山西小麦苗期耐低磷特性及遗传分析

doi:10.3864/j.issn.0578-1752.2024.05.001

Abstract

Abstract:

【Objective】In arid and semi-arid regions, the water and nutrients are scarce in the soil. The phosphorus use efficiency between different wheat genotypes varies greatly. Therefore, identification of low phosphorus-tolerant germplasm and mapping of related loci is helpful for genetic improvement of wheat. 【Method】Using 282 Shanxi wheat varieties as materials, twelve seedling morphological indicators were investigated under three phosphorus concentrations, including SDW, RDW, DW, SFW, RFW, FW, MRL, TRL, RS, RV, RD, and RN. Principal component analysis, membership function analysis, and cluster analysis were used to comprehensively evaluate the low phosphorus tolerance characteristics of different varieties at the seedling stage. On this basis, the trait evolution trend and biomass allocation at seedling stage were analyzed. At the same time, GWAS was used to identify significant loci related to the low phosphorus-related traits. 【Result】The response of different traits to low phosphorus at the seedling stage was different. Lower phosphorus concentrations led to changes in biomass allocation strategy, and shoot growth was less affected by change in phosphorus concentrations than root growth. The decrease in phosphorus concentration inhibited the growth of shoot, and SDW and SFW were significantly reduced. In contrast, low phosphorus promoted root growth, and the indicators of RDW, RFW, MRL, TRL, RV and RN increased significantly. According to the correlation analysis between D-value and morphological indicators, it was found that MRL and RD could be used as selection indicators for low phosphorus tolerance at seedling stage. Based on D-value clustering analysis, 9 low phosphorus tolerant varieties were selected, including Jinmai 46, Jinmai 61, Youmangdahongjing, Hongtumai, Hongheshang, Baikehong, Baixianmai, Huoshaotou, Baishanmai. Analysing trends in trait evolution showed that cultivars were not directly selected for their ability to tolerate low phosphorus. The ability to tolerate low phosphorus decreased first and then increased over time. Before 2010, there was a decreasing trend in the ability of varieties to tolerate low phosphorus, and after 2010, there was an increase in the ability of varieties to tolerate low phosphorus. GWAS stably detected eight loci with R²>10% in three environments, in which 1A_545074550, 2B_489279799, 6A_166899658 and 6A_273060644 were not reported previously.【Conclusion】The MRL and RD can be used as selection indicators for low phosphorus tolerance at seedling stage. A total of nine varieties were selected through comprehensive evaluation of ability in Shanxi wheat to tolerate low phosphorus during seedling stage. Association analysis detected four novel loci associated with low phosphorus tolerance on chromosomes 1A, 2B and 6A, and the results provide germplasm resources and QTL for future low phosphorus tolerance wheat breeding.

Key words: seedling stage, low phosphorus tolerance, germplasm identification, trait evolution, association analysis

WEI NaiCui, TAO JinBo, YUAN MingYang, ZHANG Yu, KAI MengXiang, QIAO Ling, WU BangBang, HAO YuQiong, ZHENG XingWei, WANG JuanLing, ZHAO JiaJia, ZHENG Jun. Seedling Characterization and Genetic Analysis of Low Phosphorus Tolerance in Shanxi Varieties[J].Scientia Agricultura Sinica, 2024, 57(5): 831-845.

0
/ / Recommend

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: https://www.chinaagrisci.com/EN/10.3864/j.issn.0578-1752.2024.05.001

https://www.chinaagrisci.com/EN/Y2024/V57/I5/831

Figures/Tables 10

Fig. 1

Table 1

Fig. 2

Fig. 3

Table 2

Fig. 4

Fig. 5

Fig. 6

Table 3

Significance loci for which PRC values of each morphological indicator were stably detected in three or more environments"

