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SSR MAPPING AND A MODIFIED-BULK SEGREGANT
ANALYSIS FOR BLOOM TIME IN SOUR CHERRY

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6/01 cJClRC/DateDuopss-ms

SSR MAPPING AND A MODIFIED-BULK SEGREGANT ANALYSIS FOR
BLOOM TIME IN SOUR CHERRY

By
Fatih Ali Canli

A DISSERTATION

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

DOCTOR OF PHILOSOPHY
Department of Horticulture

2002

ABSTRACT

SSR MAPPING AND A MODIFIED-BULK SEGREGANT ANALYSIS FOR
BLOOM TIME IN SOUR CHERRY

By

Fatih Ali C anli

Two separate projects were carried out to aid breeding studies of sour cherry
(Prunus cerasus L., 2n=4x=32). In the first project, 45 SSR markers from apple, peach,
sour cherry and sweet cherry were screened and 10 informative SSRs yielding 17
markers that were added to previously developed sour cherry linkage map of two
tetraploid sour cherry cultivars, ‘Rheinische Schattenmorelle’ (RS) and ‘Erdi Botermo’
(EB). The BB linkage map consisted of 1 18 markers in 19 linkage groups covering 337.8
cM. The average distance between two markers is 2.86 cM. The longest distance
between two adjacent markers was 20.9 cM in linkage group 17. The RS linkage map
consisted of 133 markers in 19 linkage groups covering 433.9 cM. The average distance
between two adjacent markers is 19 cM in R88. RS9 and R812 from the previous map
were combined into one linkage group with the addition of new markers. The BB and RS
consensus map consisted of 161 markers covering 442.4 cM in 19 linkage groups. The
average distance between two markers is 2.79 CM. The longest distance between two
adjacent markers was 15 cM in linkage group 19. Several SSR markers were tightly
linked to quantitative traits such as bloom time (blm2), fruit weight (fit?) and pistil death

(de), which could facilitate marker assisted selection (MAS) for these traits.

In the second project, three different approaches were used to identify markers
associated with bloom time in a sour cherry population from ‘Balaton’ x ‘Surefire’ cross.
Initially a primer pair derived from pSl41 sequence was employed. However, the primer
amplified many bands between 140 bp and 500 bp and was not useful in determining any
association. In a second approach, pchpgms3 SSR marker, which mapped to the EBI and
8cM of pSl4l probe, was tested for association with the bloom time. Bloom data was
converted into degree-days and PCR amplification products of pchpgms3 SSR marker
were tested for association. No significant relationship was detected between alleles of
the pchpgms3 and bloom time. In a third approach, a modified bulk segregant analysis in
combination with AFLP technique was used to screen two extreme phenotypic groups
from bloom time. The average number of polymorphic bands was 10 per primer pair and
the polymorphism rate ranged from 10% to 44% per primer pair. Screening of early and
late extreme groups with 156 AFLP primer pairs resulted in the identification of three
candidate bands in three different primer combinations (a 82 bp fragment in
EGG/MCAC, a 78 bp fragment in ETT/MC CG, and a 94 bp fragment in EAA/MCGT)
that were present in one extreme phenotypic group but not in the other. The
establishment of an association of bloom time with markers assists a breeding program

by allowing for selection early in the generation saving time and effort.

ACKNOWLEDGMENTS

I am very gratefiil to Dr. Ron Perry for his guidance, editorial assistance and
financial support.

I would especially like to thank my committee members, Dr. James Kelly, Dr.
David Douches and Dr. Rebecca Grumet for their guidance and helpfiil advise during the
course of my study.

I appreciate Dr. Kenneth Sink, Dr. Amy Iezzoni, Dr. Dechan Wang, Dr. Eric J
Hanson for giving me permission to use their facilities.

I also would like to thank Andre Loskutov, Veronica Vallego, Audrey Sebolt,
Chris Owens, Nathanael Hauck, Pete Callow, Zafer Makaraci, Sedat Serce, Sergey
Nesterenko, Steven Berkheimer and all the department of Horticulture community at
MSU for their help with various aspects of my project during the course of my study.

I would like to take this opportunity to thank my wife and the children for their

support, sacrifice and love over the years.

iv

TABLE OF CONTENTS

LIST OF TABLES .................................................................................. vi
LIST OF FIGURES ................................................................................ viii
CHAPTER 1: DEVELOPMENT OF A SECOND GENERATION LINKAGE MAP

FOR SOUR CHERRY USING SSR MARKERS ...................................... 1
ABSTRACT ........................................................................................... 2
INTRODUCTION .................................................................................... 4
LITERATURE REVIEW ........................................................................... 6
MATERIALS AND METHODS .................................................................. 22
RESULTS AND DISCUSSION .................................................................. 25
LITERATURE CITED ............................................................................. 49
CHAPTER 2: A MODIFIED-BULK SEGREGAN T ANALYSIS FOR BLOOM

TIME IN SOUR CHERRY ............................................................... 57
ABSTRACT .......................................................................................... 58
INTRODUCTION .................................................................................. 60
LITERATURE REVIEW .......................................................................... 61
MATERIALS AND METHODS .................................................................. 70
RESULTS AND DISCUSSION .................................................................. 74
LITERATURE CITED ............................................................................. 87
CHAPTER 3: SUMMARY AND DISCUSSIONS .............................................. 91
LITERATURE CITED ............................................................................ 100
APPENDIX ......................................................................................... 102

1.1.

1.2.

1.3.

1.4.

1.5.

1.6.

2.1.

2.2.

2.3.

2.4.

LIST OF TABLES

Chapter 1

Summary of the 45 SSR primers derived from peach, sweet cherry, sour cherry

and apple tested in current study ............................................................. 26

Segregation ratios and product sizes of informative SSR primers in sour
cherry mapping population from ‘Rheinische Schattenmorelle’ (RS) and ‘Erdi

Botermo’ (EB) ................................................................................. 29

SSR markers incorporated into linkage groups of sour cherry map from
cultivars ‘Rheinische Schattenmorelle’ and ‘Erdi Botermo’. Numbers refer to

the linkage groups .............................................................................................. 41

Common markers which were used for the assignment of sour cherry linkage

groups ........................................................................................... 43

Linkage group locations of SSR loci in maps of several Prunus species.

Numbers refer to linkage groups ............................................................. 44

QTL detected for flower and fruit traits in sour cherry cultivars
‘Rheinische Schattenmorelle’ (RS) and ‘Erdi Botermo’ (EB) by Wang et al.
(1998) and SSR markers incorporated to these QTL locations in current

study ............................................................................................ 46

Chapter 2

Degree days (DD) for bloom time and PCR amplification by pchgms3 primer

pair for each progeny in ‘Balaton’ x ‘Surefire’ population .............................. 75

Differences of least squares means for pchgms3 marker levels and years for
bloom data in ‘Balaton’ x ‘Surefire’ population. The significance of
differences between marker levels (0 versus 1) of pchgms3 SSR were tested

using three years of bloom data expressed in degree days (DD). ..................... 76

Number of polymorphic bands produced by AFLP primer pair combinations

in ‘Balaton’ x ‘Surefire’ population. ........................................................ 79

Mean phenotypic values, standard deviations (SD) and value range for the
bloom time distribution of the progenies of ‘Balaton’ x ‘Surefire’. All data

expressed as degree days (DD) .............................................................. 82

vi

Chapter 3

3.1. Recent progress in SSR mapping in Prunus ................................................ 95
Appendix
A. 1. Primer sequences and lab sources ......................................................... 103

vii

1.1.

1.2.

1.3.

LIST OF FIGURES

Chapter 1

The second-generation linkage maps of two sour cherry cultivars,

‘Rheinische Schattenmorelle’ (RS) and ‘Erdi Botermo’ (EB), generated by
addition of SSR markers to the previously constructed RFLP map

(Wang et a1. 1998). SSR markers are underlined. Lines represent anchor loci
correspondences between RS and EB linkage groups. When two linkage

groups in one cultivar are homologous to a linkage group in the other

cultivar, the shorter of the two is marked, i.e., RSS’ .................................................. 31

The consensus map of two sour cherry cultivars, ‘Rheinische Schattenmorelle’

(RS) and ‘Erdi Botermo’ (EB), constructed fi'om combined data of AF LP and

SSR markers using JoinMap with a minimum LOD of 3.0 and a

maximum recombination frequency of 0.35. SSR markers are

underlined ....................................................................................... 36

DNA fragment patterns of the SSR primer UDP41]. Arrows indicates two
segregating fiagments which were mapped to bloom time 2 (blm2) location. The
upper arrow indicates a 154 bp fragment named UDP411-154 and the lower arrow
indicates a 131 bp fragment named UDP411-131. Lanes 1-13 are progenies from
‘Rheinische Schattenmorelle’ (RS) x ‘Erdi Botermo’ (EB)

population ....................................................................................... 47

Chapter 2

2.1. Three candidate AF LP bands that are present in one group, but not in another; a-

a 94 bp band pointed by an arrow in EAA/MCGT primer combination is present

in late group, but not in early group. b- A 78 bp band pointed by an arrow in
ETT/MCCG primer combination is present in late group, but not in early group.

c- EGG /MCAC primer combination has a band pointed by an arrow at 82 bp that
present in early group, but not in late group. Lane L is 10 bp ladder; lanes 1, 2

and 3 are late group (2-19, 4—22, and 2-39, respectively); lanes 4, 5, and 6 are

early group (4-47, 2-61, and 3-24, respectively); lane S is Surefire and

lane B is Balaton ................................................................................ 81

2.2. Bloom dates of progenies of ‘Balaton’ x ‘Surefire’ in 1999. Selected progenies

2.3.

for bulk segregant analysis are shown in bold84

Bloom dates of progenies of ‘Balaton’ x ‘Surefire’ in 2000. Selected progenies
for bulk segregant analysis are shown in bold .............................................. 85

viii

LIST OF FIGURES (continued)

2.4. Bloom dates of progenies of ‘Balaton’ x ‘Surefire’ in 2001. Selected progenies
bulk segregant analysis are shown in bold ................................................... 86

CHAPTER 1

DEVELOPMENT OF A SECOND GENERATION LINKAGE MAP
FOR SOUR CHERRY USING SSR MARKERS

ABSTRACT

A second generation linkage map of two tetraploid sour cherry cultivars (Prunus
cerasus L., 2n=4x=32), ‘Rheinische Schattenmorelle’ (RS) and ‘Erdi Botermo’ (EB) was
constructed by addition of new SSR markers to a previously constructed map (Wang et
al.1998).

Forty-five SSR primer pairs from apple, peach, sour cherry and sweet cherry were
screened and 10 informative SSRs yielding 17 markers were added to the sour cherry
linkage map. All apple primers showed amplification in sour cherry, but none of them
amplified the expected size of bands and showed a complex banding pattern. Nine of 21
SSR fi'om ‘Redhaven’ peach (Prunus persica (L) Batsch) amplified fragments with the
expected size, and five were informative. The remaining SSR expressed a complex
banding pattern. Of the eight SSR primers from sour cherry, six showed a complex
banding pattern and two amplified fragments of the expected size. Only GA25 was
informative and was incorporated into the map. Out of eight peach SSR primers reported
by Sosinski et a1. (2000), one (pchgms3) was mapped to the sour cherry map. Two out of
four sweet cherry SSR markers were informative and placed into the map.

The BB linkage imp consisted of 19 linkage groups covering 337.8 cM with 118
markers. The longest linkage group was EBl covering 43 cM and the shortest was BB 6’
where two markers were mapped to the same place. The average distance between two
markers is 2.86 cM. The longest distance between two adjacent markers is 20.9 cM in
linkage group 17. The RS linkage map consisted of 133 markers in 19 linkage groups

covering 433.9 cM. The average distance between two markers is 3.26 cM. The longest

linkage group was RS8 covering 71.8 cM. The shortest linkage group was R819 in
which two markers were mapped to the same place. One group, which has only two
markers mapped to the same place, was not assigned a group number. The longest
distance between two adjacent markers was 19 cM in R88. With the addition of new
markers, the groups R89 and RS12 from the previous map (Wang et a1. 1998) were
merged and combined into one linkage group named RS9. The BB and RS consensus
map consisted of 161 markers covering 442.4 cM in 19 linkage groups. The largest
linkage group is group 9 covering 44.5 cM. The average distance between two markers
was 2.79 cM. The longest distance between two adjacent markers was 15 cM in linkage
group19.

SSR markers tightly linked to important quantitative traits in sour cherry, such as
bloom time, fruit weight and pistil death, were obtained. These markers could be utilized
as a valuable tool for trait selection. The results from current study have shown that SSR
primers are co-dominant, reproducible, highly polymorphic and have high utility for
cross-species amplification within Prunus. SSR will be the marker of choice for

comparative mapping and MAS studies in Prunus.

INTRODUCTION

Sour cherry is an allotetraploid species (P. cerasus L., 2n=4x=32) with sweet cherry
(P. avium L., 2n=2x=16) and ground cherry (P. fi-uticosa Pall, 2n=4x=32) as the
presumed ancestral species (Beaver et a1. 1995; Iezzoni and Hancock 1996). Beaver and
Iezzoni (1993) reported that sour cherry shows disomic inheritance which is
characteristic of allopolyploids.

De Condolle (1884) stated that the sour cherry originated from the region surrounding
the Caspian Sea, close to Istanbul, Turkey. Kolesnikova (1975) proposed that sour
cherries could be divided into two ecological groups, a western European group and
Middle Russian group. The former group is less winter hardy, but has better fi'uit quality
than the latter. According to some authorities this is too restrictive, and they suggested
that two areas of origin stretching from Switzerland to the Adriatic Sea and from the
Caspian Sea to the far north existed (Webster 1996). Webster (1996) suggests that sour
cherry thrives best in the areas with a Mediterranean type-climate.

Sour cherry is produced in significant quantities in about 40 countries. The majority
of the world’s sour cherries are produced in Russia (200,000 tons from commercial
orchards and 180,000 tons from home gardens, 1986), Ukraine (2,400 tons from
commercial gardens, 191,000 tons fi'om home gardens, 1993), Yugoslavia (114,594 tons,
1990), Turkey (90,000 tons, 1990) and Hungary (78,000 tons,l990) (Webster and Looney
1996). Total production in US in 1999 was 115,804 tons with Michigan having 72.5%

of the production (U SDA/NASS 1999).

There is an increasing interest in constructing linkage maps of crop plants so that
selection of DNA markers linked to a trait of interest can be used for trait selection at
early stages of cultivar development (Scorza 1996). Molecular marker-based linkage
maps have been useful for identifying and localizing important genes controlling both
qualitatively and quantitatively inherited traits in tomato (Tanksley et al. 1989). DNA
based markers can be used to identify related cultivars, to assess taxonomic relationships,
and to indirectly select tagged loci affecting qualitative and quantitative traits. They also
allow breeders to follow loci during the selection process, which helps reduce time spent
in backcross programs (Scorza 1996). When compared to phenotypic markers, DNA
based molecular markers have some advantages: they are developmentally stable,
detectable in all tissues, not affected by environmental conditions and are insensitive to

epistatic effects (Scorza 1996).

LITERATURE REVIEW

SSR’s

Simple Sequence Repeats (SSRs, also known as SSR) are a class of DNA
markers, consisting of tandem repeats of mono-, di-, tri-, tetra-, or penta-nucleotide units
that are found throughout the genomes of most eukaryotic plants (Powell et al. 1991; Lit
and Ludy 1989; Taramino and Tingey 1996). Due to the high rate of variation in the
number of repeat units, the polymorphism level shown by SSRs is high (Taramino and
Tingey 1996).

