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作物学报 ›› 2023, Vol. 49 ›› Issue (6): 1532-1541.doi: 10.3724/SP.J.1006.2023.24121

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

基于高密度遗传图谱定位大豆蛋白质含量相关的QTL

刘亭萱1,2,**(), 谷勇哲2,**(), 张之昊3, 王俊1,*(), 孙君明2,*(), 邱丽娟1,2,*()   

  1. 1长江大学, 湖北荆州 434025
    2中国农业科学院作物科学研究所/农业农村部北京大豆生物学重点实验室/农业农村部种质资源利用重点实验室, 北京 100081
    3东北农业大学农学院, 黑龙江哈尔滨 150030
  • 收稿日期:2022-05-19 接受日期:2022-10-10 出版日期:2023-06-12 网络出版日期:2022-10-20
  • 通讯作者: *邱丽娟, E-mail: qiulijuan@caas.cn;王俊, E-mail: wangjagri@yangtzeu.edu.cn;孙君明, E-mail: qiulijuan@caas.cn
  • 作者简介:刘亭萱, E-mail: 18834408825@163.com;
    谷勇哲, E-mail: guyongzhe@caas.cn第一联系人:**同等贡献
  • 基金资助:
    中国和乌拉圭联合实验室合作项目(2018YFE0116900)

Mapping soybean protein QTLs based on high-density genetic map

LIU Ting-Xuan1,2,**(), GU Yong-Zhe2,**(), ZHANG Zhi-Hao3, WANG Jun1,*(), SUN Jun-Ming2,*(), QIU Li-Juan1,2,*()   

  1. 1Yangtze University, Jingzhou 434025, Hubei, China
    2National Key Facility for Gene Resources and Genetic Improvement of MARA/Beijing Key Laboratory of Soybean Biology of MARA/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    3College of Agriculture, Northeast Agricultural University, Harbin 150030, Heilongjiang, China
  • Received:2022-05-19 Accepted:2022-10-10 Published:2023-06-12 Published online:2022-10-20
  • Contact: *E-mail: qiulijuan@caas.cn;E-mail: wangjagri@yangtzeu.edu.cn;E-mail: qiulijuan@caas.cn
  • About author:First author contact:**Contributed equally to this work
  • Supported by:
    Sino-Uruguayan Joint Laboratory(2018YFE0116900)

摘要:

大豆是重要的粮食作物和经济作物, 其籽粒蛋白约为40%, 是优质植物蛋白主要来源之一。挖掘控制大豆高蛋白数量性状位点(Quantitative trait loci, QTL)以及分子标记育种对高蛋白大豆培育具有重要的意义。本研究利用蛋白含量存在明显差异的中黄35 (Zhonghuang 35, ZH35)和中黄13 (Zhonghuang 13, ZH13)杂交构建的包含192个株系的重组自交系群体为供试材料, 通过对两亲本及RIL群体重测序, 构建了包含4879个bin标记的高密度遗传图谱, 总遗传距离为3760.71 cM, 相邻标记间的遗传距离为0.77 cM。RIL群体及亲本分别于北京顺义和河南濮阳种植, 2个环境共检测到15个蛋白含量相关QTL位点, 分布于5号、12号、15号、17号、18号、19号和20号染色体, 贡献率为4.36%~11.39%。其中, 北京顺义和河南濮阳检测到qPro-20-1qPro-20-3, 2个QTL贡献率分别为7.65%和7.58%, 重叠区域包括33个基因。本研究有助于精细定位和图位克隆大豆蛋白含量相关基因, 并为进一步培育高蛋白大豆品种提供基因资源。

关键词: 大豆, 蛋白质含量, 重组自交系, bin图谱, QTL定位

Abstract:

Soybean is an important food crop and economic crop, and its grain protein is about 40%, which is one of the main sources of high-quality vegetable protein. Mining the quantitative trait loci (QTL) that control soybean high protein and molecular marker breeding are of great significance for the breeding of high protein soybean. In this study, a recombinant inbred line population consisting of 192 lines constructed by crossing Zhonghuang 35 (ZH35) and Zhonghuang 13 (ZH13) with significant differences in protein content was used as the experimental materials. A high-density genetic map containing 4879 bin markers was constructed with the total genetic distance of 3760.71 cM and the genetic distance between adjacent markers of 0.77 cM by resequencing the two parents and the RIL population. The RIL population and its parents were grown in Shunyi, Beijing and Puyang, Henan, respectively. A total of 15 protein content-related QTL loci were detected in the two environments, which were distributed on chromosomes 5, 12, 15, 17, 18, 19, and 20, respectively. The contribution rate was 4.36%-11.39%, among which, qPro-20-1 and qPro-20-3 were detected in Shunyi, Beijing and Puyang, Henan, respectively. The contribution rates of the two QTLs were 7.65% and 7.58%, respectively, and the overlapping regions included 33 genes. This study is helpful for fine mapping and map-based cloning of soybean protein content-related genes, and provides genetic resources for further breeding of high-protein soybean varieties.

Key words: soybean, protein content, recombinant inbred lines, bin map, QTL mapping

表1

2年两代亲本及重组自交系群体蛋白质含量统计分析"

环境
Environment
中黄35
Zhonghuang 35
(mean±SD, %)
中黄13
Zhonghuang 13 (mean±SD, %)
RIL群体RIL population
最大值
Max. (%)
最小值
Min. (%)
变异系数
CV (%)
峰度
Kurtosis
偏度
Skewness
北京顺义
Shunyi, Beijing
38.24±0.17 44.99±0.25 46.00 37.51 2.48 0.22 -0.07
河南濮阳
Puyang, Henan
38.20±0.22 44.20±0.32 45.98 37.04 2.98 -0.17 0.02

图1

2个环境中F2:6群体蛋白含量表型箱型图"

图2

ZH35/ZH13重组自交系及亲本的蛋白含量表型分布特征 A: RIL群体在北京顺义的蛋白含量分布图; B: RIL群体在河南濮阳的蛋白含量分布图。ZH35: 中黄35; ZH13: 中黄13。"

图3

染色体标记分布图 X轴代表染色体编号; Y轴代表遗传距离(cM); 蓝色代表bin标记。"

表2

中黄35/中黄13的RIL群体连锁图谱的基本信息"

染色体
Chr.
上图标记数
Total marker
总图距
Total distance (cM)
平均图距
Average distance (cM)
小于5 cM gap数量/gap总数×100%
Gap less than 5 cM/gap total number×100%
Chr.01 247 169.29 0.69 100
Chr.02 264 213.61 0.81 99.62
Chr.03 301 179.80 0.60 100
Chr.04 226 205.36 0.91 99.56
Chr.05 203 177.41 0.87 100
Chr.06 290 291.38 1.00 98.62
Chr.07 235 186.79 0.79 100
Chr.08 289 216.94 0.75 100
Chr.09 245 183.47 0.75 100
Chr.10 227 199.14 0.88 100
Chr.11 214 219.26 1.02 98.13
Chr.12 205 163.48 0.80 99.02
Chr.13 126 96.81 0.77 97.62
Chr.14 204 153.72 0.75 99.51
Chr.15 402 215.29 0.54 99.75
Chr.16 209 158.00 0.76 100
Chr.17 220 192.44 0.87 99.55
Chr.18 266 198.39 0.75 99.25
Chr.19 299 176.74 0.59 100
Chr.20 207 163.40 0.79 99.52
平均Average 243.95 188.03 0.77 99.51

表3

2种环境下检测到的大豆蛋白质含量QTL"

