jurnalke3

Tree Genetics & Genomes
(2019) 15:13
https://doi.org/10.1007/s11295-019-1318-9
ORIGINAL ARTICLE
First simple sequence repeat-based genetic linkage map reveals a major
QTL for leafing time in walnut (Juglans regia L.)
Sina Kefayati 1 & Adi Surya Ikhsan 1 & Mehmet Sutyemez 2 & Aibibula Paizila 1 & Hayat Topçu 1 & Şakir Burak Bükücü 2 &
Salih Kafkas 1
Received: 25 July 2018 / Revised: 6 January 2019 / Accepted: 8 January 2019
# Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract
Walnut (Juglans regia L.) is one of the most important nut tree species. Simple sequence repeat (SSR)-based genetic linkage
maps are valuable and effective tools for studies on integrative and comparative mapping. The data available in literature on
walnut is inadequate for linkage mapping. Therefore, in the present study, we aimed to construct the first SSR-based genetic
linkage map using the ‘Chandler’ × ‘Kaplan-86’ F1 population and to identify QTL (Quantitative Trait Loci) for leafing time in
walnut. A total of 386 SSRs were mapped to 16 linkage groups (LGs) after screening 1437 SSR primer pairs. Among the mapped
markers, 166 SSRs (43.0%) were heterozygous in both the parents, whereas 113 (29.3%) and 107 (27.7%) markers were from
one of the parents that were integrated into the female and male maps, respectively. The total length of the consensus map was
1568.2 cM, with an average length of 98.0 cM. It included an average of 24.1 markers per LG, and the mean distance between
SSR markers was 4.3 cM. The Chandler maternal linkage map included 279 SSR markers of total map length 1285.8 cM,
whereas the Kaplan-86 male genetic map contained 273 SSRs of total map length 1574.4 cM. The SSR-based linkage maps
presented in this study are moderately dense and can be considered as a fundamental genetic linkage map for further genetic and
molecular studies in walnut, as well as in other Juglans species. A major QTL was identified for leafing time on LG4 explaining
52.0–68.8% of the phenotypic variation at similar positions in parental and consensus maps. The identified QTL region with
associate markers can be potential for marker-assisted selection in the future for walnut breeding programs.
Keywords Juglans regia . Linkage mapping . Microsatellite . Simple sequence repeat . Walnut
Introduction
Walnut (Juglans regia L.) belongs to the family Juglandaceae
and is mainly cultivated for its nuts and timber. It grows as a
wild or cultivated tree in a wide area ranging from
Southeastern Europe to the Caucasus (Ikhsan et al. 2016).
Walnuts contain essential nutritional components for human,
Communicated by E. Dirlewanger
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s11295-019-1318-9) contains supplementary
material, which is available to authorized users.
* Salih Kafkas
[email protected]
1
Department of Horticulture, Faculty of Agriculture, University of
Çukurova, 01330 Adana, Turkey
2
Department of Horticulture, Faculty of Agriculture, University of
Kahramanmaraş Sütçüimam, Kahramanmaraş, Turkey
such as protein and lipids at a concentration of approximately
16.7 and 66.9% on dry weight basis, respectively. Some major
proteins include prolamin, albumin, globulin, and glutelin.
Walnuts also contain alpha tocopherol, gamma tocopherol,
delta tocopherol, total carotenoid, and selenium (Sze-Tao
and Sathe 2000). From economic perspective, walnuts are
categorized as high-value nuts beside pistachio, hazelnuts,
almonds, and cashew nuts (Bernard et al. 2018). The global
total walnut production is approximately four million metric
tons, and Turkey accounts for > 5% of the production, after
China (47.7%) and USA (16.2%) (Faostat 2018).
For a long time, farmers selected the best walnuts from
seedling population and propagated them, and later walnut
scion and rootstock breeding programs were practiced during
the twentieth century. During the past 50 years, many studies
worldwide have focused on walnut breeding, genetics, and
biotechnology (Bernard et al. 2018).
The advancements in genetics, biotechnology, and genomics have provided new tools to discover alleles and genes.
13
Page 2 of 12
These tools can improve the efficiency of breeding programs
(Moose and Mumm 2008). Molecular breeding is defined as
the use of molecular markers in conjunction with linkage
maps and genomics to select plants with desirable traits based
on the genetic assays (Vinod 2006). The linkage map is valuable to obtain information regarding segregation and genetic
makeup of a species. It is constructed using the recombination
frequency of markers. This information is utilized as a foundation to determine the relative location of these markers in
the genome (Semagn et al. 2006). All the species of genus
Juglans are diploid with a karyotype of 2n = 32 and have 16
linkage groups (LGs) (Woodworth 1930); the estimated genome size of J. regia is 606 Mbp (Horjales et al. 2003). A draft
genome of walnut has also been published by Martínez-García
et al. (2016).
Numerous molecular marker systems have been used in
walnut for germplasm characterization, genetic diversity, and
genetic linkage mapping (Fjellstrom et al. 1994; Nicese et al.
1998; Woeste et al. 2002; Potter et al. 2002; Dangl et al. 2005;
Kafkas et al. 2005; Doğan et al. 2014; Luo et al. 2015; Zhu
et al. 2015). Among them, the simple sequence repeat (SSR) is
a convenient marker for DNA fingerprinting as compared
with the other molecular markers owing to its co-dominant
inheritance, large number of alleles per locus, suitability for
automation, genome-wide abundance, and the requirement of
only a small amount of DNA for the analysis (Wani et al.
2010). The SSRs have a wide range of applications, including
in studies on genetic diversity, identification of cultivar, and
construction of high-density genetic linkage maps and
marker-assisted selection (Winter et al. 2000).
To the best of our knowledge, the first SSR marker development study in walnut was by Woeste et al. (2002) using the
enrichment method in Juglans nigra. Subsequently, a few
studies have attempted to generate polymorphic SSR markers
for walnut (Victory et al. 2006; Hoban et al. 2008; Zhang et al.
2010; Qi et al. 2009, 2011; Yi et al. 2011; Chen et al. 2013;
Zhang et al. 2013; Najafi et al. 2014; Topçu et al. 2015a, b;
Ikhsan et al. 2016; Dang et al. 2016; Eser et al. 2018). Among
them, Ikhsan et al. (2016) and Eser et al. (2018) used bacterial
artificial chromosome (BAC)-end sequences generated by Wu
et al. (2012) and developed numerous polymorphic SSRs (308
and 551, respectively).
In the past, restriction fragment length polymorphism
(RFLP), random amplified polymorphic DNA (RAPD), and
isozyme markers were used to construct genetic linkage maps
in walnut (Fjellstrom and Parfitt 1994; Woeste et al. 1996;
Malvolti et al. 2001); however, the number of markers was not
sufficient to cover all the LGs, and there was no sequence information on some of the mapped markers. Recently, SNP (singlenucleotide polymorphism) and InDel-based linkage map of walnut were also published (Luo et al. 2015; Zhu et al. 2015).
Cultivar breeding programs in walnut are very slow due to
long juvenile period. Therefore, it is very important to have
Tree Genetics & Genomes
(2019) 15:13
markers linked to economically important characters for early
selection in walnut for cost-effective breeding. The development of linkage maps and the identification of markers closely
linked to QTL (quantitative trait loci) of an important phenotypic trait may provide marker-assisted selection (MAS).
Furthermore, detecting a QTL region associated with a phenotypic trait may lead to identify candidate genes. Leafing
time is an important trait that has effect on expansion of the
cultivation in walnut. Currently, there is no marker development or a QTL study in walnut on phenology of the trees.
Therefore, we aimed to construct the first SSR-based linkage
map and to perform QTL analysis for leafing time in walnut in
this study.
Materials and methods
Plant material and DNA extraction
A set of 175 segregating F1 progenies derived from a cross
between ‘Chandler’ × ‘Kaplan-86’ was used to construct the
linkage map. The F1 population was planted in Nuts
Application and Research Center (SEKAMER) of
Kahramanmaraş Sütçüimam University located in
Kahramanmaraş province of Turkey in 2009 with 6 m × 6 m
spacing.
The DNA was extracted from young leaf tissues by the
cetyl trimethylammonium bromide method (Doyle and
Doyle 1990). The concentration of DNA was measured using
a Qubit Fluorimeter (Invitrogen). The DNA samples were
subsequently diluted to a concentration of 10 ng/L for the
SSR ploymerase chain reaction (PCR) analysis.
