欢迎访问作物学报,今天是

作物学报 ›› 2022, Vol. 48 ›› Issue (3): 635-643.doi: 10.3724/SP.J.1006.2022.14008

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

利用PyBSASeq算法挖掘大豆百粒重相关位点与候选基因

王娟1(), 张彦威2, 焦铸锦1, 刘盼盼1, 常玮1,*()   

  1. 1南阳师范学院生命科学与农业工程学院, 河南南阳 473061
    2山东省农业科学院作物研究所, 山东济南 250100
  • 收稿日期:2021-01-18 接受日期:2021-04-26 出版日期:2021-06-04 网络出版日期:2021-06-04
  • 通讯作者: 常玮
  • 作者简介:E-mail: 252576027@qq.com
  • 基金资助:
    河南省科技攻关项目(192102110125);南阳师范学院青年基金项目(QN2016003)

Identification of QTLs and candidate genes for 100-seed weight trait using PyBSASeq algorithm in soybean

WANG Juan1(), ZHANG Yan-Wei2, JIAO Zhu-Jin1, LIU Pan-Pan1, CHANG Wei1,*()   

  1. 1School of Life Science and Agricultural Engineering, Nanyang Normal University, Nanyang 473061, Henan, China
    2Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, Shandong, China
  • Received:2021-01-18 Accepted:2021-04-26 Published:2021-06-04 Published online:2021-06-04
  • Contact: CHANG Wei
  • Supported by:
    Science and Technology Project of Henan Province(192102110125);Youth Foundation Project of Nanyang Normal University(QN2016003)

摘要:

基于量化染色体区间上与目标性状相关多态性位点的富集程度这一原理开发的PyBSASeq算法, 被证实更适合进行基于BSA-seq技术的复杂数量性状遗传解析。本研究采用该算法, 以‘滑皮豆’和‘齐黄26’为亲本杂交所衍生的包含149个RILs为材料, 挖掘与大豆百粒重相关位点, 共获得11个与目标性状紧密关联的候选区域, 分别位于1号、2号、4号、7号、9号、14号和16号染色体, 其中qSW4-1qSW9-1qSW9-2qSW7-1与已报道大豆百粒重QTL位置一致。候选区域共包含218个编码基因, 根据基因表达特性和单倍型分析结果, 最终获得2个与目标性状相关的候选基因Glyma.02G075000Glyma.04G082500, 分别参与蔗糖的运输和维生素E的生物合成。研究结果将有助于大豆百粒重遗传机制的阐释, 并为基于BSA-Seq技术的数量性状研究提供参考。

关键词: 大豆, 百粒重, PyBSASeq, SLAF-Seq

Abstract:

The PyBSASeq algorithm, based on quantifying the enrichment of likely trait-associated SNPs in a chromosomal interval, was proved more suitable for genetic analysis of complex quantitative traits in bulked segregant analysis. In this study, to identify the loci and candidate genes of 100-seed weight (SW) in soybean using the PyBSASeq algorithm, 149 RILs derived from the hybrid of ‘Huapidou’ and ‘Qihuang 26’ were used as materials. As a result, 11 candidate regions closely associated with SW were identified by mining the 100-grain weight related loci of soybean, which were located on chromosomes 1, 2, 4, 7, 9, 14, and 16, respectively. Among these regions, qSW4-1, qSW9-1, qSW9-2, and qSW7-1 were consistent with the QTLs for 100-seed weight reported previously, and a total of 218 coding genes were included in the candidate regions. Among these genes, Glyma.02G075000 and Glyma.04G082500 were predicted to be candidate genes using gene expressional and haplotype analysis. Bioinformatics analysis showed that the candidate genes were mainly involved in sugar transport and biosynthesis of Vitamin E. The results will be helpful to elucidate the genetic mechanism of seed weight regulation in soybean and provide a reference for the study of quantitative trait based on BSA-Seq method.

