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- QTL ANALYSIS OF FRUIT COLOR AND ESTIMATION OF
GENETIC DIVERSITY USING DNA MARKERS IN SWEET
CHERRY (Prunus avium L.)

presented by

SUNETH SITHUMINI SOORIYAPATHIRANA

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QTL ANALYSIS OF FRUIT COLOR AND ESTIMATION OF GENETIC
DIVERSITY USING DNA MARKERS IN SWEET CHERRY
(Prunus avium L.)

By

Suneth Sithumini Sooriyapathirana

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Plant Breeding, Genetics and Biotechnology

2009

ABSTRACT
QTL ANALYSIS OF FRUIT COLOR AND ESTIMATION OF GENETIC
DIVERSITY USING DNA MARKERS IN SWEET CHERRY
(Prunus avium L.)

By

Suneth Sithumini Sooriyapathirana

Fruit color is an important indicator of sweet cherry fruit maturity and
distinguishes two major market classes, e. g. yellow skin and fruit with a pink blush on
the skin, and dark mahogany colored skin and flesh. Yet, within these extremes, there is
a continuum of flesh and skin color types. The genetic control of skin and flesh color in
sweet cherry was investigated using a QTL approach with a population derived from a
cross between parents representing the two color extremes. Skin and flesh colors were
measured from the progeny using a qualitative color card rating in 2006, 2007 and
2008. In 2008, color was also evaluated quantitatively for lightness (L*), redness (a*),
and yellowness (b*). The skin and flesh color card ratings for the three years were
significantly correlated (P<0.0001) and therefore only the 2008 data were used in the
genetic analyses. Progeny segregations for the color measurements (card, L*, a*, b*)
did not fit normal distributions; instead the distributions were skewed towards the skin
color of the dark-skinned parent. A major QTL for skin and flesh color was identified
on Linkage Group (LG) 3 and three other QTLs for skin and flesh color were identified
on LGS, LG6 and LG8. However, the consistent significance of the QTL identified on
LG3 suggests the presence of a major regulatory gene for fruit color development.

The genetic diversity of sweet cherry (Prunus avium L.) germplasm historically

used in the breeding programs of Pacific North West region in North America was

studied in comparison to a subset of European sweet cherry landraces and a wild cherry
(P. avium) selection to test the hypothesis of genetic founder effect that occurred when
early settlers brought selected subset of sweet cherry germplasm from Europe to the
New World. Pacific North West sweet cherry germplasm was defined as a set of 28
landraces, parents and released cultivars. A subset of seven European sweet cherry
landraces and a single wild cherry selection were used for the comparison. The
genotypic data for all 36 sweet cherry selections were recorded for 77 DNA markers. A
total of 300 alleles were detected for 77 markers with an average of four alleles per
locus. A total of 52 unique alleles were identified and 40 of them were not present in the
Pacific North West sweet cherry germplasm. The 50% of the total alleles detected were
rare alleles and 30% of the total rare alleles were not detected in the Pacific North West
sweet cherry germplasm. The European landraces were distantly related at 25% of
genetic dissimilarity value but Pacific North West sweet cherry parents and cultivars
were separated only at 8% of genetic dissimilarity value showing the low level of
diversity compared to European sweet cherry landraces and the wild cherry selection.
This study shows that Pacific North West sweet cherry germplasm had been subjected
to genetic founder effect and implies that the introduction of new germplasm from
Europe is necessary to broaden the genetic diversity in the Pacific North West sweet

cherry germplasm.

 

COPYRIGHT

SUNETH SITHUMINI SOORIYAPATHIRANA

2009

DEDICATION

To my wife Chamila Kumari Pathirana

 

ACKNOWLEDGEMENTS

I am enormously grateful to my major professor Dr. Amy F. Iezzoni for
accepting me to her research group, for her supervision, inspiration, courteous help and
fmancial assistance throughout my study. I express my deepest gratitude to Dr. James F.
Hancock for his guidance, motivation, friendliness and financial assistance. I am very
grateful to Dr. J. Mitchell McGrath for his insight and teaching me the complex aspects
of molecular biology and genomics. I thank and sincerely appreciate Dr. Dechun Wang
for teaching me the statistical genetics and his directions throughout my research
project.

I appreciate and thank Audrey M. Sebolt for her expertise, help and kindness. I
give special thanks to Peter W. Callow for his support, generosity and friendliness. I am
very grateful to Dr. Janet M. Lewis and Karolyn A. Terpstra for offering me teaching
assistantships.

I appreciate the support rendered by Sue A. Hammar, Dr. Guorong Zhang, Dr.
Veronica A. Vallejo, Dr. Guo-Qing Song and Travis L. Stegmeir. I am also grateful to
the administrative staff, Lorri K. Busick, Rita M. House, Sherry M. Mulvaney, Joyce T.
Lockwood and Sharon Roback for their support. I also thank all the faculty, staff and
students of the Plant Breeding, Genetics and Biotechnology Graduate Program at
Michigan State University for their support, guidance and companionship throughout
my graduate student life.

I sincerely thank Dr. S. H. P. Parakrama Karunaratne and Dr. Preminda

Sarnaraweera, Faculty of Science, University of Peradeniya, Sri Lanka for their

vi

 

motivation, advices and granting study leave for me to undertake graduate research
studies at MSU.

I warmly thank my wife, Chamila Kumari Pathirana for her unwavering support,
guidance, motivation and patience. Her constant nurture and inspiration were the key
forces behind my success. I dedicate this thesis to Chamila for her love and affection.
Finally I graciously mention and thank my wife Chamila and our two little sons, Saritha

Hansana and Ranuga Sanhitha, for keeping my life happy and pleasant.

vii

TABLE OF CONTENTS

LIST OF TABLES ................................................................................................... x
LIST OF FIGURES ................................................................................................. xiii
LITERATURE REVIEW ....................................................................................... 1
GENETICS OF FRUIT SKIN AND FLESH COLOR IN SWEET CHERRY ..... 2
Importance of fruit color in cherry industry .................................................. 2
Variability of fruit color in sweet cherry ...................................................... 2
Genetics of fruit color in sweet cherry .......................................................... 3
Fruit color pigments in sweet cherry ............................................................ 3
Factors affecting color development in sweet cherry ................................... 3
Variability of fruit color in apple .................................................................. 4
Genetics of fruit color in apple ..................................................................... 4
Biochemistry and molecular genetics of fruit color in apple ........................ 5
Fruit color studies in other rosaceous crops .................................................. 6
Chapter one: Goal ......................................................................................... 8
LTERATURE CITED .................................................................................. 9
GENETIC DIVERSITY IN SWEET CHERRY GERMPLASM .......................... 13
Background ................................................................................................... 13
Origin and geographical range ...................................................................... 13
Genetic diversity ........................................................................................... 14
Breeding ........................................................................................................ 1 5
Chapter two: Goal ......................................................................................... 16
LITERATURE CITED ................................................................................. 17

CHAPTER ONE: QTL ANALYSIS OF FRUIT SKIN AND FLESH

COLOR IN SWEET CHERRY (Prunus avium L.) ................... 19
INTRODUCTION ............................................................................................... 20
MATERIALS AND METHODS ........................................................................ 22

Plant material ................................................................................................ 22
Fruit sampling and evaluation ....................................................................... 22
Statistical analysis for color measurements .................................................. 26
QTL analysis ................................................................................................. 26
RESULTS AND DISCUSSION ......................................................................... 28
Color data ...................................................................................................... 28
Color development ........................................................................................ 33
Data distribution ............................................................................................ 35

viii

 

QTL analysis ................................................................................................. 42
QTL haplotypes ............................................................................................ 52
Epistasis ........................................................................................................ 61
CONCLUSION ................................................................................................... 71
LITERATURE CITED ....................................................................................... 72

CHAPTER TWO: GENETIC DIVERSITY ANALYSIS OF SWEET
CHERRY (Prunus avium L.) CULTIVARS USING

 

DNA MARKERS .................................................................... 75
INTRODUCTION .............................................................................................. 76
MATERIALS AND METHODS ....................................................................... 78

Plant materials .............................................................................................. 78
DNA extraction and genotyping ................................................................... 78
Data analysis ................................................................................................. 79
RESULTS AND DISCUSSION ........................................................................ 82
Unique and rare alleles ................................................................................. 82
Allele diversity and cultivar heterozygosity ................................................. 82
Genetic diversity structure ............................................................................ 106
Graphical genotypes for sweet cherry cultivars ........................................... 109
A panel of cultivars for SNP discovery for P. avium ................................... 144
Related studies on genetic diversity in Prunus ............................................ 147
Marker polymorphism .................................................................................. 151
A panel of DNA markers for P. avium DNA fingerprinting ........................ 154
Use of DNA markers for diversity studies ................................................... 154
CONCLUSION ............................................................................................ 156
LITERATURE CITED ................................................................................. 157

ix

 

LIST OF TABLES

Table 1.1: Description of the color card categories for fruit skin color in sweet
cherry used for QTL analysis ................................................................. 24

Table 1.2: Description of the color card ratings for fruit flesh color in sweet
cherry used in the QTL analysis .............................................................. 25

Table 1.3: Pearson’s correlation coefficients for skin color 1 (SCl), skin color
2 (SC2), and flesh color (FC) card readings from the NY X EF
progeny in 2006, 2007 and 2008 ............................................................. 30

 

Table 1.4: Pearson’s correlation coefficients for skin color 1 (SCI), skin color
2 (SC2) and flesh color (FC) card and L*, a*, and b* values for NY
>< EF progeny evaluated in 2008 .............................................................. 31

Table 1.5: Means and standard deviations for skin color 1 (SCI), skin color 2
(SC2), and flesh color (FC) values for EF and NY in 2008 .................... 32

Table 1.6: The progression of fruit skin and flesh color over harvest data for
blush and mahogany classes of NY54 x EF progeny for year 2008 ....... 34

Table 1.7: Summary statistics and heritability of color data for year 2008 ............. 41

Table 1.8: QTLs for color card values, L*, a* and b* for SC], SC2 and FC
identified in the NY >< EF F1 population in 2008 data ............................ 44

Table 1.9: Definitions of parental haplotypes for five QTL regions on linkage
groups 3, 5, 6 and 8 ................................................................................... 55

Table 1.10: Card and a* of skin color 1 (SCI), Card and b* of skin color 2
(SC2) and Card, L8 and b* of flesh color (F C) of different genotype
classes for the major QTL on linkage group 3 at 53.7 cM. Numbers
in parenthesis are the number of progeny individuals ........................... 56

Table 1.11: L* and b* of skin color 1 (SCI), L* and a* of skin color 2 (SC2)
and a* of flesh color (FC) of different genotype classes for the
minor QTL on linkage group 3 at ~21.0 cM. Numbers in
parenthesis are the number of progeny individuals ............................... 57

Table 1.12: b* of skin color 1 (SCI) of different genotype classes for the QTL
region on linkage group 6. Numbers in brackets are the number of
progeny individuals ................................................................................ 58

Table 1.13: a* of flesh color (F C) of different genotype classes for the QTL
region on linkage group 5. Numbers in brackets are the number of
progeny individuals ................................................................................ 59

Table 1.14: a* of flesh color (FC) of different genotype classes for the QTL
region on linkage group 8. Numbers in brackets are the number of
progeny individuals ................................................................................ 60

Table 2.1: The Prunus avium groups, selections (wild, Non-PNW and PNW),
their parents, origins, number of unique alleles (U A) and % of
heterozygous loci (H) ............................................................................. 85

Table 2.2: The relative abundance of alleles with differing frequencies
detected for 77 DNA markers ................................................................ 87

Table 2.3: The percentage of heterozygous loci (H) per linkage group .................... 88

Table 2.4: The possession of marker-alleles by sweet cherry selections - LG1 ........ 89

Table 2.5: The possession of marker-alleles by sweet cherry selections - LG2 ........ 90

Table 2.6: The possession of marker-alleles by sweet cherry selections - LG3 ........ 91

Table 2.7: The possession of marker-alleles by sweet cherry selections - LG4 ........ 92

Table 2.8: The possession of marker-alleles by sweet cherry selections - LG5 ........ 93

xi

Table 2.9: The possession of marker-alleles by sweet cherry selections - LG6 ........ 94

Table 2.10:

Table 2.11:

Table 2.12:

Table 2.13:

Table 2.14:

Table 2.15:

The possession of marker-alleles by sweet cherry selections - LG7 ...... 95

The possession of marker-alleles by sweet cherry selections - LG8 ...... 96

The panel of selections/individuals for SNP detection in P. avium ....... 146

The comparison of number of alleles per SSR marker and
heterozygosity (H) of three SSR between sweet and flowering
cherries ................................................................................................... 148

The number of alleles detected for sweet and tart cherries for 6
SSR markers ........................................................................................... 150

Heterozygosity (H) and Polymorphic Information Content (PIC) of
DNA markers used in the study ............................................................. 153

xii

Figure 1.1:

Figure 1.2:

Figure 1.3:

Figure 1.4:

LIST OF FIGURES

A-L Progeny frequency distribution of color traits measured in
2008 (A) SCI card. (B) SC2 card. (C) PC card. (D) SCI L*. (E)
SC2 L*. (F) FC L*. (G) SCI a*. (H) SC2 a*. (1) PC a*. (J) SCI
b*. (K) SC2 b*. (L) FC b*. EF and NY parental values are shown.

A—D Locations of QTLs for color card and L*, a* and b* values
for SCI (darkest location of the fruit skin), SC2 (lightest location
of the fruit skin) and F C (flesh color) using the multiple QTL
mapping method. The variability explained by QTL (R2%) is
shown after the trait name of each QTL. 1-LOD and 2-LOD
support intervals of each QTL are marked by thick and thin bars,
respectively. Blank bars represent QTLs for color card QTLs.
Black bars represent QTLs for L*. Bars filled with one sided
hatch lines represent QTLs for a*. Bars filled with two sided hatch
lines represent QTLs for b*. Only linkage groups including the
QTLs are presented. (A) Linkage group 3, (B) Linkage group 5,
(C) Linkage group 6, (D) Linkage group 8. LOD scores and the
percentage variability explained by the QTLs (R2) are presented
in the Table 1.8 .................................................................................

Locations of QTLs on LG3 for color card data of 2006, 2007 and
2008 for SCl (darkest location of the fruit skin), SC2 (lightest
location of the fruit skin) and FC (flesh color) using the multiple
QTL mapping method. Blank bars represent QTLs. 1-LOD and 2-
LOD support intervals of each QTL are marked by thick and thin
bars, respectively. The percentage variability explained by the
QTL (R2) and the level of QTL significance in number of stars
(***: LOD value significant at P < 0.05 genome wide, **: LOD
value significant at P < 0.1 genome wide, * : The LOD value
significant at P <0.05 individual linkage group wide, based on
1000 permutation tests) are shown with the QTLs. ..........................

A-E. The two-way inter genomic region interactions between the
major QTL and the minor QTL regions on LG3 (A) Inter loci
interaction for SCl L* between PR41 and Ma066a (B) Inter loci
interaction for SCl b* between PR41 and Ma066a (C) Inter loci
interaction for SC2 L* between PR41 and Ma066a (D) Inter loci
interaction for SC2 a* between PR41 and Ma066a (E) Inter loci
interaction for F C a* between PR41 and Ma066a. Means denoted
by same letter within each graph are not significantly different at

xiii

...... 36

...... 45

...... 50

 

Figure 1.5:

Figure 2.1:

Figure 2.2:

Figure 2.3:

Figure 2.4:

P < 0.05 (done using Least Squares Means, General Linear Model
SAS 9.1). Units: L*, a* and b* (colorimeter reading) ............................ 63

A-C. The two-way inter genomic region interactions between the

major QTL region on LG 3 and the other QTLs on LGs 5, 6 and 8.

(A) Inter loci interaction for F C a* between PR41 on LG 3 and

PS1H3 on LG8. (B) Inter loci interaction for PC a* between PR41

on LG 3 and BPPCT026 on LG5 (C) Inter loci interaction for SCI

b"‘ between PR41 on LG 3 and UDP96-001 on LG6 Means

denoted by same letter within each graph are not significantly

different at P < 0.05 (done using Least Squares Means, General

Linear Model SAS 9.1). Units: L*, a* and b* (colorimeter

reading) ................................................................................................... 67

The number of unique and shared alleles identified in the three

groups of sweet cherry used in the study; PNW: 28 cultivars

historically used and released in the Pacific North West sweet

cherry breeding programs, Non-PNW: seven sweet cherry

cultivars from Europe that have not been used in the PNW sweet

cherry breeding programs and Wild: one forest (mazzard) cherry

(Prunus avium) selection (N Y54) ........................................................... 86

A-H. The different alleles for the markers and their relative

presence in all the linkage groups for 36 sweet cherry selections

{wild cherry (gray bar), PNW (white bar) and non-PNW (black

bar) groups}. The arrows show the alleles that do not exist in the

PNW sweet cherry cultivars and the names of the cultivars are

indicated near the arrows. A: linkage group (LG) 1, B: LG2, C:

LG3, D: LG4, E: LG5, F: LG6, G: LG7, H: LG8 .................................. 97

Dendrogram resulting from marker allele based genetic distance

analysis of 36 sweet cherry selections. Cluster analysis used

McQuitty linkage, Absolute Correlation Coefficient Distance

(Minitab 15) ............................................................................................ 107

Figure 2.4: A-H Graphical genotypes for 36 sweet cherry
cultivars. Eight linkage groups for each cultivar are shown with
two homologous chromosomes for each linkage group. The
marker positions in centi Morgan (cM) and marker names are
shown on the left. In each cell, the allele in base pairs is shown for
the SSR and gene based (PR markers and the allele name in

xiv

 

number is shown for the S-locus. 558 indicates a confirmed null
allele and 8 indicates an unconfirmed null allele. “ -” represents
the missing data. The blank cells represent the gaps in the linkage
groups. (A) Linkage group 1, (B) Linkage group 2, (C) Linkage
group 3, (D) Linkage group 4, (E) Linkage group 05, (F) Linkage
group 6, (G) Linkage group 7, (H) Linkage group 8. Each linkage
group has four pages of GGT to represent 36 sweet cherry
selections in four separate pages. Page 1 for each linkage group:
Wild cherry (N Y54) and non-PNW sweet cherry cultivars, Page 2
for each linkage group: second subset of PNW sweet cherry
cultivars, Page 3 for each linkage group: third subset of PNW
sweet cherry cultivars, Page 4 for each linkage group: fourth
subset of PNW sweet cherry cultivars ................................................... 111

XV

LITERATURE REVIEW

GENETICS OF FRUIT SKIN AND FLESH COLOR IN SWEET CHERRY

Importance of fruit color in cherry industry

Fruit color is one of the most important traits in determining consumer demand in
sweet cherry (Prunus avium L.). Dark mahogany colored sweet cherries are preferred in
North America (Turner 2008) and Europe (Werrnund and Feame 2000) and blush colored
sweet cherries are preferred in Asia (Miller et al. 1986). The color of fi'uit skin and flesh
is also important to determine the maturity level of fruits (Facteau et al. 1983). Breeding
for sweet cherry cultivars with desired fruit colors is challenging, because, the underlying

genetics of skin and flesh color traits have not been studied in detail.

