMFn160_161 0_161 H5 H6 H7 H8 EMFn134_160 4_160 0.0 EMFn134_185 42.4 0.0 SF5C08_410 42.4 SF5C08_410 ChFaM040_143 0.0 2.6 EMFn213_298 EMFn213_320 UAFv8216_161 16_161EMFn160_160 0_160 16.2 0.0 7.9 15.3 EMFn134_192 ARSFL19_295 19.2 22.2 UAFv8216_239-107 26.1 28.2 35.2 43.7 UDF004_143 ChFaM080_220 30.1 UFFa03B05_214 138 Figure 3.4 (cont’d) H5 H6 H7 H6 EMFn134_192 16.2 4_192 15.3 ChFaM017_250 ARSFL19_295 H9 Ch Ch 19.2 19.2 Ch ChFaM017_211 H10 26.1 28.2 UDF004_143 30.1 0.0 35.2 ChFaM080_220 Ch EMFn184_260 ChFaM017_147 SFGRP7_125 EM 30.1 26.1 UDF004_143 28.2 0.0 C 25.3 C ChFaM080_220 35.2 UFFa03B05_214 43.7 B05_214 MFn213_298 MFn213_320 EMFn213_298 ChFaM017_86 EMFn213_320 ARSFL19_295 UAFv8216_239-107 22.2 16_239-107 0.0 0.0 2.6 15.3 0.0 0.0 2.6 0.0 7.9 0.0 ChFaM040_143 EMFn213_298 EMFn213_320 H8 7.9 0.0 4_185 EMFn134_185 ChFaM040_143 H7 H8 H8 0.0 ChFaM017_86 7.9 ChFaM017_250 24.1 35.1 19.2 52.4 ChFaM017_147 ChFaM103_190 EMFn184_260 30.1 ChFaM103_215 ChFaM017_211 26.1 EMFn235_219 40.6 SFL19_295 SF6E02_119 139 Figure 3.4 (cont’d) H9 H10 H11 H11 SFGRP7_125 ChFaM086_440-225-198 0.0 0.0 ChFaM086_440-225-198 ChFaM088_106 0.0 0.0 0.0 P7_125 24.1 2_119 H10 5-198 ChFaM088_106 40.6 0.0 ChFaM103_215 M103_215 6.8 EMFn134_214 23.6 23.6 ChFaM093_240 CHFaM067_161 H12 31.8 ChFaM004_132 31.8 EMFn235_219 35.1 235_219 ChFaM104_220 12.4 EMFn134_214 12.4 H11 0.0 ChFaM022_305-225 40.6 UaFv9094_390 40.6 42.2 UAFv8216_101 42.2 ChFaM104_220 CHFaM067_161 27.2 29.4 31.8 ChFaM046_135 ChFaM046_157 47.2 SF5G02_250 ChFaM004_132 40.6 42.2 CHFaM067_161 27.2 ChFaM046_135 27.2 29.4 ChFaM046_157 29.4 ChFaM004_132 UaFv9094_390 UAFv8216_101 140 Ch Ch UaFv9094_390 UAFv8216_101 47.2 ChFaM103_190 52.4 EMFn134_214 M103_19012.4 23.6 ChFaM088_106 Ch 0.0 ChFaM022_305-225 0.0 6.8 ChFaM104_220 6.8 SF6E02_119 25.3 ChFaM093_240 25.3 H12 H12 47.2 SF5G02_250 SF Figure 3.4 (cont’d) EMFn230_220 0.0 ChFaM095_170 11.7 ChFaM095_170 11.7 H14 H14 H15 H1 H15 EMFn152_148 0.0 EMFn230_220 0.0 EMFn152_148 1.0 0.0 FAC001_211 0.0 ChFaM095_400 0.0 FAC001_211 1.0 0.0 16.8 12.9 12.9 ChFaM095_320 13.4 EMFn230_225 15.9 16.8 30.6 22.2 ChFam032_210 30.5 H15 H14 12.9 0.0 1.0 30.5 EMFn123_200 UFFa16H07_262 37.6 UFFa16H07_262 H16 ChFaM095_400 0.0 57.1 ChFaM095_320 13.4 EMFn152_148 15.9 0.0 FAC001_211 ChFaM061_220 EMFn225_250 57.1 ChFaM061_220 H15 H16 UFFa04G04_158 ChFaM161_190 0.0 ChFaM095_400 EMFn225_250 m032_210 _170 m032_205 30.5 16.8 EMFn123_200 EMFn230_225 16H07_262 22.2 ChFam032_210 28.0 ChFam032_205 12.9 C ChFam032_205 37.6 0.0 C ChFam032_210 28.0 ChFam032_205 28.0 EMFn117_155-127 30.6 EMFn117_155-127 EMFn230_225 22.2 152_148 01_211 220 230_225 H13 H13 ChFaM095_320 141 13.4 15.9 UFFa04G04_158 ChFaM161_190 E aM151_475 Figure 3.4 (cont’d) H17 H17 H17 H18 H18 H18 0.0 0.0 0.0 SF5C08_325 SF5C08_325 SF5C08_325 5.8 5.8 5.8 EMFn181_170 EMFn181_170 EMFn181_170 H19 H19 H19 ChFaM151_475 0.0 ChFaM151_475 0.0 0.0 ChFaM151_475 EM E EM 5.7 5.7 5.7 C C Ch 11.0 11.0 11.0 0.0 0.0 0.0 3.9 3.9 3.9 ChFaM076_139 0.0 ChFaM076_139 0.0 0.0 ChFaM076_139 Uffa20H10_247 Uffa20H10_247 Uffa20H10_247 H20 H20 H20 C C Ch 15.3 15.3 15.3 UFFa20D02_109 UFFa20D02_109 UFFa20D02_109 Uffa20H10_260 Uffa20H10_260 Uffa20H10_260 26.2 26.2 26.2 17.5 17.5 17.5 ChFaM151_223 ChFaM151_223 ChFaM151_223 H21 H22 H20 0.0 0.0 ChFaM111_176 UFFA01H05_2 ChFaM111_150 11.0 0.0 EMFvi104_127 5.7 ChFaM151_218 12.8 0H10_260 22.0 aM151_223 142 ChFaM076_103 FAC006_225 Figure 3.4 (cont’d) H21 0.0 FaM151_218 22.0 FaM076_103 H22 H22 ChFaM151_218 0.0 UFFA01H05_251 0.0 H23 H23 12.8 FAC006_225 ChFaM076_103 0.0 SF4B12 0.0 0.0 ChFaM030_205 ChFa 16.9 12.8 UFFA01H05_251 SF4B12 0.0 H24 H24 16.9 CHFaM067_176 CHFa FAC006_225 22.2 22.2 ARSFL2_200-248 143 ARSFL2_200-248 103_450 Figure 3.4 (cont’d) Tribute Map: T1 T1 T2 T2 0.0 0.0 EMFn198_169 EMFn198_169 0.0 ChFaM103_450 0.0 5.7 EMFn117_129 EMFn117_129 5.7 T3 17.4 13.3 EMFn117_188 17.4 EMFn117_157 27.5 27.5 ChFaM095_150 32.9 32.9 EMFvi104_130 13.3 ChFaM 0.0 7.3 EMFn121_243 8.8 ChFaM101_137 EMFn117_188 13.9 ChFaM101_136 13.9 EMFn117_157 UAFv8216_182-185 17.5 UAFv8216_182-185 17.5 21.9 ChFaM101_153 21.9 22.6 UAFv8216_161 23.6 ChFaM063_90 22.6 UAFv8216_161 23.6 ChFaM095_150 EMFn1 ChFaM 7.3 8.8 32.3 EMFvi104_130 ChFaM088_300 32.3 44.4 50.8 0.0 ChFaM063_106 7.3 8.8 37.8 EMFn134_160 ChFaM101_153 ChFaM063_90 EMFn134_160 44.4 EMFn160_161 EMFn160_161 50.8 EMFn160_160 EMFn160_160 ChFaM101_136 21.9 22.6 T4 0.0 EMFn235_215 16_182-185 16_161 088_300 34_160 20.0 ChFaM101_180 144 32.9 35.3 ChFaM ChFaM 20.0 ChFaM ChFaM088_300 EMFn121_243 ChFaM101_137 13.9 T ChFaM103_450 0.0 ChFaM063_106 0.0 37.8 T3 T3 SFGRP7_134-141-147 Uffa11A11_206-260 32.9 35.3 44.0 48.4 FaM063_106 Figure 3.4 (cont’d) T4 0.0 T5 EMFn235_215 0.0 ChFaM111_195 0.0 EM 13.0 Ch 24.3 Ch 29.2 20.0 ChFaM111_210 11.3 Ch 50.4 Ch 57.3 FaM101_136 ChFaM078_140 7.5 MFn121_243 FaM101_137 FaM101_153 FaM063_90 T6 UD ChFaM101_180 32.9 35.3 SFGRP7_134-141-147 Uffa11A11_206-260 44.0 EMFn121_249 48.4 ChFaM063_141-107 41.4 145 UaFv8936_410 FaM078_140 FaM111_210 FaM111_195 Fv8936_410 M078_140 Figure 3.4 (cont’d) T5 T6 T6 T7 0.0 0.0 ChFaM078_140 EMFn170_215 7.5 ChFaM111_210 11.3 13.0 6.7 ChFaM111_195 13.0 ChFaM056_112 15.9 24.3 ChFaM040_100 29.2 ChFaM040_143 24.3 25.4 29.2 32.0 41.4 EMFn170_215 0.0 ChFaM003_495 0.0 UFFa16H07_270 50.4 T6 ChFaM080_220 57.3 UDF004_143 13.0 ChFaM003_495 EMFn184_260 0.0 E 12.3 EMFn184_245 ChFaM003_400 16.2 ChFaM017_92 17.8 ChFaM017_86 E 24.7 S UFFa16H07_270 6.7 ChFaM056_112 12.3 15.9 ChFaM003_400 16.2 17.8 ChFaM040_100 25.4 24.7 ChFaM076_101 ChFaM040_143 32.0 ChFaM151_228 57.3 T7 ChFaM080_220 ChFaM076_101 SF5G02_228 ChFaM151_228 T8 UDF004_143 0.0 ChFaM003_495 6.7 0.0 EMFn170_215 50.4 UFFa16H07_270 0.0 EMFn184_260 12.3 ChFaM056_112 EMFn184_245 15.9 24.3 ChFaM040_100 29.2 