小麦粒重相关性状的QTL定位及分子标记的开发

doi:10.3864/j.issn.0578-1752.2023.21.001

中国农业科学 ›› 2023, Vol. 56 ›› Issue (21): 4137-4149.doi: 10.3864/j.issn.0578-1752.2023.21.001

• 作物遗传育种·种质资源·分子遗传学 • 上一篇下一篇

小麦粒重相关性状的QTL定位及分子标记的开发

张泽源¹(), 李玥², 赵文莎¹, 顾晶晶³, 张傲琰¹, 张海龙¹, 宋鹏博¹, 吴建辉¹, 张传量¹, 宋全昊⁴, 简俊涛⁵, 孙道杰¹(), 王兴荣²()

¹ 西北农林科技大学农学院，陕西杨凌 712100
² 甘肃省农业科学院作物研究所，兰州 730000
³ 洛阳市农林科学院，河南洛阳 471023
⁴ 驻马店市农业科学院，河南驻马店 463000
⁵ 南阳市农业科学院，河南南阳 473000

收稿日期:2023-03-28 接受日期:2023-04-20 出版日期:2023-11-01 发布日期:2023-11-06
通信作者:
孙道杰，E-mail：sunwheat@nwsuaf.edu.cn。
王兴荣，E-mail：wxr_0618@163.com
联系方式: 张泽源，E-mail：18238768351@163.com。
基金资助:
陕西省“两链”融合重点专项(2023KXJ-011)

QTL Mapping and Molecular Marker Development of Traits Related to Grain Weight in Wheat

ZHANG ZeYuan¹(), LI Yue², ZHAO WenSha¹, GU JingJing³, ZHANG AoYan¹, ZHANG HaiLong¹, SONG PengBo¹, WU JianHui¹, ZHANG ChuanLiang¹, SONG QuanHao⁴, JIAN JunTao⁵, SUN DaoJie¹(), WANG XingRong²()

¹ College of Agronomy, Northwest A&F University, Yangling 712100, Shaanxi
² Institute of Crop Science, Gansu Academy of Agricultural Sciences, Lanzhou 730000
³ Luoyang Academy of Agriculture and Forestry Sciences, Luoyang 471023, Henan
⁴ Zhumadian Academy of Agricultural Sciences, Zhumadian 463000, Henan
⁵ Nanyang Academy of Agricultural Sciences, Nanyang 473000, Henan

Received:2023-03-28 Accepted:2023-04-20 Published:2023-11-01 Online:2023-11-06

摘要/Abstract

摘要：

【目的】小麦是世界总产量第二的粮食作物，而粒重是影响小麦产量的重要因素。以和尚头（HST）和陇春23（LC23）衍生的216个家系重组自交系（recombinant inbred lines，RIL）群体为材料，基于55K SNP基因型数据，针对小麦粒重相关性状进行QTL定位，开发和验证粒长主效QTL的共分离标记，为分子标记辅助选择育种提供参考。【方法】利用小麦55K SNP芯片对亲本和RIL群体进行基因分型，构建高密度遗传连锁图谱，并与中国春参考基因组IWGSC RefSeq v1.0进行相关性分析。基于完备区间作图法对多环境粒重相关性状进行QTL定位；通过对主效QTL进行方差分析，判断不同QTL间的加性互作效应，并分析其对粒重相关性状的影响。同时，根据粒长主效QTL的共分离SNP位点开发相应的竞争性等位基因特异性PCR标记（kompetitive allele specific PCR，KASP），并在242份国内外小麦种质构成的自然群体中进行验证。【结果】构建了和尚头/陇春23 RIL群体的高密度遗传图谱，全长4 543 cM，共包含22个连锁群，覆盖小麦21条染色体，平均遗传距离为1.7 cM。遗传图谱与物理图谱具有显著相关性，Pearson相关系数为0.77—0.99（P<0.001）。共检测到51个粒重相关QTL，其中，有4个为3个及以上环境稳定表达的主效QTL，分布在2D、5A、6B和7D染色体。根据物理区间和功能标记分析主效QTL Qtkw.nwafu-2D.1和Qtkw.nwafu-7D分别为光周期基因Ppd-D1和开花基因FT-D1，方差分析表明，二者具有显著的互作效应；Qtkw.nwafu-2D.1和Qtkw.nwafu-7D优异等位基因的聚合显著提高了小麦的千粒重和粒宽。此外，根据粒长主效位点Qgl.nwafu-5A的共分离SNP开发了相应的KASP分子标记AX-111067709，该标记在242份小麦组成的自然群体中与粒长和粒重性状显著相关，在不同环境下能增加粒长3.33%—4.59%（P<0.001）和粒重5.70%—10.35%（P<0.05）。【结论】和尚头（HST）和陇春23（LC23）的粒重相关性状由多个遗传位点控制，其中，Qtkw.nwafu-2D.1和Qtkw.nwafu-7D通过加性互作效应可显著提高小麦的千粒重和粒宽。Qgl.nwafu-5A与粒重和粒长具有显著相关性，其共分离分子标记AX-11106770可应用于分子标记辅助选择育种。

