ABSTRACT YOUNG, ELISHEBA. Fruit Quality

Home , Vaccinium formosum, Vaccinium fuscatum, Vaccinium myrsinites, Vaccinium myrtilloides

ABSTRACT

YOUNG, ELISHEBA. Fruit Quality Evaluation of a Mapping Population and Single Nucleotide Polymorphic (SNP) Marker Discovery in Blueberry (Vaccinium) Species. (Under the direction of Hamid Ashrafi).

Blueberry breeders at NC State University have released several elite cultivars that have contributed to the estimated ~$70 M statewide farm-gate value. Blueberries belong to the

Ericaceae family and the genus Vaccinium with several subgenera or sections. Many commercially important cultivars released today including the parents of the population in the current study are derived from the species in section Cyanococcus. However, these

cultivars may include introgressed genetic materials from other species of other sections that

yet need to be discovered. Traditionally, selection for desirable traits is accomplished using

recurrent selection through subjective field evaluations. Although a successful means of

cultivar development, statistically only one in 10,000 seedlings is chosen as a cultivar which

requires significant time, land, and labor resources. The task is made more difficult with

increased ploidy levels. As such, there is growing interest in the development of genomic

tools that blueberry breeders can use to make selections for fruit quality attributes more

efficiently. Recently, a genetic linkage map has been used to identify quantitative trait loci

(QTL) in a diploid population segregating for chilling requirements and cold-hardiness.

However, little is known about the genetic mechanisms responsible for QTLs that control

fruit quality traits like firmness, sugar content, acidity, and berry size in a tetraploid

population of blueberries. As such, part of the research at the NC State blueberry breeding

program involves the genotyping and phenotyping of mapping populations that segregate for

fruit quality-related traits. In the current study, a tetraploid F1 population (n=344) of a cross

between cv. ‘Reveille’ and cv. ‘Arlen’ (RA) was used for phenotypic evaluations. The collected phenotypic data and SNP data generated from sequence capture technology will be

used for future genetic marker development, genetic map construction and QTL mapping. In

the current thesis, we will only discuss the phenotypic evaluations of fruit quality-related traits in the RA population that were carried out in three harvest seasons from 2016-2018.

The segregation of the traits in this population, their potential use for QTL mapping and the

lessons learned will be discussed.

In addition to being time and labor intensive, traditional breeding efforts to develop superior

blueberry cultivars via interspecific hybridization followed by backcrossing has resulted in

the development of modern cultivars that are segmental allopolyploids. The outbreeding

nature of blueberry and the use of inter- and intra-specific hybridization during the past century has generated a lot of speculation about the relationship between the founder species and the modern cultivars. With the advent of next-generation sequencing (NGS) technologies, it is currently possible to uncover their interrelation at the whole genome level at a lower cost by sequencing each founder and cultivated species. In the second study, using

Illumina sequencing, we re-sequenced 29 accessions at least 20X genome coverage. The 29 accessions were comprised of 18 different wild and cultivated species from 6 sections in

Vaccinium that represent 18 diploids (2n = 2x = 24), 8 tetraploids (2n = 4x = 48), and 3 hexaploids (2n = 6x = 72). The 18 diploids represented 11 different species including: section

Cyanococcus [V. caesariense, V. darrowii, V. elliottii, V. fuscatum, V. myrtilloides, V. pallidum, and V. tenellum]; section Batodendron [V. arboreum]; section Herpothamnus [V. crassifolium]; section Pyxothamnus [V. ovatum]; and section Polycodium [V. stamineum].

The 8 tetraploids were representative of 6 different species including: section Cyanococcus [V. angustifolium, V. corymbosum, V. formosum, and V. myrsinites]; section Hemimyrtillus

[V. arctostaphylos]; and section Pyxothamnus [V. consanguineum]. The 3 hexaploids were all classified in section Cyanococcus [V. virgatum] and are known as rabbiteye blueberries. The

re-sequencing data allowed for the discovery of single nucleotide polymorphic (SNP)

markers within and between different groups. These SNP markers are easily adaptable to

various SNP genotyping platforms that can be used in breeding programs, calculation of

minor allele frequency, defining haplotype blocks and phylogenetic analysis.

by Elisheba Young

A thesis submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the degree of Master of Science

Horticulture

Raleigh, North Carolina

2019

APPROVED BY:

______Dr. Hamid Ashrafi Dr. Penelope Veazie-Perkins Chair of Advisory Committee

______Dr. Consuello Arellano Dr. Ross Whetten

______Dr. Paul Manos External Member

DEDICATION

To God YHWH and my family.

BIOGRAPHY

As a child, I would perform experiments with plants ultimately learning that plants were not sessile, boring creatures, bending aimlessly in the wind, obedient only to the whims of sunlight and water. Instead, these plants were meticulously crafted entities that possessed deeper self-regulation than my middle-school classes let-on. This passion for plants continued into my adulthood. I graduated in 2008 from Spelman College with a degree in

Biology. While there, I took an elective in Botany course that really peaked my interest in plant sciences. I was instantly intrigued with the intricacies of plant life. This elective incorporated trips to sustainable herbal gardens during the year. In this class, I had my first exposure to the maintenance, expertise, and responsibility that horticulturalists require to have a persistent impact on their communities. I immediately began to delve deeper by researching fascinating plants in books and articles, establishing gardens on available plots of land, and signing up for nature hikes where I would identify various wild species of plants.

Although I was originally a pre-medical student and even successfully completed one year of medical school out of the country, my return to the United States due financial constraints allowed me to really consider my future career path and make a decision based on passion.

While ruminating on my next steps, I served in the capacity of AP Environmental Science and Biology teacher. In these roles, I found it extremely important to foster a deep understanding of sustainable crop production. As such I introduced community-garden projects to students wherever I taught. In 2014, I left teaching and decided on a career in plant sciences. In preparation, I interfaced with several botanists in Massachusetts who

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advised me about the best course of action for my career switch. One botanist in particular,

Dr. Adán Colón-Carmona, was very helpful and suggested I sit in his higher-level plant

physiology course at University of Massachusetts, Boston (UMASS Boston). The class and

laboratory research work were so interesting that I ended up enrolling in the course. While

there I was responsible for conducting an individual project that involved the expression of

the Lateral Organ Boundary gene LBD10 in response to auxin in wild-type and Auxin

Response Factor 7/19 double mutant Arabidopsis plants. I knew I had found a field that I loved. Upon moving to North Carolina, I asked to volunteer with Dr. Todd Wehner, a well- established cucurbit breeder at NC State University. While here, I acquired several field, greenhouse, and lab research skills that would eventually solidify my decision to become a horticulturalist. Improving the quality of the food for all communities was definitely in line with my recent career path. I applied that summer to enroll as a Master’s Student at NC State

University and was accepted into the Spring 2017 cohort under Dr. Hamid Ashrafi, a highly competent blueberry breeder. Eager to begin my research, I worked as a lab technician from

2016 until the start of my Master’s program, increasing my proficiency in performing DNA extractions, library preparations, gel electrophoresis, Quanti-IT PicoGreen ds DNA assay and qPCR analysis in addition to collecting agronomic data.

Under Dr. Ashrafi, I began work on two projects that attempted to map out the relationships between fruit quality traits and the QTLs that control them as well as the genetic relationship between and among several wild and cultivated species. The first project involved the measuring fruit quality related traits for QTL mapping in an F1 population of blueberry from

a cross between ‘Reveille’ x ‘Arlen’ cultivars. Once complete, this research will bring

breeders one step closer to identifying and locating QTLs associated with important traits

like firmness, soluble solid content, titratable acidity, size, etc. so that selections can be made

more easily for breeding. This is important because traditionally, the selection for desirable

traits is accomplished using recurrent selection through subjective field evaluations.

Although a successful means of cultivar development, statistically only one in 10,000

seedlings is chosen as a cultivar which requires significant time, land, and labor resources.

Using sequence capture combined with next-generation sequencing (NGS) technology we hope to significantly reduce the need for those resources in developing cultivars with superior fruit quality traits.

My second project involved single nucleotide polymorphic (SNP) marker discovery in a diverse panel of blueberry (Vaccinium) species. The outbreeding nature of blueberry and the use of inter- and intra-specific hybridization during the past century has generated a lot of speculation about the relationship between the founder species and the modern cultivars.

With the advent of NGS technologies, it is currently possible to uncover their interrelation at the whole genome level at a lower cost by sequencing each founder and cultivated species. In our study, using Illumina sequencing, we re-sequenced 29 accessions at 20X genome coverage. The 29 accessions were comprised of 18 different wild and cultivated species from

6 sections in Vaccinium that represent 18 diploids (2n = 2x = 24), 8 tetraploids (2n = 4x =

48), and 3 hexaploids (2n = 6x = 72). The re-sequencing data allowed for the discovery of

SNP markers within and between different groups. These SNP markers are easily adaptable

to various SNP genotyping platforms that can be used in breeding programs, calculation of

minor allele frequency, and defining haplotype blocks.

It is my desire to finish researching these projects, especially as it relates to performing QTL analysis and identifying haplotypes blocks in an F1 population of blueberries and in various

wild and cultivated species. Understanding the genetic mechanisms responsible for fruit

quality related traits via genomics and bioinformatics is crucial to the development of

superior fruits in the foreseeable future. Unlocking these mechanisms will save time, labor

costs, crop space, and most importantly better ensure the production of superior crops for the community. Pursing this research will ultimately place me one step closer to achieving my

goal of serving the community through the development of more advanced and ultimately

more sustainable breeding practices.

ACKNOWLEDGMENTS

NC STATE Ashrafi Lab:

Dr. Hamid Ashrafi (Chair of Advisory Committee)

Ashley Yow

Lauren Redpath

Rishi Aryal

David Keck

Robert Pertusson

John Nix

Anna Nelson

NC STATE Thesis Committee:

Dr. Penelope Perkins-Veazie

Dr. Ross Whetten

Dr. Consuello Arellano

Dr. Paul Manos

DUKE Manos Lab:

Dr. Paul Manos

Dr. Andrew Crowl

NC STATE Genomic Sequencing Lab

NC STATE Ag Foundation

NC STATE Department of Horticultural Science

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Special Thanks:

Dr. James Ballington

Terry Bland, Jessica Spencer & Castle Hayne Team

Chris Barnhill

Dr. Amanda Hulse-Kemp

And my husband, Dr. Austin Dixon

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TABLE OF CONTENTS

LIST OF TABLES ...... x

LIST OF FIGURES ...... xi

Chapter 1: Fruit Quality Related Trait Evaluations in a Segregating F1 Population of

Blueberries from a Cross Between ‘Reveille’ and ‘Arlen’ Cultivars ...... 1

Abstract ...... 1

Literature Review ...... 2

Materials and Methods ...... 28

Results and Discussion ...... 34

Conclusion ...... 65

References ...... 198

Chapter 2: Single Nucleotide Polymorphic (SNP) Marker Discovery in a Diverse Panel

of Blueberry (Vaccinium sp.) Species ...... 68

Abstract ...... 68

Literature Review ...... 69

Materials and Methods ...... 93

Results ...... 98

Discussion ...... 102

Conclusion ...... 110

References ...... 198

LIST OF TABLES

Table 1: Pearson correlation of all blueberry skin color values from 2017-2018 RA population ...... 193 Table 2: Bloom dates, ripening dates (first and last blue), days to anthesis (DTA) of RA individuals that differ significantly from the rest of the 2017 population, population statistics, DTA, and bloom and ripening dates of general RA population ...... 194 Table 3: Classification of species in diversity panel based on section ...... 195 Table 4: Filtering impacts on variant calls generated ...... 196 Table 5: Filtering of SNPs identified in BLAST alignment ...... 197

LIST OF FIGURES

Figure 1: Histogram of berry firmness trait segregation from 2016-2018 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Histogram of berry firmness trait segregation in RA population in 2016 (B) 2017, and (C) 2018 harvest years ...... 112 Figure 2: Correlation plot of berry firmness from 2016-2018 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Coefficient of determination and correlation between RA berry firmness in 2016 and 2017, (B) 2017 and 2018, and (C) 2016 and 2018 harvest years ...... 115 Figure 3: Top 15% RA plants producing the firmest berries averaged across 2016-2018 (A) and the top 20 RA plants producing the firmest berries with the lowest SEM values across the same time period (C). Top 15% RA plants producing the softest berries averaged across 2016-2018 (B) and the top 20 RA plants producing the softest berries with the lowest SEM values across the same time period (D) ...... 118 Figure 4: Histogram of average berry size (cm3) trait segregation in (A) 2016 and (B) 2017 in ‘Reveille’ x ‘Arlen’ (RA) population as measured by Tomato Analyzer software ...... 121 Figure 5: Correlation plot of berry size/volume (cm3) from 2016-2017 in ‘Reveille’ x ‘Arlen’ (RA) population. Coefficient of determination and correlation between RA berry size/volume (cm3) in 2016 and 2017 ...... 123 Figure 6: Correlation plot of averaged berry firmness (g/mm) measured using the Firmtech 2 and averaged berry size/volume (cm3) measured using the Tomato Analyzer from 2016-2017 in ‘Reveille’ x ‘Arlen’ (RA) population. Coefficient of determination and correlation between berry firmness (g/mm) and averaged berry size/vol. (cm3) from 2016-2017 ...... 124 Figure 7: Histogram of berry texture trait segregation from 2018 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Histogram of berry breakpoint force (N) in RA population in 2018 (B) berry force gradient (g/mm) in RA population in 2018, and (C) area under berry texture curve (N*sec) in RA population in 2018 harvest year ...... 125 Figure 8: (A) Coefficient of determination and correlation and between RA berry force gradient (g/mm) and firmness (g/mm) in 2018, (B) RA breakpoint force (N) and firmness (g/mm) in 2018, and (C) RA area under texture curve (N*sec) and firmness (g/mm) in 2018 ...... 128 Figure 9: (A) Top 15% RA plants producing stiffest berries measured using force gradient (g/mm) (indicating level of firmness) and (B) the top 15% of RA plants producing berries with the softest (least stiff) texture harvested in 2018 measured in force gradient (g/mm). (C) The top 20 RA plants producing stiffest berries measured using force gradient (g/mm). (D) The top 20 RA plants producing the softest (least stiff) berries measured using force gradient (g/mm). (E) Top 15% RA plants producing berries requiring the greatest force to pierce their skin (indicating level of firmness) and (F) the top 15% of RA

plants producing berries with the softest texture harvested in 2018 measured in breakpoint force (N). (G) The top 20 RA plants producing berries requiring the greatest force to pierce their skin measured in breakpoint force (N). (H) The top 20 RA plants producing berries requiring the least force to pierce their skin measured in breakpoint force (N). (I) Top 15% RA plants producing berries with toughest texture and (J) the top 15% of RA plants producing berries with the softest texture harvested in 2018 measured in area under the curve (N*sec). (K) The top 20 RA plants producing berries with the toughest texture measured in area under the curve (N*sec). (L) The top 20 RA plants producing berries with the softest texture measured in area under the curve (N*sec) ...... 131 Figure 10: Histogram of individual berry weight (g) trait segregation from 2017-2018 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Histogram of individual berry weight (g) trait in RA population in 2016, (B) 2017, and (C) 2018 harvest years ...... 138 Figure 11: Correlation plot of individual berry weight (g) from 2016-2018 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Coefficient of determination and correlation and between RA individual berry weight in 2016 and 2018 harvest years. (B) Coefficient of determination and correlation and between RA individual berry weight in 2017 and 2018 harvest years. (C) Coefficient of determination and correlation and between RA individual berry weight in 2017 and 2018 harvest years ...... 141 Figure 12: (A) Top 15% RA plants producing berries with the highest individual averaged berry weight (g) and (B) the top 15% of RA plants producing berries with the lowest individual averaged berry weight (g) harvested in 2016-2018. (C) The top 20 RA plants producing berries with the highest averaged individual berry weight (g) harvested in 2016-2018 with the lowest standard error of the mean (SEM). (D) The top 20 RA plants producing berries with the lowest averaged individual berry weight harvested in 2016- 2018 with the lowest SEM (g) ...... 144 Figure 13: Correlation plot of averaged individual berry weight (g) vs. size (cm3) from 2016-2017 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Coefficient of determination and correlation and between RA averaged individual berry weight and size in 2016-2017 harvest years ...... 147 Figure 14: Histogram of fruit puree percent titratable acidity trait segregation from 2017- 2018 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Histogram of fruit puree percent titratable acidity trait segregation in RA population in 2017 and (B) 2018 harvest years...... 148 Figure 15: Correlation plot of fruit puree percent titratable acidity from 2017-2018 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Coefficient of determination and correlation and between RA individual fruit puree percent titratable acidity in 2017 and 2018 harvest years ...... 150

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Figure 16: (A) Top 15% RA plants producing berries with the highest fruit puree percent titratable acidity as measured using an autotitrator and (B) the top 15% of RA plants producing berries with the lowest fruit puree percent titratable acidity harvested in 2018. (C) The top 20 RA plants producing berries with the highest fruit puree percent titratable acidity. (D) The top 20 RA plants producing berries with the lowest fruit puree percent titratable acidity ...... 151 Figure 17: Correlation plot of averaged 2017-2018 berry fruit puree pH from RA population measured using an autotitrator vs. berry fruit puree pH measured using a benchtop pH meter in 2016. (A) Coefficient of determination and correlation between averaged 2017-2018 berry fruit puree pH and berry fruit puree pH measured using a benchtop pH meter in 2016 ...... 155 Figure 18: Histogram of berry fruit puree pH segregation from 2017-2018 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Histogram of berry fruit puree pH trait segregation in RA population in 2017 and (B) 2018 harvest years ...... 156 Figure 19: Correlation plot of averaged berry fruit puree pH vs. berry puree percent titratable acidity from 2017-2018 in ‘Reveille’ x ‘Arlen’ (RA) population as measured by autotitrator. (A) Coefficient of determination and correlation between berry fruit puree pH and berry puree titratable acidity from 2017- 2018 harvest years ...... 158 Figure 20: Correlation plot of berry puree pH from 2017-2018 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Coefficient of determination and correlation between RA individual berry puree pH in 2017 and 2018 harvest years ...... 159 Figure 21: (A) Top 15% RA plants producing berries with the highest berry puree pH and (B) the top 15% of RA plants producing berries with the lowest berry pH harvested in 2018. (C) The top 20 RA plants producing berries with the highest berry puree pH. (D) The top 20 RA plants producing berries with the lowest berry puree pH ...... 160 Figure 22: Histogram of berry fruit puree percent acidity averaged value distribution from 2017-2018 in ‘Reveille’ x ‘Arlen’ (RA) population as measured by refractometer and bench top autotitrator. (A) Histogram of berry fruit puree percent acidity averaged value distribution as measured by refractometer. (B) Histogram of berry fruit puree percent acidity averaged value distribution as measured by bench top autotitrator ...... 163 Figure 23: Correlation plot of berry fruit puree percent acidity as measured by refractometer and bench top autotitrator from 2017-2018 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Coefficient of determination and correlation between berry fruit puree percent acidity as measured by refractometer and bench top autotitrator from 2017. (B) Coefficient of determination and correlation between berry fruit puree percent acidity as measured by refractometer and bench top autotitrator from 2018 ...... 165 Figure 24: (A) Top 15% RA plants producing berries with the highest fruit puree percent acidity and lowest SEM values averaged across 2017-2018 as measured by a refractometer. (B) Top 15% RA plants producing berries with the highest

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fruit puree percent acidity and lowest SEM values averaged across 2017-2018 as measured by a bench top autotitrator. (C) Bottom 15% RA plants producing berries with the lowest fruit puree percent acidity and lowest SEM values averaged across 2017-2018 as measured by a refractometer. (B) Bottom 15% RA plants producing berries with the lowest fruit puree percent acidity and lowest SEM values averaged across 2017-2018 as measured by a bench top autotitrator ...... 167 Figure 25: Histogram for berry soluble solid content (Brix %) trait segregation from 2016-2018 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Histogram of berry soluble solid content (Brix %) trait segregation in RA population in 2016, (B) 2017, and (C) 2018 harvest years ...... 170 Figure 26: Correlation plot of berry soluble solid content (Brix %) from 2016-2018 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Coefficient of determination and correlation and between RA berry soluble solid content (Brix %) in 2016 and 2017, (B) 2017 and 2018, and (C) 2016 and 2018 harvest years ...... 173 Figure 27: (A) Top 15% RA plants producing berries with the highest SSC (Brix %) averaged across 2016-2018 and (B) the top 15% RA plants producing berries with the lowest SSC (Brix %) berries averaged across 2016-2018. (C) The top 20 RA plants producing berries with the highest SSC (Brix %) and the lowest SEM values across the same time period and (D) the top 20 RA plants producing berries with the lowest SSC (Brix %) with the lowest SEM values across the same time period ...... 176 Figure 28: Histograms depicting segregation of (A) hue angle, (B) chroma, (C) color index, (D) L*, (E) a*, and (F) b* color values in 2017-2018 RA population... 179 Figure 29: (A) Top 15% RA plants producing berries with the lightest color averaged across 2017-2018 and (B) the top 15% RA plants producing berries with the darkest color averaged across 2017-2018. (C) The top 20 RA plants producing berries with the lightest color and the lowest SEM values across the same 2017-2018 time period (D) and the top 20 RA plants producing berries with the darkest color with the lowest SEM values across the same time period ..... 183 Figure 30: Histogram depicting segregation of days to anthesis in 2017 RA population .. 186 Figure 31: Bloom and ripening dates of 2017 RA population. (A) First bloom dates for 2017 RA population. (B) First ripening dates for 2017 RA population. (C) Last ripening dates for 2017 RA population ...... 187 Figure 32: Phylogenetic tree structure of 29 species/accessions based on ~1.77 million high quality SNPs using (A) RAxML and (B) SVDQuartets analytical programming. (C) RAxML bipartitions tree including branch lengths ...... 189

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CHAPTER 1:

Fruit Quality Related Trait Evaluations in a Segregating F1 Population of Blueberry

from a Cross between ‘Reveille’ and ‘Arlen’ Cultivars

ABSTRACT

Blueberry breeders at NC State University have released several elite cultivars that have

contributed to the estimated ~$70 M statewide farm-gate value. Blueberries belong to the

Ericaceae family and the genus Vaccinium. Many cultivars released today including the parents of the population in the current study are derived from the section Cyanococcus.

However, they may include introgressed genetic materials from other species of other sections that yet need to be discovered. Traditionally, selection for desirable traits is accomplished using recurrent selection through subjective field evaluations. Although a successful means of cultivar development, statistically only one in 10,000 seedlings is chosen as a cultivar which requires significant time, land, and labor resources. The task is made more difficult with increased ploidy levels. As such, there is growing interest in the development of genomic tools that blueberry breeders can use to make selections for fruit quality attributes more efficiently. Recently, genetic linkage maps and QTL makers for a diploid population segregating for chilling requirements and cold-hardiness have been developed using molecular markers. However, little is known about the genetic mechanisms responsible for QTLs that control fruit quality traits like firmness, sugar content, acidity, and berry size in a tetraploid population of blueberries. Our research involves the genotyping and

phenotyping of segregating fruit quality-related traits believed to be controlled by QTLs in a tetraploid F1 population (n=344) of a cross between cv. ‘Reveille’ and cv. ‘Arlen’ (RA) using sequence capture combined with next-generation sequencing (NGS) technology. The

collected phenotypic data and SNP data generated from sequence capture technology will be

used for future genetic marker development, genetic map construction and QTL mapping. In

the current thesis, we will only discuss the phenotypic evaluations of fruit quality-related traits in the RA population that were carried out in three harvest seasons from 2016-2018.

The segregation of the traits in this population, their potential use for QTL mapping and the

lessons learned will be discussed.

LITERATURE REVIEW

Blueberry is an economically important crop in the U.S. and elsewhere in the world. The

majority of blueberries that are produced commercially in the United States are of the species

Vaccinium corymbosum Linnaeus which is a member of the dicot family of Ericaceae (L.).

This small fruit crop has garnered popularity because of its flavor and well characterized

health benefits (ADAMS et al., 2010; BUSHWAY et al., 1983; RAHAL et al., 2013). Blueberries

contain anthocyanins and other flavonoid content that can help combat chronic diseases

including cardiovascular disorders, neurodegenerative diseases, diabetes, and cancer

(FERNANDES et al., 2013; RAHAL et al., 2013; SHI et al., 2017; SWEENEY et al., 2002). In

order to be competitive in the market, blueberry growers always seek to replace their current

cultivars with more productive and higher quality cultivars. Most of the blueberry varieties

native to the United States originated from the wild blueberries of the family Ericaceae,

subfamily Vaccinioideae (L.), genus Vaccinium and section (subgenus) Cyanococcus (L.). It

is from this parentage that many popular blueberry varieties in North Carolina derive their

ancestry (ECK AND CHILDERS, 1966). In order to maintain economic success, blueberry

breeders need to develop cultivars that are desirable to consumers. Desirable cultivars are

market specific, so it is important to identify the different agronomic traits associated with

each cultivar. Certain markets, for example, may desire cultivars that produce fruit that are

large, sweet, crisp/firm, and intensely blue (GILBERT et al., 2015; SAFTNER et al., 2008). The

survival of these cultivars may depend on entirely different traits like cold hardiness or

ripening times. Members of the Cyanococcus subgenus (also known as Cyanococcus

corymbosum L.), for instance, are desirable for breeders because they are cold-hardy plants

that ripen early (ECK AND CHILDERS, 1966).

However, due to the complex genetic background of blueberries, it may be difficult to map these desired traits in the blueberry genome in order to make selections more precisely.

Vaccinium corymbosum blueberries are autotetraploid plants, meaning that they contain four copies of one genome. The higher ploidy state is due to the doubling of the chromosome complement from an ancestor. The polyploidy nature of the crop complicates inheritance patterns which makes it difficult to find desired traits in the genome without application of new genomic tools. An additional complication is the fact that Vaccinium corymbosum blueberries are self-infertile making the development of classical bi-parental populations such as F2, back-cross and recombinant inbred lines an almost insurmountable feat

(ROWLAND et al., 2014). As such blueberry mapping studies are often performed in the F1

generation, where segregation patterns are unknown. Lastly, Vaccinium corymbosum

cultivars may include introgressed genetic materials from other species with the same section

or other sections that have yet to be discovered. Traditional methods of breeding for desirable

fruit quality traits include recurrent selection through subjective field evaluations. Such

methods, however, are time, land and labor intensive, particularly with the long juvenility

period of blueberry plants. As such, there is growing interest in the development of genomic

tools via next generation sequencing (NGS) techniques that blueberry breeders can use to

make informed decision for making selections for fruit quality attributes like firmness,

soluble solid content, color, titratable acidity, weight, and size more efficiently. To date the

use of marker assisted selection (MAS) has been limited to a few studies in blueberry

(MCCALLUM et al., 2016b; ROWLAND et al., 2014). The creation of genetic markers for theses

fruit quality traits is the first step towards increased breeding efficiency. In fact, cultivar

development can take over 15-20 years when only traditional breeding methods are

employed citing the importance of fruit quality marker development. However, for any

molecular marker techniques to be useful in identifying genetic association with any fruit

quality traits, precise phenotypic data must be measured in relatively large bi-parental or association mapping populations of blueberries. The phenotypic traits that are found to segregate in a structured or unstructured population become candidates for quantitative trait loci (QTL) analysis using genomic tools.

Blueberry Firmness Evaluations

Factors Affecting Blueberry Firmness

The firmness of a blueberry is a very important characteristic that breeders must consider

when assessing the quality and marketability of blueberries post-harvest (CANTÍN et al.,

2012). Harvested blueberries are very perishable and spoil quickly (CANTÍN et al., 2012;

CHEN et al., 2015). In fact, blueberries have a shelf life of only 1-8 weeks depending on

storage conditions (CHEN et al., 2015; DUAN et al., 2011). Depending on the species or

cultivar, blueberries possess a myriad of firmness levels. Some studies have found southern

highbush blueberries (SHB) to be of greater firmness than northern highbush blueberries

(NHB) (CAPPAI et al., 2018; EHLENFELDT AND MARTIN, 2002). Research indicates that the increased firmness of SHB may be due to introgression of genetic material from Vaccinium darrowii Camp and Vaccinium virgatum Aiton (formerly, Vaccinium ashei Reade) ancestors that possess higher firmness values (EHLENFELDT AND MARTIN, 2002). Cultivars with a large

percentage of Vaccinium angustifolium Aiton tend to possess lower firmness values (CAPPAI

et al., 2018).

