Welcome to Acta Agronomica Sinica，

Acta Agronomica Sinica ›› 2023, Vol. 49 ›› Issue (9): 2321-2330.doi: 10.3724/SP.J.1006.2023.23072

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

ALGWAS: two-stage Adaptive Lasso-based genome-wide association study

YANG Wen-Yu1,2(), WU Cheng-Xiu1, XIAO Ying-Jie1,3,*(), YAN Jian-Bing1,3   

  1. 1National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, Hubei, China
    2College of Science, Huazhong Agricultural University, Wuhan 430070, Hubei, China
    3Hubei Hongshan Laboratory, Wuhan 430070, Hubei, China
  • Received:2022-10-28 Accepted:2023-02-21 Online:2023-09-12 Published:2023-03-03
  • Supported by:
    National Natural Science Foundation of China(32201855);National Natural Science Foundation of China(32122066)

RichHTML414

35

PDF

597

Abstract:

As mainstream methods for genome-wide association analysis, mixed linear model methods have been widely used. However, the existing methods still have the problem of low detection power. In this study, a two-stage Adaptive Lasso-based genome-wide association analysis (ALGWAS) method was proposed. In the first stage, single nucleotide polymorphism (SNP) associated with target traits were screened by Adaptive Lasso, a variable selection method. In the second stage, SNPs selected from the first stage were put into the linear model as the covariates for genome-wide scanning. Compared with fastGWA, GEMMA and EMMAX, the ALGWAS method had the highest detection power and lower false discovery rate (FDR) in the simulation experiments. The above four methods were applied to genome-wide association analysis of Complete-diallel plus Unbalanced Breeding-like Inter-Cross (CUBIC) population of 1341 individuals in maize. ALGWAS method can detect the genes (ZmMADS69, ZmMADS15/31, ZmZCN8, and ZmRAP2.7) related to days to tasseling, the genes (ZmBRD1 and ZmBR2) related to plant height, and the genes (ZmUB2, ZmKRN2, and ZmCLE7) related to yield, while the other three commonly used genome-wide association analysis methods had low detection efficiency. In this study, a non-mixed linear model class of genome-wide association analysis method was proposed, which had higher detection advantage for microeffect polygenes and provided a new way for genetic analysis of complex traits.

Key words: maize, genome-wide association study, variable selection, Adaptive Lasso

Table 1

Average detection power and false discovery rate of genome-wide association study methods"

遗传力Heritability QTN数目
Number of QTN		 方法
Method		 检测功效
Detection power		 高效应QTN
检测功效
Detection power of QTN with high effect		 中等效应QTN
检测功效
Detection power of QTN with moderate effect		 低效应QTN
检测功效
Detection power of QTN with low effect		 错误发现率
False discovery rate
0.8
0.8
0.8
0.8
0.8
0.5
0.5
0.5
0.5
0.5
0.8
0.8
0.8
0.8
0.8
0.5
0.5
0.5
0.5
0.5		 20
20
20
20
20
20
20
20
20
20
50
50
50
50
50
50
50
50
50
50		 LM
ALGWAS
GEMMA
fastGWA
EMMAX
LM
ALGWAS
GEMMA
fastGWA
EMMAX
LM
ALGWAS
GEMMA
fastGWA
EMMAX
LM
ALGWAS
GEMMA
fastGWA
EMMAX		 0.811
0.802
0.482
0.457
0.446
0.808
0.562
0.442
0.382
0.370
0.763
0.513
0.270
0.250
0.244
0.600
0.208
0.158
0.134
0.129		 0.997
1.000
0.987
0.983
0.983
0.947
0.793
0.777
0.697
0.683
0.937
0.911
0.729
0.699
0.684
0.843
0.561
0.479
0.416
0.404		 0.838
0.895
0.440
0.375
0.358
0.825
0.565
0.423
0.353
0.338
0.769
0.520
0.128
0.101
0.095
0.571
0.094
0.036
0.022
0.020		 0.590
0.480
0.033
0.040
0.027
0.647
0.327
0.133
0.107
0.100
0.581
0.107
0.001
0.001
0.003
0.397
0.008
0.000
0.000
0.000		 0.853
0.040
0.066
0.074
0.050
0.811
0.105
0.070
0.074
0.069
0.780
0.065
0.025
0.030
0.020
0.708
0.118
0.045
0.060
0.063

Fig. 1

Average detection power of genome-wide association study methods based on simulated phenotype on realistic genetic structure A: days to tasseling; B: plant height; C: ear weight."