关键词: 小麦, 千粒重, QTL, KASP标记, 分子标记辅助选择育种

Abstract:

【Objective】The yield of wheat, the second-highest-yielding food product in the world, has a major impact by grain weight. This research used materials from a recombinant inbred line (RIL) population derived from Heshangtou (HST) and Longchun 23 (LC23). Based on 55K SNP genotype data, QTL mapping was performed for traits related to grain weight of wheat, and co-segregation markers of major grain length QTL were developed and verified to provide reference for molecular marker assisted selection breeding.【Method】The wheat 55K SNP microarray was used to genotype parents and RIL populations, and a high density genetic linkage map was constructed, and its correlation with Chinese spring reference genome IWGSC RefSeq v1.0 was analyzed. QTL mapping of traits related to grain weight in multiple environments based on inclusive composite interval mapping method. The analysis of variance of major effect QTLs were performed to judge the additive interaction effect among different QTLs, and to analyse its effect on traits related to grain weight. At the same time, the corresponding kompetitive allele specific PCR marker was developed according to the closely linked SNP loci of major QTL for grain length, and verified in 242 wheat accessions worldwide.【Result】In this study, a high density genetic map of Heshangtou/Longchun 23 RIL population was constructed, with full length 4 543 cM, including 22 linkage groups, covering 21 chromosomes of wheat, and the average genetic distance was 1.7 cM. There was a significant correlation between genetic map and physical map, and the Pearson correlation coefficient were 0.77-0.99 (P<0.001). A total of 51 QTLs related to grain weight were detected, among them, 4 stable major QTLs were found in multi-environments (three or more environments) and distributed on 2D, 5A, 6B and 7D chromosomes. According to the physical interval and functional markers, it is inferred that stable major QTLs Qtkw.nwafu-2D.1 and Qtkw.nwafu-7D are photoperiod gene Ppd-D1 and flowering gene FT-D1, respectively. The analysis of variance shows that there is a significant interaction between them. The favorite alleles polymerization of Qtkw.nwafu-2D.1 and Qtkw.nwafu-7D can significantly increase thousand grain weight and grain width of wheat. In addition, the corresponding KASP molecular detection marker AX-111067709 was developed based on the co-segregated SNP of the major locus Qgl.nwafu-5A for grain length, which was significantly correlated with grain length and grain weight traits in a diversity panel comprising of 242 wheat accessions, and could increase grain length by 3.33% to 4.59% and grain weight 5.70% to 10.35% in different environments (P<0.001).【Conclusion】There are several genetic loci that affect traits linked to grain weight in Heshangtou (HST) and Longchun 23 (LC23), and Qtkw.nwafu-2D.1 and Qtkw.nwafu-7D dramatically increased thousand grain weight and grain width through additive interaction effects. Qgl.nwafu-5A is significantly correlated with grain weight and grain length, and its co-segregated molecular marker AX-11106770 can be used in molecular marker assisted selection breeding.

Key words: wheat, thousand-grain weight, QTL, KASP marker, molecular marker-assisted selection breeding

张泽源, 李玥, 赵文莎, 顾晶晶, 张傲琰, 张海龙, 宋鹏博, 吴建辉, 张传量, 宋全昊, 简俊涛, 孙道杰, 王兴荣. 小麦粒重相关性状的QTL定位及分子标记的开发[J]. 中国农业科学, 2023, 56(21): 4137-4149.

ZHANG ZeYuan, LI Yue, ZHAO WenSha, GU JingJing, ZHANG AoYan, ZHANG HaiLong, SONG PengBo, WU JianHui, ZHANG ChuanLiang, SONG QuanHao, JIAN JunTao, SUN DaoJie, WANG XingRong. QTL Mapping and Molecular Marker Development of Traits Related to Grain Weight in Wheat[J]. Scientia Agricultura Sinica, 2023, 56(21): 4137-4149.

