1
Mapping and QTL analysis of Verticillium wilt resistance genes in
cotton
Hong-Mei Wang1, 2, Zhong-Xu Lin1, Xian-Long Zhang1, Wei Chen2, Xiao-Ping Guo1, *,
Yi-Chun Nie1 & Yun-Hai Li 2
(1State Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
2Key
Laboratory of Cotton Genetic Improvement of the Ministry of Agriculture, Cotton Research Institute,
Chinese Academy of Agricultural Sciences, Anyang 455112, China)
Abstract
Verticillium wilt is one of the most serious constraints to cotton production in almost all
the cotton-growing countries. In this study, ‘XinLuZao1’ (XLZ1), a susceptible cultivar
Gossypium hirsutum L. and ‘Hai7124’ (H7124), a resistant line Gossypium barbadense L.,
and their F2:3 families were used to map and study the disease index induced by
verticillium wilt. A total of 430 SSR loci were mapped into 41 linkage groups; the map
spanned 3745.9 cM and the average distance between adjacent loci was 8.71 cM. Four
and five QTLs were detected based on the disease index investigated on July 22 and
August 24 in 2004 respectively. These nine QTLs explained 10.63～28.83% of the
phenotypic variance, six of them located on D sub-genome. Two QTLs located in the
same marker intervals maybe partly explain the significant correlation of the two traits.
QTLs explaining large phenotypic variation were identified in this study, which may be
quite useful in cotton anti-disease breeding.
Keywords: cotton, molecular marker, QTL, Verticillium wilt
Cotton is one of the most important fiber crops. There are about 20 million cotton farmers,
growing about 33.5 million hectares of cotton in 70 countries around the world (James 2002).
Among the four cultivated species, two allotetraploid species, G. hirsutum (Upland cotton) and G.
barbadense (Sea-island cotton) are widely cultivated in the world, and each contributes to about
90% and 5% of the total cotton yield worldwide, respectively. Cultivated forms of G. hirsutum and
Received 9 Jul. 2006
Accepted 13 Feb. 2007
Supported by the Hi-Tech Research and Developmet (863) Program of China (2001AA241083 and 2004AA211171) and the National 863
Transgenic Projects (JY03-B-01 and JY-03-B-03).
*Authors
for correspondence. Tel: +86 (0)27 8728 3955; E-mail: <xpguo@mail.hzau.edu.cn>.
2
G. barbadense show very different traits including yield, fiber quality, disease resistance,
environmental adaptation, et al (Bolek et al. 2005). The breeding of G. hirsutum has focused on
maximum yield and broad adaptation, while breeding of G. barbadense has emphasized fiber
quality and disease resistance.
Most commercial cultivars of upland cotton are susceptible or little resistant to cotton wilt
disease caused by a soil-borne fungal pathogen called Verticillium dahliae. Verticillium wilt is
considered as a major disease in cotton production in the majority of cotton growing countries,
including China (Jian et al. 2003), U.S. (Bowman 1999) and the Mediterranean regions (Mert et al.
2005). The disease causes plant defoliation, which reduces yield and fruit quality, and contributes
to significant crop loss. The most effective and feasible way to control wilt disease up to now is to
develop new cotton varieties resistant to Verticillium wilt, using traditional breeding and
transgenic technologies (Bowman 1999; Zhang et al. 2000; Jian et al. 2003; Mert et al. 2005).
However, little progresses have been achieved because there is no resistance gene in upland
cotton.
Sea-island cotton shows high resistance to Verticillium wilt, breeders have been trying to
introgress the resistance gene from Sea-island cotton to upland cotton, but linkage drag between
the resistant trait and undesired agronomic traits severely hampers use of these lines. Upland
cotton and Sea-island cotton are sexually compatible, but partial sterility, longer maturity, and
hybrid breakdown are often observed in later generation hybrids (Stephens 1946). Nonetheless,
the high resistance of Sea-island cotton makes it an ideal candidate for providing new genetic
variation useful for improving disease resistance in upland cotton.
