基于SNP标记的小麦籽粒性状全基因组关联分析

doi:10.3864/j.issn.0578-1752.2021.10.002

中国农业科学 ›› 2021, Vol. 54 ›› Issue (10): 2053-2063.doi: 10.3864/j.issn.0578-1752.2021.10.002

• 作物遗传育种·种质资源·分子遗传学 • 上一篇下一篇

基于SNP标记的小麦籽粒性状全基因组关联分析

张芳¹(),任毅¹,曹俊梅²,李法计³,夏先春⁴,耿洪伟¹()

¹新疆农业大学农学院/农业生物技术重点实验室,乌鲁木齐 830052
²新疆农业科学院粮食作物研究所,乌鲁木齐 830091
³山东省农业科学院作物研究所/小麦玉米国家工程实验室/农业部黄淮北部小麦生物学与遗传育种重点实验室,济南 250100
⁴中国农业科学院作物科学研究所/国家小麦改良中心,北京 100081

收稿日期:2020-10-28 接受日期:2020-12-02 出版日期:2021-05-16 发布日期:2021-05-24
通讯作者: 耿洪伟
作者简介:张芳,E-mail: 1807681776@qq.com。
基金资助:
新疆自治区天山雪松计划(科技创新领军人才后备人选)(2018XS04);新疆公益性科研院所基本科研业务经费资助项目(KY2019009)

Genome-wide Association Analysis of Wheat Grain Size Related Traits Based on SNP Markers

ZHANG Fang¹(),REN Yi¹,CAO JunMei²,LI FaJi³,XIA XianChun⁴,GENG HongWei¹()

¹College of Agriculture, Xinjiang Agricultural University/Key Laboratory of Agricultural Biological Technology, Urumqi 830052
²Institute of Grain Crops, Xinjiang Academy of Agricultural Sciences, Urumqi 830091
³Crop Research Institute, Shandong Academy of Agricultural Sciences/National Engineering Laboratory for Wheat and Maize/Key Laboratory of Wheat Biology and Genetic Improvement in North Huang-Huai River Valley, Ministry of Agriculture, Jinan 250100
⁴Institute of Crop Science, Chinese Academy of Agricultural Sciences/National Wheat Improvement Center, Beijing 100081

Received:2020-10-28 Accepted:2020-12-02 Online:2021-05-16 Published:2021-05-24
Contact: HongWei GENG

摘要/Abstract

摘要：

【目的】籽粒性状是影响小麦产量的重要因素,通过对小麦籽粒性状进行全基因组关联分析,发掘控制小麦籽粒性状显著位点,为小麦籽粒性状的遗传改良研究提供理论参考。【方法】以在新疆种植的121份小麦为材料,利用小麦50K SNP芯片,对粒长、粒宽、籽粒长宽比、籽粒面积、籽粒周长和千粒重6个性状进行基于混合线性模型MLM（Q+K）的全基因组关联分析。【结果】在不同环境间6个籽粒性状均表现出广泛的表型变异,其中千粒重变异系数最大为13.91%—17.79%,各籽粒性状遗传力为0.85—0.90。多态性信息含量PIC值为0.09—0.38,最小等位基因频率MAF值为0.05—0.50。群体结构分析表明,试验所用自然群体可分为4个亚群。GWAS结果表明,共检测到592个与6个性状显著关联位点（P<0.001）,其中,涉及6个性状的29个SNP在2个及以上的环境中被重复检测到,分布于1A（5）、1B（2）、1D、2A（5）、3B、5A、5D、6B（4）、6D、7B和7D（7）染色体上,解释9.3%—22.7%的表型变异。检测到6个与粒长稳定的关联位点,分布在1A、2A和7D染色体上,解释9.9%—22.7%的表型变异;检测到2个与粒宽稳定的关联位点,分布在3B和5D染色体上,解释9.6%—12.2%的表型变异;检测到6个与籽粒长宽比稳定的关联位点,分布在2A（2）、5A、7B和7D（2）染色体上,解释10.1%—19.4%的表型变异;检测到3个与籽粒面积稳定的关联位点,分布在1A、1B和1D染色体上,解释9.9%—18.2%的表型变异;检测到6个与籽粒周长稳定的关联位点,分布在1A（2）、2A、6D和7D（2）染色体上,解释9.3%—22.6%的表型变异;检测到6个与千粒重稳定的关联位点,分布在1B、2A和6B染色体上,解释9.7%—12.9%的表型变异。挖掘到5个控制小麦籽粒性状一因多效显著关联位点,分布在1A、2A（2）和7D（2）染色体上,解释9.9%—22.7%的表型变异。【结论】本研究材料遗传多样性丰富,在自然群体中共发现29个与6个籽粒性状在2个及以上环境中稳定显著的关联位点。

