Mol Breeding (2016) 36:41
DOI 10.1007/s11032-016-0443-5
Construction of a SNP and SSR linkage map
in autotetraploid blueberry using genotyping by sequencing
Susan McCallum . Julie Graham . Linzi Jorgensen . Lisa J. Rowland .
Nahla V. Bassil . James F. Hancock . Edmund J. Wheeler . Kelly Vining .
Jesse A. Poland . James W. Olmstead . Emily Buck .
Claudia Wiedow . Eric Jackson . Allan Brown . Christine A. Hackett
Received: 31 August 2015 / Accepted: 25 January 2016 / Published online: 26 March 2016
Ó Springer Science+Business Media Dordrecht 2016
Abstract The construction of the first genetic map in
autotetraploid blueberry has been made possible by
the development of new SNP markers developed using
genotyping by sequencing in a mapping population
created from a cross between two key highbush
blueberry cultivars, Draper 9 Jewel (Vaccinium
corymbosum). The novel SNP markers were supplemented with existing SSR markers to enable the
alignment of parental maps. In total, 1794 single
nucleotide polymorphic (SNP) markers and 233
simple sequence repeat (SSR) markers exhibited
segregation patterns consistent with a random
Electronic supplementary material The online version of
this article (doi:10.1007/s11032-016-0443-5) contains supplementary material, which is available to authorized users.
S. McCallum (&) J. Graham L. Jorgensen
Cell and Molecular Science Programme, James Hutton
Institute, Dundee, Scotland, UK
e-mail: Susan.McCallum@hutton.ac.uk
L. J. Rowland
Genetic Improvement of Fruits and
Vegetables Laboratory, USDA-ARS, Beltsville, MD,
USA
N. V. Bassil
National Clonal Germplasm Repository, USDA-ARS,
Corvallis, OR, USA
J. F. Hancock
Department of Horticulture, Michigan State University,
East Lansing, MI, USA
chromosomal segregation model for meiosis in an
autotetraploid. Of these, 700 SNPs and 85 SSRs were
utilized for construction of the ‘Draper’ genetic map,
and 450 SNPs and 86 SSRs for the ‘Jewel’ map. The
‘Draper’ map comprises 12 linkage groups (LG),
associated with the haploid chromosome number for
blueberry, and totals 1621 cM while the ‘Jewel’ map
comprises 20 linkage groups totalling 1610 cM.
Tentative alignments of the two parental maps have
been made on the basis of shared SSR alleles and
linkages to double-simplex markers segregating in
both parents. Tentative alignments of the two parental
maps have been made on the basis of shared SSR
alleles and linkages to double-simplex markers segregating in both parents.
E. J. Wheeler
MBG Marketing, Berry Blue, Grand Junction, MI, USA
K. Vining
Department of Horticulture, Oregon State University,
Corvallis, OR, USA
J. A. Poland
Wheat Genetics Resource Center, Department of Plant
Pathology and Department of Agronomy, Kansas State
University, Manhattan, KS 66506, USA
J. W. Olmstead
Horticultural Sciences Department, University of Florida,
Gainesville, FL, USA
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Page 2 of 24
Keywords Vaccinium corymbosum Autotetraploid TetraploidMap Genotyping by
sequencing
Introduction
Consumer demand for blueberries is at an all-time high
due, in part, to their many recognized health benefits in
addition to increased worldwide production. Blueberry
fruit is rich in phenolic compounds, which have been
linked to improved night vision, prevention of macular
degeneration, anticancer activity, inhibition of proinflammatory molecules and reduced risk of heart
disease (Johnson and Arjmandi 2013; Kalt et al. 2007).
Blueberry polyphenols have also been shown to increase
endothelium-dependent vasodilation as assessed by
flow-mediated dilation, leading to an improvement in
overall vascular function (Rodriguez-Mateos et al.
2013). Resveratrol content, has been linked to reduced
risk of heart disease and cancer, while pterostilbene, the
primary antioxidant component of blueberries, has been
shown to have both preventive and therapeutic effects on
neurological, cardiovascular, metabolic and hematological disorders (McCormack and McFadden 2013; Hangun-Balkir and McKenney 2012; Rimando et al. 2004).
In the UK, the high consumer demand for blueberries,
combined with the lack of appropriate high-quality
cultivars suitable for UK climatic conditions, has resulted
in a real need by the industry for the rapid development of
new blueberry cultivars with high fruit and nutritional
quality and expanded fruiting season to meet the demand
for home grown soft fruit. UK blueberries supply only
5 % of demand, and projections have indicated that a rise
E. Buck C. Wiedow
The New Zealand Institute for Plant and Food Research
Limited, Private Bag 11600, Palmerston North 4442,
New Zealand
E. Jackson
General Mills Crop Biosciences, Wheat Innovation
Center, Manhattan, KS 66502, USA
A. Brown
Plants for Human Health Institute, Department of
Horticultural Science, North Carolina State University,
Kannapolis, NC, USA
C. A. Hackett
Biomathematics and Statistics Scotland, Dundee,
Scotland, UK
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Mol Breeding (2016) 36:41
of 50–100 % in blueberry production up to 10 % of
demand is feasible, given appropriate cultivars and
management practices (Brazelton 2013).
Highbush blueberry cultivars (Vaccinium corymbosum L.) occur at three ploidy levels, 2n = 2x = 24,
4x = 48 and 6x = 72 (Camp 1945; Megalos and
Ballington 1988). Many of the major blueberry
varieties cultivated around the world are autotetraploid (Qu et al. 1998; Boches et al. 2005).
Autopolyploids (polysomic polyploids) which also
include sugarcane (Saccharum spp.) and potato
(Solanum spp.) contain more than two genetically
similar (homologous) genomes, while allopolyploids
(disomic polyploids) consist of two or more distinct
(non-homologous) genomes such as wheat (Triticum
spp.), strawberry (Fragaria 9 ananassa) and rapeseed (Brassica spp.). In allopolyploids, the association
of two differentiated genomes by means of interspecific hybridization results in the observed chromosome
doubling. Autopolyploids, on the other hand, are
thought to derive from chromosome doubling of the
same genome within a single parental species (Gallais
2003). Pairing during meiosis can occur in autopolyploids between randomly chosen pairs of homologous
chromosomes (bivalents) or between more than two
homologous chromosomes (multivalents). Each chromosome has the potential to pair randomly with any of
its homologues leading to tetrasomic inheritance
where all allelic combinations (A1A2A3A4) may be
produced in equal frequencies (Soltis and Soltis 1993).
A recent genetic map has been published for interspecific diploid blueberry comprising a total of 265
markers across 12 linkage groups and has led to the
identification of quantitative trait loci for cold hardiness and chilling requirements (Rowland et al. 2012).
The number of markers has been recently increased to
318 (Reid et al. unpublished). However, the tetraploid
nature of commercial highbush blueberry makes
developing a genetic understanding of traits through
linkage mapping significantly more difficult than in
the disomic blueberry. One of the issues is the larger
number of markers required relative to a diploid map
and this has been addressed in our study by the use of
SNP markers derived from genotyping by sequencing
(GBS) of the mapping population.
While statistical methods for linkage analysis and
QTL mapping in diploid species are well developed,
polysomic analyses have advanced more slowly (Luo
et al. 2001; Hackett et al. 2001). A theoretical model of
Mol Breeding (2016) 36:41
linkage analysis of dominant markers in an autotetraploid species was developed by Hackett et al.
(1998), and this approach was later adopted by Meyer
et al. (1998) in the development of a linkage map in
potato. Luo et al. (2000) developed a model for the
prediction of marker genotypes for autotetraploid
parents by analyzing marker phenotypes from segregating data obtained from parents and progeny, and
this methodology is utilized in the software TetraploidMap (Hackett and Luo 2003; Hackett et al. 2007).
TetraploidMap has been used to develop linkage maps
in many autotetraploid crops including blackberry
(Castro et al. 2013), rose (Gar et al. 2011), alfalfa
(Julier et al. 2003) and potato (Bradshaw et al. 2008).
A full range of marker types including fully versus
partially informative as well as dominant versus
codominant markers often can be found segregating
simultaneously in an autotetraploid population (Wu
et al. 2001). The genotypes of diploid parents can be
predicted by the segregation pattern of the progeny,
but this is not always the case with autotetraploids due
to the possibility of multiple allele dosage and double
reduction (Wu et al. 2001) and they must therefore be
deduced by parental and offspring phenotypes combined (Hackett and Luo 2003). Both the occurrence
and frequency of double reduction in autopolyploids
would be expected to affect the pattern of allelic
segregation. The frequency of double reduction will
itself be affected by the position of each locus on a
chromosome with greater values found toward the
distal–proterminal regions and almost null at loci
located near centromeres (Welch 1962; Butruille and
Boiteux 2000). The estimation of recombination
frequencies using microsatellite markers (SSRs) is
up to four times more informative than with dominant
markers (Luo et al. 2001). Genetic markers and marker
pairs differ in their informativeness about recombination, measured by the Fisher information. This is
discussed in the context of autotetraploid analysis by
Hackett et al. (1998), and variances of different
configurations, equal to the inverse of the Fisher
information, are given there. For linkage map construction in an autotetraploid species, the most informative types of dominant markers are simplex
(AOOO 9 OOOO), duplex (AAOO 9 OOOO) and
double-simplex (AOOO 9 AOOO) in the parents.
These markers segregate in the progeny in expected
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41
presence/absence ratios of 1:1, 5:1 and 3:1, respectively, assuming tetrasomic inheritance (random chromosomal segregation). Other configurations, such as
AAOO 9 AOOO or AAOO 9 AAOO, are present in
higher proportions (11:1 and 35:1) and carry little
information about recombination. The precision with
which the recombination frequency between a pair of
marker can be estimated depends on the type and also
the phase (coupling or repulsion). For dominant
markers, recombination between simplex markers
linked in coupling phase has the highest precision,
while simplex markers linked in repulsion have a
lower precision. Double-simplex markers also have a
higher precision in coupling phase. Duplex-to-simplex
linkages, however, have equal precision in coupling
and repulsion phase, and so are particularly useful for
identifying sets of homologous chromosomes.
