What are Codominant vs Dominant Genetic Markers?

What are Codominant vs Dominant Genetic Markers?

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When talking about types of genetic markers, the adjective "dominant" and "codominant" are often used. I don't fully understand their definitions and found contradicting definitions.

Foll and Gagiotti (2008)

Typically reading from Foll and Gagiotti (2008), they list give two examples of markers of each type

  • Codominant markers
    • SNP
    • microsatellite
  • Dominant markers
    • RFLP
    • AFLP


This reddit post indicates that RFLP are codominant markers (unlike stated in Foll and Gagiotti (2008)).


On (random website I never heard of before), they define dominant and codominant markers based on gene expression suggesting that the terms dominant and codominant used for markers have the same definition than those used for allelic effects on the phenotype (dominance, recessivity, overdominance, etc… ).


  • What are the definitions of dominant and co-dominant genetic markers?

  • Can you please offer examples for each type and explain why they fit in one or another category?

  • Do the terms dominant and codominant have the same definition when used to describe a marker and when used to describe allelic effects on the phenotype (dominance, recessivity, overdominance, etc… )?

From wikipedia

If the genetic pattern of homozygotes can be distinguished from that of heterozygotes, then a marker is said to be co-dominant.

This definition seems to be different from the one used to explain allelic effects. For example, as you would know a SNP is basically a feature of the DNA sequence. It is more of a genotype rather than a phenotype. Markers are supposed to be phenotypes (can be a molecular phenotype too for example a marker for stem cells etc). SNPs do create allelic variants but they may or may not contribute to different phenotypes. Consequently, the allelic variants may exhibit dominance or co-dominance with respect to each other. So, going by the definition used in classical genetics, you cannot call SNP as a "dominant"/"co-dominant" marker.

That is a weird usage of the terminology but people have done it so the deed cannot be undone. I would avoid future usage of such misleading terms. Having said that SNP would fit the definition of a "co-dominant marker" if it is observed by DNA sequencing .

Personally, I would consider RFLP a technique and not a marker per se. RFLP can be used to differentiate SNPs but sometimes it may not even resolve different strongly visible phenotypes (its efficacy basically depends on the polymorphism at restriction sites). Heterozygotes however, would have a different band pattern compared to homozygotes. So it should be a "co-dominant" marker.

AFLP, as far as I know is simply PCR amplification of restriction fragments. It would be "co-dominant" for the same reasons. However, just a PCR based screen would not differentiate between homozygotes and heterozygotes and would thus be a "dominant" marker. Now again, these are techniques and not, in a true sense, "markers" whereas SNPs and microsatellites are actual DNA features. So these cannot be even compared. A technique, depending on its resolution, can be "dominant" or "co-dominant" as per these definitions.

For general interest

I would want to reiterate that these definitions, for some reasons mentioned above, are quite flawed and are therefore misleading (they may have been okay at certain point of time but are obsolete now). Please avoid their usage in your papers and reports and if possible point these flaws out, so that they do not propagate.

What are Codominant vs Dominant Genetic Markers? - Biology

Handout for Lecture 4 - C3032

mutant vs. mutation
isolation vs. identification
phenotype vs. genotype
screen vs selection
wild type vs. wild-type
penetrance vs. expressivity
homozygous vs. heterozygous
autosomal vs. sex-linked
dominant vs. codominant vs. recessive
complementation vs. noncomplementation
Punnett Square
marker mutation
conditional mutation
complementation group
synthetic mutations (duplicate genes)
lethal mutation

Useful Approaches to Genetics Problems:

Determine parental genotypes by progeny testing and test crosses.

Remember basic probability.

If P is the probability of an event occurring, then 1-p is the probability that it does not occur

for 2 independent events: and = a X b or = a + b

A Punnett Square for the double heterozygote a/+b/+:

If a and b are recessive, then 9 WT: 3 A: 3 B: 1 AB.

What if a is recessive and b dominant?

What if both a and b are dominant?

What if a is recessive and b is dominant but shows 50% penetrance in heterozygotes (homozygous animals are express the phenotype at 100% penetrance)?

What if a and b are recessive and and mutations in both are needed for the mutant phenotype?