关键词: 小麦, SNP标记, GWAS, 籽粒性状

Abstract:

【Objective】Grain traits are important factors affecting wheat yield. the significant locus of controlling wheat grain traits was explored by genome-wide association analysis of wheat grain traits, which provided a theoretical reference for the study of genetic improvement of wheat grain traits. 【Method】The genome-wide association analysis (GWAS) based on mixed linear model MLM (Q+K) was performed on 121 wheat grown in Xinjiang using wheat 50 K SNP chips for 6 traits which including grain length, grain width, grain length-width ratio, grain area, grain perimeter and 1000-grain weight.【Result】Six grain traits showed wide phenotypic variation in different environments, in which the maximum coefficient of variation of 1000-grain weight was 13.91-17.79 and the heritability of each grain trait was between 0.85-0.90. The polymorphism information content PIC value was 0.09-0.38, and the minimum allele frequency MAF value was 0.05-0.50. Group structure analysis shows that the natural groups used in the experiment can be divided into 4 subgroups. GWAS results showed that a total of 592 significant association sites (P<0.001) were detected in 6 traits, of which 29 SNPs were repeatedly detected in 2 or more environments, distributed in 1A(5), 1B(2), 1D, 2A(5), 3B, 5A, 5D, 6B(4), 6D, 7B and 7D(7) chromosomes, can explain 9.3% to 22.7% of the phenotypic variation. Six markers associated with stable grain length were detected, which distributed on 1A, 2A, and 7D chromosomes to explain the phenotypic variation of 9.9%-22.7%. Two markers associated with stable grain width were detected, which distributed on 3 B and 5 D chromosomes to explain the phenotypic variation of 9.6%-12.2%. Six markers associated with stable grain length-width ratio were detected, which distributed on 2A(2), 5A, 7B, and 7D(2) chromosomes to explain the phenotypic variation of 10.1%-19.4%. Three markers associated with stable grain area were detected, which distributed on 1A, 1B and 1D chromosome to explain the phenotypic variation of 9.9%-18.2%. Six markers with stable correlation with grain perimeter were detected, which distributed on 1A(2), 2A, 6D and 7D(2) chromosomes to explain the phenotypic variation of 9.3%-22.6%. Six markers associated with stable 1000-grain weight were detected, which distributed on 1B, 2A and 6B chromosomes to explain the phenotypic variation 9.7%-12.9%. Five dominant loci of pleiotropism with were found to control wheat grain traits, which distributed 1A, 2A(2) and 7D(2) chromosomes, explaining the phenotypic variation of 9.9%-22.7%.【Conclusion】In this study, the genetic diversity of the materials was abundant, a total of 29 multi-environment stability loci were found in natural population with 2 or more environmental associated with 6 grain traits.

Key words: wheat, SNP marker, GWAS, grain size-related traits

张芳,任毅,曹俊梅,李法计,夏先春,耿洪伟. 基于SNP标记的小麦籽粒性状全基因组关联分析[J]. 中国农业科学, 2021, 54(10): 2053-2063.

ZHANG Fang,REN Yi,CAO JunMei,LI FaJi,XIA XianChun,GENG HongWei. Genome-wide Association Analysis of Wheat Grain Size Related Traits Based on SNP Markers[J]. Scientia Agricultura Sinica, 2021, 54(10): 2053-2063.

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.0578-1752.2021.10.002

https://www.chinaagrisci.com/CN/Y2021/V54/I10/2053

图/表 7

表1

表2

表3

附表1

图1

图2

表4

SNP-GWAS检测到的产量性状相关位点"