Variability of fruit color in sweet cherry

The phenotypic diversity of fruit skin and flesh color of sweet cherry is very high.
Fruit skin and flesh colors range from dark mahogany skin and flesh (e. g. cultivar
“Bing”) and yellow skin and flesh (e.g. cultivar “Gold”). There are blushed fruit cultivars
with red/mahogany shades in yellow background and yellow flesh (e.g. cultivar
“Rainier”). The classification of sweet cherry skin and flesh into color classes is
dependent upon the level of fruit maturity. Dark skinned fruits get darker with time and
their flesh follows the same pattern of the color development in skin. In blushed fruits,
the red shades get more prominent in the skin and the flesh color remains unchanged with

maturity.

 

Genetics of fruit color in sweet cherry

Classical genetic approaches were used to understand the genetics of fruit color in
sweet cherry and postulated that the skin color is controlled by one major factor (Aa) and
one minor factor (Bb) and incomplete dominant epistasis was also suggested for the
interaction between A and B. Factor A was also proposed to be responsible for
controlling the flesh color (F ogle 1958 and Schmidt 1998). The data from European
breeding populations supported this genetic model (Hedtrich 1985; Georgiev 1985;

Rodrigues et al. 2008; and Tobutt and Boskovic 1996).

Fruit color pigments in sweet cherry

The color of cherries, either sweet or tart (P. cerasus L.) is mainly due to
anthocyanins. Red sweet cherry cultivars mainly contain Cyanidin-3-0-rutinoside (95%
of total anthocyanin) and cyanidin-3-0-g1ucoside. Red sour cherry cultivars such as
‘Balaton’ and ‘Montmorency’ have mainly Cyanidin-3-0-glucosylrutinoside and
cyaniding-3-0-rutinoside and minor quantities of cyanidin-3-0-glucoside. The blush

cultivars have carotenoids such as beta-carotene (Mulabagal et al. 2009).

Factors affecting color development in sweet cherry
Fruit skin and flesh color is affected by environment to a certain degree. The
environmental effect on the color development is higher in blush cherries than in dark

mahogany colored cherries. Application of gibberellic acid has no significant impact on

 

the fruit color in cherry (Horvitz 2003). In blush sweet cherries, UV light stimulates the
anthocyanin synthesis (Arakawa 1993). This explains the fact that the blush cherries that
are located inside the canopy are less colorful than the cherries on the outer canopy, as

leaves absorb most of the UV light before reaching the interior canopy.

Variability of fruit color in apple

The fruit color of apple (Malus x domestica) is well studied, and as apple and
cherry belong to the same family, Rosaceae, the recent advancements of fruit color
genetics in apple are applicable to study the fruit color genetics in sweet cherry. Apple
skin color has a wide array of phenotypic diversity ranging from green, yellow and dark
purple. The shaded combinations of different colors can also be seen. Lancaster (1992)
reported that combinations of carotenoids, chlorophyll and anthocyanins determine the

various skin colors in apple.

Genetics of fruit color in apple

The postulated mechanisms for genetics of skin color in apple are not in common
agreement. A single dominant gene model was suggested for dark red skin (Brown 1992
and Crane and Lawrence 1933). Klein (1958) found that anthocyanin stripes of apple skin
color are controlled by one major gene. White and Lespinasse (1986) suggested two

complementary genes, A and B. Later Lespinasse et al. (1988) proposed a three major

gene model for determination of apple skin color. Schmidt (1988) postulated additional

modifying factors.

Biochemistry and molecular genetics of fruit color in apple

The molecular studies on apple color genetics had started in the late 20th century.
A RAPD marker was found to be linked to apple skin color (Cheng et al. 1996). Apple
has Cynidin 3- O-galactoside as the major form of anthocyanin (Lancaster 1992, Tsao et
al. 2003). Many genes associated with the anthocyanin biosynthetic pathway have been
cloned from apple fruit skin; flavonone 3-hydroxylase (F3 H), dihydroflavonol reductase
(DF R), anthocyanin synthase (ANS) and UDP-glucose flavonoid 3-Oglucosyltransferase
(UFGT) (Honda et al. 2002, Kim et al. 2003). These genes have found to be light induced
and highly expressed in red apple skins. Takos et al. (2006), Espley et al. (2007) and Ban
et al. (2007) have shown that one or two MYB transcription factors are playing the
central role in apple fruit skin color genetics. MdMYBA, a cDNA encoding a putative
R2R3-MYB protein (Ban et al. 2007), regulated anthocyanin biosynthesis in apple skin,
has a huge similarity to MdMYBl which was independently discovered by Takos et al.
(2006). The only marked difference is that these two genes are differentially expressed at
young stages of the fruit growth. Another MYB gene, MdMYBI 0 found by Espley et al.
(2007) that has some significant differences in expression relative to MdMYBA or
MdMYBI. Ban et al. (2007) speculated that there would be at least two MdMYB loci
active in anthocyanin biosynthesis in apple skin. Polymorphism at the MdMYBa has been

mapped to Linkage Group 9 in apple ‘Delicious’ (Ban et al. 2007). Chagne et al. (2007)

 

 

found that red flesh and foliage color of apple co-segregated. The allele controlling the
red color has been named as Rni, has been mapped along with MdMYBI 0 to a single
locus Linkage Group 9 in apple. The expression of these MYB genes are UV light
defendant and low temperature induced. Espley et al. (2009) showed that a rearrangement
in the promoter region of MdMYBI 0, a microsatellite like structure with tandem repeats
of 23-bp sequence caused red phenotype in apple flesh and foliage. This motif is a target
for the MdMYBIO protein itself and hence provides an autocatalytic regulation. This
autocatalytic regulation ensures the accumulation of MdMYBI 0 protein and
accumulation of anthocyain throughout the plant. The MdMYB transcription factor
closely interacts with bHLH, another transcription factor that regulates anthocyanin
biosynthetic pathway genes. The specific genes targeted by transcription factor
complexes in apple have not been found. In Arabidopsis and grapes, such targets have
been reported (Borevitz et al. 2000, Tohge et al. 2005, Kobayashi et al. 2002). The
molecular genetic information of these studies has an immense importance in studying

the fruit color genetics of sweet cherry.

Fruit color studies in other rosaceous crops

Compared to the color work in apple, the skin color of peach and other rosaceous
fruits has not been studied in detail. Fruit skin and flesh color of peach has very high
phenotypic diversity; yellow to red skin and white to red flesh. Connors (1920) described
that an allele, Y, that controls white flesh is dominant to yellow (y) flesh in peach.

Beckman et a1. (2005) found allele, h, (highlighter) suppresses red color. The genetic

correlation between Y and h is not known. Beckman and Sherman (2003) showed full red
phenotype is controlled by fr. Dark red flesh is determined by a single gene, bf (Werner et
al. 1998). Skin color trait has been mapped to linkage group six (Dirlewanger et al. 2004,
Yamamoto et al. 2001). Peach color is mainly due to carotenoids and beta carotene is the

main form of carotenoid followed by beta-cryptoxanthin (Gil et al. 2002).

The correlation between carotenoid accumulation and the expression of
carotenogenic genes in Japanese Apricot (P. armem'aca L.) has been established (Kita et
al. 2007). Phytoene synthase-1 and lycopene B-cyclase expression is required for
carotenoid accumulation. Decrease in lycopene e-cyclase expression and increase in
lycopene B-cyclase causes a metabolic shift from synthesis of B-e-carotenoid to synthesis
of B, [3 carotenoid with ripening progresses. Ethylene is important for the primary
induction of Phytoene synthase-1. Kassim et al. (2009) mapped the polymorphisms of
several transcription factors and candidate genes of anthocyanin biosynthetic pathway to

QTLs in raspberry, Rubus idaeus L., another important rosaceous fruit species.

The genetics of fruit color in rosaceous crops is a fast developing area and the
advancements in apple, peach, apricot and raspberries could be applied to understand the

fruit color genetics of other rosaceous crops such as sweet cherry.

Chapter One: Goal

The aim of the Chapter One was to identify the genomic regions that are
associated with fruit skin and flesh color in sweet cherry. This study was the first attempt
to utilize the Quantitative Trait Loci (QTL) approach to dissect the genes related to fruit
color in cherry. The expected results would enable us to understand the genetic
mechanisms of fruit color in cherry and will be useful in marker assisted breeding for
sweet cherry varieties with desired fruit colors and to further unravel the molecular

genetic basis of fruit color in sweet cherry.

 

 

LITERATURE CITED

Arakawa O (1993) Effect of ultraviolet light on anthocyanin synthesis in light-colored
sweet cherry, cv. Sato Nishiki. J Japan Soc Hort Sci 62:543—546

Ban Y, Honda C, Hatsuyama Y, Igarashi M, Bessho H, Moriguchi T (2007) Isolation and
functional analysis of a MYB transcription factor gene that is a key regulator for the
development of red coloration in apple skin. Plant Cell Physiol 48:958—970

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Fogle HW (1958) Inheritance of fruit color in sweet cherries {Prunus avium). J Heredity
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11

 

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12

GENETIC DIVERSITY IN SWEET CHERRY GERMPLASM

Background

The cherry is one of the most important temperate fruit crops in the world. There
are two types of cherries. Sweet cherry (Prunus avium L.), is eaten fresh and its wild
forms (i.e. mazzards) are used as a timber source and sour cherry (Prunus cerasus L.) is
mainly used in processed food products. 375,000 hectares (Ha) of sweet cherry (with
1,896,000 Metric tons (Mt) of fruit harvest) and 248,000 Ha of tart cherry (with
1,035,000 Mt of fruit harvest) are grown worldwide (FAO 2005). The cost of production
for cherry is quite high and various breeding programs around the world are operating to
produce improved cultivars (Iezzoni 2008). The breeding for improved cultivars is
dependent upon the successful introgression of desired traits from the land races and wild

relatives of cherry.

Origin and geographical range

Sweet and sour cherries were originated in Central Asia (Vavilov 1951) and
slowly spread to parts of Europe. The natural range of cherries includes temperate regions
of Europe and south eastern Russia (Hedrick et al. 1915). Today, sweet cherry is
cultivated in more than 40 countries representing temperate to subtropical climates.
However, sour cherry is less widely spread compared to sweet cherry, and mainly grown

in Europe and USA. (Dirlewanger et al. 2007).

13

Genetic diversity

The genetic diversity of sweet cherry is represented by wild forest cherries (i.e.
mazzards), land races, cultivars, plant materials available from the crosses from the
breeding programs, other related species (i.e. sour, ground and duke cherries) and other
wild cherry species in family Rosaceae. Much of the genetic diversity is available from
the wild forms and landraces from the center of origin. The introgression of these exotic
germplasm to Pacific North West sweet cherry breeding is important to produce
improved cultivars. However, understanding the genetic distance between exotic and

Pacific North West sweet cherry gerrnplasms is important for successful introgression.

Sweet cherry is strictly self-incompatible, which promotes 100% out breeding (de
Nettancourt 2001), thus, very high genetic heterozygosity is expected within the
germplasm. However, vegetative propagation through grafting has fixed heterozygosity

within cultivars, limiting the chance events of increasing the diversity in orchards.

The genetic diversity of sweet cherry has been examined for various objectives
but none of the studies were aiming to find the genetic distance between Pacific North
West and European sweet cherry cultivars (Brettin et a1. 2000; Dirlewanger, et al. 2002).
The most studied area of the genetic diversity in sweet cherry is the diversity of S-alleles.

Sonneveld et al. (2003), De Cuyper et al. (2005), Wunch and Hormaza (2004) and

Vaughan et al. (2008) reported 31 S-alleles (S 1 -S7, 59-532) in sweet cherry.

14

Breeding

Breeding is quite slow compared to other rosaceous fruit crops like apple and
peach. The main breeding goals for sweet cherry is large fruit size, high fruit quality,
short juvenile phase, self compatibility, rain cracking resistance and pest and disease
resistance (Dirlewanger et al. 2007). Even though the classical breeding programs are
slow, many cultivars have been made available to the growers and breeders to use them
as parent materials. However, these cultivars are selections from the natural populations
or just one generation away from the wild progenitors (Iezzoni et al. 1990). The long
generation time, self incompatibility and small number of seeds per cross, make cherry

breeding a difficult task.

Recently, marker assisted breeding was introduced to address some of the
difficulties in breeding but it is still in the developing phase. The most important
accomplishment in sweet cherry breeding has been the introduction of self compatibility
through mutational breeding (Lewis and Crowe 1954) and the ability to genotype
cultivars for S-alleles by using DNA fingerprinting to select and grow sufficient number

of polleniser-trees in the cherry orchards.

15

Chapter Two: Goal

The aim of the Chapter Two was to assess the genetic diversity of Pacific
North West sweet cherry germplasm in comparison to a set of European sweet cherry
land races and a wild cherry selection which have not been introduced to the Pacific
North West sweet cherry breeding. This study used allele data from 77 DNA markers,
identified unique alleles and constructed graphical genotypes for all the Prunus avium
selections used. The future marker assisted breeding programs and genetic diversity

studies on Prunus will be immensely benefited from the findings of this study.

16

LITERATURE CITED

Brettin TS, Karle R, Crowe EL, Iezzoni AF (2000) Chloroplast inheritance and DNA
variation in sweet, sour and ground cherry. J Heredity 91 :75—79

De Cuyper B, Sonneveld T, Tobutt KR. 2005 Determining self-incompatibility
genotypes in Belgian wild cherries. Mol Ecol 14:945—955

de Nettancourt D (2001) Incompatibility and incongruity in wild and cultivated plants,
Springer, Berlin

Dirlewanger E, Claverie, J, Wunsch A, Iezzoni AF (2007) Cherry. Genome Mapping and
Molecular Breeding in Plants. Vol 4: fruits and nuts, Ed. Kole C, Springer p. 104—1 18

Dirlewanger E, Cosson P, Tavaud M, Aranzana J, Poizat C, Zanetto A, Arus P, Laigret F
(2002) Development of microsatellite markers in peach [Prunus persica (L.) Batsch]

and their use in genetic diversity analysis in peach and sweet cherry (Prunus avium
L.). Theor Appl Genet 105:127—138

F A0 (2005) Food and Agricultural Organization of the United Nations.
http://faostat.fao.org/site/336/default.aspx

Hedrick UP (1915). The history of cultivated cherries. In: Hedrick UP, Howe GH, Taylor
OM, Tubergen CB, Wellington R (eds) The Cherries of New York. JB Lyon
company, State Printers, Albany, NY pp 3964

Iezzoni AF (2008) Cherries. Temperate Fruit Crop Breeding. Ed: Hancock J F, Springer p.
151—176

Iezzoni A, Schmidt H, Albertini A (1990) Cherries (Prunus spp), in Genetic resources of
temperate fruit and nut crops. Eds. Moore JN, Ballington JR Jr, Int Soc Hortic Sci.
Wageningen, The Netherlands p. 111—173

17

Lewis D, Crowe LK (1954) Structure of the incompatibility gene. IV Types of mutation
in Prunus avium L. Heredity 8:357—363.

Sonneveld T, Tobutt KR, Robbins TP (2003) Allele-specific PCR detection of sweet
cherry self-incompatibility (S) alleles S] to S 16 using consensus and allele-specific
primers. Theor Appl Genet 107:1059—1070

Vaughan SP, Boskovic RI, Gisbert-Climent A, Russell K, Tobutt KR (2008)
Characterization of novel S-alleles from cherry (Prunus avium L.). Tree Genet
Genomes 4:531—541

Vavilov NI (1951) The origin, variation, immunity and breeding of cultivated plants.
Ronald, New York

Wunsch A, Hormaza J I (2002) Molecular characterisation of sweet cherry (Prunus avium
L.) genotypes using peach [Prunus persica (L.) Batsch] SSR sequences. Heredity
89:56—63.

18

CHAPTER ONE

QTL ANALYSIS OF FRUIT SKIN AND FLESH COLOR IN SWEET CHERRY

(Prunus avium L.)

19

INTRODUCTION

Sweet cherry exhibits a continuous range of fruit skin and flesh colors from the
dark mahogany color skinned and fleshed types, to those that have yellow skin with a red
blush and yellow flesh. This variation in sweet cherry fruit skin and flesh color is used to
classify different market types and to determine fruit maturity (Facteau et al. 1983). For
example, dark mahogany cherries such as ‘Bing’ are favored in the majority of markets
(Miller et al. 1986; Lyngstand and Sekse 1995; Wermund and F earne 2000; Crisosto et
al. 2003, Turner et al. 2007); however, blushed skinned and yellow fleshed sweet cherries

such as ‘Rainier’ are preferred in Asia.

