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Association Analysis of Charcoal Rot Disease Resistance in Soybean
Plant Pathol. J. 2019;35:189-199
Published online June 1, 2019
© 2019 The Korean Society of Plant Pathology.

Ali Ghorbanipour1, Babak Rabiei1,*, Siamak Rahmanpour2, and Seyed Akbar Khodaparast3

1Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
2Seed and Plant Improvement Institute (SPII), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
3Department of Plant Protection, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
Correspondence to: *Phone) +981333690282, FAX) +98-13-33690281
E-mail) rabiei@guilan.ac.ir

Handling Editor: Lee, Jungkwan
Received December 8, 2018; Revised February 14, 2019; Accepted February 18, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

In this research, the relationships among the 31 microsatellite markers with charcoal rot disease resistance related indices in 130 different soybean cultivars and lines were evaluated using association analysis based on the general linear model (GLM) and the mixed linear model (MLM) by the Structure and Tassel software. The results of microsatellite markers showed that the genetic structure of the studied population has three subpopulations (K=3) which the results of bar plat also confirmed it. In association analysis based on GLM and MLM models, 31 and 35 loci showed significant relationships with the evaluated traits, respectively, and confirmed considerable variation of the studied traits. The identified markers related to some of the studied traits were the same which can probably be due to pleiotropic effects or tight linkage among the genomic regions controlling these traits. Some of these relationships were including, the relationship between Sat_252 marker with amount of charcoal rot disease, Satt359, Satt190 and Sat_169 markers with number of microsclerota in stem, amount of charcoal rot disease and severity of charcoal rot disease, Sat_416 marker with number of microsclerota in stem and amount of charcoal rot disease and the Satt460 marker with number of microsclerota in stem and severity of charcoal rot disease. The results of this research and the linked microsatellite markers with the charcoal rot disease-related characteristics can be used to identify the suitable parents and to improve the soybean population in future breeding programs.

Keywords : linkage disequilibrium, microsatellite markers, pleiotropic effect, population genetic structure
Body

Soybean (Glycin max L.) is a diploid (2n = 2× = 40), annual and dicotyledon plant from the legumes family and Papilionoideae subfamily which is planted and exploited in both grain and fodder forms (Singh, 2006). Soybean has a special status among the plants due to its high level of protein and oil. Soybean has in average 40% protein and 20% oil which is an unrivaled grain in terms of nutritional value from among the important crops (Bilyeu et al., 2010). In natural and agricultural ecosystems, plants are exposed to the stresses. Various factors including biotic (insects, fungus, viruses and weeds) and abiotic factors (drought, salinity, high and low temperature, flooding and radiation) affect growth of the crop plants. Soybean is sensitive to a large number of pathogens that cause the most damage to seedling and root of plant. One of the soil-borne pathogens which attack the root and crown is the fungus Macrophomina phaseolina (Tassi) Goid which is the factor of charcoal rot pathogen or wilting. This fungus is poly-phage and pollutes more than 500 plant species in 100 families, including the monocotyledon and dicotyledon (Jana et al., 2003). In favourable conditions, this pathogens causes the burning and death of seedling, the crown rot and charcoal rot in most important crops such as soybean, cotton, corn, sunflower, sorghum and so on (Babu et al., 2007). It seems that the only practical way to control the soybean charcoal rot is to use the resistant cultivars (Mengistu et al., 2001). Therefore, identifying the resistant cultivars and mapping resistance gene helps significantly the plant breeders to progress the breeding programs for producing the disease resistant cultivars.

Association analysis which is also known as linkage disequilibrium (LD) mapping, has main advantages that mapping the genes. Since natural populations are used in association analysis, firstly, there is wider genetic diversity than the two-parents populations. Secondly, upon the population, LD mapping has high accuracy, because all meiosis events accumulated during the plant evolutionary history are taken into account, while in the ordinary method, mapping only occur in a number of cross or autogamous generations (Musial et al., 2008). Linkage disequilibrium has a important application in association mapping studies. In case of any linkage disequilibrium between the molecular marker and genes controlling a special trait, significant relationship can be determined between the marker and traits and using it in breeding programs. Therefore, association mapping is of high importance in determining the amount of linkage disequilibrium between the marker and trait (Oraguzie et al., 2007).

