What is the difference between snps and ssrs

Figure 9. The sequences are the same order of nucleotides except for the one in red. This SNP would be designated as G:T, since one organism has a G guanine at this location of the genome while the other organism has a T thymine. High-throughput systems are based on high-throughput!

Figure 9 shows typical plates that are used for SNP genotyping, which can hold 96 or samples at once. Figure 9a: Plates used for SNP genotyping. If a SNP falls on an enzyme restriction site Restriction sites, or restriction recognition sites, are locations on a DNA molecule containing specific base pairs in length sequences of nucleotides , which are recognized by restriction enzymes , primers can be designed so that a simple PCR assay discriminates the alleles following an enzyme digest.

This process converts a SNP that is part of a larger assay to one that can be genotyped individually. Three discriminant functions were built for each analysis. In both types of markers, no strong association was detected between MLG assignments and their geographic origin.

The individual posterior probabilities of assignment to a pre-defined geographic group were low for both the SSR and SNP data in all studied subpopulations. The cross-validated number of the PCs used for the discriminant analysis is shown in dark color in the bar plots on the top right of each scatterplot. With the SSR data, the two subpopulations of the Carpathians were further discriminated along the second axis, whereas the individuals from the two Alpine subpopulations largely overlapped Figure 3.

The opposite situation was observed in the SNP data, where the least overlap was observed between the two Alpine subpopulations.

In this study, we aimed to compare the utility of SNP and SSR markers for investigating the neutral population genetic structure of the basidiomycete A. Analyzing the population structure of such an organism implies also addressing several other issues, such as the contribution of different reproduction modes to its spread at large and small geographic scales, the connectivity among populations in a heterogeneous environment and their demographic history.

Our analysis revealed differences in the information that the two types of markers give about genetic structure within large and small geographic scales based on the example of two populations from mountain forests in Europe. Noteworthy, SSRs provided a higher resolution at a smaller geographic scale under a systematic sampling Carpathian population , whereas SNPs were able to differentiate the two subpopulations which were randomly sampled across a large area in the Alps.

Both types of markers revealed the presence of repeatedly occurring genotypes in the investigated populations. High levels of genotypic diversity are generally expected in populations of fungi that mainly reproduce sexually.

However, previous population genetic studies conducted in natural and managed forests in Europe showed that this is not always the case in Armillaria species Prospero et al. Because of the spread via vegetative rhizomorphs, Armillaria species may produce large genets that occupy a forest area of several hectares Bendel et al. Thus, the presence of such genets can reduce genotypic diversity.

Moreover, mating of closely related haploids produced by spatially distributed clones via basidiospores during sexual reproduction will influence population structure and consequently change statistical estimators, for example, I A and rbar D even after the clone-correction procedure. As expected due to their multi-allelic nature and usually higher level of polymorphism, SSR loci exhibited a significantly higher heterozygosity than bi-allelic SNP loci.

However, locus-specific values showed a wide range, possibly because of uneven allelic richness. Previous studies argued that both high and low numbers of alleles at SSR loci may affect the accuracy of heterozygosity estimates and consequently of population-specific fixation indices Wang, ; Fischer et al.

In our study, SSRs were selected with an emphasis on different nucleotide number and GC content in the tandem repeats. Therefore, the ascertainment bias due to the selection of genomic fragments with exclusively high levels of polymorphism should be minor.

For this reason, allelic richness and heterozygosity estimators vary considerably among loci. In Armillaria populations, heterozygosity may also be strongly influenced by the mixed mating system. In Armillaria species, spore release is intense but spore dispersal seems to be spatially limited Travadon et al.

This may lead to inbreeding processes like mating between closely related haplotypes and plasmogamy of haploid spores or mycelium with their diploid parents, which may both reduce heterozygosity. As inbreeding and outbreeding processes can occur simultaneously in Armillaria populations, heterozygosity may not accurately explain demographic processes for example, gene flow between populations or a Wahlund effect due to population subdivision in these fungi, regardless whether SNPs or SSRs are used.

