欢迎访问作物学报,今天是

作物学报 ›› 2022, Vol. 48 ›› Issue (10): 2451-2462.doi: 10.3724/SP.J.1006.2022.13052

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

基于杂交群体解析玉米籽粒大小相关性状及其配合力的分子遗传机制

李婷1,2(), 王亚鹏1,2, 董远1,2, 郭瑞士1, 李冬梅1, 唐雅伶1, 张兴华1,2, 薛吉全1,2, 徐淑兔1,2,*()   

  1. 1西北农林科技大学农学院 / 农业农村部西北旱区玉米生物与遗传改良重点实验室, 陕西杨凌 712100
    2陕西省玉米工程技术研究中心, 陕西杨凌 712100
  • 收稿日期:2021-09-07 接受日期:2022-02-25 出版日期:2022-10-12 网络出版日期:2022-03-22
  • 通讯作者: 徐淑兔
  • 作者简介:第一作者联系方式: E-mail: ltstime@163.com
  • 基金资助:
    财政部和农业农村部国家现代农业产业技术体系建设专项(玉米, CARS-02-77);陕西省重点研发计划项目(2021ZDLNY01-06)

Dissecting the genetic basis of kernel size related traits and their combining ability based on a hybrid population in maize

LI Ting1,2(), WANG Ya-Peng1,2, DONG Yuan1,2, GUO Rui-Shi1, LI Dong-Mei1, TANG Ya-Ling1, ZHANG Xing-Hua1,2, XUE Ji-Quan1,2, XU Shu-Tu1,2,*()   

  1. 1Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, College of Agronomy, Northwest A&F University, Yangling 712100, Shaanxi, China
    2Maize Engineering Technology Research Centre of Shaanxi Province, Yangling 712100, Shaanxi, China
  • Received:2021-09-07 Accepted:2022-02-25 Published:2022-10-12 Published online:2022-03-22
  • Contact: XU Shu-Tu
  • Supported by:
    China Agriculture Research System (Maize, CARS-02-77) of MOF and MARA;Key Research and Development Program of Shaanxi Province(2021ZDLNY01-06)

摘要:

配合力是育种过程中评价自交系潜力、筛选优良杂交组合的重要指标。籽粒大小相关性状是产量的重要构成因子, 解析籽粒大小相关性状及其配合力的遗传基础有助于高产玉米品种的培育。本研究以NCII遗传交配设计获得的246份玉米杂交组合为材料展开籽粒大小相关性状及其配合力的全基因组关联分析。研究表明, 粒长、粒宽、粒厚3个性状的广义遗传力分别为76.20%、86.52%和81.14%, 各性状与其配合力均呈显著正相关(0.58~0.82)。基于EMMAX (efficient mixed-model association expedited)算法检测到31、21、5个显著的SNP (single nucleotide polymorphism), 它们分别与性状、GCA (general combining ability)和SCA (special combining ability)关联, 其中10个SNP为性状与配合力共定位的。对共定位的SNP进行效应分析, 发现3个为加性效应、4个为部分显性效应、1个为超显性效应。结合公共数据库中基因注释及籽粒发育相关转录组数据, 在共定位、主效SNP位点附近共筛选到17个候选基因, 包括被报道与玉米籽粒发育相关的shrunken1emp6等。本研究结果有助于进一步解析玉米籽粒大小及其配合力的遗传机制, 可为籽粒大小相关性状的遗传改良提供参考。

关键词: 玉米, 杂交种, 籽粒大小, 配合力, 全基因组关联分析

Abstract:

Combining ability is a crucial index for evaluating elite inbred lines and selecting superior hybrids during maize breeding processes. Kernel size related traits are important components of grain yield. Therefore, dissecting the genetic basis of kernel size related traits and their combining ability is beneficial for improving maize yield. In this study, we performed genome-wide association study (GWAS) of kernel size related traits and their combining ability using 246 hybrids breed by designed as NCII mating. The broad-sense heritability of kernel length (KL), kernel width (KW), and kernel thickness (KT) was 76.20%, 86.52%, and 81.14%, respectively. The correlation coefficient between each trait and their combining ability was more than 0.58, indicating there was significant positive correlation. Based on the efficient mixed-model association expedited (EMMAX) algorithm, we identified 31, 21, and 5 significant single nucleotide polymorphisms (SNPs) associated with the best linear unbiased estimation (BLUE), general combining ability (GCA), and special combining ability (SCA) of kernel size related traits, respectively. Among these significant SNPs, 10 SNPs were co-located both kernel size related traits and their combining ability. The effect analysis of co-located significant SNPs showed that three SNPs were additive, four were partial dominance and one was overdominance. Further, 17 candidate genes were predicted from candidate regions of co-located SNPs or SNPs with large effect, such as shrunken 1, emp6 and so on. The results further dissect the genetic architecture of maize kernel size related traits and their combining ability and provide useful information for improving grain yield in maize breeding.

