欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2026, Vol. 52 ›› Issue (6): 1743-1756.doi: 10.3724/SP.J.1006.2026.55064

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

大豆株高性状全基因组关联分析及预测模型构建

唐宽强1(), 李公允1, 宋美毅1, 赵雪1, 常春玲1,2,3,4,*()   

  1. 1 齐齐哈尔大学生命科学与农林学院, 黑龙江齐齐哈尔 161006
    2 黑龙江省农业微生物制剂产业化工程技术研究中心, 黑龙江齐齐哈尔 161006
    3 黑龙江省农用生物制剂产业化协同创新中心, 黑龙江齐齐哈尔 161006
    4 黑龙江省高浓度有机废液转化及产业化概念验证中心, 黑龙江齐齐哈尔 161006
  • 收稿日期:2025-10-16 接受日期:2026-03-16 出版日期:2026-06-12 网络出版日期:2026-03-20
  • 通讯作者: * 常春玲, E-mail: changchunling0709@163.com
  • 作者简介:唐宽强, E-mail: tangkuanqiang@126.com
  • 基金资助:
    黑龙江省自然科学基金项目(YQ2024D013);黑龙江省省属本科高校“优秀青年教师基础研究支持计划”项目(YQJH2023099);黑龙江省省属高等学校基本科研业务费项目(145309211)

Genome-wide association analysis and prediction model construction for soybean plant height

Tang Kuan-Qiang1(), Li Gong-Yun1, Song Mei-Yi1, Zhao Xue1, Chang Chun-Ling1,2,3,4,*()   

  1. 1 College of Life Sciences, Agriculture and Forestry, Qiqihar University, Qiqihar 161006, Heilongjiang, China
    2 Heilongjiang Provincial Agricultural Microbial Preparations Industrialization Engineering and Technology Research Center, Qiqihar 161006, Heilongjiang, China
    3 Heilongjiang Provincial Agricultural Bio-Preparation Industrialization Collaborative Innovation Center, Qiqihar 161006, Heilongjiang, China
    4 Heilongjiang Provincial Concept Verification Center of High Concentration Organic Waste Liquid Biotransformation and Industrialization, Qiqihar 161006, Heilongjiang, China
  • Received:2025-10-16 Accepted:2026-03-16 Published:2026-06-12 Published online:2026-03-20
  • Contact: * Chang Chun-Ling, E-mail: changchunling0709@163.com
  • Supported by:
    Heilongjiang Provincial Natural Science Foundation of China(YQ2024D013);Excellent Young Teachers Program of Basic Research in Heilongjiang Province in China(YQJH2023099);Basic Scientific Research Fund for Universities in Heilongjiang Province of China(145309211)

RichHTML

3

PDF

120

摘要：

株高是影响大豆产量与抗倒伏能力的核心农艺性状, 对保障大豆高产稳产具有重要意义。对17,148份世界范围内的大豆种质资源进行全基因组关联分析, 共鉴定到139个与株高性状显著关联的区间, 其中候选基因Dt1对株高的贡献最大。在全基因组选择模型中, BayesA和rrBLUP模型优于其他模型, 基于前500~1000个SNP的预测模型在精度与成本效益间达到最优平衡。本研究鉴定的调控株高的关键基因位点, 为大豆株高的基因组选择提供了高效的预测模型, 也为大豆株高性状的分子设计育种提供了重要理论依据。

关键词: 大豆, 株高, 全基因组关联分析, 单倍型, 基因组选择

Abstract:

Plant height is a key agronomic trait that is closely associated with soybean yield potential and lodging resistance, and it is essential for achieving high and stable yields in soybean production. Here, we conducted a genome-wide association study (GWAS) and genomic selection (GS) analyses of plant height using a diverse panel of 17,148 soybean germplasm accessions with worldwide geographic representation. We identified 139 significant genomic loci associated with plant height, with the candidate gene Dt1 showing the largest genetic effect. In the GS analysis, the BayesA and rrBLUP models outperformed the other models in prediction accuracy. Further comparisons indicated that prediction models based on the top 500-1000 SNPs provided the best balance between predictive performance and cost effectiveness. Collectively, the loci and models identified here support efficient genomic selection for soybean plant height and provide a theoretical basis for molecular design breeding to improve this trait.

Key words: soybean, plant height, GWAS, haplotype, GS

表1

株高性状相关的候选位点"