性状 Trait	标记 Marker	染色体 Chr.	位置 Position (Mb)	混合线性模型MLM		环境 Environment	前人报道 Previously reported
性状 Trait	标记 Marker	染色体 Chr.	位置 Position (Mb)	P值P value	贡献率R² (%)	环境 Environment	前人报道 Previously reported
千粒重 TKW	AX-111669426	1B	375.4	5.94E^-04-7.67E^-04	10.4—10.5	E1/E4	Xwmc269-Xwmc33^[27]
	AX-111531320	2A	204.5	5.88E^-04-8.98E^-04	10.3—11.3	E2/E4
	AX-109874065	6B	34.0	7.73E^-04-9.84E^-04	9.7—10.4	E1/E2	TKW-xgwm533^[28]
	AX-110446017	6B	486.1	3.98E^-04-7.98E^-04	10.3—12.9	E1/E2	QKWpur-6B^[24]AX_110368497^[25]
	AX-110936500	6B	569.4	4.97E^-04-6.86E^-04	10.5—10.6	E1/E2
	AX-109493716	6B	573.6	4.92E^-04-6.58E^-04	11.0—11.8	E1/E2
粒长 GL	AX-94757616	1A	64.6	3.56E^-04-7.55E^-04	10.7—11.9	E2/E4
	AX-111082947	1A	473.7	1.41E^-06-9.42E^-04	9.9—22.7	E1/E2/E3/E4
	AX-94497666	2A	748.4	3.30E^-04-7.90E^-04	10.3—11.8	E1/E2/E3/E4	TaFlo2^[25-26]
	AX-111614568	7D	13.7—13.9	1.96E^-05-2.03E^-04	12.9—17.3	E3/E4	TaGS-D1^[29]
	AX-95633409	7D	33.6	4.46E^-04-8.05E^-04	10.7—11.9	E3/E4	QKLpur-7D.1^[24]
	AX-111197303	7D	47.4—49.2	2.70E^-04-8.32E^-04	10.9—12.6	E1/E4	QKL.caas-7DS ^[30] QGw.ccsu-7D.1 ^[31]
粒宽 GW	AX-108948870	3B	24.6	5.20E^-04-9.21E^-04	10.6—12.2	E1/E4	Kukri_c14642_917^[23]
粒宽 GW	AX-179477405	5D	210.6	6.78E^-04-8.33E^-04	9.6—10.6	E2/E4
粒长宽比 LWR	AX-111722425	2A	131.7	1.38E^-05-9.53E^-04	10.1—18.3	E1/E2/E3/E4
	AX-111531320	2A	204.5	3.57E^-05-3.28E^-04	13.0—17.1	E2/E3
	AX-179560109	5A	141.9	1.02E^-04-1.08E^-04	14.7—15.2	E2/E3
	AX-112286258	7B	709.1	1.48E^-05-4.09E^-04	11.1—19.4	E1/E3	QGlwr.ccsu-7B.1^[32]
	AX-111614568	7D	13.7—13.9	4.98E^-05-5.51E^-05	10.9—15.9	E1/E4	TaGS-D1^[29]
	AX-158554015	7D	403.7	6.09E^-05-6.42E^-04	10.8—15.4	E1/E2
籽粒面积 GA	AX-95176275	1A	146.6	5.92E^-04-9.82E^-04	9.9—11.2	E1/E4
	AX-179476290	1B	377.4	1.21E^-05-8.31E^-04	10.0—18.2	E3/E4	qKA1B-1^[33]
	AX-179477117	1D	220.5	1.21E^-05-8.31E^-04	10.0—18.2	E3/E4
籽粒周长 GC	AX-111082947	1A	473.7	1.34E^-06-5.56E^-04	10.1—22.6	E1/E3/E4
	AX-109382284	1A	546.8	2.27E^-06-3.55E^-04	11.8—21.3	E3/E4
	AX-94497666	2A	748.4	7.62E^-04-8.68E^-04	9.3—10.3	E1/E4	TaFlo2^[25,26]
	AX-111583179	6D	242.7	2.11E^-04-5.00E^-04	12.0—13.2	E2/E3
	AX-111614568	7D	13.7—13.9	1.04E^-04-3.72E^-04	11.7—14.2	E3/E4	TaGS-D1^[29]
	AX-111197303	7D	47.4—49.2	7.15E^-04-8.05E^-04	10.0—10.4	E1/E4	QKL.caas-7DS ^[30]QGw.ccsu-7D.1 ^[31]