性状 Traits	染色体 Chr.	物理位置 Position (Mb)	标记 Marker	环境 Environment	<BOLD>P</BOLD>值 <BOLD>P</BOLD> value	表型变异解释率 R² (%)	参考文献 References
茎叶部干重 SDW	7A	679.74	7A_679744088	MLP1	2.95E-04	5.98	[37]
				MLPAV	2.73E-04	6.41	[37]
				LP2	1.54E-04	6.66	[37]
				LPAV	1.08E-04	7.77	[37]
根部干重 RDW	2A	720.06	2A_720056407	MLP1	9.83E-04	5.04
		723.57	2A_723574066	LP1	1.20E-04	6.79
		728.13	2A_728131289	LP2	6.98E-04	5.49
	2B	771.81	2B_771807820	LP2	2.97E-05	8.32	[36]
		776.28	2B_776277662	MLP1	4.33E-04	5.67	[36]
				LP1	1.54E-06	10.25	[36]
	3A	697.27	3A_697265307	MLP1	3.35E-04	5.85	[37]
				MLPAV	2.52E-06	8.22	[37]
				LP2	1.42E-05	8.55	[37]
	6A	166.90	6A_166899658	MLP1	1.08E-06	10.26
				LP1	7.76E-04	5.35
				LP2	2.69E-04	6.23
		273.06	6A_273060644	LP2	7.72E-04	5.41
		273.49	6A_273485987	MLP1	9.90E-07	10.33
				LP1	8.29E-04	5.30
		596.50	6A_596501004	LP2	7.98E-05	7.18
		598.86	6A_598861692	MLPAV	1.19E-04	5.68
		599.26	6A_599260920	MLP1	6.01E-04	5.41
	7A	679.74	7A_679744223	MLP1	3.45E-10	16.79	[37]
				MLPAV	1.16E-05	7.21	[37]
				LP2	3.31E-04	6.08	[37]
		85.63	7A_85627950	MLP1	3.46E-12	20.69	[38]
				LP1	1.74E-04	6.50	[38]
				LPAV	1.95E-04	6.96	[38]
		85.66	7A_85658080	LPAV	1.03E-05	12.24	[38]
				MLPAV	9.70E-05	5.81	[38]
植株干重 DW	2B	485.17	2B_485169465	LPAV	1.51E-04	7.53
		489.28	2B_489279799	LP1	1.08E-06	10.09
				LP2	9.54E-04	5.26
		768.06	2B_768059955	LP1	5.27E-04	5.41	[36]
				LPAV	8.65E-04	5.99	[36]
		771.81	2B_771807820	LP2	7.56E-09	15.13	[36]
	7A	679.74	7A_679744088	MLP1	1.43E-04	6.45	[37]
				MLPAV	1.71E-04	6.75	[37]
				LPAV	8.41E-04	6.02	[37]
		679.74	7A_679744223	MLP1	2.19E-04	6.13	[37]
				LP2	3.36E-04	6.08	[37]
				LPAV	3.58E-04	6.77	[37]
茎叶部鲜重 SFW	1A	15.43	1A_15432092	MLPAV	9.54E-04	5.41
				LP1	9.40E-04	5.24
				LPAV	0.001	5.80
	7A	85.66	7A_85658291	MLP1	8.30E-04	5.28	[38]
				LP2	8.11E-04	5.16	[38]
				LPAV	2.38E-05	9.09	[38]
最大根长 MRL	1A	545.07	1A_545074550	MLP1	1.71E-08	13.23
				MLPAV	1.42E-07	11.12
				LP1	1.26E-04	6.24
		545.33	1A_545329345	MLP1	5.66E-09	14.11
				MLPAV	9.67E-08	11.41
				LP1	7.30E-05	6.63
		545.52	1A_545517460	MLP1	1.03E-09	16.52
				MLPAV	4.18E-08	13.20
				LP1	7.88E-05	6.98

