Utilization of Microsatellite Markers for a Comparative Assessment Of

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MSU Graduate Theses

Spring 2016

Utilization Of Microsatellite Markers For A Comparative Assessment Of Norton And Cynthiana, And The Linkage Map Construction Of A 'Chambourcin' X 'Cabernet Sauvignon' Population

Mia Elizabeth Mann

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UTILIZATION OF MICROSATELLITE MARKERS FOR A COMPARATIVE

ASSESSMENT OF NORTON AND CYNTHIANA, AND THE LINKAGE MAP

CONSTRUCTION OF A ‘CHAMBOURCIN’ X ‘CABERNET SAUVIGNON’

POPULATION

A Masters Thesis

Presented to

The Graduate College of

Missouri State University

In Partial Fulfillment

Of the Requirements for the Degree

Master of Science, Plant Science

Mia Mann

May 2016

UTILIZATION OF MICROSATELLITES FOR A COMPARATIVE

ASSESSMENT OF NORTON AND CYNTHIANA, AND THE LINKAGE MAP

CONSTRUCTION OF A ‘CHAMBOURCIN’ X ‘CABERNET SAUVIGNON’

POPULATION

Agriculture

Missouri State University, May 2016

Master of Science

Mia Mann

ABSTRACT

The first part of this study utilized microsatellites to comparatively assess the cultivars Norton and Cynthiana. Although isozyme and simple sequence repeat (SSR) marker analyses in 1993 and 2009 provided preliminary evidence that Norton and Cynthiana grapes are genetically identical, only five banding patterns and four microsatellite loci were reported. Microsatellites (n=185) spanning 19 linkage groups were used to compare the cultivars for a genome-wide analysis. Capillary electrophoresis results revealed Norton and Cynthiana to be identical at 98.6% of alleles. In the second part of this study, an interspecific hybrid population was generated by crossing V. interspecific hybrid ‘Chambourcin’ and V. vinifera ‘Cabernet Sauvignon’. The ultimate goal of performing this cross is to create a cultivar with the cold hardiness of ‘Chambourcin’ combined with the superior wine quality of V. vinifera ‘Cabernet Sauvignon’. Cross-population (CP) maps were generated using the statistical software JoinMap 4.1 by genotyping 90 F1 progenies using microsatellites. Map sizes ranged from 999.3 cM to 1821.9 cM and contained a maximum of 276 SSR markers.

KEYWORDS: Norton, Cynthiana, Chambourcin, Cabernet Sauvignon, microsatellite markers, hybrids, comparative assessment, linkage map, JoinMap

This abstract is approved as to form and content

______Chin-Feng Hwang, PhD Chairperson, Advisory Committee Missouri State University

iii

UTILIZATION OF MICROSATELLITE MARKERS FOR A COMPARATIVE

ASSESSMENT OF NORTON AND CYNTHIANA, AND THE LINKAGE MAP

CONSTRUCTION OF A ‘CHAMBOURCIN’ X ‘CABERNET SAUVIGNON’

POPULATION

Mia Mann

A Masters Thesis Submitted to the Graduate College Of Missouri State University In Partial Fulfillment of the Requirements For the Degree of Master of Science, Plant Science

May 2016

Approved:

______Chin-Feng Hwang, PhD

______W. Anson Elliott, PhD

______Melissa A. Remley, PhD

______Julie Masterson, PhD: Dean, Graduate College

ACKNOWLEDGEMENTS

I would like to express my sincerest gratitude to my advisor, Dr. Chin-Feng

Hwang, for accepting me into his laboratory and providing me with the many opportunities which have led to my receiving this degree. I gratefully acknowledge my committee members, Dr. Anson Elliott and Dr. Melissa Remley, for all of their support and suggestions. I would also like to thank Li-Ling Chen for her guidance and willingness to teach me the various laboratory techniques mentioned in this thesis.

Thank you to my fellow Hwang laboratory members for all of your assistance in conducting my research. I would like to express my appreciation to the Post Familie

Vineyards, Leding Vineyards, St. James Winery, Les Bourgeois Vineyards, McMurtrey

Vineyards, and the University of Arkansas Fruit Research Station for their contribution of plant samples for the comparative assessment of Norton and Cynthiana.

I am eternally grateful to my parents; my fiancé, Mark; my dog, Gunner; and my friends and family for all of their support as I went through this process. This thesis is dedicated to my mother, Susan Mann, and my grandmother, Faye Lund. Your constant support and encouragement had more of an impact than you ever could have imagined; I wouldn’t have made it this far without you.

TABLE OF CONTENTS

Introduction ...... 1 Plant Breeding ...... 2 Molecular Breeding ...... 3 Molecular Markers ...... 5 Microsatellites ...... 7 Native North American Grapevines ...... 7 Origin of Norton ...... 8 Norton Characteristics ...... 9 Similarities between Norton and Cynthiana ...... 10 Comparison of Norton and Cynthiana ...... 11 French-American Hybrids ...... 11 Chambourcin ...... 13 V. vinifera Cabernet Sauvignon ...... 14 Grapevine Breeding ...... 15 Linkage Mapping ...... 15 Study Overview ...... 17

Methods...... 19 Plant Materials ...... 19 PCR Amplification and Fragment Analysis ...... 20 Chambourcin x Cabernet Sauvignon Population Analysis ...... 22 Linkage Map Construction ...... 23

Results ...... 24 Norton and Cynthiana ...... 24 Chambourcin x Cabernet Sauvignon ...... 24

Discussion ...... 27 Comparative assessment of Norton and Cynthiana ...... 27 Chambourcin x Cabernet Sauvignon linkage map construction ...... 31

References ...... 35

LIST OF TABLES

Table 1. Location and age of leaf sample collections...... 45

Table 2. Genome-wide comparison of 185 loci in Norton and Cynthiana ...... 46

Table 3. Summary of consensus map constructed using the regression algorithm ...... 51

Table 4. Summary of Chambourcin map constructed using the regression algorithm ...... 52

Table 5. Summary of Cabernet Sauvignon map using the regression algorithm...... 53

Table 6. Summary of consensus map using the ML algorithm ...... 54

Table 7. Summary of Cabernet Sauvignon map using the ML algorithm ...... 55

Table 8. Summary of Chambourcin map using the ML algorithm ...... 56

Table 9. Summary of map comparisons using different mapping algorithms ...... 57

Table 10. Comparison of common markers in the reference maps of Vitis ...... 58

Table 11. Comparison of total distance in the reference map of Vitis ...... 59

vii

LIST OF FIGURES

Figure 1. Norton capillary electrophoresis chromatogram from Linkage Group 8 ...... 60

Figure 2. Cynthiana capillary electrophoresis chromatogram from Linkage Group 8 ...... 61

Figure 3. Cabernet Sauvignon capillary electrophoresis chromatogram from Linkage Group 8 ...... 62

Figure 4. Genetic maps constructed using the regression algorithm ...... 63

Figure 5. Genetic maps constructed using the ML algorithm ...... 73

Figure 6. Comparison of maps produced using different algorithms ...... 83

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INTRODUCTION

Grapevines have become a popular staple in agriculture due to their major product, wine. There are many different uses for grape including juice making, raisins, and table grapes but wine is by far the most popular (Mullins et al. 1992). It has been estimated that grapevines originated approximately 65 million years ago (This et al.

2006). Early records show that the cultivation of grapevines did not begin until 7,000-

8,000 years ago (Mullins et al. 1992; Terral et al. 2010). The first grapevines were cultivated in the South Caucasus (Myles et al. 2011) and viticulture and enology had spread across Europe by the first century (Mullins et al. 1992). Although wine is more of a luxury crop than a staple one, it has been extremely popular for thousands of years and made many appearances in literature, including both the new and old testaments of the bible (Mullins et al. 1992). Several mythological gods have also been dedicated to wine, such as Dionysus, Osiris, and Bacchus (Mullins et al. 1992; This et al. 2006).

Grapevines belong to the Vitaceae family, meaning they are characterized by having tendrils and flower clusters located across from leaves (Mullins et al. 1992). The majority of grape products marketed today belong to the Vitis genus (This et al. 2006).

Within this genus, there are more than 80 species, the most popular of which is Vitis vinifera (This et al. 2006). Economically, grapevines are an important woody perennial.

In the United States, there were over one million reported acres of vineyard production in

2014, as reported by the USDA. There were approximately 4.5 million tons of wines produced in the United States in 2014 averaging $767 per ton (USDA 2015). This totals to nearly $3.5 billion, making wine a very large economic contributor.

Even though the structure of DNA was just discovered in 1954, 60 years later researchers are under pressure to utilize the knowledge of DNA to increase crop production and tolerance to climatic factors (Neidle 2008; Tuberosa et al. 2008). This is made especially difficult by the changing environmental trends caused by climate change

(Tuberosa et al. 2008).

Plant Breeding

Evidence has indicated that the cultivation of plants began approximately 10,000 years ago when humans would select mutated plants which were easier to harvest (Sleper and Poehlman 2006). This plant domestication seems to have occurred in many different places around the same time period and it is unknown whether the seeds from the mutated, higher quality crops were planted for the purpose of domestication or not

(Bennett 2010). Following the initial cultivation, improvement of domesticated crops progressed slowly and crop improvement efforts did not begin until around the 18th century when the Age of Enlightenment led to a curiosity about crop improvement which humans began to act upon (Bennett 2010).

Traditional plant breeding involves the improvement of plant lines and future generations for economic improvement (Scaboo et al. 2010). Early civilization humans performed plant breeding by intentionally or unintentionally selecting seeds which were mutated, making them easier to collect. In this case, plants and seeds were selected for their ability to benefit humans, rather than the economy (Murphy 2007; Scaboo et al.

2010). This type of plant breeding has also led to a decrease in genetic diversity. The development of hybrid populations is a plant breeding method which can be used to

increase genetic diversity while still providing the same benefits (Morgante and Salamini

2003).

Plant breeding is essential because a need exists to improve crop outputs and the quality of the yields produced while using fewer inputs (Tester and Langridge 2010;

Henry and Nevo 2014). It is important to increase yield and quality in optimal and stressed conditions because the environment is changing and crops need to change with it in order to prevent an increase in inputs needed (Tester and Langridge 2010). Breeding for disease resistance in crops is also extremely important for reducing input.

Breeding must be a constantly evolving tool for crop improvement because it has to change as agriculture and organisms evolve. Breeders also need to be able to adapt to the changes in consumer demands (Collard and Mackill 2008). Breeding for improved crops will help producers adapt to a changing environment and the ever-growing population (Collard and Mackill 2008).

Molecular Breeding

Plant breeding as a scientific measure did not truly begin until after Mendel’s work became known (Scaboo et al. 2010). The speed at which molecular breeding has evolved has been very rapid, with much progress being made in the past few decades

(Somerville and Somerville 1999; Wijerathna et al. 2015). For instance, in the 1990s, researchers were just beginning to sequence the genome of model organisms such as

Arabidposis and it was believed that genome sequencing would not be widespread within a decade because of high cost (Somerville and Somerville 1999). Today, genome sequencing of new plants is extremely common and a wide variety of plant genomes have

been sequenced, including grape (Jaillon et al. 2007). Crops such as tomato (Foolad

2007) and rice (Wijerathna et al. 2015) have played an integral role in the improvement of plant and molecular breeding because they are model organisms and economic staples.

It is important to identify and genetically combat disease and climate stressors in all crops. Use of molecular markers is one of the best ways to ensure that this will happen

(Collard and Mackill 2008).

Marker-assisted selection (MAS) has revolutionized the breeding process. MAS is the process of mapping markers to a plant’s genome and identifying which markers are linked to the trait, or quantitative trait loci (QTL), of interest using statistical software

(Tester and Langridge 2010). MAS can be utilized to breed plants resistant to multiple biotic and abiotic stressors (Miklas et al. 2006). For instance, it is helpful for selecting for salt tolerant crops in areas where irrigation is needed to compensate for drought in order to prevent damage to crops from the salt left in the soil (Ashraf and Foolad 2012). In grapes, Dr. Walker of UC Davis has also successfully implemented MAS to breed grapevines resistant to nematodes and Pierce’s disease (Lund 2015).

MAS can also be utilized for gene pyramiding—a technique where markers are mapped to multiple genes controlling a trait of interest and used to ‘pyramid’ resistance genes on top of one another to combat the disease (Tester and Langridge 2010). MAS has been useful for gene pyramiding in important cereal crops such as wheat. This technique has been employed to prevent disease resistance in the crop, specifically to control rust diseases (Randhawa et al. 2013). Not only is it time and cost efficient compared to traditional breeding methods, but it is even more effective at producing crops with ideal traits (Randhawa et al. 2013).

For MAS to work, population sizes need to be sufficiently large. Furthermore, a high quality set of markers are needed for success (Collard and Mackill 2008). Many factors go into the selection of which marker to use including the complexity of analysis, frequency of differences (or polymorphisms), and cost of implementation (Staub et al.

1996). Of these factors, simple sequence repeat (SSR) markers align very well with the specified requirements (Collard and Mackill 2008). Once the population is established and the marker type selected, there are a series of needs which need to be met for MAS to be effective. Markers closely linked to the gene need to be identified and then confirmed through plant growth and phenotyping. The process also needs to be time and cost efficient enough to be worthwhile (Randhawa et al. 2013).

Molecular Markers

Molecular markers are frequently utilized in many disciplines of research ranging from animals to plants (Dekkers and Van Der Werf 2007; Walker et al. 2010). Molecular markers have been used frequently in staple crops like soybeans and corn, but are progressively being utilized in specialty crops like grapevine (Cipriani et al. 2011). In grapes, markers can be used to distinguish within and among cultivars and to assess genetic relationships (Bautista et al. 2008).

There are a multitude of different molecular markers that have been used for genetic mapping and MAS, beginning around the 1970s (Cipriani et al. 2011). Before

DNA-based markers became available, biochemical markers such as isozymes were utilized. Isozymes separate based on mutations that result in a change in the charge of an

amino acid and can be visualized through separation using gel electrophoresis (Staub et al. 1996).

DNA-based markers include restriction fragment length polymorphisms (RFLPs), randomly amplified polymorphic DNA (RAPD), amplified fragment length polymorphisms (AFLPs), single nucleotide polymorphisms (SNPs), and microsatellites

(Schlötterer 2004). RFLPs allow for a visualization of polymorphisms since they result from single nucleotide mutations that alter the cleavage sites for restriction endonucleases. This causes polymorphisms, or differences, that result in different banding patterns and can be visualized using hybridization probes and Southern blots

(Kumar 1999). RAPDs use a combination of short primer sequences of around 10 base pairs and polymerase chain reactions (PCR) to amplify DNA fragments. Gel electrophoresis can then be used to evaluate the fragment differences. However, the results gathered from RAPDs may be difficult to interpret because the short primer sequences have low specificity to DNA sequences (Walker et al. 2010). AFLPs are similar to RFLPs in that variations are seen in fragment banding patterns caused by mutations. AFLPs and RAPDs are advantageous over RFLPs because fragments are amplified using PCR—a much faster and cheaper method than the Southern blot process.

Despite their efficiency, AFLPs and RAPDs are typically dominant markers, meaning it is difficult to identify heterozygous individuals (Walker et al. 2010; Mueller and

Wolfenbarger 1999). SNPs are markers that display differences at a single nucleotide location (Vignal et al. 2002). They can be generated using next-generation sequencing techniques which can produce thousands of SNPs in a mapping population (Cipriani et al.

2011; Barba et al. 2013).