What if a and b are dominant and mutations in both are needed for the mutant phenotype?

A Genetic Approach to Studying a Biological Problem:

Screen or select for mutants defective in the phenomenon being studied.

Determine whether the phenotype breeds true and establish a mutant line.

Backcross to wild type to remove extraneous mutations.

Obtain other mutations in the same gene and deduce the phenotype in the absence of the gene.


Traditional descriptors of population genetic structure (eg FST) were developed for the analysis of population samples scored for genetic markers showing codominant inheritance and low levels of allelic diversity (typically two to four alleles per locus). A substantial body of population genetic theory appropriate for such markers has provided us with an understanding of how estimates of FST are influenced by parameters such as migration rate, population size and mutation rate (Wright, 1978 Nei, 1987). Using this knowledge, it is now possible (with caution, and making a number of assumptions) to infer biological processes such as dispersal patterns from estimates of FST calculated from traditional codominant markers (eg allozymes) in natural populations.

In recent years, a much wider range of genetic markers with significantly different properties have become available (Karp et al, 1998). These can be divided into two main classes: (1) locus-specific hypervariable microsatellite markers and (2) multilocus arbitrary fingerprinting techniques (eg randomly amplified polymorphic DNA (RAPDs), amplified fragment length polymorphisms (AFLPs)). The fact that both these classes of markers have different properties from their predecessors means that new methods of analysis are required if they are to be used to make inferences about population processes.

Microsatellite markers differ from allozyme markers in their stepwise pattern of mutation, their high mutation rates and their high levels of allelic diversity. Several recent reviews have highlighted the problems with using traditional estimators of FST derived from microsatellite data to infer such things as levels of gene flow among populations (Hedrick, 1999 Balloux et al, 2000 Balloux and Lugon-Moulin, 2002). This has stimulated the development of alternative methods that are based either on models of microsatellite evolution (eg RST Slatkin, 1995) or population assignment tests (Waser and Strobeck, 1998).

In the current paper, we focus on the analysis and interpretation of population data derived from arbitrary fingerprinting techniques such as RAPDs, ISSRs (intersimple sequence repeats) and AFLPs. These generate large numbers of polymorphic markers by the amplification or nonamplification of arbitrary DNA sequences scattered throughout the genome (Williams et al, 1990 Zietkiewicz et al, 1994 Vos et al, 1995). One clear advantage of these approaches is the large number of loci that can be scored as biallelic characters. RAPD and ISSR studies are typically based on data from 30 to 100 loci, whereas it is not unusual to score up to 250 loci in AFLP studies. The major disadvantage of using arbitrary fingerprinting techniques is the dominant nature of the data obtained (Lynch and Milligan, 1994 Harris, 1999). Of the three potential genotypic classes in a diploid individual (X1X1, X1X2, X2X2), only two character states are recorded: band presence (scored as 1=X1X1 and X1X2) and band absence (scored as 0=X2X2).

Two strategies have been adopted for utilising this dominant suite of markers to study population structure. The first involves population level analysis. It requires scoring of substantial numbers of individuals across a range of populations. Where the breeding system of the populations is known, population allele frequencies can be estimated using appropriate unbiased estimators and FST is then determined (Lynch and Milligan, 1994). Alternatively, especially where the breeding system is not known, either a Bayesian approach to estimating FST can be performed (Holsinger et al, 2002) or a genetic distance matrix based on phenotypic data can be calculated, and the variation partitioned into its among- and between-population components to calculate an analogue of FST (φst Excoffier et al, 1992). Inferences about levels of migration among populations, or population history, are then made in the normal way based on population genetic theory that relates FST to population processes.

The second strategy for utilising dominant marker data typically involves sampling a smaller number of individuals per population, but each is scored at a large number of loci. The multilocus dominant marker phenotypes of individuals are then used to generate a genetic distance matrix. This information is subjected to phenetic analysis to produce a visual representation (a tree-based diagram or principal coordinates plot (PCO)) of the genetic relationships among individuals in the sample (eg Winfield et al, 1998 Clausing et al, 2000 Drummond et al, 2000 van der Merwe et al, 2000 Sawkins et al, 2001 Barth et al, 2002 Nan et al, 2003).