Fruit firmness depends on many factors including cell wall composition and cell wall

degradation enzymes (CHEN et al., 2015). In a study performed on the V. virgatum cultivar

(cv.) ‘Brilliant’ blueberry (rabbiteye type), fruit firmness was evaluated in terms of the role

that cell wall modifications played on fruit softening during blueberry post-harvest storage

(CHEN et al., 2015). The study focused on cell wall components water soluble pectin (WSP),

chelator soluble pectin (CSP), sodium carbonate soluble pectin (SSP), hemicellulose and

cellulose. The research indicated that firmness increased during the first 7 days of storage but

gradually decreased after the seven-day period, regardless of storage temperature. It is

purported that this initial increase in firmness was due to increased levels of CSPs that had

been enriched with ionically bound pectins (CHEN et al., 2015). However, as WSP pectins

increased during storage, blueberry firmness declined. This decline occurred in parallel with

a decrease in SSP, cellulose, and hemicellulose. Regarding temperature, storing blueberries

at 5°C versus 10°C lead to lower WSP and higher SSP (enriched with covalently bound

pectins), CSP, cellulose, and hemicellulose which ultimately increased berry firmness. The

higher amounts of CSP and SSP may cause cross-linking to other cell wall polymers which results in the strengthening of the cell wall. In another words, the increasing of CSP and SSP enables increased blueberry firmness. However, the depolymerization of hemicellulose and cellulose plays a role in destabilizing the cell wall leading to decreased blueberry firmness

(CHEN et al., 2015).

The temperature and enzymatic activity of blueberry cell walls also affects the ability of

blueberries to remain firm during post-harvest storage. At 5°C there was decreased activity in

V. virgatum blueberry cell wall degrading enzymes polygalacturonase (PG), cellulase, β-

galactosidase and α-mannosidase (CHEN et al., 2015). PG hydrolyzes pectin acid and

polygalacturonic acid leading to pectin degradation and the breakdown of the cell wall.

Cellulase degrades cell wall components like cellulose and xyloglucan. β-galactosidase and

α-mannosidase removes galactosyl residues from cell wall polymers and hydrolyzes

carbohydrates, respectively. The decrease in enzymatic activity of all of these enzymes at

5°C ultimately slow softening of blueberry cell walls, leading in better firmness for extended

periods of time (CHEN et al., 2015).

Although cell wall factors affect the firmness of blueberries in numerous ways, firmness is further affected through various harvest techniques. Traditionally, blueberries have been harvested by hand. However, to reduce labor cost, increase capacity and improve productivity, many blueberry breeders and companies have switched to mechanical

harvesting techniques (XU et al., 2015). Unfortunately, such technology has come at a price.

Mechanical harvesting often leads to fruit bruising, which decreases the number of

blueberries that can be packed for fresh market. Extensive blueberry bruising leads to a

drastic decline in fruit firmness and fruit tissue oxidative browning (XU et al., 2015).

According to Brown et al. (1996), approximately 78% of blueberries harvested with a

commercial machine harvester were bruised, especially when dropped from 150 mm to 200

mm heights to the hard fruit catcher plates, like those found in harvesting machines (K.

BROWN et al., 1996). Such incidents highlight the importance of breeding blueberries for

adequate firmness qualities.

Blueberry Firmness Measurement Methodologies

The correct assessment of blueberry firmness depends on the type of firmness methodology

used. Various methods currently exist that offer ways to properly measure blueberry

firmness. Firmness can be accessed via subjective evaluations like human assessments of

bruised blueberry cross-sections, pressing the berries between the thumb and the index

fingers, and paying attention to the pop sound inside the mouth after biting through the fruit

pre- and post-harvest. Other methodologies employ the use of the BioWorks Firmtech 2

instrument, as well as the use of Texture Analyzers instruments such as TA.XTPlus (Stable

MicroSystem Ltd., Godalimng, UK).

To simulate the destructive environment of blueberries undergoing mechanical processing,

dropped fruit experiments were performed with ‘Reveille’, ‘O’Neal’, ‘Farthing’ and ‘Star’

blueberry cultivars and analyzed berry bruising using human assessment (XU et al., 2015).

After dropping the blueberries from various heights onto hard plastic surfaces, the berries

were held in 1-pint clamshells carrying 25 blueberries per cultivar at 21°C for 24 hours. After

this period, the photographs of the equatorial cross-sections of the blueberries were taken and

visually assessed according to their discoloration. Berries possessing discolored areas greater

than 20% were rated as bruised (XU et al., 2015). Of the blueberry cultivars represented in

the study, ‘Farthing’ and ‘Star’ proved to be the firmer blueberry, while ‘Reveille’ possessed

higher bruising rates. The authors do mention, however, that the bruising rate of the

‘Reveille’ control group was also much higher than that of all other genotypes in the study

(XU et al., 2015). The higher bruising rate of the ‘Reveille’ control group suggests that the

dropped experimental ‘Reveille’ berries were not as damaged by the fall as other cultivars.

This variation proves that the ability to measure firmness also depends on the genotype of the

blueberry species or cultivar being measured.

Compression devices that measure the firmness in terms of Newtons (N) of force are also

used in various small fruit firmness analyses. Over the course of two seasons, the differences

in firmness between V. corymbosum cultivars ‘Duke’ and ‘Brigitta’ harvested from Chilean

orchards was investigated with the use of the BioWorks Firmtech 2 compression device

(MOGGIA et al., 2017). The Firmtech2 device provides firmness (gf/mm) measurements of

small fruit samples by measuring the force it takes to compress samples to a certain extent

(PRUSSIA et al., 2006). In Moggia’s et al. (2017) study, the Firmtech 2 was programmed with

a force threshold of between 15 g (minimum) and 200 g (maximum) (MOGGIA et al., 2017).

For Firmtech 2 data comparison, the internal bruising (IB) of equatorial cross-sections of fruit was measured visually according to the extent of the bruised area. It is important to note, however, that IB assessments can be flawed in that the equatorial cross-sections only reveal bruising in the area being observed. Regression analysis (r2) was performed to ascertain the

relationships between firmness and IB during storage. The regression analysis revealed that

higher r2 coefficients were obtained for ‘Duke’ cultivars. However, ‘Brigitta’ blueberries failed to show any significant associations between firmness and IB and displayed slower

softening rates. In fact, the authors emphasize that a particular blueberry cultivar’s initial

firmness at harvest may not directly correlate with IB when damaged mechanically. Instead,

any fruit quality traits associated with initial firmness may actually be related to how long the

fruit stayed on the blueberry bush past its maturation date. Softer berries usually are usually

the result of advanced maturity (MOGGIA et al., 2017). These results point to the inherent

difficulty in establishing relationships between berry firmness and IB among different

cultivars and harvest conditions.

Firmness and Texture Relationships and Evaluation

Although firmness has been widely used throughout the blueberry breeding community as a

criterion for determining the post-harvest freshness and quality of blueberries, a texture

analysis may prove to be more telling of the physical state of the post-harvest berry. As with

blueberry firmness, texture and its sub-phenotypes play a large role in determining its economic success in worldwide markets. Fruit texture is a term used to describe the nature of a fruit as it pertains to its tissue layers and cell size. Blueberry texture can also depend on the total water-soluble pectin available in the cell wall and middle lamella. These and other factors ultimately affect the various sub-phenoytpes of texture, including firmness, mealiness, gumminess, and juiciness (GIONGO et al., 2013).

Firmness, in particular, has been used to describe the overall texture of blueberries. However,

the use of firmness as a parameter only measures the strength of the cell walls, connection

between the cells, skin cell size, and shape of the pericarp cell layers beneath. As such,

studies that have been solely using the Firmtech 2 machine to assess the firmness and post-

harvest freshness and quality of blueberries in terms of berry softness, may be overlooking

other berry qualities that contribute to the overall quality of stable berry skin and underlying

pericarp tissues. A texture analysis can broaden this understanding.

Research geared toward defining set of texture parameters to broaden the understanding of

blueberry softness past the surface of the skin is underway in the U.S and elsewhere in the

world. In a research study conducted by Giongo et al. (2013), various stages of blueberry

development and ripening were characterized so that different commercial cultivars could be compared to a standard. Before this study, most fruit texture analyses were performed via penetrometric (firmness/consistency measurement) techniques (GIONGO et al., 2013). To improve upon those techniques, Giongo et al. (2013) used the texture analyzer TA.XTplus used to heighten phenotyping accuracy and reliability of various blueberry cultivars. Using the TA.XTplus, the researchers attempted to present a texture profile of 49 highbush and hybrid blueberries assessed at various stages of development to post harvest, including berry maturity and post-harvest cold storage. In order to assess the compatibility of texture analyzer data to firmness data, firmness measurements were collected from a digital fruit firmness tester (TR Turoni srl, Forlì, Italy). Overall, the study presented a means by which blueberries could be characterized based on texture sub-phenotypes and stands as an improvement to previous forms of firmness/texture assessments used in the field of blueberry breeding (GIONGO et al., 2013).

Firmness and Bruising Relationships and Hyperspectral Imaging Evaluation

Many past and emerging technologies used to evaluate the firmness, overall texture, or internal bruising (IB) of blueberries are invasive and often result in the overall destruction of the blueberry sample being analyzed. The use of near infrared (NIR) hyperspectral imaging

(HSI) could potentially be used as a means of providing an in-depth look into the actual structural state of a blueberry fruit in terms of its firmness and IB, thereby providing a larger scope of its texture profile (JIANG et al., 2016; LEIVA-VALENZUELA et al., 2013). NIR HSI works by measuring the amount of light energy reflected off an object at certain wavelengths.

Bruised blueberries usually contain a lot of displaced water from ruptured cells. This

displaced water absorbs light energy. Therefore, bruised blueberries will reflect more light

and have lower reflectance spectra (JIANG et al., 2016). The use of NIR HSI technology as a

non-destructive means of detecting and quantifying blueberry bruising has been explored with 300 SHB and 1500 NHB blueberries using a bruising ratio index (BRI) (JIANG et al.,

2016). The generated BRI data were used for comparison against both firmness and human

assessment measurements. The study indicated that the use of NIR HSI technology could,

indeed, be an accurate and efficient means of detecting bruising beyond the skin or cross-

sectional surface of blueberries. However, some BRI measurements were not well correlated

with firmness, indicating that bruising is not always to best measure of firmness (JIANG et al.,

2016).

Blueberry Skin Color Evaluation

The color of a blueberry is also an important quality trait. The color of a blueberry ranges

from light blue to deep black (MATIACEVICH AND SILVA, 2012). Many blueberry cultivars possess a light colored epicuticular wax, also called bloom, on the skin that gives the fruit a powdery appearance. Lower levels of anthocyanins in the skin can also contribute to the lighter color (RETAMALES AND HANCOCK, 2012). This powdery appearance is desirable to

consumers (MATIACEVICH AND SILVA, 2012). Depending on the cultivar, the light appearance

in blueberries can sometimes be attributed to blueberry freshness. Many technologies have

been employed to properly access the surface colors blueberries. Older fruit color analytical

technologies simply involve visual assessments using the CIELAB space standard or portable

reflectance colorimeters (CARREÑO et al., 1995). Other technologies have resorted to more

elaborate techniques including the use of the CIELAB space standard in combination with

digital camera images analyzed with the Balu Toolbox in MATLAB ® software (V7)

(MATIACEVICH et al., 2013). More recent technologies include the use of colorimeters like the Konica Minolta Spectra Magic NX machine to analyze blueberry color. Colorimeters are designed to take various color measurements using the same light source, illumination method, and measurement conditions (KONICAMINOLTA, 2003). Colorimeters analyze several major parameters, including L*, a*, and b* values. Darker blueberries, lacking the powdery epicuticular wax, have lower L* values. L* values help describe the brightness of

blueberries, while a* and b* values represent chromaticity coordinates which help

communicate the predominance of certain colors (KONICAMINOLTA, 2003). If a* values are

positive, red is more prominent in the berry whereas negative a* values highlight the

presence of greenish colors. Blue components of the blueberry are expressed with negative

b* values while positive b* values indicate the presence of yellow (KONICAMINOLTA, 2003;

RETAMALES AND HANCOCK, 2012). The a* and b* values are often used in the calculation of

-1 2 2 1/2 hue angle (Ho = tan b*/a*) and chroma (Chroma = (a* +b* ) ) (KONICAMINOLTA, 2003;

NUNES et al., 2004). The hue angle begins at the +a* axis and is expressed in degrees. A hue angle of 0° is an indicator of red (+a*), 90° indicates yellow (+b*), 180° indicates green (-a*)

while 270° is indicative of blue (-b*). Chroma on the other hand begins with a value of 0 at

the center of the chromaticity color space and increase as distance increases from the center.

It is important to note, however, that chroma may not be a useful value when attempting to

distinguish between pink, red, yellow and orange groups (CARREÑO et al., 1995).

The usefulness of these color values was highlighted in an investigation of the color changes

that occur in V. corymbosum cultivar (cv.) ‘Patriot’ blueberries (NUNES et al., 2004). The L*,

a*, b*, hue and chroma values of these blueberries were collected using a reflectance

colorimeter on the stem end of blueberries after the berries were held at various storage

temperatures for different time periods. In this study, both hue and chroma evaluations

proved to be poor indicators of color change whereas L* values were efficient indicators for

loss of brightness. This is important because brightness plays a larger role in the evaluation

of color change than the actual blue color of blueberries (NUNES et al., 2004).

Blueberry Titratable Acidity and pH Evaluation

There are several blueberry qualities that play a very important role in the flavor profile of

blueberries. One such trait is the presence of certain amounts of different acids, which when combined with the natural sugars and phenolics of blueberries can have mass consumer appeal. The sweet taste of a blueberry is positively correlated with oxalic acid, citric acid, total sugars, and anthocyanidins. Sour tastes are also correlated with total acid content (BETT‐

GARBER et al., 2015). The acid profiles of blueberries are often assessed using titratable

acidity or pH measurements. Titratable acidity (TA), also referred to as total acidity,

measures the total acid concentration in food. It is a measurement determined by the

neutralization of the acid present in a known quantity of blueberries by using a standard base

(NIELSEN, 2003). The end point of the titration is either a target pH or a color change with pH sensitive dye. The pH value is also used to measure the acidity of blueberries. It is defined as the negative log (base 10) of the hydrogen ion concentration which is a function of the type

and concentration of acids and their conjugated bases present in the blueberry. A study

performed on highbush blueberries (V. corymbosum cv. ‘Elliott’) using a PHB-212 pH meter

(Omega, Engineering, Inc., Stamford, CT) revealed that, on average, fresh blueberries possess a pH value of 3.15 ± 0.06 (ALMENAR et al., 2008; KIM et al., 1995). Highbush

blueberries, in general, have high citric acid and succinic acid contents. In fact, the citric acid

content of highbush blueberries can be as much as 75% while the succinic acid content

averages around 17% (EHLENFELDT et al., 1994). For this reason, TA levels are often

expressed as percentages of citric acid. Using these parameters, the acidity of fresh

blueberries was found to be 0.84 ± 0.00% when measured by the same PHB-212 pH meter

accompanied by a glass electrode (ALMENAR et al., 2008).

When compared to pH, TA is often a better predictor of the acidic impact on flavor. TA is

believed to be correlated with sour taste and is a more effective indicator of tartness although

this perception is strongly influenced by presence of sugar (BETT‐GARBER et al., 2015;

NIELSEN, 2003). In addition, TA can help serve as a means of predicting maturity. Although

TA proves to be a very accurate means of assessing acid profiles in blueberries, the pH of blueberries can provide information on the ability of microorganisms to grow. This is important because acids and soluble solids can account for approximately 80% of decay variability in blueberries (GALLETTA et al., 1971).

In the past, many TA and pH measurements have been acquired from blueberries and other

small fruits using smaller, more compact machines that are not built for analyzing multiple

samples in an automated fashion (ALMENAR et al., 2008; TAN et al., 2018). However, more fruit research is starting to be performed using autotitrators capable of handling multiple samples. The Mettler Toledo Autotitrator has been used to acquire TA measurements by performing potentiometric titrations on 1 mL aliquots of the blueberry juice gathered from highbush crosses and rabbiteye crosses (CLARK et al., 2018). The autotitrator is also capable of collecting pH measurements (METTLERTOLEDO, 2014).

Blueberry Soluble Solid Content Evaluation

The analysis of soluble solid content (SSC) is frequently used as a means of determining the sweetness in blueberries (LEIVA-VALENZUELA et al., 2013). The most common sugars found in blueberries are glucose, fructose, and sucrose (KADER et al., 1993). Glucose and fructose are present in the highest amounts. According to one study, the average amounts of glucose and fructose varied between 6.7-7.7 grams per 100 grams of highbush blueberry (V. corymbosym L., cv. ‘Coville’ and ‘Berckley’) fruit. Sucrose consisted of only 0.12-1.14 grams per 100 grams fruit (KADER et al., 1993).

Measurements of sugar content can be collected in different ways. Outside of the more traditional human taste tests for sweetness, sugar content can also be measured via enzymatic analysis. Enzymatic analysis does not require any machinery. Instead, it relies simply on the use of enzymes, their substrates, the resulting products and the overall rate of the reaction to determine the concentration of a compound of interest (UC DAVIS, 2018). In the case of

fructose and glucose, for example, fructans are enzymatically hydrolyzed to determine

concentrations (ANDERSEN AND SØRENSEN, 1999).

The sugar content or sweetness of blueberries can also be measured electronically by using handheld refractometers or more advanced hyperspectral imaging techniques. A refractometer measures how fast light moves through a liquid like blueberry juice. A higher index of refraction indicates that there are more soluble solids, like sugar, present in the juice.

Handheld refractometers are usually able to provide measurements in terms of degrees of

Brix (°Bx). One degree of °Bx is equivalent to one gram of sucrose in 100 grams of solution

(DONGARE et al., 2015). Using a handheld optical refractometer to measure the °Bx of cultivated highbush blueberries (V. corymbosum L., cv ‘Elliot’), the average SSC was

determined to be 12.67 +/- 0.12 °Bx prior to storage (ALMENAR et al., 2008; KIM et al.,

1995). Due to loss of water content after 9 days of storage at 23°C and 18 days of storage at

10°C, the SSC rose to 15.43 and 15.57 °Bx (ALMENAR et al., 2008). When using a handheld

digital refractometer, the average SSC content of commercial varieties of highbush

blueberries (V. corymbosum) was determined to be 10.5 +/- 2.1% in Brix (LEIVA-

VALENZUELA et al., 2013). Measurements for SSC can be variable depending on the species

or cultivar.

In addition to measuring SSC using digital refractometers, hyperspectral imaging can be used

to analyze SSC in blueberries. In a 2013 study, refractometer °Bx measurements were

compared with imaging data collected from highbush blueberries (LEIVA-VALENZUELA et al.,

2013). However, the use of hyperspectral imaging to measure how well light is reflected or

absorbed through intact blueberries proved to be highly variable. It is recommended that

future hyperspectral SSC studies be performed using higher spatial and spectral resolutions

during the imaging process in addition to better lighting design (LEIVA-VALENZUELA et al.,

2013).

The SSC in blueberries can also be accurately measured using high performance liquid

chromatography (HPLC) techniques ((FORNEY et al., 2012; ROSSI et al., 2003). HPLCs work

by separating and quantifying compounds in a given sample (SU et al., 2017). In order to

monitor the nutritional composition and quality of fruit during the developmental process,

HPLC techniques were employed to measure the SSC of blueberry fruit as they ripen

(FORNEY et al., 2012). The HPLC was sensitive enough to distinguish between the different

types of sugar in the blueberry. The study revealed that most sugars in the blueberry were

glucose and fructose sugars and that the quantities of these sugars increased as fruit ripening

progressed (FORNEY et al., 2012). The use of HPLC technology for SSC measurement proves useful in studies requiring detailed analyses.

Blueberry Size and Weight Evaluation

In addition to blueberry qualities like firmness, texture, and flavor, blueberry breeders also

seek to develop blueberries that fit certain size profiles that are most desirable to consumers.

According to a study conducted with Maine wild blueberries, consumers preferred large

blueberries that were 11-12 mm in size (DONAHUE et al., 2000; JOHNSON et al., 2011). The

size of blueberry fruit may be determined by a variety of factors. A study of the size variation

occurring in rabbiteye blueberry (V. virgatum) cultivars and advanced selections revealed

that the fruit’s diameter and weight may be correlated with the cell number within the

mesocarp (JOHNSON et al., 2011). The relationships between the diameter and the cell area of

the blueberry was also investigated, but no significant relationship was found. There was also

no strong correlation between cell area and the blueberry’s weight. The relationship between

fruit size and cell number has also been observed in peach, tomato, olive, and sweet cherry

crops (JOHNSON et al., 2011). Due to the proposed importance of cell number in fruit size, research investigating the genetic mechanisms that regulate cell production has been

conducted.

Although blueberry fruit size may be controlled by genetics when cell number is considered, it is also important to note that external environmental or cultivation practices can play a major role in cell area enlargement. In fact, environmental factors or the treatment of blueberries with gibberelins (GA) can directly alter cell size. The enlargement of multiple cells in blueberries will ultimately increase the size of the berry as a whole (JOHNSON et al.,

2011).

The method by which small fruit size measurements are collected is evolving as new

technologies emerge. Size measurements for small fruits are traditionally gathered non-

invasively using calipers (BERTIN et al., 2009). However, the use of calipers for hundreds of

blueberry samples can be time consuming. The Firmtech 2 machine is another technology

that provides size measurements in addition to main purpose of gathering firmness data.

Nevertheless, it is difficult to standardize Firmtech 2 in a way that provides accurate size

measurements (PRUSSIA et al., 2006). The use of the Tomato Analyzer (TA; Van der Knaap et al., 2008) software is a means of gathering fruit shape and size measurements in a

semiautomatic, accurate and reproducible fashion (GONZALO et al., 2009; VAN DER KNAAP,

2008). The Tomato Analyzer (TA) software collects data by determining the boundaries of

the scanned images of sliced fruit. The TA software can efficiently scan over 100 images at

once and export the data to an excel file for analysis (GONZALO AND VAN DER KNAAP, 2008;

RODRÍGUEZ et al., 2010). TA software has been used to gather data on fruit shape features in

three inter-specific F2 populations of cultivated tomato (Solanum lycopersicum) (GONZALO

AND VAN DER KNAAP, 2008). Later studies using tomatoes have collected more complex morphology data like fruit shape index, blockiness, indentation area, and fruit angles along their boundaries (GONZALO et al., 2009). Eggplant studies have successfully collected similar

morphologic data in addition to more basic measurements like perimeter, area, max width

and max height (HURTADO et al., 2013). Although TA software has been used to collect

shape, size, and even color measurements from several fruit species, to date the software has

not been used to gather size data for blueberries even though the implications of

standardization and accuracy are promising.

Genotyping the Mapping Population

Once the phenotypic data surrounding fruit quality traits like firmness, texture, color, SSC,

titratable acidity, pH and size have been collected, the work of identifying the QTLs

associated with fruit quality traits can begin. The identification of QTLs associated with fruit quality traits will facilitate the identification of candidate genes underlying the phenotypic trait. Fruit quality traits are often controlled by polygenes, which can significantly complicate the breeding process. This is because the phenotypic fruit quality traits that are expressed in the population are only a partial expression of the genetic value of the individual plant. Therefore, phenotypic evaluation is not enough to identify polygenes or quantitative trait loci (QTL). QTLs associated with the phenotypic fruit quality traits of interest must be mapped in the blueberry genome. This task is accomplished when genetic markers such as simple sequence repeats (SSRs) or single nucleotide polymorphisms (SNPs) are identified that are genetically linked to QTLs of interest. The QTLs can be mapped on blueberry chromosomes using either genetic linkage analysis or association mapping (SEHGAL et al.,

2016).

In order to perform QTL mapping in a bi-parental population, two contrasting parental plants

possessing different alleles affecting a trait of interest must be crossed to develop a mapping

population. The population will then be phenotyped for the trait(s) of interest. It is best that,

when available, the mapping population is phenotyped in various environmental conditions

to estimate the G×E effects of alleles. Concurrent with phenotyping, the DNA should be

extracted from the population for genotyping and for the collection of molecular data.

Polymorphic markers like SNPs or SSRs can be generated from this data allowing for the

construction of a genetic map. Statistical programs such as QTL Cartographer, R-QTL,

Tetraploid Map, GENTSTAT, and GWASPoly are used to identify molecular markers that

are linked to specific traits of interest (ARENDS et al., 2010; BRADSHAW et al., 2008;

MCCORD et al., 2011; ROSYARA et al., 2016; SEHGAL et al., 2016; WANG, 2006).

Once markers are identified, they can be used to segregate the blueberry mapping population

into individuals that have the markers present in their genomes and those individuals where

the markers are absent. The markers also serve as a means of segregating mapping

populations to signify differences in phenotypic traits. Any significant differences between the phenotypic means of the groups, points to the fact that the marker locus in question is linked to a QTL associated with the trait of interest (SEHGAL et al., 2016).

Therefore, construction of a genetic linkage map is prerequisite for conducting a QTL

analysis. This linkage map will help identify marker positions and the relative genetic distances of those markers along the chromosomes. To determine the distance between markers recombination fractions are calculated using the frequency of recombinant genotypes (not parental genotypes) existing in the mapping population. If there is a low recombination frequency between two markers, then the markers are assumed to be situated more closely on the chromosome (SEHGAL et al., 2016). This linkage map will aid breeders

in making more efficient marker assisted selections.

The Importance of Molecular Marker Technology and the Recent Advances in

Molecular Marker Development in Blueberry Using Genotyping by Sequencing (GBS)

Making selections is more easily accomplished with the use of molecular markers. Instead of

relying on the visualization of phenotypic traits and pedigree history, selections can be made

by identifying genetic markers associated with desired traits. Nowadays, thousands or

millions of molecular markers can be developed in a short period of by NGS and

Bioinformatics pipelines. NGS provides a methodology that allows for the sequencing of

genetic material in a safe, quick, and cost-effective manner (OLWEN, 2017).

Various marker types are employed to create genetic maps for subsequent QTL analysis.

Marker types are selected based on the genome of the sample being studied. Although SSRs

are used as markers to help make better selections in complex autotetraploid blueberry

population, the use of single nucleotide polymorphisms (SNPs) can often be more effective

due to its increased throughput capability, abundance, and ubiquity in the genome

(MAMMADOV et al., 2012). SNPs are aberrant variations within a DNA sequence at one

nucleotide that serves as a marker for the genomic sequence under study. Both SSR and SNP

markers have been used to generate a genetic map that helps breeders make better blueberry

plant selections that are not always visually obvious (MCCALLUM et al., 2016a). In this study,

the desired cold tolerance trait is examined. Traits like cold-hardiness are quantitative and are expressed in several different ways making visual selection challenging (NAIK et al., 2007).

Construction of genetic maps can provide breeders with information concerning how such

traits are passed on from parent to offspring plants. McCallum et al. (2016) attempted to

construct the first genetic linkage map in freeze-tolerant autotetraploid V. corymbosum

blueberries. In this study ‘Draper’ and ‘Jewel’ blueberry cultivars were utilized as parental

plants to produce an F1 test population. ‘Draper’ is a northern highbush plant with high chilling requirements, while ‘Jewel’ is a southern highbush with low chilling requirements.

Both parents were mapped using SSRs and SNPs generated by genotyping by sequencing

(GBS) (MCCALLUM et al., 2016a). SSRs and SNPs are used to help estimate the

recombination frequencies of certain sections of chromosomes. The knowledge of

recombination frequencies helps establish the location of the desired traits near these SSR

and SNP makers. This information can help map out the location of cold-hardy genes of

interest. Using NGS methods, over 109,000 SNPs putative SNPs were located (MCCALLUM

et al., 2016a). The heterozygous SNPs were screened. Those that segregated in 1:1 or 5:1 ratio was selected in the ‘Draper’ x ‘Jewel’ population. A total of 1,101 of the qualifying

SNPs were found for ‘Draper’ and 673 were located for ‘Jewel’. Using this SNP data and the

233 SSRs identified, a total of 12 linkage maps were created for the ‘Draper’ x ‘Jewel’ population (MCCALLUM et al., 2016a). The data from these genetic linkage maps make it

possible for breeders to better estimate segregation patterns in their populations.