SSR are valuable markers due to their multiallelic nature, co-dominance,
abundance and extensive genome coverage. They are easy to detect by PCR and require a
small amount of template DNA. However, there are some disadvantages such as the time
requirement for cloning and sequencing to identify useful SSR markers and high-
resolution gels required to separate close alleles and score these markers (Powell et al.
1991)

Plant SSRs were first isolated and cloned fi'om tropical species (Condit and
Hubbell 1991). On average, there is a microsatellite in every 33-kb in plant nuclear
genomes whereas they are found approximately every 6 kb in mammals (Wang et al.
1994). Copy number of these repeats varies among individuals and provides the basis for
the polymorphism(s) used in selection studies (Condit and Hubbell 1991 ).

Broun and Tanksley (1996) screened tomato (Lycopersicon esculentum
Mill.)genomic libraries with seventeen synthetic probes of 2 to 5 base pair tandem

repeats. GAn and GTn sequences were most frequent in the tomato genome (estimated to

be every 1.2 Mb). ATTn and GCCCn were estimated to be every 1.4 Mb and 1.5 Mb,
respectively.

Condit and Hubbell (1991) studied the dinucleotide repeats in some tropical trees
and reported that two-base repeats are abundant in plants providing a large number of
polymorphic markers for studies of plant population genetics. They found that GT and
AG repeat regions were abundant in six plant species genome studied and they estimated
that there were 5x103 to 3x105 AC and AG repeats per genome.

Wang et al. (1994) reported that based on their survey in the Genebank, there was
1 SSR every 64.6 kb in DNA of monocotyledons versus 1 every 21.2 kb in DNA of
dicotyledons. Mono-, di-, and tetranucleotide repeats were all located in non-coding
regions. Fifty-seven percent of the trinucleotide SSRs containing GC base pairs were
located in coding regions in algae. Ma et al. (1996) reported that there was
approximately one (AC)n microstallite in every 292 kbp and one (AG)n microsatellite
every 212 kbp in wheat (T riticum aestivum L.). The trinucleotide repeats (TCT)n and
(TTG)n were 10 times less common than the two dinucleotide repeats tested and
tetranucleotide tandem repeats were rare. Many of the SSRs had more than 10 repeats
and maximum repeat number for (TCT)n was more than 50 in wheat (Ma et al. 1996).

SSRs Imve been assigned to the Arabidopsis linkage map, providing Sequence
Tagged Sites (STSs) to relate physical and recombinational genetic maps (Powell et al.
1996).

Akkaya et al. (1995) reported that 40 SSR loci were mapped in a soybean
(Glycine max) mapping population that consisted of 60 F2 plants from a cross between

cultivars ‘Clark’ and ‘Harasoy’. Although evidence of some clustering of SSR loci was

also reported, a good overall coverage of the genome was obtained in soybean (Powell et
al. 1996).

Guilford et al. (1997) demonstrated that (GT)15 and (GA)15 repeats are abundant
in apple (Malusxdomestica Borkh.), occurring about every 190-kb and 120 kb,
respectively. They have shown that SSRs isolated fi'om a small insert library enriched for
(GA) repeats contained numbers of repeats ranging fi'om 7 to 39. Primers designed for
SSR loci, which amplified these repeats in 21 different apple cultivars. The majority of
the markers were highly polymorphic, diploid and showed single Mendelian inheritance.
Twenty-five percent of the markers generated complex banding patterns agreeing with
the amplification of more than one locus. They have also demonstrated that only three
SSR markers were sufficient to differentiate between all 21 apple cultivars (Guilford et
al. 1997).

Yamamoto et al. (2001) reported that SSRs derived fi'om apple are conserved
in pear and can be used to identify polymorphism in pear based on a study of
36 pear accessions. All SSRs derived from apple amplified fragments in all pear
accessions tested (Yamamoto et al. 2001). All pear accessions were differentiated by
using seven SSR loci that generated 79 alleles.
SSR’s in Prunus

Cipriani et al. (1999) reported the sequence of 17 primer pairs of SSR loci cloned
and sequenced fiom two genomic libraries of peach ‘Redhaven’ that were enriched for
AC/GT and AG/ CT repeats, respectively. Ten out of 17 loci showed Mendelian
inheritance in a backcross population but the remaining seven SSRs did not segregate.

The evaluation of these SSRs in ten peach genotypes has shown that 15 are polymorphic

having 24 alleles each. The mean heterozygosity averaged on all loci was 0.32 which is
significantly higher than that reported for isozymes and RFLPs and RADPs (Cipriani et
al. 1999). In addition, 59% (10) of these SSRs demonstrated cross-species amplification
with other Prunus species (Cipriani et al. 1999).

The isolation and sequencing of nine additional SSRs were reported from two
genomic libraries of peach cultivar ‘Redhaven’ enriched for AC/GT and AG/CT repeats
respectively. Seventeen of these SSRs showed Mendelian inheritance. An assay of
polymorphism in 50 peach and nectarine cultivars showed that heterozygosity ranged
from 0.04-0.74 with a mean of 0.47. SSR appeared for 2-8 alleles per locus (Testolin et
a1. 2000).

Sosinski et al. (2000) reported the identification of SSR loci in peach by screening
a pUC8 genomic library and a kZAPII leaf cDNA library in addition to database
searches. Their findings indicated that CT repeats occur every 100 kb, CA repeats every
420 kb and AGG repeats every 700kb in the peach genome. PCR primers were designed
from SSR containing clones to amplify these regions (Sosinski et al. 2000). Sosinski et
al. (2000) evaluated SSR polymorphism in 28 peach cultivars which showed one, two
and four alleles per primer pair at the expected size for each locus. Five of these SSRs
segregated in intraspecific peach-mapping crosses. Furthermore, Sosinski et al. (2000)
tested SSRs for cross species amplification for use in comparative mapping both within
the Rosaceae and with also unrelated species, Arabidopsis thaliana L. The SSR markers
were found to be highly polymorphic, abundant and transportable between peach
cultivars. Moreover, SSRs developed in Rosaceae species are useful for cross species

amplification and may have utility in both intra and inter-family comparative mapping

analysis. Heterozygosity values ranged from 21% to 56%, with an average value of 45%.
These polymorphic SSR markers were used for DNA fingerprinting of 28 peach
cultivars. All eight polymorphic markers were needed to discriminate between 28
cultivars.

Downey and Iezzoni (2000) studied the genetic diversity of black cherry
germplasm (Prunus serotina Ehrh) using SSR markers developed from sequences of
other Prunus species; peach, sweet cherry and sour cherry. Four primer pairs were
sufficient to identify 54 putative alleles for the 66 black cherry accessions assayed
(Downey and Iezzoni 2000)

Cantini et al. (2001) used 10 SSR primer pairs to fingerprint 59 cherry accessions
from Geneva, NY. The Geneva cherry accessions showed high levels of polymorphism
with 4 to 16 putative alleles amplified per primer pair and heterozygosity values ranging
from 67.9% to 100%. The 10 primer pairs differentiated between all 59 cherry
accessions but two.

Abbot et al. (2000) reported that CT repeats are present in at least one in every
100 kb in peach, as compared to one in every 120 kb in apple (Guilford et al. 1997) and
one in every 225 kb in rice (Wu and Tanksley 1993). CA repeats are less frequent (every
420 kb) in peach compared to apple (190 kb) and rice (480 kb). Markers generated from
microstaellite sequences are highly polymorphic, transportable and abundant. SSR would
be very useful for genetic mapping, map merging and cultivar identification in peach (Wu
and Tanksley 1993).

Godoy and Jordano (2001) utilized SSRs for the exact identification of source trees

that were produced by seed dispersed by animals. SSRs were used to identify the source

10

tree (Prunus mahaleb) for 82.1% of the seeds collected. Remaining seeds came from
other populations. Seed dispersal distances ranged from 0 to 316 m with about 62% of
the seeds delivered within 15 m of the source trees.
Comparative mapping

Linkage maps generated in Prunus species can be compared using common markers
that have been placed on all Prunus linkage maps. Comparative mapping offers
important benefits for genome analysis. DNA probes can be used across-species in the
same taxonomic family, increasing the number of genetic markers available. If the
linkage maps are co-linear, the location of common single gene or Quantitative Trait Loci
(QTL) in one species may predict results in other species (Paterson 1995). The use of the
same SSR primers across species depends on conservation of primer sites flanking SSRs
between related taxa. Cross-species amplification of SSR alleles with the same primers
would increase value of these markers (Powell et al. 1996). Some studies have shown
cross-species amplification indicating primer sequence conservation such as in rice (Wu
and Tanksley 1993), grape (Thomas and Scott 1993) and Citrus spp (Kijas et al. 1995).
Szewc-McFadden et al. (1996) reported that SSRs are abundant in Brassica spp. and
these markers are conserved among the closely related species. Seventeen out of 21 SSR
primer pairs amplified in the three Brassica species studied (Szewc-McFadden et al.
1996). Kijas et al. (1995) tested two sequence tagged SSR tagged sites (STSs) in citrus
and related species and reported that they obtained amplification across species.
Preliminary results in Prunus suggest that SSRs are fiequently conserved among cherry,

peach and almond (Abbott et a1 .2000).

ll

With the increasing number of common loci identified in a series of Prunus
species, the maps could be combined and homologous areas and regions of
translocations, insertions and deletions detected. This would provide information on gene
order conservation. Then studies of “synteny” in Prunus could potentially be extended
to other species in the Rosaceae (Baird et al. 1996).

Assessment of genetic diversity in Prunus with molecular markers

Developing microsatellite markers requires sequencing and this hinders their
broader application. An alternative approach was suggested by Wu et al. (1994). This
approach, named random amplified microsatellite polymorphism (RAMP), includes the
random amplification of microsatellites in combination with RAPD markers. The use of
RAMP does not require sequencing and yields a larger number of bands per primer
combination (Wu et al. 1994). Cheng et a1. (2001) studied the genetic diversity of peach
(Prunus persica (L.) Batsch) cultivars based on RAMP. Genetic relationships were
assessed among 26 common peach cultivars (P. persica var. vulgaris Maxim), 12
nectarine cultivars (P. persica var. nectarina Maxim), and three flat peach cultivars (P.
persica var. platycarpa Bailey) by using ten combinations of primers producing 82
polymorphic bands. Cluster analysis from RAMP data resulted in groupings which were
consistent with the regions of origin of cultivars and classification of the cultivars.

Casas et al. (1999) employed 80 Random Amplified Polymorhic DNA (RAPD)
primers to assess the genetic diversity of forty-one genotypes fiom both commercial
Prunus rootstocks and clones from the breeding program at Aula Dei Experimental
Station, Zaragoza, Spain. Seven RAPD primers produced a combined classification of

the whole set of rootstock clones. Their analysis was successful in clustering rootstocks

12

according to the classification based on morphological descriptors widely used to
characterize Prunus clones. Manubens et al. (1999) clearly distinguished cultivars using
AFLP fingerprinting for assessment of eight peach and six nectarine varieties. This
technique was found to be more reliable than traditional assessment of agronomic traits
of the adult plant. Goulao et al. (2001) employed seven AFLP and six inter-simple
sequence (ISSR) primers for phenetic characterization of plum cultivars, resulting in
amplification of 379 and 270 products, respectively. These markers are valuable in
identification of specific genotypes and analysis of phenetics of plum (Prunus domestica
L).

The use of AFLPs with its high polymorphism are useful for identification and
genome analysis of sweet cherry cultivars (Struss et al. 2001). Ten out of 18 primer
combinations were informative generating up to 80 bands for each primer pair. Seven to
33% of the amplified bands were polymorphic and all 38 cherry cultivars were clearly
identified.

Bartolozzi et al. (1998) utilized 37 RAPD markers to study the genetic relatedness of
17 almond [Prunus dulcis (Mill.) D.A.Webb, syn. P. amygdalus, Batsch; P. communis
(1.) Archangeli] cultivars fi'om California and found that genetic diversity in almond is
limited even if it is an obligate outcrossing cultivar. Three groups of cultivar origins can
be distinguished with RAPD analysis: progeny derived from interbreeds of early
California genotypes, bud-sport mutations, and progeny derived from crosses of
California germplasm with genotypes originating from outside of California (Bartolozzi

et al. 1998).

13

QTL analysis and Marker Assisted Selection (MAS)

The genetic complexity of quantitative traits; ranging from an infinite number of
genes with tiny effects to few genes with large effects has long been discussed. Current
QTL mapping data suggests that few genes account for most of the variation with a
greater number of genes responsible for smaller amount of the variance in many plant
populations (Paterson 1995). High density genetic maps allow breeders to analyze the
genome of an organism, indicating QTL locations affecting any characteristic that could
be measured (Paterson 1995). Algorithms for QTL mapping in a wide range of
experimental designs, including F2, backcross, recombinant inbred and many other
population designs were developed (Knapp et al. 1991; Carbonell et al. 1992). These
algorithms all have been used to testing correlation between marker genotypes and
quantitative phenotypes (Paterson 1995).

The effectiveness of molecular markers in marker assisted selection (MAS) depends
on the linkage of the marker to the gene of interest. The closer the linkage between a
marker and a gene, the more efficient the selection (Baird et al. 1996).

Bloom date, harvest date, fruit weight and pistil freeze tolerance and other
quantitative traits were studied in a sour cherry mapping population (Wang 1998). Three
markers, which mapped to linkage group 1, were found to be associated with harvest date
and two unlinked markers were associated with fi'uit weight.

Osborn et al. (1987) identified RFLP markers linked to genes controlling the soluble
solids (88) content in tomato fruit by screening the F2 population for the RFLP genotype

for 88 content. Analysis of variance of 88 content for different RFLP genotypic classes

14

indicated that RFLP alleles at one of the loci were significantly associated with variation
for 88 content. Tanksley and Hewitt (1988) found that 88 content for tomato cultivars
could be improved by indirect selection for the linked RFLP markers. In this case, due to
the low heritability of SS, MAS based on molecular markers can maximize heritability
and increase gain from selection for QTL (Knapp 1994).

Lu et al. (1999) stated that a codominant AF LP marker, EAA/MCATIO, co-
segregates with the primary source of resistance to root-knot nematodes (Meloidogyme
incognita and hi. javam'ca) in rootstock cultivars of peach They cloned two allelic DNA
fragments of this AF LP marker, then sequenced and converted to STSs. Four nucleotide
differences (i.e. one addition and three substitutions) were observed between the two
clones.

Hormaza (1999) developed an approach for early selection in sweet cherry
combining RAPDs with embryo culture, which accelerates the breeding process. In their
study, they used this approach to assess, with certainty, the paternity of embryos obtained
after mixed pollinations with pollen of three sweet cherry cultivars.

Tao et al. (2000) searched for molecular markers for self-compatiblity
by using the information about S-ribonucleases (S-RNases) of other Prunus species. By
using oligonucleotide primers designed from conserved regions of Prunus S-RNases in
PCR-amplification of five self-incompatible and six self-compatible cultivars, they found
that self-compatible cultivars have a common band of approximate 1.5 kbp when
genomic DNA is digested with HindlII and probed with the cDNA encoding S'-RNase of
sweet cherry. They concluded that self-compatible cultivars possess a common S-RNase

allele which can be utilized as a molecular marker for self-compatibility.

QTL analysis in Prunus

The genetics of late blooming in almond were studied using a DNA pooling
technique to identify RAPD markers linked to a late blooming (Lb) gene (Ballester et al.
2001). The researchers were able to identify three RAPD markers associated with the Lb
gene.

