Welcome to Acta Agronomica Sinica,

Acta Agronomica Sinica ›› 2022, Vol. 48 ›› Issue (3): 635-643.doi: 10.3724/SP.J.1006.2022.14008


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 Online:2021-06-04 Published:2021-06-04
  • Contact: CHANG Wei E-mail:252576027@qq.com;weichang0@126.com
  • Supported by:
    Science and Technology Project of Henan Province(192102110125);Youth Foundation Project of Nanyang Normal University(QN2016003)


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

Fig. 1

Distribution histogram for 100-seed weight of the RIL population and parents"

Table 1

Summary of sSNP related to 100-seed weight trait in soybean"

Numbers of QTLs
Total number of SNP
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

Fig. 2

QTL mapping for 100-seed weight in soybean using PyBSASeq method"

Table 2

Summary of QTLs related to 100-seed weight trait in soybean"

QTL 染色体
Confidence interval (bp)
N_s/N_t 阈值
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

Fig. 3

Relative expression profiling of candidate genes for 100-seed weight trait during seed development period in soybean Underline gene: the candidate gene for 100-seed weight of soybean. DAF: days after flowering."

Table 3

Summary of candidate genes for 100-seed weight trait in soybean"

QTL 基因
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

Fig. 4

Haplotype analysis of candidate genes for 100-seed weight trait in soybean"

[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] CHEN Ling-Ling, LI Zhan, LIU Ting-Xuan, GU Yong-Zhe, SONG Jian, WANG Jun, QIU Li-Juan. Genome wide association analysis of petiole angle based on 783 soybean resources (Glycine max L.) [J]. Acta Agronomica Sinica, 2022, 48(6): 1333-1345.
[2] YANG Huan, ZHOU Ying, CHEN Ping, DU Qing, ZHENG Ben-Chuan, PU Tian, WEN Jing, YANG Wen-Yu, YONG Tai-Wen. Effects of nutrient uptake and utilization on yield of maize-legume strip intercropping system [J]. Acta Agronomica Sinica, 2022, 48(6): 1476-1487.
[3] YU Chun-Miao, ZHANG Yong, WANG Hao-Rang, YANG Xing-Yong, DONG Quan-Zhong, XUE Hong, ZHANG Ming-Ming, LI Wei-Wei, WANG Lei, HU Kai-Feng, GU Yong-Zhe, QIU Li-Juan. Construction of a high density genetic map between cultivated and semi-wild soybeans and identification of QTLs for plant height [J]. Acta Agronomica Sinica, 2022, 48(5): 1091-1102.
[4] LI A-Li, FENG Ya-Nan, LI Ping, ZHANG Dong-Sheng, ZONG Yu-Zheng, LIN Wen, HAO Xing-Yu. Transcriptome analysis of leaves responses to elevated CO2 concentration, drought and interaction conditions in soybean [Glycine max (Linn.) Merr.] [J]. Acta Agronomica Sinica, 2022, 48(5): 1103-1118.
[5] PENG Xi-Hong, CHEN Ping, DU Qing, YANG Xue-Li, REN Jun-Bo, ZHENG Ben-Chuan, LUO Kai, XIE Chen, LEI Lu, YONG Tai-Wen, YANG Wen-Yu. Effects of reduced nitrogen application on soil aeration and root nodule growth of relay strip intercropping soybean [J]. Acta Agronomica Sinica, 2022, 48(5): 1199-1209.
[6] WANG Hao-Rang, ZHANG Yong, YU Chun-Miao, DONG Quan-Zhong, LI Wei-Wei, HU Kai-Feng, ZHANG Ming-Ming, XUE Hong, YANG Meng-Ping, SONG Ji-Ling, WANG Lei, YANG Xing-Yong, QIU Li-Juan. Fine mapping of yellow-green leaf gene (ygl2) in soybean (Glycine max L.) [J]. Acta Agronomica Sinica, 2022, 48(4): 791-800.
[7] LI Rui-Dong, YIN Yang-Yang, SONG Wen-Wen, WU Ting-Ting, SUN Shi, HAN Tian-Fu, XU Cai-Long, WU Cun-Xiang, HU Shui-Xiu. Effects of close planting densities on assimilate accumulation and yield of soybean with different plant branching types [J]. Acta Agronomica Sinica, 2022, 48(4): 942-951.
[8] DU Hao, CHENG Yu-Han, LI Tai, HOU Zhi-Hong, LI Yong-Li, NAN Hai-Yang, DONG Li-Dong, LIU Bao-Hui, CHENG Qun. Improving seed number per pod of soybean by molecular breeding based on Ln locus [J]. Acta Agronomica Sinica, 2022, 48(3): 565-571.
[9] ZHOU Yue, ZHAO Zhi-Hua, ZHANG Hong-Ning, KONG You-Bin. Cloning and functional analysis of the promoter of purple acid phosphatase gene GmPAP14 in soybean [J]. Acta Agronomica Sinica, 2022, 48(3): 590-596.
[10] ZHANG Guo-Wei, LI Kai, LI Si-Jia, WANG Xiao-Jing, YANG Chang-Qin, LIU Rui-Xian. Effects of sink-limiting treatments on leaf carbon metabolism in soybean [J]. Acta Agronomica Sinica, 2022, 48(2): 529-537.
[11] YU Tao-Bing, SHI Qi-Han, NIAN-Hai , LIAN Teng-Xiang. Effects of waterlogging on rhizosphere microorganisms communities of different soybean varieties [J]. Acta Agronomica Sinica, 2021, 47(9): 1690-1702.
[12] SONG Li-Jun, NIE Xiao-Yu, HE Lei-Lei, KUAI Jie, YANG Hua, GUO An-Guo, HUANG Jun-Sheng, FU Ting-Dong, WANG Bo, ZHOU Guang-Sheng. Screening and comprehensive evaluation of shade tolerance of forage soybean varieties [J]. Acta Agronomica Sinica, 2021, 47(9): 1741-1752.
[13] CAO Liang, DU Xin, YU Gao-Bo, JIN Xi-Jun, ZHANG Ming-Cong, REN Chun-Yuan, WANG Meng-Xue, ZHANG Yu-Xian. Regulation of carbon and nitrogen metabolism in leaf of soybean cultivar Suinong 26 at seed-filling stage under drought stress by exogenous melatonin [J]. Acta Agronomica Sinica, 2021, 47(9): 1779-1790.
[14] ZHANG Ming-Cong, HE Song-Yu, QIN Bin, WANG Meng-Xue, JIN Xi-Jun, REN Chun-Yuan, WU Yao-Kun, ZHANG Yu-Xian. Effects of exogenous melatonin on morphology, photosynthetic physiology, and yield of spring soybean variety Suinong 26 under drought stress [J]. Acta Agronomica Sinica, 2021, 47(9): 1791-1805.
[15] ZENG Wei-Ying, LAI Zhen-Guang, SUN Zu-Dong, YANG Shou-Zhen, CHEN Huai-Zhu, TANG Xiang-Min. Identification of the candidate genes of soybean resistance to bean pyralid (Lamprosema indicata Fabricius) by BSA-Seq and RNA-Seq [J]. Acta Agronomica Sinica, 2021, 47(8): 1460-1471.
Full text



No Suggested Reading articles found!