Marker Assisted Selection

The development of genetic markers can aid breeders in performing marker assisted breeding

(MAB) via genetic screening methods. Traditionally, plants with desirable traits are selected

based on their phenotypic appearance. However, traits like fruit size, SSC, titratable acidity,

skin color, plant resistance to certain biotic and abiotic stresses such as mummy berry

resistance and cold-hardiness are quantitative and may be controlled by several genes

(BERTIN et al., 2009; CAPPAI et al., 2018; ETIENNE et al., 2002; HANCOCK et al., 2008; NAIK

et al., 2007; ROWLAND et al., 2014; SURIYAPPERUMA AND KOSKE, 1995). Breeding for desired fruit quality traits using MAB can help breeders make accurate selections, thereby decreasing the amount of time that has to be spent on the development cultivars.

A setback to MAB breeding is the difficulty associated with collecting accurate data on the parents of the breeding population. Since most blueberry cultivars used for commercial

distribution are tetraploid, there is a large amount of genetic variability associated with

quantitative traits in both parents of a bi-parental population. The environmental conditions that occur in these populations can also affect the data collected from the genotype evaluations (LOBOS AND HANCOCK, 2015). Therefore, it is wise to evaluate the genotypes in

a wide variety of environments to gain a more accurate depiction of the adaptation that is

occurring in the population (LOBOS AND HANCOCK, 2015). Once breeders correct for genetic

and environmental variability, and once genetic maps and markers are constructed for a

population, MAB can commence.

Current and Future Phenotypic and Genotypic Research in Blueberry Breeding

The ability to use markers for better trait selection within the complex genome of blueberries

is the goal of my current research. Majority of fruit quality related traits are controlled by many genes with major and minor effects in different locations of the genome. These are considered quantitative traits loci (QTLs). Many traits in the current study, including

firmness, color, soluble solid content, acid content, and fruit size have been studied in other

crop plants such as tomato, potato, cherry, peach, and apple (AMPOMAH-DWAMENA et al.,

2012; BRADSHAW et al., 2008; ETIENNE et al., 2002; MIGICOVSKY et al., 2016; SALIBA-

COLOMBANI et al., 2001; SOORIYAPATHIRANA et al., 2010; VOLZ et al., 2003). QTL studies concerning fruit color, for example, have been performed on sweet cherry (Prunus avium L.)

and tomato (Lycopersicon esculentum, var. cerasiforme (Dun.) Gray x L. esculentum Mill.)

(Saliba-Colombani et al., 2001; Sooriyapathirana et al., 2010). Fruit quality traits like titratable acidity and pH have been studied in peach (Prunus persica) for their putative relationships to QTLs (Etienne et al., 2002). To date, QTLs associated with many of these fruit quality traits have not yet been identified in the blueberry genome (CAPPAI et al., 2018).

Any research furthering the discovery of fruit quality traits associated with QTLs in

blueberries would, therefore, be novel and of vast importance to the blueberry breeding

community.

In order to identify these fruit quality traits in blueberry, a cross was made in 2011 between

two tetraploid blueberry cultivars, ‘Reveille’ and ‘Arlen’, that segregate for many traits. The

F1 population (n = 344) that was developed from the ‘Reveille’ × ‘Arlen’ cross was

transplanted in a blueberry grower’s farm in 2013 and left to grow to maturity so that both

phenotypic and genotypic assessments could be made for future QTL analysis.

In 2016, 2017, and 2018 we collected fruit samples from the entire population. Multiple fruit

quality related phenotypic traits were measured including, fruit size, fruit weight, color,

titratable acidity, soluble solids, firmness and texture. A three-year analysis of general phenotypic trait trends was performed between 2016-2018. In 2016, leaf samples were collected, and DNA was extracted from the entire population. Using a target sequence capture sequencing approach, the extracted DNAs were subjected to genotyping via RAPiD

GENOMICS sequence capture combined with NGS technology. The collected data will be used to construct a genetic map by our collaborators at Plants for Human Health Institute in near future. The scope of construction of the genetic linkage map and the QTL mapping is out of the scope of this thesis. A QTL analysis will be performed using this genetic map and the collected phenotypic measurements. The data collected in this study will allow breeders to genetically track desired quantitative agronomic traits for more efficient marker assisted selection of superior blueberry plants. However, this portion of our mapping research is primarily concerned with the initial DNA extraction from the population and the collection of population phenotypic data via advanced instrumentation to accomplish near to precise phenotyping, including the innovative use of the Tomato Analyzer (v.3.0; Athens, GA),

TA.XTPlus texture analyzer (Stable MicroSystem Ltd., Godalimng, UK), Firmtech 2

(BioWorks, KS, USA), Mettler Toledo G20S Compact Titrator (Mettler Toledo, Columbus,

Ohio), and Konica Minolta CR-5 Chroma Meter (Konica Minolta, Inc., Tokyo, Japan) in blueberry data collection.

MATERIALS AND METHODS

Plant Material

All fruit quality related studies were conducted during the 2016-2018 harvest seasons. In spring 2012 a cross was made between V. corymbosum cv. ‘Reveille’ as pistillate parent and

V. corymbosum cv. ‘Arlen’ as staminate parent. In November 2012, ~500 F1 seeds were sown

in nursery trays and were placed under fine mist for 30 days. After germination, the seedlings were kept under the mist irrigation when they were 1-inch tall. Subsequently, individual seedlings were transplanted into 4-inch pots filled with ground pine bark as substrate. The

seedlings were maintained in greenhouses with controlled temperature during day and night

with no supplemental light. In December 2013, a total of 400 seedlings were transplanted

into the soil at Barnhill’s farm, located in Ivanhoe, NC (Sampson County). The seedlings

were planted one foot apart on the row and five feet apart between the rows. Both ‘Reveille’

and ‘Arlen’ are SHB unpatented blueberries that were released by the NC State blueberry

breeding program. In spring 2016, 364 surviving and vigorous 3-year-old plants, hereafter

RA population, were selected, assigned a number and tagged from 1-364. In 2016, the blueberries from all RA individuals were hand-harvested at full maturity in a 3 weeks period, when first ripe during late-May through mid-June. Fully ripe berries (n = 10-50) were randomly picked from each RA plant and placed into separate 1-pint clamshells. Any visibly damaged and or/unripe fruit were discarded from the clamshells before transporting the fruit into the lab. The same harvest process was followed during late-May and early-June of the

2017 harvest season and late-May to late-June of the 2018 harvest season. All newly grown leaf sample material from the RA population was harvested from each individual bush in

April 2016.

Storage Conditions

Blueberry samples (n = 10-50) were transported in clamshells from Barnhill’s farm to North

Carolina State University (Raleigh, NC) and immediately placed in 4°C cold room overnight

for processing.

Fruit Quality Measurements

Fruit Firmness

Blueberry firmness (g/mm) of fruit was measured using the Firmtech 2 (BioWorks, Wamego,

KS, USA) compression device. Firmtech 2 force thresholds were set at a minimum of 50

gms and maximum of 150 gms (PRUSSIA et al., 2006). Load cell speed was set at 12 mm/s

with a table speed of 0.79 mm/s. A rubber ball (diameter 15.82 mm) was used a reference for

calibration because it provides less variability than live fruit throughout multiple uses

(PRUSSIA et al., 2006). All berries were measured while positioned with stem scar and calyx

parallel to the plate as to reduce variability of differing calyx sizes and shapes. Ten berry

samples (n = 10) were measured for firmness from each RA individual.

Fruit Size

The perimeter and area of the latitudinal and longitudinal cross-section of (n = 10) berries were assessed using the Tomato Analyzer (v.3.0; Athens, GA) software as described

(BREWER et al., 2007; RODRÍGUEZ et al., 2010). Latitudinal (n = 5) and longitudinal (n = 5)

cross-sections were placed flesh down on a glass plate for scanning. The resulting JPG file was then hand-edited using the Gimp (v.2.8; Berkley, CA) to make the image suitable for

Tomato Analyzer. Tomato Analyzer was then used to analyze the dimensions of the blueberry cross-sections.

Fruit Texture Analyzer

In the 2018 harvest season, an analysis of blueberry fruit texture was performed on blueberry fruit (n = 10) from each RA individual using a TA.XTplus texture analyzer (Stable

MicroSystem Ltd., Godalimng, UK). The TA.XTplus measures the mechanical force displacement of objects using a 5 kg loading cell and a 4 mm cylindrical flat head probe as previously described (GIONGO et al., 2013). Force displacement is assessed by allowing the probe to pierce the skin of blueberries stabilized on a stand with their transverse/lateral side facing the probe. The accompanying software generated mechanical profile graphs based on force (N) and time (sec) and also gathered data based on breakpoint force (N), area under the force by time curve (N*sec), and force gradient (g/mm), also known as Young’s elasticity module (GIONGO et al., 2013). The parameters set for the measurement of force included a

2.5 mm/sec pre-test speed, 2.5 mm/sec test speed of 2.5 mm/sec, 5 mm/sec post-test speed, trigger force of 5 g, and stop plot at trigger return. The parameter for strain was set at 90%.

Fruit Weight

The mean fruit weight of 10 ripe representative blueberries harvested from each RA individual was measured in grams using a standard laboratory scale to two decimal points.

Individual blueberry weight was calculated as the average of 10 berry samples.

Fruit Percent Titratable Acidity and pH

In 2017 and 2018 harvest seasons, percent titratable acidity and pH of blueberry fruit puree were measured using the automatic Mettler Toledo G20S Compact Titrator (Mettler Toldeo,

Columbus, OH). Readings were collected from samples containing 2 grams of the homogenized blueberry puree mixed with 60 mL of water. A titrant solution of 0.1 NaOH mol/L was used until the equivalence point was reached. The endpoint value was set at pH

8.2. Percent titratable acidity (% TA) or citric acid meq was calculated as:

% TA = (mL NaOH Consumption)*(0.1 N NaOH)*(0.064 meq citric acid)*100 /

(mass (g) of puree sample)

% TA = meq citric acid

In 2016, before the automatic Mettler Toledo G20S Compact Titrator was available, the blueberry fruit puree pH was analyzed using a benchtop Fisher Scientific AB15 Plus pH meter (Fisher Scientific, Singapore). Readings were collected from the juice puree of 10 homogenized blueberries.

In 2016-2018, percent titratable acidity was also measuring using a handheld ATAGO PAL-

BX|ACID F5 Refractometer (ATAGO, USA, Inc., Bellevue, WA). Approximately 10

blueberry fruit were harvested from each RA population individual and were homogenized with the handheld Fisher Scientific Homogenizer 150 (Fisher Scientific, Pittsburg, PA). The resulting puree was analyzed for percent titratable acidity.

Fruit Total Soluble Solid Content

Approximately 10 blueberry fruit were harvested from each RA population individual and were homogenized with the handheld Fisher Scientific Homogenizer 150 (Fisher Scientific,

Pittsburg, PA). The soluble solid content (Brix %) of the resulting puree was analyzed with a handheld ATAGO PAL-BX|ACID F5 Refractometer (ATAGO, USA, Inc., Bellevue, WA).

Fruit Color

Blueberry fruit skin color was analyzed using a Konica Minolta CR-5 Chroma Meter (Konica

Minolta, Inc., Tokyo, Japan)). A 100 mL beaker was filled to the 80 mL line with whole blueberries to ensure minimal light passage through the berries. Three readings per sample were taken per sample. The colorimeter generated values of L* (lightness), a* and b*

(chromaticity coordinate values for red/green and blue/yellow color, respectively) which were used for chroma (C) and hue angle (H) calculations (KALT et al., 1995; MCGUIRE,

1992):

C = + 2 2 ϴ = (tan-1 (b/a)√𝑎𝑎 / 6.2832)𝑏𝑏 * 360

H = 180 + ϴ, if a < 0 and b < 0

H = 360 + ϴ, if a > 0 and b < 0

Color Index = (360 – Hue Angle) / (Chroma + L)

A hue angle of 0° is an indicator of red (+a*), 90° indicates yellow (+b*), 180° indicates

green (-a*) while 270° is indicative of blue (-b*). Chroma on the other hand begins with a value of 0 at the center of the chromaticity color space and increase as distance increases from the center.

Bloom and Ripening Date Recording

In 2017 and 2018, blooming dates were recorded as the date of the first, the last bloom and percent blooming for the duration of flowering in weakly intervals from late February to early June. Ripening was measured only in 2017 as the first fruit that turned blue, the last fruit that turned blue and percent ripening for the duration of fruit development from early

May to mid-June.

Blueberries that were completely blue with little to no signs of blush were considered fully ripe. Ripening could not be accurately recorded during the 2018 harvest season due to an in-

farm trial incident in 2017.

Statistical Analysis

All statistical analyses were performed using Microsoft ® Excel ® 2016 MSO (v. 1901;

Redmond, Washington) and JMP ® Pro (v. 14.1.0; Cary, NC).

RESULTS AND DISCUSSION

Fruit Firmness

Population Segregation for Fruit Firmness using Firmtech 2

In 2016, the fruit firmness (FF) of 320 berry samples was analyzed using the Firmtech 2 instrument at the NC State Castle Hayne Research Station. The individual FF of a berry was determined by taking the average of 10 berry samples per RA individual. The FF ranged from a minimum 119.00 g/mm (RA 276) to a maximum 286.00 g/mm (RA 231)

(http://bit.ly/supplemental_thesis_data Figure/Table 9). The average FF was 198.47 ± 29.63

g/mm. In 2017, the FF of 310 berry samples were analyzed using the Firmtech 2 instrument

at the NC State blueberry genomics lab. The FF ranged from a minimum 105.54 g/mm (RA

297) to a maximum 321.82 g/mm (RA 153). The average FF was 186.72 ± 31.69 g/mm

(http://bit.ly/supplemental_thesis_data Figure/Table 9). In 2018, the FF of 298 berry samples

were analyzed using the Firmtech 2 instrument at the NC State blueberry genomics lab. The

FF ranged from a minimum 113.43 g/mm (RA 97) to a maximum 261.83 g/mm (RA 219).

The average FF was 160.45 ± 22.11 g/mm (http://bit.ly/supplemental_thesis_data

Figure/Table 9).

Due to the variability of the environmental conditions in the three years of the experiment,

only 236 RA individuals consistently had data across all three harvest years (2016-2018). In

all three years, segregation of the FF showed a continuous distribution in the RA population

suggesting its usefulness for QTL analysis (Figure 1). In 2016, the average FF ranged from a

minimum 119.00 g/mm (RA 276) to a maximum 279.00 g/mm (RA 178). The average FF of

the RA population in 2016 was 193.83 ± 28.67 g/mm (Figure 1). Although no data were

available for cv. ‘Arlen’, the average FF for cv. ‘Reveille’ in 2016 was 211.00 g/mm (M.

Mainland, Pers. Comm.) which was higher than the average RA population. In a bi-parental population when the average of a measured trait is larger than the value of either parent, it indicates that a large portion of the population is constituted by a larger number of transgressive segregants. This was not the case in the RA population during the 2016 harvest seasons. In 2017, the FF ranged from a minimum 105.54 g/mm (RA 297) to 289.20 g/mm

(RA 208), and the average FF of the population was 183.79 ± 28.67 g/mm (Figure 1). When compared to the average firmness of the RA population in 2017, the average firmness of

‘Reveille’ (170.00 g/mm) in 2017 was also lower (Figure 1) indicating that, a large portion of the population is constituted by a larger number of transgressive segregants. The average FF of the ‘Arlen’ parent was not available during the 2017 harvest year. In 2018, the population firmness ranged from a minimum 113.43 g/mm (RA 97) to a maximum 261.83 g/mm (RA

219). The average FF of the population in 2018 was 160.30 g/mm which was lower than both

2016 and 2017 population averages (160.24 ± 22.61 g/mm) (Figure 1). The berries of the

2018 harvest year were also softer than ‘Reveille’ (172.93 g/mm) and ‘Arlen’ (163.39 g/mm) from the same 2018 harvest season (Figure 1). There was increased rainfall from May-June

(ripening period) of 2018 (217.68 mm) compared to that of 2016 (160.27 mm) and 2017

(170.69 mm), which has been shown to cause faster over-ripening, skin splitting, and the attraction of decay organisms, which can ultimately result in soft berries (BOYETTE, 1993;

NC CLIMATE OFFICE, 2018). The softer berries produced in the population during the 2018

harvest are more susceptible to mechanical damage and are therefore less desirable to breeders (SAFTNER et al., 2008; XU et al., 2015).

Correlation Analysis of 2016-2018 Fruit Firmness Data (Firmtech 2)

The linear regression analysis indicated that there was a significant and positive correlation for the fruit firmness trait among the three harvest years (2016-2018) (Figure 2). The coefficient of correlation (r) was highest between 2016 and 2017 data (r = 0.54, n = 236, p <

0.01) (Figure 2 A) whereas the correlation between 2017 and 2018 data (r = 0.36, n = 236, p

< 0.01) and 2016 and 2018 data (r = 0.39, n = 236, p < 0.01) were relatively low but positive and significant (Figure 2 A, C). The lowered correlation between either 2016 or 2017 firmness data with the data of 2018 may be indicative of increased rainfall during 2018

(217.68 mm) compared to that of 2016 (160.27 mm) and 2017 (170.69 mm) leading to skewed fruit firmness data in that year.

Firmness Trends in Individual RA Plants

Firmness trends in specific RA plants, particularly those plants possessing extreme trait values on either end of the firmness spectrum, were also identified in the population for future bulk segregant analysis (BSA). An examination of the average firmness in individual

RA across all three years revealed that certain plants in the population stayed consistently in the top or bottom 15% of the population (Figure 3 A, C). From this group of individuals, the top 20 plants with the firmest berries were identified (Figure 3 C). Only those berries possessing high firmness averages and low standard errors of the mean (SEM) values across

all three years were selected, including RA 220, 150, and 104 (255.98 ± 6.5 g/mm; 235.23

+/- 2.71 g/mm; 232.46 ± 9.58 g/mm). These firmness values are all significantly higher than the ‘Reveille’, ‘Arlen’ and the RA population averages from 2016, 2017, and 2018. The 20 plants producing the softest berries with the lowest average firmness across the 2016-2018 period were also identified, including RA 38, 24, and 64 (140.44 ± 2.94 g/mm; 142.54 ± 6.46 g/mm; 147.30 ± 2.20 g/mm) (Figure 3 D). Over the three years of the experiment, the bottom

20 RA plants with the softest berries possessed firmness values that were lower than the population averages and all available parental values.

Population Segregation for Size using Tomato Analyzer

In 2016, the fruit size (FS) of 330 berry samples was analyzed using Tomato Analyzer (v.3.0;

Athens, GA) software. The Tomato Analyzer scanned 10 berries per RA individual. The FS ranged from 0.62 cm3 (RA 335) to 3.97 cm3 (RA 296). The average FS was 1.82 ± 0.80 cm3.

In 2017, the FS of 192 berry samples were also analyzed using Tomato Analyzer (v.3.0;

Athens, GA) software. The FS ranged from 0.71 cm3 (RA 341) to 4.71 cm3 (RA 308). The average FS was 1.82 ± 0.59 cm3 (http://bit.ly/supplemental_thesis_data Figure/Table 25).

Due to the variability of the environmental conditions in the two years of the experiment, only 181 RA individuals consistently had data across all two years. The FS data were collected during the harvest season in 2016 and 2017. In 2016, the average FS of 5 latitudinal fruit cross-sections ranged from 0.52 cm3 (RA 255) to 3.97 cm3 (RA 296). The average FS of

the RA population in 2016 was 1.94 ± 0.66 cm3. In 2017, the average FS of 5 latitudinal fruit

cross-sections ranged from 0.71 cm3 (RA 341) to 4.71 cm3 (RA 308). The average FS of the

RA population in 2017 was 1.83 ± 0.58 cm3 (Figure 4). ‘Reveille’ and ‘Arlen’ data were not available during 2016-2017 harvest years. When the FS from both years was averaged, segregation of the FS showed a continuous distribution in the population suggesting its usefulness for QTL analysis.

Correlation Analysis of 2016-2017 Size Data (Tomato Analyzer)

The linear regression analysis of FS across the years suggests that there was a significant and positive correlation for the FS trait among the 2016-2017 harvest years (r = 0.38, n = 181, p

< 0.01) (Figure 6). The moderate correlation coefficient (r) from the 2016 and 2017 analysis indicates that size may be a fairly heritable trait. The heritability of the size trait has also been observed in other blueberry studies (FERRÃO et al., 2018).

Correlation Analysis of 2016-2017 Fruit Firmness Data (Firmtech 2) and Fruit Size Data

(Tomato Analyzer)

Due to the variability of environmental conditions in the two years of the experiment, only

127 individuals consistently had data across all two years for firmness and size correlation

studies. The linear regression analysis suggests that there was a significant and negative

correlation for fruit firmness and average berry size by volume among the 2016-2017 harvest

years (r = -0.35, n = 126, p < 0.01) (Figure 6). This correlation suggests that smaller blueberries are firmer. The firmness of smaller berries may be related to the lower number of cells per unit area of smaller berries. According to texture analysis studies, crispier fruit is related to lower numbers of cells per unit area (BLAKER et al., 2014; MANN et al., 2005).

Size Trends in Relation to Firmness in RA Individuals

Although the general relationship between fruit firmness and berry size was negative, the top and bottom 33% of individuals in the RA population producing the firmest and largest berries were identified among the 127 RA individuals in the population, for future BSA studies

(http://bit.ly/supplemental_thesis_data Figure/Table 17). RA 312, 118, and 305 were among

the top 20% of the population producing the firmest fruit with the largest volume in the 2016-

2017 harvest (217.56 g/mm and 2.19 cm3; 211.73 g/mm and 2.27 cm3; 208.72 g/mm and 2.05

cm3) (http://bit.ly/supplemental_thesis_data Figure/Table 17). In contrast, RA 31, 36, and 53

individuals produced the softest berries with the smallest volumes in the 2016-2017 harvest

(153.97 g/mm and 1.68 cm3; 156.62 g/mm and 1.70 cm3; 157.73 g/mm and 1.59 cm3)

(http://bit.ly/supplemental_thesis_data Figure/Table 17). The size of berries are highly

correlated with consumer likeability in that larger berries are generally better liked than

smaller berries (SAFTNER et al., 2008). Therefore, the top 20% of the RA population

producing large blueberries with firmer textures may be of interest to breeders seeking

berries that are firm enough to withstand mechanical harvest and also large enough to be

desired by consumers (SAFTNER et al., 2008; XU et al., 2015).

Fruit Texture

Population Segregation for the Fruit Texture

Texture analysis was performed as a means to more accurately characterize the firmness of

berries past the surface of the skin over the course of multiple years. However, due to limited

harvest and access to the TA.XTplus texture analyzer instrument during the 2016 and 2017

harvest years, fruit texture data were only compiled from 2018 and from 210 out of 344

individuals in that year.

Trait segregation was assessed using the three available texture analyzer measurements

breakpoint force (N), force gradient (g/mm), and area under the force by time curve (N*sec).

Breakpoint force is a measure of the strength of the blueberry. It is a measurement of the

force returned at a specific amount of compression. Therefore, breakpoint force is a measure

of the hardness, firmness, and softness of the berry (TEXTURE TECHNOLOGIES, 2019). The force gradient is a measure of the stiffness or pliability of the berry. The area of the curve can be divided into two sections: positive and negative area under data the curve. The positive area under the data curve is a measure of the toughness of the berry. It represents the amount of energy that is required to masticate the berry. The area under the negative portion of the curve is the work of adhesion, or the stickiness of the berry (TEXTURE TECHNOLOGIES, 2019).

Similar to Firmtech 2 measurements, all measured texture related attributes were continuously distributed across the RA population (Figure 7), indicating the multigenic,

quantitative nature of the trait.

In 2018, the breakpoint force (N) ranged from a minimum 2.16 N (RA 109) to a maximum

5.83 N (RA 341). The average breakpoint force of the fruit of the RA population was 3.28 N, which was higher than the ‘Arlen’ average (2.70 N) suggesting that the RA population was harder or firmer in texture. The population average in 2018 was lower (softer or less firm) than ‘Reveille’ (4.08N) from that same year (Figure 7 A). These results are comparable to the firmness trends from 2018, in that the average firmness of the population was lower than that of ‘Reveille’. In 2018, the force gradient (g/mm) ranged from a minimum 44.79 g/mm (RA

326) to a maximum 160.42 g/mm (RA 331). The average force gradient value in the RA population was 80.79 g/mm. Results for force gradient (g/mm) were also comparable to the

2018 firmness data. Like the trend observed in the 2018 firmness data, the RA population average for force gradient (80.79 g/mm) was lower than ‘Arlen’ (85.29 g/mm) and ‘Reveille’

(108.06 g/mm), suggesting that there were more individuals in the RA population with lower force gradients in berry texture (Figure 7 B). Berries with lower force gradients are less stiff and more pliable. The values for area under the force by time curve (N*sec) ranged from a minimum 4.42 N (RA 137) to a maximum 12.44 N (RA 341). The RA population average for area under the force by time curve was 8.35 N*sec, which was higher than ‘Arlen’

(7.42N*sec) but lower than the ‘Reveille’ (10.98N*sec) (Figure 7 C). These data suggest that the texture of the RA population berries was slightly tougher than ‘Arlen’ but less tough than

‘Reveille’.

Correlation Analysis of 2018 Texture Trait Profile Data

The texture analyzer results from the 2018 harvest year were compared to the 2018 firmness

measurements gathered using the Firmtech 2 machine to capture any positive or negative

correlations via linear regression analysis. The highest positive correlation existed between

force gradient (g/mm) and firmness (g/mm) values (r = 0.60, n = 210, p < 0.01) (Figure 8 A).

The relatively strong correlation between the two datasets indicates that texture analyzer

results are relatively compatible with firmness data. Though not as well, breakpoint force (N)

and firmness (g/mm) were also positively correlated and significant (r = 0.49, n = 210 p <

0.01) (Figure 8 B). Lastly, there was a positive and significant correlation between the force

by time curve (N*sec) and firmness (g/mm) values (r = 0.45, n = 210, p < 0.01) (Figure 8 C).

According to the linear regression analysis, force gradient measurements of texture may

potentially be the best means of assessing blueberry fruit firmness, as the two measurements

were the most strongly correlated. However, higher force gradient measurements can be

caused by overripe berries that are harder to pierce which conflict with higher force gradient

measurements caused be firmer berries. Therefore, care should be taken to exclude overripe

berries during sample collection.

Fruit Texture Trends in Individual RA Plants

Several RA plants possessing stiffer/crispier textures (as indicated by high force gradient

values) were identified in the population for BSA during the 2018 harvest year (Figure 9 A,

C, E, G, I, K). The top 15% of the population with the firmest/toughest berry textures as analyzed by the TA.XTPlus texture analyzer were selected and compared to the top 15% of

the population with firmest berry textures measured by the Firmtech 2 machine. RA 76, 156,

and 207 were among the top 15% of the population displaying both tough and firm textures

as verified by the TA.XTPlus and Firmtech 2. Alternatively, RA 277 and 291 were among the bottom 15% of RA the population with the weakest and softest berries (Figure 9 B, D, F,

H, J, L). The berries identified in this analysis represent the extremes of the population and

would therefore be useful in future bulk BSA studies.

Fruit Weight

Population Segregation for Fruit Weight

In 2016, the fruit weight (FW) of 329 berry samples was analyzed using a standard

laboratory scale to two decimal points at the NC State Castle Hayne Research Station. The

individual weight of a berry was determined by taking the average of a cup of berries. The

FW ranged from a minimum 0.27 g (RA 60) to a maximum 3.13 g (RA 305)

(http://bit.ly/supplemental_thesis_data Figure/Table 43). The average FW was 1.69 ± 0.48 g.

In 2017, the FW of 313 berry samples were analyzed using a standard laboratory scale to two

decimal points at the NC State blueberry genomics lab. The FW ranged from a minimum

0.43 g (RA 152) to a maximum 3.38 g (RA 308). The average FW was 1.41 ± 0.47 g

(http://bit.ly/supplemental_thesis_data Figure/Table 44). In 2018, the FW of 236 berry

samples were analyzed using a standard laboratory scale to two decimal points at the NC

State blueberry genomics lab. The FW ranged from a minimum 0.28 g (RA 227) to a

maximum 3.15 g (RA 340). The average FS was 1.65 ± 0.46 g

(http://bit.ly/supplemental_thesis_data Figure/Table 45).

Due to the variability of the environmental conditions in the three years of the experiment, only 200 RA individuals consistently had FW data across all three harvest years (2016-2018).

In all three years, segregation of the FW showed a continuous distribution in the RA population suggesting its usefulness for QTL analysis (Figure 10). In 2016, the average weight of a berry per cup ranged from a minimum 0.27 g (RA 60) to a maximum 2.98 g (RA

340) per berry. The average weight of the RA population in 2016 was 1.70 ± 0.42 g. The average weight of ‘Reveille’ in 2016 was 1.73 g (M. Mainland, Pers. Comm.) which was slightly higher than the average weight of the population. In 2017, the average weight of 10 berries ranged from a minimum 0.52 g (RA 155) to a maximum 2.47 g (RA 60) per berry.