Table 3

Fig. 7

References 38

[1]	QIU H B, MEI X P, LIU C X, WANG J G, WANG G Q, WANG X, LIU Z, CAI Y L. Fine mapping of quantitative trait loci for acid phosphatase activity in maize leaf under low phosphorus stress. Molecular Breeding, 2013, 32(3): 629-639. doi: 10.1007/s11032-013-9895-z
[2]	HAN Z S, LIU Y L, DENG X, LIU D M, LIU Y, HU Y K, YAN Y M. Genome-wide identification and expression analysis of expansin gene family in common wheat (Triticum aestivum L.). BMC Genomics, 2019, 20(1): 101. doi: 10.1186/s12864-019-5455-1
[3]	栗振义, 张绮芯, 仝宗永, 李跃, 徐洪雨, 万修福, 毕舒贻, 曹婧, 何峰, 万里强, 李向林. 不同紫花苜蓿品种对低磷环境的形态与生理响应分析. 中国农业科学, 2017, 50(20): 3898-3907. doi: 10.3864/j.issn.0578-1752.2017.20.006.
	LI Z Y, ZHANG Q X, TONG Z Y, LI Y, XU H Y, WAN X F, BI S Y, CAO J, HE F, WAN L Q, LI X L. Analysis of morphological and physiological responses to low Pi stress in different alfalfas. Scientia Agricultura Sinica, 2017, 50(20): 3898-3907. doi: 10.3864/j.issn.0578-1752.2017.20.006. (in Chinese)
[4]	YUAN Y Y, GAO M G, ZHANG M X, ZHENG H H, ZHOU X W, GUO Y, ZHAO Y, KONG F M, LI S S. QTL mapping for phosphorus efficiency and morphological traits at seedling and maturity stages in wheat. Frontiers in Plant Science, 2017, 8: 614. doi: 10.3389/fpls.2017.00614 pmid: 28484481
[5]	袁园园, 董贝, 曹晓慧, 郑洪蕊. 黄淮麦区小麦成株期磷高效基因型的鉴定和筛选. 麦类作物学报, 2017, 37(1): 56-65.
	YUAN Y Y, DONG B, CAO X H, ZHENG H R. Identification and screening of high phosphorus use efficiency genotypes of wheat at adult stage in Huang-Huai wheat area. Journal of Triticeae Crops, 2017, 37(1): 56-65. (in Chinese)
[6]	柏栋阴, 冯国华, 张会云, 陈荣振, 王晓军. 低磷胁迫下磷高效基因型小麦的筛选. 麦类作物学报, 2007, 27(3): 407-410, 415.
	BAI D Y, FENG G H, ZHANG H Y, CHEN R Z, WANG X J. Screening of wheat genotypes with high phosphorus efficiency under low phosphorus stress. Journal of Triticeae Crops, 2007, 27(3): 407-410, 415. (in Chinese)
[7]	阳显斌, 张锡洲, 李廷轩, 吴德勇. 小麦磷素利用效率的品种差异. 应用生态学报, 2012, 23(1): 60-66.
	YANG X B, ZHANG X Z, LI T X, WU D Y. Differences in phosphorus utilization efficiency among wheat cultivars. Chinese Journal of Applied Ecology, 2012, 23(1): 60-66. (in Chinese)
[8]	DHARMATEJA P, KUMAR M, PANDEY R, MANDAL P K, BABU P, BAINSLA N K, GAIKWAD K B, TOMAR V, KRANTHI KUMAR K, DHAR N, ANSARI R, SAIFI N, YADAV R. Deciphering the change in root system architectural traits under limiting and non-limiting phosphorus in Indian bread wheat germplasm. PLoS ONE, 2021, 16(10): e0255840. doi: 10.1371/journal.pone.0255840
[9]	刘露露, 汪军成, 姚立蓉, 孟亚雄, 李葆春, 杨轲, 司二静, 王化俊, 马小乐, 尚勋武, 李兴茂. 不同春小麦品种耐低磷性评价及种质筛选. 中国生态农业学报, 2020, 28(7): 999-1009.
	LIU L L, WANG J C, YAO L R, MENG Y X, LI B C, YANG K, SI E J, WANG H J, MA X L, SHANG X W, LI X M. Evaluation of low phosphorus tolerance and germplasm screening of spring wheat. Chinese Journal of Eco Agriculture, 2020, 28(7): 999-1009. (in Chinese)