物理位置
Physical position (bp)		 SNP数目
SNP number		 SNP名称
Name of SNP		 GWAS模型
GWAS model		 候选位点/基因
Candidate locus or gene
1: 4537369-4737369 1 ss715579566 Fast3VmrMLM CH1-1 [57]
2: 4462767-6836648 4 ss715582840 Fast3VmrMLM, FarmCPU PH23-1 [58]
2: 6712100-6912100 2 ss715583766 Fast3VmrMLM, FarmCPU PH23-1 [58]
2: 10255896-10553962 2 ss715580919 FarmCPU PH17-11 [59]
2: 12142977-12342977 1 ss715581055 FarmCPU PH33-3 [60]
2: 40658231-40858231 1 ss715582649 Fast3VmrMLM PH26-9 [61]
2: 42434287-43540684 2 ss715583006 FarmCPU PH26-9 [61]
3: 3011469-3211469 1 ss715585099 Fast3VmrMLM Glyma.03G106900 (GmMRF2) [22]
4: 4094550-4294550 1 ss715588091 FarmCPU PH33-4 [60]
4: 4685042-4885042 1 ss715588851 FarmCPU PH33-4 [60]
5: 4655898-4855898 1 ss715590521 Fast3VmrMLM PH26-4 [61], PH26-5 [61]
5: 24093817-24293817 1 ss715590209 FarmCPU PH26-4 [61], PH17-2 [59]
5: 35986874-38089147 5 ss715591542 Fast3VmrMLM, FarmCPU PH26-1 [61], PH24-1 [62], PH24-2 [62]
5: 39041972-39241972 1 ss715592108 FarmCPU PH26-1 [61], PH24-1 [62], PH24-2 [62]
5: 42019049-42219049 1 ss715591621 Fast3VmrMLM, FarmCPU PH26-1 [61], PH24-1 [62], PH24-2 [62]
6: 19813355-20733420 3 ss715593833 Fast3VmrMLM, FarmCPU Glyma.06G207800 (E1) [20]
6: 50946599-51146599 1 ss715595278 FarmCPU PH27-1 [63]
7: 4818294-5018294 1 ss715598270 FarmCPU PH18-6 [50], PH13-9 [64], PH19-5 [50],
PH8-g2 [65]
7: 8576256-8776256 1 ss715598865 Fast3VmrMLM PH19-5 [50], CH2-1 [66]
7: 10325142-10525142 1 ss715595759 FarmCPU CH2-1 [66]
8: 13345038-13545038 1 ss715599459 FarmCPU Glyma.08G163900 (GmDW1) [16]
8: 42962862-43162862 1 ss715602049 Fast3VmrMLM PH1-g3 [56]
9: 32325584-32525584 1 ss715603525 Fast3VmrMLM PH17-4 [59]
10: 44634052-46739345 12 ss715607448 Fast3VmrMLM, FarmCPU, MLM Glyma.10G221500 (E2) [12,53],
PH19-2 [50], PH31-2 [51], PH5-g1.2 [12]
11: 7571209-7771209 1 ss715611131 FarmCPU PH24-5 [62], PH24-6 [62], PH26-6 [61],
PH26-7 [61]
11: 8164638-8364638 1 ss715611223 Fast3VmrMLM PH26-6 [61], PH26-8 [61], PH20-1 [67]
11: 10250509-10491700 2 ss715608747 Fast3VmrMLM, FarmCPU PH26-8 [61], PH20-1 [67]
11: 10829924-11029924 1 ss715608798 Fast3VmrMLM PH26-8 [61], PH20-1 [67], PH20-2 [67]
11: 11092462-11292462 1 ss715609445 Fast3VmrMLM PH26-8 [61], PH20-1 [67], PH20-2 [67]
11: 15578644-15778644 1 ss715609882 FarmCPU PH26-8 [61]
11: 18962168-19162168 1 ss715609800 Fast3VmrMLM PH3-g12 [68], Glyma.11G026400
(GmILPA1) [15]
12: 4929086-5897709 6 ss715613141 Fast3VmrMLM, FarmCPU, MLM Glyma.12G073900 (Tof12) [54]
12: 39054817-39848127 3 ss715612990 Fast3VmrMLM, FarmCPU Glyma.12G224600 (Rin1) [14]
13: 24323315-24523315 1 ss715614116 FarmCPU CH1-2 [57]
13: 32362674-32574603 4 ss715615299 FarmCPU PH5-6 [69]
13: 35325581-38601835 5 ss715615977 Fast3VmrMLM, FarmCPU, MLM Glyma.13G287600/Glyma.13G288000 (GA2oxs) [8], PH6-g12 [56]
14: 2423374-2623374 1 ss715618109 FarmCPU PH33-6 [60]
15: 10654448-11332649 3 ss715620250 Fast3VmrMLM, FarmCPU PH26-10 [52]
15: 13716717-14915803 4 ss715620652 Fast3VmrMLM, FarmCPU PH26-10 [61], PH13-3 [64]
16: 1250020-1450020 1 ss715623432 FarmCPU PH13-5 [64]
16: 2502942-2702942 1 ss715623842 FarmCPU PH2-2 [70], PH6-6 [71], PH3-4 [44]
16: 3487595-3687595 1 ss715624811 FarmCPU PH5-9 [69]
16: 5699540-5899540 1 ss715625240 FarmCPU PH1-g17 [56]
16: 31450146-31650146 1 ss715624412 FarmCPU PH26-16 [61], PH25-2 [72], PH5-13 [69],
PH7-g3 [73]
17: 36483136-37013821 5 ss715627165 FarmCPU PH33-7 [60]
18: 48304610-48504610 1 ss715631373 Fast3VmrMLM PH26-14 [61]
18: 49914203-50114203 1 ss715631551 Fast3VmrMLM PH26-14 [61]
18: 51949642-52149642 1 ss715631757 Fast3VmrMLM, FarmCPU PH26-14 [61]
19: 1189196-1389196 1 ss715633103 FarmCPU PH3-5 [44], PH6-7 [71]
19: 43640270-48280446 29 ss715635425 Fast3VmrMLM, FarmCPU, MLM Glyma.19G194300 (Dt1) [12,47-48], PH3-1 [44], PH4-4 [45], PH4-g1 [46]
19: 49180763-49434685 2 ss715635917 FarmCPU PH4-4 [45], PH9-1 [74]
20: 276574-476574 1 ss715637977 FarmCPU PH1-g26 [56]
20: 36946555-37146555 1 ss715637802 FarmCPU PH1-g27 [56]