The average weight of the RA population in 2017 was 1.40 ± 0.43 g. The average weight of

‘Reveille’ in 2017 was 2.20 g which was higher than the average weight of the population and close to the population’s maximum weight (Figure 10 A). ‘Arlen’ data were not available during the 2017-2018 harvest season. In 2018, the average weight of 10 berries ranged from a minimum 0.28 g (RA 227) to a maximum 3.15 g (RA 340). The average weight of the 2018

RA population was 1.64 ± 0.46 g (Figure 10 B). The average weight of ‘Reveille’ in 2018 was 1.24 g (M. Mainland, Pers. Comm.) which was lower than the average weight of the population.

Correlation Analysis of 2016-2018 Fruit Weight Data

The linear regression analysis of FW across the years suggests that there was a significant

and positive correlation for the fruit weight trait among the 2016-2018 harvest years. The

coefficient of correlation (r) for 2016 vs. 2017, 2017 vs. 2018, and 2016 vs. 2017 analyses

were all relatively similar (r = 0.44, n = 200, p < 0.01; r = 0.47, n = 200, p < 0.01; and r =

0.45, n = 200, p < 0.01, respectively) (Figure 11). The coefficient of correlation establishes a

positive correlation between weight values from year to year. The relatively high correlation

coefficient (r) from the 2017 and 2018 analysis indicates that weight may be a fairly heritable

trait. The heritability of the weight trait and the observed continuous trait distribution have

been seen previously in blueberries (CELLON et al., 2018) and other crops such as tomato

(RAI et al., 2016), pepper (NAEGELE et al., 2016), apples (ALSPACH AND ORAGUZIE, 2002),

peach (BISCARINI et al., 2017; QUILOT et al., 2005) and apricot, a woody perennial like

blueberry (SALAZAR et al., 2013).

Fruit Weight Trends in Individual RA Plants

The top and bottom 15% of individuals in the RA population producing the heaviest and

lightest berries were identified among 200 RA individuals in the population for future BSA

studies (Figure 12). When averaged over the 2016-2018 harvest year, RA 276, 18 and 330

were identified for producing the heaviest individual berries (2.41 ± 0.09 g; 2.41 ± 0.12 g;

2.39 ± 0.07 g) (Figure 12 A, C). In contrast, RA 244, 155, and 347 individuals produced the

lightest individual berries (0.75 ± 0.05 g; 0.80 ± 0.15 g; 0.65 g; 1.03 ± 0.07 g) (Figure 12 B,

D). Weight is highly correlated with size (DE SOUZA et al., 2012; HORTYŃSKI et al., 1991;

JOHNSON et al., 2011), indicating that the berries identified for existing on either side of the weight spectrum should possess extremely high or low berry diameters. The size of berries are highly correlated with consumer likeability in that larger berries are generally better liked than smaller berries (SAFTNER et al., 2008). Therefore, berries possessing higher weights may

be more desired by consumers. This trend of heavier berries possessing larger sizes (volume)

was observed in the population during the 2016-2017 harvest years. The linear regression

analysis of averaged FW and fruit size (FS) suggests that there was a significant and positive

correlation for the FW and FS trait (r = 0.80, n = 123, p < 0.01) (Figure 13). FW and FS are

well correlated. When assessing individual berries in the population, the top 15% of RA

plants producing the heaviest berries also produced the berries with the largest volumes. RA

18, 325, and 330 are among the top 15% of the population with the heaviest and largest

individual berries (2.50 g and 2.94 cm3; 2.38 g and 2.86 cm3; 2.31 g and 2.35 cm3)

(http://bit.ly/supplemental_thesis_data Figure/Table 41).

Percent Titratable Acidity Measurements by Titration

Population Segregation for Titratable Acidity

In 2017, the fruit percent titratable acidity (FTA) of 279 berry samples was analyzed using an

autotitraor. The FTA ranged from a minimum 0.19% TA (RA 11) to a maximum 1.97% TA

(RA 340) (http://bit.ly/supplemental_thesis_data Figure/Table 62). The average FTA was

0.50 ± 0.30% TA. In 2018, the FTA of 293 berry samples were analyzed. The FTA ranged

from a minimum 0.16% TA (RA 287) to a maximum 1.80% TA (RA 333). The average FTA

was 0.74 ± 0.32% TA (http://bit.ly/supplemental_thesis_data Figure/Table 63). In 2016 FTA

measurements via autotitration methods were not available.

Due to the variability of the environmental conditions in the two years of the experiment,

only 237 RA individuals consistently had data across the two harvest years (2017-2018). In

both years, segregation of the FTA showed a continuous distribution in the RA population

suggesting its usefulness for QTL analysis (Figure 14 A). However, the 2017 distribution

was slightly skewed towards lower titratable acidity values. Gibson et al. (2013) observed

that the titratable acidity of blueberries significantly decreases in overripe berries (GIBSON et

al., 2013). Lobos and Hancock (2015) have noted that early floral development, which is

positively correlated with early ripening, can occur as a result of changing climate conditions

like warmer early spring temperatures (LOBOS AND HANCOCK, 2015). The RA population was exposed to a relatively warmer late winter period (mid-January through mid-March) that occurred in 2017 (54.7°F), when compared to the previous year in 2016 (48.1°F) (NC

CLIMATE OFFICE, 2018). During the 2018 harvest season, the distribution of the titratable acidity trait was also continuous (Figure 14 B). In 2017 fruit, FTA ranged from 0.18-1.97%

TA. The average FTA of the RA population in 2017 was 0.60% TA. In 2017, the average

FTA of population was higher than both parents (Reveille = 0.33% TA and Arlen = 0.47%

TA) (Figure 14 A). In 2018 fruit, FTA ranged from 0.16-1.80% TA. The average FTA of the

RA population in 2018 was 0.73% TA. The titratable acidity of ‘Reveille’ was 0.69% TA, which is slightly lower than the population average. The percent titratable acidity of ‘Arlen’

in 2018 was 0.40% TA, which is significantly lower than the population average. Titratable

acidity is an important trait considered by breeders because it provides information about the

amount of citric acid in blueberries, which can affect the human perception of fruit tartness.

However, it is difficult to assess its effect on flavor profiles without taking soluble solid

content account (GILBERT et al., 2015; SAFTNER et al., 2008). As a result, the ratio of sugar to

acid is a better indicator of the balance between fruit acidity and sweetness.

Correlation Analysis of 2017-2018 Fruit Titratable Acidity

The linear regression analysis showed that there was a weak, but significant, positive correlation for the FTA trait among 2017 and 2018 harvest years (r = 0.28, n = 237, p < 0.01)

(Figure 15), suggesting that this trait may be affected by environmental factors. These results indicate that titratable acidity is not a highly heritable trait and should be used for QTL analysis with caution.

Fruit Titratable Acidity Trends in Individual RA Plants

RA plants producing fruit with extremely high and low FTA acidity over the two-year harvest period were identified for further consideration in NC State blueberry breeding program. RA 153, 189, and RA 333 were among the top 15% of the population with fruit possessing high FTA (1.38 ± 0.15% TA; 1.59 ± 0.16% TA; 1.87 ± 0.07% TA) (Figure 16 A,

C). According to Bett-Garber et al. (2015) blueberry FTA is positively correlated with sour tastes (BETT‐GARBER et al., 2015). RA 113, 118, and 131 were among the bottom 15% of the

population with the lowest FTA values (0.17 ± 0.01% TA; 0.22 ± 0.03% TA; 0.23 ± 0.04%

TA) (Figure 16 B, D).

Population Segregation for fruit puree pH

In 2016, the fruit puree pH of 198 berry samples was analyzed using a bench top pH meter because the Mettler Toledo bench top autotitrator was not yet available to the NC State blueberry genomics lab. The pH ranged from a minimum 1.06 (RA 165) to a maximum 4.74

(RA 256) (http://bit.ly/supplemental_thesis_data Figure/Table 65). The average pH was 3.07

± 0.67. Standardization procedures and accuracy were improved during the 2017-2018

harvest years with the use of an autotitrator that was able to measure the initial pH of the

sample solution. This instrument was not available in 2016. In 2017, the fruit puree pH of

281 berry samples were analyzed using this automated titrator. The fruit puree pH ranged

from a minimum 3.06 (RA 37) to a maximum 4.98 (RA 167). The average pH was 3.72 ±

0.36 (http://bit.ly/supplemental_thesis_data Figure/Table 62). In 2018, the fruit puree pH of

270 berry samples were analyzed using this automated titrator. The fruit puree pH ranged

from a minimum 3.33 (RA 240) to a maximum 4.86 (RA 258). The average pH was 3.85 ±

0.29 (http://bit.ly/supplemental_thesis_data Figure/Table 63).

Due to the variability of the environmental conditions in the two years of the experiment,

only 222 RA individuals measured using the autotitrator consistently had data across the two

harvest years (2017-2018). The berry pH data gathered by the benchtop pH meter in 2016

were not used in the segregation analysis due to the availability of the autotitrator during the

2017-2018 harvest year, which allowed for better standardization procedures. Additionally,

the pH values in 2016 were poorly correlated with the averaged 2017-2018 berry puree data

gathered using the autotitrator (r = 0.08, n = 123, p < 0.01) (Figure 17). These data suggest

that the autotitrator pH data were not well correlated with the benchtop pH data (Figure 17).

The segregation of the fruit puree pH in 2017 was slightly skewed towards lower pH values

(Figure 18 A). In 2017 fruit puree, the pH ranged from a minimum 3.06 (RA 37) to a

maximum 4.98 (RA 167). The average pH of the population in 2017 was 3.72 ± 0.37 (Figure

18 A). The pH of ‘Reveille’ in 2017 was 3.85, which was slightly higher than the population

average. The pH of ‘Arlen’ was 3.42, which was lower than the population average. In 2018,

the pH values were continuously distributed in the population (Figure 18 B). In 2018, the pH of fruit puree ranged from a minimum 3.33 (RA 240) to a maximum 4.86 (RA 258). The average fruit puree pH of the population in 2018 was 3.85 ± 0.30 (Figure 18 B). The average pH of ‘Reveille’ in 2018 was 3.85 ± 0.11 (n = 4), which was very close to the 2017 population average and very close to the population average in 2018. The pH of ‘Arlen’ in

2018 was 4.08, which was higher than the population average in 2018. The pH of ‘Arlen’ in

2018 was also higher than the pH of ‘Arlen’ in 2017, indicating possible environmental influence or differences in maturity of the ‘Arlen’ samples at the time of testing (Figure 18

B). Similar to FTA data, we observed highly variable results from year to year. Moreover, when trying to determine the tartness perception on blueberry flavor, titratable acidity measurements are often more useful in the determination of desired flavor profiles (BETT‐

GARBER et al., 2015; NIELSEN, 2003). The pH of a solution is a poor indicator of tartness

because it is difficult to detect the actual flavor of certain acids in a solution when only

relying on the presence of free protons. For example, adding hydrochloric acid to water will

yield a flavorless solution despite the abundance of hydrogen ions whereas the addition of

citric acid to water will yield a more sour tasting solution at the same pH (DA CONCEICAO

NETA et al., 2007). The titration process, however, relies on the removal of protons with the

addition of a base leaving only the anions of different acidic molecules like citric acid anions

in the solution. Depending on the type, these acidic molecules contribute to the sour taste of

solutions (DA CONCEICAO NETA et al., 2007; FURGUSON AND MARTIN, 2017; PEYNAUD et al.,

1996). In the present study, the correlation between the pH of the blueberry puree and titratable acidity was significant and negative (r = -0.76, n = 208, p < 0.01) (Figure 19). Bett-

Garber et al. (2015) observed that the pH of blueberry juice was negatively correlated to sour tastes, which are related to high FTA values (BETT‐GARBER et al., 2015; DA CONCEICAO

NETA et al., 2007). Although often negatively correlated, there is not a completely

predictable relationship between pH and FTA. The pH of a solution depends only on the

ability of the acids present in a solution to dissociate and not the amount of acids present,

which is what titratable acidity measures (AUSTRALIAN WINE RESEARCH INSTITUTE, 2019).

Correlation Analysis of 2017-2018 Fruit Puree pH

The correlation analysis suggested that there was a significant and positive correlation between fruit puree pH in 2017 and 2018 harvest years (r = 0.40, n = 222, p < 0.01) (Figure

20).

Fruit puree pH Trends in Individual RA Plants

The top 15% of the RA population producing fruit with the highest average pH and the bottom 15% of the RA population producing fruit with the lowest pH were identified for future use in the NC State blueberry breeding program (Figure 21). RA 113, 131, and 229 were among the individuals with the highest pH values which were 4.68 ± 0.06, 4.74 ± 0.05, and 4.51 ± 0.02, respectively (Figure 21 A, C). RA 81, 299, and 338 were among the individuals with the lowest pH values which were 3.38 ± 0.08, 3.34 ± 0.06, and 3.31 ± 0.10, respectively (Figure 21 B, D).

Percent Titratable Acidity Measured by Handheld Refractometer

FTA measured by a handheld refractometer was compared with the data obtained from a bench top autotitrator. Our objective was to determine if the handheld refractometer with dual functionality can be an adequate replacement for timely process of fruit puree by a bench top autotitrator. In 2016, the FTA was only measured by the handheld refractometer

(n = 279); therefore, no comparison could be made between refractometer data and bench top autotitrator. However, in 2017 and 2018 the data were present for both refractometer and our benchtop autotitrator. The data were compiled from the available 252 and 259 out of 344 RA individuals in 2017 and 2018, respectively.

Fruit Acidity Comparison Between Refractometer and Bench Top Autotitrator in 2017

The FTA percentage of ‘Reveille’ as measured by the refractometer was 0.19% TA in 2017 and the acidity percentage of ‘Arlen’ was 0.37% TA. The FTA of ‘Reveille’ as measured by

the bench top autotitrator was 0.33% TA in 2017 and the FTA of ‘Arlen’ was 0.47% TA. In

2017, the FTA of berry fruit puree as measured by refractometer ranged from a minimum

0.07% TA (RA 7) to a maximum 1.50% TA (RA 189). The average FTA of the RA population in 2017 as measured by the refractometer was 0.45 ± 0.25% TA, which was higher than both ‘Reveille’ and ‘Arlen’. The FTA of berry fruit puree as measured by bench top autotitrator ranged from a minimum 0.19% TA (RA 113) to a maximum 1.96% TA (RA

340). The average FTA of the RA population in 2017 as measured by the benchtop

refractometer was 0.50 ± 0.30% TA, which was higher than both ‘Reveille’ and ‘Arlen’ measured by the same instrument. The average FTA of the 2017 RA population as measured

by bench top refractometer was also very similar to the autotitrator FTA measurement

(http://bit.ly/supplemental_thesis_data Figure/Table 54).

The population distribution of FTA values as measured via refractometer in 2017 was similar to the population distribution of FTA values as measured via bench top autotitration, in that both possessed data that was continuous but slightly skewed towards lower FTA (Figure 22).

The correlation analysis comparing the FTA values gathered using the refractometer and the bench top autotitrator suggested that there was a significant and strong positive correlation in the 2017 harvest year (r = 0.86, n = 252, p < 0.05) (Figure 23).

The 2017 data suggest that the FTA trait, as measured by handheld refractometer can be used in lieu of autotitrator where this instrument is not available or when a breeder would like to make a rapid selection in the field.

Fruit Percent Titratable Acidity Comparison Between Refractometer and Bench Top

Autotitrator in 2018

The FTA of ‘Reveille’ as measured by the refractometer was 0.52% TA in 2018 and the FTA of ‘Arlen’ was 0.25% TA. The FTA of ‘Reveille’ as measured by the benchtop autotitrator was 0.68% TA in 2018 and the FTA of ‘Arlen’ was 0.40% TA. In 2018, the FTA of berry fruit puree as measured by refractometer ranged from a minimum 0.1% TA (RA 287) to a maximum 1.44% TA (RA 333). The FTA of the RA population in 2018 as measured by the refractometer was 0.48 ± 0.24% TA, which was slightly lower than ‘Reveille’ but higher than

‘Arlen’. The FTA of berry fruit puree as measured by bench top autotitrator ranged from a minimum 0.16% TA (RA 287) to a maximum 1.80% TA (RA 333). The average FTA of the

RA population in 2018 as measured by the benchtop autotitrator was 0.74 ± 0.32% TA, which was higher than both ‘Reveille’ (0.69% TA) and ‘Arlen’ (0.47% TA) as measured by the same instrument. The average FTA of the 2018 RA population as measured by bench top refractometer was lower than the autotitrator FTA measurement. However, both minimum and maximum FTA were identified in the same RA individual (RA 287 and RA 333, respectively) (http://bit.ly/supplemental_thesis_data Figure/Table 56).

The population distribution of FTA values as measured via refractometer in 2018 was similar to the population distribution of FTA values as measured via bench top autotitration, in that both possessed distribution trends that were continuous (Figure 22).

The correlation analysis comparing the FTA values gathered using the refractometer and the bench top autotitrator suggested that there was a significant and strong positive correlation in the 2018 harvest year (r = 0.95, n = 259, p < 0.01) (Figure 23).

The 2018 data suggests that the FTA trait, as measured by handheld refractometer can be used in lieu of autotitrator where this instrument is not available or when a breeder would like to make a rapid selection in the field.

Comparison Between Refractometer and Bench Top Autotitrator Fruit Puree Percent Acidity

Trends in Individual RA Plants

Data for averaged fruit puree percent acidity gathered using both the refractometer and bench top autotitrator were compiled from the available 167 out of 344 RA individuals in 2017-

2018. The top 15% of the RA population with the highest average FTA and the bottom 15% of the RA population with the lowest FTA were identified for future use in the NC State blueberry breeding program (Figure 24). The top and bottom percent acidity values represented the highest and lowest citric acid content in the fruit puree. The values measured by both the refractometer and bench top autotitrator were considered in the analysis. RA 333,

168, and 157 were among the individuals with the highest FTA values. The FTA of the fruit

from RA 333, 168, and 157 measured using the refractometer were 1.44% TA, 0.74 ± 0.09%

TA, and 0.65 ± 0.04% TA, respectively (Figure 24 A, B). Comparatively, the FTA of the fruit from the same RA individuals measured using the bench top autotitrator were 1.87 ±

0.07% TA, 0.88 ± 0.03% TA, and 0.85 ± 0.09% TA, respectively. RA 113, 167, and 19 were among the individuals with the lowest FTA values when measured by both the refractometer and benchtop autotitrator. The lowest FTA values measured from RA 113, 167, and 19 fruit using the refractometer were 0.12 ± 0.02% TA, 0.15 ± 0.03% TA, and 0.15 ± 0.02% TA, respectively (Figure 24 C, D). Comparatively, the lowest values collected from the same RA individuals using the bench top autotitrator were 0.17 ± 0.01% TA, 0.24 ± 0.03% TA, and

0.25 ± 0.05% TA, respectively. The similar trends in these and other RA individuals suggest that the FTA trait, as measured by handheld refractometer can be used in lieu of autotitrator when this instrument is not available or when a breeder would like to make a rapid selection in the field.

Fruit Soluble Solid Content

Population Segregation for Fruit Soluble Solid Content

In 2016, the fruit soluble solid content (FSSC) of 310 berry samples was analyzed using a handheld refractometer. FSSC values are expressed as Brix percentages. The FSSC in the RA population ranged from a minimum 7.80% (RA 271 and 362) to a maximum 19.7% (RA

248) in 2016 (http://bit.ly/supplemental_thesis_data Figure/Table 80). The average FSSC was 11.89 ± 2.06%. In 2017, the FSSC of 267 berry samples were analyzed. The FSSC ranged from a minimum 7.40% (RA 187) to a maximum 18.50% (RA 135 and 265). The

average FSSC was 12.72 ± 1.99% (http://bit.ly/supplemental_thesis_data Figure/Table 80).

In 2018, the FSSC of 265 berry samples were analyzed. The FSSC ranged from a minimum

7.90 % (RA 244) to a maximum 15.50% (RA 335). The average FSSC was 11.52 ± 1.51%

(http://bit.ly/supplemental_thesis_data Figure/Table 80).

Due to the variability of the environmental conditions in the three years of the experiment,

only 180 RA individuals consistently yielded data across all three harvest years (2016-2018).

In all three years, segregation of the FSSC trait showed a continuous distribution in the RA population suggesting its usefulness for QTL analysis. Cellon et al. (2018) has also observed this continuous distribution pattern in a population of tetraploid blueberries (CELLON et al.,

2018). In 2016, the average FSSC ranged from a minimum 7.80% (RA 271 and 362) to a

maximum 17.60% (RA 235). The average FSSC of the RA population in 2016 was 11.94 ±

2.04%. The average FSSC of ‘Reveille’ in 2016 was 11.08% (M. Mainland, Pers. Comm.) which was slightly lower than the average FSSC of the population. No parental FSSC data for and ‘Arlen’ were available in 2016 (Figure 25 A). In 2017, the average FSSC ranged from a minimum 7.60% (RA 146) to a maximum 18.5% (RA 265). The average FSSC of the

RA population in 2017 was 12.65 ± 1.90%. The average FSSC of ‘Reveille’ in 2016 was

13.1% which was slightly higher than the average FSSC of the population. The average

FSSC of ‘Arlen’ (10.80%) was lower than the RA population average during the 2017 harvest year (Figure 25 B). In 2018, the average FSSC ranged from a minimum 7.90% (RA

244) to a maximum 15.40% (RA 48). The average FSSC of the RA population in 2018 was

11.54 ± 1.45%. The average FSSC of ‘Reveille’ in 2018 was 11.70% which was slightly

higher than the average FSSC of the population. The average FSSC of ‘Arlen’ (10.80%) was

lower than the RA population average during the 2017 harvest year (Figure 25 C).

Soluble solid content (SSC) is a major fruit quality trait that plays an important role in consumer desirability due to its contribution to the perception of sweetness. According to

Beaudry et al. (1992) and Saftner et al. (2008), acceptable levels of blueberry FSSC content, measured in Brix % units, are usually greater than 10% (BEAUDRY, 1992; SAFTNER et al.,

2008). In the present study, all RA population averages were consistently above 10% during each harvest year between 2016 and 2018 (n = 180) (Figure 25). However, it should be noted

that more sugar does not always indicate that berries will be sweeter (GILBERT et al., 2015).

Factors such anthocyanin content can distort SSC readings when measured via refraction. In

addition, sweetness perception is often affected by titratable acidity (SAFTNER et al., 2008).

As a result, the ratio of sugar to acid is a better indicator of the balance between fruit acidity

and sweetness.

Correlation Analysis of 2016-2018 Fruit SSC Data

The linear regression analysis of FSSC across the years suggests that there was no correlation

for the FSSC trait among the 2016-2018 harvest years suggesting low trait heritability. The

coefficient of correlation (r) for 2016 vs. 2017, 2017 vs. 2018, and 2016 vs. 2018 analyses

were all low (r = 0.123, n = 180, p <0.01; r = 0.248, n = 180, p <0.01; and r = 0.067, n = 180,

p <0.01, respectively) (Figure 26). The low heritability of the FSSC trait has been observed

in other blueberry studies and also in other crops like peach (CELLON et al., 2018; DE SOUZA

et al., 1998). The heritability of the FSSC trait is believed to be largely affected by

environmental factors (BALLINGER et al., 1963; BALLINGTON, 1984; GILBERT et al., 2015;

SAFTNER et al., 2008).

Fruit SSC Trends in Individual RA Plants

The top and bottom 15% of individuals in the RA population producing the heaviest and

lightest berries were identified among the available 180 RA individuals in the population, for

future BSA studies (Figure 27). When averaged over the 2016-2018 harvest years, RA 205,

255 and 239 were identified for producing the berries with the highest FSSC (15.80 ± 1.15%;

14.97 ± 0.81%; 14.30 ± 0.72%) (Figure 27 A, C). In contrast, RA 55, 27, and 324 individuals produced berries with the lowest FSSC (9.60 ± 0.31%; 9.83 ± 0.52%; 10.00 ± 0.35%) (Figure

27 B, D). RA plants producing fruit with these extremely high and low FCCS values will be subject to further consideration in NC State blueberry breeding program.

Blueberry Skin Color

Population Segregation for Fruit Skin Color

The powdery appearance of blueberries is an attractive feature of blueberries, making the skin color trait important to blueberry breeders and consumers (MATIACEVICH AND SILVA,

2012). The production of highly marketable lighter skinned blueberries depends on the

correct analysis of various color measurements, including hue, chroma, color index, L*, a*, and b* values. Segregation analysis for these various color traits were performed during the

2017-2018 harvest years. In 2017, the fruit skin color (FSC) of 315 berry samples were

analyzed using a bench top colorimeter (http://bit.ly/supplemental_thesis_data Figure/Table

87). In 2018, the FSC of 294 berry samples were analyzed using a bench top colorimeter

(http://bit.ly/supplemental_thesis_data Figure/Table 86). Due to the variability of the

environmental conditions in the two years of the experiment, only 266 RA individuals

consistently had data across both harvest years (2017-2018). In both years, segregation of the

FSC (especially when measured in terms of chroma, L*, and b* values) showed a continuous

distribution in the RA population suggesting its usefulness for QTL analysis (Figure 28). The

averaged chroma value in the RA population in 2017-2018 ranged from a minimum 2.35

(RA 29) to a maximum 6.63 (RA 153). The average chroma value of the RA population was

4.39 ± 0.75, which was higher than ‘Reveille’ (4.18) in 2017. Chroma values for ‘Reveille’ in

2018 and ‘Arlen’ in 2017-2018 were not available for comparison. Because chroma is a measure of the color’s intensity or distance from gray, both measurements indicated that the color of the berries from the RA population were more vivid than ‘Reveille’. Chroma values in comparison to visual assessment, however, have not been as useful as other color measurements (i.e. L* values) in determining the brightness of fruit (NUNES et al., 2004). The averaged L* values of the RA population in 2017-2018 ranged from a minimum 22.06 (RA

29) to a maximum 33.35 (RA 340).

The L* value of the RA population (27.90) when averaged across 2017-2018 was higher than the 2017 ‘Reveille’ parent (24.93) (M. Mainland, Pers. Comm.). Higher L* values translate to lighter colors. L* values have been cited as the efficient indicators of brightness, with chroma being less useful in comparison with visual assessment (NUNES et al., 2004). The

segregation of the L* value trait showed a continuous distribution in the population. Such

continuous distribution patterns signify its usefulness for QTL analysis.

The hue angle and color index are both measurements that take multiple color measurements

into consideration and, therefore, may be of use in determining the brightness of blueberry

skin color. Hue angle provides an indication of the actual color of the blueberry skin on the

CIELAB color space wheel, with 180°, 270°, and 360° indicating a bluish-green, blue, and reddish-purple hue, respectively (MCGUIRE, 1992). When averaged across the 2017-2018

harvest seasons, the hue angle of the RA population ranged from a minimum 183.03° (RA

271) to a maximum 355.10° (RA 63). The hue angle in the RA population (266.20°) when

averaged across 2017-2018 was very similar to ‘Reveille’ (266.90°) in 2017. No ‘Reveille’

hue angle data were available in 2018 and no ‘Arlen’ data were available in 2017-2018

(Figure 28). The average hue angle of the RA population and ‘Reveille” were very close to the true blue hue angle 270° (MCGUIRE, 1992). The hue angle trait is segregating across the population and shows a continuous distribution, a feature that makes it useful for QTL analysis. However, it would be difficult to assess for FSC brightness using hue angle because

L*values are not considered in the calculation.

The color index of blueberries takes all the collected color measurement values (L*, a*, b*,

chroma, and hue angle) into account. As such, it could be a useful tool in evaluating the color

and brightness of blueberries. The trait does segregate across the 2017-2018 RA population

and shows a continuous distribution. When averaged across the 2017-2018 harvest seasons,

the color index of the RA population ranged from a minimum 0.19 (RA 65) to a maximum

5.68 (RA 271). The average color index of the RA population was 2.93, which was lower

than ‘Reveille’ (3.51) in 2017. No ‘Reveille’ data were available in 2018 and no ‘Arlen’ data

were available in 2017-2018 (Figure 28). When compared to the color index of red table

grapes [CIRG = (180 – hue angle) / (chroma + L)], the color index places the color of the RA

population berries between red and dark violet grape samples (CARREÑO et al., 1995).

