RTM-GWAS方法应用于大豆RIL群体百粒重QTL检测的功效

doi:10.3864/j.issn.0578-1752.2020.09.004

Abstract

Abstract:

【Objective】To thoroughly dissect the QTL system conferring 100-seed weight in a recombinant inbred lines population, the restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS) method was compared with other mapping methods for method optimization, which will provides basis for further exploration of candidate gene system and molecular marker-assisted design breeding. 【Method】A recombinant inbred line population consisting of 427 lines derived from a cross between Kefeng-1 and NN1138-2 was tested for its 100-seed weight under three environments. A total of 3 683 SNPLDBs (SNP linkage disequilibrium blocks) composed of 39 353 SNPs were applied to QTL mapping using three different mapping procedures, including the composite interval mapping (CIM) method, the mixed linear model (MLM-GWAS) method and the RTM-GWAS method, and the best mapping procedure was selected for the analysis of the 100-seed weight genetic system in NJRIKY population through comparing their detection power, including the detected number of QTLs and total phenotypic variation explained. 【Result】The 100-seed weight of Kefeng-1 and NN1138-2 were 9.0 g and 17.9 g, respectively, showing significant difference. The genotypic coefficient of variation and heritability of the trait were 12.4% and 85.4%, respectively. These results indicated that the population was suitable for genetic analysis of 100-seed weight trait. The RTM-GWAS procedure performed the best with the largest number of QTLs (57) explaining the most phenotypic variation (PVE=70.78%), while a total of 14 and 6 QTLs contributing 56.47% and 18.47% phenotypic variation were detected using CIM and MLM-GWAS, respectively. The 57 QTLs detected from the RTM-GWAS distributed on 19 chromosomes, of which 41 QTLs overlapped with 81 QTLs identified from 30 bi-parental populations in the literature. Furthermore, the PVE of 57 QTLs ranged from 0.03% to 7.57%, of which 16 QTLs were novel ones, including one large contribution major QTL Sw-09-2 (PVE>3%). Furthermore, 20 QTLs had significant interaction effect with environment. A total of 36 candidate genes were annotated from 37 QTLs through χ² test between SNPLDB markers and SNPs harboring in the predicted genes, of which 4 candidate genes were from the large contribution QTLs and other 32 candidate genes were from the small contribution QTLs. These candidate genes were included in different biological processes, of which 13 candidate genes were grouped in seed development directly, and the remaining candidate genes were grouped into different functions, such as transport, transcriptional regulators, etc., indicating that these genes from different biological pathways regulate the expression of 100-seed weight trait in NJRIKY together. 【Conclusion】Among the three different mapping procedures, RTM-GWAS procedure is the most powerful one which can provide a relatively thorough detection of 100-seed weight QTLs in NJRIKY population, therefore, it is more suitable for QTL mapping in bi-parental population such as RIL population. The candidate genes with various functions jointly regulated the complex expression of 100-seed weight trait.

Key words: soybean [Glycine max (L.) Merr.], 100-seed weight, QTL (quantitative trait locus), recombinant inbred lines population, restricted two-stage multi-locus genome-wide association analysis

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.

0
/ / Recommend

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

URL: https://www.chinaagrisci.com/EN/10.3864/j.issn.0578-1752.2020.09.004

https://www.chinaagrisci.com/EN/Y2020/V53/I9/1730

Figures/Tables 9

Fig. 1

Table 1

Table 2

Table 3

Fig. 2

Table 4

Table 5

The QTLs associated with 100-seed weight in NJRIKY population"

