Scientia Agricultura Sinica ›› 2015, Vol. 48 ›› Issue (22): 4408-4416.doi: 10.3864/j.issn.0578-1752.2015.22.002

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

The Genetic Constitution of Transgressive Segregation of the 100-Seed Weight in A Recombinant Inbred Line Population NJRSXG of Soybean

ZHANG Ying-hu1,2, MENG Shan1, HE Jian-bo1, WANG Yu-feng1, XING Guang-nan1, ZHAO Tuan-jie1, GAI Jun-yi1   

  1. 1 Soybean Research Institute, Nanjing Agricultural University/National Center for Soybean Improvement/MOA Key Laboratory for Biology and Genetic Improvement of Soybean (General)/National Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing 210095
    2 Institute of Agricultural Sciences in Jiangsu Coastal Areas, Yancheng 224002, Jiangsu
  • Received:2015-06-12 Online:2015-11-16 Published:2015-11-16

Abstract: 【Objective】 The present study aimed at exploring the QTL and their allele effects of the 100-seed weight in a recombinant inbred line (RIL) population for revealing the genetic mechanism of transgressive segregation, and providing guidance for breeding for different seed size types in soybeans. 【Method】 The RIL (recombinant inbred line) population NJRSXG derived from a cross between Xinjin 2 and Gantai 2-2 was tested and measured for their 100-seed weight in five environments from 2009 to 2011. The genetic map with 400 SSR markers was established for QTL mapping using the mixed model based composite interval mapping (MCIM) method in QTL Network V2.1 software. Based on the QTL mapping, the QTL-allele constitutions of each line of the RIL population was obtained, and the corresponding QTL-allele matrix was established. 【Result】 The 100-seed weight of the parents, Xinjin 2 and Gantai 2-2, were 16.92 g and 14.14 g, respectively, with those of the RILs ranging from 12.09 g to 25.01 g, showing an obvious transgressive segregation. The genotypic coefficient of variation (GCV) was 16.06%, and the overall heritability was 96.17% from a joint dataset analysis. Ten additive QTL and nine epistatic QTL pairs were detected in the joint dataset using an MCIM method. The phenotypic variation explained by additive QTL was ranged from 0.69% to 14.93%, and the four major QTLs, i.e., Sw-05-2, Sw-08-1, Sw-12-1 and Sw-17-1, were 6.91%, 14.93%, 7.80%, and 5.01%. The Sw-13-3 with both additive effect and epistasis effects had not been reported before. The phenotypic variance of epistatic QTL pairs was small, and ranged from 0.31% to 3.44%. The genetic components of 100-seed weight was estimated from the mapping results, and the analysis of variance for the joint dataset, the genetic contribution due to additive QTL, epistatic QTL pairs, and collective unmapped minor QTL were 47.91%, 13.06%, and 35.19% of the phenotypic variation, respectively. From the mapping procedure, the allele effects of all the loci were also obtained, and the genetic constitution of the QTL-alleles for each line of the RIL population and its parents was used to establish the QTL-allele matrix. The two parents have seven and three loci with positive additive effects respectively; therefore, this is a complementary pair of parents. No lines of the RIL population had all positive alleles or all negative alleles, indicating a hidden potential of recombination, and the lines with larger seed weight had more positive alleles, while the lines with smaller seed weight had more negative alleles, which implies that recombination among loci was the major cause for transgressive segregation of the 100-seed weight in the RIL population. In addition, it was also found that there was still a possibility to improve the 100-seed weight through further recombination. 【Conclusion】 The transgressive segregation of 100-seed weight can be found in a RIL population; 10 additive QTL and 9 epistatic QTL were detected using the joint dataset tested under five environments. The transgressive segregation was caused by recombination among loci between parents, and the potential for further improvement through recombination among RILs still exists.

Key words: soybean, recombinant inbred lines (RIL), 100-Seed weight, linkage mapping, QTL-allele matrix

