小麦主栽品种济麦22与良星99的基因组序列多态性比较分析

doi:10.3724/SP.J.1006.2020.01009

作物学报 ›› 2020, Vol. 46 ›› Issue (12): 1870-1883.doi: 10.3724/SP.J.1006.2020.01009

• 作物遗传育种·种质资源·分子遗传学 • 上一篇下一篇

小麦主栽品种济麦22与良星99的基因组序列多态性比较分析

杨正钊(), 王梓豪, 胡兆荣, 辛明明, 姚颖垠, 彭惠茹, 尤明山, 宿振起^*(), 郭伟龙^*()

中国农业大学农学院 / 农业生物技术国家重点实验室 / 杂种优势研究与利用教育部重点实验室, 北京 100193

收稿日期:2020-01-15 接受日期:2020-06-02 出版日期:2020-12-12 网络出版日期:2020-11-25
通讯作者: 宿振起,郭伟龙
基金资助:
国家重点研发计划项目(2018YFD0100803);国家自然科学基金项目(31701415);中央高校基本科研业务费专项资助

Comparative analysis of the genomic sequences between commercial wheat varieties Jimai 22 and Liangxing 99

YANG Zheng-Zhao(), WANG Zi-Hao, HU Zhao-Rong, XIN Ming-Ming, YAO Ying-Yin, PENG Hui-Ru, YOU Ming-Shan, SU Zhen-Qi^*(), GUO Wei-Long^*()

College of Agronomy and Biotechnology / State Key Laboratory for Agrobiotechnology / Key Laboratory of Crop Heterosis and Utilization, Ministry of Education, China Agricultural University, Beijing 100193, China

Received:2020-01-15 Accepted:2020-06-02 Published:2020-12-12 Published online:2020-11-25
Contact: SU Zhen-Qi,GUO Wei-Long
Supported by:
National Key Research and Development Program of China(2018YFD0100803);National Natural Science Foundation of China(31701415);Chinese Universities Scientific Fund.

摘要/Abstract

摘要：

济麦22和良星99是我国黄淮冬麦区和北部冬麦区大面积推广的高产小麦品种, 也是目前小麦杂交育种的重要亲本。虽然济麦22和良星99的来源和系谱不同, 但在重要农艺、产量等性状上存在较高的相似性。为了从全基因组水平研究其遗传组成的异同, 本研究采用Illumina HiSeq2500测序平台对上述两个品种进行了全基因组测序(平均测序深度为5.8×), 并系统地比较了两个品种拷贝数变异(CNV)、单核苷酸多态性(SNP)和插入/缺失(InDel)的序列差异。与中国春参考基因组序列相比, 两个品种除了具有总长466 Mb的共有CNV变异区间外, 济麦22和良星99的特有CNV变异区间的总长分别为91 Mb和45 Mb, 这些特有CNV区间主要集中在2B和4B染色体上; 济麦22和良星99间存在1,547,371个SNP差异位点和137,817个InDel差异位点。以差异SNP分布规律为依据, 在全基因组水平鉴定出济麦22和良星99间存在14.2%的差异多态性热点区间, 这些区间集中分布在1D、2B和4B染色体上。通过对5个控制小麦株高和穗长基因的序列分析, 发现有2个位于多态性热点区间的基因在品种间存在移码突变。本研究为利用重测序数据在基因组水平上比较小麦品种间遗传差异提供了重要参考, 同时揭示了济麦22和良星99在全基因组的遗传相似区间和差异区间, 为今后小麦育种改良中更好利用济麦22与良星99提供了重要遗传信息。

关键词: 小麦, 济麦22, 良星99, 全基因组重测序, SNP, CNV

Abstract:

Jimai 22 and Liangxing 99 are high-yield wheat varieties widely planted in the North Huang-Huai Rivers Valley Winter Wheat Zone and Northern Winter Wheat Zone in China, and are currently used as important parents in wheat breeding programs. Although the origins and pedigrees of Jimai 22 and Liangxing 99 are different, they are highly similar in many important agronomic traits, yield-associated traits, and so on. To identify the genomic differences between the two varieties, we performed whole-genome sequencing using the Illumina HiSeq2500 platform, with an average sequencing depth of 5.8×. We aligned the raw sequencing data against the Chinese Spring reference genome and identified the difference of copy-number variation (CNV) regions, single-nucleotide polymorphisms (SNPs) and InDels in sequence between the two varieties. Lengths of 466 Mb CNV intervals were shared by the two varieties. The lengths of cultivar-specific CNV intervals in Jimai 22 and Liangxing 99 were 91 Mb and 45 Mb, respectively, and these intervals are mainly located on chromosomes 2B and 4B. Beyond the CNV intervals, 1,547,371 SNPs and 137,817 InDels were different between the two cultivars. Based on the distribution of SNP densities in the intervals, we identified the polymorphic hotspot regions on chromosomes 1D, 2B, and 4B, making up 14.2% of the whole genome. The sequences of five previous cloned dwarf genes and spike length related genes were investigated, and two genes located in the polymorphic hotspot regions were detected with the frame shift variations. This study provides an important guidance for evaluating the genetic differences between two wheat varieties in the genomic level, and also identified both genetic similarity regions and polymorphic hotspot regions between Jimai 22 and Liangxing 99, which provided a valuable genetic information for future genetic improvement utilizing Jimai 22 and Liangxing 99 as parents.