Microsatellites

Microsatellites, or SSRs, are very valuable in molecular breeding because of their

PCR-derived, polymorphic, and co-dominant nature (Merdinoglu et al. 2005). SSR markers are often used in V. vinifera genetic analyses (Blondon et al. 2004) but have become increasingly used in other grapevine species due to their high interspecies transferability (Doligez et al. 2006; Li et al. 2013). They have been implemented for rootstock identification (Lin and Walker 1998), survey of germplasms (Giannetto et al.

2010), comparison of cultivars (Lefort and Roubelakis-Angelakis 2001), and breeding for resistance (Riaz et al. 2009). In addition, several SSR-based linkage maps have been developed that have allowed for the identification of quantitative trait loci (QTLs) controlling agronomic traits and can be used for MAS to improve the efficiency of grape breeding (Doucleff et al. 2004; Riaz et al 2009).

Native North American Grapevines

Very few native North American species can be seen in commercial production today but they are frequently seen as the rootstocks on many V. vinifera vines to protect from fungal disease outbreaks due to their high level of resistance. Breeding of interspecific hybrids has also been used to confer resistance upon more popular cultivated varieties. Of the many Vitis species growing throughout the world, the majority of them are native to North America (Aradhya et al. 2003). Grapevines which are native to North

America include V. arizonica, V. aestivalis, V. cinerea, V. labrusca, V. riparia, V. rupestris, and Muscadinia rotundifolia (Stafne et al. 2015). Vitis aestivalis has become

one of few native Vitis species utilized in the grape industry today for its resistance characteristics (This et al. 2006; Stafne et al. 2015).

Of the many native species used for hybrid breeding, V. aestivalis has the most potential for warding off environmental stresses such as disease and cold temperatures

(Wagner 1996). Although other native species such as V. labrusca, V. riparia, and V. rotundifolia display these qualities individually, V. aestivalis is the only vine which displays both characteristics (Wagner 1996).

Origin of Norton and Cynthiana

‘Norton is a V. aestivalis-derived cultivar with ambiguous origins. Norton is believed to have been developed by Dr. Daniel Norborne Norton. Based on early records and correspondences, Dr. Norton developed the cultivar (originally known as ‘Norton’s

Virginia Seedling’) in one of his Virginian vineyards (Ambers and Ambers 2004). The cultivar is believed to be the result of a cross between Bland and a native V. aestivalis vine performed unintentionally by D.N. Norton (Ambers 2013). In a letter, Dr. Norton described the development of the ‘Norton’ cultivar through emasculation of ‘Bland’ and pollination with ‘Pinot Meunier’. However, ‘Norton’ bears a very strong resemblance to

V. aestivalis so it is believed that the ‘Bland’ clusters were pollinated when the flowers were not yet receptive to pollen and bags were likely not applied to protect the clusters from interfering factors. Therefore, V. aestivalis pollen likely traveled to the emasculated

‘Bland’ clusters through wind or insects and pollinated the flowers during a time when they were receptive to pollen. However, ‘Bland’ is no longer in existence so if it is

involved in the parentage of ‘Norton’, it would be nearly impossible to verify this speculation (Ambers 2013).

Similarly to Norton, Cynthiana grape cultivars are also described to be largely derived from V. aestivalis (Parker et al. 2005, 2009; Stover et al. 2009). Cynthiana was reportedly sent to the Prince of Flushing in New York by someone who discovered it in the woods of Arkansas (Hendrick 1908). The cultivar was then conveyed to Hermann,

Missouri to be grown in the vineyards there (Hendrick 1908).

However, as previously stated, the precise origin of the two cultivars can only be hypothesized. Norton and Cynthiana vines are very popular in Missouri and Arkansas, respectively. Early records report that Norton was introduced in Missouri vineyards in the late 1840s while Cynthiana was introduced in the late 1850s (Husmann 1883; Hendrick

1908). Since this time, it has been speculated that Norton and Cynthiana are actually the same cultivar (Hendrick 1908).

Norton Characteristics

‘Norton’ produces a dry, red wine and displays fungal resistance and winter hardiness characteristics (Reisch et al. 1993; Ali et al. 2011). Due to its ability to withstand these environmental conditions, Norton has become increasingly popular in the

Midwestern United States. Since its discovery in Virginia, ‘Norton’ was quickly established in vineyards west of the state and is commonly found in Midwest states such as Missouri and Arkansas (Hussmann 1883). Norton has become so popular in Missouri that Missouri is the leading producer of this cultivar (Robinson et al. 2012). Out of 500 total acres of Norton planted in the United States, the majority is constituted by vineyards

in Missouri with 300 acres planted in the state (Ambers 2013). In 2014, it was reported that Missouri had a total of 1,700 acres bearing grapes. Thus, Norton’s production makes up approximately 18% of Missouri’s total grape acreage (USDA 2015).

Similarities and Differences of Norton and Cynthiana

Many phenotypical similarities have been noted between Norton and Cynthiana.

The two cultivars display similar cluster, berry, and peduncle sizes (Main and Morris

2004). They also display resistance to many different fungal diseases, such as powdery and downy mildew, and a variety of berry rots which can severely damage vineyards across the world (Harris 2012). Another likeness is the difficulty of rooting ability from dormant hardwood cuttings (Galet 1998; Keeley et al. 2003), and a high sensitivity to sulfur spray (unpublished data). The vines are cold hardy, withstanding a temperature range as low as -32°C, and require a long growing season (~125 days) to fully ripen

(Dami et al. 2005). The two cultivars produce a dry, red wine with a high titratable acidity (8.5 to 13 g/L) which may be attributed to the high amount of malic acid present within the fruit (Main and Morris 2004).

Some phenotypical differences, however, exist between Norton and Cynthiana.

For instance, differences in the ideal soil type have been noted. Though they both thrive in sandy soils, Cynthiana favors a loam soil better than Norton and Norton favors clay and gravelly soils better than Cynthiana (Hendrick 1908; Harris 2012). Differences between the fruit and wine quality of Norton and Cynthiana have also been identified

(Hendrick 1908). It has been stated that, if grown beside one another in the vineyard, enough differences can be seen to discredit the cultivars being the same and that

Cynthiana is the superior cultivar (Hendrick 1908). Some researchers have accepted

Norton and Cynthiana as the same cultivar (Morris and Main 2010), but many growers and wine-makers still assert that distinctions exist in their respective viticultural performance and enological quality (Hendrick 1908).

Comparison of Norton and Cynthiana

Reisch et al. (1993) provided preliminary evidence that Norton and Cynthiana are genetically indistinguishable using isozyme analysis. This study evaluated five biochemical markers across seven samples of Norton, two samples of Cynthiana, and one sample of Melody which was used as a control. The results from this study revealed similar banding patterns for Norton and Cynthiana but the use of only five banding patterns provides a low resolution view of the genome.

Similarly, in a study by Parker et al. (2009), four microsatellite loci were used to identify Norton and Cynthiana as genetically synonymous cultivars. However, this is an extremely low number of microsatellites and testing a larger number of microsatellites in order to carry out a genome-wide assessment may help to better confirm or refute conclusions made from isozyme analyses.

French-American Hybrids

French-American hybrids are developed from crosses between native American and V. vinifera grapes. Most of these hybrids were developed by breeders in France as a method of combating fungal diseases such as phylloxera without sacrificing the wine quality (Wagner 1996; Pollefeys and Bousquet 2003). French-American hybrid breeders

developed the hybrids using traditional methods, meaning it took many years to get a final product (Wagner 1996; Reynolds 2015). Native American rootstocks could be grafted onto V. vinifera to protect against phylloxera but this method did not protect against fungal diseases such as downy and powdery mildew. French growers also planted

American varieties as an attempt to avoid pest and disease problems but the wine produced from these vines were far too inferior and low quality to continue production

(Wagner 1996). Thus, French-American hybrids were developed to provide natural protection from these diseases without sacrificing the wine quality. (Reynolds 2015). The development of successful French-American hybrids saved growers a great deal of money spent to combat the biotic and abiotic stressors present in France (Wagner 1996).

Although developed in France and bred for European conditions, the native grape contributions to French-American hybrid grapes makes them suitable for growth in both

France and North America (Wagner 1996). As a result, they have been planted more frequently in the United States. The cold hardy characteristics they carry along with the high quality wine produced makes them an overall suitable wine for the Eastern and

Midwest United States (Wagner 1996; Pollefeys and Bousquet 2003). Despite their beneficial qualities, very little molecular profiling has been done with them (Pollefeys and Bousquet 2003).

Chambourcin

Chambourcin is a French-American hybrid which was developed by Johannès

Sevye in France and became available on the market in 1963 (Galet 1979; Scheef 1991).

According to the Vitis International Variety Catalogue (VIVC), the parents of

Chambourcin are Seyve Villard 12-417 and Chancellor (Maul and Eibach 2003). It is a hybrid with good wine qualities and is becoming very popular in Missouri vineyards

(Scheef 1991). It also displays moderate cold hardiness, withstanding temperatures as low as -20° F, and has a long growing season (Dami et al. 2005; Homich et al. 2016). The cold hardiness of Chambourcin is often impacted by early frost and freezing events which fall during the vine’s long growing season before acclimation can occur (Zhang and

Dami 2012). However, Chambourcin is more tolerant of disease and cold temperatures than V. vinifera and cluster thinning can be implemented for optimal productivity and prevention of winter injuries (Zhang and Dami 2012; Reynolds 2015).

The pedigree of Chambourcin is extremely complex because many generations of crosses were often made before a final French-American hybrid was complete (Reynolds

2015). According to the VIVC, Chambourcin’s pedigree goes back up to eight generations on the mother’s side and seven generations on the father’s side (Maul and

Eibach 2003). The pedigree of Chambourcin includes contributions from V. vinifera, V. rupestris, V. labrusca, V. riparia, V. labruscana, V. aestivalis, and V. cinerea (Maul and

Eibach 2003).

Vitis vinifera ‘Cabernet Sauvignon’

Vitis vinifera is a popular European grape which can be utilized for eating and drinking (Riaz et al. 2004). The species has been the largest contributor to the improvement of grapevines (Olmo 1995). Although V. vinifera is the only grapevine originating from Europe, there are over 10,000 V. vinifera cultivars present today

(Mullins et al. 1992; Olmo 1995; Aradhya et al. 2003). It was cultivated from the wild

European grape V. vinifera L. ssp. sylvestris (Zohary 1995). However, very few wild vinifera vines are still in existence, as the majority have been cultivated in some way

(Olmo 1995; Zohary 1995).

Cabernet Sauvignon is a V. vinifera cultivar developed by crossing Cabernet

Franc and Sauvignon Blanc (Myles et al. 2011). The parentage of the cultivar was identified in 1996 at UC Davis (Bowers and Meredith 1997). It is also a half-sibling to

Merlot, who shares Cabernet Franc as a parent (Boursiquot et al. 2009). Cabernet

Sauvignon produces an acidic, red wine which is high in tannins (Robinson et al. 2012).

The vine originated in the Bordeaux region of France but has spread across the world

(Kolpan et al. 1996). Out of 10,000 V. vinifera cultivars, Cabernet Sauvignon is one of the most popular globally (Mullins et al. 1992; Riaz et al. 2004). As of 2010, there were over 77,000 acres of Cabernet Sauvignon planted in California alone, making it the most popular red wine variety in the state (Robinson et al. 2012). Like most V. vinifera cultivars, Cabernet Sauvignon displays low disease resistance and is susceptible to cold temperatures (Reisch et al. 1993).

Grapevine Breeding

Grape molecular breeding is important because grapes are woody perennials and require a great deal of time and money to grow out (Lodhi et al. 1995). As a result, researchers have been working to understand the grape genome since the 1990s (Lodhi et al. 1995) The first grape linkage map was published in 1995 using isozyme, RFLP, and

RAPD markers (Lodhi et al. 1995) and MAS efforts in grapevine were initiated by Dalbó et al. (2001).

The development of a hybrid grape population involves a series of steps. The first step is the emasculation of the female grape clusters. Emasculation is performed by removing the male portion of the grape flowers without harming the female portions.

Paper bags are then used to cover the emasculated clusters to prevent accidental pollination from occurring (Eibach and Töpfer 2015). Pollen must then be collected from the intended male parent and dried. The dried pollen is used to pollinate the emasculated clusters. For optimal yield, pollen should be applied when the stigma is secreting fluid and the clusters recovered with bags (Eibach and Töpfer 2015). Once berries have reached veraison, seeds can be extracted. The seeds are placed into a container of water and those which float to the top are discarded because this indicates poor embryo development. A cold stratification period of approximately 2.5 months at 4°C is used to provide the seeds with a dormant period (Eibach and Töpfer 2015).

Linkage Mapping

A linkage map is essentially a “road map” of the genome which is generated, or mapped, using molecular markers (Paterson 1996). Linkage mapping is established on the basis that genes are aligned along chromosomes and crossing-over, or recombination, may occur between them (Azhaguvel et al. 2008). Linkage between genes is determined by evaluating the frequency of recombination in order to estimate their positions relative to one another on the chromosome (Sanders and Bowman 2012). The first linkage map was constructed by Alfred Sturtevant in 1911 using Drosophila melanogaster (Sanders and Bowman 2012). In the first linkage maps published by Sturtevant and Morgan, the map distance was equal to recombination frequency (Liu 1998). In linkage maps today,

mapping functions are utilized to convert recombination frequencies to distances for mapping (Reyes-Valdés 2003). Since the development of the first linkage map, two primary mapping functions have emerged: Haldane’s and Kosambi’s. Haldane’s mapping function differs from Morgan’s because it takes double crossovers between loci into account (Ott 1991). Kosambi’s mapping function differs from both because it takes double crossovers and interference into account (Ott 1991). Interference can happen when the occurrence of a crossover event affects the probability of other crossover events occurring on the chromosome (Huehn 2011).

For genetic mapping to occur a sufficient population size must be obtained and informative markers must be available (Young 1994; Isobe and Tabata 2010). The informative markers are then screened across the population and a mapping software is used to generate a genetic map for each parent which can then be integrated into one map

(Abbott 2008). Multiple mapping software programs are in existence today which can be used for map development (Kang 2003). JoinMap is a popular mapping software which was developed in order to integrate linkage maps (Stam 1993; Isobe and Tabata 2010).

JoinMap has two mapping algorithms the user can choose from which are 1) the regression mapping algorithm and 2) the Monte Carlo maximum likelihood algorithm.

The regression algorithm is useful to construct maps with less than 50 markers on each linkage group since it works by adding markers one at a time based on how informative they are. This can cause the program to run slowly if too many markers are being screened per linkage group (Van Ooijen 2006). JoinMap provides the option of using either Haldane’s or Kosambi’s mapping functions when using the regression algorithm

(Van Ooijen 2006). The Monte Carlo algorithm is ideal for mapping if over 50 markers

are present on a linkage group. However, any errors or missing genotype data can cause issues if the map distance is too small (Van Ooijen 2006).

To date, many linkage maps have been constructed for grape interspecific hybrid populations (Grando et al. 2003; Doucleff et al. 2004; Lowe and Walker 2006; Moreira et al. 2011). A pseudo-test cross approach must be used for grape linkage mapping because grapes are highly heterozygous (Costantini et al. 2009). The linkage maps produced from grape populations have been useful for identifying QTLs for a variety of traits including downy mildew (Blasi et al. 2011; Moreira et al. 2011), powdery mildew (Hoffman et al.

2008; Riaz et al. 2011), seedlessness (Doligez et al. 2002; Mejía et al. 2007), and berry weight (Fischer et al. 2004; Cabezas et al. 2006).

