Crops ›› 2020, Vol. 36 ›› Issue (6): 189-196.doi: 10.16035/j.issn.1001-7283.2020.06.028

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Mining the Elite Allele of Resistance of Cercospora sojina Hara Race 1 in Soybean Resources

Wang Caijin(), Di Wenjing, Ma Shumei, Wang Yang()   

  1. Department of Modern Agriculture and Ecological Environment,Heilongjiang University, Harbin 150080, Heilongjiang, China
  • Received:2020-04-25 Revised:2020-10-12 Online:2020-12-15 Published:2020-12-09
  • Contact: Wang Yang E-mail:1613115170@qq.com;2004084@hlju.edu.cn

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Abstract:

In order to provide theoretical basis for molecular breeding of resistance varieties by mining the elite allele and carrier materials of Cercospora sojina Hara race 1 in soybean resources, the natural population composed of 205 soybean resources was to identify the disease resistance for Cercospora sojina Hara race 1 under the artificial condition. Population structure and genetic diversity was estimated on the basis of 117 simple sequence repeat (SSR) markers. Association mapping for soybean resistance was performed by GLM model and MLM model. The results showed that the value of coefficient of variation for the resistance in 205 resources was 20.90%. We identified 7 marker-trait associations which explained variance from 7.58% to 16.06%. Thirty-six positive alleles were revealed. Satt244-230 (26.16), Satt142-154 (21.94) and Satt244-186 (20.19) had the highest positive phenotypic effect, and their carrier varieties were 12C8646, 12C8670 and 12C6175, which were all wild resources. Satt142-189 (8.94) had the highest positive phenotypic effect in varieties, which was carried by seven varieties breeding in Heilongjiang province and the typical carrier material was Dongnong 43. The above information could be used for marker-assisted selection breeding and anti-source screening.

Key words: Soybean resources, Cercospora sojina Hara, Race 1, Resistance, Elite allele

Table 1

A total of 205 soybean resources of different origins"

类型Type 来源Origin 材料Material
A







 黑龙江省育成品种
（96份）






 北豆5, 合丰16, 合丰23, 合丰25, 合丰29, 合丰32, 合丰34, 合丰36, 合丰39, 合丰41, 合丰42, 合丰44, 合丰47, 合丰50, 合丰51, 合丰52, 合丰53, 合丰54, 合丰55, 合丰56, 合农59, 合农61, 黑农37, 黑农40, 黑农44, 黑农48, 黑农51, 黑农52, 黑农58, 黑农68, 华疆2, 华疆4号, 金源55, 垦豆18, 垦丰14, 垦丰16, 垦丰17, 垦丰20, 垦丰22, 垦丰23, 垦农26, 垦农28, 绥农22, 绥农24, 北丰11, 北丰14, 北疆1号, 东农42, 东农43, 东农47, 东农48, 东农49, 东农50, 东农51, 东农52, 东农53, 东农55, 丰收06, 丰收24, 丰收25, 合丰05, 合丰22, 合丰30, 合丰35, 合丰40, 合丰45, 合丰57, 合农62, 合农63, 黑河10, 黑河11, 黑河12, 黑河14, 黑河15, 黑河16, 黑河18, 黑河19, 黑河20, 黑河21, 黑河22, 黑河23, 黑河24, 黑河25, 黑河26, 黑河39, 黑河43, 黑河44, 黑河49, 黑河50, 黑河51, 黑农69, 绥农4号, 绥农25, 绥农31, 红丰3, 垦豆25
B

 外省育成品种
（34份）
 吉林1, 吉林2, 吉林11, 吉林12, 吉林14, 吉林17, 吉林18, 吉林19, 吉林25, 吉林28, 吉林29, 吉林30, 吉林32, 安丰1, 九农1, 九农2, 九农9, 白农1, 铁丰5, 铁丰22, 铁丰25, 辽豆3, 丹豆1,
丹豆2, 开育8, 通农8, 紫花4, 通农4, 长农5, 吉育95, 长农18, 长农24, 长农26, 长农27
C
 地方品种
（13份）		 小白脐, 满地金, 玉石豆, 牛毛青, 一窝蜂, 黄毛豆, 龙油太, 黑滚豆, 茶色豆, 青扎豆, 蓑衣领, 嘟噜豆, 元宝金
D





