222 [14] comparing macromolecules Schable, N. A., Fischer, R. U., and Glenn, T. C. (2002). Tetranucleotide microsatellite DNA loci from the dollar sunfish (Lepomis marginatus). Mol. Ecol. Notes 2, 509–511. Tautz, D. (1989). Hypervariability of simple sequences as a general source for polymorphic DNA markers. Nucleic Acids Res. 17, 6463–6471. Weber, J. L., and May, P. E. (1989). Abundant class of human DNA polymorphisms which can be typed using the polymerase chain reaction. Am. J. Human Genet. 44, 388–396. Weissenbach, J., Gyapay, G., Dib, C., Vignal, A., Morissette, J., Millasseau, P., Vaysseix, G., and Lathrop, M. (1992). A second-generation linkage map of the human genome. Nature 359, 794–801. Zane, L., Bargelloni, L., and Patarnello, T. (2002). Strategies for microsatellite isolation: A review. Mol. Ecol. 11, 1–16. Zhu, Y., Queller, D. C., and Strassmann, J. E. (2000). A phylogenetic perspective on sequence evolution in microsatellite loci. J. Mol. Evol. 50, 324–338. [14] Use of Microsatellites for Parentage and Kinship Analyses in Animals By Michael S. Webster and Letitia Reichart Abstract Microsatellite markers are quickly becoming the molecular marker of choice for studies of parentage and kinship in animals. In this chapter, we review methods and give protocols for screening potential microsatellite markers, as well as protocols for genotyping individuals with useful markers once they have been identified. In addition, we explain how microsatellites can be used to assess parentage and kinship, give basic analytical methods, and briefly review more sophisticated approaches that can be used to circumvent many of the problems that arise in any real empirical study. Introduction The application of molecular genetic methods to the study of natural populations has allowed researchers to directly examine kinship and parent–offspring relationships and thereby ushered in a revolution in our understanding of mating systems and social behavior. During the early phase of this ‘‘molecular revolution,’’ most researchers used protein allozymes or multilocus DNA fingerprinting (Burke, 1989). Microsatellites have become the marker of choice for studies of kinship and parentage. METHODS IN ENZYMOLOGY, VOL. 395 Copyright 2005, Elsevier Inc. All rights reserved. 0076-6879/05 $35.00 [14] microsatellites for parentage and kinship analyses in animals 223 Microsatellites are tandem repeats of short (
2–6 bp) genetic elements, in which differences between alleles are primarily in the number of repeats (Goldstein and Schlötterer, 1999; Jarne and Lagoda, 1996). These markers provide a powerful approach to analyses of parentage and kinship, with many advantages over other approaches (Webster and Westneat, 1998). The principal advantage of microsatellites over multilocus approaches is that microsatellites are codominant markers, so heterozygotes can be distinguished from homozygotes. This allows for exact genotyping and more precise genetic comparisons among individuals. In addition, microsatellite loci have high mutation rates, and as a consequence, a large number of alleles typically exist at a single locus. This allows for highly powerful analyses of kinship because unrelated individuals will be unlikely to share alleles. Finally, because microsatellite analyses are polymerase chain reaction (PCR) based, only small amounts of DNA are needed, and highly degraded DNA can be used. This allows for DNA to be used from nontraditional sources, including feces and hair (Constable et al., 2001; Morin et al., 2001), feathers (Pearce et al., 1997), and museum skins (Bouzat et al., 1998). Despite these advantages, microsatellites also carry a number of significant disadvantages that must be weighed against the benefits. First, the primers used to amplify microsatellites tend to be fairly species specific (i.e., the primers that work for species X may not work for species Y). As a consequence, primers often need to be isolated for each study species, which can be very laborious, particularly for taxa with a relatively low frequency of microsatellites (Primmer et al., 1997). This is beginning to change, as a growing number of species now have microsatellites available (see any issue of Molecular Ecology Notes), and techniques are now available to facilitate the process of microsatellite isolation (Zane et al., 2002). Second, because microsatellite analyses are PCR based, mutations in primer regions can lead to nonamplifying ‘‘null alleles’’ (Ishibashi et al., 1996; Paetkau and Strobeck, 1995). These can pose problems for parentage assignments (Pemberton et al., 1995) because a parent and offspring who share a null allele will appear as a mismatch. Techniques for estimating the frequency of null alleles