QTL	标记 Marker	-lgP	贡献率 R² (%)	QTL	标记 Marker	-lgP	贡献率 R² (%)
Sw-11-2	Gm11_BLOCK_5228756_5269867	56.22	7.57*	Sw-05-1	Gm05_BLOCK_28628661_28825701	7.42	0.82*
Sw-09-2	Gm09_BLOCK_34358756_34538440	39.72	5.12*	Sw-04-1	Gm04_BLOCK_10110270_10257050	6.78	0.74
Sw-04-7	Gm04_BLOCK_39928569_39957258	29.72	3.72*	Sw-06-1	Gm06_BLOCK_5723355_5918489	6.58	0.71
Sw-08-1	Gm08_16468204	28.90	3.61	Sw-06-3	Gm06_30304358	6.52	0.71*
Sw-10-1	Gm10_BLOCK_41285429_41285653	26.41	3.27	Sw-15-2	Gm15_25471398	6.31	0.68
Sw-18-5	Gm18_59805347	24.23	2.98*	Sw-16-5	Gm16_BLOCK_23266112_23462965	5.98	0.64
Sw-13-1	Gm13_31358574	23.33	2.86*	Sw-02-1	Gm02_BLOCK_2190737_2384686	5.72	0.61*
Sw-17-1	Gm17_BLOCK_2938402_3123352	20.28	2.45	Sw-04-4	Gm04_BLOCK_19626939_19826251	5.51	0.59
Sw-04-9	Gm04_BLOCK_40868266_40917625	19.75	2.38	Sw-03-2	Gm03_BLOCK_41020781_41042956	5.12	0.54
Sw-16-1	Gm16_BLOCK_12959169_13153966	16.33	1.94	Sw-03-1	Gm03_3970385	4.52	0.47
Sw-01-2	Gm01_BLOCK_55629182_55799927	15.79	1.87*	Sw-15-3	Gm15_36895087	4.48	0.46
Sw-18-1	Gm18_BLOCK_1922631_2035120	15.72	1.86*	Sw-06-2	Gm06_20892550	4.34	0.45*
Sw-14-4	Gm14_BLOCK_44088379_44288183	15.18	1.79*	Sw-04-2	Gm04_BLOCK_10815779_11015107	3.97	0.40
Sw-14-1	Gm14_1033115	14.10	1.65	Sw-10-3	Gm10_BLOCK_41542940_41691982	3.68	0.37
Sw-07-1	Gm07_BLOCK_1944869_1966911	12.09	1.40	Sw-01-1	Gm01_BLOCK_12639088_12838656	3.65	0.36
Sw-09-1	Gm09_BLOCK_264001_463636	11.95	1.38*	Sw-12-2	Gm12_BLOCK_37972975_37975786	3.46	0.34*
Sw-15-1	Gm15_BLOCK_9079223_9250661	11.21	1.29*	Sw-14-2	Gm14_BLOCK_13649138_13846641	3.19	0.31
Sw-04-8	Gm04_BLOCK_40184350_40334324	10.84	1.24	Sw-14-3	Gm14_BLOCK_42062012_42261888	2.98	0.29
Sw-18-2	Gm18_36907653	10.77	1.23	Sw-04-11	Gm04_BLOCK_47626591_47646641	2.94	0.28
Sw-10-2	Gm10_41392627	10.09	1.15	Sw-16-2	Gm16_15575581	2.92	0.28
Sw-08-2	Gm08_BLOCK_19121233_19314940	9.71	1.10	Sw-04-10	Gm04_41805915	2.65	0.25
Sw-09-3	Gm09_BLOCK_39036252_39210642	9.27	1.04	Sw-16-4	Gm16_BLOCK_21928405_22126811	2.54	0.24
Sw-11-1	Gm11_BLOCK_767896_842085	8.80	0.99	Sw-04-6	Gm04_BLOCK_26906733_27091677	2.54	0.24*
Sw-12-1	Gm12_BLOCK_35239969_35240158	8.77	0.98	Sw-18-4	Gm18_56862453	2.13	0.19
Sw-17-2	Gm17_BLOCK_7387475_7572446	8.11	0.90*	Sw-04-3	Gm04_BLOCK_17249185_17373282	2.03	0.18
Sw-16-3	Gm16_BLOCK_17551378_17602014	8.03	0.89	Sw-05-2	Gm05_30058805	1.83	0.16*
Sw-18-3	Gm18_BLOCK_56236139_56414021	8.00	0.89	Sw-12-3	Gm12_BLOCK_38208501_38296133	1.61	0.13*
Sw-02-2	Gm02_BLOCK_48637411_48823121	7.59	0.84	Sw-20-1	Gm20_236372	1.52	0.13
Sw-04-5	Gm04_BLOCK_23431433_23626782	7.59	0.84*	共计Total	57		70.78

Table 5

Fig. 3

Table 6

The candidate gene system of 100-seed weight annotated from the detected QTLs using RTM-GWAS procedure"

