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Acta Agronomica Sinica ›› 2021, Vol. 47 ›› Issue (5): 847-859.doi: 10.3724/SP.J.1006.2021.04176

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

Detecting QTL system of root hydraulic stress tolerance index at seedling stage in soybean

WANG Wu-Bin1, TONG Fei1,2, KHAN Mueen-Alam1, ZHANG Ya-Xuan1, HE Jian-Bo1, HAO Xiao-Shuai1, XING Guang-Nan1, ZHAO Tuan-Jie1, GAI Jun-Yi1,*()   

  1. 1Soybean Research Institute, Nanjing Agricultural University / National Center for Soybean Improvement / MOA Key Laboratory for Biology and Genetic Improvement of Soybean (General), Ministry of Agriculture and Rural Affairs / National Key Laboratory of Crop Genetics and Germplasm Enhancement / Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing 210095, Jiangsu, China
    2Xuzhou Institute of Agricultural Sciences of Xuhuai Region of Jiangsu, Xuzhou 221131, Jiangsu, China
  • Received:2020-07-31 Accepted:2020-11-13 Online:2021-05-12 Published:2020-12-23
  • Contact: GAI Jun-Yi E-mail:sri@njau.edu.cn
  • Supported by:
    Fundamental Research Funds for the Central Universities(KYZZ201901);National Natural Science Foundation of China(31601325);MOE Program for Changjiang Scholars and Innovative Research Team in University(PCSIRT_17R55);China Agriculture Research System(CARS-04);Jiangsu JCIC-MCP

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Abstract:

Soybean is an important source of plant protein and vegetable oil in the world. Drought is one of the important environmental stress factors affecting soybean yield. To explore the genetic base of drought tolerance in soybean, a nested association mapping population composed of two sets of recombinant inbred lines with a common parent in a total of 429 lines was investigated for leaf proline content under PEG simulated drought stress. The genetic system of root hydraulic stress tolerance index (RHSTI) was analyzed using the RTM-GWAS (restrictive two-stage multilocus genome-wide association study). The results showed that there were significant differences in RHSTI among the three parents under two different environments in spring and summer, and among the nested association mapping population with the variation range of 0.11-2.94 and 0.03-1.93, respectively. The heritability values of Line and Line×Environment were 37.9% and 60.1%, respectively, indicating that the variation of RHSTI was greatly affected by the environment. Using the RHSTI data and 6137 SNPLDB markers, a total of 45 main effect QTLs were detected on 18 chromosomes, which could explain a total of 37.58% phenotypic variation, including 7 large contribution QTLs with R 2 more than 1%. Among them, 34 QTLs with QTL×Environment effect explained 12.50% of the phenotypic variation. Combined with the transcriptome data under PEG stress, totally 38 differentially expressed genes were identified within a QTL ± 500 kb, which can be grouped into different biological categories, including ABA responders, stress responders, transcription factors, development factors, protein metabolism factors, unknown functions and others, with stress responses, transcription factors and development factors as the major parts. In summary, the results indicated that the drought tolerance of soybean was a complex quantitative trait, with the complex genetic basis controlling by multiple loci, multiple genes and interaction with the environment. The present results can lay the foundation of molecular breeding for drought tolerance in soybean.

Key words: soybean, root hydraulic stress tolerance index, proline content, QTL, nested association mapping population

Table 1

Information of drought tolerance QTLs reported in the literatures"

作图群体
Mapping population		 评价指标
Indicator		 QTL数目
QTL number		 染色体(QTL数目)a
Chromosome (QTL number)		 参考文献
Reference
Benning/PI 416937 萎蔫指数Canopy width 7 2(1) 4(1) 5(1) 12(1) 14(1) 17(1) 19(1) [11]
KS4895/Jackson 萎蔫指数Canopy width 4 8(1) 13(1) 14(1) 17(1) [12]
93705KS4895/Jackson 萎蔫指数Canopy width 13 2(3) 4(1) 5(2) 6(2) 8(1) 14(1) 17(3) [13]
08705KS4895/Jackson 萎蔫指数Canopy width 7 9(1) 11(2) 12(1) 16(1) 17(2) [13]
KS4895/ PI 424140 萎蔫指数Canopy width 4 11(1) 17(1) 19(2) [13]
Benning/PI 416937 萎蔫指数Canopy width 4 2(3) 19(1) [13]
NN1138-2/Kefeng 1 耐旱系数Drought index 10 1(1) 5(2) 6(1) 7(1) 12(1) 16(1) 17(2) 20(1) [14]
Benning/PI 416937 叶片电导率Hydraulic conductance 4 3(1) 5(1) 10(1) 12(1) [15]
S-100/Tokyo 水分利用率Water use efficiency 3 4(2) 19(1) [16]
Young/PI 416937 水分利用率Water use efficiency 6 12(2) 16(3) 18(1) [17]
QTL元分析Meta-QTL 26 2(10) 5(3) 11(4) 12(2) 17(6) 19(3) [18]