are straightforward (Brookfield, 1996), but not necessarily very sensitive. The analysis of parentage and kinship via microsatellites typically involves PCR amplification of a locus from a number of individuals followed by gel electrophoresis to distinguish alleles of different size. Depending on how PCR products are labeled, electrophoresis can be done on an automated sequencer, which greatly facilitates scoring and comparison of individual genotypes. In this chapter, we provide general methodologies for microsatellite analysis, including protocols of the methods employed in our 224 comparing macromolecules [14] laboratory. In addition, we provide a brief outline of analytical approaches to assess parentage and other types of kinship relationship from microsatellite genotypes. Testing and Optimizing Loci There are two alternative sources for potentially useful microsatellite loci. First, one may test microsatellite primers isolated from other species. The likelihood that a particular primer pair will work for the target species appears rather stochastic, although some microsatellites show highly conserved flanking/primer regions (Rico et al., 1996; Slate et al., 1998; Zardoya et al., 1996), and detailed comparisons show that the probability that primers from one species work for another increases if they are closely related (Primmer et al., 1996). Second, one may isolate microsatellite markers from the genome of the study organism itself. Methods for isolating useful microsatellites are beyond the scope of this chapter but are reviewed elsewhere (Zane et al., 2002). In our lab, we have had good success using the method of Hammond et al. (1998) to develop and screen genomic libraries enriched for simple sequence repeats (SSRs). Once a set of potentially useful loci have been identified, it is necessary to optimize PCR conditions for the study organism, because conditions often vary from one organism to the next. After optimization, useful primers should show one (homozygotes) or two (heterozygotes) bands per individual on agarose gels, although in reality two bands will often blur together to form a single fuzzy band. Useful loci will show variation across individuals in the population, and this variation can often be detected on an agarose test gel (Fig. 1). The general approach for optimizing PCR conditions is to run multiple reactions under varying conditions and then visualize the resulting products on agarose gels stained with ethidium bromide (EtBr). Typical PCR recipes include a pair of microsatellite primers to be tested/optimized, 10X PCR buffer, salt (MgCl2 or KCl), Taq polymerase, and deoxyribonucleic triphosphates (dNTPs). No recipe will optimize all microsatellite primers, and PCR conditions will likely vary for each pair. The most useful strategies for optimizing loci involve testing different annealing temperatures (increasing or decreasing 1 or 2 at a time) and varying salt concentrations (usually in the range 0.5–3.0 mM). Varying concentrations of dNTPs, primers, and/or Taq polymerase can affect the quality of the PCR products obtained. Manipulating these components typically allows complete optimization of loci. Below, we give the standard protocol used in our lab for optimization of PCR conditions (Protocol 1). [14] microsatellites for parentage and kinship analyses in animals 225 Fig. 1. Two 3% mini–agarose test gels for amplification of (A) a single microsatellite optimized for Bicknell’s thrush (Catharus bicknelli), and (B) a single microsatellite optimized for milkweed (Ascelpias sp.). Lane one in each gel contains a 100-bp DNA ladder used to estimate product sizes present in the remaining lanes. Large arrows (on right) indicate location of variable microsatellite bands. Both gels depict polymerase chain reaction (PCR) products for highly polymorphic loci ready for visualization on a 4.5% polyacrylamide gel to determine individual genotypes. PROTOCOL 1 Optimizing PCR Conditions for Microsatellites Using Cold PCR Cold PCR is a general PCR procedure in which the reaction begins at room temperature and then the temperature is raised to 94–96 for DNA denaturation. The initial denaturation period is followed by a specific number of cycles of denaturation, annealing, and extension. After finishing a specified number of cycles, the program finishes with a final extension (72 ) period. Annealing temperatures can range from 45 to 65 and will vary for each primer set. The following list is a cold PCR protocol used in our laboratory. 226 [14] comparing macromolecules 1. For each individual to be run, pipette 1
