Scientia Agricultura Sinica ›› 2017, Vol. 50 ›› Issue (18): 3450-3460.doi: 10.3864/j.issn.0578-1752.2017.18.002

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

QTL Mapping for Main Root Length and Lateral Root Number in Soybean at the Seedling Stage in Different N, P and K Environments

LIANG HuiZhen1, DONG Wei1, XU LanJie1, YU YongLiang1, YANG HongQi1, TAN ZhengWei1, XU Yang2, CHEN XinWei3   

  1. 1Sesame Research Center, Henan Academy of Agricultural Sciences, Zhengzhou 450002; 2 3Shangqiu Academy of Agriculture and Forestry Sciences, Shangqiu 476000, HenanNanyang Academy of Agricultural Sciences, Nanyang 473083, Henan;
  • Received:2017-03-06 Online:2017-09-16 Published:2017-09-16

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Abstract: 【Objective】Main root length (MRL) and lateral root number (LRN) are important root traits. It is important to develop the gene resources and reveal the genetic mechanisms of MRL and LRN, and identify quantitative trait loci (QTL) associated with root traits in soybean, including main-effect QTLs, epistatic effects and QTL × environment interactions, meanwhile, map the main-effect QTLs, epistatic effects and QTL × environment interactions in different N, P and K environments. 【Method】A total of 447 RILs derived from a cross between cultivated Jindou23 as the female and native variety HuibuzhiZDD02315 as the male were used as materials. Thirty seeds from each of the RILs and their parents were covered with pasteurized paper, and cultivated in CK (nonfertilized condition), NPK (normal fertilization conditions) and 1.5NPK (high fertilization conditions) at 20-28℃ in 2015 and 2016, and a complete random design with three replications was used in this study. Root traits were measured at V2 stage. Epistatic QTLs and QTL × environment interactions were performed using WinQTLCart 2.5 and QTLNETwork 2.1. 【Result】Twenty-four QTLs for MRL and LRN were detected on chromosomes 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16 and 17 using CIM method in this study. The variation accounted for by each of these twenty- four QTLs ranged from 8.52% to 43.62%. These QTLs showed additive effect. Two QTLs for MRL and LRN were detected by MCIM, which showed an additive effect. Another two pairs of additive × additive epistatic effects QTLs for MRL and LRN were detected, including one pair of major QTLs and non-major QTL additive × additive epistatic effects, and one pair of non-major QTLs and non-major QTL additive × additive epistatic effects. Two pairs of QTL interaction for MRL explained 1.53% and 1.95% of the phenotypic variation, and two pairs of QTL interaction for LRN explained 2.47% and 1.13% of the phenotypic variation. Two QTLs were simultaneously detected on the same chromosome using two WinQTLCart 2.5 and QTLNETwork 2.1. Nine QTLs were simultaneously detected in three environments. The QTL for MRL was all mapped on chromosome 6 in 2015 (including NPK and 1.5NPK) and 2016 (including 1.5NPK). The QTL for LRN was all detected on chromosome 5 in 2015 (including NPK and 1.5NPK) and 2016 (including CK), another QTL for LRN was all mapped on chromosome 17 in 2015 (including CK and NPK) and 2016 (including NPK). 【Conclusion】MRL and LRN absorb less NPK at seedling stage in soybean, so farmers should minimize the use of NPK in agricultural production. MRL and LRN were controlled by the same controlled gene and specific gene in NPK treatments. Some QTLs were not simultaneously identified in different NPK environments as the related genetic mechanism is comparatively complex. Additive effects, additive × environment interactions and additive × additive epistatic effects are important genetic factors in MRL and LRN formation and inheritance. One each QTL for MRL and LRN was all detected by CIM and MCIM; one stable gene for MRL and LRN existed in interval markers between Satt442-Satt296 and Satt521-GMABABR.

Key words: soybean, nitrogen, phosphorus and potassium, main root length, lateral root number, quantitative trait loci, epistatic effects