Single nucleotide polymorphisms (SNPs) are by far
the most abundant mutations identified between
related DNA molecules. As a result of the advent of
rapid and affordable second generation sequencing
technologies, SNPs have become increasingly important as markers in plant research on both a fundamental
and applied basis (Ward et al. 2013). Genotyping by
sequencing (GBS) has enabled the discovery of large
numbers of SNPs and the subsequent creation of high
density linkage maps in a range of diploid fruit species,
including raspberry, grape, blackcurrant, apple, sweet
cherry, and peach (Ward et al. 2013; Barba et al. 2014;
Russell et al. 2014; Gardner et al. 2014; Guajardo et al.
2015; Bielenberg et al. 2015). SNP markers can be
identified from short reads generated by next generation sequencing (NGS) either by aligning to a reference
genome or by de novo assembly (Nielsen et al. 2011).
Although there is a draft whole genome assembly for
blueberry under preparation (Reid et al. unpublished),
this was not available at the time of this study, making
de novo assembly necessary.
To date, there is no publically available genetic
linkage map for autotetraploid blueberry for QTL
mapping of economically important traits such as fruit
and nutritional quality. The generation of such a
genetic linkage map is a critical step toward providing
the framework for blueberry crop improvement
through marker-assisted breeding, and we describe
here the development of the first linkage map in the
autotetraploid blueberry V. Corymbosum.
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Methods
Mol Breeding (2016) 36:41
Genotyping by sequencing (GBS) library
construction and sequencing
Plant material
The mapping population is an F1 population generated
from a cross between two tetraploid cultivars: the
northern highbush ‘Draper’ released by Michigan State
University in 2002 and the southern highbush ‘Jewel’
which was released by the University of Florida in 1999.
The cross was made at Michigan Blueberry Growers Marketing and seedlings were propagated at
Michigan State University. ‘Draper’ is an early to
mid-season cultivar producing premium quality, firm
and sweet fruit with superior shelf life. ‘Jewel’ is an
early to mid-season cultivar from a high-yielding plant
producing very large, slightly tart fruit. The mapping
population segregates for a number of key phenotypic
traits and is comprised of 99 individuals.
DNA isolation
Young actively growing blueberry leaves were collected from the two parental cultivars, Draper and
Jewel, and the 99 available progeny. Between 30 and
50 mg of leaf tissue was placed in a cluster tube
(Corning Life Sciences, Tewksbury, MA, USA)
containing a 4-mm stainless steel bead (McGuire
Bearing Company, Salem, OR, USA) and samples
were frozen in liquid nitrogen prior to storage at
-80 °C until extraction. Frozen tissue was homogenized using a RetschÒ MM301 Mixer Mill (Retsch
Inc. Hann, Germany) at a frequency of 30 cycles/s and
three 30 s bursts. Two different DNA extraction
methods were used, depending on how the DNA was
to be utilized. For microsatellite analysis, the DNA
was isolated using commercially available cell lysis
and protein precipitation solutions (Qiagen, Hilden,
Germany) detailed in Gilmore et al. (2011) following
the Qiagen DNeasy mini plant kit protocol. DNA was
quantified based on absorbance at 260 nm with the
Wallac Victoc 3V, 1420 Multilabel Counter (Perkin
Elmer, Massachusetts, USA). For construction of the
GBS library, DNA was isolated with the E-Z 96Ò Plant
DNA extraction kit (Omega Bio-Tek, Norcross, GA,
USA) as previously described (Gilmore et al. 2011)
and concentrations determined with a Quant-iTTM
PicogreenÒ dsDNA Assay kit (Invitrogen, Eugene,
OR, USA).
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GBS libraries from the two parents and progeny were
constructed as described by Ward et al. (2013) and
Elshire et al. (2011). Briefly, a set of 96 barcoded
adapters were generated from complementary
oligonucleotides with a ApeK1 overhang sequence
and unique barcodes terminate with 4–8 bp on the 30
end of its top strand and a 3-bp overhang on the 50 end
of its bottom strand complementary to the ‘sticky’ end
generated by ApeK1. The common adapter has only an
ApeK1 compatible sticky end. Oligonucleotides comprising the top and bottom strands of each barcode
adapter and a common adapter were diluted (separately) in TE (50 mM each) and annealed in a
thermocycler (95 °C, 2 min; ramp down to 25 °C by
0.1 °C/s; 25 °C, 30 min; 4 °C hold). DNA samples
(100 ng in a volume of 10 ll) were added to individual
wells containing barcoded adaptors, dried and then
digested for 2 h at 75 °C with ApeKI (New England
Biolabs, Ipswitch, MA) in 20 ll volumes containing
16 NEB Buffer 3 and 3.6 U ApeKI. Adapters were
then ligated to sticky ends by adding 30 ll of a solution
containing 1.66 ligase buffer with ATP and T4 ligase
(640 cohesive end units) (New England Biolabs) with
incubation at 22 °C for 1 h followed by heating to
65 °C for 30 min to inactivate the T4 ligase. Sets of 48
or 96 digested DNA samples, each with a different
barcode adapter, were combined (5 ll each) and
purified using a commercial kit (QIAquick PCR
Purification Kit; Qiagen, Valencia, CA) according to
the manufacturer’s instructions. DNA samples were
eluted in a final volume of 50 ll Restriction fragments
from each library were then amplified in 50 ll volumes
containing 2 ll pooled DNA fragments, 16Taq Master
Mix (New England Biolabs), and 25 pmol, each, of the
following primers: 5’-AATGATACG GCGACCACC
GAGATCTACACTCTTTCCCTACACGACGCTCT
TCCGATCT and 5’-CA AGCAGAAGACGGCATA
CGAGATCGGTCTCGGCATTCCTGCTGAACCGC
TCTTCCG ATCT. Temperature cycling consisted of
72 °C for 5 min, 98 °C for 30 s followed by 18 cycles
of 98 °C for 30 s, 65 °C for 30 s, 72 °C for 30 s with a
final Taq extension step at 72 °C for 5 min (Elshire
et al. 2011). These amplified sample pools constitute a
sequencing ‘library.’ Annealed and quantitated unique
barcode and common adapters were provided by the
Mol Breeding (2016) 36:41
Buckler Lab for Maize Genetics and Diversity, Cornell
University (Ithaca, NY, USA). The GBS libraries were
submitted to the Oregon State University Center for
Genome Research and Biocomputing (CGRB) core
facilities (Corvallis, OR, USA) for quantitation using a
QubitÒ fluorometer (Invitrogen, Carlsbad, CA, USA).
The size distribution of the libraries was confirmed to
range between 180 and 250 bp with the Bioanalyzer
2100 HS-DNA chip (Agilent Technologies, Santa
Clara, CA, USA). Libraries were diluted to 10 nM
based on QubitÒ readings, and quantitative PCR
(qPCR) used to quantify the diluted libraries. From
each pooled library, 15.5 pM was loaded onto a single
lane for single-end IlluminaÒ sequencing of 101 cycles
with the HiSeqTM 2000 and analyzed using the version
3 cluster generation and sequencing kits.
Putative SNP markers were identified using the
Universal Network Enabled Analysis Kit (UNEAK)
pipeline implemented in TASSEL, as a reference
genome sequence was not available to map reads for
SNP analysis (Lu et al. 2013). Sequence reads were
trimmed to 64 bp ‘‘tags’’ and SNPs were represented
by a minimum of five sequences, with SNP loci having
a minimum minor allele frequency C5 %. Stringent
filtering was applied to select markers with less than
10 % missing data.
Development of SSR markers
SSR markers were derived from a number of sources
including those previously reported from two
expressed sequence tag (EST) libraries developed
from cold-acclimated (CA) and non-acclimated (NA)
floral buds (Rowland et al. 2003; 2008; Dhanaraj et al.
2004; Boches et al. 2005) of ‘Bluecrop,’ GenBank
EST sequences (GVC for GenBank-derived V. corymbosum); 454 sequenced ESTs (VCB); Sanger-sequenced ESTs developed by the New Zealand
Institute for Plant & Food Research Ltd (Pr prefix);
and genomic sequences from the largest NGS scaffolds of the W85-20 genome assembly (KAN prefix
for Kannapolis where the genome is being assembled).
Sources and sequences of the majority of SSR markers
mapped are described in more detail in Rowland et al.
(2014) with any additional marker sequence information and GenBank accession numbers detailed in the
supplementary Table (S1). SSR markers were first
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screened over parents and a subset of progeny to
identify alleles that were either present in one parent
only and segregating 1:1 or 5:1, or present in both
parents and segregating 3:1, respectively. Multi-allelic
SSRs which were polymorphic between the parents
also were included for further analysis. The whole
population was then genotyped with these markers and
any SSRs with C25 % missing data excluded.
PCR conditions and SSR genotyping
PCRs and capillary electrophoresis platforms varied
slightly according to which of the three laboratories
(James Hutton Institute, USDA-ARS-NCGR and
University of Florida,) genotyped the population. At
the James Hutton Institute, a typical 25-ll reaction
contained 25 ng template DNA, 1.0 lM forward and
reverse primer, 0.2 mM dNTPs, 1 9 PCR buffer
(containing 10 mm Tris–HCl, 1.5 mm MgCl2,
50 mm KCl, pH 8.3) and 0.1 units Taq polymerase
(Roche, Basel, Switzerland). PCR was performed
using a Perkin Elmer 9700 Thermal Cycler as follows:
5 min at 95 °C, then 30 s at 95 °C, 30 s at 57 °C and
45 s at 72 °C for 40 cycles followed by 10 min at
72 °C. PCR products were resolved and visualized on a
1.5 % agarose gel to assess success of amplification,
then analyzed on an ABI 3730 DNA sequence analyzer
(Applied Biosystems, Foster City, USA) using Rox
500 (Applied Biosystems) as an internal size standard.
Allele sizes were visualized using GeneMapper software version 3.7 (Applied Biosystems).