Despite the importance of fruit skin and flesh color in sweet cherry, the genetic
control is not well understood. Fogle (1958) and Schmidt (1998) concluded that red skin
color is dominant to yellow and proposed the presence of one major (A/a) and one minor
gene (B/b) that exhibit epistasis. A/a was also suggested to control flesh color where A-
and aa would confer mahogany and yellow flesh, respectively. The dominance of
mahogany over yellow was supported by data from European breeding populations
(Hedtrich 1985; Georgiev 1985; Rodrigues et al. 2008; and Tobutt and Boskovic 1996).
However, collectively these studies also suggested that the genetic control of cherry skin
and flesh color must involve additional minor genes to account for the wide range in

color (from light yellow, pinks, reds, to dark mahogany).

2O

To further investigate the genetic control of fruit skin and flesh color in cherry, an
existing sweet cherry linkage mapping population that was segregating for these traits,
and the available linkage map (Olmstead et al. 2008) were used for QTL analysis. The
mapping population was a pseudo testcross between the blush and yellow fleshed
Emperor Francis (EF) and dark mahogany skinned and fleshed New York 54 (NY). To
facilitate a QTL approach, fruit color was quantified using L*, a* and b* color metrics
where L* represents lightness, a* represents red/greenness, and b* represents
blue/yellowness. This L*, a* and b* colorimetric system has been used to quantify color
pigments in sweet cherries (Crisosto et al. 2003, Clayton and Biasi 2003, Usenik et al.
2005), other Prunus species (Gil et al. 2002, Kita et al. 2007) and many other plant
samples to include apple (Espley et al. 2007), tomato (Sacks and Francis 2001), and

wheat (Zhang et al. 2008).

The objective of this study was to determine the genetic control of fruit skin and

flesh color in sweet cherry utilizing a QTL approach.

21

MATERIALS AND METHODS

Plant material

The QTL analysis was based on a sweet cherry mapping population of 190
pseudo-testcross progeny individuals (~equal numbers from reciprocal crosses) from a
cross between a landrace variety ‘Emperor Francis’ (EF), and a wild ‘mazzard’ sweet

cherry ‘New York 54’ (NY). A subset of 94 progeny individuals from this population
were grafted onto Giesla® 6, a semi-dwarfing precocious rootstock, to provide a clonal

replicate. Both the original seedling population and the grafted subset were planted at the
Michigan State University Clarksville Horticultural Research Station, Clarksville, Mich.,
USA. In 2006 and 2007 all the evaluations were from fruits fi'om the original seedlings.
However, in 2008 a spring freeze killed the majority of the flowers on trees of the
original population, and fruits were only evaluated from a subset of 94 individuals
planted in a grafted plot that did not undergo freeze damage. This entire plot of 94
individuals was netted one week prior to fruit harvest to protect the ripening fruit from
bird damage and an electric fence was installed around the perimeter of the plot to deter

raccoons.

Fruit sampling and evaluation

Five fruits (one to four on the original seedlings if fewer fruit were available)
were sampled from the trees. Fruit maturity was judged by observing the luster or
dullness of the appearance of cherry fruit skin. However, because of the difficulty in

judging maturity, each progeny individual was harvested multiple times, approximately

22

twice a week for a maximum of four harvest times. The data from the multiple harvests
of each tree were compared using ANOVA to identify the maximum color potential to be

used in QTL analysis.

In all three years, color card readings were recorded from the darkest location of
the fi'uit skin (skin color 1, SCI), lightest location (skin color 2, SC2) and flesh color
(FC). Nine (0-8) and five (1-5) color card categories were used to qualitatively measure
skin color and flesh color respectively (Table1.1 and Table 1.2). Color card categories for
skin color were defined according to colors previously identified for the Sweet Cherry
Maturity Index which was manufactured by Colorcurve Systems, Inc (, East Lansing,
Mich.) and color chips from The Flower Council of Holland (FCH), Leiden, The Royal
Horticultural Society (RHS), London. Color card categories for flesh color were defined
according to Washington State University’s Sweet Cherry Flesh Color Index and The

FCH Leiden, The RHS, London.

SCI, SC2, and FC were quantitatively evaluated for lightness (L*), redness (a*),
and yellowness (b*) using a spectrophotometer (CM-2002, Minolta, Tokyo, Japan). L*
measures the range from black (lower values) to white (higher values), a* measures the
range from red (higher values) to green (lower values), and b* measures the range from

blue (lower values) to yellow (higher values).

23

Table 1.1: Description of the color card categories for fruit skin color in sweet cherry

 

 

used for QTL analysis
Correspondent
Color card Color Color classification in RHS
color category
categorya description . Color Chartb
m sweet cherry
maturity index3
0 Translucent - White — 155 D
1 Pale yellow - Yellow — 10 A
2 Orange - Grayish orange — 170 D
3 Light red - Red — 39 A
4 Red 1 Grayish red — 179 A
5 Dark red 2 Grayish red — 181 A
6 Light mahogany 3 Grayish purple — 183 A
7 Mahogany 4 Grayish purple — 187 B
8 Dark Mahogany 5 Grayish purple— 187 A

 

aSweet Cherry Maturity Index, Agricultural Engineering Department, Michigan State

University, East Lansing, MI 48824. Manufactured by Colorcurve Systems, Inc. Color

card categories 1-3 were not included as this Index was developed for dark colored

cherries.

bThe flower council of Holland, Leiden; The Royal Horticultural Society (RHS), London

24

Table 1.2: Description of the color card ratings for fruit flesh color in sweet cherry used

in the QTL analysis

 

Color card category in the
Color description

Color classification in RHS

 

sweet cherry flesh color Color Chartb
indexa
1 Clear to pale yellow Yellow — 11 A
2 Pale pink Red — 37 A
3 Red Grayish orange - 170 D
4 Mahogany Grayish red — 182 A
5 Dark mahogany Grayish purple — 187 A

 

3‘Washington State University’s Sweet Cherry Flesh Color Index

bThe flower council of Holland, Leiden; The Royal Horticultural Society (RHS), London

25

Statistical analysis for color measurements

The descriptive statistics of the color data were calculated using the Univariate
procedure of SAS version 9.1 (SAS Institute 2006). The differences in color
measurements between the two parents were compared using a t-test (P < 0.05). Pearson
correlations for SCI, SC2, and PC using the color card data for 2006, 2007 and 2008 and
the L* a* and b* data from 2008 were calculated using the CORR procedure of SAS
version 9.1 (SAS Institute 2006). In 2008, estimates of broad-sense heritability were
calculated from those seedlings for which measurements were taken from both the
original seedling and the grafted replicate using an analysis of variance (ANOVA) Broad-

sense heritability was estimated by using variance components with the formula,
2 2 2 2 2 . . . 2 .
H =O'g / (O'g +6ng / 1‘), where 0'g IS the genetlc variance of progeny, 0'ng IS the

interaction variance between progeny and plot, and r is the number of plots (i.e. seedling

plot and grafted replicate).

QTL analysis

A consensus map of the two individual maps, NY and EF (Olmstead et al. 2008)
was used for the QTL analysis. The consensus linkage map has a total of 197 markers,
including 102 simple sequence repeat (SSR) markers, 61 amplified fragment length
polymorphism (AFLP) markers, 27 gene-derived markers, and 7 sequence related
amplified polymorphism (SRAP) markers. QTL analysis was done using MapQTL 5.0
(Van Ooijen 2004). Kruskal Wallis nonparametric test, interval mapping (IM), and
multiple QTL mapping (MQM) were performed for each trait. In MQM, the markers

closest to the peak of the QTL detected by IM were used as cofactors. The LOD

26

thresholds were estimated with 1,000 permutation tests for each trait. The QTLs with
LOD values higher than the genome wide threshold at P < 0.05 were considered most
significant, but QTLs with LOD values higher than genome wide threshold at P < 0.1 and
QTLs with LOD values higher than individual linkage group level at P < 0.05 were also
reported. QTLs with differing thresholds were reported as the use of phenotypic data that
are not normally distributed, can result in unusually high LOD thresholds (Li et al. 2006
and Buil et al. 2005) leading to some real QTLs undetected. The linkage maps and QTL

positions were drawn using MapChart (Voorrips 2002).

27

RESULTS AND DISCUSSION

Color data

Color card readings for SCI, SC2, and FC from the individuals in the linkage
mapping population were significantly correlated across all three years (P < 0.0001)
(Table 1.3) indicating that there was minimal inter-year variation in color. For 2008, the
color card, L*, a* and b* values from the 94 seedlings in the clonally replicated mapping
population subset were all significantly correlated for both skin (SCI and SC2) and flesh
color (FC) (P < 0.0001; Table 1.4). In particular, color card readings for SCI, SC2, and
FC exhibited strong significant negative correlations to L* and b*. This reflects
increases in darkness (- L*) and increases in blueness (- b*) in the dark mahogany fruit
types. The significant correlations across the three fruit measurements suggest that there

is a common genetic mechanism controlling skin and flesh color.

For skin color (SCI and SC2), a* was negatively correlated with the color card
data; however, for PC, a* and the card color data were positively correlated. This
suggests that a different genetic mechanism may contribute to the variation in a* in the

skin versus the flesh.

EF and NY exhibited significantly different color values for all traits except for

SC2 a* (Table 1.5). This similarity between the two parents for a* reflects the fact that

redness is not so important in lighter side (non blush or yellow) of EF. Collectively these

28

results indicate that the red — green vector (a*) alone, does not adequately describe the

quantitative variation in the cherry fruit and skin color.

29

Table 1.3: Pearson’s correlation coefficients for skin color 1 (SCI), skin color 2 (SC2),

and flesh color (FC) card readings from the NY X EF progeny in 2006, 2007 and 2008

 

Trait 2006 vs. 2007 2006 vs. 2008 2007 vs. 2008

 

3C1 0.80a(138)b

0.78 (85) 0.84 (89)
SC2 0.77 (138) 0.79 (85) 0.85 (89)
FC 0.88 (138) 0.82 (80) 0.91 (85)

 

aall the values are significant at P < 0.0001

b . . . . .
The number of 1nd1v1duals in each comparison.

30

Table 1.4: Pearson’s correlation coefficients for skin color 1 (SCI), skin color 2 (SC2)

and flesh color (F C) card and L*, a*, and b* values for NY >< EF progeny evaluated in

 

 

2008

= a “a == a a z

8 8 a g 8 8 9. g a a 8

0.87K

SCI card -0.90 -0.90 0.95 -0.84 -0.63 -0.91 0.88 -0.84 0.49 -0.86
SCI L* 0.90 0.97 -0.84 0.86 0.49 0.88 -0.73 0.72 -0.34 0.80
SCI a* 0.96 -0.90 0.86 0.70 0.92 -0.86 0.82 -0.36 0.88
SCI b* -0.89 0.88 0.56 0.92 -0.79 0.78 -0.37 0.85
SC2 card 088 -0.63 -0.93 0.90 -0.85 0.49 -0.88
SC2 L* 0.44 0.94 -0.76 0.76 -0.38 0.81
SC2 a* 0.61 -0.77 0.68 -0.34 0.70
SC2 h* -0.86 0.83 -0.43 0.90
FC card 087 0.53 089
PC L* -0.48 0.89
FC a* -0.37

 

xAll the values are significant at P < 0.0001. Each comparison represents 1861 to 1865

individual fruits

31

Table 1.5: Means and standard deviations for skin color 1 (SCI), skin color 2 (SC2), and

flesh color (FC) values for EF and NY in 2008

 

 

Tissue Traitz EFy NY
SCl 8.0 b (0.0)
Card 3.5 a (0.9)

L* 47.4 a (5.1) 27.3 b (0.9)
a* 35.4 a (2.9) 7.0 b (2.8)
b* 21.7 a (3.0) 0.6 b (0.6)

SC2 Card 1.3 a (0.5) 7.8 b (0.4)
L* 69.1 a (6.0) 27.7 b (1.1)
a* 7.0 a (10.3) 9.0 a (4.0)
b* 35.3 a(5.7) 1.3b(1.1)

FC Card 1.0 a (0.0) 4.8 b (0.4)
L* 40.8 a (5.5) 20.7 b (2.6)
a"‘ 3.8 a (1.6) 9.7 b (3.7)
b* 26.7 a (1.9) 2.6 b (1.6)

 

y Means denoted by same letters in the same row are not significantly different at P <

0.0001.

ZUnits: card (color card categories), L*, a* and b* (colorimeter reading)

32

Color development

In 2008, the pattern of skin and flesh color development for the parents and
progeny were evaluated over four harvest dates to identify the colorimetric values that
best represented the maximum color potential of each individual. The skin color metrics
for dark mahogany fruits and flesh color metrics of all the fruits exhibited little
differences across all four harvest dates (Table 1.6). However, for EF, the majority of the
skin color values were significantly different among the four harvest dates. The changes
in the EF skin measurements indicated that the fi'uit skin was becoming less yellow and
this change was accompanied by a significant increase in the red blush on the fruit by the
last harvest date. However, the flesh color of EF did not exhibit a parallel increase in red
pigmentation. Instead, the EF flesh color remained yellow highlighting the importance of
carotenoid pigments in the EF blush type cherries compared to the anthocyanin pigments

in the red fleshed cherries.

Due to the different final colors between the blush and mahogany cherry types,
the progression of color development across harvest date was evaluated using separate
groups of seedlings that represented these two color classes. The overall trend was for
decreases in L*, a* and b* and increases in card values over time (Table 1.6) that
represented a darkening of the fruit skin and flesh. The minimum L*, a* and b* and
maximum card values for each seedling were used in the QTL analysis as it represented

the maximum color maturity for each seedling.

33

Table 1.6: The progression of fruit skin and flesh color over harvest data for blush and

mahogany classes of NY54 x EF progeny for year 2008

 

 

. S #
Fruit color class Location COM" metric June 20 June 23 June 26 June 30
Blush SCl Card 4.75 a 5.0 b 5.4 c 5.3 c
L* 37.3 a 35.1 b 33.8 c 33.5 c
a* 28.4 a 26.1 b 24.2 c 21.7 d
b* 13.2 a 10.8 b 9.8 c 8.4 d
Mahogany SCI Card 7.8 a 7.9 b 7.9 b 7.9 b
L* 29.2 a 28.7 b 28.0 c 28.4 c
a* 12.6 a 9.6 b 7.3 c 5.7 d
b* 2.0 a 1.3 b 0.9 c 0.6 d
Blush SC2 Card 3.03 a 3.8 b 3.8 b 4.1 c
L* 47.5 a 43.6 b 42.9 b 41.2 c
a“ 22.5 a 24.8 b 23.0 c 23.5 c
b* 20.1 a 18.0b 16.9b 15.1 c
Mahogany SC2 Card 7.6 a 7.8 b 7.7 b 7.8 b
L* 29.5 a 29.2 a 28.5 b 29.0 b
a* 14.1a ll.4b 9.2c 8.1d
b* 2.9a I.8b l.8b 1.5b
Blush PC Card 1.5 a 1.1 b 1.1 b 1.2 b
L* 33.4 a 35.8 a 35.7 a 35.8 a
a* 4.3 a 3.4 b 4.4 c 4.4 c
b* l7.3a 15.1 b 16.5c 15.4d
Mahogany FC Card 4.2 a 4.4 b 4.5 b 4.4 b
L"‘ 21.8a 2I.Ia 21.4a 21.6a
a* 11.7 a 8.0 b 7.8 b 8.6 c
b* 4.6 a 2.4 b 3.6 c 3.7 c

 

sMeans followed by different letters within the same row are significantly different at P <

0.05 across the rows. The least square (LS) means are shown here (calculated from

General Linear Model Procedure)

#Harvest days for 2008 fruiting season, growing degree days calculated from January 1,
2008 with a base temperature of 4.4 C (June 20: 723.2, June 23: 763.8, June 26: 811.3

and June 30: 872.9) and SUnits: card (color card categories), L*, a* and b*

34

Data distribution

The pattern of the data distribution was examined for the minimum L*, a* and b*
and maximum card values of all the individuals for 2008 data. The progeny values for all
the color traits were not normally distributed (Figure 1.1) and the Kolmogorov-Smimov
Normality Coefficients (KS) were all significant (P < 0.01) (Table 1.7). In addition, the
color card and F C L* and b* distributions in particular suggested a 9:7 ratio characteristic
of a two locus interaction. These skewed distributions were consistent with the
suggestion of Fogle (195 8) and Schmidt (1998) that there is at least one major gene
controlling the genetic variation in fruit color and possible epistasis. Transgressive
segregants were identified for SC2 a* and F C a* whereas for all the other color traits, the
progeny had phenotypic values intermediate to the parents. For SC2 a* and F C a* there
was an abundance of progeny individuals that had redness values above that of the red
fi'uited NY parent. The transgressive segregants identified for SC2 and F C a* were
consistent with the correlation results that suggested a* in the skin and flesh is under

different genetic control than the color measured by the color card, L* and b*.

Broad sense heritability estimates (H2) for all the traits except SC2 a* were higher
than 0.80 (Table 1.7). This suggests that the intensity of the red blush on the skin of the
light colored cherries may be more sensitive to environmental conditions than the overall
skin and flesh color. In particular, the intensity of the red blush is was reduced on fruit
from the original seedling block, possibly due to less light interception and slightly
immature fruit, compared to the fruit harvested from the clonal orchard where the netting

permitted us to harvest fruits at optimum maturity.