[10]	孔忠新, 杨丽丽, 张政值, 马正强. 小麦耐低磷基因型的筛选. 麦类作物学报, 2010, 30(4): 591-595.
	KONG Z X, YANG L L, ZHANG Z Z, MA Z Q. Screening of wheat germplasms tolerant to low phosphorus. Journal of Triticeae Crops, 2010, 30(4): 591-595. (in Chinese)
[11]	DHARMATEJA P, YADAV R, KUMAR M, BABU P, JAIN N, MANDAL P K, PANDEY R, SHRIVASTAVA M, GAIKWAD K B, BAINSLA N K, TOMAR V, SUGUMAR S, SAIFI N, RANJAN R. Genome-wide association studies reveal putative QTLs for physiological traits under contrasting phosphorous conditions in wheat (Triticum aestivum L.). Frontiers in Genetics, 2022, 13: 984720. doi: 10.3389/fgene.2022.984720
[12]	周思远, 毕惠惠, 程西永, 张旭睿, 闰永行, 王航辉, 毛培钧, 李海霞, 许海霞. 小麦耐低磷相关性状的全基因组关联分析. 植物遗传资源学报, 2020, 21(2): 431-445. doi: 10.13430/j.cnki.jpgr.20190509001
	ZHOU S Y, BI H H, CHENG X Y, ZHANG X R, RUN Y H, WANG H H, MAO P J, LI H X, XU H X. Genome-wide association study of low-phosphorus tolerance related traits in wheat. Journal of Plant Genetic Resources, 2020, 21(2): 431-445. (in Chinese)
[13]	SAFDAR L B, UMER M J, ALMAS F, UDDIN S, SAFDAR Q T A, BLIGHE K, QURAISHI U M. Gene, 2021, 768: 145301. doi: 10.1016/j.gene.2020.145301
[14]	WANG J, SUN J H, MIAO J, GUO J K, SHI Z L, HE M Q, CHEN Y, ZHAO X Q, LI B, HAN F P, TONG Y P, LI Z S. A phosphate starvation response regulator Ta-PHR1 is involved in phosphate signalling and increases grain yield in wheat. Annals of Botany, 2013, 111(6): 1139-1153. doi: 10.1093/aob/mct080
[15]	OUYANG X, HONG X, ZHAO X Q, ZHANG W, HE X, MA W Y, TENG W, TONG Y P. Knock out of the phosphate2 gene TaPHO2-A1 improves phosphorus uptake and grain yield under low phosphorus conditions in common wheat. Scientific Reports, 2016, 6: 29850. doi: 10.1038/srep29850
[16]	GUO C J, GUO L, LI X J, GU J T, ZHAO M, DUAN W W, MA C Y, LU W J, XIAO K. TaPT2, a high-affinity phosphate transporter gene in wheat (Triticum aestivum L.), is crucial in plant Pi uptake under phosphorus deprivation. Acta Physiologiae Plantarum, 2014, 36(6): 1373-1384. doi: 10.1007/s11738-014-1516-x
[17]	ZHENG X W, LIU C, QIAO L, ZHAO J J, HAN R, WANG X L, GE C, ZHANG W Y, ZHANG S W, QIAO L Y, ZHENG J, HAO C Y. The MYB transcription factor TaPHR3-A1is involved in phosphate signaling and governs yield-related traits in bread wheat. Journal of Experimental Botany, 2020, 71(19): 5808-5822. doi: 10.1093/jxb/eraa355
[18]	EL MAZLOUZI M, MOREL C, ROBERT T, YAN B F, MOLLIER A. Phosphorus uptake and partitioning in two durum wheat cultivars with contrasting biomass allocation as affected by different P supply during grain filling. Plant and Soil, 2020, 449(1): 179-192. doi: 10.1007/s11104-020-04444-0