Dirlewanger et al. (1999) mapped QTL controlling fi'uit quality in peach using an
F 2 population. The QTL for almost all qualitative components were on two linkage
groups and the fraction of the total variation in each trait explained by the QTL was very
high and accounted for up to 90 % of the variation of some characters. All the detected
QTL displayed the same effect as the parental phenotypes for productivity, fi'esh weight,
pH, quinic acid, sucrose and sorbitol content. On the contrary, some QTL for maturity
date, titratable acidity, malic and citric acids and fructose, showed the same effect as
parental phenotypes, but others displayed the opposite effect.

Wang et al. (2000) reported QTL analysis of flower and fruit traits in sour cherry
using the RFLP map of EB and R8. The location and effects of QTL for eight traits and
eleven putatively significant QTL (LOD > 2.4) were detected for six characters (bloom
time, ripening date, % pistil death, % pollen germination, fi'uit weight, and soluble solid
concentration). The percentage of phenotypic variation explained by a single QTL varied
from 12.9 % to 25.9 %. The QTL for flower traits (bloom time, % pistil death and %
pollen germination) were mapped to the same linkage group, BB 1.

Mapping in polyploids
Although a linkage map in sour cherry could provide broad potential advantages,

linkage map construction in sour cherry is lagging compared to other Prunus species due

to its polyploid origin. Construction of linkage maps in polyploids is difficult. There are
large numbers of genotypes for each primer pair expected in a segregating population and
these genotypes cannot always be identified by their banding patterns. Secondly, the
genome constitution (allopolyploidy versus autopolyploidy) in many polyploids is not
clearly understood (Wu et al. 1992). To overcome the difficulty of mapping in
polyploids, Wu et al. (1992) proposed the use of Single Dose Restriction Fragments
(SDRF). In the sour cherry mapping population, informative markers will be those that
are Single Dose Restriction Fragments (SDRFs) in one or both parents [i.e., (+--- x ----),
(--- x +--), or (+-- x +---), segregating 1:1, 1:1, or 3:1 respectively] (Wu et al. 1992;
Hemmat et al. 1994; Sorrells 1992). To identify SDRFs with a confidence level of 98 %
in the four ploidy levels, a population size of at least 75 is needed (Wu et al. 1992).

Software programs have been developed to aid with the mapping. JOINMAP was
developed by Piet Starn at the center for Plant Breeding and Reproduction Research,
Wagenigen, The Nederlands (Starn 1993). Like MAPMAKER (Lincoln et al. 1992),
JOINMAP can construct maps of single crosses, but it also has advantages of merging
maps obtained fi'om distinct experiments and published recombination frequencies that
are important in comparative mapping. Unlike MAPMAKER, JOINMAP can also be
used with markers segregating in various ratios (3:1, 1:1) within the same cross (Baird et
al. 1996).
Current status of mapping in Prunus

Linkage mapping was first initiated with diploid species due to the relative
simplicity compared to polyploids. Linkage maps of peach (Chaparo et a1. 1994;

Rajapackse et al. 1995), peach X almond (Foolad et al. 1995), peach X P. davidiana

l7

(Dirlewenger and Bodo 1994), almond (P. dulcis) and (V iruel et al. 1995), sweet cherry
(Stockinger et al. 1996) were conducted.

Rajapakse et al. (1995) reported the construction of a genetic linkage map of
peach. The map consisted of 47 markers covering 332 cM (RFLP, RAPD and
morphological markers) based on 71 F2 individuals derived from ‘New Jersey Pillow’
and KV77119. Fooled et al. (1995) constructed a linkage map of a dwarf peach selection
(54P455) and an almond cultivar ‘Padre’ cross with 107 markers. Markers were assigned
to nine different linkage groups covering 800 cM (11 markers remained unlinked). Viruel
et al. (1995) constructed two linkage maps in almond using RFLP’s. Eight linkage
groups were constructed with the 93 heterozygous loci in ‘Ferragnes’ and eight linkage
groups were constructed with 69 loci heterozygous in ‘Tuano’. Dirlewanger and Bodo
(1994) constructed a linkage map of peach with RAPD markers where eight linkage
groups were identified. Lu et al. (1998) constructed a linkage map of peach rootstocks
with (AFLP) markers in 55 F2 individuals of the cross Lovell x Nemared. They have
scored 169 AF LP markers from 21 different primer combinations and assigned 153
markers to 15 linkage groups covering 1297 cM with the average interval of 9.1 cM.
Dirlewanger et al. (1998) constructed a linkage map of peach from an intraspecific F2
population consisting of 249 markers including four agronomic characters
(peach/nectarine, flat/round fiuit, acid/non-acid fiuit, and pollen sterility) and one
isoenzyme, 92 RAPD, 50 RFLP, eight inter-microsatellite amplification (IMA), and 115
AFLP markers. The amp will be useful in the detection of QTL’s for controlling acid and
sugar content, consists of 11 linkage groups covering 712 cM with the average density of

4.5 cM. The mapping population was generated from a flat non-acid peach, ’Fejalou

l8

Jalousia(R)' and an acid round nectarine 'Fantasia' (Dirlewanger et al. 1998). .100me et
al. (1998) constructed a saturated linkage map for Prunus using an almond x peach F2
progeny with 246 markers (11 isozymes and 235 RFLPs) covering distance of 491 cM
with the average map density of 2.0 cM/marker. The map had only four gaps of 10 cM.
RFLPs come from 213 probes from the genomic and cDNA libraries of almond, peach,
P. ferganensis, cherry, plum and apple, with an additional 16 almond probes of known
genes. The order of locus on the map was almost identical and distances did not differ
significantly among an intra specific almond map sharing 67 anchor loci.

Dettori et al. (2001) constructed a linkage map of a BC] progeny (Prunus persica
x (P. persica x P. ferganensis)) consisiting of 109 loci (74 RFLPs, 17 SSRs, l6 RAPDs,
and two morphological traits) covering 521 cM on 10 linkage groups with an average
distance between markers of 4.8 cM. JOINMAP 2.0 software was used to integrate loci
segregating in five different ratios. Two monogenic traits, flesh adhesion (F/f) and leaf
glands (E/e) were placed on the map. Homologies were found among the respective
linkage groups. No relevant differences were observed in the linear order of the common
loci (Dettori et a1. 2001)

A second-generation linkage map was constructed for almond using RAPD and
SSR markers (Joobeur et al. 2000). Fifty-four RAPD markers and SSRs were added to
the molecular map previously constructed with 120 RFLPs and seven isozyme genes.
Polymorphism was detected in six of the eight Prunus SSRs studied leading these to be
mapped. All markers placed on the 8 linkage groups were previously identified resulting

in a 5% increase to the previous map fiom 415 cM to 457 cM.

An RF LP genetic linkage map of two tetraplo id sour cherry cultivars, ‘Rheinische
Schattenmorelle’ (RS) and ‘Erdi Botermo’ (EB) was developed from the crosses of these
two cultivars (Wang et al. 1998). The RS linkage map consists of 19 linkage groups
covering 461.6 cM and EB linkage map consists of 16 linkage groups covering 279.2 cM.
Fifty-three markers mapped in both parents allowed for the identification of 13 sets of
homologous linkage groups. Homoeologous relations could not be determined since only
15 of the probes detected duplicate loci. Fifty-nine of the markers on the linkage maps
were identified with probes, which are employed in other Prunus linkage maps.

Jauregui et al. (2001) reported developing a linkage map using an interspecific F2
population between almond and peach with selected markers of eight linkage groups
from previously developed Prunus maps. Contrary to expected eight linkage groups in
Prunus, markers studied mapped to seven linkage groups and markers of groups 6 and 8
in previous maps formed a single group. By studying pollen fertility and chromosome
behavior of meiosis in F1 generation, the presence of a reciprocal translocation between
‘Garfi’ almond and ‘Nemared’ peach was suggested (Jauregui et al. 2001).

Shimada et al. (2000) developed a genetic linkage map using 133 F2 plants from an
intraspecific cross among peach cultivars in Japan. The map of the rootstock cultivar,
'Akame', and the ornamental peach, 'Juseitou' contained 83 markers consisting of 41
RAPD, 30 AFLP, and Inter-SSR, PCR-RFLP markers and also three morphological trait
loci, brachytic dwarf (dw), red leaf (Gr) and narrow leaf (nl). The map had ten linkage
groups ranging in length from 17 to 244 cM and covered more than 960 cM. The
morphological characteristic, n1 co-segregated with the dw locus. DNA markers found to

be linked to Gr and dw loci, could be utilized in peach breeding.

20

The expanded Prunus genetic linkage map constructed from peach and almond
covers 1,144 cM (Bliss et al. 2002). Sour cherry linkage map, being tetraploid, should be
two times the length of the peach map. However the published map covers only one
fourth of the expected length due the difficulty of having informative markers in
tetraploids compared to diploids (Wang et al. 1998). The objective of this study is to
identify informative SSR markers and incorporate these markers onto the sour cherry
map. Incorporation of informative SSR markers may lead to identification of
homoeologous linkage groups in sour cherry that would be very valuable tools for
comparative mapping studies in Prunus. Additionally, if mapped close to the QTL of
important traits in sour cherry, these SSR markers would be valuable tools in MAS for

these traits.

21

MATERIALS AND METHODS

Mapping population, plant material and DNA extraction

The sour cherry mapping population is a ‘pseudotestcross’ in which informative
markers are those that are homozygous recessive in one parent and heterozygous in the
other parent and segregate 1 : l (Hemmat et al. 1994). Eighty-four progeny from crosses
of ‘Rheinische Schattenmorelle’ (RS) x ‘Erdi Botermo’ (BB) were used as a mapping
population. EB and RS were chosen as parents because they differ from each other for
important horticultural traits such as fi'uit firmness, fi'uit color, pistil fi'eeze susceptibility,
cold hardiness, bloom date and fertility. Additionally, these parents originated from
different geographical regions (Germany and Hungary, respectively) (Wang et al. 1998).
Young unfolded leaves were collected from trees of the mapping population located at
the Clarksville Horticultural Experiment Station of Michigan State University. Leaves
were frozen at —80 0C overnight and lyophilized for 2-3 days. DNA isolation was
conducted according to Stockinger et al. (1996).
SSR primers

The information on 45 SSR primer pairs from ‘Redhaven’ peach (Testolin et al.
2000), apple (Guilford et al. 1997) and sweet cherry (Sosinski et a1. 2000) was used in
this study. Sequences of peach primers (pchgms and pchcms series) and sweet cherry
primers (P808E08, P812A02, PSOlHO3 and P807A02) were provided by Sosinski et al.
(2000). 80m cherry SSR primers were derived from a small-insert genomic DNA library

(A. Iezzoni, Horticulture Dept., Michigan State University, East Lansing, Michigan).

22

Annealing temperature for each primer pairs.

To find the optimum and highest annealing temperature for each primer, a
Stratagene Robocycler with a temperature gradient was used. EB was used as the
template DNA in the PCR mixture in optimization since the genomic library was
constructed from this parent. The reaction and a 123 bp ladder was run on a 0.9%
agarose gel to determine the highest optimum annealing temperature to reduce the
change of mismatching and confirm the size of the amplified fragment. The gel was
stained with ethidium bromide (0.5 mg/ul) for 15 minutes and rinsed with double
distilled water for one minute.

Screening primers for polymorphism using PCR

After determining the optimum annealing temperature for each primer pair,
another DNA amplification reaction was set up with each primer pair and both parents
and 12 progeny to find the primers that identify segregating fragments. Five ul of the
PCR products was first run on a 0.9 % agorose gel to verify amplification. To identify
the presence of polymorphism, 4 ul of each remaining reaction was run on a 4 %
polyacrylamide gel and the bands was detected by using the DNA silver staining protocol
of Promega (Promega Corporation, Madison , WI).

PCR with informative markers

After identifying SSR primers that were polymorphic, another DNA amplification
reaction was conducted on the remaining progeny in the mapping population as follows;
1X PCR buffer, 0.2 mM of dNT P’s, 2.5 mM of MgCl2, 50 ng DNA, 0.6 unit TAQ DNA
polymerase enzyme( Boehringer Mannheim Biochemicals) and ddH2O was added to a

volume of 25 pl. DNA amplification reactions were performed in a thermocycler (model

23

9600; Perkin Elmer Applied Biosystems, Inc., Foster City, California). The amplification
products were separated by electrophoresis for 2.5 h at 80 W on a 6% polyacrylamide
sequencing gel (Bio-Rad), then silver stained with sequence staining kit by Promega and
sizes were estimated using a 10 bp ladder (Gibco BRL).
Scoring, X2 analysis and map construction

The primers, which showed polymorphisms based on size in the polyarcylamide
gel, were scored for the absence or presence of a band in the mapping population.

In the mapping population, informative markers are those that are SDRFs in one or both
parents [i.e., (+--x----), (----x+---), or (+---x+---), segregating 1:1, 1:1, or 3:1,
respectively] (Wu et al. 1992; Hemmat et al. 1994; Sorrells 1992). Fragments which
differed between both parents were tested for fit to a 1:1 (presencezabsence) ratio.
Fragments which are present in both parents were tested for fit to a 3:1 (presence :
absence) ratio. Those markers, which fitted the appropriate ratios at the 5% level, were
used in linkage analysis. The SSR data of 84 progenies was added to the previously
constructed RFLP data (Wang et a1. 1998). A linkage amp was generated from the

RFLP, and SSR data with JOINMAP V2.0 (Starn 1993).

24

RESULTS AND DISCUSSION

Forty-five SSR primer pairs were tested to find informative markers in sour
cherry (Table 1.1). Ten of these primer pairs were informative (Table 1.2), yielding l7
SDRF. A second generation linkage map of two tetraploid sour cherry cultivars,
‘Rheinische Schattenmorelle’ (RS) and ‘Erdi Botermo’ was constructed (Fig. 1.1) by the
addition of new SSR markers to a map previously constructed by Wang et al. (1998).

The revised EB linkage map consisted of 19 linkage groups covering 337.8 cM
with 118 markers. Seventeen markers remained unlinked. The longest linkage group is
EBl covering 43 cM and the shortest is EB6’ where two markers were mapped to the
same place. The average distance between two markers is 2.9 cM. The longest distance
between two adjacent markers is 20.9 cM in linkage group 17 (Fig. 1.1). The published
EB linkage map published by Wang et a1. (1998) consisted of 95 SDRF in 16 groups
covering 279.2 cM. The incorporation of 23 new markers provided a 20% (58.6 cM)
increase over the length of the previous map. Seventeen markers remained unlinked.
Marker order was mostly conserved when compared to the earlier map. The average
distance (CM/marker) between two loci decreased from 3.5 to 2.9 cM in the EB map.

The revised RS linkage map consisted of 133 markers in 17 linkage groups
covering 433.9 cM. The average distance between two markers is 3.26 cM. The longest
linkage group is RS8 covering 71.8 cM. The shortest linkage groups are R819 where two
markers were mapped to the same place. The longest distance between two adjacent
markers is 19 cM in RS8. Twenty-six markers were unlinked. The addition of new

markers to the linkage map would incorporate these unlinked markers to the linkage map.

25

Table 1.1. Summary of the 45 SSR primers derived from peach, sweet cherry, sour

cherry and apple tested in current study.