Key words: maize, hybrids, kernel size, combining ability, genome-wide association study

表1

籽粒大小相关性状的基本描述统计及方差分析"

来源 Source 粒长 KL 粒宽 KW 粒厚 KT
均值±标准差 Mean ± SD 12.62 ± 0.66 7.55 ± 0.44 4.30 ± 0.23
变异系数 CV 5.21% 5.78% 5.38%
偏度 Skewness 0.08 0.30 0.24
峰度 Kurtosis 0.15 0.41 -0.25
母本一般配合力 GCA female 2.22*** 0.26*** 1.55***
父本一般配合力 GCA male 25.86*** 3.89*** 6.60***
特殊配合力 SCA 0.56*** 0.08*** 0.18***
母本一般配合力×环境 GCA female × Environment 0.44 0.05** 0.11
父本一般配合力×环境GCA male × Environment 0.57 0.04 0.22*
特殊配合力×环境 SCA × Environment 0.35 0.04 0.09
基因型方差 $\sigma _{\text{g}}^{2}$ 0.31 0.17 0.04
基因型×环境方差 $\sigma _{\text{ge}}^{2}$ 0.02 0.00 0.00
广义遗传力 H2 (%) 76.20 86.52 81.14

图1

籽粒大小相关性状及其配合力的相关性分析 A: 不同环境下籽粒大小性状的相关性分析; B: 籽粒大小与其配合力的相关性分析。*、**和***分别表示在P < 0.05、P < 0.01和P < 0.001水平显著; KL: 粒长; KW: 粒宽; KT: 粒厚; XY: 旬邑; YL: 榆林; BLUE: 最佳线性无偏估计; GCA: 一般配合力; SCA: 特殊配合力。"

图2

籽粒大小相关性状及其配合力的显著SNP分布及统计 缩写同图1。"

表2

籽粒大小相关性状及其一般配合力、特殊配合力显著关联的SNP"