[19]	黄晨晨, 宋晓, 黄绍敏, 张珂珂, 岳克. 不同磷效率小麦根系形态、磷素转运及产量的差异分析. 华北农学报, 2021, 36(2): 169-175. doi: 10.7668/hbnxb.20191728
	HUANG C C, SONG X, HUANG S M, ZHANG K K, YUE K. Analysis of differences in root morphology, phosphorus transport and yield of wheat with different phosphorus efficiency. Acta Agriculturae Boreali-Sinica, 2021, 36(2): 169-175. (in Chinese) doi: 10.7668/hbnxb.20191728
[20]	赵佳佳, 乔玲, 武棒棒, 葛川, 乔麟轶, 张树伟, 闫素仙, 郑兴卫, 郑军. 山西省小麦苗期根系性状及抗旱特性分析. 作物学报, 2021, 47(4): 714-727. doi: 10.3724/SP.J.1006.2021.01048
	ZHAO J J, QIAO L, WU B B, GE C, QIAO L Y, ZHANG S W, YAN S X, ZHENG X W, ZHENG J. Seedling root characteristics and drought resistance of wheat in Shanxi province. Acta Agronomica Sinica, 2021, 47(4): 714-727. (in Chinese) doi: 10.3724/SP.J.1006.2021.01048
[21]	ZHENG X W, QIAO L, LIU Y, WEI N C, ZHAO J J, WU B B, YANG B, WANG J L, ZHENG J. Genome-wide association study of grain number in common wheat from Shanxi under different water regimes. Frontiers in Plant Science, 2021, 12: 806295. doi: 10.3389/fpls.2021.806295
[22]	REDDY V R P, ASKI M S, MISHRA G P, DIKSHIT H K, SINGH A, PANDEY R, SINGH M P, GAYACHARAN, RAMTEKEY V, PRITI, RAI N, NAIR R M. Genetic variation for root architectural traits in response to phosphorus deficiency in mungbean at the seedling stage. PLoS ONE, 2020, 15(6): e0221008. doi: 10.1371/journal.pone.0221008
[23]	ZHANG Z W, ERSOZ E, LAI C Q, TODHUNTER R J, TIWARI H K, GORE M A, BRADBURY P J, YU J M, ARNETT D K, ORDOVAS J M, BUCKLER E S. Mixed linear model approach adapted for genome-wide association studies. Nature Genetics, 2010, 42(4): 355-360. doi: 10.1038/ng.546 pmid: 20208535
[24]	ZHANG X H, WARBURTON M L, SETTER T, LIU H J, XUE Y D, YANG N, YAN J B, XIAO Y J. Genome-wide association studies of drought-related metabolic changes in maize using an enlarged SNP panel. Theoretical and Applied Genetics, 2016, 129(8): 1449-1463. doi: 10.1007/s00122-016-2716-0 pmid: 27121008
[25]	VANDAMME E, ROSE T, SAITO K, JEONG K, WISSUWA M. Integration of P acquisition efficiency, P utilization efficiency and low grain P concentrations into P-efficient rice genotypes for specific target environments. Nutrient Cycling in Agroecosystems, 2016, 104(3): 413-427. doi: 10.1007/s10705-015-9716-3
[26]	WANG X R, SHEN J B, LIAO H. Acquisition or utilization, which is more critical for enhancing phosphorus efficiency in modern crops? Plant Science, 2010, 179(4): 302-306. doi: 10.1016/j.plantsci.2010.06.007
[27]	NIU Y F, CHAI R S, JIN G L, WANG H, TANG C X, ZHANG Y S. Responses of root architecture development to low phosphorus availability: A review. Annals of Botany, 2013, 112(2): 391-408. doi: 10.1093/aob/mcs285 pmid: 23267006