QTL	解释率 R² (%)	候选基因 Candidate gene	SNP数目 No. of SNPs	GO注释 GO description
Sw-01-2	1.87	Glyma01g45220	6	胚胎发育Embryo Development
Sw-02-1	0.61	Glyma02g02860	1	PSII相关的光收集复合物II过程PSII associated light-harvesting complex II process
Sw-02-2	0.84	Glyma02g43960	2	种子休眠时胚胎发育终止Embryo development ending in seed dormancy
Sw-03-1	0.47	Glyma03g04020	1	多糖分解过程Polysaccharide catabolic process
Sw-03-2	0.54	Glyma03g33360	1	麦芽糖代谢过程Maltose metabolic process
Sw-04-1	0.74	Glyma04g11582	1	转运Transport
Sw-04-7	3.72	Glyma04g34070	1	响应镉离子Response to cadmium- ion
Sw-04-9	2.38	Glyma04g34660	1	脂肪酸代谢过程Fatty acid catabolic process
Sw-04-10	0.25	Glyma04g35511	1	转录的调节,依赖DNA Regulation of transcription, DNA-dependent
Sw-04-11	0.28	Glyma04g41810	2	蛋白质糖基化Protein glycosylation
Sw-05-1	0.82	Glyma05g23100	1
Sw-06-1	0.71	Glyma06g07880	2	种子休眠时胚胎发育终止Embryo development ending in seed dormancy
Sw-07-1	1.40	Glyma07g02940	3	ATP分解过程ATP catabolic process
Sw-08-1	3.61	Glyma08g21620	3	分生组织生长调节Regulation of meristem growth
Sw-08-2	1.10	Glyma08g24950	1	细胞生长Cell growth
Sw-09-1	1.38	Glyma09g00430	4	响应脱落酸Response to abscisic acid stimulus
Sw-09-3	1.04	Glyma09g32540	2	毒素分解过程Toxin catabolic process
Sw-10-1,	3.27	Glyma10g32920	2	种子发育Seed development
Sw-10-2	1.15		2
Sw-10-3	0.37	Glyma10g33160	1	花器官的形成Floral organ formation
Sw-11-1	0.99	Glyma11g01405	4	鸟苷四磷酸代谢过程Guanosine tetraphosphate metabolic process
Sw-11-2	7.57	Glyma11g07430	1	葡糖醛酸木聚糖代谢过程Glucuronoxylan metabolic process
Sw-12-1	0.98	Glyma12g31670	2	细胞过程规则Abaxial cell fate specification
Sw-12-3	0.13	Glyma12g35020	4	转录正调控Positive regulation of transcription
Sw-13-1	2.86	Glyma13g28260	1
Sw-14-1	1.65	Glyma14g01790	1	多糖生物合成过程Polysaccharide biosynthetic process
Sw-14-3	0.29	Glyma14g33820	1	花发育调节作用Regulation of flower development
Sw-14-4	1.79	Glyma14g35260	1	核转录mRNA分解代谢过程Nuclear-transcribed mRNA catabolic process
Sw-15-1	1.29	Glyma15g12410	4	蛋白质糖基化Protein glycosylation
Sw-16-5	0.64	Glyma16g20730	1	过渡金属离子转运Transition metal ion transport
Sw-17-1	2.45	Glyma17g04360	1
Sw-17-2	0.90	Glyma17g09961	3
Sw-18-1	1.86	Glyma18g02940	1	转录调控Regulation of transcription
Sw-18-3	0.89	Glyma18g46517	2
Sw-18-4	0.19	Glyma18g47240	1	表皮细胞分裂调节Regulation of epidermal cell division
Sw-18-5	2.98	Glyma18g50760	3	多糖生物合成过程Polysaccharide biosynthetic process
Sw-20-1	0.13	Glyma20g00565	4
共计Total	54.12	36