0
/ / 推荐

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

链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2023.21.001

https://www.chinaagrisci.com/CN/Y2023/V56/I21/4137

图/表 9

表1

图1

表2

图2

表3

粒重相关性状的部分QTL"

性状 Trait	QTL	环境 Environment	左侧标记 Left marker	右侧标记 Right marker	物理位置 Physical interval (Mb)	LOD	贡献率 PVE (%)	加性效应¹⁾ Add
千粒重TKW	Qtkw.nwafu-2A	E1\E2	AX-169336957	AX-109601471	94.16—136.56	3.47—7.93	5.22—7.42	1.45—2.12
	Qtkw.nwafu-2D.1	E1\E2\E4\BLUP	AX-111696354	AX-109755068	28.09—47.89	4.33—11.89	7.19—12.92	-2.44—-1.42
	Qtkw.nwafu-2D.2	E4\BLUP	AX-110373068	AX-109710757	148.29—197.50	3.58—4.16	5.69—5.80	-1.75—-1.01
	Qtkw.nwafu-7D	E1\E2\E3\E4\BLUP	AX-110826147	AX-111618969	65.50—75.23	4.63—10.71	7.53—13.26	1.55—2.46
粒长 GL	Qgl.nwafu-2A.1	E2\BLUP	AX-109402024	AX-169336957	86.97—94.16	3.88—6.74	3.74—8.05	0.06—0.09
	Qgl.nwafu-4B	E3\BLUP	AX-110076607	AX-109852046	11.43—12.82	4.03—5.6	6.14—8.06	0.08—0.28
	Qgl.nwafu-5A	E1\E2\E3\BLUP	AX-111067709	AX-110670888	0.65—0.75	3.93—59.43	4.56—17.86	0.09—0.81
	Qgl.nwafu-6B	E2\BLUP	AX-110927266	AX-110464369	135.98—173.59	5.06—7.10	5.10—8.78	-0.10—-0.07
粒宽 GW	Qgw.nwafu-2D.1	E2\E3\BLUP	AX-111096297	AX-109755068	32.97—47.89	4.41—10.14	7.28—12.36	-0.12—-0.04
	Qgw.nwafu-4B	E1\BLUP	AX-110076607	AX-94699353	11.43—19.76	3.93—4.10	5.50—7.91	0.03—0.10
	Qgw.nwafu-5A.1	E3\BLUP	AX-111067709	AX-110670888	0.65—0.75	4.06—5.17	5.97—6.57	0.03—0.12
	Qgw.nwafu-7D.2	E2\E4\BLUP	AX-111843581	AX-111618969	67.45—75.23	4.76—16.00	7.85—20.13	0.05—0.10
籽粒长宽比LWR	Qlwr.nwafu-2A	E3\E4\BLUP	AX-109287352	AX-111258161	613.88—626.08	4.54—7.01	5.41—11.90	0.02—0.05
	Qlwr.nwafu-2D	E1\E2\BLUP	AX-109422526	AX-109755068	35.02—47.89	8.32—12.93	7.68—14.51	0.03—0.06
	Qlwr.nwafu-4A	E1\E2\BLUP	AX-110629864	AX-111561234	72.00—129.02	5.53—9.57	5.75—8.11	-0.04—-0.03
	Qlwr.nwafu-4D	E3\BLUP	AX-110015970	AX-109181699	33.04—35.23	3.30—4.90	2.54—8.16	-0.04—-0.02
	Qlwr.nwafu-6B.2	E1\E2\E3\BLUP	AX-110603992	AX-95659567	455.92—559.34	4.40—9.13	4.87—10.48	-0.04—-0.03
	Qlwr.nwafu-7D	E1\E2\BLUP	AX-109866327	AX-111618969	61.80—75.23	4.12—12.57	4.67—12.16	-0.05—-0.03

