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Acta Agronomica Sinica ›› 2024, Vol. 50 ›› Issue (3): 623-632.doi: 10.3724/SP.J.1006.2024.34091

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

Genome-wide association analysis and candidate genes predication of leaf characteristics traits in soybean (Glycine max L.)

WANG Qiong1(), ZHU Yu-Xiang1,2, ZHOU Mi-Mi1, ZHANG Wei1, ZHANG Hong-Mei1, CEHN Xin1, CEHN Hua-Tao1,*(), CUI Xiao-Yan1,*()   

  1. 1Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences / Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Nanjing 210014, Jiangsu, China
    2College of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, Jiangsu, China
  • Received:2023-05-25 Accepted:2023-09-13 Online:2024-03-12 Published:2023-10-07
  • Contact: *E-mail: cht@jaas.ac.cn; E-mail: cxy@jaas.ac.cn
  • Supported by:
    Natural Science Foundation of Jiangsu Province(BK20220740);Jiangsu Seed Industry Revitalizing Project(JBGS[2021]057);Key Research and Development Program of Jiangsu Province(BE2022328);Jiangsu Agricultural Science and Technology Innovation Fund(CX(22)5002)

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Abstract:

Leaf shape and vertical distribution of soybean affect canopy structure, photosynthetic efficiency, and yield. The existence of different leaf shapes and sizes on the same plant, which is known as heterophylly, has been observed in many flowering plant species. Yet, the genetic characteristics and genetic basis of heterophylly in soybean remain unknown. In this study, leaf characteristics such as leaf length, leaf width, leaf shape index, and heterophylly index were investigated in 283 soybean germplasm resources for two consecutive years in Nanjing, Jiangsu Province. A total of 181 related loci were detected by genome-wide association study (GWAS), among which 18 loci could be repeatedly detected in two environments or among multiple traits. Using the loci associated with leaf characteristics, we integrated the GWAS approach with the expression profiling data and gene-based association and functional annotation of orthologs in Arabidopsis to identify candidate genes involved in leaf development in soybean. The known soybean leaf shape regulatory gene Ln (Glyma.20G116200) was found upstream of locus Chr20:36152820. In addition, two candidate genes (Glyma.19G192700 and Glyma.19G194100) were identified near the related locus Chr19:45155943 on chromosome 19, homologous genes of growth-regulating factor 4 (GRF4), and LITTLE ZIPPER 3 (ZPR3), respectively. These results lay a solid foundation for expanding our understanding of the genetic mechanism of heterophylly in soybean.

Key words: soybean, leaf characteristics, heterophylly, GWAS, SNP markers

Fig. 1

Schematic representation of the design in the experiment"

Table 1

Statistical analysis of leaf characteristics traits of soybean association panel"

环境
Environment		 性状
Trait		 最小值
Min.		 最大值
Max.		 平均值±标准差
Mean±SD		 变异系数
CV (%)
2021南京
2021 Nanjing		 上部叶长 Upper leaf length 7.00 18.00 11.25±1.94 0.17
上部叶宽 Upper leaf width 2.50 9.70 5.66±1.41 0.25
上部叶形 Upper leaf shape 1.36 3.24 2.05±0.42 0.20
下部叶长 Lower leaf length 4.00 12.10 7.44±1.42 0.19
下部叶宽 Lower leaf width 2.80 8.03 4.94±1.00 0.20
下部叶形 Lower leaf shape 1.12 2.14 1.50±0.22 0.15
异形叶指数 Heterophylly leaf index 0.90 2.11 1.36±0.25 0.18
2022南京
2022 Nanjing		 上部叶长 Upper leaf length 6.67 17.23 11.28±1.86 0.16
上部叶宽 Upper leaf width 3.53 11.17 6.13±1.39 0.23
上部叶形 Upper leaf shape 1.38 2.90 1.90±0.32 0.17
下部叶长 Lower leaf length 4.83 15.13 9.24±1.58 0.17
下部叶宽 Lower leaf width 2.90 9.60 5.72±1.29 0.23
下部叶形 Lower leaf shape 1.20 2.91 1.67±0.35 0.21
异形叶指数 Heterophylly leaf index 0.68 1.76 1.16±0.19 0.16

Fig. 2

Distribution of leaf characteristics traits of soybean association panel"

Table 2

Correlation coefficients of leaf characteristics traits"

性状
Trait		 相关系数
Correlation coefficient		 P值
P-value
上部叶长 Upper leaf length 0.41 6.10E-09
上部叶宽 Upper leaf width 0.61 1.02E-19
上部叶形 Upper leaf shape 0.64 6.19E-22
下部叶长 Lower leaf length 0.26 4.24E-04
下部叶宽 Lower leaf width 0.36 9.75E-07
下部叶形 Lower leaf shape 0.59 4.49E-18
异形叶指数 Heterophylly leaf index 0.53 1.73E-13

Fig. 3

Correlation coefficients among leaf characteristics traits in soybean *, **, and *** mean significant correlation at the 0.05, 0.01, and 0.001 probability levels, respectively."