However, the use of color index as a trait for future QTL studies should be approached with

caution as there is no well-established standard for suitable blueberry color index (MCGUIRE,

1992).

Correlation Analysis of 2017-2018 Color Measurements

All color traits from 2017 and 2018 were correlated with one another using a Pearson

correlation analysis. Color values that were related to one another (sharing similar variables

in their formula) like 2017 chroma versus b* values were well correlated (r = 1.00, n = 266, p

= 0), as expected (Table 1). Color index and chroma from both 2017 and 2018 were negatively correlated (r = -0.40, n = 266, p < 0.01 and r = -0.41, n = 266, p <0.01,

respectively) (Table 1). These negative correlations suggest that higher L* values (lighter

blueberry skin) correspond to lower color index values. However, more research is necessary

to determine the standards and usefulness of the color index in the determination of blueberry

color and brightness.

Brightness Trait Trends in Individual RA Plants

Although it is difficult to determine the usefulness of several color measurements when

determining the brightness of blueberries, L* values are used as the standard means of

measurement. As such, the top 15% of the RA population producing the berries with the

highest L* values (with low SEM) and the bottom 15% of the RA population producing berries with the lowest L* values (with low SEM) were identified for BSA studies (Figure

29). In particular, RA 161, 263, and 340 (L* = 32.10 ± 1.08, L* = 32.58 ± 0.35, L* = 33.35 ±

0.85) were among the brightest blueberries while RA 59, 138, and 167 (L* = 22.45 ± 1.06,

L* = 24.20 ± 0.26, L* = 23.03 ± 0.87), were among the darkest blueberries (n = 266) (Figure

29). The brightest berries identified within the RA population are of particular interest to the

NC State breeding program.

Bloom and Ripening

Population Segregation for Days to Anthesis

In 2016, bloom data were not available but last-ripe (last-blue) data were collected from 289

RA individuals. The earliest last-blue date was May 26th (RA 120) and the latest date was

June 27th (RA 303, 320, 327-330, 340, and 342). In 2017, bloom data were collected from

335 RA individuals and last-ripe data were collected from 317 RA individuals. The earliest bloom date was March 7th and the latest was April 19th. The earliest last-blue date was May

21st (RA 212) and the latest date was June 15th (RA 266). In 2018, bloom data were collected

from 353 RA individuals. The earliest bloom date was April 4th. Of the 353 RA individuals,

212 bloomed on April 4th including RA 1-3, 345, and 356. The latest bloom date was May

30th. A total of 30 RA individuals bloomed on this date, including RA 152, 173, 220, and

252. Due to farm conditions at the Barnhill location, ripening data from 2018 was not

available (http://bit.ly/supplemental_thesis_data Figure/Table 91 and 94). With the data

available, only the bloom and last-blue dates of 311 RA individuals were analyzed for days

to anthesis (DTA) in 2017. The days to anthesis (DTA) ranged from a minimum 41 days

(RA 128) to a maximum 90 days (RA 172 and 221) (http://bit.ly/supplemental_thesis_data

Figure/Table 91 and 94). The average DTA for the RA population was 58 days (Table 2,

Figure 30). Approximately 55% of RA individuals fully ripened around 56-59 days after first

bloom. In general, ‘Reveille’ is an early blooming cultivar that bloomed around April 3rd in

2017 and has been observed to bloom up to 2 weeks earlier (W. T. Bland, Pers. Comm.).

‘Arlen’ is a late blooming cultivar that did not bloom until several weeks later (W. T. Bland,

Pers. Comm.). Although there were extremes on either of the distribution plot, the 311 RA

plants surveyed did not display clear segregation patterns for DTA in the population (Figure

30). This was primarily due to the fact that blooming can happen quickly over the time span

of a few days.

Bloom and Ripening Trends in Individual RA Plants

In 2017, 41 late blooming and late ripening RA individuals were identified in the population.

Of the 41 RA individuals, RA 301, 264, 294, 306, 364, and 363 bloomed on the latest date

(4/19/2017) and ripened during the latest week (6/11/2017 – 6/14/2017) (Figure 31 A, B;

Table 2 A). In total, it took RA 301, 264, 294, 306, 364, and 363 only 53-56 days from first bloom to last blue, which is less than the average DTA of the RA population (58.28 ± 7.98 days) (Table 2). These individuals may be of use in future BSA studies in the NC State breeding program. The development of late blooming cultivars that also ripen late a priority for North Carolina blueberry breeders. Late blooming blueberry cultivars that expose their flowers later in the spring or summer are less likely to be damaged by early spring frosts.

Blueberry cultivars that ripen later in the spring or summer allow breeders to extend their growing season. RA 113, 139, 210, 232, and 307 were identified as early blooming and early ripening individuals that could be in danger of later frosts during the spring season. These

RA individuals began blooming on 3/7/2017 and showed the first signs of ripening from

5/12/2017 - 5/15/2017 (Figure 31 A, B; Table 2 A). On average, bloom to berry ripening took 68 days, which is approximately 10 days longer than the DTA of the RA population

(58.28 ± 7.98 days). In terms of possessing the least desirable traits, these early blooming and early ripening individuals may be of interest in BSA studies.

CONCLUSION

The majority of the fruit quality traits were segregating within the RA population, indicating their potential usefulness as ideal candidates for future QTL analysis. Traits for firmness, texture, weight, color (specifically L* values), which displayed continuous distribution of the

trait, are of particular interest. In addition, the high heritability of some of these traits such as

fruit weight and size, as indicated by relatively significant coefficients of correlation,

increases the chance of identifying the same QTLs in every year.

Although titratable acidity and SSC were well distributed across the RA population, the data

suggests that they are low heritable traits that may result in spurious rather than consistent

QTLs in different years. These traits are highly susceptible to environmental changes.

Due to the limited bloom data available in the present study, it is difficult to determine

whether the days to anthesis trait will segregate in the RA population (continuous distribution

patterns were not observed in 2017) limiting the ability to identify the bloom trait of the RA

population as a QTL candidate. However, a few RA individuals with desirable late blooming

and late ripening traits were singled out for future bulk segregant analysis.

The relationship between fruit quality traits measured via different protocols is also useful in

enhancing blueberry fruit quality studies. The assessment of firmness via both Firmtech 2

and texture analyzer instruments provided insight on the effectiveness and interchangeability

of each instrument. Both instruments produced texture values that were well correlated with

each other. However, more research is needed to confirm this correlation as data were collected from only one year.

The relationships between the titratable acidity values measured by the autotitrator versus the handheld refractometer was also of interest. Both traits, despite slight differences, displayed similar segregation patterns and low correlations between harvest years. Both traits, despite the means of obtaining the measurement were highly correlated with each other, suggesting that the autotitrator could be used in lieu of the handheld refractometer, thereby providing a more accurate and automated means of obtaining titratable acidity values.

The collected phenotypic data will be used with genotypic data gathered from the RA population. The genotyping data has been generated using RAPiD GENOMICS capture- sequence technology. It will be used to map the location of the fruit quality related traits

(identified as QTL candidates in present study) on blueberry chromosomes. This molecular data will aid in the marker development of QTLs controlling fruit quality traits which breeders will be able to use for marker assisted selection.

CHAPTER 2:

Single Nucleotide Polymorphic (SNP) Marker Discovery in a Diversity Panel of

Blueberry (Vaccinium) Species

ABSTRACT

Cultivated southern highbush blueberries are among the high-value crops in North Carolina

with an estimated annual ~$70 M farm-gate value. Blueberry contains many beneficial

components like flavonoids, which can help combat cardiovascular disorders,

neurodegenerative diseases, diabetes, and cancer thus contributing significantly to the current

popularity of the crop. Blueberries are members of the Ericaceae family and include several

subgenera or sections. Vaccinium section Cyanococcus is native to North America, and all

species of this section have contributed to the genetic background of most commercially

important cultivars. Traditional breeding efforts to develop superior blueberry cultivars

began in 1908 and, as a result, many of today’s cultivars are the product of interspecific

hybridization followed by backcrossing. Consequently, modern cultivars are segmental

allopolyploids, which share a complex ancestry resulting from the intercrossing of different

wild accessions and cultivated varieties. The outbreeding nature of blueberry and the use of

inter- and intra-specific hybridization during the past century has generated a lot of speculation about the relationship between the founder species and the modern cultivars.

With the advent of next-generation sequencing (NGS) technologies, it is currently possible to

uncover their interrelation at the whole genome level at a lower cost by sequencing each

founder and cultivated species. In this study, using Illumina sequencing, we re-sequenced 29

accessions at least 20X genome coverage. The 29 accessions were comprised of 18 different wild and cultivated species from 6 sections in Vaccinium that represent 18 diploids (2n = 2x

= 24), 8 tetraploids (2n = 4x = 48), and 3 hexaploids (2n = 6x = 72). The 18 diploids represented 11 different species including: section Cyanococcus [V. caesariense, V. darrowii,

V. elliottii, V. fuscatum, V. myrtilloides, V. pallidum, and V. tenellum]; section Batodendron

[V. arboreum]; section Herpothamnus [V. crassifolium]; section Pyxothamnus [V. ovatum];

and section Polycodium [V. stamineum]. The 8 tetraploids were representative of 6 different

species including: section Cyanococcus [V. angustifolium, V. corymbosum, V. formosum, and

V. myrsinites]; section Hemimyrtillus [V. arctostaphylos]; and section Pyxothamnus [V.

consanguineum]. The 3 hexaploids were all classified in section Cyanococcus [V. virgatum]

and are known as rabbiteye blueberries. The re-sequencing data allowed for the discovery of single nucleotide polymorphic (SNP) markers within and between different groups. These

SNP markers are easily adaptable to various SNP genotyping platforms that can be used in breeding programs, calculation of minor allele frequency, defining haplotype blocks and phylogenetic analysis.

LITERATURE REVIEW

Cultivated southern highbush blueberries are phylogenetically complex plants that are ranked

among the high value crops in North Carolina with an estimated annual ~$70 M farm-gate

value. Blueberry contains many beneficial components like flavonoids, which can help

combat cardiovascular disorders, neurodegenerative diseases, diabetes and cancer thus

contributing significantly to the current popularity of the crop. Blueberries are members of

the Ericaceae family which includes several subgenera or sections. According to Vander

Kloet (1988) the Vaccinium genus, specifically, includes many sections including

Cyanococcus, Batodendron, Herpothamnus, Hemimyrtillus, Pyxothamnus and Polycodium

(VANDER KLOET, 1988). Section Cyanococcus has been of particular interest to blueberry

breeders. Native to North America, many species of this section have contributed to the

genetic background of most commercially available Vaccinium corymbosum southern

highbush (SHB) blueberry species. By introgressing traits like blueberry stem canker

resistance from wild North Carolina V. corymbosum, early ripening and stem blight resistance from V. angustifolium, and reduced chilling requirements and improved adaptability to warmer climates from V. darrowii, V. tenellum, and V. virgatum, the NC State

University breeding program was successful in breeding SHB berries that are well adapted to the unique climate of North Carolina (Ballington, 2007). Such trait introgressions were accomplished by generations of inter- and intra-specific hybridizations followed by backcrossing, a traditional breeding technique that has been used since 1908 to develop superior blueberry cultivars. Many traits like early ripening and adaptability to warmer climates still exist in several North Carolina grown blueberries (Ballington, 2007). NC

State’s breeding program has since grown to include blueberry cultivars that have been hybridized with wild and cultivated blueberries from across the northeastern and southeastern

United States in addition to other countries including the Costa Rica and the Republic of

Georgia. Although, such breeding practices have enabled NC State to release several successful and well adapted SHB cultivars in the 1970s, many modern cultivars in various

breeding programs are often segmental allopolyploids that share a complex ancestry. The

relationships and genetic diversity resulting from the intercrossing of various wild accessions

and cultivated varieties with commercial blueberries during the past century has generated a

lot of speculation about the relationship between the founder species and the modern

cultivars. In fact, detailed information concerning these relationships and the genetic

diversity existing among these founder species and cultivars is largely unknown.

Regionality of Selected Blueberry Species from NC State Blueberry Breeding Program

The NC State blueberry breeding program has amassed a collection of blueberry species to

develop cultivars that are suitable for commercial production and adaptation to North

Carolina. This collection of blueberry species has been found to adapt well to a range of

environments spanning Canada, eastern and central United States and further abroad. In

particular, 18 species have been selected for their uniquely distinct properties and

adaptabilities. Many of these species have been found to be native to Canada, including V.

myrtilloides Michaux, V. angustifolium Aiton, V. pallidum Aiton, V. stamineum Linnaeus, V. corymbosum Linnaeus and V. ovatum Pursh (Vander Kloet, 1988). The V. myrtilloides

Michaux species, in particular, is native to the region of central Labrador to Vancouver

Island in Canada while the other aforementioned Canada native blueberries can also be found extending southward to the United States. For example, V. angustifolium, for example, extends from southern Manitoba to Minnesota and south to northern Illinois, Pennsylvania, and Delaware. This species is also native to the mountains of Virginia and West Virginia

(Vander Kloet, 1988). The V. pallidum species is native to southern Ontario Canada but

extends south to Minnesota, Maine, Georgia, Alabama, Arkansas, eastern Oklahoma and southeastern Kansas (rare in latter two locations). Vaccinium stamineum is native to southeastern Ontario down to central Florida, west to eastern Texas, far eastern Oklahoma, and the southeastern tip of Kansas. The V. corymbosum species ranges from the Saint

Lawrence Valley to Quebec City and east the southwest portion of Nova Scotia. The V. corymbosum species, popular for its stem blight resistance, also grows naturally in northeastern Illinois, northern Indiana, south central Michigan, Florida, Texas, and

Oklahoma. Vaccinium ovatum can be found along the Pacific coast from central British

Columbia to central California along the Pacific coast (Vander Kloet, 1988).

Several blueberry species collected in the NC State blueberry program are found exclusively in the United States. Such species include V. elliottii Chapman, V. tenellum Aiton, V. crassifolium Andrews, V. arboreum Marshall, V. myrsinites Lamarck, V. darrowii Camp, V. virgatum Andrews (formerly V. ashei Reade), V. formosum Andrews, V. fuscatum Aiton, and

V. caesariense Mackenzie. The V. elliottii species is native to Virginia, North Carolina and

South Carolina. From the Carolinas, V. elliottii's range extends west to Texas (excluding

Oklahoma) and south to Florida (AGRICULTURE, 2018d). Used in breeding programs for improved adaptability to warm climates, V. tenellum, extends from southeastern Virginia to southern Georgia then west to Alabama (Ballington, 2007; Vander Kloet, 1988). Similar to

V. tenellum, V. crassifolium ranges from southeastern Virginia to southeastern South

Carolina and Savannah, Georgia. The V. arboreum species is also native to southern Virginia but extends further south to central Florida, then west to eastern Texas, central Oklahoma,

and southeastern Missouri. Vaccinium myrsinites is found further south along the coastal

plains of South Carolina to Southern Florida and the Florida Panhandle. Vaccinium darrowii,

like V. tenellum, aids in warm climate adaptability, and ranges from southwestern Georgia, to

southern Alabama, southeastern Louisiana, and Florida (Ballington, 2007; Vander Kloet,

1988). The V. virgatum species is used for similar reasons and is native to North Carolina,

South Carolina, Georgia, Florida, then west to Texas (AGRICULTURE, 2018d; BALLINGTON,

2007). Vaccinium formosum is native to Maryland and extends south along the east coast to

Florida, and then west to Alabama (AGRICULTURE, 2018b; SYSTEM, 1983). Vaccinium

fuscatum appears across a large portion of the United States. Also known as the black

highbush blueberry, V. fuscatum is native to the majority of the east coast beginning in Maine

but extending west to Michigan and Indiana and south to Mississippi, Alabama, Georgia and

Florida. The V. fuscatum species is also native to Arkansas, Louisiana, Oklahoma, and Texas

(AGRICULTURE, 2018a). Lastly, V. caesariense, a species widely used as the reference

genome in most recent blueberry genomics studies was classified by authors (ROWLAND et

al., 2014) as diploid V. corymbosum, can be found all along the east coast from Maine to

Florida (AGRICULTURE, 2018c; BIAN et al., 2014).

International blueberries also collected in the NC State breeding program include V.

arctostaphylos Linnaeus and V. consanguineum Klotzsch. V. arctostaphylos was collected

from the wild in the Republic of Georgia (System, 2004). The V. consanguineum has been

located in Central America, including Honduras, Costa Rica, and Panama (Tropicos, 2018).

Allopolyploidy and Autopolyploidy in Vaccinium Species

Distinction between Allopolyploids and Autopolyploids

When attempting to uncover the relationships that exist within and between a diverse panel

of wild and cultivated species, like those in the NC State blueberry program, it is necessary to

consider the patterns of inheritance that exist among them. This is especially true when

considering the multiple ploidy levels that exist within this diversity panel. Approximately

70% of all angiosperms, like the Vaccinium genus, experience polyploidization (Wu et al.,

2001). Understanding whether a species is allopolyploid or autopolyploid can shed light on

how genes are passed on from parent to progeny ultimately leading to either the stability of a

species or gradual evolution of one species into something new and different.

Allopolyploids are hybrid organisms that are created from the union of two similar but

different species. The allopolyploid hybrid is the result of the hybridization of the gametes

from these two different species and the subsequent doubling of that hybrid's chromosomes

through spontaneous or artificial means (Griffiths, 2002; Wu et al., 2001). This doubling of

the genome allows allopolyploids to successfully accomplish bivalent pairing of homologous

during meiosis (Wu et al., 2001). Without this chromosome doubling, the genome would only include single copies of the chromosomes donated from the parental gametes which may be too dissimilar to pair, resulting in sterility of the hybrid (Griffiths, 2002).

Although the original allopolyploid hybrid was created from the union of two parents from separate species, disomic inheritance patterns are possible and usually occur. Due to the

chromosome doubling that occurs with allopolyploid hybrids, the bivalent homologous

pairing of chromosomes from the same ancestor can occur (Wu et al., 2001). This inheritance

pattern has led to some confusion concerning similarities with autopolyploids.

Autopolyploids result solely from the genome doubling of an individual created from the gametes of two parents of the same species. Unlike allotetraploids, the homologous chromosomes of autopolyploids can pair with a variety of homologous pairs (MCCALLUM et

al., 2016a; WU et al., 2001). This is possible because the autopolyploid results from the

doubled genome derived from the merging of two same-species ancestors. The chromosomes donated from the two parents are very similar. The ability of these chromosomes to pair with multiple homologous pairs can lead to the formation of multivalents and therefore polysomic inheritance patterns, instead of strictly bivalent patterns like allopolyploids (MCCALLUM et

al., 2016a; WU et al., 2001).

Studies have been done in order to more confidently distinguish between autopolyploids and

allopolyploids. Wu et al. (2001) performed simulation studies that attempted to characterize

gene segregation patterns that occurred in the progeny of autotetraploids by estimating two

meiotic parameters (Wu et al., 2001). The first parameter described the preference of homologous chromosome pairing that is characteristic of allopolyploids while the second parameter described the degree of double reduction that characteristically occurs in autopolyploids. Double reduction occurs when the progeny of two parents is created from a gamete that carries two chromatids from the same chromosome. Crossing-over and non-

disjunction are the main factors that contribute to double reduction. Normally, such an event

does not occur, but becomes more common with multivalents like autopolyploids (Mather,

1936). This simulation model presented by Wu et al. (2001) becomes useful especially when

establishing marker genotypes of parents through established segregation patterns with

tetraploids. Unlike simple diploids, tetraploids complicate inheritance patterns because their

alleles can possess multiple-dosage levels and tetraploids can undergo double reduction

during meiosis (Wu et al., 2001).

In additional to creating a model that helps more accurately classify allopolyploids and autopolyploids, Wu et al. (2001) helped provide a framework for the construction of genetic maps (Wu et al., 2001). The parameters required in the simulation model are known to affect both the allele and genotype frequencies of genes in populations. As such, the preferential pairing factor and the frequency of double reduction are important considerations when considering multiple families in a population and evolutionary polyploid genetics studies like the blueberry accession study at hand (Wu et al., 2001).

The ability to distinguish between autopolyploid and allopolyploid inheritance patterns is important in blueberries genomics, specifically. Krebs and Hancock (1989) noted that the origins of various polyploid species in Vaccinium subsection Cyanococcus resulting from the inter- and intraspecific hybridization among diploids is widely unknown. At the time, there was uncertainty surrounding whether or not those species were autopolyploids, allopolyploids, or segmental allopolyploids (KREBS AND HANCOCK, 1989b). In the past, the

genus Vaccinium was widely considered to be allopolyploid in nature (CAMP, 1942; CAMP,

1945). More recent evidence, however, suggests that polyploid evolution in Vaccinium can

occur via autopolyploidy as well (Qu et al., 1998). According to Lyrene et al. (2003),

autotetraploid segregation ratios are observed in both the tetraploid V. angustifolium and the

tetraploid V. corymbosum (Draper and Scott, 1971; Hall and Aalders, 1963; Lyrene et al.,

2003). Qu and Hancock (1997) have also supplied evidence to support that V. corymbosum and the tetraploid V. corymbosum x V. darrowii hybrid also display autopolyploid like tetrasomic inheritance patterns (Lyrene et al., 2003; Qu and Hancock, 1997).

Although many polyploid Vaccinium blueberry species seem to display autopolyploid inheritance patterns, there are many that seem to embody both autopolyploid and allopolyploid characteristics. The tetraploid V. corymbosum, for example, has been shown to display tetrasomic inheritance patterns characteristic of autotetraploids even though during meiosis bivalent pairing chiefly occurs, a trait usually shared by allotetraploids (KREBS AND

HANCOCK, 1989b; QU AND HANCOCK, 1997; WU et al., 2001). This ploidy duality can also be

observed in the interspecific tetraploid blueberry hybrid US 75. The US 75 hybrid resulted

from the hybridization of V. darrowii, a diploid, evergreen, lowbush blueberry from the

southeastern United States and V. corymbosum, a tetraploid, deciduous, highbush blueberry

from the northern regions of the United States (Qu and Hancock, 1997). Although this unique

hybrid was derived from two morphologically and geographically distinct blueberry species,

US 75 may not be strictly classified as an allotetraploid, which are usually derived from two

distinct species. US 75 also displays autotetraploid characteristics due its tetrasomic

inheritance patterns (Qu and Hancock, 1995; Qu and Hancock, 1997). These tetrasomic

inheritance patterns could be due to bivalent formations from random selections of four

homologous chromosomes during meiosis (Qu and Hancock, 1997). Therefore,

classifications of ploidy level cannot be made on the basis of bivalent versus multivalent

chromosomal configurations alone.

In terms of autopolyploid Vaccinium adaptation and evolution, some evidence suggests that genomic diploidization may be a driving force (Qu and Hancock, 1997). Diploidization

occurs when chromosomal mutations and structural rearrangements cause chromosome

pairings to be limited to only specific sets of homologous chromosomes. As a result, the

number of bivalents increases throughout the generations. Diploidization can also occur

when, instead of quadrivalents, bivalents are formed via the random pairing of four

homologous chromosomes. This was the case with both V. corymbosum and US 75, which

accounts for the reason both blueberry types showed mostly bivalent pairing (indicative of

allopolyploids) but displayed tetrasomic inheritance (indicative of autopolyploids) (Qu and

Hancock, 1997). Such diploidization ultimately leads to the chromosomal divergence and/or

changes in pairing control alleles that can change the tendency of a species to randomly pair

chromosomes or preferentially pair chromosomes (Qu and Hancock, 1997).

The fact that blueberry species like V. corymbosum can display autopolyploid-like tetrasomic

inheritance patterns but with allopolyploid-like bivalent pairing among four homologues may

explain how many blueberry species have evolved over time. Such characteristics may allow

for diploid species, like V. darrowii, to easily transfer new genes, via unreduced gametes,

into related autopolyploid blueberry species, like V. corymbosum. The generation of 2n

gametes via reduction is significant in blueberry breeding and for the transmission of genes

across multiple ploidy levels (Lyrene et al., 2003). The transfer of 2n gametes makes it

possible for the creation of tetraploids and hexaploids from lower ploidy level progenitors

thereby sparking evolutionary events. This phenomenon may account of the reason that V.

corymbosum’s geographic range from Florida to Maine overlaps with that of many diploid

Vaccinium species that originated in the southeastern United States and moved northward to

Maine (Lyrene et al., 2003; Qu and Hancock, 1997; Vander Kloet, 1988). The use of the

unreduced 2n gametes that can form from V. darrowii in a cross with the hexaploid V.

virgatum (Rabbiteye) also played a major role in the creation of the pentaploid southern

highbush blueberry (Lyrene et al., 2003). Eventually, the commercially successful tetraploid

southern highbush blueberry hybrid was created via tetraploid highbush and diploid V.

darrowii crosses where the V. darrowii progenitor of this hybrid donated unreduced 2n gametes (Lyrene et al., 2003; Sharpe and Darrow, 1960).

Although 2n gametes probably play a large role in Vaccinium evolution, this does not indicate that the diploid is necessarily a direct ancestor of any autotetraploid in a particular diploid-tetraploid pairing. However, this genetic introgression does seem to occur when diploid and tetraploid species grow together in the same geographic range (Lyrene et al.,

2003).

Contributions to Polyploid Inheritance, Diversity and Evolution Studies

Autotetraploid Studies

Studies seeking to establish the relationships, genetic diversity, and population structure

existing in and between wild and cultivated species must deal with the inheritance patterns of

the species being examined, especially in the case of polyploids. With the advent of

molecular markers, attempts have been made to construct genetic linkage maps of polyploid

using various types of markers including, isoenzyme (KREBS AND HANCOCK, 1989a), ,

RAPDs (ARUNA et al., 1993), RFLPs (HAGHIGHI AND HANCOCK, 1992), and SSRs (BIAN et al., 2014; BOCHES et al., 2006; BREVIS et al., 2008; MCCALLUM et al., 2016a). In recent

years, advances have even been made using SNP markers coupled with next-generation sequences (NGS) technologies. However, many of these studies have employed the use of single dose dominate markers, also known as simplex markers (HACKETT et al., 2014;

HACKETT et al., 2013; MCCALLUM et al., 2016a). These simplex markers will segregate in

progeny population in a 1:1 ratio. An improvement on this method involves the use of

codominant SSR markers. Lou et al. (2001) attempted to improve upon this model by

identifying co-dominant and dominant genetic markers in a full-sib autotetraploid potato. The

study, however, does not consider quadrivalent or trivalent with univalent chromosomal

pairings which can occur in autotetraploids, like many blueberry species. Instead

chromosomal segregation involving the random pairing of four homologous chromosomes

yielding two bivalents was the only criteria examined (Luo et al., 2001).

In the case of autotetraploid blueberries, McCallum et al. (2016) generated the first linkage

map for the autotetraploid blueberry V. corymbosum (MCCALLUM et al., 2016a). During

meiosis, the homologous chromosomes in these tetraploids could form pairs of bivalents or

multivalents. When the chromosomes in a tetraploid pair randomly with any available

homologues creating a situation where all allelic combinations can be produced in equal

frequencies, tetrasomic inheritance can be assumed. This is not always the case with

autotetraploid inheritance. In the McCallum study, however, tetrasomic inheritance is

assumed as evidenced by the types of dominant markers used the research (MCCALLUM et al., 2016a). Assuming that the random chromosomal segregation of tetrasomic inheritance is in play, the most informative dominant markers for autotetraploid species are simplex

(AOOO × OOOO), duplex (AAOO × OOOO), and double-simplex (AOOO × AOOO) markers which segregate in the progeny in 1:1. 5:1, and 3:1 ratios, respectively (MCCALLUM

et al., 2016a). The recombination frequencies of the chromosomes are best estimated with the highest precision using dominant simplex markers and double-simplex markers that are

linked in coupling phases. If attempting to identify sets of homologous chromosomes, linked

duplex-to-simplex markers can be used to estimate recombination frequencies when linked in both coupling and repulsion phases (MCCALLUM et al., 2016a).

Utilizing genotype by sequencing technology (GBS), McCallum et al. (2016) generated the

1,312 SNP and 171 SSR markers that were ultimately used in the creation of the

autotetraploid V. corymbosum map for northern highbush ‘Draper’ and southern highbush

‘Jewel’ highbush cultivars. At the time a reference genome was not available to map reads

for SNP analysis so putative SNP markers were identified via the use of the TASSEL

software package which included the Universal Network Enabled Analysis Kit (UNEAK)

pipeline (MCCALLUM et al., 2016a). To date, these blueberry cultivar maps stand as the first autotetraploid blueberry genetic maps created using GBS technology.