表4

参考文献 42

[1]	庄巧生. 产量潜力改良中国小麦品种改良及系谱分析. 北京: 中国农业出版社, 2003: 498-519.
	ZHUANG Q S. Wheat Improvement and Pedigree Analysis in China. Beijing: Chinese Agricultural Press, 2003. (in Chinese)
[2]	GODFRAY H C, BEDDINGTON J R, CRUTE I R, HADDAD L, LAWRENCE D, MUIR J F, PRETTY J, ROBINSON S, THOMAS S M, TOULMIN C. Food security: The challenge of feeding 9 billion people. Science, 2010,327(5967):812-818. doi: 10.1126/science.1185383
[3]	XIN F, ZHU T, WEI, S W, HAN Y C, ZHAO Y, ZHANG D Z, MA L J, 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):12-25. doi: 10.1038/s41598-019-55410-5
[4]	ROSEGRANT M W, AGCAOILI S M. Global and regional food demand, supply and trade prospects to 2010. Washington: International Food Policy Research Institute Press, 1995: 61-84.
[5]	REYNOLDS M, BONNETT D, CHAPMAN S C, FURBANK R T, MANE’S Y, MATHER D E, PARRY M A J. Raising yield potential of wheat I overview of a consortium approach and breeding strategies. Journal of Experimental Botany, 2011,62(2):439-452. doi: 10.1093/jxb/erq311
[6]	BENNETT M D, SMITH J B. Nuclear DNA amounts in angiosperms. Philosophical transactions of the royal society of London, 1976,274(933):227-274.
[7]	冯建英, 温阳俊, 张瑾, 章元明. 关联分析方法的研究进展. 作物学报, 2016,42(7):945-956.
	FENG J Y, WEN Y J, ZHANG J, ZHANG Y M. Advances on methodologies for genome-wide association studies in plants. Acta Agronomica Sinica, 2016,42(7):945-956. (in Chinese)
[8]	BROOKES A J. The essence of SNPs. Gene, 1999,234(2):177-186. doi: 10.1016/S0378-1119(99)00219-X
[9]	RAFALSKI A. Applications of single nucleotide polymorphisms in crop genetics. Current Opinion in Plant Biology, 2002,5(2):94-100. doi: 10.1016/S1369-5266(02)00240-6
[10]	郑德波, 杨小红, 李建生, 严建兵, 张士龙, 贺正华, 黄益勤. 基于SNP标记的玉米株高及穗位高QTL定位. 作物学报, 2013,39(3):549-556.
	ZHENG D B, YANG X H, LI J S, YAN J B, ZHANG S L, HE Z H, HUANG Y Q. QTL identification for plant height and ear height based on SNP mapping in maize (Zea mays L.). Acta Agronomica Sinica, 2013,39(3):549-556. (in Chinese)
[11]	WANG S C, WONG D, FORREST K, ALLEN A, CHAO S, HUANG B E, MACCAFERRI M, SALVI S, MILNER S G, CATTIVELLI L, MASTRANGELO A M, WHAN A, STEPHEN S, BARKER G, WIESEKE R, PLIESKE J, IWGSC, LILLEMO M, MATHER D, APPELS R, DOLFERUS R, GUEDIRA G B, KOROL A, AKHUNOVA A R, FEUILLET C, SALSE J, MORGANTE M, POZNIZK C, LUO M C, DVORAK J, MORELL M, DUBCOVSKY J, GANAL M, TUBEROSA R, LAWLEY C, MIKOULITCH I, CAVANAGH C, EDWARDS K, HAYDEN M, AKHUNOV E. Characterization of polyploid wheat genomic diversity using a high-density 90, 000 single nucleotide polymorphism array. Plant Biotechnology Journal, 2014,12:787-796. doi: 10.1111/pbi.2014.12.issue-6
[12]	吴凯. 农作物SNP芯片技术及其在分子育种中的应用. 山西农业科学, 2018,46(4):670-672.
	WU K. Research progress on crop SNP chip technology and its application in molecular breeding. Journal of Shanxi Agricultural Sciences, 2018,46(4):670-672. (in Chinese)
[13]	LIU J J, LUO W, QIN N, DING P, ZHANG H, YANG C C, MU Y, TANG H P, LIU Y X, LI W, JIANG Q T, CHEN G Y, WEI Y M, ZHENG Y L, LIU C J, LAN X J, MA J. A 55 K SNP array-based genetic map and its utilization in QTL mapping for productive tiller number in common wheat. Theoretical and Applied Genetics, 2018,131:2439-2450. doi: 10.1007/s00122-018-3164-9
[14]	CAMPBELL K G, BERGMAN C J, GUALBERTO D G, ANDERSON J A, GIROUX M J, HARELAND G, FULCHER G, SORRELLS M E, FINNEY P L. Quantitative trait loci associated with kernel traits in a soft×hard wheat cross. Crop Science, 1999,39:1184-1195. doi: 10.2135/cropsci1999.0011183X003900040039x
[15]	AMMIRAJU J S S, DHOLAKIA B B, SANTRAH D K, LAGU M D, DHALIWAL H S, RAO V S, GUPTA V S, RANJEKAR P K. Identification of inter simple sequence repeat (ISSR) markers associated with seed size in wheat. Theoretical and Applied Genetics, 2001,102:726-732. doi: 10.1007/s001220051703
[16]	BRESEGHELLO F, SORRELLS M E. QTL analysis of kernel size and shape in two hexaploid wheat mapping populations. Field Crops Research, 2007,101:172-179. doi: 10.1016/j.fcr.2006.11.008
[17]	LI X J, LI L Q, WANG H, SORRELLS M E. Quantitative trait loci analysis for kernel length and width in wheat. Journal of Northwest Agriculture and Forestry University, 2009,37(3):95-100.
[18]	PANTALIAÕ G F, NARCISO M, GUIMARÃES C, CASTRO A, COLOMBARI J M, BRESEGHELLO F, RODRIGUES L, VIANELLO R P, BORBA T O, BRONDANI C. Genome wide association study for grain yield in rice cultivated under water deficit. Genetica, 2016,144:651-664. doi: 10.1007/s10709-016-9932-z
[19]	YANO K, YAMAMOTO E, AYAL K, TAKEUCHI H, LO P C, HU L, YAMASAKI M, YOSHIDA S, KITANO H, HIRANO K, MATSUOKA M. Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice. Nature Genetic, 2016,48:927-936. doi: 10.1038/ng.3596
[20]	WU X, LI Y X, SHI Y S, SONG Y C, ZHANG D F, LI C H, BUCKLER E S, LI Y, ZHANG Z W, WANG T Y. Joint-linkage mapping and GWAS reveal extensive genetic loci that regulate male inflorescence size in maize. Plant Biotechnology, 2016,14:1551-1562.
[21]	ATANASOV K E, BARQUERO B L, TIBURCIO A F, RUBEN A. Genome wide association mapping for the tolerance to the polyamine oxidase inhibitor guazatine in Arabidopsis thaliana. Front Plant Science, 2016,7:588-589.