Table 2

Descriptive statistics of root hydraulic stress tolerance index (RHSTI) in the MTZ population"

环境
Environment		 M Z T MTZ
组中值(频数) Value of class mid-point (frequency) 变幅
Range		 平均数
Mean		 变异系数
CV (%)		 遗传力
h2
0.1 0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.7 >1.9
春季Spring 0.35 1.05 0.95 1 24 159 128 57 15 9 2 1 9 0.11-2.94 0.69 13.2 97.2
夏季Summer 0.17 1.04 0.96 38 56 75 100 61 34 20 5 0 2 0.03-1.93 0.65 12.1 97.9
平均值Mean 0.26 1.05 0.95 2 41 115 159 55 19 5 6 0 5 0.09-2.80 0.68 12.7 37.9

Fig. 1

Drought performance of three parents under PEG simulated stress The letters on the left in the figure represent the positions of three parents in the blue box on the right. Abbreviations are the same as those given in Table 2."

Table 3

Joint analysis of variance of root hydraulic stress tolerance index (RHSTI) under two environments in the MTZ population"

变异来源
Source of variation		 F值
F-value
家系 Line 50.54**
环境 Environment 0.80
区组(环境) Block (Environment) 94.89**
家系×环境 Line × Environment 30.38**

Table 4

Identified QTLs conferring root hydraulic stress tolerance index (RHSTI) in MTZ population"