Key words: wheat, Jimai 22, Liangxing 99, whole-genome resequencing, SNP, CNV

杨正钊, 王梓豪, 胡兆荣, 辛明明, 姚颖垠, 彭惠茹, 尤明山, 宿振起, 郭伟龙. 小麦主栽品种济麦22与良星99的基因组序列多态性比较分析[J]. 作物学报, 2020, 46(12): 1870-1883.

YANG Zheng-Zhao, WANG Zi-Hao, HU Zhao-Rong, XIN Ming-Ming, YAO Ying-Yin, PENG Hui-Ru, YOU Ming-Shan, SU Zhen-Qi, GUO Wei-Long. Comparative analysis of the genomic sequences between commercial wheat varieties Jimai 22 and Liangxing 99[J]. Acta Agronomica Sinica, 2020, 46(12): 1870-1883.

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: https://zwxb.chinacrops.org/CN/10.3724/SP.J.1006.2020.01009

https://zwxb.chinacrops.org/CN/Y2020/V46/I12/1870

图/表 19

附图1

附图2

表1

图1

表2

表3

表4

表5

图2

图3

图4

图5

图6

图7

表6

表7

表8

表9

图8

参考文献 27

[1]	何中虎, 庄巧生, 程顺和, 于振文, 赵振东, 刘旭 . 中国小麦产业发展与科技进步. 农学学报, 2018,8(1):99-106.
	He Z H, Zhuang Q S, Cheng S H, Yu Z W, Zhao Z D, Liu X . Wheat production and technology improvement in China. J Agric, 2018,8(1):99-106 (in Chinese).
[2]	张婷, 逯腊虎, 张伟, 杨斌, 袁凯, 史晓芳 . 黄淮麦区300个小麦种质的主要农艺性状比较. 小麦研究, 2018,39(1):13-20.
	Zhang T, Lu L H, Zhang W, Yang B, Yuan K, Shi X F . Comparison of main agronomic traits of 300 wheat germplasms in Huanghuai Wheat Region. J Wheat Res, 2018,39(1):13-20 (in Chinese).
[3]	曹廷杰, 王西成, 赵虹 . 国审小麦新品种周麦18号丰产性、稳产性及适应性分析. 中国农业科技导报, 2007,9(1):39-41.
	Cao T J, Wang X C, Zhao H . Analysis on the yield potential, stability and adaptability of national authorized wheat variety Zhoumai 18. Rev China Agric Sci Technol, 2007,9(1):39-41 (in Chinese with English abstract)
[4]	盖红梅, 李玉刚, 王瑞英, 李振清, 王圣健, 高峻岭, 张学勇 . 鲁麦14对山东新选育小麦品种的遗传贡献. 作物学报, 2012,38:954-961. doi: 10.3724/SP.J.1006.2012.00954
	Gai H M, Li Y G, Wang R Y, Li Z Q, Wang S J, Gao J L, Zhang X Y . Genetic contribution of Lumai 14 to novel wheat varieties developed in Shandong province. Acta Agron Sin, 2012,38:954-961 (in Chinese with English abstract)
[5]	傅晓艺, 郭进考, 刘艳滨, 史占良, 单子龙, 韩然, 何明琦 . 利用石4185为骨干亲本培育高产小麦新品种. 种子, 2017,36(12):95-99.
	Fu X Y, Guo J K, Liu Y B, Shi Z L, Shan Z L, Han R, He M Q . New wheat varieties with high yield of core parent breeding by using Shi 4185. Seed, 2017,36(12):95-99 (in Chinese).
[6]	凌宏清 . 小麦及其近缘种基因组测序研究进展与发展趋势. 麦类作物学报, 2016,36:397-403.
	Ling H Q . Progress and perspectives of the genome sequencing in wheat and its relatives. J Triticeae Crops, 2016,36:397-403 (in Chinese with English abstract)
[7]	Uauy C . Wheat genomics comes of age. Curr Opin Plant Biol, 2017,36:142-148. doi: 10.1016/j.pbi.2017.01.007 pmid: 28346895
[8]	殷贵鸿, 李根英, 何中虎, 刘建军, 王辉, 夏先春 . 小麦新品种济麦22抗白粉病基因的分子标记定位. 作物学报, 2009,35:1425-1431. doi: 10.3724/SP.J.1006.2009.01425
	Yin H G, Li G Y, He Z H, Liu J J, Wang H, Xia X C . Molecular mapping of powdery mildew resistance gene in wheat cultivar Jimai 22. Acta Agron Sin, 2009,35:1425-1431 (in Chinese with English abstract)
[9]	赵广军, 张英肖 . 高产稳产冬小麦品种良星99栽培技术. 小麦研究, 2006,27(3):12-13.
	Zhao G J, Zhang Y X . Cultivation technique of Liangxing 99, a winter wheat variety with high and stable yield. J Wheat Res, 2006,27(3):12-13 (in Chinese)