At the USDA-ARS-NCGR, PCRs contained 1 9 reaction buffer, 2 mM MgCl2, 0.2 mM dNTPs, 0.50 lM
fluorescent M13 primer (Shuelke 2000), 0.12 lM
forward primer, 0.50 lM reverse primer, 0.075 units
of GoTaqÒ DNA Polymerase (Promega Corporation,
Madison, WI, USA), and 4.5 ng genomic DNA in a
total volume of 15 lL (Rowland et al. 2014). At the
University of Florida, a 10-ll PCR contained 20 ng
template DNA, 0.75 lM forward primer, 0.3 lM
reverse primer, 0.3 lM M13 fluorescent tag primer
(Shuelke 2000), 0.2 mM dNTPs, 2 mM MgCl2,
1 9 GoTaq PCR buffer and 0.025 units GoTaq DNA
polymerase (Promega Corp, Madison, WI). In all three
laboratories, band polymorphisms were scored in
individuals as present (1), absent (0) or missing data
(9), where markers failed to amplify in an individual.
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Linkage map construction
Our mapping strategy involved constructing maps for
the two parents separately using simplex and duplex
markers, and then aligning these approximately using
simplex to double-simplex coupling linkages. Details
of the theory for dominant markers can be found in
Hackett et al. (1998) and Meyer et al. (1998).
In an allotetraploid species, simplex and doublesimplex show the same expected segregation ratios as
above, however duplex markers either do not segregate, or segregate in a 3:1 presence/absence ratio
depending on the chromosomal pairing. This could
form the basis of a test for the type of ploidy,
especially in a large population. However, an alternative is to compare the proportion of simplex coupling
and repulsion linkages: In an allopolyploid, these
should be detected in equal frequencies, while in an
autotetraploid few repulsion linkages will be detected
(Wu et al. 1992; Al-Janabi et al. 1992).
TetraploidMap was used to explore the joint allele
frequencies for each SSR separately, in order to assess
the most likely parental genotypes and the goodness of
fit between the observed and expected joint allele
frequencies. The SSRs were also tested to see whether
there was evidence of offspring genotypes that could
only be obtained from double reduction (e.g., if the
parental genotypes are inferred to be ABCC 9
DDDD, then an offspring with phenotype BD can only
arise from double reduction in the first parent), however
none were found. If no parental genotype model gave a
satisfactory fit to the observed joint frequencies, then
the alleles were analyzed separately, as dominant
markers, but labeled as a, b, etc., to show the origin.
Linkage maps were constructed separately for
markers segregating in ‘Draper’ followed by those
segregating in ‘Jewel.’ The marker sets in ‘Draper’
were assembled by combining SSR markers polymorphic in ‘Draper’ with GBS markers suggested by a
preliminary filter to be heterozygous in ‘Draper,’
homozygous in ‘Jewel’ and segregating in a 1:1 or 5:1
ratio in the progeny, based on a Chi-square test of
goodness of fit. Markers for which there was a
significant lack of fit (p \ 0.001) were excluded. This
was then repeated to identify markers heterozygous in
‘Jewel’ and homozygous in ‘Draper’ for ‘Jewel’ map
construction.
Each set of markers was clustered into linkage
groups using group average clustering with a distance
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Mol Breeding (2016) 36:41
measure based on the significance level of a Chisquare test for independent segregation as described
by Luo et al. (2001) in the context of a tetraploid potato
analysis. Any co-segregating markers were excluded.
The TetraploidMap software (Hackett et al. 2007) was
used to calculate recombination frequencies and LOD
scores between all pairs of markers, for each cluster
separately. As the resultant linkage groups, were too
large to be ordered within TetraploidMap the pairwise
recombination frequencies and LOD scores for independence for each linkage group were analyzed with
JoinMap 4 (Van Ooijen 2006). The linkage groups
were checked one group at a time within JoinMap to
confirm linkage and eliminate any markers with LOD
scores less than four to any of the others in that linkage
group, and the remaining markers were ordered using
its regression mapping algorithm, with default parameters. The software MapChart (Voorrips 2002) was
then used to display the linkage maps which were
numbered randomly.
Double-simplex markers from the GBS analysis
were not used in the cluster analysis, as they have low
Fisher information unless they are in coupling phase
(Meyer et al. 1998). However, they were tested for
coupling associations with the simplex markers from
both ‘Draper’ and ‘Jewel’ using a Chi-square test for
independent segregation and inferring association if
the test for independent segregation was rejected with
significance p \ 0.001. Linkage groups from ‘Draper’
and ‘Jewel’ were then aligned on the basis of at least
one double-simplex marker showing an association
with simplex SNPs in both the ‘Draper’ and the
‘Jewel’ groups.
Results
Genotyping by sequencing libraries
The GBS library constructed from the ‘Draper’ 9
‘Jewel’ mapping population generated 153M singleend reads. The number of reads ranged from a
minimum of 259,300 in progeny CF89 to 2.7 million
in CF9 and averaged 1.5 million reads per sample
(Fig. S1). Unique tag counts ranged from a low of
145,038 tags in CF89 to 596,130 tags per individual
sequenced in this lane and were 422,463 on average.
The proportion of unique tags per read was 30 % on
average across all the individuals sequenced and was
Mol Breeding (2016) 36:41
highest in individuals with the lowest number of
reads: CF89, CF90 and CF95 (Fig. S1). Using the
UNEAK pipeline, 109,044 putative SNPs were
identified. CF89, CF90 and CF95 had the highest
percent of missing data at 49, 40 and 27 % of the
SNPs identified. Filtering to exclude any SNPs with
C10 % missing values resulted in 17,846 SNPs.
Heterozygous SNPs (5224 in ‘Draper’ and 6533 in
‘Jewel’) were further screened to select those which
exhibited only one allele segregating in a 1:1 or 5:1
ratio (1101 in ‘Draper’ and 673 in ‘Jewel’) based on
a chi-square goodness of fit test, excluding those with
significant distortion (p \ 0.001). SNP counts were
only considered missing where 0/0 read depths were
recorded.
SSR marker analysis
A total of 295 SSR primer pairs were tested for
polymorphism between the parents of the mapping
population. Nine primer pairs failed to amplify and
products of a further 53 primers were homozygous in
both parents. From the 233 primer pairs successfully
scored, a total of 402 markers were mapped. Of
these, 207 were codominant SSR markers, with two
or more alleles segregating, while the remaining 195
were individual bands for products from an SSR
primer pair that were scored as dominant markers.
With codominant markers, such as SSRs, it is easy to
detect the presence of double reduction (two sister
chromatids recovered from a single gamete). However, there were no offspring genotypes identified in
this dataset that could be obtained only through
double reduction.
Comparison of the frequency of simplex coupling
and repulsion linkages
In a cross between allopolyploids, an analysis of
simplex markers assuming disomic inheritance
should be equally likely to detect coupling and
repulsion linkages. However, for ‘Draper’ (1143
simplex markers), there were 2214 pairs with coupling recombination frequency less than 0.1, however
none with a repulsion recombination frequency less
than 0.1. For ‘Jewel’ (681 simplex markers), there
were 810 pairs with coupling recombination frequency less than 0.1, but again none with a repulsion
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recombination frequency less than 0.1. Similar
comparisons at recombination frequencies of 0.2
and 0.25 showed very small number of repulsion
linkages in comparison with the coupling linkages.
We were therefore confident in our analysis of this
cross using an autotetraploid rather than an allopolyploid model.
Linkage map construction
The initial clustering analysis of 1260 markers identified 12 linkage groups in ‘Draper.’ After calculation
of recombination frequencies and LOD scores with
TetraploidMap, these markers were used to construct
linkage maps at a minimum LOD score of 4 in
JoinMap. The resulting map had a total length of 1621
cM and comprised 785 mapped markers, among which
33 were co-dominant SSRs, 52 were dominant SSRs
and 700 were SNPs (Table 1). Linkage group coverage
ranged from 38 to 104 markers with the average
number of markers per LG being 65 and the number of
markers mapped across all 12 linkage groups totaling
785. The largest gap between markers was 10.9 cM
(LG 7). Clusters formed in JoinMap were separated
based on a minimum LOD score of 4, and this resulted
in an average loss of markers in ‘Draper’ of 15.5 per
linkage group; in ‘Jewel,’ an average of 3 markers per
group was discarded.
Fewer markers (856) were available for the initial
cluster analysis for ‘Jewel’ map, than for ‘Draper’.
This resulted in the ‘Jewel’ groups being more
fragmented than for ‘Draper’ and the clustering
analysis in TetraploidMap produced 32 large groupings with a further 12 smaller groups of between 2 and
7 markers. The larger groups detected and groups
containing markers linking ‘Jewel’ to ‘Draper’ were
then ordered into linkage maps.