QTL 连锁不平衡区段
SNPLDB		 染色体
Chr.		 位置
Position		 等位变异数目
AN		 主效QTL Main effect QTL QTL与环境互作 QTL×Env a 已报道位点
Reported QTLs
-lg P R2(%) -lg P R2(%)
RHSTI1.1 Gm01_4742892 Gm01 4742892 2 15.4 0.57 12.2 0.44
RHSTI2.1 BLOCK_2_27517291_27687530 Gm02 27517291-27687530 3 18.1 0.72 4.0 0.15
RHSTI2.2 BLOCK_2_39672439_39870477 Gm02 39672439-39870477 3 40.6 1.66 4.4 0.17 mqCanopy wilt-018[18]
RHSTI2.3 BLOCK_2_46859465_47055418 Gm02 46859465-47055418 3 22.0 0.88 NS
RHSTI2.4 BLOCK_2_51400285_51574802 Gm02 51400285-51574802 3 18.8 0.74 6.1 0.24
RHSTI3.1 BLOCK_3_2456930_2505542 Gm03 2456930-2505542 3 14.7 0.58 10.2 0.40
RHSTI3.2 BLOCK_3_43400584_43484318 Gm03 43400584-43484318 3 16.5 0.65 33.4 1.36
RHSTI4.1 BLOCK_4_41364990_41553577 Gm04 41364990-41553577 3 17.1 0.68 2.5 0.10
RHSTI6.1 BLOCK_6_4265210_4332096 Gm06 4265210-4332096 2 21.0 0.79 4.3 0.14
RHSTI6.2 BLOCK_6_4951013_5150438 Gm06 4951013-5150438 3 17.4 0.69 NS
RHSTI6.3 BLOCK_6_13682742_13879752 Gm06 13682742-13879752 3 15.1 0.59 NS
RHSTI7.1 BLOCK_7_945093_1143184 Gm07 945093-1143184 3 16.0 0.63 10.9 0.43
RHSTI7.2 Gm07_27902201 Gm07 27902201 2 35.3 1.39 NS
RHSTI7.3 BLOCK_7_37870093_38067672 Gm07 37870093-38067672 3 15.8 0.62 9.3 0.36
RHSTI8.1 Gm08_42822124 Gm08 42822124 2 18.8 0.70 14.6 0.54
RHSTI9.1 BLOCK_9_5045930_5095878 Gm09 5045930-5095878 3 21.4 0.85 11.9 0.47
RHSTI9.2 Gm09_5673327 Gm09 5673327 2 15.3 0.56 2.2 0.06
RHSTI9.3 BLOCK_9_7453368_7651586 Gm09 7453368-7651586 3 25.5 1.02 NS
RHSTI9.4 BLOCK_9_14835645_14840806 Gm09 14835645-14840806 2 18.5 0.69 3.5 0.11
RHSTI10.1 BLOCK_10_10109602_10137279 Gm10 10109602-10137279 3 18.1 0.72 NS
RHSTI10.2 BLOCK_10_39940500_40129301 Gm10 39940500-40129301 3 24.1 0.96 4.4 0.17 Drought tolerance 6-3[13]
RHSTI10.3 BLOCK_10_40440079_40629141 Gm10 40440079-40629141 3 15.3 0.60 12.2 0.48 Drought tolerance 6-3[13]
RHSTI10.4 BLOCK_10_47418374_47616648 Gm10 47418374-47616648 3 19.7 0.78 3.5 0.14
RHSTI11.1 BLOCK_11_26241368_26440039 Gm11 26241368-26440039 3 15.0 0.59 3.1 0.12 mqCanopy wilt-020[18]
mqCanopy wilt-009[18]
RHSTI11.2 BLOCK_11_29079685_29279006 Gm11 29079685-29279006 3 20.1 0.80 16.6 0.66 mqCanopy wilt-020[18]
RHSTI12.1 BLOCK_12_11206224_11381903 Gm12 11206224-11381903 3 16.3 0.64 11.6 0.45
RHSTI12.2 BLOCK_12_11687384_11857574 Gm12 11687384-11857574 2 73.0 3.09 NS
RHSTI13.1 BLOCK_13_3657693_3729944 Gm13 3657693-3729944 3 14.4 0.57 13.3 0.52
RHSTI13.2 BLOCK_13_35892463_36008531 Gm13 35892463-36008531 3 15.5 0.61 4.1 0.16
RHSTI13.3 BLOCK_13_41064207_41264076 Gm13 41064207-41264076 2 18.1 0.67 NS
RHSTI14.1 BLOCK_14_2645749_2661861 Gm14 2645749-2661861 3 19.9 0.79 5.8 0.22
RHSTI14.2 BLOCK_14_46766676_46936602 Gm14 46766676-46936602 3 20.9 0.83 4.7 0.18 Canopy wilt 2-5[13]
RHSTI15.1 Gm15_38751520 Gm15 38751520 2 34.1 1.34 NS Canopy wilt 2-5[13]
RHSTI15.2 BLOCK_15_49313308_49446734 Gm15 49313308-49446734 2 18.0 0.67 42.6 1.70
RHSTI17.1 BLOCK_17_9162497_9323658 Gm17 9162497-9323658 3 11.7 0.46 5.7 0.22 Drought index 1-9[14]
Canopy wilt 1-3[12]
Canopy wilt 3-10[13]
RHSTI17.2 BLOCK_17_10274061_10289190 Gm17 10274061-10289190 3 59.2 2.51 NS
RHSTI17.3 BLOCK_17_32103917_32299960 Gm17 32103917-32299960 3 14.1 0.55 NS
RHSTI18.1 BLOCK_18_1411394_1609397 Gm18 1411394-1609397 3 25.0 1.00 3.1 0.12
RHSTI18.2 BLOCK_18_4798286_4951218 Gm18 4798286-4951218 3 12.6 0.50 6.4 0.25
RHSTI18.3 BLOCK_18_13133051_13329765 Gm18 13133051-13329765 3 18.3 0.72 7.0 0.27
RHSTI19.1 BLOCK_19_1291865_1491855 Gm19 1291865-1491855 3 17.2 0.68 32.0 1.29
RHSTI19.2 Gm19_17194130 Gm19 17194130 2 12.9 0.47 3.2 0.10
RHSTI19.3 BLOCK_19_23574474_23596371 Gm19 23574474-23596371 2 15.4 0.56 NS
RHSTI19.4 BLOCK_19_35661675_35858974 Gm19 35661675-35858974 3 17.6 0.70 8.3 0.32
RHSTI20.1 BLOCK_20_35376980_35573994 Gm20 35376980-35573994 3 18.7 0.74 4.0 0.15
总计Total 45 123 37.58 12.50

Fig. 2

Genetic analysis of RHSTI in the MTZ population A: Manhattan plot of GWAS for RHSTI in MTZ population. B: Quantile-quantile plot of GWAS for RHSTI in MTZ population. C: the effects of 123 alleles on 45 RHSTI loci; the bars above the horizontal axis represent positive values while the bars below the horizontal axis represent negative values. D: the QTL-allele matrix of RHSTI in the MTZ population; the horizontal axis represents the lines of MTZ in ascending order according to RHSTI from the left side to the right side; the vertical axis is the QTL of RHSTI for each line and each horizontal indicates one allele with its effects expressed as the color thickness; the cells with warm colors indicate positive alleles; while the cells with cool colors indicate negative alleles the depth of the color indicates the size of the allele effect; E: the genetic structure and phenotype value of the three parents along with the elite progeny; the numbers in and outside of the parentheses represent the total numbers of negative and positive alleles, respectively; P1, P2, P3, and EP represent M8206, Zhengyangbaimaopingding, Tongshanbopihuangdoujia, and elite progeny pyramiding all the most favorable alleles, respectively. F: GO functional classification of the candidate genes of RHSTI."