序号
Number
性状
Trait
SNP标记
SNP marker
染色体
Chr.
位置
Position
等位基因
Allele
P
P-value
模型
Model
表型解释率
R2 (%)
1 KL_BLUE Affx-291427653 2 172,900,382 A/C 1.17E-05 加性Additive 2.65
2 KL_BLUE Affx-291399192 2 174,990,230 C/T 6.00E-05 显性Dominance 5.20
3 KL_BLUE Affx-291415239 2 188,723,861 A/G 1.23E-04 加性Additive 3.36
4 KL_BLUE Affx-291421576 3 4,667,059 C/T 1.17E-04 显性Dominance 0.59
5 KL_BLUE Affx-291388159 4 161,189,573 G/T 9.25E-05 显性Dominance 7.87
6 KL_BLUE Affx-291414096 5 19,152,693 G/A 1.12E-04 显性Dominance 7.50
7 KL_BLUE Affx-291382102 5 45,936,956 A/T 6.34E-05 显性Dominance 0.75
8 KL_BLUE Affx-291430485 5 199,891,583 T/C 3.77E-05 显性Dominance 0.84
序号
Number
性状
Trait
SNP标记
SNP marker
染色体
Chr.
位置
Position
等位基因
Allele
P
P-value
模型
Model
表型解释率
R2 (%)
9 KL_GCA Affx-88981164 2 109,561,515 G/A 2.62E-05 加性Additive 25.52
10 KL_GCA Affx-291438859 2 147,075,651 C/T 1.25E-04 加性Additive 7.29
11 KL_GCA Affx-291381936 2 172,197,055 G/A 7.39E-05 加性Additive 13.35
12 KL_GCA Affx-291427653 2 172,900,382 A/C 7.93E-07 加性Additive 0.80
13 KL_GCA Affx-291383897 2 173,344,526 G/C 1.86E-05 加性Additive 0.90
14 KL_GCA Affx-291399192 2 174,990,230 C/T 3.45E-05 加性Additive 0.03
15 KL_GCA Affx-291400614 2 177,112,609 C/T 4.06E-05 加性Additive 1.11
16 KL_GCA Affx-291415239 2 188,723,861 A/G 4.76E-06 加性Additive 0.42
17 KL_GCA Affx-291419252 2 194,294,110 T/C 9.17E-06 加性Additive 0.02
18 KL_SCA Affx-291414096 5 19,152,693 G/A 6.68E-05 显性Dominance 3.54
19 KW_BLUE Affx-291445199 1 180,428,491 C/T 1.16E-04 显性Dominance 10.90
20 KW_BLUE Affx-158946352 1 224,324,098 G/A 5.83E-05 加性Additive 0.65
21 KW_BLUE Affx-291438736 1 292,427,178 C/A 4.06E-05
5.00E-11
加性Additive
显性Dominance
28.23
0.07
22 KW_BLUE Affx-291386203 2 28,660,634 C/A 3.98E-05
2.31E-05
加性Additive
显性Dominance
3.79
0.44
23 KW_BLUE Affx-291438094 2 235,818,735 C/G 1.09E-05 显性Dominance 0.07
24 KW_BLUE Affx-291404824 3 1,338,197 C/A 2.84E-05
8.58E-06
加性Additive
显性Dominance
6.41
12.47
25 KW_BLUE Affx-291426461 4 35,122,656 T/C 3.23E-05 显性Dominance 0.11
26 KW_BLUE Affx-159061154 5 2,145,982 T/G 8.59E-05 显性Dominance 0.15
27 KW_BLUE Affx-291435207 7 277,626 A/G 5.33E-05 加性Additive 12.96
28 KW_BLUE Affx-291438357 9 38,415,742 T/C 8.18E-05 显性Dominance 0.18
29 KW_GCA Affx-291445199 1 180,428,491 C/T 1.34E-04 加性Additive 16.83
30 KW_GCA Affx-158946352 1 224,324,098 G/A 1.36E-04 加性Additive 6.47
31 KW_GCA Affx-158948129 1 235,021,563 C/T 7.38E-05 加性Additive 10.69
32 KW_GCA Affx-291438736 1 292,427,178 C/A 4.28E-06 加性Additive 16.97
33 KW_GCA Affx-291385173 2 5,147,139 T/C 3.83E-05 加性Additive 1.67
34 KW_GCA Affx-291420744 5 80,480,359 A/C 3.51E-05 加性Additive 7.50
35 KW_GCA Affx-291401044 9 129,309,960 A/G 2.72E-05 加性Additive 8.28
36 KW_SCA Affx-291436386 2 27,498,155 C/T 1.11E-04 显性Dominance 2.92
37 KT_BLUE Affx-291413666 1 118,986,201 A/C 8.08E-05 显性Dominance 7.95
38 KT_BLUE Affx-159032175 1 262,226,149 T/C 6.04E-06 显性Dominance 0.37
39 KT_BLUE Affx-291390855 1 262,368,074 C/T 4.17E-05
1.60E-05
加性Additive
显性Dominance
12.47
7.33
40 KT_BLUE Affx-158839028 1 280,195,356 C/A 7.78E-05 显性Dominance 3.39
41 KT_BLUE Affx-291410051 1 285,592,051 C/T 5.51E-05 显性Dominance 4.21
42 KT_BLUE Affx-291443747 2 23,623,257 G/A 8.77E-05 加性Additive 25.58
43 KT_BLUE Affx-291388256 2 195,694,458 C/T 1.19E-04 显性Dominance 0.34
44 KT_BLUE Affx-291401517 2 235,207,369 G/A 1.26E-05 显性Dominance 0.26
45 KT_BLUE Affx-291441620 3 85,891,477 T/C 6.06E-06 显性Dominance 2.57
46 KT_BLUE Affx-291422512 9 11,615,037 G/A 1.36E-04 显性Dominance 0.53
47 KT_BLUE Affx-291445448 9 15,315,557 A/C 8.53E-05 显性Dominance 3.25
48 KT_BLUE Affx-291438777 9 107,428,822 G/A 2.59E-05 显性Dominance 0.04
49 KT_BLUE Affx-291378767 10 144,701,539 C/A 1.09E-04 显性Dominance 2.76
50 KT_GCA Affx-291399215 1 104,625,663 C/T 3.57E-05 加性Additive 21.69
序号
Number
性状
Trait
SNP标记
SNP marker
染色体
Chr.
位置
Position
等位基因
Allele
P
P-value
模型
Model
表型解释率
R2 (%)
51 KT_GCA Affx-291390855 1 262,368,074 C/T 7.69E-05 加性Additive 10.71
52 KT_GCA Affx-291418355 3 1,713,800 A/G 1.18E-04 加性Additive 9.51
53 KT_GCA Affx-291442108 4 12,611,068 G/A 9.26E-05 加性Additive 0.03
54 KT_GCA Affx-291425118 5 37,099,257 C/T 1.14E-04 加性Additive 4.83
55 KT_SCA Affx-291399798 3 11,407,826 G/A 9.19E-05 显性Dominance 3.52
56 KT_SCA Affx-291422512 9 11,615,037 G/A 4.99E-05 显性Dominance 5.84
57 KT_SCA Affx-291438777 9 107,428,822 G/A 8.20E-05 显性Dominance 1.66