表3

图3

表4

图4

图5

参考文献 41

[1]	SHEWRY P R, HEY S J. The contribution of wheat to human diet and health. Food and Energy Security, 2015, 4(3): 178-202. doi: 10.1002/fes3.64 pmid: 27610232
[2]	LI S D, WANG L, MENG Y N, HAO Y F, XU H X, HAO M, LAN S Q, ZHANG Y J, LV L J, ZHANG K, PENG X H, LAN C X, LI X P, ZHANG Y L. Dissection of genetic basis underpinning kernel weight-related traits in common wheat. Plants (Basel, Switzerland), 2021, 10(4): 713.
[3]	SHIFERAW B, SMALE M, BRAUN H J, DUVEILLER E, REYNOLDS M, MURICHO G. Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Security, 2013, 5(3): 291-317. doi: 10.1007/s12571-013-0263-y
[4]	HU J M, WANG X Q, ZHANG G X, JIANG P, CHEN W Y, HAO Y C, MA X, XU S S, JIA J Z, KONG L R, WANG H W. QTL mapping for yield-related traits in wheat based on four RIL populations. Theoretical and Applied Genetics, 2020, 133(3): 917-933. doi: 10.1007/s00122-019-03515-w pmid: 31897512
[5]	ZHANG J J, SHE M Y, YANG R C, JIANG Y J, QIN Y B, ZHAI S N, BALOTF S, ZHAO Y, ANWAR M, ALHABBAR Z, JUHÁSZ A, CHEN J S, LIU H, LIU Q E, ZHENG T, YANG F, RONG J K, CHEN K F, LU M Q, ISLAM S, MA W Y. Yield-related QTL clusters and the potential candidate genes in two wheat DH populations. International Journal of Molecular Sciences, 2021, 22(21): 11934. doi: 10.3390/ijms222111934
[6]	KUMAR A, MANTOVANI E E, SEETAN R, SOLTANI A, ECHEVERRY-SOLARTE M, JAIN S, SIMSEK S, DOEHLERT D, ALAMRI M S, ELIAS E M, KIANIAN S F, MERGOUM M. Dissection of genetic factors underlying wheat kernel shape and size in an Elite×Nonadapted cross using a high density SNP linkage map. Plant Genome, 2016, 9(1): 55.
[7]	CAO J J, SHANG Y Y, XU D M, XU K L, CHENG X R, PAN X, LIU X, LIU M L, GAO C, YAN S N, YAO H, GAO W, LU J, ZHANG H P, CHANG C, XIA X C, XIAO S H, MA C X. Identification and validation of new stable QTLs for grain weight and size by multiple mapping models in common wheat. Frontiers in Genetics, 2020, 11: 584859. doi: 10.3389/fgene.2020.584859
[8]	LIU H, ZHANG X T, XU Y F, MA F F, ZHANG J P, CAO Y W, LI L H, AN D G. Identification and validation of quantitative trait loci for kernel traits in common wheat (Triticum aestivum L.). BMC Plant Biology, 2020, 20(1): 529. doi: 10.1186/s12870-020-02661-4
[9]	PANG Y L, LIU C X, WANG D F, ST AMAND P, BERNARDO A, LI W H, HE F, LI L Z, WANG L M, YUAN X F, DONG L, SU Y, ZHANG H R, ZHAO M, LIANG Y L, JIA H Z, SHEN X T, LU Y, JIANG H M, WU Y Y, LIU S B. High-resolution genome-wide association study identifies genomic regions and candidate genes for important agronomic traits in wheat. Molecular Plant, 2020, 13(9): 1311-1327. doi: S1674-2052(20)30221-5 pmid: 32702458
[10]	XIN F, ZHU T, WEI S, HAN Y, ZHAO Y, ZHANG D, MA L, DING Q. QTL mapping of kernel traits and validation of a major QTL for kernel length-width ratio using SNP and bulked segregant analysis in wheat. Scientific Reports, 2020, 10(1): 25. doi: 10.1038/s41598-019-56979-7 pmid: 31913328
[11]	MA J, ZHANG H, LI S Q, ZOU Y Y, LI T, LIU J J, DING P Y, MU Y, TANG H P, DENG M, LIU Y X, JIANG Q T, CHEN G Y, KANG H Y, LI W, PU Z E, WEI Y M, ZHENG Y L, LAN X J. Identification of quantitative trait loci for kernel traits in a wheat cultivar Chuannong16. BMC Genetics, 2019, 20(1): 77. doi: 10.1186/s12863-019-0782-4 pmid: 31619163
[12]	QU X R, LI C, LIU H, LIU J J, LUO W, XU Q, TANG H P, MU Y, DENG M, PU Z E, MA J, JIANG Q T, CHEN G Y, QI P F, JIANG Y F, WEI Y M, ZHENG Y L, LAN X J, MA J. Quick mapping and characterization of a co-located kernel length and thousand-kernel weight-related QTL in wheat. Theoretical and Applied Genetics, 2022, 135(8): 2849-2860. doi: 10.1007/s00122-022-04154-4 pmid: 35804167
[13]	YANG Y, AMO A, WEI D, CHAI Y M, ZHENG J, QIAO P F, CUI C G, LU S, CHEN L, HU Y G. Large-scale integration of meta-QTL and genome-wide association study discovers the genomic regions and candidate genes for yield and yield-related traits in bread wheat. Theoretical and Applied Genetics, 2021, 134(9): 3083-3109. doi: 10.1007/s00122-021-03881-4 pmid: 34142166
[14]	张香宇. 小麦RHL32籽粒发育相关基因克隆及其与TaRPP13L1多效性功能初析[D]. 杨凌: 西北农林科技大学, 2022.
	ZHANG X Y. Cloning of gene related to grain development in wheat RHL32 and pleiotropic function dissection of the candiated genes and TaRPP13L1[D]. Yangling: Northwestern Agriculture and Foresty University, 2022. (in Chinese)
[15]	YANG F P, LIU G Y, WU Z Y, ZHANG D X, ZHANG Y F, YOU M S, LI B Y, ZHANG X H, LIANG R Q. Cloning and functional analysis of TaWRI1Ls, the key genes for grain fatty acid synthesis in bread wheat. International Journal of Molecular Sciences, 2022, 23(10): 5293. doi: 10.3390/ijms23105293
[16]	朱雪成. 春化和光周期基因在江苏小麦品种中的分布及其对农艺性状的效应分析[D]. 扬州: 扬州大学, 2019.
	ZHU X C. Distribution of vernalization and photoperiod genes in Jiangsu wheat cultivars and their effects on agronomic traits[D]. Yangzhou: Yangzhou University, 2019. (in Chinese)
[17]	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.). Theoretical and Applied Genetics, 2007, 115(5): 721-733. doi: 10.1007/s00122-007-0603-4
[18]	NISHIDA H, YOSHIDA T, KAWAKAMI K, FUJITA M, LONG B, AKASHI Y, LAURIE D A, KATO K. Structural variation in the 5′ upstream region of photoperiod-insensitive alleles Ppd-A1a and Ppd-B1a identified in hexaploid wheat (Triticum aestivum L.), and their effect on heading time. Molecular Breeding, 2013, 31(1): 27-37. doi: 10.1007/s11032-012-9765-0
[19]	PUGSLEY A T. A genetic analysis of the spring-winter habit of growth in wheat. Australian Journal of Agricultural Research, 1971, 22(1): 21. doi: 10.1071/AR9710021
