Return to Main Index page
A. What are microsatellites?A. What are microsatellites?
B. What uses do microsatellites serve?
C. How we develop microsatellite primers?
D. How do we screen DNA with species-specific or heterospecific primers?
E. What data-analysis tools are available?Go to primer on microsatellites on Dave McDonald's web page
Mutation process: Microsatellites are useful genetic markers because they tend to be highly polymorphic. It is not uncommon to have human microsatellites with 20 or more alleles and heterozygosities (Hexp = gene diversity, D) of > 0.85. Why are they so variable? The reason seems to be that their mutations occur in a fashion very different from that of "classical" point mutations (where a substitution of one nucleotide to another occurs, such as a G substituting for a C). The mutation process in microsatellites occurs through what is known as slippage replication. If we envision the repeat units (e.g., an AC dinucleotide repeat) as beads on a chain, we can imagine that during replication two strands could slip relative positions a bit, but still manage to get the zipper going down the beads. One strand or the other could then be lengthened or shortened by addition or excision of nucleotides. The result will be a novel "mutation" that comprises a repeat unit that is one bead longer or shorter than the original. The idea that adding or subtracting one repeat is likely easier than adding or subtracting two or more beads is the basis for using the Stepwise Mutation Model (SMM) as opposed to the Infinite Alleles Model (IAM). An advantage of the SMM (at least in theory) is that the difference in size then conveys additional information about the phylogeny of alleles. Under the IAM the only two states are "same" and "different". Under the SMM we have a potential continuum of different similarities (same size, similar in size, very different in size). If, however, the SMM does not hold, then we may be worse off using it -- it may actually be highly misleading. Even if the underlying mutation process is largely stepwise, it is not difficult to see how drift might affect the distribution of allele sizes in a way that would almost entirely invalidate the SMM (visualize this by examining Figs. 6.1 and 6.2 in Lecture 6).
Advantages of microsatellites as genetic markers:
Limits to utility of microsatellites: Microsatellite DNA is probably rarely useful for higher-level systematics. That is because the mutation rate is too high. Across highly divergent taxa two problems arise. First, the microsatellite primer sites may not be conserved (that is the primers we use for Species A may not even amplify in Species B). Second, the high mutation rate means that homoplasy becomes much more likely -- we can no longer safely assume that two alleles identical in state are identical by descent (from a common, meaning shared not abundant, ancestor). As a concrete example imagine two species, each with an AC19 allele that occurs at high frequency. If the populations diverged long ago it becomes increasingly likely that the way those alleles arose took different pathways (e.g., in one species the AC19 arose from an ancestor that went from AC18 to AC19 to AC20 then back to AC19; in the other species the ancestral AC18 went to AC19 and stayed there. Any inferences we make about the species relationships based on the AC19 similarity would be misleading). The identity in state does not correspond to the identity by descent that provides (reliable) phylogenetic signal. A further potential drawback of using microsatellites is that we tend to have relatively few loci to work with (4-20). In some situations, that raises the probability of having a bias due to forces such as selection acting on one or more loci that may give a misleading impression relative to the true pattern of change for the genome as a whole.
1) Extract DNA from tissue (wide variety of possible methods depending upon tissue type)
2) Fragment the genome. Cut our genomic DNA into suitable size fragments with restriction enzymes. Generally, restriction enzymes that produce mean fragment sizes in the range of 300-600 bp are the desired goal.
3) Insert. Insert the fragments into plasmids. This step allows cloning of the fragments -- producing many copies of the 300-600 bp pieces we have inserted in the plasmids. To get a slightly more detailed idea of how plasmids act as cloning vectors, look up the boldface terms in the glossary of terms page. PUC19 is a commonly used plasmid for this sort of analysis. Why PUC19? The restriction sites in PUC19 are known (so that the ligated DNA fragments can later be cut out) and it replicates well in a bacterial culture.
4) Plate the plasmids on a nylon membrane.
5) Probe the membrane with labeled oligonucleotides of desirable repeats (e.g., AC10).
6) Culture the positive clones (the plasmid-fragments that bonded with the oligo probes).
7) Cut the insert out of the plasmids with restriction enzymes and run them out on an agarose gel.
8) Probe. Use Southern transfers to probe the digest again with labeled oligos. This serves:
10) Select. Analyze the sequence to check for "good" primer sites and useful repeat length (generally at least 8 repeats and it is often best to have more -- depending upon our intended application we may want long pure repeats or we may be interested in shorter interrupted repeats, which may have lower mutation rates). Criteria that enter into primer selection include:
11) Order the locus-specific primers (generally these will be 20-30 bp sections of the flanking regions not immediately adjacent to the repeat unit).