SSR genotyping
The SSR analysis was carried out using a three-primer strategy for inexpensive PCR amplification according to the method described by Schuelke (2000). Eighteen bases of M13
universal primer (5′ TGTAAAACGACGGCCAGT 3′) was
added to each of the forward primers. The universal M13
primer was fluorescently labeled with FAM, VIC, NED, or
PET dye. The PCR amplification was performed using the
Applied Biosystems® Veriti® 96-Well Thermal Cycler.
A total of 1437 published SSR primer pairs were screened
in six progenies and in the parents (Table 1). Thereafter, the
segregated SSR loci were analyzed across the whole population. The PCR and cycling conditions were similar to those
described in the studies that developed the SSR primer pairs.
The capillary electrophoresis of PCR products was performed
in the ABI 3130xl (Applied Biosystems) genetic analyzer instrument. A total of 9.7 μL of Hi-Di formamide and 0.3 μL of
500 LIZ size standard with 1 μL of PCR product were loaded
to each well. The aliquots were denatured for 5 min at 95 °C
Tree Genetics & Genomes
(2019) 15:13
Page 3 of 12
Table 1
The number of tested, segregated, and mapped SSR loci with their origins, acronyms, and references
No
Origin
Acronym
References
No. of tested
primer pairs
No. of
segregated loci
No. of
mapped loci
Species
Source
1
2
3
4
5
6
7
8
9
10
11
12
13
14
J. nigra
J. nigra
J. nigra
J. nigra
J. nigra
J. nigra
J. cinerae
J. regia
J. regia
J. regia
J. regia
J. nigra
J. mandshurica
J. regia
Genomic
Genomic
Genomic
Genomic
Genomic
Genomic
Genomic
EST
EST
EST
EST
Genomic
Genomic
EST
WGA
WGA
WGA
WGA
WGA
WGA
JCIN
ZMZ
WEST
ZMZ
JRCV
WAC
JMP
BFUJR
Woeste et al. (2002)
Dangl et al. (2005)
Foroni et al. (2005)
Victory et al. (2006)
Robichaud et al. (2006)
Ross-Davis et al. (2008)
Hoban et al. (2008)
Qi et al. (2009)
Zhang et al. (2010)
Qi et al. (2011)
Yi et al. (2011)
Topçu (2012)
Chen et al. (2013)
Zhang et al. (2013)
30
12
1
2
4
5
13
43
41
18
30
7
50
76
5
5
1
1
0
2
4
8
12
0
10
1
12
22
2
4
1
1
0
1
3
7
10
0
9
1
10
18
15
16
17
18
19
J. regia
J. regia
J. nigra
J. regia
J. regia
Genomic
BAC-end
Genomic
Genomic
ABRII
WJR
WGA
CUJR
Najafi et al. (2014)
Chen et al. (2014)
Topçu et al. (2015a)
Topçu et al. (2015b)
13
12
37
185
0
5
8
94
0
5
6
73
BAC-end
BAC-end
JRHR
JRHR
Ikhsan et al. (2016)
Eser et al. (2018)
307
551
1437
164
405/150a
504
123
112
386
20
J. regia
Total SSR loci
a
13
150 were analyzed from 405 segregated loci
and run on the ABI 3130xl automated sequencer using a 36-cm
capillary array and POP7 polymer. GeneMapper version 4.0
was used to determine allele size. When both parents were
heterozygous (ab × cd, ef × eg, and hk × hk), the markers were
termed “common,” and when one of the parents was heterozygous (nn × np and lm × ll), the markers were termed “parental.”
Genetic linkage map construction and comparison
with walnut genome
The double pseudo-testcross mapping approach was followed
to construct genetic linkage maps independently for each parent. The co-dominant nature of SSR markers enabled us to
construct the consensus map. The following five different
segregation patterns were scored: ab × cd and ef × eg with
1:1:1:1 segregation, hk × hk with 1:2:1 segregation, and
lm × ll and nn × np with 1:1 segregation. 1:1:1:1 and 1:2:1
segregations are common markers used in both the parental
maps, whereas 1:1 segregated markers are parental markers,
lm × ll is a maternal marker, and nn × np a paternal marker. All
data analyses were performed using the JoinMap version 4.1
software (Van Ooijen 2011a). The markers were tested for
segregation distortion (SD) by the chi-square test, before using
them in linkage mapping. Subsequently, the LGs of the
markers were compared at various logarithm of the odds
(LOD) thresholds (from 2 to 10) to evaluate the stability of
the resulting LGs. Finally, an LOD threshold of 6 was chosen
to construct the LGs of walnut using the SSR markers. The
SSR markers were ordered in two steps by regression mapping with default parameters. Firstly, the markers in the LGs
were ordered without highly skewed markers (χ2 > 0.01), and
the resulting order of markers in the LGs was used as reference
in the second step, where all the markers were used in mapping. The markers causing severe discrepancies during ordering were removed from the map. The map distances were
calculated in centimorgans (cM) using Kosambi’s function
(Kosambi 1944). The final version of the genetic maps was
drawn using the MapChart version 2.2 software (Voorrips
2002). To determine the possible causes of SD, we analyzed
the distorted markers by the allelic and zygotic SD tests as
described by Li et al. (2011).
The genetic positions of the mapped SSRs in this study
were compared to with their physical positions in walnut genome published by Martínez-García et al. (2016). SSR sequences flanking by forward and reverse primers were
searched against the walnut genome using the BLAST function of National Center for Biotechnology Information
(NCBI), and only the best matching one was presented.
13
Page 4 of 12
Phenotyping and QTL analysis for leafing time
Leafing time of the progenies and the parents were determined
during three consecutive growing seasons (2015, 2016, and
2017) based on the walnut descriptor (IPGRI 1994). Leafing
time was recorded as the number of days from January 1st of
each year. MapQTL v5.0 (Van Ooijen 2004) was used to analyze QTL data. An interval mapping approach was initially
used to detect putative QTLs in each parental and consensus
maps. The LOD thresholds at the genome-wide level were
determined by running 1000 permutation tests. The
“Automatic Cofactor Selection” tool was used iteratively to
identify the strongest marker cofactors for each year. Then,
multiple QTL model mapping (MQM) was performed. The
Kruskal–Wallis (KW) test was performed to detect associations between markers and leafing time.
Results
SSR marker segregation
A total of 1437 SSRs were screened for segregation, 504
(35.1%) markers were initially selected based on their segregation pattern, and 386 (26.9%) markers were successfully
mapped. Among the 1437 SSR primer pairs screened, 161
(11.2%) were developed from J. nigra, Juglans cinerae, and
Juglans mandshurica, of which, 39 (24.2%) were segregated
and 29 (18.0%) were mapped. The remaining 1276 SSR loci
were developed from J. regia, of which, 720 (56.4%) were
segregated and 357 (28.0%) were mapped. Only 504 SSR loci
of the 759 segregated loci were analyzed in the F1 population
(Table 1).
A total of 166 (43.0%) out of 386 mapped SSRs were
heterozygous in both the parents, and the segregation types
were as follows: 61 ab × cd (1:1:1:1), 83 ef × eg (1:1:1:1), and
22 hk × hk (1:2:1). The number of mapped parental markers
was 113 (29.3%) and 107 (27.7%) in the female (‘Chandler’)
and male (‘Kaplan-86’) maps, respectively. About three fourth
of the common SSR markers were obtained from the BACend sequences.
Construction of parental and consensus maps
The consensus and parental genetic linkage maps spanned 16
LGs (Fig. 1). In the consensus genetic linkage map, 386 SSR
markers were successfully mapped in the ‘Chandler’ × ‘Kaplan86’ F1 population. A high percent of the mapped markers were
parental markers (57.0%; lm × ll and nn × np), and the remaining were common markers (43.0%; ab × cd, ef × eg, and
hk × hk). The mapped SSRs in the consensus map, their primer
sequences, repeat motifs, amplified alleles in ‘Chandler’ and
Tree Genetics & Genomes
(2019) 15:13
‘Kaplan-86’ parents, segregation types, their LGs, positions,
and ki-kare values were given in Online Resource 1.