The shape of the tree or PCO plot then forms the basis for making population genetic inferences about the populations from which the individuals were derived. For instance, grouping of individuals from different populations in the same cluster of a tree has been interpreted as evidence for gene flow between these populations location of individuals from different populations in mutually exclusive clusters has been taken as evidence for genetic isolation of the populations.

The representation of genetic relationships among individuals using these techniques is commonly undertaken. However, the interpretation of the resulting trees in terms of the genetic behaviour of the populations involved is not straightforward, and is not based on any formal quantitative population genetic model or theory. Despite this, the use of phenetic diagrams using dominant data to infer population genetic processes appears to be gaining widespread and uncritical acceptance.

The objective of this paper is to emphasise some potential pitfalls inherent in this practice. To do this, we present a simple illustrative example. We explore how the commonly used neighbour joining tree-based approach for analysing arbitrary fingerprinting data relates to known information on population structure. We show how variation in the extent of genetic differentiation among populations, the number of markers scored and the interaction of these factors affect tree topology. Our purpose is not to provide guidelines for population genetic interpretation of phenetic trees, but rather to emphasise some factors that make this process problematic when practised in isolation.

What’s The Difference Between Codominance and Incomplete Dominance?

Even though Mendel played an integral part in observing dominant relationships, codominant and incomplete dominant relationships are considered to be non-Mendelian inherit ​ ​ ance patterns.

What Is Codominance?

In a codominant relationship, neither allele is recessive or masked by the other allele (which make the pair that code a characteristic). Blending plays a role in a codominant relationship, and both alleles are equally expressed, and their features are both present (and seen) in the phenotype.

In a way, you could think of codominance like “co-parenting,” where each parent plays an equal role. In a codominant relationship, both alleles are passed down from one generation to the next, rather than being bred out.

How Does Incomplete Dominance Differ?

We know what complete dominance is and incomplete (or partial) dominance may be a lot like it sounds. Incomplete dominance refers to when one allele for a certain trait is not entirely dominant over its counterpart (the other allele). The offspring end up with a combined phenotype.

The traits of each parent are neither dominant or recessive and a third phenotype results. The alleles don’t actually blend, but the traits appear to be mixed, so many people refer to the result of incomplete dominance as “blended.”

As you can see codominant and incomplete dominant relationships are very similar. While one has actual blending going on in the offspring, the other appears to be you can see how some people might assume they are the same, right?

A simple way to explain the differences between the two is that in incomplete dominance, the traits of the offspring are unique and similar to the dominant traits (but still a trait of its own). Such as black feathers and white feathers produce silver feathered offspring.

A codominant relationship will produce offspring that has both traits visible. You can get a better idea of how this works in the examples below.

Comparison of DNA Isolation and Dominant and Co-dominant Molecular Markers to Reveal the Genetic Sex of Gallus domesticus (Domestic Chickens)

Purpose: The quality of DNA and reliability of molecular markers are crucial for the success of Polymerase Chain Reaction (PCR) based genetic sex determination. This study was aimed at investigating the optimum conditions for isolation of DNA from chicken blood and the reproducibility of dominant and co-dominant sex markers to be validated as a tool for successful sexing in avian research.

Research Method: Efficacy of six different extraction procedures including manual and solution based commercial purification kit were evaluated with different combinations of initial blood, lysis buffer and protein denaturant in relation to the DNA yield and purity. Three primer sets namely CHD1, HUR 0423 and HUR 0424 were evaluated by PCR.

Findings: The study results showed that 10µl of initial blood volume yields a significantly high DNA yield with high purity. Dominant marker HUR0424 showed to be a reliable marker system for the genetic sexing of domestic chickens over co-dominant markers.

Research Limitation: For the accuracy of the results, protocols had to be followed at the same time and using same sample to avoid any errors.

Originality/ Value: PCR based sexing is considered, the most accurate and inexpensive method and hence validation of the method is important for success of future avian research.

MorphMarket Blog

In the Reptile hobby, we classify mutant genes based on how they interact to morph the animal's appearance from its wild form. An animal may carry zero, one, or two copies of a mutant gene. If it only carries one gene (i.e., half the pair), we refer to it as the heterozygous form since it has the mutant gene paired with a the normal/wild type gene. When the pair is present, the animal is said to be homozygous for that gene.