35

Figure 1.1: A-L Progeny frequency distribution of color traits measured in 2008 (A) SC1
card. (B) SC2 card. (C) FC card. (D) SCI L*. (E) SC2 L*, (F) F C L*, (G) SCI a*. (H)
SC2 a*. (1) PC a*. (J) SC 1 b*. (K) SC2 b*. (L) FC b*. EF and NY parental values are

shown.

36

Fig 1.1 Cont.
A: Skin Color 1 Card

   

B: Skin Color 2 Card

1,50, , *--NY54
a l

5401
.2

3307
“5 l
.20)

201..- ._..m, .-
4 5

1 2 3 6 7 8

Color card category

C: Flesh Color Card

Number of Individua
S 8

 

o
l
l

3
Color card category

37

Fig 1.1 Cont.
D:3SOkin Color 1 Lightness (L*)

 

 

a

a

5

.2

£20 -. 7777777777777777777777777

a l

“a ‘ NY54 EF

1.. L .,

E10

2 0.1:: II: lllIl , ‘ . ,m ‘
18 20 22 24 26 28 30 32 34 36 38 40 41

Lightness (L*)

E: Skin Color 2 Lightness (L*)

“30‘

E f NY54

E 1

£20 1

5 1

g .

5103 , , 7
g 1 l 1

z 0 111...“, III] II“ llllll llllll “H .11, .. -Jl.

15 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63
Lightness (L*)

F: Flesh Color Lightness (L*)

 

30...,

.2

5

IE

~520er

E NY54

’5 . EF

5- 1

.810»

E .1 ”

2 II II
0111.11,.11111 8,II.1;.51.:.mflll II“...

14 16 20 22 26 28 32 35 38

8Lightness (L*)

38

Fig 1.1 Cont.
G. azfikin Color 1 Redness (a*)

I , , , ,
:Oil EF
III:..1.I.J.IIIIIII,.I.L. 1
15 17 19 21 23 25 27 29
1Redness (a*)

_ n—-
o M

(I!

Number of Individuals

H: Skin Color 2 Redness (a*)

N
O

NY54

l
151
I

Number of Individuals

 

Illlalll II 1 1.

I
l I l

-5 -11357911131517 23
Redness(a*)

I: Flesh Color Redness (a*)
25
l NY54

N
O

p—o
M

   
 

—.
O

‘_._l
1 A 24.

   

Number of Individuals
LII
F1
1:1

     

‘ll 1"";

.~ 5‘1:

,

1:1,

‘ 5 ’2‘» LI!
’1’ T' '7” 'I" ' 'T '1

0 1 2 3 4 5 6 7 8 9 10 11
Redness (a*)

 

O

39

Fig 1.1 Cont.
J: ggin Color 1 Yellowness (b*)

‘ NY54

A
O

N
O

i
l
l

0 . I-I.n.m.lfil.n.fllllll.fl..._fln,fll.n-._nfl,n,i , ,2-..-, .,

0 2 4 6 8101214161820212325272931
Yellowness (b*)

Number of Individuals
0

K: Skin Color 2 Yellowness (b*)

  

£1 NY54
3
£40
.2
E301
h-I
320 -
.8 EF
5101 w . _ .
J1
0.1rmlfllurmm, ,II..
012 3 4 5 6 7 8 91011121314151617181920
Yellowness (b*)
L: Flesh Color Yellowness (b*)
50,1 ,. 5
£
a
E40 NY54
.2
'6
E.
'0-
O
E ,. EF
5 - + I

 

-1012 3 5 7 8 9101112131415161718
Yellowness(b*)

40

Table 1.7: Summary statistics and heritability of color data for year 2008

 

Broad

 

Mean Kolmogorov-Smirnov
Color 3 3
Tissue (Standard sense Minimum Maximum Normality Coefficient
metric . . .
h
Deviation) "'mb'my (KS)***
2
(H )
SCI Card 6.6 (1.5) 0.96 2.0 8.0 0.1
L"I 31.4 (4.2) 0.82 17.9 54.1 0.2
a“ 15.9 (9.9) 0.95 0.7 42.3 0.1
b* 5.5 (5.7) 0.90 -0.5 34.3 0.2
SC2 Card 5.8 (2.2) 0.95 1.0 8.0 0.2
L” 36.0 (9.4) 0.89 15.0 99.2 0.2
a" 16.4 (9.1) 0.74 -5.1 38.1 0.1
b* 9.2 (8.9) 0.96 -0.4 45.9 0.2
PC Card 2.8 (1.8) 0.98 1.0 5.0 0.2
L“ 28.6 (8.3) 0.94 3.6 61.9 0.1
3* 6.7 (3.9) 0.86 -0.5 22.3 0.1
b* 9.7 (7.1) 0.96 -0.9 30.9 0.1

 

$Units: card (color card categories), L* (lightness), a* (red/greenness) and b*

(yellow/blueness) (colorimeter readings)

*** KS is significant at P < 0.01. Significant KS indicates the deviation from normality.

41

QTL analysis
For 2008 color card and L*, a* and b* data, significant QTLs detected for SC]

card, SCI a*, SC2 card, SC2 b*, FC card, FC L* and FC b* on linkage group (LG) 3 at
53.7 cM and the average variability (R2) explained by these QTLs was 87.2% and ranged
from 78.4 % for SC2 b* to 94.7% for FC L* and F C b* (Table 1.8, Figure 1.2A)
indicating that there is a major QTL on LG3 at 53.7 cM for anthocyanin pigmentation.

Significant QTLs for five other color metrics were also located on LG3, however the

predicted peak positions ranged from 12.8 cM for PC a* to 40.8 for SC2 a*. The average

R2 for these QTLs was 25.6% and ranged from 13.5% for SC2 L* to 44% for PC a*

(Table 1.8, Figure 1.2A). This suggests that there is at least one additional color QTL

located on LG3.

To test whether the major QTL on LG3 was significant in 2006 and 2007, QTL
analysis was done for these two years using the color card data for SC], SC2 and FC and

(Figure 1.3). Significant QTLs detected for SCI card, SC2 card and FC card for 2006 and

2007 data on linkage group (LG) 3 at 53.7 cM as in 2008 data. The average R2 for these

color card QTLs were 85.6% and ranged from 73.8% for 2007 SC2 card and 93.8% for
2007 PC card. The identification of a QTL on LG3 at 53.7 cM for two additional years

validated our finding that there is a major fruit color QTL on that genomic location.

For the 2008 color card and L*, a* and b* data, three more significant color QTLs

were identified, one for SCI b* and two for F C a*. For SC 1 b*, a QTL was detected on

42

LG6 with an R2 of 42.8% (Table 1.8, Figure 1.2C). For FC a*, two additional QTLs

were detected on LG5 and LG8 (Table 1.8, Figures 1.2 B and D). The QTL on LG5 and
LG8 explained 18.7% and 44.0% of the phenotypic variation, respectively. The
identification of additional QTLs for FC a* is consistent with phenotypic data that

suggested PC a* reflected a different color pattern compared to the other color metrics.

43

Table 1.8: QTLs for color card values, L*, a* and b* for SCI, SC2 and FC identified in

the NY X EF F1 population in 2008 data

 

QTL Peak position (closest

 

118:1: :2:ng ggfigge marker and T x E bin map LOD R2b
posrtlona)

SCI Card 3 53.7 (PR41, 3:37) 13.3* 87.3
L* 3 21.0 (UDP97-403, 3:12) 4.87”” 21.2

3* 3 53.7 (PR41, 3237) 7.9* 80.0

b* 3 21.0 (UDP97-403 3:12) 60*" 26.0

b* 6 15.0 (UDP96-001 ~6Z25) 4.1"I 42.8

SC2 Card 3 53.7 (PR41, 3:37) 135* 87.5
L* 3 21.0 (UDP97-403 3212) 3.0* 13.5

8* 3 40.8 (UDP98-416) 3.9”“ 23.3

13* 3 53.7 (PR41, 3237) 7.7** 78.4

FC Card 3 53.7 (PR41, 3237) 28.1* 94.7
L"K 3 53.7 (PR41, 3237) 11.2* 86.2

a* 3 12.8 (EAC-MCTA-360) 35* 20.8

3* 5 27.8 (EAT-MCCC-285 ~5221) 3.2* 18.7

3* 8 83.3 (PSIH3 — unknown) 4.5'” 44.0

I)" 3 53.7 (PR41, 3237) 13.7* 87.9

 

aQTL peak position is expressed in cM and the closest marker and T X E bin map

position is indicated in bracket. QTLs were estimated using multiple QTL mapping
(MQM) method of MapQTL 5.0

sz, percentage of phenotypic variation explained by the QTL

*** The LOD value significant at P < 0.05 based on 1000 genome wide permutation tests
** The LOD value significant at P < 0.1 based on 1000 genome wide permutation tests
* The LOD value significant at P <0.05 based on 1000 individual linkage group wide

permutation tests

44

Figure 1.2: A-D Locations of QTLs for color card and L*, a* and b* values for SCI
(darkest location of the fruit skin), SC2 (lightest location of the fruit skin) and FC (flesh
color) using the multiple QTL mapping method. The variability explained by QTL (R2%)
is shown after the trait name of each QTL. l-LOD and 2-LOD support intervals of each
QTL are marked by thick and thin bars, respectively. Blank bars represent QTLs for color
card QTLs. Black bars represent QTLs for L*. Bars filled with one sided hatch lines
represent QTLs for a*. Bars filled with two sided hatch lines represent QTLs for b*. Only
linkage groups including the QTLs are presented. (A) Linkage group 3, (B) Linkage
group 5, (C) Linkage group 6, (D) Linkage group 8. LOD scores and the percentage

variability explained by the QTLs (R2) are presented in the Table 1.8

45

Fig 1.2 Cont. A

L

G3

/ EPPCU5090

I—q

 

AMPAIOI
T\ MA034

/ UDAp-429
:, MA066a
EAC-MCTA-98
UDP97-403
\ EAC-MCTA-l65
‘\ PMS30
‘\ BPPCT039
‘\ EAC-MCTA-79
:~ EPDCU3083
\ UDP98-416
:\ EMPaS12
\ CPDCT037
~~ EPPB4221-PR41
2. Ma039a

 

IQ EAA-MCTT-525
PR1 10

 

73.3

79.3 \
80.8 /

EAT-MCTC-SSO

_/ EMPA014
_\ EMPaS02

 

 

89.4 N

U, EAT-MCCC-350

Q‘ EPPCU9994-PR74

[:1-I

9133138

fl-I

lHOS

r[IDS

46

1:1-1

P193138

(HOS

 

 

L
I

"[238

 

 

 

 

;
\
\
\
\521
\n
\ra
\
\
8

\ \

\

\

\

\

\

§m

\S

\19

N

\

I-rj
l 69 69 if; 8:
=- 2 F ‘7

Fig 1.2 Cont. B

LG5

 

0.0

 

7.7

 

12.8

 

20.7

 

 

IIIIII

I

 

75.5

1

1
t

/

\

\

 

(ITlll

8

EPPCU096 1

EPPCU9168
MEI-EM1-850

BPPCT026

EAT-MCCC-278
UDP96-0 19

EPPCU0863-PR84
BPPCT037
EMPaS 1 1
EAA-MCAC-440
CPDCT028

PR90
EAA-MCAC-340
EPDCU5 1 83
PR26

EPPB42 1 6-PR5 1
EPPB4230
CPDCT022
BPPCT014

47

9331

Fig 1.2 Cont.

LG6

 

 

 

 

 

 

0.0 PR85
4.0 EPPCU3855-PR72
9.5 MEI-EM2-575

1 1.9 EMPaSOl

16.5 UDP96-001

19.2 EAT-MCTC-375

25.3 \g/ BPPCT008

28.3 Ma014a

31.9 — PR127

33.3 \ / EAA-MCCC-I60

35.7 e/ EAC-MCTA-225

37.8 \r/ CPPCT029

37.9 \—/ UDA-47l

39.6 \—/ nBPPCT006

40.3 \:/ EMPA004

43.0 \ / CPSCT029

44.7 \P/ EAT-MCTC-272

449 /=\ BPPCT009

48.5 \ / CPPCT023

50.1 \:/ EgggU2828

EPPCU3090

——I

Ill

1111 ll]

EAA-MCAC-420
EAA-MCCC-I30
UDAp-4 1 3

‘/ EAT-MCCC-IOO

/ UDP98-021
\ EPPB4227

/ MaO40a

PrPFT
/ PR93 EPPCU4092-PR56

/ EAC-MCTA-350
/ EAT-MCTC-I42
\ EAC-MCTA-185

 

 

82.4

CI

18::

PR121

48

QIDS

Fig 1.2 Cont. D LG8

 

EAA-MCAA-398

4.3 \_/ EAA-MCCT-280

 

pchgms49

8:5 /"‘\ EAA-MCCT-225

 

EAA-MCCC-l 32

 

EPPCU4726

 

33.4 \r

=

EAA-MCCT-410

/ CPPCT006
PR25/22
\ EPDCU3516

33.7 /—~
34.6 / \ EAA-MCAA-322

4;

.U‘

/
l

4:.
\0
be
I
ll

Ur
1.»
\O

\[

I

/ EAA-MCAC-258
/ MA023a

:/ EAT-MCTC-425
_/ EAA-MCTT-315
__\ EAT-MCTC-188

EAA-MCCC-425
\ EAT-MCTC-3l2

EAT-MCTC-330
EAT-MCTC-315

PR27

 

MEI-EM2-475

 

78.3

PSIH3

 

87.3

 

104.1

EAA-MCCT-345

 

111.3

 

EAT-MCTC-S 10

 

 

121.9

MEI-EM2-570

49

\ EAA-MCAC-430 MD201 a

93d

Figure 1.3: Locations of QTLs on LG3 for color card data of 2006, 2007 and 2008 for
SCI (darkest location of the fruit skin), SC2 (lightest location of the fruit skin) and FC
(flesh color) using the multiple QTL mapping method. Blank bars represent QTLs. 1-
LOD and 2-LOD support intervals of each QTL are marked by thick and thin bars,
respectively. The percentage variability explained by the QTL (R2) and the level of QTL
significance in number of stars (***: LOD value significant at P < 0.05 genome wide, **:
LOD value significant at P < 0.1 genome wide, * : The LOD value significant at P <0.05
individual linkage group wide, based on 1000 permutation tests) are shown with the

QTLs.

50

LG3

0.0 wo~ EPPCU9994-PR74

6,7 \_/ EPPCU5090

AMPAIOl

 

_\ MA034
18.9 \ / UDAp-429

MA066a
EAC-MCTA-98
UDP97-403

_\ EAC-MCTA-165

29.9 /_\ PMS30

_\ BPPCT039
_\ EAC-MCTA-79

 

EPDCU3083

/'l\ UDP98-416

44.0 /2\ EMPas12

\ CPDCT037

50.7 ’T\ EPPB4221-PR41

MaO39a

 

/C\ EAA-MCTT-SZS
/ \

PR110

EAT-MCTC-550

 

73.3

79.3 \
80.8 /

_/ EMPA014
"\ EMPaSOZ

 

 

89.4 N

U/ EAT-MCCC-350

N N “N
§as§383§§
\reoo

sesamsgga
infinmwinOE NO
geez-sgann
o 99””
mflawfifi-j-r-r
aaaaaaere
go \000 \O
E’opafidPHN-fl
seriouboPOPPS
J**~O*J******

gei-

**

51

QTL haplotypes

To further investigate the allele effects of the color QTLs, the progeny individuals
were sorted by their parental QTL haplotypes that were defined by the allelic states of at
least two linked markers (Table 1.9). The choice of the two markers to represent the QTL
haplotype was based on the fact that they should flank the QTL and at least one marker
should be heterozygous in one parent. For example, the NY and EF QTL haplotypes for
the QTL on LG 3 at 53.7 cM were defined by the alleles for AFLP marker, EAC-MCTA-
79 and SSR, MaO39a located at 35.6 cM and 54.2 cM, respectively. EAC-MCTA-79 is
heterozygous in NY and homozygous in EF. To facilitate the genetic notation, other
allele (i.e. cannot see in the gel and can be considered as a null allele) was designated as
‘$$’ following the format used for FlerTL (Bink et al. 2008), a sofiware used in
pedigree based QTL analysis using multiple populations. Only EF is heterozygous for

MaO39a.

For the two QTLs on LG3 and LG6, there were four parental QTL haplotypes (a,
b, c and d) as each parent was heterozygous for haplotypes. However, for LG5 and LG8
there were three parental QTL haplotypes as NY was homozygous for the LG5 QTL

haplotype and EF was homozygous for the LG8 haplotype.

The progeny color trait means were then calculated for each of the QTL
haplotypes. For example, for the QTL on LG3 at 53.7 cM, four progeny classes were
defined as “ac”, “ad”, “be”, and “bd” and the trait means were calculated from 29, 23, 23,

and 14 progeny individuals, respectively (Table 1.10). Those progeny individuals that

52

received the LG3 “a” haplotype from NY had intensified color, most notably it increased
darkness (card) and reduced redness (a*) in SCI, increased darkness (card) and increased
blueness (b*) in SC2 and increased darkness (card and L*) and increased blueness (b*) in
FC as many of the fruits were approaching mahogany color in both skin and flesh. The
lighter colored fruit had the LG3 “b” haplotype from NY. As NY has dark mahogany
skin and flesh, this finding for the major QTL on LG3, supports the prior observation that

mahogany fruit (skin and flesh) is dominant to yellow fi'uit.

Both EF and NY were heterozygous for the SC] L*, SCI b*, SC2 L*, SC2a* and
PC a* QTL haplotypes on LG3 at ~21.0 cM (Table 1.11). Those progeny received “a”
haplotype from NY had increased darkness (L*) and increased blueness (b*) in SCI and
increased lightness (L*) and reduced redness (a*) in SC2. But the same haplotype “a”
increased the redness (a*) in PC suggesting that flesh color is under different genetic

control.