Fig. 2

Manhattan plots for days to tasseling of CUBIC population"

Fig. 3

Manhattan plots for plant height of CUBIC population"

Fig. 4

Manhattan plots for ear weight of CUBIC population"

Fig. 5

Quantitle-quantitle plot of genome-wide association study methods A: days to tasseling; B: plant height; C: ear weight."

Table 2

ALGWAS detected the known gene location and its corresponding peakSNP location"

基因名称
Gene name		 基因 ID
Gene ID		 基因位置
Gene location		 peakSNP位置
peakSNP location		 P值
P-value
ZmMADS69 GRMZM2G171650 Chr. 3: 159,022,119..159,050,063 Chr. 3: 158,013,626 8.434874E-09
ZmMADS15/31 GRMZM2G553379 Chr. 5: 6,993,294..7,011,505 Chr. 5: 6,684,616 1.667779E-08
ZmZCN8 GRMZM2G179264 Chr. 8: 123,028,887..123,033,675 Chr. 8: 121,955,506 1.638071E-12
ZmRAP2.7 GRMZM2G700665 Chr. 8: 131,575,389..131,581,816 Chr. 8: 131,013,374 6.050159E-10
ZmBR2 GRMZM2G315375 Chr. 1: 20,2334,824..202,342,008 Chr. 1: 202,814,745 1.595160E-09
ZmBRD1 GRMZM2G103773 Chr. 1: 249,371,977..249,376,239 Chr. 1: 249,462,103 3.165227E-21
ZmZCN8 GRMZM2G107829 Chr. 8: 123,028,887..123,033,675 Chr. 8: 124,009,899 2.306359E-10
ZmBAM1d GRMZM2G043584 Chr. 1: 30,516,319..30,522,796 Chr. 1: 27,500,593 1.373829E-04
ZmYIGE1 GRMZM2G008490 Chr. 1: 51,075,171..51,161,917 Chr. 1: 58,798,749 4.593378E-05
ZmUB2 GRMZM2G160917 Chr. 1: 188,213,876..188,220,983 Chr. 1: 192,566,425 1.078588E-05
ZmKRN2 GRMZM2G125656 Chr. 2: 17,742,986..17,750,216 Chr. 2: 17,280,224 6.421449E-06
ZmCLE7 GRMZM2G372364 Chr. 4: 7,568,824..7,572,604 Chr. 4: 16,087,155 7.774088E-06
ZmKRN6 GRMZM2G119714 Chr. 6: 94,188,754..94,201,186 Chr. 6: 90,084,654 8.809443E-05
[1] Zhang Y M, Mao Y C, Xie C Q, Smith H, Luo L, Xu S Z. Mapping quantitative trait loci using naturally occurring genetic variance among commercial inbred lines of maize (Zea mays L.). Genetics, 2005, 169: 2267-2275.
doi: 10.1534/genetics.104.033217
[2] Yu J M, Pressoir G, Briggs H W, Vroh B I, Yamasakiet M, Doebley J F, McMullen M D, Gaut B S, Nielsen D M, Holland J B, Kresovich S, Buckler E S. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet, 2006, 38: 203-208.
doi: 10.1038/ng1702 pmid: 16380716
[3] Kang H M, Zaitlen N A, Wade C M, Kirby A, Heckerman D, Daly M J, Eskin E. Efficient control of population structure in model organism association mapping. Genetics, 2008, 178: 1709-1723.
doi: 10.1534/genetics.107.080101 pmid: 18385116
[4] 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
[5] Zhang Z W, Ersoz E, Lai C Q, Todhunter R J, Tiwari H K, Gore M A, Bradbury P J, Yu J, Arnett D K, Ordovas J M, Buckler E S. Mixed linear model approach adapted for genome-wide association studies. Nat Genet, 2010, 42: 355-360.
doi: 10.1038/ng.546 pmid: 20208535
[6] 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