 

 

Primer Sequence Source Comments

01a6 (Guilford et Apple Complex banding pattern
a1. 1997)

02b1 (Guilford et Apple Complex banding pattern
al. 1 997)

23fl (Guilford et Apple Complex banding pattern
a11997)

26c6 (Guilford et Apple Complex banding pattern
a11997)

Pchcmsl (Sosinski et al. Peach Complex banding pattern
2000)

Pchcms2 (Sosinski et al. Peach Not polymorphic
2000)

Pchcms3 (Sosinski et al. Peach Not polymorphic
2000)

Pchgmsl (Sosinski et al. Peach Many bands between 140 and 300bp with
2000) complex banding pattern

PchgmsS (Sosinski et a1. Peach Insufficient PCR product
2000)

Pchgms3 (Sosinski et al. Peach Informative (see Table 1.2)
2000)

Pchgms2 (Sosinski et al. Peach Insufficient PCR product
2000)

B4G3 (Appendix, Peach Insufficient PCR product
Table l)

P801H03 (Sosinski et al. Sweet Insufficient PCR product
2000) cherry

PSO7A02 (Sosinski et al. Sweet Insufficient PCR product
2000) cherry

P808E08 (Sosinski et al. Sweet Informative (see Table 1.2)
2000) cherry

P812A02 (Sosinski et al. Sweet Informative (see Table 1.2)
2000) cherry

PSO9F08 (Joobeur et al. Sweet Amplified complex bands between 250-140 bp.
2000) cherry

 

26

Table 1.1. (cont’d).

 

 

Marker Sequence Source Comments
Reference
GA25(PceGA (Cantini et al. Sour Informative (see Table 1.2)
25) 2001) cherry
GA65 (Appendix, Sour Amplified 6 bands ranging from 340bp to
(PceGA65) Table 1.1) cherry 247 bp, band patterning is not consistent, not
workable
GA50 (Appendix, Sour Amplified bands between 178 to 143, not
(PceGA50) Table 1.1) cherry scorable
GA57 (Appendix, Sour Amplified many bands of not expected size
(PceGA57) Table 1.1) cherry between 500 and 130 bp
GA55 (Appendix, Sour Amplified many bands of not expected size
(PceGA55) Table 1) cherry between 500 and 130 bp
GA26 (Appendix, Sour Amplified many bands between 500 and
(PceGA26) Table 1) cherry 100bp
GA77 (Appendix, Sour Informative (see Table 1.2)
(PceGA77) Table l) cherry
GA34(PceGA (Downey and Sour Informative (see Table 1.2)
34) Iezzoni 2000) cherry
UDP98-24 (Testolin et al. Peach There are complex bands 73 to 64 bp, not
2000). expected size, very faint to be scored
UDP98-22 (Testolin et al. Peach Informative (see Table 1.2)
2000)
UDP98-410 (Testolin et al. Peach Informative (see Table 1.2)
2000).
UDP98-41 l (Testolin et al. Peach Informative (see Table 1.2)
2000)
UDP98—412 (Testolin et al. Peach Amplified many bands of not expected size
2000). between 500 and 90 bp
UDP98-414 (Testolin et al. Peach Amplified many bands
2000)
UDP98-416 (Testolin et al. Peach Insufficient PCR product

2000)

27

Table 1.1. (cont’d).

 

 

Marker Sequence Som‘ce Comments

UDP98-409 (Testolin et al. Peach A 160 bp band, not polymorphic
2000). 163 bp not polymorphic

UDP96-018 (Testolin et al. Peach Amplified bands of not expected size about
2000). 500 bp

UDP96-008 (Testolin et al. Peach Informative (see Table 1.2)
2000)

UDP96-003 (Testolin et al. Peach Amplified very close 3-4 bands at 100bp of
2000). not scorable nature

UDP96-001 (Testolin et a1. Peach A 130bp band, not polymorphic
2000). A 118bp band segregating 10:1

A 105bp band segregating 2:1

UDP96—005 (Testolin et al. Peach Amplified many bands between 250 and 500
2000). bp

UDP96-019 (Testolin et al. Peach Amplified many bands between 250 and 500
2000) bp

UDP97-403 (Testolin et al. Peach 150bp can not be separated form 149b band,
2000). not scorable

100bp not polymorphic

UDP98-405 (Testolin et al. Peach Informative (see Table 1.2)
2000)

UDP98-406 (Testolin et al. Peach A 99bp band segregating 2:1
2000). A 97bp band, not polymorphic

UDP98-407 (Testolin et al. Peach Amplified many bands larger than 500 bp
2000)

p814] (Appendix, Sweet Amplified many bands between 500 and
Table A. l) cherry l40bp.

 

28

Table 1.2 Segregation ratios and product sizes of informative SSR primers in sour cherry
mapping population from ‘Rheinische Schattenmorelle’ (RS) x ‘Erdi Botermo’ (EB) .

 

 

Primer Plant source Reference Product size RS EB Ratio
GA34 Som' cherry (Downey and 184 - +
(PceGA34) Iezzoni 2000) 175 + - 1 :1
170 + - 1:1
161 + - 1 :1
143 + +
P812A02 Sweet cherry (Sosinski et al. 178 - + 1:1
2000) 167 - + 1:1
162 + - l :1
160 + +
148 - + 1:1
PSO8E08 Sweet cherry (Sosinski et al. 188 - + 1:1
2000) 184 + +
175 + +
Pchgms3 Peach (Sosinski et al. 189 - + 1 :1
2000) 1 82 + +
178 + + 3:1
174 + - l :1
GA25 Sour cherry (Cantini et al. 199 - + 1:1
(PceGA25) 2001) 187 - + 1 :1
l 74 + + 3: 1
162 + +
UDP96-008 Peach (Testolin et al. 158 + -
2000) 155 + +
148 - +
139 - + 1:1
135 + +
128 + -
UDP98-405 Peach (Testolin et al. 112 + - 1:1
2000) 105 + +
103 - +
100 + +
97 - +
UDP98-22 Peach (Testolin et al. 104 + - l :1
2000) 98 + 1:1
90 + +
UDP98-410 Peach (Testolin et al. 139 + - 1:1
2000) 134 + - 2'1
131 - +
UDP98-411 Peach (Testolin et al. 164 - + 1:2
2000) 154 + + 3:1
1 50 + +
131 + + 3:1

 

- = absence of a band, + = presence of a band.

29

Figure 1.1., pages 31, 32 and 33. The second-generation linkage maps of two
sour cherry cultivars, ‘Rheinische Schattenmorelle’ (RS) and ‘Erdi Botermo’ (EB),
generated by addition of SSR markers to the previously constructed RFLP map (Wang et
al. 1998). SSR markers are underlined. Lines represent anchor loci correspondences
between RS and EB linkage groups. When two linkage groups in one cultivar are
homologous to a linkage group in the other cultivar, the shorter of the two is marked, i.e.,
RSS’.

30

 

 

 

 

 

 

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With the addition of new markers, R89 and R812 from the previous map
combined into one linkage group as RS9 (Fig. 1.1). The previous RS linkage map (Wang
et al. 1998) possesses 126 SDRF assigned to 19 linkage groups covering 461.6 cM. A six
percent decrease (27.7 CM) in the map distance was observed. This is caused by the fact
that linkage group 9 and 12 from the earlier imp combined into one named group 9.
Twenty-six markers remained unlinked. Marker order was generally conserved. The
average distance (CM/marker) between two loci decreased from 4.3 to 3.3 cM in the RS
map. Counterparts of fourteen RS linkage groups homologous to the EB linkage groups
were detected (Fig. 1.1).

The revised EB and RS consensus imp (Fig. 1.2) consisted of 161 markers
covering 442.4 cM in 19 linkage groups. Forty-nine markers remained unlinked. The
largest linkage group is group 9 covering 44.5 cM. The shortest linkage group is group19
covering 5.7 cM. The average distance between two markers is 2.79 cM. The longest
distance between two adjacent markers is 16.9 cM in linkage group 8 (Fig. 1.1). The
previous EB and RS consensus map (Wang et al. 1998) consisted of 144 SDRF in 16
groups covering 443.1 cM. The incorporation of 17 new markers did not change the
length of the previous map significantly. The average distance (CM/marker) between two
loci decreased from 3.07 to 2.8 cM in the revised map. Marker order in the new map was

mostly conserved when compared to the previous EB and RS consensus map.

34

Figure 1.2., pages 36 and 37. The consensus map of two sour cherry cultivars,
‘Rheinische Schattenmorelle’ (RS) and ‘Erdi Botermo’ (EB), constructed from combined
data of AFLP and SSR markers using JoinMap with a minimum LOD of 3.0 and a
maximum recombination frequency of 0.35. SSR markers are underlined.

35

Groupt

0.04\

6.3 -
8.4 N
8.9 "

f

/'

\-

p—

145 '*

18.7nH

27.5
29.0
29.8
30.7
31.7
37.0
42.2

Grou
0.0
2.9
4.7
6.9
7.7
9.9

11.2

12.8

13.7

15.8

17.7

18.2

21.1

21.8

26.6

Fig. 1.2.

 

EF167b

EF172b
EF1678
8601

EF172c

CPM12b CPM12a

QDQmS3—189 EF194d
EF19406 EF194C
EF19402

EF194b EF19403
EF19404 EF19401
EF1948

EF19405

P8141

ems
p5

CPM30a
EF78a

EF64b

EFTBc
CPMZb
EF62b §A25-187
EF130b
CPMZOe
GA25-174
CPMZa
EF149a
EF149b
EF64c EF53a
EF78b
pch2023

A34-175

 

Groupz Group3
0.0 4 \ EF128b 0.0 I P§12Aog-148
6.2 EF176h
73 ~ .. EF133c
9,1— ._ PL686
11.4 — -l EF178 093411-154
13.6 — .- DP411-131
14.9 .. ~AC27a
211— >— EF1288
22,9 +L—A621
24.5 a —l Olec EF169b
29.3 r -l CPM9O Hsp4
31.9 N / CPM59
32.9 ~ - m
333 J \ EF173b
36,9 _. — EF108c
Group6 Group?
0.0 CPM39a o.o 4‘ \AGwa
EF162b CPMZOc
3-9 EF159a
5.2 EF176e EF162a
73 CPMzaa EF176m
9,3 EF176I
10-5 CPM23“ 11,5— l—uomos-nz
13.3 CPM39d 13,3 — —- CPM488
15,1 EF159b
16.6 EF152b 16.3__EF185
17.3 CPM104 EF152a
PM48b
21,0 CPMZOd 20.8'\ (C
' 23_2-’~EF176t
24,7 4 \ EF176n
32.3 x r EF187h
34.1 ~AGtoc
36.5 W EF187c
37.1 4“ P827

36

 

Group4

\ EF71
— EF182b

0.0 J
2.3 a

7,8 -‘ — EF127

H cmsaa CPM 58b
r CPM 53b

17.2 ‘1
18.6

 

215 a I— EF158b

Groups

0.04

3.4 ""
5.5 —‘

V EF143b

e—EF‘I439
—PS41

I—EF156b
Pru2 EF143c
EF143d

PExtta

22.4 ~
247-j
27.3 e

r EF111
~EF1458
~EF145b

33.0 N
34.1 "‘
35.6 "1

33] ~ —- EF76

 

43.1“ “-Extlb

Group9 Group10 Group11 Group12 Group13

 

 

 

 

 

 

 

 

onfi‘w 0.01\EF169a 0.04‘5F106b 0.01NLLD_5___§P 10-13 0.01‘A68
9.4.: b31769 8.5—0-P 12A 2173
J 13~3‘—EF129 139~—EF483
“-8 ’Emm‘ 15.4~»—IEF179 EF176¢ 4-}: 12A02167 15.0-1-QDP41Q134 '
19_2 j—EFWSd
20,0 ~L—EF48b
23,3 - 0. EF1760
25.6 —HEF156c L
27.6 A EF1588
31.6 x rCPM57a
32.2 4 \CPMB
34.4 — -EF1581
373 - — EF176§
40.2 —+ —-A66
44.5—LEF1589
Group“ Group15 Group16 Group17 Group18
0,0 EF1080 EF1738 0.0 fl \ EF187g 0,0 87H2b 0.0 A \ A640!) 0.0 J \AG40a
1_ B7H23 —
3.3-AEF187I EF1871 2 2'37 “610°
4.8 - - EF1870
9_5 CPMZSb CPM230 9.2 EF157
11.4 a — EF1876
— EF187'
13.8 r 1 14,4—L-0PM39c
17_4——EF187d 1e_3——CPM39b
Group19
L. 0.0 EF133b
26.1 ~ W EFSO
3,3 EF1330

5.7 DP411-164

Fig. 1.2 (cont’d)

37

Homologous relations for linkage groups were identified using 60 bridging
markers heterozygous in both parents (Fig. 1.1). Fifteen EB linkage groups homologous
to the RS linkage groups were identified. RS counterparts of EB linkage groups 3, 12, 13
and 14 were not identified. EB counterpart of RS linkage group 16 was also not
identified (Fig. 1.1).

Two EB linkage groups were homologous to RS6. The longer of two was named
EB6 and the shorter was named EB6’. Two RS linkage groups were homologous to EBS.
The longer one was named RSS and the shorter was named RSS’. Similarly, two RS
linkage groups were homologous to EB7. The longer one was named RS7 and the
shorter was named RS7’ (Fig. 1.1). In all three cases, the two linkage groups
homologous to the same linkage group of the other parent may become one linkage group
when the map is saturated as stated by Wang et. al. (1998).

Four of the apple primers published by Guilford et al. (1997) were tested in sour
cherry. All of the apple primers tested (01a6, 02b1, 23fl and 2666) showed PCR
amplification in sour cherry (Table 1.1). SSRs isolated from apple did amplify alleles of
expected size in pear (Yamamato et al. 2001) and in peach (60%) (Sosinski et al. 2000).
In our study, none of the primers amplified the expected size of bands in sour cherry and
they showed a complex banding pattern amplifying many bands in sour cherry (Table
1.1). Apple SSR primers gave similar results in apricot as (Sosinski et al. 2000) in sour
cherry. Although 80% of the apple primers amplified in apricot, they all showed a
complex banding pattern.

Twenty-one out of the 26 SSR primers developed from ‘Redhaven ‘ peach

cultivar (Testolin et al. 2000) tested in this study showed PCR amplification in sour

38

cherry (Table 1.1). UDP96-010, UDP96-013, UDP96-015, UDP97-401 and UDP98-408
SSR primers were not studied since they were reported to show no amplification in sour
cherry (Cipriani et al. 1999). Of the 21 SSR primers tested, only nine (UDP98-024,
UDP98-406, UDP98-411, UDP-96-001, UDP-97-403, UDP98-405, UDP-98—406,
UDP98-410 and UDP98-411) amplified bands of the expected size in sour cherry. Five
of these nine markers also resulted in useful SDRF and four of them were incorporated
into the sour cherry linkage map. Although UDP98-22 SSR primers amplified
polymorphic bands and segregated 1:1 (Table 1.2), it did not map on any of the linkage
groups. The sour cherry linkage map is not saturated. The lack of a saturated map in
sour cherry limited the mapping of potentially useful marker such as UDP98-22. Similar
limitations were concluded by Sosinski et al. (2000) for a peach map. They reported that
five out of ten SSR they studied were informative, however only one of them (pchgmsl)
was incorporated into the peach map due to lack of saturation. The termining 18 peach
SSR primers showed a complex banding pattern with amplification of many bands per
primer pair and were not useful for genetic and mapping studies in our sour cherry
mapping population (Table 1.1).

All peach SSR primers reported by Sosinski et al. (2000) demonstrated PCR
amplification in sour cherry. The pchgmsl presented a complex banding pattern between
140-300bp. PchcmsZ and pchcms3 were not polymorphic. PchgmsZ, pchgmsS and
B4G3 showed weak amplification and were not suitable as primers. The pchcmsl
amplification product displayed a complex banding pattern resulting in many bands of
unexpected size. Pchgms3 was informative, exhibiting SDRF and mapped to the sour

cherry linkage map (Table 1.1). Since pchcms4 and pchcmsS were reported to show a

39

complex banding pattern in sour cherry (Sosinski et al. 2000), these two markers were
excluded in this study.