Table 6

References 40

[1]	SMITH T J, CAMPER H M . Effect of seed size on soybean performance. Agronomy Journal, 1970,67(5):681-684. doi: 10.1002/(SICI)1097-0215(19960904)67:5&lt;681::AID-IJC15&gt;3.0.CO;2-8 pmid: 8782658
[2]	BURRIS J S, EDJE O T, WAHAB A H . Effects of seed size on seedling performance in soybeans: II. seedling growth and photosynthesis and field performance1. Crop Science, 1973,13(2):207.
[3]	ZENG Z . Precision mapping of quantitative trait loci. Genetics, 1994,136(4):1457-1468. pmid: 8013918
[4]	FUJII K, SAYAMA T, TAKAGI K, TAKAGI K, KOSUGE K, OKANO K, KAGA A, ISHINOTO M . Identification and dissection of single seed weight QTLs by analysis of seed yield components in soybean. Breeding Science, 2018,68(2):177-187. doi: 10.1270/jsbbs.17098 pmid: 29875601
[5]	ZHANG Y, LI W, LIN Y, ZHANG L, WANG C, XU R . Construction of a high-density genetic map and mapping of QTLs for soybean (Glycine max) agronomic and seed quality traits by specific length amplified fragment sequencing. BMC Genomics, 2018,19(1):641. doi: 10.1186/s12864-018-5035-9 pmid: 30157757
[6]	ZHOU Z, JIANG Y, WANG Z, GOU Z, LYU J, LI W, YU Y, SHU L, ZHAO Y, MA Y, FANG C, SHEN Y, LIU T, LI C, LI Q, WU M, WANG M, WU Y, DONG Y, WAN W, WANG X, DING Z, GAO Y, XIANG H, ZHU B, LEE S H, WANG W, TIAN Z . Resequencing 302 wild and cultivated accessions identifies genes related to domestication and improvement in soybean. Nature Biotechnology, 2015,33(4):408-414. doi: 10.1038/nbt.3096 pmid: 25643055
[7]	ZHANG H, HAO D, SITOE H M, YIN Z, HU Z, ZHANG G, YU D, SINGH R . Genetic dissection of the relationship between plant architecture and yield component traits in soybean (Glycine max) by association analysis across multiple environments. Plant Breeding, 2015,134(5):564-572.
[8]	CONTRERAS-SOTO R I, MORA F, DE OLIVEIRA M A, HIGASHI, W, SCAPIM C A, SCHUSTER I A . Genome-wide association study for agronomic traits in soybean using SNP markers and SNP-based haplotype analysis. PLoS ONE, 2017,12(2):e0171105. doi: 10.1371/journal.pone.0171105 pmid: 28152092
[9]	HU Z, ZHANG D, ZHANG G, KAN G, HONG D, YU D . Association mapping of yield-related traits and SSR markers in wild soybean (Glycine soja Sieb. and Zucc.). Breeding Science, 2014,63(5):441-449. doi: 10.1270/jsbbs.63.441 pmid: 24757383
[10]	ZHANG J, SONG Q, CREGAN P B, JIANG G . Genome-wide association study, genomic prediction and marker-assisted selection for seed weight in soybean (Glycine max). Theoretical and Applied Genetics, 2016,129(1):117-130. doi: 10.1007/s00122-015-2614-x pmid: 26518570
[11]	SONAH H, O'DONOUGHUE L, COBER E, RAJCAN I, BELZILE F . Identification of loci governing eight agronomic traits using a GBS-GWAS approach and validation by QTL mapping in soya bean. Plant Biotechnology Journal, 2015,13(2):211-221. doi: 10.1111/pbi.12249 pmid: 25213593
[12]	YAN L, HOFMANN N, LI S, FERREIRA M E, SONG B, JIANG G, REN S, QUIGLEY C, FICKUS E, GREGAN P, SONG Q . Identification of QTL with large effect on seed weight in a selective population of soybean with genome-wide association and fixation index analyses. BMC Genomics, 2017,18(1):529. doi: 10.1186/s12864-017-3922-0 pmid: 28701220
[13]	WANG J, CHU S, ZHANG H, ZHU Y, CHENG HAO, YU D . Development and application of a novel genome-wide SNP array reveals domestication history in soybean. Scientific Reports, 2016,6:20728. doi: 10.1038/srep20728 pmid: 26856884
[14]	COPLEY T R, DUCEPPE M O, O'DONOUGHUE L S . Identification of novel loci associated with maturity and yield traits in early maturity soybean plant introduction lines. BMC Genomics, 2018,19(1):167. doi: 10.1186/s12864-018-4558-4 pmid: 29490606