Study Overview

The first study utilized SSR markers to compare Norton and Cynthiana at each of their 19 chromosomes to determine if they are genetically identical cultivars. Prior to this study, a genetic map was constructed of a V. aestivalis-derived ‘Norton’ and V. vinifera

‘Cabernet Sauvignon’ population by testing 600 SSR markers—359 of which were informative markers that are polymorphic for Norton in 19 chromosomes. A total of 185 markers, about 10 markers from each linkage group, were randomly selected and screened using capillary electrophoresis, and the resulting banding patterns were compared between Norton and Cynthiana.

For the second study, a V. interspecific hybrid ‘Chambourcin’ x V. vinifera

‘Cabernet Sauvignon’ population was developed in May 2013. The seeds produced from this cross were harvested fall 2013. Following germination, DNA was extracted from

seedling leaf tissue and capillary electrophoresis was used to identify true-hybrids. The crosses made typically result in some self-fertilized seedlings so true-hybrid testing is often necessary. Out of 215 seedlings tested, 150 were determined to be interspecific hybrids. Once the true hybrids were identified, 1,205 SSR markers were tested for polymorphism on six confirmed hybrid progeny and the two parents. Three hundred sixty markers were determined to be polymorphic and were subsequently screened across the first 94 progeny. The fragment data was genotyped and a linkage map was constructed using JoinMap 4.1.

MATERIALS AND METHODS

To compare Norton and Cynthiana, a total of 185 polymorphic markers—seven to ten from each of the 19 linkage groups in the Norton map—were randomly selected and screened across 8 total leaf samples. DNA was isolated from the leaf samples using a

Qiagen kit and DNA fragments were amplified using PCR. The fragments produced from

185 primers were analyzed using capillary electrophoresis to determine fragment lengths.

To improve the speed and cost efficiency of SSR genotyping, seven to twelve fluorescent-labeled microsatellite loci, depending on their size range, were multiplexed and evaluated simultaneously during capillary electrophoresis.

In order to construct a linkage map for a Chambourcin x Cabernet Sauvignon population, the population was developed at the Missouri State Fruit Experiment Station

(MSFES) and true hybrids were identified using capillary electrophoresis following DNA extraction. A set of 1,205 SSR markers were tested for polymorphisms using six confirmed true hybrid progeny and the parents. Polymorphic markers were screened across the hybrid population and utilized for linkage map construction in JoinMap 4.1.

Plant Materials

Four Norton samples were obtained from Missouri Vineyards, three Cynthiana samples were obtained from Arkansas vineyards and one Cabernet Sauvignon sample was obtained from a vineyard in Missouri (Table 1). The original cutting source of the St.

James ‘Norton’ leaf sample is Double A Vineyards in Fredonia, New York. The cutting

source of the ‘Norton’ sample from Missouri State Fruit Experiment Station (MSFES) is a block planted at Stone Hill Vineyard in Hermann, Missouri in the 1860s.

In May 2013, a cross was made between Chambourcin and Cabernet Sauvignon following the emasculation and pollination protocol described by Adhikari et al. (2014).

Seeds were collected from the clusters during harvest and placed into a container of water. Any seeds which floated to the top were removed and discarded. The remaining seeds were cold stratified for three months at 4°C. Germination was performed as outlined in Adhikari et al. (2014).

DNA was extracted from plant leaf materials following the extraction protocol described by Adhikari et al. (2014). Liquid nitrogen was used to grind approximately 100 mg of grape leaf tissue until it became a fine powder. A DNeasy Plant Mini Kit (Qiagen,

Valencia, CA) was used to isolate DNA following the protocol provided by Qiagen.

DNA concentrations were assessed using a NanoDrop spectrophotometer (Thermo Fisher

Scientific, Waltham, MA). DNA was diluted to 15 ng/μL and stored at 4°C when not in use.

PCR Amplification and Fragment Analysis

Microsatellite marker alleles were amplified using PCR following the protocol described by Adhikari et al. (2014). The total volume of the PCR reaction was 10 μL, consisting of:

 2 μL of 15 ng of template DNA

 1.8 μL of a primer mix containing 0.1 μM of forward and 2 μM of reverse primer

 1 μL 2 μM WellRed M13 primer

 0.2 μL 25 mM MgCl2

 5 μL AmpliTaq GoldR 360 Master Mix buffer (Life Technologies, Grand Island, NY)

The following touchdown PCR method was used to amplify the DNA:

 Initial denaturation: 10 min at 95° C,

 10 touchdown cycles of:

o Denaturation: 94° C for 30 sec

o Annealing: Initial temperature of 62° C for 30 sec, decreasing by 1° C in each consecutive cycle

o Extension: 72° C for 1 min where annealing temperature was decreased by 1° C at each cycle

 24 cycles of:

o Denaturation: 94° C for 30 sec

o Annealing: 56° C for 30 sec

o Extension: 72° C for 1 min

 Final extension: 72°C for 7 min.

Four μL of the resulting PCR products were loaded onto a 1.5% agarose gel to confirm the success of the reactions and evaluate the amount of PCR required for capillary electrophoresis (Bio-Rad, Hercules, CA).

A GenomeLab GeXP genetic analysis system, otherwise known as capillary electrophoresis (Beckman Coulter, Brea, CA), was used to determine allele sizes. The system uses a GenomeLab GeXP Genetic Analysis software, Fragment Analysis Module, to evaluate fragment sizes. Fragment lengths were analyzed and interpreted for all SSR markers utilized for the comparative assessment and linkage map construction. A control

DNA size standard 400 ladder and Sample Loading Solution was combined with PCR

products prior to capillary electrophoresis. A multiplex capillary electrophoresis program was implemented to evaluate seven to twelve PCR products simultaneously.

Chambourcin x Cabernet Sauvignon Population Analysis

Following germination, seedlings were tested using fragment analysis to determine if they were F1 interspecific hybrids. Leaf samples were collected from each seedling to be used for DNA isolation. PCR was performed on the extracted DNA using five different SSR markers (FAM15, FAM35, VrZAG62, VVS2, FAM75, and FAM115).

Gel electrophoresis was then implemented in order to verify the presence of PCR product and to assess sample quantities to be used for capillary electrophoresis. The verified interspecific hybrids were then transferred into larger pots and eventually transferred to the vineyard. DNA from interspecific hybrids was stored at -20°C for later use in population analysis.

Prior to testing microsatellites for polymorphisms, a preliminary test was run to determine the presence or absence of a band by running PCR using the two parents. Gel electrophoresis was used to evaluate PCR products for band presence. SSR markers which displayed bands for both Chambourcin and Cabernet Sauvignon were tested for polymorphism using six of the confirmed interspecific hybrid progeny and the two parents. The confirmed polymorphic markers were utilized for population analysis on the first 90 Chambourcin x Cabernet Sauvignon hybrid progeny and the two parents.

Capillary electrophoresis was used for allelic size determination during true hybrid identification, polymorphic marker testing and population analysis.

Linkage Map Construction

Microsatellite results from population analysis were genocoded following the

JoinMap segregation codes for a cross pollinated (CP) population (, ,

, , ). Genotyped results were transferred from MS-Excel to

JoinMap 4.1 (Van Ooijen 2006) for mapping. Three hundred eighteen loci were evaluated across 90 individuals in the population. Loci genotype frequencies were sorted by amounts of missing data and those with a substantial amount missing were excluded from map construction. Markers were also evaluated for similarity and markers with a similarity greater than 0.97 were also excluded.

The ‘recombination frequency’ grouping parameter was used for map construction and confirmed through re-evaluation using the ‘independence LOD parameter’. The recombination frequency threshold range began at 0.250 and ended at

0.050, decreasing stepwise by 0.05. Both the regression mapping algorithm and the maximum likelihood (ML) mapping algorithm were used to generate parental and consensus maps. Kosambi’s mapping function was used with the regression mapping algorithm. Parental nodes were constructed for regression mapping using the “Create

Maternal and Paternal Population Nodes” function in JoinMap. Parental maps were automatically constructed when using the ML algorithm. Chromosomes were assigned to linkage groups based on ESTs present in the linkage groups. A reference framework of

Vitis was used to identify chromosome numbers for linkage groups which did not contain

ESTs (Doligez et al. 2006). MapChart (Voorips 2002) was utilized to export all maps.

RESULTS

Norton and Cynthiana

Of the 185 markers (740 alleles) evaluated, Norton and Cynthiana fragment lengths were revealed to be identical for nearly all markers (Table 2). Ten alleles (1.4%) showed differences between Norton and Cynthiana. Differences in fragment sizes never exceeded one base pair. A comparison of the fragment peak patterns between Norton and

Cynthiana revealed many similarities and few differences. Slight differences in peak height may have been caused by differences in sample disbursement. A further comparison of Norton and Cynthiana peak patterns to Cabernet Sauvignon peak patterns revealed significant differences in Cabernet Sauvignon (Fig. 1-3).

Chambourcin x Cabernet Sauvignon

Out of 215 Chambourcin x Cabernet Sauvignon seedlings tested, 150 were revealed to be true hybrids following fragment analysis. The results from the preliminary tests to determine band presence of microsatellite markers (n=1,205) using gel electrophoresis are as follows:

 Both parents—952

 Chambourcin only—20

 Cabernet Sauvignon only—24

 No band—209

Six hybrid progeny and the two parents were used to screen 952 markers for polymorphisms using capillary electrophoresis. Three hundred sixty-three polymorphic

markers were identified and deemed suitable for use in population analysis. Following population analysis, 318 of these polymorphic markers produced ratios suitable for linkage evaluation in JoinMap. The following totals (n=318) were recorded for CP marker segregation types and utilized for mapping in JoinMap:

 —85

 —73

 —17

 —91

 —52

Using the regression mapping algorithm, 276 markers were mapped in the consensus map, 214 in the map for Chambourcin, and 194 in the Cabernet Sauvignon map. These maps spanned 1160.0 cM, 999.3 cM, and 1076.5 cM, respectively. The parental maps were aligned along either side of the consensus map (Fig. 4). Markers not mapped were either ungrouped or excluded due to similarity or high amounts of missing data. The linkage group covering the largest distance in the consensus map was linkage group 18 and spanned 96.2 cM (Fig 4; Table 3). Linkage groups 9 and 7 were the largest in Chambourcin and Cabernet Sauvignon, respectively (Fig 4; Table 4, 5). The average gap in the consensus map was 4.20 cM. In the Chambourcin and Cabernet Sauvignon maps, the average gaps were 4.67 cM and 5.55 cM, respectively (Table 3-5). Twenty- three markers were excluded from the Chambourcin map, 21 from the Cabernet

Sauvignon map, and 17 from the consensus map due to high similarity or distortion determined using Chi-square analysis (p=0.01).

Two hundred sixty-nine markers were mapped in the consensus, 226 markers in the Chambourcin, and 201 markers in the Cabernet Sauvignon maps produced using the maximum likelihood mapping algorithm. The maps covered a genetic distance of 1821.9 cM, 1774 cM, and 1643.4 cM, respectively. Both parental maps were able to be aligned with the consensus map (Fig. 5). Linkage group 14 spanned the largest distance in both the consensus map and the Cabernet Sauvignon map (Fig. 5; Table 6, 7). Linkage group

10 was the longest group in the map for Chambourcin. The average gaps in the consensus, Chambourcin, and Cabernet Sauvignon maps were 6.77 cM, 7.85 cM, and

8.18 cM, respectively (Table 6-8). Twenty-four markers were excluded from ML mapping due to high similarity or distortion determined using Chi-square analysis

(p=0.01).

DISCUSSION

Comparative Assessment of Norton and Cynthiana

The use of 185 SSR markers spanning 19 linkage groups in this study proved to be a reasonable approach for the genetic analysis between Norton and Cynthiana. All of the PCR products were successfully amplified and the use of multiplex capillary electrophoresis allowed for a quick and efficient investigation of the microsatellite loci within two genomes. First testing the six standard markers on each sample provided an initial idea of the DNA quality, as well as the expected final results.

The fragment sizes and peak patterns for Norton and Cynthiana revealed undeniable similarities and very minute differences. Conversely, the data revealed significant differences between Norton/Cynthiana and Cabernet Sauvignon. Since the data collected did not show significant differences between the Norton and Cynthiana cultivars, this is solid evidence that the two cultivars are genetically identical within these

185 loci. The slight differences in fragment length observed between Norton and

Cynthiana were likely the result of computational errors from the capillary array.

Variation in fragment length for the same primer was never so markedly different between Norton and Cynthiana that it could be labeled significant. Quite often variations in base pair values were the result of rounding, though the fragment lengths may not have differed by more than a tenth of a base pair. Furthermore, it is not uncommon for clones to display some genetic variations. Clonal evaluations of Cabernet Sauvignon using SSR markers have revealed some fragment differences between the clones despite being the same cultivar (Moncada et al. 2006). It has also been suggested that differences seen in

clones may be caused by transposable elements in somatic cells (Carrier et al. 2012).

Although Norton and Cynthiana were similar to one another, polymorphisms could be seen in Cabernet Sauvignon. These results support the initial hypothesis that Norton and

Cynthiana are the same cultivar.

The identical results between Norton and Cynthiana were largely expected due to the results of isozyme analysis. The isozyme analysis data were identical at all five banding patterns tested, leading researchers to believe Norton and Cynthiana were indistinguishable (Reisch et al. 1993). Due to the advancement of technology and identification of hundreds of microsatellites, the results provided by isozyme analysis represent a low resolution comparison of the two genomes. A more detailed investigation of the genomes would leave less room for uncertainty. This comparative assessment using microsatellites provided an effective method for analyzing the genomes of the two cultivars by utilizing capillary electrophoresis. Capillary electrophoresis has proven to be a reliable method for DNA sequencing and sample identification (Huang et al. 1992). The high resolution results produced by capillary electrophoresis provided a more accurate and reliable conclusion than simply using isozyme or gel electrophoresis banding patterns.

Although many growers and wine makers have asserted that Norton and

Cynthiana are different, the most recent documentation of these differences dates back to

1908 (Hendrick 1908). This source states:

“The botanical differences between the two varieties are not greater than might be attributed to environment, soil, climate, and culture; but side by side the two grapes ripen at different times, and the quality of the fruit, and more particularly of the wine is such that the varieties must be considered as distinct. The distinction should be maintained for Cynthiana is the better of the two.”

Furthermore, a publication by Husmann, the owner of the Missouri vineyard where

Norton and Cynthiana were first planted side by side, stated in 1883:

“[Cynthiana] resembled the Norton so much in growth and foliage, that I supposed it to be identical with it, until it bore fruit, and more especially when I made wine from it, when the difference became very apparent. This seeming identity has prevented dissemination…but the bunch is generally heavier, with broader shoulders, the berry somewhat larger, sweeter, and less astringent, and the wine is not quite as dark, less rough and astringent…”

In defense of these statements, a variety of hypotheses can be formed to explain the phenotypic differences between the two cultivars. For instance, the soil texture and/or quality could have differed between the locations where Norton and Cynthiana were grown within the same vineyard. Soil texture can affect water retention and thus water availability to the grapevine (Van Leeuwen et al. 2009). Water availability is important in grapevines because it can directly affect the amount of sugar and water held within the berry—factors which are key in wine-making (Tramontini et al. 2012). Soil quality can influence berry harvest and can affect phenolic components such as anthocyanin (de

Andrés-de Prado et al. 2007). Anthocyanin differences could explain the color differences noted between Norton and Cynthiana wine by Husmann (1883) (Sacchi et al.

2005). Potassium levels in the soil also have the ability to affect pH in grapes and wine

(Jackson and Lombard 1993).