[28]	MENG X X, LIU N, ZHANG L, YANG J, ZHANG M F. Genotypic differences in phosphorus uptake and utilization of watermelon under low phosphorus stress. Journal of Plant Nutrition, 2014, 37(2): 312-326. doi: 10.1080/01904167.2013.852225
[29]	FUKAKI H, TASAKA M. Hormone interactions during lateral root formation. Plant Molecular Biology, 2009, 69(4): 437-449. doi: 10.1007/s11103-008-9417-2 pmid: 18982413
[30]	LIN D L, YAO H Y, JIA L H, TAN J F, XU Z H, ZHENG W M, XUE H W. Phospholipase D-derived phosphatidic acid promotes root hair development under phosphorus deficiency by suppressing vacuolar degradation of PIN‐FORMED2. The New Phytologist, 2020, 226(1): 142-155. doi: 10.1111/nph.v226.1
[31]	秦晓梁. 小麦选育过程中根冠比值和异速生长关系的演化[D]. 兰州: 兰州大学, 2013.
	QIN X L. Evolution of root to shoot ratio and allometric relationship in demostration and selection of wheat[D]. Lanzhou: Lanzhou University, 2013. (in Chinese)
[32]	SANDAÑA P, PINOCHET D. Ecophysiological determinants of biomass and grain yield of wheat under P deficiency. Field Crops Research, 2011, 120(2): 311-319. doi: 10.1016/j.fcr.2010.11.005
[33]	李朴芳. 人工选择压力下麦类作物株型塑性及其逆境适应机制[D]. 兰州: 兰州大学, 2014.
	LI P F, Plant architecture plasticity and its response to adverse stresses in dryland Triticeae crops under artificial selection pressure[D]. Lanzhou: Lanzhou University, 2014. (in Chinese)
[34]	李朴芳, 程正国, 赵鸿, 张小丰, 李冀南, 王绍明, 熊友才. 旱地小麦理想株型研究进展. 生态学报, 2011, 31(9): 2631-2640.
	LI P F, CHENG Z G, ZHAO H, ZHANG X F, LI J N, WANG S M, XIONG Y C. Current progress in plant ideotype research of dryland wheat (Triticum aestivum L.). Acta Ecologica Sinica, 2011, 31(9): 2631-2640. (in Chinese)
[35]	田中伟, 樊永惠, 殷美, 王方瑞, 蔡剑, 姜东, 戴廷波. 长江中下游小麦品种根系改良特征及其与产量的关系. 作物学报, 2015, 41(4): 613-622.
	TIAN Z W, FAN Y H, YIN M, WANG F R, CAI J, JIANG D, DAI T B. Genetic improvement of root growth and its relationship with grain yield of wheat cultivars in the Middle-Lower Yangtze River. Acta Agronomica Sinica, 2015, 41(4): 613-622. (in Chinese) doi: 10.3724/SP.J.1006.2015.00613
[36]	WU F K, YANG X L, WANG Z Q, DENG M, MA J, CHEN G Y, WEI Y M, LIU Y X. Identification of major quantitative trait loci for root diameter in synthetic hexaploid wheat under phosphorus-deficient conditions. Journal of Applied Genetics, 2017, 58(4): 437-447. doi: 10.1007/s13353-017-0406-5 pmid: 28887804
[37]	SU J Y, XIAO Y M, LI M, LIU Q Y, LI B, TONG Y P, JIA J Z, LI Z S. Mapping QTLs for phosphorus-deficiency tolerance at wheat seedling stage. Plant and Soil, 2006, 281(1): 25-36. doi: 10.1007/s11104-005-3771-5
[38]	GUO Y, KONG F M, XU Y F, ZHAO Y, LIANG X, WANG Y Y, AN D G, LI S S. QTL mapping for seedling traits in wheat grown under varying concentrations of N, P and K nutrients. Theoretical and Applied Genetics, 2012, 124: 851-865. doi: 10.1007/s00122-011-1749-7 pmid: 22089330