[15]	JING Y, ZHAO X, WANG J, TENG W, QIU L, HAN Y, LI W . Identification of the genomic region underlying seed weight per plant in soybean (Glycine max L. Merr.) via high-throughput single- nucleotide polymorphisms and a genome-wide association study. Frontiers in Plant Science, 2018,9:1392. doi: 10.3389/fpls.2018.01392 pmid: 30369935
[16]	HE J, MENG S, ZHAO T, XING G, YANG S, LI Y, GUANG R, LU J, WANG Y, XIA Q, YANG B, GAI J . An innovative procedure of genome-wide association analysis fits studies on germplasm population and plant breeding. Theoretical and Applied Genetics, 2017,130(11):2327-2343. doi: 10.1007/s00122-017-2962-9 pmid: 28828506
[17]	LI S, CAO Y, HE J, ZHAO T, GAI J . Detecting the QTL-allele system conferring flowering date in a nested association mapping population of soybean using a novel procedure. Theoretical and Applied Genetics, 2017,130(11):2297-2314. doi: 10.1007/s00122-017-2960-y pmid: 28799029
[18]	ZHANG Y, HE J, WANG Y, XING G, ZHAO J, LI Y, YANG S, PALMER R G, ZHAO T, GAI J . Establishment of a 100-seed weight quantitative trait locus-allele matrix of the germplasm population for optimal recombination design in soybean breeding programmes. Journal of Experimental Botany, 2015,66(20):6311-6325. doi: 10.1093/jxb/erv342 pmid: 26163701
[19]	MENG S, HE J, ZHAO T, XING G, LI Y, YANG S, LU J, WANG Y, GAI J . Detecting the QTL-allele system of seed isoflavone content in Chinese soybean landrace population for optimal cross design and gene system exploration. Theoretical and Applied Genetics, 2016,129(8):1557-1576. doi: 10.1007/s00122-016-2724-0 pmid: 27189002
[20]	ZHANG Y, HE J, WANG H, WANG H, MENG S, XING G, LI Y, YANG S, ZHAO J, ZHAO T, GAI J . Detecting the QTL-allele system of seed oil traits using multi-locus genome-wide association analysis for population characterization and optimal cross prediction in soybean. Frontiers in Plant Science, 2018,9:1793. doi: 10.3389/fpls.2018.01793 pmid: 30568668
[21]	ZHANG Y, HE J, MENG S, LIU M, XING G, LI Y, YANG S, YANG J, ZHAO T, GAI J . Identifying QTL-allele system of seed protein content in Chinese soybean landraces for population differentiation studies and optimal cross predictions. Euphytica, 2018,214(9), 157.
[22]	KHAN M A, TONG F, WANG W, HE J, ZHAO T, GAI J . Analysis of QTL-allele system conferring drought tolerance at seedling stage in a nested association mapping population of soybean [Glycine max (L.) Merr.] using a novel GWAS procedure. Planta, 2018,248(4):947-962. doi: 10.1007/s00425-018-2952-4 pmid: 29980855
[23]	MACKAY I, POWELL W . Methods for linkage disequilibrium mapping in crops. Trends in Plant Science, 2007,12(2):57-63. doi: 10.1016/j.tplants.2006.12.001 pmid: 17224302
[24]	MALOSETTI M, VAN EEUWIJK F A, BOER M P, CASAS A M, ELIA M, MORALEJO M, BHAT P R, RAMSAY L, MOLINA CONO J L . Gene and QTL detection in a three-way barley cross under selection by a mixed model with kinship information using SNPs. Theoretical and Applied Genetics, 2011,122(8):1605-1616. doi: 10.1007/s00122-011-1558-z pmid: 21373796
[25]	PAN L, HE J, ZHAO T, XING G, WANG Y, YU D, CHEN S, GAI J . Efficient QTL detection of flowering date in a soybean RIL population using the novel restricted two-stage multi-locus GWAS procedure. Theoretical and Applied Genetics, 2018,131(12):2581-2599. doi: 10.1007/s00122-018-3174-7 pmid: 30167759
[26]	RIYAN C, LIM J E, SAMOCHA K E, SOKOLOFF G, ABNEY M, SKOL A D, PALMER A A . Genome-wide association studies and the problem of relatedness among advanced intercross lines and other highly recombinant populations. Genetics, 2010,185(3):1033. doi: 10.1534/genetics.110.116863 pmid: 20439773