[7] Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3000 shared controls. Nature, 2007, 447: 661-678.
doi: 10.1038/nature05911
[8] Li H, Peng Z Y, Yang X H, Wang W D, Fu J J, Wang J H, Han Y J, Chai Y C, Guo T T, Yang N, Liu J, Warburton M L, Cheng Y B, Hao X M, Zhang P, Zhao J Y, Liu Y J, Wang G Y, Li J S, Yan J B. Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat Genet, 2013, 45: 43-50.
doi: 10.1038/ng.2484 pmid: 23242369
[9] Huang X H, Wei X H, Sang T, Zhao Q, Feng Q, Zhao Y, Li C Y, Zhu C R, Lu T T, Zhang Z W, Li M, Fan D L, Guo Y L, Wang A, Wang L, Deng L W, Li W J, Lu Y Q, Weng Q J, Liu K Y, Huang T, Zhou T Y, Jing Y F, Li W, Lin Z, Buckler E S, Qian Q, Zhang Q F, Li J Y, Han B. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet, 2010, 42: 961-969.
doi: 10.1038/ng.695 pmid: 20972439
[10] Xiao Y J, Liu H J, Wu L J, Warburton M L, Yan J B. Genome- wide association studies in maize: praise and stargaze. Mol Plant, 2017, 10: 359-374.
doi: 10.1016/j.molp.2016.12.008
[11] 彭勃, 赵晓雷, 王奕, 袁文娅, 李春辉, 李永祥, 张登峰, 石云素, 宋燕春, 王天宇, 黎裕. 玉米叶向值的全基因组关联分析. 作物学报, 2020, 46: 819-831.
doi: 10.3724/SP.J.1006.2020.93063
Peng B, Zhao X L, Wang Y, Yuan W Y, Li C H, Li Y X, Zhang D F, Shi Y S, Song Y C, Wang T Y, Li Y. Genome-wide association studies of leaf orientation value in maize. Acta Agron Sin, 2020, 46: 819-831. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2020.93063
[12] 谢磊, 任毅, 张新忠, 王继庆, 张志辉, 石书兵, 耿洪伟. 小麦穗发芽性状的全基因组关联分析. 作物学报, 2021, 47: 1891-1902.
doi: 10.3724/SP.J.1006.2021.01078
Xie L, Ren Y, Zhang X Z, Wang J Q, Zhang Z H, Shi S B, Geng H W. Genome-wide association study of pre-harvest sprouting traits in wheat. Acta Agron Sin, 2021, 47: 1891-1902. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2021.01078
[13] 杨飞, 张征锋, 南波, 肖本泽. 水稻产量相关性状的全基因组关联分析及候选基因筛选. 作物学报, 2022, 48: 1813-1821.
doi: 10.3724/SP.J.1006.2022.12047
Yang F, Zhang Z F, Nan B, Xiao B Z. Genome-wide association analysis and candidate gene selection of yield related traits in rice. Acta Agron Sin, 2022, 48: 1813-1821. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2022.12047
[14] Lippert C, Listgarten J, Liu Y, Kadiel C M, Davidson R I, Heckerman D. FaST linear mixed models for genome-wide association studies. Nat Methods, 2011, 8: 833-835.
doi: 10.1038/nmeth.1681 pmid: 21892150
[15] Listgarten J, Lippert C, Kadie C M, Davidson R I, Eskin E, Heckerman D. Improved linear mixed models for genome-wide association studies. Nat Methods, 2012, 9: 525-526.
doi: 10.1038/nmeth.2037 pmid: 22669648
[16] Loh P R, Bhatia G, Gusev A, Finucane H K, Bulik-Sullivan B K, Pollack S J. Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis. Nat Genet, 2015, 47: 1385-1392.
doi: 10.1038/ng.3431 pmid: 26523775