Another hypothesis to explain the differences between the two cultivars is that the

Norton and Cynthiana wines being compared were different ages. Norton wine is said to peak between 8 and 10 years after bottling (Pollack 2011). If the bottling dates differed, this could have resulted in differences in taste when comparing the two. Wines bottled in different years could also have variations caused by differing climate/environmental factors between the two years. Summers which are warmer than normal can result in a

lighter colored wine (Main and Morris 2007). Seasons with higher or lower than normal rainfall can also affect the wine produced as it has the ability to affect almost every aspect of berry quality including phenolics, degrees Brix, titratable acidity and pH (Jackson and

Lombard 1993).

As stated above, there are a variety of factors and management techniques which could alter berry and wine quality. It is difficult to determine the factors which contributed to the differences noted between Norton and Cynthiana over 100 years ago.

The main solution to this problem would be to perform a controlled evaluation comparing the two side by side while ensuring that the soil quality and texture are the same in all locations and that any wines used to compare the vines were produced in the same year.

The origin of Norton and Cynthiana is largely unknown, although it has been suggested that Norton originated prior to Cynthiana (Husmann 1883). A study on the origin of Norton presumed it to be the older of the two cultivars and thus the original cultivar if they are the same (Ambers and Ambers 2004). Despite hypothesized origins of the two cultivars, growers in Missouri are more prone to call the cultivar ‘Norton’ while

Arkansas growers are likely to use the term ‘Cynthiana’ for what is now thought to be the same cultivar. These reasons may cause complications to arise when determining what the identical cultivars should be called. It is unlikely that either ‘Norton’ or ‘Cynthiana’ will be used universally when deciding how to name and market the products of these two cultivars. Consequently, if these results are taken into consideration, then the terms

‘Norton’ and ‘Cynthiana’ should be accepted as the same and used interchangeably.

‘Chambourcin’ x ‘Cabernet Sauvignon’ Linkage Map Construction

Linkage maps have become very useful in genomics research because they assist in and allow for QTL mapping, marker-assisted selection (MAS), and cultivar comparisons such as the one mentioned previously. The cross made between V. interspecific hybrid ‘Chambourcin’ and V. vinifera ‘Cabernet Sauvignon’ allowed for the production of the first genetic map developed from Chambourcin. Chambourcin is a

French-American hybrid which was originally developed in France but has recently developed popularity in the Midwestern United States due to its wine quality and moderate disease resistance.

Prior to this study, very little work had been carried out to investigate the genetic information available in Chambourcin. The consensus maps produced here covered 1,160 cM and 1,821.9 cM, depending on the algorithm used, and utilized a maximum of 276 out of 318 total SSR markers. Fewer SSR markers were included in the parental maps than the consensus maps regardless of the algorithm used (Table 3-8). This could be explained by the exclusion of markers for both maps, markers for

Chambourcin, and markers for Cabernet Sauvignon during map construction.

Markers with these segregation types were not used for parental map construction due to an inability to garner linkage information from these marker types when evaluating the parents individually. Grapevine chromosome numbers were determined for the linkage groups using EST markers which have known locations. Linkage groups within the consensus maps which did not contain any EST markers utilized the reference map for

Vitis to determine chromosome numbers (Doligez et al 2006).

A comparison of the maps produced using the regression and ML algorithms revealed both similarities and differences. Marker order was conserved in many chromosomes although some differences were seen in chromosomes 1, 2, 8, 13, 15, and

16 (Fig. 6). Linkage groups in the maps produced using the ML algorithm revealed genetic distances which were larger than those seen in the maps produced from regression mapping (Table 9; Fig. 6). These differences were likely caused by the sensitivity the ML algorithm displays to errors within the dataset, which can cause linkage group distances to increase (Hackett and Broadfoot 2003; Cheema and Dicks

2009). The larger distances in the ML mapping also resulted in a larger average gap than what is seen in the regression map.

Another large difference between regression mapping and ML mapping was that the ML map showed better linkage group coverage on chromosome 18 in Chambourcin than the corresponding linkage group in the regression map (Fig. 4, 5). In the map produced from regression mapping, an extra linkage group was required to provide equivalent coverage and thus two separate linkage groups were needed for Chambourcin to align with the two ends of chromosome 18 in the consensus map (Fig. 4). This was likely because the linkage groups covered opposite ends of the chromosome and, as a result, the regression mapping algorithm was unable to identify substantial linkage between the two groups to incorporate them into one linkage group. However, the ML algorithm was able to identify linkage between the two ends of chromosome 18 and thus displayed better alignment with chromosome 18 in the consensus map (Fig. 5).

The map constructed using the ML algorithm contained 7 fewer SSRs than the regression map (Table 9). This was likely due to the removal of more distorted markers in

the ML map. Prior to the construction of the final consensus maps, the maps were evaluated with and without distorted markers (p=0.01) included. The separate maps were then compared to evaluate how the removal of distorted markers affected the marker order in the maps. Distorted markers which did not affect marker order were included in the final map. The map produced using the ML algorithm was highly sensitive to distorted markers than the regression map. As a result, the ML map had to be produced without distorted markers because of the large affect they had on marker order in the map.

The consensus map obtained using the regression algorithm was also used for comparison with a framework linkage map constructed for Vitis (Doligez et al. 2006).

The comparison of these maps revealed consistencies between the consensus map and the reference framework map. However, the consensus covered only 0.86 of the distance that the framework map covered between the outermost markers shared between the maps

(Table 10). The consensus map also covered a shorter total distance than the reference map, although it contained a higher total number of markers (Table 11; Doligez et al.

2006).

Despite containing more markers than the reference map, other linkage maps have been produced using Vitis which contain larger numbers of SSRs than the map produced here (Welter et al. 2007; Vezzulli et al. 2008). Since Chambourcin contains a substantial amount of V. vinifera within its pedigree, this could have affected the number of polymorphic SSR markers available for a population developed by crossing Chambourcin with another V. vinifera cultivar. The low marker numbers in the map also could have been caused by an insubstantial number of progeny. A larger population is always better

when linkage mapping because it provides more opportunities for recombination to occur and for the mapping program to identify linkage. The population used for map construction contained only 90 progeny. The plates used for capillary electrophoresis will hold 96 total samples, which limits the number of progeny that can be tested at one time.

Ideally, 94 progeny and the two parents would have been screened using the identified polymorphic markers. However, four plants died while population analysis was being conducted and only 90 total samples had a complete data set. Fortunately, these 90 progeny represent only one part of the Chambourcin x Cabernet Sauvignon population and there are more progeny left to be tested. Additionally, this population is also being expanded to approximately 300 true hybrids. Once the remaining population is tested, the linkage map will likely improve because there will be more opportunities for recombination.

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Table 1. Location and age of leaf sample collections

Cultivar Vineyard Vineyard Location Year planted

Norton St. James St. James, MO 1986

Norton Les Bourgeois Rocheport, MO Late 1980s

Norton McMurtrey Mountain Grove, MO 1984

Norton* Missouri State Fruit Experiment Mountain Grove, MO 2011 Station

Cynthiana Post Altus, AR ~1890

Cynthiana Leding Altus, AR ~1920

Cynthiana University of Arkansas Fruit Clarksville, AR ~1980 Research Station

Cabernet Missouri State Fruit Experiment Mountain Grove, MO 2008 Sauvignon Station

Table 2. Genome-wide comparison of 185 loci in Norton and Cynthiana

Chr. Primer Norton Cynthiana Cab. Sauv.

1 FAM79 146/148 146/148 146/156 VMCNG2g7 91/112 91/112 110/110 UDV-055 163/165 163/165 167/167 VMC9D3 198/200 198/200 179/206 VMC8D1 198/221 198/221 217/221 VVIO61 228/231 228/231 234/234 VVIF52 260/265 260/265 257/257 VVIS21 276/290 276/290 282/282 VMC9F2 289/313 289/313 214/289 VVIP60 310/332 310/332 307/315

2 FAM24 266/270 266/270 270/277 FAM140 256/265 256/265 251/266 VMC3B10 86/121 86/121 123/127 VMC7g3 119/149 119/149 119/136 UDV-109 131/142 131/142 131/154 VMC6F1 140/142 140/142 134/140 VMC5G7 191/213 191/213 198/200 VRIP93 199/208 199/208 197/199 VVMD34 240/244 240/244 240/248 VVIU20 366/384 365/383 366/388

3 FAM102 147/160 147/160 150/150 FAM138 207/212 207/212 204/204 FAM030 278/283 278/283 285/285 VVIN54 100/106 100/106 100/100 VMC3F3 127/135 127/135 131/137 UDV-093 147/164 147/164 163/168 VMC1G7 243/260 243/260 254/264 VVMD36 278/291 278/291 253/262 CF1608 281/293 281/293 284/284 ctg0171 297/303 297/303 297/320

4 FAM46 143/148 143/148 143/143 FAM126 202/222 202/222 192/201 FAM02 203/226 203/226 193/203 FAM38 224/232 224/232 226/230 VMC2E10 53/55 53/55 57/59 VMC7h3 131/143 131/143 135/161 VVIP37 117/129 117/129 149/154 VVIP77 180/186 180/186 186/191 CTG6983 254/277 254/277 244/254 VVIR46 377/381 377/381 379/385

Table 2 continued

Chr. Primer Norton Cynthiana Cab. Sauv.

5 FAM10 100/134 100/134 103/131 FAM72 166/176 166/176 174/174 FAM12 368/377 368/377 355/364 VVC71 83/99 83/99 96/96 VMC3B9 100/101 100/101 90/105 ctg6305 158/163 158/163 161/175 VRIP89 159/186 159/186 159/159 PSCtg199_2 177/223 177/223 185/208 ssrVrZAG79 250/254 250/254 246/246 VVIN33 283/285 283/285 283/291

6 FAM110 287/311 287/311 287/287 FAM78 296/306 296/306 306/306 FAM40 315/318 315/318 316/326 VVIM43 88/94 88/94 85/101 VMC2F10 95/114 95/114 95/105 VVC07 98/119 98/119 98/98 VMC4G6 124/140 124/140 124/130 UDV-085 128/152 128/152 133/138 VMCNg1h11 238/261 238/261 238/238 VVIP28 246/252 246/252 248/261

7 FAM13 191/196 191/196 197/197 FAM115 315/336 315/336 327/330 VVIV36 156/171 156/171 155/161 VMC16F3 178/185 178/185 176/187 ssrVrZAG62 181/205 181/204 189/195 VVCS1H059O18F1-1 191/194 191/194 189/194 Vamu111-CS 194/196 194/196 196/196 Psctg45_2 203/211 203/211 202/204 VVMD06 212/215 212/215 212/212 VVMD7 236/246 236/246 239/239

8 FAM59 166/169 166/169 190/190 FAM113 246/263 246/263 256/263 FAM55 270/273 270/273 278/278 FAM16 326/329 326/329 329/329 FAM76 395/399 395/399 395/395 UDV-125 112/116 112/116 97/138 VMC1b11 175/177 175/177 185/185 VMC5h2 195/212 195/212 195/195 VMC2F12 193/232 193/232 212/249 VMC1e8 225/229 225/229 224/224

Table 2 continued

Chr. Primer Norton Cynthiana Cab. Sauv.

9 FAM35 139/140 139/140 153/164 EST2B07 246/252 246/252 246/246 VMC6d12 147/162 147/162 181/183 FAM42 370/398 370/398 380/395 VMC2E11 86/94 86/94 94/98 VMC3G8 160/164 160/164 162/172 CD009354 183/185 183/185 183/195 SC8_0141_028 219/229 219/229 223/223 VVIU37 228/230 228/230 237/237 VVIO52 375/377 375/377 384/384

10 FAM148 392/395 392/395 398/398 VMC2E8 64/69 64/69 67/77 VMCZAG67 136/138 136/138 123/136 VRZAG67 142/144 142/144 129/142 VRIP64 149/160 149/160 139/158 VVIV37 147/163 147/163 166/166 VmcSsrVrZAG025 239/242 239/242 228/239 ctg5592 213/223 213/223 219/224 VRIP25 239/242 239/242 228/239 VVIH01 246/247 246/247 245/260

11 EST8c01b 151/154 151/154 156/171 FAM149 342/345 342/345 345/351 FAM07* 348/360 347/360 356/356 FAM73 376/389 376/389 364/385 VVS2 135/137 135/137 141/154 ctg0393 195/197 195/197 195/199 VVCS1H091D05F1-1* 259/271 258/270 270/270 CF6881 280/293 280/293 268/288 SC8_0118_063 307/320 307/320 300/303 ctg3410 316/337 316/337 338/338

12 FAM71 181/184 181/184 180/182 VVCS1H084C16R1-1 94/98 94/98 98/101 VVCS1H078D22R1-1 97/112 97/112 97/97 UDV-120 155/166 155/166 136/152 VMC8g6 144/152 144/152 162/166 VMCNG1G4 171/183 171/183 174/180 ctg3230 193/198 193/198 202/215 VMC2H4 203/214 203/214 214/221 CTG0863 227/240 227/240 226/261 C004 310/315 310/315 315/315

Table 2 continued

Chr. Primer Norton Cynthiana Cab. Sauv.

13 EST10h11 154/162 154/162 156/156 SCE_0071_014 144/173 144/173 177/181 VVIC51 175/177 175/177 166/186 PSCTG231_2 179/187 179/187 193/193 NS01 183/188 183/188 183/183 VMC3D12 214/216 214/216 205/225 SC8_0053_001 234/241 234/241 226/234 VMCNG4el0.1 247/259 247/259 248/252 VMC9H4 271/283 271/283 272/276 CTG7356 279/298 279/298 279/279

14 FAM90 371/377 371/377 375/375 VMC2H12 112/116 112/116 95/95 VVCh14-9 101/104 101/104 106/106 VMCNG1G1.1 192/200 192/200 175/229 VVCh14-18 180/184 180/184 178/178 VVC34 184/187 184/187 201/207 VVC62 184/188 184/188 184/204 VRIP112 235/240 235/240 224/229 VVIN94* 273/280 273/279 294/294 FAM44 110/116 110/116 116/116

15 FAM105 271/277 271/277 277/279 VVIM42-2 87/89 87/89 85/85 ctg4274 283/293 283/293 277/279 VMC5G8 297/299 297/299 313/321 SC8_0040_088 360/371 360/371 334/334 VVIQ61 364/366 364/366 362/368 VVIV67 335/337 335/337 369/377

16 FAM36 357/375 357/375 371/381 UDV-009 143/158 143/158 141/177 UDV-052 150/182 150/182 160/184 VMC1E11 203/205 203/205 195/199 GB007D01 214/222 214/222 205/205 CTG7620 227/230 227/230 226/226 VVMD5 233/247 233/247 231/240 ctg7933 242/251 242/251 242/246 ctg9366 266/269 266/269 263/266 ctg2141 358/367 358/367 361/361

Table 2 continued

Chr. Primer Norton Cynthiana Cab. Sauv.