[27]	王永军, 喻德跃, 章元明, 陈受宜, 盖钧镒 . 重组自交系群体的检测调整方法及其在大豆NJRIKY群体的应用. 作物学报, 2004,30(5):413-418.
	WANY Y J, YU D Y, ZHANG Y M, CHEN S Y, GAI J Y . Method of evaluation and adjustment of recombinant inbred line population and its application to the soybean RIL population NJRIKY. Acta Agronomica Sinica, 2004,30(5):413-418. (in Chinese)
[28]	盖钧镒, 邱家驯, 赵团结 . 大豆品种南农493-1和南农1138-2与其衍生新品种的亲缘关系及其育种价值分析. 南京农业大学学报, 1997,20(1):1-8.
	GAI J Y, QIU J X, ZHAO T J . An analysis of genetic relationship of Nannong493-1 and Nannong 1138-2 with their derivative cultivars and their potential in future breeding. Journal of Nanjing Agricultural University, 1997,20(1):1-8. (in Chinese)
[29]	MURRAY M G, THOMPSON W F . Rapid isolation of high molecular weight plant DNA. Nucleic Acids Research, 1980,8(19):4321-4325. doi: 10.1093/nar/8.19.4321 pmid: 7433111
[30]	ROBERTS A, MCMILLAN L, WANG W, PARKER J, RUSYN I, THREADGILL D . Inferring missing genotypes in large SNP panels using fast nearest-neighbor searches over sliding windows. Bioinformatics, 2007,23(13):401-407. doi: 10.1093/bioinformatics/btm220 pmid: 17646323
[31]	YU J M, PRESSOIR G, BRIGGS W H, BI I V, YAMASAKI M, DOEBLEY J F, MCMULLEN M D, GAUT B S, NIELSEN D M, HOLLAND J B, KRESOVICH S, BUCKLER E S . A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics, 2006,38(2):203-208. doi: 10.1038/ng1702 pmid: 16380716
[32]	BRADBURY P J, ZHANG Z, KROON D E, CASSTEVENS T M, RAMDOSS V, BUCKER E S . TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics, 2007,23(19):2633-2635. doi: 10.1093/bioinformatics/btm308 pmid: 17586829
[33]	STRIMMER K . fdrtool: A versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics, 2008,24(12):1461-1462. doi: 10.1093/bioinformatics/btn209 pmid: 18441000
[34]	GAI J . Quantitative Inheritance//MALOY S, HUGHES K. eds. Brenner’s Encyclopedia of Genetics. 2nd ed. San Diego: Academic Press, 2013: 18-21.
[35]	LU X, XIONG Q, CHENG T, LI Q, LIU X, BI Y, LI W, ZHANG W, MA B, LAI Y, DU W, MAN W, CHEN S, ZHANG J . A PP2C-1 allele underlying a quantitative trait locus enhances soybean 100-seed weight. Molecular Plant, 2017,10(5):670-684. doi: 10.1016/j.molp.2017.03.006 pmid: 28363587
[36]	GAI J, WANG Y, WU X, CHEN S . A comparative study on segregation analysis and QTL mapping of quantitative traits in plants-with a case in soybean. Frontiers of Agriculture in China, 2007,1(1):1-7. doi: 10.1007/s11703-007-0001-3
[37]	ZHANG W K, WANG Y J, LUO G Z, ZHANG J, HE C, WU X, GAI J, CHEN S . QTL mapping of ten agronomic traits on the soybean (Glycine max L. Merr.) genetic map and their association with EST markers. Theoretical and Applied Genetics, 2004,108(6):1131-1139. doi: 10.1007/s00122-003-1527-2 pmid: 15067400
[38]	HAN Y, LI D, ZHU D, LI H, LI X, TENG W, LI W . QTL analysis of soybean seed weight across multi-genetic backgrounds and environments. Theoretical and Applied Genetics, 2012,125(4):671-683. doi: 10.1007/s00122-012-1859-x pmid: 22481120
[39]	CHEN Q, ZHANG Z, LIU C, XIN D, QIU H, SHAN D, SHAN C, HU G . QTL analysis of major agronomic traits in soybean. Agricultural Sciences in China, 2007,6(4):399-405.
[40]	WANG P, ZHAO Y, LI Z, HSU C, LIU C, FU L, HOU Y, DU Y, XIE S, ZHANG C, GAO J, CAO M, HUANG X, ZHU Y, TANG K, WANG X, TAO W, XIONG Y, ZHU J . Reciprocal regulation of the TOR kinase and ABA receptor balances plant growth and stress response. Molecular Cell, 2018,69(1):100-112. doi: 10.1016/j.molcel.2017.12.002 pmid: 29290610