图1

群体结构分析 a: 株高分布; b: 群体结构Admixture分析(K = 11); c: 系统进化树分析; d: 11个亚群株高差异分析, 按亚群株高平均值从大到小排序, 不同小写字母表示差异显著性(P < 0.05); e: 各亚群连锁不平衡分析。图c-e中颜色标注与图b保持一致, 相同颜色代表同一亚群。"

图2

基于Fast3VmrMLM (a)、FarmCPU (b)和MLM (c)模型的大豆株高全基因组关联分析 PH: 株高; CH: 冠层高度; Fast3VmrMLM: 快速三维多变量随机混合线性模型; FarmCPU: 固定和随机模型循环概率联合法; MLM: 混合线性模型。"

图3

基因表达及单倍型分析 a-f: Glyma.19G194300、Glyma.10G221500、Glyma.12G073900、Glyma.13G287600、Glyma.13G288000和Glyma.06G207800在不同器官不同时期的表达情况。g-k: 19号染色体43.64-48.28 Mb、10号染色体44.63-46.74 Mb、12号染色体4.93-5.90 Mb、13号染色体35.33-38.60 Mb和6号染色体19.81-20.73 Mb区间内不同单倍型的株高表型分析。各单倍型序列以窄字体标注于对应图下方。TPM: 每百万读取次数中某个转录本的占比。"

图4

不同模型的株高表型预测表现 a-d: BayesA模型、rrBLUP模型、DNN模型和SVR模型预测株高与实测株高的比较; e-h: 前98、500、1000和5000个与株高紧密关联的SNP位点构建的BayesA模型, 其预测株高与实测株高的比较; i-l: 前98、500、1000和5000个与株高紧密关联的SNP位点构建的rrBLUP模型, 其预测株高与实测株高的比较。rrBLUP: 岭回归最佳线性无偏预测; BayesA: 贝叶斯A; SVR: 支持向量回归; DNN: 全连接神经网络。"

表2

不同预测模型的性能比较"

指标
Metric		 贝叶斯A
BayesA		 岭回归最佳线性无偏预测
rrBLUP		 深度神经网络
DNN		 支持向量回归
SVR
平均绝对百分比误差MAPE (%) 15.08±0.27 15.37±0.25 16.29±2.35 18.53±2.97
对称平均绝对百分比误差SMAPE (%) 14.22±0.17 14.49±0.16 15.25±1.77 17.29±2.63
平均绝对误差MAE 12.18±0.15 12.41±0.12 13.01±2.33 15.07±3.51
均方误差MSE 294.30±11.85 305.69±11.50 333.41±134.86 476.21±258.61
皮尔逊相关系数r 0.82±0.01 0.81±0.01 0.79±0.04 0.71±0.07

图5

基于不同数量SNP标记的株高表型预测 a-j: 基于GWAS筛选的前98, 500, 1000和5000个显著关联SNP位点及全基因组SNP位点, 分别构建BayesA模型(a-e)和rrBLUP模型(f-j), 对2014年种植于山西的914份大豆材料株高进行预测, 并与实测株高结果比较; k-t: 基于GWAS筛选的前98、500、1000和5000个显著关联SNP位点及全基因组SNP位点, 分别构建BayesA模型(k-o)和rrBLUP模型(p-t), 对2013年种植于北京的932份大豆材料株高进行预测, 并与实测株高结果比较。"

