Research Report

Comparative Analysis of Genetic Structure of by SSR and AFLP  

Xiaoyong Xie1 , Jinxiang Zhong2 , Sifa Li3
1 Key Laboratory of Fishery Ecology and Environment, Guangdong Province, South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou, 510300, China
2 Ocean and Fishery Technical Extension Center of Guangdong, Guangzhou, 510220, China
3 Shanghai Ocean University, Shanghai, 201306, China
Author    Correspondence author
Genomics and Applied Biology, 2018, Vol. 9, No. 4   doi: 10.5376/gab.2018.09.0004
Received: 08 May, 2018    Accepted: 22 Jun., 2018    Published: 29 Jun., 2018
© 2018 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:

Xie X.Y., Zhong J.X., and Li S.F., 2018, Comparative analysis of genetic structure of Nile tilapia by SSR and AFLP, Genomics and Applied Biology, 9(4): 19-23 (doi: 10.5376/gab.2018.09.0004)


The genetic structure data of Nile tilapia from SSR assay were compared with that from AFLP. The ratio of polymorphic sites percentage from SSR to AFLP averaged 1.683 17, the average ratio of population specific Fst indices was 1.249 87, and the ratio of gene diversity in group averaged 1.676 60, respectively in 5 populations. The ratio of genetic distance among 5 populations of Nile tilapia from SSR to AFLP averaged 3.972 02, whereas the ratio of genetic identity averaged 0.937 92. The results about SSR and AFLP could provide reference for other similar studies in this field.

Genetic structure; SSR; AFLP; Comparison


In recent decades, research on genetic diversity and genetic structure based on DNA was very active. Marker techniques of RAPD, RFLP, AFLP, SSR, and ISSR were widely used. Especially AFLP and SSR have been widely used in genetic diversity, genetic structure and other research fields. SSR technique is often referred to as microsatellite marker, with the characteristics of well-repeated, highly polymorphic. And it has been proved by many researches that it could identify a large number of alleles in the genome accurately and efficiently (Zhao et al., 2014). Compared with other markers, it could reveal the overall characteristics of the whole genome (Song et al., 2009). AFLP is also called amplified fragment length polymorphism, which combined with the accuracy of RFLP and the high efficiency of PCR. Compared with other molecular markers, it has the incomparable advantages (Li et al., 2013). And it is considered to be the most abundant polymorphic technology (Zhang et al., 2013), has relatively obvious advantages (Guan et al., 2013). And AFLP was used to analyze the genetic diversity of wild naked carp populations in different tributaries by Wang et al. (2015). However, there were relatively few reports on the differences and relationships between the results of SSR and AFLP. At present, little is known about the relationship between genetic diversity and genetic structure data based on SSR and AFLP. The comparative analysis of SSR and AFLP markers is almost the blind spot of current research on molecular marker application.


Team of Professor Li Sifa from Shanghai Ocean University introduced 5000 Oreochromis niloticus in 1994, which has undergone genetic improvement for about 10 years (Xie et al., 2007; 2011). In this study, SSR and AFLP molecular markers were used to compare and analyze the genetic structure data in the breeding process of Oreochromis niloticus. In order to deepen the understanding of the selection of molecular markers and the data of genetic structure analysis, these two molecular marker methods and their degrees were compared.


1 Results and Analysis

1.1 Calculation of genetic diversity ratio within population based on SSR and AFLP

The results of genetic diversity ratio in population were calculated by SSR and AFLP in this study (Table 1). According to the two methods, the ratio of polymorphic sites in five selected generations was between 1.603 25 and 1.856 16, with an average of 1.683 17. The ratio of Fst in five selected generations was between 1.081 61 and 1.399 44, with an average of 1.249 87. The ratio of gene diversity in the population was between 1.587 65 and 1.758 33, with an average of 1.676 60.


Table 1 Ratio of genetic diversity of Nile tilapia based on SSR and AFLP


1.2 Ratio of similarity coefficient of Nei based on SSR and AFLP

The results of ratio of similarity coefficient of Nei and of genetic distance in groups among 5 populations were calculated based on SSR and AELP (Table 2). Genetic distance in groups among 5 populations was between 2.824 10 and 6.920 63, with an average of 3.972 02±1.328 94 (Nei, 1972). Genetic similarity coefficient was between 0.928 10 and 0.945 45, with an average of 0.937 92±0.005 77 (Nei, 1972).