[1]    孙广玉, 何庸, 张荣华, 张代平. 大豆根系生长和活性特点的研究. 大豆科学, 1996, 15(14): 317-321.
Sun G Y, He Y, Zhang R H, Zhang D P. Studies on growth and activities of soybean root. Soybean Science, 1996, 15(14): 317-321. (in Chinese)
[2]    Jiang C, Gao X, Liao L, Harberd N, Fu X. Phosphate starvation root architecture and anthocyanin accumulation responses are modulated by the gibberellins-DELLA signaling pathway in Arabidopsis. Plant Physiology, 2007, 145: 1460-1470.
[3]    Bi Y M, Wang R L, Zhu T, Rothstein S J. Global transcription profiling reveals differential responses to chronic nitrogen stress and putative nitrogen regulatory components in Arabidopsis. BMG Genomics, 2007, 8: 281-297.
[4]    周蓉, 王贤智, 陈海峰, 张晓娟, 单志慧, 吴学军, 蔡淑平, 邱德珍, 周新安, 吴江生. 大豆倒伏性及其相关性状的QTL分析. 作物学报, 2009, 35(1): 57-65.
Zhou R, Wang X Z, Chen H F, Zhang X J, Shan Z H, Wu X J, Cai S P, Qiu D Z, Zhou X A, Wu J S. QTL analysis of lodging and related traits in soybean. Acta Agronomica Sinica, 2009, 35(1): 57-65. (in Chinese)
[5]    Tar’an B, Warkentin T, Somers D J, Miranda D, Vandenberg A, Blade S, Woods S, Bing D, Xue A, DeKoeyer D, Penner G. Quantitative trait loci for lodging resistance, plant height and partial resistance to mycosphaerella blight in field pea (Pisum sativum L.). Theoretical and Applied Genetics,2003, 107: 1482-1491.
[6]    杨秀红, 吴宗璞, 张国栋. 不同年代大豆品种根系性状演化的研究. 中国农业科学, 2001, 34(3): 292-295.
Yang X H, Wu Z P, Zhang G D. Evolution of root characters of soybean varieties of different ages. Scientia Agricultura Sinica, 2001, 34(3): 292-295. (in Chinese)
[7]    Sanchez P A, Salinas J G. Low input technology for managing oxisols and ultisols in tropical America. Advances in Agronomy, 1981, 34: 279-406.
[8]    NOuri M, Stephen C M, Tom G, Max D C. Hybrid and nitrogen influence on pearl millet production in Nebraska: yield, growth, and nitrogen uptake, and nitrogen use efficiency. Agronomy Journal, 1999, 91: 737-743.
[9]    Price A H, Steele K A, Moore B J, Jones R G W. Upland rice grown in soil-filled chambers and exposed to contrasting water-deficit regimes: II. Mapping QTL for root morphology and distribution. Field Crops Research, 2002, 76: 25-43.
[10]   Narang R A, Bruene A, Altmann T. Analysis of phosphate acquisition efficiency in different Arabidopsis accessions. Plant Physiology, 2000, 124: 1786-1799.
[11]   程琳琳. 中国农田生态系统钾素平衡与钾肥需求[D]. 北京: 中国农业大学, 2007.
Cheng L L. Potassium balance and demand of potassium in farmland ecosystem in China[D]. Beijing: China Agricultural University, 2007. (in Chinese)
[12]   Cho Y I, Jiang W Z, Chin J H, Piao Z Z, Cho Y G, Mccouch S R, Koh H J. Identification of QTLs associated with physiological nitrogen use efficiency in rice. Molecules and Cells, 2007, 23(1): 72-79.
[13]   Lian X M, Xing Y Z, Yan H, Xu C G, Li X H, Zhang Q F. QTLs for low nitrogen tolerance at seedling stage identified using a recombinant inbred line population derived from an elite rice hybrid. Theoretical and Applied Genetics, 2005, 112: 85-96.
[14]   杨树明, 曾亚文, 王荔, 杜娟, 普晓英, 杨涛. 不同生长环境下水稻氮、磷、钾利用相关性状的QTL定位分析. 植物营养与肥料学报, 2015, 21(4): 823-835.
Yang S M, Zeng Y W, Wang L, Du J, Pu X Y, Yang T. Identification of QTL traits on N, P and K utilization in rice under different growth environments. Journal of Plant Nutrition and Fertilize, 2015, 21(4): 823-835. (in Chinese)
[15]   Zhu J M, Mickelson S M, Kaeppler S M, Lynch J P. Detection of quantitative trait loci for seminal root traits in maize (Zea mays L.) seedlings grown under differential phosphorus levels. Theoretical and Applied Genetics, 2006, 113: 1-10.
[16]   苏顺宗, 徐刚, 刘丹, 吴玲, 张啸, 任志勇, 聂治, 林海建, 高世斌. 两种磷水平下玉米苗期根系性状的QTL定位. 玉米科学, 2013, 21(4): 33-37.