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

环境 Environment	亲本 Parent (g)			重组自交系群体 RIL
环境 Environment	科丰1号 Kefeng-1	南农1138-2 NN1138-2	平均值 Mean	最小值 Min.	最大值 Max.	遗传变异系数GCV(%)	遗传率 h²(%)
12JP	8.5	15.7	11.4	7.5	16.0	13.5	93.3
13JP	8.8	14.9	11.6	7.6	20.6	15.5	92.5
13SF	9.8	23.2	13.9	9.2	19.5	15.1	93.9
平均 Mean	9.0	17.9	12.3	8.6	17.1	12.4	85.4

变异来源 Source of variation	自由度 DF	均方 MS	F值 F value	P值 P value
环境 Environment	2	2308.47	101.66	<0.0001
重复（环境）Replication(Environment)	6	20.08	12.59	<0.0001
区组（环境×重复）Block(Environment×Replication)	180	1.34	1.78	<0.0001
家系 Line	426	23.66	6.81	<0.0001
家系×环境 Line×Environment	851	3.50	4.67	<0.0001
误差 Error	2245	0.75
合计 Total	3710

定位方法 Method	检测的QTL Detected QTLs	表型变异解释率 PVE (%)	已报道的QTL Reported QTLs
RTM-GWAS
Mapped QTL	57(19)	70.78	41(84)
LC major QTL	5(5)	23.30
SC major QTL	52(19)	47.48
QTL×Env.	20(13)	4.20
Unmapped QTL		14.52
CIM
Mapped QTL	14(8)	56.47	13(16)
MLM-GWAS
Mapped QTL	6(3)	18.47	6(18)

标记Marker	LOD/-lgP	表型变异解释率PVE (%)	SoyBase QTL
CIM
Gm04_BLOCK_15191938_15271503	3.67	2.94	5-2
Gm04_BLOCK_36700318_36900222	3.08	2.55	36-15
Gm04_BLOCK_39069193_39185415	2.93	2.37	36-15
Gm04_BLOCK_38868989_39068925	2.45	2.01	36-15
Gm04_BLOCK_29674388_29831299	1.96	1.59	20-2
Gm08_BLOCK_19121233_19314940	4.11	3.41	7-1,34-1,34-13,36-1,37-8
Gm09_BLOCK_35812609_35851884	6.81	5.76	40-4
Gm09_36859571	6.97	5.86	40-4
Gm10_BLOCK_49435374_49524464	4.44	3.69
Gm11_3955010	7.78	6.91	37-9
Gm11_BLOCK_5228756_5269867	12.39	11.03	21-1
Gm14_BLOCK_29877119_30044908	2.36	1.90	23-1,13-2
Gm15_BLOCK_29781764_29980832	3.69	2.95	6-1,36-12
Gm18_54211355	4.12	3.50	3-3
总计Total		56.47
MLM-GWAS
Gm04_BLOCK_23431433_23626782	3.72	3.36	20-2,5-2,36-15,45-3
Gm11_BLOCK_4611160_4662697	3.27	2.88	37-9
Gm11_BLOCK_5228756_5269867	4.03	3.69	37-9,21-1,22-2,49-2
Gm11_20972740	3.12	2.73	21-1,20-4,20-3,11-1,32-1,4-1,10-3,36-11
Gm18_BLOCK_16884478_16885174	3.40	3.03	2-5
Gm18_45987500	3.17	2.78	27-1
总计Total		18.47

Detection Power of RTM-GWAS Applied to 100-Seed Weight QTL Identification in a Recombinant Inbred Lines Population of Soybean