Sweet cherry SSR primers were also tested (Table 1.1) in sour cherry. Two of
them were informative (P808608 and PS 12AO2) and were incorporated into the sour
cherry map (Table 1.3).

The primer designed fiom pSl41 probe was determined not to be usefill as it
amplified many bands between 140 bp and 500 bp. This probe had mapped to a region in
the sour cherry linkage map group] and that was associated with bloom time (Table 1.1).

Five of the SSR primers (GA26, GA65, GASO, GA57 and M55) developed fi'om
sour cherry (Amy Iezzoni, Department of Horticulture, Michigan State University, East
Lansing, MI) showed a complex banding pattern. GA34 amplified 8 bands between 143
and 184 bp, but was not reproducible (Table 1.1). GA77 produced three bands, one being
polymorphic, however it did not segregate in 1:1 or 3:1 ratio (Table 1.1). Of the sour
primers tested, GA25 produced an expected ratio that was applicable to the sour cherry
linkage map (Table 1.3).

Bliss et al. (2002) reported mapping four SSR loci in a Prunus map based on an
interspecific cross between almond and peach. They mapped pchgmsl and GA34 to
group 2, GA77 to group 4 and P812A02 to group 8. Since GA77 did not produce a band
segregating at a ratio of 1:1 and 3:], this loci also was not mapped in the sour cherry map.
P812A02 (GK12A02) was mapped to group EBll (Fig. 1.1) and EB and RS3 (Fig. 1.2)

of sour cherry map.

40

Table 1.3 SSR markers incorporated into linkage groups of sour cherry map from
cultivars ‘Rheinische Schattenmorelle’ (RS) and ‘Erdi Botermo’ (EB). Numbers refer to

 

 

the linkage groups.

SSR primer Sequence reference Linkage groups Linkage Linkage
in BB and RS groups in RS groups in
consensus map map EB map

UDP98-410 (Testolin et al. 2000) 12 12

UDP98-410 (Testolin et al. 2000) 12 12

UDP98-411 (Testolin et al. 2000) 19 2
UDP98-411 (Testolin et al. 2000)

UDP98-411 (Testolin et al. 2000) 2
pchpgms3 (Sosinski et a1. 2000) 12
pchpgms3 (Sosinski et al. 2000) 1 1

P812A02 (Sosinski et al. 2000) 11 11
P812A02 (Sosinski et al. 2000) 11 11
P812A02 (Sosinski et al. 2000) 3 3

UDP98-405 (Testolin et all. 2000) 7 7

UDP96-008 (Testolin et all. 2000) 9 9

GA25 (Cantini et al. 2001) 5

(PceGA25)

GA25 (Cantini et al. 2001) 5 5

(PceGA25)

PSO8E08 (Sosinski et al. 2000) 15 15 15

 

41

Joobeur et al. (2000) included six SSRs in the almond map. They mapped
pchgms3 and P8918 into group 1, pchgmsl into group 2, PSIZeZ to group4, PS7a2 into
group 6 and P88e8 into group 7 of the almond map (Table 1.4). In sour cherry, pchgms3
was placed on EBl, PSlZeZ to BB] 1, and PS8e8 into EBlS (Table 1.4). The pchgmsB
locus mapped into group 1 in both almond and sour cherry maps. PS7e2 could not be
mapped in sour cherry map due to low DNA amplification and pchgmsl was also not
mapped due to lack of DNA amplification. PSSe8 mapped to group 15 of sour cherry
map, where it mapped group 7 of almond map. PSIZeZ mapped to REM and EB3 and
RS3 of the sow cherry map, where it mapped to group 4 of almond map and to group 8 of
almond x peach map.

GA34 amplification products were very hard to score due to closeness of the
alleles to each other and not always reproducible. These markers were mapped into
group2 and group5 of EB and RS consensus map. The map location of the GA34 marker
in sour cherry is in good agreement with the almond and peach map (Bliss et al. 2002),
where it mapped between CPM59 and CPM90 markers in both maps. The map location
of peach pchgms3 marker in sour cherry agrees with the results of Joobeur et al. (2000),
where it mapped into the middle section of linkage group 1 on both maps.

The linkage groups in the previous sour cherry map (Wang et al. 1998) and in the
revised map were numbered according to suspected homology to the almond x peach
map (Bliss et al. 2002) and the almond map (Joobeur et al. 2000). Six linkage groups in
sour cherry share two or more common markers (Table 1.4) with the corresponding
linkage groups in the almond x peach map and in the almond map. These results suggest

that these six linkage groups of sour cherry might be homologous to the corresponding

42

linkage groups in the almond x peach map and the almond map. The distances between
common markers shared between these maps are generally consistent. For example,
markers Pru2 and Extl were mapped 44.6 cM apart in sour cherry (Fig. 1.2) and 41 cM
apart in the almond x peach map. CPM39 and CPM20 were mapped 21 cM apart in sour
cherry (Fig. 1.2) and 25.2 cM apart in the almond x peach map. However,
inconsistencies in map distances between shared markers also exist. For example,
markers PLG86 and CPM59 were mapped 50.4 cM apart in the almond x peach map,
however, only 22.8 cM apart in sour cherry (Fig. 1.2). These conclusions about the
homology relations are preliminary until more common markers are incorporated to these
maps.
Table 1.4. Common markers which were used in the assignment of sour cherry linkage
groups.

Linkage group Shared markers of the sour cherry map with the corresponding linkage

groups in the almond x peach map (Bliss et al. 2002) and the almond
map (Joobeur et al. 2000)

 

pchgms3, CPM12

GA34, AC27, AG21, Ole1,PLG86, CPM59, CPM90
CPM58, CPM53

CPM20, CPM2

CPM20, CPM39, CPM23

CPM67, CPM48,AG10, CPM64

Ext], Pru2

OOQONUI-bN—d

Eight out of 26 SRR markers developed by Testolin et al. (2000) were assigned
into a peach map constructed with F2 progenies from cultivars ‘Akame’ and ‘ Jeseitou’
(Yamamoto et al. 2001) When compared to our results, six of the eight SSR markers
(UDP96-01, UDP96-03, UDP96-05. UDP9619, UDP98406, and Udp-409) also amplified

in sour cherry, however none of them were informative which prevented mapping (Table

l).

43

Table 1.5. Linkage group locations of SSR loci in maps of several Prunus species.

Numbers refer to linkage groups.

 

 

SSR Sour Almond Peach Peach Peach Peach Apricot
marker cherry (Joobeur and (Sosinski (Dettori (Yarnamo (Hurtado

et al. almond et al. et al. to et al. et al.

2000) (Bliss et 2000) 2001) 2001) 2002)

al.
2002)

Pchgmsl c.b 2 2 1 - - 7
Pchgms3 1 1 - + - - -
pchgms4 - - - - - - 4
pchgmsS - - - - - - 1
pchcmsS - - - - - - 3
GA34“ 5, 2 - 2 - - - -
(3.4.251b 5 - - - - - -
GA77"kc p.n.s - 4 - - - -
P812A02 3, 11 4 8 - - -
PSO7A02 w.a 6 - — - - -
PSO8E08 1 5 7 - - - ~ -
PSO9F08 c.b 1 - - - - -
UDP-405 7 - - — - - -
UDP-008 9 - - — 3 -
UDP-410 12 - - - 2 - 4
UDP-4ll 2 , 19 - - - 2 - 4
UDP-022 I.f - - - 1 - -
UDP-408 n.a - - - - 1 -
UDP-OOS c.b - - - - 5 2
UDP-004 - - - - - 6 -
UDP-019 c.b - - - - 3 -
UDP-OOl p.n.s - - - 6 3 -
UDP-Ol 5 n.a - - - 8 3 -
UDP-406 p.n.s - - - 2 7 4
UDP-OIO - 6 - 3
UDP-409 n.p - - - 8 3 2
UDP-018 c.b - - - 1 - 2
UDP-013 n.a - - - 2 — 4
UDP-401 n.a - - - 5 - -
UDP-412 c.b - - - 6 - -
UDP-024 c.b - - - 4 - -
UDP-415 n.a - - - 7 - -
UDP-003 c.b - - - 4 -
UDP-416 w.a - - - 6 -

 

n.a = no amplification, n.p = not polymorphic

, c.b = complex banding pattern,

p.n.s = polymorphic but no SDRF, w.a = weak amplification, I.f = informative. *a,b,
and c are also called PceGA34, PceGA25 and PceGA77, respectively

44

Dettori et al. (2001) reported that 17 out of 26 peach microstellites tested were
polymorphic in peach and incorporated into a peach genetic linkage map in a backcross
progeny (Prunus persica x (P. persica x P. ferganensis) (Table 1.5). UDP-411 mapped
into linkage group 2 in both sour cherry and peach map. UDP-008 mapped into linkage
group 9 of the sour cherry map and linkage group 3 of the peach map. UDP-410 mapped
into linkage group 12 of sour cherry map and into linkage group 2 of the peach map.

Out of 45 SSR primer pairs, 22 % (10 loci) were found to be informative yielding
17 informative SDRF that were incorporated in the map EB and RS consensus map in
current study. This is comparable to the results in apricot where out of 45 SSR screened,
13 (28%) loci were mapped (Hurtado et al. 2002).

QTL locations for six fi'uit and flower traits were detected by Wang et al. (1998).
In current study, SSR markers were mapped to the locations of QTL detected earlier
(Table 1.6). Two peach SSR markers UDP41 1-154 and UDP411-131 (Fig. 1.3) were
linked to the bloom time (blm2) location, at the distances of 4.5 cM and 2.3 0M,
respectively (Table 1.6). The same peach markers are also tightly linked to fi'uit weight
QTL (wa), at the distances of 4.5 0M and 2.30M respectively (Table 1.6). The pchgms3-
189 marker was mapped to 8.4 cM of the P8141 which located in bloom time (bImI)
area. The same marker is also tightly linked to EF194c marker at a distance of 0.8 cM
which is located in pistil death (de) area (Table 1.6). UDP405-112 marker mapped 11.1
cM distance of the AGIOb marker which is the closest marker to soluble solids
concentration (ssc2) QTL location (Table 1.6). SSR markers obtained are horticulturally
very important. Being tightly linked to important traits and highly polymorphic, these

SSR markers will be utilized for breeding for these traits saving considerable time and

45

resources. A negative correlation was found between bloom time and percent pistil death
(r = - 0.25) and also a negative correlations exits between bloom time and fruit weight (r
= - 0.45) (Wang et al. 1998). The existence of correlation between these traits firrther
increases the value of these markers enabling breeder to select more than one trait at the

same time.

Table 1.6. QTL detected for flower and fruit traits in sour cherry cultivars ‘Rheinische
Schattenmorelle’ (RS) and ‘Erdi Botermo’ (EB) by Wang et al. (1998) and SSR markers
incorporated to these QTL locations in current study.

 

 

Trait QTL Linkage R2 Nearest Nearest SSR SSR
group RF LP marker distance
maker(s) to RFLP
marker
Bloom time Blml EB] 19.9 P8141 pchgms3-189 8.4 cM
Blm2 EB and 22.3 PLG86 UDP411-154 4.5 cM
R82 UDP411-13l 2.3 cM
Pistil death (%) Pdl EBI 12.9 EF194c pchgms3-189 0.8 cM
Pd2 R88 14.3 EF156b
Pollen Pgr EB] 17.0 EF146
germination(%)
Ripening time Rpl RS4 21.5 EF158b
Rp2 EB and 25.9 CPM20e
RS 6
Fruit weight (g) le EB4 13.7 EF182a
Fw2 EB and 15.5 PL086 UDP411-154 4.5 cM
RS 2 UDP411- 131 2.30M
Soluble solids Sscl BB7 16.5 AGIOb UDP405-112 11.ch
concentration
8302 R86 13.1 EF159a

 

R2 = amount of phenotypic variance explained by QTL (Coefficient of determination).

46

1 2 3 4 5 6 _7 8 9 10 11 12 ‘

 

   

Fig. 1.3. DNA fragment patterns of the SSR primer UDP411. Arrows indicates two
segregating fragments which were mapped to bloom time 2 (blm2) location. The upper
arrow indicates a 154 bp fiagment named UDP411-154 and the lower arrow indicates a
131bp fragment named UDP41 1-131. Lanes 1-13 are progenies from ‘Rheinische
Schattenmorelle’ (RS) x ‘Erdi Botermo’ (EB) population.

The expanded Prunus genetic linkage map constructed from peach and almond
covers 1144 cM (Bliss et al. 2002). The sour cherry linkage map, being tetraploid (2n =
4x = 32), should have 16 linkage groups covering two times of the length of the peach
map. Mapping has a drawback in sour cherry due to the requirement for SDRF in a
tetraploid state, which limits the availability of informative markers. However, new SSR
primer pairs were recently published by Aranzana et al. (2002), Dirlewanger et al. (2002)
and Wang et a1. (2002). Incorporation of new markers should extend the current sour

cherry map and bring the linkage group number down to 16 groups.

47

The results in this study clearly indicate that SSR developed in other Prunus
species have good utility in sour cherry, and are transportable into Prunus species. 88R
are distributed throughout the sour cherry genome. Having cross-species amplification,
they are highly useful for comparative mapping analysis. Further incorporation of
currently unavailable SSR loci into sour cherry map will likely provide an excellent
source for identification of homoeologous linkage groups in sour cherry (Wang et al.
1998). With the availability of more SSR markers and an increased number of common
SSR loci mapped in Prunus species, it should be possible to identify homologous areas,
and regions of translocations, insertions, or deletions. Such data would provide

information on gene order conservation in Prunus and the family Rosaceae.

48

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49

Bowers JE, Dangl GS, Vignani R, Meredith CP (1996) Isolation and
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56

CHAPTER 2

A MODIFIED-BULK SEGREGANT ANALYSIS FOR BLOOM TIME IN SOUR
CHERRY

57

ABSTRACT

Developing genetic markers linked to bloom time in sour cherry (Prunus cerasus
L., 2n=4x=32) is very important, because utilization of markers will help the indirect
selection of varieties for desirable bloom time in early generations, saving time and
effort. Three different approaches were used to identify markers associated with bloom
time in a sour cherry population derived from crosses between two sour cherry cultivars,

‘Balaton’ and ‘Surefire’.

 

In first method, a primer pair designed from the sequence of an AFLP probe
p814] that mapped to the linkage group 1 of ‘Erdi Botermo’ (EB) (Chapter 1) was used
to find markers associated with the bloom time. The primer amplified too many bands
between 140 bp and 500 bp, and therefore did not distinguish any specific association
with the trait.

In a second approach, the pchpgms3 SSR marker, which mapped to the EBl at
8.4 0M distance from the p8141 probe, was tested for association with bloom time.
Bloom data was converted into degree-days and tested for association with PCR
amplification products of the pchpgms3 SSR marker, (189bp, l76bp and l74bp) which
were polymorphic in the progeny (the progeny set is shown in table 2.2). There was no
significant relationship between alleles amplified by pchpgms3 primer and bloom time
data in sour cherry.

In a third strategy, a modified bulk segregant analysis in combination with AF LP
technique was used to screen progenies from two extreme phenotypic classes for bloom

time. The average number of polymorphic bands was 10.7 per primer pair and the

58

percentage of polymorphism ranged from 10% to 44% for primer pair combinations.
Screening of early and late extreme groups with 156 AF LP primer pairs resulted in the
identification of three candidate bands in three different primer combinations (a 82 bp
fiagment in EGG/MCAC primer pair combination, a 78 bp fragment in ETT/MCCG
primer pair combination, and a 94 bp fragment in EAA/MCGT primer pair combination).
These candidate bands were present in an early bloom time group but not in late group or

versa visa.