Although the McCallum et al. (2016) study was successful in introducing the first genetic map for tetraploid V. corymbosum blueberry cultivars, allele dosage was not considered due to lack of enough GBS read depth. The read depth coverage generated in the GBS data allowed only for the determination of presence or absence of alleles. The coverage was not enough to determine allele dosage (MCCALLUM et al., 2016a). Uitdewilligen et al. (2013)

developed a GBS method for a highly heterozygous autotetraploid potato (Solanum

tuberosum) that considered the many alternative allele copy number states that can exist in polyploids. In order to conduct accurate assessments of allele copy number of SNPs in autotetraploid potatoes, a read depth of approximately 60-80x, at minimum, is recommended

(Uitdewilligen et al., 2013).

Allopolyploid Mapping Studies

The characterization of allopolyploid genetic relationships and evolution in blueberries has

been difficult due to the ambiguity surrounding its inheritance patterns (Bell et al., 2009). As

mentioned, allopolyploid blueberries that are derived from two different parent species have

been known to display the tetrasomic inheritance patterns of autopolyploids (Qu and

Hancock, 1995; Qu and Hancock, 1997). Adding to the ambiguity, allotetraploid simplex and

double-simplex SNP makers display the same expected ratios as autotetraploids (MCCALLUM et al., 2016a).

Distinguishing among allopolyploids in blueberries could potentially be accomplished by identifying the segregation patterns of various types of dominant markers. Wu et al. (1992) performed a study to detect and estimate linkage in polyploids using single-dose restriction fragments (SDRF) (Wu et al., 1992). The research provides evidence to support that allopolyploids possessing a single allele marker (single-dose) will display a 1:1

(presence:absence) segregation pattern in the progeny. Allopolyploids possessing two alleles for the same marker (double-dose) will segregate in a 3:1 ratio if the two markers are on homoeologous chromosomes. They will not segregate at all if the two marker alleles are on homologous chromosomes. Autopolyploids with the same double-dose markers will segregate at higher 5:1 ratios (Wu et al., 1992).

Allopolyploidy in blueberry can also be detected when the proportion between simplex coupling to repulsion linkage is considered because, in allopolyploids, the proportions equal.

In autotetraploids, very few repulsion linkages are detected (MCCALLUM et al., 2016a).

The unique changes that occur in the allopolyploid genome as a result of interspecific

hybridizations is also useful when characterizing the inheritance patterns and evolution of

polyploids. To better understand the processes involved in the genome evolution of

allopolyploids, Feldman and Levy (2009) examined the allopolyploid events surrounding

several species of Aegilops and Triticum wheat (Feldman and Levy, 2009). When compared

to their diploid parents, natural and synthetic wheat allopolyploids contain 2-10% less DNA.

This loss of DNA content is believed to occur immediately following the formation of the allopolyploid in question. As a result of this allopolyploidization, non-coding, low copy, and high copy DNA sequences are eliminated (Feldman and Levy, 2009).

Some allopolyploids, like the wheat species in the Feldman and Levy (2009) study, have been known to "select" the most efficient gene combinations from their different-species parents resulting in a new organism that is not necessarily an intermediate between its diploid parents (Feldman and Levy, 2009). Such an ability to create genome-asymmetry may contribute to the allopolyploid's ability to evolve overtime.

The fact that allopolyploids merge the genomes of two distinct species can often lead to convergent evolution. Allopolyploidization allows for the introgression of chromosomal segments from two different genomes leading to the introduction of a new recombinant genome into the population. The chromosomes within this newly formed genome can undergo various re-patterning events due to intra- and inter-genomic translocations (Feldman and Levy, 2009).

The evolution of allopolyploid cotton has also been studied by Flagel et al. (2012) using an assembly of over 4 million expressed sequence tags (ESTs) sequences. The assembly was comprised of ESTs from two allotetraploid Gossypium cotton species and representative

progenitor diploids. The study demonstrates that following allopolyploid genome duplication

cotton genes undergo increased rates of molecular evolution. The study also shows that

allopolyploid cotton can exchange genes non-reciprocally between homoeologous genes.

Ultimately, the study provided a method to determine the genome of origin for allopolyploids

(Flagel et al., 2012).

According to Flagel et al. (2012), allopolyploid evolution can occur immediately following genome duplication on the chromosomal level. When allopolyploid genes are doubled during allopolyploidization, the genes involved may either evolve independently or evolve together as they interact. This process is mediated by different forms of duplicate gene sequence homogenization, like gene conversion (Flagel et al., 2012).

Molecular evolution in allotetraploids can be determined using the coding regions of the species in question. After coding regions of Gossypium cotton were determined, Flagel et al.

(2012) calculated the rates of synonymous (dS) and nonsynonymous (dN) divergence/mutation within and between the collected cotton species (Flagel et al., 2012).

The dN/dS ratio serves as an indicator of the selection pressures that operate on the protein composition of a gene. If there are elevated dN/dS ratios specific for the species, positive selection may be at play. Such elevated dN/dS ratios are expected between two allotetraploid species that diverged from common ancestors and not necessarily between the two diploid progenitor species (Flagel et al., 2012). However, due to the small sample size per taxon used in the study, only minor differences in dN/dS ratios were observed indicating very little

evolution happening on both allotetraploid and diploid levels. Regardless, rates of sequence evolution between allotetraploid and diploid progenitor genomes indicated that there was indeed a shared history between the parental and allotetraploid homoeologs (Flagel et al.,

2012).

Lastly, Flagel et al. (2012) also demonstrated that SNP markers could be used to establish the origins of allotetraploids. When the SNP variants of the proposed diploid progenitor species are identified they can be compared to the proposed allotetraploid derivatives. The diploid polymorphisms can be used to establish that the diploid SNPs most likely occurred prior to the creation of the allotetraploids in the study (Flagel et al., 2012).

Past Vaccinium Inheritance Research and Limitations

The complexity of the blueberry genome as it relates to its state as an allopolyploid or autopolyploid makes the determination of its inheritance and evolution patterns challenging.

This is turn complicates the assessment of genetic relationships, diversity, and population structure existing among wild and cultivated blueberry species that prevents breeders from making the most efficient selection decisions. Despite such complications, strides towards uncovering the complex and varied inheritance patterns of blueberries of various ploidy levels have been made so that relationships between diverse species could be better understood. Although not used specifically to distinguish relationships among polyploids, allozyme markers were one of the first marker types used to establish the genetic

relationships existing among diploid blueberries within the Cyanococcus section. Vaccinium section Cyanococcus is a highly diverse group exhibiting high levels of variation within and among the species that are in the section (BRUEDERLE AND VORSA, 1994). Using starch gel electrophoresis, 11 polymorphic loci were identified to help establish relationships among the species in the study and to compare the findings with previously conducted morphological studies. Clustering of similar species were observed in a dendrogram created using the allozyme markers and most clustering patterns were consistent with morphological data

(BRUEDERLE AND VORSA, 1994).

The use of isoenzyme markers is one of the earliest molecular means of determining the inheritance patterns of polyploids and genetic relationships among blueberry species and

cultivars. Using the segregation ratios of four enzyme loci in highbush V. corymbosum

samples, the potential origin and segregation patterns of four V. corymbosum cultivars were

observed (KREBS AND HANCOCK, 1989a). The observed segregation ratios were relatively

consistent with the tetrasomic inheritance, codominant allelic expression (for dimeric

enzymes) and random chromosome segregation expected of a species with an autopolyploid

origin (KREBS AND HANCOCK, 1989a).

Other early molecular techniques for establishing the relationships between blueberry species

and cultivars include the use of restriction fragment length polymorphic (RFLP) and random

amplified polymorphic DNA (RAPD) markers. RFLP markers have been used to study the inheritance patterns and variability in highbush blueberry genomes like V. corymbosum, V.

angustifolium, V. darrowii, and V. virgatum. The study used 23 restriction enzymes to

establish organelle inheritance patterns (HAGHIGHI AND HANCOCK, 1992). Although the analysis suggested that the region of the chloroplast genome surveyed in the study had not changed significantly among the sample species, there was still a significant amount of variation existing between the species potentially due to widespread hybridization by breeders (HAGHIGHI AND HANCOCK, 1992).

RAPD and simple sequence repeat (SSR) markers used in tandem have been implemented to

establish relationships between blueberry species of various ploidy levels, like the diploid V.

darrowii, tetraploid V. corymbosum, and hexaploid V. virgatum Read (LEVI AND ROWLAND,

1997). The study was able to use the RAPD and SSR markers to show that the various

species grouped according to ploidy level. However, marker data were not sufficient enough

to determine the relationships among groups, like V. corymbosum, potentially due to the high

amount of heterozygous markers existing in the selected cultivar (LEVI AND ROWLAND,

1997).

Using SSR markers with the help of NGS technology, Bian et al. (2014) studied such

relationships in 150 cultivated accessions from Vaccinium section Cyanococcus. These

diverse cultivated accessions included northern highbush (NHB) (primarily V. corymbosum), southern highbush (SHB) (V. corymbosum introgressed with one or more native southern

USA species), lowbush (V. angustifolium), half-high hybrid, and rabbiteye (V. virgatum) primary blueberry species. Diploid, tetraploid, pentaploid and aneuploid blueberries were all

represented in this selected group of 150 cultivated accessions (BIAN et al., 2014). The SSRs

were developed using the 2013 draft of the diploid blueberry genome from W85-20 (V. caesariense or diploid V. corymbosum). The study successfully aided in the identification of over 43,000 SSRs of which 42 genomic SSRs and EST-SSR markers were used in the genotyping of the 150 blueberry accessions used in the study.

The Bian et al. (2014) study successfully uncovered the genetic diversity and relationships among the collected 150 blueberry accessions by means of various methodologies. Using

SSR markers, several thousand allele phenotypes were identified, and the mean Shannon normalized index (HSh) and mean expected heterozygosity (He) was accessed. Using a paired

t test for the selected SSR markers, these measurements showed that there was greater

genetic diversity in rabbiteye blueberries than in SHB (BIAN et al., 2014). SHB, in turn,

displayed greater genetic diversity than NHB. The study found inter- and intra-specific levels of stratification between the accessions and identified rabbiteye blueberry (V. virgatum) as

genetically distinct. Bian et al. also found that ploidy variation was correlated with genetic

distinctiveness (BIAN et al., 2014).

A neighbor-joining cluster analysis revealed the formation of two clades. The first clade was comprised of rabbiteye, pentaploid and aneuploids, diploid V. corymbosum, V. darrowii, lowbush, and two northern highbush blueberry selections. The second clade included most of the highbush blueberry selections. These results demonstrate how the introgression of species like V. darrowii and other southern species into the highbush population has greatly altered

the population’s genome, thereby expanding highbush blueberry genetic diversity (BIAN et al., 2014).

Genetic relationships within and between species were also observed in the Bian et al. (2014) study. Hexaploid rabbiteye blueberry accessions, for example, were genetically distinct

(displayed great genetic distance) from both diploid and tetraploid species (BIAN et al.,

2014). Diploid V. corymbosum was also quite distinct from tetraploid V. corymbosum. In fact, the diploid V. corymbosum was more closely related to species in the same ploidy group, like V. darrowii. This indicates that the tetraploid V. corymbosum has significantly evolved from the diploid V. corymbosum as a result of past gene flow between wild diploid species from the same geographic location (BIAN et al., 2014).

Although the use of SSR markers in the Bian et al. study (2014) has demonstrated its usefulness in the detection of blueberry genetic variation, the use of single nucleotide polymorphism (SNP) markers has proven even more beneficial due to their abundance in the genome coupled with their capacity for ultra-high-throughput automation with the help of

NGS technologies (Mammadov et al., 2012). SNP marker technology has been used to help identify popular American blueberry cultivars that are most suitable to European climates

(CAMPA AND FERREIRA, 2018). The study involved the use of 5,255 SNP markers supplied by

GBS data to identify the relationships that existed between 70 popular American blueberry cultivars. These cultivars were assessed based on their chilling requirements and flowering season. With the use of the 5,255 SNPs and the UPGMA clustering method, a dendrogram

was created. Clustering among the 70 cultivars occurred on the basis of similar ploidy levels

and also chilling requirements demonstrating the successful use of SNP data to identify

relationships among different cultivars (CAMPA AND FERREIRA, 2018). As evidenced by these

and other studies, with the advent of NGS technologies and the use of polymorphic markers,

it is currently possible to uncover the interrelation of various diversity panels at the whole

genome level at a lower cost by sequencing founder and cultivated species.

Current Vaccinium Research

Despite many advancements, studies seeking to uncover more complex relationships from a

larger blueberry diversity panel consisting of wild and cultivated accessions using SNPs have

not yet been completed. Although genetic maps have been constructed for two cultivated

species in Vaccinium section Cyanococcus using several hundred SNPs and steps have been made to establish the relationships, genetic diversity, and population structure of many cultivated accessions of the same blueberry section using SSRs, our study aims to shed light on the relationships of both cultivated and wild accessions from multiple Vaccinium sections.

This is important because most of the commercially successful cultivars on the market have been derived from wild species representing various Vaccinium sections and their haplotype blocks and generational passage is widely unknown.

To this end, this study attempts to discover the haplotype blocks of 6 sections of Vaccinium representing multiple ploidy levels of both wild and cultivated species. SNP makers will be used to construct future genetic maps instead of SSRs due to their abundance in the genome

coupled with their capacity for ultra-high-throughput automation (Mammadov et al., 2012).

SNP markers will also be created with the use of the V. caesariense reference genome, a resource not available during the McCallum et al. (2016) study.

In this study, using Illumina sequencing, we re-sequenced 29 blueberry accessions at on average 20X genome coverage for diploid species, 40X for tetraploid and hexaploid species.

The 29 accessions were comprised of 18 different wild and cultivated species from 6 sections in Vaccinium that represent 18 diploids (2n = 2x = 24), 8 tetraploids (2n = 4x = 48), and 3 hexaploids (2n = 6x = 72). The 18 diploids represented 11 different species including: section

The 8 tetraploids were representative of 6 different species including: section Cyanococcus

[V. angustifolium, V. corymbosum, V. formosum, and V. myrsinites]; section Hemimyrtillus

[V. arctostaphylos]; and section Pyxothamnus [V. consanguineum]. The 3 hexaploids were all classified in section Cyanococcus [V. virgatum] and are known as rabbiteye blueberries. The re-sequencing data allowed for the discovery of SNP markers within and between different groups. These SNP markers are easily adaptable to various SNP genotyping platforms that can be used in breeding programs, calculation of minor allele frequency, defining haplotype blocks, and phylogenetic analysis.

MATERIALS AND METHODS

Plant Materials

The 29 blueberry accessions in this study served as representatives of 18 different wild and cultivated species (Table 3) from across the eastern United States and abroad. The 29 accessions were previously collected by Dr. Jim Ballington (North Carolina State University,

Raleigh, NC, USA) and were maintained as live plants in Sandhills (Jackson Springs, NC),

Ideal Tract Farm (Castle Hayne, NC), Piedmont (Salisbury, NC) research stations or in the greenhouses of the Department of Horticultural at NC State (Raleigh, NC). The accessions were originally collected from Maine (V. myrtilloides), New Jersey (V. caesariense), Virginia

(V. pallidum), West Virginia (V. angustifolium), North Carolina (V. crassifolium and V. fuscatum), South Carolina (V. crassifolium, V. elliottii, V. formosum, V. myrsinites, V. tenellum, and V. virgatum), Georgia (V. elliottii), Florida (V. elliottii and V. virgatum), Costa

Rica (V. consanguineum), and the Republic of Georgia (V. arctostaphylos).

The sampling included species representing 6 sections in Vaccinium that encompasses 18 diploids (2n = 2x = 24), 8 tetraploids (2n = 4x = 48), and 3 hexaploids (2n = 6x = 72). The 18 diploids represented 11 different species from section Cyanococcus, Batodendron,

Herpothamnus, Hemimyrtillus, Pyxothamnus and Polycodium. The 8 tetraploids were representative of 6 different species from section Cyanococcus, Hemimyrtillus, and

Pyxothamnus. The 3 hexaploids were all classified section Cyanococcus.

DNA Extraction

The total DNA of 29 blueberry accessions was extracted from 0.50-1.00 g of leaf tissue using

the modified CTAB method as described by Rowland, et al. (2016) (ROWLAND, 2016).

Library Preparation and Genome-Resequencing

The genomic DNA was sheared with Covaris® instrument (Woburn, MA) to achieve a 200-

300 bp fragment size. The size of sheared DNA was verified using a TapeStation instrument

(Agilent, City, State) prior library construction. The Illumina short-read libraries were made using the NEXTflex® Rapid DNA-Seq Kit, Version 15.10 (Bioo Scientific, Austin, TX) according to manufacturer’s instruction. The libraries were size selected using the Blue

Pippin instrument (Sage Scientific, MA) to be 400-600 bp including the 123 bp bar coded adapters. The individual libraries were quantified using KAPA qPCR library quantification kit (KK2102) (Boston, MA) and CFX96 qPCR (BioRad, Hercules, CA). The size of each

DNA library was verified by a Tape Station Instrument (Santa Clara, CA) before pooling.

Normalized libraries were pooled according to the ploidy level of individuals. For instance, the libraries of eight diploid species were pooled together to run in one lane of HiSeq X (two pools). Accordingly, 4 tetraploid (two pools) and 3 hexaploid accessions/cultivars (one pool) were pooled separately to run on each lane of Illumina. The constructed libraries were sent to

Novogene (Sacramento, CA) for sequencing using Illumina HiSeq X 150 paired-end sequencing. The accession W85-20 (V. caesariense) genome assembly was used as the reference to map the reads to it.

Data Processing

The raw reads were imported into CLC Workbench V. 11 (Qiagen, Denmark) and any remaining adapters were trimmed. The trimmed reads were mapped to the reference genome with the following parameters: 0.7 length fraction, 0.85 similarity fraction/percent identity, and random mapping. The CLC mapping files were exported as ‘BAM’ files, and subsequently they were transferred into a Linux server for further processing.

Variant Call Analysis

In order to discover the SNPs in the diversity panel, all ‘BAM’ files were merged into an all files merged ‘BAM’ file using SAMtools (v.0.1.7a) (LI et al., 2009). The SAMtools was used to generate pileup files for each accession/cultivar in the diversity panel. The pileup files were further parsed and filtered by three in-house Perl scripts as described previously (A.

Hulse-Kemp, Pers. Comm.). Using a fourth in-house Perl script the pileup files of accessions/cultivars were merged with the all genotypes pileup file to create a variant call table (genotypes table) with all variant positions in rows and genotypes arranged in columns.

The genotypes table file can be easily formatted to standard Variant Call Format (VCF) file format if needed. SNP positions were determined using a series of criteria that have been implemented in a Perl script. The user can determine these criteria by Standard Inputs (stdin) in the command line. Briefly, in the diversity panel, if two individuals are homozygous with alternate allele, that position was selected as a simplex marker. The script also allows the user to increase the stringency of the SNP discovery by selecting more than two (up to 4)

genotypes to be homozygous with different alleles. For instance, the ‘A’ base vs ‘G’ base has

to be seen in at least four genotypes (‘A’-‘A’ vs. ‘G’-‘G’). The flanking sequence of each

SNP, comprising 50 bases on each side of SNP position, were extracted by an in-house Perl script.

SNP Flanking Sequences Analysis

The flanking sequences were aligned to Illumina transcriptome assembly sequences of

‘Arlen’, ‘Legacy’, ‘NC3104’, ‘O’Neal’, and ‘Premier’ blueberry cultivars using the NCBI

Basic Local Alignment Search Tool (BLAST) on a local server (MADDEN, 2013). The

flanking sequences were also aligned with PacBio iso-seq high quality transcriptome

assemblies of ‘O’Neal’ and ‘Premier’ cultivars. The BLAST hits from the generated report

were parsed using an expectation cut off value of 20, identity cutoff value of 40, and an

overlap cutoff value of 100 (KOZIK, 2005). Only the flanking sequences of SNPs with 99% identity between the subject (accessions) and query (SNP flanking sequence of W85-20 reference) and possessing 100% length of query were chosen for primers design and phylogenetic tree development.

Phylogeny Estimation

A phylogenetic tree was generated in collaboration with the Manos lab at Duke University using a dataset of the generated 1,767,905 high quality SNPs extracted from the BLAST

results. A phylogeny was estimated using a maximum likelihood (ML) approach in RAxML

(v.8.2) (STAMATAKIS, 2014). Using the provided SNP molecular data, the RAxML program

generated a phylogenetic tree. Due to the highly variable nature of the SNP-only dataset, parameters for ascertainment bias correction were set to correct for the fact that the data consisted of only highly variable sites and a gamma model of rate heterogeneity (using the ‘-

m ASC_GTRGAMMA’ command). ML searches were run using 10 distinct starting trees

and 100 rapid bootstrap replicates to estimate support for relationships.

To account for the incomplete lineage sorting (ILS) that may occur within the Vaccinium

section Cyanococcus, a potentially young group, a “species tree” was estimated under the coalescent using singular value decomposition quartet species tree estimation (SVDQuartets)

(CHIFMAN AND KUBATKO, 2014). Not accounting for ILS could potentially lead to invalid

phylogeny. Using concatenated SNP dataset as input, the SVDQuartets estimation was

executed in PAUP* (v.4a142) (SWOFFORD, 2002). All possible quartets were evaluated and

100 bootstrap replicates were run to assess support at each phylogenetic tree node.

RESULTS

SNP Marker Development Evaluations

Variant Call Analysis of All Twenty-Nine Genotypes

A variant call spreadsheet (genotype table) was used to identify SNP markers (Table 4). The average read depth (~20X coverage) of the resequencing data in the current study was enough for the generation of single-dose simplex markers

(http://bit.ly/supplemental_thesis_data Figure/Table 105). Simplex markers were defined as

positions where two individuals from the diversity panel possessed alleles that were

homozygous and different from each other. Using this filtering parameter along with minimal

and maximal coverage constraints as defined by this study (minimum depth 3, maximum

depth 1000), 20,238,008 SNPs were identified when all 29 accessions/cultivars are screened

for variant calls. Changing the filtering parameter to require four genotypes to have alleles

homozygous with the alternate alleles (e.g. two genotypes with “C” and two genotypes with

“G” alleles) reduced the number of SNPs to 14,800,476. The number of identified SNPs was further decreased when the depth of coverage parameters is made more stringent (minimum depth 20, maximum depth 1000). Screening the same 29 accession/cultivar genotypes, the

SNP number decreased from 20,238,008 to 15,734,682 when at least two genotypes in the panel are required to be homozygous and opposite of each other. It decreases even further to

13,314,965 when at least four genotypes are required to be homozygous and opposite to the reference allele.

Variant Call Analysis of Eighteen Diploid Genotypes

The variant call table was split to include only 18 diploid genotypes. As a result, diploid blueberry genotypes screened separately under the same parameters (two genotypes homozygous and opposite to the reference; minimum coverage depth 3, maximum coverage

1000) revealed 19,672,735 SNPs which was only slightly less (565,273 SNPs) than the ~20.2 million SNPs identified when all 29 diversity panel genotypes were screened (Table 4). This indicates that most of the diversity within the 29 accessions/cultivars was due to the presence of the diploids in the panel. Filtered under the same coverage depth (minimum 3 and maximum 1000) but requiring four genotypes to be homozygous and opposite of each other

(e.g. two genotypes with the “C” allele and two genotypes with the “G” allele), the SNP count decreased to 13,360,832. More stringent coverage parameters (minimum depth 20, maximum depth 1000) lowered the SNP count to 14,171,926 and 11,617,882 when at least two and four genotypes were required to be homozygous and opposite on each other, respectively (Table 4).

Variant Call Analysis of Eight Tetraploid Genotypes

Compared to the diploids, the tetraploids blueberry genotypes represented even less of the diversity in the panel possessing SNP counts of 9,663,151 and 2,551,255 when at least two and four genotypes were required to be homozygous and opposite of each other, respectively

(minimum coverage depth 3, maximum coverage depth 1000) (Table 4). Under the same parameters, differing only in the increased stringency of minimum and maximum depth coverage (minimum depth 20, maximum depth 1000), tetraploid SNP counts decreased to

5,426,145 and 2,075,357, respectively (Table 4). However, when the V. arctostaphylos (a genetically distinctive tetraploid blueberry species native to the Republic of Georgia) genotype was removed from the tetraploid screening, the SNP counts decreased by at least

25% when filtered using a minimum depth of 3 and maximum depth of 1000. These tetraploid SNP counts decreased by at least 41% when filtered using a minimum depth of 20 and a maximum depth of 1000. This reduction in SNP number resulting from the absence of

V. arctostaphylos suggests that a significant portion of the diversity in the tetraploid genotypes was due to the presence of this foreign blueberry species (Table 4).

Variant Call Analysis of Three Hexaploid Genotypes

The hexaploid blueberry genotypes were also screened separately to investigate the amount of diversity that exists among them. When the allele of at least two genotypes were required to be homozygous and opposite of each other (alternate alleles), 503,283 SNPs were identified (minimum coverage depth 3, maximum coverage depth 1000) (Table 4). When the coverage stringency was increased to a minimum depth 20 and maximum depth 1000, the

SNP count decreased significantly to 78,172. SNP data filtered according to four genotypes possessing alleles that are homozygous and opposite to the reference were not available, as there were only three hexaploid blueberry genomes in the diversity panel (Table 4).

The ~20.2 million SNPs developed from the least stringent parameters (two genotypes homozygous and opposite of each other; minimum coverage depth 3, maximum coverage

1000) were used to extract the flanking sequence (50 bp on each side of SNP position) from

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the diploid blueberry genome assembly. The extracted flanking sequences were further

filtered to use only the high-quality SNPs for the phylogenetic analysis of the diversity panel.

Selection of Markers for Phylogenetic Tree Construction

SNPs in conserved regions of the genome were selected by using the BLAST tool to align the flanking sequences of the SNP positions to all transcriptome data available in blueberry genomics laboratory at NC State (MADDEN, 2013). These data include the Illumina

transcriptome assembly sequences of `Arlen`, `Legacy`, `NC3104`, `O’Neal`, and `Premier`

as well as PacBio Iso-Seq data of `O’Neal` blueberry cultivars. The BLAST results were

parsed to obtain 3,644,719 alignment hits. The alignment hits were filtered for 100 bp

alignment length (one position less than flanking sequence length) and 99% identity between

subject (transcriptome sequences) and query sequence. A total of 1,767,905 SNP positions

were selected that met the filtering criteria (Table 5).

Phylogenetics Estimation Results

Maximum likelihood analysis of the concatenated data matrix recovered maximal support

(BS = 100) for all nodes in the phylogeny (Figure 32). Similarly, SVDQuartets found

maximal support for all relationships. Topologies from these analyses are largely in

agreement. For example, both confirm the monophyly of Vaccinium sect. Cyanococcus and

recover four main clades within this section. Both analyses agree on the placement of V.

myrtilloides and V. angustifolium as sister to each other and the earliest branch in

Cyanococcus. The hexaploid V. virgatum accession form a strongly supported clade in both

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analyses and are inferred as the next diverging lineage. The third clade consists of both

highbush (V. elliottii) and lowbush taxa. The ML tree indicates a monophyletic V. elliottii

while SVDQuartets failed to recover this. The fourth clade similarly consists of highbush and

lowbush taxa. Species-level relationships within this clade, however, differ between the two

analyses. Placement of the non-Cyanococcus taxa are consistent between ML and species-

tree analyses except for V. ovatum and V. consanguineum.

DISCUSSION

SNP Marker Development

The ~1.77 high-quality SNPs identified in this study provide the framework for generating a phylogenetic hypothesis for many wild species and modern blueberry cultivars at the whole genome level. Many of the modern cultivars have resulted from widespread intra- and interspecific hybridization and backcrossing which has led to the ancestral ambiguity.

Several attempts have been made to resolve the ambiguity existing among blueberry species and cultivars. The present study contributes to this line of research by providing relationship data from the analysis of 18 different species (representing from both wild and cultivated

Vaccinium sp.) gathered using the ~1.77 million SNPs identified in the genic regions of several cultivars (‘Arlen’, ‘O’Neal’, ‘Legacy’, ‘Premier’, and NC 3014). In the past, various genetic markers have been used to uncover the relationships between blueberry cultivars and species. Previous studies have used allozyme data, 11 polymorphic loci were used to identify the relationships among 8 common blueberry species (BRUEDERLE AND VORSA, 1994)

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including V. caesariense, V. darrowii, V. elliottii, V. tenellum, and V. myrtilloides. More

recently, 5,255 SNP markers resulting from genotyping by sequencing were used to assess

the relationship existing between 70 popular cultivated blueberries (CAMPA AND FERREIRA,

2018). However, the present study is novel in its use of nearly 2 million SNPs and an even

more diverse panel of wild and cultivated blueberry species to analyze such relationships.