Recessive type genes don't produce a visual change unless the animal is carrying the full pair of the mutant gene. An example is Piebald. A "Het" Piebald Ball Python looks just like a wild or normal Ball Python. When a breeder is not sure if the animal is carrying a recessive gene, they can refer to it as being "possible het", possibly with a percentage of likelihood based on the parents (e.g., "66% possible het").

Genes which are Dominant produce visual effects even in the heterozygous form. This means offspring with only a single parent carrying the gene can have a mutated appearance. In true Dominance, the same altered visual effect is achieved with either a single gene or pair of genes. The Pinstripe in Ball Pythons is a well known Dominant gene, which is indistinguishable in het or "super" (homozygous) form.

Incomplete Dominance is when the heterozygous and homozygous forms each produce different morphed appearances. For example, the Lesser Platinum gene produces a lighter colored appearance in heterozygous form, and the solid white Blue Eyed Lucy (BEL) in homozygous form. Breeders who discover a new Dominant gene cannot say that it is Incompletely Dominant until they produce a super form which differs from the Heterozygous one. Without knowing if this is even possible, it can take many generations to "prove out" the gene's true nature.

The Reptile community has commonly used the term Codominant to refer to genes that produce a different appearance in homozygous and heterozygous forms. Incomplete Dominance is however, a more accurate biological term in most cases. Incomplete Dominance describes genes that produce a blended effect. For example, red and white genes producing pink (or any other color) are incompletely dominant. By contrast, Codominant describes genes which produce an unblended effect. An example would be red and white genes producing an appearance of red and white. It might appear to be pink at a distance, but it is really comprised of white and pink hairs, scales, etc. Most of the reptile genes which have been referred to as Codominant have the blended effect, and thus would be more accurately categorized as Incomplete Dominants.

Here's a short video which illustrates these differences.

Where do forms of Dominance show up on MorphMarket? For each species, you can browse the ads by traits or morphs. On this index page, you'll find the traits color-coded by dominance. Warmer colors represent recessives and cooler colors represent the dominants. We further divide the ads using different colors for possible, het and homozygous or super forms. Grayscale indicates traits that are not directly inheritable.

You can find learn more about genetics using the links on our resources page.



We use the HGDP–CEPH Human Genome Diversity Cell Line Panel presented by C ann et al. (2002) to identify regions of the human genome that may be influenced by selection. The last version of this data set consists of 1056 individuals from 53 subpopulations, which were scored for 835 microsatellites. On the basis of the results of our realistic simulation study, we chose to use the same 19 continental populations from Africa, Europe, and Asia to minimize the false-positive rate. We kept only microsatellites that were strictly di-, tri-, and tetranucleotidic, which led us to select 106 dinucleotides, 127 trinucleotides, and 327 tetranucleotides, leading to a total of 560 markers. To further minimize the detection of false positives we adopted the best strategy identified by our simulations and conducted separated analyses for each of the three types of markers and grouped the results. We used the same cutoff value of 0.99 as in the simulated data set. We found 131 loci under selection: 86 were detected as being under directional selection and 45 under balancing selection. This represents 23% of the studied loci and is much higher than the false-positive rate estimated from the simulation study that considered similar demographic and sampling scenarios (4.5%). These results suggest that a high number of loci have been subject not only to directional (15%) but also to balancing selection (8%) in the course of human evolution.

We identified the microsatellite loci that are located within a gene whose position is well defined, using the NCBI UniSTS database ( We found eight microsatellites close to known genes under directional selection of which two were located on the X chromosome and 10 known genes under balancing selection, all located on autosomes (Table 8). We then used the Online Mendelian Inheritance in Man (OMIM) database ( to establish the putative function of the 18 genes identified using NCBI and established that 15 genes (8 under balancing and 7 under directional selection) are referenced as implicated in a genetic disease. These results are in accordance with those of C lark et al. (2003) who showed that the genes under selection are overrepresented in this database.