[22]	BHATTA M, SHAMANIN V, SHEPELEV S, BAENZIGER P S, POZHERUKOVA V, POTOTSKAYA I, MORGOUNOV A. Marker-trait associations for enhancing agronomic performance, disease resistance, and grain quality in synthetic and bread wheat accessions in western Siberia. Genes, 2019,10:1-44. doi: 10.3390/genes10010001
[23]	SUN C W, ZHANG F Y, YAN X F, ZHANG X F, DONG Z D, CUI D Q, CHEN F. Genome-wide association study for 13 agronomic traits reveals distribution of superior alleles in bread wheat from the Yellow and Huai Valley of China. Plant Biotechnology Journal, 2017,15:953-969. doi: 10.1111/pbi.2017.15.issue-8
[24]	DABA S D, TYAGI P, BROWN G G, MOHAMMADI M. Genome-wide association studies to identify loci and candidate genes controlling kernel weight and length in a historical united states wheat population. Front Plant Science, 2018,9:1045-1059. doi: 10.3389/fpls.2018.01045
[25]	LI F J, WEN W E, LIU J D, ZHANG Y, CAO S H, HE Z H, RASHEED A, JIN H, ZHANG C, YAN J, ZHANG P Z, WAN Y X, XIA X C. Genetic architecture of grain yield in bread wheat based on genome-wide association studies. BMC Plant Biology, 2019,19:168-187. doi: 10.1186/s12870-019-1781-3
[26]	SAJJAD M, MA X L, KHAN S H, SHOAIB M, SONG Y H, YANG W L, ZHANG A, LIU D C. TaFlo2-A1, an ortholog of rice Flo2, is associated with thousand grain weight in bread wheat. BMC Plant Biology, 2017. DOI 10.1186/s12870-017-1114-3.
[27]	王瑞霞, 张秀英, 伍玲, 王瑞, 海林, 游光霞, 闫长生, 肖世和. 不同生态环境下冬小麦籽粒大小相关性状的QTL分析. 中国农业科学, 2009,42(2):398-407.
	WANG R X, ZHANG X Y, WU L, WANG R, HAI L, YOU G X, YAN C S, XIAO S H. QTL analysis of grain size and related traits in winter wheat under different ecological environments. Scientia Agricultura Sinica, 2009,42(2):398-407. (in Chinese)
[28]	张坤普, 徐宪斌, 田纪春. 小麦籽粒产量及穗部相关性状的QTL定位. 作物学报, 2009,35(2):270-278.
	ZHANG K P, XU X B, TIAN J C. QTL mapping for grain yield and spike related traits in common wheat. Acta Agronomica Sinica, 2009,35(2):270-278. (in Chinese)
[29]	ZHANG Y J, LIU J D, XIA X C, HE Z H. TaGS-D1, an ortholog of rice OsGS3, is associated with grain weight and grain length in common wheat. Molecular Breeding, 2014,34(3):1097. doi: 10.1007/s11032-014-0102-7
[30]	LI F J, WEN W E, HE Z H, LIU JD, JIN H, GENG H W, YAN J, ZHANG P Z, WAN Y X, XIA X C. Genome-wide linkage mapping of yield related traits in three Chinese bread wheat populations using high-density SNP markers. Theoretical and Applied Genetics, 2018,131:1903-1924. doi: 10.1007/s00122-018-3122-6
[31]	MIR R R, KUMAR N, JAISWALI 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, 2012,29:963-972. doi: 10.1007/s11032-011-9693-4
[32]	KUMARI S, JAISWAL V, MISHRA V K, PALIWAL R, BALYAN H S, GUPTA P H. QTL mapping for some grain traits in bread wheat (Triticum aestivum L.). Physiology Molecular Biology Plants, 2018,24(5):909-920. doi: 10.1007/s12298-018-0552-1
[33]	XIN F, ZHU T, WEI S W, HAN Y C, ZHAO Y, ZHANG D Z, MA L J, 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, 2020,10:25-37. doi: 10.1038/s41598-019-56979-7
[34]	GILL B S, APPELS R, OTHAOBERHOLSTER A M B, BUELL C R, BENNETZEN J L B, CHALHOUB B, CHUMLEY F, DVORAK J, IWANAGA M, KELLER B, LI W L, MCCOMBIE W R, OGIHARA Y, QUETIER F, SASAKI T. A workshop report on wheat genome sequencing: international genome research on wheat consortium. Genetics, 2004,168(2):1087-1096. doi: 10.1534/genetics.104.034769
[35]	DHOLAKIA B B, AMMIRAJU J S S, Singh H, LAGU M D, RODER M S, RAO V S, DHALIWAL H S, RANJEKAR P K, GUPTA V S. Molecular marker analysis of kernel size and shape in bread wheat. Plant Breeding, 2003,122(5):392-395. doi: 10.1046/j.1439-0523.2003.00896.x
[36]	GEGAS V C, NAZARI A, GRIFFITHS S, SIMMONDS J, FISH L, ORFORD S, SAYERS L, DOONAN J H, SNAPE J W. A genetic framework for grain size and shape variation in wheat. The Plant Cell, 2010,22(6):1046-1056. doi: 10.1105/tpc.110.074153
[37]	陈佳慧, 兰进好, 王晖, 王道峰, 林琪, 田纪春. 小麦籽粒构型性状与粒重的相关性分析. 中国种业, 2010,8:57-59.
	CHEN J H, LAN J H, WANG H, WANG D F, LIN Q, TIAN J C. Correlation analysis of wheat grain configuration traits and grain weight. China Seed Industry, 2010,8:57-59. (in Chinese)
[38]	余曼丽, 赵林姝, 郭会君, 古佳玉, 李军辉, 谢永盾, 赵世荣, 刘录祥. 小麦籽粒性状的QTL定位. 麦类作物学报, 2014,34(8):1029-1035.
	YU M L, ZHAO L S, GUO H J, GU J Y, LI J H, XIE Y D, ZHAO S R, LIU L X. QTL mapping for kernel traits in wheat. Journal of Triticeae Crops, 2014,34(8):1029-1035. (in Chinese)
[39]	马艳明, 冯智宇, 王威, 张胜军, 郭营, 倪中福, 刘杰. 新疆冬小麦品种农艺及产量性状遗传多样性分析. 作物学报, 2020,46(8):1-11.
	MA Y M, FENG Z Y, WANG W, ZHANG S J, GUO Y, NI Z F, LIU J. Genetic diversity analysis of winter wheat landraces and modern bred varieties in Xinjiang based on agronomic traits. Acta Agronomica Sinica, 2020,46(8):1-11. (in Chinese)
[40]	CABRAL A L, JORDAN M C, LARSON G, SOMERS D J, HUMPHREYS D G, MCCARTNEY C A. Relationship between QTL for grain shape, grain weight, test weight, milling yield, and plant height in the spring wheat cross RL4452/AC domain. PLoS ONE, 2018,13(1):1-32.
[41]	MUHAMMAD J, ALI A, GUL A, GHAFOOR A, NAPAR A A, IBRAHIM A M H, NAVEED N H, YASIN N A, KAZI A M. Genome-wide association studies of seven agronomic traits under two sowing conditions in bread wheat. BMC Plant Biology, 2019,19:149-167. doi: 10.1186/s12870-019-1754-6
[42]	CAO S H, XU D A, HANIF M, XIA X C, HE Z H. Genetic architecture underpinning yield component traits in wheat. Theoretical and Applied Genetics, 2020,178(1):539-551.