The average read depth (~20X coverage) of the resequencing data in the current study was

enough for the generation of single-dose simplex markers, which can help establish the

presence or absence of a given allele. This is an important preliminary step in the

identification of SNP markers. However, many of the species in the diversity panel have

complicated hybridization histories with species of various ploidy levels. This event has led

to the creation of diverse diploids, tetraploids and hexaploids. The tetraploids and hexaploids

in the panel, for example, have an added layer of complexity because they can exist as

autopolyploids, allotetraploids, or segmental allopolyploids. These genomic states can

potentially be identified by studying their inheritance patterns using double-simplex markers.

In order to develop double-simplex markers, a minimum coverage of 60-80X may be

required (MCCALLUM et al., 2016a; UITDEWILLIGEN et al., 2013). However, allotetraploid

and autotetraploid double-simplex markers (e.g. AOOO x AOOO) may segregate in similar expected 3:1 ratios (if the random chromosomal segregation assumed) (MCCALLUM et al.,

2016a).

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The development of SNP data from the present study can aid in the development of markers

that can be implemented for use in determining ancestral haplotype blocks which have

widespread implications in blueberry breeding. The SNP data can also be used to provide a

preliminary frame work of the phylogenetic relationships existing among the species in the

panel of this present study.

Phylogenetic Estimation

The use of ~1.77 million high-quality SNPs to generate phylogenetic trees positively

contributes to existing phylogenetic studies conducted on diverse blueberry populations.

Previous research has employed the use of relatively small numbers allozyme, RAPD, and

SSR markers in order to establish relationships among diverse blueberry species or cultivars.

However, many of these assessments lead to the creation of neighbor-joining (NJ)

dendrograms that only consider marker similarities among the taxa in question (BIAN et al.,

2014; BOCHES et al., 2006; BREVIS et al., 2008). In addition, evolutionary models are not

considered in the analysis. In order to accommodate for the high amount of molecular data

used in the present study (~1.77 million SNPs) RAxML and SVDQuartets analysis were implemented to provide a stronger preliminary validation for the grouping of several species/accessions in the phylogenetic tree. The SVDQuartets analysis adds additional layer of confidence by considering the incomplete lineage sorting that may occur with the younger

Cyanococcus section of blueberries. The multiple ploidy levels, possible genomic recombination events, and hybridizations that many species in the diversity panel may have

experienced limit the inferences that can be made from either phylogenetic tree analysis in

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the current study. However, the use of such a large data set (~1.77 million SNPs) did help

validate the existence of well-established clades as well as propose new hypotheses about the

relationships existing among the groups.

Several aspects of the resultant topologies from the RAxML and SVDQuartets analysis

agreed with previous phylogenetic studies performed on diverse blueberry populations. In

general, the Cyanococcus section forms a very large and diverse clade including V.

caesariense, V. corymbosum, V. darrowii, V. elliottii, V. formosum, V. fuscatum, V.

myrsinities, V. pallidum and V. tenellum. Many of these species have formed the same

clustering groups in other phylogenetic studies. In the Bruederle and Vorsa (1994) allozyme

study, V. atrococcum (also referred to as V. fuscatum Gray), V. caesariense, V. darrowii¸ V.

elliottii, V. tenellum, and V. vacillans (also referred to as V. pallidum) all formed a very large

clade (BRUEDERLE AND VORSA, 1994; UTTAL, 1987; VANDER KLOET, 1983). In addition to

this similarity was the clustering of V. fuscatum and V. caesariense into a smaller subclade in

the present SNP study and in the Bruederle and Vorsa (1994) allozyme study. According to

Vander Kloet, V. caesariense, V. elliottii, and V. atrococcum (referred to as V. fuscatum in

the present study) should be classified in the V. corymbosum taxon (BRUEDERLE AND VORSA,

1994; VANDER KLOET, 1980). However, the allozyme study only inferred an affinity between

the three species, neither confirming or disproving Vander Kloet’s postulate (BRUEDERLE

AND VORSA, 1994). In the present study, the same affinity between V. caesariense, V.

elliottii, and V. fuscatum may exist, as they are grouped within the Cyanococcus section,

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although the data is not conclusive enough to suggest or disprove the argument that the two

species should be classified as V. corymbosum.

Many of the species in the diversity panel also formed clusters based on ploidy level and

species. Vaccinium virgatum, for example, is hexaploid and forms its own clade. Tetraploid

species like V. corymbosum and V. formosum also clustered together in a relatively large

clade. Unlike the V. virgatum clade, this clade was not monophyletic. Vaccinium fuscatum

(formerly V. atrococcum), V. caesariense, and V. pallidum (formerly V. vacillans) were also

interspersed among tetraploid V. corymbosum and V. formosum species in the tree created by

RAxML analysis. These results may indicate that V. corymbosum and V. formosum may

share a common ancestry with V. fuscatum, V. caesariense, and V. pallidum. The clustering

of V. fuscatum with V. caesariense, both highbush species, also occurred in previous

phylogenetic studies (BRUEDERLE AND VORSA, 1994). The clustering of tetraploids V. corymbosum and V. formosum into distinct clades were more defined in the SVDQuartets estimation, which takes evolutionary models into account. The clades in this tree were more well defined and monophyletic, except for V. pallidum which is positioned as the sister of V. corymbosum. The placement of V. pallidum suggests V. corymbosum may have a shared genetic history with V. pallidum due to potential hybridization events in the past.

Recent studies utilizing SNPs from GBS data have also generated topologies where blueberry taxa have clustered according to ploidy level (CAMPA AND FERREIRA, 2018).

106

Other monophyletic groups based on the ploidy and species were formed in both RAxML

and SVDQuartets topologies. The diploid V. elliottii (PI 346621 and PI 346623) species

formed a monophyletic tree with a sister V. elliottii (NC 95-1-6). The separation of the three

V. elliottii species may have been due to the different geographic growing conditions. The diploids V. arboreum and V. stamineum also formed separate monophyletic clades. The diploid V. darrowii species also formed its own monophyletic clade with the tetraploid V. myrsinites as a sister, suggesting shared history. The diploid V. crassifolium formed its own clade with the tetraploid V. arctostaphylos as a sister. Interestingly, the tetraploid V.

arctostaphylos was one of the most geographically distinct species. V. arctostaphylos was

originally collected from the Republic of Georgia. Correctly analyzing the nature of

relationship between V. arctostaphylos may be outside the scope of this study.

V. myrtilloides was the closest related clade outside of the Cyanococcus clade in both SNP and allozyme studies. The grouping of the diploid V. elliottii into its own clade within the

Cyanococcus group was also consistent with the clustering found in the same dendrogram created using allozyme data (BRUEDERLE AND VORSA, 1994). In addition, in this study V. tenellum shared a clade with V. darrowii. However, unlike the Bruederle and Vorsa (1994) study, V. tenellum, in this study, was not a sister to V. darrowii in the RAxML or

SVDQuartets phylogenetic tree, perhaps due to the considerations of a large amount of molecular data available to RAxML and SVDQuartets programming and also the evolutionary modeling inherent in SVDQuartets programming, specifically.

107

Various clades also revealed geographic patterns. Vaccinium myrtilloides and V.

angustifolium may have formed their own clade as a result of their shared geography. The V.

myrtilloides species is diploid and V. angustifolium is tetraploid but both species are native to

the state of Maine in the northeastern United States. Diploids V. darrowii, V. tenellum, and V.

elliottii are all in a large clade with the tetraploid V. myrsinites. These species are all native to

the southeastern United States. However, it is possible that these species have formed a clade

simply because they are each other’s closest relatives. Additional research must be conducted

in order to establish if the clade formed because of the hybridization and the gene flow events

that occur in the wild when different species share the same geographic proximity.

When comparing the phylogenetic analysis resulting from our SNP data to other southern

highbush (SHB) studies using SSR data (BREVIS et al., 2008), many similarities were present.

For example, in both cases the SHB cultivar ‘O’Neal’ (V. corymbosum) was a part of the same major clade containing the SHB cultivar ‘Reveille’ (V. corymbosum) (BREVIS et al.,

2008). A pedigree analysis performed alongside the SSR analysis mirrored the same result

(BREVIS et al., 2008).

The fact that, in the present SNP study, the next three major clades immediately outside of

the group containing the SHB ‘O’Neal’, ‘Reveille’, and ‘Arlen’ (V. corymbosum) includes V.

darrowii, V. virgatum, and V. angustifolium suggests that these species are potentially genetic

contributors to SHB blueberries. This preliminary hypothesis was supported by the reported

expected genetic contributions of V. darrowii, V. virgatum, and V. angustifolium to SHB

108

species/cultivars (BREVIS et al., 2008). However, the pedigree-based neighbor-joining dendrogram estimated with genetic distances places ‘O’Neal’ and ‘Reveille’ in the same major clade with ‘Arlen’ on a separate genetic clade. This is potentially due to the fact that

‘Arlen’ contains a larger genetic contribution from V. darrowii than both ‘O’Neal’ and

‘Reveille’ combined (BREVIS et al., 2008). The separation of ‘Arlen’ from ‘Reveille’ and

‘O’Neal’ also occurs in a separate neighbor-joining dendrogram based on the Dice distance of 42 SSR markers (BIAN et al., 2014). This difference in grouping is also evident in the

SVDQuartets phylogenetic tree from the present SNP study. While the three V. corymbosum

cultivars remain close, ‘Arlen’ breaks away from ‘O’Neal’ and ‘Reveille’.

To build upon the current phylogenetic research using SNP data, several methodologies

should be implemented in the future. Additional wild species should be sampled from

multiple geographic locations in order to better substantiate the gene flow that may be occurring between different species that cluster together in the same clade. Also, the species/cultivar hybridization that occurs in the wild should be compared with the hybridization that occurs in more controlled experimental settings to ensure that plant samples gathered from both environments can be analyzed interchangeably. Further studies should also be conducted with cultivars that were not as thoroughly hybridized as the V.

corymbosum cultivars ‘Arlen’, ‘O’Neal’, and ‘Reveille’. The use of natural tetraploid populations may aid in the generation of a less compromised phylogenetic estimate. The

‘Arlen’, ‘O’Neal’, and ‘Reveille’ cultivars have undergone several generations of complex

breeding which complicates their genomes. As a result of such extensive hybridization, these

109

cultivars have experienced gene flow and recombination events making it difficult to assess

their relationship to the other less complex species and accessions in this diversity panel.

Cultivars with less diverse backgrounds such as those that were selected from the wild,

should be used in future studies. Future studies should also include the use of more samples

per species to observe if clades continue to form according to species similarity. Lastly, in

order to test the usefulness of this SNP data to uncover the relationships within and between

wild and cultivated species, a set of full-length genes conserved among the diversity panel should be used as data for phylogenetic estimations. If similar topologies are observed, the use of SNPs for this type of analysis will be better substantiated.

CONCLUSION

The use of high-quality SNP markers has proven beneficial in the establishment of the relationships existing within and between various species of wild and cultivated blueberries.

The present study has employed the use of ~1.77 million SNPs found among both wild and cultivated accessions that are well distributed across the coding regions of genome. With the use of this molecular data, similarities among different species becomes evident. Many clades formed on the basis of species similarity, ploidy level, geographic distribution and shared genetic history. In most cases, members of the same ploidy level clustered in the same clade. However, species like V. pallidum (2x), V. myrsinites (4x), V. angustifolium (4x), and

V. arctostaphylos (4x) clustered outside of their own ploidy level groups. This clustering may be due to geographical proximity suggesting gene flow and hybridization, as may be the case

110

with V. myrtilloides (2x) and V. angustifolium (4x) (both native to Maine, USA). Other reasons for clustering may be due to a shared genetic relationship between these species.

These and other reasons for shared proximity require further research.

The SNP data gathered from the 29 accessions in this diversity panel can be used to for marker development so that haplotype blocks for individual species and cultivars in the panel can be established. The creation of such haplotype maps will be useful in determining the evolutionary phylogeny and relationships between the wild and cultivated species that exist in the background of many of the highly hybridized species that breeders use in several popular cultivars. Understanding the history and relationships existing between and among these species will enable more efficient breeding.

FIGURES

111

Figure 1: Histogram of berry firmness trait segregation from 2016-2018 in ‘Reveille’ x

112

A Harvest 2016 Berry Firmness

40%

30%

20%

Population Avg. Percent RA Population 10% (n=237)

0% 100 125 150 175 200 225 250 275 300 325 Firmness (g/mm)

B Harvest 2017 Berry Firmness

40% Reveille

30%

20%

Population Avg. Percent RA Population 10% (n=237)

0% 100 125 150 175 200 225 250 275 300 325 Firmness (g/mm)

113

C Harvest 2018 Berry Firmness

40%

Reveille

30%

Arlen 20%

Population Avg. Percent RA Population 10% (n=237)

0% 100 125 150 175 200 225 250 275 300 325 Firmness (g/mm)

114

Figure 2: Correlation plot of berry firmness from 2016-2018 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Coefficient of determination and correlation between RA berry firmness in

115

A Harvest 2016 vs 2017 Berry Firmness 300

250

200

150

100 2016 Firmness (g/mm) 2016 Firmness 50

0 0 50 100 150 200 250 300 350 2017 Firmness (g/mm) n = 237 R = 0.52 R² = 0.2738

116

B Harvest 2017 vs 2018 Berry Firmness 350

300

250

200

150

100 2017 Firmness (g/mm) 2017 Firmness

0 0 50 100 150 200 250 300 350 400 2018 Firmness (g/mm) n = 237 R = 0.22 R² = 0.1082

C Harvest 2016 vs 2018 Berry Firmness 300

250

200

150

100 2016 Firmness (g/mm) 2016 Firmness 50

0 0 50 100 150 200 250 300 350 400 2018 Firmness (g/mm) n = 237 R = 0.38 R² = 0.1477

117

Figure 3: Top 15% RA plants producing the firmest berries averaged across 2016-2018 (A) and the top 20 RA plants producing the firmest berries with the lowest SEM values across the

same time period (C). Top 15% RA plants producing the softest berries averaged across

2016-2018 (B) and the top 20 RA plants producing the softest berries with the lowest SEM

values across the same time period (D).

118

A Top 15% Percent Firmest Berries from RA 2016-2018 300

250

200

150

100

Average Firmness RA of Each Firmness Average 50

0 76 220 150 155 104 124 261 355 246 223 262 281 195 191 312 135 208 360 159 Firmest RA Individuals

B Top 15% Percent Softest Berries from RA 2016-2018 300

250

200

150

100

Average Firmness RA of Each Firmness Average 50

0 92 24 98 64 96 69 36 29 298 294 287 283 296 285 300 292 286 263 189 Softest RA Individuals

119

C Top 20 Firmest Berries from RA 2016-2018 300

250

200

150

100

Average Firmness RA of Each Firmness Average 50

0 11 13 76 82 220 150 104 124 219 261 223 207 262 281 168 195 312 135 309 208 Firmest RA Individuals

D Top 20 Softest Berries 2016-2018 300

250

200

150

100

Average Firmness RA of Each Firmness Average 50

0 2 6 38 24 64 37 75 53 26 69 19 36 277 290 283 285 311 343 189 301 Softest RA Individuals

120

Figure 4: Histogram of average berry size (cm3) trait segregation in (A) 2016 and (B) 2017

in ‘Reveille’ x ‘Arlen’ (RA) population as measured by Tomato Analyzer software.

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A Average 2016 Individual Berry Volume

40%

Population Avg. 30% (n=181)

20%

10% Percent RA Population

0% 0.3 0.7 1.1 1.5 1.9 2.3 2.7 3.1 3.5 3.9 4.3 4.7 5.1 Average Berry Volume (cm3)

B Average 2017 Individual Berry Volume

40% Population Avg. (n=181)

30%

20%

10% Percent RA Population

0% 0.3 0.7 1.1 1.5 1.9 2.3 2.7 3.1 3.5 3.9 4.3 4.7 5.1 Average Berry Volume (cm3)

122

2016 vs 2017 Individual Berry Volume 4.00

3.50

3.00

2.50

2.00

1.50

1.00 2016 Berry (cm3) Volume 2016 Berry 0.50

0.00 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 2017 Berry Volume (cm3) n =181 R = 0.38 R² = 0.1465

Figure 5: Correlation plot of berry size/volume (cm3) from 2016-2017 in ‘Reveille’ x

‘Arlen’ (RA) population. Coefficient of determination and correlation between RA berry size/volume (cm3) in 2016 and 2017.

123

A Harvest 2016-2017 Average Berry Firmness vs. Volume 3.5 )

3 3

2.5

1.5

Average Average Berry Volume (cm 0.5

0 0 50 100 150 200 250 300 Average Berry Firmness (g/mm) n = 127 R = -0.35 R² = 0.1258

Figure 6: Correlation plot of averaged berry firmness (g/mm) measured using the Firmtech 2 and averaged berry size/volume (cm3) measured using the Tomato Analyzer from 2016-2017 in ‘Reveille’ x ‘Arlen’ (RA) population. Coefficient of determination and correlation between berry firmness (g/mm) and averaged berry size/vol. (cm3) from 2016-2017.

124

Figure 7: Histogram of berry texture trait segregation from 2018 in ‘Reveille’ x ‘Arlen’

(RA) population. (A) Histogram of berry breakpoint force (N) in RA population in 2018 (B) berry force gradient (g/mm) in RA population in 2018, and (C) area under berry texture curve

(N*sec) in RA population in 2018 harvest year.

125

A Harvest 2018 Berry Breakpoint Force 30%

25%

20% Arlen

15%

Percent RA Pop. 10% Reveille Population Avg. 5% (n=210)

0% 1.85 2.15 2.45 2.75 3.05 3.35 3.65 3.95 4.25 4.55 4.85 Breakpoint Force (N)

B Harvest 2018 Berry Force Gradient 35% Population Avg. 30% (n=210)

25%

20%

15% Arlen

RA Percent Population 10% Reveille 5%

0% 35 45 55 65 75 85 95 105 115 125 135 145 Force Gradient (g/mm)

126

C Harvest 2018 Area Under Berry Texture Curve 35% Arlen 30%

25%

20%

15%

10% Percent RA Population Population Avg. 5% (n=210) Reveille

0% 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 Area Under Texture Curve (N*sec)

127

Figure 8: (A) Coefficient of determination and correlation and between RA berry force

gradient (g/mm) and firmness (g/mm) in 2018, (B) RA breakpoint force (N) and firmness

(g/mm) in 2018, and (C) RA area under texture curve (N*sec) and firmness (g/mm) in 2018.

128

A 2018 Gradient vs Firmness 250

200

150

100 Firmness (g/mm)

0 0 20 40 60 80 100 120 140 160 Gradient (g/mm) n = 210 R = 0.60 R² = 0.3594

B 2018 Break Point Force vs Firmness 250

200

150

100 Firmness (g/mm)

50 R =

0 0 1 2 3 4 5 6 Break Point Force (N) n = 210 R = 0.49 R² = 0.2359

129

C 2018 Area Under Texture Curve vs. Firmness 250

200

150

100 Firmness (g/mm)

0 0 2 4 6 8 10 12 14 Area Under Texture Curve (N*Sec) n = 210 R = 0.45 R² = 0.1987

130

Figure 9: (A) Top 15% RA plants producing stiffest berries measured using force gradient

(g/mm) (indicating level of firmness) and (B) the top 15% of RA plants producing berries with the softest (least stiff) texture harvested in 2018 measured in force gradient (g/mm). (C)

The top 20 RA plants producing stiffest berries measured using force gradient (g/mm). (D)

The top 20 RA plants producing the softest (least stiff) berries measured using force gradient

(g/mm). (E) Top 15% RA plants producing berries requiring the greatest force to pierce their

skin (indicating level of firmness) and (F) the top 15% of RA plants producing berries with

the softest texture harvested in 2018 measured in breakpoint force (N). (G) The top 20 RA

plants producing berries requiring the greatest force to pierce their skin measured in breakpoint force (N). (H) The top 20 RA plants producing berries requiring the least force to pierce their skin measured in breakpoint force (N). (I) Top 15% RA plants producing berries

with toughest texture and (J) the top 15% of RA plants producing berries with the softest

texture harvested in 2018 measured in area under the curve (N*sec). (K) The top 20 RA plants producing berries with the toughest texture measured in area under the curve (N*sec).

(L) The top 20 RA plants producing berries with the softest texture measured in area under

the curve (N*sec).

131

A Top 15% Population w/ Highest Berry Force Gradient 2018 160 140 120 100 80

RA 60 40 20 0 76 85 10 43 82 364 195 362 167 319 156 222 260 335 251 309 145 207 149 123 280 233 160 255 183 348 182 103 221 225 214 208 Berry Force Gradient (g/mm) for Each Each for (g/mm) Gradient Force Berry RA Individuals Producing Berries w/ Highest Force Gradient

B Bottom 15% Population w/ Lowest Berry Force Gradient 2018 160 140 120 100 80

RA 60 40 20 0 9 55 36 47 50 95 63 25 16 93 326 293 129 277 232 263 109 297 289 226 276 246 291 262 117 294 270 274 332 144 273 254 Berry Force Gradient (g/mm) for Each Each for (g/mm) Gradient Force Berry RA Individuals Producing Berries w/ Lowest Force Gradient

132

C Top 20 RA Individuals w/ Highest Berry Force Gradient 2018 160 140 120 100 80 60 RA 40 20 0 76 364 195 362 167 319 156 222 260 335 251 309 145 207 149 123 280 233 160 255

Berry Force Gradient (g/mm) Each for (g/mm) Gradient Force Berry RA Individuals Producing Berries w/ Highest Force Gradient

D Bottom 20 RA Indviduals w/ Lowest Berry Force Gradient 2018 160 140 120 100 80 60

RA 40 20 0 55 36 47 50 95 326 293 129 277 232 263 109 297 289 226 276 246 291 262 117 RA Individuals Producing Berries w/ Lowest Force Gradient

Berry Force Gradient (g/mm) for Each Each for (g/mm) Gradient Force Berry

133

E Top 15% Population w/ Highest Berry Breakpoint Force 2018 5.00 4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 6 76 10 85 55 79 98 60 Berry Breakpoint Force (N) for Each RA Each for (N) Force Breakpoint Berry 195 347 103 364 319 149 304 252 260 223 251 255 123 243 362 114 214 280 199 207 229 222 110 241 RA Individuals Producing Berries w/ Highest Breakpoint Force

F Bottom 15% Population w/ Lowest Berry Breakpoint Force 2018 5.00 4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 1 95 39 32 23 93 73 19 64 74 24 37 63 18 109 326 342 158 295 290 262 143 246 161 181 273 232 328 205 293 340 281

Berry Breakpoint Force (N) for Each RA Each for (N) Force Breakpoint Berry RA Individuals Producing Berries w/ Lowest Breakpoint Force

134

G Top 20 RA Individuals w/ Highest Berry Breakpoint Force 2018 5.00 4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 6 76 10 85 55 79 195 347 103 364 319 149 304 252 260 223 251 255 123 243

Berry Breakpoint Force (N) for Each RA Each for (N) Force Breakpoint Berry RA Individuals Producing Berries w/ Highest Breakpoint Force

H Bottom 20 RA Individuals w/ Lowest Berry Breakpoint Force 2018 5.00 4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 1 95 39 32 23 93 73 19 64 74 109 326 342 158 295 290 262 143 246 161

Berry Breakpoint Force (N) for Each RA Each for (N) Force Breakpoint Berry RA Individuals Producing Berries w/ Lowest Breakpoint Force

135

I Top 15% Population w/ Highest Area Under Curve 2018 14

0 6 85 76 55 98 88 38 79 10 40 364 123 195 255 252 280 103 110 156 319 304 362 223 215 149 237 347 164 241 335 297 251 Area Under Curve (N*sec) RA Each (N*sec) for Curve Area Under RA Individuals Producing Berries w/ Highest Area Under Curve

J Bottom 15% Popultation w/ Lowest Area Under Curve 2018 14

0 1 95 39 32 23 93 73 19 64 74 24 37 63 18 109 326 342 158 295 290 262 143 246 161 181 273 232 328 205 293 340 281 Area Under Curve (N*sec) RA Each (N*sec) for Curve Area Under RA Individuals Producing Berries w/ Lowest Area Under Curve

136

K Top 20 RA Individuals w/ Highest Area Under Curve 2018 14

0 6 85 76 55 98 88 38 79 364 123 195 255 252 280 103 110 156 319 304 362

Area Under Curve (N*sec) RA Each (N*sec) for Curve Area Under RA Individuals Producing Berries w/ Highest Area Under the Curve

L Bottom 20 Population w/ Lowest Area Under Curve 2018 14

0 1 93 95 32 73 39 64 19 23 63 24 326 158 109 342 262 295 273 181 232

Area Under Curve (N*sec) RA Each (N*sec) for Curve Area Under RA Individuals Producing Berries w/ Lowest Area Under the Curve

137

Figure 10: Histogram of individual berry weight (g) trait segregation from 2017-2018 in

‘Reveille’ x ‘Arlen’ (RA) population. (A) Histogram of individual berry weight (g) trait in

138

A Harvest 2016 Individual Berry Weight (g) Population Avg. (n=200) 25%

20%

15%

10%

Percent RA Population 5% Reveille

0% 0.1 0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9 2.1 2.3 2.5 2.7 2.9 3.1 Weight (g)

B Harvest 2017 Individual Berry Weight (g)

25% Population Avg. (n=200) 20%

15%

10%

Percent RA Population 5% Reveille

0% 0.1 0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9 2.1 2.3 2.5 2.7 2.9 3.1 Weight (g)

139

C Harvest 2018 Individual Berry Weight (g)

25% Population Avg. (n=200) 20% Reveille

15%

10%

Percent RA Population 5%

0% 0.1 0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9 2.1 2.3 2.5 2.7 2.9 3.1 Weight (g)

140

Figure 11: Correlation plot of individual berry weight (g) from 2016-2018 in ‘Reveille’ x

‘Arlen’ (RA) population. (A) Coefficient of determination and correlation and between RA individual berry weight in 2016 and 2018 harvest years. (B) Coefficient of determination and

correlation and between RA individual berry weight in 2017 and 2018 harvest years. (C)

Coefficient of determination and correlation and between RA individual berry weight in 2017

and 2018 harvest years.

141

A Harvest 2016 vs 2017 Individual Berry Weight 3.500

3.000

2.500

2.000

1.500

2016 Weight (g) 2016 Weight 1.000

0.500

0.000 0 0.5 1 1.5 2 2.5 3 2017 Weight (g) n = 200 R = 0.4406 R² = 0.1941

B Harvest 2017 vs 2018 Individual Berry Weight 3

2.5

1.5

2017 Weight (g) 2017 Weight 1

0.5

0 0 0.5 1 1.5 2 2.5 3 3.5 2018 Weight (g) n = 200 R = 0.4652 R² = 0.2164

142

C Harvest 2016 vs 2018 Individual Berry Weight 3.500

3.000

2.500

2.000

1.500 2016 Weight (g) 2016 Weight 1.000

0.500

0.000 0 0.5 1 1.5 2 2.5 3 3.5 2018 Weight (g) n = 200 R = 0.4489 R² = 0.2015

143

Figure 12: (A) Top 15% RA plants producing berries with the highest individual averaged

berry weight (g) and (B) the top 15% of RA plants producing berries with the lowest

individual averaged berry weight (g) harvested in 2016-2018. (C) The top 20 RA plants

producing berries with the highest averaged individual berry weight (g) harvested in 2016-

2018 with the lowest standard error of the mean (SEM). (D) The top 20 RA plants producing

berries with the lowest averaged individual berry weight harvested in 2016-2018 with the

lowest SEM (g).

