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, 441-857, Republic of Korea 2 Busan National University Hospital, Medical Research Institute, 10 ami-dong 1ga Busan, 602-739, 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 (e-mail : kangst@rda.go.kr)