ChFaM003_400 16.2 17.8 ChFaM017_92 ChFaM017_86 25.4 ChFaM076_101 24.7 SF5G02_228 32.0 146 ChFaM151_228 ChFaM040_143 T8 T8 UaFv8936_410 M111_210 M111_195 0.0 0.0 T7 C C a18H04_121 a18H04_143 aM104_220 aM030_500 Figure 3.4 (cont’d) T9 T10 T9 0.0 ChFaM072_350 0.0 6.3 ChFaM072_385 6.3 T10 UFfa18H04_121 ChFaM072_350 0.0 0.0 UFfa18H04_143 2.2 2.2 T11 T11 UFfa18H04_121 0.0 ChFaM104_220 0.0 UFfa18H04_143 ChFaM104 Ch 0.0 ChFaM030_500 8.8 Ch 7.3 ChFaM030 ChFaM072_385 8.8 T13 UFFa16H07_256 24.9 24.9 0.0 T12 ChFaM104_220 8.8 ChFaM030_500 0.0 7.3 ChFaM104_197 22.5 32.5 ChFaM004_230 7.3 ChFaM018_450 0.0 ChFam 21.0 FAC001 ChFaM004_230 T12 0.0 T14 UFFa16H07_256 T11 0.0 T12 ChFaM104_197 147 ChFaM018_460 ChFaM031_138-190 Figure 3.4 (cont’d) T14 T13 18_450 0.0 18_460 22.5 T15 T14 ChFam032_210 0.0 ChFaM018_450 0.0 EMFn170_215 0.0 ChFam032_210 0.0 13.5 T16 T15 ChFaM056_112 13.5 EMFn170_208 0.0 EMFn170_215 0.0 ChFaM094_120 18.1 T17 T18 ChFaM098_226 34.0 34.0 EMFn213_310 0.0 0.0 EMFn213_320 3.8 T16 0.0 ChFaM09 FAC001_211 31_138-190 ChFaM031_138-190 32.5 Fn170_215 EMFn170 ChFaM056_112 18.1 FAC001_211 21.0 21.0 ChFaM018_460 T16 3.9 ChFaM09 ChFaM07 Uffa20H1 EMFn170_208 T17 T18 T19 FaM056_112 34.0 ChFaM094_120 0.0 EMFn213_310 0.0 ChFaM076_139 3.8 18.1 EMFn213_320 3.9 Uffa20H10_247 ChFaM098_226 148 0.0 EMFn1 Figure 3.4 (cont’d) T18 T19 T19 FaM076_139 310 0.0 ChFaM076_139 EMFn198_189 0.0 0.0 320 3.9 a20H10_247 T20 T20 EMFn198_189 ChFaM147_210 0.0 0.0 Uffa20H10_247 EMFn198_161 17.3 EMFn198_161 EMFn181_240 26.6 ChFaM085_155 26.6 EMFn181_240 17.3 27.5 ChFaM085_155 27.5 T21 T22 T21 T22 0.0 0.0 ChFam011_129 7.8 ChFam011_129 0.0 UFFa03B05_210 ChFaM103_124 4.5 UFFa03B05_210 0.0 EMFvi136_159 4.5 7.8 ChFaM147_210 ChFaM103_124 EMFvi136_159 16.8 16.8 SF6E02_149 149 SF6E02_149 Appendix 3.4 Multiplex segregation ratios of SSR markers The markers with distorted segregation ratios were tested to see if they fit into any of the multiplex segregation ratios as described by Lerceteau-Kohler et al. (2003). Markers segregating in only one parent were tested for multiplex ratios 3:1 (disomic, SD-single dose x ND- null dose), 7:1 (disomic, TD-triple dose x ND), 15:1 (disomic, 4D- four dose x ND), and 11:3 (octosomic DD-double dose x ND). Markers segregating in both parents were tested for multiplex ratios: 7:1 (disomic, DD x SD), 15:1 (disomic TD x SD), and 25:3 (octosomic, DD x SD). Only 15% (13 out of 88 markers with distortion) fit into multiplex segregation ratios at the 0.05 level. Five markers that segregated in both parents fit into the multiplex disomic ratio 7:1. Out of these, two also fit into the ratio 25:3. Six markers segregating in ‘Honeoye’ fit multiplex segregation ratio 3:1 and three of these also fit into 11:3 ratio. Two markers segregating in ‘Tribute’ fit the multiplex disomic ratio 3:1. Lerceteau-Kohler et al. (2003) could not fit 28 (out of 892) markers into any segregation ratios. Of the remaining, 72 fit into both simplex and multiplex ratios. 8% of the markers they used fit into multiplex ratios, whereas, in this study, 5% of the markers fit multiplex ratios. Overall, 65% of the markers in the ‘Honeoye’ × ‘Tribute’ fit simplex segregation ratio indicating that the genome is largely diplodized, as reported by LerceteauKohler et al. (2003). Only 33% of the markers displayed multiplex segregation ratios, indicative of polysomic inheritance. 150 Table 3.7 Multiplex segregation ratios of SSR markers with segregation distortion. Locus Seg.type h- kk X2 (7:1) simplex (SD × SD) Present in both parents X2 (3:1) multiplex disomic (DD × SD) UDF004_130 ChFaM040_100 ChFaM151_210 EMFn181_221 ChFaM104_197 98 145 144 92 126 15 27 29 17 26 8.3 7.9 6.3 5.1 5 0.062 1.608 2.874 0.955 2.947 Locus Seg.type lm ll X2 (1:1) X2 (3:1) simplex (SD × ND) multiplex disomic (DD × ND) Present in Honeoye ChFaM148_161 UFFA01H05_250 UFFa04G04_162 ChaM093_725 Uffa14A11_114 Uffa11A11_225 116 78 40 38 34 33 35 30 11 21 18 18 43.5 21.3 16.5 4.9 4.9 4.4 0.267 0.444 0.320 3.531 2.564 2.882 Locus Seg.type np nn X2 (1:1) X2 (3:1) simplex (SD × ND) multiplex disomic (DD × ND) 38.3 14.7 0.020 3.600 Present in Tribute ChFaM104_196 ChFaM147_210 111 81 36 39 151 X2 (25:3) multiplex octosomic (DD × SD) 0.774 2.716 Df 1 1 1 1 1 X2 (11:3) multiplex octosomic (DD × ND) 0.275 2.586 0.001 Df X2 (11:3) multiplex octosomic (DD × ND) 0.818 Df 1 1 1 1 1 1 1 1 1 Appendix 3.5 Colinearity in Octoploid map Homeologous groups could be identified for 29 out of 34 groups based on comparisons with the diploid map of Sargent et al. (2011), octoploid map of Sargent et al. (2012), and the F. vesca genome sequence (www.strawberrygenome.org; Shulaev et al., 2011). Only one group (HT15) had markers from more than one diploid group (VI and VI). This group has only 3 markers covering 15.9 cM, but it is possible that it represents a translocation at this location. In his denser map, Sargent et al. (2009) observed duplicated loci in homeologous groups I and VI. Sargent et al. (2012) suggested that there was “almost complete colinearity” between diploid and octoploid marker locations, except for two chromosomal inversions on three homeologs (I, III, and IV). In their earlier paper, Sargent et al. (2009) also reported that there were some regions on the octoploid map where the marker order was not collinear with the diploid. For example, CVFCT032 and BFAC045 were not collinear with respect to other markers in group RG3-A and BFACT002, and EMFn214 and CFVCT015 were not collinear on group RG2-B. RousseauGueutin et al., (2008) also suggested that overall the marker colinearity is conserved between diploid and octoploid genome. However, they identified two regions (homeolog II and IV) where there were potential inversions. In our ‘Honeoye’ × ‘Tribute’ map, linkage group (HT15) consisted of markers from two different homeologs (IV and VI), which could be due to a translocation. When we compared the physical locations of markers on the pseudochromosomes of the diploid physical map (www.strawberrygenome.org) to the octoploid, we observed much less colinearity than was described by Sargent et al. (2009 and 2011). 