Key words: soybean, 100-seed weight, PyBSASeq, SLAF-Seq

图1

RIL群体及亲本百粒重分布直方图"

表1

大豆百粒重相关sSNP汇总表"

染色体
Chromosome
QTL数量
Numbers of QTLs
SNP总数
Total number of SNP
QTL/SNP
(%)
P值范围
Range of P-value
1 9 57 15.79 1.16×10-4-6.71×10-3
2 7 408 1.72 3.07×10-3-5.99×10-3
4 33 233 14.16 1.57×10-3-9.36×10-3
7 41 163 25.15 9.51×10-4-8.03×10-3
9 8 136 5.88 1.45×10-3-8.13×10-3
14 73 170 42.94 9.29×10-5-8.74×10-3
16 10 239 4.18 6.71×10-4-8.46×10-3
总数Total 181 5110 3.54 9.29×10-5-9.36×10-3

图2

基于PyBSASeq算法定位的大豆百粒重相关QTL位点"

表2

大豆百粒重相关QTL位点汇总"

QTL 染色体
Chromosome
置信区间
Confidence interval (bp)
N_s/N_t 阈值
Threshold
标记/位置
Marker/position
P
P-value
qSW1-1 1 3353593-4295069 0.80 0.20 Marker4145223/4003887 1.16×10-3
qSW2-1 2 6277409-6795574 0.46 0.15 Marker6292178/6353955 3.07×10-3
qSW4-1[39,40] 4 6745068-7489088 0.84 0.13 Marker6412385/6990477 1.57×10-3
qSW7-1[41,42,43] 7 7022337-13703737 1.00 0.33 Marker5915595/8069918 9.51×10-4
qSW7-2 7 36784876-38304878 1.00 0.10 Marker5871955/37004467 1.82×10-3
qSW9-1[13] 9 13567640-13932246 0.67 0.33 Marker4706727/13932246 1.45×10-3
qSW9-2[44,45,46] 9 44196936-44232803 0.60 0.20 Marker4584679/44196936 2.06×10-3
qSW9-3 9 47099980-47940126 1.00 0.33 Marker4755711/47109866 3.11×10-3
qSW14-1 14 8672359-12688053 1.00 0.09 Marker5172395/11147027 2.93×10-4
qSW14-2 14 41729998-42423216 1.00 0.13 Marker5029131/41946443 9.29×10-5
qSW16-1 16 33408123-35629720 0.83 0.17 Marker3332493/33871753 6.71×10-4

图3

种子发育不同时期大豆百粒重相关候选基因特异性表达结果 带有下画线的基因: 大豆百粒重候选基因。DAF: 开花后天数。"

表3

大豆百粒重相关候选基因汇总表"

QTL 基因
Gene
位置
Position
功能注释
Annotation
qSW2-1 Glyma.02G073200 Chr02:6399972..6405250 α-1,4-岩藻糖基转移酶 Alpha-1,4-fucosyltransferase
qSW2-1 Glyma.02G075000 Chr02:6535991..6539362 蔗糖转运蛋白(SUT1) Sugar transport protein (SUT1)
qSW4-1 Glyma.04G082300 Chr04:6945685..6946469 维生素E生物合成(生育酚环化酶)Vitamin E biosynthesis (tocopherols)
qSW4-1 Glyma.04G082500 Chr04:6948445..6954177 维生素E生物合成(生育酚环化酶)Vitamin E biosynthesis (tocopherols)
qSW7-2 Glyma.07G200700 Chr07:36897410..36898244 HSP20家族蛋白 HSP20 family protein
qSW9-2 Glyma.09G249500 Chr09:47056885..47062577 羧肽酶C Carboxypeptidase C
qSW14-1 Glyma.14G101900 Chr14:10079123..10080100 ncRNA
qSW14-2 Glyma.14G170900 Chr14:42298358..42306613 β-D-木糖苷酶7相关 β-D-xylosidase 7-related
qSW16-1 Glyma.16G176000 Chr16:33732712..33735090 葡萄糖基/葡萄糖醛基转移酶Glucosyl/glucuronosyl transferases

图4

候选基因单倍型分析 *, P < 0.05; **, P < 0.01"