表3

BLUE与配合力(一般配合力和特殊配合力)共定位SNP的效应分析"

序号
Number
性状
Trait
SNP名称
SNP marker
染色体
Chr.
位置
Position
显性度
Dominance effect index
效应
Effect
1 KL_BLUE, KL_GCA Affx-291427653 2 172,900,382 0.03 加性 Additive
2 KL_BLUE, KL_GCA Affx-291399192 2 174,990,230 -0.12 加性 Additive
3 KL_BLUE, KL_GCA Affx-291415239 2 188,723,861 -0.55 部分显性 Partial dominance
4 KL_BLUE, KL_SCA Affx-291414096 5 19,152,693 2.64 超显性 Overdominance
5 KW_BLUE, KW_GCA Affx-291445199 1 180,428,491
6 KW_BLUE, KW_GCA Affx-158946352 1 224,324,098 -0.08 加性Additive
7 KW_BLUE, KW_GCA Affx-291438736 1 292,427,178 -0.57 部分显性 Partial dominance
8 KT_BLUE, KT_GCA Affx-291390855 1 262,368,074
9 KT_BLUE, KT_SCA Affx-291422512 9 11,615,037 -0.60 部分显性 Partial dominance
10 KT_BLUE, KT_SCA Affx-291438777 9 107,428,822 -0.57 部分显性 Partial dominance

图3

性状与配合力共定位SNP的不同等位基因的表型差异分析 **和***分别表示在P < 0.01和P < 0.001水平显著。缩写同表1。"

图4

候选区间内在籽粒中表达基因的功能富集分析 A: GO分析; B: KEGG通路分析。BP: 生物过程; CC: 细胞组分; MF: 分子功能。"

表4

玉米粒长、粒宽、粒厚及其配合力(一般配合力和特殊配合力)的候选基因和功能注释"