Using the association mapping, Iqbal et al. (2001) identified six QTLs for resistance against the sudden death syndrome (SDS) disease in soybean based on the ANOVA method. By using the SSR markers, Wang et al. (2008) identified twelve QTLs related to resistance against the powdery mildew in soybean by association analysis. Li et al. (2010) and Wu et al. (2011) identified 45 QTLs related to the resistance to Phytophthora distributed on 15 chromosomes by using the association analysis. Fusari et al. (2012) and Iquira et al. (2015), identified the QTLs for resistance to Sclerotinia stem rot disease in sunflower and soybean respectively by using the mixed linear model, they identified a candidate gene which justified 20% of phenotypic variance. Through association analysis, Sun et al. (2014) identified four alleles of Satt 634-133, Satt 634-149, Satt 222-168 and Satt 301-190 related to the partial resistance to Phytophthora in soybean. Sonah et al., (2015) between one and eight genomic loci associated with seed weight were identified. Coser et al. (2017), new sources for charcoal rot disease resistance were identified from both field and greenhouse screening from maturity groups I, II, and III. Five significant single nucleotide polymorphism (SNP) and putative candidate genes related to abiotic and biotic stress responses were reported from the field screening; while greenhouse screening revealed eight loci associated with eight candidate gene families, all associated with functions controlling plant defense response.

It seems that genes controlling some morphological attributes and a few number of diseases in soybean have been identified based on the consistency analysis as well as association analysis but genes or QTLs controlling the resistance to the charcoal rot disease in soybean have not been yet mapped. In this study, SSR markers linked to some important traits related to the charcoal rot disease were identified by association analysis in Iranian soybean germplasm.

Materials and Methods

Plant materials and phenotypic evaluations

To evaluate the resistance of different soybean genotypes to charcoal rot, the seeds of 130 soybean genotypes from different maturity groups were planted in two separate experiments as a randomized complete block design with three replications at the research field of Seed and Plant Improvement Research Institute (SPIRI), Karaj Iran, during two years, 2014 and 2015. The plant materials of this study were part of the Iranian soybean germplasm achived from SPIRI (Table 1). The seeds of each genotype were planted in four lines of 2.5 m with a distance of 50 cm between lines and 10 cm between plants. Primary plowing and disk were carried out at a depth of 30 cm and 15 cm, respectively, and the ground levelling was done by the trowel. Nitrogen fertilizer with 150 kg/ha criterion according to the soil test was added to the plots in equal proportions in three stages, before planting, flowering and podding stage. The first irrigation was carried out 3 days before planting and the next irrigations were done once a week. Weed control was carried out manually on several occasions.

The genotypes were inoculated with the pathogen at flowering stage employing Tesso and Ejeta (2011) method with some modifications. For contamination in field conditions, isolate S8, isolated and purified from infected soybean plants at SPIRI field, was propagated on a potato dextrose agar (PDA) culture medium to obtain a three-day culture. Seven mm discs made from the fungus colony margin were placed in the center of 9 cm petri dishes containing a new PDA culture medium. Then, in sterile conditions, four sterilized toothpicks were placed in each petri with the same intervals and on two sides of the mycelium disk. The petri dishes were stored in dark conditions at 30°C for 7 days. After the toothpicks were covered with mycelium colonies and fungal microsclerotia, they were transferred to the field for inoculation of plants at the flowering stage. To inoculate, some holes were firstly created on the stems horizontally by an awl which were in the diameter of the toothpick and the contaminated toothpicks were secondly inserted into the plant stem. To determine the resistance and susceptibility of soybean genotypes to charcoal rot disease, the traits including pod weight, grain weight, 100 grain weight, grain yield, number of microsclerota in stem, amount of charcoal rot disease (I) and severity of charcoal rot disease (S) were measured. Amount of charcoal rot disease (I) and severity of charcoal rot disease (S) were calculated based on Eqs. (1) and (2), respectively.


I=Σn/N×100

Where n is the number of plants with symptoms of the disease and N is the total number of evaluated plants (Cardoso et al., 2004).