[10]	Qu Y F, Wu P P, Hu J H, Chen Y X, Shi Z L, Qiu D, Li Y H, Zhang H J, Zhou Y, Yang L, Liu H W, Zhu T Q, Liu Z Y, Zhang Y M, Li H J . Molecular detection of the powdery mildew resistance genes in winter wheats DH51302 and Shimai 26. J Integr Agric, 2020,19:931-940. doi: 10.1016/S2095-3119(19)62644-4
[11]	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
[12]	Li H, Durbin R . Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 2009,25:1754-1760. doi: 10.1093/bioinformatics/btp324 pmid: 19451168
[13]	Appels R, Eversole K, Feuillet C, Keller B, Rogers J, Stein N, Pozniak C J, Stein N, Choulet F, Distelfeld A, Eversole K, Poland J, Rogers J, Ronen G, Sharpe A G, Pozniak C, Ronen G, Stein N, Barad O, Baruch K, Choulet F, Keeble-Gagnere G, Mascher M, Sharpe A G, Ben-Zvi G, Josselin A A, Stein N, Mascher M, Himmelbach A, Choulet F, Keeble-Gagnere G, Mascher M, Rogers J, Balfourier F, Gutierrez-Gonzalez J, Hayden M, Josselin A A, Koh C, Muehlbauer G, Pasam R K, Paux E, Pozniak C J, Rigault P, Sharpe A G, Tibbits J, Tiwari V, Choulet F, Keeble-Gagnere G, Mascher M, Josselin A A, Rogers J, Spannagl M, Choulet F, Lang D, Gundlach H, Haberer G, Keeble-Gagnere G, Mayer KFX, Ormanbekova D, Paux E, Prade V, Simkova H, Wicker T, Choulet F, Spannagl M, Swarbreck D, Rimbert H, Felder M, Guilhot N, Gundlach H, Haberer G, Kaithakottil G, Keilwagen J, Lang D, Leroy P, Lux T, Mayer KFX, Twardziok S, Venturini L, Appels R, Rimbert H, Choulet F, Juhasz A, Keeble-Gagnere G, Choulet F, Spannagl M, Lang D, Abrouk M, Haberer G, Keeble-Gagnere G, Mayer KFX, Wicker T, Choulet F, Wicker T, Gundlach H, Lang D, Spannagl M, Lang D, Spannagl M, Appels R, Fischer I, Uauy C, Borrill P, Ramirez-Gonzalez R H, Appels R, Arnaud D, Chalabi S, Chalhoub B, Choulet F, Cory A, Datla R, Davey M W, Hayden M, Jacobs J, Lang D, Robinson S J, Spannagl M, Steuernagel B, Tibbits J, Tiwari V, van Ex F, Wulff BBH, Pozniak C J, Robinson S J, Sharpe A G, Cory A, Benhamed M, Paux E, Bendahmane A, Concia L, Latrasse D, Rogers J, Jacobs J, Alaux M, Appels R, Bartos J, Bellec A, Berges H, Dolezel J, Feuillet C, Frenkel Z, Gill B, Korol A, Letellier T, Olsen O A, Simkova H, Singh K, Valarik M, van der Vossen E, Vautrin S, Weining S, Korol A, Frenkel Z, Fahima T, Glikson V, Raats D, Rogers J, Tiwari V, Gill B, Paux E, Poland J, Dolezel J, Cihalikova J, Simkova H, Toegelova H, Vrana J, Sourdille P, Darrier B, Appels R, Spannagl M, Lang D, Fischer I, Ormanbekova D, Prade V, Barabaschi D, Cattivelli L, Hernandez P, Galvez S, Budak H, Steuernagel B, Jones J D G, Witek K, Wulff B B H, Yu G, Small I, Melonek J, Zhou R, Juhasz A, Belova T, Appels R, Olsen O A, Kanyuka K, King R, Nilsen K, Walkowiak S, Pozniak C J, Cuthbert R, Datla R, Knox R, Wiebe K, Xiang D, Rohde A, Golds T, Dolezel J, Cizkova J, Tibbits J, Budak H, Akpinar B A, Biyiklioglu S, Muehlbauer G, Poland J, Gao L, Gutierrez-Gonzalez J, N'Daiye A, Dolezel J, Simkova H, Cihalikova J, Kubalakova