欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2023, Vol. 49 ›› Issue (9): 2321-2330.doi: 10.3724/SP.J.1006.2023.23072

• 作物遗传育种·种质资源·分子遗传学 •    下一篇

基于Adaptive Lasso的两阶段全基因组关联分析方法

杨文宇1,2(), 吴成秀1, 肖英杰1,3,*(), 严建兵1,3   

  1. 1作物遗传改良全国重点实验室, 湖北武汉 430070
    2华中农业大学理学院, 湖北武汉 430070
    3湖北洪山实验室, 湖北武汉 430070
  • 收稿日期:2022-10-28 接受日期:2023-02-21 出版日期:2023-09-12 网络出版日期:2023-03-03
  • 通讯作者: *肖英杰, E-mail: yxiao25@mail.hzau.edu.cn
  • 作者简介:杨文宇, E-mail: yangwenyurain@126.com
  • 基金资助:
    国家自然科学基金项目(32201855);国家自然科学基金项目(32122066)

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 Published:2023-09-12 Published online:2023-03-03
  • Supported by:
    National Natural Science Foundation of China(32201855);National Natural Science Foundation of China(32122066)

RichHTML

47

PDF

5508

摘要：

作为进行全基因组关联分析的主流方法, 混合线性模型类方法得到了广泛的应用。但是, 现有方法仍存在检测功效不高的问题。本文提出一种基于Adaptive Lasso的2阶段全基因组关联分析方法(two-stage Adaptive Lasso-based genome-wide association analysis, ALGWAS), 该方法在第1阶段通过变量选择方法Adaptive Lasso筛选出与目标性状相关联的单核苷酸多态性位点(single nucleotide polymorphism, SNP), 第2阶段将第1阶段筛选出的SNP作为协变量放入线性模型中进行全基因组扫描。在模拟实验中, ALGWAS方法与3种常用的全基因组关联分析方法fastGWA、GEMMA和EMMAX相比具有最高的检测功效, 同时具有较低的错误发现率(false discovery rate, FDR)。将以上4种方法应用到包含1341份材料的玉米CUBIC (Complete-diallel plus Unbalanced Breeding-like Inter-Cross)群体的全基因组关联分析中, ALGWAS方法可检测到与开花期相关基因ZmMADS69、ZmMADS15/31、ZmZCN8和ZmRAP2.7, 与株高相关基因ZmBRD1和ZmBR2, 与产量相关基因ZmUB2、ZmKRN2和ZmCLE7等, 而其他3种常用的全基因组关联分析方法检测功效较低。本研究提出了一种非混合线性模型类的全基因组关联分析方法, 对解析微效多基因决定的复杂遗传性状具有更高的检测效率, 为基因挖掘提供了新的途径。

关键词: 玉米, 全基因组关联分析, 变量选择, Adaptive Lasso

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

表1

基于从头模拟表型的不同全基因组关联分析方法的平均检测功效和错误发现率"

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

图1

基于真实性状遗传结构模拟表型的不同全基因组关联分析方法的检测功效 A: 抽雄期; B: 株高; C: 穗重。"

图2

CUBIC群体抽雄期的曼哈顿图"

图3

CUBIC群体株高的曼哈顿图"

图4

CUBIC群体穗重的曼哈顿图"

图5

不同全基因组关联分析方法的QQ图 A: 抽雄期; B: 株高; C: 穗重。"

表2

ALGWAS方法检测到的已知基因位置及其对应的peakSNP位置"

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] 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.
[2] 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.
[3] 刘恩波, 陈静, 李红星, 于宁宁, 任佰朝, 赵斌, 刘鹏, 张吉旺. 遮阴改变源-库平衡和调节碳水化合物代谢进而抑制夏玉米幼穗发育[J]. 作物学报, 2026, 52(6): 1891-1901.
[4] 梁进宇, 尹嘉德, 王红丽, 张国平, 侯慧芝, 董博, 马明生. 基于无人机高光谱和机器学习的旱地饲用玉米叶片氮含量估测[J]. 作物学报, 2026, 52(6): 1788-1801.
[5] 唐宽强, 李公允, 宋美毅, 赵雪, 常春玲. 大豆株高性状全基因组关联分析及预测模型构建[J]. 作物学报, 2026, 52(6): 1743-1756.
[6] 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.
[7] 韩亚鑫, 何冠华, 张小琼, 张登峰, 李永祥, 刘旭洋, 王天宇, 黎裕, 邹华文, 李春辉. 基于RNA-Seq和BSA-Seq联合分析挖掘玉米侧根密度基因资源[J]. 作物学报, 2026, 52(5): 1341-1352.
[8] 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.
[9] 孙淑凤, 许振南, 黄嘉鑫, 翁建峰, 李新海. 玉米MAPK家族全基因组鉴定及其对拟轮枝镰孢菌感染的响应[J]. 作物学报, 2026, 52(5): 1291-1308.
[10] 张宁宁, 滕雨菲, 任娜娜, 魏兴卓, 闫书豪, 樊可心, 王永宏, 陈文康, 张兴华, 朱万超, 徐淑兔, 薛吉全. 201份玉米自交系抗旱表型评价及可塑性分析[J]. 作物学报, 2026, 52(5): 1309-1325.
[11] 杨扬, 常诗惠, 田红丽, 易红梅, 王璐, 任洁, 范亚明, 刘亚维, 王凤格, 赵久然. 不同生态区国审玉米品种的遗传多样性分析[J]. 作物学报, 2026, 52(5): 1352-1364.
[12] 张鸿蓉, 王菲儿, 李盼, 仇海龙, 朱静, 赵连豪, 南运有, 何蔚, 樊志龙, 胡发龙, 柴强, 殷文. 减量20%灌水与25%有机肥替代化肥提高青贮玉米产量的光合特性[J]. 作物学报, 2026, 52(5): 1487-1500.
[13] 蔡宏玮, 于爱忠, 姜科强, 王鹏飞, 王玉珑, 霍建喆, 庞小能, 尹波, 尚永盼. 干旱灌区有机肥替代部分化肥促进甜玉米产量提升的关键机制[J]. 作物学报, 2026, 52(4): 1166-1180.
[14] 田红丽, 杨扬, 范亚明, 易红梅, 郭丹丹, 王凤格, 赵久然. 适于玉米品种鉴定的一套三等位变异SNP新型标记组合[J]. 作物学报, 2026, 52(4): 993-1005.
[15] 杨亚莉, 徐明睿, 马越飞, 海艺蕊, 刘凯栋, 刘万茂, 孙颖. 玉米根尖及整根响应缺铁的转录组比较研究[J]. 作物学报, 2026, 52(4): 1006-1021.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备15008789号-2