The total length of the consensus map was 1568.2 cM with
an average of 98.0 cM per LG. The number of SSR markers in
the 16 LGs of walnut varied from 10 (LG15) to 35 (LG3), with
an average of 24.1. The LG3, LG2, LG4, and LG10 were the
longest LGs with 135.5, 134.0, 126.7, and 122.2 cM, respectively. The shortest LGs were LG15 (68.3 cM), LG13
(68.9 cM), and LG16 (75.2 cM). The average distance between the SSR markers was 4.3 cM in the consensus map,
and it ranged from 2.7 cM (LG7) to 6.8 cM (LG15). The
average number of markers per cM (marker density) was
0.25, and LG7 was the densest group with 0.37 markers.
There were only two gaps ≥ 20 cM in LG2 and LG9 in the
consensus map (Table 2; Fig. 1).
In the ‘Chandler’ maternal map, 279 SSR markers were
successfully mapped in 16 LGs (Fig. 1). The LG7 (31), LG5
(30), and LG3 (29) had the highest number of markers, whereas, LG16 (3) and LG15 (4) included the lowest number of
SSR markers. The average number of markers per LG was
17.4. The number of mapped common markers was 166
(59.5%), and LG7 had the highest number (25). The total
map length of female map was 1285.8 cM, with an average
of 80.4 cM. The LG length ranged between 20.2 cM (LG16)
and 136.0 cM (LG3). The average marker distance ranged
from 3.0 cM (LG7) to 13.0 cM (LG15), with an average of
5.3 cM. The average number of markers per cM was between
0.08 (LG15) and 0.34 (LG7), with an average of 0.21. There
were five gaps ≥ 20 cM in LG4, LG8, LG9, LG11, and LG14
in the ‘Chandler’ map (Table 2; Fig. 1).
In ‘Kaplan-86’ male parent, 273 SSR markers were successfully mapped to 16 LGs, with an average of 17.1 markers. The
number of mapped markers was between 8 (LG15) and 28
(LG7). The percent of mapped common markers was 60.8%,
and LG7 had the highest rate (89.3%). The total map length was
1574.4 cM, with an average of 98.4 cM. The LG13 (47.9 cM)
was the shortest, whereas LG4 (141.1 cM), LG2 (133.9 cM),
LG3 (130.6 cM), LG5 (128.3 cM), and LG10 (118.4 cM) were
the longest in the ‘Kaplan-86’ map. Furthermore, LG7 (3.6),
LG16 (4.0), and LG13 (4.4) were the densest groups, whereas
LG15 (8.5), LG2 (8.1), and LG12 (8.1) were the sparsest
groups. The average marker distance was 6.0 cM, and the mean
marker density was 0.18. There were nine gaps ≥ 20 cM in
LG2, LG3, LG4, LG6, LG7, LG9, LG12, and LG15 in the
Kaplan-86 male genetic linkage map (Table 2; Fig. 1).
Comparison with walnut genome
Comparison of the SSRs placed in walnut genome revealed
marker order for most of the SSRs (Online Resource 1).
Within each LG, only a few markers were mapped to orders
that differed from that of the walnut genome. SSRs mapped in
different orders within LGs were observed in LG4, LG6, LG7,
(2019) 15:13
Tree Genetics & Genomes
LG-1
Chandler-1
CUJRD462
JRHR229003
JMP14
JRHR215674
JRHR225292
JRHR220524
JRHR217037
JRHR227701
JRHR211565
JRHR213554
JRHR227686b
BFUJR307
JRHR214565
JRHR224068
CUJRD465
CUJRA436
BFUJR301
41,98
54,74
57,66
59,17
62,70
69,55
70,80
79,68
81,85
84,99
88,67
90,25
44,02
78,94
JRHR227554
90,07
92,85
JRHR214565
JRHR224068
JRHR207191
JRHR231559b
CUJRD005a
JRHR228611
31,17
36,57
42,54
CUJRA202
CUJRB450
JRHR223519a
50,27
WGA5
66,13
69,77
71,85
74,18
78,86
84,11
89,76
JRHR214968
JRHR228874
JRCV197782
CUJRA218
JRHR217463
JRHR219728
JRHR213185
JRHR211118
11,00
JRHR212022
19,01
JRHR229542
25,57
31,34
CUJRB321
JRHR223570a
39,61
45,16
JRHR223867
WGA202
55,42
59,07
65,01
JRHR221447
CUJRB468
JRHR230226
77,98
80,87
83,81
85,02
95,37
97,63
98,11
103,46
107,16
113,71
JRHR224834
JRHR231293
CUJRD481
JRHR212270
JRHR227143
JRHR224661
JRHR221565a
JRHR223824
BFUJR184
JRHR219017
128,33
JRHR227391
Kaplan86-9
LG-9
0,00
3,99
5,41
7,12
10,99
JRHR225892
JRHR219358x
JRHR215811
JRHR219358y
JRHR223168
32,24
38,88
47,24
48,55
54,65
59,72
60,94
62,13
64,55
66,29
68,12
68,80
78,17
79,92
82,15
90,35
JRHR211835
JRZMZ22
WJR265
CUJRB103a
JRHR229981
JRHR222846
JRHR212067
JRHR220864b
JRHR223139
CUJRA481
CUJRB421
JRHR226204
JRHR230927
JRHR226414b
CUJRA211
JRHR213115
0,00
4,08
5,46
11,16
JRHR225892
JRHR219358x
JRHR215811
JRHR223168
33,93
40,59
49,08
56,42
61,63
63,05
66,54
68,30
70,14
JRHR211835
JRZMZ22
WJR265
JRHR229981
JRHR222846
JRHR212067
JRHR223139
CUJRA481
CUJRB421
82,40
96,62
133,96
JRHR230119
Chandler-6
0,00
7,44
9,09
12,91
16,26
19,30
23,44
25,72
29,24
30,68
33,08
34,31
45,06
48,88
53,61
60,98
72,41
78,07
0,00
4,56
8,49
9,50
16,85
18,35
19,55
21,56
29,09
32,99
45,48
48,66
JRHR225191
JRHR216529
CUJRB317
JRHR214591
JMP27
BFUJR207
CUJRD439
JRHR215253
CUJRB453
CUJRA004
JRCV195023
BFUJR19
JRHR230142
CUJRB202
CUJRA482
CUJRB490
JRHR227393
JRCV195682
BFUJR228
JRHR218062
JRHR213682
94,48
JRHR224559
JRHR227769
JRHR229053b
JRHR209244
JRHR220176
JRHR215620
JRHR206788a
JRHR224700
JRHR218066
CUJRC006
JRHR211468
JRHR227905
CUJRB220
JRHR230927
65,69
69,31
71,77
76,63
80,93
JMP16
JRHR206716x
JRHR206716y
CUJRA405
JRHR226237
JRHR213115
95,57
JRHR214370
0,00
2,60
5,71
7,47
9,62
12,41
15,30
20,01
28,90
32,92
36,27
JRHR223518
WJR100
JRHR222130
JCINB157
WEST1552
JRHR215590
JRHR228223
JRHR230245
JRHR229800
WEST566
JRHR224311
52,00
56,94
59,01
62,14
67,12
BFUJR121
JRHR226028
CUJRB414
WGA171
WAC167
LG-13
0,00
2,67
5,08
8,21
9,89
11,98
14,74
17,60
22,22
29,85
34,75
38,09
38,13
41,14
45,18
47,79
53,80
58,73
60,80
63,93
68,92
JRHR227206
JRHR223518
WJR100
JRHR222130
JCINB157
WEST1552
JRHR215590
JRHR228223
JRHR230245
JRHR229800
WEST566
JRHR227734
JRHR224311
BFUJR240
BFUJR195
WGA70
BFUJR121
JRHR226028
CUJRB414
WGA171
WAC167
Kaplan86-13
0,00
2,39
4,93
8,39
10,06
14,96