17 VMC2H3 77/79 77/79 81/87 VVCS1H068D03F1-1 83/106 83/106 87/98 VMC3C11.1 93/104 93/104 96/108 CTG8270 141/149 141/149 157/157 VMC9G4 153/160 153/160 170/172 ctg9346 141/149 141/149 157/158 ctg6954 229/235 229/235 222/230 ctg5672 228/236 228/236 244/244 ctg6344 268/276 268/276 271/279 AF3283 249/254 249/254 265/267

18 FAM132 137/140 137/140 136/136 FAM100 171/182 171/182 191/193 FAM75 161/177 161/177 174/174 FAM06 288/316 288/316 298/298 VMC2B1 97/107 97/107 107/107 UDV-130 125/133 125/133 138/153 VVCS1H066N21R1-1* 156/160 156/159 159/163 VVIU04 170/173 170/173 170/170 UDV737 294/309 294/309 290/300 B004 389/397 389/397 394/394

19 FAM15 119/124 119/124 109/115 UDV029 77/91 77/91 85/97 VMC3b7.2 97/128 97/128 103/103 UDV023 205/224 205/224 195/201 VMC5e9 201/208 201/208 195/218 PSCTG196_2 284/309 284/309 289/297 VVIN04 360/363 360/363 367/367 VVIV33 351/373 351/373 344/356

Table 3. Summary of consensus map size and marker distribution constructed using the regression algorithm

Linkage group Length Number of Average gap (cM) SSRs (cM) 1 52.8 21 2.51 2 68.1 13 5.24 3 15.5 7 2.21 4 90.0 23 3.91 5 65.3 19 3.44 6 45.2 10 4.52 7 84.4 14 6.03 8 73.1 11 6.65 9 67.6 12 5.63 10 93.8 20 4.69 11 59.6 12 4.97 12 44.6 12 3.72 13 31.0 9 3.44 14 69.3 29 2.39 15 48.9 6 8.15 16 36.8 11 3.35 17 71.8 17 4.22 18 96.2 18 5.34 19 46.0 12 3.83 Total 1160.0 276 4.20

Table 4. Summary of the Chambourcin (P1) map size and marker distribution constructed using the regression algorithm

Linkage group Length Number of Average gap (cM) SSRs (cM) 1 39.1 18 2.17 2 66.4 8 8.30 3 13.5 5 2.70 4 70.8 16 4.43 5 64.5 12 5.38 6 43.9 9 4.88 7 23.8 5 4.76 8 73.9 11 6.72 9 82.5 12 6.88 10 77.0 17 4.53 11 61.1 11 5.55 12 39.1 9 4.34 13 31.9 9 3.54 14 64.5 21 3.07 15 47.4 6 7.90 16 28.6 10 2.86 17 61.9 12 5.16 18 57.0 13 4.38 19 52.4 10 5.24 Total 999.3 214 4.67

Table 5. Summary of the Cabernet Sauvignon map size and marker distribution constructed using the regression algorithm

Linkage group Length (cM) Number of Average gap SSRs (cM) 1 83.7 13 6.44 2 57.3 9 6.37 3 12.4 4 3.10 4 80.2 16 5.01 5 70.7 17 4.16 6 45.5 7 6.50 7 103.1 13 7.93 8 23.8 5 4.76 9 70.0 14 5.00 10 76.2 15 5.08 11 46.0 8 5.75 12 54.0 10 5.40 13 40.3 6 6.72 14 73.1 14 5.22 15 32.8 5 6.56 16 40.3 10 4.03 17 67.2 12 5.60 18 97.7 12 8.14 19 2.2 4 0.55 Total 1076.5 194 5.55

Table 6. Summary of consensus map size and marker distribution using the Maximum Likelihood algorithm

Linkage group Length Number of Average gap (cM) SSRs (cM) 1 127.5 21 6.07 2 68.9 12 5.74 3 19.0 7 2.71 4 143.8 22 6.54 5 115.1 18 6.39 6 61.5 10 6.15 7 146.0 14 10.43 8 97.1 11 8.83 9 110.9 13 8.53 10 156.0 19 8.21 11 72.8 12 6.07 12 63.5 12 5.29 13 34.0 9 3.78 14 196.0 28 7.00 15 51.7 5 10.34 16 48.0 11 4.36 17 98.9 17 5.82 18 150.4 18 8.36 19 60.8 10 6.08 Total 1821.9 269 6.77

Table 7. Summary of the Cabernet Sauvignon (P2) map size and marker distribution using the Maximum Likelihood algorithm

Linkage group Length Number of Average gap (cM) SSRs (cM) 1 90.5 11 8.23 2 67.6 11 6.15 3 13.2 5 2.64 4 136.8 16 8.55 5 139.6 17 8.21 6 50.5 8 6.31 7 131.9 12 11.0 8 93.3 8 11.7 9 113.2 11 10.3 10 105.2 15 7.01 11 44.9 9 4.99 12 61.1 11 5.55 13 17.8 5 3.56 14 250.5 17 14.7 15 27.7 4 6.93 16 46.3 11 4.21 17 86.0 12 7.17 18 118.5 12 9.88 19 48.8 6 8.13 Total 1643.4 201 8.18

Table 8. Summary of the Chambourcin (P1) map size and marker distribution using the Maximum Likelihood algorithm

Linkage group Length Number of Average gap (cM) SSRs (cM) 1 119.4 20 5.97 2 65.0 7 9.29 3 14.1 5 2.82 4 150.8 20 7.54 5 90.6 13 6.97 6 63.6 9 7.07 7 134.8 9 15.0 8 97.3 11 8.85 9 97.4 11 8.85 10 196.0 17 11.5 11 77.1 11 7.01 12 61.2 9 6.8 13 36.7 9 4.08 14 134.5 23 5.85 15 47.6 5 9.52 16 48.7 10 4.87 17 88.3 12 7.36 18 178.1 14 12.7 19 72.8 11 6.62 Total 1774 226 7.85

Table 9. Summary of the linkage group comparison using different mapping algorithms

Regression Maximum Likelihood Linkage Group Total distance Number of Total distance Number of SSRs SSRs 1 52.8 21 127.5 21 2 68.1 13 68.9 12 3 15.5 7 19.0 7 4 90.0 23 143.8 22 5 65.3 19 115.1 18 6 45.2 10 61.5 10 7 84.4 14 146.0 14 8 73.1 11 97.1 11 9 67.6 12 110.9 13 10 93.8 20 156.0 19 11 59.6 12 72.8 12 12 44.6 12 63.5 12 13 31.0 9 34.0 9 14 69.3 29 196.0 28 15 48.9 6 51.7 5 16 36.8 11 48.0 11 17 71.8 17 98.9 17 18 96.2 18 150.4 18 19 46.0 12 60.8 10 Total 1160.0 276 1821.9 269

Table 10. Comparison of the Chambourcin x Cabernet Sauvignon regression consensus map and a Vitis reference map using distances between common markers in the maps

Chromosome First common Last Vitis Consensus Ratio marker common reference map distance marker map distance (cM) (cM) 1 VVIF52 VRZAG29 55.7 35.2 0.63 2 VVMD34 VMC8C2 50.9 58.8 1.16 3 UDV043 VVIB59 22.9 6.7 0.29 4 VMCNG1F1.1 VRZAG83 73.5 89.5 1.22 5 VVMD27 VMC4C6 65.5 50.6 0.77 6 VMC2H9 VVIM43 51.8 41.0 0.79 7 UDV011 VVIV04 83.3 72.4 0.87 8 VMC6G8 VVIB66 57.4 58.1 1.01 9 VMC3G8 VVIQ52 49.1 39.3 0.80 10 VVIH01 UDV063 65.0 59.0 0.91 11 VMCNG2H1 VVIB19 34.5 34.2 0.99 12 VMC4H9 VMC8G9 17.7 16.7 0.94 13 VVIC51 VMC9H4 20.3 11.3 0.56 14 VMC1E12 VVIN70 70.9 66.9 0.94 15 VVIP33 VMC4D9 21.2 15.6 0.74 16 VMC1E11 VVMD5 42.8 25 0.58 17 VMC3C11.1 VVIB09 43.5 41.8 0.96 18 VMC2A3 VMC7F2 98.6 73.8 0.75 19 VMC3B7 UDV127 27.8 24.6 0.88 Total 952.4 820.5 0.86

Table 11. Comparison of the Chambourcin x Cabernet Sauvignon regression consensus map and the Vitis reference map using total linkage group distances

Linkage Group Vitis reference map Consensus map Ratio distance (cM) distance (cM) 1 83.6 52.8 0.63 2 78.0 68.1 0.87 3 59.2 15.5 0.26 4 82.3 90.0 1.1 5 65.5 65.3 0.97 6 69.1 45.2 0.65 7 99.7 84.4 0.85 8 90.6 73.1 0.81 9 90.3 67.6 0.75 10 80.1 93.8 1.2 11 72.5 59.6 0.82 12 75.9 44.6 0.59 13 83.5 31.0 0.37 14 92.3 69.3 0.75 15 32.6 48.9 1.5 16 76.9 36.8 0.48 17 53.7 71.8 1.3 18 127.0 96.2 0.76 19 72.3 46.0 0.64 Total 1485.1 1160 0.78

Fig. 1. Norton capillary electrophoresis chromatogram from Linkage Group 8

Fig. 2. Cynthiana capillary electrophoresis chromatogram from Linkage Group 8

Fig. 3. Cabernet Sauvignon capillary electrophoresis chromatogram from Linkage Group 8

1_P1 1 1_P2

0.0 VMC8D1 4.0 Vchr1b VVIF52 VMC8D1 6.2 Vchr1c 0.0 0.0 9.7 VMC7g5 7.9 VMC8D1 10.1 VVIN61 17.3 Vchr1b 10.7 ctg5664 19.9 Vchr1c 12.0 CTG5955 20.7 VVIN61 12.4 VVIP60 22.3 VMC7g5 13.3 VMCNG1H7 25.7 CTG5955 13.8 ctg8034 VMC3G9 25.8 VVIP60 22.4 VVIN61 14.0 VVIB94 26.0 CTG1774 26.3 ctg5664 16.0 C008 27.4 VMC7g5 20.0 AF8125 27.6 ctg8034 22.0 VRZAG29 28.0 VMC3G9 34.2 VVIP60 28.0 VVC19 29.2 C008 36.5 ctg5664 28.4 VVCS1H024F14R1-1 29.7 VMCNG1H7 38.1 VMC3G9 31.0 VVIF52 32.3 VVIB94 39.7 VMCNG1H7 39.1 VVCS1H051N07R1-1 35.2 VRZAG29 37.7 AF8125 41.1 VVCS1H024F14R1-1 48.7 VVIB94 46.3 VVC19 53.1 AF8125 52.4 VVIQ57 52.8 VVCS1H051N07R1-1 60.0 VVC19

65.3 VVIQ57

81.8 VMC4F8 83.7 FAM79

2_P1 2 2_P2

0.0 VMC7g3 0.0 VMC8C2 0.0 VMC8C2

4.7 VMC7g3 4.7 VMC7g3

10.5 SC8_0146_010 13.5 HCF045+046(FAM24) 14.1 HCF045+046(FAM24) 16.4 VMC6b1 17.7 SC8_0146_010 18.0 SC8_0146_010 18.3 SC8_0146_026

23.7 VMC6b1 23.8 VMC6b1 26.2 SC8_0508_004 27.5 VRZAG93

34.0 SC8_0508_004 36.0 VRZAG93 36.7 VVIO55 36.9 VVIO55

43.0 VMC6F1 42.8 VMC6F1 46.0 Vchr2a 49.0 Vchr2a 51.8 Vchr2a

57.0 VVMD34 57.3 VVMD34 58.8 VVMD34

66.4 FAM108 68.1 FAM108

Figure 4. Genetic maps for Chambourcin (P1), Cabernet Sauvignon (P2), and the consensus using the regression algorithm

3_P1 3 3_P2

0.0 FAM61 0.0 FAM61 0.0 UDV-043

5.0 VMC8F10 5.1 VMC8F10

7.6 UDV-043

9.1 VMC3F3

10.3 UDV-043 10.2 VVMD36

12.4 VVMD36 12.4 ctg0171 VMC3F3 13.5 ctg0171 13.3 13.8 VVMD36 14.3 VVIB59

15.5 ctg0171

4_P1 4 4_P2

0.0 VRZAG83 0.0 VRZAG83 0.0 FAM02 1.2 CTG6983 5.7 VVIP37 5.4 VVIP37 9.8 FAM02 7.1 ctg0624 11.2 CTG6983 10.3 CTG6983 14.5 VRIP83 13.0 VVIP37 15.8 ctg0624 15.8 ctg0624 18.7 B002 22.2 VVMD32 21.9 VVMD32 20.9 VMC8B11 27.5 B002 30.0 Vchr4a VMCNG1F1-1 28.9 VMC8B11 31.7 VRZAG21 30.9 VMC8B11 31.8 VVIN75 33.2 VVIN75 37.0 VVIN75 31.9 SSRLG4-3 39.3 VRZAG21 38.8 VRZAG21 36.9 VVCS1H069I06F1-1 40.1 VVCS1H069I06F1-1 39.6 SSRLG4-3 42.1 VVCS1H069I06F1-1 44.3 SC8_0107_070 47.2 ctg5780 50.9 SC8_0107_070 53.3 VVCS1H034J03F1-1 VVCS1H034J03F1-1 53.1 54.8 VMC7h3 FAM129 FAM129 59.6 60.0 60.1 ctg1026 61.0 ctg1026 62.9 ctg1026 63.4 BM438035 64.3 ctg6426 66.8 BM438035 69.7 ctg5780 70.8 VMC7h3 71.6 VMC7h3 75.6 ctg5780 80.2 VMCNG1F1-1

89.5 VMCNG1F1-1 90.0 Vchr4a

Figure 4 continued

5_P1 5 5_P2

0.0 VVC06 0.0 UDV-041 0.0 VVC06 3.2 FAM12 3.5 FAM12 6.7 VVC06 6.7 VRIP47 7.8 VVMD27 Vchr5c 9.6 VVMD27 10.1 FAM12 10.3 FAM10 14.4 VVII52 Vchr5c 14.7 VVMD27 16.8 VRIP47 20.1 ssrVrZAG79 19.8 FAM10 22.5 UDV-041

27.1 VVIN33 26.6 ssrVrZAG79 30.4 ctg6305 29.8 UDV-053 32.2 ctg6305 33.4 UDV-053 32.9 ctg6305 35.2 UDV-053 38.1 Vchr5a 38.2 VMC6e10 41.3 VMC9B5 39.4 VVIV21 43.8 Vchr5a 43.1 VMC9B5 43.2 VVIN33 44.3 VMC6e10 44.9 FAM09 48.9 Vchr5a 49.6 VMC6e10 51.6 VVIN33 51.1 FAM09 55.1 VVIV21 55.9 VMC9B5

62.0 VVIN40 62.3 VVIN40 64.5 VMC4C6 65.3 VMC4C6 67.4 VVIN40 70.7 VMC4C6

6_P1 6 6_P2

0.0 VMC2H9 0.0 VMC2H9 0.0 VMC2H9 2.7 VMC2G2 4.7 VMC5C5 6.1 VMC5C5 5.8 VMC2G2 8.2 VMC2G2 10.0 VMC2F10 12.3 Vchr6a

14.5 Vchr6a 15.1 VMC2F10

18.3 VVMD21 19.5 VMC2F10

24.1 VVMD21

28.9 VVMD21

33.9 VVS02 35.6 VVS02 37.0 VVIM43 37.4 VMCNG4B9 37.7 VVS02 39.9 VMCNG4B9 41.0 VVIM43

43.9 VVC07 45.2 VVC07 45.5 VVIM43

Figure 4 continued

7_P1 7 7_P2

0.0 VMC16F3 0.0 VMC16F3 0.0 VMC16F3 5.7 VVIV36 6.1 VRZAG62 10.1 VMC5H5 10.8 UDV-011 10.2 VVMD31 12.1 VVMD06 17.5 UDV-011

23.8 VVMD06 23.7 VMC5H5 25.3 VVMD31

33.1 VMC5H5 35.2 VVIV36 36.2 VVMD31 39.7 FAM19

49.5 VVIV36 52.1 FAM19 55.3 VMC8D11 60.5 FAM116 64.9 VVCS1H069K09R1-1 67.0 VVCS1H069K09R1-1 71.9 VMC8D11 77.0 Chr7V004 79.8 FAM115 79.4 FAM116 83.2 VVIV04 84.4 VVCS1H097B13R1-1