Table 2 Ratio of similarity coefficient of Nei (upper right) and of genetic distance (lower left) in groups


2 Discussion

2.1 Technical characteristics and influencing factors of SSR and AFLP markers

Analysis of population genetic diversity and genetic structure is the basis of utilization and protection of biological germplasm resources. Domestic and overseas scholars carried out a detailed study on the genetic diversity of various Bio-economies by using different methods, such as morphological features, genetic relationship and biochemical indexes. And molecular markers provide a deeper technique for the analysis of biological genetic diversity. Among all kinds of molecular marker techniques, SSR sequence has no protein coding function in genome, and they are rarely limited by the pressure of natural selection during long biological evolution. Thus, SSR has the characteristics of high mutation rate among individuals and populations. In the case of different primer combinations and multiple pairs of primers, AFLP could detect much polymorphic loci, which is very sensitive to the variation reaction among DNA samples. Its sensitivity could detect tiny genetic diversity among closely related biological DNA samples. In some kinds of successful development of SSR Markers, such as bean research field, SSR even surpasses AFLP to become the mainstream technique of population genetic diversity analysis (Liu et al., 2014). Association application of SSR and AFLP is adopted in the research of high demand, which proves the validity of SSR and AFLP in genetic diversity analysis (Du et al., 2003; Wang et al., 2006; Wang et al., 2012; Li et al., 2014).


2.2 Further comparison between SSR and AFLP markers

Most of the current studies stayed at using SSR or AFLP alone to analyze genetic diversity within and among populations of different species. There were few reports about association application of SSR and AFLP t. As for the depth of the study, it was lack of the further comparative analysis of the SSR and AFLP results, and lack of the horizontal comparison of related research. To some extent, it limited the reference and application value of the research results. Further analysis of the results of different markers could help to understand the genetic structure of the population completely and thoroughly. Xie (2014) used this method for the first time to make a pioneering exploration (Xie et al., 2014; Xie and Li, 2014). The genetic diversity ratio within population calculated by SSR and AFLP showed that the level of genetic diversity obtained by SSR was higher. This conclusion was similar to that of other studies (Yuan et al., 2000; Jiang et al., 2007), which indicated that SSR has higher resolution in genetic diversity analysis than AFLP. And in this study, we found that the index of polymorphic loci based on SSR method was 1.68317 times higher than that based on AELP method; Population specific Fst based on SSR was 1.24987 times higher than that based on AELP; Average genetic diversity index in population was 1.67660 times higher than that based on AELP. The results of this study could provide methods and reference for other similar research. The results of this study provided the basis for population genetic diversity assessment, and researchers could estimate the results of genetic diversity studies using one of the SSR or AFLP methods based on this ratio.


Jiang et al. (2007) compared the genetic diversity in peanut genotypes with bacterial wilt resistance through SSR and AELP technique, and found that SSR analysis showed the genetic distance of 31 peanut genotypes ranged from 0.12 to 0.94, with an average of 0.53. The genetic distance of the 31 peanut genotypes obtained by AELP ranged from 0.06 to 0.57, with an average of 0.25. In this study, the ratio of genetic distance among 5 selected populations calculated by SSR method and AFLP method was in the range of 2.824 10 and 6.92063, with an average of 3.97202 (Nei, 1972), whereas the ratio of genetic identity was between 0.928 10 and 0.945 45, with an average of 0.937 92 (Nei, 1972). This study showed that the genetic distance between populations based on SSR analysis was greater than that based on AFLP analysis, which was consistent with the results of Jiang (2007). As for the detailed ratio, the average genetic distance obtained by Jiang (2007) based on SSR was 2.12 times of that obtained by AFLP, which less than the ratio 3.97202 in this study. The difference was mainly related to the genetic relationship between populations, as well as the technical characteristics of the two markers. The object of this study was 5 populations of Nile tilapia, and it was known that relationship among populations closely. The research object of Jiang (2007) was 31 cultivated peanut genotypes materials with different resistance to bacterial wilt. Therefore, there was a great difference between genetic distance and genetic identity among populations. About the characteristics of molecular marking techniques, AELP was aimed at restriction endonuclease fragment from the entire genome, selective amplification with different primer combinations. The genetic diversity of the detection was the change of the restriction site in the genome or the variation of the DNA sequence length in the enzyme fragment. This result was reliable and stable (Wang et al., 2006). Essentially, SSR is a simple repeat sequence scattered in the eukaryote genomes, mainly distributed in the non-coding region. Because of the different base composition of repeat sequence, and the range of variation of repeat number is very large, it shows high polymorphism. However, SSR sequences are easily influenced by convergent variation and parallel evolution during biological evolution, which bring errors to the analysis of genetic relationships in the assessment of genetic diversity (Xu and Wang, 2001). Therefore, molecular markers used in the analysis of genetic distance and genetic identity among populations should be carefully selected according to the genetic relationships between materials. In the aspect of horizontal comparison and evaluation of the results, the study of genetic diversity ratio in SSR/AFLP population has a good reference value.