l of genomic DNA into a well-labeled (individual, primer pair, date) PCR tube. 2. Make up the following master mix: Final Initial concentration Per sample concentration Item Sterile water PCR buffer II (no MgCl2) dNTP mix (2.5 mM) Forward primer Reverse primer MgCl2 Taq polymerase — 10 2.5 mM 5
M 5
M 25 mM 5 U/
l 11.8
l 2.5
l 1.5
l 2.5
l 2.5
l 3.0
l 0.2
l 1 0.15 mM 0.50
M 0.50
M 3.00 mM 1.00 Units 3. Pipette 24.0
l of the master mix into each of the tubes with the 1.0-
l DNA template, for a total volume of 25.0
l. 4. Cycle the PCRs as follows: 1 cycle: 30 cycles: 1 cycle: 94 for 3 min 94 for 60 s X for 60 s (annealing temperature to be varied) 72 for 45 s 72 for 5 min The annealing temperature is to be varied on different runs. Our lab usually does reactions at 50 , 55 , and 60 for each primer pair. Some primer pairs might require other annealing temperatures. 5. Run the products out on a 2% or 3% agarose/1x TBE buffer (1 TBE buffer diluted from 10 TBE stock, 1 M Tris, 0.9 M boric acid, 0.02 M EDTA) minigel and stain with ethidium bromide (EtBr) to test for useful amplification. 6. If PCR products are not satisfactory (multiple bands per lane, little or no product, etc.), repeat procedure using different annealing temperatures. Improved PCR results might also result from altering concentration of salt (MgCl2) and/or polymerase. An alternative PCR procedure for primer testing and optimization is ‘‘touchdown PCR’’ (TD-PCR). Under normal PCR protocols, primers can produce spurious bands caused by nonspecific binding of the primers. These spurious bands can increase scoring difficulty and make a locus less useful for genotyping. Don et al. (1991) developed TD-PCR to help eliminate [14] microsatellites for parentage and kinship analyses in animals 227 spurious bands and increase the quantity of target DNA produced. For TD-PCR, cycles begin with a very high annealing temperature, well above the expected annealing temperature. The TD-PCR program is designed to decrease the annealing temperature in small increments (e.g., 1 ) every second cycle to the expected annealing temperature (the ‘‘touchdown’’ temperature). Once the reaction reaches the touchdown temperature, 10 cycles are run at this annealing temperature before final extension. This method reduces the number of spurious bands because less nonspecific annealing occurs at higher temperatures, so only the target region should amplify during early cycles, exponentially increasing the amount of target DNA available in later cycles. TD-PCR can be used with a ‘‘cold start’’ for initial denaturation (i.e., Taq polymerase added to reactions at room temperature before cycling). However, Roux (2003) recommends that TD-PCR be used with a ‘‘hot-start’’ reaction in which Taq polymerase is not added to the reaction until samples are near denaturation temperature (at least 85 ), thereby avoiding most low-temperature priming altogether. TD-PCR has been used for optimizing primers in few parentage analyses (but see Hughes et al. [2003]) and has been used mostly for primer pairs that are difficult to amplify. Roux (2003) suggests that TD-PCR can be used as a faster method for primer optimization because it could potentially reduce the number of test PCRs needed to optimize PCR conditions for a primer pair. TD-PCR may be most useful when testing primer combinations across species, because the likelihood of nonspecific binding increases with slight differences in homologous regions being amplified. Once PCR products have been obtained, they should be visualized on a mini–agarose gel (test gel), which is a quick way to assess whether PCR conditions produce clear bands and whether a microsatellite is polymorphic (i.e., useful for genotyping). Mini–agarose gels (2% or 3% agarose in 1 TBE buffer, weight by volume) are useful for visualizing test PCR products (see Protocol 1). Test gels should include amplification products from 10 to 20 individuals and should reveal one or two clear bands per individual (from homozygotes and heterozygotes, respectively). Highly polymorphic loci are most useful for kinship analyses because the precision of relatedness estimates increases as the level of polymorphism increases. Visualizing PCR Products and Determining Individual Genotypes After microsatellite primers have been optimized, several methods can be used to visualize PCR products and determine individual genotypes at each locus. The three most common methods use radioactivity, silver staining, or fluorescent markers. Radioactivity and silver staining have been used most often, and protocols for each of these methods are described elsewhere 228 comparing macromolecules [14] (Strassmann et al., 1996; Tegelström, 1992). The most popular method for visualization and scoring is to fluorescently end-label one PCR primer of a pair and quantify the size of PCR products on an automated sequencer. Products