Nonetheless, at large and small scales, we observed a heterozygote deficit at most loci for both types of genetic markers, suggesting a predominance of inbreeding processes.

However, the high abundance of rhizomorphs Tsykun et al. Therefore, we can assume that this population is driven by inbreeding processes along with clonal spreading via rhizomorphs. However, long-distance spore dispersal cannot be excluded and is indirectly supported in our study by the low number of private alleles, the lack of a strong structure between subpopulations within mountain ranges regardless of sampling design or spatial scale and the low differentiation between the geographically distant Carpathian and Alpine populations see below.

Pairwise F ST values between the studied fungal populations and subpopulations, even between geographically distant ones like Alpine vs Carpathian , were low 0. It is known that F ST is very sensitive to the level of within-population variation, resulting in suspiciously low values in SSR studies and a consequent underestimation of the level of population divergence Brumfield et al.

This suggests low overall population differentiation due to extensive gene flow among populations. Although such high gene flow may be present between the two subpopulations of the rather continuous Carpathian primeval forests, it is less realistic between the two Alpine subpopulations and between the Alpine and Carpathian populations, which are separated by the Alps and a large geographic distance, respectively.

An alternative explanation for the low F ST values may be a common glacial refugium of the A. Recently, several authors for example, Jost, ; Meirmans and Hedrick, have criticized the use of F ST as a measure of population differentiation.

Since this estimator seems to be negatively correlated with the number of alleles per locus, F ST tends to have values towards zero in populations with high allelic richness and thus underestimates the actual divergence between populations Jost, In a comparative study by Fischer et al. In particular, one should not rely on absolute values because it can reflect rather the highly polymorphic nature of the markers than a real whole-genome differentiation of populations.

In our study, we used a limited number of SNPs, which were selected because they exhibited a sufficient level of polymorphism. Thus, this specific set of SNPs might induce an ascertainment bias and show a higher population differentiation than genome-wide SNPs that contain many low-frequency alleles. Regardless of the overall low absolute F ST values, for both types of markers, the genetic differentiation between distant populations Carpathian vs Alpine was substantially higher than the one between subpopulations of the same mountain region.

Using SNPs, the highest differentiation was observed between the most distant subpopulations that are separated by a high mountain range, that is, between both Carpathian subpopulations and the subpopulation of the South Alps. The geographically distant populations that is, Carpathian and Alpine showed a clear separation with both types of markers.

SSRs and SNPs, however, gave different signals within the two populations sampled at different spatial scales. The DAPC analysis, however, was able to define genetic components that differ between the Carpathian subpopulations, suggesting a weak subdivision.

Because in this region the landscape barriers for spore dispersal are relatively weak for example, low mountain relief and small distance between the studied forests , genetic exchange between these subpopulations is a realistic scenario. However, the clustering with SSRs showed also that fungal populations sampled within a small-scaled area might have a complex genetic structure. The mainly monocultural beech forests of the Carpathians seem to harbor a more homogenous A.

It is important to note that these SNPs were initially selected from housekeeping genes present in the genomes of five fungal species other than Armillaria Dutech et al. Therefore, SNPs in such conserved genes may rather reflect long-term divergence among populations than recent processes.

Apparently, the two Carpathian subpopulations have not yet diverged enough to reveal nucleotide differences in the genes considered.

In contrast to using SSRs, the two large-scaled subpopulations that are separated by a high mountain range North and South of Alpine population were assigned to two different clusters using SNPs. This suggests that the Alpine mountain range left its traces on the long-term divergence of the northern and southern A.

The presence of only one genetic cluster in the large-scaled Alpine population based on SSRs might be at least partially due to the particular sampling design applied. The two Alpine subpopulations mainly share the same alleles at all 17 SSR loci. Thus, a random sampling of distant individuals at a large spatial scale may not accurately reveal local population allele frequencies to infer subpopulation structure with SSRs.