性状 Trait	环境 Environment	平均值 Mean	范围 Range	标准差 SD	变异系数 CV (%)	偏度 Ske.	峰度 Kur.
千粒重 TKW	2017	22.92	14.86—32.28	4.08	17.79	-0.18	-0.43
	2018	35.55	23.13—49.03	4.95	13.91	0.05	-0.02
	2019	37.84	19.15—54.15	5.62	14.85	-0.02	0.41
粒长 GL	2017	6.44	5.66—7.59	0.34	5.24	0.54	0.88
	2018	6.63	5.67—7.48	0.31	4.66	0.20	0.54
	2019	6.61	5.78—7.51	0.31	4.69	0.16	0.06
粒宽 GW	2017	3.14	2.61—3.61	0.24	7.52	-0.39	-0.28
	2018	3.15	2.61—3.65	0.22	6.95	-0.19	-0.65
	2019	3.27	2.69—3.64	0.18	5.50	-0.43	0.03
长宽比 LWR	2017	2.09	1.75—2.67	0.19	9.12	0.74	0.20
	2018	2.13	1.81—2.58	0.16	7.39	0.59	-0.06
	2019	2.03	1.75—2.41	0.13	6.40	0.68	0.43
籽粒面积 GA	2017	15.82	11.88—19.59	1.54	9.74	-0.11	0.16
	2018	16.24	12.55—21.34	1.59	9.76	0.23	0.02
	2019	16.67	12.65—20.84	1.38	8.28	-0.01	0.63
籽粒周长 GC	2017	16.61	14.52—18.62	0.76	4.56	0.07	0.67
	2018	16.78	14.61—19.11	0.72	4.30	0.22	0.58
	2019	17.27	15.73—19.80	0.72	4.17	0.25	0.44