3 Materials and Methods

3.1 Experiment materials

Team of Professor Li Sifa from Shanghai Ocean University introduced 5, 000 Oreochromis niloticus in 1994, which was used as a base group (F0). Since 1996, the system breeding of superior species has been carried out, and a new generation of selective breeding population has been produced every year. Each generation was marked as F1~F9. Experiment materials of this study were selected from 5 generations of reserved F0, F6~F9 for breeding of superior species of Oreochromis niloticus. Selected 20 samples from each generation randomly, cut tissue of caudal fin, numbered respectively, and then preserved with 95% ethanol for DNA extraction (Xie et al., 2011).


3.2 Molecular marker analysis

3.2.1 DNA extraction

The DNAs of 100 individuals were extracted from 5 generations of experimental materials by routine phenol/chloroform extracting method. Then agarose gel electrophoresis was used to detect the extracted DNA samples.


3.2.2 Reaction system and program of SSR and AFLP

In this study, the total volume of liquid in the SSR reaction system was 25 μL, which contained about 50 ng genomic DNA, 3 μL buffer solution (10 mmol/LTris-HCl, pH9.0, 50 mmol/LKCl, 3.0 mmol/L MgCl2, and 0.001% gelatin), 0.1 mmol/LdNTP, 0.2 μmol/L primer concentration, 1.0 U Taq enzyme (Biostar International). Executed SSR reaction program: pre-denatured 4 min at 94°C, then denatured 30 s per cycle at 94°C, annealed 30 s, and extended 30 s at 72℃. After 35 cycles, the final extension step was carried out at 72℃ for 10 min (Xie et al., 2007). AFLP experiment referred to Vos (1995), in which EcoRⅠ (6 recognition base sites GAATCC) and MseⅠ (4 recognition base sites TTAA) were selected as the restriction endonuclease. Both endonuclease and ligase bought from New England Biolab (Xie et al., 2011).


3.2.3 Data statistics and analysis

The "0-1" identification and labeling system was used to observe whether the amplified bands were labeled in the gel electrophoresis atlas obtained from the amplified products of DNA fragments. If there were amplified bands, marked "1", if there were no amplified bands, marked "0", recorded the location of the labeled bands as well. POPGENE software (Yeh et al., 1999, was used to calculate average genetic diversity, according to the method of Nei (1973). Genetic identity and genetic distance among populations before and after the correction of the deviation were calculated, according to the methods of Nei (1972) and Nei (1978). ARLEQUIN software (Schneider et al., 2000, was used to calculate the Fst, according to the method of Weir and Cockerham (1984) (Xie et al., 2007; 2011). The ratio of polymorphic loci, population specific Fst indices, average genetic diversity, genetic identity, and genetic distance among populations were calculated by SSR and AFLP respectively.


Authors’ contributions

XXY was the executor of this study who completed the design and study of experiment, carried out data statistics and analysis, and drafted and revised the manuscript. He's also the head of one of the funding projects. ZJX participated in the manuscript revision. LSF was the designer and director of another funding project. All the authors have read and approved of the final manuscript.



This study was supported by a grant from the National Key Technologies R & D Program of China during the 10th Five-Year Plan Period (2001BA505B0513) and the Central Level, Scientific Research Institutes for Basic R & D Business Special Fund (South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences) (2007TS04). Thanks to Shanghai Ocean University for providing Nile tilapia materials for this study.



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