Table 3

GWAS signals of leaf characteristics traits"

信号
Signal		 标记
Marker		 染色体
Chr.		 位置
Position		 P值
P-value		 性状
Trait
5_10.7 Chr05:10705738 5 10705738 7.47E-07 2021下部叶长 Lower leaf length in 2021
5_10.7 Chr05:10705738 5 10705738 1.93E-06 2021下部叶宽 Lower leaf width in 2021
10_50.7 Chr10:50760191 10 50760191 2.01E-06 2021上部叶形 Upper leaf shape in 2021
10_50.7 Chr10:50760191 10 50760191 1.10E-06 2021异形叶指数 Heterophylly index in 2021
13_39.7 Chr13:39721130 13 39721130 1.53E-06 2022上部叶宽 Upper leaf width in 2022
13_39.7 Chr13:39772387 13 39772387 8.95E-06 2021上部叶形 Upper leaf shape in 2021
16_32.2 Chr16:32235624 16 32235624 3.05E-06 2021上部叶长 Upper leaf length in 2021
16_32.2 Chr16:32236735 16 32236735 1.41E-06 2021下部叶长 Lower leaf length in 2021
17_40.9 Chr17:40908625 17 40908625 9.14E-06 2022 上部叶长 Upper leaf length in 2022
17_40.9 Chr17:40908625 17 40908625 2.91E-06 2022下部叶长 Lower leaf length in 2022
19_23.9 Chr19:23901295 19 23901295 2.43E-06 2021上部叶宽 Upper leaf width in 2021
19_23.9 Chr19:23901508 19 23901508 6.92E-06 2022上部叶宽 Upper leaf width in 2022
19_45.1 Chr19:45151266 19 45151266 6.05E-06 2022上部叶形 Upper leaf shape in 2022
19_45.1 Chr19:45155931 19 45155931 9.51E-06 2022上部叶宽 Upper leaf width in 2022
19_45.1 Chr19:45155943 19 45155943 2.45E-07 2021上部叶形 Upper leaf shape in 2021
19_45.1 Chr19:45155943 19 45155943 2.84E-08 2021异形叶指数 Heterophylly index in 2021
20_19.2 Chr20:19295767 20 19295767 8.08E-06 2022上部叶形 Upper leaf shape in 2022
20_19.2 Chr20:19295767 20 19295767 3.39E-06 2022下部叶形 Lower leaf shape in 2022

Fig. S1

Manhattan plots for GWAS of leaf characteristics traits"

Fig. 4

Relative expression pattern of candidate genes Glyma.19G192700"