[1] Liu S L, Zhang M, Feng F, Tian Z X. Toward a “Green revolution” for soybean. Mol Plant, 2020, 5:688-697.
doi: 10.1093/mp/sss011
[2] Liu Z X, Li H H, Fan X H, Huang W, Yang J Y, Wen Z X, Li Y H, Guan R X, Guo Y, Chang R Z, Wang D C, Chen P Y, Wang S M, Qiu L J. Phenotypic characterization and genetic dissection of nine agronomic traits in Tokachi nagaha and its derived cultivars in soybean (Glycine max (L.) Merr.). Plant Sci, 2017, 256:72-86.
doi: 10.1016/j.plantsci.2016.11.009
[3] 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.
pmid: 1979039
[4] Pathan S M, Vuong T, Clark K, Lee J D, Shannon J G, Roberts C A, Ellersieck M R, Burton J W, Cregan P B, Hyten D L, Nguyen H T, 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
[5] Ramamurthy R K, Jedlicka J, Graef G L, Waters B M. Identification of new QTLs for seed mineral, cysteine, and methionine concentrations in soybean [Glycine max(L.) Merr.]. Mol Breed, 2014, 34:431-445.
doi: 10.1007/s11032-014-0045-z
[6] Kulkarni K P, Asekova S, Lee D H, Bilyeu K, Song J T, Lee J D. Mapping QTLs for 100-seed weight in an interspecific soybean cross of Williams 82 (Glycine max) and PI 366121(Glycine soja). Crop Past Sci, 2017, 68:148-155.
doi: 10.1071/CP16246
[7] Zhou Z K, Jiang Y, Wang Z, Gou Z H, Lyu J, Li W Y, Yu Y J, Shu L P, Zhao Y J, Ma Y M, Fang C, Shen Y T, Liu T F, Li C C, Li Q, Wu M, Wang M, Wu Y S, Dong Y, Wan W T, Wang X, Ding Z L, Gao Y D, Xiang H, Zhu B G, Lee S H, Wang W, Tian Z X. Resequencing 302 wild and cultivated accessions identifies genes related to domestication and improvement in soybean. Nat Biotechnol, 2015, 33:U408-U125.
[8] Jeong N, Suh S J, Kim M H, Lee S, Moon J K, Kim H S, Lee S, Moon J K, Kim H S, Jeong S C. Ln is a key regulator of leaflet shape and number of seeds per pod in soybean. Plant Cell, 2012, 24:4807-4818.
doi: 10.1105/tpc.112.104968
[9] Lu X, Xiong Q, Cheng T, Li Q T, Liu X L, Bi Y D, Li W, Zhang W K, Ma B, Lai Y C, Du W G, Man W Q, Chen S Y, Zhang J S. PP2C-1 allele underlying a quantitative trait locus enhances soybean l00-seed weight PP2C-1 allele underlying a quantitative trait locus enhances soybean l00-seed weight. Mol Plant, 2017, 10:670-684.
doi: 10.1016/j.molp.2017.03.006
[10] Karikari B, Chen S X, Xiao Y T, Chang F G, Zhou Y L, Kong J J, Bhat J A, Zhao T J. Utilization of interspecific high density genetic map of RIL population for the QTL detection and candidate gene mining for 100-seed weight in soybean. Front Plant Sci, 2019, 10:1001.
doi: 10.3389/fpls.2019.01001
[11] Hina A, Cao Y C, Song S Y, Li S G, Sharmin R A, Elattar M A, Bhat J A, Zhao T J. Glycine max L.) Glycine max L.). Int J Mol Sci, 2020, 21:1040.
doi: 10.3390/ijms21031040
[12] Jing Y, Zhao X, Wang J Y, Teng W L, Qiu L J, Han Y P, Li W B. Glycine max L.) via high-throughput single-nucleotide polymorphisms and a genome-wide association study Glycine max L.) via high-throughput single-nucleotide polymorphisms and a genome-wide association study. Front Plant Sci, 2018, 9:1392.