152 The figure below shows a comparison between the physical marker locations on the diploid map and the ‘Honeoye’ × ‘Tribute’ octoploid groups. Markers from diploid homeolog I were found in 6 octoploid linkage groups, markers from homeolog II were found in 7 groups, markers from homeolog III were found in 5 groups, markers from homeolog IV were found in 4 groups, markers from homeologs V and VI were found in 4 groups each, and markers from homeolog VII were found in one group. BLASTs with the primer sequences placed ChFaM098 in diploid group II, even though it was placed in homeolog III in the octoploid map by Sargent et al. (2012), and ChFaM098 cosegregated with EMFN170 and EMFn202, both present in homeolog III. Some of the markers that were located close together on the diploid pseudochromosome (Shulaev et al., 2011) and in the diploid map (Sargent et al., 2004, 2006, 2011) were found in separate groups in the octoploid. For example, diploid group I has EMFn128 and ChFaM081 next to each other separated by 10,00,000 bp. However, in the octop loid map, EMFn128 was located on two linkage groups HT21 and HT29, while ChFaM081 was located in group HT32. In diploid homeolog II, ChFaM103 was separated from UFFa03B05 by 500,000 bp on the diploid physical map; however, these two markers cosegregated in only one octoploid group (HT23), even though ChFaM103 was found on two of other octoploid linkage groups (HT5 and HT11). Diploid homeologous group II had the highest density of markers in our octoploid map, and therefore was the most useful linkage group to evaluate colinearity. There were several locations on the octoploid map where the marker order was not collinear with the diploid. For example, diploid group II had the marker order SFGRP7, EMFn235, EMFn121, while the homeolog HT8 has the order EMFn235_215, SFGRP7_134/141/147, and EMFn121_249. Similarly, the marker order ChFaM103, UFFaB05, ChFaM088, UaFV8216, and EMFn134 in the diploid group was 153 not repeated in the octoploid HT5 which had markers in the order: ChFaM103_450, UaFV8216_182/185, UaFv8216_161, ChFaM088_300, and EMFn134_160. We also found instances where closely linked diploid markers were found on different linkage groups in the octoploid. Based on comparison with Sargent et al. (2011) diploid SSR map, UFFa04G04 belongs to homeologous group VI, while EMFn225 belongs to homeologous group V in Sargent et al. (2012 submitted) octoploid map. Sargent et al (2004) observed that 2 out of the 75 SSRs segregated at two loci in their octoploid map, and Sargent et al. (2009, 2011) found several additional SSR markers that mapped to 2 locations. For example, CFVC032 mapped to homeologs IV and VII in the octoploid ‘Redgauntlet’ map, although it was located in group III in the diploid map. Similarly, EMFn181 markers mapped to homeologs III, IV, V, and VI on the ‘Redgauntlet’ map, but were located on group V in the diploid map. We also found 4 alleles that amplified from EMFn181; however only one of them assembled into linkage groups, so it is not known whether the other alleles would have mapped to different homeologs. Only 130 out of the 258 markers used to assemble our map were placed in linkage groups and as a result our octoploid map was much less dense than that of Sargent et al. (2006, 2009, 2011). However, the separation of closely linked markers into separate groups and the differences we observed in marker order between the diploid and the octoploid suggests that colinearity between the two ploidy levels in our mapping population is much more limited than that described by Sargent and his group. It is unknown why our data provide such a different picture about colinearity, unless a more dense map of ‘Honeoye’ × ‘Tribute’ will combine many of our smaller linkage groups and provide greater evidence of colinearity. 154 One of the factors limiting the density of our map was that we were unable to accurately evaluate allele dosage (van Djik et al. 2010) because polyacrylamide gel electrophoresis was used to visualize PCR amplicons. 155 Figure 3.5 Comparison of ‘Honeoye’ × ‘Tribute’ linkage groups (HT1-HT34) with diploid linkage groups (I-VII) developed based on physical distances on pseudochromosomes. Diploid map distances are in x 10,00,000 bp. Octoploid distances are in cM. The markers on the octoploid linkage groups are color coded to indicate whether they segregate in ‘Honeoye’ (red), in ‘Tribute’ (green), or both (blue). Marker names are abbreviated to include the first letter of the SSR locus name and the band size. Original marker names are shown in Figure 3.1 156 Figure 3.5 (cont’d) HT29 0.0 E_162 5.9 HT7 C_192 0 1 2 6 4 7 HT30 0.0 C_139 3.9 U_247 I HT8 HT31 HT1 C_440 0.0 A_301 0.0 HT9 II HT2 HT10 III 0.0 HT11 HT3 E_148 IV 0.0 HT12 C_106 V E_214 12.4 C_225 13.3 U_136 15.5 E_225 0.0 E_169 E_215 C_305 C_495 E_185 17.1 S_125 0.0 0.0 0.0 0.0 ChFaM072 SFGRP7 UDF004 EMFvi136 15.4 EMF 3.5 1.8 10.2 16.7 C_230 22.3 C_240 25.3 U_270 6.7 C_132 23.4 C_205 27.0 C_197 29.7 ChFaM086 7.4 C_500 30.7 EMFn170 14.4 E_188 U_390 30.9 C_219 31.6 C_400 UFFa16H07 15.9 9.3 E_192 16.2 U_101 32.9 E_157 E_240 36.6 EMF 23.4 U_262 C_180 C_315 36.8 20.0 37.9 ChF 23.5 C_161 C_152 22.1 EMFn128 22.2 12.2 39.7 U_239 C_95 EMFn235 38.4 10.8 ChF S_119 23.7 ChFaM081 13.2 U_256 C_101 24.1 42.4 EMFn12125.4 12.1 C_135 27.2 UaF 26.2 A_205 44.8 