[20]	YAN L, FU D, LI C, BLECHL A, TRANQUILLI G, BONAFEDE M, SANCHEZ A, VALARIK M, YASUDA S, DUBCOVSKY J. The wheat and barley vernalization gene VRN3 is an orthologue of FT. Proceedings of the National Academy of Sciences of the United States of America, 2006, 103(51): 19581-19586.
[21]	SHIMADA S, OGAWA T, KITAGAWA S, SUZUKI T, IKARI C, SHITSUKAWA N, ABE T, KAWAHIGASHI H, KIKUCHI R, HANDA H, MURAI K. A genetic network of flowering-time genes in wheat leaves, in which an APETALA1/FRUITFULL-like gene, VRN1, is upstream of FLOWERING LOCUS T. The Plant Journal, 2009, 58(4): 668-681. doi: 10.1111/j.1365-313X.2009.03806.x pmid: 19175767
[22]	MORENO-SEVILLA B, BAENZIGER P S, PETERSON C J, GRAYBOSCH R A, MCVEY D V. The 1BL/1RS translocation: Agronomic performance of F₃-derived lines from a winter wheat cross. Crop Science, 1995, 35(4): 1051-1055. doi: 10.2135/cropsci1995.0011183X003500040022x
[23]	王兴荣, 张彦军, 苟作旺, 李玥, 陈伟英, 祁旭升. 甘肃“和尚头”小麦调查报告. 甘肃农业科技, 2015(12): 49-52.
	WANG X R, ZHANG Y J, GOU Z W, LI Y, CHEN W Y, QI X S. Investigation report of wheat Gansu “Heshangtou”. Gansu Agricultural Science and Technology, 2015(12): 49-52. (in Chinese)
[24]	袁俊秀, 杨文雄. 丰产广适优质春小麦新品种——陇春23号. 麦类作物学报, 2009, 29(4): 740.
	YUAN J X, YANG W X. Longchun 23, a new spring wheat variety with high yield, wide adaptability and high quality. Journal of Triticeae Crops, 2009, 29(4): 740. (in Chinese)
[25]	王建康, 盖钧镒. 利用杂种F₂世代鉴定数量性状主基因-多基因混合遗传模型并估计其遗传效应. 遗传学报, 1997, 24(5): 432-440.
	WANG J K, GAI J Y. Identification of major gene and polygene mixed inheritance model and estimation of genetic parameters of a quantitative trait from F₂ progeny. Acta Genetica Sinica, 1997, 24(5): 432-440. (in Chinese)
[26]	ALLEN P, BENNETT K. SPSS statistics version 22: A practical guide. 2014.
[27]	MENG L, LI H H, ZHANG Y L, WANG J K. QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. The Crop Journal, 2015, 3(3): 269-283. doi: 10.1016/j.cj.2015.01.001
[28]	OOIJEN J, VAN'T VERLAAT J W, TOL J, DALÉN J, BUREN J, MEER J M, KRIEKEN J V, KESSEL J, VAN O, VOORRIPS R, HEUVEL L. JoinMap®4, Software for the calculation of genetic linkage maps in experimental populations. Biology, 2006.
[29]	VOORRIPS R E. MapChart: Software for the graphical presentation of linkage maps and QTLs. Journal of Heredity, 2002, 93(1): 77-78. doi: 10.1093/jhered/93.1.77 pmid: 12011185
[30]	ZHAO G Y, ZOU C, LI K, WANG K, LI T B, GAO L F, ZHANG X X, WANG H J, YANG Z J, LIU X, JIANG W K, MAO L, KONG X Y, JIAO Y N, JIA J Z. The Aegilops tauschii genome reveals multiple impacts of transposons. Nature Plants, 2017, 3(12): 946-955. doi: 10.1038/s41477-017-0067-8
[31]	YU M, MAO S L, HOU D B, CHEN G Y, PU Z E, LI W, LAN X J, JIANG Q T, LIU Y X, DENG M, WEI Y M. Analysis of contributors to grain yield in wheat at the individual quantitative trait locus level. Plant Breeding, 2018, 137(1): 35-49. doi: 10.1111/pbr.2018.137.issue-1
[32]	RÖDER M S, HUANG X Q, BÖRNER A. Fine mapping of the region on wheat chromosome 7D controlling grain weight. Functional & Integrative Genomics, 2008, 8(1): 79-86.
[33]	HUANG X Q, CÖSTER H, GANAL M W, RÖDER M S. Advanced backcross QTL analysis for the identification of quantitative trait loci alleles from wild relatives of wheat (Triticum aestivum L.). Theoretical and Applied Genetics, 2003, 106(8): 1379-1389. doi: 10.1007/s00122-002-1179-7
[34]	MIR R R, KUMAR N, JAISWAL V, GIRDHARWAL N, PRASAD M, BALYAN H S, GUPTA P K. Genetic dissection of grain weight in bread wheat through quantitative trait locus interval and association mapping. Molecular Breeding, 2012, 29(4): 963-972. doi: 10.1007/s11032-011-9693-4
[35]	CHEN Z Y, CHENG X J, CHAI L L, WANG Z H, BIAN R L, LI J, ZHAO A J, XIN M M, GUO W L, HU Z R, PENG H R, YAO Y Y, SUN Q X, NI Z F. Dissection of genetic factors underlying grain size and fine mapping of QTgw.cau-7D in common wheat (Triticum aestivum L.). Theoretical and Applied Genetics, 2020, 133(1): 149-162. doi: 10.1007/s00122-019-03447-5
[36]	LIU H Y, SONG S, XING Y Z. Beyond heading time: FT-like genes and spike development in cereals. Journal of Experimental Botany, 2019, 70(1): 1-3. doi: 10.1093/jxb/ery408 pmid: 30590673
[37]	韩领锋, 亢玲, 张博, 王宪国, 王中华, 张晓科, 陈东升. 小麦光周期基因在我国不同麦区中的组成分布. 麦类作物学报, 2016, 36(12): 1617-1622.
	HAN L F, KANG L, ZHANG B, WANG X G, WANG Z H, ZHANG X K, CHEN D S. Composition and distribution of wheat photoperiod genes in different wheat regions in China. Journal of Triticeae Crops, 2016, 36(12): 1617-1622. (in Chinese)
[38]	MOCKLER T, YANG H Y, YU X H, PARIKH D, CHENG Y C, DOLAN S, LIN C. Regulation of photoperiodic flowering by Arabidopsis photoreceptors. Proceedings of the National Academy of Sciences of the United States of America, 2003, 100(4): 2140-2145.
[39]	WEI J, FANG Y, JIANG H, WU X T, ZUO J H, XIA X C, LI J Q, STICH B, CAO H, LIU Y X. Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat. BMC Plant Biology, 2022, 22(1): 288. doi: 10.1186/s12870-022-03677-8 pmid: 35698038
[40]	吕波. 植物开花基因FT的遗传转化及其参与开花调控的研究[D]. 泰安: 山东农业大学, 2014.
	LÜ B. Genetic transformation of plant flowering gene FT and its involvement in flowering control[D]. Taian: Shandong Agricultural University, 2014. (in Chinese)
[41]	LI T, DENG G B, SU Y, YANG Z, TANG Y Y, WANG J H, ZHANG J Y, QIU X, PU X, YANG W Y, LI J, LIU Z H, ZHANG H L, LIANG J J, YU M Q, WEI Y M, LONG H. Genetic dissection of quantitative trait loci for grain size and weight by high-resolution genetic mapping in bread wheat (Triticum aestivum L.). Theoretical and Applied Genetics, 2022, 135(1): 257-271. doi: 10.1007/s00122-021-03964-2