Su S Z, Xu G, Liu D, Wu L, Zhang X, Ren Z Y, Nie Z, Lin H J, Gao S B. QTL mapping for root traits of maize seedling grown at two phosphorus levels. Journal of Maize Sciences, 2013, 21(4): 33-37. (in Chinese)
[17]   蔡红光, 刘建超, 米国华, 袁力行, 陈晓辉, 陈范骏, 张福锁. 田间条件下控制玉米开花前后根系性状的QTL定位. 植物营养与肥料学报, 2011, 17(2): 317-324.
Cai H G, Liu J C, Mi G H, Yuan L X, Chen X H, Chen F J, Zhang F S. QTL mapping for root traits around flowering stage of maize under field condition.Plant Nutrition and Fertilizer Science,2011, 17(2): 317-324. (in Chinese)
[18]   An D G, Su J Y, Liu Q Y, Zhu Y J, Tong Y P, Li J M, Jing R L, Li B, Li Z S. Mapping QTLs for nitrogen uptake in relation to the early growth of wheat (Triticum aestivum L.). Plant and Soil, 2006, 284: 73-84.
[19]   Su J Y, Xiao Y M, Li M, Liu Q Y, Li B, Tong Y P, Jia J Z, Li Z S. Mapping QTLs for phosphorus-deficiency tolerance at wheat seedling stage. Plant and Soil, 2006, 281: 25-36.
[20]   Li Z X, Ni Z F, Peng H R, Liu Z Y, Nie X L, Xu S B, Liu G, Sun Q X. Molecular mapping QTLs for root response to phosphorus deficiency at seedling stage in wheat (Triticum aestivum L.). Progress in Natural Science, 2007, 17(10): 1177-1184.
[21]   刘莹, 盖钧镒, 吕慧能. 大豆根区逆境耐性的种质鉴定及其与根系性状的关系. 作物学报, 2005, 31(9): 1132-1137.
Liu Y, Gai J Y, Lü H N. Identification of rhizosphere abiotic stress tolerance and related root traits in soybean [Glycine max (L.) Merr.]. Acta Agronomica Sinica, 2005, 31(9): 1132-1137. (in Chinese)
[22]   King C A, Purcell L C, Brye K R. Differential wilting among soybean genotypes in response to water deficit. Crop Science, 2009, 49: 290-291.
[23]   Lee G J, CARTER J T G, Villagarcia M R, LI Z, ZHOU X, GIBBS M O, Boerma H R. A major QTL conditioning salt tolerance in S-100 soybean and descendent cultivars. Theoretical and Applied Genetics, 2004, 109(8): 1610-1619.
[24]   BIANCHI-HALL C M, ARELLANO C, BOERMA H R, PARROTT W A, HUSSEY R S, ASHLEY D A, BAILEY M A, CARTER T E, RUFTY T W, MIAN M A R. Aluminum tolerance associated with quantitative trait loci derived from soybean PI416937. Crop Science, 2000, 40(2): 538-545.
[25]   朱向明, 韩秉进. 供氮水平对苗期大豆根系吸水特性的影响. 土壤与作物, 2013, 2(4): 173-176.
Zhu X M, Han B J. Root water uptake characteristics of soybean seedlings under different levels of nitrogen supply. Soil and Crop, 2013, 2(4): 173-176. (in Chinese)
[26]   Zhang D, Song H N, Cheng H, Hao D R, Wang H, Kan G Z, Jin H X, Yu D Y. The acid phosphatase-encoding gene GmACP1 contributes to soybean tolerance to low-phosphorus stress. Plos Genetics, 2014, 10(1): e1004061.
[27]   苏辉, 李志刚, 宋书宏. 低磷胁迫下大豆主要农艺性状的QTL定位. 西北农业学报, 2009, 18(1): 98-101, 116.
Su H, Li Z G, Song S H. Molecular mapping of QTLs major agronomic traits in soybean (Glycine max L.) under phosphorus deficiency stress. Acta Agriculturae Boreali-occidentalis Sinica, 2009, 18(1): 98-101, 116. (in Chinese)
[28]   Beebe S E, Rojas-Pierce M, Yan X L, Blair M W, Pedraza F, Munoz F M, Tohme J, Lynch J P. Quantitative trait loci for root architecture traits correlated with phosphorus acquisition in common bean. Crop Science, 2006, 46: 413-423.
[29]   Liang H Z, Yu Y L, Yang H Q, Xu L J, Dong W, Du H, Cui W W, Zhang H Y. Inheritance and QTL mapping of related root traits in soybean at the seedling stage. Theoretical and Applied Genetics, 2014, 127: 2127-2137.
[30]   Wang S C, Basten C J, Zeng Z B. Windows QTL Cartographer 2.5 User Manual. Department of Statistics, North Carolina State University, Raleigh, NC, 2005.
[31]   Yang J, Zhu J. Predicting superior genotypes in multiple environments based on QTL effects. Theoretical and Applied Genetics, 2005, 110: 1268-1274.
[32]   周蓉, 陈海峰, 王贤智, 伍宝朵, 陈水莲, 张晓娟, 吴学军, 杨中路, 邱德珍, 江木兰, 周新安. 大豆幼苗根系性状的QTL分析. 作物学报, 2011, 37(7): 1151-1158.