amplified using labeled primers should be run on agarose test gels, as the dye-labeled primers may require slight alterations to PCR conditions (e.g., annealing temperature), and because PCRs sometimes (and inexplicably) fail. Several fluorescent dye labels are available for end-labeling primers, and final PCR products are visualized using automated sequencers (e.g., Applied Biosystems, Inc. [ABI], Prism models) and gel scanners. For visualizing PCR products on an ABI 377, our lab typically orders end-labeled primers with FAM (blue), HEX (yellow), and TET (green) fluorescent markers, and we also run an internal size standard in each lane (TAMRA, red). By varying locus color, multiple loci can be run on a single gel and products for each locus are easily distinguished. Our standard protocol for running PCR products on an automated sequencer is given in Protocol 2. PROTOCOL 2 Running Fluorescently Labeled PCR Products on the ABI 1. Run optimized PCR protocol with fluorescently end-labeled forward primer. Run 5
l of PCR product on mini–agarose test gel to double-check product amplification. The fluorescent dyes are light sensitive, so PCR products (and labeled size standards) should be kept out of light at all times (wrap PCR tubes in aluminum foil). 2. Pour (4.5%) polyacrylamide gel. Mix 45 ml automatrix polyacrylamide (National Diagnostics SequaGel Automatrix 4.5) and 5 ml 10 TBE. Just before pouring gel, add 250
l 10% ammonium persulfate (APS) and 30
l TEMED. Add entire solution (45 ml) to syringe and pour gel. 3. Sample preparation for 48 lanes: Sample preparation is done on ice. Loading buffer (90
l) is mixed by adding 50
l formamide, 24
l blue dye, and 16
l TAMRA. Add 1.5
l of loading buffer mix to each sample tube (one tube per sample). Then 0.8–1.0
l of PCR product is added into each sample tube. (Note: The amount of PCR product can be decreased or diluted if fluorescence is too strong for scoring purposes.) After PCR product has been added to the loading buffer, heat shock the samples at 96 for 5 min to denature DNA. After heat shocking, samples are immediately placed on ice. 4. Loading samples: We load 1.0 –1.2
l of sample in each lane. Samples can be loaded one at time into a shark-tooth comb or can be [14] microsatellites for parentage and kinship analyses in animals 229 Fig. 2. Genotyper chromatogram profiles of microsatellite loci illustrating varying degrees of scoring difficulty. The top panel shows locus T2 amplified from three individual Bicknell’s thrush (two heterozygotes and one homozygote [bottom profile] are shown). This locus is relatively easy to score because of the single large ‘‘peak’’ characterizing each allele. The bottom panel shows locus Dca24 amplified from two individual black-throated blue warblers (Dendroica caerulescens; one homozygote [upper profile] and one heterozygote [lower profile] are shown). This locus is more difficult to score because of the complex profiles and lower amplification. 230 comparing macromolecules [14] loaded by using a membrane comb protocol developed by the Gel Company. Our lab uses the membrane comb technique because samples can be loaded quickly and have a higher probability of running straight (compared to when using a traditional shark-tooth comb). 5. Microsatellite gels are called GeneScan runs in the ABI system and are run for 2.5–3.0 h. Data are automatically recorded by the system and saved in a gel file that will be used for analysis. GeneScan and GenoTyper (ABI) are used to analyze microsatellite data from an ABI automated sequencer (i.e., assign base-pair sizes to individual alleles at each locus). GeneScan is used to align lanes, assign size standards for each lane, and then extract lanes for use in GenoTyper, which is then used to assign sizes to bands (PCR products) for each individual at each locus. The ease of scoring allele sizes from sequencer profiles can vary dramatically across loci (Fig. 2). We offer suggestions on how to score microsatellite alleles using these software packages (Protocol 3). PROTOCOL 3 Tips for Scoring Gels with ABI Software After gel data are collected from a run, GeneScan is used to view the gel file. Adjust gel contrast for the size standard (red) and for the color of the labeled microsatellite products run on the gel. Then track lanes to ensure the lane assignment matches the correct lane. Lanes should be tracked with 70% confidence or higher. After tracking lanes and checking alignment, lanes can be extracted. After extracting lanes, an analysis window will list each lane to be analyzed with a size standard. To analyze lanes, first choose the color of the labeled microsatellite, then choose one to two lanes as size standard lanes for comparison of all microsatellite