性状 Trait	均方MS				F值F value			遗传力 h²
性状 Trait	基因型 Genotype	环境 Environment	基因型×环境 G×E	误差 Error	基因型 Genotype	环境 Environment	基因型×环境 G×E	遗传力 h²
千粒重TKW	157.46	17050.92	27.47	4.43	35.54***	3848.31***	6.20***	0.85
粒长GL	0.60	2.74	0.10	0.02	37.54***	170.47***	6.18***	0.86
粒宽GW	0.29	1.63	0.04	0.01	22.98***	131.03***	2.84***	0.89
长宽比LWR	0.16	0.96	0.02	0.00	40.15***	236.77***	4.53***	0.90
籽粒面积GA	14.19	52.10	1.96	0.53	26.59***	97.66***	3.68***	0.87
籽粒周长GC	3.28	36.25	0.52	0.14	23.40***	258.40***	3.71***	0.86

性状Trait	千粒重TKW	粒长GL	粒宽GW	长宽比LWR	籽粒面积GA
粒长GL	0.36***
粒宽GW	0.93***	0.22*
长宽比LWR	-0.64***	0.42***	-0.78***
籽粒面积GA	0.90***	0.64***	0.88***	-0.41***
籽粒周长GC	0.58***	0.94***	0.49***	0.15ns	0.83***

染色体 Chromosome	标记数量 No. of markers	物理长度 Physical distance (Mb)	标记密度Density of marker	遗传多样性Genetic diversity	最小等位基因频率MAF		多态信含量 PIC
染色体 Chromosome	标记数量 No. of markers	物理长度 Physical distance (Mb)	标记密度Density of marker	遗传多样性Genetic diversity	平均 Mean	范围 Range	平均 Mean	范围 Range
1A	2544	602.45	0.24	0.35	0.25	0.05—0.50	0.28	0.09—0.38
1B	1469	746.65	0.29	0.34	0.25	0.05—0.50	0.27	0.09—0.38
1D	1486	498.09	0.20	0.36	0.26	0.05—0.50	0.29	0.09—0.38
2A	2682	787.57	0.31	0.38	0.29	0.05—0.50	0.3	0.09—0.38
2B	2177	802.80	0.32	0.37	0.27	0.05—0.50	0.29	0.09—0.38
2D	1308	655.89	0.26	0.37	0.27	0.05—0.50	0.29	0.09—0.38
3A	2252	750.52	0.29	0.46	0.37	0.25—0.50	0.35	0.30—0.38
3B	2275	868.70	0.34	0.42	0.33	0.17—0.50	0.33	0.24—0.38
3D	1201	626.21	0.25	0.36	0.27	0.05—0.50	0.29	0.09—0.38
4A	2063	750.43	0.29	0.46	0.39	0.26—0.50	0.36	0.30—0.38
4B	1327	672.85	0.26	0.39	0.29	0.11—0.50	0.31	0.18—0.38
4D	817	521.81	0.20	0.39	0.29	0.05—0.50	0.31	0.09—0.38
5A	2251	709.53	0.28	0.49	0.43	0.35—0.50	0.37	0.35—0.38
5B	1605	713.57	0.28	0.46	0.38	0.26—0.50	0.35	0.29—0.38
5D	1373	573.30	0.22	0.41	0.31	0.16—0.50	0.32	0.23—0.38
6A	2315	625.50	0.24	0.49	0.43	0.34—0.50	0.37	0.35—0.38
6B	1305	725.17	0.28	0.45	0.37	0.21—0.50	0.35	0.28—0.38
6D	1330	498.56	0.19	0.45	0.36	0.20—0.50	0.34	0.27—0.38
7A	2407	745.91	0.29	0.48	0.41	0.31—0.50	0.36	0.34—0.38
7B	1332	750.19	0.29	0.45	0.37	0.22—0.50	0.35	0.28—0.38
7D	1354	643.15	0.25	0.44	0.32	0.19—0.50	0.33	0.26—0.38
A基因组A genome	16514	4971.91	0.30	0.44	0.37	0.05—0.50	0.34	0.09—0.38
B基因组 B genome	11490	5279.93	0.46	0.41	0.32	0.05—0.50	0.32	0.09—0.38
D基因组 D genome	8869	4017.01	0.45	0.40	0.30	0.05—0.50	0.31	0.09—0.38
总计Total	36873	14268.85	0.39	0.42	0.33	0.05—0.50	0.32	0.09—0.38