RichHTML

PDF

Abstract

Cite this article

share this article

Figures/Tables 9

References 40

Related Articles 15

Metrics

Comments

Recommended 0

[1]	LI YiLing,PENG XiHong,CHEN Ping,DU Qing,REN JunBo,YANG XueLi,LEI Lu,YONG TaiWen,YANG WenYu. Effects of Reducing Nitrogen Application on Leaf Stay-Green, Photosynthetic Characteristics and System Yield in Maize-Soybean Relay Strip Intercropping [J]. Scientia Agricultura Sinica, 2022, 55(9): 1749-1762.
[2]	JIANG FenFen, SUN Lei, LIU FangDong, WANG WuBin, XING GuangNan, ZHANG JiaoPing, ZHANG FengKai, LI Ning, LI Yan, HE JianBo, GAI JunYi. Geographic Differentiation and Evolution of Photo-Thermal Comprehensive Responses of Growth-Periods in Global Soybeans [J]. Scientia Agricultura Sinica, 2022, 55(3): 451-466.
[3]	YAN Qiang,XUE Dong,HU YaQun,ZHOU YanYan,WEI YaWen,YUAN XingXing,CHEN Xin. Identification of the Root-Specific Soybean GmPR1-9 Promoter and Application in Phytophthora Root-Rot Resistance [J]. Scientia Agricultura Sinica, 2022, 55(20): 3885-3896.
[4]	ZHAO DingLing,WANG MengXuan,SUN TianJie,SU WeiHua,ZHAO ZhiHua,XIAO FuMing,ZHAO QingSong,YAN Long,ZHANG Jie,WANG DongMei. Cloning of the Soybean Single Zinc Finger Protein Gene GmSZFP and Its Functional Analysis in SMV-Host Interactions [J]. Scientia Agricultura Sinica, 2022, 55(14): 2685-2695.
[5]	REN JunBo,YANG XueLi,CHEN Ping,DU Qing,PENG XiHong,ZHENG BenChuan,YONG TaiWen,YANG WenYu. Effects of Interspecific Distances on Soil Physicochemical Properties and Root Spatial Distribution of Maize-Soybean Relay Strip Intercropping System [J]. Scientia Agricultura Sinica, 2022, 55(10): 1903-1916.
[6]	JunYi GAI,JianBo HE. Major Characteristics, Often-Raised Queries and Potential Usefulness of the Restricted Two-Stage Multi-Locus Genome-Wide Association Analysis [J]. Scientia Agricultura Sinica, 2020, 53(9): 1699-1703.
[7]	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.
[8]	XiaoShuai HAO,MengMeng FU,ZaiDong LIU,JianBo HE,YanPing WANG,HaiXiang REN,DeLiang WANG,XingYong YANG,YanXi CHENG,WeiGuang DU,JunYi GAI. Genome-Wide QTL-Allele Dissection of 100-Seed Weight in the Northeast China Soybean Germplasm Population [J]. Scientia Agricultura Sinica, 2020, 53(9): 1717-1729.
[9]	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.
[10]	ZHAO DeQiang,LI Tong,HOU YuTing,YUAN JinChuan,LIAO YunCheng. Benefits and Marginal Effect of Dry Matter Accumulation and Yield in Maize and Soybean Intercropping Patterns [J]. Scientia Agricultura Sinica, 2020, 53(10): 1971-1985.
[11]	PANG Ting,CHEN Ping,YUAN XiaoTing,LEI Lu,DU Qing,FU ZhiDan,ZHANG XiaoNa,ZHOU Ying,REN JianRui,WANG Tian,WANG Jin,YANG WenYu,YONG TaiWen. Effects of Row Spacing on Dry Matter Accumulation, Grain Filling and Yield Formation of Different Nodulation Characteristic Soybeans in Intercropping [J]. Scientia Agricultura Sinica, 2019, 52(21): 3751-3762.
[12]	LIAN Yun,LI HaiChao,LI JinYing,WANG JinShe,WEI He,LEI ChenFang,WU YongKang,LU WeiGuo. Distribution of Soybean Cyst Nematode Resistance Allele Rhg1 and Rhg4 in Huang-Huai Soybean Varieties [J]. Scientia Agricultura Sinica, 2019, 52(15): 2559-2566.
[13]	NIU Lu, ZHAO QianQian, YANG Jing, XING GuoJie, ZHANG Wei, HE HongLi, YANG XiangDong. Over-Expression of Yeast PAC1 Confers Enhanced Resistance to Soybean mosaic virus in Transgenic Soybean [J]. Scientia Agricultura Sinica, 2018, 51(2): 217-225.
[14]	WANG DaGang, LI Kai, ZHI HaiJian. Progresses of Resistance on Soybean Mosaic Virus in Soybean [J]. Scientia Agricultura Sinica, 2018, 51(16): 3040-3059.
[15]	ZHOU Li, FU ZhiDan, DU Qing, CHEN Ping, YANG WenYu, YONG TaiWen. Effects of Reduced N Fertilization on Crop N Uptake, Soil Ammonia Oxidation and Denitrification Bacteria Diversity in Maize/Soybean Relay Strip Intercropping System [J]. Scientia Agricultura Sinica, 2017, 50(6): 1076-1087.