97.6 FAM115 100.8 VVCS1H097B13R1-1 103.1 VVIV04

8_P1 8 8_P2

0.0 VMC6G8 0.0 VMC6G8 0.0 VMC6G8 3.4 ctg6142 7.5 ctg6142 11.8 ctg6142 NS04 16.3 NS04 15.3 17.9 NS04 SC8_1853_004 21.9 SC8_1853_004 20.9 23.4 SC8_1853_004 23.8 VMC5G6.1 25.4 VMC5G6.1 27.0 VMC5G6.1

45.0 FAM82 45.3 FAM82

49.8 VMC1e8 50.3 VMC1e8 52.3 VMC3c9 53.1 VMC3c9

57.3 VVIB66 58.1 VVIB66

VMC2H10 71.1 FAM76 70.0 73.9 VMC2H10 73.1 FAM76

Figure 4 continued

9_P1 9 9_P2

0.0 VVCS1H081G06F1-1 0.0 FAM26 0.0 VMC1C10 1.1 FAM35 5.3 FAM35 5.4 FAM26 8.1 CD009354 12.3 CD009354 14.1 VMC1C10 18.0 VVCS1H081G06F1-1 19.6 FAM26 20.2 VVIU37 21.4 FAM35 23.4 VMC3G8 26.1 VMC3G8

37.2 VVIU37

42.4 VMC5C1 41.9 VMC5C1

49.7 VMC4H6 52.9 SC8_0069_047 52.1 VMC4H6 56.8 VVCS1H016F02F1-1 55.9 SC8_0069_047 59.7 UDV-040 59.9 VVCS1H016F02F1-1 VMC2A9-1 61.8 VMC5C1 62.7 VVIQ52 61.2 65.2 VMC4H6 63.4 UDV095 68.1 SC8_0069_047 67.6 VVIC72 65.9 UDV-040 70.9 VVCS1H016F02F1-1 70.0 VVIQ52 74.1 UDV-040 75.6 VVIQ52

82.5 VVIC72

10_P1 10 10_P2

0.0 CTG6661 0.0 CTG6661 0.0 UDV-063 7.5 SC8_0060_099 7.4 UDV-063 9.8 UDV-063 8.2 SC8_0060_099 11.0 VVIP09 11.0 VVIP09 11.6 ctg5592 11.5 VVIV37 11.5 VVIV37 13.4 ctg5592 13.8 ctg5592 22.4 VMC8A4 23.9 VMC3E11.2 23.5 VMC3E11.2 23.2 VMC3E11.2 25.2 VMC8A4 25.8 VMC8A4 26.3 UDV-059 28.4 VMC8D3 27.9 VMC8D3 28.5 VMC8D3 29.8 UDV-059 30.4 UDV-059

39.2 VMC2A10 43.7 VRIP25 42.6 VMC2A10 41.6 VRIP25 45.9 VMC2A10 44.7 VRIP25 46.5 VMCZAG67 49.0 UDV-101 47.2 UDV-101 VMCZAG67 52.8 VMCZAG67 50.9 57.2 AJ1061 60.8 VVIH01 58.5 AJ1061 62.2 AJ1061 63.0 SC8_0282_007 66.4 VVIH01 68.1 SC8_0282_007 68.7 SC8_0282_007 70.6 VVIH01 72.6 FAM33 77.0 VVCS1H066N21R1-1 76.3 FAM33 76.2 UDV050

93.8 UDV050

Figure 4 continued

11_P1 11 11_P2

0.0 FAM125 0.0 FAM125 0.0 FAM125

10.8 PSCTG159_2 12.2 SC8_0118_063 13.4 SC8_0118_063 13.7 PSCTG159_2

19.9 PSCTG159_2 21.0 ctg0393 22.7 ctg0393 23.5 C_26866_1 VVIB19 25.2 25.4 VVIB19 29.1 Vchr11a 30.2 ctg0393 31.4 VVIB19 33.7 VVS2 33.0 VVS2 35.3 EST8c01b 36.3 EST8c01b 38.3 VVS2 40.1 EST8c01b 42.7 VVMD25 44.9 VVMD25 46.0 VVMD25

53.9 ctg3410 55.4 ctg3410 56.1 VVCS1H078C12R1-1 57.7 VVCS1H078C12R1-1 59.6 VMCNg2h1 61.1 VMCNg2h1

12_P1 12 12_P2

0.0 CTG0382 0.0 VVCS1H084C16R1-1 0.0 Vchr12b 1.6 VMC3B8 2.1 Vchr12a 4.8 Vchr12a 4.7 VMC3B8 4.1 CTG0382 6.6 CTG0382 VVCS1H061H16R1-1 6.6 VMC3B8 8.2 Vchr12a 10.7 Vchr12b 13.1 VVCS1H084C16R1-1

19.0 ctg3230 19.5 ctg3230 22.6 VMC4H9 22.4 ctg3230 25.3 VMC4H9 28.1 ctg0863 30.3 ctg0863

34.2 FAM22 35.7 ctg0863 36.8 VMC8G9 39.1 UDV-120 39.2 FAM22 42.0 VMC8G9 44.6 UDV-120

47.9 FAM22 51.1 VMC8G9 54.0 UDV-120

Figure 4 continued

13_P1 13 13_P2

0.0 NS01 0.0 NS01 0.0 UDV-088

8.7 VVIC51 8.5 VVIC51

11.8 SC8_0100_062 12.6 SC8_0100_062 14.1 PSCTG231_1 14.0 PSCTG231_1

17.6 PSCTG231_2 17.4 PSCTG231_2 20.0 VMC9H4 19.8 VMC9H4

23.4 VVIC51

SC8_0053_001 27.0 SC8_0053_001 26.4 CTG9373 28.3 28.9 SC8_0100_062 30.3 CTG9373 31.0 CTG7356 31.9 CTG7356 34.3 VMC9H4

40.2 SC8_0053_001 40.3 CTG9373

14_P1 14 14_P2

0.0 SC8_0128_067 0.0 VMC1E12 0.0 CTG6140

5.8 SC8_0128_067 8.2 VMC2H12 11.7 VMC2b11 18.2 VMC2b11 16.6 VMC2H5 22.4 VMC2H5 24.6 VVCS1H038P11F1-1 VRIP112 20.9 27.1 VRIP112 VMC6c10 22.6 VMC1E12 22.4 28.2 VMC6c10 34.9 VMC5B3 28.4 VMC5B3 41.1 VVCh14-47 28.6 SC8_0128_067 41.7 VMC2a5 30.8 VMC2H12 34.1 VVCh14-47 42.0 VVMD24 35.2 VVCS1H094K22F1-1 42.3 VVCS1H094K22F1-1 37.5 CTG6530 44.7 CTG6530 39.2 VVCh14-28 47.7 VVCh14-28 40.8 VVCh14-27 VVCh14-27 48.1 44.8 VMC2H5 43.2 VVCh14-2 VVCh14-10 50.6 VVCh14-2 VVCh14-10 47.4 VVCS1H038P11F1-1 43.5 UDV095 51.6 UDV095 49.3 VVCh14-20 56.8 VVCh14-20 50.2 VMC6c10 49.6 CTG5882 57.2 CTG5882 50.4 UDV025 57.3 VVCh14-25 55.4 VVIN94 57.7 VMC6E1 57.7 VMC5B3 58.7 VVIN70 58.6 UDV025 61.4 FAM90 59.7 VVCh14-24 64.9 VVCh14-24 64.5 VVCh14-23 62.6 VVCh14-23 65.4 VVCh14-23 63.6 VVIN94 65.8 UDV025 66.9 VVIN70 67.4 VVCh14-25 VMC6E1 69.3 FAM90 71.0 VMC2a5 73.1 VVCh14-28

Figure 4 continued

15_P1 15 15_P2

0.0 VVIB63 0.0 FAM105 0.0 FAM105

5.6 VVIP33 7.1 ctg4274 9.4 VVIB63

13.7 VVIP33 13.6 VVIP33

18.5 VMC4D9.2 17.4 VVIB63 18.9 ctg4274 20.6 ctg4274

29.3 VMC4D9.2

32.8 VMC4D9.2

41.1 SC8_0040_088

47.4 UDV-012 48.9 SC8_0040_088

16_P1 16 16_P2

0.0 VVC05 0.0 FAM36 0.0 VVC05 1.5 VVC05

4.4 VMC1E11

7.6 VMC1E11 7.3 VMC1E11 8.8 Vchr16b

11.4 Vchr16b 12.3 Vchr16b 13.5 CTG0567 14.7 CTG0567 16.3 CTG0567 18.1 CTG1282

21.1 VVMD37 22.5 SC8_0091_083 23.0 CTG1282 23.5 CTG1282 24.9 VVMD5 26.5 SC8_0091_083 SC8_0091_083 27.9 VMC5a1 27.0 28.6 ctg7933 28.8 VVMD37 30.9 VVMD37 32.6 VVMD5

36.4 ctg7933 35.8 VVMD5 36.8 VMC5a1 40.1 ctg7933 40.3 VMC5a1

Figure 4 continued

17_P1 17 17_P2

0.0 VMC3C11.1 0.0 VMC3C11.1 0.0 VMC3C11.1 4.1 FAM97 5.3 VVIS63 VMC2H3 7.1 VVIS63 5.2 VVIS63 9.7 FAM97 8.0 11.8 VMC2H3

19.0 VMC2H3 19.4 VMC3A9 20.5 VVIN73 24.5 VVIN73 24.1 VVCS1H068D03F1-1 26.1 VMC3A9 28.5 VVCS1H068D03F1-1 32.6 Vchr17a 34.5 FAM83 ctg5672 35.1 ctg6954 35.9 VMC3A9 35.1 ctg9346 41.9 ctg9346 39.3 ctg6954 39.1 VVIB09 42.8 FAM83 41.8 VVIB09 42.9 ctg5672 Vchr17a 44.0 47.8 VVIV08 48.4 ctg6954 49.3 VVIB09 52.8 VVIV08 53.5 CTG6354

60.8 VVIV08 61.9 CTG6354 62.5 VMC2A1

66.9 VMC2A1 67.2 CTG0557

71.8 CTG0557

18_P1 18 18_P2

0.0 FAM74 0.0 VVCS1H066N21R1-1 0.0 VMC2A3 4.5 VVCS1H066N21R1-1 6.4 VMC2A3 FAM74 9.2 SCU10 5.1 SCU10 11.0 VMCNg1b9 11.1 FAM74 9.6 16.7 SCU10 19.2 VMCNg1b9

29.9 FAM112 31.2 CB915120 33.5 UDV-117 34.3 CB915120 37.1 VVIP08 39.0 CB915120 41.0 FAM112 45.1 VVIP08 45.8 UDV-117 46.3 UDV-117 53.1 VMC2D2 55.2 VMC2D2

72.2 Vvin191--S 0.0 Vvin191--S 74.4 VVIN16 73.0 UDV737 2.2 VVIN16 76.0 UDV730 73.9 UDV736 3.8 UDV730 76.5 UDV736 76.9 VMC7F2 6.5 UDV737 77.9 UDV737 8.1 VMC7F2 80.2 VMC7F2

FAM117 90.0 FAM117 19.9 UDV-108 91.2 96.2 UDV-108 97.7 UDV-108

Figure 4 continued

19_P1 19 19_P2

0.0 UDV127 0.0 UDV127 0.0 UDV127 1.1 PSCTG81_1 SC8_0142_035 PSCTG196_2 4.8 PSCTG81_1 2.2 6.6 SC8_0142_035 7.5 PSCTG196_2 7.8 VMC1A7

12.6 VMC1A7 13.6 SC8_0142_035 14.2 VMC6C7

19.1 VMC6C7

23.7 VVIM03 24.1 SCU011 24.6 VMC3b7.2 29.5 VVIM03 27.9 B003 29.9 SCU011 30.4 VMC3b7.2 33.7 B003

43.1 VVIU09 46.0 FAM15 48.9 VVIU09

52.4 FAM15

Figure 4 continued

1_P1 1 1_P2

0.0 VVIF52 0.0 VVIF52 0.0 VMC8D1

15.7 VMC8D1 15.7 VMC8D1 28.1 Vchr1b 31.7 Vchr1c 29.9 Vchr1b 37.2 VVIN61 27.8 VVIN61 34.1 Vchr1c 39.9 VMC7g5 VMC7g5 40.4 VVIN61 33.7 44.5 CTG5955 44.7 VMC7g5 45.7 CTG1774 VVIP60 41.7 VVIP60 50.2 CTG5955 46.9 ctg5664 45.4 ctg5664 51.6 CTG1774 VVIP60 48.7 VMC3G9 49.4 VMC3G9 54.1 ctg5664 51.7 VMCNG1H7 52.2 VMCNG1H7 56.9 VMC3G9 53.9 ctg8034 59.8 VMCNG1H7 55.1 VVIB94 65.6 ctg8034 58.5 C008 66.3 VVIB94 68.5 VVIB94 63.2 AF8125 72.2 AF8125 71.5 C008 69.0 VRZAG29 75.6 AF8125 81.6 VVC19 80.1 VRZAG29 82.0 VVC19 89.7 VVC19 90.5 VVIQ57 VVCS1H024F14R1-1 96.3 98.1 VVIQ57 104.4 VVCS1H024F14R1-1

119.4 VVCS1H051N07R1-1

127.5 VVCS1H051N07R1-1

2_P1 2 2_P2

0.0 VMC7g3 0.0 VMC8C2 0.0 VMC8C2

5.2 VMC7g3 5.2 VMC7g3

10.4 SC8_0146_010

16.3 VMC6b1 16.1 HCF045+046(FAM24) 19.7 SC8_0146_010 19.2 HCF045+046(FAM24) 20.7 SC8_0146_026 23.9 SC8_0146_010 25.0 SC8_0146_026 27.4 SC8_0508_004 26.4 VMC6b1 28.6 VRZAG93 31.3 VMC6b1

38.7 SC8_0508_004 40.0 VRZAG93 VVIO55

46.3 VRZAG93 VVIO55

51.6 VMC6F1 52.2 VMC6F1 56.2 Vchr2a 55.7 Vchr2a 58.6 Vchr2a

65.0 VVMD34 67.6 VVMD34 68.9 VVMD34

Figure 5. Genetic maps for Chambourcin (P1), Cabernet Sauvignon (P2), and the consensus using the ML algorithm