JRHR227206
JRHR223518
WJR100
JCINB157
JRHR222130
JRHR215590
29,80
38,17
41,22
45,25
47,86
JRHR229800
JRHR227734
BFUJR240
BFUJR195
WGA70
Chandler-14
JRHR223038a
JRHR226096
JRCV195815
44,20
BFUJR68
65,66
70,41
JRHR209503
JMP23
85,19
88,29
89,29
92,96
95,58
103,61
107,94
110,54
JRHR214968
CUJRB401
JCINB110
CUJRA218
JRHR231751
JRHR219728
JRHR226652
JRHR213185
133,86
JRHR230119
JRHR230345
7,31
JRHR223215
18,99
CUJRA432
CUJRA002
WEST1172
WGA76
JRZMZ46
JRHR230736a
WGA136
JRHR226941
CUJRA212
JRHR222705
WEST1528
JRHR224229
JRHR223541a
JRHR211298
JRHR220903
JRHR218863
JMP28
JRHR221483
JRCV195258
JRZMZ7
JRHR230254
WGA225
JRHR227244
JRHR227798
JRHR227254
BFUJR10
CUJRB462
35,89
41,58
44,44
45,74
47,57
51,97
54,55
67,10
70,65
74,46
77,18
80,58
94,51
97,39
101,88
103,47
105,89
107,38
108,99
110,97
112,25
114,17
116,33
128,13
129,33
136,05
JRHR225191
CUJRB317
JRHR214591
BFUJR207
CUJRD439
CUJRB453
CUJRA004
JRCV195023
JRHR230142
BFUJR19
CUJRB202
JRHR227393
75,02
JRHR213682
92,66
JRHR224559
0,00
4,10
4,17
4,33
4,53
5,64
8,99
14,21
16,16
16,41
17,92
19,97
22,88
36,84
45,30
47,44
51,61
53,00
54,19
54,81
58,28
65,08
69,56
71,21
73,06
75,74
77,76
79,87
81,31
87,12
91,78
BFUJR45
JMP40
JRHR227328a
LG-14
0,00
4,21
5,25
12,84
14,33
17,16
24,43
27,85
31,07
36,50
39,89
42,82
45,91
52,94
62,34
65,58
67,98
72,59
76,80
79,75
92,32
JRHR229053b
JRHR209244
JRHR220176
JRHR215620
JRHR206788a
JRHR218066
CUJRD437
JRHR211468
CUJRC006
JRHR224349
CUJRB309
JRHR227905
CUJRB220
WEST1000
JMP16
JRHR206716x
JRHR206716y
CUJRA405
JRHR226237
CUJRB483
JRHR214370
111,95
114,51
118,42
BFUJR45
JMP40
JRHR227328a
Kaplan86-14
JRHR217272
0,00
JMP29
0,00
JMP29
7,00
JRHR224565
8,25
JRHR223609
8,25
JRHR223609
14,41
JRHR228722
43,12
JRHR219598
52,70
57,67
59,65
62,77
66,08
72,78
JRHR224485
JRHR227284
JRHR213537
CUJRB111
JRHR218268
JRHR221078
0,00
0,00
7,13
17,98
29,58
33,45
38,26
39,66
42,33
43,34
45,05
49,91
52,22
58,78
60,48
66,22
69,67
73,20
76,02
76,66
79,46
93,88
96,79
101,22
102,79
105,21
106,69
108,31
110,28
111,48
113,49
115,64
115,69
127,68
128,82
135,46
23,36
28,10
33,37
35,53
JRHR227219
JRHR217272
JRZMZ44
JRHR224565
23,33
28,24
33,27
JRHR227219
JRHR217272
JRZMZ44
43,12
JRHR228722
42,49
JRHR228722
58,17
CUJRB485
58,04
CUJRB485
67,92
JRHR220269
67,88
JRHR220269
75,61
85,09
87,29
90,36
92,34
95,50
98,74
105,48
JRHR219598
JRHR224485
JRHR215899
JRHR227284
JRHR213537
CUJRB111
JRHR218268
JRHR221078
75,65
85,12
87,32
90,39
92,37
95,53
98,77
105,51
JRHR219598
JRHR224485
JRHR215899
JRHR227284
JRHR213537
CUJRB111
JRHR218268
JRHR221078
JRHR230345
JRHR223215
CUJRA432
CUJRB206
CUJRA002
CUJRD458a
WEST1172
WGA76
JRZMZ46
JRHR230736a
WGA136
JRHR226941
JRHR228966a
CUJRA450
CUJRA212
JRHR222705
WEST1528
JRHR224229
JMP41
JRHR223541a
JRHR211298
JRHR220903
JRHR218863
JMP28
JRHR221483
JRCV195258
JRZMZ7
JRHR230254
WGA225
JRHR227244
JRHR227798
JRHR230901
JRHR227254
BFUJR10
CUJRB462
JRHR230345
7,30
JRHR223215
28,76
32,27
37,49
38,98
42,50
44,27
51,55
58,82
60,52
67,07
70,95
74,53
77,60
CUJRB206
CUJRA002
CUJRD458a
WEST1172
JRZMZ46
JRHR230736a
JRHR226941
JRHR228966a
CUJRA450
CUJRA212
JRHR222705
WEST1528
JMP41
96,20
99,15
103,91
109,08
110,88
113,88
118,11
128,57
130,65
JRHR211298
JRHR220903
JRHR218863
JRCV195258
JRZMZ7
WGA225
JRHR230901
BFUJR10
JRHR227254
JRHR225189
JRHR225644
JRHR223134
JRHR231304
JRHR231764
JRHR222064
JRHR223323
CUJRA420
CUJRB124
JRHR223658
JRHR228555
CUJRB409
JRHR215721
JRHR214460
JRHR218769
JRHR217314
CUJRA318
JRHR224330
JRHR209936
84,15
86,99
92,48
97,85
99,27
CUJRB012
JRHR209732
WEST795
BFUJR41
JRHR225564
Chandler-11
0,00
3,32
4,39
7,37
8,00
8,88
11,12
14,34
17,24
20,72
JRHR218910
JRZMZ27
CUJRB304
WJR281
JRHR231426
JRHR223620
JRHR213218
JRHR229196a
JRHR215944
JRHR231669
41,46
CUJRC307
48,76
CUJRA448
Chandler-15
0,00
4,16
4,21
4,37
4,60
5,74
9,24
14,62
16,57
18,53
46,17
55,33
57,58
61,82
63,19
64,40
65,05
68,63
75,56
80,55
82,09
83,84
87,40
89,98
91,50
97,06
98,06
101,87
JRHR209249
JRHR229430b
JRHR229430a
JRHR228663
WJR061
CUJRB305
WJR142
JRHR226814
JRHR225264
JRHR215854
CUJRA108
JRHR227273
JRHR216158
JRHR218204
JRHR220931
JRHR217811
JRHR222528
JRHR204187
JRHR210714
CUJRB301
JRHR229439
JRHR225377
JRHR229425
JRHR212831
JRHR212973
JRHR221425
BFUJR154
JRHR230167
0,00
23,05
27,84
29,29
31,57
35,66
40,90
47,96
52,27
54,20
55,11
59,61
63,17
72,18
79,82
84,46
90,41
Kaplan86-11
LG-11
WEST1656
JRHR222084
JRHR218910
JRZMZ27
CUJRB304
JRHR213218
JRHR231426
WJR281
JRHR223620
JRHR229196a
JRHR215944
JRHR231669
JRHR225870
JRHR222595
CUJRB114
JRHR229070
JRHR229220
CUJRC307
CUJRB412
CUJRA448
JRHR226534
JMP32
0,00
10,11
17,42
20,78
21,94
24,36
25,82
26,77
28,26
31,84
34,42
37,70
41,21
45,67
51,30
53,72
56,87
58,87
65,16
66,15
70,01
77,64
LG-15
JRHR212264
JRHR225189
JRHR225644
JRHR223134
JRHR231304
JRHR231764
JRHR222064
JRHR223323
CUJRA420
CUJRB124
JRHR223658
JRHR228555
CUJRD322
CUJRB409
JRHR215721
JRHR214460
JRHR218769
JRHR217314
CUJRA318
JRHR229793
JRHR224330
JRHR209936
CUJRC207
CUJRB210
CUJRA461
JRHR216284
JRHR221458
CUJRB012
JRHR209732
WEST795
JRHR225564
BFUJR41
JRHR227079a
0,00
12,35
18,20
28,80
30,01
33,60
35,97
38,95
39,82
42,28
43,57
45,79
46,83
47,85
49,96
54,47
57,29
62,74
68,28
68,29
75,56
88,19
96,75
98,49
100,30
101,96
104,43
109,27
112,58
118,69
120,18
126,67
LG-8
Chandler-8
Kaplan86-7
JRHR209249
JRHR229430b
JRHR229430a
JRHR228663
WJR061
CUJRB305
WJR142
JRHR226814
JRHR225264
JRHR216872
JRHR215854
JRHR225235
JRHR229295
CUJRA108
JRHR227273
JRHR216158
JRHR218204
JRHR220931
JRHR217811
JRHR222528
JRHR204187
JRHR210714
JRHR223692b
CUJRB301
JRHR229439