序号
Number
性状
Trait
SNP标记
SNP marker
染色体
Chr.
候选基因
Candidate gene
注释
Description
1 KT_GCA Affx-291399215 1 GRMZM2G025157 Putative pentatricopeptide repeat-containing protein
2 KW_BLUE, KW_GCA Affx-291445199 1 GRMZM2G085474 Thiolase1 (thl1)
3 KW_BLUE, KW_GCA Affx-158946352 1 GRMZM2G031572 Putative serine peptidase S28 family protein
4 KW_GCA Affx-158948129 1 GRMZM2G464680 Xyloglucan galactosyltransferase KATAMARI 1
5 KT_BLUE, KT_GCA Affx-291390855 1 GRMZM2G022997 Inositol hexakisphosphate and diphosphoinositol- pentakisphosphatekinase VIP2
6 KW_BLUE, KW_GCA Affx-291438736 1 GRMZM2G450920 Protein SCAR2
7 KT_BLUE Affx-291443747 2 GRMZM2G505380 Protein O-glucosyltransferase 1
8 KL_GCA Affx-88981164 2 GRMZM2G158043 Pullulanase-type starch debranching enzyme1 (zpu1)
9 KL_BLUE, KL_GCA Affx-291427653 2 GRMZM2G014106 Enhancer of polycomb-like transcription factor
10 KL_GCA Affx-291381936 2 GRMZM2G104546 Aspartate kinase homoserine dehydrogenase 2
11 KL_BLUE, KL_GCA Affx-291399192 2 AC203957.3_FG004 BZIP-transcription factor 40
12 KL_BLUE, KL_GCA Affx-291415239 2 GRMZM2G048392 Empty pericarp6 (emp6)
13 KW_BLUE Affx-291404824 3 GRMZM2G175218 Beta amylase4 (amyb4)
14 KL_BLUE, KL_SCA Affx-291414096 5 GRMZM2G081221 Protein phosphatase 1 regulatory inhibitor
15 KW_BLUE Affx-291435207 7 GRMZM5G800488 Laccase20 (lac20)
16 KT_BLUE, KT_SCA Affx-291422512 9 GRMZM2G089713 Sucrose synthase (shrunken1)
17 KT_BLUE, KT_SCA Affx-291438777 9 GRMZM2G048472 Homogentisate phytyltransferase VTE2-1
[1] Troyer A F. Development of Hybrid Corn and the Seed Corn Industry. Handbook of Maize. New York: Springer, 2009. pp 87-114.
[2] Zhang R Y, Xu G, Li J S, Yan J B, Li H H, Yang X H. Patterns of genomic variation in Chinese maize inbred lines and implications for genetic improvement. Theor Appl Genet, 2018, 131: 1207-1221.
doi: 10.1007/s00122-018-3072-z
[3] Sprague G F, Tatum L A. General vs specific combining ability in single crosse of corn. J Am Soc Agron, 1942, 34: 923-932.
doi: 10.2134/agronj1942.00021962003400100008x
[4] Reif J C, Gumpert F M, Fischer S, Melchinger A E. Impact of interpopulation divergence on additive and dominance variance in hybrid populations. Genetics, 2007, 176: 1931-1934.
pmid: 17507673
[5] Chen J X, Zhou H, Xie W B, Xia D, Gao G J, Zhang Q L, Wang G W, Lian X M, Xiao J H, He Y Q. Genome-wide association analyses reveal the genetic basis of combining ability in rice. Plant Biotechnol J, 2019, 17: 2211-2222.
doi: 10.1111/pbi.13134
[6] Geng X L, Sun G F, Qu Y J, Sarfraz Z, Jia Y H, He S P, Pan Z E, Sun J L, Iqbal M S, Wang Q L. Genome-wide dissection of hybridization for fiber quality-and yield-related traits in upland cotton. Plant J, 2020, 104: 1285.
[7] Zhou Z Q, Zhang C S, Lu X H, Wang L W, Hao Z F, Li M S, Zhang D G, Yong H J, Zhu H Y, Weng J F. Dissecting the genetic basis underlying combining ability of plant height related traits in maize. Front Plant Sci, 2018, 9: 1117.
[8] Lu X, Zhou Z Q, Yuan Z H, Zhang C S, Hao Z F, Wang Z H, Li M S, Zhang D G, Yong H J, Han J N, Li X H, Weng J F. Genetic dissection of the general combining ability of yield-related traits in maize. Front Plant Sci, 2020, 11: 788.
[9] Liu X G, Hu X J, Li K, Liu Z F, Wu Y J, Feng G, Huang C L, Wang H W. Identifying quantitative trait loci for the general combining ability of yield-relevant traits in maize. Breed Sci, 2021, 71: 217-228.