The development of molecular markers provides a new opportunity in development upland
cotton cultivars with high resistance to Verticillium wilt. With the assistance of tightly linked
marker(s) toVerticillium wilt resistance, it is possible to transfer resistance gene(s) from Sea-island
cotton to upland cotton and reduce linkage drag as little as possible. Several research groups have
developed linkage maps of cotton using different mapping populations and different molecular
markers (Rong et al. 2004; Nguyen et al. 2004; Lin et al. 2005; Han et al. 2006) , and 20
chromosome have been identified. Recently, Wang et al. (2006) assigned the other six sub-genome
linkage group to corresponding chromosomes by translocation and FISH mapping. Some
important traits in cotton such as agronomic and fiber quality traits have been mapped (Jiang et al.
3
1998; Shappley et al. 1998; Ulloa and Meredith 2000; Lacape et al. 2003; Mei et al. 2004, Lin et
al. 2005), which facilitate the development of markers associating with Verticillium wilt
resistance.
Some progresses have been achieved in mapping Verticillium wilt resistance gene/QTLs in
cotton. In intraspecific populations, Qi et al. (2001) identified a RAPD marker with a distance of
12.4 cM and explained 12.1% phenotypic variance. Wang et al. (2005) detected three QTLs
significantly related to resistance to V. dahliae, accounting for 14.15%, 3.45% and 18.78% of the
phenotypic variance, respectively. The pathogen they used was all Anyang strain with moderated
invasion ability. In interspecific populations, Gao et al. (2003) identified three QTLs for
Verticillium wilt resistance using Tianmen strain, a pathogen with high invasion ability. These
QTLs explained 15.39%, 54.11% and 57.18% of the phenotypic variance, respectively. Du et al.
(2004) found a SSR marker related to Verticillium wilt resistance by BSA method using Anyang
strain, the locus had a distance of 13.1 cM with Verticillium wilt resistance gene and explained
50.1% phenotypic variance. Bolek et al. (2005) mapped some related traits affected by
Verticillium wilt using BSA method and found three QTLs having large effect on resistance to
Verticillium wilt. From the above results, it indicated that the inheritance of Verticillium wilt
showed quantitative character in intraspecific populations and qualitative character in interspecific
populations.
In this study, we used a whole genome screening strategy to mapping QTLs related to
Verticillium wilt resistance for further MAS in cotton. An interspecific population derived form
Gossypium hirsutum L. cv. XinLuZhao 1 (XLZ1), a cultivar highly sensitive to Verticillium wilt,
and G. barbadense L cv. Hai 7124 (H7124), a cultivar highly resistant to Verticillium wilt was used
for QTL analysis. The pathogen used in this study was Anyang strain.
Results
Trait segregation and correlations in F2:3 families
The field experiments demonstrated that XLZ1 was highly susceptible to Verticillium wilt
disease, and the average diseases index (DI) was 58.6. H7124 was highly resistant to Verticillium
wilt disease with an average DI of 2.3.
The DI of F3 families ranged from 0 to 58.0 on July 22 and from 2.3 to 67.0 on August 24
respectively (Figure 1). It showed that Verticlllium wilt resistance is a quantitative trait, however,
4
transgressive segregation towards increased tresistance (low DI value) was observed. Correlation
analysis indicated that the two traits were significantly positive correlated (r= 0.76, P< 0.0001).
Linkage analysis and map construction
A total of 1142 SSR markers were analyzed between the two parents, and 508 polymorphic
markers were detected. After linkage analysis, 430 SSR loci assembled to 41 linkage groups
ranging from 4.1 to 344.1 cM in length and from 2 to 38 in loci number. The resulting linkage
groups were numbered LG1-LG41 in descending order of the length, and 24 linkage groups were
assigned to 19 chromosomes. The map covered a total of 3745.9 cM, and the average distance
between adjacent markers was 8.71 cM (Fig 2).
QTLs associated with Verticillium wilt resistance (DI)
A composite interval mapping approach was used to scan the whole genome and to estimate
the number of putative QTLs in F2:3 families with a LOD score of more than 3.0. Five and four
QTLs were detected for disease scoring at July 22 and August 24, respectively (Table 1). Among
the 9 QTLs, 6 were located in the D sub-genome, the other three QTLs were not assigned to
corresponding chromosome because of less anchoring markers (Fig 2).