doi: 10.3389/fpls.2018.01392 pmid: 30369935
[13] Zhang Y W, Li W, Lin Y H, Zhang L F, Wang C J, Xu R. Glycine max) agronomic and seed quality traits by specific length amplified fragment sequencing Glycine max) agronomic and seed quality traits by specific length amplified fragment sequencing. BMC Genomics, 2018, 19:641.
doi: 10.1186/s12864-018-5035-9
[14] Michelmore R W, Paran I, Kesseli R V. Identification of markers linked to disease resistance genes by bulked segregant analysis: a rapid method to detect markers in specific genomic regions by using segregating populations. Proc Natl Acad Sci USA, 1991, 88:9828-9832.
doi: 10.1073/pnas.88.21.9828
[15] Zou C, Wang P X, Xu Y B. Bulked sample analysis in genetics, genomics and crop improvement. Plant Biotechnol J, 2016, 14:1941-1955.
doi: 10.1111/pbi.2016.14.issue-10
[16] Yang Z M, Huang D Q, Tang W Q, Zheng Y, Liang K J, Cutler A J, Wu W R. Mapping of quantitative trait loci underlying cold tolerance in rice seedlings via high-throughput sequencing of pooled extremes. PLoS One, 2013, 8:e68433.
doi: 10.1371/journal.pone.0068433
[17] Mansur L M, Orf J, Lark K G. Glycine max L. Merr) Glycine max L. Merr). Theor Appl Genet, 1993, 86:914-918.
doi: 10.1007/BF00211041 pmid: 24193997
[18] Yi B, Chen Y N, Lei S L, Tu J X, Fu T D. Bnms1) in Brassica napus L Bnms1) in Brassica napus L. Theor Appl Genet, 2006, 113:643-650.
doi: 10.1007/s00122-006-0328-9
[19] Watanabe S, Xia Z J, Hideshima R, Tsubokura Y, Sato S, Yamanaka N, Takahashi R, Anai T, Tabata S, Kitamura K, Harada K. GIGANTEA gene is involved in soybean maturity and flowering GIGANTEA gene is involved in soybean maturity and flowering. Genetics, 2011, 188:395-407.
doi: 10.1534/genetics.110.125062 pmid: 21406680
[20] Whipple C J, Kebrom T, Weber A L, Yang F, Hall D, Meeley R, Schmidt R, Doebley J, Brutnell T P, Jackson D P. Grassy tillers1 promotes apical dominance in maize and responds to shade signals in the grasses. Proc Natl Acad Sci USA, 2011, 108:E506-E512.
doi: 10.1073/pnas.1102819108
[21] Schneeberger K, Ossowski S, Lanz C, Juul T, Petersen A H, Nielsen K L, Jorgensen J E, Weigel D, Andersen S U. SHOREmap: simultaneous mapping and mutation identification by deep sequencing. Nat Methods, 2009, 6:550-551.
doi: 10.1038/nmeth0809-550 pmid: 19644454
[22] Austin R S, Vidaurre D, Stamatiou G, Breit R, Provart N J, Bonetta D, Zhang J F, Fung P, Gong Y C, Wang P W. Arabidopsis genes Arabidopsis genes. Plant J, 2011, 67:715-725.
doi: 10.1111/tpj.2011.67.issue-4
[23] Abe A, Kosugi S, Yoshida K, Natsume S, Takagi H, Kanzaki H, Matsumura H, Yoshida K, Mitsuoka C, Tamiru M, Innan H, Cano L, Kamoun S, Terauchi R. Genome sequencing reveals agronomically important loci in rice using MutMap. Nat Biotechnol, 2012, 30:174-178.
doi: 10.1038/nbt.2095
[24] Takagi H, Uemura A, Yaegashi H, Tamiru M, Abe A, Mitsuoka C, Utsushi H, Natsume S, Kanzaki H, Matsumura H, Saitoh H, Yoshida K, Cano L M, Kamoun S, Terauchi R. MutMap-Gap: whole-genome resequencing of mutant F2 progeny bulk combined with de novo assembly of gap regions identifies the rice blast resistance gene Pii. New Phytol, 2013, 200:276-283.