性状 Traits	环境 Environment	亲本Parents		平均值±标准差 Mean ± SD	最小值 Min	最大值 Max	w检验 w-test	P	遗传力 H²
性状 Traits	环境 Environment	和尚头HST	陇春LC23	平均值±标准差 Mean ± SD	最小值 Min	最大值 Max	w检验 w-test	P	遗传力 H²
千粒重 TKW (g)	E1	46.77	39.56**	43.22±6.74	23.85	59.74	0.97	0.24	0.81
	E2	45.81	39.00**	41.64±5.85	24.09	54.73	0.97	0.07
	E3	48.03	39.32**	43.94±5.53	26.32	57.71	0.97	0.07
	E4	47.93	46.82	42.02±6.87	23.36	60.69	0.98	0.75
	BLUP	46.33	41.47**	42.75±4.04	30.53	52.94	0.98	0.32
粒长 GL (mm)	E1	7.26	6.69**	6.99±0.58	4.95	8.92	0.99	0.98	0.58
	E2	6.53	6.10**	6.36±0.28	5.52	7.19	0.98	0.63
	E3	7.47	7.09**	8.03±0.93	5.59	10.29	0.96	0.00
	E4	6.41	6.02**	6.20±0.56	4.38	7.39	0.84	0.00
	BLUP	6.91	6.65 **	6.91±0.24	6.29	7.71	0.98	0.72
粒宽 GW (mm)	E1	3.31	3.07	3.15±0.31	2.10	3.99	0.98	0.63	0.56
	E2	3.01	2.88	2.95±0.15	2.41	3.34	0.96	0.01
	E3	3.64	3.42*	3.79±0.42	2.46	4.87	0.96	0.00
	E4	3.22	3.21	3.01±0.30	1.97	3.53	0.85	0.00
	BLUP	3.27	3.18	3.23±0.11	2.85	3.55	0.97	0.29
籽粒长宽比 LWR	E1	2.19	2.18	2.22±0.15	1.88	2.98	0.96	0.00	0.84
	E2	2.17	2.12*	2.16±0.13	1.89	2.53	0.96	0.00
	E3	2.05	2.07	2.12±0.13	1.77	2.61	0.98	0.52
	E4	1.99	1.88**	2.06±0.14	1.72	2.69	0.97	0.31
	BLUP	2.11	2.08**	2.14±0.09	1.88	2.47	0.98	0.63