C_157 29.4 EMFn152 C_300 32.0 49.2 C_158 E_130 16.6 S_134 32.9 EMFn115 16.9 E_80 35.1 E_219 52.1 U_206 35.3 C_220 54.9 EMFn202 26.1 C_215 HT19 HT20 19.6 ChFaM103 27.8 HT22 40.6 HT21 HT23 HT24 ARSFL98 C_189 U_214 E_249 44.0 UFFa03B05 20.1 C_38543.7 61.7 ChFaM088 22.5 S_250 C_141 47.2 48.4 UAFv8216 HT34 22.7 HT32 HT33 C_350 68.1 EMFn134 C_190 22.8 52.4 E_298 C_143 E_208 E_157 U_210 E_2 0.0 0.0 0.0 0.0 0.0 0.0 UaFv9094 25.1 E_320 2.6 HT25 HT26 HT27 HT28 HT29 HT30 C_124 4.5 C_176* C_196 7.2 E_140 E_189 0.0 C_129 0.0 0.0 C_1 11.7 ChFaM098 0.0 15.3 E_215 0.0 C_117 31.6 C_223 7.8 E_192 0.0 E_162 S_149 C_139 E_159 A_295 16.8 0.0 0.0 0.0 0. C_120 18.1 U_247 3.9 E_157 C_192 5.8 5.9 C_110 14.6 S_410 24.2 13.5 U_143 28.2 C_112 35.2 C_220 12.8 U_260 15.3 34.0 C_226 27.5 C_155 C_165 30.6 E_1 157 25. HT7 HT9 HT1 0 0.0 .0 HT8 HT10 HT2 Figure 3.5 (con t’d) C_305 E_169 A_301 II0.0 0.0 0.0 HT9 HT11 HT7 HT3 HT10 HT12 HT8 HT4 HT11 HT1 HT9 HT5 HT12 HT2 HT10 HT6 HT3 HT11 HT4 HT12 HT11 HT8 HT5 HT12 HT3 VIE_148 0.00.0 S_125 C_495 E_215 0.0 III S_125 C_305 0.0 IV E_185 C_495 0.0 V C_450 0.00.0 C_495 S_125 0.0 E_185 0.0 0.0 A_301 0.0 C_106 0.00.0 E_215 E_148 0.00.0 E_169 C_106 0.0 C_106 0.00.0 C_305 C_92 0.0 0.0 U_270 C_1 E_185 U_270 6.7 U_270 6.7 E_2 9.7 C_147 8.6 E_243 9.7 EMFn123 9.6 C_1 10.7 C_137 hFaM072 SFGRP7 EMFvi136 15.4 EMFn184 1.8 10.2 16.7 E_214 12.4 E_214 10.7 E_188 12.4 UDF004 3.1 C_225 12.6 10.0 E_260 E_188 13.3 13.1 C_225 .3 EMFn117 C_400 C_400 U_13615.9 15.9 E_192 15.5 C_400 E_192 16.2 U_136 15.9 .5 16.2 E_157 17.1 7.2 E_225 17.1 E_157 16.2 C_1 17.3 E_192 E_225 17.2 C_139 17.5 17.3 U_182 C_180 20.0 C_180 20.0 hFaM086 C_23022.2 22.3 14.4 C_152 C_152 U_239 22.1 U_239 C_230 22.5 1 22.1 22.2 22.3 EMFn170 22.2 C_152 C_1 22.5 U_239 C_153 S_228 24.1 S_119 S_119 22.7 24.1 24.1 U_161 23.6 C_132 24.8 23.4 S_119 C_132 24.8 23.4 C_9 C_101 C_101 FFa16H07 25.4 25.4 C_90 C_101 25.4 C_135 C_135 2 27.2 C_205 27.2 C_205 27.0 EMFn181 27.0 C_250 23.4 C_135 27.4 C_197 29.3 29.7 C_197 29.3 C_500 29.7 30.7 C_1 C_157 C_157 4 29.4 C_500 .7 C_111 29.4 ChFaM078 23.5 C_157 U_390 30.9 MFn128 C_219 32.0 EMFn235 C_158 32.6 C_158 32.3 U_390 C_21932.0 32.0 30.9 31.6 C_158 E_130 .6 2.6 S_134 10.8 32.9 E_130 C_300 S_134 32.9 ChFaM046 23.7 U_101 32.9 E_219 hFaM081 U_101 E_240 32.9 36.6 E_219 35.3 E_219 34.8 35.1 E_240 .6 U_206 35.1 E_245 35.1 EMFn121 12.1 35.3 U_206 UaFv8936 36.8 26.2 U_262 C_315 U_262 37.9 C_315 36.8 .9 E_160 37.8 39.7 C_215 C_95 38.4 HT1 HT3 HT5 C_161 C_215 39.7 40.6 40.6 HT2 C_215 .4 MFn152 C_95 40.6 HT4 C_161 U_256 42.4 U_256 43.4 3.4 MFn115 C_189 42.4 U_214 U_214 C_189 43.7 43.7 E_249 U_214 44.0 43.7 E_249 44.0 A_205 E_161 44.8 44.4 A_205 .8 S_250 S_250 2 47.2 EMFn202 S_250 C_141 26.1 48.4 C_141 47.2 48.4 HT4 C_300 49.2 C_300 .2 ChFaM103 19.6 E_160 50.8 ARSFL98 27.8 C_190 E_80 C_190 52.4 52.1 52.4 C_190 HT19 UFFa03B05 HT20 HT21 0.0 HT22 0.0 HT23 0.0 HT24 0.0 E_8020.1 .1 A_301 E_148 C_106 52.4 C_106 C_45 0.0 C_220 54.9 C_220 ChFaM088 22.5 54.9 UAFv8216 22.7 C_176* 9.7 C_176* 59.7 HT23 61.7 EMFn134 22.8 61.7 C_385 9.7 E_243 C_385 C_137 10.7 UaFv9094 E_208 E_157 U_210 E_214 E_220 0.0 0.0 25.1 C_143 0.0 0.0 0.0 0.0 12.4 C_225 E_298 13.3 EMFn225 33.9 C_350 2.6 68.1 C_350 68.1 U_136 E_320 15.5 4.5 E_225 C_124 17.1 C_139 17.5 17.3 U_18 C_196 7.2 C_230 22.5 22.3 C_153 ChFaM060 U_16 38.1 11.7 23.6 C_132 C_170 23.4 C_90 24.8 ChFaM098 31.6 C_205 27.0 A_295 15.3 C_197 29.3 29.7 C_500 S_149 30.7 C_111 16.8 C_120 18.1 U_390 30.9 C_219 31.6 C_30 32.3 158 U_101 32.9 E_240 36.6 S_410 24.2 U_262 C_315 36.8 37.9 E_16 37.8 C_161 39.7 C_95 38.4 U_143 28.2 U_256 42.4 E_155 30.6 6.7 II Figure 3.5 (cont’d) HT19 III IV HT20 V C_143 0.0 HT21 HT1 E_208 VI 0.0 A_301 0.0 0.0 UDF004 10.2 16.7 EMFvi136 EMFn184 15.4 18.1 9.6 13.3 10.0 15.5 C_120 EMFn170 14.4 EMFn123 C_225 EMFn117 U_136 17.1 U_143 23.4 23.5 23.7 C_220 26.2 35.2 C_500 C_219 E_240 C_315 C_95 44.8 26.1 hFaM103 FFa03B05 hFaM088 AFv8216 MFn134 aFv9094 22 23 27.8 ARSFL98 HT19 8 C_223 36.8 U_262 U_256 HT28 C_143 0.0 HT29 E_208 0.0 E_192 0.0 5.9 HT30 28.2 E_162 C_120 0.0 18.1 C_192 0.0 7.2 C_139 U_247 24.2 3.9 29 30 32 39 C_300 E_80 HT21 hFaM098 0 C_205 HT22 54.9 61.7 0.0 HT27 HT20 27.0 A_205 49.2 52.1 EMFn202 12 A_ 15.3 E_225 42.4 MFn235 MFn121 EMFn181 ChFaM078 30.7 ChFaM046 31.6 C_226 34.0 36.6 UaFv8936 37.9 38.4 E_ 0 E_ 0.0 E_148 2.6 S_410 24.2 28.2 E_157 0.0 C_196 7.2 FGRP7 HT22 HT2 EMFn225 33.9 68.1 E_298 E_157 0.0 E_320 2.6 HT31 ChFaM060 38.1 C_196 HT25 HT26 0.0 0.0 C_220 HT23 C_385 C_350 U_210 0.0 4.5 A_295 15.3 16.8 C_440 E_215 0.0 C_117 S_410 5.8 U_143 C_124 HT2 S_149 0.0 E_157 U_260 15.3 C_165 C_220 35.2 34.0 C_226 13.5 C_112 159 25.3 C_240 12.8 Figure 3.5 (cont’d) HT32 HT33 III 0.0 E_189 IV 14.6 0.0 C_129 7.8 0.0 E_140 HT34 E_159 HT25 V HT27 VI C_110 E_215 0.0 UDF004 HT26 16.7 EMFvi136 27.5 HT13 9.6 10.0 EMFn184 15.4 C_155 HT14 HT15 EMFn123 EMFn117 0.0 C_140 7.5 C_210 11.3 23.4 23.5 S_325 23.7 26.2 E_170 C_195 C_117 5.8 E_157 HT16 EMFn181 ChFaM078E_250 0.0 ChFaM046 UaFv8936 HT17 C_112 13.5 EMFn170 0.0 12.8 5.8 17.5 U_109 13.4 15.9 U_158 C_190 C_400 12.9 0.0 0.0 U_260 C_320 ARSFL98 F_225 31.0 C_219 34.1 EMFn225 33.9 41.4 HT18 U_410 160 38.1 ChFaM060 30.5 E_200 0.0 C 22.5 EMFn202 0.0 C_210 C_223 0.0 C 32.5 C Figure 3.5 (cont’d) HT4 HT5 HT6 HT13 106 0.0 C_106 214 9.7 10.7 E_243 C_137 17.3 C_139 17.5 U_182 22.5 24.8 C_153 C_90 23.6 U_161 V 230 132 197 29.3 390 EMFvi136 101 V C_450 C_92 C_140 0.0 7.5 C_147 C_210 11.3 E_260 C_195 12.6 C_111 EMFn184 15.4 32.3 37.8 161 0.0 8.6 0.0 22.7 S_325 5.8 HT15 E_170 U_109 0.0 E_250 13.4 15.9 U_158 C_190 0.0 C_250 31.0 34.1 EMFn123 EMFn117 E_245 34.8 HT7 HT8 HT9 U_410 HT10 HT13 HT14 HT15 E_161 44.4 EMFn181 ChFaM078 E_160 50.8 0.0 E_169 E_215 C_305 0.0 0.0 0.0 ChFaM046 C_140 S_325 