Both EF and NY were also heterozygous for the SCI b* QTL haplotype on LG6
(Table 1.12). Those progeny that received the “d” haplotype from EF had a reduced b*
value (increased blueness) compared to those that received the “c” haplotype. However,
this effect was only present for those individuals that had the “b” haplotype from NY, not

the “a” haplotype. This suggests that the QTL alleles on LG6 may interact.

For the FC a* QTLs on LG5 and LG8 only one of the parents was heterozygous.

NY was homozygous for the PC a* QTL haplotype on LG5 (Table 1.13). Therefore the

53

6‘ 9,

trait means were calculated from those progeny individuals that received either the c or
“d” haplotypes from EF. Those progeny individuals that received the “c” haplotype from
EF had increased redness compared to those progeny that received the “d” haplotype. EF
was homozygous for the PC a* QTL haplotype on LG8 (Table 1.14). Therefore the trait
means were calculated from those progeny individuals that received either the “a” or “b”

haplotype from NY. The presence of the “a” haplotype as opposed to the “b” haplotype

was associated with an increase in red color.

54

Table 1.9: Definitions of parental haplotypes for five QTL regions on linkage groups 3, 5,
6 and 8

 

 

 

 

 

 

 

 

 

 

Linkage group Parent Haplotype Molecular marker
EAC-MCTA-79 MaO39a
NY a 79" 170
3 at 53.7 cM b my 170
EF c $$ 220
d $$ 170
MA066a BPPCT039a
NY a 149 138
3 at ~21.0 cM b 142 133
EF c 142 145
d 142 138
BPPCT026 UDP96-019
NY a 164 202
5 EF c 164 205
d 170 202
EMPaSOl UDP96-001
NY a 228 129
6 b 222 131
EF 0 228 129
d 232 115
MD201a PS1H3
NY a 250 280
8 b 230 270
EF 0 230 270

 

xAllele fragment size in bp y$$z Confirmed null allele for marker, EAC-MCTA-79

55

Table 1.10: Card and a* of skin color 1 (SCI), Card and b* of skin color 2 (SC2) and
Card, L8 and b* of flesh color (F C) of different genotype classes for the major QTL on

linkage group 3 at 53.7 cM. Numbers in parenthesis are the number of progeny

 

 

individuals
. b
Color Metric
Halo e
p typ a SCI SC2 FC
Combination

 

Card a* Card b* Card L* b*

 

ac(29) 7.3a 8.2a 6.8a 4.0a 3.5a 21.0a 4.5a

ad(23) 7.3a 7.2a 6.9a 3.0a 3.7a 21.2a 3.7a

be (23) 6.2 b 15.0 b 5.2 b 9.8 b 2.1 b 27.8 b 9.9 b

bd(14) 6.7b 13.6 b 6.0 b 6.8 b 2.3 b 25.7 b 8.9 b

 

Means denoted by the same letters within column are not significantly different at P <

0.05 (done using Least Squares Means, General Linear Model SAS 9.1)

aa, b, c, and d are the haplotypes as defined in Table 1.9

bUnits: card (color card categories), L*, a* and b* (colorimeter reading)

56

Table 1.11: L* and b* of skin color 1 (SCI), L* and a* of skin color 2 (SC2) and a* of
flesh color (FC) of different genotype classes for the minor QTL on linkage group 3 at

~21.0 cM. Numbers in parenthesis are the number of progeny individuals

 

Color Metricb
Haplotype

 

a SCl SC2 F C
Combination

 

L* b* L* a* a*

 

ac (14) 27.3 a 0.3 a 26.4 a 4.7 a 4.4 a
ad (32) 28.7 a 2.9 b 31.4 b 7.2 b 3.6 a
be (13) 30.4 a 4.8 b 32.1 b 9.2 b 2.4 b

bd (09) 29.9 a 3.9 b 32.2 b 14.2 b 1.6 b

 

Means denoted by the same letters within column are not significantly different at P <

0.05 (done using Least Squares Means, General Linear Model SAS 9.1)

aa, b, c, and d are the haplotypes as defined in Table 1.9

b . . . .
Unrts: card (color card categorres), L*, a* and b* (colorrmeter readlng)

57

Table 1.12: b* of skin color 1 (SCI) of different genotype classes for the QTL region on

linkage group 6. Numbers in brackets are the number of progeny individuals

 

 

 

 

Color Metricb
Haplotype SCl
Combinationa
b*
ac (20) 2.7 a
ad (30) 3.1 a
be (18) 3.2 a
bd (18) 2.2 a

 

Means denoted by the same letters within column are not significantly different at P <

0.05 (done using Least Squares Means, General Linear Model SAS 9.1)

aa, b, c, and d are the haplotypes as defined in Table 1.7

bUnits: card (color card categories), L*, a* and b* (colorimeter reading)

58

Table 1.13: a* of flesh color (F C) of different genotype classes for the QTL region on

linkage group 5. Numbers in brackets are the number of progeny individuals

 

 

 

 

Color Metricb
Haplotype FC
Combinationa
3*
ac (33) 3.7 a
ad (38) 2.8 a

 

Means denoted by the same letters within column are not significantly different at P <

0.05 (done using Least Squares Means, General Linear Model SAS 9.1)

21a, c, and d are the haplotypes as defined in Table 1.7

bUnits: card (color card categories), L*, a* and b* (colorimeter reading)

59

Table 1.14: a* of flesh color (F C) of different genotype classes for the QTL region on

linkage group 8. Numbers in brackets are the number of progeny individuals

 

 

 

 

Color MetricF
Haplotype
a F C
Combination
3*
ac (31) 3.0 a
bc (33) 3.5 a

 

Means denoted by the same letters within column are not significantly different at P <

0.05 (done using Least Squares Means, General Linear Model SAS 9.1)

‘1a, b, c, and d are the haplotypes as defined in Table 1.7

bUnits: card (color card categories), L*, a* and b* (colorimeter reading)

60

Epistasis

The bimodal pattern of some of the progeny distributions suggested the possibility
of epistasis. To investigate this further, the major QTL on LG3 at 53.7 cM was
considered as the major factor and the QTLs on LG3 at ~21.0 cM, LGs 5, 6 and 8 were
considered separately as the second factor. The trait values for the different allelic states

were determined using markers from the QTL peak positions.

The mean SCI L* and SC2 L* values for genotypic classes defined by PR41 (the
marker selected to represent major QTL on LG3) and Ma066a (the marker selected to
represent minor QTL on LG3) were not different indicating there is no epistatic
interaction for SCl L* and SC2 L*. The mean SCI b* values for PR41 genotypes when
Ma066a was homozygous were greater than the mean SCI b* values for PR41 genotypes
when Ma066a was heterozygous. This indicates the epistatic interaction for SCI b*
between major and minor QTLs on LG3. The mean SC2 a* was highest for those
progeny individuals that were heterozygous for PR41 and Ma066a and lowest for those
progeny individuals that werehomozygous for PR 41 and heterozygous for Ma066a. This
suggests a possible epistatic interaction for SC2 a* between major and minor QTLs on
LG3. The mean FC a* values for PR41 genotypes when Ma066a was heterozygous were
greater than the mean PC a* values for PR41 genotypes when Ma066a was homozygous.
This indicates the epistatic interaction for SC 1 b* between major and minor QTLs on

LG3 (Figure 1.4).

61

The mean F C a* values for those progeny individuals that were heterozygous for
PR41 (a marker near the LG3 QTL peak) were similar, irrespectively of differences in the
QTL allelic states for LG8 and LG5. However, for those progeny that were homozygous
for PR41, the allelic states for PS1H3 (LG8 PC a* QTL) and BPPCT026 (LG5 PC a*
QTL) did result in different color outcomes. This suggests that there may be an epistatic
interaction between the QTL(s) on LG3 and the QTLs on LG5 and LG8. In contrast, for
the LG6 SC 1 b* QTL, a maximum trait value was obtained for those progeny that had the

131 and 129 bp alleles for UDP96-001, irrespective of the allelic state at the PR41 locus

(Figure 1.5).

62

Figure 1.4: A-E. The two-way inter genomic region interactions between the major QTL
and the minor QTL regions on LG3 (A) Inter loci interaction for SC] L* between PR41
and Ma066a (B) Inter loci interaction for SC] b* between PR41 and Ma066a (C) Inter
loci interaction for SC2 L“ between PR41 and Ma066a (D) Inter loci interaction for SC2
a* between PR41 and Ma066a (E) Inter loci interaction for FC a* between PR41 and
Ma066a. Means denoted by same letter within each graph are not significantly different
at P < 0.05 (done using Least Squares Means, General Linear Model SAS 9.1). Units: L*,

a* and b* (colorimeter reading)

63

Fig 1.4 Cont.
A 32.0
SCl L*

16.0 5

0.0

LGO3: PR41 ;
1

LGO3: Ma066a;

5.0 T

SCl b*

2.5 '

304" 29.83 a

 

 

 

 

.__ 4 .
337/337 337/312 337/337 337/312 1
1

142/142 142/149 1‘
Allele/fragement (bp)

 

 

111—1

 

0.0
LGO3: PR41;

LGO3: Ma066a;

 

337/337l337/312 337/337‘ 337/312

142/142 142/149
Allele/fragment (bp)

64

Fig 1.4 Cont.

a

 

 

 

 

 

33.9
C 34-0 7 28.2 a
SC2 L* 1
1
17.0 1
1
0.0 1 ‘7 1 fi—V “1
LGO3zPR41; 337/337 1 337/337 1 337/312 ;
LGO3: Ma066a: 142/142 1 142/149
Allele/fragme nt (bp)
D
14.0 ~
SC2 a* 1 a
1 8.2
7.0 '1 5.4C
0.0 , — ~ 1 -

 

 

 

1

LGO3: PR41;1 337/337 i
LGO3: Ma066a; 1

337/312 1 337/337 1 337/312 ;

142/142
Allele/fragme nt (bp)

 

 

 

 

 

 

 

 

1
1

 

 

 

 

 

 

 

 

142/149

65

29.7

 

 

 

 

6.7

 

 

 

 

Vi

1

Fig 1.4 Cont.

E
5.0 *

FC a*

2.2

 

1.7

4.1

 

 

 

 

 

 

 

 

 

0.0 1 :
1 1

LGO3:PR41; 337/337 1 337/312 1 337/337 J

LGO3: Ma066a;1 142/142

 

 

66

3.8

 

 

 

 

337/312 1

142/149
Allele/fragme nt (bp)

Figure 1.5: A-C. The two-way inter genomic region interactions between the major QTL
region on LG 3 and the other QTLs on LGs 5, 6 and 8. (A) Inter loci interaction for PC a*
between PR41 on LG 3 and PSIH3 on LG8. (B) Inter loci interaction for PC a"‘ between
PR41 on LG 3 and BPPCT026 on LG5 (C) Inter loci interaction for SC] b* between
PR41 on LG 3 and UDP96-001 on LG6 Means denoted by same letter within each
graph are not significantly different at P < 0.05 (done using Least Squares Means,

General Linear Model SAS 9.1). Units: L*, a* and b* (colorimeter reading)

67

A

U E U11

1
L008. PSIH3; 270/270 270/280 270/270 1 270/280
1
LOW-PR41; 1 337/337 337/312

Allele/fragment (bp)

3
3.8 -
4'0 7 2.8a 3.1a 3.08
FC a* U U 1:1
2.0 -
0.0 Q - , 1

LGDS:BPPCF026;1 164/164 1 164/170 ‘ 164/164 1 164/170

11303: PR41; 337/337 337/312
Allele/fragme nt (bp)

B

C"

C 40 b
4'0 23212,7a25a 4.0b 27a 34
SC1b* 1 ' ' '
210 1:1 1:1 1:1 H H 15¢
1 o tn tn ox : as 1 tn . tn 1 ex
N v—' v— N N '-‘ F‘ N
[(1)62UDP96-001;.: C 1 C : C C 1C C 1C
1 ON 1 ON 1 I—I 1 v—( o 1 a 1 .— ,._1
1 2 1 2 2 2 1 2 1 2 1 2 2 1
WWW“; 337/337 337/312

Allele/fmgme nt (bp)

68

Identification of QTLs for fruit skin and flesh color in sweet cherry is important
for marker assisted breeding and to discover the underlying genes. Two previous studies
by Fogle (1958) and Schmidt (1998) showed that, skin and flesh color in sweet cherry is
a major genie trait. The bimodal pattern of data distribution for fruit color and the higher
presence of mahogany skinned and red fleshed individuals in the segregating progenies
are in agreement with the these studies. But higher heritability values for all the color
metrics except red-green vector of lighter side of the blush cherries (SC2 a*), explain the
very high genetic control of the fruit color in sweet cherry. However, this study suggests

that minor genes may also control the variation for skin and flesh color in this cross.

The higher correlation between skin and flesh color is in agreement with Fogle
(1958) and Schmidt (1998). However, SC2 a* is not correlated to any other color metric.
In blush fruits, SC2 a* is not very important as this location is yellow in color. But in
mahogany color fruits, a* is important but less important compared to lightness/darkness
(L*) and yellowness/blueness (b*) as the visual color is very dark blackish purple and
that kind of color is more explained by L* and b*. This implies that SC2 a* is more

subjected to environmental effects than any other color vector.

The color development pattern of the skin and flesh with fruit maturity was in
agreement with the previous study by Usenik et al. (2005) except in our study we
observed that redness, a*, is also decreasing with time. However, Usenik’s study didn’t

include any blush varieties and segregating populations, but the dark varieties he used,

69

Van, Sunburst and Elisa have different a* development compared to our dark fruited

variety, NY54.

The QTL analysis for fi'uit skin and flesh color in sweet cherry is challenging
because the frequency distributions significantly deviate from normality. This has two
consequences on the LOD score estimations; an increased Type 1 error rate and the
inflated LOD values to levels where they cannot be compared between the different traits
(Buil, 2005). The present study encountered these two problems and the MapQTL 5.0
manual (Van Ooijen, 2004) suggested that single marker analysis-Kruskalis Wallis Test
could be used to verify the QTLs derived under such conditions. The QTLs presented in
this paper were verified with that procedure but care must be taken when interpreting the

inflated LOD scores for QTLs.

The detection of a major QTL on LG3 for all the color metrics suggest that,
pigmentation is controlled by a major gene on LG3. Four other QTLs on LGs 3, 5, 6 and
8 suggest that genetic control of mm color is also controlled by other genes with minor
effects. However, very high R2 values for QTLs on LG3 suggest that the QTL on LG3 is
the major gene. Previous studies suggested an epistatic interaction between one major
and one minor gene. The present study is in agreement with those hypotheses and also
suggested that the epistatic interactions would be more complex with different allelic

states.

70

CONCLUSION

This study identified a major QTL for skin and flesh color of sweet cherry on LG
3 and four minor QTLs on LG3, LG5, LG6 and LG8. The genomic regions and parental
haplotypes in the QTL regions will serve as a basis for future fine mapping and candidate
gene studies designed to determine the genetic change underlying the color QTL. In
breeding perspective, this QTL information will expedite the production of sweet cherry

varieties with desired skin and flesh colors.

71

LITERATURE CITED

Bink MCAM, Boer MP, ter Braak CJ F, Jansen J, Voorrips RE, van de Weg WE (2008)
Bayesian analysis of complex traits in pedigreed plant populations. Euphytica
161:85—96

Buil A, Dyer TD, Almasy L, Blangero J (2005) Smoothing of the bivariate LOD score for
non-normal quantitative traits. BMC Genet 6 (Suppl 1):Sl 11

Clayton M, Biasi WV, Agar IT, Southwick SM, Mitcham EJ (2003) Postharvest quality
of 'Bing’ cherries following preharvest treatment with hydrogen cyanamide, calcium
ammonium nitrate, or gibberellic acid. HortScience 38:407—411

Crisosto CH, Crisosto GM, Metheney P (2003) Consumer acceptance of Brooks and Bing
cherries is mainly dependent on fruit SSC and visual skin color. Postharvest Biol
Tech 28: 1 59—167

Espley RV, Hellens RP, Puterill J, Kutty-Amma S, Allan AC (2007) Red coloration in
apple fruit is due to the activity of a MYB transcription factor, MdMYBIO. Plant
Journal 49:414—427

Facteau TJ, Chestnut NE, Rowe KE (1983) Relationship between fruit weight, firmness,
and leaf/fruit ratio in Lambert and Bing sweet cherries. Can J Plant Sci 63:763—765

Fogle HW (1958) Inheritance of fruit color in sweet cherries {Prunus avium). J Heredity
49:294—298

Georgiev VS (1985) Some results with sweet cherry breeding in the research institute for
fruit growing in Kustendil, Bulgaria. Acta Hortic 169:73—78

Gil M1, Tomas-Barberan FA, Hess-Pierce B, Kade, AA (2002) Antioxidant capacities,
phenolic compounds, carotenoids, and vitamin C contents of nectarine, peach and
plum cultivars from California. J Agric Food Chem 50:4976—4982

Hedtrich RT (1985) Sweet cherry breeding at the Swiss Federal Research Station I.
Results of fruit characters and flowering period inheritance. Acta Hortic 169:51—62

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Kita M, Kato M, Ban Y, Honda C, Yaegaki H, Ikoma Y, Moriguchi T (2007) Carotenoid
accumulation in Japanese apricot (Prunus mume Siebold & Zucc.): molecular

analysis of carotenogenic gene expression and ethylene regulation. J Agric Food
Chem. 55:3414—3420

Li M, Boehnke M, Abecasis GR, Song PX (2006) Quantitative trait linkage analysis
using Gaussian copulas. Genetics 173:2317—2327

Lyngstad L, Sekse L (1995) Economic aspects of developing a high quality sweet cherry
product in Norway. Acta Hortic 379:313—320

Miller DC, Casavant KL, Buteau RJ (1986) An analysis of Japanese consumer
preferences for Pacific Northwest and Japanese sweet cherries. Research Bulletin XB
Washington State University, Agricultural Research Center. pp1—15

Olmstead JW, Sebolt AM, Cabrera A, Sooriyapathirana SS, Hammer S, Iriarte G, Wang
D, Chen CY, Van Der Knapp E, Iezzoni AF (2008) Construction of an intra-specific
sweet cherry (Prunus avium L.) genetic linkage map and synteny analysis with the
Prunus reference map. Tree Genet Genomes 4:897—910

Rodrigues LC, Morales MR, Artur Joao Bartolo Femandes AJ B and Ortiz JM (2008)
Morphological characterization of sweet and sour cherry cultivars in a germplasm
bank at Portugal. Genet Resourse Crop Evol 552593—601

Sacks EJ and Francis DM (2001) Genetic and environmental variation for tomato flesh
color in a population of modern breeding lines. J Am Soc Hortic Sci 26:221—226

Schmidt H (1998) On the basis of fruit color in sweet cherry. Acta Hortic 468:77—81

Tobutt KR, Boskovic R (1996) A cherry gene database. Acta Hortic 410: 147—1 54

Turner J, Seavert C, Colonna A, Long LE (2008) Consumer sensory evaluation of sweet
cherry cultivars in Oregon, USA. Acta Hortic 795:781—786

Usenik V, Kastelec D, Stampar F (2005) Physicochemical changes of sweet cherry fruits
related to application of gibberellic acid. Food Chemistry 90:663—671

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Van Ooijen J W (2004) MapQTL® 5, sofiware for the mapping of quantitative trait loci in
experimental populations. Kyazma BV, Wageningen, Netherlands

Voorrips RE (2002) MapChart®: Software for the graphical presentation of linkage maps
and QTLs. J Heredity 93:77—78

Wermund U, Feame A (2000) Key challenges facing the cherry supply chain in the UK.
Acta Hortic 536:613—624

Zhang W, Chao S, Manthey F, Chicaiza O, Brevis JC, Echenique V, Dubcovsky J (2008)
QTL analysis of pasta quality using a composite microsatellite and SNP map of
durum wheat. Theor Appl Genet 11721361-1377

74

CHAPTER TWO
GENETIC DIVERSITY ANALYSIS OF SWEET CHERRY (Prunus avium L.)