[1]    Hao D R, Cheng H, Yin Z T, Cui S Y, Zhang D, Wang H, Yu D Y. Identification of single nucleotide polymorphisms and haplotypes associated with yield and yield components in soybean (Glycine max) landraces across multiple environments. Theoretical and Applied Genetics, 2012, 124(3): 447-458.
[2]    黄中文, 赵团结, 喻德跃, 陈受宜, 盖钧镒. 大豆产量有关性状QTL的检测. 中国农业科学, 2009, 42(12): 4155-4165.
Huang Z W, Zhao T J, Yu D Y, Chen S Y, Gai J Y. Detection of QTLs of yield related traits in soybean. Scientia Agricultura Sinica, 2009, 42(12): 4155-4165. (in Chinese)
[3]    王凤敏, 赵双进, 王静华, 谷峰, 赵青松, 杨春燕, 张孟臣, 秦君. 河北省不同时期育成大豆品种产量构成因子分析. 大豆科学, 2014, 33(6): 830-836.
Wang F M, Zhao S J, Wang J H, Gu F, Zhao Q S, Yang C Y, Zhang M C, Qin J. Analysis on the yield components of soybean cultivars released in defferent stages of Hebei province. Soybean Science, 2014, 33(6): 830-836. (in Chinese)
[4]    Maughan P J, Maroof M A S, Buss G R. Molecular-marker analysis of seed weight: Genomic locations, gene action, and evidence for orthologous evolution among three legume species. Theoretical and Applied Genetics, 1996, 93(4): 574-579.
[5]    Moose S P, Mumm R H. Molecular plant breeding as the foundation for 21st century crop improvement. Plant Physiology, 2008, 147(3): 969-977.
[6]    Zhang Y H, He J B, Wang Y F, Xing G N, Zhao J M, Li Y, Yang S P, Palmer R G, Zhao T J, Gai J Y. Establishment of a 100-seed weight quantitative trait locus-allele matrix of the germplasm population for optimal recombination design in soybean breeding programmes. Journal of Experimental Botany, 2015.
[7]    Dargahi H, Tanya P, Somta P, Abe J, Srinives P. Mapping quantitative trait loci for yield-related traits in soybean (Glycine max L.). Breeding Science, 2014, 64(4): 282-290.
[8]    Kato S, Sayama T, Fujii K, Yumoto S, Kono Y, Hwang T Y, Kikuchi A, Takada Y, Shiraiwa T, Ishimoto M. A major and stable QTL associated with seed weight in soybean across multiple environments and genetic backgrounds. Theoretical and Applied Genetics, 2014, 127(6): 1365-1374.
[9]    汪霞, 徐宇, 李广军, 李河南, 艮文全, 章元明. 大豆百粒重QTL定位. 作物学报, 2010, 36(10): 1674-1682.
Wang X, Xu Y, Li G J, Li H N, Gen W Q, Zhang Y M. Mapping quantitative trait loci for 100-seed weight in soybean (Glycine max L. Merr). Acta Agronomica Sinica, 2010, 36(10): 1674-1682. (in Chinese )
[10]   孙亚男, 仕相林, 蒋洪蔚, 孙殿军, 辛大伟, 刘春燕, 胡国华, 陈庆山. 大豆百粒重QTL的上位效应和基因型×环境互作效应. 中国油料作物学报, 2012, 34(6): 598-603.
Sun Y N, Shi X L, Jiang H W, Sun D J, Xin D W, Liu C Y, Hu G H, Chen Q S. Epistatic effects and qE interaction effects of QTLs for 100-seed weight in soybean. Chinese Journal of Oil Crop Sciences, 2012, 34(6): 598-603. (in Chinese)
[11]   Gai J Y, Chen L, Zhang Y H, Zhao T J, Xing G N, Xing H. Genome-wide genetic dissection of germplasm resources and implications for breeding by design in soybean. Breeding Science, 2012, 61(5): 495-510.
[12]   Hanson C, Robinson H, Comstock R. Biometrical studies of yield in segregating populations of Korean Lespedeza. Agronomy Journal, 1956, 48(6): 268-272.
[13]   Schmutz J, Cannon S B, Schlueter J, Ma J, Mitros T, Nelson W, Hyten D L, Song Q, Thelen J J, Cheng J, Xu D, Hellsten U, May G D, Yu Y, Sakurai T, Umezawa T, Bhattacharyya M K, Sandhu D, Valliyodan B, Lindquist E, Peto M, Grant D, Shu S, Goodstein D, Barry K, Futrell- Griggs M, Abernathy B, Du J, Tian Z, Zhu L, Gill N, Joshi T, Libault M, Sethuraman A, Zhang X C, Shinozaki K, Nguyen H T, Wing R A, Cregan P, Specht J, Grimwood J, Rokhsar D, Stacey G, Shoemaker R C, Jackson S A. Genome sequence of the palaeopolyploid soybean. Nature, 2010, 463(7278): 178-183.