3_P1 3 3_P2

0.0 FAM61 0.0 FAM61 0.0 UDV-043

4.8 VMC8F10 4.8 VMC8F10

9.8 VMC3F3 10.7 UDV-043 10.7 UDV-043 10.9 VVIB59 VVMD36

12.9 VVMD36 13.2 ctg0171 14.1 ctg0171

16.6 VMC3F3 17.3 VVIB59 VVMD36

19.0 ctg0171

4_P1 4 4_P2

0.0 Vchr4a VMCNG1F1-1 0.0 Vchr4a VMCNG1F1-1 0.0 VMCNG1F1-1

12.8 ctg5780 15.7 ctg5780 20.0 BM438035 ctg5780 24.1 BM438035 18.6 22.3 ctg6426 26.7 ctg6426 23.4 ctg1026 31.2 ctg6426 28.4 ctg1026 33.5 ctg1026 30.3 FAM129 33.2 VMC7h3 FAM129 35.7 40.7 VMC7h3 48.3 VMC7h3 56.9 SC8_0107_070 60.2 VVCS1H034J03F1-1 63.4 SC8_0107_070 VVCS1H034J03F1-1 66.9 68.9 SSRLG4-3 74.9 SSRLG4-3 81.0 SSRLG4-3 79.3 VVCS1H069I06F1-1 76.6 VVCS1H069I06F1-1 VVCS1H069I06F1-1 82.1 83.6 VRZAG21 83.7 VRZAG21 VVIN75 83.5 VRZAG21 85.8 VVIN75 87.9 VVIN75 VMC8B11 96.3 VMC8B11 97.8 99.2 VMC8B11 B002 97.4 B002 100.0 102.7 B002 107.2 VVMD32 107.1 VVMD32 113.8 VVIP37 118.3 VVIP37 VVIP37 122.7 124.8 VRZAG83 129.9 VRZAG83 130.9 ctg0624 136.2 VRIP83 136.8 CTG6983 142.4 VRIP83 137.2 ctg0624 143.6 ctg0624 143.8 CTG6983 150.8 CTG6983

Figure 5 continued

5_P1 5 5_P2

0.0 VVC06 0.0 VVC06 0.0 VVC06 3.5 FAM12 3.5 FAM12 3.5 FAM12 VVMD27 VVII52 VVMD27 VVII52 8.8 6.9 10.6 VVMD27 Vchr5c Vchr5c 10.5 VRIP47 9.4 VRIP47 22.5 ssrVrZAG79 30.6 ssrVrZAG79 36.0 UDV-053 33.5 UDV-041 41.9 ctg6305 42.9 UDV-041 49.1 VVIN33 VMC6e10 Vchr5a 55.3 UDV-053 60.2 FAM09 63.2 ctg6305 65.3 VMC9B5 73.5 VVIV21 74.6 UDV-053 76.8 VVIN33 86.6 VMC6e10 84.5 ctg6305 88.2 VVIN40 87.2 Vchr5a 90.6 VMC4C6 88.9 FAM09 VMC9B5 94.4 99.6 VVIV21 104.6 VVIN33 112.1 VVIN40 112.9 VMC6e10 115.1 VMC4C6 114.2 Vchr5a 117.6 FAM09 123.5 VMC9B5

136.1 VVIN40 139.6 VMC4C6

6_P1 6 6_P2

0.0 VMC2H9 0.0 VMC2H9 0.0 VMC2H9 3.5 VMC2G2 VMC5C5 6.0 VMC2G2 8.5 VMC2G2 7.7 VMC5C5 11.9 VMC5C5 11.9 VMC2F10

16.5 Vchr6a

20.4 VVMD21 22.4 VMC2F10 24.5 Vchr6a

33.0 VMC2F10 33.0 VVMD21

42.4 VVS02 44.6 VMCNG4B9 45.6 VVMD21 46.9 VVS02 48.5 VMCNG4B9 VVIM43 51.4 VVS02 50.5 52.7 VVIM43 54.9 VVIM43

61.5 VVC07 63.6 VVC07

Figure 5 continued

7_P1 7 7_P2

0.0 VVIV04 0.0 VVIV04 0.0 VVIV04 3.5 FAM115 1.8 VVCS1H097B13R1-1 2.3 VVCS1H097B13R1-1 5.7 Chr7V004 4.6 FAM115 5.7 FAM115 13.0 VVCS1H069K09R1-1 10.8 Chr7V004 20.3 FAM116 26.3 VMC8D11 30.7 VVCS1H069K09R1-1 31.3 FAM116

41.1 VMC8D11 48.3 VVCS1H069K09R1-1

67.0 FAM19 70.4 VVIV36 73.9 FAM19 81.9 VVIV36 85.9 VVMD31 92.6 VVMD31 89.4 VMC5H5 93.3 VVIV36 94.3 VMC5H5 99.3 VVMD31 VMC5H5 107.4 UDV-011 109.4 VMC16F3 110.5 UDV-011

120.6 VMC16F3

131.9 VMC16F3 134.8 VVMD06

146.0 VVMD06

8_P1 8 8_P2

0.0 FAM76 0.0 FAM76 0.0 VMC2H10 3.6 VMC2H10 3.6 VMC2H10

10.1 VVIB66 14.7 VMC3c9 17.9 VVIB66 22.1 VVIB66 23.8 VMC3c9 29.2 VMC3c9 29.3 VMC1e8 33.9 VMC1e8

40.8 FAM82 43.7 FAM82

VMC5G6.1 66.5 VMC5G6.1 67.4 68.8 VMC5G6.1 70.4 SC8_1853_004 72.0 SC8_1853_004 73.0 SC8_1853_004 78.0 NS04 77.1 NS04 79.3 NS04 80.5 ctg6142

89.0 ctg6142 93.8 ctg6142 93.3 VMC6G8 97.3 VMC6G8 97.1 VMC6G8

Figure 5 continued

9_P1 9 9_P2

0.0 VMC1C10 0.0 VMC1C10 0.0 VMC1C10 7.2 FAM26 6.5 FAM26 5.9 FAM26 FAM35 9.5 FAM35 7.7 FAM35 15.4 CD009354 14.4 CD009354

31.5 VVIU37 32.2 VVIU37 33.8 VMC3G8 34.7 VMC3G8

67.7 VMC5C1 72.4 VMC4H6 72.5 VMC5C1 75.8 SC8_0069_047 77.3 VMC5C1 80.5 VVCS1H016F02F1-1 84.0 UDV-040 82.2 VMC4H6 86.2 VVIQ52 86.4 SC8_0069_047 92.1 VVCS1H016F02F1-1 91.9 VMC4H6 UDV-040 97.4 VVIC72 95.6 97.0 SC8_0069_047 99.7 VVIQ52 103.8 VVCS1H016F02F1-1 107.2 UDV-040 110.9 VVIC72 113.2 VVIQ52

10_P1 10 10_P2

0.0 UDV050 0.0 UDV050 0.0 UDV050 5.1 FAM33 8.2 VVIH01 11.7 SC8_0282_007

29.1 AJ1061 36.4 FAM33 41.4 VMCZAG67 42.5 UDV-101 49.7 VRIP25 58.7 VVIH01 54.5 VMC2A10 62.2 SC8_0282_007 68.0 UDV-059 71.5 AJ1061 71.7 VMC8D3 81.9 VMCZAG67 75.1 VMC3E11.2 83.2 UDV-101 78.6 VMC8A4 91.0 VRIP25 95.2 VMC2A10 92.6 ctg5592 113.6 UDV-059 109.1 VVIH01 117.2 VMC8D3 105.2 UDV-063 112.6 SC8_0282_007 121.9 VMC3E11.2 114.0 AJ1061 124.2 VMC8A4 122.5 VMCZAG67 136.1 ctg5592 132.3 VRIP25 141.6 VVIP09 135.9 VMC2A10 143.4 VVIV37 159.2 UDV-059 145.2 UDV-063 162.8 VMC8D3 147.5 SC8_0060_099 168.7 VMC3E11.2 156.0 CTG6661 169.8 VMC8A4 179.6 ctg5592 183.1 VVIP09 184.2 VVIV37 185.3 UDV-063 187.6 SC8_0060_099 196.0 CTG6661

Figure 5 continued

11_P1 11 11_P2

0.0 VMCNg2h1 0.0 VMCNg2h1 0.0 VVMD25

6.0 ctg3410 6.0 ctg3410 7.4 EST8c01b 9.4 VVCS1H078C12R1-1 9.4 VVCS1H078C12R1-1 9.6 VVS2

16.8 VVIB19 17.9 ctg0393 19.1 C_26866_1 23.5 VVMD25 23.5 VVMD25 28.8 PSCTG159_2 30.0 SC8_0118_063 32.1 EST8c01b 33.3 EST8c01b 34.5 VVS2 35.8 VVS2

43.5 VVIB19 ctg0393 44.9 FAM125 46.7 VVIB19 46.4 47.6 C_26866_1 51.4 ctg0393

57.5 PSCTG159_2 59.7 SC8_0118_063 62.6 PSCTG159_2 66.0 SC8_0118_063

72.8 FAM125 77.1 FAM125

12_P1 12 12_P2

0.0 CTG0382 0.0 Vchr12b 0.0 Vchr12b 2.3 VMC3B8 4.7 CTG0382 VVCS1H061H16R1-1 4.7 CTG0382 VVCS1H061H16R1-1 7.2 Vchr12a 7.0 VMC3B8 7.0 VMC3B8 9.5 Vchr12a 10.7 Vchr12a

17.7 VVCS1H084C16R1-1

22.4 VVCS1H084C16R1-1

27.5 ctg3230 30.0 VMC4H9 32.4 VMC4H9

36.5 ctg3230

40.9 ctg3230 41.5 ctg0863

48.2 ctg0863 50.1 ctg0863

54.2 FAM22 56.4 FAM22 57.6 FAM22 57.6 VMC8G9 58.9 VMC8G9 61.2 UDV-120 60.6 VMC8G9 61.1 UDV-120 63.5 UDV-120

Figure 5 continued

13_P1 13 13_P2

0.0 NS01 0.0 NS01 0.0 VVIC51

6.0 SC8_0100_062

10.9 VVIC51 10.9 VVIC51 11.9 VMC9H4 14.0 SC8_0100_062 15.4 SC8_0100_062 16.9 PSCTG231_1 17.9 PSCTG231_1 17.8 SC8_0053_001 CTG9373

PSCTG231_2 20.3 20.8 PSCTG231_2 22.6 VMC9H4 22.7 VMC9H4

29.2 SC8_0053_001 29.8 SC8_0053_001 31.4 CTG9373

34.0 CTG9373 34.0 CTG7356

36.7 CTG7356

14_P1 14 14_P2

0.0 UDV095 VVCh14-2 17.1 FAM90 UDV095 VVCh14-2 UDV095 34.4 VVCh14-23 0.0 0.0 48.5 VVIN70 58.3 VVIN94 66.8 UDV025 51.2 FAM90 VMC6E1 VVCh14-20 102.1 VVCh14-24 67.9 CTG5882 102.8 VVCh14-23 75.1 VVCh14-10 110.1 VVIN70 77.3 VVCh14-28 VVCh14-27 115.2 VVIN94 80.8 CTG6530 119.6 UDV025 VMC6E1 VVCh14-20 VVMD24 VVCS1H094K22F1-1 121.3 83.1 VMC2a5 CTG5882 VVCh14-25 84.2 VVCh14-47 127.3 VVCh14-10 90.1 VMC5B3 129.2 VVCh14-28 VVCh14-27 96.4 VMC6c10 133.3 CTG6530 100.0 VRIP112 136.0 VVMD24 109.2 VMC2H5 136.3 VVCS1H094K22F1-1 118.2 VMC2b11 136.6 VMC2a5 134.5 SC8_0128_067 137.8 VVCh14-47 144.3 VMC5B3 170.1 VVCh14-24 153.1 VMC6c10 171.2 VVCh14-23 154.9 VRIP112 172.3 UDV025 159.2 VVCS1H038P11F1-1 174.6 VMC6E1 VVCh14-25 164.8 VMC2H5 181.1 VVCh14-28 173.4 VMC2b11 189.0 VVMD24 186.0 VMC2H12 190.1 VMC2a5 189.0 SC8_0128_067 198.6 VMC5B3 196.0 VMC1E12 209.7 VMC6c10 VRIP112 214.4 VVCS1H038P11F1-1 220.4 VMC2H5 240.7 VMC2H12 243.6 SC8_0128_067 250.5 VMC1E12

Figure 5 continued

15_P1 15 15_P2

0.0 SC8_0040_088 0.0 SC8_0040_088 0.0 VMC4D9.2

15.7 ctg4274

23.0 VVIP33

28.2 VMC4D9.2 28.2 VMC4D9.2 27.7 VVIB63 30.4 ctg4274

37.2 ctg4274

41.7 VVIP33

46.5 VVIP33 47.6 VVIB63

51.7 VVIB63

16_P1 16 16_P2

0.0 VVC05 0.0 FAM36 0.0 FAM36 1.1 VVC05 1.1 VVC05

7.2 VMC1E11 8.3 VMC1E11 8.3 VMC1E11

12.9 Vchr16b 15.5 Vchr16b 17.0 Vchr16b 16.4 CTG0567

20.8 CTG0567

24.2 CTG0567

27.2 CTG1282 29.2 CTG1282 30.1 CTG1282 31.0 SC8_0091_083 33.4 SC8_0091_083 34.7 SC8_0091_083 35.6 VVMD37 VVMD37 38.2 VVMD37 37.5

41.6 VVMD5 42.9 VVMD5 42.8 VVMD5

46.3 VMC5a1 ctg7933 47.5 VMC5a1 47.5 VMC5a1 48.7 ctg7933 48.0 ctg7933

Figure 5 continued

17_P1 17 17_P2

0.0 CTG6354 0.0 CTG0557 0.0 CTG0557 1.1 VVIV08 6.0 VMC2A1 6.0 VMC2A1

21.4 VVIB09 23.5 CTG6354 23.7 ctg6954 24.7 VVIV08 24.7 VVIV08 30.8 ctg5672 33.1 Vchr17a 35.4 FAM83 37.2 VVIB09 36.9 ctg9346 41.1 VVIB09 44.6 ctg6954 41.9 ctg6954 43.6 VMC3A9 51.2 ctg5672 53.3 Vchr17a 55.4 FAM83 54.5 VVCS1H068D03F1-1 56.9 ctg9346 57.9 VVIN73 58.2 VVCS1H068D03F1-1 59.0 VMC3A9 61.9 VVIN73 65.6 VMC2H3 63.1 VMC3A9

73.0 VMC2H3 74.2 FAM97 79.7 VVIS63 81.1 VMC2H3 80.1 VVIS63 82.8 FAM97 86.0 VMC3C11.1 88.3 VMC3C11.1 91.7 VVIS63

98.9 VMC3C11.1

18_P1 18 18_P2

0.0 UDV-108 0.0 UDV-108 6.8 FAM117 0.0 UDV-108 13.1 VMC7F2 18.8 VMC7F2 8.9 FAM117 14.6 UDV737 21.2 UDV737 UDV730 23.4 UDV736 18.9 24.4 VMC7F2 VVIN16 24.1 UDV730 21.2 27.8 UDV737 23.4 Vvin191--S 25.6 VVIN16 27.2 Vvin191--S 28.9 UDV736

52.2 VMC2D2 59.7 UDV-117 68.7 VMC2D2 72.3 CB915120 81.1 VVIP08 83.4 UDV-117 89.0 FAM112 92.6 CB915120 103.6 VVIP08 103.7 SCU10 107.2 UDV-117 105.2 FAM74 110.6 FAM112 114.3 VVCS1H066N21R1-1 112.9 CB915120 VMCNg1b9 119.8 118.5 VMC2A3 122.2 SCU10 127.7 FAM74 138.4 VMCNg1b9 140.7 SCU10 146.2 VVCS1H066N21R1-1 150.2 FAM74 150.4 VMC2A3

178.1 VVCS1H066N21R1-1

Figure 5 continued

19_P1 19 19_P2

0.0 UDV127 0.0 UDV127 0.0 UDV127 2.3 PSCTG196_2 3.4 PSCTG81_1 SC8_0142_035 8.9 VMC1A7 PSCTG196_2 10.0 PSCTG81_1 SC8_0142_035 15.5 VMC1A7 PSCTG196_2 16.6 SC8_0142_035