The severity of disease (S) is based on the rate of colour changes in the plant tissue (stem).


S=(Hd/Ht)×100

Which Hd is the height of the stem discoloration or the length of the lesion, and Ht is the total height of the stem measured (Mengistu et al., 2007). The ruler was used to measure the length of the lesion caused by the fungus.

Genotypic evaluations

For genotypic evaluations, 3 to 4 newly-developed leaves were taken from each bush at five-leaf stage and wrapped up on a thin aluminium foil and put on the liquid nitrogen container. After transfixing the samples, they were powdered together with liquid nitrogen in proclaim pounder and 5 mg were poured into the 2 ml tubes and kept in −80°C. The genomic DNA was extracted by cetyl trimethyl ammonium bromide (CTAB) as reported by Saghai-Maroof et al. (1984). Quantification and qualification of the extracted DNA was determined by electrophoresis (97 v for 45 min) on 1% agarose gel and DNA samples were diluted about 20–30 ng/μl. The characteristics of 31 SSR markers (Table 2) were extracted from soybase database (www.soybase.org). Polymerase chain reaction (PCR) was carried out using the Eppendorf thermocycler in volume of 15 μl including: 2 μl genomic DNA, 1.5 μl PCR buffer (10×), 0.5 μl dNTPs (1 mM), 1 μl of each forward and reverse primers, 1.2 μl magnesium chloride (15 mM), 0.1 μl Tag DNA polymerase and 7.7 μl sterilized distilled water. Thermal cycles were including: one cycle for initial denaturing stage in 95°C for 5 min followed by 35 thermal cycles as denaturation in 94°C for 30 s, annealing in 45–60°C (based on the optimum temperature of each primer) for 30 s and primer extension in 72°C for 45 sec and finally after the end of the 35 three-stages thermal cycles, one cycle for final extension in 72°C for 5 min. The PCR products were separted by horizontal agarose gel elctrophoresis and the gels were stained by AgNO3 and finally the observed bands for each of the studied genotypes were scored.

Data analysis

To perform the association mapping, structure analysis was firstly conducted to construct the genetic structure matrix of the studied genotypes using the STRUCTURE software (Pritchard et al., 2000). Since there was no prior information on population structure, so the optimum number of groups or sub-populations (K) was determined by simulation, so that K was considered from 1 to 10 and simulation was conducted with period length of 100000 burn in and 100000 Markov Chain Monte Carlo (MCMC) repetition and the optimum K was determined using Evanno et al. (2005) method. Then, membership percentage of each genotype in each group was determined by Spataro et al. (2011) method. Based on this method, a genotype is attributed to a group when its membership percentage is more than 70% (0.7), but if the membership percentage is obtained less than 69% (0.69), it is considered as a mixed genotype. Data obtained from the population structure (Q matrix) and breaking it into two or more sub-populations, was extracted from this software. Finally, the association mapping was conducted using two different statistical models, GLM and MLM, with the data set of phenotypic matrix, genotypic matrix, structure matrix (Q) and kinship matrix by TASSEL 4.1.2 software (Bradbury et al., 2007).

Results and Discusion

Descriptive statistics

Descriptive statistics of the measured traits including minimum, maximum, mean, range and cofficient of variation (CV) for the studied population is shown in Table 3. The highest CV was observed for grain yield and amount of charcoal rot disease (23.12% and 21.82%, respectively). Furthermore, CV for the other traits was also more than 10%. These results shows that the studied soybean germplasm has a high diversity for the all measured traits that can be useful for the association analysis. Because in the association analysis, genetic factors related to phenotypic variations are searched in more diverse populations than those derived from the crossing of two pure lines, the occurrence of recombinant events during the evolutionary history of these highly diverse populations, which are usually several generations more further from their common ancestry, cause to the failure of the linkage disequilibrium blocks in the genome (Oraguzie et al., 2007). In the other words, all the meiosis events accumulated over the evolutionary history of the plant are considered in the association mapping, while in the conventional mapping populations, the meiosis events occurs only in a few intercourse generations or self-pollination (Oraguzie et al., 2007). Therefore, it seems that to be necessary the existence of a high variation in the studied populations for clarity and accuracy of the results (Oraguzie et al., 2007). This variation was observed in the studied population in this research. Wang et al. (2006) and Vikram (2007) in similar studies in soybean reported the high levels of diversity for their studied populations.