Here is an example of a microsatellite sequence for
scrub-jays that contains a repeat unit and forward and reverse primer sites.
From beginning of forward primer to end of reverse
primer, the above is 131 bp Repeat is CA11
The repeat unit is highlighted in red, while the forward and reverse primers are highlighted in blue and green. We would send out an order for the primer sequences (in our case we add an additional 19 bp M13 tail, which allows us to attach fluorescent nucleotides/dNTPs to our amplified product in the PCR). A laser in our sequencer/automated genotyper then detects the fluorescence, which is how we visualize the bands that constitute the allelic data we hope to gather and analyze.
Strassmann et al. (1996) has a more detailed run-through of much of this section.
1) Extract the DNA. One often begins by somehow breaking up the tissue (e.g., by grinding in liquid nitrogen). Alternatives for the extraction process include classic phenol-chloroform extractions, salt-based extractions, and a variety of commercial kits. We are getting rid of proteins and other non-DNA tissue components in this step. A typical analysis might include extracting DNA from each of the individuals in a local population of 30 individuals.
2) Amplify. We add a very small amount of each of our 30 samples of extracted DNA to a PCR cocktail for amplification in a thermocycler. This is a "magic" step that has revolutionized molecular biology. We start with almost no DNA and wind up with enough that we can see it on a gel! Various "cocktail" recipes exist -- they typically contain the thermophilic bacterial enzyme Taq polymerase (essential), the dNTP mix (nucleotides that will allow massive replication of our target DNA), magnesium chloride, and the fluorescently labeled dNTPs (these will bind to the specially added M13 or T3 tail and light up under the laser and make bands of DNA alleles show up on the gel).
3) Load. We load our 30 amplified products in separate lanes in a large vertical polyacrylamide gel. We also load several lanes with a DNA ladder -- known-size fragments of amplified DNA of known quantity/concentration. A common ladder is lambda phage cut with restriction enzymes to yield a series of fragments. The newer capillary sequencers don't use a gel.
4) Run the sequencer. We run the amplified product through the sequencer until all the alleles have had time to run by the laser, which illuminates the fluorescent nucleotides and makes bands light up on the gel (or go digital-direct to the computer). The sequencer generates both an analog image (for older, gel-based sequencers) and digitally stored data concerning the size of the fragments.
5) Optimize (variations on Steps 2-4). It
often takes considerable fiddling to get the PCR conditions right for a
particular combination of primer, DNA, thermocycler and sequencer. Major
variables in optimization include:
temperature (the primer sequence will have a predicted melting temperature but what actually works may be higher or lower),
the PCR-programmed times for denaturing, annealing and extending steps
magnesium chloride concentrations
Alternative methods of visualization include "hand-built" polyacrylamide sequencing gels with silver-staining, CyberGreen staining, ethidium bromide staining or radioactive labeling. Many of these involve nasty chemicals (EtBr) or radioactivity, so we feel fortunate to be using a relatively clean, safe procedure.
Fig. 8.1. Stylized diagram of an electrophoretic gel for microsatellites.
A current draws amplified DNA down
"lanes" in the polyacrylamide gel. The fragments can then be separated by size (bp = base pairs) and individuals
can be genotyped for their allelic composition (homozygote or heterozygote for one or more alleles). Here
the left-hand lane has a "ladder" of known-size fragments, the second lane has the DNA from one individual
(genotype bc) and the third lane has the DNA from a second individual (genotype ad). Running multiple loci
provides a wealth of genetic information about individuals, populations or species.
Fig. 8.2. Representative microsatellite and gender probe gel.
DNA was amplified by PCR and run out on a Li-Cor
automated sequencer for scoring by fragment size (number of base pairs). The individuals are WY black bears.
E. How do we analyze the allelic
information? For a slightly more detailed description go to
the Genetic analysis
You can also download my Word document on Web Genetic software. Luikart and England (1999) provides an (older) overview of approaches. For use of alternative markers see papers (mostly from TREE) by Sunnucks (2000), Mueller and Wolfenbarger (1999; AFLP), Campbell et al. (2003; AFLP) and Brumfield et al. (2003; SNPs - single nucleotide polymorphisms).