doi: 10.1111/nph.2013.200.issue-1
[25] Takagi H, Tamiru M, Abe A, Yoshida K, Uemura A, Yaegashi H, Obara T, Oikawa K, Utsushi H, Kanzaki E, Mitsuoka C, Natsume S, Kosugi S, Kanzaki H, Matsumura H, Urasaki N, Kamoun S, Terauchi R. MutMap accelerates breeding of a salt-tolerant rice cultivar. Nat Biotechnol, 2015, 33:445-449.
doi: 10.1038/nbt.3188
[26] Zheng W J, Wang Y, Wang L L, Ma Z B, Zhao J M, Wang P, Zhang L X, Liu Z H, Lu X C. Pi65(t), a novel broad-spectrum resistance gene to rice blast using next-generation sequencing Pi65(t), a novel broad-spectrum resistance gene to rice blast using next-generation sequencing. Theor Appl Genet, 2016, 129:1035-1044.
doi: 10.1007/s00122-016-2681-7
[27] Liu S Z, Yeh C T, Tang H M, Nettleton D, Schnable P S. Gene mapping via bulked segregant RNA-Seq (BSR-Seq). PLoS One, 2012, 7:e36406.
doi: 10.1371/journal.pone.0036406
[28] Haase N J, Beissinger T, Hirsch C N, Vaillancourt B, Deshpande S, Barry K, Buell C R, Kaeppler S M, de Leon N. Shared genomic regions between derivatives of a large segregating population of maize identified using bulked segregant analysis sequencing and traditional linkage analysis. G3: Genes Genom Genet, 2015, 5:1593-1602.
[29] Mascher M, Jost M, Kuon J E, Himmelbach A, Assfalg A, Beier S, Scholz U, Graner A, Stein N. Mapping-by-sequencing accelerates forward genetics in barley. Genome Biol, 2014, 15:R78.
doi: 10.1186/gb-2014-15-6-r78
[30] Campbell B W, Hofstad A N, Sreekanta S, Fu F, Kono T J Y, O’Rourke J A, Vance C P, Muehlbauer G J, Stupar R M. Fast neutron-induced structural rearrangements at a soybean NAP1 locus result in gnarled trichomes. Theor Appl Genet, 2016, 129:1725-1738.
doi: 10.1007/s00122-016-2735-x
[31] Dobbels A A, Michno J M, Campbell B W, Virdi K S, Stec A O, Muehlbauer G J, Naeve S L, Stupar R M. An induced chromosomal translocation in soybean disrupts a KASI ortholog and is associated with a high-sucrose and low-oil seed phenotype. G3: Genes Genom Genet, 2017, 7:1215-1223.
[32] Dong Z M, Chen L, Li Z, Liu N X, Zhang S C, Liu J, Liu B Q. sdf-1) in soybean by SLAF-seq method sdf-1) in soybean by SLAF-seq method. Euphytica, 2020, 216:103.
doi: 10.1007/s10681-020-02633-7
[33] Takagi H, Abe A, Yoshida K, Kosugi S, Natsume S, Mitsuoka C, Uemura A, Utsushi H, Tamiru M, Takuno S, Innan H, Cano L M, Kamoun S, Terauchi R. QTL-seq: rapid mapping of quantitative trait loci in rice by whole genome resequencing of DNA from two bulked populations. Plant J, 2013, 74:174-183.
doi: 10.1111/tpj.2013.74.issue-1
[34] Mansfeld B N, Grumet R. QTLseqr: an R package for bulk Segregant analysis with next-generation sequencing. Plant Genome, 2018, 11:180006.
doi: 10.3835/plantgenome2018.01.0006
[35] Zhang J B, Panthee D R. PyBSASeq: a simple and effective algorithm for bulked segregant analysis with whole-genome sequencing data. BMC Bioinf, 2020, 21:99.