Genetic structure of the population

In genetic studies, the population structure which is used to explain the relationships of individuals within and between the populations, provides a perspective on the evolutionary relationships of individuals in a population. Moreover, in the ideal association analysis, there should be no structure in the studied population, indeed, the population should not be structurally divided into subgroups, since the existence of the structure in the studied population could be a deterrent to achieve the reliable results. If the effects of population structure and kinship relationships are not considered in the association analysis, false positive results will arise (Breseghello and Sorrells, 2006). Therefore, understanding the population structure as a prerequisite in association mapping can avoid false positive relationships between markers and traits (Pritchard et al., 2000). In this research, the genetic structure of the studied population and the proper number of sub-population were used as covariate in conducting association analysis based on the Bayesian method in STRUCTURE software (Porras-Hurtado et al., 2013). The results showed that there are three probable sub-populations (K = 3) in the studied germplasm (Fig. 1), which was considered as the optimal K for estimating the population structure and the membership matrix in each sub-population (Q matrix). The results indicated that (Fig. 2) among the total of 130 studied genotypes, 9 genotypes (6.92%) had the mixed structure (the membership probability of each sub-population is less than 0.69), 34 genotypes (26.15%) belonged to the first structure (red), 43 genotypes (33.07%) to the second structure (blue) and 44 genotypes (33.84%) to the third structure (green).

Linkage disequilibrium

In assoction mapping where the quantitative trait loci (QTLs) are mapped based on the linkage disequilibrium (LD) in addition to combining the population structure, the extent of LD in the genome is also very important (Al-Maskri et al., 2012). In this study, 25.8% of the markers had a significant R2 and greater than 0.1 (R2 ≥ 0.1, P-value ≤ 0.01) (Fig. 3). The linkage disequilibrium in the studied genetic population allows association mapping analysis. The factors increasing the amount of LD are system of autogamy, epistasis, genomic alterations, genetic drift, genetic isolation, population structure, small size of population, selection and degree of kinship, while alternating (allogamy), gene transformation, high levels of recombinant and mutation, as well as periodic mutations, are factors that decrease the LD levels (Al-Maskri et al., 2012; Gupta et al., 2005). Slatkin (1999) reported that the multiallelic markers (such as microsatellite) are more likely to achieve a meaningful LD than the bi-allelic markers (such as DArT, SNP, etc.). Remington et al. (2001) also observed a relatively higher range of LDs between SSR markers than SNP markers.

Association mapping with GLM and MLM models

To identify the linked markers to evaluated traits in the studied soybean genotypes, the association mapping was performed based on the general linear model (GLM) dependent on the Q matrix (the membership probability of each individual to each subgroup) and the mixed linear model (MLM) dependent on Q + K matrix (K: kinship relationship matrix) using TASSEL software ver. 3. Based on the results of the GLM, 31 markers showed a significant relationship with the evaluated traits, of which 14 relationships were significant at the probability level of 5% and the others were significant at the probability level of 1%. The associated and significant markers in GLM method were including the relationships between 2 markers with severity of disease, 3 markers with grain yield, 4 markers with each of the traits of number of microsclerota in stem and the amount of charcoal rot disease, 5 markers with pod weight, 6 markers with grain weight and 7 markers with 100 grain weight (Table 4). In contrast, in the MLM model, which uses more information than the GLM model, 35 significant relationships were identified among the studied markers and traits at the probability levels of 5% and 1%, including the relationships of 3 markers with the grain yield, 4 markers with severity of disease, 5 markers with pod weight, number of microsclerota in stem and amount of charcoal rot disease, 6 markers with grain weight and 7 markers with 100 grain weight (Table 4).