E_250 0.0 0.0 0.0 UaFv8936 6.7 E_170 5.8 C_210 7.5 E_188 13.1 C_195 11.3 15.9 E_157 17.2 U_158 13.4 C_180 20.0 C_190 15.9 U_109 C_152 17.5 22.1 25.4 C_135 27.2 C_157 29.4 32.0 E_130 32.6 S_134 32.9 EMFn225 U_206 33.9 35.3 43.4 H S_228 41.4 23.4 23.5 23.7 26.2 0.0 17.5 VI 27.4 9.6 C_300 10.0 E_160 HT14 C_189 E_249 ChFaM060 38.1 41.4 44.0 U_410 C_141 48.4 47.2 HT HT16 C_495 0.0 U_270 0.0 C_2 C_400 C_101 C_158 31.0 34.1 24.1 35.1 F_2 C_2 40.6 S_250 52.4 161 59.7 C_176* Figure 3.5 (cont’ d) HT15 HT21 HT16 HT22 HT17 HT23 HT18 HT24 E_298 0.0 0.0 C_400 E_157 0.0 0.0 C_210 U_210 0.0 0.0 C_450 E_220 0.0 0.0 E_250 E_320 2.6 C_124 4.5 VI C_196 7.2 C_170 11.7 C_320 12.9 U_158 3.4 A_295 C_190 9.6 15.3 5.9 S_149 16.8 EMFn123 EMFn184 EMFn117 10.0 C_460 22.5 S_410 24.2 EMFn181 ChFaM078 ChFaM046 UaFv8936 F_225 C_219 31.0 34.1 30.5 E_200 HT7 HT8 E_155 30.6 C_138 32.5 0.0 13.1 33.9 0.0 HT15 HT16 E_157 32.6 7.5 C_210 11.3 5.8 S_325 E_250 0.0 U_109 C_18 E_130 HT18 32.9 35.3 C_210 C_40044.00.0 43.4 0.0 C_189 48.4 E_170 C_195 17.5 0.0 E_21 HT17 EMFn225 C_140 0.0 ChFaM060 38.1 0.0 20.0 HT14 E_188 17.2 HT13 E_169 13.4 15.9 S_13 U_20 C E_24 C_14 C_320 12.9 C_176* 59.7 U_158 C_190 22.5 162 41.4 U_410 31.0 34.1 F_225 C_219 30.5 E_200 C 32.5 C Figure 3.5 (cont’d) HT19 HT20 HT21 HT22 HT23 HT24 VII C_143 E_208 E_157 E_298 E_320 C_196 15.3 18.1 C_120 24.2 28.2 35.2 A_295 0.0 U_210 4.5 C_124 16.8 E_2 C_1 30.6 EMFn213 0.0 2.6 0.0 11.7 16.4 0.0 0.0 7.2 0.0 E_1 S_149 S_410 U_143 C_220 34.0 C_226 163 Appendix 3.6 Table 3.8 QTL regions associated with remontancy (rem) in MI, OR, CA, MN, and MD in 2005, 2006, and 2011 in ‘Honeoye’ × ‘Tribute’ population. The position of the highest peak is represented where the significant regions were spread over a range. Only regions with significant LOD values are represented. Significant LOD value at p ≤ 0.05 was determined from 1000 permutations with the dataset. Trait rem MI2005 Group 2 2 5 5 6 7 7 7 16 16 19 19 32 34 Position 6 61.7 2 13 7 5 15.1 26.2 0 20 16 32.2 5 4 Nearest Locus EMFn152_148 ChFaM072_385 ChFaM103_450 UAFv8216_182/185 ChFaM017_147 EMFn198_169 EMFn117_188 EMFvi104_130 ChFaM147_210 FAC006_225 UDF004_143 ChFaM080_220 EMFn115-140 ChFam011_129 Locus position 0 61.7 0 17.5 8.6 0 13.1 32.6 0 31 28.2 35.2 0 0 % Expl. 54.4 54.4 54.4 54.4 54.4 54.4 54.4 54.4 54.4 54.4 54.4 54.4 63.9 17 rem MI2006 3 6 7 16 16 32 32 34 0 4 7 0 15 0 14 7 ChFaM088_106 ChFaM017_92 EMFn117_188 ChFaM147_210 EMFn117_157 EMFn115-140 ChFaM081_110 EMFvi136_159 0 0 13.1 0 17.2 0 14.6 7.8 53.6 53.6 53.6 53.6 53.6 63.7 63.7 10.1 rem MI2011 1 1 1 16.5 26.5 37.6 UDF004_136 ChFaM129_500/190 ChFaM040_315 15.5 30.7 37.9 51.3 51.3 51.3 164 Table 3.8 (cont’d) Group Position Nearest Locus 1 2 3 3 5 6 7 7 7 7 7 10 13 22 30 34 51.2 31 2 33.9 27.6 29.4 4 18.2 28.2 38.6 55.4 14.7 12.3 4.6 3.9 2 EMFvi104_80 ChFam032_205 ChFaM088_106 UAFv8216_101 ChFaM088_300 ChFaM017_250 EMFn198_169 EMFn117_157 EMFvi104_130 ChFaM111_189 ChFaM111_176 ChFaM003_400 ChFaM111_195 EMFn213_320 Uffa20H10_247 ChFam011_129 Locus position 52.1 27 0 32.9 32.3 27.4 0 17.2 32.6 43.4 59.7 15.9 11.3 2.6 3.9 0 rem OR2005 1 1 1 5 6 6 7 7 9 19 22 30 32 34 4 18.5 42.4 5 3 31.4 5 15.1 42.4 8 15.3 2 5 0 ARSFL8_301 UDF004_136 ARSFL98_205 ChFaM103_450 ChFaM017_92 EMFn184_245 EMFn198_169 EMFn117_188 SF5G02_250 ChFaM040_143 ARSFL19_295 ChFaM076_139 EMFn115-140 ChFam011_129 0 15.5 44.8 0 0 34.8 0 13.1 47.2 0 15.3 0 0 0 54.6 54.6 54.6 54.6 46.1 36.4 54.6 54.6 30.9 63 54.6 54.6 54.6 14.1 rem OR2011 1 1 2 2 3 13.3 44.8 23.1 54.9 0 ChFaM098_225 ARSFL98_205 ChFam032_205 ChFaM061_220 ChFaM088_106 13.3 44.8 27.3 54.9 0 55.2 56 56 56 56 Trait 165 % Expl. 51.3 51.3 51.3 51.3 51.3 48.8 51.3 51.3 51.3 51.3 51.3 51.3 51.3 51.3 51.3 39.7 Table 3.8 (cont’d) Group Position Nearest Locus 3 4 5 6 6 6 7 7 9 10 10 16 18 18 30 34 23.4 16.7 1 8.6 18.6 32.4 4 19.2 47.2 2 24.9 0 2 22.5 2 7.8 ChFaM004_132 ChFaM101_139 ChFaM103_450 ChFaM017_147 SF5G02_228 EMFn184_245 EMFn198_169 EMFn117_157 SF5G02_250 ChFaM003_495 ChFaM076_101 ChFaM147_210 ChFaM018_450 ChFaM018_460 ChFaM076_139 EMFvi136_159 Locus position 23.4 17.3 0 8.6 22.7 34.8 0 17.2 47.2 0 25.4 0 0 22.5 0 7.8 rem CA2005 1 1 1 5 6 7 7 19 23 30 32 34 4 14.3 41.4 5 3 2 15.1 8 15.3 2 4 0 ARSFL8_301 UDF004_136 ChFaM040_95 ChFaM103_450 ChFaM017_92 EMFn198_169 EMFn117_188 ChFaM040_143 ARSFL19_295 Uffa20H10_247 EMFn115-140 ChFam011_129 0 15.5 38.4 0 0 0 13.1 0 15.3 3.9 0 0 54.6 54.6 54.6 54.6 46.1 54.6 54.6 63 54.6 54.6 54.6 14.1 rem MN2005 2 5 6 7 7 7 9 62.7 46.4 0 2 16.1 26.2 2 ChFaM072_385 EMFn160_161 ChFaM017_92 EMFn198_169 EMFn117_157 EMFvi104_130 ChFaM022_305/225 61.7 44.4 0 0 17.2 32.6 0 56.6 56.6 56.6 56.6 56.6 56.6 35.9 Trait 166 % Expl. 56 56 56 56 47.9 56 56 56 56 56 56 56 56 56 45.4 69.7 Table 3.8 (cont’d) Trait rem MD2005 Group Position Nearest Locus 16 1 ChFaM147_210 16 19 FAC006_225 19 23 UDF004_143 32 8 ChFaM081_110 34 3 ChFam011_129 5 5 ChFaM103_450 5 20.5 UAFv8216_182/185 6 3 ChFaM017_92 7 3 EMFn198_169 7 16.1 EMFn117_157 7 26.2 EMFvi104_130 9 0 ChFaM022_305/225 10 3 ChFaM003_495 10 15.7 ChFaM003_400 16 5 ChFaM147_210 16 19 FAC006_225 19 10 ChFaM040_143 19 23 UDF004_143 32 5 EMFn115-140 34 6 EMFvi136_159 167 Locus position 0 31 28.2 14.6 0 0 17.5 0 0 17.2 32.6 0 0 15.9 0 31 0 28.2 0 7.8 % Expl. 56.6 56.6 56.6 62.3 15.1 47.7 47.7 47.7 47.7 47.7 47.7 29.2 47.7 47.7 47.7 47.7 47.7 47.7 57.2 65.5 Appendix 3.7 Table 3.9 QTL regions associated with weeks of flowering in MI, OR, and CA in 2005, 2006, and 2011 in ‘Honeoye’ × ‘Tribute’ population. The position of the highest peak is represented where the significant regions were spread over a range. Only regions with significant LOD Significant LOD value at p ≤ 0.05 was determined from 1000 values are represented. permutations