Genes under natural selection

Littorina saxatilis:

To present an application to AFLPs, we reanalyzed the Littorina saxatilis data set of W ilding et al. (2001), studied also by G rahame et al. (2006). The data consist of 290 polymorphic AFLP loci, surveyed in four different rocky shores in Britain: Thornwick Bay, Flamborough (TH) Filey Brigg (FY) Old Peak (OP) and Robin Hood's Bay (RB). In this region L. saxatilis is found as two morphological forms (“H” and “M”) that show good evidence of partial reproductive isolation. One set of individuals of each morphological form was sampled in each shore, with the exception of the RB shore where two sets of M were sampled. Each of the eight resulting samples is composed of 43–51 individuals.

In each shore two hypotheses can explain the observed divergence between the two morphological forms (G rahame et al. 2006): an allopatric divergence followed by a secondary contact or a primary parapatric divergence (W ilding et al. 2001). In both cases populations are likely to be exchanging genes only in the region of contact, and using the eight populations in a single analysis would lead to a violation of the demographic model assumed by our inference method. This is also supported by the neighbor-joining tree constructed by W ilding et al. (2001) from the loci they identified as neutral: populations were clustered by site (they also constructed a tree using all loci, which led to a grouping of populations by morphotypes H and M).

W ilding et al. (2001) used a modified version of the Fdist model (B eaumont and N ichols 1996) to detect selection from dominant markers. They analyzed three data sets, corresponding to the three shores where both morphotypes were sampled, each one containing two populations. One potential problem of the B eaumont and N ichols (1996) method is the necessity to estimate Nm from the data set to perform simulations with this target value. However, the estimation of Nm assumes neutrality and is overestimated in the presence of directional selection. To avoid this problem, they used an iterative procedure whereby the mean FST calculated from the full data set is used as input of a first Fdist run, and then it is iteratively modified as outlier loci are removed. After four such steps, W ilding et al. (2001) retained only loci that were lying above the 0.99 quantile in all three H–M comparisons and identified 15 loci under selection.

We made the same three analyses of each two-population data set using our method. The Bayesian model we used takes explicitly into account the loci under selection in the estimation of FST coefficients in Equation 3 and, therefore, does not suffer from the problem mentioned above. B eaumont and B alding (2004) compared the critical P-values between the Bayesian method and Fdist by matching the false-positive rate of 6800 neutral loci. They showed that a level of 1% for Fdist is equivalent to a level of 10% for the Bayesian model. Here, the sensitivity study above indicates that a 10% level for the informal criterion used by B eaumont and B alding (2004) is equivalent to a cutoff value of 0.7 for the posterior probability estimated by our reversible-jump version of the method. We identified 13 loci with a probability >0.7 and they all belong to the list of 15 loci identified by W ilding et al. (2001). The two missing loci are named “A37” and “F11” by W ilding et al. (2001) and, according to our method, both are identified as outlier in only two of the data sets. More precisely, the A37 locus has a posterior probability of only 0.53 in the Filey data set, and the F11 locus has a posterior probability of 0.65 in the Old Peak data set. These loci are at the lower tail of the allele-frequency distribution estimated by W ilding et al. (2001) in two of the three data sets considered. If we were to use a cutoff value of 0.65 instead of 0.7 we would include the F11 locus in the list of selected loci but also an additional marker not found by W ilding et al. (2001).

We also analyzed these three sets of two populations as a single data set of six populations to investigate the influence of the violation of the demographic model assumed by our method. Using a cutoff value of 0.99, all 13 loci found in the pairwise analyses are identified as outliers, but we also find 4 additional loci. The results of the simulations of the spatial expansion model suggest that these loci could be false positives due to the violation of the demographic model assumed. As was the case for the human data set, these 4 loci have a posterior estimate of α situated at the tail of the distribution of α-values for loci with a posterior probability >0.99. More precisely, the maximum estimated value of α for these 4 additional loci is 1.89, while most of the loci identified as outliers (7 of 13) in the pairwise analyses have a posterior estimate of α greater than this value.