JRHR225377
WGA118
JRHR229425
JRHR219542
JRHR212831
JRHR212973
JRHR221425
BFUJR154
JRHR230167
0,00
Kaplan86-4
LG-4
Chandler-4
5,83
16,41
17,63
21,31
23,65
26,54
27,56
29,87
31,34
34,37
35,27
37,47
41,89
44,73
50,23
55,07
62,81
0,00
0,00
LG-7
0,00
4,10
4,17
4,33
4,53
5,64
8,99
14,21
16,16
16,41
17,92
19,97
22,88
36,83
45,30
47,43
51,62
53,00
54,17
54,80
58,33
65,13
69,70
70,30
71,61
73,21
75,84
77,16
77,86
80,09
81,44
86,82
87,84
91,57
JRHR209249
JRHR229430b
JRHR229430a
JRHR228663
WJR061
CUJRB305
WJR142
JRHR226814
JRHR225264
JRHR216872
JRHR215854
JRHR225235
JRHR229295
CUJRA108
JRHR227273
JRHR216158
JRHR218204
JRHR220931
JRHR217811
JRHR222528
JRHR204187
JRHR210714
JRHR223692b
JRHR229439
JRHR225377
WGA118
JRHR219542
JRHR212831
JRHR212973
JRHR221425
JRHR230167
Kaplan86-10
JRHR227769
JRHR229053b
JRHR209244
JRHR220176
JRHR215620
JRHR206788a
JRHR224700
JRHR218066
CUJRD437
JRHR211468
CUJRC006
JRHR224349
CUJRB309
JRHR227905
CUJRB220
WEST1000
JMP16
JRHR206716x
JRHR206716y
CUJRA405
JRHR226237
CUJRB483
JRHR214370
Kaplan86-3
LG-3
0,00
Chandler-7
0,00
8,75
12,83
17,95
19,65
25,72
29,38
31,02
33,47
34,62
42,95
49,55
LG-10
0,00
4,62
8,45
9,46
16,89
18,31
19,60
21,41
28,44
31,89
34,97
40,30
43,67
46,43
49,48
56,67
66,04
69,28
71,67
76,29
80,50
83,45
96,01
115,65
118,21
122,12
Chandler-13
13,06
16,55
Kaplan86-6
0,00
7,50
9,01
12,83
16,36
18,08
19,72
23,65
26,06
29,58
30,99
33,37
34,53
43,00
45,27
49,08
49,65
53,81
61,18
72,59
77,99
Chandler-10
JRHR206750
57,61
LG-6
JRHR225191
JRHR216529
CUJRB317
JRHR214591
JMP27
CUJRD439
JRHR215253
CUJRB453
CUJRA004
JRCV195023
BFUJR19
JRHR230142
CUJRA482
CUJRB490
JRCV195682
BFUJR228
JRHR218062
JRHR213682
0,00
WEST1656
10,12
JRHR222084
21,55
24,46
27,02
28,52
34,56
38,32
41,45
45,92
51,54
53,97
57,12
65,41
70,26
77,89
CUJRB304
JRHR213218
WJR281
JRHR223620
JRHR215944
JRHR231669
JRHR225870
JRHR222595
CUJRB114
JRHR229070
JRHR229220
CUJRB412
JRHR226534
JMP32
JRHR228739
JRHR219141
CUJRB223
WEST1464
CUJRA124
JRHR213725
WEST5
JRHR226285
CUJRA206
JRHR223389b
JRHR223389a
CUJRD495
CUJRB467
JRHR214359
JRCV198129
JRHR230765
BFUJR247
0,00
12,33
22,72
27,87
29,32
31,74
33,24
35,70
38,00
41,02
45,15
48,14
52,45
54,66
55,55
59,77
63,26
71,49
79,66
81,70
84,28
85,44
90,24
JRHR220384
JRHR218355
JRHR225388
JRHR217036
JRHR211717
JRHR223592
WGA321
JCINB159
BFUJR276
JRCV197566
JRHR213012
JRHR223947
WGA111
WGA167
JRHR226683
WEST156
Kaplan86-15
Chandler-16
0,00
0,00
CUJRD410
9,76
JRHR204109
20,18
JRHR223464
0,00
JRHR231614
0,00
JRHR231614
16,65
JRHR218727
16,51
JRHR218727
36,01
JRHR226648
34,66
38,32
38,59
40,29
WGA139
CUJRD104
JRHR226648
JRHR226860
34,21
37,95
39,91
WGA139
CUJRD104
JRHR226860
52,09
JRHR227267
56,65
62,31
63,84
68,30
JRHR227267
JMP6
CUJRD487
JRHR227832
56,75
62,28
63,81
68,28
JRHR227267
JMP6
CUJRD487
JRHR227832
JRHR218727
JRHR212264
JRHR225644
JRHR225189
36,23
38,62
43,54
47,47
49,88
52,14
59,47
62,56
69,22
74,73
82,03
JRHR222064
JRHR223323
CUJRB124
JRHR228555
CUJRD322
JRHR215721
JRHR218769
JRHR217314
CUJRA318
JRHR229793
JRHR209936
100,81
CUJRC207
109,81
111,89
113,56
114,90
122,59
128,00
133,83
141,07
JRHR221458
JRHR216284
CUJRB210
CUJRA461
JRHR209732
WEST795
JRHR225564
JRHR227079a
Kaplan86-8
JRHR228739
CUJRB417
JRHR219141
CUJRB223
WEST1464
CUJRA124
CUJRC105
JRHR213725
JRHR226241
WEST5
CUJRB441
JRHR226285
CUJRA206
JRHR223389b
JRHR223389a
CUJRD495
CUJRB467
JRHR214359
JRCV198129
CUJRB004
JRHR230765
JRHR210057
BFUJR247
Chandler-12
0,00
5,60
10,02
14,88
25,30
32,70
37,60
42,86
47,89
52,58
59,67
60,66
63,40
68,29
69,63
70,44
0,00
12,32
LD-2017
0,00
JRHR206750
JRHR223038a
JRHR226096
JRHR207652
JRHR207191
JRHR231559b
CUJRD005a
JRCV195815
JRHR228611
CUJRA202
BFUJR68
CUJRB450
JRHR223519a
JRHR209503
JMP23
WGA5
JRHR214968
CUJRB401
JCINB110
CUJRA218
JRHR228874
JRHR231751
JRCV197782
JRHR217463
JRHR219728
JRHR226652
JRHR213185
LD-2015
CUJRB103a
JRHR229981
JRHR222846
JRHR212067
JRHR220864b
JRHR223139
CUJRA481
JRHR226204
JRHR230927
JRHR226414b
CUJRA211
JRHR213115
60,68
63,77
65,25
69,18
47,89
7,11
11,10
17,00
22,25
0,00
13,06
16,55
22,34
29,45
33,44
39,35
44,20
44,60
53,53
57,61
58,90
64,91
65,66
70,42
72,54
85,62
88,30
89,28
92,36
94,30
95,59
95,97
100,67
104,81
108,30
110,68
LD-2016
42,63
48,38
53,75
54,99
56,15
58,61
60,41
62,82
72,03
73,90
76,12
84,29
JRHR225892
JRHR215811
JRHR219358y
CUJRD462
WGA79
JRHR227283
JRHR215674
CUJRD008
JRHR225292
JRHR220524
JRHR217037
JRHR227701
JRHR211565
JRHR213554
Chandler-3
Kaplan86-2
LG-2
JRHR207652
LD-2015
JRHR211118
JRHR231297
JRHR212022
BFUJR46
JRHR229542
CUJRB321
JRHR227122
JRHR230046
JRHR223570a
JRCV194742
JRHR223867
WGA202
JRHR207496
JRHR221447
JRHR215859
CUJRB468
JRHR226085
JRHR230226
JRHR224834
JRHR231293
JRHR212270
CUJRD481
JRHR223559b
JRHR227143
JRHR224661
JRHR221565a
JRHR226041
JRHR223248
JRHR228709b
JRHR223824
JRHR227033
BFUJR184
JRHR219017
JRHR227391
Chandler-9
0,00
5,50
7,06
18,66
21,26
31,86
40,70
44,21
Kaplan86-5
LG-5
0,00
6,84
11,10
14,59
17,41
22,47
25,72
27,14
28,33
33,80
36,33
41,08
45,12
50,46
52,38
54,11
56,88
58,94
68,75
70,83
74,13
75,28
76,30
83,81
85,77
86,17
87,15
89,47
90,29
91,03
92,20
94,86
99,96
116,24
Chandler-2
0,00
JRHR214458
JRHR213696
JRHR217215
LD-2017
JRHR211118
JRHR231297
JRHR212022
BFUJR46
JRHR229542
CUJRB321
JRHR227122
JRHR230046
JRCV194742
JRHR223867
WGA202
JRHR207496
JRHR221447
JRHR215859
CUJRB468
JRHR226085
JRHR224834
JRHR231293
JRHR212270
JRHR223559b
JRHR227143
JRHR224661
JRHR221565a
JRHR226041
JRHR223248
JRHR228709b
JRHR223824
JRHR227033
BFUJR184
JRHR219017
Kaplan86-1
0,00
4,60
9,07
LD-2016
56,72
59,63
61,13
64,68
71,81