M, Safar J, Vrana J, Berges H, Bellec A, Vautrin S, Alaux M, Alfama F, Adam-Blondon A F, Flores R, Guerche C, Letellier T, Loaec M, Quesneville H, Pozniak C J, Sharpe A G, Walkowiak S, Budak H, Condie J, Ens J, Koh C, Maclachlan R, Tan Y, Wicker T, Choulet F, Paux E, Alberti A, Aury J M, Balfourier F, Barbe V, Couloux A, Cruaud C, Labadie K, Mangenot S, Wincker P, Gill B, Kaur G, Luo M, Sehgal S, Singh K, Chhuneja P, Gupta O P, Jindal S, Kaur P, Malik P, Sharma P, Yadav B, Singh N K, Khurana J, Chaudhary C, Khurana P, Kumar V, Mahato A, Mathur S, Sevanthi A, Sharma N, Tomar R S, Rogers J, Jacobs J, Alaux M, Bellec A, Berges H, Dolezel J, Feuillet C, Frenkel Z, Gill B, Korol A, van der Vossen E, Vautrin S, Gill B, Kaur G, Luo M, Sehgal S, Bartos J, Holusova K, Plihal O, Clark M D, Heavens D, Kettleborough G, Wright J, Valarik M, Abrouk M, Balcarkova B, Holusova K, Hu Y, Luo M, Salina E, Ravin N, Skryabin K, Beletsky A, Kadnikov V, Mardanov A, Nesterov M, Rakitin A, Sergeeva E, Handa H, Kanamori H, Katagiri S, Kobayashi F, Nasuda S, Tanaka T, Wu J, Appels R, Hayden M, Keeble-Gagnere G, Rigault P, Tibbits J, Olsen O A, Belova T, Cattonaro F, Jiumeng M, Kugler K, Mayer K F X, Pfeifer M, Sandve S, Xun X, Zhan B, Simkova H, Abrouk M, Batley J, Bayer P E, Edwards D, Hayashi S, Toegelova H, Tulpova Z, Visendi P, Weining S, Cui L, Du X, Feng K, Nie X, Tong W, Wang L, Borrill P, Gundlach H, Galvez S, Kaithakottil G, Lang D, Lux T, Mascher M, Ormanbekova D, Prade V, Ramirez-Gonzalez R H, Spannagl M, Stein N, Uauy C, Venturini L, Stein N, Appels R, Eversole K, Rogers J, Borrill P, Cattivelli L, Choulet F, Hernandez P, Kanyuka K, Lang D, Mascher M, Nilsen K, Paux E, Pozniak C J, Ramirez-Gonzalez R H, Simkova H, Small I, Spannagl M, Swarbreck D, Uauy C . Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science, 2018, 361: eaar7191. doi: 10.1126/science.aav0887 pmid: 30262485
[14]	Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R . The sequence alignment/map format and SAMtools. Bioinformatics, 2009,25:2078-2079. doi: 10.1093/bioinformatics/btp352 pmid: 19505943
[15]	van der Auwera G A, Carneiro M O, Hartl C, Poplin R, Del Angel G, Levy-Moonshine A, Jordan T, Shakir K, Roazen D, Thibault J, Banks E, Garimella K V, Altshuler D, Gabriel S, Depristo M A . From FastQ data to high-confidence variant calls: The genome analysis toolkit best practices pipeline. Curr Protoc Bioinform, 2013,43:10-11.
[16]	Cingolani P, Platts A, Wang L L, Coon M, Nguyen T, Wang L, Land S J, Lu X, Ruden D M . A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly, 2014,6:80-92. doi: 10.4161/fly.19695 pmid: 22728672
[17]	Quinlan A R, Hall I M . BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 2010,26:841-842. doi: 10.1093/bioinformatics/btq033 pmid: 20110278
[18]	Peng J, Richards D E, Hartley N M, Murphy G P, Devos K M, Flintham J E, Beales J, Fish L J, Worland A J, Pelica F, Sudhakar D, Christou P, Snape J W, Gale M D, Harberd N P . ‘Green revolution’ genes encode mutant gibberellin response modulators. Nature, 1999,400:256-261. doi: 10.1038/22307 pmid: 10421366