allele sizes in each lane. To assign size standards, ABI will provide a chromatogram of base-pair sizes for bands produced by TAMRA. You will view the bands (as peaks in a chromatogram) and assign base-pair sizes for size standard peaks on your gel by comparing the chromatogram provided by ABI with your gel chromatogram. Assigning size standards is important because the size standard determines how well allele sizes can be compared between individuals at a locus. After size standards have been selected for all lanes, analyze lanes. GeneScan data must now be imported into GenoTyper for further analysis. Import all lanes from the gel file you want to view. Select the color of the fluorescently labeled microsatellite and you will see a [14] microsatellites for parentage and kinship analyses in animals 231 window with a chromatogram of all the lanes. Zoom in on the section of the gel where you see the highest concentration of peaks (this should be the microsatellite region and will correspond to the base-pair size estimated using test gels). After zooming in, select the lighting bolt symbol on the right-hand side of the screen. This will show individual chromatograms for each lane (Fig. 2). Now, create a category with the color of the labeled microsatellite, and label the two highest peaks on your chromatogram. After the program labels the peaks, the researcher checks the labeled peaks and designs scoring criteria for identifying homozygotes and heterozygotes. Typically, the highest peaks (most fluorescence) represent an allele, because the microsatellite region should have produced the most product during PCR analysis and should produce the strongest signal. The key to assigning allele sizes is to look for patterns between individuals and to identify patterns produced by each allele. Allelic patterns will be consistent. Also, the type of repeat region (i.e., dinucleotide or trinucleotide) produced for each microsatellite locus is known and the distance between allele sizes can be estimated. By using this criteria, some false alleles can be avoided. Each individual will have one or two peaks (homozygote or heterozygote) per locus. Some problems can arise during scoring if a ‘‘false allele’’ is produced. A false allele is a shadow peak (i.e., a peak that looks as if it could be an allele). This peak can be identified after viewing peaks for lots of individuals and will show up as a third peak for heterozygotes who clearly have two distinct alleles. The most difficult part of scoring is creating criteria to assign allele sizes. Once allele size criteria are assigned, at least 10 individuals should be run on every gel to ensure that allele sizes are assigned correctly. Consistently rerunning these individuals will allow you to compare individuals across gels and detect any problem gels. Analysis of Microsatellite Genotype Data As microsatellites have become more popular for parentage and population studies, the number and sophistication of analytical approaches has increased exponentially. Here, we briefly review some of the most basic analyses necessary and give references for more sophisticated approaches. This chapter focuses on analyses of parentage or kinship and does not include the large number of analytical approaches that have been developed to estimate population structure and other population parameters from microsatellite data (Sunnucks, 2000). Before conducting an analysis of kinship or parentage, one should characterize the genetic variation (polymorphism) seen at each locus 232 comparing macromolecules [14] Fig. 3. Histogram showing distribution of allele sizes for a two microsatellite loci, (A) Msp6 and (B) Msp10, assayed in a population of splendid fairy wrens (Malurus splendens) sampled in 1998 (n = 290 adults). [14] microsatellites for parentage and kinship analyses in animals 233 (Fig. 3). The frequency of each allele P (xi) can be calculated from the total population of adults genotyped ( xi = 1.00), and the expected frequency of heterozygotes (he) can be calculated as (Nei, 1987): X ðxi Þ2 he ¼ 1  This expected frequency can be compared to the observed frequency of heterozygotes (ho) using a standard goodness-of-fit test and a continuity correction suggested for Hardy-Weinberg tests (Lessios, 1992), and more sophisticated tests of significance are available (e.g., see approach employed in GENEPOP at http://wbiomed.curtin.edu.au/genepop). A significant difference between he and ho suggests the presence of a null (i.e., nonamplifying) allele, and in these cases the frequency of the null allele (r) can be estimated. Under the assumption of no null homozygotes, the frequency of the null allele is given by (Brookfield, 1996; Krauss, 2000; Summers and Amos, 