73,07
73,85
82,33
84,49
87,44
91,13
92,72
Chandler-5
0,00
6,78
11,13
14,53
17,48
22,45
25,57
26,99
33,54
35,97
40,74
44,71
50,10
51,90
53,83
56,31
67,70
69,82
73,17
75,25
82,51
84,49
84,89
85,95
88,27
89,09
89,77
91,00
93,62
98,77
JRHR214458
JRHR213696
JRHR217215
CUJRD462
WGA79
JRHR229003
JMP14
JRHR227283
JRHR215674
CUJRD008
JRHR220524
JRHR225292
JRHR217037
JRHR227701
JRHR211565
JRHR213554
JRHR227686b
BFUJR307
JRHR227554
JRHR214565
JRHR224068
CUJRD465
CUJRA436
BFUJR301
LD-2015
17,30
22,53
25,43
35,81
0,00
4,41
8,56
17,44
19,70
23,32
26,36
29,01
37,26
40,57
LD-2017
JRHR214458
JRHR213696
JRHR217215
LD-2016
0,00
4,44
8,65
13
Page 5 of 12
0,00
9,88
20,03
21,64
24,27
26,43
36,51
43,82
45,03
CUJRB417
JRHR219141
CUJRA124
CUJRC105
JRHR213725
JRHR226241
CUJRB441
JRHR223389b
JRHR223389a
63,79
JRHR214359
72,57
76,27
CUJRB004
JRHR210057
LG-12
0,00
5,58
9,84
14,67
18,29
25,46
32,90
37,65
43,28
43,94
48,33
53,34
60,34
61,20
63,93
68,83
70,17
71,02
79,82
Kaplan86-12
JRHR220384
JRHR218355
JRHR225388
JRHR217036
JRHR225846
JRHR211717
JRHR223592
WGA321
JCINB159
JRHR228256
BFUJR276
JRCV197566
JRHR213012
JRHR223947
WGA111
WGA167
JRHR226683
WEST156
WGA256
LG-16
0,00
10,49
17,90
19,99
20,42
20,44
31,32
34,56
37,68
41,45
53,43
57,85
61,22
65,15
67,50
73,46
75,25
CUJRD410
JRHR204109
JRHR223464
JRHR229005
JRCV195263
JRZMZ37
CUJRC001
JRHR228864
JRHR228869a
CUJRB102
WGA104
CUJRB113
JRHR223601
BFUJR49
JRZMZ31
CUJRC002
CUJRC319
0,00
5,72
9,95
14,87
18,67
JRHR220384
JRHR218355
JRHR225388
JRHR217036
JRHR225846
26,30
JRHR211717
38,85
WGA321
45,52
JRHR228256
56,34
JRCV197566
64,45
JRHR213012
88,91
WEST156
97,40
WGA256
Kaplan86-16
0,00
7,34
9,46
9,89
9,91
20,79
24,03
27,15
30,92
42,90
47,32
50,69
54,62
56,97
62,93
64,72
JRHR204109
JRHR223464
JRHR229005
JRCV195263
JRZMZ37
CUJRC001
JRHR228864
JRHR228869a
CUJRB102
WGA104
CUJRB113
JRHR223601
BFUJR49
JRZMZ31
CUJRC002
CUJRC319
Fig. 1 The maternal (left), paternal (right), and consensus (middle) simple sequence repeat (SSR)-based genetic linkage map of walnut using
‘Chandler’ × ‘Kaplan-86’ F1 population. On the left is map distance in
centimorgans (cM), and on the right are marker names. The markers in
bolds are common markers. Gaps larger than ≥20 cM were shown as
black bars on LGs. A LOD threshold of 6 was chosen to construct the
linkage groups. QTLs for leafing time in 2015, 2016, and in 2017 are
shown as bars
LG8, LG10, LG11, LG13, and in LG15 at different positions.
Some SSRs were matched with the same scaffold which were
in different LGs. The scaffold jcf7180001222243 matched to
both LG1 and LG7, the scaffold jcf7180001222243 matched
to both LG1 and LG12, and the scaffold jcf7180001222257b
matched to both LG4 and LG11.
linkage maps contained 52 out of 279 (18.6%) and 35 out of
273 (12.4%) skewed markers. Among the 61 distorted
markers in the consensus map, 27 were common markers
and 34 were parental markers. Among the parental markers,
25 were on the female map, whereas nine were on the male
map (Table 2). The skewed markers in the parental and consensus maps were randomly distributed to 11 LGs. The LG1,
LG9, LG13, LG15, and LG16 had perfect marker distribution
without distorted markers.
In the consensus map, LG4 (42.4%), LG2 (32.1%), LG8
(30.4%), and LG6 (27.3%) had the highest percent of skewed
Distribution of distorted markers
In the consensus map, 61 out of 386 (15.8%) SSR markers
were distorted, whereas the ‘Chandler’ and ‘Kaplan-86’
15
52.1
16
20.2
Total
1285.8
Average 80.4
90.3
89.8
136
99.3
98.8
78.1
91.8
90.4
84.3
95.6
48.8
70.4
67.1
72.8
1
2
3
4
5
6
7
8
9
10
11
12
13
14
4
3
279
17.4
0.08
0.15
0.21
5.3
0.22
0.18
0.21
0.24
0.30
0.23
0.34
0.19
0.18
0.19
0.25
0.23
0.24
0.14
Marker
density
13.0
6.7
4.5
5.6
4.7
4.1
3.3
4.3
3.0
5.3
5.6
5.3
4.1
4.4
4.2
7.3
Marker
distance (cM)
18.6
0.0
0.0
0.0
50.0
6.9
58.3
13.3
27.8
22.6
29.4
0.0
22.2
8.3
6.3
0.0
10.0
68.3
75.2
1568.2
98.0
92.7
134
135.5
126.7
116.2
94.5
90.6
90.2
90.4
122.1
77.6
79.8
68.9
105.5
10
17
386
24.1
24
28
35
33
34
22
34
23
21
26
22
19
21
17
%
LG length No. of
Distortion (cM)
markers
Consensus
4.3
6.8
4.4
3.9
4.8
3.9
3.8
3.4
4.3
2.7
3.9
4.3
4.7
3.5
4.2
3.3
6.2
Marker
distance (cM)
0.24
0.14
0.22
0.25
0.20
0.25
0.26
0.29
0.23
0.37
0.25
0.23
0.21
0.28
0.23
0.30
0.16
Marker
density
15.8
0.0
0.0
0.0
32.1
5.7
42.4
14.7
27.3
20.6
30.4
0.0
19.2
13.6
5.3
0.0
11.8
68.3
64.7
1574.4
98,4
92.9
133.9
130.6
141.1
128.3
92.7
101.9
76.3
96.6
118.4
77.9
97.4
47.9
105.5
8
16
273
17.1
17
16
24
23
21
14
28
12
15
24
16
12
11
16
%
LG length No. of
Distortion (cM)
markers
Kaplan-86
6.0
8.5
4.0
5.5
8.4
5.4
6.1
6.1
6.6
3.6
6.4
6.4
4.9
4.9
8.1
4.4
6.6
Marker
distance (cM)
0.18
0.12
0.25
0.18
0.12
0.18
0.16
0.16
0.15
0.27
0.16
0.16
0.20
0.21
0.12
0.23
0.15
Marker
density
12.4
0.0
0.0
0.0
12.5
0.0
26.1
9.5
42.9
14.3
33.3
0.0
20.8
18.8
8.3
0.0
12.5
%
Distortion
Page 6 of 12
20
16
29
24
30
18
31
17
15
18
12
16
16
10
LG length No. of
(cM)
markers
LGs
Chandler
Table 2 Lengths of linkage groups, number of markers, average distance between markers and number of markers per cM (marker density), and percentage of distorted markers in the parental and
consensus maps of Chandler × Kaplan-86 F1 population
13
Tree Genetics & Genomes
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Tree Genetics & Genomes
(2019) 15:13
Page 7 of 12
markers, whereas LG12 (5.3%) and LG3 (5.7%) had a low
percent of SD. The highest SD in the female map was observed in LG4 (58.3%) and LG2 (50.0%), followed by LG8
(29.4%), LG6 (27.8%), LG7 (22.6%), and LG10 (22.2%).