[19]	Beales J, Turner A, Griffiths S, Snape J W, Laurie D A . A pseudo-response regulator is misexpressed in the photoperiod insensitive Ppd-D1a mutant of wheat(Triticum aestivum L.). Theor Appl Genet, 2007,115:721-733. doi: 10.1007/s00122-007-0603-4
[20]	柴岭岭 . 普通小麦2D染色体株高和穗长QTL的精细定位与图位克隆. 中国农业大学博士学位论文, 北京, 2019. pp 38-39.
	Chai L L . Fine Mapping and Map-based Cloning of QTL Controlling Plant Height and Spike Length on Chromosome 2D in Common Wheat (Triticum aestivum L.). PhD Dissertation of China Agricultural University, Beijing, China, 2019. pp 38-39 (in Chinese with English abstract).
[21]	Chai L L, Chen Z Y, Bian R L, Zhai H J, Cheng X J, Peng H R, Yao Y Y, Hu Z R, Xin M M, Guo W L, Sun Q X, Zhao A J, Ni Z F . Dissection of two quantitative trait loci with pleiotropic effects on plant height and spike length linked in coupling phase on the short arm of chromosome 2D of common wheat ( Triticum aestivum L.). Theor Appl Genet, 2018,131:2621-2637. doi: 10.1007/s00122-018-3177-4 pmid: 30267114
[22]	Zhao Z H, Sun H G, Song W, Lu M, Huang J, Wu L F, Wang X M, Li H J . Genetic analysis and detection of the gene MlLX99 on chromosome 2BL conferring resistance to powdery mildew in the wheat cultivar Liangxing 99. Theor Appl Genet, 2013,126:3081-3089. doi: 10.1007/s00122-013-2194-6 pmid: 24061485
[23]	邹景伟, 邱丹, 孙艳玲, 郑超星, 李静婷, 吴培培, 武小菲, 王晓鸣, 周阳, 李洪杰 . Pm52——小麦品种良星99抗白粉病基因的有效性. 作物学报, 2017,43:332-342. doi: 10.3724/SP.J.1006.2017.00332
	Zou J W, Qiu D, Sun Y L, Zheng C X, Li J T, Wu P P, Wu X F, Wang X M, Zhou Y, Li H J . Pm52: effectiveness of the gene conferring resistance to powdery mildew in wheat cultivar Liangxing 99. Acta Agron Sin, 2017,43:332-342 (in Chinese with English abstract). doi: 10.3724/SP.J.1006.2017.00332
[24]	Wu P P, Hu J H, Zou J W, Qiu D, Qu Y F, Li Y H, Li T, Zhang H J, Yang L, Liu H W, Zhou Y, Zhang Z J, Li J T, Liu Z Y, Li H J . Fine mapping of the wheat powdery mildew resistance gene Pm52 using comparative genomics analysis and the Chinese Spring reference genomic sequence. Theor Appl Genet, 2019,132:1451-1461. doi: 10.1007/s00122-019-03291-7 pmid: 30719526
[25]	Wang Y, Song F, Zhu J, Zhang S, Yang Y, Chen T, Tang B, Dong L, Ding N, Zhang Q, Bai Z, Dong X, Chen H, Sun M, Zhai S, Sun Y, Yu L, Lan L, Xiao J, Fang X, Lei H, Zhang Z, Zhao W . GSA: genome sequence archive. Genom Proteom Bioinf, 2017,15:14-18. doi: 10.1016/j.gpb.2017.01.001
[26]	National genomics data center members and partners. Nucleic Acids Res, 2020,48:D24-D33. doi: 10.1093/nar/gkz913 pmid: 31702008
[27]	Wang W X, Wang Z H, Li X T, Ni Z F, Hu Z R, Xin M M, Peng H R, Yao Y Y, Sun Q X, Guo W L . SnpHub: an easy-to-set-up web server framework for exploring large-scale genomic variation data in the post-genomic era with applications in wheat. GigaScience, 2020, 9:giaa060. doi: 10.1093/gigascience/giaa060 pmid: 32501478