Associations between the ‘Draper’ and ‘Jewel’
maps were identified utilizing double-simplex SNP
markers (heterozygous in both parents). Where possible these associations were confirmed by SSRs
mapped to linkage groups in both parents. Twenty
linkage groups from ‘Jewel’ were aligned with
‘Draper’ maps by this approach. These 20 groups
consisted of 536 markers, comprising 59 codominant
SSR markers and 27 dominant markers and 450 SNP
markers from the GBS dataset. Linkage group coverage ranged from 7 to 62 markers with the average
number of markers per LG being 27. The total number
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Page 8 of 24
Mol Breeding (2016) 36:41
Table 1 Marker segregation ratio of SSRs, dominant markers and genotyping by sequencing SNP markers mapped to each linkage
group in blueberry ‘Draper’
Linkage
group
No. of markers
assigned
No. of mapped
markers
Total length
(cM)
No. of codominant
SSRs
No. of dominant
SSRs
No. of
SNPs
1
51
43
123
3
7
33
2
74
42
83
5
5
32
3
109
65
138
3
4
58
4
91
56
117
5
6
45
5
135
85
145
3
4
78
6
93
66
142
0
1
65
7
53
38
165
2
0
36
8
86
58
149
3
4
51
9
93
56
107
1
1
54
10
145
104
166
4
8
92
11
12
180
150
88
84
153
133
2
2
7
5
79
77
1260
785
1621
33
52
700
Total
This table shows the number of markers assigned to each chromosome, the number placed on the map and the source of the mapped
markers as co-dominant SSRs, dominant individual SSR bands and SNPs from genotyping by sequencing
Table 2 Marker segregation ratio of SSRs, dominant markers and genotyping by sequencing SNP markers mapped to each linkage
group in blueberry ‘Jewel’
Linkage
group
No. of markers
assigned
No. of mapped
markers
Total length
(cM)
No. of codominant
SSRs
No. of dominant
SSRs
No. of
SNPs
1
55
51
122
8
3
2
48
38
87
3
1
40
34
3a
70
45
116
5
1
39
22
3b
28
23
62
1
0
4a
18
10
40
3
0
7
4b
21
18
64
2
1
15
5
40
37
114
3
5
29
6a
36
35
95
1
1
33
6b
9
7
61
3
1
3
7
27
15
67
2
1
12
8a
8b
38
23
31
18
92
86
4
2
4
1
23
15
9
15
8
19
0
0
8
10a
36
29
122
5
0
24
10b
18
16
103
3
0
13
11a
50
44
49
5
1
38
11b
27
22
107
2
2
18
11c
17
13
51
0
0
13
12a
70
62
94
3
3
56
12b
Total
19
14
59
4
2
8
665
536
1610
59
27
450
This table shows the number of markers assigned to each chromosome, the number placed on the map and the source of the mapped
markers as co-dominant SSRs, dominant individual SSR bands and SNPs from genotyping by sequencing
123
Mol Breeding (2016) 36:41
Page 9 of 24
41
Table 3 Marker linkages aligning ‘Draper’ and ‘Jewel’ maps
Linkage
group
Linked SSRs between ‘Draper’ and ‘Jewel’
Double-simplex
marker
Closest ‘Draper’
marker
Closest ‘Jewel’
marker
1
Pr031818824c/Pr031818824a, KAN-11151a/
KAN-11151b, Pr032475745b/Pr032475745a
TP108922
TP77102
TP20020
TP83559
TP59202
TP73709
TP29647
ATDGK
TP59531
TP7071
TP54290
Pr032475737b
TP86460
TP55121
Pr032475737b
TP91693
TP111210
TP55569
TP52124
TP36095
TP112121
TP86849
TP105172
TP62542
TP7029
TP90423
TP3055
TP37864
TP109905
TP68096
TP66717
TP62382
TP45278
TP118110
TP103341
TP32755
TP13796
TP44158
TP49265
TP91050
TP105172
TP55896
TP110093
GVC-SL247b
TP41833
2
GVC-SL247b/GVC-SL247a, Pr032605975b,
Pr031818828, CER6-2a/CER6-2b
3a
No SSRs
TP23056
TP27951
TP20232
3b
No SSRs
TP27957
TP112016
TP54867
TP103628
TP99861
TP92762
4a
Pr032475739a/Pr032475739b
KAN-286
TP49297
TP22373
TP97091
GVC-NA961b
TP11546
4b
5
6a
No SSRs
AP1-2
No SSRs
TP102286
TP96618
TP96642
TP86460
TP117648
TP117626
TP20145
TP69426
TP69527
TP47172
TP39778
TP106905
TP65717
TP79854
TP3551
TP81900
TP52344
TP106208
TP20057
TP16108
TP1394
TP48725
TP10972
TP52998
TP72318
TP61784
TP13525
TP86684
TP113820
TP35552
TP4962
TP10142
TP18926
TP26674
TP36725
TP17580
TP33829
TP75554
TP37985
TP29801
TP105555
TP104871
6b
No SSRs
TP4962
TP60675
GVC-V64f07b
7
GVC-C52d01
TP41311
TP24505
TP91420
KAN-11199c
TP98745
TP110283
TP64338
TP40861
Kan-2133
TP89358
TP104443
TP78565
TP81095
TP99925
TP65231
GVC-C652a
TP104443
TP882
TP90876
TP99925
TP27733
TP8643
TP35521
TP23870
TP97411
8a
8b
Pr031818819, KAN-656d/KAN-656a,
123
41
Page 10 of 24
Mol Breeding (2016) 36:41
Table 3 continued
Linkage
group
9
10a
10b
11a
11b
Linked SSRs between ‘Draper’ and ‘Jewel’
No SSRs
CA94b/CA94a, Pr032754266b, Pr032605974
CHI-2c/CHI-2a
VCC_j1a/VCC_j1b, GVC-NA619a, VCC-j9c/
VCC-j9a/VCC-j9b, Pr031818815a/Pr031818815b,
VCB-BH-1DV1YKa/VCB-BH-1DV1YKb
GVC-C62h04
Double-simplex
marker
Closest ‘Draper’
marker
Closest ‘Jewel’
marker
TP79857
TP13658
TP58542
TP37911
TP83726
TP97198
TP79321
TP21301
TP56333
TP16831
TP116768
TP25475
TP36317
TP4796
TP53798
TP23332
TP11103
TP13041
KAN-1030b
CA94a
TP59352
TP96509
TP25793
TP76010
TP13175
TP9274
TP39388
TP18300
TP44089
TP67524
TP105022
TP92002
TP39404
TP71386
TP83735
TP109340
TP49816
TP92083
TP102378
TP66262
TP86367
TP60515
TP70042
TP58394
TP 38254
TP116162
TP111874
TP92083
TP18300
TP91338
TP17427
TP71386
TP106097
TP28408
TP66262
TP112113
TP65878
TP37508
TP20096
TP45143
TP84923
TP15023
TP10805
TP8875
Vcc-j9a
TP14041
TP100828
TP61870
TP10482
TP35491
TP116232
TP29352
TP87859
TP4248
TP10321
TP7206
TP118034
KAN-157a
TP8875
TP7397
TP74315
TP107006
11c
No SSRs
TP96858
TP9985
12a
CA794a/CA794b
TP67110
TP48691
TP11939
TP100598
TP95103
TP97478
TP90155
TP8345
TP10687
TP4893
TP103841
TP27068
TP48650
TP37238
TP102542
TP10868
TP92430
TP18150
TP26302
TP26100
TP14075
12b
VCC-k4a/VCC-k4b, GVC-C22g08a/GVC-C22g08b, VCC-i2b
This table shows the closest linked simplex marker on ‘Draper’ and ‘Jewel’ to each of the double-simplex (DS) markers (underlined
bold markers in Fig. 1) as well as the individual SSR markers which mapped to both parents (linked line between markers in Fig. 1)
123
Mol Breeding (2016) 36:41
Draper1
0
KAN-11151b
48
49
50
53
58
60
61
62
64
66
68
69
70
VCB-C09467a
TP21913
KAN-430
TP66406
TP16488
TP111210
TP73519 CA855
TP74601
CA344
TP77102
TP65523
TP118334
TP140 Pr031818824c
TP83186
TP115798
TP48136
TP115023
TP59202
GVC-C78b
TP75621
TP80084
TP37898
TP107547
TP49993
TP45023 TP58157
TP33974
TP43077
78
Pr032475745b
82
84
TP70593 TP79503
TP100369
87
90
91
TP55121
TP99730
ATDGK1-3
95
TP54290
18
20
26
29
33
34
36
37
39
41
42
43
45
105
107
109
123
TP51992
TP21341
TP62897
Pr032754270a
Page 11 of 24
Jewel1
0
2
7
9
11
12
13
14
15
17
18
19
25
27
29
31
34
35
36
40
Pr032475743
TP76523
GVC-V24d10b
KAN-11151a
TP77044
TP12388
TP68938
TP96725 TP2208
TP72590 TP112282
TP108503 TP65047
TP13136
TP58518
TP67352
TP95218
TP40460
TP75195 TP90799
TP105869
TP20020
TP99353
TP96396
45
47
48
49
50
55
56
58
61
TP82022
TP104128
TP33921
TP59531 TP55569
TP25456
GVC-C278
TP4195
TP24636
TP73709
65
TP109470
68
71
72
Pr032475745a
Pr031818824a
TP87508
77
TP67359
82
84
GVC-C600b
TP60254
88
TP109706
91
93
TP14818
TP109245
96
TP38086
99
TP110243
102
104
Pr031818820
TP28237
108
KAN-11067
113
Pr032475737b
122
Pr032475737a
Fig. 1 Genetic linkage map constructed in the tetraploid
blueberry population, ‘Draper’ 9 ‘Jewel.’ Underlined and bold
markers show the closest linked double-simplex markers for
each of the parents, while SSR markers linking parental maps
are shown with a line between the maps (Table 3)
of markers mapped in ‘Jewel’ was 536 covering
1610 cM, with the largest gap between markers being
27.2 cM (LG 11b). Table 2 gives details of the
markers mapped to each linkage group, and Table 3
shows the double-simplex markers and SSRs on which
the alignments are based, and the closest simplex
marker from each of ‘Draper’ and ‘Jewel’ to the
double-simplex marker. The linkage maps are shown
in Fig. 1.
Draper2
0
TP65030
8
TP46312
58
59
61
62
67
68
70
72
CER6-2b
TP105994
TP71935
TP105172
TP17740 Pr031818827a
TP109905
GVC-SL247b
TP11435
TP48964
GVC-SL247a GVC-SL247c
TP103341 TP36095
TP71729
TP90423
TP45955 TP27827
TP44158
TP11039
Pr031818828
TP88746
TP48802
Pr032605975c
TP22699
TP62382 TP108957
TP21522
TP40828
Pr032605975a TP34184
TP66713 Pr032605975b
TP26258 KAN-1007
TP42449
TP33648
TP74187
TP53919
83
TP1798
16
21
22
25
26
28
29
30
31
32
33
34
35
37
39
41
43
45
50
51
54
55
41
Jewel2
0
TP45278
17
19
TP49265
TP55896
29
30
34
35
36
38
39
40
41
42
45
47
49
51
54
57
58
60
61
63
68
71
74
76
79
80
81
85
87
TP68010
TP112121
TP32755
TP67652
TP62542
CER6-2a
TP98336
GVC-SL247b TP39993
TP11191 TP4905
TP37254 TP38854
TP100758 TP41833
TP3055
TP81471 TP113473
TP99585
TP68096
TP56599
TP19675
TP110196
TP62208
Pr031818828
TP53035
TP2599
TP106924
TP112748
TP35239
TP721
TP81982 TP104336
TP16789
KAN-43a
Fig. 1 continued
Discussion
We report the first application of GBS in blueberry.