[1] Du H P, Fang C, Li Y R, et al. Understandings and future challenges in soybean functional genomics and molecular breeding. J Integr Plant Biol, 2023, 65: 468-495.
doi: 10.1111/jipb.13433
[2] SoyStat. 2025, http://www.soystats.com.
[3] Zhu Z, Wang Y L, Liu S L, et al. Genomic atlas of 8105 accessions reveals stepwise domestication, global dissemination, and improvement trajectories in soybean. Cell, 2025, 188: 6519-6535.e15.
doi: 10.1016/j.cell.2025.09.007
[4] Assefa T, Otyama P I, Brown A V, et al. Genome-wide associations and epistatic interactions for internode number, plant height, seed weight and seed yield in soybean. BMC Genomics, 2019, 20: 527.
doi: 10.1186/s12864-019-5907-7 pmid: 31242867
[5] Fang C, Dong L D, Zhou J C, et al. Toward a Green Revolution in soybean: the role of ultra-high-density planting. J Integr Plant Biol, 2026, 68: 297-301.
doi: 10.1111/jipb.70079
[6] Liu S L, Zhang M, Feng F, et al. Toward a “Green Revolution” for soybean. Mol Plant, 2020, 13: 688-697.
doi: 10.1016/j.molp.2020.03.002
[7] Lipka A E, Tian F, Wang Q S, et al. GAPIT: genome association and prediction integrated tool. Bioinformatics, 2012, 28: 2397-2399.
pmid: 22796960
[8] Wang X, Li M W, Wong F L, et al. Increased copy number of gibberellin 2-oxidase 8 genes reduced trailing growth and shoot length during soybean domestication. Plant J, 2021, 107: 1739-1755.
doi: 10.1111/tpj.v107.6
[9] Qin C, Li Y H, Li D L, et al. PH13 improves soybean shade traits and enhances yield for high-density planting at high latitudes. Nat Commun, 2023, 14: 6813.
doi: 10.1038/s41467-023-42608-5
[10] Ping J Q, Liu Y F, Sun L J, et al. Dt2 is a gain-of-function MADS-domain factor gene that specifies semideterminacy in soybean. Plant Cell, 2014, 26: 2831-2842.
doi: 10.1105/tpc.114.126938
[11] Zhou Z K, Jiang Y, Wang Z, et al. Resequencing 302 wild and cultivated accessions identifies genes related to domestication and improvement in soybean. Nat Biotechnol, 2015, 33: 408-414.
doi: 10.1038/nbt.3096 pmid: 25643055
[12] Fang C, Ma Y M, Wu S W, et al. Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean. Genome Biol, 2017, 18: 161.
doi: 10.1186/s13059-017-1289-9 pmid: 28838319
[13] Kou K, Su T, Wang Y P, et al. Natural variation of the Dt2 promoter controls plant height and node number in semi-determinant soybean. Mol Breed, 2021, 41: 40.
doi: 10.1007/s11032-021-01235-y
[14] Li S C, Sun Z H, Sang Q, et al. Soybean reduced internode 1 determines internode length and improves grain yield at dense planting. Nat Commun, 2023, 14: 7939.
doi: 10.1038/s41467-023-42991-z pmid: 38040709
[15] Sun J Q, Huang S Y, Lu Q, et al. UV-B irradiation-activated E3 ligase GmILPA1 modulates gibberellin catabolism to increase plant height in soybean. Nat Commun, 2023, 14: 6262.
doi: 10.1038/s41467-023-41824-3
[16] Li Z F, Guo Y, Ou L, et al. Identification of the dwarf gene GmDW1 in soybean (Glycine max L.) by combining mapping-by-sequencing and linkage analysis. Theor Appl Genet, 2018, 131: 1001-1016.
doi: 10.1007/s00122-017-3044-8
[17] Zhao C, Ma J J, Zhang Y H, et al. The miR166 mediated regulatory module controls plant height by regulating gibberellic acid biosynthesis and catabolism in soybean. J Integr Plant Biol, 2022, 64: 995-1006.
doi: 10.1111/jipb.v64.5
[18] Sun J Z, Zhang X M, Feng J H, et al. The transcription factor GmFULc regulates soybean plant height by binding the promoter of a gibberellin-responsive gene. Plant Physiol, 2025, 197: kiaf021.
doi: 10.1093/plphys/kiaf021
[19] Xue Y G, Zhang Y T, Shan J M, et al. Growth repressor GmRAV binds to the GmGA3ox promoter to negatively regulate plant height development in soybean. Int J Mol Sci, 2022, 23: 1721.
doi: 10.3390/ijms23031721
[20] Xia Z J, Watanabe S, Yamada T, et al. Positional cloning and characterization reveal the molecular basis for soybean maturity locus E1 that regulates photoperiodic flowering. Proc Natl Acad Sci USA, 2012, 109: E2155-E2164.