CULTIVARS USING DNA MARKERS

75

INTRODUCTION

Sweet cherry (Prunus avium L.), belonging to family Rosaceae, is an important
temperate fruit crop. P. avium originated in central Asia and Europe where wild cherry,
also P. avium, is an important timber tree. The wild cherries, landraces and improved
cultivars represent the genetic diversity of P. avium (Iezzoni et al. 2008). Early settlers
brought sweet cherry seeds and budwood to the New World from Europe. Most probably
they would have brought a small number of selected land races from Europe. Early
settlers in the New World selected the best sweet cherry seedlings such as “Bing” and
landraces such as “Lambert” from the original material for the large scale planting. The
advanced selections and the original material of sweet cherry brought to the New World
represent the sweet cherry germplasm in the Pacific North West (PNW) region in North
America. The PNW sweet cherry germplasm therefore, may have undergone genetic
founder effect when early settlers selected seeds and budwood from the natural habitat to
carry with them. Previous studies suggested that the PNW sweet cherry germplasm may
have a narrow genetic base. The low genetic polymorphism was reported by Stockinger
et al. (1996) and Gerlach and Stosser (1997) with randomly amplified polymorphic DNA
(RAPD) markers, and Beaver et al. (1995) and Granger (1993) with isozyme markers.
But no studies have been conducted to assess the genetic founder effect with more
comprehensive simple sequence repeat (SSR) and gene based markers. If the genetic
founder effect could be assessed with DNA markers with reference to the current
genomic information of P. avium, it would provide a strong platform for germplasm
enhancement and crop improvement of sweet cherry and tart cherry (P. cerasus), for

whom P. avium was one of the two parents. We took the advantage of DNA markers and

76

linkage maps available from various studies for Prunus avium and other Prunus species
(Clarke and Tobutt 2003; Dirlewanger et al. 2002; Joobeur et al. 1998; Dirlewanger et al.
2004; Olmstead et al. 2008) which could be used to estimate and visualize the genetic

diversity at genome level.

In the present study, the DNA polymorphisms among PNW sweet cherry
germplasm (defined by 28 landraces and cultivars historically used and released in PNW
region) (abbreviated as PNW from this point onwards), seven European sweet cherry
land races which were not used in PNW sweet cherry breeding (abbreviated as non-PNW
from this point onwards) and one wild cherry selection (New York 54) from Germany
were compared using DNA markers to test the hypothesis of genetic founder effect in
PNW sweet cherry germplasm. The specific objectives of this study were to, 1. Assess
the genetic founder effect in PNW sweet cherry germplasm, diversity and genomic
relationships among PNW, non-PNW and wild sweet cherry germplasm groups. 2.
Examine the level of heterozygosity and allele diversity across the cherry genome. 3.
Define a subset (panel of six individuals) of P. avium selections for single nucleotide
polymorphism (SNP) detection to develop high throughput DNA markers. 4.
Recommend a panel of few DNA markers that can effectively be used for in-house P.

avium DNA fingerprinting purposes without any ambiguity.

77

MATERIALS AND METHODS

Plant materials

Thirty-six P. avium selections were chosen for the study that represented PNW
sweet cherry germplasm (defined by 28 landraces and cultivars historically used and
released in PNW breeding programs), seven European sweet land races which were not
used in PNW sweet cherry breeding and one wild cherry selection (New York 54) (Table

2.1).

Leaves were collected from trees growing at Michigan State University’s
Clarksville Horticultural Research Station, Clarksville, Michigan and the North West
Horticultural Research Station, Traverse City, Michigan or Washington State
University’s Irrigated Agricultural Research Center, Prosser, Washington. Immature and
actively growing leaf samples were collected from all the selections in early spring,
placed immediately in dry ice, moved to the laboratory and frozen for 24 hours at 80 0C.
The frozen leaf samples were freeze dried for 48 to 72 hours and stored at -20°C until

DNA extraction.

DNA extraction and genotyping

DNA was extracted using the cetyl trimethylammonium method described by
Stockinger et al. (1996). PCR conditions were as in Olmstead et al. 2008 except for the
EMPA and EMPAS markers, where, touch-down PCR temperature profile was used

(Clarke and Tobutt 2003). The S-locus was genotyped using the S-RNase allele-specific

78

primers, S1 -S6 (Sonneveld et al. 2001), S7-Sg and 8.2 (Sonneveld et al. 2003). The gene or
expression sequence tag (EST) based markers (PR markers) were genotyped according to
the method described by Olmstead et al. (2008). SSR markers were size separated in 6%
denaturing poly acrylamide gels and visualized using silver stain (Bio-Rad Laboratories,

Hercules, CA, USA). The S-RNase and PR markers were resolved in 4% agarose gels.

Data analysis

The different alleles for each marker were identified by fragment base pair (bp)
size for SSR and PR markers and as nominal data for the S—locus. Some of the sweet
cherry selections were related, e. g. parents or grandparents, and the marker genotypes for
these individuals were checked to verify that these genotypes were consistent with the
pedigree relationships (see Table 2.1 for pedigree information). Additionally, three
populations were available from the crosses between New York 54 (N Y54) and Emperor
Francis (EF) (NY x EF: 190 individuals, Olmstead et al. 2008), Powdery Mildew
Resistant-l (PMRl) and Rainier (PMRl x Rainier: 108 individuals), PMRl and Bing
(PMRl x Bing: six individuals) and PMRl and Van (PMRl x Van: five individuals)
(Olmstead 2001). These crosses were used to validate the inheritance pattern of the
alleles and to detect the presence of null alleles. The confirmed null alleles were

designated as $8 and unconfirmed null alleles which potentially could be homozygous

were designated as $ following the format used for FlerTL® (Bink et al. 2008). The

markers, whose putative alleles showed inconsistent segregation patterns, duplicate or

multiple loci, or stutter bands/smears were not included in the analysis.

79

The unique alleles (UA) for each cultivar and for PNW and non-PNW groups
were identified. The percentage of heterozygous loci (% H) for each sweet cherry
selection was calculated as the proportion of heterozygous loci out of total number of
marker loci scored for that cultivar. The frequency of each allele of marker (p,) was
calculated and used to estimate heterozygosity (H) and polymorphic information content
(PIC) for the 77 DNA markers. The H and PIC were calculated as explained in Botstein
(1980) and Shete et al. (2000) using the following formula: Pi is the frequency of 14"

allele, and n is the number of alleles.

H =1— :piz
i=1

n n—l n
PIC = 1-21912 -1 2 21712ij 1
i=1

i=1 j=i+l

The allele frequencies were classified to identify the rare alleles and to see their

presence or absence in the PNW sweet cherry germplasm.

Allele data were converted to the binary format (1: presence of the allele and O:
absence of the allele for a given P. avium selection) and a dendrogram was constructed
using the UPGMA method of McQuitty linkage (McQuitty and Koch 1975) and Absolute
Correlation Coefficient Distance (Minitab 15.0) to show the genetic relatedness among

the 36 selections.

80

The distribution of alleles for marker loci along the linkage groups of P. avium
(Olmstead et al. 2008) was examined to identify the genomic areas where non-PNW and
wild type alleles were detected. The %H for all the linkage groups were examined for
PNW, non-PNW and wild P. avium groups. The graphical genotypes (GGT) for all the
linkage groups (LG) of all 36 selections were illustrated to visually represent the alleles

of all the loci across the sweet cherry genome.

81

RESULTS AND DISCUSSION

Unique and rare alleles

A total of 300 alleles were identified for 77 DNA markers from the 36 sweet
cherry selections (Table 2.2 and Figure 2.1). 52 unique alleles (UA) (an allele only found
in a single sweet cherry selection) out of 300 alleles were identified, 40 of which were
not detected in the PNW sweet cherry germplasm. The wild selection, NY54, had the
highest number (13) of UA followed by the European landraces, Ambrunus (had nine
UA) and Cristobalina (had six UA) (Table 2.1). The higher presence of UA outside the
PNW sweet cherry germplasm support our hypothesis that PNW sweet cherry germplasm
had been undergone the genetic founder effect when early settlers brought sweet cherry

germplasm to the New World.

The frequency for all 300 alleles showed that rare alleles (those alleles with
frequency of less than 0.20) were more common (147) and notably 44 out of the 147
alleles (30%) were absent in the PNW sweet cherry germplasm (Table 2.2 and Figures
2.1 and 2.2). The 30% absence of rare alleles also validates the hypothesis of genetic

founder effect in PNW sweet cherry germplasm.

Allele diversity and cultivar heterozygosity
A total of 256, 253 and 112 alleles were detected in PNW, non-PNW and wild
sweet cherry groups respectively. All three groups shared 31% of total alleles detected)

(Figure 2.1). PNW and non-PNW group shared 74% of total alleles showing that they

82

were evolutionary more related than their individual relationships to wild cherry. The

PNW and non-PNW groups shared 32% of total alleles with wild cherry (Figure 2.1).

The percentage of heterozygous loci (%H) was highest in EF. This bias resulted
from the initial marker screening to identify allele polymorphism was based on EF and
wild cherry, NY54 (Olmstead et al. 2008). However, all the %H values for the other 34
selections and groups were not significantly different (Table 2.1) (statistical analysis is
not shown). This is due to the facts that P. avium maintain higher level of genetic
heterozygosity through the reproductive mechanism of self incompatibility and the
heterozygosity for cultivars with no progeny or pedigree data available cannot be exactly
determined due to the presence of possible null alleles. Therefore, the cultivar %H is not
a good parameter to test the hypothesis of genetic founder effect. The % H for individual
linkage groups (LG) of PNW, non-PNW and wild P. avium groups were also compared
and only LG8 for PNW and non-PNW had lower level of H compared to other LGs and
also compared to the LG8 of NY54 (Table 2.3). This could lead to the hypothesis that
LG8 may contain many important agronomic traits and therefore, subjected to more

intense selection in the processes of domestication and breeding (Table 2.3).

A total of 44 alleles were detected from the eight European landraces and wild
cherry which were not present in the PNW sweet cherry germplasm (Tables 2.1, 2.4-2.11
and Figure 2.1). The genomic locations of these 44 alleles were identified and their
distribution among the eight P. avium linkage groups were graphically displayed along

with other alleles (Figure 2.2). A total of l 1, 7, 5, 4, 5, 7, 2 and 3 alleles which were

83

novel to PNW sweet cherry breeding germplasm were detected on LGs 1, 2, 3, 4, 5, 6, 7
and 8 respectively. These allele numbers indicate that LGs l to 6 has undergone the
genetic founder effect than that of LGs 7 and 8. In figure 2.2 on each LG, the names of
European landraces or NY54 which provide the novel alleles are shown. LGs 1, 2 and 6
showed that UA were widely spread along the LGs. Implying the missing DNA diversity
as a whole LG8. LGs 7 and 8 were least diverse in terms of UA. Only wild cherry, NY54

and Katalin brought UA to LG7, and NY54, l9-21B and Eugenia brought UA to LG8.

84

Table 2.1: The Prunus avium groups, selections (wild, Non-PNW and PNW), their parents, origins, number
of unique alleles (UA) and % of heterozygous loci (H)

 

 

 

 

Group Selection Parent 1 Parent 2 Origin UA H Class H
Wild NY54 U3 U Germany 13 49.4 49.4
l9-2 l B U U Ukraine 1 36.9
Ambrunes U U Spain 9 50.1
Cristobalina U U Spain 6 39.4
1:311” Eugenia U U Nb_ Europe 4 54.9 46-5
Katalin U U Hungary 4 43.8
Krupnoplodnaya U U Romania 1 50.6
Windsor U U N. Europe 2 49.8
Emperor Francis U U N. Europe 4 79.8
Benton Stella Beaulieu USA 0 57.4
Bing Black-Republican U USA 0 63.7
Brooks Rainier Early-Burlat USA 0 48.6
Chelan Stella Beaulieu USA 0 49.0
Chinook Bing Gil-Peck USA 0 52.2
Glacier Stella Early-Burlat USA I 50.8
Lambert U U USA 1 45.2
Lapins Van Stella USA 0 50.9
Napoleon U U Germany 0 59.1
Newstarc U U Canada 0 49.7
PC7147-009 Stella U USA I 49.3
PC7903-002 PC7147-4 PC7 146-1 1 USA 0 49.6
PC8007-002 Glacier Cashmere USA 0 43.6
PNW PMR-l u U USA 0 42.7 53:6
Rainier Bing Van USA 0 58.1
Regina Schneiders Rube Germany 4 54.9
Same V-l 6U l4U U Canada 0 51.]
Schmidt U U Germany 0 69.3
Schneiders U U Germany 0 52.2
Selah P8-79 Stella USA 0 47.4
Stellac Lambert JI242U Canada 0 63.3
Summitc Van Sam Canada 0 39. l
Sweetheartc Van Newstar Canada 0 51 .4
Tieton Stella Early-Burlat USA I 56.9
Ulster Schmidt Lambert USA 0 55.3
Vanc Empress-Eugenie U Canada 0 58. 1
Vice Bing Schmidt Canada 0 52.1

 

aU: Unknown, bN: Northern, cConsidered as PNW germplasm (closely related to other PNW selections)

85

Figure 2.1: The number of unique and shared alleles identified in the three groups of
sweet cherry used in the study; PNW: 28 cultivars historically used and released in the
Pacific North West sweet cherry breeding programs, Non-PNW: seven sweet cherry
cultivars from Europe that have not been used in the PNW sweet cherry breeding

programs and Wild: one forest (mazzard) cherry (Prunus avium) selection (N Y54)

 

86

Table 2.2: The relative abundance of alleles with differing frequencies detected for 77

DNA markers

 

Number of alleles of each

Number Of alleles class that are not present in

Allele frequency class within each class

 

 

PNW
<0.20 147 44
0.20 - <0.40 77 0
0.40 - <0.60 50 0
0.60 - <0.80 22 0
0.80 - <1.00 4 0
Total number of alleles 300 44

 

87

Table 2.3: The percentage of heterozygous loci (H) per linkage group

 

 

 

Number
LG of loci PNW Non-PNW Wild (N Y54) Total
per LG
1 13 54.8 50.6 30.8 48
2 12 50.5 34.4 58.3 38
3 9 68.9 54.7 22.2 52
4 8 43.4 44.9 37.5 36
5 11 60.6 62.8 50.0 51
6 9 60.2 53.1 66.7 39
7 10 61.3 49.0 70.0 61
8 5 27.4 35.0 60.0 28
Total 77 53.4 48.1 49.4 44

 

88

Table 2.4: The possession of marker-alleles by sweet cherry selections - LGI

 

 

Map Position M k Allele Number of selections If Number of selections possesing the
(cM) ar er (bp) possesing the allele allele is less than 3, selection names
0.0 CPPCTOI6 171 13
ISO 22
188 4
190 l Katalin
198 1 Regina
200 ] Ambrunus
204 l Katalin
208 l6
1 .0 EMPAOOI 1 3 5 l Cristobalina
MS I PC7147-009
150 5
155 I]
158 l PMR-l
160 I3
I65 2|
37.8 EMPAOOS 225 l Cristobalina
240 4
245 I7
255 27
45.1 EPDCUSIOO 184 7
I95 2 EF, Schmidt
I98 7
202 1 Cristobalina
47.7 UCD-CH3l I45 24
150 12
155 7
I65 12
l 70 l Krupnoplodnaya
50.0 PR33 244 25
250 2 Windsor, Sam
266 24
63 .6 PCeGA59 184 28
190 26
64.9 CPSCT027 204 33
216 5
218 23
220 l Ambrunus
85.1 PMS67 I48 26
160 9
162 4
165 22
88.1 PRIOI 146 l NY54
152 36
158 6
l06.6 CPPCTOl9 180 3]
I86 8
188 l Ambrunus
I90 1 EF
110.0 EMPAOI l 240 32
245 12
250 l Ambrunus
116.0 EPPB42 l 3 132 l EF
140 34
I46 5