doi: 10.1270/jsbbs.20008
[10] 刘文童, 监立强, 郭晋杰, 赵永锋, 黄亚群, 陈景堂, 祝丽英. 玉米穗部性状及其一般配合力的关联分析. 植物遗传资源学报, 2020, 21: 706-715.
Liu W T, Jian L Q, Guo J J, Zhao Y F, Huang Y Q, Chen J T, Zhu L Y. Association analysis of ear-related traits and their general combining ability in maize. J Plant Genet Resour, 2020, 21: 706-715. (in Chinese with English abstract)
[11] Li C H, Li Y X, Sun B C, Peng B, Liu C, Liu Z Z, Yang Z Z, Li Q C, Tan W W, Zhang Y. Quantitative trait loci mapping for yield components and kernel-related traits in multiple connected RIL populations in maize. Euphytica, 2013, 193: 303-316.
doi: 10.1007/s10681-013-0901-7
[12] Peng B, Li Y X, Wang Y, Liu C, Liu Z Z, Tan W W, Zhang Y, Wang D, Shi Y S, Sun B C. QTL analysis for yield components and kernel-related traits in maize across multi-environments. Theor Appl Genet, 2011, 122: 1305-1320.
doi: 10.1007/s00122-011-1532-9 pmid: 21286680
[13] Liu M, Tan X L, Yang Y, Liu P, Zhang X X, Zhang Y C, Wang L, Hu Y, Ma L L, Li Z L. Analysis of the genetic architecture of maize kernel size traits by combined linkage and association mapping. Plant Biotechnol J, 2020, 18: 207-221.
doi: 10.1111/pbi.13188
[14] Lan T R, He K H, Chang L G, Cui T T, Zhao Z X, Xue J Q, Liu J C. QTL mapping and genetic analysis for maize kernel size and weight in multi-environments. Euphytica, 2018, 214: 119.
[15] Li T, Qu J Z, Tian X K, Lao Y H, Wei N N, Wang Y H, Hao Y C, Zhang X H, Xue J Q, Xu S T. Identification of ear morphology genes in maize (Zea mays L.) using selective sweeps and association mapping. Front Genet, 2020, 11: 747.
[16] 倪先林, 张涛, 蒋开锋, 杨莉, 杨乾华, 曹应江, 文春阳, 郑家奎. 杂交稻特殊配合力与杂种优势、亲本间遗传距离的相关性. 遗传, 2009, 31: 849-854.
Ni X L, Zhang T, Jiang K F, Yang L, Yang Q H, Cao Y J, Wen C Y, Zheng J K. Correlations between specific combining ability, heterosis and genetic distance in hybrid rice. Hereditas, 2009, 31: 849-854. (in Chinese with English abstract)
[17] Knapp S J, Stroup W W, Ross W M. Exact confidence intervals for heritability on a progeny mean basis 1. Crop Sci, 1985, 25: 192-194.
doi: 10.2135/cropsci1985.0011183X002500010046x
[18] Murray M G, Thompson W F. Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res, 1980, 8: 4321-4326.
pmid: 7433111
[19] Danecek P, Auton A, Abecasis G, Albers C A, Banks E, Depristo M A, Handsaker R E, Lunter G, Marth G T, Sherry S T. The variant call format and VCFtools. Bioinformatics, 2011, 27: 2156-2158.
doi: 10.1093/bioinformatics/btr330 pmid: 21653522
[20] Ayres D L, Darling A, Zwickl D J, Beerli P, Holder M T, Lewis P O, Huelsenbeck J P, Ronquist F, Swofford D L, Cummings M P. BEAGLE: an application programming interface and high- performance computing library for statistical phylogenetics. Syst Biol, 2012, 61: 170-173.
doi: 10.1093/sysbio/syr100
[21] Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet, 2012, 44: 821-824.
doi: 10.1038/ng.2310
[22] Huang X H, Yang S H, Gong J Y, Zhao Y, Feng Q, Gong H, Li W J, Zhan Q L, Cheng B Y, Xia J H. Genomic analysis of hybrid rice varieties reveals numerous superior alleles that contribute to heterosis. Nat Commun, 2015, 6: 6258.
[23] Gao X Y, Starmer J, Martin E R. A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet Epidemiol, 2008, 32: 361-369.
doi: 10.1002/gepi.20310