with the dataset. Trait Group Position Nearest Locus Weeks-MI2005 3 24.4 ChFaM004_132 Locus position 23.4 Weeks-MI2006 1 2 3 3 5 6 6 6 7 7 9 9 9 10 13 26 32 32 37.9 11 2 9 3 5 13.6 28.4 5 25.2 8 22.1 42.4 3 41.3 2 0 9 ChFaM040_315 EMFn230_225 ChFaM088_106 EMFn134_214 ChFaM103_450 ChFaM017_147 EMFn184_260 ChFaM017_250 EMFn198_169 EMFn117_157 ChFaM022_305/225 ChFaM046_152 SF5G02_250 ChFaM003_495 UFFa03B05_214/175 ChFam011_117 EMFn115-140 ChFaM081_110 37.9 17.1 0 12.4 0 8.6 12.6 27.4 0 17.2 0 22.1 47.2 0 43.7 0 0 14.6 24.4 65.6 24 24 24.3 15.6 25 24.4 25 25 24.7 20.3 23.9 25 24.7 36.4 24.7 25 Weeks MI2011 1 2 5 6 6 7 16.5 28 6 0 34.8 3 UDF004_136 ChFam032_205 ChFaM103_450 ChFaM017_92 EMFn184_245 EMFn198_169 15.5 27 0 0 34.8 0 45.1 45.6 43.8 35.9 15.8 47 168 % Expl. 20 Table 3.9 (cont’d) Group Position Nearest Locus 7 9 9 9 10 20 22 29 34 11 9 27.2 42.4 31.4 2 13.6 3 5 EMFn117_188 ChFaM022_305/225 ChFaM046_135 SF5G02_250 ChFaM151_228/113 EMFn170_208 ARSFL19_295 ChFaM107_192 EMFvi136_159 Locus position 13.1 0 27.2 47.2 32 0 15.3 5.9 7.8 Weeks OR2005 5 5 19 19 10 20.5 14 27 UAFv8216_182/185 UAFv8216_161 ChFaM040_143 UDF004_143 17.5 23.6 0 28.2 28.4 28.7 49.8 61.1 Weeks CA2005 1 1 2 2 3 3 4 5 6 7 7 8 9 10 12 13 13 15 16 18 20 20 8 29.5 1 67.7 2 25.4 23.5 49.4 12.6 5 58.4 35.3 41.4 30.4 2 7.5 23.3 7 30 1 7 33.1 ChFaM098_225 ChFaM129_500/190 EMFn152_148 ChFaM072_350 ChFaM088_106 ChFaM004_132 ChFaM101_153 EMFn160_160 EMFn184_260 EMFn198_169 ChFaM111_176 Uffa11A11_206/260 SF5G02_250 ChFaM151_228/113 EMFn134_185 ChFaM111_210 ChFaM111_195 UFFa04G04_158 FAC006_225 ChFaM018_450 EMFn170_208 ChFaM098_226 13.3 30.7 0 68.1 0 23.4 22.4 50.8 12.6 0 59.7 35.3 47.2 32 0 7.5 11.3 13.4 31 0 0 34 54.5 29.9 30.8 30 31.1 31.8 32.6 32.8 30.2 44.5 31.3 30.2 32.2 23.9 29.5 28.7 31.3 29.9 32.2 29.4 23 29.1 Trait 169 % Expl. 46.4 39.9 42.2 43.5 45 35.7 39.4 37.6 59.6 Table 3.9 (cont’d) Trait Weeks OR2011 Group 21 22 26 28 29 32 Position 24.2 13.6 0 8 4 3 Nearest Locus SF5C08_410 ARSFL19_295 ChFam011_117 ChFaM098_165 ChFaM107_192 EMFn115-140 Locus position 24.2 15.3 0 15.3 5.9 0 % Expl. 30.7 32.6 26.8 27.3 30.4 31.7 1 1 2 2 2 3 3 4 4 5 5 5 7 7 7 9 9 15 16 19 19 22 34 18.5 31.6 1 23.1 57.9 2 24.4 0 11.7 21.5 37.3 50.8 3 30.2 59.7 17 26.1 4 8 3 32.2 9.6 4 UDF004_136 ChFaM080_219 EMFn152_148 ChFam032_205 ChFaM061_220 ChFaM088_106 ChFaM004_132 ChFaM063_106 ChFaM101_137 UAFv8216_161 EMFn134_160 EMFn160_160 EMFn198_169 EMFvi104_130 ChFaM111_176 ChFaM046_152 ChFaM046_135 EMFn225_250 ChFaM147_210 ChFaM040_143 ChFaM080_220 ARSFL19_295 EMFvi136_159 15.5 31.6 0 27 54.9 0 23.4 0 10.7 23.6 37.8 50.8 0 32.1 59.7 22.1 27.2 0 0 0 35.5 15.3 7.8 32.2 31.4 32 32.2 30.6 32 29.1 31.7 31.1 31.6 14.8 23.1 32.2 32.2 35.8 30.5 30.8 16.1 32 30.7 29.5 30.1 24.3 170 Appendix 3.8 Table 3.10 QTL regions associated with flowering at 17°C, 20°C, and 23°C in ‘Honeoye’ × ‘Tribute’ population. in ‘Honeoye’ × ‘Tribute’ population. The position of the highest peak is represented where the significant regions were spread over a range. Only regions with significant LOD values are represented. Significant LOD value at p ≤ 0.05 was determined from 1000 permutations with the dataset. ChFaM129_500/190 EMFn152_148 EMFn230_225 ChFam032_205 UFFa16H07_262 ChFaM072_385 EMFn134_214 EMFn198_169 SF5G02_250 ChFaM076_101 ChFaM151_228/113 ChFaM147_210 FAC006_225 UDF004_143 ChFaM080_220 ChFaM094_120 Uffa20H10_247 ChFam011_129 Locus position 30.7 0 17.1 27 36.8 61.7 12.4 0 47.2 25.4 32 0 31 28.2 35.2 18.1 3.9 0 % Expl. 47.3 45.9 46.6 46.9 46.6 45.2 44.7 46.1 41.4 44.5 44.7 40.4 47.5 45 43.3 14.5 40.2 31.9 ChFaM129_500/190 EMFn152_148 ChFaM088_106 ChFaM004_132 ChFaM103_450 EMFvi104_130 30.7 0 0 23.4 0 32.6 43 42.5 44 39.2 33.4 42.3 Trait Group Position Nearest Locus Total Flowers at 17°C 1 2 2 2 2 2 3 7 9 10 10 16 16 19 19 20 30 34 23.5 4 17.1 24.1 33 63.7 14.4 4 39.4 20.9 30.4 14 24 18 35.2 18.1 2 5 Total Flowers at 20°C 1 2 3 3 5 7 25.5 6 5 25.4 0 30.2 171 Table 3.10 (cont’d) Trait ChFaM111_189 SF5G02_250 ChFaM147_210 Uffa20H10_247 ChFam011_129 Locus position 43.4 47.2 0 3.9 0 % Expl. 43.9 44.1 45 37.3 32.4 ARSFL8_301 ChFaM063_106 EMFn117_157 ChFaM040_143 UDF004_143 EMFn170_208 ChFaM094_120 ARSFL19_295 Uffa20H10_247 ChFam011_129 0 0 17.2 0 28.2 0 18.1 15.3 3.9 0 49.1 46.6 48.8 47.7 47.1 12.2 19.7 47.4 47.1 50.3 Position Nearest Locus 7 9 16 30 34 Total Flowers at 23°C Group 50.4 45.4 14 3.9 4 1 4 7 19 19 20 20 22 30 34 5 0 15.1 12 29.2 6 18.1 14.6 2 6 172 REFERENCES 173 REFERENCES Aharoni, A., O’Connell, A.P. 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(2011) Quantitative trait loci and underlying candidate genes controlling agronomical and fruit quality traits in octoploid strawberry (Fragaria × ananassa). Theoretical and Applied Genetics 123:755-778 179 CHAPTER 5 CONCLUSIONS AND FUTURE RESEARCH 180 The goal of this research was to measure the interaction between heat tolerance and photoperiod sensitivity in regulating flowering in the octoploid strawberry. Previous research had shown that photoperiodic response of strawberry genotypes is modified by the ambient temperature (Darrow, 1936; Durner et al., 1984; Serçe and Hancock, 2005a; Weebadde et al., 2008; Sonsteby and Heide, 2008; Bradford et al., 2010). This research demonstrated that a population segregating for remontancy also segregated for heat tolerance/sensitivity. Flower initiation in plants growing under unfavorable photoperiod (long days) depended on their relative temperature (17°C, 20°C, 23°C) tolerance. Although both the parents (‘Honeoye’ and ‘Tribute’) were heat sensitive and had fewer flowers at 23°C than at 17°C, the progeny included both heat tolerant and sensitive responses. The extent of heat tolerance varied among the progeny. In