[21] Chen L Y, Nan H Y, Kong L P, et al. Soybean AP1 homologs control flowering time and plant height. J Integr Plant Biol, 2020, 62: 1868-1879.
doi: 10.1111/jipb.v62.12
[22] Zhang J L, Chen L, Cai Y P, et al. A novel MORN-motif type gene GmMRF2 controls flowering time and plant height of soybean. Int J Biol Macromol, 2023, 245: 125464.
doi: 10.1016/j.ijbiomac.2023.125464
[23] Brown A V, Conners S I, Huang W, et al. A new decade and new data at SoyBase, the USDA-ARS soybean genetics and genomics database. Nucleic Acids Res, 2021, 49: D1496-D1501.
[24] Crossa J, Pérez-Rodríguez P, Cuevas J, et al. Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci, 2017, 22: 961-975.
doi: S1360-1385(17)30184-X pmid: 28965742
[25] Alemu A, Åstrand J, Montesinos-López O A, et al. Genomic selection in plant breeding: key factors shaping two decades of progress. Mol Plant, 2024, 17: 552-578.
doi: 10.1016/j.molp.2024.03.007 pmid: 38475993
[26] Li M, Zhang Y W, Zhang Z C, et al. A compressed variance component mixed model for detecting QTNs and QTN-by- environment and QTN-by-QTN interactions in genome-wide association studies. Mol Plant, 2022, 15: 630-650.
doi: 10.1016/j.molp.2022.02.012
[27] Wang J T, Chen Y, Shu G P, et al. Fast3VmrMLM: a fast algorithm that integrates genome-wide scanning with machine learning to accelerate gene mining and breeding by design for polygenic traits in large-scale GWAS datasets. Plant Commun, 2025, 6: 101385.
doi: 10.1016/j.xplc.2025.101385
[28] Song Q J, Hyten D L, Jia G F, et al. Development and evaluation of SoySNP50K, a high-density genotyping array for soybean. PLoS One, 2013, 8: e54985.
doi: 10.1371/journal.pone.0054985
[29] Song Q J, Hyten D L, Jia G F, et al. Fingerprinting soybean germplasm and its utility in genomic research. G3: Genes Genomes Genet, 2015, 5: 1999-2006.
[30] Liu Y C, Du H L, Li P C, et al. Pan-genome of wild and cultivated soybeans. Cell, 2020, 182: 162-176.e13.
doi: S0092-8674(20)30618-8 pmid: 32553274
[31] Liu Y C, Zhang Y, Liu X N, et al. SoyOmics: a deeply integrated database on soybean multi-omics. Mol Plant, 2023, 16: 794-797.
[32] Chen S F. Fastp 1.0: an ultra-fast all-round tool for FASTQ data quality control and preprocessing. iMeta, 2025, 4: e70078.
doi: 10.1002/imt2.v4.5
[33] Van der Auwera G, O’Connor B. Genomics in the cloud: using Docker, GATK, and WDL in Terra. California, CA: O’Reilly Media, Inc., 2020.
[34] Alexander D H, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res, 2009, 19: 1655-1664.
doi: 10.1101/gr.094052.109 pmid: 19648217
[35] Price M N, Dehal P S, Arkin A P. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol, 2009, 26: 1641-1650.
doi: 10.1093/molbev/msp077 pmid: 19377059
[36] Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet, 2007, 81: 559-575.
doi: 10.1086/519795 pmid: 17701901
[37] Wang J B, Zhang Z W. GAPIT version 3: boosting power and accuracy for genomic association and prediction. Genom Proteom Bioinform, 2021, 19: 629-640.
doi: 10.1016/j.gpb.2021.08.005
[38] Fan J W, Shen Y T, Chen C, et al. A large-scale integrated transcriptomic atlas for soybean organ development. Mol Plant, 2025, 18: 669-689.
doi: 10.1016/j.molp.2025.02.003
[39] Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet, 2012, 44: 821-824.
doi: 10.1038/ng.2310 pmid: 22706312
[40] Pérez P, de los Campos G. Genome-wide regression and prediction with the BGLR statistical package. Genetics, 2014, 198: 483-495.
doi: 10.1534/genetics.114.164442 pmid: 25009151
[41] Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in Python. J Mach Learn Res, 2011, 12: 2825-2830.
[42] Liu Y, Wang D L, He F, et al. Phenotype prediction and genome-wide association study using deep convolutional neural network of soybean. Front Genet, 2019, 10: 1091.
doi: 10.3389/fgene.2019.01091 pmid: 31824557
[43] Wickham H. Ggplot2: Elegant Graphics for Data Analysis. New York, NY: Springer New York, 2009.
[44] Mansur L M, Orf J H, Chase K, et al. Genetic mapping of agronomic traits using recombinant inbred lines of soybean. Crop Sci, 1996, 36: 1327-1336.