 

Table 2.5: The possession of marker-alleles by sweet cherry selections - LGZ

 

 

Map Position Marker Allele Number of selections If Number of selections possesing the
(cM) (bp) possesing the allele allele is less than 3, selection names
0.0 MA069a 105 l Wnidsor
120 36
126 I4
I30 I Lambert
1.5 CPSCT038 I90 32
I92 9
204 I0
4.] UDA-059 134 28
138 8
I I BPPCTO34 225 I8
235 I2
250 2 Schmidt, Ulster
255 I6
12.7 MA005c I90 26
I98 25
I58 UDAp-46I 159 I5
I78 32
23.7 BPPCTOOZ I68 I NY54
I84 36
185 9
186 14
32.4 MA007a 93 I NY54
104 I9
110 I0
I I6 22
I26 I Cristobalina
40.7 U DA-005 2 I 8 35
222 22
224 I
46.4 UCD-CHIZ 175 26
I80 I I
I82 8
I86 I I9-2 I B
190 4
I92 I I
55.7 PCeGA34 135 I2
I43 9
I45 I NY54
155 I EF
I65 I NY54
59.7 CPSCTO37 I90 7
195 I3
2 IO 32

 

90

Table 2.6: The possession of marker-alleles by sweet cherry selections - L03

 

 

Map Position Marker Allele Number of selections If Number of selections possesing the
(CM) (bp) possesing the allele allele is less than 3, selection names
0.0 EPPCU599O 185 27
195 28
0.1 PaClTA4 I40 29
143 27
23.2 PMS30 I32 4
I42 24
152 7
I62 7
I70 7
175 17
25.8 BPPCT039 128 I Cristobalina
134 15 -..
138 14
140 2 Ambrunus, Regina
I45 23
32.4 EPDCU3083 145 24
153 26
34.1 UDP98-416 200 I8
210 33
39.4 CPDCTO37 145 6
1 5 5 1 Cristobalina
158 2 Ambrunus, Windsor
160 10
165 8
I70 25
47.5 MA039a I70 34
216 l l
220 4
222 1 Regina
72.6 EMPAOM 220 I Ambrunus
225 10
230 27
232 3 Cristobalina, Napoleon, Lambert
233 8
234 4
235 8

 

91

Table 2.7: The possession of marker-alleles by sweet cherry selections - LG4

 

 

Map Position Marker Allele Number of selections If Number of selections possesing the
(cM) (bp) possesing the allele allele is less than 3, selection names
0.0 EPPCU3664 I 15 II
122 31
125 6
130 9
6.4 EMPAOIS 220 14
222 13
225 1 NY54
240 9
13.1 AMPAIIO 135 34
138 14
33.8 BPPCT040 120 1 1
125 12
128 1 EF
1 30 l Cristobalina
I35 9
145 17
45.0 UDP97-402 1 18 I NY54
I22 I NY54
126 3 Cristobalina, EF, Stella
I30 5
138 6
50.0 M 12a 180 33
185 10
190 l Eugenia
59.5 UDA-037 423 5
425 l l
431 20
73.7 UDA-027 135 36
137 16

 

92

Table 2.8: The possession of marker-alleles by sweet cherry selections - LGS

 

 

 

Map Position Marker Allele Number of selections lf Number of selections possesing the
(cM) (bp) possesing the allele allele is less than 3, selection names
0.0 EPPCUO961 I46 20
148 27
150 15
7.7 EPPCU9168 162 13
I64 8
I68 25
20.7 BPPCT026 164 24
170 17
I78 5
I86 14
38.4 UDP96-019 202 22
205 22
47.4 BPPCT037 137 5
142 13
145 15
148 18
155 4
157 1 Windsor
49.0 EMPaS] 1 68 16
78 22
88 3 NY54, 19-21B, Ambrunus
108 4
112 10
61.9 EPDCU5183 120 20
125 2 Cristobalina, Schneiders
140 4
145 I Krupnoplodnaya
150 8
65.0 CPDCT016 150 16
160 33
70.5 EPPB4230 253 9
254 7
255 1 Ambrunus
256 24
260 7
73.4 CPDCT022 145 3 NY54, Regina, Tieton
150 19
155 l I
158 l Ambrunus
165 7
175 6
75.5 BPPCT014 190 22
192 3 Eugenia, EF, Schmidt

195

—
kl!

93

Table 2.9: The possession of marker-alleles by sweet cherry selections - LG6

 

 

Map Position Marker Allele Number of selections If Number of selections possesing the
(cM) (bp) possesing the allele allele is less than 3, selection names
0.0 EMPaSOI 222 5
228 27
232 18
240 1 Glacier
4.5 UDP96-001 110 1 Ambrunus
115 7
129 28
131 15
13.5 BPPCT 008 90 8
97 31
100 4
36.6 CPPCT023 170 7
I71 5
44 EPPCU3090 172 25 NY54
180 1
185 27
50.8 UDP98-021 102 27
112 24
118 1 NY54
51.3 EPPB4227 120 1 Tieton
125 1 NY54
130 19
I35 32
145 4
59.4 MA040a 210 20
215 1 Eugenia
225 '12
240 6
66.8 S-Rnase S-1 9
S-2 6
S-3 18
S-4 21
S-5 2 Krupnoplodnaya, PC7903-002
S-6 3 NY54, Ambrunus, Eugenia
S-7 1 Eugenia
S-9 10
S-12 2 Katalin, Schneiders

 

94

Table 2.10: The possession of marker-alleles by sweet chen'y selections - LG7

 

 

Map Position Marker Allele Number of selections If Number of selections possesing the
(cM) (bp) possesing the allele allele is less than 3, selection names
0.0 CPPCT022 245 25
250 26
252 3 NY54, Chinook, Regina
13.1 UDAp-407 205 15
215 30
217 2 PC8007-002, PMR—I
14.0 CPSCT026 178 17
180 5
14.5 UDAp-401 260 21
265 14
270 5
295 7
15.5 EPDCU2931 132 15
146 3 Napoleon, Windsor, Lambert
148 5
150 1 Katalin
152 23
160 1 NY54
30.3 CPPCT033 145 10
148 12
149 4
150 13
152 3 EF, Schmidt, Vic
158 3
164 13
38.2 PMS2 130 8
142 22
146 24
165 4
42.4 PS8e08 172 8
181 27
186 17
45.0 PCHCMS2 670 18
730 28
49.6 EPDCU3392 110 12
I 15 I6
123 7
129 20
135 5

 

95

Table 2.11: The possession of marker—alleles by sweet cherry selections - L08

 

 

Map Position Marker Allele Number of selections lf Number of selections possesing the
(cM) (bp) possesing the allele allele is less than 3, selection names
0.0 pchgms49 1 56 35
168 I 1
170 3 Benton, Chelan, Tieton
173 2
13.4 EPPCU4726 160 34
I62 2
24.4 CPPCT006 188 4
190 29
204 1 Regina
206 2 NY54, 19-21B
208 14
54.6 MD201a 230 34
250 6
80.8 PSlH3 270 34
272 5
275 9
280 1 NY54
285 1 Eugenia

 

96

Figure 2.2: A-H. The different alleles for the markers and their relative presence in all the
linkage groups for 36 sweet cherry selections {wild cherry (gray bar), PNW (white bar)
and non-PNW (black bar) groups}. The arrows show the alleles that do not exist in the
PNW sweet cherry cultivars and the names of the cultivars are indicated near the arrows.

A: linkage group (LG) 1, B: LG2, C: LG3, D: LG4, E: LG5, F: LG6, G: LG7, H: LG8

97

Fig 2.2 Cont. A: Linkage group 1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   
    
 

 

 

 

 

 

 

 

14bbp
116.0 cM 140bp F _
EPPB4213 132m) .
110.0 cM 32222 E‘— Ambr‘fm‘
EMPAOII 240bp 1 -
190bp
106.6 cM 188bp—i é—Ambrunus
CPPCT019 186bp_.
180bp :— -
_:1
88.1 cM PR 101 $3? 1.__.L .
-1 146bp ,- :NY54
85.1 cM }232P 1:, “
PMS67 160bp_
14ng E .
64. 9 cM 3233p h <——Ambrunus 1
CPSCT027 2166‘” E
18313" — '
p — 1
63.6 cM GA59 184bp— _
50.0 cM PR33 32222 .3— '
244bn - -
___1_7_0bp 1 <— Krupnoplodnaya
165bp ,-
37898‘1131 12331-1:
- p "—21:-
145bp l—F J
202b *—-Cristobalina
45.1 CM 198153;:
EPDCUSIOO 1951:1511:
l84bp :-
25 5bp _
37-8 CM 245bp _ I
EMPAOOS _ 240bp 1 :2
225bp é—Cristobalina
165bp 3
1601311
1.0 CM 158bp
EMPAOOI 155.121).
150bp
_____ <— Cristobalina
204pr” 4—Katalin
0.0 CM 28%)?) <—-Ambrunus
CPPCW #19011: {—181.11
188bp
180bp 3
l7lbp 1 IF! 1 _ __ .
0 10 20 3O 40
lNon-PNW alleles El PNW alleles fl Mazzard alleles Number of alleles

98

Fig 2.2 Cont. B: Linkage group 2

 

 

 

 

 

  
 

 

 

 

 

 

 

 

 

 

59.7 CM 3222;: _ —
CPSCT037 1 90bp
“165bp“; 4—NY54
55.7 cM 155bp_____
PCeGA34 145bp ‘1- <—NY54
'143bp —:1
l35bp _:
..,1?2b12. '
190bp
46-4 0M ' 186bp 4—19-21B
UCD-CH12 182bp
180bp
175bp — n
40.7 cM 3.2.2.41)me
UDA-OOS 222bp
218bp -
, 126bp I <—Crist0balina
32.4 cM “6131’. . '
MA007a 3110bp
104bp ,— J

93bp ”h <—NY54
l86bp ‘-:I

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

23'7 CM 185bp -:

BPPCT002 184bp " n
168bp MS—NY54

15.8 cM 178122,

UDAp461 159bp h:

12.7 CM 4 198bp T— 1

MA005c 190bp % 111111111
255bp _:m

11.0 CM 2501319 :1

BPPCT034 235bp F:
225bp

4.1cM _l38bp_ :1

UDA-059 134bp —
204bp h:

1.5 cM
7 192bp_ :18:

CPSCT038 190bp .

0.0 cM ”Ob? F’

M 069 126bp _ _:1

a a 120bp. ; m
105bp F‘—Windsor 4| 1 1 4‘
0 10 20 30 4O
INon-PNW alleles El PNW alleles I Mazzard alleles Number Of alleles

99

Fig 2.2 Cont. C: Linkage group 3

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  
  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

72.6 cM
EMPA014
I
47.5 cM
MaO39a
J
39.4 cM 11----..-..---..
CPDCT037 15312423- 4——Ambrunus
$13:pr <-——'Cristobalina
145bp .
34.1 cM 121.9212- i
UDP98-416 200bp J- I
32.4 cM 111153131311- ‘
EPDCU3083 l45bp ‘ " '
1-1.4.5991) '
25.8 cM __ 11410191311
BPPCT039 1128911.. 1111-:
134bp J
128bpw 4—Cristobalina
12§b8 '
1170581-
23.2 cM __.1.62.bp---:'
PMS30 3135321212 E
114% 1
'13sz E
0.1cM PaClTA4 5313.12.11i i
40bp 4- I
0.0 cM 111125119111,— '
EPPCU5990 185bp _ 11"
0 10 20 30 40

I Non-PNW alleles E1 PNW alleles Mazzard alleles Number of alleles

100

Fig 2.2 Cont. D: Linkage group 4

 

 

 

 

 

 

 

 

7326M Wl37bp _:::1
43lbp _:1
59'5CM 425bp ‘3
UDA-037 3. 4
423bp
190bp I<—-Eugenia
50'0 CM 185bp -:1
M12a ...... ,
180bp — J
138bp :1
130bp 4:
45.0 cM -
UDP97-402 1126b!) .3

122bp m<———NY54
118bp HRH—NY54
145bp -:m
l35bp -:
33,8 cM 130bp I"—'Cristobalina
BPPCT040 128bp :1

125bp 7-:

 

 

 

 

 

13.1cM 138bp -:

AMPA110 135bp — i
240bp i:m

6-4 CM 225bp STE—NY54

EMPAOlS ~

 

EPPCU3 664

 

0.0 cM l25bp
—

1‘1pr -:

 

0 10 20 30 40
INon-PNW alleles El PNW alleles I Mazzard alleles Number Of alleles

101

Fig 2.2 Cont. E: Linkage group 5

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

75557 1333" r
BPPCT014 190bp _ _
175bp :n
165bp -:
73'4 CM 158bp I<-—Ambrunus
CPDCT022 155bp -:
150bp _ I
145bp :Il
260bp_ I:
256b _ II
70.5 CM ”2551): i4—Ambrunus
EPPB4230 254bp 4.:
25%;; :m
65.0 cM 160pr24 I
CPDCT016 150bp _:ml
150bp :11:
61.9 CM 423:: ”I é—Krupnoplodnaya
EPDCU5183 125131) :1
12%;; T I
112bp :2
.1.08bp F33
49-0 CM , 88bp -l‘—NY54,19-21B,Ambrunus
68bp -:
157bp ls—Windsor
4155bp -:l
474 cM l48bp ,, —:mm
BPPCT037 _ 145bp 1-:I
1421,97:
137bp I:
38.4 cM 205bp 7 — 1
UDP96-019 202bp ‘ 5557777
1861313 —:1
20.7 cM ,178bp #2
BPPCT026 “170bp I:
164bp
7.7 cM 123? :— W"
P,.-:'
EPPCU9168 162bp :4
0.0 cM 150pr I:
EPPCUO961 .148bp . “1..
146bp — I y
0 10 20 30 40

Number of alleles
INon-PNW alleles D PNW alleles I Mazzard alleles

102

Fig 2.2 Cont. F: Linkage group 6

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

.5-12 F3
S-9 -:
S-7 I<——Eugenia
66.8 cM Sf6 .- ‘—— NY54, Ambrunus, Eugenia
S—Rnase S-5 I]
S-4 -_ fl
S-27 —1111
s-1 i:
240bp 44':
59.4 cM 225bp .2111
Ma040a 21 prg 1 I ‘——Eugenia
210bp 1- *1
.. 1.4.5.152 :2
51.3 cM .1352?» ‘
EPPB4227 .130bP .1 I“
.I.21.5_b_p_..1-<-—NY54
l20bp 121
50.8 cM 1181’? .im T—NY54
UDP98-021 ”1712,1594: fl
102bp _; *1
44.0 cM 1 18,51,313. —: fl
EPPCU3090 _ 1,80bp I ‘—NY54
l72bp [1 m
36.6 cM 17lbp Fm
CPPCT023 170bp —m
13.5 CM . lOObpr :1
BPPCT008 97bp 1 fl
90bp
131bp‘ —:m
4.5 CM 129bp — W
UDP96-001 ”115bp " ::
110bp Ih—Ambrunus
240bp :1
00 cM 232bp E
EMPASO] 228bp ., ii 111
222bp 4 , .
0 10 20 30 40
Number of alleles
I Non-PNW alleles D PNW alleles ll Mazzard alleles

Fig 2.2 Cont. G: Linkage group 7

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

135bp E
49.6 cM 129bp 1 '
EPDCU3392 123138—31
115bp
110bp -:
45.0 cM 730bp ‘ -
PCHCMS2 H670bp
4244 cM 441486bp :
P88e08 1811’10 . .111.
172bp -:
165bp -:
38.2 cM 146bp — 1
PMSZ l42bp
130bp —m
4164bp4 I:1
1581,51) I-
303 cM 15sz :'
CPPCT033 150bp -:
149bp _]
11.58.61 -:7
145bp —:
160bp Il<—NY54
.15.pr — 4
15.5 CM 44150bp I4—Katalin
EPDCU2931 1481915 F:
146bp4_-:]
132bp —:I
295bp 44::
14.5 cM 270bp Cm
UDAp-40l 265bp -:
260bp - 1
14.0 cM 180bp -:m
CPSCT026 l78bp ———-‘—‘——m
13.1cM 217"? :’
UDAp-407 2151’? _ "
205bp _:m
0.0 cM 2521’? 1:“
CPPCT022 2501913 .
245bp 4 _: 4 4
0 10 20 3O 40
Number of alleles
I Non-PNW alleles D PNW alleles I Mazzard alleles

104

Fig 2.2 Cont. H: Linkage group 8

 

285bp I_Eugenia

280bp l+—NY54

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

80.8 cM
PS1H3 E7_51_me-::l
272bp I:
270158 -
54.6 CM 2501),) u
MD201a .1 .3
250515 I I
208bp - I
206913 .4—NY54, 19-2113
24.4cM .
CPPCTOO6 204bp J
190bp ‘- 4
188bp I]
13.4 cM
EPPCU4726
0.0 cM
pChgms49
0 10 20 30 40
Number of alleles

INon-PNW alleles El PNW alleles I Mazzard alleles

105

Genetic diversity structure

The phylogenetic relationships among the 36 cherry selections were determined
using the data from the 77 DNA markers (Figure 2.3). At ~75% of genetic similarity
value, the selections could be classified into 8 clusters (A-H). The clusters A and D
exclusively represent European selections. NY54, a wild cherry is genetically dissimilar
from all the other ones studied and has 70% genetic similarity to two Spanish landraces,
Cristobalina and Ambrunus. NY54, Cristobalina and Ambrunus as a single cluster

(cluster A) were separated from the rest of the cultivars at 40% of genetic dissimilarity

 

value. The cluster E contains Vic, whose parents are Schmidt and Bing. In this study, Vic
clustered with Schmidt. The clusters B, C, G and H include PNW sweet cherry breeding
germplasm. Grouping of two European selections, Windsor and Eugenia that do not have
any known pedigree relationship to the PNW cultivars, with clusters C and B, suggest the
close genetic relationship of these selections to the parents that have been used in the

PNW sweet cherry breeding.