doi: 10.1186/s12859-020-3435-8
[36] Pan Q C, Xu Y C, Li K, Peng Y, Zhang W, Li W Q, Li L, Yan J B. The genetic basis of plant architecture in 10 maize recombinant inbred line populations. Plant Physiol, 2017, 175:858-873.
doi: 10.1104/pp.17.00709
[37] Zhao X, Teng W L, Li Y H, Liu D Y, Cao G L, Li D M, Qiu L J, Zheng H K, Han Y P, Li W B. Loci and candidate genes conferring resistance to soybean cyst nematode HG type 2.5.7. BMC Genomics, 2017, 18:462.
doi: 10.1186/s12864-017-3843-y pmid: 28615053
[38] Severin A J, Woody J L, Bolon Y T, Joseph B, Diers B W, Farmer A D, Muehlbauer G J, Nelson R T, Grant D, Specht J E. Glycine max: a guide to the soybean transcriptome Glycine max: a guide to the soybean transcriptome. BMC Plant Biol, 2010, 10:160.
doi: 10.1186/1471-2229-10-160 pmid: 20687943
[39] Copley T R, Duceppe M O, O’Donoughue L S. Identification of novel loci associated with maturity and yield traits in early maturity soybean plant introduction lines. BMC Genomics, 2018, 19:167.
doi: 10.1186/s12864-018-4558-4 pmid: 29490606
[40] Hacisalihoglu G, Burton A L, Gustin J L, Eker S, Asikli S, Heybet E H, Ozturk L, Cakmak I, Yazici A, Burkey K O, Orf J, Settles A M. Quantitative trait loci associated with soybean seed weight and composition under different phosphorus levels. J Integr Plant Biol, 2018, 60:46-55.
[41] Teng W, Han Y, Du Y, Sun D, Zhang Z, Qiu L, Sun G, Li W. Glycine max L. Merr.) Glycine max L. Merr.). Heredity, 2009, 102:372-380.
doi: 10.1038/hdy.2008.108 pmid: 18971958
[42] Yan L, Li Y H, Yang C Y, Ren S X, Chang R Z, Zhang M C, Qiu L J. Glycine max × Glycine soja Glycine max × Glycine soja. Plant Breed, 2014, 133:632-637.
doi: 10.1111/pbr.2014.133.issue-5
[43] Contreras-Soto R I, Mora F, de Oliveira M A R, Higashi W, Scapim C A, Schuster I. A Genome-wide association study for agronomic traits in soybean using SNP markers and SNP-based haplotype analysis. PLoS One, 2017, 12:e0171105.
doi: 10.1371/journal.pone.0171105
[44] Hao D R, Cheng H, Yin Z T, Cui S Y, Zhang D, Wang H, Yu D Y. Glycine max) landraces across multiple environments Glycine max) landraces across multiple environments. Theor Appl Genet, 2012, 124:447-458.
doi: 10.1007/s00122-011-1719-0
[45] Mian M A R, Bailey M A, Tamulonis J P, Shipe E R, Carter T E, Parrott W A, Ashley D A, Hussey R S, Boerma H R. Molecular markers associated with seed weight in two soybean populations. Theor Appl Genet, 1996, 93:1011-1016.
doi: 10.1007/BF00230118 pmid: 24162474
[46] Vieira A, de Oliveira A, Soares T, Schuster I, Piovesan N, Martinez C, de Barros E, Moreira M. Use of the QTL approach to the study of soybean trait relationships in two populations of recombinant inbred lines at the F7 and F8 generations. Brazil J Plant Physiol, 2006, 18:281-290.
doi: 10.1590/S1677-04202006000200004
[47] 金彦龙, 李艳军, 张新宇, 孙杰, 薛飞. 利用SLAF-Seq结合BSA方法分子标记‘小白冬麦’抗白粉病基因mlxbd. 西北农业学报, 2019, 28:914-921.
