Chief EditorJ. S. Sandhu
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Full Research Article
Genetic Analysis and Quantitative Trait Locus Mapping using the Major Gene Plus Polygene Model for Soybean [Glycine max (L.) Merr.] Main Quality Trait
First Online 31-10-2022|
Methods: In this study, 236 F2 generation plants and a derivative group were constructed by using Jiyu50 and Jinong18, obtained from Jilin Province. Combining three years of phenotypic and molecular detection data, using the ICIM method, one-dimensional scanning detected 24 QTLs related to protein and fat content, two-dimensional ICIM analysis of F2:3 families detected 7 pairs of epistatic QTLs associated with fat content.
Result: Based on a mixed model for major genes and polygenes, the C model was determined the optimal genetic model for protein and fat content, with genetic rates of multiple genes of 71.15% and 79.15%, respectively. In the detection of high-oil molecular markers, the detection coincidence degree was Sat_238 > Satt 100> Satt 150 > Satt 636> Sat_287 = Sat_342, while this was Satt150 = Sat_342> Satt100 > Satt 636> Sat_287> Sat_238 for high-protein markers. The QTL localization results showed that 9 microeffect QTLs related to protein content and 7 microeffect QTLs related to fat content were detected. Isolation analysis results were generally similar to those of QTL mapping. One stable QTL associated with protein content and two stable QTLs associated with fat content were identified as being of some application value in soybean molecular marker-assisted breeding.
As can be seen from the above examples, many QTLs are known to be related to soybean protein and fat content and they are scattered throughout most of the linkage groups. However, the number and location of QTLs detected in populations of different genetic backgrounds differ significantly (Zhen et al., 2011). Therefore, the analysis of QTL stability among different generations will help improve the efficiency of soybean protein and the fat content selection and accelerate the breeding process (Shen et al., 2001). In this study, QTL mapping of soybean protein and fat content was carried out using the ICIM method of QTL IciMapping v3.0 software and F2 and F2:3, F3:4 derived populations were obtained by the hybridization of soybean Ji Yu 50 and Jinong 18. The genetic development of soybean protein and fat content was analyzed using the major gene and polygene mixed genetic model. This will provide the theoretical basis for the selection of molecular markers in the breeding of high-quality soybean varieties.
MATERIALS AND METHODS
In this study, soybean variety Jiyu 50 (female parent, Ji Shen bean 2001015) was hybridized with Jinong 18 (male parent, Ji Shen bean 2006) in the experimental field of Jilin Agricultural University in the summer of 2013 to obtain the F0 generation. In October 2015, an F1 individual plant with 450 seeds was obtained. In October 2016, 236 F2 individual plants were obtained. In October 2017, F3 strains were obtained from 236 individual plants from the F2 generation. In October 2018, F3:4 strains were obtained from the F3 strains.
According to the soybean public genetic map published (Song et al., 2004), 380 pairs of SSR primers were primarily confirmed from the soybean database SoyBase (http://soybase.agron.iastate.edu) and synthesized by Changchun KuMei Co., Ltd. (Changchun, China).
Genetic analysis methods
Genetic analysis of quality traits was carried out using the primary gene and polygene mixed genetics model with the five generation joint separation analysis method (P1, F1, P2, F2 and F2: 3) (Wang et al., 1998).
Soybean protein and fat content
The soybean protein and fat content of Jiyu 50, Jinong 18, the F2 generation and their derivative groups was determined by a near infrared grain quality analyzer (BUCHI NIRLab N-200 MCS 100) (Switzerland) between 2016~2018.
DNA extraction and SSR analyses
Genomic DNA of the soybean was isolated from the leaf tissue by the CTAB method (Wang and Fang, 2002; Li et al., 2017). Polymerase chain reaction (PCR) was performed in a 15 μL volume containing 0.6 μL genomic DNA (50 ng/μL), 0.6 μL dNTP mixtures (10 mM), 0.6 μL SSR primer (25 μM), 1.5 μL 10X PCR buffer (contain Mg2+), 0.15 μL Taq polymerase (5 U/μL) and 10.95 μL double-distilled water. The PCR conditions were 4 min at 94°C; followed by 35-40 cycles of 45 s at 94°C, 30 s at 50°C and 30 s at 72°C; then 8 min at 72°C (Nazima et al., 2018). After amplification, the PCR products were mixed with a loading buffer, denatured for 5 min at 94°C and kept at 0°C. The denatured PCR products were separated on 8% (w/v) denaturing polyacrylamide gel and visualized by silver staining (Sanguinetti et al., 1994).