Individual QTLs detected were as follows:
DI for 7.22: five QTLs, designated as q7.22-1, q7.22-2, q7.22-3, q7.22-4 and q7.22-5, were
detected and explained 10.63% and 28.83% of the phenotypic variation, respectively. q7.22-4 that
explaining the maximum phonotypic variation located on LG18, an unassigned linkage group; the
H7124 allele increased phenotypic value by 1.36. q7.22-2 and q7.22-3 located on the same
chromosome, but in different linkage groups.
DI for 8.24: four QTLs, designated as q8.24-1, q8.24-2, q8.24-3 and q8.24-1, were localized
and explained 14.95% and 26.63% of the phenotypic variation, respectively. q8.24-1, the QTL
explaining the maximum phonotypic variation, located on Chr24; the H7124 allele increased
phenotypic value by 2.29. q8.24-2 and q8.24-3 located on the same linkage group (chromosome).
q7.22-1 and q8.24-2 (Figure 3A), q7.22-4 and q8.24-4 located within the same marker
interval, respectively, which maybe explained the significant correlation between the two traits.
However, those QTLs within different marker intervals may be special QTL for DI of single data
(Figure 3B).
5
Discussion
One of the difficulties in cotton breeding for cultivars with resistance to Verticillium wilt is
the uncontrollable environmental impacts on the happening of the Verticillium wilt disease. In this
experiment, the study of cotton resistance to Verticillium wilt was performed in a field heavily
infested on purpose with pathogenic strain in order to control the disease happening condition and
ensure the consistency. The average of disease index of the two stages was used to determine the
DI of each F2:3 families on Verticillium wilt happening fastigium. This gave us more confidence on
the analysis of cotton resistance to Verticillium wilt disease.
The development of modern molecular biology offers us more advantages to explore the
genetic and molecular mechanisms of cotton resistance to disease. In previous reports, only
limited markers were used in their researches (Fang et al.2001; Qi et al 2001; Gao et al. 2003; Du
et al. 2004). In our experiment, we employed more than 1000 SSR markers to study the cotton
resistance to Verticillium wilt disease in the F2:3 population of an interspecific hybrid between a
susceptible variety XLZ1 and a resistant line H7124. After constructing linkage groups and
scanning the whole genome, in total, 9 QTLs for disease resistance were detected. Among them, 6
QTLs located in D sub-genome, which consisted with other reports that D sub-genome contributed
a lot to traits variations including yield, fiber quality and disease resistance (Jiang et al. 1998;
Wright et al. 1998; Mei et al. 2004; Lin et al. 2005).
Verticillium wilt of cotton (Gossypium hirsutum), induced by Verticillium dahliae, is a
widespread disease present in most cotton producing areas. The best long-term approach to control
the infection and disease appears to be the use of resistant cultivars. Although great progresses
have been achieved in cultivars development with resistance to Verticillium wilt, markers
associated with Verticillium wilt are still essential for marker assisted selection (MAS) to
accelerate resistant breeding in cotton.
MAS would be more effective when environmental effects confound direct selection. Since
the cost to evaluate the Verticillium wilt disease is high, MAS would be more likely to show the
greatest gain. To conduct marker-assisted selection in plant breeding, the detection of QTL that
account for a significant amount of phenotypic variation is always desired, otherwise the MAS
may not be cost-effective to be used in regular basis by breeders. Markers close to genomic
regions with QTL that explain only a small fraction of the phenotypic variability may not be
6
considered useful by plant breeders in a selection process. The two QTLs detected in this study
that explained a high fraction phenotypic variation; what’s more, each QTL was responsible for
one DI, so they would be effective in MAS for disease-resistant lines in cotton breeding programs.
Those QTLs with minor effects could be as assistant ones, and those QTLs common for different
DIs could improve the MAS efficiency.
Material and methods
Plant material and field experiments
An F2 population of 76 fertile individuals was developed from one F1 plant between a
susceptible cultivar XLZ1 and a resistant line H7124 in 2002. XLZ1 and H7124 were kindly
supplied by the Chinese Cotton Germplasm Resource Center. The F2:3 families were obtained
through individual self-cross of each F2 plant in 2003.
A non-defoliant, moderate pathogenic Anyang strain of Verticillium dahliae was supplied by
the Plant Protection Department of the Cotton Research Institute, CAAS (Anyang, China). The
Verticillium wilt resistance of the F2:3 families and their parents were evaluated in a field, heavily
infested with Anyang strain in the year 2004, at the Cotton Research Institute Anyang. The trial
was designed with two replicates and 15 plants per block; before planting, field was inoculated
with culture medium of cotton seed for Verticillium pathogen with amount of 0.5% of the total
weight of farming soil.