[1] Wilson R F. Soybean:Market Driven Research Needs. New York: Springer New York, 2008. pp 3-15.
[2] 冯锋, 张志楠, 谷勇哲, 何俊卿, 田志喜. 提升我国大豆供给能力路径刍议. 中国科学院院刊, 2022, 37: 1281-1289.
Feng F, Zhang Z N, Gu Y Z, He J Q, Tian Z X. Discussion on approaches to improving soybean supply capacity in China. Bull Chin Acad Sci, 2022, 37: 1281-1289 (in Chinese with English abstract).
[3] 田志喜, 刘宝辉, 杨艳萍, 李明, 姚远, 任小波, 薛勇彪. 我国大豆分子设计育种成果与展望. 中国科学院院刊, 2018, 33: 915-922.
Tian Z X, Liu B H, Yang Y P, Li M, Yao Y, Ren X B, Xue Y B. Update and prospect of soybean molecular module-based designer breeding in China. Bull Chin Acad Sci, 2018, 33: 915-922 (in Chinese with English abstract).
[4] Reinhardt D, Kuhlemeier C. Plant architecture. EMBO Rep, 2002, 3: 846-851.
doi: 10.1093/embo-reports/kvf177 pmid: 12223466
[5] 赵团结, 盖钧镒, 李海旺, 邢邯, 邱家驯. 超高产大豆育种研究的进展与讨论. 中国农业科学, 2006, 39: 29-37.
Zhao T J, Gai J Y, Li H W, Xing H, Qiu J X. Advances in breeding for super high-yielding soybean cultivars. Sci Agric Sin, 2006, 39: 29-37 (in Chinese with English abstract).
doi: 10.3864/j.issn.0578-1752.at-2005-6132
[6] Gao J, Yang S, Cheng W, Fu Y, Leng J, Yuan X, Jiang N, Ma J, Feng X. GmILPA1, encoding an APC8-like protein, controls leaf petiole angle in soybean. Plant Physiol, 2017, 174: 1167-1176.
doi: 10.1104/pp.16.00074 pmid: 28336772
[7] 彭玉华, 杨国保, 吴琳, 吴宇, 朱国富. 大豆叶形垂直分布类型在产量改良中的应用. 中国油料作物学报, 1999, 21(1): 14-17.
Peng Y H, Yang G B, Wu L, Wu Y, Zhu G F. Application of vertical leaf shape distribution to soybean yield improvement. Chin J Oil Crop Sci, 1999, 21(1): 14-17 (in Chinese with English abstract).
[8] Chen Q, Liu B, Ai L, Yan L, Lin J, Shi X, Zhao H, Wei Y, Feng Y, Liu C, Yang C, Zhang M. QTL and candidate genes for heterophylly in soybean based on two populations of recombinant inbred lines. Front Plant Sci, 2022, 13: 961619.
doi: 10.3389/fpls.2022.961619
[9] Fang C, Ma Y, Wu S, Liu Z, Wang Z, Yang R, Hu G, Zhou Z, Yu H, Zhang M, Pan Y, Zhou G, Ren H, Du W, Yan H, Wang Y, Han D, Shen Y, Liu S, Liu T, Zhang J, Qin H, Yuan J, Yuan X, Kong F, Liu B, Li J, Zhang Z, Wang G, Zhu B, Tian Z. 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
[10] Han Y, Zhao X, Liu D, Li Y, Lightfoot D A, Yang Z, Zhao L, Zhou G, Wang Z, Huang L, Zhang Z, Qiu L, Zheng H, Li W. Domestication footprints anchor genomic regions of agronomic importance in soybeans. New Phytol, 2016, 209: 871-884.
doi: 10.1111/nph.13626 pmid: 26479264
[11] Lu S, Dong L, Fang C, Liu S, Kong L, Cheng Q, Chen L, Su T, Nan H, Zhang D, Zhang L, Wang Z, Yang Y, Yu D, Liu X, Yang Q, Lin X, Tang Y, Zhao X, Yang X, Tian C, Xie Q, Li X, Yuan X, Tian Z, Liu B, Weller J L, Kong F. Stepwise selection on homologous PRR genes controlling flowering and maturity during soybean domestication. Nat Genet, 2020, 52: 428-436.
doi: 10.1038/s41588-020-0604-7
[12] Zhou Z, Jiang Y, Wang Z, Gou Z, Lyu J, Li W, Yu Y, Shu L, Zhao Y, Ma Y, Fang C, Shen Y, Liu T, Li C, Li Q, Wu M, Wang M, Wu Y, Dong Y, Wan W, Wang X, Ding Z, Gao Y, Xiang H, Zhu B, Lee S H, Wang W, Tian Z. 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
[13] Paterson A H, Brubaker C L, Wendel J F. A rapid method for extraction of cotton (Gossypium spp.) genomic DNA suitable for RFLP or PCR analysis. Plant Mol Biol Rep, 1993, 11: 122-127.
doi: 10.1007/BF02670470
[14] Bolger A M, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 2014, 30: 2114-2120.
doi: 10.1093/bioinformatics/btu170 pmid: 24695404
[15] Zhang W, Xu W, Zhang H, Liu X, Cui X, Li S, Song L, Zhu Y, Chen X, Chen H. Comparative selective signature analysis and high-resolution GWAS reveal a new candidate gene controlling seed weight in soybean. Theor Appl Genet, 2021, 134: 1329-1341.
doi: 10.1007/s00122-021-03774-6 pmid: 33507340
[16] Browning B L, Zhou Y, Browning S R. A one-penny imputed genome from next-generation reference panels. Am J Hum Genet, 2018, 103: 338-348.
doi: S0002-9297(18)30242-8 pmid: 30100085
[17] Kang H M, Sul J H, Service S K, Zaitlen N A, Kong S Y, Freimer N B, Sabatti C, Eskin E. Variance component model to account for sample structure in genome-wide association studies. Nat Genet, 2010, 42: 348-354.