144

A Top 15% Berries w/ Highest Weight 2016-2018 3.50

3.00

2.50

2.00

1.50

1.00

Average Average Weight (g) of EachRA 0.50

0.00 1 3 18 15 57 26 25 89 16 19 38 42 340 276 330 332 338 296 278 325 293 156 280 290 287 328 282 297 323 283 RA Individuals Producing Berries w/ Highest Weight

B Top 15% Berries w/ Lowest Weight 2016-2018 3.50

3.00

2.50

2.00

1.50

1.00

Average Average Weight (g) of EachRA 0.50

0.00 32 10 47 66 244 155 341 227 254 241 197 347 137 208 243 196 167 149 250 154 110 205 248 204 307 201 150 193 111 267 RA Individuals Producing Berries w/ Lowest Weight

145

C Top 20 Berries w/ Highest Weight 2016-2018 3.50

3.00

2.50

2.00

1.50

1.00

Average Average Weight (g) of EachRA 0.50

0.00 1 18 26 25 16 38 42 276 330 332 296 278 325 293 280 287 282 297 323 283 RA Individuals Producing Berries w/ Highest Weight

D Top 20 Berries w/ Lowest Weight 2016-2018 3.50

3.00

2.50

2.00

1.50

1.00

Average Average Weight (g) of EachRA 0.50

0.00 32 10 47 66 244 155 254 241 197 347 167 149 154 110 205 204 201 150 193 111 RA Individuals Producing Berries w/ Lowest Weight

146

A Harvest 2016-2017 Averaged Individual Berry Weight vs Size 4

3.5 ) 3 3

2.5

1.5

1 2017 Avg. Size Avg. Size (cm 2016 - 2017 0.5

0 0 0.5 1 1.5 2 2.5 3 2016-2017 Avg. Weight (g) n = 123 R = 0.80 R² = 0.6445

Figure 13: Correlation plot of averaged individual berry weight (g) vs. size (cm3) from 2016-

2017 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Coefficient of determination and correlation and between RA averaged individual berry weight and size in 2016-2017 harvest years.

147

Figure 14: Histogram of fruit puree percent titratable acidity trait segregation from 2017-

2018 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Histogram of fruit puree percent titratable

acidity trait segregation in RA population in 2017 and (B) 2018 harvest years.

148

A Harvest 2017 Fruit Puree Percent Titratable Acidity Reveille 60%

50%

40% Population Avg. 30% (n=237)

20%

Percent RA Population Arlen 10%

0% 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Percent Titratable Acidity

B Harvest 2018 Fruit Puree Percent Titratable Acidity 60%

50% Arlen

40% Population Avg. (n=237) 30%

20% Percent RA Population 10% Reveille

0% 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Percent Titratable Acidity

149

Harvest 2017 vs 2018 Fruit Puree Pecent Titratable Acidity 2.50

2.00

1.50

1.00

0.50 2017 Titratable Acidity (citric acid meq) acid (citric Acidity Titratable 2017 0.00 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2018 Titratable Acidity (citric acid meq) n = 237 R = 0.284 R² = 0.0805

Figure 15: Correlation plot of fruit puree percent titratable acidity from 2017-2018 in

‘Reveille’ x ‘Arlen’ (RA) population. (A) Coefficient of determination and correlation and between RA individual fruit puree percent titratable acidity in 2017 and 2018 harvest years.

150

Figure 16: (A) Top 15% RA plants producing berries with the highest fruit puree percent

titratable acidity as measured using an autotitrator and (B) the top 15% of RA plants producing berries with the lowest fruit puree percent titratable acidity harvested in 2018. (C)

The top 20 RA plants producing berries with the highest fruit puree percent titratable acidity.

(D) The top 20 RA plants producing berries with the lowest fruit puree percent titratable

acidity.

151

A Top 15% Population w/ Highest Fruit Puree Percent Titratable Acidity 2017-2018 2.00 1.80 1.60 1.40 1.20 1.00

RA 0.80 0.60 0.40 0.20 0.00 37 76 34 81 36 35 58 333 188 136 257 240 241 277 168 181 226 157 RA Individuals Producing Berries w/ Highest Percent Titratable Acidity Berry Percent Titratable Acidity (using autotitrator) for Each for autotitrator) (using Acidity Titratable Percent Berry

152

B Bottom 15% Population w/ Lowest Fruit Puree Titratable Acidity 2017-2018 2.00 1.80 1.60 1.40 1.20 1.00

RA 0.80 0.60 0.40 0.20 0.00 7 9 3 12 22 11 19 40 97 46 94 50 39 113 118 131 167 255 243 229 287 219 230 207 261 250 278 215 111 208 288 263 259 318 193 245 RA Individuals Producing Berries w/ Lowest Percent Titratable Acidity Berry Percent Titratable Acidity (using autotitrator) for Each Each for autotitrator) (using Acidity Titratable Percent Berry

C Top 20 RA w/ Highest Titratable Acidity 2017-2018 (Low SEM) 2.00 1.80 1.60 1.40 1.20 1.00 0.80 0.60 0.40 0.20 0.00 92 76 36 82 89 333 189 153 136 338 241 277 133 246 168 221 181 226 157 218 Berry Titratable Acidity (using autotitrator) for RA Each for autotitrator) (using Acidity Titratable Berry RA Individuals Producing Berries w/ Highest Percent Titratable Acidity

153

D Bottom 20 RA w/ Lowest Titratable Acidity 2017-2018 (Low SEM) 2.00 1.80 1.60 1.40 1.20 1.00 0.80 0.60 0.40 0.20 0.00 9 12 40 94 50 39 Berry Titratable Acidity (using autotitrator) for RA Each for autotitrator) (using Acidity Titratable Berry 113 118 131 167 255 243 229 230 207 261 250 278 215 288 RA Individuals Producing Berries w/ Lowest Percent Titratable Acidity

154

Harvest 2017-2018 Averaged Berry pH (Autotitrator) vs. A Berry pH (Benchtop pH Meter) 4.5 4 3.5 3 2.5 2 1.5 1

2016 pH (Handheld (Handheld 2016pH Meter)pH 0.5 0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 2017-2018 Avg. pH (Autotitrator) n = 123 R = 0.08 R² = 0.0072

Figure 17: Correlation plot of averaged 2017-2018 berry fruit puree pH from RA population measured using an autotitrator vs. berry fruit puree pH measured using a benchtop pH meter in 2016. (A) Coefficient of determination and correlation between averaged 2017-2018 berry fruit puree pH and berry fruit puree pH measured using a benchtop pH meter in 2016.

155

Figure 18: Histogram of berry fruit puree pH segregation from 2017-2018 in ‘Reveille’ x

‘Arlen’ (RA) population. (A) Histogram of berry fruit puree pH trait segregation in RA

population in 2017 and (B) 2018 harvest years.

156

A Harvest 2017 Berry Fruit Puree pH

40%

30% Arlen Reveille 20%

Population Avg. 10% Percent RA Population (n=222)

0% 3.1 3.3 3.5 3.7 3.9 4.1 4.3 4.5 4.7 4.9 pH

B Harvest 2018 Berry Fruit Puree pH

40% Population Avg. (n=222)

30%

20% Arlen

10% Percent RA Population Reveille

0% 3.1 3.3 3.5 3.7 3.9 4.1 4.3 4.5 4.7 4.9 pH

157

Harvest 2017-2018 Averaged A Fruit Puree pH vs. Fruit Percent Titratable Acidity 1.2

0.8

0.6 (Autotitrator) 0.4 2018 Avg. Avg. 2018 Acidity Percent Titratable - 0.2 2017

0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 2017-2018 Avg. pH (Autotitrator) n = 208 R = -0.76 R² = 0.5703

Figure 19: Correlation plot of averaged berry fruit puree pH vs. berry puree percent titratable acidity from 2017-2018 in ‘Reveille’ x ‘Arlen’ (RA) population as measured by autotitrator.

(A) Coefficient of determination and correlation between berry fruit puree pH and berry puree titratable acidity from 2017-2018 harvest years.

158

A 2017 vs 2018 Berry pH 5.5

4.5

4 2017 Berry pH 2017 Berry

3.5

3 3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 5 2018 Berry pH n = 222 R = 0.40 R² = 0.1599

Figure 20: Correlation plot of berry puree pH from 2017-2018 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Coefficient of determination and correlation between RA individual berry puree pH in 2017 and 2018 harvest years.

159

Figure 21: (A) Top 15% RA plants producing berries with the highest berry puree pH and

(B) the top 15% of RA plants producing berries with the lowest berry pH harvested in 2018.

RA plants producing berries with the lowest berry puree pH.

160

A Top 15% Berries w/ Highest pH 2017-2018 5.00 4.50 4.00 3.50 3.00 2.50 2.00

Berry Each RA pH for Berry 1.50 1.00 0.50 0.00 7 11 19 12 46 10 131 113 167 229 118 219 214 258 243 307 250 239 261 208 207 265 237 238 314 347 111 281 259 230 278 222 204 247 RA Individuals Producing Berries w/ Highest pH

B Bottom 15% Berries w/ Lowest Titratable Acidity 2017-2018 5.00 4.50 4.00 3.50 3.00 2.50 2.00 1.50 Berry Each RA pH for Berry 1.00 0.50 0.00 37 81 17 92 58 18 28 34 32 38 77 89 20 55 36 82 21 188 189 338 299 333 340 252 241 240 277 153 218 257 156 323 136 345 RA Individuals Producing Berries w/ Lowest pH

161

C Top 20 Berries w/ Highest pH 2017-2018 (LOW SEM) 5.00 4.50 4.00 3.50 3.00 2.50 2.00 1.50 Berry Each RA pH for Berry 1.00 0.50 0.00 11 12 131 113 229 118 219 250 261 208 207 237 314 347 111 281 259 278 204 247 RA Individuals Producing Berries w/ Highest pH

D Bottom 20 Berries w/ Lowest pH 2017-2018 (LOW SEM) 5.00 4.50 4.00 3.50 3.00 2.50 2.00 1.50 Berry Each RA pH for Berry 1.00 0.50 0.00 81 17 58 18 28 34 32 77 20 55 338 299 252 240 277 153 218 156 323 345 RA Individuals Producing Berries w/ Lowest pH

162

Figure 22: Histogram of berry fruit puree percent acidity averaged value distribution from

2017-2018 in ‘Reveille’ x ‘Arlen’ (RA) population as measured by refractometer and bench

top autotitrator. (A) Histogram of berry fruit puree percent acidity averaged value distribution as measured by refractometer. (B) Histogram of berry fruit puree percent acidity

averaged value distribution as measured by bench top autotitrator.

163

Harvest 2017-2018 Averaged Fruit Puree Percent Titratable A Acidity (Refractometer) Arlen 50%

40% Population Avg. (n=167) 30%

20%

Percent RA Population 10% Reveille

0% 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Percent Titratable Acidity (Refractometer)

B Harvest 2017-2018 Averaged Fruit Puree Percent Titratable Acidity (Autotitrator) 50% Reveille 40% Arlen

30%

20%

Population Avg. Percent RA Population 10% (n=167)

0% 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Percent Titratable Acidity (Autotitrator)

164

Figure 23: Correlation plot of berry fruit puree percent acidity as measured by refractometer

and bench top autotitrator from 2017-2018 in ‘Reveille’ x ‘Arlen’ (RA) population. (A)

Coefficient of determination and correlation between berry fruit puree percent acidity as

measured by refractometer and bench top autotitrator from 2017. (B) Coefficient of

determination and correlation between berry fruit puree percent acidity as measured by

refractometer and bench top autotitrator from 2018.

165

A Harvest 2017 Berry Fruit Puree Percent Acidity Benchtop Autotitrator vs. Refractometer 1.6

1.4

1.2

0.8

0.6

0.4

0.2

0 0 0.5 1 1.5 2 2.5

2017 Percent Titratable Acidity (Refractometer) 2017 Percent Titratable Acidity (Autotitrator) n = 252 R = 0.86 R² = 0.7317

B Harvest 2018 Berry Fruit Puree Percent Acidity Benchtop Autotitrator vs. Refractometer 1.6

1.4

1.2

0.8

0.6

0.4

0.2

0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

2018 Percent Titratable Acidity (Refractometer) 2018 Percent Titratable Acidity (Autotitrator) n = 259 R = 0.95 R² = 0.8985

166

Figure 24: (A) Top 15% RA plants producing berries with the highest fruit puree percent acidity and lowest SEM values averaged across 2017-2018 as measured by a refractometer.

(B) Top 15% RA plants producing berries with the highest fruit puree percent acidity and lowest SEM values averaged across 2017-2018 as measured by a bench top autotitrator. (C)

Bottom 15% RA plants producing berries with the lowest fruit puree percent acidity and lowest SEM values averaged across 2017-2018 as measured by a refractometer. (B) Bottom

15% RA plants producing berries with the lowest fruit puree percent acidity and lowest SEM

values averaged across 2017-2018 as measured by a bench top autotitrator.

167

Top 15% Population w/ Highest Averaged Percent Acidity (2017-2018) A (Low SEM) (Refractometer) 2.50

2.00

1.50

1.00

0.50

0.00 2 8 55 36 38 65 77 25 24 333 134 299 277 168 354 157 334 217 253 103 RA Individuals Producing Berries w/ Highest Averaged Percent Acidity Berry Berry Fruit Puree PercentAcidity for Each RA

Top 15% Population w/ Highest Averaged Percent Acidity (2017-2018) B (Low SEM) (Autotitrator) 2.50

2.00

1.50

1.00

0.50

0.00 2 76 36 47 38 15 21 333 338 241 168 221 181 226 157 154 334 134 146 253 RA Individuals Producing Berries w/ Highest Averaged Percent Acidity Berry Berry Fruit Puree PercentAcidity for Each RA

168

Bottom 15% Population w/ Lowest Averaged Percent Acidity C (2017-2018) (Low SEM) (Refractometer) 2.50

2.00

1.50

1.00

0.50

0.00 9 19 45 48 52 64 87 83 113 167 255 261 250 282 215 111 208 263 100 114 RA Individuals Producing Berries w/ Lowest Averaged Percent Acidity

Berry Berry Fruit Puree PercentAcidity for Each RA

Bottom 15% Population w/ Lowest Averaged Percent Acidity D (2017-2018) (Low SEM) (Autotitrator) 2.50

2.00

1.50

1.00

0.50

0.00 9 19 40 94 50 39 48 113 167 255 243 229 207 261 250 215 288 284 268 296

Berry Berry Fruit Puree PercentAcidity for Each RA RA Individuals Producing Berries w/ Lowest Averaged Percent Acidity

169

Figure 25: Histogram for berry soluble solid content (Brix %) trait segregation from 2016-

2018 in ‘Reveille’ x ‘Arlen’ (RA) population. (A) Histogram of berry soluble solid content

170

A 2016 Berry Soluble Solid Content

Population Avg. 50% (n=180)

40%

30%

20% Percent RA Population 10% Reveille

0% 6 8 10 12 14 16 18 Brix %

B 2017 Berry Soluble Solid Content

50% Arlen

40% Population Avg. (n=180)

30%

20% Percent RA Population 10% Reveille

0% 6 8 10 12 14 16 18 Brix %

171

C 2018 Berry Soluble Solid Content Arlen Reveille 50%

40%

30%

20%

Percent RA Population Population Avg. 10% (n=180)

0% 6 8 10 12 14 16 18 Brix %

172

Figure 26: Correlation plot of berry soluble solid content (Brix %) from 2016-2018 in

‘Reveille’ x ‘Arlen’ (RA) population. (A) Coefficient of determination and correlation and between RA berry soluble solid content (Brix %) in 2016 and 2017, (B) 2017 and 2018, and

173

A 2016 vs 2017 Berry Soluble Solid Content 20

8 2016 Brix % 6

0 0 2 4 6 8 10 12 14 16 18 20 2017 Brix % n = 180 R = 0.123 R² = 0.0151

B 2017 vs 2018 Berry Soluble Solid Content 20

8 2017 Brix % 6

0 0 2 4 6 8 10 12 14 16 18 2018 Brix % n = 180 R = 0.248 R² = 0.0616

174

C 2016 vs 2018 Berry Soluble Solid Content 20

8 2017 Brix % 6

0 0 2 4 6 8 10 12 14 16 18 2018 Brix % n = 180 R = 0.067 R² = 0.0045

175

Figure 27: (A) Top 15% RA plants producing berries with the highest SSC (Brix %) averaged across 2016-2018 and (B) the top 15% RA plants producing berries with the lowest

SSC (Brix %) berries averaged across 2016-2018. (C) The top 20 RA plants producing

berries with the highest SSC (Brix %) and the lowest SEM values across the same time period and (D) the top 20 RA plants producing berries with the lowest SSC (Brix %) with the

lowest SEM values across the same time period.

176

A Top 15% Berries w/ Highest SSC 2016-2018 18

Average Average Brix of Each % RA 4

0 87 94 47 10 24 50 83 44 205 193 255 347 239 201 233 149 243 364 265 207 210 260 215 159 256 228 235 RA Individuals Producing Berries w/ Highest SSC

B Top 15% Berries w/ Lowest SSC 2016-2018 18

Average Average Brix of Each % RA 4

0 4 6 55 27 35 23 45 60 39 18 29 95 146 324 320 286 282 261 312 288 322 240 263 323 272 113 280 RA Individuals Producing Berries w/ Lowest SSC

177

C Top 20 Berries w/ Highest SSC 2016-2018 (Low SEM) 18 16 14 12 10 8 6 4 Average Average Brix of Each % RA 2 0 87 94 47 10 24 50 83 44 205 193 255 347 239 201 233 149 243 364 215 260 RA Individuals Producing Berries w/ Highest SSC

D Top 20 Berries w/ Lowest SSC 2016-2018 (Low SEM) 18 16 14 12 10 8 6 4 Average Average Brix of Each % RA 2 0 4 6 55 27 35 23 39 324 320 286 282 261 312 288 322 240 263 323 272 113 RA Individuals Producing Berries w/ Lowest SSC

178

Figure 28: Histograms depicting segregation of (A) hue angle, (B) chroma, (C) color index,

(D) L*, (E) a*, and (F) b* color values in 2017-2018 RA population.

179

A Harvest 2017-2018 Averaged Hue Angle Population Avg. (n=266) 40%

30%

2017 Reveille 20%

Percent RA Population 10%

0% 255 257 259 261 263 265 267 269 271 273 275 277 Hue Angle

B Harvest 2017-2018 Averaged Chroma Values

40% Population Avg. (n=266)

30%

20%

Percent RA Population 10% 2017 Reveille

0% 1.8 2.2 2.6 3 3.4 3.8 4.2 4.6 5 5.4 5.8 6.2 6.6 7 Chroma

180

C Harvest 2017-2018 Averaged Color Index

40% Population Avg. (n=266)

30%

20%

2017

Percent RA Population 10% Reveille

0% 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 Color Index

D Harvest 2017-2018 Averaged L* (D65) Values

40% Population Avg. (n=266)

30%

20%

Percent RA Population 10% 2017 Reveille

0% 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5 30.5 31.5 32.5 33.5 L*(D65)

181

E Harvest 2017-2018 Averaged a*(D65) Values

Population Avg. 40% (n=266)

30%

20%

Percent RA Population 10% 2017 Reveille

0% -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 a*(D65)

F Harvest 2017-2018 Averaged b*(D65) Values

40% Population Avg. (n=266) 30% 2017 Reveille 20%

Percent RA Population 10%

0% -7.25 -6.75 -6.25 -5.75 -5.25 -4.75 -4.25 -3.75 -3.25 -2.75 -2.25 b*(D65)

182

Figure 29: (A) Top 15% RA plants producing berries with the lightest color averaged across

2017-2018 and (B) the top 15% RA plants producing berries with the darkest color averaged across 2017-2018. (C) The top 20 RA plants producing berries with the lightest color and the

lowest SEM values across the same 2017-2018 time period (D) and the top 20 RA plants

producing berries with the darkest color with the lowest SEM values across the same time

period.

183

A Top 15% Berries w/ Lightest Color (High L* Value) 2017-2018

40 35 30 25 20 15 10 5 Average Average L* Vslue of Each RA 0 76 98 42 92 340 161 207 197 230 193 257 179 192 334 134 196 262 269 225 223 RA Individuals Producing Berries w/ Lightest Color

B Top 15% Berries w/ Darkest Color (Low L* Value) 2017-2018 40 35 30 25 20 15 10

Average Average L* Value of Each RA 5 0 4 6 29 93 89 97 33 172 150 138 123 296 111 321 314 220 300 110 247 282 RA Individuals Producing Berries w/ Darkest Color

184

C Top 20 Berries w/ Lightest Color (High L* Value) 2017-2018 (Low SEM) 40 35 30 25 20 15 10 5 Average Average L* Valueof Each RA 0 98 22 49 340 263 161 207 197 243 193 245 257 249 208 334 196 269 215 223 333 RA Individuals Producing Berries w/ Lightest Color (Low SEM)

D Top 20 Berries w/ Darkest Color (Low L* Value) 2017-2018 (Low SEM) 40 35 30 25 20 15 10 5 Average Average L* Valueof Each RA 0 7 59 93 89 53 97 82 167 138 278 146 296 321 314 354 285 110 308 247 295 RA Individuals Producing Berries w/ Darkest Color (Low SEM)

185

A Harvest 2017 Days to Anthesis

60% 55% 50% 45% 40% Population 35% Avg. (n=311) 30% 25% 20%

Percent RA Population 15% 10% 5% 0% 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 Days

Figure 30: Histogram depicting segregation of days to anthesis in 2017 RA population.

186

Figure 31: Bloom and ripening dates of 2017 RA population. (A) First bloom dates for 2017

RA population. (B) First ripening dates for 2017 RA population. (C) Last ripening dates for

2017 RA population.

187

A 2017 First Bloom Dates

6/24/2017 6/19/2017 6/14/2017 6/9/2017 6/4/2017 5/30/2017 5/25/2017 5/20/2017 5/15/2017 5/10/2017 5/5/2017 4/30/2017 4/25/2017 4/20/2017 4/15/2017

First Bloom Bloom Dates First 4/10/2017 4/5/2017 3/31/2017 3/26/2017 3/21/2017 3/16/2017 3/11/2017 3/6/2017 3/1/2017 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117 121 125 129 133 137 141 145 149 153 157 161 165 169 173 177 181 185 189 193 197 201 205 209 213 217 221 225 229 233 237 241 245 249 253 257 261 265 269 273 277 281 285 289 293 297 301 305 309 313 317 321 325 329 333 337 341 345 349 353 357 361 RA Individuals

B 2017 First Ripe Dates

6/24/2017 6/19/2017 6/14/2017 6/9/2017 6/4/2017 5/30/2017 5/25/2017 5/20/2017 5/15/2017 5/10/2017 5/5/2017 4/30/2017 4/25/2017 4/20/2017 4/15/2017 4/10/2017 First Ripening Dates Ripening First 4/5/2017 3/31/2017 3/26/2017 3/21/2017 3/16/2017 3/11/2017 3/6/2017 3/1/2017 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117 121 125 129 133 137 141 145 149 153 157 161 165 169 173 177 181 185 189 193 197 201 205 209 213 217 221 225 229 233 237 241 245 249 253 257 261 265 269 273 277 281 285 289 293 297 301 305 309 313 317 321 325 329 333 337 341 345 349 353 357 361 RA Individuals

188

C 2017 Last Ripe Dates

6/24/2017 6/19/2017 6/14/2017 6/9/2017 6/4/2017 5/30/2017 5/25/2017 5/20/2017 5/15/2017 5/10/2017 5/5/2017 4/30/2017 4/25/2017 4/20/2017 4/15/2017 4/10/2017 Last Ripening Dates 4/5/2017 3/31/2017 3/26/2017 3/21/2017 3/16/2017 3/11/2017 3/6/2017 3/1/2017 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117 121 125 129 133 137 141 145 149 153 157 161 165 169 173 177 181 185 189 193 197 201 205 209 213 217 221 225 229 233 237 241 245 249 253 257 261 265 269 273 277 281 285 289 293 297 301 305 309 313 317 321 325 329 333 337 341 345 349 353 357 361 RA Individuals

189

Figure 32: Phylogenetic tree structure of 29 species/accessions based on ~1.77 million high- quality SNPs using (A) RAxML and (B) SVDQuartets analytical programming. (C) RAxML

bipartitions tree including branch lengths.

190

A B

191

192

Table 1: Pearson correlation of all blueberry skin color values from 2017-2018 RA population.

Color Values

2017 L*(D65) 2017 a*(D65) 2017 b*(D65) 2017 Chroma 2017 Hue Angle 2017 Color Index 2018 L*(D65) 2018 a*(D65) 2018 b*(D65) 2018 Chroma 2018 Hue Angle 2018 Color Index 1

2018 Color Index

1 -0.8732

2018 Hue Angle

1 0.1054 -0.4076

2018 Chroma

1 -0.9998 -0.118 0.416

2018 b*(D65)

1 0.571 -0.5744 -0.2699 0.5078

2018 a*(D65)

1 -0.438 -0.3608 0.3669 -0.0573 -0.4072

2018 L*(D65)

1 -0.0656 0.0667 0.018 -0.0185 -0.0069 0.037

2017 Color Index

1 -0.9075 -0.092 0.0557 0.1168 -0.1173 0.0126 0.0451

2017 Hue Angle

1 -0.0824 -0.2315 0.2352 -0.2868 -0.3297 0.3292 0.0876 -0.2283

2017 Chroma

1 -0.9971 0.094 0.2144 -0.2377 0.2826 0.326 -0.3254 -0.0846 0.2265

2017 b*(D65)

1 0.496 -0.4629 0.2086 -0.0662 -0.13 0.2236 0.1095 -0.1119 0.0317 0.0477

2017 a*(D65)

1 -0.259 -0.5996 0.6147 -0.009 -0.396 0.3703 -0.269 -0.2611 0.2643 -0.0412 -0.1591

2017 L*(D65)

193

Table 2: Bloom dates, ripening dates (first and last blue), days to anthesis (DTA) of RA individuals that differ significantly from the rest of the 2017 population, population statistics,

DTA, and bloom and ripening dates of general RA population.

RA Early Bloom, First Blue Early Bloom Date First Blue Date DTA 113 3/7/2017 5/15/2017 69 139 3/7/2017 5/14/2017 68 210 3/7/2017 5/12/2017 66 232 3/7/2017 5/14/2017 68 307 3/7/2017 5/15/2017 69 RA Early Bloom, Last Blue (latest) Early Bloom Date Last Blue Date DTA 145 3/7/2017 6/4/2017 89 172 3/7/2017 6/5/2017 90 221 3/7/2017 6/5/2017 90 RA Late Bloom, First Blue Late Bloom Date First Blue Date DTA 92 4/14/2017 5/15/2017 31 244 4/14/2017 5/15/2017 31 324 4/14/2017 5/15/2017 31 360 4/14/2017 5/15/2017 31 RA Late Bloom, Last Blue (latest) Late Bloom Date First Blue Date DTA 301 4/19/2017 6/11/2017 53 264 4/19/2017 6/12/2017 54 294 4/19/2017 6/12/2017 54 306 4/19/2017 6/12/2017 54 364 4/19/2017 6/12/2017 54 363 4/19/2017 6/14/2017 56 RA Population Stats Days to Anthesis Maximum DTA 90.00 Minimum DTA 41.00 Average DTA 58.28 Standard Deviation DTA 7.98 Bloom and Ripening Dates Dates Earliest Bloom 3/7/2017 Latest Bloom 4/19/2017 Earliest Ripening 5/12/2017 Latest Ripening 6/15/2017

194

Table 3: Classification of species in diversity panel based on section.

Sections: Cyanococcus Batodendron Herpothamnus Hemimyrtillus Pyxothamnus Polycodium Species: V. angustifolium (4x) V. arboreum (2x) V. crassifolium (2x) V. arctostaphylos (4x) V. consanguineum (4x) V. stamineum (2x) V. caesariense (2x) V. ovatum (2x) V. corymbosum (4x) V. darrowii (2x) V. elliottii (2x) V. formosum (4x) V. fuscatum (2x) V. myrsinites (4x) V. myrtilloides (2x) V. pallidum (2x) V. tenellum (2x) V. virgatum (6x)

195

Table 4: Filtering impacts on variant calls generated. Minimum Depth 3, Minimum Depth 20, Maximum Depth 1000 Maximum Depth 1000

Genotype 2 genotypes 4 genotypes 2 genotypes 4 genotypes Tables homozygous homozygous homozygous homozygous and and and and opposite opposite opposite opposite SNPs Among 20,238,008 14,800,476 15,734,682 13,314,965 All 29 Genotypes SNPs Among 19,672,735 13,360,832 14,171,926 11,617,822 18 Diploid Genotypes SNPs Among 9,663,151 2,551,255 5,426,145 2,075,357 8 Tetraploid Genotypes SNPs Among 7,243,000 922,895 3,175,730 620,465 7 Tetraploid Genotypes w/o Vaccinium arctostaphylos SNPs Among 503,283 N/A 78,172 N/A 3 Hexaploid Genotypes

196

Table 5: Filtering of SNPs identified in BLAST alignment.

Filtering Method & Parameters Resulting SNP Count BLAST parser output filtered 99% Identity 3,644,719 using in-house parsed result 100 Length filter In-house high-quality SNP - 1,767,905 extractor filter

197

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