环境
Environment
位点名称
QTL name
染色体
Chr.
定位区间
Mapping interval (bp)
QTL位置
Position (cM)
阈值
LOD
加性效应
Additive
effect
贡献率
R2 (%)
报道位点
Reported locus
北京顺义
Shunyi, Beijing
qPro-15-1 15 bin3524-bin3532 71.51 3.05 0.33 5.13 [40][41]
qPro-17-1 17 bin4087-bin4090 19.41 2.71 -0.31 4.63 New
qPro-17-2 17 bin4095-bin4110 28.11 3.67 -0.36 6.21 New
qPro-18-1 18 bin4523-bin4526 170.21 2.57 -0.31 4.44 New
qPro-18-2 18 bin4525-bin4529 175.91 4.14 -0.39 7.26 New
qPro-19-1 19 bin4807-bin4811 130.71 2.57 -0.31 4.38 [42]
qPro-19-2 19 bin4816-bin4821 140.61 3.41 -0.36 5.77 [42]
qPro-19-3 19 bin4824-bin4829 147.41 2.85 -0.33 4.88 New
qPro-20-1 20 bin5059-bin5063 161.01 4.48 -0.40 7.65 New
河南濮阳
Puyang, Henan
qPro-5-1 5 bin1167-bin1170 84.81 2.67 -0.38 4.83 [39]
qPro-5-2 5 bin1161-bin1164 91.41 4.82 -0.50 8.51 [36] [37] [38]
qPro-5-3 5 bin1154 100.71 3.50 -0.43 6.29 [36] [36] [38]
qPro-12-1 12 bin2906-bin2911 95.01 3.42 0.42 5.94 New
qPro-20-2 20 bin5046-bin5049 150.61 4.14 -0.46 7.28 New
qPro-20-3 20 bin5057-bin5060 159.11 4.33 -0.47 7.58 New

图4

重叠区域中标记bin5059与bin5060的基因型与表型相关性分析 (A): bin5059的基因型与表型相关性分析; (B): bin5060的基因型与表型相关性分析。图中横坐标为基因型, 低蛋白记为a和A, 高蛋白记为b和B, 纵坐标为蛋白含量。**: P < 0.01。"

表4

重叠区间内候选基因的功能注释"

基因名称
Gene name
功能注释
Functional annotation
Glyma.20G237700 α/β-水解酶折叠蛋白 Alpha/beta-Hydrolase fold-containing protein
Glyma.20G237800 植物转化酶/果胶甲酯酶抑制剂 Plant invertase/pectin methylesterase inhibitor
Glyma.20G238000 脂质生物合成过程 Lipid biosynthetic process
Glyma.20G238400 WD40重复蛋白 WD40 repeat-containing protein
Glyma.20G238500 预测PRP38剪接因子 Predicted PRP38-like splicing factor
Glyma.20G238600 线粒体靶向单链DNA结合蛋白 Mitochondrially targeted single-stranded DNA binding protein
Glyma.20G238700 蛋白质去磷酸化; 蛋白质丝氨酸/苏氨酸磷酸酶活性
Protein dephosphorylation; protein serine/threonine phosphatase activity
Glyma.20G238800 锌指(C3HC4型环指)家族蛋白; BRCT结构域蛋白/RING/U-box蛋白
Zinc finger (C3HC4-type RING finger) family protein; BRCT domain-containing protein/RING/U-box protein
Glyma.20G238900 磷酸甘油酸变位酶家族蛋白 Phosphoglycerate mutase family protein
Glyma.20G239000 干旱响应家族蛋白; 干旱诱导的19蛋白(Di19), 锌结合
Drought-responsive family protein; rought induced 19 protein (Di19), zinc-binding
Glyma.20G239100 细胞内蛋白质转运; 囊泡介导的转运 Intracellular protein transport; vesicle-mediated transport
基因名称
Gene name
功能注释
Functional annotation
Glyma.20G239200 蛋白质转运; 导入α亚型 Protein transport; importin alpha isoform
Glyma.20G239300 多核苷酸转移酶, 核糖核酸酶H样超家族蛋白; 3¢-5¢外切酶活性
Polynucleotidyl transferase, ribonuclease H-like superfamily protein; 3¢-5¢ exonuclease activity
Glyma.20G239600 分泌相关的RAS超家族2; 细胞内蛋白质转运
Secretion-associated RAS super family 2; intracellular protein transport
Glyma.20G239700 PsbP样蛋白2; 光系统II氧进化复合物 PsbP-like protein 2; photosystem II oxygen evolving complex
Glyma.20G239800 跨膜转运; ATP酶活性, 与物质的跨膜运动相耦合
Transmembrane transport; ATPase activity, coupled to transmembrane movement of substances
Glyma.20G240000 碳水化合物代谢过程; β-N-乙酰氨基己糖苷酶活性
Carbohydrate metabolic process; beta-N-acetylhexosaminidase activity
Glyma.20G240200 多核苷酸转移酶, 核糖核酸酶H样超家族蛋白
Polynucleotidyl transferase, ribonuclease H-like superfamily protein
Glyma.20G240300 谷胱甘肽代谢过程; 谷胱甘肽转移酶活性 Glutathione metabolic process; glutathione transferase activity
Glyma.20G240400 质体转录活性17; 钴胺素合成蛋白 Plastid transcriptionally active 17; cobalamin synthesis protein
Glyma.20G240500 裂解和多腺苷酸化特异性因子(CPSF) A亚基蛋白; 通过剪接体剪接mRNA
Cleavage and polyadenylation specificity factor (CPSF) A subunit protein; mRNA splicing, via spliceosome
Glyma.20G240600 α/β-水解酶超家族蛋白; 脂质代谢过程 Alpha/beta-Hydrolases superfamily protein; lipid metabolic process
Glyma.20G240700 核糖体L29家族蛋白; 核糖体的结构成分 Ribosomal L29 family protein; structural constituent of ribosome
Glyma.20G240800 果胶裂解酶样超家族蛋白; 果胶分解代谢过程 Pectin lyase-like superfamily protein; pectin catabolic process
Glyma.20G240900 脂质生物合成过程 Lipid biosynthetic process