Zhou R, Chen H F, Wang X Z, Wu B D, Chen S L, Zhang X J, Wu X J, Yang Z L, Qiu D Z, Jiang M L, Zhou X A. QTL analysis of root traits of soybean at seedling stage. Acta Agronomica Sinica, 2011, 37(7): 1151-1158. (in Chinese)
[33]   王珍. 大豆SSR遗传图谱构建及重要农艺性状QTL分析[D]. 南宁: 广西大学, 2004.
Wang Z. Construction of soybean SSR based map and QTL analysis important agronomic traits [D]. Nanning: Guangxi University, 2004. (in Chinese)
[34]   梁慧珍. 大豆子粒性状的遗传及QTL分析[D]. 杨凌: 西北农林科技大学, 2006.
Liang H Z. Genetic analysis and QTL mapping of seed traits in soybean [Glycine max (L.) Merr] [D]. Yangling: Northwest A＆F University, 2006. (in Chinese)
[35]   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)
[36]   McCouch S R, Cho Y G, Yano M, Paul E, Blinstrub M, Morishima H, Kinoshita T. Report on QTL nomenclature. Rice Genetics Newsletter, 1997, 14: 11-14.
[37]   张志勇, 汤菊香, 王素芳, 王清连. 氮磷钾对植物侧根生长发育的影响及其生理机制. 广东农业科学, 2009, 36(5): 89-92.
Zhang Z Y, Tang J X, Wang S F, Wang Q L. Effects of N, P, K on growth and development of plants and its physiological mechanisms. Guangdong Agricultural Sciences, 2009, 36(5): 89-92. (in Chinese)
[38]   王金社, 李海旺, 赵团结, 盖钧镒. 重组自交家系群体4对主基因加多基因混合遗传模型分离分析方法的建立. 作物学报, 2010, 36(2): 191-201.
Wang J S, Li H W, Zhao T J, Gai J Y. Establishment of segregation analysis of mixed inheritance model with four major genes plus polygenes in recombinant inbred lines population. Acta Agronomica Sinica, 2010, 36(2): 191-201. (in Chinese)
[39]   Zhang Z H, Yu S B, Yu T, Huang Z, Zhu Y G. Mapping quantitative trait loci (QTLs) for seedling-vigor using recombinant inbred lines of rice (Oryza sativa L.). Field Crops Research, 2005, 91: 161-170.
[40]   Li Z K, Luo L J, Mei H W, Wang D L, Shu Q Y, Tabien R, Zhong D B, Ying C S, Stansel J W, Khush G S, Paterson A H. Overdominant epistatic loci are the primary genetic basis of inbreeding depression and heterosis in rice: I. Biomass and grain yield. Genetics, 2001, 158: 1737-1753.
[41]   Carlborg O, Haley C S. Epistasis: too often neglected in complex trait studies. Nature Reviews Genetics, 2004, 5: 618-625.
[42]   Salas P, Oyarzo-Llaipen J, Wang D, Chase K, Mansur L. Genetic mapping of seed shape in three populations of recombinant inbred lines of soybean [Glycine max (L.) Merr.]. Theoretical and Applied Genetics, 2006, 113(8): 1459-1466.
[43]   Orf J H, Chase K, Jarvik T, Mansur L M, Cregan P B, Adler F R, Lark K G. Genetics of soybean agronomic traits: I. Comparison of three related recombinant inbred populations. Crop Science, 1999, 39(6): 1642-1651.
[44]   Csanadi G, Vollmann J, Stift G, Lelley T. Seed quality QTLs identified in a molecular map of early maturing soybean. Theoretical and Applied Genetics, 2001, 103(6/7): 912-919.
[45]   Reinprecht Y, Poysa V, Yu K, Rajcan I, Ablett G, Pauls K. Seed and agronomic QTL in low linolenic acid, lipoxygenase-free soybean (Glycine max (L.) Merrill) germplasm. Genome, 2006, 49(12): 1510-1527.
[46]   Brensha W, Kantartzi S, Meksem K, Grier R, Barakat A, Lightfoot D, Kassem M. Genetic analysis of root and shoot traits in the ‘essex’ by ‘forrest’ recombinant inbred line (RIL) population of soybean [Glycine max (L.) Merr.]. Journal of Plant Genome Sciences, 2012, 1(1): 1-9.
[47]   Lian Q, Xiaohui C, Mantong M, Xiaolong Y, Hong L. QTL analysis of root traits as related to phosphorus efficiency in soybean. Annals of Botany, 2010, 106(1): 223-234.
[48]   Manavalan L, Prince S, Musket T, Chaky J, Deshmukh R. Vuong T, Song L, Cregan P, Nelson J, Shannon J, Specht J, Nguyen H. Identification of novel QTL governing root architectural traits in an interspecific soybean population. PLoS ONE, 2015, 10(3): e0120490.
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