Construction of the molecular genetic map
PCR amplification banding patterns identical to those of the male parent were recorded as “1”, those identical to the female parent were recorded as “2”, heterozygous banding patterns as “3” and missing banding patterns as “–”. Mapmaker Exp 3.0 software (The Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA) was used for map construction with the “Group” command to perform interlocking analysis and grouping of markers (Wang 2009). If the number of linkage markers was less than 8, the “Compare” command was used to sort and if the number was greater than 8, the “Ripple” command was used to sort (Schneider et al. 1997, Liu et al., 2000). The error detection level was set at 1% and the recombination rate was converted to genetic distance (cM) using the Kosambi function. Win QTL Cart 2.5 software (Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA) was used to draw the genetic linkage map.
Mapping quantitative trait loci
The Inclusive Composite Interval Mapping algorithm was applied to determine the protein and fat content of the F2 and F3 segregation groups by QTL IciMapping v3.0 software (Institute of Crop Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China). The scanning step was 1.0 cM. The probabilistic levels of stepwise regression variables were 0.01 and 0.02, respectively. LOD≥2.0 (LR = 9.66) was used as the threshold for QTL mapping and effect estimation (Zhang et al., 2008).
RESULTS AND DISCUSSION
The protein and fat content was shown to vary greatly between the parents and to have a near normal distribution and a wide distribution frequency in F2 population (Fig 1); this was typical of a quantitative genetic model. Protein content variation was 37.29%~44.50% and the average value was 40.90% in F2. The protein content is the separation of mid parent, biased towards the female parent. Regarding fat content in the F2 group, the variation was 17.31%~23.34% and the average value was 19.73% (Table 1). The fat content is also the separation of mid parent, biased towards the female parent. Super parent isolated single plants were detected in the offspring for both protein and fat content.
Genetic analysis of soybean protein and fat content
Based on analysis of the major gene plus polygene genetic model, the likelihood function and the Akaike information criterion (AIC) value of the protein and fat content under different genetic models were obtained by the IECM algorithm. According to the principle of minimum AIC value, C and E-2 were preliminarily determined as alternative models for protein content, C and E-1 as alternative models for fat content. Further fitness test results showed that the C model (polygenic genetic model) was the most suitable model for protein and fat content (Supplementary Table 1 and Table 2) and the polygenic heritability was 71.15% and 79.15%, respectively.
QTL mapping of soybean protein and fat content
In this study, 380 pairs of SSR primers were used to screen the polymorphic primers between the parents. The results showed that 118 pairs of primers showed polymorphism in the parents and the polymorphism rate was 31.05%. SSR-PCR was carried out on 236 individual plants of F2 isolated population using polymorphic primers. Finally, a SSR linked genetic map containing 102 markers was constructed. Fourteen QTLs related to protein content were detected, which were distributed in six linkage groups, including 4 (C2), 6 (A1), 12 (G), 13 (C1), 17 (M) and 22 (F). Among them, four QTLs had a phenotypic variation of more than 20% and one was stable in 2 years. Ten QTLs related to fat content were detected that were distributed in five linkage groups, including 1 (A1), 4 (C2), 12 (G), 17 (M) and 22 (F). Among them, one stable QTL in three continuous years was detected in the 12 (G) linkage group Sat_287~Sat_342 marker interval and one stable QTL in two continuous years was detected in the (C2) linkage group Satt100~Sat_238 marker interval. Additionally, three major QTLs related to soybean fat content were also detected (Supplementary Table 3 and Fig 2).
Fig 2: Location of additivity effect QTLs on linkage groups. Note: Red triangles represent QTL of protein content in F2 generation. Blue triangles represent QTL of protein content in F2:3 families. Purple triangles represent QTL of protein content in F3:4 families. Red diamonds represent QTL of fat content in F2 generation. Blue diamonds represent QTL of fat content in F2:3 families. Purple diamonds represent QTL of fat content in F3:4 families.