Table 5

Analysis the candidate genes of root hydraulic stress tolerance index (RHSTI) combined the results of GWAS and RNA-seq"

基因
Gene		 QTL 对照
Control		 处理
Treatment		 A S T D PM UF O
Glyma.01G044100 RHSTI1.1 4.63315 26.99810 √
Glyma.02G171100 RHSTI2.1 1.65710 35.83620 √
Glyma.02G196500 RHSTI2.2 13.39870 54.69290 √ √ √ √
Glyma.02G250200 RHSTI2.3 10.60250 154.59800 √ √ √
Glyma.02G308100 RHSTI2.4 20.68510 89.04400 √
Glyma.03G023900 RHSTI3.1 10.74360 39.75440 √
Glyma.03G202400 RHSTI3.2 46.58860 2.89863 √
Glyma.04G179500 RHSTI4.1 615.19900 78.03500 √ √ √
Glyma.06G054700 RHSTI6.1 0.27378 132.47900 √
Glyma.06G063500 RHSTI6.2 5.58627 26.31760 √
Glyma.06G165100 RHSTI6.3 16.17140 3.43912 √
Glyma.07G013900 RHSTI7.1 119.39400 5.21799 √
Glyma.07G208700 RHSTI7.3 59.19030 196.58300 √ √
Glyma.08G316700 RHSTI8.1 8.69897 187.91100 √
Glyma.09G051700 RHSTI9.1 0.00001 40.02450 √
Glyma.09G071600 RHSTI9.3 5.55258 142.93600 √ √ √
Glyma.10G081700 RHSTI10.1 18.56970 69.58230 √ √ √ √
Glyma.10G169300 RHSTI10.2 3.97157 66.91020 √
Glyma.10G179200 RHSTI10.3 32.64080 6.27830 √
Glyma.10G251900 RHSTI10.4 248.40400 501.60600 √
Glyma.11G171400 RHSTI11.1 8.60429 102.58700 √
Glyma.11G207000 RHSTI11.2 1.99196 20.33400 √
Glyma.12G112100 RHSTI12.1 64.63140 29.13040 √
基因
Gene		 QTL 对照
Control		 处理
Treatment		 A S T D PM UF O
Glyma.12G117000 RHSTI12.2 3.83243 33.14560 √
Glyma.13G078400 RHSTI13.1 49.82870 14.74430 √
Glyma.13G272000 RHSTI13.2 56.77120 135.84100 √
Glyma.13G333100 RHSTI13.3 44.62830 847.65000 √
Glyma.14G190800 RHSTI14.2 36.08840 9.31428 √
Glyma.15G222200 RHSTI15.1 19.02410 1.79038 √
Glyma.15G267900 RHSTI15.2 2.69447 34.25220 √
Glyma.17G113100 RHSTI17.1 79.26370 26.09940 √
Glyma.17G122900 RHSTI17.2 46.38730 23.03860 √
Glyma.17G202600 RHSTI17.3 0.73104 40.50930 √
Glyma.18G018600 RHSTI18.1 9.17402 136.37000 √ √
Glyma.19G014800 RHSTI19.1 31.60140 7.32146 √
Glyma.19G100600 RHSTI19.3 53.01000 13.26570 √
Glyma.19G109400 RHSTI19.4 25.26790 129.41800 √
Glyma.19G112700 RHSTI20.1 119.13700 37.92940 √

Table 6

QTL clusters of multiple drought tolerance indicators within 500 kb in MTZ population"

序号
Order		 QTL 染色体
Chr.		 物理位置
Physical position		 序号
Order		 QTL 染色体
Chr.		 物理位置
Physical position
1 RHSTI3.2/RRL3.5 Gm03 43400584-43484318 10 RRL14.1/RPDW14.2 Gm14 47717307-48064975
2 RSL4.2/RPL4.1 Gm04 44742587-45097286 11 RRL15.1/RSL15.1 Gm15 11105948-11421179
3 RHSTI6.3/RRL6.2 Gm06 13682742-13879752 12 RHSTI15.2/RRL15.4 Gm15 48985599-49446734
4 RHSTI7.3/RRL7.2 Gm07 37870093-38067672 13 RHSTI17.3/RRL17.2 Gm17 32103917-34132465
5 RHSTI10.3/RRL10.2 Gm10 40440079-40629141 14 RHSTI18.1/RPL18.1 Gm18 1411394-2099117
6 RRL10.3/RPDW10.1 Gm10 50470068-50478746 15 RHSTI19.2/RSL19.3 Gm19 16552234-17194130
7 RPL13.2/RPDW13.1 Gm 13 22001544-22395896 16 RHSTI19.4/RRL19.3 Gm19 35661675-35924257
8 RHSTI13.2/RSL13.1 Gm 13 35347991-36008531 17 RRL19.5/RPDW19.1 Gm19 37678898-38456131
9 RRL13.6/RPL13.1 Gm 13 43533843-43640816 18 RHSTI20.1/RPL20.1 Gm20 35319103-35573994
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