[17] Jiang L D, Zheng Z L, Qi T, Kemper K E, Wray N R, Visscher P M, Yang J. A resource-efficient tool for mixed model association analysis of large-scale data. Nat Genet, 2019, 51: 1749-1755.
doi: 10.1038/s41588-019-0530-8 pmid: 31768069
[18] Maher B. Personal genomes: the case of the missing heritability. Nature, 2008, 456: 18-21.
[19] Visscher P. Sizing up human height variation. Nat Genet, 2008, 40: 489-490.
doi: 10.1038/ng0508-489 pmid: 18443579
[20] Yang J, Benyamin B, McEvoy B P, Gordon S, Henders A K, Nyholt D R, Madden P A, Heath A C, Martin N G, Montgomery G W, Goddard M E, Visscher P M. Common SNPs explain a large proportion of the heritability for human height. Nat Genet, 2010, 42: 565-569.
doi: 10.1038/ng.608 pmid: 20562875
[21] Song B, Mott R, Gan X. Recovery of novel association loci in Arabidopsis thaliana and Drosophila melanogaster through leveraging INDELs association and integrated burden test. PLoS Genet, 2018, 14: e1007699.
[22] Zhang Y W, Tamba C L, Wen Y J, Li P, Ren W L, Ni Y L, Gao J, Zhang Y M. mrMLM v4.0.2: an R platform for multi-locus genome-wide association studies. Genom Prot Bioinfor, 2020, 18: 481-487.
[23] Yang N, Lu Y L, Yang X H, Huang J, Zhou Y, Ali F H, Wen W W, Liu J, Li J S, Yan J B. Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel. PLoS Genet, 2014, 10: e1004573.
[24] Liu H J, Wang X Q, Xiao Y J, Luo J Y, Qiao F, Yang W Y, Zhang R Y, Meng Y J, Sun J M, Yan S J, Peng Y, Niu L Y, Jian L M, Song W, Yan J L, Li C H, Zhao Y X, Liu Y, Warburton M L, Zhao J R, Yan J B. CUBIC: an atlas of genetic architecture promises directed maize improvement. Genome Biol, 2020, 21: 20.
doi: 10.1186/s13059-020-1930-x
[25] Lande R, Thompson R. Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics, 1990, 124: 743-756.
doi: 10.1093/genetics/124.3.743 pmid: 1968875
[26] Yu J M, Holland J B, McMullen M D, Buckler E S. Genetic design and statistical power of nested association mapping in maize. Genetics, 2008, 178: 539-551.
doi: 10.1534/genetics.107.074245 pmid: 18202393
[27] Tibshirani R. Regression shrinkage and selection via the lasso. J Royal Statist Society, 1996, 58: 267-288.
[28] Zou H. The adaptive lasso and its oracle properties. J Am Statist Assoc, 2006, 101: 1418-1429.
doi: 10.1198/016214506000000735
[29] Liang Y M, Liu Q, Wang X F, Huang C, Xu G H, Hey S, Lin H Y, Li C, Xu D Y, Wu L S, Wang C L, Wu W H, Xia J L, Han X, Lu S J, Lai J S, Song W B, Schnable P S, Tian F. ZmMADS69 functions as a flowering activator through the regulatory module and contributes to maize flowering time adaptation. New Phytol, 2019, 221: 2335-2347.
doi: 10.1111/nph.2019.221.issue-4
[30] Makarevitch I, Thompson A, Muehlbauer G J, Springer N M. Brd1 gene in maize encodes a brassinosteroid C-6 oxidase. PLoS One, 2012, 7: e30798.