addition, almost all heat tolerant genotypes had very few runners at high temperatures. Bradford et al. (2010) previously concluded that runner formation was favored by high temperatures and long photoperiod; however, they studied three genotypes (‘Honeoye’, Tribute’, and RH30) that had heat sensitive floral responses. All the heat tolerant progeny had the remontant phenotype when grown under field conditions in MI, and most were remontant in OR. This suggests that heat tolerance is required for repeat flowering in the Midwestern US during the warm summer. This hypothesis is supported by the previous work of Weebadde et al. (2008) who observed a higher percentage of remontant progeny in the cooler western states (CA and OR) than in the warmer eastern states (MI, MD, MN). Only one genotype (HT43) had a heat tolerant floral response in the greenhouse, remontant phenotype in the field, and produced a significant number of runners. In a clonally propagated crop like strawberry, runner production is an important trait and remontant genotypes typically do not produce many runners making it necessary to use labor intensive methods of 181 micropropagation or crown separation. Therefore, the genotype HT43 combines two desirable traits: heat tolerance and ability to propagate vegetatively. An octoploid linkage map for ‘Honeoye’ x ‘Tribute’ was developed using SSR markers. The SSR markers selected were derived from diploid and octoploid Fragaria species (F. x ananassa, F. vesca, F. viridis, and F. nubicola), and a large majority of the mapped SSRs were generated from ESTs. Although the map was not as dense as other recently published SSR maps (ZorillaFonatessi et al. 2011; Sargent et al., 2011), it was the first one that targeted a population segregating for remontancy, and therefore provided a framework for identifying QTL associated with remontancy and heat tolerance. Phenotypic observations taken on the population growing in three temperature conditions in a greenhouse were used to identify regions in the genome that are associated with flowering at higher temperatures (23°C). In addition, remontancy vs. non-remontancy was assessed in the same progeny previously grown at 5 locations (MI, MN, MD, OR, CA) in 2005, at MI in 2006, and at MI and OR in 2011 to identify QTL associated with remontancy. Availability of phenotypic observations from multiple environmental conditions and multiple regions ensured that stable loci would be identified. Locations of remontancy QTL were compared with the heat tolerance QTL to identify whether the remontancy phenotype is associated with heat tolerance. QTL for flowering at 23°C (heat tolerance) were identified on 8 linkage groups and out of these, the QTL on groups HT7 and HT19 overlapped with the remontancy QTL for all 5 states. In addition, remontancy QTL from different environmental conditions and years overlapped on several linkage groups, and some QTL specific to eastern and western states were identified. This observation was similar to Weebadde et al (2008) who reported that some QTL are specific 182 to geographical locations and some are common to multiple environments. However, this study identified many more QTL than Weebadde et al. (2008) using the same phenotypic data. Numerous other inheritance studies have also suggested that the remontant trait is under complex, multigenic control (Barritt et al. 1982; Shaw, 2003; Serçe and Hancock, 2005b; Shaw and Famula, 2005). The small population sizes used in our study may have caused statistical artifacts that resulted in the identification of spurious QTL in individual experiments (Beavis, 1998; Xu, 2003; Holland, 2007). In addition, quantitative traits are known to be greatly affected by environmental conditions (Patterson et al., 2003; Collard et al., 2005; Kenis et al., 2008). We used fifteen different phenotypic data sets from replicated populations growing under multiple environmental conditions in the field and in the greenhouse to identify the most robust QTL. In addition, we developed an independent population from the same parents used by Weebadde et al. (2008) and identified QTL using phenotypic data from both the populations to confirm our data and identify robust chromosomal regions associated with our trait of interest. This is the approach recommended by many authors including Lander and Kruglyak (1995) and Pelgas et al. (2011). The phenotypes associated with the markers flanking the hea t tolerant QTL were compared to see if the phenotypic data supported the hypothesis that the presence of these markers resulted in the heat tolerant floral response. Five markers were identified where the presence of the allele produced the heat tolerant floral response; however, flower numbers associated with only one of these (EMFn170_208) was significant. Since there was considerable variance in flower number with many genotypes producing no flowers at all, all 5 of these markers should be tested using larger population sizes to determine how robust they are for marker-assisted selection. 