doi: 10.2135/cropsci1996.0011183X003600050042x
[45] Lee S H, Bailey M A, Mian M A, et al. Identification of quantitative trait loci for plant height, lodging, and maturity in a soybean population segregating for growth habit. Theor Appl Genet, 1996, 92: 516-523.
doi: 10.1007/BF00224553 pmid: 24166318
[46] Sonah H, O’Donoughue L, Cober E, et al. Identification of loci governing eight agronomic traits using a GBS-GWAS approach and validation by QTL mapping in soya bean. Plant Biotechnol J, 2015, 13: 211-221.
doi: 10.1111/pbi.12249 pmid: 25213593
[47] Liu B H, Watanabe S, Uchiyama T, et al. The soybean stem growth habit gene Dt1 is an ortholog of Arabidopsis TERMINAL FLOWER1. Plant Physiol, 2010, 153: 198-210.
doi: 10.1104/pp.109.150607
[48] 刘路平, 胡雪洁, 祁金, 等. 大豆生育期基因E1和E2的启动子克隆及其表达模式分析. 中国农业科学, 2025, 58: 840-850.
doi: 10.3864/j.issn.0578-1752.2025.05.002
Liu L P, Hu X J, Qi J, et al. Cloning of the promoters and analysis of expression patterns of maturity genes E1 and E2in soybean. Sci Agric Sin, 2025, 58: 840-850 (in Chinese with English abstract).
[49] Yu H, Bhat J A, Li C D, et al. Genome-wide survey identified superior and rare haplotypes for plant height in the north-eastern soybean germplasm of China. Mol Breed, 2023, 43: 22.
doi: 10.1007/s11032-023-01363-7
[50] Wang D, Graef G L, Procopiuk A M, et al. Identification of putative QTL that underlie yield in interspecific soybean backcross populations. Theor Appl Genet, 2004, 108: 458-467.
doi: 10.1007/s00122-003-1449-z pmid: 14504749
[51] Li W X, Zheng D H, Van K, et al. QTL mapping for major agronomic traits across two years in soybean (Glycine max L. Merr.). J Crop Sci Biotechnol, 2008, 136: 125.
[52] Bhat J A, Karikari B, Adeboye K A, et al. Identification of superior haplotypes in a diverse natural population for breeding desirable plant height in soybean. Theor Appl Genet, 2022, 135: 2407-2422.
doi: 10.1007/s00122-022-04120-0 pmid: 35639109
[53] Watanabe S, Xia Z J, Hideshima R, et al. A map-based cloning strategy employing a residual heterozygous line reveals that the GIGANTEA gene is involved in soybean maturity and flowering. Genetics, 2011, 188: 395-407.
doi: 10.1534/genetics.110.125062 pmid: 21406680
[54] Li Y Q, Dong Y S, Wu H Y, et al. Positional cloning of the flowering time QTL qFT12-1 reveals the link between the clock related PRR homolog with photoperiodic response in soybeans. Front Plant Sci, 2019, 10: 1303.
doi: 10.3389/fpls.2019.01303
[55] Lu S J, Dong L D, Fang C, et al. Stepwise selection on homeologous PRR genes controlling flowering and maturity during soybean domestication. Nat Genet, 2020, 52: 428-436.
doi: 10.1038/s41588-020-0604-7
[56] Zhang J P, Song Q J, Cregan P B, et al. Genome-wide association study for flowering time, maturity dates and plant height in early maturing soybean (Glycine max) germplasm. BMC Genom, 2015, 16: 217.
doi: 10.1186/s12864-015-1441-4
[57] Keim P, Diers B W, Olson T C, et al. 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
[58] Reinprecht Y, Poysa V W, Yu K F, et al. Seed and agronomic QTL in low linolenic acid, lipoxygenase-free soybean (Glycine max (L.) Merrill) germplasm. Genome, 2006, 49: 1510-1527.
doi: 10.1139/g06-112 pmid: 17426766
[59] Kabelka E A, Diers B W, Fehr W R, et al. Putative alleles for increased yield from soybean plant introductions. Crop Sci, 2004, 44: 784-791.
doi: 10.2135/cropsci2004.7840
[60] Kim K S, Diers B W, Hyten D L, et al. Identification of positive yield QTL alleles from exotic soybean germplasm in two backcross populations. Theor Appl Genet, 2012, 125: 1353-1369.
doi: 10.1007/s00122-012-1944-1 pmid: 22869284
[61] Sun D S, Li W B, Zhang Z C, et al. Quantitative trait loci analysis for the developmental behavior of Soybean (Glycine max L. Merr.). Theor Appl Genet, 2006, 112: 665-673.
doi: 10.1007/s00122-005-0169-y
[62] Chen Q S, Zhang Z C, Liu C Y, et al. QTL analysis of major agronomic traits in soybean. Agric Sci China, 2007, 6: 399-405.
doi: 10.1016/S1671-2927(07)60062-5