59

INTRODUCTION

Consistent yield is one of main objectives of sour cherry breeding programs
(Iezzoni 1996). In some cherry growing regions, such as Michigan, where 72.5 % of the
sour cherries in US are produced (U SDA/NASS 1999), low temperature damage to
flower buds and flowers is the most common factor reducing yield (Thompson 1996). ,3.
Therefore, cold hardiness of sour cherry flower buds is one of the most important
breeding objectives for these cold production regions (Iezzoni 1996). A delay in the

spring floral bud development could decrease crop loss from a spring freeze (Iezzoni

 

1996). Therefore, the development of new later blooming varieties would avoid some of
the loss due to spring fieeze injury.

Bloom time in cherry is a quantitative trait, but has high broad sense heritability
(0.91) (Wang et al. 2000) probably due to low number of genes controlling the trait.
Identification of markers linked to Quantitative Trait Loci (QTL) controlling bloom time
in sour cherry could expedite the development of new cultivars or improvement of
current cultivars with late blooming characteristics using marker assisted selection

(MAS).

60

Si

ge

LITERATURE REVIEW

QTL identification strategies

DNA markers are essential tools in plant genetics with particular value in gene
mapping and marker assisted selection. Genetic markers linked with QTL may enable
indirect selection of complex traits. Molecular markers have been successfirlly used to
map individual genetic factors or QTL controlling complex traits (Hurtado et al. 2002;
Ballester et al. 2001; Dirlewanger et al. 1999). While such experiments are usefirl, they
require large populations, and are labor-intensive (Wang and Paterson 1994).
Construction of separate linkage maps to identify QTL for each complex trait in many
different populations is frequently not feasible (Miklas et al. 1996).

More efficient alternatives to the construction of saturated linkage maps for
identifying QTL have been developed. Bulked segregant analysis (BSA) (Michelmore et
al. 1991) and selective genotyping (Lander and Botstein 1989) have been used to identify
markers linked to targeted QTL. In these approaches, polymorphic markers are evaluated
across two DNA pools (BSA method) or groups of lines (selective genotype method):
One DNA pool or group of lines consists of the most resistant (one-extreme) and the
other the most susceptible (other extreme) lines within the population. Markers that 00-
segregate within groups with the trait of interest are mapped across the entire population.
Thus, only a few select markers are mapped and analyzed for association with the
specific quantitative trait (Miklas et al. 1996). Chen et al. (1994) used the selective

genotyping approach with the objective of rapidly locating putative resistance loci.

61

 

DNA pooling based only upon phenotypic information, or BSA has been
employed mostly in the analysis of simply inherited traits. Wang and Paterson (1994)
assessed the utility of phenotype-based DNA pools for tagging QTL in F2, backcross
(BC), recombinant inbred (RI), and doubled haploid (DH) populations. The effects of
population size, portion of population selected, magnitude of the phenotypic effect of
individual QTL alleles (QTL allele effect), and effects of both dominance and deviations
from Mendelian segregation ratios, are taken into consideration. It was suggested that
BC populations are better than F2 populations, but less efficient than R1 or DH
populations, for “tagging” QTL using phenotype-based DNA pools. The use of
phenotype-based DNA pools might be successful in tagging QTL of very large effect, but
is unlikely to permit comprehensive identification of the majority of QTL affecting a
complex trait (Wang and Paterson 1994). To tag QTL using phenotype-based DNA
pools, Wang and Paterson (1994) recommended several considerations, including (1) the
use of crosses having a wide variation; (2) the use of large populations (3) the use of
homozygous populations, i.e., R1 or DH lines; and (4) the replication of phenotypic
evaluations, facilitated by the use of homozygous populations, but also possible by using
F2 or BC —derived lines for phenotype evaluations (Wang and Paterson 1994).

Koester et al. (1993) used F2 and F3 progeny derived from crosses between the
near isogenic lines (NIL) and the recurrent parent of maize to identify QTL controlling
days to flowering and plant height. Mansur et al. (1993) used recombinant inbred lines
(RIL) exhibiting an extreme phenotype for each trait (eg. early and late plants maturity)
by having two bulked DNA samples prepared for each trait. When an RFLP marker was

linked to a QTL, one parental allele predominated in the bulked DNA from a particular

62

 

phenotype; while the other allele was associated with the opposite phenotype. Chalmers
et al. (1993) successfully used DH populations of barley in combination with RAPDs to
identify molecular markers linked to genes controlling the milling energy (ME)
requirement. Their work involved the construction of bulks by combining DNA from DH
families representing the extreme members of the distribution for ME. Miklas et a1.
(1996) investigated the use of selective mapping to expedite identification of RAPDs
associated with QTL conditioning bean golden mosaic virus (BGMV) or common
bacterial blight (CBB) resistance using RIL populations of common bean (Phaseolus
vulgaris L.). They used a BSA of as few as three individuals and tested 101 RAPDs
identified as polymorphic between the parents tested across resistant vs. susceptible bulks
for BGMV reaction. Fourteen of 22 RAPDs which were selectively mapped because
they co-segregated among lines within bulks, and were linked with seven of the nine
QTL. BSA and selective genotyping was equally effective and less costly than
completely classifying the entire population with each marker.

Use of AFLP’S in Prunus:

Amplified fragment length polymorphism (AF LP) is used increasingly in a
variety of genetic analyses due to its suitability for high-throughput analyses (Cervera et
al. 2000). Cervera et al. (2000) evaluated an AFLP system and found it to be a powerful
tool in forest tree genetics for genetic variability studies, genome mapping purposes and
fingerprinting of different tree species such as Prunus spp., Eucalyptus spp., Quercus

spp., Populus spp., and Pinus spp, .

63

Campalans et al. (2001) used the cDNA-AF LP (amplified restriction fiagment
polymorphism derived technique for RNA fingerprinting) method to find differentially
expressed genes during dehydration of ahnond.

AFLPs are useful for identification and genome analysis of sweet cherry cultivars
(Struss et al. 2001). Struss et a1. (2001) found that ten out of 18 primer combinations
were informative. Seven to thirty-three of the amplified bands were polymorphic and all
38 sweet cherry cultivars were clearly identified. Goulao et al. (2001) stated that AF LP
and ISSR techniques are usefirl for identification of genotypes and analysis of phenetic
relationships in plum (Prunus domestica L.). They used six ISSR and seven AF LP
primers resulting in the amplification of 270 and 379 fragments, respectively. Several
cultivars fall the same group with both AF LP and ISSR analysis. The phenetic
classification fiom the two methods were similar (r = 0.73, for the diploid group) but,
ISSR had better reproducibility and a higher percentage of polymorphisms (87.4% vs.
62.8%) (Goulao et al. 2001). Manubens et al. (1999) employed AFLP fingerprinting for
assessment of peach and nectarine (Prunus persica ssp nucipersia) varieties and
distinguished eight peach and six nectarine varieties more consistently when compared to
traditional identification based on assessment of agronomic traits of the adult plant.

Shimada et al. (1999) studied the AF LP system for usefirlness in obtaining
information on reproducibility, efficiency and frequency of polymorphisms, in the peach
using fluorescein isothiocyanate (FITC) and biotin detection systems. An almost
identical band pattern was obtained from different methods of DNA extraction and

between replications. AFLP analysis resulted in 2.5 polymorphic bands between 'Akame

and Juseitou' per primer, which is 20 times more than those obtained by RAPD analysis

64

leading to discrimination of closely related cultivars. They concluded that AFLP analysis
is a useful system for cultivar identification, the parentage, and trapping work in peach.

Lu et al. (1999) found that a co-dominant AFLP marker, EAA/MCATIO, co-
segregates with the primary source of resistance to root-knot nematodes (Meloidogyme
incognita and hi. javanica) in rootstock cultivars of peach. Two allelic DNA fragments of
this AF LP marker were cloned, then sequenced and converted to sequence tagged sites
(8T8). Four nucleotide differences (i.e. one addition and three substitutions) were
observed between the two clones. Then they evaluated the STS marker system for peach
germplasm improvement by PCR-amplifying germplasm with the Mij3F/Mij1R primer
pair and then digesting with Sau3 Al. The banding patterns of the EAA/MCATIO STS
markers were able to distinguish among the three genotypes - homozygous resistant,
heterozygous resistant and homozygous susceptible - in the 'Lovell' x 'Nemared' cross.
Moreover, the results of the rootstock survey were consistent with nematode infection
response of each rootstock.
Bulk segregant analysis

The conventional method of locating and comparing QTL requires a segregating
population of plants where each one is genotyped with molecular markers. Another
approach is to group plants according to the phenotype of the trait of interest and test for
differences in allele fi'equency between the population bulks: bulk-segregant analysis
(BSA) (Michelmore et al. 1991). A marker that is polymorphic between the parents of
the population and closely-linked to a major QTL regulating a trait of interest will 00-
segregate with that QTL. For example a marker will co-segregate with the phenotype of

a trait if the QTL has a major effect. If two extreme groups are analde with the

65

polymorphic marker, the frequency of the two marker alleles present within each of the
two bulks will deviate significantly from the 1:1 ratio expected for most populations.
Since, in many species, chromosomal locations of many markers was determined, the
location of linked QTL could be deduced without genotyping each individual in a
segregating population. This method was used in composite populations of maize to
locate QTL effecting yield under drought conditions (Quarrie et al. 1999).

Bentolila and Hanson ( 2001) used BSA to identify markers closely linked to the
restorer of fertility (Rf) locus in petunia in a large BCl population produced fi'om two
different parental lines carrying Rf. They were able to identify an amplified fragment
length polymorphism (AF LP) marker that co-segregates with Rf.

Decousset et a1. (2000) employed BSA in a BC2 population segregating for the
de-HI photoperiod response gene and were able to identify six AF LP markers closely
linked to the de-Hl gene.

Smiech et al. (2000) used BSA with RAPD’s to identify markers to distinguish
between resistant and susceptible forms tomato (Lycopersicon esculentum Mill.). They
stated that 28 out of 271 primers produced polymorphism which were tested for linkage
to the resistance phenotype. They were able to identify 5 primers enabling them to
distinguish between resistant and susceptible forms in a F2 segregating progeny
developed from resistant x susceptible parents for tomato spotted wilt virus. They
concluded that the selection of TSWV resistant individuals can be facilitated by MAS.

Badenes et al. (2000) used BSA with RAPD markers to identify markers linked to
male sterility and self-compatibility in apricot. Their screening of 228 primers yielded a

marker linked to male-fertility (M4-950) but none to S alleles. With a second approach of

66

 

 

the screening of primers in a subset of seedlings, they were able to identify two markers
linked to the Sc allele and three markers linked to male-fertility.

Dong et al. (2000) used BSA with the AFLP technique to identify molecular
markers linked to the therrnosensitive genie male sterility (TGMS) gene in a F2
population of a cross between a TGMS indica mutant, TGMS-VNl, and a fertile indica
line, CH1 of rice (Oryza sativa) . Out of 200 AF LP primer combinations surveyed, they
identified four AFLP markers (E2/M5-600, E3/Ml6-400, E5/M12-600, and E5/M12-200)
linked to the TGMS gene in the coupling phase.

Wise et al. (1999) employed BSA with AF LP analysis to identify DNA markers
closely linked to the Rf8 locus, which mediates partial fertility restoration of T-cytoplasm
maize. They stated that these findings would help a better understanding of mechanisms
of nuclear-directed mitochondrial RNA processing and fertility restoration.

Yu and Wise (2000) identified three markers linked to the Pea crown-rust
resistance cluster using AF LP-based BSA in pea (Lathyrus sativus).

The Beta (B) locus eflects fruit beta-carotene content in tomato (Lycopersicon
esculentum Mill.) (Zhang and Stomme12000). Zhang and Stommel (2000) employed
BSA with 1018 random primers for RAPD analysis and 64 primer pairs for AF LP
analysis in an F2 population segregating for B, and identified polymorphic bands which
distinguished two bulked DNA samples. One single 100 bp AFLP amplified band
distinguished the NILs and co-segregated with Beta modifier (MOB) and was shown to
be closely linked to the locus.

Lecouls et al. (1999) used BSA with RAPD analysis to identify markers linked to

the Mal gene (controls a high and wide-spectrum resistance to root-knot nematode) using

67

segregating progenies crossed by host parents. They were able to identfy four dominant
coupling-phase markers from a total of 660 lO-base primers tested.

A single recessive gene, ana, produces the anasazi pattern of partly colored
seedcoats in common bean (Bassett et al. 2000). Bassett et al. (2000) identified
molecular markers linked in coupling to the Ana (0M9 (200), 5.4 0M) gene using BSA.
Bloom time studies in Prunus

Flowering time is generally considered to be inherited quantitatively, however, a
single gene controlling late flowering in a qualitative manner was identified in progenies
tracing back to a single mutant in almond (Company et al. 1999). The effect of this allele
in almond pro genies is modified by quantitatively inherited minor genes.

Wang et aL (2000) reported QTL analysis of flower and fi'uit traits in sour cherry
using the RFLP map of EB and RS. They estimated the location and effects of QTL for
eight traits. They reported that they detected eleven putatively significant QTL (LOD >
2.4) for six characters (bloom time, ripening date, % pistil death, % pollen germination,
fruit weight, and soluble solid concentration) and the percentage of phenotypic variation
explained by a single QTL varied from 12.9 % to 25.9 %. QTL for flower traits (bloom
time, % pistil death and % pollen germination) were mapped to the same linkage group,
BE]. A negative correlation was found between bloom time and percent pistil death (r =
- 0.25) (Wang et al. 2000).

Ballester et al. (2001) studied the genetics of late bloom in almond. BSA was
used to in an F1 population to identify RAPD markers linked to the Lb gene, which is
located on the linkage group 4. They were able to identify three RAPD markers

associated with the Lb gene. One of them (OKP101350) placed at 5.4 cM from Lb and

68

possibly can be used as a selective marker for flowering time. Plants with Lb allele
bloomed about two weeks later and this allele had dominant gene action.

Bloom time QTL were also identified in the peach map (Yamamato et al. 2001).
Six QTL were located in 4 different linkage groups. One of the QTL mapped in group 6
and another 3 QTL into group 3. The last bloom time QTL located was at the end of the
group 1.

Low temperature damage to floral organs in spring is the most common factor in
reducing the yield in some regions (Thompson 1996). Therefore, the development of
new late blooming varieties would avoid some of the loss due to spring freeze injury.
Identification of markers linked to bloom time QTL in sour cherry could expedite the
development new cultivars with late blooming characteristics using MAS. The objective
of this study is to search for such candidate markers associated with bloom time in sour
cherry using different approaches, which includes testing of the genetic markers that are
mapped close to the bloom time QTL for association with bloom time (Chapter 1) and

BSA.

69

MATERIALS AND METHODS

Plant material and bloom time

Bloom time was scored on approximately 200 progenies from a cross between
two sour cherry cultivars, ‘Balaton’ x ‘Surefire’, for three consecutive (1999-2001) years.
Bloom time was recorded as the time when approximately 50% of the flowers were open.
‘Balaton’ x ‘Surefire’ population was chosen because this cross displayed considerable
variation in bloom time (Figures 2.2, 2.3 and 2.4). Temperature readings were obtained
fiom the Clarksville Horticultural Experiment Station. Bloom date data was converted
into degree days (DD) from January 1 with a base temperature of 4.4 °C (Table 2.2). The
positive differences of hourly temperature readings from 4.4 °C were summed to
calculate daily heat unit accumulations.