To establish which of the two approaches is the most appropriate one, we modified the simulation scheme presented above to incorporate a different demographic scenario. More precisely, instead of simulating the six populations under an island model, we simulated first three populations from this model (for the three shores) and then, from each one of them, generated allele frequencies for two populations (corresponding to the two different morphotypes). This demographic history mimics the neighbor-joining tree constructed by W ilding et al. (2001) from the loci they identified as neutral. We chose simulation parameters to obtain data sets close to the real one. We simulated 290 such loci and 50 individuals in each population. We used FST = 0.05 between the ancestral population and the three intermediate populations and FST = 0.03 between the intermediate populations and the six populations sampled. The ancestral allele frequencies were simulated from a beta distribution with both parameters equal to 0.5 and we chose FIS = 0.5. We added selection to 20 loci, using α = 2.5.

We performed the same analysis on this data set as with the real one: 17 loci over the 20 loci under selection had a posterior probability >0.7 in all three pairwise analyses. We did not detect any false positives and all 3 false-negative loci were identified as outliers in two of the three analyses. We then carried out an analysis with the six populations as a single data set and identified all 20 loci as selected with a posterior probability >0.99. However, we also identified 4 additional false-positive loci. The maximum estimated value of α for these 4 additional loci is 2.02, while only 11 of the 20 true outlier loci have a posterior estimate of α greater than this value. These results suggest that, under this particular demographic model, it is better to carry out pairwise analyses instead of a single global one. Moreover, it seems that the best strategy is to identify as selected all loci that are outliers in at least two of the three pairwise analyses. Indeed, if we use such an approach, then we retrieve all 20 loci under selection without identifying any false positives. Note that we can obtain the same result even if we raise the cutoff probability to 0.78.

Applying this approach to the periwinkle data set of W ilding et al. (2001), we identify as selected all 15 loci originally found by them and also 6 additional outliers. We obtain the same result even if we raise the cutoff probability to 0.81. Thus, our analyses suggest that a total of 21 loci are influenced by selection in this species.


Dispersal is one of the central processes in the dynamics and evolution of plant populations. The spatial dynamics of plant populations are determined to a large degree by the movement of seeds. At regional scales, seed dispersal ranges will set the possibilities for colonization of new sites, and will influence the probability of extinction of local populations (the rescue effect Brown & Kodric-Brown 1977 ). Among-population dispersal is also an important determinant of metapopulation structure and will define the units within which we consider dynamics and evolution ( Husband & Barrett 1996 ). For example, can we regard the local population as the unit of dynamics, or do we need to consider the dynamics of neighbouring populations if we are to achieve a complete understanding of the local dynamics? If we observe a disequilibrium at the local population level, can we understand this in the light of metapopulation dynamics and regional equilibria ( Olivieri et al. 1990 Antonovics et al. 1994 Husband & Barrett 1996 )? Furthermore, dispersal is an important issue for several applied topics, including viability analysis for populations of fragmented species ( Ellstrand 1992 Ellstrand & Elam 1993 Ouborg 1993 ), evaluation of the risks of escape of genetically modified organisms into natural populations ( Ellstrand & Hoffman 1990 ) and control of epidemic diseases and invasions of exotic species ( Hengeveld 1989 Williamson 1996 ).

If we are to find answers to the questions within this diverse array of topics, some quantification of dispersal is vital. However, quantifying dispersal, and especially long-distance dispersal, has always been one of the more difficult tasks in plant population biology. Several approaches can be found in the literature. Many studies measure the actual distance over which individual propagules disperse and construct a frequency distribution. Dispersal distances are measured by trapping seeds at various distances from the source ( Huiskes et al. 1995 Ruckelshaus 1996 Thiede & Augspurger 1996 ), by recapturing marked and released propagules ( Johansson & Nilsson 1993 ) or by using artificial analogues of dispersal propagules ( Nilsson et al. 1991 ). Another approach tries to predict dispersal distances by measuring the aerodynamic properties of seeds in wind tunnel experiments ( Van Dorp et al. 1996 ) and making diffusion models ( Greene & Johnson 1996 Cain et al. 1998 ). Both these approaches typically yield leptokurtic dispersal curves, with the majority of seeds dispersing over very short distances and only a very small proportion dispersing over longer ranges. For instance, in a seed trap experiment with Lupinus texensis 95% of the seeds dispersed less than 2 m, less than 0.5% of the seeds dispersed between 3.2 and 3.4 m, and no dispersed seeds were detected beyond 3.5 m ( Schaal 1980 ). While the maximal detected dispersal distance may vary between species and experiments, the general rule is that long-distance dispersal is very rare. However, comparison of observed colonization rates with rates predicted from dispersal curves drawn up using the methods described above suggests that the magnitude of long-distance dispersal is frequently underestimated ( Van Dorp 1996 Cain et al. 1998 ).