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

引物编号 Primer ID	引物序列 Primer sequence (5'-3')	退火温度 Annealing temperature (℃)	用途 Purpose
TraesCS2D02G055700_F	CAGGTCGAGACAGAGAACAA	56	测序引物 Sequencing primers
TraesCS2D02G055700_R	ATCGAGCCCCTCAATTTCAT	58	CNV标记特异引物 Specific primers for CNV markers
TraesCS2D02G051500_F	TCAGCTCAGGGTTATCAAGC
TraesCS2D02G051500_R	TTGGGTGCATTTTTCAGTCC
2B_LX99_CNV1_F	AGCAGGTAATCCACACCAAA
2B_LX99_CNV1_R	TGGAGAACCCACTTATCCAA
2B_LX99_CNV2_F	AGCAGGTAATCCACACCAAA
2B_LX99_CNV2_R	AATCCACACCAAATCCCCAT
2B_COM_CNV_F	CACCCTAGATACAACCGAGG
2B_COM_CNV_R	ATGTCACCGATCTTCTGAGC
2D_COM_CNV_F	ATGATCACGATGGACCTAGC
2D_COM_CNV_R	TGCCACGAAGATTTAGAGGA

表型 Phenotype	济麦22 Jimai 22		良星99 Liangxing 99		差异显著性 Statistical significance of the difference
表型 Phenotype	均值±标准误差 Mean ± SE	样本量 Sample counts	均值±标准误差 Mean ± SE	样本量 Sample counts	P-value (t-test)
旗叶长Flag leaf length (cm)	16.78±0.36	30	16.94±0.40	30	0.76
旗叶宽Flag leaf width (cm)	1.88±0.024	30	1.85±0.039	30	0.53
每穗小穗数Spikelet number per spike	21.87±0.45	30	21.80±0.28	30	0.91
每穗可育小穗数Fertile spikelet per spike	21.23±0.50	30	21.17±0.14	30	1.00
每穗不育小穗数Infertile spikelet per spike	0.63±0.14	30	0.63±0.23	30	0.90
穗长Spike length (cm)	9.57±0.13	30	10.43±0.15	30	3.98E-5 ^**
株高Plant length (cm)	75.05±0.57	30	80.23±0.75	30	1.90E-6 ^**
穗粒数Grain number per spike	52.23±0.73	30	55.20±0.35	30	0.31
千粒重Thousand seed weight (g)	49.20±0.33	6	48.30±0.03	6	0.66
粒长Grain length (cm)	6.43±0.07	6	6.53±0.03	6	0.39
粒宽Grain width (cm)	3.25±0.06	6	3.30±0.02	6	0.58

基因组 Genome	济麦22中的纯合 SNP个数 Number of homozygous SNPs in Jimai 22	良星99中的纯合 SNP个数 Number of homozygous SNPs in Liangxing 99	两者基因型相同的纯合SNP个数 Number of homozygous SNPs with the same genotype	两者基因型不同的纯合 SNP个数 Number of homozygous SNPs with different genotypes
A	6,405,063	6,395,080	6,194,047	301,206
B	8,033,789	7,781,369	7,248,448	1,148,764
D	887,462	883,555	818,951	97,401
全基因组 Whole genome	15,326,314	15,060,004	14,261,446	1,547,371

基因组 Genome	济麦22中的纯合 InDel个数 Number of homozygous InDels in Jimai 22	良星99中的纯合 InDel个数 Number of homozygous InDels in Liangxing 99	两者基因型相同的纯合InDel个数 Number of homozygous InDels with the same genotype	两者基因型不同的纯合 InDel个数 Number of homozygous InDels with different genotypes
A	464,369	459,599	438,410	28,732
B	562,658	543,395	492,112	95,316
D	116,485	115,570	104,345	13,769
全基因组Whole genome	1,143,512	1,118,564	1,034,867	137,817

基因组 Genome	济麦22的特有CNV区间 Specific CNV region in Jimai 22		良星99的特有CNV区间 Specific CNV region in Liangxing 99		两品种的共有CNV区间 Common CNV regions in the two cultivars
基因组 Genome	长度 Length (Mb)	%	长度 Length (Mb)	%	长度 Length (Mb)	%
A	8	0.06	4	0.03	135	0.96
B	80	0.57	33	0.23	226	1.61
D	3	0.02	8	0.06	105	0.75
全基因组Whole genome	91	0.65	45	0.32	466	3.31