[24] Liu H J, Wang Q, Chen M J, Ding Y H, Yang X R, Liu J, Li X H, Zhou C C, Tian Q L, Lu Y Q. Genome-wide identification and analysis of heterotic loci in three maize hybrids. Plant Biotechnol J, 2020, 18: 185-194.
doi: 10.1111/pbi.13186
[25] 渠建洲, 冯文豪, 张兴华, 徐淑兔, 薛吉全. 基于全基因组关联分析解析玉米籽粒大小的遗传结构. 作物学报, 2022, 48: 304-319.
doi: 10.3724/SP.J.1006.2022.13002
Qu J X, Feng W H, Zhang X H, Xu S T, Xue J Q. Dissecting the genetic architecture of maize kernel size based on genome-wide association study. Acta Agron Sin, 2022, 48: 304-319. (in Chinese with English abstract)
[26] Chen J, Zeng B, Zhang M, Xie S J, Wang G K, Hauck A, Lai J S. Dynamic transcriptome landscape of maize embryo and endosperm development. Plant Physiol, 2014, 166: 252-264.
doi: 10.1104/pp.114.240689
[27] Huerta-Cepas J, Forslund K, Coelho L P, Szklarczyk D, Jensen L J, von Mering C, Bork P. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol, 2017, 34: 2115-2122.
doi: 10.1093/molbev/msx148 pmid: 28460117
[28] Yu G C, Wang L G, Han Y Y, He Q Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS, 2012, 16: 284-287.
doi: 10.1089/omi.2011.0118
[29] Chettoor A M, Yi G, Gomez E, Hueros G, Meeley R B, Becraft P W. A putative plant organelle RNA recognition protein gene is essential for maize kernel development. J Integr Plant Biol, 2015, 57: 236-246.
doi: 10.1111/jipb.12234
[30] Qu Z, Li L Z, Luo J Y, Wang P, Yu S B, Mou T M, Zheng X F, Hu Z L. QTL mapping of combining ability and heterosis of agronomic traits in rice backcross recombinant inbred lines and hybrid crosses. PLoS One, 2012, 7: e28463.
[31] Qi H H, Huang J, Zheng Q, Huang Y Q, Shao R X, Zhu L Y, Zhang Z X, Qiu F Z, Zhou G C, Zheng Y L. Identification of combining ability loci for five yield-related traits in maize using a set of testcrosses with introgression lines. Theor Appl Genet, 2013, 126: 369-377.
doi: 10.1007/s00122-012-1985-5
[32] 王博新, 王亚辉, 陈朋飞, 冯志前, 郝引川, 张仁和, 张兴华, 薛吉全. 源于陕A群、陕B群玉米自交系在不同密度条件下配合力分析. 作物学报, 2017, 43: 1328-1336.
doi: 10.3724/SP.J.1006.2017.01328
Wang B X, Wang Y H, Chen P F, Feng Z Q, Hao Y C, Zhang R H, Zhang X H, Xue J Q. Combining ability of maize inbred lines from Shaan A group and Shaan B group under different density conditions. Acta Agron Sin, 2017, 43: 1328-1336. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2017.01328
[33] Jiang Y, Schmidt R H, Zhao Y, Reif J C. A quantitative genetic framework highlights the role of epistatic effects for grain-yield heterosis in bread wheat. Nat Genet, 2017, 49: 1741-1746.
doi: 10.1038/ng.3974 pmid: 29038596
[34] Li C H, Li Y X, Sun B C, Peng B, Liu C, Liu Z Z, Yang Z Z, Li Q C, Tan W W, Zhang Y. Quantitative trait loci mapping for yield components and kernel-related traits in multiple connected RIL populations in maize. Euphytica, 2013, 193: 303-316.
doi: 10.1007/s10681-013-0901-7
[35] Raihan M S, Liu J, Huang J, Guo H, Pan Q C, Yan J B. Multi-environment QTL analysis of grain morphology traits and fine mapping of a kernel-width QTL in Zheng 58 × SK maize population. Theor Appl Genet, 2016, 129: 1465-1477.
doi: 10.1007/s00122-016-2717-z
[36] Liu Y, Wang L W, Sun C L, Zhang Z X, Zheng Y L, Qiu F Z. Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments. Theor Appl Genet, 2014, 127: 1019-1037.
doi: 10.1007/s00122-014-2276-0