图5

SoyBase网站候选区间内基因在种子不同发育时期表达量"

[1] Huang J H, Ma Q B, Cai Z D, Xia Q J, Cheng Y B. Identification and mapping of stable QTLs for seed oil and protein content in soybean [Glycine max (L.) Merr]. J Agric Food Chem, 2020, 68: 6448-6460.
doi: 10.1021/acs.jafc.0c01271
[2] Wang J, Chen P, Wang D, Shannon G, Zeng A, Orazaly M, Wu C. Identification and mapping of stable QTL for protein content in soybean seeds. Mol Breed, 2015, 35: 92-102.
doi: 10.1007/s11032-015-0285-6
[3] Pathan S M, Vuong T, Clark K, Lee J D, Sleper D A. Genetic mapping and confirmation of quantitative trait loci for seed protein and oil contents and seed weight in soybean. Crop Sci, 2013, 53: 765-774.
doi: 10.2135/cropsci2012.03.0153
[4] Wilcox J R. Increasing seed protein in soybean with eight cycles of recurrent selection. Crop Sci, 1998, 38: 1536-1540.
doi: 10.2135/cropsci1998.0011183X003800060021x
[5] Keim P, Diers B W, Olson T C, Shoemaker R C. RFLP mapping in soybean: association between marker loci and variation in quantitative traits. Genetics, 1990, 126: 735-742.
doi: 10.1093/genetics/126.3.735 pmid: 1979039
[6] Du W J, Wang M, Fu S X, Yu D Y. Mapping QTLs for seed yield and drought susceptibility index in soybean (Glycine max L.) across different environments. J Genet Genomics, 2009, 36: 721-731.
doi: 10.1016/S1673-8527(08)60165-4
[7] Pipolo C V, Pipolo A E, Haleem A H, Boerma H R, Sinclair T R. Identification of QTLs associated with limited leaf hydraulic conductance in soybean. Euphytica, 2012, 186: 679-686.
doi: 10.1007/s10681-011-0535-6
[8] Powell W, Morgante M, Andre C, Hanafey M, Vogel J, Tingey S, Rafalski A. The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Mol Breed, 1996, 2: 225-238.
doi: 10.1007/BF00564200
[9] Apuya N R, Frazier B L, Keim P, Roth E J, Lark K G. Restriction fragment length polymorphisms as genetic markers in soybean, Glycine max (L.) Merrill. Theorl Appl Genet, 1988, 75: 889-901.
[10] Keim P S, Diers B W, Olson T C, Shoemaker R C. RFLP mapping in soybean: association between marker loci and variation in quantitative traits. Genetics, 1990, 126: 735-742.
doi: 10.1093/genetics/126.3.735 pmid: 1979039
[11] Keim P, Schupp J M, Travis S E, Clayton K, Zhu T, Shi L, Ferreira A, Webb D M. A high-density soybean genetic map based on AFLP markers. Crop Sci, 1997, 37: 537-543.
doi: 10.2135/cropsci1997.0011183X003700020038x
[12] Akkaya M S, Shoemaker R C, Specht J E, Bhagwat A A, Cregan P B. Integration of simple sequence repeat DNA markers into a soybean linkage map. Crop Sci, 1995, 35: 1439-1445.
doi: 10.2135/cropsci1995.0011183X003500050030x
[13] Cregan P B, Jarvik T, Bush A L, Kaya N, Vantoai T T, Lohnes D G, Chung J. An integrated genetic linkage map of the soybean genome. Crop Sci, 1999, 39: 1464-1490.
doi: 10.2135/cropsci1999.3951464x