22.8 VMC6C7

30.7 VMC6C7

38.3 VVIM03 38.9 SCU011 39.4 VMC3b7.2 42.6 B003 47.8 VVIM03 46.5 VVIU09 48.4 SCU011 48.8 FAM15 48.9 VMC3b7.2 52.5 B003 57.9 VVIU09 60.8 FAM15

69.4 VVIU09 72.8 FAM15

Figure 5 continued

1_Regression 1_ML 2_Regression 2_ML

0.0 VVIF52 7.9 VMC8D1 17.3 Vchr1b 0.0 VVIF52 19.9 Vchr1c 0.0 VMC8C2 15.7 VMC8D1 0.0 VMC8C2 20.7 VVIN61 4.7 VMC7g3 29.9 Vchr1b 5.2 VMC7g3 22.3 VMC7g5 13.5 HCF045+046(FAM24) 34.1 Vchr1c 16.1 HCF045+046(FAM24) 25.7 CTG5955 17.7 SC8_0146_010 40.4 VVIN61 19.7 SC8_0146_010 25.8 VVIP60 18.3 SC8_0146_026 44.7 VMC7g5 20.7 SC8_0146_026 26.0 CTG1774 23.7 VMC6b1 50.2 CTG5955 26.4 VMC6b1 26.3 ctg5664 34.0 SC8_0508_004 51.6 CTG1774 VVIP60 38.7 SC8_0508_004 27.6 ctg8034 36.0 VRZAG93 54.1 ctg5664 40.0 VRZAG93 VVIO55 28.0 VMC3G9 36.7 VVIO55 56.9 VMC3G9 51.6 VMC6F1 29.2 C008 43.0 VMC6F1 59.8 VMCNG1H7 58.6 Vchr2a 29.7 VMCNG1H7 49.0 Vchr2a 65.6 ctg8034 32.3 VVIB94 58.8 VVMD34 68.9 VVMD34 68.5 VVIB94 35.2 VRZAG29 68.1 FAM108 71.5 C008 37.7 AF8125 75.6 AF8125 41.1 VVCS1H024F14R1-1 80.1 VRZAG29 46.3 VVC19 89.7 VVC19 52.4 VVIQ57 98.1 VVIQ57 52.8 VVCS1H051N07R1-1 104.4 VVCS1H024F14R1-1

127.5 VVCS1H051N07R1-1

3_Regression 3_ML 4_Regression 4_ML

0.0 FAM61 0.0 VRZAG83 5.4 VVIP37 5.1 VMC8F10 0.0 FAM61 0.0 CTG6983 9.8 FAM02 7.6 UDV-043 4.8 VMC8F10 6.6 ctg0624 10.3 CTG6983 13.3 VMC3F3 10.7 UDV-043 7.6 VRIP83 13.8 VVMD36 16.6 VMC3F3 14.5 VRIP83 17.3 VVIB59 VVMD36 15.8 ctg0624 14.3 VVIB59 19.0 VRZAG83 15.5 ctg0171 19.0 ctg0171 21.9 VVMD32 27.5 B002 25.6 VVIP37 28.9 VMC8B11 37.0 VVIN75 36.7 VVMD32 38.8 VRZAG21 43.8 B002 39.6 SSRLG4-3 46.1 VMC8B11 42.1 VVCS1H069I06F1-1 50.9 SC8_0107_070 58.0 VVIN75 53.1 VVCS1H034J03F1-1 60.2 VRZAG21 60.0 FAM129 64.5 VVCS1H069I06F1-1 62.9 ctg1026 68.9 SSRLG4-3 64.3 ctg6426 66.8 BM438035 80.4 SC8_0107_070 71.6 VMC7h3 83.6 VVCS1H034J03F1-1 75.6 ctg5780 89.5 VMCNG1F1-1 90.0 Vchr4a 103.1 VMC7h3 113.5 FAM129 115.4 ctg1026 117.1 ctg6426 119.8 BM438035 128.1 ctg5780

143.8 VMCNG1F1-1 Vchr4a

Figure 6. A comparison of linkage groups in the Chambourcin x Cabernet Sauvignon consensus maps generated using different mapping algorithms available in JoinMap 4.1

5_Regression 5_ML 6_Regression 6_ML

VVC06 0.0 0.0 VMC2H9 0.0 UDV-041 FAM12 0.0 VMC2H9 3.5 4.7 VMC5C5 6.7 VVC06 VVMD27 VVII52 6.0 VMC2G2 5.8 VMC2G2 10.1 FAM12 8.8 Vchr5c 7.7 VMC5C5 14.4 VVII52 Vchr5c 10.5 VRIP47 12.3 Vchr6a 14.7 VVMD27 15.1 VMC2F10 16.5 Vchr6a 16.8 VRIP47 22.4 VMC2F10 19.8 FAM10 24.1 VVMD21 26.6 ssrVrZAG79 30.6 ssrVrZAG79 32.2 ctg6305 33.5 UDV-041 33.0 VVMD21 35.2 UDV-053 35.6 VVS02 41.3 VMC9B5 37.4 VMCNG4B9 43.8 Vchr5a 41.0 VVIM43 46.9 VVS02 44.3 VMC6e10 45.2 VVC07 48.5 VMCNG4B9 44.9 FAM09 52.7 VVIM43 51.6 VVIN33 55.3 UDV-053 55.1 VVIV21 61.5 VVC07 62.3 VVIN40 63.2 ctg6305 65.3 VMC4C6 73.5 VVIV21 76.8 VVIN33

86.6 VMC6e10 87.2 Vchr5a 88.9 FAM09 94.4 VMC9B5

112.1 VVIN40 115.1 VMC4C6

7_Regression 7_ML 8_Regression 8_ML

0.0 VMC16F3 0.0 VVMD06 0.0 VMC6G8 0.0 VMC6G8

10.8 UDV-011 7.5 ctg6142 8.1 ctg6142 12.1 VVMD06 16.3 NS04 17.8 NS04 21.9 SC8_1853_004 23.7 VMC5H5 SC8_1853_004 25.4 VMC16F3 25.4 VMC5G6.1 24.1 25.3 VVMD31 28.4 VMC5G6.1 35.2 VVIV36 39.7 FAM19 38.6 UDV-011 45.3 FAM82 51.7 VMC5H5 50.3 VMC1e8 VMC3c9 55.3 VMC8D11 53.4 VVMD31 53.1 56.3 FAM82 60.5 FAM116 58.1 VVIB66 64.9 VVCS1H069K09R1-1 64.1 VVIV36 70.0 VMC2H10 67.8 VMC1e8 77.0 Chr7V004 72.1 FAM19 73.1 FAM76 73.3 VMC3c9 79.8 FAM115 79.2 VVIB66 83.2 VVIV04 84.4 VVCS1H097B13R1-1 93.5 VMC2H10 97.1 FAM76

115.3 VVCS1H069K09R1-1 119.7 VMC8D11 125.7 FAM116 135.2 Chr7V004 141.4 FAM115 144.2 VVCS1H097B13R1-1 146.0 VVIV04

Figure 6 continued

9_Regression 9_ML 10_Regression 10_ML

0.0 CTG6661 0.0 FAM26 0.0 VMC1C10 7.4 UDV-063 0.0 CTG6661 1.1 FAM35 6.5 FAM26 8.2 SC8_0060_099 8.5 SC8_0060_099 8.1 CD009354 7.7 FAM35 11.0 VVIP09 10.7 UDV-063 12.5 VVIV37 15.4 CD009354 11.5 VVIV37 20.2 VVIU37 13.8 ctg5592 14.3 VVIP09 23.4 VMC3G8 23.9 VMC3E11.2 19.9 ctg5592 32.2 VVIU37 25.2 VMC8A4 31.8 VMC8A4 33.8 VMC3G8 28.4 VMC8D3 34.1 VMC3E11.2 42.4 VMC5C1 29.8 UDV-059 38.7 VMC8D3 49.7 VMC4H6 42.6 VMC2A10 42.4 UDV-059 52.9 SC8_0069_047 44.7 VRIP25 56.8 VVCS1H016F02F1-1 49.0 UDV-101 60.8 VMC2A10 59.7 UDV-040 50.9 VMCZAG67 65.0 VRIP25 62.7 VVIQ52 58.5 AJ1061 72.8 UDV-101 67.6 VVIC72 72.5 VMC5C1 66.4 VVIH01 74.0 VMCZAG67 68.1 SC8_0282_007 82.2 VMC4H6 76.3 FAM33 86.4 SC8_0069_047 84.5 AJ1061 92.1 VVCS1H016F02F1-1 93.8 UDV050 93.8 SC8_0282_007 95.6 UDV-040 97.3 VVIH01 99.7 VVIQ52 110.9 VVIC72 119.6 FAM33

156.0 UDV050

11_Regression 11_ML 12_Regression 12_ML

0.0 FAM125 0.0 FAM125 0.0 VVCS1H084C16R1-1 0.0 Vchr12b 2.1 Vchr12a 4.7 VMC3B8 4.7 CTG0382 VVCS1H061H16R1-1 6.6 CTG0382 VVCS1H061H16R1-1 7.0 VMC3B8 Vchr12b Vchr12a 12.2 SC8_0118_063 10.7 10.7 13.0 SC8_0118_063 13.7 PSCTG159_2 15.3 PSCTG159_2 19.5 ctg3230 22.7 ctg0393 22.4 VVCS1H084C16R1-1 23.5 C_26866_1 25.2 C_26866_1 25.3 VMC4H9 25.4 VVIB19 26.4 ctg0393 29.3 VVIB19 30.3 ctg0863 30.0 VMC4H9 33.0 VVS2 35.3 EST8c01b 36.5 ctg3230 38.3 VVS2 39.2 FAM22 40.7 EST8c01b 42.7 VVMD25 42.0 VMC8G9 44.6 UDV-120 ctg0863 49.3 VVMD25 48.2

53.9 ctg3410 56.1 VVCS1H078C12R1-1 57.6 FAM22 VMCNg2h1 59.6 60.6 VMC8G9 63.3 VVCS1H078C12R1-1 63.5 UDV-120 66.8 ctg3410

72.8 VMCNg2h1

Figure 6 continued

13_Regression 13_ML 14_Regression 14_ML

0.0 VMC1E12 0.0 VMC1E12 5.8 SC8_0128_067 6.9 SC8_0128_067 0.0 NS01 0.0 NS01 8.2 VMC2H12 9.9 VMC2H12 8.5 VVIC51 10.9 VVIC51 18.2 VMC2b11 22.5 VMC2b11 12.6 SC8_0100_062 15.4 SC8_0100_062 22.4 VMC2H5 31.2 VMC2H5 14.0 PSCTG231_1 17.9 PSCTG231_1 24.6 VVCS1H038P11F1-1 36.7 VVCS1H038P11F1-1 17.4 PSCTG231_2 20.8 PSCTG231_2 27.1 VRIP112 41.1 VRIP112 19.8 VMC9H4 22.7 VMC9H4 28.2 VMC6c10 42.9 VMC6c10 26.4 SC8_0053_001 29.2 SC8_0053_001 34.9 VMC5B3 51.6 VMC5B3 28.3 CTG9373 31.4 CTG9373 41.1 VVCh14-47 58.1 VVCh14-47 31.0 CTG7356 34.0 CTG7356 41.7 VMC2a5 59.4 VMC2a5 42.0 VVMD24 59.7 VVCS1H094K22F1-1 42.3 VVCS1H094K22F1-1 59.9 VVMD24 44.7 CTG6530 62.6 CTG6530 47.7 VVCh14-28 66.7 VVCh14-27 VVCh14-28 48.1 VVCh14-27 68.7 VVCh14-10 50.6 VVCh14-2 VVCh14-10 VVCh14-25 CTG5882 74.7 51.6 UDV095 VVCh14-20 VMC6E1 56.8 VVCh14-20 76.4 UDV025 57.2 CTG5882 80.8 VVIN94 57.3 VVCh14-25 85.8 VVIN70 57.7 VMC6E1 93.2 VVCh14-23 58.6 UDV025 93.9 VVCh14-24 59.7 VVCh14-24 62.6 VVCh14-23 63.6 VVIN94 144.8 FAM90 66.9 VVIN70 69.3 FAM90

196.0 VVCh14-2 UDV095

15_Regression 15_ML 16_Regression 16_ML

0.0 FAM105 0.0 VVIB63 0.0 FAM36 0.0 FAM36 1.5 VVC05 1.1 VVC05

5.3 VVIP33 VMC1E11 7.6 8.3 VMC1E11 9.4 VVIB63 12.3 Vchr16b 13.7 VVIP33 14.6 ctg4274 15.5 Vchr16b 16.3 CTG0567

20.6 ctg4274 20.8 CTG0567 23.5 VMC4D9.2 23.0 CTG1282 26.5 SC8_0091_083 29.3 VMC4D9.2 28.8 VVMD37 29.2 CTG1282

32.6 VVMD5 33.4 SC8_0091_083 36.4 ctg7933 36.8 VMC5a1 37.5 VVMD37

42.8 VVMD5

47.5 VMC5a1 48.9 SC8_0040_088 48.0 ctg7933 51.7 SC8_0040_088

Figure 6 continued

17_Regression 17_ML 18_Regression 18_ML

0.0 VMC3C11.1 0.0 VMC3C11.1 0.0 VVCS1H066N21R1-1 0.0 VMC2A3 5.3 VVIS63 VMC2A3 4.2 VVCS1H066N21R1-1 7.2 VVIS63 6.4 9.7 FAM97 11.1 FAM74 11.8 VMC2H3 16.1 FAM97 16.7 SCU10 VMC2H3 24.5 VVIN73 17.8 19.2 VMCNg1b9 22.7 FAM74 26.1 VMC3A9 28.2 SCU10 28.5 VVCS1H068D03F1-1 35.8 VMC3A9 30.6 VMCNg1b9 32.6 Vchr17a 37.0 VVIN73 39.0 CB915120 34.5 FAM83 ctg5672 40.7 VVCS1H068D03F1-1 41.0 FAM112 35.1 ctg9346 42.0 ctg9346 45.1 VVIP08 39.3 ctg6954 43.5 FAM83 46.3 UDV-117 41.8 VVIB09 45.6 Vchr17a 55.2 VMC2D2 57.8 CB915120 52.8 VVIV08 47.7 ctg5672 61.4 FAM112 53.5 CTG6354 54.3 ctg6954 72.2 Vvin191--S 67.0 UDV-117 66.9 VMC2A1 57.8 VVIB09 74.4 VVIN16 69.3 VVIP08 71.8 CTG0557 74.2 VVIV08 76.0 UDV730 75.3 CTG6354 76.5 UDV736 81.7 VMC2D2 77.9 UDV737 92.9 VMC2A1 80.2 VMC7F2 98.9 CTG0557 91.2 FAM117 96.2 UDV-108

123.2 Vvin191--S 124.8 VVIN16 126.3 UDV730 127.0 UDV736 129.2 UDV737 131.6 VMC7F2 143.6 FAM117 150.4 UDV-108

19_Regression 19_ML

0.0 UDV127 4.8 PSCTG81_1 6.6 SC8_0142_035 0.0 UDV127 7.5 PSCTG196_2 8.9 VMC1A7 PSCTG196_2 7.8 VMC1A7 10.0 PSCTG81_1 SC8_0142_035 14.2 VMC6C7 23.7 VVIM03 22.8 VMC6C7 24.1 SCU011 24.6 VMC3b7.2 38.3 VVIM03 27.9 B003 38.9 SCU011 43.1 VVIU09 39.4 VMC3b7.2 46.0 FAM15 42.6 B003 57.9 VVIU09 60.8 FAM15

Figure 6 continued