Phenotyping
The reaction of the plant towards the inoculated pathogen was rated on a scale from 1 to 5
modified from Hunter et al. (1968): (1) no disease symptoms, (2) slight wilting and unilateral
discoloration of lower leaves, (3) moderate wilting involving more than one-half of the plant, (4)
severe wilting involving more than one-half of the plant, and (5) dead due to wilt. The disease
index (DI) was calculated as following:
DI = [∑(Ni×i)/(N×4)]×100; i=0~5, Ni = plant number of reaction i
Each plant was rated separately and the mean values from the two replicates were subjected
to statistical analyses. The evaluation was done on July 22 and August 24.
DNA extraction and SSR analysis
Genomic DNA was extracted followed the procedures described by Paterson et al. (1993).
The sequences of 1142 SSR primers (BNL, JESPR and TMH series) were obtained from
7
CottonDB (http://algodon.tamu.edu/cgi-bin/ace/searches/browser). The SSR reaction was
performed by using the method described by Wu et al. (2003).
Data analysis
Trait histogram was drawn by Excel 2003; correlation between traits was performed using
SAS software (SAS Institute Inc. 1999).
Linkage analysis was performed using Mapmaker Exp/3.0b (Lander et al. 1987). To identify
linkage groups, pairwise comparisons and the grouping of markers were performed using the
“Group” command at a maximum recombination fraction of 40cM and a minimum LOD score
above 4.0. To establish the most likely order within each linkage group, the “order” command was
used and the remaining markers were added into a frame map using the “try” command. The order
of markers was confirmed using the “ripple” command. Alternatively, a framework of markers
was generated using the “compare” command and the best order was confirmed. Recombination
fractions were converted to map distances in cM using the Kosambi mapping function (Kosambi
1944).
With Windows QTL Cartographer (Version 2), composite interval mapping (CIM), was used
to analyze the association between markers and traits (Wang et al. 2003). Model 6 with a window
size of 10 cM was used to scan the genome at 2-cM intervals. Five markers were selected as
cofactors, using the forward–backward regression method of stepwise regression. A stringent
LOD threshold ≥3.0 was set to identify the presence of putative QTLs.
Assignment of linkage groups to chromosomes
Possible assignment of linkage groups to specific chromosomes of the tetraploid genome was
based on bridge SSR loci common to this study and some published works (Liu et al. 2000;
Lacape et al. 2003; Mei et al. 2003; Rong et al. 2004; Nguyen et al. 2004; Han et al. 2004; Wang
et al. 2006).
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Fig.1 Histogram of DI in F3 families
Fig. 2 A genetic linkage map of tetraploid cotton based on 430 SSRs included 41 linkage groups, and spaned
3745.9 cM. The average distance between adjacent markers was 8.71 cM. QTLs for Verticillium wilt
resistance (DI) were indicated by boxes. Boxes to the left of each linkage group identify QTLs where ‘XLZ’
alleles have a positive additive effect, whereas boxes to the right identify QTLs where ‘H7124’ alleles have a
positive additive effect. The triangle showed the LOD peak of QTL.
10
Fig.3 Graphic display of QTLs related to resistance to Verticillium wilt in cotton by WinQTLCart2.0 based
on the interspecific population of XLZ1×H7124. (a) Common QTL for the two traits; (b) QTL only for one
trait (DI of 7.22)
Table 1. QTLs of disease-resistance detected by CIM using WinQTLCart V2.0
QTL Namea
LG/Chr
Marker Interval
LOD
Position on map
ab
d
R2
q7.22-1
q7.22-2
q7.22-3
q7.22-4
q7.22-5
q8.24-1
q8.24-2
q8.24-3
q8.24-4
LG5-Chr16
LG9-Chr26
LG16-Chr26
LG18
LG26
LG3-Chr24
LG5-Chr16
LG5-Chr16
LG26
BNL2441~BNL2766
BNL3867-3~BNL1605
BNL3368~BNL3537
BNL1706-2~BNL1706-1
BNL1673~BNL2894
BNL3017~JESPR305
BNL2441~BNL2766
BNL2766~BNL3065
BNL1673~BNL2894
3.81
3.32
4.01
3.66
3.78
3.12
4.11
4.03
3.49
125.81
23.11
38.61
85.31
44.11
20.01
121.41
137.81
48.11
7.61
9.27
-7.79
-1.36
6.39
-2.29
11.55
9.47
8.03
-4.21
-6.69
7.07
-6.23
3.09
14.35
-9.70
-11.56
7.49
11.55
10.63
12.01
28.83
11.81
26.63
17.01
17.32
14.95
a. Individual QTLs were designed with "q" and evaluation date of DI followed by lowercase
letters for more than one QTL affecting the trait.