A number of common markers were also identified for different traits with both GLM and MLM models in this study. For example, the SSR marker Sat_252 had significant relationships with pod weight, 100 grain weight and amount of charcoal rot disease, Sat_238 with pod weight, grain weight and grain yield, Satt512 with pod weight and 100 grain weight, S63880-CB with grain weight and grain yield, Satt079 with grain weight and 100 seeds weight, Sat_124 with grain weight, 100 seeds weight and grain yield, Satt359, Satt190 and Sat_169 with number of microsclerota in stem, amount of charcoal rot disease and severity of charcoal rot disease, Sat_416 with number of microsclerota in stem and amount of charcoal rot disease and Satt460 with traits of number of microsclerota in stem and amount of charcoal rot disease. This result can probably be due to the pleiotropic effects or tight linkage of the genomic regions involved in controlling these traits (Jun et al., 2008). The identification of common markers is very important in plant breeding since the simultaneous selection of several traits is possible (Tuberosa et al., 2002). Moreover, the significant relationships between several markers with a specific trait was also showed in this research (Table 4). For example, the relationship between Sat_404 and Satt361 markers with the pod weight, Sct_028 and Satt460 markers with grain weight and Satt607, Sat_357 and Satt644 markers with 100 grain weight, indicating the quantitative and polygenic inherentance of the evaluated traits. On the other hand, low values of the coefficients of determination (R2) for most of the linked markers also confirms the same issue and shows the determination of some variances in these traits through identified genetic locations and, therefore, the greater effect of the environment (relative to genetic effects) on variation of these traits. In general, considering the constraints of the linkage mapping method, such as the lack of availability of dispersed populations and the long time required to create them, the association analysis method by eliminating these limitations provides researchers with appropriate marker information (Oraguzie et al., 2007).

The results of the present study showed the effectiveness of the association mapping method in identifying markers linked to the evaluated traits in the studied soybean genotypes. Evidently, it is necessary to validate the markers identified and associated with relevant traits in large populations with a higher level of diversity as well as in different environments, in order to ensure their relevance to the related traits, and thus to increase the efficiency these markers will increase in various breeding programs such as marker assisted selection (MAS). Neale and Savolainen, (2004) showed that genetic locations selected by the association analysis have important advantages such as involving adequate levels of nucleotide diversity and also the ability accurately phenotypic evaluations that can be used in MAS. Several studies have previously been conducted to identify genetic locations associated with resistance to charcoal rot disease in different plants by different molecular markers. Olaya et al. (1996) showed that the resistance to charcoal rot disease in soybean was controlled by two genes with complete dominance, called mp-1 and mp-2. They also identified two RAPD markers related to resistance. Miklas et al. (1998) identified three QTLs associated with resistance to charcoal rot disease in beans by association mapping. Yuan et al. (2002) showed that Satt294 marker on C1 linkage group, Satt440 on I linkage group and Satt337 on K linkage group are associated with seed yield in soybean. Hernández-Delgado et al. (2009) showed that the resistance to charcoal rot in beans is controlled by two dominant genes with epistatic effects. Sun et al. (2014) by association analysis identified four SSR alleles, Satt 634-133, Satt 634-149, Satt 222-168 and Satt 301-190, which were associated with a slight resistance to phytophthora disease in soybean.

Acknowledgments

We thanks from the University of Guilan, Rasht, Iran, and the Seed and Plant Improvement Research Institute (SPIRI), Karaj, Iran, for their financial support of this research.

Figures
Fig. 1. Bilateral charts for determining the number of sub-populations in the studied soybean genotypes (K=3) based on microsatellite markers.
Fig. 2. Bayesian model based cluster analysis for 130 different soybean genotypes using 31 microsatellite loci (K=3). Each color indicate one sub-population or cluster. Vertical axis show the membership coefficient of each genotype into clusters.
Fig. 3. Linkage disequilibrium plot (LD plot). Diameter upper and lower are indicating linkage disequilibrium and p-value for each pair of marker, respectively.
Tables

Soybean genotypes evaluated to charcoal rot disease (Macrophomina phaseolina) in field conditions in this research