[14] Liu N X, Li M, Hu X B, Ma Q B, Mu Y H, Tan Z Y, Xia Q J, Zhang G Y, Nian H. Construction of high-density genetic map and QTL mapping of yield-related and two quality traits in soybean RILs population by RAD-sequencing. BMC Genomics, 2017, 18: 466-479.
doi: 10.1186/s12864-017-3854-8 pmid: 28629322
[15] Varshney R K, Nayak S N, May G D, Jackson S A. Next- generation sequencing technologies and their implications for crop genetics and breeding. Trends Biotechnol, 2009, 27: 522-530.
doi: 10.1016/j.tibtech.2009.05.006 pmid: 19679362
[16] Joosen R V L, Arends D, Willems L A J, Ligterink W, Jansen R C, Hilhorst H W M. Visualizing the genetic landscape of Arabidopsis seed performance. Plant Physiol, 2012, 158: 570-589.
doi: 10.1104/pp.111.186676
[17] Yu H H, Xie W B, Wang J, Xing Y Z, Xu C G, Li X H, Xiao J H, Zhang Q F. Gains in QTL detection using an ultra-high density SNP map based on population sequencing relative to traditional RFLP/SSR markers. PLoS One, 2011, 6: e17595.
doi: 10.1371/journal.pone.0017595
[18] Song W B, Wang B B, Hauck A L, Dong X M, Li J P, Lai J S. Genetic dissection of maize seedling root system architecture traits using an ultra-high density bin-map and a recombinant inbred line population. J Integr Plant Biol, 2015, 58: 266-279.
doi: 10.1111/jipb.12452
[19] Cheng Y B, Ma Q B, Ren H L, Xia Q J, Song E L, Tan Z Y, Li S X, Zhang G Y, Nian H. Fine mapping of a phytophthora- resistance gene RpsWY in soybean (Glycine max L.) by high- throughput genome-wide sequencing. Theor Appl Genet, 2017, 130: 1041-1051.
doi: 10.1007/s00122-017-2869-5
[20] Jiang N F, Shi S L, Shi H, Khanzada H, Wassan G M, Zhu C L, Peng X S, Yu Q Y, Chen X R, He X P, Fu J R, Hu L F, Xu J, Ou-Yang L J, Sun X T, Zhou D H, He H H, Bian J M. Mapping QTL for seed germinability under low temperature using a new high-density genetic map of rice. Front Plant Sci, 2017, 8: 1223-1232.
doi: 10.3389/fpls.2017.01223 pmid: 28747923
[21] Yu T, Lei Y, Lu H F, Zhang B, Li Y F, Liu C, Ge T L, Liu Y L, Han J N, Li Y H, Qiu L J. QTL analysis for plant height and fine mapping of two environmentally stable QTLs with major effects in soybean. J Integr Agric, 2022, 21: 933-946.
doi: 10.1016/S2095-3119(21)63693-6
[22] Pan L Y, He J B, Zhao T J, Xing G N, Wang Y F, Yu D Y, Chen S Y, Gai J Y. Efficient QTL detection of flowering date in a soybean RIL population using the novel restricted two-stage multi-locus GWAS procedure. Theor Appl Genet, 2018, 131: 2581-2599.
doi: 10.1007/s00122-018-3174-7 pmid: 30167759
[23] Wang W B, Liu M F, Wang Y F, Li X L, Cheng S X, Shu L P, Yu Z P, Kong J J, Zhao T J, Gai J Y. Characterizing two inter-specific bin maps for the exploration of the QTLs/genes that confer three soybean evolutionary traits. Front Plant Sci, 2016, 7: 1248-1264.
doi: 10.3389/fpls.2016.01248 pmid: 27602037
[24] Allen G C, Flores-Vergara M A, Krasnyanski S, Kumar S, Thompson W F. A modified protocol for rapid DNA isolation from plant tissues using cetyltrimethylammonium bromide. Nat Protoc, 2006, 1: 2320-2325.