b. Positive additive effects indicated that the H7124 allele increased phenotypic value.
20
18
Mean
Mean
DI of 7.22
14
DI of 8.24
12
10
8
6
4
2
0
04.
99
5.
00
-9
.99
10
.0
01
15 4.9
9
.0
019
.99
20
.0
02
25 4.99
.0
02
30 9.99
.0
03
35 4.99
.0
03
40 9.99
.0
04
45 4.99
.0
04
50 9.99
.0
05
55 4.99
.0
05
60 9.99
.0
06
65 4.99
.0
070
.00
Number of Lines
16
The Desease Index
Fig.1
11
LG1 (Chr19)
LG2
BNL678
LG3 (Chr24)
BNL3935
LG4 (Chr10)
BNL3017
LG5(Chr16)
BNL3300
16.6
10.8
9.0
22.6
BNL3992
BNL2448
6.5
BNL3147-2
4.1
TMHE20
JESPR305
29.3
13.1
BNL4016
BNL1665
BNL1746
9.8
BNL3895
10.7
30.3
BNL3250
BNL2641
33.3
13.7
BNL1597
BNL256
5.4
26.0
BNL3638
BNL3793
BNL3442-2
BNL632
11.1
36.8
32.9
19.1
26.4
BNL3500
7.3
BNL3449
JESPR23
10.6
16.0
BNL2821
2.0
3.6
6.7
3.7
BNL3347
JESPR218
JESPR236
BNL3535
BNL3662
BNL3426
BNL3418
11.7
JESPR158
2.7
2.9
5.1
10.6
14.3
15.1
JESPR127
BNL3084
JESPR33
29.0
2.7
2.7
6.2
BNL1053
BNL3798
JESPR1
13.9
BNL2499
BNL2655
BNL2568
4.9
BNL3598-1
JESPR118
BNL2786-1
BNL2786-2
2.8
2.7
4.8
0.7
BNL2616
BNL1521
BNL2961
TMHF09
7.9
JESPR-302
9.8
1.3
1.3
7.7
14.4
9.9
BNL390
BNL3602-2 14.4
BNL3875
19.7
15.2
10.6
JESPR157
BNL1154
BNL2662
BNL3279
JESPR65-2
BNL169
JESPR235
2.1
JESPR171
BNL3660
BNL3993
BNL3948
24.7
BNL2597
BNL3602-1
13.4
10.8
BNL4096
7.2
2.7
6.1
2.7
BNL3811
BNL3903-2
BNL3903-1
BNL3569
BNL852
1.5
0.7
7.2
BNL2650
BNL2667
BNL2681
BNL3402
10.6
2.7
4.0
BNL285
BNL1611
JESPR181
9.8
JESPR37
13.7
BNL2715
12.2
BNL3452
BNL1026
BNL2766
TMHB09
JESPR297
BNL3065
6.9
25.2
JESPR102
BNL3008
BNL2734
BNL2986
BNL580
BNL3232
JESPR128
BNL3799
BNL3287
JESPR237
BNL2441
21.4
BNL1513
BNL3860
19.0
BNL1878
BNL3977
4.0
2.4
1.6
27.6
16.2
JESPR29
BNL3649
BNL1551
BNL2634
12.4
12.4
12.2
29.3
8.7
2.0
BNL3071
TMHP14
BNL1580
13.2
11.6
19.1
9.2
7.6
BNL3348
2.4
2.4
BNL3790
BNL387
BNL500
5.7
2.0
BNL1161
5.6
4.8
0.7
5.4
3.5
3.5
2
3.4
6.6
9.9
3.4
BNL3563
BNL1664-2
DI for 7.22
DI for 8.24
BNL3496
BNL3923
12
LG6 (Chr2)
LG7 (Chr25)
BNL2877
2.7
5.4
BNL3436
BNL3972
BNL3661
19.1
9.0
17.2
3.5
BNL3411
BNL1404
JESPR135