No. Genotype Maturity groupa Codeb No. Genotype Maturity groupa Codeb
1 AGS 358 (3) II 2176 33 Si-bi-va- 1207 II 2038
2 AGS 359 (4) III 3072 34 A 3237 II 2019
3 Hartwig III 3061 35 A 3935 II 2020
4 Gloy III 3062 36 Columbus II 2022
5 2-L.80-5914 III 3057 37 Union II 2034
6 B-R22 Bijelina I 1173 38 Stressland-B III 3034
7 LN 89-3394 II 2149 39 Stressland-C III 2043
8 L.D 9 II 2079 40 GN3074 III 3074
9 Kenwood II 2099 41 Pek - Cak - taj III 3031
10 Fowler III 3045 42 Swift III 3026
11 TN 4.94 III 3037 43 G.3× Hamilton (10) V Sh8
12 Manacon III 3022 44 DPX × Yougetsu (2) V Sh18
13 Fowler III 3045 45 DPX × Yougetsu (3) V Sh19
14 Cysne II 2093 46 DPX × Darby (2) V Sh31
15 Sort 62 II 2064 47 DPX × Darby (3) V Sh32
16 Sort 126 S.M.A.B II 2065 48 Williams × DPX (6) V Sh40
17 Wars zawska II 2044 49 Hamilton × Sahar (3) V Sh47
18 Bonus II 2041 50 Hamilton × Nemaha (6) V Sh55
19 Clean II 2052 51 9242 II 2004
20 Stressland-A II 2055 52 S 24 - 92 II 2005
21 5601-46-6-1 C II 2056 53 CX 232 II 2006
22 Harbinskaia 111-3994/56 II 2060 54 Karbine I 1096
23 Bean – Comet B II 2061 55 Harbinskaia 3971 B I 1097
24 Delsoy 4210 III 3017 56 Dikmanova - Cierna I 1098
25 Comet (NRM) B I 1160 57 Dornburger I 1099
26 B-R23 Bijelina I 1162 58 Banjaluka B II 1100
27 Bijelina 54/68 I 1163 59 Harasoy I 1090
28 NS-16 B I 1139 60 Motte IV 4001
29 B-R3 (Bijelina) I 1140 61 K.S 4895 IV 4007
30 Grangelb I 1118 62 Essex IV 4009
31 Mishel II 2042 63 AGS 381 (10) IV 4010
32 Calland II 2047 64 TN 5.95 V 5001
65 Delsoy 4710 V 5002 98 NE-3297 II 2133
66 EJC (Edi. Jappan) V 5003 99 ST.Pazova 54/18 II 2162
67 Telar V 5004 100 N.S-L-11-6 II 2161
68 Nekador V 5005 101 Mercory II 2147
69 Hatcheson V 5006 102 Roanak II 2125
70 Cliford V 5007 103 Pance Vacka B II 2123
71 Hood V 5008 104 L.52 II 2165
72 Kaspian V 5009 105 Sort 126 S.M.A.B II 2164
73 Sari V 5010 106 ERFurt II 2163
74 AGS 346 (2) V 5011 107 VINIMK 9186 II 2117
75 AGS (5) V 5012 108 PA 83 II 2098
76 AGS 367 (6) V 5013 109 VESTAG 97 II 2097
77 AGS 364 (8) V 5014 110 Hack II 2095
78 AGS 380 (9) V 5014 111 Hadgson II 2027
79 Doles V 5018 112 CM - 1070 II 2012
80 GN2050 V 5020 113 S - 12 - 49 II 2013
81 DI 74 V 17F-1 114 S.R.F × Columbus II 2016
82 D42.I4 III 17F-4 115 Budgoszkasz 061 II 2118
83 Linford III 17F-13 116 Rounest II 2119
84 Clean III 17F-14 117 Poplu - 18 - 35 II 2028
85 LH-2500 III 17F-15 118 Tokyo Brown II 2029
86 M 7 III 17F-16 119 Century 84 II 2030
87 TN 6.90 III 2130 120 RCAT ANGORA II 2007
88 T 215 II 2171 121 S19 - 90 II 2009
89 Kabalovskaja B II 2167 122 Black Tokyo II 2062
90 Kabalovskaja II 2166 123 Cul.9132 I 1047
91 8-L.65-3266 II 2157 124 AP - 1394 I 1098
92 Black Hawck II 2156 125 PRO - 280 I 1064
93 Illinoi II 2155 126 S 14 - H 4 I 1065
94 L.2 II 2152 127 SENTRY I 1066
95 S3-941-8-1-8 II 2142 128 Spirit II TU38
96 L.8 II 2140 129 Salin II TU309
97 Darby II 2138 130 Interprise II 1067
I, II, III, IV and V are very early, early, intermediate, late and very late maturity, respectively.
The codes of soybean genotypes in plant gene bank of Seed and Plant Improvement Research Institute, (SPIRI), Karaj, Iran