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

性状 Traits	CK			MLP			LP
性状 Traits	变幅 Range	均值 Mean	变异系数 CV (%)	变幅 Range	均值 Mean	变异系数 CV (%)	变幅 Range	均值 Mean	变异系数 CV (%)
茎叶部干重SDW (mg)	6.13-32.31	13.71	27.04	3.98-18.01	10.87	19.87	7.24-27.05	12.78	18.77
根部干重RDW (mg)	1.41-7.47	3.83	32.73	1.12-10.49	3.86	25.74	2.46-38.74	5.37	55.15
植株干重DW (mg)	8.51-39.78	17.55	26.75	5.10-24.23	14.73	19.70	10.81-60.23	18.15	24.73
茎叶部鲜重SFW (mg)	77.69-356.73	158.54	29.20	49.80-214.49	142.33	19.58	69.39-241.62	139.49	17.39
根部鲜重RFW (mg)	27.66-103.15	54.70	32.99	15.13-91.01	55.43	24.91	23.58-107.42	67.53	23.40
植株鲜重FW (mg)	110.81-459.88	213.24	29.13	64.93-305.50	197.76	19.89	99.60-343.94	207.02	18.06
最大根长MRL (cm)	3.10-21.40	10.53	24.67	3.65-17.20	11.30	19.36	4.95-20.70	14.19	17.00
总根长TRL (cm)	7.00-67.19	32.99	33.63	5.39-62.68	32.97	26.16	16.88-88.69	50.90	27.01
根表面积RS (cm²)	1.31-8.74	4.73	32.53	0.90-7.87	4.65	24.47	2.67-10.07	6.27	22.76
根体积RV (cm³)	0.02-0.18	0.07	37.13	0.02-0.14	0.07	29.12	0.03-0.15	0.08	25.45
根直径RD (cm)	0.37-1.03	0.61	19.85	0.38-1.06	0.62	17.42	0.33-0.90	0.49	17.19
根尖数RN	3.33-31.00	9.43	41.90	2.83-18.83	8.60	31.45	5.17-37.17	16.51	33.15

性状 Traits	D值_MLP D values _MLP	D值_LP D values _LP	性状 Traits	D值_MLP D values _MLP	D值_LP D values _LP
茎叶部干重SDW	0.277**	0.079	最大根长MRL	0.155*	-0.222**
根部干重RDW	0.068	-0.121	总根长TRL	0.150*	-0.121
植株干重DW	0.229**	0.012	根表面积RS	0.055	-0.148*
茎叶部鲜重SFW	0.177**	-0.036	根体积RV	-0.044	-0.11
根部鲜重RFW	0.035	-0.131*	根直径RD	-0.214**	0.156*
植株鲜重FW	0.138*	-0.079	根尖数RN	0.149*	-0.056

Seedling Characterization and Genetic Analysis of Low Phosphorus Tolerance in Shanxi Varieties