Jin Y L, Li Y J, Zhang X Y, Sun J, Xue F. Genetic mapping of resistance gene to powdery mildew mlxbd in winter wheat ‘Xiao Bai’ using a combination of SLAF-seq and BSA. Acta Agric Boreali-Occident Sin, 2019, 28:914-921 (in Chinese with English abstract).
[48] 徐剑文, 刘剑光, 赵君, 王希睿, 肖松华. 利用BSA-seq发掘棉花适宜机采的果枝长度相关QTL. 棉花学报, 2019, 31:319-326.
Xu J W, Liu J G, Zhao J, Wang X R, Xiao S H. The identification of QTL associated with cotton fruit branch length suitable for mechanized harvest utilizing BSA-seq. Cotton Sci, 2019, 31:319-326 (in Chinese with English abstract).
[49] 王伟, 刘凡, 任莉, 徐理, 陈旺, 曾令益, 黄炳文, 方小平. 采用SLAF-seq技术开发甘蓝型油菜霜霉病抗性SNP位点. 中国油料作物学报, 2016, 38:555-562.
Wang W, Liu F, Ren L, Xu L, Chen W, Zeng L Y, Huang B W, Fang X P. Resistance SNP development to downy mildew in Brassica napus using SLAF-seq technique. Chin J Oil Crop Sci, 2016, 38:555-562 (in Chinese with English abstract).
[50] 贾秀苹, 卯旭辉, 岳云, 陈炳东, 梁根生, 王兴珍. 利用BSA-Seq方法鉴定向日葵耐盐候选基因. 中国油料作物学报, 2018, 40:777-784.
Jia X P, Mao X H, Yue Y, Chen B D, Liang G S, Wang X Z. Identification of major salt-tolerant genes via BSA-Seq method in sunflower. Chin J Oil Crop Sci, 2018, 40:777-784 (in Chinese with English abstract).
[51] Wang G Y, Chen B, Du H S, Zhang F L, Zhang H Y, Wang Y Q, He H J, Geng S S, Zhang X F. Genetic mapping of anthocyanin accumulation-related genes in pepper fruits using a combination of SLAF-seq and BSA. PLoS One, 2018, 13:e0204690.
doi: 10.1371/journal.pone.0204690
[52] Libault M, Farmer A, Brechenmacher L, Drnevich J, Langley R J, Bilgin D D, Radwan O, Neece D J, Clough S J, May G D, Stacey G. Bradyrhizobium japonicum infection Bradyrhizobium japonicum infection. Plant Physiol, 2010, 152:541-552.
doi: 10.1104/pp.109.148379 pmid: 19933387
[53] Scofield G N, Hirose T, Gaudron J A, Upadhyaya N M, Ohsugi R, Furbank R T. OsSUT1, leads to impaired grain filling and germination but does not affect photosynthesis OsSUT1, leads to impaired grain filling and germination but does not affect photosynthesis. Funct Plant Biol, 2002, 29:815-826.
doi: 10.1071/PP01204 pmid: 32689529
[54] Aldape M J, Elmer A M, Chao W S, Grimes H D. Identification and characterization of a sucrose transporter isolated from the developing cotyledons of soybean. Arch Biochem Biophys, 2003, 409:243-250.
pmid: 12504891
[55] Seguin P, Turcotte P, Tremblay G, Pageau D, Liu W C. Tocopherols concentration and stability in early maturing soybean genotypes. Agron J, 2009, 101:1153-1159.
doi: 10.2134/agronj2009.0140
[56] Shang X G, Zhu L J, Duan Y J, Guo W Z. A cotton alpha 1,3-/4-fucosyltransferase-encoding gene, FucT4, plays an important role in cell elongation and is significantly associated with fiber quality. Mol Genet Genomics, 2020, 295:1141-1153.
doi: 10.1007/s00438-020-01687-5
[57] Harmoko R, Yoo J Y, Ko K S, Ramasamy N K, Hwang B Y, Lee E J, Kim H, Lee K J, Oh D B, Kim D Y, Lee S, Li Y, Lee S Y, Lee K O. Oryza sativa) Oryza sativa). New Phytol, 2016, 212:108-122.
doi: 10.1111/nph.14031 pmid: 27241276