The LG12 (6.3%), LG3 (6.9%), and LG11 (8.3%) had a low
percent of SD markers in the female map. The highest percent
of SD in the ‘Kaplan-86’ linkage map was determined in LG6
(42.9%) and LG8 (33.3%), followed by LG4 (26.1%) and
LG10 (20.8%) (Table 2).
To reveal the possible causes of SD, we subjected the
distorted markers to the allelic and zygotic SD tests. Of the
61 skewed markers, none of the markers showed a significant
zygotic SD, and all of them were gametic. A total of 44 SD
markers exhibited maternal allelic SD, whereas 20 showed
paternal allelic SD (Online Resource 2).
QTL analysis for leafing time
The phenotypic data and statistical values for leafing time are
summarized in Table 3. There were phenotypic differences
between Chandler and Kaplan-86 for leafing time, and
Kaplan-86 had an earlier leafing time than Chandler. The
leafing time differed 11, 8, and 9 days between the parents
in 2015, 2016, and 2017 growing seasons, respectively. The
F1 progeny phenotypes were normally distributed (Fig. 2),
highlighting the polygenic control of the trait. QTL analysis
was performed in each season using both parental and consensus maps. One major QTL for leafing time was identified in
LG4 for all growing seasons in all genetic linkage maps (Fig.
1 and Table 4). In the Chandler map, the QTL explained from
52.0 to 68.8% of phenotypic variation (PV) with an associated
LOD score that ranged from 23.5 (in 2015) to 39.1 (in 2017).
The closest marker was CUJRB012 in all growing seasons,
while JRHR209732 was the second closest marker in 2015. In
the Kaplan-86 map, the QTL explained from 58.6 to 68.4% of
PV with an associated LOD score that ranged from 24.1 (in
2015) to 33.4 (in 2017). The JRHR209732 was the closest
marker in all growing seasons. In the consensus map, the
QTL explained from 53.6 to 68.8% of PV with an associated
LOD score that ranged from 23.7 (in 2015) to 39.1 (in 2017).
Table 3 Mean, maximum, and minimum values, standard deviations of
leafing time evaluated in 2015, 2016, and 2017 in ‘Chandler’ × ‘Kaplan86’ F1 population in walnut
Year
2015
2016
2017
a
F1 progenies
Parents
Mean
Max
Min
SDa
Chandler
Kaplan-86
114
99
104
123
109
117
101
87
94
4.47
3.58
5.47
117
104
107
106
96
98
Standard deviation
13
JRHR209732 was the closest marker in 2015, while the
CUJRB012 was the closest marker in 2016 and in 2017, respectively. The Kruskal–Wallis test was highly significant
showing a high association between markers (JRHR209732
and CUJRB012) and leafing time (Table 4).
Discussion
SSR markers
The SSR primer pairs published previously were used to construct the first SSR-based map of walnut in the present study.
Ninety-eight genomic SSRs that were extracted from J. nigra
(Woeste et al. 2002; Dangl et al. 2005; Foroni et al. 2005;
Victory et al. 2006; Robichaud et al. 2006; Ross-Davis et al.
2008; Topçu 2012; Topçu et al. 2015a) were evaluated, and
16.2% were mapped. Three of the 13 SSR markers (23.1%)
generated from J. cinerae by Hoban et al. (2008) were mapped.
Chen et al. (2013) generated five SSR loci from J. mandshurica,
and 10 (20.0%) were mapped in the present study. Qi et al.
(2009, 2011) generated 61 SSRs from EST sequences, and seven (11.5%) were mapped in this study. Zhang et al. (2010, 2013)
and Yi et al. (2011) also used EST sequences and developed 117
and 30 EST-SSRs, and 28 (23.9%) and 9 (30.0%) were mapped
in the present study, respectively. Najafi et al. (2014) used the
fast isolation by AFLP of sequences containing repeats method
to develop genomic SSRs; however, the 14 markers were neither polymorphic nor mapped in the present study. Topçu et al.
(2015b) used the enrichment method to develop genomic SSRs
from J. regia, and 73 (39.5%) out of 185 SSR markers were
mapped. Chen et al. (2014), Ikhsan et al. (2016), and Eser et al.
(2018) generated the SSRs from the BAC-end sequences of
walnut deposited in GenBank by Wu et al. (2012). They developed 12, 307, and 551 SSRs, and we mapped 5 (41.7%), 123
(40.1%), and 112 (20.3%) markers, respectively, in the
‘Chandler’ × ‘Kaplan-86’ F1 population. We used only 150
segregated SSRs from the study by Eser et al. (2018) by choosing mostly common markers for use in both the parental maps.
The remaining 255 segregated ones by Eser et al. (2018) were
parental markers, which need to be genotyped across the whole
F1 population for finding their positions in a further study.
A total of 1276 SSR markers from J. regia were analyzed
in the present study, and 357 (28.0%) were mapped. The remaining 161 SSRs were from other Juglans species, and 29
(18.0%) of them were mapped. Therefore, the percent of
mapped markers in the SSRs from J. regia was higher than
that from other Juglans species; this was expected. A total of
1068 genomic SSRs and 208 EST-SSRs from J. regia were
tested, and the rate of mapped markers was higher in the SSRs
from genomic sequences (32.0%) than that from the EST
(21.2%) due to higher polymorphism of the genomic SSRs
than the EST. This finding is consistent with previous results,
13
Page 8 of 12
Tree Genetics & Genomes
(2019) 15:13
Fig. 2 Frequency distribution of leafing time in ‘Chandler’ × ‘Kaplan-86’ F1 population in 2015, 2016, and 2017. The values of two parents are
indicated by arrows. C, Chandler; K, Kaplan-86
and the lower level of polymorphism of EST-SSRs might be
due to the selection against variation in the conserved regions
of the EST-SSRs (Scott et al. 2000; Chabane et al. 2005;
Zhang et al. 2014).
About 75% of the common SSR markers were obtained
from three SSR development studies. The BAC-end sequences generated by Wu et al. (2012) were used by Eser
et al. (2018) and Ikhsan et al. (2016) to develop numerous
polymorphic SSR markers in walnut. Eser et al. (2018) generated about half (50.6%) of the common markers used in the
present study, followed by Ikhsan et al. (2016) with 24.1%
common markers, and Topçu et al. (2015b) generating 21
mapped common SSR markers.
Genetic linkage maps and comparison with walnut
genome
A two-way pseudo-testcross strategy is mainly used to construct an individual genetic linkage map in out-crossing species using the F1 mapping populations due to their highly
Table 4
Identified QTLs for leafing time in the parental and consensus maps using ‘Chandler’ × ‘Kaplan-86’ F1 population in walnuta
Year
Chandler
2015
2016
2017
Kaplan-86
2015
2016
2017
Consensus
2015
2016
2017
heterozygous genomes (Grattapaglia and Sederoff 1994).
The first genetic linkage map was reported by Fjellstrom and
Parfitt (1994) using RFLP markers and an interspecific backcross of (J. hindsii × J. regia) × J. regia with 63 progenies.
They constructed 12 LGs of walnut with 42 RFLP loci.
Further, Woeste et al. (1996) used the same population to
expand the genetic map by RAPD markers. They increased
the number of segregated markers to 107, which were
assigned to 15 LGs. Malvolti et al. (2001) used the ‘Lara
480’ × ‘Chandler 1036’ F1 population with 82 progenies to
construct a genetic map based on isozyme and RAPD
markers. A total of 47 markers were mapped to 11 LGs of
the female map, whereas 29 markers were mapped to 10 LGs
of the male parent map. These studies were the preliminary
attempts to construct LGs of walnut with inadequate number
of markers whose sequence information was not available.