doi: 10.1038/ng.548 pmid: 20208533
[18] Kim Y S, Kim S G, Lee M, Lee I, Park H Y, Seo P J, Jung J H, Kwon E J, Suh S W, Paek K H, Park C M. HD-ZIP III activity is modulated by competitive inhibitors via a feedback loop in Arabidopsis shoot apical meristem development. Plant Cell, 2008, 20: 920-933.
doi: 10.1105/tpc.107.057448
[19] Hewezi T, Maier T R, Nettleton D, Baum T J. The Arabidopsis microRNA396-GRF1/GRF3 regulatory module acts as a developmental regulator in the reprogramming of root cells during cyst nematode infection. Plant Physiol, 2012, 159: 321-335.
doi: 10.1104/pp.112.193649 pmid: 22419826
[20] Horiguchi G, Kim G T, Tsukaya H. The transcription factor AtGRF5 and the transcription coactivator AN3 regulate cell proliferation in leaf primordia of Arabidopsis thaliana. Plant J, 2005, 43: 68-78.
doi: 10.1111/j.1365-313X.2005.02429.x pmid: 15960617
[21] Kim J H, Kende H. A transcriptional coactivator, AtGIF1, is involved in regulating leaf growth and morphology in Arabidopsis. Proc Natl Acad Sci USA, 2004, 101: 13374-13379.
doi: 10.1073/pnas.0405450101
[22] Kim J H, Choi D, Kende H. The AtGRF family of putative transcription factors is involved in leaf and cotyledon growth in Arabidopsis. Plant J, 2003, 36: 94-104.
doi: 10.1046/j.1365-313X.2003.01862.x
[23] Liang G, He H, Li Y, Wang F, Yu D. Molecular mechanism of microRNA396 mediating pistil development in Arabidopsis. Plant Physiol, 2014, 164: 249-258.
doi: 10.1104/pp.113.225144 pmid: 24285851
[24] Sun P, Zhang W, Wang Y, He Q, Shu F, Liu H, Wang J, Wang J, Yuan L, Deng H. OsGRF4 controls grain shape, panicle length and seed shattering in rice. J Integr Plant Biol, 2016, 58: 836-847.
doi: 10.1111/jipb.v58.10
[25] Li S, Gao F, Xie K, Zeng X, Cao Y, Zeng J, He Z, Ren Y, Li W, Deng Q, Wang S, Zheng A, Zhu J, Liu H, Wang L, Li P. The OsmiR396c-OsGRF4-OsGIF1 regulatory module determines grain size and yield in rice. Plant Biotechnol J, 2016, 14: 2134-2146.
doi: 10.1111/pbi.12569 pmid: 27107174
[26] Li S, Tian Y, Wu K, Ye Y, Yu J, Zhang J, Liu Q, Hu M, Li H, Tong Y, Harberd N P, Fu X. Modulating plant growth-metabolism coordination for sustainable agriculture. Nature, 2018, 560: 595-600.
doi: 10.1038/s41586-018-0415-5
[27] Rodriguez R E, Mecchia M A, Debernardi J M, Schommer C, Weigel D, Palatnik J F. Control of cell proliferation in Arabidopsis thaliana by microRNA miR396. Development, 2010, 137: 103-112.
doi: 10.1242/dev.043067 pmid: 20023165
[28] Bhagsari A S, Brown R H. Leaf photosynthesis and its correlation with leaf area. Crop Sci, 1986, 26: 127-132.
doi: 10.2135/cropsci1986.0011183X002600010030x
[29] Sarlikioti V, De Visser P H, Buck-Sorlin G H, Marcelis L F. How plant architecture affects light absorption and photosynthesis in tomato: towards an ideotype for plant architecture using a functional-structural plant model. Ann Bot, 2011, 108: 1065-1073.
doi: 10.1093/aob/mcr221
[30] Tischner T, Allphin L, Chase K, Orf J H, Lark K G. Genetics of seed abortion and reproductive traits in soybean. Crop Sci, 2003, 43: 464-473.
doi: 10.2135/cropsci2003.0464
[31] Jeong N, Moon J K, Kim H S, Kim C G, Jeong S C. Fine genetic mapping of the genomic region controlling leaflet shape and number of seeds per pod in the soybean. Theor Appl Genet, 2011, 122: 865-874.
doi: 10.1007/s00122-010-1492-5 pmid: 21104397
[32] Fang C, Li W, Li G, Wang Z, Zhou Z, Ma Y, Shen Y, Li C, Wu Y, Zhu B, Yang W, Tian Z. Cloning of Ln gene through combined approach of map-based cloning and association study in soybean. J Genet Genomics, 2013, 40: 93-96.
doi: 10.1016/j.jgg.2013.01.002
[33] Jeong N, Suh S J, Kim M H, 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
[34] Li Y, Hou Z, Li W, Li H, Lu S, Gan Z, Du H, Li T, Zhang Y, Kong F, Cheng Y, He M, Ma L, Liao C, Li Y, Dong L, Liu B, Cheng Q. The legume-specific transcription factor E1 controls leaf morphology in soybean. BMC Plant Biol, 2021, 21: 531.
doi: 10.1186/s12870-021-03301-1 pmid: 34773981
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