Selection of bulks

Two groups of three plants were selected from each extreme of the bloom time
distribution of ‘Balaton’ x ‘Surefire’ population for selective genotyping. Three
progenies, 3-24, 4-47 and 2-61, were selected as early group because they were the
earliest flowering individuals over three years (Fig. 2.2, 2.3, and 2.4). The other three
progenies, 4-22, 2-19 and 2-39, were selected as the late group since they were the latest
flowering progenies for three years (Fig. 2.2, 2.3, and 2.4). These selected progenies
carry enough flowers (about 10 flowers) to assess the bloom time accurately. Two
groups were screened with a total of 156 AFLP primer pair combinations (Table 2.1).
DNA extraction

Young unfolded leaves were obtained from trees of the ‘Balaton’ x ‘Surefire’

population located at Clarksville Horticultural Experiment Station of Michigan State

70

University and were brought to the laboratory in a cooler and frozen at —80 OC overnight
and lyophilized for 2-3 days. DNA isolation was conducted according to Stockinger et
a1. (1996).
Primers and PCR conditions for approaches 1 and 2

A primer pair (5’-GGCTCCTACCCATCTAACTGTGA—3’, S’GTCCCGTGCT
TTTCCCATTC-3’) was designed from the sequence of a RFLP probe p8141, which is a
clone, derived from sweet cherry stylar cDNA (Iezzoni and Brettin 1998). The primer
sequence for pchpgms3 SSR primer was given by Sosinski et aL (2000). These two
primer pairs were PCR-amplified as follows: 1X PCR buffer, 0.2 mM of dNTP’s, 2.5
mM of MgCl2, 50 ng DNA, 0.6 unit Tag DNA polymerase enzyme (Boehringer
Mannheim Biochemicals) and ddH2O to a volume of 25 ul. PCR reactions were
performed in a therrnocycler (model 9600; Perkin Elmer Applied Biosystems, Inc., Foster

City, California). The PCR products were electrophoresed for 2.5 hrs at 80W on a

6%polyacrylamide gel with a 38 X 50 cm Sequi-Gen GT sequencing cell (BioRad

Laboratories Inc., Hercules, CA, USA). Silver staining was conducted with a
commercial kit (Promega # Q4132) according to instructions. Fragment sizes were
estimated using a 10 bp ladder (Gibco BRL).
Modified -BSA and AFLP procedure for third aproach

The DNA pooling technique proposed by Michelmore et al. (1991) with a
modification was used to find candidate markers that are present in one group but not in
the other. The modification was made by not mixing the DNA of plants from the same

group and keeping them separate.

71

AFLP markers were used because they do not have a very high development
cost, genotyping cost is moderate, and produces more bands (up to 100 per gel) per gel
than any other markers.

Digestion, adapter ligation, preamplification, and selective amplification were
done as described (V 05 et al. 1995; Barrett and Kidwell 1998), except with the following
modifications described by Hazen et al. (2002); 2 pl of restriction ligation product was
combined with 25 ng of Msel and EcoRI, 0.5 mM dNTPs, 1X PCR buffer (10 mM Tris-
HCl, pH 7.2 50 mM KCl, and 0.1% Triton X-100), 0.5 U Taq polymerase, 1.5 mM
MgC12, total volume 20 pl. Preamplification was done with the following thermocycler
profile [94 .C 2 min — 26 cycles (94 .C l min, 56 .C l min, 72 .C 1 min) - 72 .C 5 min].
The PCR product from preamlification was diluted six times with sterile water. One
microliter of the dilute preamplification product was added to 19 pl of the following
cocktail (25 ng EcoRI primer, 30 ng MseI primer, 0.4 mM dNTPs, 1X PCR buffer, 0.4 U
Taq polymerase, 1.5 mM MgC12) and selective amplification was carried out with the
following profile [94 .C 2 min — 12 cycles with annealing temperatures decreasing by 0.7
.C each step (94 .C 30 sec, 65 .C 30 sec, 72 .C 1 min) -— 23 cycles (94 .C 30 sec, 56 .C 30
sec, 72 .Clmin) — 72 .C 2 min]. The screening of early and late bulks was done by 156
AFLP primer combinations (Table 2.1).

Electrophoresis

The selective amplification products were separated by electrophoresis for 2.5 hrs

at 80 W on a 6% polyacrylamide sequencing gel on a 38 X 50 cm Sequi-Gen GT

sequencing cell (BioRad, Hercules, CA), then silver stained with sequence staining kit by

72

Promega (# Q4132) and sizes were estimated using a 10 bp ladder (Gibco BRL #10821-

015).

73

 

RESULTS AND DISCUSSION

Three different approaches were used to identify markers linked to bloom time in
sour cherry population from the ‘Balaton’ x ‘Surefire’ cross.

In the first approach, a primer pair designed fiom p814] sequence was used to
find a marker associated with the bloom time. The p814] probe mapped in a region of
(HM!) the linkage group 1 of EB, which explained the 19.9 % of the phenotypic variation
(Wang et al. 2000). Therefore, a primer designed from the sequence of p814] probe
could have been a usefiil marker in selecting for bloom time in early generations.
However, the primer designed from the sequence amplified many bands between 140 bp
and 500 bp and no specific band was available to test for association with bloom time.

In a second approach, the pchpgms3 SSR marker, which mapped to the EBl
linkage group at 8.4 cM from the p8141 probe (Chapter 1, F ig.1.1) was tested for
association with bloom time. Bloom data was converted into degree days (Table 2.1) and
tested for association with PCR amplification products of the pchpgms3 SSR marker
(189bp, 176bp and l74bp) which was polymorphic between the parents and in the
progeny. The significance of differences between marker levels (0 versus 1) of pchgms3
SSR were tested (Table 2.2) using three years of bloom data expressed in degree days
(DD) (Table 2.1). The model (YD-km = Mean + Mli +M2,- + M31, + Yearm + Eijkm) also
included year to eliminate the effect of missing data (Table 2.1) and a total of 74
observations were used (Table 2.1) . Degree of freedom is 68 (74 — 6 = 68). There were
no significant differences between marker levels of alleles amplified by pchpgms3 primer

and bloom time (Table 2.2).

74

Table 2.1. Degree days (DD) for bloom time and PCR amplification by pchgms3 primer
pair for each progeny in ‘Balaton’ x ‘Surefire’ population.

 

 

Plant Marker DD
pchgms3 pchgms3 pchgms3 l 999 2000 2001 Average
-189bp -176bp -l 74bp

4-47 0 l 0 389.0 212.2 178.9 260.0
3-24 0 1 1 418.0 212.2 163.6 264.6
2-61 1 1 0 406.5 223.1 173.0 267.5
3-20 0 1 0 430.5 218.1 173.0 273.8
2-31 1 l 0 457.5 235.5 201.2 298.0
2-29 0 l 0 474.0 235.5 187.4 298.9
2-44 0 1 0 474.0 235.5 201.2 303.5
1-66 1 0 1 474.0 251.3 187.4 304.2
2-32 0 1 0 474.0 259.3 201.2 311.5
2-54 0 1 0 474.0 243.5 217.5 311.6
4-56 0 1 0 517.0 223.1 201.2 313.7
2-56 0 l 1 494.5 251.3 201.2 315.6
4-14 0 1 1 494.5 251.3 201.2 315.6
3-37 1 l 0 474.0 287.6 201.2 320.9
3-59 0 l 1 494.5 267.4 201.2 321.0
342 0 l 1 494.5 251.3 217.5 321.1
2-45 1 0 1 517.0 267.4 217.5 333.9
4-35 0 l 1 544.5 235.5 234.9 338.3
3-50 1 l 0 517.0 267.4 234.9 339.7
2-33 0 l 0 494.5 201.2 347.8
446 0 1 1 544.5 287.6 217.5 349.8
4-54 0 1 0 574.5 287.6 234.9 365.6
Balaton 1 l 0 494.5 251 .3 372.9
Surefire 0 l 1 544.5 287.6 416.0
1-25 1 0 1 457.5 457.5
1-26 0 0 1 474.0 474.0
1-42 0 l 1 517.0 517.0
1-27 1 1 0 574.5 574.5
3-66 0 1 0 616.5 616.5

 

1 = presence of the marker; 0 = absence of the marker, DD = degree days from January 1

with a base temperature of 4.4 °C.

75

 

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76

Ballester et al. (2001) reported a QTL for bloom time on linkage group 4 in the
almond map tlmt explained 79% of the phenotypic variation. Three RAPD markers were
identified in almond in association with bloom time using BSA, with one at a 5.4 cM
distance from the late blooming gene (Lb) which could be used in MAS. Incorporation of
more common co-dominant markers, such as SSRs, between a sour cherry and an ahnond
map might have potential for evaluation of association of these markers with bloom time
assuming the gene order is conserved between the two species. Currently the sour cherry
and peach x almond hybrids map share two common RFLP markers in this linkage group
namely; CPM58 and CPM53 (Chapter 1). Recently Dettori et al. (2001) incorporated
two SSR markers to the linkage 4 of peach; UDP96-003 and UDP98-024. The UDP-003
is only 7 cM away fiom the FG3 marker which is located also next to the bloom time
QTL of the almond map. Unfortunately UDP96-003 marker could not be incorporated
into sour cherry linkage map due to the complex banding pattern. Although UDP98-024
marker was polymorphic and informative, it did not map due to the low saturation in the
sour cherry map (Chapter 1).

Yamamato et al. (2001) reported that they have identified six bloom time QTL in
peach map locating in four different linkage groups. One of the QTL mapped in group 6
and another three into group 3. Linkage group 3 contains four 88R loci recently
incorporated, however, none of these markers were informative in sour cherry (Chapter
1). The last bloom time QTL located at the end of the group 1 in their study as in the
case of sour cherry (Wang et al. 1998) where a microsatelite marker, pchgms3, was
incorporated (Chapter 1). However no association was found in our study between this

marker and bloom time (Chapter 1).

77

In the third method, a modified BSA in combination with AFLP technique was
used to identify candidate markers associated with bloom time. The selected individuals
used for bulk analysis are indicated on Figures 2.2-2.4. There were 156 AF LP primer
pair combinations, which were used to screen early and late bulks of the population to
find candidate markers present in one bulk but not in the other. AFLP primer
combinations resulted in 1 to 34 polymorphic bands for each primer pair (Table 2.2). The
average number of polymorphic bands was 10.65 per primer pair and the polymorphism
rate ranged from 10% (ECA/MCCT and EGG/MCAT) to 44% (EAA/MCGA) per primer
pair combination.

Screening of early and late bulks with 156 AFLP primer pair combinations
resulted in the identification of three candidate bands in three different primer
combinations that were present in early bulk but absent in late bulk or visa versa. The
EGG/MCAC primer combination amplified a band at 82 bp that was present in the early
group but not in the late group (Fig. 2.1-0). The Balaton parent also had the band of the
early group; ‘Surefire’ did not possess this band.

ETT/MCCG primer combination resulted in an amplified band of 78 bp, which is
present in the late group, but not in the early group (Fig. 2.1-b). The parent, ‘Balaton’,
also had the band as the late group; ‘Surefire’ did not have this band.

EAA/MCGT primer combination amplified a band at 94 bp, which is present in

the late group, but not in the early group (Fig. 2.1-a).

78

Table 2.3 Number of polymorphic bands produced by AFLP primer pair combinations in
‘Balaton’ x ‘Surefire’ population.

 

 

Primer No. of Primer No. of Primer No. of
combination polymor. combination polymor. combination polymor.
bands bands bands
ECA MCGC 7 ECC MCGG na EGG MCCC 3
MCT C 2 MCGC 10 MCCG na
MCCG na MCAA 7 MCGC l 1
MCAA 5 MCGG 10 MCTC 12
MCGT na MCGA 22 MCAA 12
MCGA 26 MCAT na MCGT 6
MCAT 14 MCTC 28 MCGG 2
MCAG na MCCG 15 MCGA 5
MCCT 7 MCCC 10 MCAT 3
MCTA 16 MCCT na MCAG na
MCCA na MCAG na MCCT na
MCCG 10 MCTA 6 MCT A na
MCCC 10 MCCA 4 MCCA 7
MCAC 6 MCAC 5 MCAC 10
MCTT 1 1 MCTT 9 MCTT 2
MCGT 24 MCAG 7
ETT MCAC 8 ECT MCGC na EAT MCTC 3
MCTT na MCTC 4 MCCC na
MCTC 12 MCAA l3 MCTA 3
MCCC 1 l MCCC 21 MCTC 13
MCTA 9 MCCG 15 MCAA na
MCTC 9 MCGG 12 MCGT 5
MCAT 14 MCGA na MCGA na
MCAA na MCAT na MCGG na
MCGT 6 MC AG na MCCA na
MCAT 14 MCCT 2 MCAG 14
MCGG 1 l MCTA 7 MCCG 9
MCCA 19 MCCA 7 MCGC na
MCAG 13 MCGT na MCAT na
MCCG 1 5 MCAC 26 MCGA na
MCGC 10 MCTT 18 MCCT 1 1
MCGA 24 MCAC l4
MCGG 9

 

79

Table 2.2. (cont’d)

 

 

Primer No. of Primer No. of Primer No. of
combination polymor. combination polymor. combination polymor.
bands bands bands
EAG MCGA 5 EGC MCTC 3 EAA MCCG 22
MCGG 23 MCGA 6 MCCC 9
MCTC 12 MCAT 4 MCAA 12
MCAG 6 MCAA 5 MCTC 8
MCCT 1 MCTC 8 MCAT 8
MCCA 2 MCAG 5 MCGC 1 8
MCTA na MCCT 3 MCTA 6
MCGT 1 7 MCTA l4 MCCA na
MCAT 1 8 MCCA 7 MCAG na
MCGC 2 MCGT na MCCT na
MCCG 10 MCGC na MCGT 18
MCAA 8 MCCC na MCGG 21
MCCC l6 MCGG 2 MCGA 34
MCAC 9 MCAC 7 MCAC na
MCTT na MCTT l4 MCTT 6
MCCG 12
EAC MCAT 7 EAC MCTC na EAC MCAC 26
MCCC na MCGT na MCTT l9
MCAG na MCCG na MCTC na
MCCT 4 MCGC na
MCTA na MCGA na
MCCA 10 MCCG na
MCAA na MCGA na

 

na = data is not available, polymor. = polymorphic.

80

a. EAA/MCGT combination b. ETT/MCCG combination c. EGG/MCAC combination

pun. ._ . w.—

 

L[12 3 ][4 5 6]L [1 2 3][4 5 6] L LSB[123][456]

Figure 2.1 a-c. Three candidate AFLP bands that are present in one group, but not in
another; a 94 bp band pointed by an arrow in BAA/MCGT primer combination is present
in late group, but not in early group. A 78 bp band pointed by an arrow in ETT/MCCG
primer combination is present in late group, but not in early group. EGG IMCAC primer
combination has a band pointed by an arrow at 82 bp that present in early group, but not
in late group. Lane L is 10 bp ladder; lanes 1, 2 and 3 are late group (2-19, 4-22, and 2-
39, respectively); lanes 4, 5, and 6 are early group (4-47, 2-61, and 3-24, respectively);
lane 8 is Surefire and lane B is Balaton.

81

The significant relationship between these AF LP markers and the bloom time
could be confirmed by genotyping the whole ‘Balaton’ and ‘Surefire’ with these markers
and testing for a statistically significant relationship. Then, informative AFLP markers
could be converted into STS as explained in Chapter 3 and be utilized in MAS for late
blooming.

For a better understanding of the genetics of bloom time, the AFLP markers
obtained here could be incorporated into the existing sour cherry linkage map in BB and
R8 population as explained in Chapter 3. The incorporation of these markers into the
map will provide usefiil linkage information between these AFLP markers. If these
markers are not closely linked and if each one Imps independent of each other or into
different linkage groups, then this information may allow us to have better understanding
of the number of genes controlling bloom time. The amount of variation explained by the
locations of the AF LP markers could also be calculated using QTL CARTOGRAPHER
(Basten et al. 1997). If these candidate markers are closely linked or map in to the same
location, this might indicate that they are part of the same QTL.