Characteristics of the studied microsatellite markers in this research

Marker Chromosome number Forward sequence Reverse sequence Annealing temperature (°C)
S63880-CB 2 AGTCCTCCTCGCCAACAACAAC TTCATTTCATTTCCAAGCGGGT 63.9
S60211-TB 2 GAAGATCCTAACACGATGGCCG TTCGTTGTTTCCTTCATTGCCG 62.7
Satt644 2 TATGCCTCAAACCACAAA CAGGCCACCATTTTTCTT 45.4
Satt041 2 TGTTGTGTGGCTTTATTATT TTAAGGTGGGATATGGTC 46.7
Sat_169 2 ATTCGTTAAATACTCCACATCAATA TTATGCTTTGTTGTTTTTCAGTT 49.1
Satt546 2 TGAGCGATCAAGAAGCACTTA TTTGGATCGCATAACACTTTA 53.8
Satt022 3 GGGGGATCTGATTGTGTATTTTACCT CGGGTTTCAAAAAACCATCCTTAC 60.8
Satt646 4 GCGGGGTATGAATTAATTAATGTAGAAT GCGCCTTCAAAAACTAATGACATATCAT 56.7
Satt190 4 GGGAGTGTGAACTTACATTGTCT GGGCCTTGAATTTTGTGCTAT 55.9
Satt607 4 GCGGTTTCATCTGCAGTGTATTATTAT GCGCCACTTAATTATTTCAGATTAATT 63.9
Satt161 4 GGGTATATCAACATATCTTCACCTTTTT GGGCTGCTTGTTAATGTTTTGTAGA 64.9
Sat_404 4 GCGGATGCGCTTAGCCAGAAGATGAGT GCGTTCTCCCCCAATGTACATACTTCTACCA 60.1
Satt661 4 TGATATGAGCAATGTAGTTCCTCT TCCATGAAAAAGAAGTTAGAATAGC 55.4
Satt361 4 GCGCGGTCAATGAATCGGGAGACAC GCGGTTTTCAGCGTTATTAAGTTTTG 63.3
Sat_077 4 GACACTTGTGGAATTACTCA GGGTTGAAGACTTAAATTTGAAATCTCT 50.4
Satt399 4 AAGCCAACCTTATAATTCTTTCAT ATATGGGCTTACTTACCCATCATAGA 55.7
Sat_416 4 GCGTTAGTTTTTATTTTTAAGTTGA GCGACATTGTGTGAGGGTAA 51.8
Sat_357 4 GCGAGGGTTTAAGGTGTAGGTTGT GCGCACCGCTTTTGTTTCTTTTTG 60.1
Satt512 5 AACGTCTTCAAGTCAAGTGCCTACA GCCCACATAGTTTTCATTTTTCTCCA 62.1
Sct_028 6 TCGCCGGTACAAAAG CGAATGAACAAACAAACA 44.2
Satt307 6 GCGCTGGCCTTTAGAAC GCGTTGTAGGAAATTTGAGTAGTAAG 57.9
Satt079 6 AGTCGAAGATACACAATTAGAT CTTTTAGACACAAATTTATCACT 47.9
Satt460 6 GCGCGATGGGCTGTTGGTTTTTAT GCGCATACGATTTGGCATTTTTCTATTG 59.1
Satt202 6 GGAATGCATGAGTATTAACCTCTTAT GGGCTAACGAACATGTAACTTATCAAC 57.9
Sat_252 6 GCGTTTTTCTGTCATGTCTTTGAATTTT GCGGCAGGTCTCATACAAGTCATCATCT 59.1
Sat_238 6 GCGCTGAGACGTTTGTGATTGTTTGTAA GCGGTGCATGCAATTCTAGTTAGATATT 63.9
Satt359 11 GCGAGAAAATAATCCTGCTCAAG GCGTTTAAGTCCAATAACAAAGATAAC 65.9
Sat_218 12 GCGCACGTTAAATGAACTGGTATGATA GCGGGCCAAAGAGGAAGATTGTAAT 63.4
Sat_287 14 GCGCATTCCCTATTTTTTAGAATGAGTAAAT GCGCCATAATCAGCATGCACACCTTATATGG 73.8
Satt691 15 GCGAAAGATAAAAAGTAGATTGAAA GCGCTCCTAAATCCAAATGAATC 60.8
Sat_124 19 GGGTCCATTCCACTTTTTGTACAATAT GGGAGTTCAAACATCCATTAGTGGTATA 63.4