染色体 Chromosome	连锁群 Linkage group	遗传长度 Genetic length (cM)	Bin标记数量 No. of Bin	标记数量 No. of marker	Bin标记密度 Bin density (Bin/cM)	最大遗传距离 Max genetic distance (cM)
1A	LG1A	165.15	161	1059	1.03	9.21
1B	LG1B	198.34	153	1388	1.30	14.02
1D	LG1D	133.99	46	377	2.91	19.57
2A	LD2A	211.24	140	937	1.51	15.13
2B	LG2B	237.42	175	1295	1.36	11.76
2D	LG2D	286.16	109	464	2.63	20.80
3A	LG3A	259.25	174	1008	1.49	14.16
3B	LG3B	249.84	165	1331	1.51	15.08
3D	LG3D	251.05	94	638	2.67	18.11
4A	LG4A	209.20	108	641	1.94	11.16
4B	LG4B	122.92	69	256	1.78	13.47
4D	LG4D	180.03	112	442	1.61	18.11
5A	LG5A	247.18	174	816	1.42	22.91
5B	LG5B	247.07	172	760	1.44	10.86
5D	LG5D	305.92	91	293	3.36	17.21
6A	LG6A	183.40	101	727	1.82	17.92
6B	LG6B	160.84	138	953	1.17	17.20
6D	LG6D	215.15	90	713	2.39	31.82
7A	LG7A.1	217.90	148	1104	1.47	10.74
7A	LG7A.2	15.94	19	82	0.84	3.30
7B	LG7B	206.74	151	901	1.37	14.96
7D	LG7D	238.28	82	344	2.91	15.93
A基因组 A genome		1509.26	1025	6374	1.47	22.91
B基因组 B genome		1423.17	1023	6884	1.39	17.20
D基因组 D genome		1610.57	624	3271	2.58	31.82
总计 Total		4543.00	2672	16529	1.70	31.82

源Source	自由度df	千粒重TKW	粒宽GW	粒长GL	籽粒长宽比LWR
Qtkw.nwafu-2D.1 (2D)	1	76.83***	39.88***	3.80	61.42***
Qtkw.nwafu-7D (7D)	1	71.32***	44.50***	1.62	96.21***
环境Environment (E)	4	2.27	195.79***	205.2***	35.32***
2D×7D	1	26.75***	11.51**	0.33	26.98***
2D×E	4	1.74	4.59**	4.21**	1.41
7D×E	4	0.73	0.74	0.06	1.52
2D×7D×E	4	0.817	1.81	1.10	0.86
误差Error	813	30.21	0.08	0.35	0.01