基于SNP标记的小麦籽粒性状全基因组关联分析

Genome-wide Association Analysis of Wheat Grain Size Related Traits Based on SNP Markers

RichHTML

PDF

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 42

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	陈吉浩, 周界光, 曲翔汝, 王素容, 唐华苹, 蒋云, 唐力为, $\boxed{\hbox{兰秀锦}}$, 魏育明, 周景忠, 马建. 四倍体小麦胚大小性状QTL定位与分析[J]. 中国农业科学, 2023, 56(2): 203-216.
[2]	严艳鸽, 张水勤, 李燕婷, 赵秉强, 袁亮. 葡聚糖改性尿素对冬小麦产量和肥料氮去向的影响[J]. 中国农业科学, 2023, 56(2): 287-299.
[3]	徐久凯, 袁亮, 温延臣, 张水勤, 李燕婷, 李海燕, 赵秉强. 畜禽有机肥氮在冬小麦季对化肥氮的相对替代当量[J]. 中国农业科学, 2023, 56(2): 300-313.
[4]	古丽旦,刘洋,李方向,成卫宁. 小麦吸浆虫小热激蛋白基因Hsp21.9的克隆及在滞育过程与温度胁迫下的表达特性[J]. 中国农业科学, 2023, 56(1): 79-89.
[5]	王浩琳,马悦,李永华,李超,赵明琴,苑爱静,邱炜红,何刚,石美,王朝辉. 基于小麦产量与籽粒锰含量的磷肥优化管理[J]. 中国农业科学, 2022, 55(9): 1800-1810.
[6]	唐华苹,陈黄鑫,李聪,苟璐璐,谭翠,牟杨,唐力为,兰秀锦,魏育明,马建. 基于55K SNP芯片的普通小麦穗长非条件和条件QTL分析[J]. 中国农业科学, 2022, 55(8): 1492-1502.
[7]	马小艳,杨瑜,黄冬琳,王朝辉,高亚军,李永刚,吕辉. 小麦化肥减施与不同轮作方式的周年养分平衡及经济效益分析[J]. 中国农业科学, 2022, 55(8): 1589-1603.
[8]	刘硕,张慧,高志源,许吉利,田汇. 437个小麦品种钾收获指数的变异特征[J]. 中国农业科学, 2022, 55(7): 1284-1300.
[9]	王洋洋,刘万代,贺利,任德超,段剑钊,胡新,郭天财,王永华,冯伟. 基于多元统计分析的小麦低温冻害评价及水分效应差异研究[J]. 中国农业科学, 2022, 55(7): 1301-1318.
[10]	职蕾,者理,孙楠楠,杨阳,Dauren Serikbay,贾汉忠,胡银岗,陈亮. 小麦苗期铅耐受性的全基因组关联分析[J]. 中国农业科学, 2022, 55(6): 1064-1081.
[11]	秦羽青,程宏波,柴雨葳,马建涛,李瑞,李亚伟,常磊,柴守玺. 中国北方地区小麦覆盖栽培增产效应的荟萃（Meta）分析[J]. 中国农业科学, 2022, 55(6): 1095-1109.
[12]	蔡苇荻,张羽,刘海燕,郑恒彪,程涛,田永超,朱艳,曹卫星,姚霞. 基于成像高光谱的小麦冠层白粉病早期监测方法[J]. 中国农业科学, 2022, 55(6): 1110-1126.
[13]	宗成, 吴金鑫, 朱九刚, 董志浩, 李君风, 邵涛, 刘秦华. 添加剂对农副产物和小麦秸秆混合青贮发酵品质的影响[J]. 中国农业科学, 2022, 55(5): 1037-1046.
[14]	马鸿翔, 王永刚, 高玉姣, 何漪, 姜朋, 吴磊, 张旭. 小麦抗赤霉病育种回顾与展望[J]. 中国农业科学, 2022, 55(5): 837-855.
[15]	冯子恒,宋莉,张少华,井宇航,段剑钊,贺利,尹飞,冯伟. 基于无人机多光谱和热红外影像信息融合的小麦白粉病监测[J]. 中国农业科学, 2022, 55(5): 890-906.