[37] Dinges J R, Colleoni C, James M G, Myers A M. Mutational analysis of the pullulanase-type debranching enzyme of maize indicates multiple functions in starch metabolism. Plant Cell, 2003, 15: 666-680.
pmid: 12615940
[38] Zheng Y X, Yuan F, Huang Y Q, Zhao Y F, Jia X Y, Zhu L Y, Guo J J. Genome-wide association studies of grain quality traits in maize. Sci Rep, 2021, 11: 9797.
[39] Liu J, Huang J, Guo H, Lan L, Wang H Z, Xu Y C, Yang X H, Li W Q, Tong H, Xiao Y J. The conserved and unique genetic architecture of kernel size and weight in maize and rice. Plant Physiol, 2017, 175: 774-785.
doi: 10.1104/pp.17.00708
[40] Zhang K, Wang F, Liu B Y, Xu C Z, He Q X, Cheng W, Zhao X Y, Ding Z H, Zhang W, Zhang K W. ZmSKS13, a cupredoxin domain-containing protein, is required for maize kernel development via modulation of redox homeostasis. New Phytol, 2021, 229: 2163-2178.
doi: 10.1111/nph.16988 pmid: 33034042
[1] 段灿星, 崔丽娜, 夏玉生, 董怀玉, 杨知还, 胡清玉, 孙素丽, 李晓, 朱振东, 王晓鸣. 玉米种质资源对拟轮枝镰孢与禾谷镰孢穗腐病的抗性精准鉴定与分析[J]. 作物学报, 2022, 48(9): 2155-2167.
[2] 张超, 杨博, 张立源, 肖忠春, 刘景森, 马晋齐, 卢坤, 李加纳. 基于QTL定位和全基因组关联分析挖掘甘蓝型油菜收获指数相关位点[J]. 作物学报, 2022, 48(9): 2180-2195.
[3] 张振博, 屈馨月, 于宁宁, 任佰朝, 刘鹏, 赵斌, 张吉旺. 施氮量对夏玉米籽粒灌浆特性和内源激素作用的影响[J]. 作物学报, 2022, 48(9): 2366-2376.
[4] 郭瑶, 柴强, 殷文, 范虹. 玉米密植光合生理机制及应用途径研究进展[J]. 作物学报, 2022, 48(8): 1871-1883.
[5] 王沙沙, 黄超, 汪庆昌, 晁岳恩, 陈锋, 孙建国, 宋晓. 小麦籽粒大小相关基因TaGS2克隆及功能分析[J]. 作物学报, 2022, 48(8): 1926-1937.
[6] 王天波, 赫文学, 张峻铭, 吕伟增, 梁雨欢, 卢洋, 王雨露, 谷丰序, 宋词, 陈军营. 人工老化玉米种胚ROS产生及ATP合成酶亚基mRNA完整性研究[J]. 作物学报, 2022, 48(8): 1996-2006.
[7] 夏秀忠, 张宗琼, 杨行海, 荘洁, 曾宇, 邓国富, 宋国显, 黄欲晓, 农保选, 李丹婷. 广西水稻地方品种核心种质芽期耐盐性全基因组关联分析[J]. 作物学报, 2022, 48(8): 2007-2015.
[8] 裴丽珍, 陈远学, 张雯雯, 肖华, 张森, 周元, 徐开未. 有机物料还田对夏玉米穗位叶光合性能及氮代谢的影响[J]. 作物学报, 2022, 48(8): 2115-2124.
[9] 杨迎霞, 张冠, 王梦梦, 陆国清, 王倩, 陈锐. 基于高通量测序技术的转基因玉米GM11061分子特征研究[J]. 作物学报, 2022, 48(7): 1843-1850.
[10] 王丹, 周宝元, 马玮, 葛均筑, 丁在松, 李从锋, 赵明. 长江中游双季玉米种植模式周年气候资源分配与利用特征[J]. 作物学报, 2022, 48(6): 1437-1450.
[11] 杨欢, 周颖, 陈平, 杜青, 郑本川, 蒲甜, 温晶, 杨文钰, 雍太文. 玉米-豆科作物带状间套作对养分吸收利用及产量优势的影响[J]. 作物学报, 2022, 48(6): 1476-1487.
[12] 陈静, 任佰朝, 赵斌, 刘鹏, 张吉旺. 叶面喷施甜菜碱对不同播期夏玉米产量形成及抗氧化能力的调控[J]. 作物学报, 2022, 48(6): 1502-1515.
[13] 徐田军, 张勇, 赵久然, 王荣焕, 吕天放, 刘月娥, 蔡万涛, 刘宏伟, 陈传永, 王元东. 宜机收籽粒玉米品种冠层结构、光合及灌浆脱水特性[J]. 作物学报, 2022, 48(6): 1526-1536.
[14] 肖颖妮, 于永涛, 谢利华, 祁喜涛, 李春艳, 文天祥, 李高科, 胡建广. 基于SNP标记揭示中国鲜食玉米品种的遗传多样性[J]. 作物学报, 2022, 48(6): 1301-1311.
[15] 崔连花, 詹为民, 杨陆浩, 王少瓷, 马文奇, 姜良良, 张艳培, 杨建平, 杨青华. 2个玉米ZmCOP1基因的克隆及其转录丰度对不同光质处理的响应[J]. 作物学报, 2022, 48(6): 1312-1324.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 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 .
[2] 王春梅;冯祎高;庄丽芳;曹亚萍;亓增军;别同德;曹爱忠;陈佩度. 普通小麦近缘物种黑麦1R、簇毛麦1V及鹅观草1Rk#1染色体特异分子标记的筛选[J]. 作物学报, 2007, 33(11): 1741 -1747 .
[3] 杨燕;赵献林;张勇;陈新民;何中虎;于卓;夏兰琴. 四个小麦抗穗发芽分子抗性标记有效性的验证与评价[J]. 作物学报, 2008, 34(01): 17 -24 .
[4] 王东;于振文;王旭东. 硫素对冬小麦籽粒蛋白质积累的影响[J]. 作物学报, 2003, 29(06): 878 -883 .
[5] 赵翔;汪延良;王亚静;王西丽;张骁. 盐胁迫条件下外源Ca2+对蚕豆气孔运动及质膜K+通道的调控[J]. 作物学报, 2008, 34(11): 1970 -1976 .
[6] 叶小利;李学刚;李加纳. 甘蓝型油菜种皮黑色素形成机理的研究[J]. 作物学报, 2002, 28(05): 638 -643 .
[7] 徐宁;程须珍;王素华;王丽侠;赵丹. 以地理来源分组和利用表型数据构建中国小豆核心种质[J]. 作物学报, 2008, 34(08): 1366 -1373 .
[8] 孟金陵;刘后利. 连续自交对甘蓝型油菜(Brassica napus L.)胚胎发育的影响[J]. 作物学报, 1986, 12(02): 79 -86 .
[9] 李常保;刘艳华;杜长青;孔令让. 普通小麦与粗山羊草正反交育性机理的胚胎学研究[J]. 作物学报, 2002, 28(02): 170 -174 .
[10] 胡洪凯;马尚耀;石艳华. 谷子(Setaria italica)显性雄性不育基因的发现[J]. 作物学报, 1986, 12(02): 73 -78 .