106

Figure 2.3: Dendrogram resulting from marker allele based genetic distance analysis of
36 sweet cherry selections. Cluster analysis used McQuitty linkage, Absolute Correlation

Coefficient Distance (Minitab 15)

107

 

Similarity

100.00 86.83 73.65 60.48
(A) NY54
Ambrunes

Cristob alina J
(B) BF

Eugenia
Lapins ..
Sweetheart—} —
Rainier
Van I
Selah
Bing —
Nap ole on - :—
Chinook

Lambert

Newstar J l —
Stella

Ulster
PC7147-009

Summit I —

Sam
,.___Windm_
(D) Katalin

Schneiders I
Renae...
(E) l9-2lB
Schmidt-———l
Vic-———J
F Krupnoplod 4
IIIIENIIZIIImImagflolgHII
G Benton
( ) Glacier ]
PCSN'LOOZ 7

Brooks
(H) Tieton I

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

108

Graphical genotypes for sweet cherry cultivars

The genetic diversity analysis used DNA markers that covered all eight sweet
cherry linkage groups. Thus it was possible to visualize the linkage group heterozygosity
for the 36 sweet cherry selections and present it as graphical genotypes (GGT) (Figure
2.4). The GGT illustrate the marker alleles for the eight linkage groups using the map
positions from the consensus linkage group based on the data of Olmstead et al. (2008).

The GGT can be used to search for those selections that have marker alleles that are 4

 

linked to favorable QTL alleles. For example, Zhang et al. (2009) identified two QTLs
for fruit size on LG2 and LG6 segregating in the NY54 x EF mapping population. r
BPPCT034 was found to be linked to the LG2 fruit size QTL. In our study four alleles

were identified for BPPCT034 (PIC of 0.6) indicating that this would be a good marker

candidate for fine mapping and validation of the QTL. EPPCU3090 on LG6 was

associated with the second QTL identified. In our study three alleles were identified for

EPPCU3090 (PIC value of 0.4) therefore, it is also a useful marker to further investigate

the LG6 fruit size QTL. Similarly the GGT could be correlated to mapped genes and

QTLs to get more insight for the marker haplotype information which will be useful in

marker assisted breeding and future functional genomic studies. The only drawback in

these GGT is that the exact allele phase (i.e. coupling and repulsion) for markers for

some P. avium selections cannot be represented as for those selections; marker data for

segregating progeny populations are not available.

GGT for sweet cherry cultivars show the allelic states and genomic landscape

with respect to studied DNA markers and the consensus linkage map positions. However,

109

few mapped QTL are available to fully utilize this resource. The extensive phenotyping
for important traits in multiple years and locations is necessary to assign breeding values
to the linked alleles and marker allele haplotypes. This will allow the GGT to be used in

marker assisted breeding and comparative genomics in family Rosaceae.

110

 

Figure 2.4: A-H Graphical genotypes for 36 sweet cherry cultivars. Eight linkage groups
for each cultivar are shown with two homologous chromosomes for each linkage group.
The marker positions in centi Morgan (cM) and marker names are shown on the left. In
each cell, the allele in base pairs is shown for the SSR and gene based (PR markers and
the allele name in number is shown for the S-locus. $$ indicates a confirmed null allele

6‘ 9,

and $ indicates an unconfirmed null allele. represents the missing data. The blank
cells represent the gaps in the linkage groups. (A) Linkage group 1, (B) Linkage group 2,
(C) Linkage group 3, (D) Linkage group 4, (E) Linkage group 05, (F) Linkage group 6,
(G) Linkage group 7, (H) Linkage group 8.

Each linkage group has four pages of GGT to represent 36 sweet cherry selections in
four separate pages. Page 1 for each linkage group: Wild cherry (N Y54) and non-PNW
sweet cherry cultivars, Page 2 for each linkage group: second subset of PNW sweet

cherry cultivars, Page 3 for each linkage group: third subset of PNW sweet cherry

cultivars, Page 4 for each linkage group: fourth subset of PNW sweet cherry cultivars

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143

A panel of cultivars for SNP discovery for P. avium

The transition from SSR to SNP markers suitable for high throughput genotyping
platforms requires the sequencing of a set of selections known to represent the genetic
diversity in the germplasm of interest. The results from the SSR diversity study can be
used to select such a panel of selections. In our case, goal was to identify six P. avium
selections as a “SNP detection panel”. Such a panel must represent 90% allele diversity,
should have wide phenotypic diversity and if possible should have progenies and
established linkage maps to further study the genetics. However, still there is a room for
polymorphism loss by selecting only six selections but defining a core set is not possible
without a comprehensive marker survey and it is not currently feasible to have more than

six individuals in the SNP detection panels due to high cost of DNA sequencing.

Table 2.12 lists the six P. avium selections for SNP detection panel along with
other relevant information. The number of UA is the key criterion which shows the
amount of genetic diversity bringing in to the SNP detection panel. The selections that
have more than 3 UA were considered for the panel. The S-genotype was also considered
because it and was the marker locus with highest PIC (Table 2.15). As far as possible
many S-alleles were recovered in the panel. The cluster positions according to the
dendrogram (Figure 2.3) of each selection were considered and as far as possible, most
distantly clustered ones were chosen. The individual LG level diversity was also
considered and Figure 2.2 and Tables 2.4-2.11 were useful for this purpose as it shows
which sweet cherry selections bring UA for each locus and LG. All the eight LGs were

represented with UA in selecting the SNP panel. The relevant published information such

144

 

 

as linkage maps and QTLs were considered last to facilitate the quick understanding and
application of the new SNP which will be developed based on this panel. Selections EF,
NY54, Ambrunus, Cristobalina, Eugenia and Katalin are suggested for a SNP detection

panel based on this study. The suggested SNP detection panel brings 40 out of total 52

UA detected in the study and also brings 6 S-alleles (82, S3, S4, S6, S7 and S12).

145

Table 2.12: The panel of selections/individuals for SNP detection in P. avium

 

P. avium

selection

Number
of UA

s.
genotype

The LGs where
UA located

Published information and

remarks

 

EF

4

S354

1,4

Founder parent of many PNW
cultivars, LG map available
(Olmstead et al. 2008), fruit
size (Zhang et al. 2009 in
press) and mu color QTLs
reported (this thesis)

 

NY54

13

$256

1,2,4, 5,6, 7,8

Wild cherry, LG map available
(Olmstead et al. 2008), fruit
size (Zhang et al. 2009 in
press) and fruit color QTLs
reported (this thesis)

 

Ambrunus

S356

1,3, 5,6

Bring highest number of UA
and reported for high fruit
quality (Bernalte et al. 1998)

 

Cristobalina

S356

1,2, 3,4

Important to bring DNA
diversity for natural self

compatibility

 

Eugenia

S387

4,6,8

Important to bring DNA
diversity from LG8 (No other
sweet cherry selections used in
the study does not bring UA to
LG8)

 

Katalin

54512

1,7

Important to bring DNA
diversity from LG7 (No other
sweet cherry selections used in
the study does not bring UA to
LG7)

 

146

 

 

Related studies on genetic diversity in Prunus

This study identified 300 alleles for 77 DNA markers for 36 sweet cherry
selections with the range of two to nine alleles per locus with an average of five. Struss et
al. (2003) identified 48 alleles from 15 SSR markers for 15 sweet cherry cultivars.
Boritzki et al. (2000) used ten AFLP markers to characterize 128 sweet cherry accessions
but only 128 fragments were found to be polymorphic out of total 712 fragements
amplified. This shows the suitability of SSR and gene based markers over AF LP in
diversity studies in sweet cherry. Wunsch and Hormaza (2002) used 34 peach SSRs to
study 76 sweet cherry cultivars and amplified 24 SSRs but only 14 were polymorphic.
Wunch and Hormaza (2004) used 12 SSR markers to fingerprint 28 Spanish sweet cherry
genotypes and found 42 informative alleles; which were able to completely classify the
genotypes. This clearly shows the present study mined more alleles compared to these
previous studies. Ohta et al. (2005) used 85 SSR markers to characterize 144 individuals
of flowering cherries (Prunus subgenus Cerasus), 29 SSR were successfully amplified
and they found mean number of alleles per locus of 17.3. Three SSR markers were
common to Ohta et al. (2005) and present study (Table 2.13) and for all three SSR, more
alleles detected compared to the number of alleles detected in sweet cherry indicating that

flowering cherry is more genetically diverse than sweet cherry.

147

Table 2.13: The comparison of number of alleles per SSR marker and heterozygosity (H)

of three SSR between sweet and flowering cherries

 

Number of alleles detected in
Number of alleles detected . .
SSR Markers . . 144 flowenng cherry accessmns
1n 36 sweet cherry selections
(Ohta et al. 2005)

 

UDP96-001 4 1 l
MAOO7a 6 29
PMS67 4 23

 

148

Cantini et al. (2001) identified 107 alleles for 59 accessions of tetraploid tart
cherries using 10 SSR markers, the number of alleles on locus ranged from 4 to 16 and
86% of the alleles had less than 0.2 of frequency. The present study found only 55%
alleles had a frequency less than 0.2 of frequency. Even though, these parameters are not
readily comparable between sweet and tart cherries, there were 6 SSR markers common
for study of genetic diversity in tart cherry (Cantini et a1. 2001) and the present study
(Table 2.14). Except for PS8e8, all the other SSRs exhibit a greater number of alleles in

tart cherries compared to sweet cherries.

149

Table 2.14: The number of alleles detected for sweet and tart cherries for 6 SSR markers

 

 

Number of alleles Number of alleles detected in 59
Misl:rs detected in 36 sweet tetraploid tart cherry accessions
cherry cultivars (Cantini et al. 2001)

P88e08 4 4
PMSZ 5 8
PMS30 7 11
PMS3 7 16
PceGA59 2 10
PMS67 5 13

 

150

In current study only nine S-alleles were reported, however, to date, 32 S-alleles
have been reported in sweet cherry [Sonneveld et al. (2003), De Cuyper et al. (2005),

Wunsch and Hormaza (2004) and Vaughan et al. (2008)]. De Cuyper et al. (2005) found

17 S-alleles in Belgium wild cherries With the S—alleles S3 (26%), S] (16%) and S2 (13%)
the most common and S1 -S7, S9, $12-$16 also present in that sweet cherry germplasm. S10
and Sn-Szz were unique to wild cherries. In the present study, nine S alleles reported (S 1-
S7, S9 and S 12) and S4 is the most abundant (29%), S3 (25%), S9 (14%) and SI (13%). 813-
S16 were not detected in the present study and they occurred in less than 2% in Belgium
sweet cherries. Wunsch and Hormaza (2004) reported three new S-alleles ($23-$25) and
Vaughan et al. (2008) reported six new S-alleles in wild cherries (527-832) and these

alleles were not detected by our study or by De Cuyper et al. (2005). Schuster et al.
(2007) studied S-allele genotypes of 149 sweet cherry cultivars and clones in Turkey and

found 13 different S-alleles and 40 genotypes (i.e. S-allele combinations). The present
study identified nine S-alleles and 15 genotypes with S3S4 been the highest represented
genotype; S386 is the highest represented genotype. Whereas, S3S4 and S153 are also

prominently present in sweet cherry accessions assessed in Turkey (Schuster et al. 2007).

Marker polymorphism
300 alleles were found using 77 DNA markers in 36 P. avium selections. The

minimum number of alleles for a marker locus was two, the average was four and
maximum was nine (for the S-locus: S 1, 52, S3, S4, S5, S6, S7, S9 and S12). The average

heterozygosity (H) and the polymorphic information content (PIC) for all the markers

151

were 0.5 and range was 0.63. The markers which have PIC equal or greater than 0.5 were
adequate for linkage mapping, QTL analysis and diversity studies compared to the
markers which have PIC less than 0.5. Table 2.15 presents the DNA markers and H and
PIC in the studied set of sweet cherry cultivars. This was an important resource to select

markers for marker assisted breeding and population genetic studies.

152

Table 2.15: Heterozygosity (H) and Polymorphic Information Content (PIC) of DNA
markers used in the study

 

 

Locus H PIC Locus H PIC
AMPAIIO 0-33 0-23 EPPCU599O 0.50 0.45

BPPCT002 0.50 0.45
1 . .4
BPPCTOO8 0.34 0.31 EPPCU9 63 056 0 9

 

 

 

BPPCT014 0.55 0.51 "”23 0'40 0'35
BPPCT026 0.70 0.64 MAOOSC 0'50 0'38
BPPCT034 0.68 0.62 MAOO7a 0-65 0-53
BPPCT037 0.75 0.70 MaO39a 0.38 0.34
BPPCT039 0.67 0.61 Ma040a 0.60 0.54
BPPCT040 0.76 0.72 Ma069a 0.36 0.31
CPDCTO|6 0.46 0.42 MD201a 0.16 0.15
CPDCT022 0.73 0.68 PACITA4 0.50 0.46
CPDCT037 0.67 0.63 PCeGA34 0.60 0.55
CPPCT006 0.57 0.52 PceGA59 0.50 0.37
CPPCT016 0.73 0.68 PCHCMSZ 0.44 0.34
cppcr019 0.29 0.26 pchgms49 0.38 0.35
CPPCT022 0.54 0.44 PMS2 0.66 0.59
CPPCT023 0.46 0.41 PMS30 0.76 0.72
CPPCT033 0.82 0.80 PMS67 0.64 0.58
CPSCT026 0.33 0.29 PRIOI 0.18 0.17
CPSCT027 0.55 0.47 PR33 0.53 0.42
CPSCT038 0.47 0.42 pslh3 0.42 0.39
EMPAOOI 0.73 0.68 PS8eO8 0.58 0.51
EMPAOOS 0.52 0.44 S-RNase 0.81 0.78
EMPAOI 1 0.42 0.38 uco-cmz 0.68 0.65
EMPAOI4 0.71 0.68 UCD-CH31 0.70 0.65
EMPAOIS 0.67 0.62 UDA-005 0.45 0.36
EMPaSOI 0.56 0.48 UDA-027 0.35 0.29
EMPaS] l 0.68 0.63 11071037 0.56 0.48
EPDCU2931 0.60 0.55 UDA-059 0.26 0.23
EPDCU3083 0.50 0.46 UDAp-401 0.68 0.64
EPDCU3392 0.76 0.72 UDAp-407 0.41 0.35
EPDCU5 100 0.62 0.58 UDAp-46l 0.43 0.38
EPDCU5183 0.60 0.55 UDP96-001 0.55 0.48
EPPB4213 0.16 0.15 UDP96-Ol9 0.50 0.38
EPPB4227 0.51 0.45 110997-402 0.70 0.65
EPPB4230 0.65 0.6] UDP98-021 0.52 0.42
EPPCUO961 0.62 0.55 UDP98-416 0.40 0.32
EPPCU3090 0.52 0.40 Mean 0.54 0.48
EPPCU3664 0.61 0.57 Standard 016 0_ '6
EPPCU4726 0.06 0.06 deV'atlon

 

 

153

A panel of DNA markers for P. avium DNA fingerprinting

The identification of a minimum number of markers that can differentiate all 36
selections used in this study would be very important for future DNA fingerprinting
studies in sweet cherry such as cultivar identification and to solve cultivar mix ups in
nurseries and orchards. Based on PIC data (Table 2.15) and the ability to clearly
differentiate the fragment size, a subset of five markers (CPPCT016, PMS30, S-locus,
EPPCUO961 and UCD-CH12) were selected from the 77 markers through several
iterations of cluster analysis. The alleles for these five markers together can differentiate

all the 36 cultivars in a dendrogram without any ambiguity and overlapping.

Use of DNA markers for diversity studies

The use of DNA markers is currently the most popular approach to study the
genetic diversity in living organisms. However, this approach is limited by the
availability of a sufficient number of markers that provide genome-wide coverage. In
sweet cherry linkage maps are available (Joobeur et al. 1998; Dirlewanger et al. 2004;
Olmstead et a1. 2008) from which a genome-wide set of DNA markers could be selected.
However, sometimes, not all the DNA markers, especially some SSRs, are useful for
allele mining in a diverse set of cultivars as SSR alleles can be difficult to resolve due to
stutter bands or smears in the gels. The solution is to identify a subset of markers for
which alleles can be confidently identified and if possible verified in segregating
populations. There are instances, when some markers amplify multiple alleles which
cannot be used to mine alleles and the strange alleles which do not agree with Mendelian

genetics in pedigree relationships for the studied cultivars. Such markers have to be

154

discarded from diversity studies. In rare cases, confirmed null alleles (symbol: $$) could

be used as normal alleles when confirmatory evidences are available from progeny data.

155

CONCLUSION

The genetic diversity of 28 PNW, seven non-PNW and one wild sweet cherry
groups were analyzed using 77 DNA markers. A total of 300 alleles were identified with
an average of four alleles per locus to test the hypothesis of genetic founder effect. A
total of 52 unique alleles were identified and 40 of them were not present in the PNW
sweet cherry germplasm. A total of 157 rare alleles were identified and 44 of them were
absent in PNW sweet cherry germplasm. These results indicate that early settlers brought
a limited subset of sweet cherry germplasm to the New World and incorporation of
germplasm from the natural habitat would broaden the genetic diversity to provide a

better platform for sweet cherry breeding.

156

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