We have generated thousands of SNPs located across
the V. corymbosum genome and utilized a proportion
of them to construct the first genetic map in tetraploid
blueberry, indeed in any polyploid woody perennial
fruit crop. Genotyping of an F1 population enabled the
genetic mapping of 785 SNP markers at LOD 4 or
above to the 12 linkage groups of ‘Draper,’ and 536 to
20 linkage groups in ‘Jewel.’ Mapping of homologous
chromosomes awaits future enlargement of the marker
dataset and we are confident that GBS will provide
such resources in a future study. However the GBS
data obtained was not sufficient in terms of read depth
coverage to call allele dosage and so was used to
determine presence or absence of alleles only.
We found that excluding SNP markers with[10 %
missing data led to a decrease in the number of SNPs
available for linkage map construction and this is
consistent with previous reports in grape (Barba et al.
2014), sweet cherry (Guajardo et al. 2015) and black
raspberry (Bushakra et al. 2015). In blueberry, the
123
41
Page 12 of 24
Fig. 1 continued
123
Mol Breeding (2016) 36:41
Jewel3a
0
2
5
8
11
13
14
17
18
19
20
21
22
23
24
26
29
30
32
34
35
39
47
51
52
54
55
57
58
59
61
64
67
TP26949
TP70291
TP3224
TP74612
TP95384
TP27248
TP17735
Pr032754271a
TP35242
TP26544
TP110323 TP81898
TP82477
TP116340
TP10775
TP15419
TP3066
TP18995
TP18352 TP20232
TP75070
TP48286
TP14086
TP108998
VCC_K4b
TP3853
TP98897
TP68171
TP100237
TP117392
TP48250
TP11035
KAN-118a
TP34440
KAN-718b
76
77
TP40276
TP7594
84
TP41772
89
TP18182
99
TP38894
103
TP12701
108
111
112
TP77295
TP6452
TP33471
116
GVC-NA961a
Draper3
0
TP60753
10
12
13
15
TP72836
TP31938
TP60401
TP892
21
24
25
Pr032605976a
TP15700
GVC-NA741
31
33
TP94869
TP90607
38
41
42
47
48
52
54
57
58
59
64
65
66
67
68
71
72
73
74
75
77
78
79
80
83
84
87
92
95
96
101
105
110
116
TP18323
TP30737
TP111807 TP99861
TP99238 TP89330
VCB-C06669
TP80421
TP68945
FLS-2
TP2213 TP112016
TP27951 TP37109
TP8278 TP37969
TP56611
TP7244
FLS-3 TP12763
TP66585
TP1978 TP114677
TP71680
TP77275
TP43461 TP104831
TP74341 TP23903
TP91332
TP48619
TP70435
TP30043
TP22608
TP88895
TP96943
TP97463
TP58885
TP68357
TP34118
TP5996 TP22008
TP45061
TP24735
TP14149 TP29029
TP111987
TP43187
TP80372
Pr032475741a
124
126
TP91783
TP33302
130
KAN-789b
134
TP75653
138
TP86092
61
62
63
Jewel3b
0
TP43662
4
TP31591
8
TP96708
11
TP94639
15
TP72189
22
26
28
30
31
36
37
39
40
41
44
46
47
48
50
TP47338
TP58920
TP60773
TP37999
TP54405
TP107305
TP92762
TP50133
TP95994
TP88665
TP93446
TP54570
TP54867
KAN-2584
TP74880
56
58
TP50442
TP109277
61
TP7556
Mol Breeding (2016) 36:41
Page 13 of 24
Jewel4a
0
TP14469
3
5
KAN-503c
Pr032475739a
14
15
TP11546
TP44932
22
TP22373
30
36
39
40
Draper4
0
16
Jewel4b
KAN-254
TP60388
TP67880
TP116959
82
83
84
85
86
87
88
89
91
92
93
94
95
97
98
100
106
117
0
Pr031818824b
5
TP72468
8
10
TP29122
TP53410
14
TP16839
19
GVC-C78a
25
TP51164
29
TP97420
32
34
CA855a
TP878
39
TP96642
43
44
46
48
50
TP69426
TP7416
TP69527
TP91901
TP102509
54
TP65540
64
TP108314
KAN-458
KAN-286
33
37
39
41
43
45
49
53
54
59
62
63
67
71
72
74
76
77
80
81
41
TP96618
GVC-NA961b
TP20145
TP24935
TP16647
TP89853
TP88172
Pr032475739b
TP117626
TP69846 IP5PII-1c
TP72721
TP95955
TP50486 TP105001
TP14197
TP76652
TP50176 TP13286
TP112173 TP103091
TP88111 TP68304
TP72553 Pr031818820
TP22385 TP5637
TP94729 TP74609
TP43937
TP39794
TP101410 TP64266
KAN-11067
TP3426 TP52815
TP77746
TP91921
TP81081
TP11103 TP113917
TP96627
TP29160 TP49297
TP80904
TP8239 TP42927
VCC_J1c
TP33148
TP31473
Pr032475737c
TP42138
KAN-118b
KAN-286
Fig. 1 continued
number of SNPs (109,044) identified by the nonreference guided UNEAK pipeline was much larger
than that identified by a reference-guided pipeline in
black raspberry (57,238) constructed using the same
protocol and sequenced in the same IlluminaÒ run.
Number of SNPs identified in the separate studies on
sweet cherry and grape were lower than those in
blueberry, at 11,854 and 42,172 respectively.
Excluding SNP markers with [10 % missing data in
blueberry led to a 6-fold reduction in the number of
SNPs (17,846 SNPs) and 7.2-fold reduction in black
raspberry (7911 SNPs). Using this same arbitrary
filtering parameter of excluding SNP markers with
[10 % missing data, 1.4-fold and 2.5-fold reductions
in the number of SNPs were observed in the diploid
grape (16,833 SNPs) and sweet cherry (8476 SNPs)
123
41
Page 14 of 24
Mol Breeding (2016) 36:41
Draper5
0
AOMT-3
6
TP79854
28
31
35
36
38
40
41
43
45
46
48
49
51
52
53
54
55
56
57
58
59
60
62
63
64
65
67
69
71
74
76
78
79
81
82
84
85
88
94
97
98
101
102
104
105
107
108
114
115
118
120
121
125
129
131
134
137
145
Fig. 1 continued
123
TP23111
TP56952
TP58580
TP77682
TP28513
TP113820 TP107949
TP94363 TP36205
TP52627
TP16108
TP15250
TP15626
TP101178 TP12673
TP42824 TP15231
TP21052 TP1922
TP13321 TP112800
TP58625 TP24892
TP52344 TP47629
TP22878
TP21262 TP10972
AP1-2
TP111816 TP110757
TP15641 TP28367
TP49598
TP61784
TP73842 TP58115
TP89124 TP18151
TP38269
TP74136
CDF-5
TP94844 TP91269
TP24290 KAN-987b
TP104076
TP5482
TP50917 TP9449
TP32774
TP39228
TP82863 TP19208
TP44475 KAN-987a
TP99293
TP77535
TP106394 Pr032754271b
TP83149
TP9374
TP23320
TP39778
TP51162
TP101021
TP110849
TP47832 TP43789
TP52081
TP91781
TP41705
TP59881
TP30031
Pr032475744b
TP36941
TP3472
TP68383
TP31718
TP38138
TP27546 TP32569
TP26069
Jewel5
0
TP3551
10
TP106905
14
AP1-2
20
TP43058 TP80292
TP106208
24
27
30
31
33
34
38
39
42
44
47
48
50
TP1394
TP99603
TP91905
TP33771
TP20867 TP52998
TP13245
TP93505
TP24195
Pr032754272c TP13525
Pr032754268
TP102609 TP19883
TP52862
TP100289
55
TP12124
59
TP31009
64
KAN-1107b
67
KAN-1107a
78
80
TP45101
GVC-NA800
83
TP3552
86
88
91
93
94
TP29148
GVC-NA172
TP65407
TP34270
TP67319
99
TP110038
106
TP23277
114
Pr032475740c
Mol Breeding (2016) 36:41
Page 15 of 24
Jewel6a
0
TP110946
8
11
13
14
17
19
TP47056
TP95617
TP72538
TP51655
TP114253
TP94271
24
26
28
30
31
34
35
36
38
42
43
46
TP45214
VCC_S10d
TP84056 TP19936
TP14064 TP18926
TP37985
TP7778
TP15543
TP35016
TP28465
TP64690
TP27258
TP15633
55
TP17580
58
TP56115
61
TP74231
69
71
74
75
76
77
TP104871
TP21619
TP56156
TP45093
TP100463 TP44464
TP28913
84
85
TP18773
KAN-337b
90
TP36232
95
TP11483
Draper6
0
TP36946
6
TP48003
10
13
15
17
18
20
21
23
TP25800
TP2903
TP16404
TP68115
TP17472
TP50920
TP95065
TP82166
27
29
TP69624
TP97642
49
51
52
56
57
62
64
65
66
70
71
72
74
75
76
77
78
79
80
81
82
83
84
85
87
88
90
93
94
96
102
104
106
110
111
112
119
121
123
124
126
130
133
137
139
142
TP47585
TP56718
TP86240
TP40711
TP75554
TP98773 TP94775
TP91003
TP28841
TP78948 TP30229
TP80935
TP25523
TP89077
TP18528
TP60125
TP50454
TP98824
TP22889 TP111587
TP85406 TP116301
TP36725
TP5913
TP112470
TP60675 TP2621
TP105555
TP10142
TP7663
TP109691
TP15586 TP41516
TP24322
TP3136
TP72207
TP97148
TP92184 TP75418
TP28909
TP3278
TP54730
TP48047
TP2584
TP27511
TP115154
TP71621
KAN-1555a
TP1534
TP14942
TP18935
TP741
TP54474
TP76630
41
Jewel6b
0
TP116908
15
KAN-66b
18
TP80345
32
KAN-66a
39
GVC-V64f07c
46
TP40006
61
GVC-V64f07b
Fig. 1 continued
123
41
Page 16 of 24
Mol Breeding (2016) 36:41
Draper7
0
Fig. 1 continued
123
TP84990
9
TP95276
12
TP24098
19
20
22
23
KAN-11049
TP42086
TP33184
TP21609
28
TP111278
32
TP69989
42
TP113682
47
TP32909
50
52
TP90136
TP97379
55
TP40535
59
TP84979
70
TP108334
73
TP5956
76
TP65890
82
TP41100
88
TP47922
91
TP24505
96
TP85206
99
GVC-V52d01
107
TP65223 TP64338
110
TP42195
115
117
TP24719
TP53859
120
TP33605
127
TP90029
135
136
TP66812
TP67917
141
TP10786
148
TP58606
153
155
TP5635
TP108533
158
TP90328
165
TP2827
Jewel7
0
GVC-NA398b
4
TP52753
7
KAN-2133
16
TP107607
19
21
23
26
TP35268
TP102061
TP91420
TP10107
30
TP8085
33
TP965
37
TP20267
43
GVC-V52d01
52
TP6869
62
TP34540
67
TP10274