1997): r ¼ ðhe  ho Þ=ð1 þ he Þ For parentage studies, the most basic analysis is based on a simple mendelian principle: At any given locus, an offspring should possess one allele inherited from its mother and another allele inherited from its father. Therefore, it should be possible to identify the biological parents of an offspring by comparing its genotype to that of potential parents. Adults who do not match the offspring (i.e., do not possess an allele found in the offspring) can be excluded as biological parents, and the combination of two parents (male and female) should explain all of the alleles found in the offspring (although sometimes a single mismatch may be explained by mutation). To properly analyze parentage using microsatellites (or any other codominant marker), one must understand the probability that an offspring would match an adult by chance. This probability can be high for loci with relatively few alleles and for common alleles at more polymorphic loci. Several methods for estimating this probability have been developed (Dodds et al., 1996; Jamieson, 1994; Selvin, 1980; Usha et al., 1995), and some of these address various problems that may arise in any real study. For example, the probability of a random match can be corrected for systems in which population substructure leads to some potential parents being genetically related to each other (Double et al., 1997; Waits et al., 2001). In many research projects, one parent will be known (often the mother, e.g., in mammals where maternity is not ambiguous), but the other (the father) will not, so the biological father will be a male who possesses all of the offspring’s nonmaternal alleles. In these cases, the average probability 234 comparing macromolecules [14] of paternal exclusion (Pej) for each polymorphic locus can be calculated as follows (Jamieson, 1994): X X X X ðxi Þ2 þ ðxi Þ3 þ 2 ðxi Þ4  3 ðxi Þ5 Pej ¼ 1  2 X 2 X X ðxi Þ3 þ2 ðxi Þ2 þ3 ðxi Þ2 This is the probability, averaged over all i alleles at the jth locus, that a randomly chosen non-sire male will not possess the paternal allele found in an offspring (i.e., will not match), given that the mother of the offspring is known with certainty. It is also possible to calculate the total probability of exclusion (Pet), which is the probability that a randomly chosen male will not possess the paternal allele of an offspring at one or more of the loci surveyed (i.e., it is equal to one minus the probability that the male will match at all loci surveyed): Pet ¼ 1  Pð1  Pej Þ Consideration of these probabilities makes clear the difference between a parentage exclusion analysis and a parentage assignment analysis. In the former, one is interested in determining whether a particular adult is or is not a biological parent of a particular offspring. Such comparisons are meaningful only if the probability of sharing alleles by chance is low. Thanks to the high polymorphism at most microsatellites, in most cases a small number of loci will give a very low probability of sharing by chance. For an assignment analysis, on the other hand, one is interested in determining which of the many possible adults in the population are the biological parents of an offspring. In this case, the offspring is being compared to multiple adults and some of those adults will likely match by chance. Consider, for example, the situation in which the mother of an offspring is known but the father can be any male in the population, and the offspring is compared to multiple males at several microsatellites. If the probability that the offspring will falsely match a nonparent male at all loci is 0.01, the probability that at least one of 100 males will match the offspring by chance is 0.634. Thus, false matches are a serious concern in parentage assignment studies, and consequently, several microsatellite markers should be used. Though straightforward, parentage analyses can be complicated by a number of factors, including null alleles, genotype scoring errors, mutations, and incomplete sampling. Many of these problems can be addressed in the analysis. For example, in exclusion analyses, null alleles can create apparent mismatches between a potential sire and an offspring, but these can be recognized (because the adult male and the offspring will appear to [14] microsatellites for parentage and kinship analyses in animals 235 be homozygous for different alleles) and ignored. Similarly, incomplete sampling of candidate parents can create analysis problems, but Neff et al. (2000a,b) discuss ways to deal with this problem, including guidelines on the number of loci to use and ways of estimating the number of parents contributing to a brood. It is also possible to reconstruct the