[63] Alcivar A, Jacobson J, Rainho J, et al. Genetic analysis of soybean plant height, hypocotyl and internode lengths. J Agric Food Environ Sci, 2007, 1: 1-20.
[64] Specht J E, Chase K, Macrander M, et al. Soybean response to water: a QTL analysis of drought tolerance. Crop Sci, 2001, 41: 493-509.
doi: 10.2135/cropsci2001.412493x
[65] Prince S J, Vuong T D, Wu X L, et al. Mapping quantitative trait loci for soybean seedling shoot and root architecture traits in an inter-specific genetic population. Front Plant Sci, 2020, 11: 1284.
doi: 10.3389/fpls.2020.01284 pmid: 32973843
[66] Mansur L M, Lark K G, Kross H, et al. Interval mapping of quantitative trait loci for reproductive, morphological, and seed traits of soybean (Glycine max L.). Theor Appl Genet, 1993, 86: 907-913.
doi: 10.1007/BF00211040 pmid: 24193996
[67] Gai J Y, Wang Y J, Wu X L, et al. A comparative study on segregation analysis and QTL mapping of quantitative traits in plants: with a case in soybean. Front Agric China, 2007, 1: 1-7.
doi: 10.1007/s11703-007-0001-3
[68] Contreras-Soto R I, Mora F, de Oliveira M A R, et al. 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
[69] Lee S H, Bailey M A, Mian M A R, et al. Molecular markers associated with soybean plant height, lodging, and maturity across locations. Crop Sci, 1996, 36: 728-735.
doi: 10.2135/cropsci1996.0011183X003600030035x
[70] Mansur L M, Orf J, Lark K G. Determining the linkage of quantitative trait loci to RFLP markers using extreme phenotypes of recombinant inbreds of soybean (Glycine max L. Merr.). Theor Appl Genet, 1993, 86: 914-918.
doi: 10.1007/BF00211041 pmid: 24193997
[71] Lark K G, Chase K, Adler F, et al. Interactions between quantitative trait loci in soybean in which trait variation at one locus is conditional upon a specific allele at another. Proc Natl Acad Sci USA, 1995, 92: 4656-4660.
doi: 10.1073/pnas.92.10.4656 pmid: 7753859
[72] Guzman P S, Diers B W, Neece D J, et al. QTL associated with yield in three backcross-derived populations of soybean. Crop Sci, 2007, 47: 111-122.
doi: 10.2135/cropsci2006.01.0003
[73] Li R C, Jiang H W, Zhang Z G, et al. Combined linkage mapping and BSA to identify QTL and candidate genes for plant height and the number of nodes on the main stem in soybean. Int J Mol Sci, 2020, 21: 42.
doi: 10.3390/ijms21010042
[74] Orf J H, Chase K, Jarvik T, et al. Genetics of soybean agronomic traits: I. comparison of three related recombinant inbred populations. Crop Sci, 1999, 39: 1642-1651.
doi: 10.2135/cropsci1999.3961642x
[75] Liu C, Tian Y, Liu Z X, et al. Identification and characterization of long-InDels through whole genome resequencing to facilitate fine-mapping of a QTL for plant height in soybean (Glycine max L. Merr.). J Integr Agric, 2022, 21: 1903-1912.
doi: 10.1016/S2095-3119(21)63675-4
[76] Niu M R, Tian K W, Chen Q, et al. A multi-trait GWAS-based genetic association network controlling soybean architecture and seed traits. Front Plant Sci, 2024, 14: 1302359.
doi: 10.3389/fpls.2023.1302359
[77] Clark C B, Zhang D J, Wang W D, et al. Identification and mapping of a recessive allele, dt3, specifying semideterminate stem growth habit in soybean. Theor Appl Genet, 2023, 136: 258.
doi: 10.1007/s00122-023-04493-w
[78] Chang F G, Guo C Y, Sun F L, et al. Genome-wide association studies for dynamic plant height and number of nodes on the main stem in summer sowing soybeans. Front Plant Sci, 2018, 9: 1184.
doi: 10.3389/fpls.2018.01184 pmid: 30177936
[79] Wang L S, Li H Y, He M L, et al. GIGANTEA orthologs, E2 members, redundantly determine photoperiodic flowering and yield in soybean. J Integr Plant Biol, 2023, 65: 188-202.
doi: 10.1111/jipb.v65.1
[80] Wan Z, Liu Y X, Guo D D, et al. CRISPR/Cas9-mediated targeted mutation of the E1 decreases photoperiod sensitivity, alters stem growth habits, and decreases branch number in soybean. Front Plant Sci, 2022, 13: 1066820.
doi: 10.3389/fpls.2022.1066820