TMHA12
BNL2812
BNL1681
24.0
BNL1047
BNL3547
BNL3512
BNL4060 22.9
JESPR179
BNL2651
18.4
4.8
BNL4094
7.7
4.7
BNL3190
5.4
0.7
4
3.3
1.3
4
5
BNL2635
BNL1410
6.6
BNL520
BNL3413
3.3
JESPR101
0.7
BNL3971 2.7
2
BNL2706-2 1
8.3
TMHK08
5.4
BNL3806-1 7.6
TMHK19
BNL3806-2 3.0
JESPR224
5.4
JESPR227
2.7
JESPR215
6.2
BNL3538
BNL3405
BNL2762
13.2
BNL3937
BNL3655
BNL3103
BNL3264
18.2
BNL1169
BNL1417
13.8
17.5
BNL3558
10.5
4.0
0.7
0.7
2
3.4
2.7
18.0
BNL3599
BNL3867-2
BNL3510
BNL3816
BNL840
JESPR92
BNL3435
30.2
JESPR167
13.0
BNL220
BNL2632
BNL1721
TMHN16
BNL3282
TMHP20
5.6
25.8
BNL3592
BNL1408
BNL1595
3.1
1.3
5.5
BNL2652
BNL1079
BNL4079
TMHE17
BNL2768
7.2
BNL3867-1
19.9
14.1
BNL3598-2
BNL3479
BNL2967
5.5
13.1
JESPR270
BNL1040
6.2
BNL2621
13.7
14.5
JESPR178
BNL150
20.9
0.9
5.5
12.2
BNL2895
BNL2691
BNL3523
13.5
TMHJ04
21.1
BNL2544
JESPR245
BNL3590
26.5
19.7
BNL1605
15.5
24.6
6.2
BNL243
BNL3867-3
14.0
BNL2569
3.4
4.1
2.7
3.4
4.7
BNL116
BNL1151
23.9
15.4
LG10 (Chr18)
5.6
BNL3545-2
22.3
0.7
3.3
LG9 (Chr12)
BNL3431
BNL584
9.3
3.4
LG8 (Chr11)
BNL4041
BNL2578
BNL3261
13
LG11 (Chr6)
LG12 (Chr23)
BNL2823
LG13 (Chr9)
LG14 (Chr8)
BNL2750
BNL3173
LG15 (Chr14)
BNL3255
BNL3267
7.9
BNL597
22.6
24.2
28.8
17.2
29.7
JESPR114
11.7
BNL3443-1
BNL3650
0.7
3.4
9.3
BNL2741
12.2
9.0
BNL1064
BNL1065
BNL3812
BNL3292
10.9
BNL2993
BNL4099
BNL2884
8.1
9.9
27.5
JESPR110
JESPR208-1
2.0
9.9
TMHO06
7.6
BNL3140
BNL1579
3.4
BNL3987
13.3
3.1
BNL4028
BNL1043
JESPR290
BNL1414
JESPR208-2
BNL1030
BNL219-2
BNL354
1.3
3.4
2
4.1
3.8
8.5
10.0
17.1
BNL1317
BNL2977
32.4
16.6
TMHD20
BNL3145
BNL4012
10.6
BNL2882
17.3
13.3
BNL3257
5.8
0.7
2
BNL1664-1
BNL2538
BNL3658
BNL1607
11.8
BNL3099
18.2
11.6
BNL1672-1
BNL3511
2.9
BNL4004
TMHB04
18.4
BNL2847
8.1
BNL3034
18.2
4.1
19.8
BNL3582-2
BNL3582-1
7.6
JESPR151
JESPR274
23.3
JESPR-13
3.0
BNL3534
10.6
TMHD02
BNL3031
BNL1672-2
12.5
16.0
BNL1902
TMHN07
11.7
BNL3359
LG16 (Chr26)
LG17
LG18
BNL358
BNL3414
18.5
30.6
BNL2906
BNL2771
BNL3368
10.0
7.9
BNL3443-2
19.5
22.2
BNL2557
4.5
3.0
3
1.3
1.1
9.9
BNL3423-2