RichHTML

PDF

Abstract

Cite this article

share this article

Figures/Tables 10

References 38

Related Articles 15

Metrics

Comments

Recommended 0

[1]	XIONG ChuWen, GUO ZhiBin, ZHOU QiangHua, CHENG YanBo, MA QiBin, CAI ZhanDong, NIAN Hai. Function Analysis of the Soybean Transcription Factor NAC1 in Tolerance to Low Phosphorus [J]. Scientia Agricultura Sinica, 2024, 57(3): 442-453.
[2]	ZHOU WenQi, ZHANG HeTong, HE HaiJun, GONG DianMing, YANG YanZhong, LIU ZhongXiang, LI YongSheng, WANG XiaoJuan, LIAN XiaoRong, ZHOU YuQian, QIU FaZhan. Candidate Gene Localization of ZmDLE1 Gene Regulating Plant Height and Ear Height in Maize [J]. Scientia Agricultura Sinica, 2023, 56(5): 821-837.
[3]	DONG YanYu, XU BiYu, DONG ZeYu, WANG LuYao, CHEN JinWen, FANG Lei. Genome-Wide Identification and Interspecific Comparative Analysis of the EXO70 Gene Family in Cotton [J]. Scientia Agricultura Sinica, 2023, 56(23): 4621-4634.
[4]	KAYOUMU MiReZhaTiJiang, WUMAIERJIANG XiErAiLi, LI XiaoTong, WANG XiangRu, GUI HuiPing, ZHANG HengHeng, ZHANG XiLing, DONG Qiang, SONG MeiZhen. Screening of Low Phosphorus Tolerant Germplasm in Cotton at Seedling Stage and Comprehensive Evaluation of Low Phosphorus Tolerance [J]. Scientia Agricultura Sinica, 2023, 56(21): 4150-4162.
[5]	GAO GuangLiang, ZHANG KeShan, ZHAO XianZhi, XU GuoYang, XIE YouHui, ZHOU Li, ZHANG ChangLian, WANG QiGui. Identification of Molecular Markers Associated with Goose Egg Quality Through Genome-Wide Association Analysis [J]. Scientia Agricultura Sinica, 2023, 56(19): 3894-3904.
[6]	MA YanMing, LOU HongYao, ZHANG ShengJun, WANG Wei, GUO Ying, NI ZhongFu, LIU Jie. Genome-Wide Association Analysis of Yield Traits in Xinjiang Winter Wheat Germplasm [J]. Scientia Agricultura Sinica, 2023, 56(18): 3487-3499.
[7]	LI ZhouShuai,DONG Yuan,LI Ting,FENG ZhiQian,DUAN YingXin,YANG MingXian,XU ShuTu,ZHANG XingHua,XUE JiQuan. Genome-Wide Association Analysis of Yield and Combining Ability Based on Maize Hybrid Population [J]. Scientia Agricultura Sinica, 2022, 55(9): 1695-1709.
[8]	ZHI Lei,ZHE Li,SUN NanNan,YANG Yang,Dauren Serikbay,JIA HanZhong,HU YinGang,CHEN Liang. Genome-Wide Association Analysis of Lead Tolerance in Wheat at Seedling Stage [J]. Scientia Agricultura Sinica, 2022, 55(6): 1064-1081.
[9]	LIU Jin,HU JiaXiao,MA XiaoDing,CHEN Wu,LE Si,JO Sumin,CUI Di,ZHOU HuiYing,ZHANG LiNa,SHIN Dongjin,LI MaoMao,HAN LongZhi,YU LiQin. Construction of High Density Genetic Map for RIL Population and QTL Analysis of Heat Tolerance at Seedling Stage in Rice (Oryza sativa L.) [J]. Scientia Agricultura Sinica, 2022, 55(22): 4327-4341.
[10]	CHEN Xu,HAO YaQiong,NIE XingHua,YANG HaiYing,LIU Song,WANG XueFeng,CAO QingQin,QIN Ling,XING Yu. Association Analysis of Main Characteristics of Bur and Nut with SSR Markers in Chinese Chestnut [J]. Scientia Agricultura Sinica, 2022, 55(13): 2613-2628.
[11]	WANG Juan, MA XiaoMei, ZHOU XiaoFeng, WANG Xin, TIAN Qin, LI ChengQi, DONG ChengGuang. Genome-Wide Association Study of Yield Component Traits in Upland Cotton (Gossypium hirsutum L.) [J]. Scientia Agricultura Sinica, 2022, 55(12): 2265-2277.
[12]	YAN YongLiang,SHI XiaoLei,ZHANG JinBo,GENG HongWei,XIAO Jing,LU ZiFeng,NI ZhongFu,CONG Hua. Genome-Wide Association Study of Grain Quality Related Characteristics of Spring Wheat [J]. Scientia Agricultura Sinica, 2021, 54(19): 4033-4047.
[13]	ShuGuang LI,YongCe CAO,JianBo HE,WuBin WANG,GuangNan XING,JiaYin YANG,TuanJie ZHAO,JunYi GAI. Genetic Dissection of Protein Content in a Nested Association Mapping Population of Soybean [J]. Scientia Agricultura Sinica, 2020, 53(9): 1743-1755.
[14]	LiYuan PAN,JianBo HE,JinMing ZHAO,WuBin WANG,GuangNan XING,DeYue YU,XiaoYan ZHANG,ChunYan LI,ShouYi CHEN,JunYi GAI. Detection Power of RTM-GWAS Applied to 100-Seed Weight QTL Identification in a Recombinant Inbred Lines Population of Soybean [J]. Scientia Agricultura Sinica, 2020, 53(9): 1730-1742.
[15]	JianBo HE,FangDong LIU,WuBin WANG,GuangNan XING,RongZhan GUAN,JunYi GAI. Restricted Two-Stage Multi-Locus Genome-Wide Association Analysis and Its Applications to Genetic and Breeding Studies [J]. Scientia Agricultura Sinica, 2020, 53(9): 1704-1716.