2) Microsatellite specific measures (mostly relying on SMM, Stepwise Mutation Models)
(delta mu squared) of Goldstein et al. 1995
DSW of Shriver et al. (1995)
RST of Slatkin (1995) as implemented by Goodman (1997)
of Michalakis and Excoffier (1996)
Beerli, P., and J. Felsenstein. 1999. Maximum likelihood estimation of migration rates and population numbers of two populations using a coalescent approach. Genetics 152: 763-773.
Blouin, M.S. 2003. DNA-based methods for pedigree reconstruction and kinship analysis in natural populations. Trends Ecol. Evol. 18: 503-511.
Brumfield, R.T., P. Beerli, D.A. Nickerson, and S.V. Edwards. 2003. The utility of single nucleotide polymorphisms in inferences of population history. Trends Ecol. Evol. 18: 249-256.
Campbell, D., P. Duchesne, and L. Bernatchez. 2003. AFLP utility for population assignment studies: analytical investigation and empirical comparison with microsatellites. Mol. Ecol. 12: 1979–1991.
Chesser, R.K., and R.J. Baker. 1996. Effective sizes and dynamics of uniparentally and diparentally inherited genes. Genetics 144: 1225-1235.
Davies, N., F.X. Villablanca, and G.K. Roderick. 1999. Determining the source of individuals: multilocus genotyping in nonequilibrium population genetics. Trends Ecol. Evol. 14: 17-21.
Evett, I.W., and B.S. Weir. 1998. Interpreting DNA Evidence: Statistical Genetics for Forensic Scientists. Sinauer Associates, Sunderland, MA.
Goldstein, D. B., A.R. Linares, L.L. Cavalli-Sforza, and M.W. Feldman. 1995. Genetic absolute dating based on microsatellites and the origin of modern humans. PNAS USA 92: 6723-6727.
Goodman, S.J. 1997. RST Calc: a collection of computer-programs for calculating estimates of genetic differentiation from microsatellite data and determining their significance. Mol. Ecol. 6: 881-885.
Hughes, C.R. 1998. Integrating molecular techniques with field methods in studies of social behavior: a revolution results. Ecology 79: 383-399.
McDonald, D.B., and W.K. Potts. 1997. Microsatellite DNA as a genetic marker at several scales. pp. 29-49 In Avian Molecular Evolution and Systematics (D. Mindell, ed.). Academic Press, New York.
Michalakis, Y., and L. Excoffier. 1996. A generic estimation of population subdivision using distances between alleles with special reference for microsatellite loci. Genetics 142: 1061-1064.
Mueller, U.G., and L.L. Wolfenbarger. 1999. AFLP genotyping and fingerprinting. Trends Ecol. Evol. 14: 389-394.
Parker, P.G., A.A. Snow, M.D. Schug, G.C. Booton, and P.A. Fuerst. 1998. What molecules can tell us about populations: choosing and using molecular markers. Ecology 79: 361-382.
Piertney, S.B., A.D.C. MacColl, P.J. Bacon, P.A. Racey, X. Lambin, and J.F. Dallas. 2000. Matrilineal genetic structure and female-mediated gene flow in red grouse (Lagopus lagopus scoticus): An analysis using mitochondrial DNA. Evol. 54: 279-289
Pritchard, J.K., M. Stephens, and P. Donnelly. 2000. Inference of population structure using multilocus genotype data. Genetics 155: 945-959.
Rannala, Bruce, and J.L. Mountain. 1997. Detecting immigration by using multilocus genotypes. PNAS 94: 9197-9201.
Selkoe, K.A., and R.J. Toonen. 2006. Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecol. Letters 9: 615-629.
Shoemaker, J.S. et al. 1999. Bayesian statistics in genetics -- a guide for the uninitiated. Trends Genet. 15: 354-358.
Shriver, M.D., L. Jin, E. Boerwinkle, R. Deka, R.E. Ferrell, and R. Chakraborty. 1995. A novel measure of genetic distance for highly polymorphic tandem repeat loci. Mol. Biol. Evol. 12: 914-920.
Slatkin, M. 1995. A measure of population subdivision based on microsatellite allele frequencies. Genetics 139: 457-462.
Strassmann, J.E., Solis, C.R., Peters, J.M., and Queller, D.C. 1996. Strategies for finding and using highly polymorphic DNA microsatellite loci for studies of genetic relatedness and pedigrees. Pp. 163-180In Molecular Zoology: Advances, Strategies and Protocols (J.D. Ferraris, and S.R. Palumbi, eds.). John Wiley and Sons, New York. [See also detailed protocols on pp. 528-549].
Sunnucks, P. 2000. Efficient genetic markers for population biology. Trends Ecol. Evol. 15: 199-203.