1. Analysis of Quantitative Trait Loci for Seed Coat Cracking from Two Soybean Populations.
Sung-Taeg Kang*1, Hyeun-Kyeung Kim2, Min-Jung Seo1, Jung-Kyung Moon1 , Hong-Tae Yun*1, Suk-Ki Lee1 , Yeong-Ho Lee1, Si-Ju Kim1
1 National Institute of Crop Science, RDA, 209 Seodun-Dong, Kwonsun-Gu, Suwon, , Republic of Korea
2 Busan National University Hospital, Medical Research Institute, 10 ami-dong 1ga Busan, , Republic of Korea
INTRODUCTION
MATERIALS & METHODS
Seed Coat Cracking (SCC) showing cracks in the surface of seed coat, causes serious effect on seed quality in soybean. However, breeding for resistance to SCC is difficult due to the complicated genetic behavior and environmental interaction. The objective of this research was to improve breeding efficiency for resistance to SCC based on DNA marker in soybean. In this study, SSR markers were used to identify the genomic regions significantly associated with the quantitative trait loci (QTL) that controls SCC in two segregating F2:11 RIL populations,'Keunolkong' /'Shinpaldal kong’ (K/S) and 'Keunolkong' / 'Iksan10' (K/I). The two populations were phenotyped at two locations in Suwon and Yeoncheon, Korea.
Material : F2 derived F11 RILs , 2 populations
- 117 Keunolkong / Shinpaldalkong, 115 Keunolkong/Iksan10
Field experiment
Location (year) : Suwon (2005), Yeoncheon (2005)
Plot arrangement : Randomized Complete Block Design with Two Rep.
Traits : SCC, Flowering (R1), Maturity (R7)
DNA marker analysis : PCR, Silver-Staining(or Agarose)
- Linkage mapping : Mapmanager Macintosh program (LOD 3.0)
- QTL analysis : SAS program (v. 9.1)
satt276
0.0
satt050
28.5
satt385
48.9
satt545
61.3
satt236
86.0 1 A1
satt177
satt187
54.3 A2
satt411
satt212
20.1
satt151
57.4
satt045
satt263
74.1
satt204
76.8
satt268
79.1
satt117
84.0
satt483
89.8
satt185
102.0
satt231
133.3
satt553
174.7 2 3 E
satt596
satt287
38.8
satt285
44.6
sct065
78.5
satt132
81.1
satt529
86.7
satt406
91.4
satt183
109.5
satt215
121.3
satt547
171.3 J
satt173
satt592
30.0
satt243 O
satt197 B1
satt184
satt580
50.0
satt147
95.5
D1a
satt216
satt157
21.4
satt266
40.6
satt537
59.8
satt189
73.9
satt506
79.4
satt141
83.9
satt350
90.7
satt546
104.4
satt172
115.4
satt274
129.3
satt459
137.6
D1b
satt571
satt496
20.0
satt292
52.1
satt148
81.8
satt440
111.5 I
satt137
satt417
10.2
satt167
14.0
satt260
41.8
satt196
60.8 K
satt495
satt238
18.9
satt398
33.7
satt278
45.7
satt418
61.7
satt313
77.7
sct010
97.6
satt166
109.0
satt229
120.8
satt513
136.9
satt373
139.8 L
satt100
satt460
4.7 C2
satt453
41.5
8.0
satt371
45.9
satt160
satt114
57.7
satt335
74.0
satt554
114.9 F
satt590
satt567
13.4
satt245
30.3
satt175
43.4
satt336 M
satt108
60.7
9.4
12.3
satt414
16.1
satt380
19.8
27.5
sct_001
41.0
satt431
75.6
125.6
satt458
satt014
9.6
satt372
22.0
satt154
37.1
satt397
65.6
satt389
78.7
satt226
sat_022
123.9 D2
satt577
satt126
33.3
satt556
81.5
satt020
86.5
satt066
92.8
satt063
105.5 B2
satt296
45.5
61.4
91.3
105.2
17.3
satt523
24.4
25.4
28.0
30.5
satt462
40.2
satt497
54.6
sct_010
82.8
satt076
86.4
satt527
91.9
92.9
115.0
133.5
- POP1 : Keunolkong / Shinpaldalkong
- POP2 : Keunolkong / Iksan10
Flowering
Maturity
’05, Suwon
’05, Yeoncheon
’01, Milyang
R2(%) 25 50
Seed Coat Cracking
RESULTS
Table 1. Range and means of SCC (unit: %), broad-sense heritability estimates for the parents and populations for individual environment and across two environments.
Suwon
Yeoncheon
Over Env.
Population 1
(Keunolkong x Shinpaldalkong)
Population 2
(Keunolkong x Iksan 10)
Parents P1
4.8
17.0
10.8 P2
0.0
1.0
0.5
Population
Minimum
Maximum
80.5
96.5
88.5
74.5
78.5
76.5
Mean
10.5
17.9
14.2
14.6
22.5
18.6 h2
92.6
93.5
80.9
75.6
87.5
82.8
Fig. 2. The location of QTLs associated with flowering (red), maturity (blue) and SCC(green) in two populations.  QTLs significance(R2 value) are represented with vertical bars. ① Suwon ② Yeoncheon ③ Milyang
Table 2. One-way ANOVA for QTLs associated with SCC across environments in two
populations
Suwon
Yeoncheon LG
marker P
R2 (%)
Allelic Means AA BB
Population 1 (Keunolkong x Shinpaldalkong)
D1b
satt172
ns　
2.0　
7.9　
12.1　
***
10.9
10.7
25.2
satt216 **
5.0
14.9
9.8
12.0
25.9
satt157 *
5.2
7.2
15.0
6.5
12.4
23.8 I
satt496
11.1
5.1
16.2
12.3
10.1
24.9 J
satt406
7.6
5.5
23.1
11.6
satt529
7.7
15.1
5.6
4.8
21.4
12.2
satt215
7.5
14.7
5.4
3.7
21.8
13.4 K
satt196
7.0　
13.7　
7.8　
9.9
25.0 O
satt243
4.1
13.5
7.0
22.0
Population 2 (Keunolkong x Iksan 10) B2
satt556
5.7
9.5
17.8
16.7
26.3
11.5
8.0
20.0
26.7 D2
satt372
8.5
9.7
19.9
17.3
28.8
satt183
18.8
9.4
3.0　
25.6　
18.4　
Population 1(Keunolkong x Shinpaldalkong)
Population 2(Keunolkong x Iksan 10)
Fig. 1. Frequency distribution of lines for SCC at Suwon & Yeoncheon environments in 2005.
SUMMARY
§ Probability : *** P < 0.001, ** P < 0.01, P < 0.05
QTLs on D1b, D2, and I were commonly associated with SCC at two locations. Especially QTL tightly linked with Satt216 on LG D1b was commonly associated with SCC across populations and locations. Results showed that major QTLs for SCC have been identified on LG D1b, D2, and I. And these major QTLs for SCC may be used for minimizing soybean SCC through effective marker-assisted selection (MAS).
Corresponding Author : DR. SUNG-TAEG KANG (