genotype of unsampled parents, particularly if a large number of offspring have been sampled from a brood/litter (Jones, 2001). Marshall et al. (1998) give a very lucid account of the difficulties of inferring parentage from genotype data and describe a likelihood approach for assigning parentage that circumvents many such problems. These authors have developed a software package (CERVUS) for conducting such analyses, and this software is quickly becoming widely used in parentage studies. However, although an extremely useful advance over simple exclusion analyses, in our experience the likelihood approach will often assign parentage to an adult who, for other (nongenetic) reasons, is very unlikely to be a true parent (Prodöhl et al., 1998). This is because the likelihood approach does not take nongenetic data into account, and because males who are homozygous for alleles possessed by an offspring will have an increased probability of being assigned as the sire. Therefore, we find it useful to closely scrutinize the results of a likelihood analysis (i.e. the output from CERVUS) to ensure that each paternity assignment makes biological sense (Prodöhl et al., 1998; Webster et al., 2004). Neff et al. (2001) developed a Bayesian approach that can objectively do this by including nongenetic data in the calculation of previous and posterior probabilities. Though not widely used, this method holds great promise for the analysis of parentage because it may reduce the chances of assigning parentage to a biologically implausible adult. Finally, in many studies researchers will be interested in kinship relationships outside of the parent–offspring relationship. Determining such relationships from genetic data can prove challenging because any given category of relationship can show considerable variation in the degree of genetic similarity between any two individuals, and so different categories of relationship may overlap broadly in genetic similarity scores (Blouin et al., 1996; Queller and Goodnight, 1989; Webster and Westneat, 1998). Nevertheless, estimates of kinship can be robust if a number of loci are used in the analysis, and several methods for inferring relatedness/kinship have been developed (Blouin, 2003). The most widely used approach is to use a software package (RELATEDNESS) that employs a likelihood approach to calculate the most likely category of relationship for two individuals based on their genotypes and the distribution of genotypes in the population (Goodnight and Queller, 1999). 236 comparing macromolecules [14] References Blouin, M. S. (2003). 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In ‘‘Molecular Approaches to Ecology and Evolution’’ (R. DeSalle and B. Schierwater, eds.), pp. 7–35. Birkhäuser, Boston. Zane, L., Bargelloni, L., and Patarnello, T. (2002). Strategies for microsatellite isolation: A review. Mol. Ecol. 11, 1–16. Zardoya, R., Vollmer, D., Craddock, C., Streelman, J. T., Karl, S., and Meyer, A. (1996). Evolutionary conservation of microsatellite flanking regions and their use in resolving the phylogeny of cichlid fishes (Pisces: Perciformes). Proc. R. Soc. Lond. B. 263, 1589–1598. [15] Use of Capillary Array Electrophoresis Single-Strand Conformational Polymorphism Analysis to Estimate Genetic Diversity of Candidate Genes in Germplasm Collections By David N. Kuhn and Raymond J. Schnell Abstract Capillary array electrophoresis single-strand conformation polymorphism (CAE-SSCP) analysis provides a reliable high-throughput method to genotype plant germplasm collections. Primers designed for highly conserved regions of candidate genes can be used to amplify DNA from plants in the collection. These amplified DNA fragments of identical length are turned into useful markers by assaying sequence differences by CAE-SSCP analysis. Sequence differences affect the electrophoretic mobility of singlestranded DNA under non-denaturing conditions. By collecting the mobility data for both strands assayed at two temperatures, alleles can be defined by mobility alone. For a germplasm collection with an unknown number of alleles at a locus, such mobility data of homozygotes can be used to determine the number of unique alleles without the necessity of cloning and sequencing each allele. Introduction The assessment of genetic diversity of germplasm collections and the genotyping of individuals for identification are important goals in the maintenance of such collections. As collections have grown, identification of parents for future breeding programs or for expanding current research populations has become more difficult, requiring sufficient polymorphic METHODS IN ENZYMOLOGY, VOL. 395 Copyright 2005, Elsevier Inc. All rights reserved. 0076-6879/05 $35.00