Marker-assisted selection of soybean protein and fat content
High fat and high protein molecular markers were detected using six stable SSR markers (Satt 100, Sat_287, Satt 150, Satt 636, Sat_238 and Sat_342) related to soybean protein and fat content in 108 soybean materials from Biotechnology Center of Jilin Agricultural University. The detection coincidence degree of high-oil molecular markers was (high to low): Sat_238 (95.12%) > Satt 100 (87.80%) > Satt150 (75.61%) > Satt636 (71.95%) > Sat_287 = Sat_342 (68.29%). The detection coincidence degree of high-protein molecular markers was (high to low): Satt150 = Sat_342 (84.00%) > Satt 100 (77.33%) > Satt636 (70.67%) > Sat_287 (52%) > Sat_238 (50.33%) (Table 2 and Fig 3).
Comparison of major gene plus polygene model analysis with QTL mapping for soybean main quality traits
The quantitative analysis of genetic traits and molecular marker loci can be carried out on the genetic traits of quantitative traits and the results of the two analyses can be used to confirm each other. The inheritance of protein content was shown to follow a polygene genetic model according to the results of the fifth generation (P1, F1, P2, F2 and F2:3) populations. Therefore, it should be possible to locate several QTLs with LODs of similar sizes. QTL mapping showed that nine minor QTLS with similar LOD sizes were detected in soybean F2 and F2:3 populations (phenotypic variation rate <10%) and only one major QTL was detected (phenotypic variation rate >10%) protein content. The inheritance of fat content was also shown to follow a polygene genetic model according to the results of the fifth generation (P1, F1, P2, F2 and F2:3) populations. In the F2 and F2:3 populations, seven minor QTLs with similar LOD sizes were detected and only two major QTLs. In general, the results of the model analysis are similar to those of QTL mapping. Our findings are also consistent with those reported by Xu (2006) and Wang (2001), but differ to those reported by Zheng et al. (2007). This may be because the isolation analysis method can only detect genes with strong effects in QTL mapping analysis, while other genes are classified as micro-polygenes. Thus, the number of major genes detected in QTL mapping usually exceeds the number of major genes detected by model analysis, which is consistent with the findings of Wang (2000) and Xu (2006). Additionally, because the population used in our study was the early F2 and F2:3 populations after hybridization, not the stable RIL population, the genetic parameters were less relative to the RIL population and the F2 data used for statistical analysis did not represent average values. Thus, the experimental results were affected by the environment, so should be verified by extending the parental analysis.
Marker-assisted selection of soybean main quality traits
Using the six pairs of SSR markers, which were localized and stable in relation to soybean protein and fat content, 108 soybean seed resources were analysed for high oil and high protein. The detection coincidence degree of SSR markers exceeded 50%, with a maximum of 95.12%. We found that the Satt100 marker was closely linked to the fat content and was identified as a marker of repeated positioning more than twice, which is consistent with previous studies (Hou et al., 2014). The marker related to fat content was stable under different genetic background conditions, indicating that it could be used in marker-assisted selection breeding for high oil and protein content in soybean. The detection coincidence degree was high (>90%) for Sat_238, but was lower (<90%) for the other five markers (Satt100, Satt150, Sat_342, Satt636 and Sat_287). This could be because the protein and fat content are quantitative traits controlled by multigenes. Therefore, future work should further investigate markers that are closely linked with protein and fat content (Yang et al., 2008). Additionally, although we identified QTLs that were stable in different generations, we did not verify the stability under different environmental conditions. Further testing and verification is under way to confirm the selection effect of these molecular markers.
Using the six pairs of SSR markers, which were localized and stable in relation to soybean protein and fat content, 108 soybean seed resources were analysed for high oil and high protein. The detection coincidence degree of SSR markers exceeded 50%, with a maximum of 95.12%. We found that the Satt100 marker was closely linked to the fat content and was identified as a marker of repeated positioning more than twice. The marker related to fat content was stable under different genetic background conditions, indicating that it could be used in marker-assisted selection breeding for high oil and protein content in soybean.
Conflict of interest
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