[31] Xing A Q, Gao Y F, Ye L F, Zhang W P, Cai L C, Ching A, Llaca V, Johnson B, Liu L, Yang X H, Kang D M, Yan J B, Li J S. A rare SNP mutation in Brachytic2 moderately reduces plant height and increases yield potential in maize. J Exp Bot, 2015, 66: 3791-3802.
doi: 10.1093/jxb/erv182 pmid: 25922491
[32] Yang N, Liu J, Gao Q, Gui S T, Chen L, Yang L F, Huang J, Deng T Q, Luo J Y, He L J, Wang Y B, Xu P W, Peng Y, Shi Z, Lan L, Ma Z Y, Yang X, Zhang Q Q, Bai M Z, Li W, Liu L, Jackson D, Yan J B. Genome assembly of a tropical maize inbred line provides insights into structural variation and crop improvement. Nat Genet, 2019, 51: 1052-1059.
doi: 10.1038/s41588-019-0427-6 pmid: 31152161
[33] Luo Y, Zhang M L, Liu Y, Liu J, Li W Q, Chen G S, Peng Y, Jin M, Wei W J, Jian L M, Yan J, Fernie A R, Yan J B. Genetic variation in YIGE1 contributes to ear length and grain yield in maize. New Phytol, 2022, 234: 513-526.
doi: 10.1111/nph.v234.2
[34] Du Y F, Liu L, Peng Y, Li M F, Li Y F, Liu D, Li X W, Zhang Z X. UNBRANCHED3 expression and inflorescence development is mediated by UNBRANCHED2 and the distal enhancer, KRN4, in maize. PLoS Genet, 2020, 16: e1008764.
[35] Chen W K, Chen L, Zhang X, Yang N, Guo J H, Wang M, Ji S G, Zhao X Y, Yin P F, Cai L C, Xu J, Zhang L L, Han Y J, Xiao Y N, Xu G, Wang Y B, Wang S H, Wu S, Yang F, Jackson D, Cheng J K, Chen S H, Sun C Q, Qin F, Tian F, Fernie A R, Li J S, Yan J B, Yang X H. Convergent selection of a WD40 protein that enhances grain yield in maize and rice. Science, 2022, 375: e7985.
[36] Liu L, Gallagher J, Arevalo E D, Chen R, Skopelitis T, Wu Q, Bartlett M, Jackson D. Enhancing grain-yield-related traits by CRISPR-Cas9 promoter editing of maize CLE genes. Nat Plants, 2021, 7: 287-294.
doi: 10.1038/s41477-021-00858-5 pmid: 33619356
[37] Jia H T, Li M F, Li W Y, Liu L, Jian Y N, Yang Z X, Shen X M, Ning Q, Du Y F, Zhao R, Jackson D, Yang X H, Zhang Z X. A serine/threonine protein kinase encoding gene KERNEL NUMBER PER ROW6 regulates maize grain yield. Nat Commun, 2020, 11: 988.
doi: 10.1038/s41467-020-14746-7 pmid: 32080171
[38] Zeng Z B. Precision mapping of quantitative trait loci. Genetics, 1994, 136: 1457-1468.
doi: 10.1093/genetics/136.4.1457 pmid: 8013918
[39] Li H H, Ye G Y, Wang J K. A modified algorithm for the improvement of composite interval mapping. Genetics, 2007, 175: 361-374.
doi: 10.1534/genetics.106.066811 pmid: 17110476
[1] AI Rong, ZHANG Chun, YUE Man-Fang, ZOU Hua-Wen, WU Zhong-Yi. Response of maize transcriptional factor ZmEREB211 to abiotic stress [J]. Acta Agronomica Sinica, 2023, 49(9): 2433-2445.
[2] HUANG Yu-Jie, ZHANG Xiao-Tian, CHEN Hui-Li, WANG Hong-Wei, DING Shuang-Cheng. Identification of ZmC2s gene family and functional analysis of ZmC2-15 under heat tolerance in maize [J]. Acta Agronomica Sinica, 2023, 49(9): 2331-2343.