183 Flower initiation in plants is a complex process that has been shown to involve many induction pathways (photoperiodic, circadian, autonomous, developmental, ambient temperature, vernalization) with multiple genes functional in every pathway in both dicots (Arabidopsis) and monocots (rice, wheat, barley) (Boss et al., 2004; Henderson and Dean, 2004; Greenup et al., 2009). Although homologs of several Arabidopsis flowering genes have been identified in strawberry (Mouhu et al., 2009), the process of flower induction and differentiation in perennial plants is complicated by extended juvenility, dormancy, and repeated reversion of shoot apical meristem from floral to vegetative states (Albani and Coupland 2010). Homologs of the Arabidopsis genes such as LFY, AP1, TFL1, FT have been identified in perennial crops like apple (Hattasch et al., 2008), poplar (Igasaki et al., 2008), grape (Carmona et al., 2007), and citrus (Nishikawa et al., 2010). However, in most cases, there are multiple homologs of the Arabidopsis gene that have additional divergent functions in flower development (Hattasch et al. 2008; Mimida et al., 2009). These findings suggest that there might be multiple homologs of the Arabidopsis flowering genes in the octoploid strawberry. The complexity of the process and the numerous loci involved in the process of flower initiation is probably reflected in the fact that QTL associated with remontancy were identified in multiple linkage groups in the ‘Honeoye’ × ‘Tribute’ population. The immediate next step in this project should be validating the markers associated with heat tolerance on a wider panel of heat tolerant/sensitive and remontant/non-remontant genotypes. Once the markers are identified as tightly linked to heat tolerance, they may be developed for use in capillary electrophoresis for high throughput genotyping. In addition, more tightly linked markers should be added to those linkage groups that did not have dense marker coverage but had significant heat tolerance and remontancy QTL. The existing diploid and octoploid SSR 184 maps provide an excellent resource for selecting markers that are likely to map in the targeted linkage groups. The availability of the F. vesca genome sequence (Shulaev et al., 2011) provides an additional resource for designing markers for specific homeologous groups. Mouhu et al. (2009) identified several homologs of Arabidopsis flowering genes in strawberry. SSR markers derived from these EST sequences can be mapped to the ‘Honeoye’ × ‘Tribute’ linkage map to see if they collocate with the remontancy or heat QTL. The marker-trait associations may also be tested in populations derived from other remontant genotypes to determine whether they share the same QTL, and to identify new QTL of interest that can be pooled together when developing remontant cultivars. Several populations derived from remontant genotypes (‘Seascape’, RH30, and ‘Fort Laramie’) are available and have been evaluated for remontant vs non remontant phenotype in MI and OR in 2011. These populations would be the ideal for screening the marker-trait associations. The populations include: ‘Earliglow’ × ‘Seascape’, ‘Seascape’ × ‘Honoeye’, ‘Seascape’ × MSU56, MSU49 × ‘Seascape’, MSU56 × RH30, ‘Earliglow’ × RH30, MSU49 × RH30, ‘Honeoye’ × RH30, ‘Fort Laramie’ × MSU49, ‘Fort Laramie’ × Earliglow, ‘Fort Laramie’ × ‘Honeoeye’ and ‘Fort Laramie’ × MSU56. Fruit quality is the most important trait when developing a horticultural fruit crop. The ‘Honeoye’ × ‘Tribute’ population has been evaluated for fruit quality traits in 2011 and repeat phenotypic observations will be collected over the next two years. The SSR linkage map developed in this study may be used to identify fruit quality QTL in this population and can be compared with the fruit quality QTL identified in F. × ananassa selection lines ‘232’ and ‘1392’ 185 (Zorilla-Fonatesi et al. 2011). In addition, the fruit quality assessments will be useful in selecting the heat tolerant, remontant genotypes for cultivar development. In conclusion, the results provided here demonstrate that seasonal patterns of flowering in strawberry may be more strongly regulated by temperature than photoperiod. In cultivars that have historically been considered short day plants, mid-summer temperatures may actually be more important in regulating mid-summer flowering than photoperiod. In this study, remontant genotypes had varied levels of heat tolerance. However, all the genotypes that were remontant in the warmer midwestern environment were heat tolerant, indicating that heat tolerance is an important attribute for a plant to continue flowering. Five alleles associated with heat tolerance were identified. These alleles are potential candidates for validation on a larger panel and subsequent use in marker-assisted breeding. Availability of tightly linked markers to important phenotypic traits will allow the strawberry breeders to take advantage of the benefits of marker assisted breeding (Collard et al., 2005). All breeding programs are limited by the amount of land available for phenotypic evaluations of crosses. Since heat tolerance is a primary trait necessary for strawberry cultivars developed for the midwestern market, the ability to use markers to screen seedlings for heat tolerance will make selection much more efficient. Seedlings could be screened for heat tolerance before they are field planted, allowing the breeders to use the available land for phenotypic evaluation of only the heat tolerant progeny. The markers could also be used in parent selection to identify non-remontant genotypes that are carrying some QTL for remontancy but do not flower in midto late summer because they are heat sensitive. In addition, availability of markers will allow breeders to reliably select for a trait that is affected by environmental conditions. 186 The heat tolerant progeny from ‘Honeoye’ × ‘Tribute’ will likely be used to introduce heat tolerance in future crosses, and markers associated for heat tolerance have immediate application in markerassisted breeding (after validation). 187 REFERENCES 188 REFERENCES Albani, M.C., Coupland, G. (2010) Comparative analysis of flowering in annual and perennial Plants. Current topics in Developmental Biology 91: 323-348 Barritt, B.H., Bringhurst, R.S., Voth, V. 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