At the same time these rare long-distance dispersal events have great biological relevance. They determine both the possibilities of colonization of new sites and the structure of metapopulations, and may also contribute to gene flow among populations and thus influence the distribution of genetic variation. Although conventional population genetic wisdom says that the exchange of one migrant per generation is enough to prevent strong differentiation among two populations ( Ellstrand & Elam 1993 ) (although allele frequencies may still be quite different Wright 1978 ), the methods available to study dispersal are generally insufficiently sensitive to measure this low-frequency dispersal rate reliably, thus illustrating the limitations of such methods.

The search for a solution to this problem led Silvertown (1991) to suggest that the study of dispersal would greatly benefit from integration of ecological and population genetic approaches. The argument was that if it is very difficult to estimate long-distance dispersal rates by following individual propagules, then we should approach the problem from the other end, by studying the (population genetic) consequences of dispersal, rather than dispersal events themselves. Part of Silvertown’s plea was based on the potential offered by the use of sophisticated molecular marker techniques, which at that time were emerging rapidly. The question then was: ‘How can these techniques help us understand dispersal, and, especially, will they lead to qualitatively new insights?’ The central question addressed in this paper is the way in which the population genetic approach, through the use of molecular markers, may indeed be able to help the study of dispersal.

Any attempt to integrate ecological and population genetic approaches to study dispersal should start with a definition of dispersal, as distinct from gene flow. Dispersal and gene flow, although clearly related, have different meanings, which could be confused. Therefore, throughout this paper dispersal will refer to the dispersal of seeds (or other propagules able to establish themselves), while gene flow will refer to the movement of genes and thus may involve both seed and pollen migration.

We begin this review by discussing a few characteristics of molecular markers that are relevant to the study of dispersal. We then discuss the two basic population genetic approaches to this topic: the indirect and direct methods. In the indirect method, gene flow is inferred from the distribution of genetic variation among (sub)populations, and dispersal rates are then calculated from gene flow levels. Specific applications are needed to achieve this and two, genetic dispersal curves and separation of seed dispersal from pollen flow, are presented here. The direct method of estimating gene flow and dispersal, and the help of molecular markers in this approach, is then discussed. We conclude with a comparison of the type of information that can be obtained from ecological and the two population genetic approaches, and discuss prospects for the application of molecular markers in dispersal research.

What are Codominant vs Dominant Genetic Markers? - Biology

Some commonly used types of genetic markers are

  • RFLP (or Restriction fragment length polymorphism)
  • SSLP (or Simple sequence length polymorphism)
  • AFLP (or Amplified fragment length polymorphism)
  • RAPD (or Random amplification of polymorphic DNA)
  • VNTR (or Variable number tandem repeat) polymorphism, SSR (or Simple sequence repeat)
  • SNP (or Single nucleotide polymorphism)
  • STR (or Short tandem repeat)
  • SFP (or Single feature polymorphism)
  • DArT (or Diversity Arrays Technology)
  • RAD markers (or Restriction site associated DNA markers)

They can be further categorized as dominant or co-dominant. Dominant markers allow for analyzing many loci at one time, e.g. RAPD. A primer amplifying a dominant marker could amplify at many loci in one sample of DNA with one PCR reaction. Co-dominant markers analyze one locus at a time. A primer amplifying a co-dominant marker would yield one targeted product. Dominant markers, as RAPDs and high-efficiency markers (like AFLPs and SMPLs), allow the analysis of many loci per experiment without requiring previous information about their sequence.

Codominant markers (RFLPs, microsatellites, etc.) allow the analysis of only a locus per experiment, so they are more informative because the allelic variations of that locus can be distinguished. As a consequence, you can identify linkage groups between different genetic maps but, for their development it is necessary to know the sequence (which is still expensive and is considered one of their down sides).


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