BNL3423-1
17.7
3.4
3.4
BNL3792
6.1
BNL1379
21.6
33.4
3.4
BNL3408
28.6
BNL2725
JESPR136
BNL1706-2
21.3
BNL3482
BNL2495
15.0
BNL2486
18.4
BNL1706-1
BNL1318
18.4
BNL1045-1
BNL830
BNL300
BNL3090
BNL3085-2
10.4
4.7
2.7
2.7
3.4
2.7
2
0.7
0.7
4.7
8.3
11.0
JESPR231
8.4
7.6
TMHE03
BNL786
BNL1350
BNL3445
BNL3441
BNL3537
9.3
3.3
4.7
11.1
BNL2609-1
BNL3807
BNL3881
BNL206
BNL4015
TMHE18
JESPR63
BNL3849
LG20 (Chr15)
4.0
2
3.3
BNL3392
BNL2611
7.7
4.8
LG19 (Chr3)
BNL1418
BNL2564-2
BNL3902
TMHN20
BNL1666
BNL2646
JESPR180-2
JESPR298
JESPR205
JESPR180-1
BNL2700
BNL4080
TMHG11
BNL2920
14
LG21
LG22 (Chr13)
LG23
BNL1394
BNL3308
18.3
BNL3472
JESPR175
BNL1438
BNL2449
BNL4007
JESPR12
7.6
BNL3319
BNL3415
3.6
BNL2921
BNL2564-1
JESPR289
BNL3910
JESPR90
BNL2827
BNL4095
BNL3778
BNL3085-1
3.3
2.7
1.3
1.3
4
3
3.2
7.6
29.2
2.1
4
2.7
0.7
LG24 (Chr4)
JESPR107
BNL3886
22.3
22.2
BNL3259
BNL4047
5.5
9.2
12.5
BNL3994
4.7
TMHK01
TMHD03
17.7
LG25
JESPR230
14.6
7.6
BNL1041
BNL1122
BNL3871
TMHJ19
24.6
27.0
BNL3888
5.4
10.3
24.9
BNL1604-2
BNL236
BNL1667
JESPR243
BNL3580
0.7
5.6
14.5
BNL2572
JESPR153-2
9.9
BNL1604-1
LG26
LG27
LG28 (Chr9)
BNL2732
BNL2717
5.2
24.9
BNL1162
9.0
LG30
10.5
12.3
BNL2772
10.8
JESPR65-1
BNL3874
9.2
BNL1707
21.9
BNL3958
BNL3241
4.0
BNL1673
22.9
BNL3601
4.5
10.9
TMHA14
BNL2709
TMHF17
4.8
9.9
11.0
BNL3015
BNL2471
BNL3955
BNL2706-1
BNL4003
JESPR195
BNL2443
BNL3371
1.5
0.7
0.7
2.7
9.7
4.6
JESPR66
6.8
8.3
BNL2182
JESPR232
BNL3626
6.9
BNL1038
JESPR50
3.2
LG29 (Chr8)
BNL1670-1
JESPR42
BNL2894
LG31
LG32
JESPR246
BNL4030-2
BNL4030-1
3.6
LG33
LG34 (Chr23)
LG35
BNL3383
BNL1655
2.7
3.6
13.0
11.9
BNL1688
BNL4092
4.8
BNL448
24.2
27.5
BNL3545-1
BNL3644
JESPR156
BNL891
BNL686
BNL3442-1
BNL3281-2
LG38
LG39
6.9
13.3
16.0
4.8
JESPR292
BNL3627
LG40
BNL4059
BNL1042
BNL1022
BNL1646
BNL3989
BNL1080
BNL4017
6.7
BNL1510
BNL673
LG37 (Chr8)
BNL226
7.4
3.9
2.9
17.6
14.6
10.2
LG36 (Chr3)
8.5
BNL2865
BNL3043
JESPR300
LG41
4.1
BNL3873
JESPR220
15
Fig. 2
DI of 7.22
DI of 7.22
DI of 8.24
DI of 8.24
LG5-Chr16
(A)
Fig.3
LG16-Chr26
(B)