doi: 10.1038/nprot.2006.384 pmid: 17406474
[25] Wang L X, Wang J, Luo G L, Yuan X X, Gong D, Hu L L, Chen H L, Wang S H, Chen X, Cheng X Z. Construction of a high- density adzuki bean genetic map and evaluation of its utility based on a QTL analysis of seed size. J Integr Agric, 2021, 20: 1753-1761.
doi: 10.1016/S2095-3119(20)63343-3
[26] Shen Y T, Du H L, Liu Y C, Ni L B, Wang Z, Liang C Z, Tian Z X. Update soybean Zhonghuang 13 genome to a golden reference. Sci China Life Sci, 2019, 62: 1257-1260.
doi: 10.1007/s11427-019-9822-2 pmid: 31444683
[27] Liu D Y, Ma C X, Hong W G, Huang L, Liu M, Liu H, Zeng H P, Deng D J, Xin H, Song J, Xu C H, Sun X W, Hou X L, Wang X W, Zheng H K. Construction and analysis of high-density linkage map using high-throughput sequencing data. PLoS One, 2014, 9: e98855.
doi: 10.1371/journal.pone.0098855
[28] McCouch S R, Cho Y G, Yano M, Paul E, Blinstrub M, Morishima H, Kinoshita T. Report on QTL nomenclature. Rice Genet Newsl, 1997, 14: 11-12.
[29] 董骥驰, 杨靖, 郭涛, 陈立凯, 陈志强, 王慧. 基于高密度Bin图谱的水稻抽穗期QTL定位. 作物学报, 2018, 44: 938-946.
doi: 10.3724/SP.J.1006.2018.00938
Dong J C, Yang J, Guo T, Chen L K, Chen Z Q, Wang H. QTL mapping for heading date in rice using high-density bin map. Acta Agron Sin, 2018, 44: 938-946 (in Chinese with English abstract).
doi: 10.3724/SP.J.1006.2018.00938
[30] Hori K, Kobayashi T, Shimizu A, Sato K, Takeda K, Kawasaki S. Efficient construction of high-density linkage map and its application to QTL analysis in barley. Theor Appl Genet, 2003, 107: 806-813.
pmid: 12838391
[31] Xu X Y, Zeng L, Tao Y, Vuong T, Wan J R, Boerma R, Noe J, Li Z L, Finnerty S, Pathan S M, Shannon J G, Nguyen H T. Pinpointing genes underlying the quantitative trait loci for root-knot nematode resistance in palaeopolyploid soybean by whole genome resequencing. Proc Natl Acad Sci USA, 2013, 110: 13469-13474.
doi: 10.1073/pnas.1222368110 pmid: 23898176
[32] Karikari B, Li S G, Bhat J A, Cao Y, Kong J J, Yang J Y, Gai J Y, Zhao T J. Genome-wide detection of major and epistatic effect QTLs for seed protein and oil content in soybean under multiple environments using high-density bin map. Int J Mol Sci, 2019, 20: 979-1000.
doi: 10.3390/ijms20040979
[33] Ma Y J, Ma W Y, Hu D Z, Zhang X N, Yuan W J, He X H, Kan G Z, Yu D Y. QTL mapping for protein and sulfur-containing amino acid contents using a high-density bin-map in soybean (Glycine max L. Merr.). J Agric Food Chem, 2019, 67: 12313-12321.
doi: 10.1021/acs.jafc.9b04497
[34] Huang J H, Ma Q B, Cai Z D, Xia Q J, Li S X, Jia J, Chu L, Lian T X, Nian H, Cheng Y B. Identification and mapping of stable QTLs for seed oil and protein content in soybean [Glycine max (L.) Merr.]. J Agric Food Chem, 2020, 68: 6448-6460.
doi: 10.1021/acs.jafc.0c01271