Mol Breeding (2016) 36:41
Jewel8a
0
Pr032475742a
12
TP22681
15
TP66351
18
TP113036
21
24
26
27
GVC-V22a02c TP107925
GVC-V31e03c
GVC-V31e03f
TP100411
32
TP38166
39
TP2436
43
44
46
48
50
52
53
54
57
61
62
64
KAN-453
TP24949
TP1063
TP69790
CA23
TP80027
TP3071
TP33294
TP32396
TP27557
TP16606
TP7787
68
TP33044
73
74
GVC-C652a
TP81095
79
82
83
85
KAN-11199c
TP44704
COP1-1
TP89358
92
TP13388
Page 17 of 24
Draper8
0
Jewel8b
Pr032475742c
10
TP4846
19
21
TP55108
TP67027
25
TP27931
35
TP16570
39
TP3183
51
TP19384
60
63
65
66
69
70
71
74
75
77
79
83
85
86
89
91
95
99
100
101
108
109
112
115
116
TP58374
Pr031818819
TP65568
TP66946
TP83726 TP6839
GVC-NA824b TP27013
TP13255
GVC-NA824a TP108387
TP40617
TP882 VCB-C00694
TP66989
TP63835
TP104332
TP104242 TP26964
TP4146 TP60587
TP7864 TP51789
TP106957
KAN-656a
TP64012
TP23870
TP66483 TP78565
TP77630
TP90448
TP6890
TP27733
TP107118 TP96838
TP9029
TP40861 TP71690
KAN-11199c
121
TP71592 TP65231
126
129
130
131
TP38778 TP13658
TP91842
TP64038
TP44060
135
137
TP96010
TP57776
140
TP85344
148
TP24094
72
73
41
0
CA169
11
13
Pr031818819
TP97411
19
TP90876
23
TP97198
32
KAN-656d
36
TP8643
40
TP16244
48
50
TP58524
TP96121
54
TP76686
59
TP58155
65
TP35861
68
TP43484
72
TP69817
75
TP5320
82
TP39216
86
TP36560
Fig. 1 continued
respectively, though other differences between the
studies may have come into play here. The relatively
large number of SNPs identified in blueberry
compared with black raspberry, constructed and
sequenced simultaneously, along with the higher rate
of SNP reduction in SNP markers with[10 % missing
123
Page 18 of 24
41
Draper9
0
TP72627
8
11
14
16
17
18
19
20
21
23
24
28
30
32
33
TP44811
TP61265
TP61584
TP58607
TP116768
TP55311
TP4796 TP61338
TP61427 TP111033
TP108810
TP94021
TP97881
TP30010
TP73792
KAN-1233c
TP6332
40
TP21024
43
TP40683
46
49
52
54
58
59
64
66
68
TP59871
TP108347
TP51564
TP21301
TP28501
TP108763
TP54526
TP43709 TP86297
TP55173
TP3244 TP5575
TP31131 TP112269
TP67666
TP61744
TP4286
TP109214
TP18160 TP4251
TP77515 CA642
TP22445
TP5485
TP74524
TP110674
TP63855
TP38839 TP51466
TP66674
TP82202
TP69922
TP40780
TP69661
TP64171
TP55422
69
70
72
73
74
76
77
79
80
81
82
84
86
88
90
91
95
101
107
Mol Breeding (2016) 36:41
Jewel9
0
TP59595
7
9
11
13
TP25475 TP117191
TP53798
TP56333
TP13041 TP19094
19
TP38680
Fig. 1 continued
data is noteworthy and might have resulted from the
autopolyploidy of the blueberry, sub-optimal DNA
quality, and/or use of a non-reference-guided SNP
detection pipeline necessitated by the absence of a
reference genome. The average proportion of unique
tags to reads per sample in blueberry (30 %) was
almost twofold higher than that observed in black
raspberry (17 %) (Bushakra et al. unpublished). We
believe this is due to the presence of two genomes per
locus in blueberry when compared to the single diploid
genome of black raspberry. Since autopolyploidy
caused higher amount of variation compared with
the diploid, a corresponding higher coverage is needed
123
to obtain well-supported SNP calls with[5 supporting
reads.
Of the 17,846 blueberry SNPs evaluated for
mapping in this study, 785 (4.4 %) were mapped in
‘Draper’ and 450 SNPs (2.5 %) were mapped in
‘Jewel.’ Although a large number of markers have
been excluded in this study, such loses are in line with
previous studies and we decided that priority should be
given to the quality of markers used over the
quantity. In sweet cherry, of 8476 SNPs selected for
mapping, 443 SNPs (5 %) were mapped in the
maternal parent ‘Rainier,’ and 474 SNPs (5.6 %) in
the paternal parent, ‘Riverdel’ (Guajardo et al. 2015).
In grape, of 16,833 SNPs, linkage maps were
constructed with 1146 SNPs (6.8 %) in V. rupestris
B38 and 1215 SNPs (7.2 %) in ‘Chardonnay,’ while in
black raspberry 399 (5 %) of the 7911 SNPs were
mapped in the parents (Bushakra et al. 2015).
All four parental homologues were identified across
four linkage groups in ‘Draper’ while a further five
linkage groups recovered three homologues
(Table S2). In ‘Jewel’ however, only three homologues could be resolved in four linkage groups with
eight more recovering only two (Table S2). We are not
aware of any group calling allele dosage in a tetraploid
crop using genotyping by sequencing. A recent study
by Rocher et al. (2015) in tetraploid alfalfa (Medicago
sativa) found only 23 % of SNP loci were deemed as
having reliable genotype calls in 50 % of plant
samples but sequencing depth was not sufficient for
tetraploid allele dosage. A sequencing depth of
between 60 and 809 has been estimated as appropriate
in heterogeneous cultivars of tetraploid potato in order
to establish reliable allele dosage (Uitdewilligen et al.
2013).
Due to the small population size, only tentative
linkages were assigned in this study and best alignments reported between parents were based on this. It
will be necessary to repeat this study in a larger
population of at least 275 seedlings to obtain greater
confidence in identifying markers which are truly
duplex, which would give an increased power to detect
linkages. Another area for improvement is the number
of markers required to cover all 96 chromosomes in
blueberry. With one simplex marker per 5 cM and
assuming an average chromosome length of
*100 cM, would require a total of 1920 simplex
markers. Then, in order to align homologous chromosomes together, a further 2 9 12 sets of duplex
Mol Breeding (2016) 36:41
Page 19 of 24
Jewel10a
0
TP67524
15
KAN-628a
21
23
TP60515
TP19800
26
TP92083
30
TP61010
35
36
TP59352
CA94b
41
TP34524
51
TP90349
64
KAN-991a
72
TP24823
76
77
80
82
84
86
88
91
93
TP39404
TP76010
TP102378
TP41374
TP39388
TP92780
TP94297
TP92779
Pr032754266b
100
101
103
106
TP38254
TP39244
Pr032605974
TP108095
110
TP14851
113
TP78815
117
TP20664
122
TP223
Draper10
0
8
10
13
16
18
24
27
30
32
34
35
36
37
38
39
40
43
44
46
49
51
52
53
54
55
56
57
58
59
60
61
62
65
66
68
69
71
72
73
75
78
80
81
82
84
86
87
88
92
94
96
98
99
100
102
103
104
105
109
112
114
116
117
118
126
127
128
130
131
132
133
134
136
137
138
141
142
145
146
150
151
155
157
166
TP69119
TP66816
TP45782
TP91338
TP44089
TP79910
TP117839
TP109340
TP111874
TP67061
TP33401
TP59534
TP104406
TP112113
TP106097
TP86367
TP11930
TP80632
TP43023
TP80984
TP94489
TP10691
TP93226 TP47143
TP116759
TP22485
TP15274 TP92183
TP74877
TP11137
CHI-2a TP68592
TP90779 TP110896
TP94763
KAN-319a GVC-V52c09a
CA94a
TP18373
TP106205
TP52654
TP62284
TP23128 CA787
TP5681 TP6054
TP9274
TP5325
TP26698
TP104344
KAN-1030a
KAN-4737b
TP3210
TP76203 TP1354
KAN-4737a
TP43328 TP10302
TP31240
TP19217 TP4416
TP76236
TP5971
TP74501 TP54534
TP22450
TP99554 TP62194
TP25793 TP52215
TP84065
TP76532
TP21993 TP99641
TP75149
KAN-1030b
TP33589
TP6410
TP92002
TP69465
TP12951
TP50682
TP29575
Pr032754266b
TP67598
Pr032754267b
TP29804
TP10287
TP58394
TP75016
TP25988
TP35518
Pr032605974
TP47780
TP93329
TP74710
TP19263
TP58599
TP49816
TP40028
TP27389
41
Jewel10b
0
CHI-2c
23
Pr032754269
30
31
33
TP35878
TP65878
TP7726
39
41
42
TP24678
TP6813
TP28408
46
TP60410
50
TP40172
60
TP5283
73
TP17427
81
VCC_B3
89
TP113527
96
TP2343
103
TP27265
Fig. 1 continued
123
41
Page 20 of 24
Jewel11a
0
6
8
10
11
12
13
15
16
17
18
19
21
23
24
25
26
28
29
30
32
33
37
40
41
42
43
44
47
48
49
TP104400 TP111410
TP110784
TP10482
TP101494 TP42218
TP25721
Tp90901 TP43631
TP38943 TP76233
TP95021
TP94478
TP59149
Pr031818815a
TP117940
VCB-BH-1DVIYKb TP80736
VCC_j9c VCC_J1a
TP28540 TP65644
TP34187
GVC-NA619b TP45143
TP87818
TP44099 TP71582
TP72871
TP43100
TP77008
TP76704
TP14041 GVC-NA619a
TP42453
TP8228 TP10805
TP19706 TP77409
TP15376
TP29352
TP37687
TP10321
TP61028
Fig. 1 continued
123
Mol Breeding (2016) 36:41
Draper11
0
5
6
11
12
13
14
15
18
19
20
21
22
23
24
25
26
27
28
29
30
31
33
34
37
43
46
53
54