小麦粒重相关性状的QTL定位及分子标记的开发

QTL Mapping and Molecular Marker Development of Traits Related to Grain Weight in Wheat

RichHTML

PDF

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 41

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	彭海霞, 卡得艳, 张天星, 周梦蝶, 吴林楠, 辛转霞, 赵惠贤, 马猛. 过量表达小麦TaCYP78A5增加花器官的大小[J]. 中国农业科学, 2023, 56(9): 1633-1645.
[2]	魏永康, 杨天聪, 臧少龙, 贺利, 段剑钊, 谢迎新, 王晨阳, 冯伟. 基于无人机多光谱影像特征融合的小麦倒伏监测[J]. 中国农业科学, 2023, 56(9): 1670-1685.
[3]	张旭, 韩金妤, 李晨晨, 张丹丹, 吴启蒙, 刘胜杰, 焦韩轩, 黄硕, 李春莲, 王长发, 曾庆东, 康振生, 韩德俊, 吴建辉. 结合基因关联和转录组分析鉴定小麦成株期抗条锈病位点YrZ501-2BL的候选基因[J]. 中国农业科学, 2023, 56(8): 1429-1443.
[4]	韩紫璇, 房静静, 武雪萍, 姜宇, 宋霄君, 刘晓彤. 长期秸秆配施化肥下土壤团聚体碳氮分布、微生物量与小麦产量的协同效应[J]. 中国农业科学, 2023, 56(8): 1503-1514.
[5]	马胜兰, 况福虹, 林洪羽, 崔俊芳, 唐家良, 朱波, 蒲全波. 秸秆还田量对川中丘陵冬小麦-夏玉米轮作体系土壤物理特性的影响[J]. 中国农业科学, 2023, 56(7): 1344-1358.
[6]	李儒香, 周恺, 王大川, 李巧龙, 向奥妮, 李璐, 李苗苗, 向思茜, 凌英华, 何光华, 赵芳明. 水稻CSSL-Z481代换片段携带的穗部性状QTL分析及次级代换系培育[J]. 中国农业科学, 2023, 56(7): 1228-1247.
[7]	南瑞, 杨玉存, 石芳慧, 张礼宁, 米彤茜, 张立强, 李春艳, 孙风丽, 奚亚军, 张超. 小麦源库优异种质的鉴定与源库类型的划分[J]. 中国农业科学, 2023, 56(6): 1019-1034.
[8]	常春义, 曹元, Ghulam Mustafa, 刘红艳, 张羽, 汤亮, 刘兵, 朱艳, 姚霞, 曹卫星, 刘蕾蕾. 白粉病对小麦光合特性的影响及病害严重度的定量模拟[J]. 中国农业科学, 2023, 56(6): 1061-1073.
[9]	王箫璇, 张敏, 张鑫尧, 魏鹏, 柴如山, 张朝春, 张亮亮, 罗来超, 郜红建. 不同磷肥对砂姜黑土和红壤磷库转化及冬小麦磷素吸收利用的影响[J]. 中国农业科学, 2023, 56(6): 1113-1126.
[10]	郑文燕, 常源升, 何平, 何晓文, 王森, 高文胜, 李林光, 王海波. ‘鲁丽’×‘红1#’苹果杂交群体全基因组KASP标记开发及验证[J]. 中国农业科学, 2023, 56(5): 935-950.
[11]	王建锋, 成嘉欣, 舒伟学, 张艳茹, 王晓杰, 康振生, 汤春蕾. 小麦条锈菌效应蛋白Hasp83在条锈菌致病性中的功能分析[J]. 中国农业科学, 2023, 56(5): 866-878.
[12]	郭燕, 井宇航, 王来刚, 黄竞毅, 贺佳, 冯伟, 郑国清. 基于无人机影像特征的冬小麦植株氮含量预测及模型迁移能力分析[J]. 中国农业科学, 2023, 56(5): 850-865.
[13]	樊志龙, 胡发龙, 殷文, 范虹, 赵财, 于爱忠, 柴强. 干旱灌区春小麦水分利用特征对绿肥与麦秸协同还田的响应[J]. 中国农业科学, 2023, 56(5): 838-849.
[14]	王脉, 董清峰, 高珅奥, 刘德政, 卢山, 乔朋放, 陈亮, 胡银岗. 小麦苗期根系性状的全基因组关联分析与优异位点挖掘[J]. 中国农业科学, 2023, 56(5): 801-820.
[15]	贾晓昀, 王士杰, 朱继杰, 赵红霞, 李妙, 王国印. 陆地棉高密度遗传图谱的构建及产量相关性状的QTL定位[J]. 中国农业科学, 2023, 56(4): 587-598.