 野生资源
（62份）




 12C6211, 12C8757, 12C8760, 12C6208, 11C2638, 12NC1844, 12C8745, 12NC1806, 12NC1846 , 12NC1808, 12C8607, 12C6002, 12NC1848, 12NC1810, 12NC1854, 12C8619, 12NC1812, 12C8670, 12C8643, 12C8646, 12C6181, 12C6175, 12C8697, 12C8649, 12C8603, 12C8592, 12NC1838, 12NC1820, 12NC1822, 12C8631, 12C8595, 12C8769, 12C8598, 12C8601, 12C8721, 12NC1818, 12NC1816, 12NC1864, 12NC1814, 12NC1860, 12NC1858, 12C6166, 12NC1834, 12C8742, 12C5984, 12NC1842, 12NC1802, 12NC1804, 12C8589, 12C8766, 12NC1866, 12C8772, 12C8709, 12C6214,
12C8604, 12C6169, 12C8667, 12NC1852, 12C6061, 12C8625, 12C6277, 12NC1836

Table 2

Cercospera sojina identification grading criteria"

分级
Grade		 病情指数
Disease index		 抗性评价
Resistance evaluation
1 0~20 高抗（HR）
2 21~40 抗病（R）
3 41~60 中抗（MR）
4 61~80 感病（S）
5 80以上 高感（HS）

Fig.1

Distribution of 117 SSR markers on the chromosomes of soybean"

Table 3

Resistance identification results of different origin soybean resources"

来源
Origin		 数量
No.		 HR R MR S HS 病情指数范围
Range of disease index (%)		 平均值
Average (%)		 变异系数
Coefficient of variation (%)
A 96 1 22 53 20 0 12~72 52.17 21.76
B 34 0 1 15 18 0 40~68 59.41 9.61
C 13 0 2 4 6 1 22~82 57.54 24.87
D 62 0 15 28 19 0 28~68 53.66 22.47

Fig.2

Distribution of relative kinship value of 205 soybean resources"

Fig.3

Population structure of 205 soybean resources A: Estimation of LnP(D) value in population; B: Estimation of ?K value in population; C: Group structure diagram"

Fig.4

Cluster heat map analysis results of 205 soybean resources"

Table 4

Marker loci associated with resistance and their explained phenotypic variation"

模型
Model		 位点
Marker		 染色体
Chromosome		 位置
Position		 P R2 (%)
GLM Satt387 3 36 576 654 0.0369 8.96
Satt233 8 17 298 012 0.0367 9.05
Satt332 11 22 137 813 0.0056 9.17
Satt142 12 36 043 054 0.0189 16.06
Satt309 13 1 736 305 0.0481 7.58
Satt244 16 33 327 246 0.0200 13.09
Satt431 16 35 718 476 0.0421 13.58
MLM Satt332 11 22 137 813 0.0197 9.17

Table 5

Phenotypic effect of some positive alleles at loci significantly associated with resistance"