小麦主栽品种济麦22与良星99的基因组序列多态性比较分析

Comparative analysis of the genomic sequences between commercial wheat varieties Jimai 22 and Liangxing 99

RichHTML

PDF

摘要/Abstract

引用本文

使用本文

图/表 19

参考文献 27

相关文章 15

Metrics

本文评价

推荐阅读 0

关于本刊

在线期刊

作者中心

相关单位

联系我们

SNP突变类型 Types of SNP mutations	多态性热点区间 Differential genetic region		相似区间 Similar genetic region		全基因组 Whole genome region
SNP突变类型 Types of SNP mutations	数量 Counts	每Mb密度 Density per Mb	数量 Counts	每Mb密度 Density per Mb	数量 Counts	每Mb密度 Density per Mb
T>A	33,545	17.52	1701	0.15	35,246	2.62
A>T	33,868	17.69	1713	0.15	35,581	2.64
G>C	49,065	25.62	2020	0.17	51,085	3.79
C>G	49,521	25.86	1956	0.17	51,477	3.82
A>C	47,712	24.91	2089	0.18	49,801	3.70
T>G	47,862	24.99	1888	0.16	49,750	3.69
G>T	65,670	34.29	3204	0.28	68,874	5.11
C>A	66,047	34.49	3226	0.28	69,273	5.14
A>G	200,697	104.80	6364	0.55	207,061	15.37
T>C	201,236	105.08	6540	0.57	207,776	15.42
G>A	289,933	151.40	11,276	0.98	301,209	22.36
C>T	290,292	151.59	11,151	0.96	301,443	22.38
转换Transition	982,158	512.88	35,331	3.06	1,017,489	75.53
颠换Transversion	393,290	205.37	17,797	1.54	411,087	30.51
全部Total	1,375,448	718.25	53,128	4.60	1,428,576	106.04

InDel类型 Types of InDel	InDel长度 Length of InDel	差异区间 Differential genetic region		相似区间 Similar genetic region		全基因组 Whole genome region
InDel类型 Types of InDel	InDel长度 Length of InDel	数量 Count	每Mb密度 Density per Mb	数量 Count	每Mb密度 Density per Mb	数量 Count	每Mb密度 Density per Mb
在济麦22中插入, 在良星99中丢失 Insertion in Jimai 22, deletion in Liangxing 99	1	34,031	17.77	7723	0.67	41,754	3.10
	2	7259	3.79	3017	0.26	10,276	0.76
	3	2664	1.39	1242	0.11	3906	0.29
	4	1685	0.88	843	0.07	2528	0.19
	5	701	0.37	419	0.04	1120	0.08
	6	883	0.46	370	0.03	1253	0.09
	7	453	0.24	225	0.02	678	0.05
	8	476	0.25	216	0.02	692	0.05
	9	363	0.19	153	0.01	516	0.04
	≥10	4131	2.16	1599	0.14	5730	0.43
	全部Total	52,646	27.49	15,807	1.37	68,453	5.08
在良星99中插入, 在济麦22中丢失 Insertion in Liangxing 99, deletion in Jimai 22	1	34,083	17.80	7796	0.67	41,879	3.11
	2	7315	3.82	3037	0.26	10,352	0.77
	3	2690	1.40	1190	0.10	3880	0.29
	4	1710	0.89	785	0.07	2495	0.19
	5	746	0.39	420	0.04	1166	0.09
	6	832	0.43	356	0.03	1188	0.09
	7	412	0.22	227	0.02	639	0.05
	8	509	0.27	208	0.02	717	0.05
	9	402	0.21	183	0.02	585	0.04
	≥10	3993	2.09	1637	0.14	5630	0.42
	全部Total	52,696	27.52	15,835	1.37	68,531	5.09

变异所在区间 Region of variations	错义突变 Missense mutation		同义突变 Synonymous mutation		移码突变 Frame-shift mutation		提前终止突变 Stop-gained mutation		终止子丢失突变 Stop-lost mutation
变异所在区间 Region of variations	数量 Count	%	数量 Count	%	数量 Count	%	数量 Count	%	数量 Count	%
差异区间Different region	2981	51.3	2431	41.9	304	5.2	68	1.2	16	0.3
相似区间Similar region	348	44.7	330	42.4	94	12.1	6	0.8	0	0
全部Total	3329	50.6	2761	42.0	398	6.1	74	1.1	16	0.2