58
64
68
70
72
74
78
80
84
86
88
94
96
98
99
100
103
104
105
106
107
111
114
115
117
118
121
122
123
127
128
129
132
133
134
136
137
138
139
140
141
142
145
147
149
153
TP58236
TP14543 TP7397
TP93962
TP50470
TP76829
TP60817
TP112772
TP61746
TP67259 TP102642
TP15023
TP50952
TP28606 TP113812
TP39560
TP8806 TP57253
TP21675
TP61870
TP115753 TP85982
VCC_j9a
GVC-NA247a TP112120
TP20096
TP45836
TP5131
Pr031818815b
VCC_J1b
TP69388 VCC_j9b
TP111627
TP5121
TP48068
TP25021
VCB-BH-1DVIYKa
GVC-NA619a
TP105618
TP16520
TP114747
TP113018
TP90902
TP102153
TP103977
TP59988
TP118034
TP23807
TP4248
TP6038 TP78423
TP9986
TP9985
TP104690
TP96041
TP4241
TP61655
TP48553
TP116700
TP116232
TP9859
TP94960
TP56191 TP22854
TP21385 TP6393
TP101402
TP27999
TP96422
TP19309
TP112763
TP65565
TP81792
TP115074 TP21386
TP97606
TP14166
TP41775
GVC-V62h04
KAN-1593
TP59962
TP74568
TP24843
TP86290
TP45201
TP56198
TP24775
TP17291
Jewel11b
0
Pr032475738
27
30
33
34
35
36
38
40
41
42
44
45
47
48
50
55
TP96133
TP103028
TP109639
TP30539
TP55997
TP67409
TP42928
TP71604
TP90658
TP15600
TP43657
TP3322
GVC-V62h04
TP21283
TP79660
TP3686
67
TP22481
77
TP74315
83
TP62324
93
TP69019
107
KAN-157a
Jewel11c
0
2
5
7
TP72828
TP19704
TP15142
TP49752
13
TP19899
17
TP105585
26
TP41753
30
32
TP109980
TP91358
38
TP93387
42
TP107006
47
TP43716
51
TP92486
Mol Breeding (2016) 36:41
Fig. 1 continued
Page 21 of 24
Jewel12a
0
8
10
12
13
14
15
16
19
21
22
25
26
27
28
30
32
33
34
35
37
38
39
40
41
42
43
45
47
48
49
50
52
54
55
56
57
59
60
61
63
66
67
69
70
72
74
76
79
94
TP97478
TP62475 TP42800
TP76995
TP115165
TP27068
TP8905
TP102542 TP118091
TP11939
TP10687 TP26100
TP74776
TP60976
TP19805
TP77512
TP83375
TP106340
TP102138
TP64920
TP106898 TP112887
TP11906
TP88091 TP32377
TP18089
VCC_J3a
TP37696 VCC_J3b
TP3327 TP44931
TP33689
TP39044 TP60038
TP52206
TP64381
TP101375
TP110247
TP95286 TP104469
TP101929
TP102229
TP24005
TP18610
TP42461 TP86995
TP32781
TP112776 TP32528
GVC-NA41
TP9058
TP104769
TP55260
GVC-C668
Pr031818816b
TP55259
TP64332
TP43663
TP6922
TP71982
TP105168
CA794a
markers (480 in total) would give an overall requirement of *2500 markers. Only next generation
sequencing technologies could supply these and we
have demonstrated their potential in this study.
The linkage map developed for ‘Draper’ and
‘Jewel’ required the power of both TetraploidMap
Draper12
0
5
15
18
30
33
37
38
40
44
46
47
48
50
54
57
59
60
61
63
65
66
67
68
71
72
73
74
75
76
78
79
81
84
86
87
91
92
93
95
96
97
98
99
100
102
104
105
106
107
108
110
111
113
115
116
117
119
121
125
126
128
133
TP2378
TP26302
TP57154
TP71448
TP28786
TP109482 TP56551
TP64641
TP18785
TP75932
VCC_K4b CDF-7
TP76296
TP42528
VCC_I2b
TP15069 TP78724
TP84010
TP109463
TP109772
TP7373
TP51212
TP44488 TP89192
CA794b
CA483 CA794a
TP108244
TP87097
TP81077 TP3588
TP88208
GVC-V22g08b TP56621
TP91414
TP53879
TP52274
TP90837 TP7512
TP103841
TP23850
TP9358
TP50688 TP21295
TP3281 TP39024
TP65086 TP106877
TP116710
TP22365
TP92239
TP37238
TP39659
TP113146
TP67977
TP11603
TP115710 TP99108
TP77554
TP48691 TP32416
TP113948
TP77696
TP95103
TP20165
TP112669 TP71152
TP86380
TP37201
TP4621
TP115419 TP3764
TP18150 TP87963
TP11473
TP26721 TP114357
TP71436
TP10833 TP11768
TP42038
TP72741
TP8345
TP51127
41
Jewel12b
0
KAN-662b
24
KAN-1099
29
30
37
TP85295
TP14075
TP33976 VCC_K4a
VCC_I2b
TP82443
40
TP43291
43
45
VCB-C12195
TP47009
48
50
GVC-V22g08a
TP51767
59
TP71362
34
and JoinMap 4 to order markers correctly. There
were too many markers in this dataset to analyze
using TetraploidMap in isolation as in its current
format TetraploidMap cannot handle large enough
datasets for all analyses required (Hackett et al.
2007).
123
41
Page 22 of 24
Traditional blueberry breeding has been complicated by a relatively long juvenile period (approximately 3 years) and polyploid nature of the plant as
well as by severe inbreeding depression preventing the
development of inbred lines (Die and Rowland 2013).
Phenotypic selection of blueberry is often complicated
by incomplete expression of traits through a combination of environmental factors including: climate,
soil type and abiotic and biotic pressures (Collard et al.
2005; Winter and Kahl 1995). Blueberry breeding
programmes would benefit from the further development of genomic and genetic resources such as the
map developed in this study, to enable identification of
genetic markers for key breeding traits. Such markers
will then be available for implementation of markerassisted breeding (MAB), as is currently being applied
by breeders of other woody perennial fruit crops.
MAB is particularly important to improve the effectiveness of selection of parental plants for crosses and
for early generational seedling selection, to eliminate
offspring with undesirable traits before they are field
planted. A key advantage of MAB is that selection
based on molecular markers is not affected by the
environment or by the developmental stage of plants.
Generation of a genetic linkage map is a critical
step toward providing the framework for knowledgebased crop improvement. Traits that are difficult to
phenotype, such as the chilling requirement and cold
tolerance of individual progeny could be more accurately predicted with the allocation of appropriate
markers. Marker-assisted breeding is a powerful aid to
in genetic improvement particularly when combining
traits like such as adaptation and season extension with
other significant traits including fruit quality and pest
and disease resistance. Woody perennials, like blueberry, are especially suitable for improvement via
marker-assisted breeding because of their long generation times, high heterozygosity, self-incompatibility,
inbreeding depression, and polyploidy of commercial
types (Russell et al. 2014; Barba et al. 2014; Guajardo
et al. 2015) and this study is a step towards achieving
this goal in blueberry.
Acknowledgments This work was funded through
Horticulture Link (HL0190), and all contributing partners are
gratefully acknowledged. Project collaboration with the
Specialty Crop Research Initiative-funded project (Grant
2008-51180-04861 entitled ‘Generating genomic tools for
blueberry improvement’) has provided valuable access to
plant material, genetic resources and advice. Support for this
123
Mol Breeding (2016) 36:41
work from the Scottish Government’s Rural and Environment
Science and Analytical Services Division (RESAS) is gratefully
acknowledged. Mention of trade names or commercial products
in this publication is solely for the purpose of providing specific
information and does not imply recommendation or
endorsement by the James Hutton Institute or any of the other
agencies involved in this research. We thank all reviewers for
their comments and Sue Gardiner for assisting in preparation of
this ms.
References
Al-Janabi SM, Honeycutt RJ, McClelland M, Sobral BWS
(1992) A genetic linkage map of Saccharum spontaneum
L. ‘SES 208’. Genetics 134:1249–1260
Barba P, Cadle-Davidson L, Harriman J, Glaubitz JC, Brooks S,
Hyma K, Reisch B (2014) Grapevine powdery mildew
resistance and susceptibility loci identified on a high-resolution SNP map. Theor Appl Genet 127:73–84
Bielenberg DG, Rau B, Fan S, Gasic K, Abbott A.G, Reighard
GL, Okie WR, Wells CE (2015) Genotyping by sequencing
for SNP-based linkage map construction and QTL analysis
of chilling requirement and bloom date in peach [Prunus
persica (L.) Batsch]. PLoS ONE 10(10):e0139406
Boches PS, Bassil NV, Rowland LJ (2005) Microsatellite
markers for Vaccinium from EST and genomic libraries.
Mol Ecol Notes 5:657–660
Bradshaw JE, Hackett CA, Pande B, Waugh R, Bryan GJ (2008)
QTL mapping of yield, agronomic and quality traits in
tetraploid potato (Solanum tuberosum subsp. tuberosum).