位点-等位变异
Locus-allele		 表型效应
ai		 材料数
No.		 典型载体材料
Typical material		 位点-等位变异Locus-allele 表型效应
ai		 材料数
No.		 典型载体材料Typical material
Satt332-219 16.16 7 12C8670 Satt387-211 7.19 4 12C8670
Satt332-248 1.37 109 东农43 Satt387-235 5.52 6 12NC1820
Satt332-240 0.43 34 12NC1838 Satt387-209 5.32 16 12C8646
Satt142-154 21.94 2 12C8670 Satt387-217 3.69 4 12C8667
Satt142-124 10.61 3 12C8721 Satt387-204 2.82 19 元宝金
Satt142-189 8.94 8 东农43 Satt387-197 1.00 101 东农43
Satt142-196 5.27 3 12NC1838 Satt431-265 18.16 2 12C8667
Satt142-165 5.08 7 12NC1820 Satt431-189 12.63 4 12C6175
satt142-150 3.65 7 12C8646 Satt431-254 9.13 2 12NC1838
satt142-163 1.94 3 12C6181 Satt431-234 8.51 16 东农43
Satt142-177 0.80 93 黑农44 Satt431-220 8.13 6 12C8646
Satt244-230 26.16 1 12C8646 Satt431-245 5.33 5 12C8670
Satt244-186 20.19 1 12C6175 Satt431-228 0.64 82 12NC1820
Satt244-178 3.22 72 东农43 Satt309-134 4.34 18 12C8646
Satt233-204 16.21 2 12C8760 Satt309-144 1.57 56 黑农48
Satt233-208 3.46 8 12C8646 Satt309-155 1.23 10 黑河15
Satt233-214 3.01 25 黑农48 Satt309-138 0.70 17 12C8643
Satt233-220 2.88 39 东农43 Satt309-147 0.32 45 东农43
[1] 雷金芝, 郭彪, 仲召生, 等. 江滨农场大豆灰斑病发生情况调查. 现代化农业, 2006(1):13-14.
[2] Pham A T, Harris D K, Buck J, et al. Fine mapping and characterization of candidate genes that control resistance to Cercospora sojina K. Hara in two soybean germplasm accessions. PLoS ONE, 2015,10(5):1-20.
[3] Mian M A R, Missaoui A M, Walker D R, et al. Frogeye leaf spot of soybean:a review and proposed race designations for isolates of hara. Crop Science, 2008,48(1):14-24.
[4] 丁俊杰, 顾鑫, 杨晓贺, 等. 黑龙江省大豆灰斑病菌生理小种及遗传关系分析. 中国农业科学, 2012,45(21):4377-4387.
[5] 黄桂潮, 霍虹, 张再兴, 等. 大豆灰斑病菌(Cercospoora Sojina Hara)生理小种鉴定结果初报. 大豆科学, 1984(3):231-235.
[6] 马淑梅. 2006~2010年黑龙江省大豆灰斑病菌生理小种监测及部分主栽品种抗性鉴定. 大豆科学, 2011,30(3):450-454.
[7] 万建民. 作物分子设计育种. 作物学报, 2006(3):455-462.
[8] Sun J T, Guo N, Lei J, et al. Association mapping for partial resistance to Phytophthora sojae in soybean (Glycine max (L.) Merr.). Journal of Genetics, 2014,93(2):355-363.
doi: 10.1007/s12041-014-0383-y pmid: 25189230
[9] 魏崃, 薛永国, 王伟威, 等. 应用关联分析鉴定大豆对腐霉菌的抗性基因. 大豆科学, 2013,32(3):295-301.
[10] 冯莉君. 新的大豆孢囊线虫抗虫基因的发掘和鉴定. 科学之友, 2013(3):160-162.
[11] 张文慧, 陈庆山, 杨庆凯, 等. 大豆灰斑病1号生理小种抗性基因的SSR标记分析. 大豆科学, 2004,23(3):169-173.
[12] 董伟, 杨庆凯, 沈义国, 等. 大豆灰斑病1号生理小种抗性基因的RAPD标记. 高技术通讯, 1999(10):48-51.
[13] 董亚楠, 魏崃, 丁俊杰, 等. 应用关联分析挖掘大豆对灰斑病12号生理小种的抗性位点. 大豆科学, 2016,35(3):402-406.
[14] 马淑梅, 李宝英. 东北春大豆灰斑病菌生理小种鉴定结果初报. 植物病理学报, 1997,27(2):180.
[15] 马淑梅. 大豆种质资源对灰斑病抗性评价和广谱抗源鉴定. 中国农学通报, 2011,27(17):260-264.
[16] Doyle J J, Doyle J L. Isolation of plant DNA from fresh tissue. Focus, 1990,12:13-15.
doi: 10.1103/PhysRevFocus.12.13
[17] 时宽玉, 洪德林. 6个水稻杂交组合与其亲本的SSR标记多态性及其应用. 南京农业大学学报, 2005(4):1-5.
[18] Liu K, Liu S. Muse Power Marker:an integrated analysis environment for genetic marker analysis. Bioinformatics, 2005,21:2128-2129.
doi: 10.1093/bioinformatics/bti282 pmid: 15705655
[19] Pritchard J K, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics, 2000,155:945-959.
pmid: 10835412
[20] Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE:a simulation study. Molecular Ecology, 2005,14(8):2611-2620.
pmid: 15969739
[21] Bradbury P J, Zhang Z, Kroon D E, et al. TASSEL:software for association mapping of complex traits in diverse samples. Bioinformatics, 2007,23(19):2633-2635.
doi: 10.1093/bioinformatics/btm308 pmid: 17586829
[22] Hardy O, Vekemans X. SPAGeDi:a versatile computer program to analyse spatial genetic structure at the individual or population levels. Molecular Ecology Notes, 2002,2:618-620.
doi: 10.1046/j.1471-8286.2002.00305.x
[23] 文自翔, 赵团结, 郑永战, 等. 中国栽培和野生大豆农艺品质性状与SSR标记的关联分析Ⅱ. 优异等位变异的发掘. 作物学报, 2008,34(8):1339-1349.
doi: 10.3724/SP.J.1006.2008.01339
[24] Rouf M, Jason B, Tarek J, et al. Identification of soybean genotypes resistant to Cercospora sojina by field screening and molecular markers. Plant Disease, 2009,93(4):408-411.
doi: 10.1094/PDIS-93-4-0408 pmid: 30764228
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