In contrast, scattered sampling at large scale did not affect the discrimination power of SNPs. This is most likely because differences among geographically distant populations in SNP loci were fixed along an evolutionary time scale, making it easier to detect population-specific allele frequencies even with a scattered random sampling. Our results are in agreement with those of a study on the global migration patterns of the pathogenic crop fungus Mycosphaerella graminicola Banke and McDonald, The authors found that SSRs were sensitive to detect recent 50— years migration events between North and South American populations, whereas protein-coding loci were not.

Based on these and on our results, we conclude that SSRs have a higher resolution for genetically and spatially close populations as in the Carpathian subpopulations with extensive gene flow. In contrast, SNPs in housekeeping genes seem to be more appropriate for phylogeographic large-scale studies as in the Alpine subpopulations. However, this conclusion should be treated with caution, because our study design does not allow disentangling the possible confounding effects of sampling scale and population-specific demography on marker performance.

In summary, the present study revealed differences on inferences of population genetic structure of the fungus Armillaria cepistipes at different spatial scales when using two different marker types SSRs and SNPs.

SSRs were found to be better suited for detecting structure in populations at a small spatial scale with a systematic and continuous sampling design as shown in the example of the Carpathian population. The patterns observed in the SNP markers rather reflect ancient divergence of distant and naturally separated populations, being less sensitive to sampling design as shown in the example of the Alpine population.

A full factorial sampling design and a higher genomic resolution would help to strengthen the reliability of the obtained results.

Nevertheless, both marker types were suitable for detecting the weak genetic structure of the two fungal populations considered. Agapow P-M, Burt A. Indices of multilocus linkage disequilibrium. Mol Ecol Notes 1 : — Banke S, McDonald B. Migration patterns among global populations of the pathogenic fungus Mycosphaerella graminicola. Mol Ecol 14 : — Secrets of the subterranean pathosystem of Armillaria. Mol Plant Pathol 12 : — Article Google Scholar. Microsatellite markers for the diploid basidiomycete fungus Armillaria mellea.

Mol Ecol Resour 9 : — Contrasting patterns of genetic diversity and population structure of Armillaria mellea sensu stricto in the Eastern and Western United States. Phytopathology : — Genetic population structure of three Armillaria species at the landscape scale: a case study from Swiss Pinus mugo forests. Mycol Res : — Mtailed primers improve the readability and usability of microsatellite analyses performed with two different allele-sizing methods.

Biotechniques 31 : The utility of single nucleotide polymorphisms in inferences of population history. Trends Ecol Evol 18 : — Genetic exchange between diploid and haploid mycelia of Armillaria gallica. Mycol Res 99 : — Comparative performance of single nucleotide polymorphism and microsatellite markers for population genetic analysis. J Hered : — Infect Genet Evol 7 : — Genetic analysis reveals efficient sexual spore dispersal at a fine spatial scale in Armillaria ostoyae , the causal agent of root-rot disease in conifers.

Fungal Biol : — Forest Pathol 46 : — Conserv Genet Resour 4 : — Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis. Mol Ecol 11 : — Detecting loci under selection in a hierarchically structured population.

Heredity : — Estimating genomic diversity and population differentiation — an empirical comparison of microsatellite and SNP variation in Arabidopsis halleri.

BMC Genom 18 : Foll M, Gaggiotti O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a bayesian perspective. Genetics : — Population genetics of fungal diseases of plants. Parasite 15 : — Similar partitioning of variation at population and sub population level has been reported in case of rice [ 32 ].

Though, the broad pattern of distribution of varieties in the PCoA plots was similar with both the markers, but a closer look revealed three major clusters for rice varieties in case of SNP markers, such grouping was not found in case of SSR markers. Similar observation has been reported in case of wheat with SNP markers [ 33 ]. The proportion of variance explained by first three coordinates in case of SNP Therefore, it may be concluded on the basis of this study that for genetic diversity analysis HvSSR markers were more effective.