Minimum, maximum, mean ± standard deviation (SD), range and phenotypic coefficient of variation of the measured traits in the 130 soybean genotypes studied in this research

Trait Maximum Minimum Mean ± SD Range CV (%)
Pod weight (g) 1.27 0.26 0.61 ± 0.08 1.01 12.4
Grain weight (g) 0.38 0.07 0.16 ± 0.05 0.31 13.34
100 grain weight (g) 30.33 5.8 14.39 ± 3.03 29.75 18.14
Grain yield (g/plant) 40.10 6.77 15.25 ± 3.11 33.37 23.12
Number of microsclerota in stem 200.33 0 54.69 ± 9.39 200.33 10.88
Amount of charcoal rot disease (%) 80.83 0 23.16 ± 4.21 80.83 21.82
Severity of charcoal rot disease (%) 99.17 0 40.43 ± 7.67 99.17 19.34

Microsatellite markers linked to evaluated traits in the studied soybean population using the association mapping based on GLM and MLM models

Trait Marker GLM model MLM model

P-value R2 F-value P-value
Pod weight Sat_404 0.03 0.08 5.16 0.02
Satt361 0.02 0.09 5.62 0.01
Sat_252 0.004 0.11 8.68 0.003
Sat_238 0.03 0.08 4.96 0.02
Satt512 0.04 0.08 4.17 0.04
Grain weight S63880-CB 0.03 0.11 4.53 0.03
Sct_028 0.04 0.06 4.12 0.04
Satt079 0.002 0.11 9.19 0.002
Sat_238 0.02 0.13 4.62 0.03
Satt460 0.02 0.09 4.32 0.03
Sat_124 0.004 0.11 8.33 0.004
100 grain weight Satt607 0.04 0.09 4.8 0.03
Sat_357 0.01 0.1 6.75 0.01
Satt079 0.03 0.08 4.53 0.03
Sat_252 0.004 0.12 7.69 0.006
Satt644 0.004 0.12 7.34 0.007
Satt512 0.01 0.1 5.47 0.02
Sat_124 0.004 0.11 8.48 0.004
Grain yield S63880-CB 0.05 0.07 3.87 0.05
Sat_238 0.03 0.08 5.27 0.02
Sat_124 0.002 0.11 9.65 0.002
Number of microsclerota in stem Satt359 0.006 0.1 4.69 0.01
Satt190 0.01 0.09 5.98 0.01
Sat_416 0.01 0.11 5.16 0.02
Satt460 0.06 0.08 6.56 0.01
Sat_169 0.009 0.1 5.9 0.01
Amount of charcoal rot disease Satt359 0.004 0.09 5.95 0.01
Satt190 0.06 0.07 4.39 0.03
Sat_416 0.007 0.12 4.79 0.03
Sat_252 0.009 0.1 5.87 0.01
Sat_169 0.002 0.13 7.2 .008
Severity of charcoal rot disease Satt359 0.01 0.11 5.69 0.01
Satt190 0.01 0.07 3.78 0.05
Satt460 0.03 0.1 5.52 0.02
Sat_169 0. 3 0.05 7.16 0.008
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August 2019, 35 (4)
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