The bloom time data for ‘Balaton’ x ‘Surefire’ population exhibited continuous
variation in all three years, which is typical of quantitative inheritance. Distributions of
flowering time of progenies in all three years were normal (Fig. 2.2, 2.3 and 2.4) and the
means (Table 2.4) were similar to the mid-parent values. The absence of a bimodal
distribution suggests that there is no major dominant gene for bloom time in ‘Balaton’ x
‘Surefire’ population. In contrary, the bloom time distribution in ahnond showed a
bimodal distribution due to the presence of a major dominant Lb gene (Company et al.

1999). The difference in bloom time for ‘Balaton’, and ‘Surefire’ (43.10 DD) was not

82

significant (P < 0.05). Parental values were not the extremes (Tables 2.1 and 2.4) and
transgressive segregation was observed for bloom time distribution of the progenies (Fig.
2.2, 2.3 and 2.4).

Table 2.4. Mean phenotypic values, standard deviations and value range for the bloom

time distribution of the progenies of ‘Balaton’ x ‘Surefire’. All data expressed as degree
days.

 

 

Year Mean SD Max. Min.

1999 470.3 125.6 616.0 389.0
2000 261.8 28.4 339.0 212.2
2001 200.5 15.3 273.6 163.6

 

DD = degree days from January 1 with a base temperature of 4.4 °C.
SD = standard deviation.

From a breeding standpoint, availability of informative markers associated with
bloom time is very important, because utilization of markers will help selection of
varieties for bloom time early in the generation, saving time and effort. These candidate
markers may also be incorporated into existing Prunus maps and lead to isolation of

genes for bloom time.

83

 

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LITERATURE CITED

Apostol J, Iezzoni AF (1992) Sour cherry breeding and production in Hungary.
Fruit Variety Journal 16:11-15

Badenes ML, Hurtado MA, Sanz F, Archelos DM, Burgos L, Egea J, Llacer G
(2000) Searching for molecular markers linked to male sterility and self-
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90

CHAPTER 3

SUMMARY AND DISCUSSIONS

91

SUMMARY AND DISCUSSIONS

Two projects were carried out to aid breeding studies of sour cherry (Prunus
cerasus L., 2n=4x=32). In the first project, 45 primer pairs developed fiom the
sequences of Prunus nricrosatellites (simple sequence repeats, SSR) were screened and
10 informative SSR yielding 17 markers were added to a previously developed sour
cherry linkage map of two tetraploid sour cherry cultivars, ‘Rheinische Schattenmorelle’
(R8) and ‘Erdi Botermo’ (EB).

The previously published EB linkage map (Wang et al. 1998) consisted of 95
SDRF in 16 linkage groups covering 279.2 cM. With the addition of new markers the
current linkage map consists of 118 SDRF in 18 linkage groups covering 337.8 cM.
Seventeen markers remained unlinked. Therefore the addition of new markers provided a
20% (58.6 cM) increase over the length of the previous map. The previous RS linkage
map (Wang et al. 1998) consisted of 126 SDRF assigned to 19 linkage groups covering
461.6 cM. The current linkage map now consists of 133 SDRF assigned to 19 linkage
groups covering 433.9 cM. With the addition of new markers, a 27.7 CM decrease in the
map distance was observed. This is caused by the fact that with the addition of the new
markers, linkage groups 9 and 12 fi'om the old map were combined into one linkage
group named group 9 in the current map. Twenty-six markers were unlinked. The
expanded Prunus genetic linkage map constructed fiom peach and almond covers 1144
cM (Bliss et al. 2002). The sour cherry linkage map, being tetraploid, should be twise the

length of the peach map.

92

Homologous relations between EB and RS linkage groups were identified using
60 bridging markers heterozygous in both parents. Fifteen EB linkage groups
homologous to the R8 linkage groups were identified. RS counterparts of EB linkage
groups 3, 12, 13 and 14 were not identified. An EB counterpart of RS linkage group 16
was also not identified (Fig. 1.1, Chapter 1).

Ideal markers are those which have two segregating bands mapping to two
different linkage groups (Wang et al. 1998). In the current map, groups 2 and 19 could
be homoeologous, since the markers amplified by the UDPll and F133 mapped into
different linkage groups at 6 0M and 5.7 0M apart in linkage groups 2 and 19,
respectively (Fig. 1.1). The other suspected homeologous relation between group 17 and
18 detected by AG40 and CPM39 RFLP markers (Fig. 1.1) has already been reported by
Wang et al. (1998). However, more data is needed to make more precise conclusions.

In the current study, SSR markers were incorporated to the locations QTL for six
traits that were detected earlier by Wang et al. (1998) (Table 1.5, Chapterl). Two SSR
markers, UDP41 1-154 and UDP411-131, were incorporated into linkage group 2 (Fig.
1.1, Chapter 1) and were tightly linked to bloom time location (blm2) at the distances of
4.5 0M and 2.3 cM, respectively (Table 1.5, Chapter 1). The same peach markers were
also linked to ma weight (wa) location with the same distances as above (Table 1.5,
Chapter 1). A significant negative correlation exists between bloom time and fruit weight
(r = -0.45) (Wang et a1. 1998). The pchgms3-189 marker was mapped 8.4 cM from the
p8141, which is located in bloom time location (bImI) (Table 1.5, Chapter 1). The
pchgms3-189 marker was also tightly linked to pistil death (pdl) location at a distance of

0.8 0M (T able 1.5, Chapter 1). A negative correlation was detected between bloom time

93

and pistil death (Wang et al. 1998). The UDP405-112 marker was mapped 11.1 cM fi'om
the AGIOb marker, which is located in soluble solids (ssc2) area (Table 1.5, Chapter 1).
SSR markers, which were mapped into the QTL areas, are very valuable. The
correlations mentioned above further increase the value of these SSR markers in sour
cherry breeding by allowing breeders to select for different traits at the same time. The
use of these SSR markers in MAS would make very important contributions to breeding
of these traits.

Although SSRs are highly polymorphic, co-dominant and reproducible, very
limited numbers are available for Prunus mapping due to the expensive and time
consuming SSR development procedure. Recently, 41 SSR primer pair sequences from
peach were published by Dirlewanger et al. (2002b). However, the number of SSRs
available in Prunus falls considerably short of the goal to cover the complete genome for
mapping purposes, which limits the use of these markers in MAS. A new methodology
developed by Wang et al. (2002) provides a better alternative for Prunus mapping,
because their approach combines high-throughput AFLP mapping, which allows quick
map development, followed by targeted SSR development in AFLP marked region of
interest. First, the region of interest is mapped with marker-dense AFLP mapping (or
bulk-segregant analysis), then SSR linked to trait was developed by using these AFLP
markers as a probe in a bacterial artificial chromosome (BAC) library. This methodology
allows rapid identification of SSR loci that are tightly linked to the traits of interest.

Recently, 93 new SSR primer pairs were published by Aranzana et al. (2002),
Dirlewanger et al. (2002b) and Wang et al. (2002) (Table 3.1). Furthermore, a

presentation made at the 2002 Plant and Animal Genome conference indicates that 40

94

more SSR primer sequences are expected to be published in near future by Kimura et a1.
(2002) (Table 3.1).

Table 3.1. Recent progress in SSR mapping in Prunus.

 

Species SSR information Reference

Peach 41 SSR primer pair sequences (Dirlewanger et al. 2002)
available

Peach 17 SSR primer pair sequences (Wang et al. 2002)
available

Peach 35 SSR primer pair sequences (Aranzana et al. 2002)
available

Peach 58 SSR markers placed in a peach (Aranzana et al. 2001)
map

Peach 40 SSR were developed and half of (Kimural et al. 2002)
them mapped in peach map

Sweet cherry SSR incorporated into sweet cherry (Dirlewanger et al. 2002a)
map

 

Cross species amplification of SSR primers in the current study and in other
studies mentioned in Chapter 2 indicates that SSRs are highly polymorphic, transportable
and frequently conserved in Prunus species and in the Rosaceae family. These provide
an excellent tool for intra and inter family comparative mapping analysis and markers can
be used for cross species amplification increasing the number of SSR markers available.
SSRs in sour cherry are dispersed throughout the genome and not clustered in specific
areas of the genome. The increased availability of SSR, their co-dominant nature and
transportability makes SSR choice of markers and powerful tools for fiiture comparative
mapping, breeding and MAS studies in Prunus. Due to its diploid genome and small
genome size, peach is a good candidate to be a model plant in Rosaceae for comparative
mapping and for positional cloning of important genes (Sosinski et al. 2000). Including
SSR markers into Prunus maps will provide a fiamework of anchor points, leading to

map alignment and establishment of positions of QTLs and known genes (Aranzana et a1.

95

 

2002). With the increased number of SSRs mapped in Prunus species, these anchor loci
will enable a comparison of genome organization in the genus and will lead to
establishment of a consensus map for the genus.

The objective of the second project was to identify candidate markers associated
with bloom time in sour cherry using the ‘Balaton’ x ‘Surefire’ population. Several
approaches were used such as testing the association between bloom time and markers
that were mapped into bloom time locations in sour cherry or BSA. Low temperature
damage to flowers is the most common factor in reducing the yield in some cherry
growing regions (Thompson 1996), such as Michigan. Therefore, the development of
new late blooming varieties to avoid the loss due to spring freeze injury is one of the
most important breeding objectives for these cold regions (Iezzoni 1996)

Bloom time in cherry is a quantitative trait, but is highly heritabile (0.91) (Wang
et al. 2000). Identification of markers linked to bloom time QTL in sour cherry could
expedite the development of new cultivars or improvement of current cultivars with late
blooming characteristics using MAS.

Bloom time data was collected from ‘Balaton’ and ‘Surefire’ population as
explained (Chapter 2). The high heritability of the bloom time and the normal
distribution of this trait in ‘Balaton’ and ‘Surefire’ population over three years suggest
that bloom time is controlled by few number of genes with additive affect. If a dominant
gene with major effect was involved, it would show bimodal distribution as in the case of
almond (Ballester et al. 2001). As expected from the high heritability value, the bloom
times of the progeny were consistent over three years and not significantly affected by the

environmental conditions.

96

In the first approach, a primer pair designed fi'om the sequence of a RFLP marker
p8141 was employed to find a fi'agment, which is associated with the bloom time in
‘Balaton’ and ‘Surefire’ population. The p814l is a clone derived from sweet cherry
stylar cDNA (Iezzoni and Brettin 1998). The pSl4l probe mapped in the region of
bloom time QTL (blmI) in the linkage group 1 of EB, which explained the 19.9 % of the
phenotypic variation (Wang et al. 2000). However the primer pair amplified a complex
banding pattern and no specific band was available to test possible association with the
bloom time and PCR amplification products of the primer pair.

In a second approach, the pchpgms3 SSR marker, which was mapped at a
distance of 8.4 cM from the p8141 probe (10 0M in EB-RS consensus map) in BB]
(Chapter 1, Figure1.l), was tested for association with the bloom time. However, no
significant relationship between alleles amplified by the pchpgmsB primer and bloom
time was observed (Table 2.4, Chapter2). The large distance between marker location
and the trait my invalidate its use as a diagnostic marker of QTL for bloom time in sour
cherry. Another reason could be the low number of observations used in this experiment.
Ballester et al. (2001) stated that the RAPD marker OKP101350 located 5.4 cM fiom the
late blooming gene (Lb) could be a valuable diagnostic marker for late blooming, since
the band was present in almost all late blooming plants and absent in most of the earlier-
blooming plants in almond. Therefore, 10 cM distance from the trait of interest may not
be useful whereas 5 cM may be a more practical distance to work with (Rajapakse et al.
1995).

In the third approach, screening of early and late extreme groups for bloom time

with 156 AFLP primer pairs resulted in the identification of three candidate bands in

97

three different primer combinations (a 82 bp band in EGG/MCAC, a 78 band in
ETT/MCCG, and a 94 bp band in BAA/MCGT, Fig. 2.1, Chapter 2) that were present in
the early bulk but not in the late or present in the late group but not in the early. After
significant relationship between one or more of this candidate bands and bloom time is
confirmed in the whole population, then these markers could be used in MAS.

Although AF LP markers are highly polymorphic and reliable in sour cherry, they
are very expensive and time consuming to be used in MAS. Therefore, as a next step,
candidate markers developed here could be converted into sequence tagged sites (STS)
by excising the bands fi‘om gels, cloning and sequencing. An alternative approach also
could be targeted SSR development (Wang et al. 2002) from the region(s) of interest after
the candidate AF LP markers developed in this study are incorporated into the sour cherry
map.

The candidate AF LP markers developed in the current study could be
incorporated into the sour cherry map after the association with bloom time is confirmed
in the entire population by genotyping all progenies of ‘Balaton’ and ‘Surefire’ with the
candidate markers. Provided that candidate markers yield SDRF segregating 1:1 or 3:1 in
EB and RS population and show Mendelian inheritance, these markers could be
incorporated into the sour cherry map.

In conclusion, the AF LP markers obtained in chapter 2 and the SSR markers
mapped to bloom time location (Chapter 1) are very important for MAS in sour cherry
breeding and utilization of these markers will allow selection of new late blooming
varieties earlier in generation resulting in saving of time, effort and resources.

Incorporation of these markers into EB and RS map allow QTL analysis for bloom time

98

to be performed. Therefore, the amount of variation explained by each location can be
calculated. This information will give a better understanding of genetic basis of bloom

time and will help estimating the number of genes that control bloom time in sour cherry.

99

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100

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using RFLP markers. Theor. Appl. Genet. 97:1217-1224

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101

APPENDIX

102

Table A.1. Primer sequences and lab sources.

 

 

 

 

 

 

 

 

 

 

 

 

Primer pair Orientationa Sequence (5’ to 3’) Lab source
pSl4l F GGCTCCTACCCATCTAACTGTGA A. Iezzoni"
R GTCCCGTGCTTI‘TCCCATTC
pchpgms3 F ACGCbl‘ATGTCCGTACACTCTCCATG Sosinski et
R CAACCTGTGATTGCTCCTATTAAAC al. 2000
GA65 F GAGAAGCATCCAATGGCAAAGTT A. Iezzoni
(PceGA6Q R CGGGGGCATCAATATACCTCAA
GA50 F TTCCGTCCGAAGAAATGATTCA A. Iezzoni
(PceGASO) R TAACTAATGCAGCAGAGCACA
GA57 F CTTTCAGAACACGAGGCATAGTC A. Iezzoni
(PceGA57) R GTGTGGAATTGTGAGCGGATAA
GA55 F GGTACCGGGGGCATCACAC A. Iezzoni
(PceGA55) R GTGTGGATTTGTGAGCGGATAA
GA26 F CTTGCAGCTAGCTAGAGTGGTTTT A. Iezzoni
(PceGA26) R GTGTGGAATTGTGAGCGGATAA
GA77 F CCTTACCACTGGCATCATCA A. Iezzoni
(PceGA77) R CAGCTGAGCAGGCAACAAAA
B4G3 F CATTGTTCATGGGAGGAATT A.G.
R AGAACATCCCTAAAGGAGCA Abbottd

 

 

 

aF = forward, R = reverse.
l’I'his fourth nucleotide, a C in this study, is a G in the original pchpgms3 (Sosinski et a,

2000).

cA. Iezzoni, Horticulture Dept., Michigan State University, East Lansing, MI, USA.
dA.G. Abbott, Department of Biological Science, Clemson University, Clemson,

SC,USA.

103