[35] Wang J, Mao L, Zeng Z Q, Yu X B, Lian J Q, Feng J, Yang W Y, An J G, Wu H Y, Zhang M G, Liu L Z. Genetic mapping high protein content QTL from soybean ‘Nanxiadou 25’ and candidate gene analysis. BMC Plant Biol, 2021, 21: 388.
doi: 10.1186/s12870-021-03176-2 pmid: 34416870
[36] Patil G, Vuong T D, Kale S, Valliyodan B, Deshmukh R, Zhu C S, Wu X L, Bai Y H, Yungbiuth D, Lu F, Kumpatla S, Shannon J G, Varshney R K, Nguyen H T. Dissecting genomic hotspots underlying seed protein, oil, and sucrose content in an interspecific mapping population of soybean using high-density linkage mapping. Plant Biotechnol J, 2018, 16: 1939-1953.
doi: 10.1111/pbi.12929 pmid: 29618164
[37] Mansur L M, Orf J H, Chase K, Jarvik T, Cregan P B, Lark K G. Genetic mapping of agronomic traits using recombinant inbred lines of soybean. Crop Sci, 1996, 36: 1327-1336.
doi: 10.2135/cropsci1996.0011183X003600050042x
[38] Mao T T, Jiang Z F, Han Y P, Teng W L, Zhao X, Li W B. Identification of quantitative trait loci underlying seed protein and oil contents of soybean across multi-genetic backgrounds and environments. Plant Breed, 2013, 132: 630-641.
doi: 10.1111/pbr.12091
[39] Li X Y, Xue H, Zhang K X, Li W B, Ning H L. Mapping QTLs for protein and oil content in soybean by removing the influence of related traits in a four-way recombinant inbred line population. J Agric Sci, 2020, 158: 659-675.
[40] Wang W, Sun Y, Yang P, Cai X, Yang L, Ma J, Ou Y, Liu T, Ali I, Liu D. A high density SLAF-seq SNP genetic map and QTL for seed size, oil and protein content in upland cotton. BMC Genomics, 2019, 20: 599-610.
doi: 10.1186/s12864-019-5819-6 pmid: 31331266
[41] Zhang J P, Wang X Z, Lu Y M, Bhusal S J, Song Q J, Cregan P B, Yen Y, Brown M, Jiang G L. Genome-wide scan for seed composition provides insights into soybean quality improvement and the impacts of domestication and breeding. Mol Plant, 2018, 11: 460-472.
doi: S1674-2052(17)30386-6 pmid: 29305230
[42] Kaleri A A, Li L, Zhang Y, Liu W, Jiang C, Zhang Y, Liu C, Kaleri H A, Nizamani M M, Mehmood A, Bahadur S, Li W X, Ning H. Recognition of QTL for seed protein and oil content in two soybean recombinat inbred lines populations. J Anim Plant Sci, 2021, 31: 1669-1685.
[43] 邱红梅, 厉志, 于妍, 高淑芹, 马晓萍, 郑宇宏, 孟凡凡, 侯云龙, 王跃强, 王曙明. 基于元分析的大豆含硫氨基酸相关基因挖掘与信息学分析. 中国油料作物学报, 2015, 37: 141-147.
Qiu H M, Li Z, Yu Y, Gao S Q, Ma X P, Zheng Y H, Meng F F, Hou Y L, Wang Y Q, Wang S M. Mining and analysis of genes related to sulfur-containing amino acids in soybean based on meta-QTL. Chin Oil Crop Sci, 2015, 37: 141-147. (in Chinese with English abstract)
[44] Li Z W, Meyer S, Essig J S, Liu Y, Schapaugh M A, Muthukrishnan S, Hainline B E, Trick H N. High-level expression of maize γ-zein protein in transgenic soybean (Glycine max). Mol Breed, 2005, 16: 11-20.
doi: 10.1007/s11032-004-7658-6
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