[58] Pedersen C T, Loke I, Lorentzen A, Wolf S, Kamble M, Kristensen S K, Munch D, Radutoiu S, Spillner E, Roepstorff P, Thaysen-Andersen M, Stougaard J, Dam S. Lotus japonicus for basic and applied glycoprotein research Lotus japonicus for basic and applied glycoprotein research. Plant J, 2017, 91:394-407.
doi: 10.1111/tpj.13570
[59] 路子显, 曲建波. 大豆热激蛋白与内源激素变化的研究. 大豆科学, 1998, 17:318-325.
Lu Z X, Qu J B. Study on variations of heat shock protein and endogenous hormone. Soybean Sci, 1998, 17:318-325 (in Chinese with English abstract).
[60] Anurag D, Sweta D, Rishi S, Saurabh B, Singh A K, Pinky A, Parida S K, Tyagi A K. An efficient strategy combining SSR markers- and advanced QTL-Seq-driven QTL mapping unravels candidate genes regulating grain weight in rice. Front Plant Sci, 2016, 7:1535.
pmid: 27833617
[61] Jiang P, Gao J S, Mu J, Duan L, Li X. Triticum timopheevi Triticum timopheevi. J Appl Genet, 2020, 61:151-162.
doi: 10.1007/s13353-020-00539-7
[62] Minic Z, Do C T, Rihouey C, Morin H, Lerouge P, Jouanin L. Arabidopsis Arabidopsis. J Exp Bot, 2006, 57:2339-2351.
doi: 10.1093/jxb/erj205
[63] Rehman H M, Nawaz M A, Bao L, Shah Z H, Lee J M, Ahmad M Q, Chung G, Yang S H. Genome-wide analysis of Family-1 UDP-glycosyltransferases in soybean confirms their abundance and varied expression during seed development. J Plant Physiol, 2016, 206:87-97.
doi: 10.1016/j.jplph.2016.08.017
[1] 陈玲玲, 李战, 刘亭萱, 谷勇哲, 宋健, 王俊, 邱丽娟. 基于783份大豆种质资源的叶柄夹角全基因组关联分析[J]. 作物学报, 2022, 48(6): 1333-1345.
[2] 杨欢, 周颖, 陈平, 杜青, 郑本川, 蒲甜, 温晶, 杨文钰, 雍太文. 玉米-豆科作物带状间套作对养分吸收利用及产量优势的影响[J]. 作物学报, 2022, 48(6): 1476-1487.
[3] 王炫栋, 杨孙玉悦, 高润杰, 余俊杰, 郑丹沛, 倪峰, 蒋冬花. 拮抗大豆斑疹病菌放线菌菌株的筛选和促生作用及防效研究[J]. 作物学报, 2022, 48(6): 1546-1557.
[4] 于春淼, 张勇, 王好让, 杨兴勇, 董全中, 薛红, 张明明, 李微微, 王磊, 胡凯凤, 谷勇哲, 邱丽娟. 栽培大豆×半野生大豆高密度遗传图谱构建及株高QTL定位[J]. 作物学报, 2022, 48(5): 1091-1102.
[5] 李阿立, 冯雅楠, 李萍, 张东升, 宗毓铮, 林文, 郝兴宇. 大豆叶片响应CO2浓度升高、干旱及其交互作用的转录组分析[J]. 作物学报, 2022, 48(5): 1103-1118.
[6] 彭西红, 陈平, 杜青, 杨雪丽, 任俊波, 郑本川, 罗凯, 谢琛, 雷鹿, 雍太文, 杨文钰. 减量施氮对带状套作大豆土壤通气环境及结瘤固氮的影响[J]. 作物学报, 2022, 48(5): 1199-1209.
[7] 王好让, 张勇, 于春淼, 董全中, 李微微, 胡凯凤, 张明明, 薛红, 杨梦平, 宋继玲, 王磊, 杨兴勇, 邱丽娟. 大豆突变体ygl2黄绿叶基因的精细定位[J]. 作物学报, 2022, 48(4): 791-800.
[8] 李瑞东, 尹阳阳, 宋雯雯, 武婷婷, 孙石, 韩天富, 徐彩龙, 吴存祥, 胡水秀. 增密对不同分枝类型大豆品种同化物积累和产量的影响[J]. 作物学报, 2022, 48(4): 942-951.
[9] 杜浩, 程玉汉, 李泰, 侯智红, 黎永力, 南海洋, 董利东, 刘宝辉, 程群. 利用Ln位点进行分子设计提高大豆单荚粒数[J]. 作物学报, 2022, 48(3): 565-571.
[10] 周悦, 赵志华, 张宏宁, 孔佑宾. 大豆紫色酸性磷酸酶基因GmPAP14启动子克隆与功能分析[J]. 作物学报, 2022, 48(3): 590-596.
[11] 董衍坤, 黄定全, 高震, 陈栩. 大豆PIN-Like (PILS)基因家族的鉴定、表达分析及在根瘤共生固氮过程中的功能[J]. 作物学报, 2022, 48(2): 353-366.
[12] 张国伟, 李凯, 李思嘉, 王晓婧, 杨长琴, 刘瑞显. 减库对大豆叶片碳代谢的影响[J]. 作物学报, 2022, 48(2): 529-537.
[13] 宋丽君, 聂晓玉, 何磊磊, 蒯婕, 杨华, 郭安国, 黄俊生, 傅廷栋, 汪波, 周广生. 饲用大豆品种耐荫性鉴定指标筛选及综合评价[J]. 作物学报, 2021, 47(9): 1741-1752.
[14] 曹亮, 杜昕, 于高波, 金喜军, 张明聪, 任春元, 王孟雪, 张玉先. 外源褪黑素对干旱胁迫下绥农26大豆鼓粒期叶片碳氮代谢调控的途径分析[J]. 作物学报, 2021, 47(9): 1779-1790.
[15] 张明聪, 何松榆, 秦彬, 王孟雪, 金喜军, 任春元, 吴耀坤, 张玉先. 外源褪黑素对干旱胁迫下春大豆品种绥农26形态、光合生理及产量的影响[J]. 作物学报, 2021, 47(9): 1791-1805.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!