株高基因/QTL Plant height gene/QTL	基因编号 Gene ID	染色体 Chromosome	区间 Region	基因功能注释 Function annotation	序列差异类型 Variation type
Rht-B1	TraesCS4B02G043100	4B	多态性热点区间 Polymorphic hotspot regions	编码DELLA蛋白 DELLA protein-coding gene	无 None
Rht-D1	TraesCS4D02G040400	4D	相似区间 Genetic similar region	编码DELLA蛋白 DELLA protein-coding gene	无 None
Ppd-D1	TraesCS2D02G079600	2D	相似区间 Genetic similar region	编码PRRs蛋白 PRRs protein-coding gene	无 None
QPht/Sl.cau-2D.1	TraesCS2D02G051500	2D	多态性热点区间 Polymorphic hotspot regions	编码WRKY转录因子 WRKY transcription factor protein-coding gene	InDel
QPht/Sl.cau-2D.1	TraesCS2D02G055700	2D	多态性热点区间 Polymorphic hotspot regions	编码GRAS转录因子 GRAS transcription factor protein-coding gene	InDel, SNP

[1]	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.
[2]	胡川, 赵凯男, 黄修利, 吴金芝, 任开明, 王贺正, 付国占, 黄明, 李友军. 一次灌溉下耕作方式和氮肥用量对旱地小麦产量和品质的影响[J]. 作物学报, 2026, 52(6): 1830-1846.
[3]	陈雪燕, 何华川, 李政嘉, 董新盼, 李藕琪, 刘小云, 李丹萍, 陈志伟, 刘国霞, 吕胜源, 吴印莹, 赵振东, 曹新有, 万何平. 水培盐碱复合胁迫下‘济麦60’苗期根系有机酸分泌动态变化及其转录调控机制[J]. 作物学报, 2026, 52(6): 1859-1875.
[4]	高沛阳, 李瑾璇, 董宇奎, 石玉, 张振, 张永丽. 测墒补灌下小麦分蘖发生和成穗对施氮量的响应[J]. 作物学报, 2026, 52(6): 1847-1858.
[5]	张献丰, 郭利建, 李康春, 孔斌雪, 刘玉芳, 车卓, 杨德龙. 小麦ABHD6基因家族鉴定与粒重功能标记开发[J]. 作物学报, 2026, 52(6): 1711-1727.
[6]	翟胜男, 曹新有, 李豪圣, 李吉虎, 李法计, 刘金栋, 夏先春, 吕莹莹, 马瑞峰, 王颖, 耿洪伟, 刘建军. 小麦Pod-A1、Pod-D1和Pod-2D位点等位变异对籽粒过氧化物酶活性的遗传效应分析[J]. 作物学报, 2026, 52(6): 1593-1603.
[7]	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.
[8]	王壮壮, 武紫君, 张永新, 张芯源, 袁丽雪, 陈如雪, 刘世举, 段剑钊, 冯伟, 王同朝, 王永华. 豫东南黏壤潮土区水氮优化协同提高冬小麦产量和氮素利用效率[J]. 作物学报, 2026, 52(5): 1501-1521.
[9]	何万龙, 耿洪伟, 张飞飞, 米克热阿依·阿巴白克热, 罗紫洋, 李鹏程, 周钊宇, 程宇坤. 基于深度学习的小麦重要病害图像识别系统的研究[J]. 作物学报, 2026, 52(5): 1401-1417.
[10]	张振, 冯连杰, 石玉, 于振文, 张永丽. 节水补灌下不同穗型小麦产量形成差异研究[J]. 作物学报, 2026, 52(5): 1522-1535.
[11]	侯思宇, 王国璀, 韦金贵, 谢玮欣, 殷文, 樊志龙, 柴强, 胡发龙. 绿肥配施化学氮肥对西北干旱灌区小麦干物质积累及产量形成的影响[J]. 作物学报, 2026, 52(4): 1208-1219.
[12]	尚云秋, 赵竹, 陈欢, 丁永刚, 乔玉强, 李玮, 张向前, 曹承富, 杜世州. 长期定位耕作方式对雨养小麦籽粒灌浆和产量形成的影响[J]. 作物学报, 2026, 52(4): 1236-1250.
[13]	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.
[14]	乔宇馨, 李程越, 康晓玉, 张鑫琪, 贾绍辉, 刘倩, 曹亚丽, 史鑫蕊, 郝兴宇, 李萍. 基于APSIM模型的长期免耕秸秆覆盖对旱地小麦增产效应研究[J]. 作物学报, 2026, 52(4): 1181-1192.
[15]	李灿, 张喜伟, 朱博涛, 张沛沛. 小麦GSK激酶TaSK41的功能分析及互作蛋白的筛选[J]. 作物学报, 2026, 52(3): 677-687.