Recently, Zhu et al. (2015) constructed a genetic linkage
map of walnut using the ‘Yuan Lin’ × ‘Qing Lin’ F1 population with 84 progenies using the SNP and InDel markers. The
female map included 2395 markers, whereas the male map
LG
GW
LG4
LG4
LG4
4.0
2.9
3.1
LG4
LG4
LG4
LG4
LG4
LG4
cM
LOD
PV%
Closest markers
Position cM
Kruskal–Walls
test significancec
82.8–90.0
70.0–92.0
75.8–86.2
23.5b
33.9b
39.1b
52.0
64.9
68.8
CUJRB012 and JRHR209732
CUJRB012
CUJRB012
84.2 and 87.0
84.2
84.2
*******
*******
*******
3.1
3.0
3.0
114.9–127.6
105.8–133.0
114.9–129.0
24.1b
27.6b
33.4b
63.5
58.6
68.4
JRHR209732
JRHR209732
JRHR209732
122.6
122.6
122.6
*******
*******
*******
3.1
3.1
3.2
104.4–112.3
95.2–108.4
102.0–108.4
23.7b
34.3b
39.1b
53.6
63.0
68.8
JRHR209732
CUJRB012
CUJRB012
109.3
104.4
104.4
*******
*******
*******
a
The table indicates genome-wide (GW) LOD thresholds, QTL region in the LG (cM), the closest linked markers, their map positions in cM, the
estimated LODs, and the percentages (%) of the total phenotypic variance (PV) explained at the QTL peak
b
Genome-wide significant level was at p < 0.00005
c
Significant level at p < 0.00005
Tree Genetics & Genomes
(2019) 15:13
contained 448 markers. They also constructed consensus map
with 2577 markers along 16 LGs. There were several LGs
with inadequate number of markers in the parental maps,
such as LG11 in female map and LG3 and LG13 in male
map, which included only one marker. SNP markers were
also used by Luo et al. (2015) to build SNP-based linkage
map of walnut using ‘Chandler’ × ‘Idaho’ F1 population.
The authors constructed only consensus map with 1525 SNP
markers whose sequence information was not published. The
number of mapped markers changed between 139 (LG3) and
13 (LG15). Herein, we report the first SSR-based genetic linkage map of walnut using the ‘Chandler’ × ‘Kaplan-86’ F1
population with 175 progenies. The consensus map is a moderately dense map with 387 markers covering all the 16 LGs
of walnut. A SSR-based genetic linkage maps developed in
the present study can be a valuable tool for future genetic and
molecular breeding studies in walnut. The map also can help
compare and integrate linkage maps from different populations of walnut as well as defining chromosomal location of
markers in genome-wide association studies (GWAS).
Comparisons of the consensus map developed in this study
with the walnut genome sequence (Martínez-García et al.
2016) supported the genetic position and marker order of most
of the markers mapped. In addition, some inconsistencies between linkage maps and scaffold positions could be due to
minor misassembles in the walnut genome or the possibility
that our use of the best matching marker position on the walnut scaffolds for each SSR did not provide an accurate comparison. Comparison between walnut linkage maps and the
walnut genome revealed extensive collinearity, but some
markers mapped in different orders. Those kinds of inverse
orders were also observed in different tree species (Klagges
et al. 2013; Guajardo et al. 2015; Calle et al. 2018), when the
authors compared genetic maps with genome sequence of a
plant species. The presence of a well-matched scaffold in different LGs of walnut genetic map may indicate regions that
are duplicated. Chromosomal level assembly of walnut genome may help to answer inconsistencies between linkage
maps and scaffold positions in this study, which is underway
in our lab.
Distorted markers
SD is a prevalent genetic phenomenon in the genetic linkage
analysis of plants. It is explained by the deviation of genotypic
frequencies of a locus from the expected Mendelian ratio
(Lyttle 1991; Ma et al. 2014). Van Ooijen (2011b) suggests
retaining the skewed markers in the analysis in order to understand the reason for SD better. Several studies have also demonstrated that the exclusion of SD markers in the linkage analysis leads to the failure of analysis in significant parts of a LG
(Liebhard et al. 2003; Beltramo et al. 2016; Khodaeiaminjan
et al. 2018). We also used SD markers in the present study with
Page 9 of 12
13
the exception of those significantly affecting marker order or
distance.
SD in walnut was first reported by Zhu et al. (2015) who
constructed a genetic linkage map of walnut using the SNP
and InDel markers. The integrated map included 2577
markers, and 761 (29.5%) were skewed markers. The integrated SSR linkage map in the present study consisted lower rate
(15.8%) of SD markers. The main reason of higher SD reported by Zhu et al. (2015) can be due to lower number of progenies in their analysis. In the present study, 175 progenies
were used, whereas Zhu et al. (2015) performed their analysis
using 84 progenies. The genetic distance between parents can
be another reason for high SD, because Kianian and Quiros
(1992) suggested that wider genetic relationship between parents might lead to increased SD. The results of the allelic and
zygotic SD tests in this study suggested that all the skewed
markers might have resulted from gametic selections, because
none of markers showed a significant zygotic SD. Ma et al.
(2014) and Tan et al. (2016) also reported similar results in the
F1 populations when they applied the allelic and zygotic tests.
A major QTL for leafing time in walnut
Walnut breeding is difficult and time-consuming due to long
juvenile period and high heterozygosity. MAS could identify
young seedlings with desirable traits in a breeding program.
Leafing time is one of the most important traits in walnut that
can limit the expansion of the cultivation. Bud burst together
with leafing time is under strong genetic control; however, this
trait is also environmentally driven by photoperiod and temperature (Charrier et al. 2011; Cooke et al. 2012). The leafing
time in our progeny showed a broadly normal distribution
during three consecutive years. Transgressive segregation
was observed, with a few individuals showing phenotypes
more extreme than the parents (Fig. 2). Cultivars with late
leafing time are likely less susceptible to the spring frost and
could be more suitable for cold environmental conditions. On
the other hand, genotypes with early leafing time could be
more suitable to warm environmental conditions, where they
can have early nut maturity, a very desirable trait for producers
and for walnut industry. QTL mapping is a powerful method
to identify genomic regions controlling this trait.
The first SSR genetic linkage map in walnut identified a
major QTL for leafing time in this study. Collard et al.
(2005) suggested that a QTL can be classified as ‘major’ if it
can account for > 10% of the PV. A more enhanced definition
of ‘major’ requires that the QTL be stable across multiple
seasons or locations (Marinoni et al. 2018). Based on these
criteria, we found a QTL region in LG4 detected in three consecutive years in all genetic maps explaining up to 68.8% of
PV. The JRHR209732 and CUJRB012 markers had high associations with leafing time, and they can be considered potential markers for MAS in walnut breeding programs after
13
Page 10 of 12
validating them in a germplasm collection and in different
segregating populations. Improving the current linkage maps
to high-density saturated linkage maps in the future may help
to identify genes controlling leafing time in walnut.
Conclusions
To the best of our knowledge, this is the first study to reveal
moderately dense SSR-based linkage map of walnut using the
‘Chandler’ × ‘Kaplan-86’ F1 population. The SSRs are still
the marker of choice in the genetic linkage mapping studies
due to their co-dominant nature that enables map comparison
and integration of different populations. We generated a complete genetic map of 16 LGs in walnut, which can be considered as a framework genetic linkage map for further studies on
comparative and integrative linkage maps, QTL mapping,
marker-assisted selection (MAS), and genome-wide association studies (GWAS) in walnut. This is also the first report to
discover a major QTL region on LG4 for leafing time in walnut representing a promising tool in walnut breeding. Future
plan will be to get high-density genetic linkage maps to develop markers closely linked to leafing time for MAS and to
identify genes controlling this trait in walnut.
Acknowledgments This work was supported by the Scientific and
Technological Research Council of Turkey (Project No. TUBİTAKTOVAG 214O140) and the Çukurova University Scientific Research
Projects Unit (Project Nos: ZF2013YL43, ZF2013YL48, FDK20154851).
Data archiving statement The details of mapped SSR markers were
given in Online Resource file 1 and in Fig. 1.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
interest.
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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