[81] Jing Y, Zhao X, Wang J, et al. Identification of loci and candidate genes for plant height in soybean (Glycine max) via genome-wide association study. Plant Breed, 2019, 138: 721-732.
doi: 10.1111/pbr.v138.6
[82] Spielmeyer W, Ellis M H, Chandler P M, Semidwarf (sd-1), “green revolution” rice, contains a defective gibberellin 20-oxidase gene. Proc Natl Acad Sci USA, 2002, 99: 9043-9048.
doi: 10.1073/pnas.132266399 pmid: 12077303
[83] Sasaki A, Ashikari M, Ueguchi-Tanaka M, et al. A mutant gibberellin-synthesis gene in rice. Nature, 2002, 416: 701-702.
doi: 10.1038/416701a
[84] Peng J R, Richards D E, Hartley N M, et al. ‘Green revolution’ genes encode mutant gibberellin response modulators. Nature, 1999, 400: 256-261.
doi: 10.1038/22307
[85] Liu X L, Huang M, Fan B, et al. Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet, 2016, 12: e1005767.
doi: 10.1371/journal.pgen.1005767
[86] Gao P F, Zhao H N, Luo Z, et al. SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding. Brief Bioinform, 2023, 24: bbad349.
doi: 10.1093/bib/bbad349
[87] Montesinos-López O A, Montesinos-López A, Pérez-Rodríguez P, et al. A review of deep learning applications for genomic selection. BMC Genom, 2021, 22: 19.
doi: 10.1186/s12864-020-07319-x
[88] Salesse-Smith C E, Sharwood R E, Busch F A, et al. Increased Rubisco content in maize mitigates chilling stress and speeds recovery. Plant Biotechnol J, 2020, 18: 1409-1420.
doi: 10.1111/pbi.13306 pmid: 31793172
[89] Jarquín D, Crossa J, Lacaze X, et al. A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor Appl Genet, 2014, 127: 595-607.
doi: 10.1007/s00122-013-2243-1 pmid: 24337101
[1] 陈檬瑶, 陈强, 史晓蕾, 索文英, 张孟臣, 杨春燕, 闫龙, 张锴, 孟庆民. 大豆籽粒油分含量QTL定位及KASP标记开发[J]. 作物学报, 2026, 52(7): 1985-1996.
[2] 赵辉, 黄义文, 买春艳, 景鹏飞, 孙海艳, 吴培培, 于立强, 李辉利, 周阳, 郭宪瑞, 张宏军. 小麦抗倒伏相关性状全基因组关联分析[J]. 作物学报, 2026, 52(7): 1943-1953.
[3] 刘峰, 董宏伟, 张越, 李冉, 孙若男, 刘佳城, 孙树杰, 张旭东, 韩清芳. 玉豆间作减氮稳产的光合机制：光合产物转化效率提升[J]. 作物学报, 2026, 52(7): 2095-2108.
[4] 金昱何, 王雪菲, 徐张一娃, 缪怡宁, 蒋云杰, 伊莹, 缪德麟, 朱静仪, 钟一帆, 陈铭亨, 方芳, 刘鹏. 外源激素对低温胁迫下大豆叶片叶绿素荧光参数及抗氧化酶系统的影响[J]. 作物学报, 2026, 52(6): 1817-1829.
[5] 张献丰, 郭利建, 李康春, 孔斌雪, 刘玉芳, 车卓, 杨德龙. 小麦ABHD6基因家族鉴定与粒重功能标记开发[J]. 作物学报, 2026, 52(6): 1711-1727.
[6] 习千辉, 徐梓瑗, 刘梦梦, 王宏艺, 郎凯琳, 井震海, 陈锋, 赵磊. 小麦籽粒铜含量的全基因组关联分析及候选基因预测[J]. 作物学报, 2026, 52(6): 1604-1617.
[7] 毛嘉琦, 黄朋雨, 赵佳佳, 郑兴卫, 武棒棒, 郝宇琼, 屈非, 刘成, 马朋涛, 郑军. 山西小麦品种白粉病抗性评价及抗病基因分子检测[J]. 作物学报, 2026, 52(6): 1669-1681.
[8] 彭佳泺, 李颖, 李丹丹, 杨军宁, 郭学峰, 张文姣, 俞晓雪, 周亚荣, 王振玉, 王彩香, 马雄风, 宿俊吉. 陆地棉I类LBD家族成员鉴定及GhLBD6调控开花期的功能和单倍型分析[J]. 作物学报, 2026, 52(6): 1682-1697.
[9] 姚术, 郭凯悦, 翟慧慧, 姚佳慧, 邓文琪, 闫玲, 黄驰, 高阳, 俞嫣然, 赵振邦, 李英慧, 王晓波, 李佳佳. 大豆苗期耐低铁综合评价及优异种质筛选[J]. 作物学报, 2026, 52(5): 1373-1387.
[10] 杨欣雨, 崔文涛, 迪力尼格尔·阿力木, 汪凯翔, 吴鹏昊, 任姣姣. 玉米穗上叶片数全基因组关联分析和全基因组选择[J]. 作物学报, 2026, 52(5): 1573-1590.
[11] 闫安, 蒋昆炜, 王蓉圆, 田林, 张璐, 王韵, 徐建龙. 水稻剑叶小维管束数基因SVN7的鉴定与克隆[J]. 作物学报, 2026, 52(5): 1364-1372.
[12] 张晴, 杨昱, 郭茜, 岳霈尧, 殷丛丛, 牛景萍, 赵晋忠, 杜维俊, 岳爱琴. 大豆GmARA6a的克隆及响应盐胁迫的功能分析[J]. 作物学报, 2026, 52(2): 480-493.
[13] 鲁雅妮, 丁超杰, 张煜, 杜习军, 齐学礼, 胡琳, 许为钢. 河南省200份小麦品种苗期茎基腐病抗性鉴定与全基因组关联分析[J]. 作物学报, 2026, 52(2): 363-375.
[14] 李诗晴, 王茜, 王素华, 张耀文, 王丽侠. 绿豆种质资源苗期耐盐性鉴定及相关基因发掘[J]. 作物学报, 2026, 52(2): 376-388.
[15] 李云香, 郭千纤, 侯万伟, 张小娟. 引进ICARDA小麦苗期根系抗旱性状的全基因组关联分析[J]. 作物学报, 2025, 51(9): 2387-2398.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] 李绍清, 李阳生, 吴福顺, 廖江林, 李达模. 水稻孕穗期在淹涝胁迫下施肥的优化选择及其作用机理[J]. 作物学报, 2002, 28(01): 115 -120 .
[2] 王兰珍;米国华;陈范骏;张福锁. 不同产量结构小麦品种对缺磷反应的分析[J]. 作物学报, 2003, 29(06): 867 -870 .
[3] 杨建昌;张亚洁;张建华;王志琴;朱庆森. 水分胁迫下水稻剑叶中多胺含量的变化及其与抗旱性的关系[J]. 作物学报, 2004, 30(11): 1069 -1075 .
[4] 袁美;杨光圣;傅廷栋;严红艳. 甘蓝型油菜生态型细胞质雄性不育两用系的研究Ⅲ. 8-8112AB的温度敏感性及其遗传[J]. 作物学报, 2003, 29(03): 330 -335 .
[5] 王永胜;王景;段静雅;王金发;刘良式. 水稻极度分蘖突变体的分离和遗传学初步研究[J]. 作物学报, 2002, 28(02): 235 -239 .
[6] 王丽燕;赵可夫. 玉米幼苗对盐胁迫的生理响应[J]. 作物学报, 2005, 31(02): 264 -268 .
[7] 田孟良;黄玉碧;谭功燮;刘永建;荣廷昭. 西南糯玉米地方品种waxy基因序列多态性分析[J]. 作物学报, 2008, 34(05): 729 -736 .
[8] 胡希远;李建平;宋喜芳. 空间统计分析在作物育种品系选择中的效果[J]. 作物学报, 2008, 34(03): 412 -417 .
[9] 王艳;邱立明;谢文娟;黄薇;叶锋;张富春;马纪. 昆虫抗冻蛋白基因转化烟草的抗寒性[J]. 作物学报, 2008, 34(03): 397 -402 .
[10] 郑希;吴建国;楼向阳;徐海明;石春海. 不同环境条件下稻米组氨酸和精氨酸的胚乳和母体植株QTL分析[J]. 作物学报, 2008, 34(03): 369 -375 .
京ICP备15008789号-2