SSR marker in genetic diversity analyses have been a powerful tool because these markers are neutral, multi-allelic and co-dominant in nature Lapitan et al. In the present study SSR markers support the grouping but with SNP marker no such clear distinction between the two subpopulations was observed. There are three seasons for growing rice in India viz.

The pedigree of modern varieties also shows that autumn rice has been frequently used as one of the parent in the development of modern varieties, such as Ratna, Manoharsali and Annada Table S1 has been used for development of Shanti, Himalaya-2, Nagarjuna, Chandan, Kapilee and Luit. This may be another reason for less distinction between the two subpopulations.

At population level, no clear population structure for the rice varieties was observed either with SSR or SNP markers which may be due to large genetic variation or frequent intermixing of rice varieties in rice crossing programme across the regions. The genetic structures of populations have been previously reported in rice [ 37 - 41 ].

Fifteen population cluster and large number of admixture varieties with SNP indicated that population structure can be better explained with SNP markers, because in released varieties of Indian rice to create variation large number of diverse parents has been used.

Since admixture is the representation of diverse parents, which themselves have diverse ancestry in breeding history and domestication, may be main reason for variation present in the population [ 42 ]. Since, in the SNP based population structure rice varieties appeared subdivided in more clusters than SSR, indicated ability of SNP marker system in delineating population structure at fine level in crops.

However, the unique features of SNP markers such as, abundance in the genome, ability to generate polymorphism due to variation at single base level and their development from the conserved single-copy rice genes [ 19 ], enabled these markers to present different diversity spectrum as well as the population structure in Indian rice varieties as compared to the SSR markers. At the population structure level SNP markers showed better genetic relatedness with more population number whereas at the diversity level SSR showed better grouping samples even at trait level.

For this reason, SNP markers should be preferably used for determination of population structure in crops. Moreover, SNP markers are mostly derived from genes, as a result genetic diversity assessed using these markers reveals functional variations and may be potentially exploited for the marker trait association studies. Additionally, SNP markers in the present study were derived from the genomic region of rice having synteny with wheat genome and therefore may be equally useful for assessing genetic diversity, population structure and other marker based studies in wheat.

Details of rice samples showed trait based grouping with SSR markers. AOMVA analysis between Indica rice population varieties and aus rice population 29 varieties after removing hybrid rice 1 variety sample based on SSR marker. F-statistics analysis between Indica rice population varieties and aus rice population 29 varieties after removing hybrid rice 1 variety sample based on SSR marker.

AOMVA analysis between Indica rice population varieties and aus rice population 29 varieties after removing hybrid rice 1 variety sample based on SNP marker. F-statistics analysis between Indica rice population varieties and aus rice population 29 varieties after removing hybrid rice 1 variety sample based on SNP marker. We are grateful to Dr Jonathan K Stiles, Professor, Morehouse School of Medicine, Atlanta Ga for manuscript editing and anonymous reviewers for their critical inputs to improve the manuscript.

Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Simple sequence repeat SSR and Single Nucleotide Polymorphic SNP , the two most robust markers for identifying rice varieties were compared for assessment of genetic diversity and population structure.

Introduction Rice Oryza sativa L. Statistical Analyses The SSR profiles were scored based on the size bp of fragments amplified across all the varieties.

Results The present study was conducted on indica rice varieties which included DUS tested as well as released and notified varieties from eighteen major rice growing states of India and varieties released and notified by Central Varietal Release and Notification Committee CVRC of India. Download: PPT. Figure 1. Figure 2. Figure 3. Figure 4. Figure 5. Figure 6. Supporting Information.

Table S1. Table S2. Table S3. Table S4. Figure S1. Acknowledgments We are grateful to Dr Jonathan K Stiles, Professor, Morehouse School of Medicine, Atlanta Ga for manuscript editing and anonymous reviewers for their critical inputs to improve the manuscript.

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