Theor Appl Genet 116:193–211
Brazelton C (2013) World Acreage and Production. North
American Blueberry Council. http://www.chilealimentos.
com/2013/phocadownload/Aprocesados_congelados/nabc_
2012-world-blueberry-acreage-production.pdf. Accessed 9
Feb 2015
Bushakra JM, Bryant DW, Dossett M, Vining KJ, Van Buren R,
Gilmore BS, Lee J, Mockler TC, Finn CE, Bassil NV
(2015) A genetic linkage map of black raspberry (Rubus
occidentalis) and the mapping of Ag4 conferring resistance
to the aphid Amphorophora agathonica. Theor Appl Genet
128(8):1631–1646
Butruille DV, Boiteux LS (2000) Selection–mutation balance in
polysomic tetraploids: impact of double reduction and
gametophytic selection on the frequency of subchromosomal localization of deleterious mutations. Proc Natl
Acad Sci USA 97:6608–6613
Camp WH (1945) The North American blueberries with notes
on other groups of Vacciniaceae. Brittonia 5:203–275
Castro P, Stafne ET, Clark JR, Lewers KS (2013) Genetic map
of the primocane-fruiting and thornless traits of tetraploid
blackberry. Theor Appl Genet 126(10):2521–2532
Collard BCY, Jahufer MZZ, Brouwer JB, Pang ECK (2005) An
introduction to markers, quantitative trait loci (QTL)
mapping and marker-assisted selection for crop improvement: the basic concepts. Euphytica 142:169–196
Dhanaraj AL, Slovin JP, Rowland LJ (2004) Analysis of gene
expression associated with cold acclimation in blueberry
Mol Breeding (2016) 36:41
floral buds using expressed sequence tags. Plant Sci
166:863–872
Die JV, Rowland LJ (2013) Superior cross-species reference
genes: a blueberry case study. PLoS ONE 8(9):e73354
Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K,
Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity
species. PLoS ONE 6(5):e19379
Gallais A (2003) Quantitative genetics and breeding methods in
autopolyploid plants. INRA Editions, Paris
Gar O, Sargent DJ, Tsai CJ, Pleban T, Shalev G, Byrne DH,
Zamir D (2011) An Autotetraploid Linkage Map of Rose
(Rosa hybrida) Validated Using the Strawberry (Fragaria
vesca) Genome Sequence. PloS ONE 6(5):e20463
Gardner KM, Brown P, Cooke TF, Cann S, Costa F, Bustamante
C, Velasco R, Troggio M, Myles S (2014) Fast and costeffective genetic mapping in apple using next-generation
sequencing. G3: Genes|Genomes|Genet 4(9):1681–1687
Gilmore BS, Bassil NV, Hummer KE (2011) DNA extraction
protocols from dormant buds of twelve woody plant genera. J Am Pomol Soc 65:201–207
Guajardo V, Solis S, Sagredo B, Gainza F, Munoz C, Gasic K,
Hinrichsen P (2015) Construction of high density sweet
cherry (Prunus avium L.) linkage maps using microsatellite
markers and SNPs detected by genotyping-by-sequencing
(GBS). PLoS ONE 10(5):e0127750
Hackett CA, Luo ZW (2003) TetraploidMap: construction of a
linkage map in autotetraploid species. J Hered 94:358–359
Hackett CA, Bradshaw JE, Meyer RC, McNicol JW, Milbourne
D, Waugh R (1998) Linkage analysis in autotetraploid
species: a simulation study. Genet Res Camp 71:143–154
Hackett CA, Bradshaw JE, McNicol JW (2001) Interval mapping of QTLs in autotetraploid species. Genetics
159:1819–1832
Hackett CA, Milne I, Bradshaw JE, Luo ZW (2007) TetraploidMap for Windows: linkage map construction and
QTL mapping in autotetraploid species. J Hered
98:727–729
Hangun-Balkir Y, McKenney L (2012) Determination of
antioxidant activities of berries and resveratrol. Green
Chem Lett Rev 5(2):147–153
Johnson SA, Arjmandi BH (2013) Evidence for anti-cancer
properties of blueberries: a mini review. Anticancer Agents
Med Chem 13(8):1142–1148
Julier B, Flajoulot S, Barre P, Cardinet G, Santoni S, Huguet T,
Huyghe C (2003) Construction of two genetic linkage maps
in cultivated tetraploidalfalfa (Medicago sativa) using
microsatellite and AFLP markers. BMC Plant Biol 3:9
Kalt W, Joseph JA, Shukitt-Hale B (2007) Blueberries and
human health. A review of the current research. J Am
Pomol Soc 61:151–160
Lu F, Lipka AE, Glaubitz J, Elshire R, Cherney JH, Casler MD,
Buckler ES, Costich DE (2013) Switchgrass genomic
diversity, ploidy, and evolution: novel insights from a
network-based SNP discovery protocol. PLoS Genet
9(1):e1003215
Luo ZW, Hackett CA, Bradshaw JE, McNicol JW, Milbourne D
(2000) Predicting parental genotypes and gene segregation
for tetrasomic inheritance. Theor Appl Genet 100:
1067–1073
Page 23 of 24
41
Luo ZW, Hackett CA, Bradshaw JE, McNicol JW, Milbourne D
(2001) Construction of a genetic linkage map in tetraploid
species using molecular markers. Genetics 157:1369–1385
McCormack D, McFadden D (2013) A review of Pterostilbene
antioxidant activity and disease modification. Oxid med
Cell Longev 2013:575482
Megalos BS, Ballington JR (1988) Unreduced pollen frequencies versus hybrid production in diploid- tetraploid Vaccinium crosses. Euphytica 39:271–278
Meyer RC, Milbourne D, Hackett CA, Bradshaw JE, McNicol
JW, Waugh R (1998) Linkage analysis in tetraploid potato
and association of markers with quantitative resistance to
late blight (Phytophthora infestans). Mol Gen Genet
259:150–160
Nielsen R, Paul JS, Alberechtsen A, Song YS (2011) Genotype
and SNP calling from next generation sequencing data. Nat
Rev Genet 12:443–451
Qu L, Hancock JF, Whallon JH (1998) Evolution in an
autopolyploid group displaying predominantly bivalent
pairing at meiosis: genomic similarity of diploid Vaccinium darrowii and autotetraploid Vaccinium corymbosum (Ericaceae). Am J Bot 85:698–703
Rimando AM, Kalt W, Magee JB, Dewey J, Ballington JR
(2004) Resveratrol, pterostilbene, and piceatannol in Vaccinium berries. J Agric Food Chem 52(15):4713–4719
Rocher S, Jean M, Castonguay Belzile F (2015) Validation of
genotyping by sequencing analysis in populations of tetraploid Alfalfa by 454 sequencing. PLoS ONE
10(6):e0131918
Rodriguez-Mateos A, Rendeiro C, Bergillos-Meca T, Tabatabaee S, George TW, Heiss C, Spencer JPE (2013) Intake
and time dependence of blueberry flavonoid-induced
improvements in vascular function: a randomized, controlled, double-blind, crossover intervention study with
mechanistic insights into biological activity. Am J Clin
Nutr 98:1179–1191
Rowland LJ, Mehra S, Dhanaraj AL, Ogden EL, Arora A (2003)
Identification of molecular markers associated with cold
tolerance in blueberry. Acta Hort 625:59–69
Rowland LJ, Dhanaraj AL, Naik D, Alkharouf N, Matthews B,
Arora R (2008) Study of cold tolerance in blueberry using
EST libraries, cDNA microarrays, and subtractive
hybridization. HortSci 43:1975–1981
Rowland LJ, Bell D, Alkharouf N, Bassil N, Drummond F,
Beers L, Buck E, Finn C, Graham J, McCallum S, Hancock
J, Polashock J, Olmstead J, Main D (2012) Generating
genomic tools for blueberry improvement. J Fruit Sci
12:276–287
Rowland LJ, Ogden EL, Bassil N, Buck EJ, McCallum S,
Graham J, Brown A, Wiedow C, Campbell AM, Haynes
KG, Vinyard BT (2014) Construction of a genetic linkage
map of an interspecific diploid blueberry population and
identification of QTL for chilling requirement and cold
hardiness. Mol Breed 34:2033–2048
Russell J, Hackett C, Hedley P, Liu H, Milne L, Bayer M,
Marshall D, Jorgensen L, Gordon S, Brennan R (2014) The
use of genotyping by sequencing in blackcurrant (Ribes
nigrum): developing high-resolution linkage maps in species without reference genome sequences. Mol Breed
33:835–849
123
41
Page 24 of 24
Shuelke M (2000) An economic method for the fluorescent
labelling of PCR fragments. Nat Am Inc 18:233–234
Soltis DE, Soltis PS (1993) Molecular data and the dynamic
nature of polyploidy. Crit Rev Plant Sci 12:243–273
Uitdewilligen J, Wolters AM, D’Hoop B, Borm T, Visser R, Eck
H (2013) A next generation sequencing method for genotyping-by-sequencing of highly heterozygous autotetraploid potato. PLoS ONE 8(5):e62355
Van Ooijen JW (2006) JoinMap Ò 4; Software for the calculation of genetic linkage maps in experimental populations.
Kyazma BV, Wageningen
Voorrips RE (2002) MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered 93:77–78
Ward JA, Bhangoo J, Fernández-Fernández F, Moore P,
Swanson JD, Viola R, Velasco R, Bassil N, Weber CA,
123
Mol Breeding (2016) 36:41
Sargent DJ (2013) Saturated linkage map construction in
Rubus idaeus using genotyping by sequencing and genome-independent imputation. BMC Genom 14:2
Welch JE (1962) Linkage in autotetraploid maize. Genetics
47:367–396
Winter P, Kahl G (1995) Molecular marker technologies for
plant improvement. World J Microbiol Biotechnol
11:438–448
Wu KK, Burnquist W, Sorrells ME, Tew TL, Moore PH, Tanskley SD (1992) The detection and estimation of linkage in
polyploids using single-dose restriction fragments. Theor
Appl Genet 83:294–300
Wu R, Gallo-Meager M, Littell RC, Zeng ZB (2001) A general
polyploidy model for analyzing gene segregation in
outcrossing tetraploid species. Genetics 159:869–882