[14]   苏成付, 赵团结, 盖钧镒. 不同统计遗传模型QTL定位方法应用效果的模拟比较. 作物学报, 2010, 36(7): 1100-1107.
Su C F, Zhao T J, Gai J Y. Simulation comparisons of effectiveness among QTL mapping procedures of different statistical genetic models. Acta Agronomica Sinica, 2010, 36(7): 1100-1107. (in Chinese)
[15]   Yang J, Hu C, Hu H, Yu R, Xia Z, Ye X, Zhu J. QTLNetwork: Mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics, 2008, 24(5): 721-723.
[16]   Wang S, Basten C, Zeng Z. Windows QTL cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC, 2012,
[17]   Voorrips R E. MapChart: Software for the graphical presentation of linkage maps and QTLs. The Journal of Heredity, 2002, 93(1): 77-78.
[18]   Han Y P, Li D M, Zhu D, Li H, Li X, Teng W, Li W. QTL analysis of soybean seed weight across multi-genetic backgrounds and environments. Theoretical and Applied Genetics, 2012, 125(4): 671-683.
[19]   Zhang W K, Wang Y J, Luo G Z, Zhang J S, He C Y, Wu X L, Gai J Y, Chen S Y. QTL mapping of ten agronomic traits on the soybean (Glycine max L. Merr.) genetic map and their association with EST markers. Theoretical and Applied Genetics, 2004, 108(6): 1131-1139.
[20]   Hoeck J A, Fehr W R, Shoemaker R C, Welke G A, Johnson S L, Cianzio S R. Molecular marker analysis of seed size in soybean . Crop Science, 2003, 43(1): 68-74.
[21]   Kim H K, Kim Y C, Kim S T, Son B G, Choi Y W, Kang J S, Park Y H, Cho Y S, Cho I S. Analysis of quantitative trait loci (QTLs) for seed size and fatty acid composition using recombinant inbred lines in soybean. Journal of Life Science, 2010, 20(8): 1186-1192.
[22]   Panthee D R, Pantalone V R, West D R,Saxton A M, Sams C E. Quantitative trait loci for seed protein and oil concentration, and seed size in soybean. Crop Science, 2005, 45(5): 2015-2022.
[23]   Hyten D L, Pantalone V R, Sams C E, Saxton A M, Landau-Ellis D, Stefaniak T R, Schmidt M E. Seed quality QTL in a prominent soybean population . Theoretical and Applied Genetics, 2004, 109(3): 552-561.
[24]   Buckler E S, Holland J B, Bradbury P J, Acharya C B, Brown P J, Browne C, Ersoz E, Flint-Garcia S, Garcia A, Glaubitz J C, Goodman M M, Harjes C, Guill K, Kroon D E, Larsson S, Lepak N K, Li H, Mitchell S E, Pressoir G, Peiffer J A, Rosas M O, Rocheford T R, Romay M C, Romero S, Salvo S, Sanchez Villeda H, da Silva H S, Sun Q, Tian F, Upadyayula N, Ware D, Yates H, Yu J, Zhang Z, Kresovich S, McMullen M D. The genetic architecture of maize flowering time. Science, 2009, 325(5941): 714-718.
[25]   Flint-garcia S A, Thornsberry J M, Buckler E S T. Structure of linkage disequilibrium in plants. Annu Review of Plant Biology, 2003, 54(1): 357-374.
[26]   晁毛妮, 郝德荣, 印志同, 张晋玉, 宋海娜, 张怀仁, 褚珊珊, 张国正, 喻德跃. 大豆生物量与产量组分间的相关及关联分析. 作物学报, 2014, 40(1): 7-16.
Chao M N, Hao D R, Yin Z T, Zhang J Y, Song H N, Zhang H R, Chu S S, Zhang G Z, Yu D Y. Correlation and association analysis between biomass and yield components in soybean. Acta Agronomica Sinica, 2014, 40(1): 7-16. (in Chinese)
[27]   Korir P, Zhang J, Wu K J, Zhao T J, Gai J Y. Association mapping combined with linkage analysis for aluminum tolerance among soybean cultivars released in Yellow and Changjiang River Valleys in China. Theoretical and Applied Genetics, 2013, 126(6): 1659-1675.
[28]   Kim H, Xing G N, Wang Y F, Zhao T J, Yu D Y, Yang S P, Li Y, Chen S Y, Palmer R G, Gai J Y. Constitution of resistance to common cutworm in terms of antibiosis and antixenosis in soybean RIL populations. Euphytica, 2014, 196(1): 137-154.
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