[3] BAI Yan, GAO Ting-Ting, LU Shi, ZHENG Shu-Bo, LU Ming. A retrospective analysis of the historical evolution and developing trend of maize mega varieties in China from 1982 to 2020 [J]. Acta Agronomica Sinica, 2023, 49(8): 2064-2076.
[4] WANG Xing-Rong, ZHANG Yan-Jun, TU Qi-Qi, GONG Dian-Ming, QIU Fa-Zhan. Identification and gene localization of a novel maize nuclear male sterility mutant ms6 [J]. Acta Agronomica Sinica, 2023, 49(8): 2077-2087.
[5] WANG Juan, XU Xiang-Bo, ZHANG Mao-Lin, LIU Tie-Shan, XU Qian, DONG Rui, LIU Chun-Xiao, GUAN Hai-Ying, LIU Qiang, WANG Li-Ming, HE Chun-Mei. Characterization and genetic analysis of a new allelic mutant of Miniature1 gene in maize [J]. Acta Agronomica Sinica, 2023, 49(8): 2088-2096.
[6] WEI Jin-Gui, GUO Yao, CHAI Qiang, YIN Wen, FAN Zhi-Long, HU Fa-Long. Yield and yield components of maize response to high plant density under reduced water and nitrogen supply [J]. Acta Agronomica Sinica, 2023, 49(7): 1919-1929.
[7] LI Rong, MIAN You-Ming, HOU Xian-Qing, LI Pei-Fu, WANG Xi-Na. Effects of nitrogen application on decomposition and nutrient release of returning straw, soil fertility, and maize yield [J]. Acta Agronomica Sinica, 2023, 49(7): 2012-2022.
[8] MEI Xiu-Peng, ZHAO Zi-Kun, JIA Xin-Yao, BAI Yang, LI Mei, GAN Yu-Ling, YANG Qiu-Yue, CAI Yi-Lin. Heat-inducible transcription factor ZmNF-YC13 regulates heat stress response genes to improve heat tolerance in maize [J]. Acta Agronomica Sinica, 2023, 49(7): 1747-1757.
[9] CHANG Li-Juan, LIANG Jing-Gang, SONG Jun, LIU Wen-Juan, FU Cheng-Ping, DAI Xiao-Hang, WANG Dong, WEI Chao, XIONG Mei. Event-specific PCR detection method of transgenic maize ND207 and its standardization [J]. Acta Agronomica Sinica, 2023, 49(7): 1818-1828.
[10] ZHANG Zhen-Bo, JIA Chun-Lan, REN Bai-Zhao, LIU Peng, ZHAO Bin, ZHANG Ji-Wang. Effects of combined application of nitrogen and phosphorus on yield and leaf senescence physiological characteristics in summer maize [J]. Acta Agronomica Sinica, 2023, 49(6): 1616-1629.
[11] MA Juan, ZHU Wei-Hong, LIU Jing-Bao, YU Ting, HUANG Lu, GUO Guo-Jun. Multi-locus genome-wide association study and prediction for general combining ability of maize ear length [J]. Acta Agronomica Sinica, 2023, 49(6): 1562-1572.
[12] LI Lu-Lu, MING Bo, GAO Shang, XIE Rui-Zhi, WANG Ke-Ru, HOU Peng, XUE Jun, LI Shao-Kun. Characteristic difference in grain in-field drydown between maize cultivars with various maturation [J]. Acta Agronomica Sinica, 2023, 49(6): 1643-1652.
[13] WANG Yu-Long, YU Ai-Zhong, LYU Han-Qiang, LYU Yi-Tong, SU Xiang-Xiang, WANG Peng-Fei, CHAI Jian. Effects of green manure replanting and returning after wheat on following year’s maize root traits and water use efficiency in oasis irrigation area [J]. Acta Agronomica Sinica, 2023, 49(5): 1350-1362.
[14] LI Hui, WANG Xu-Min, LIU Miao, LIU Peng-Zhao, LI Qiao-Li, WANG Xiao-Li, WANG Rui, LI Jun. Water and nitrogen reduction scheme optimization based on yield and nitrogen utilization of summer maize [J]. Acta Agronomica Sinica, 2023, 49(5): 1292-1304.
[15] ZHOU Hai-Ping, ZHANG Fan, CHEN Kai, SHEN Cong-Cong, ZHU Shuang-Bing, QIU Xian-Jin, XU Jian-Long. Identification of rice blast resistance in xian and geng germplasms by genome- wide association study [J]. Acta Agronomica Sinica, 2023, 49(5): 1170-1183.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] Li Shaoqing, Li Yangsheng, Wu Fushun, Liao Jianglin, Li Damo. Optimum Fertilization and Its Corresponding Mechanism under Complete Submergence at Booting Stage in Rice[J]. Acta Agronomica Sinica, 2002, 28(01): 115 -120 .
[2] Wang Lanzhen;Mi Guohua;Chen Fanjun;Zhang Fusuo. Response to Phosphorus Deficiency of Two Winter Wheat Cultivars with Different Yield Components[J]. Acta Agron Sin, 2003, 29(06): 867 -870 .
[3] YANG Jian-Chang;ZHANG Jian-Hua;WANG Zhi-Qin;ZH0U Qing-Sen. Changes in Contents of Polyamines in the Flag Leaf and Their Relationship with Drought-resistance of Rice Cultivars under Water Deficiency Stress[J]. Acta Agron Sin, 2004, 30(11): 1069 -1075 .
[4] Yan Mei;Yang Guangsheng;Fu Tingdong;Yan Hongyan. Studies on the Ecotypical Male Sterile-fertile Line of Brassica napus L.Ⅲ. Sensitivity to Temperature of 8-8112AB and Its Inheritance[J]. Acta Agron Sin, 2003, 29(03): 330 -335 .
[5] Wang Yongsheng;Wang Jing;Duan Jingya;Wang Jinfa;Liu Liangshi. Isolation and Genetic Research of a Dwarf Tiilering Mutant Rice[J]. Acta Agron Sin, 2002, 28(02): 235 -239 .
[6] WANG Li-Yan;ZHAO Ke-Fu. Some Physiological Response of Zea mays under Salt-stress[J]. Acta Agron Sin, 2005, 31(02): 264 -268 .
[7] TIAN Meng-Liang;HUNAG Yu-Bi;TAN Gong-Xie;LIU Yong-Jian;RONG Ting-Zhao. Sequence Polymorphism of waxy Genes in Landraces of Waxy Maize from Southwest China[J]. Acta Agron Sin, 2008, 34(05): 729 -736 .
[8] HU Xi-Yuan;LI Jian-Ping;SONG Xi-Fang. Efficiency of Spatial Statistical Analysis in Superior Genotype Selection of Plant Breeding[J]. Acta Agron Sin, 2008, 34(03): 412 -417 .
[9] WANG Yan;QIU Li-Ming;XIE Wen-Juan;HUANG Wei;YE Feng;ZHANG Fu-Chun;MA Ji. Cold Tolerance of Transgenic Tobacco Carrying Gene Encoding Insect Antifreeze Protein[J]. Acta Agron Sin, 2008, 34(03): 397 -402 .
[10] ZHENG Xi;WU Jian-Guo;LOU Xiang-Yang;XU Hai-Ming;SHI Chun-Hai. Mapping and Analysis of QTLs on Maternal and Endosperm Genomes for Histidine and Arginine in Rice (Oryza sativa L.) across Environments[J]. Acta Agron Sin, 2008, 34(03): 369 -375 .
Add: No. 80 Xueyuan South Rd, Beijing 100081, P. R. China E-mail: zwxb301@caas.cn
Supported by Beijing Magtech Co.Ltd