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1 January 2000 Genetic Variations Revealed by Microsatellite Markers ina Small Population of the Sika Deer (Cervus nippon)on Kinkazan Island, Northern Japan
Hidetoshi B. Tamate, Ayumi Okada, Masato Minami, Nobumasa Ohnishi, Hiroshi Higuchi, Seiki Takatsuki
Author Affiliations +

Genetic variations within a population of the Japanese sika deer, Cervus nippon, on Kinkazan Island were studied by microsatellite analysis. Seventeen pairs of polymerase chain reaction primers designed for several species of ungulates successfully amplified polymorphic microsatellite DNA in sika deer. About 20% of the Kinkazan population was sampled and genotyped for nine diagnostic microsatellite loci. Alleles at those loci in the Kinkazan population were found to be under the Hardy-Weinberg equilibrium. To determine whether the Kinkazan deer have a reduced level of genetic variability, an average heterozygosity in the population was calculated and compared with the values determined for other populations from Hyogo, Yamaguchi, Shimane, Tsushima, and Nagasaki. Neither the observed nor the expected heterozygosity in the Kinkazan deer significantly differed from that in the other populations. Our result indicated that, despite its small population size, the Kinkazan deer preserve extensive microsatellite variations.


The sika deer (Cervus nippon) is a common form of wild-life that was once distributed over the Japanese Islands. Its habitat has been fragmented during this century mainly due to the destruction of forests caused by human activities. As a consequence, local populations have been isolated and confined to relatively small areas. Most of the local populations are supposed to have experienced bottlenecking as a result of overexploitation and/or mass mortality occurring during severe winters.

Since gene flows between local populations rarely occur at present, the loss of genetic variation that may lead to local extinction has been anticipated in the Japanese sika deer. However, the genetic status of populations has not been studied well because of a lack of appropriate genetic markers. Molecular markers such as randomly amplified polymorphic DNAs (RAPD) and restriction fragment length polymorphism (RFLP) are not always diagnostic for detecting genetic variations within a population of sika deer because the genetic variability of those markers is reduced to an undetectable level in some cases (Tamate et al., 1995a, 1995b). Mitochondrial DNA sequences are good markers to track down the population history, although they are only informative for maternal lineages (Nagata et al., 1995; Tamate et al., 1998).

To overcome such difficulties, microsatellite markers have been sought for the genus Cervus. Microsatellite DNA consists of reiterated short sequences tandemly arrayed, and it usually provides genetic data with a greater information content than do mitochondrial DNA haplotypes or isoenzyme markers (Bruford et al., 1996). Previous studies demonstrated that artiodactyl microsatellite loci are conserved across closely related species (DeWoody et al., 1995; Pemberton et al., 1995; Engel et al., 1996; Kühn et al., 1996; Talbot et al., 1996; Slate et al., 1998). Some of those loci, from bovine and ovine microsatellites, were successfully used in population-genetic analysis for the sika deer (Nagata et al., 1998) and in the study of hybridization between the red deer and sika deer (Abernethy, 1994).

More intricate genetic analyses such as individual-based studies would require a number of diagnostic markers that detect genetic polymorphism within a single population. Screening of microsatellite markers has been continued for this purpose and, to date, 47 microsatellite loci have been found to be polymorphic in the sika deer in Scotland (Slate et al., 1998). However, it is not known whether those markers are diagnostic for detecting variations in a population of the Japanese sika deer.

In the present study, we took further effort to increase the number of informative markers for the Japanese sika deer. For this purpose, we screened microsatellite loci that were reported previously for other artiodactyls. To test the usefulness of the markers, we made an attempt to detect genetic variations in a small island population of the Japanese sika deer.



Sampling localities are shown in Fig. 1. We collected samples from 109 deer from Kinkazan Island, a small island (960 ha) located off the northeastern coast of Japan (38°17′ N, 141°39′ E). During the collection, we took special care not to injure the animals, as described previously (Minami et al., 1992). Blood was taken from live-captured animals and subjected to DNA extraction by the standard method (Sambrook et al., 1984). We also collected DNA samples from the muscles of hunter-killed deer at the following localities in Japan (Fig. 1): Hyogo (n=26), Yamaguchi (n=24), Shimane (n=20), Tsushima (n=23), and Nagasaki (n=21). Those samples were used as representatives of “reference populations” to the Kinkazan population.

Fig. 1

Map showing locations of the sampled populations of the sika deer.


Polymerase Chain Reaction

To screen diagnostic markers, we selected 25 sets of primer pairs for loci that have been reported to be polymorphic in Cervidae (DeWoody et al., 1995; Pemberton et al., 1995; Engel et al., 1996; Kuhn et al., 1996; Talbot et al., 1996). Among them, twelve sets of primers were designed for bovine loci: BL42, BM203, BM888, BM4107, BM4208, BMC1009, BOVIRBP, ETH225, Haut14, Hel6, INRA040, and IOBT965. We also tested three ovine primer sets (OarFCB193, MAF65, and VH110), five caprine primer sets (SRCRSP2, SRCRSP3, SRCRSP6, SRCRSP9, and SRCRSP10), and five primer sets designed for the white-tailed deer (Odocoileus virginianus) (Cervid 1, Cervid 2, Cervid 3, Cervid 4, and Cervid 14). Except those primers, we used eleven sets of bovine primers (TGLA48, TGLA53, TGLA57, TGLA75, TGLA122, TGLA126, TGLA227, TGLA263, AGLA293, MGTG4B, and MGTG7) that were provided with the StockMarks™ for Cattle from the Applied Biosystem Division, Perkin-Elmer-Cetus, USA (ABI/PEC).

The polymerase chain reaction (PCR) was performed in a total volume of 20 μl, using the primer pair and 100 ng of genomic DNA. The reaction mixture contained 0.8 μM of each primer, 200 μM each of dATP, dCTP, dGTP, and dTTP, 0.4 μM of [F]-dCTP and 0.025 units of TaKaRa Taq DNA polymerase (Takara, Japan) in buffer consisting of 10 mM Tris-HCl (pH 8.3), 50 mM KCl, and 1.5 mM MgCl2. The [F]-dCTPs R6G-dCTP (green, 100 μM), R110-dCTP (blue, 100 μM), and TAMRA-dCTP (yellow, 100 μM) were obtained from ABI/ PEC (product number 402793). The primer sequences are listed in Table 1. Thirty cycles of a series of reactions at 96°C (denaturation) for 30 sec, 56°C (annealing) for 1 min, and 72°C (elongation) for 30 sec were carried out in a PE9600 thermal cycler (ABI/PEC). The annealing temperature was reduced to 46°C when BM888 marker was amplified. The first denaturation step at 96°C was extended to 2.5 min, and the final elongation step at 72°C was extended to 10 min.

Table 1

Microsatellite markers tested in the study.



After PCR, an aliquot of 0.5 μl for each locus, was taken from the products and mixed with 12 μl of deionized formamide and 0.5 μl of ROX350 marker. Gel electrophoresis was run in an ABI-PRISM 310 Genetic Analyzer (ABI/PEC) for 30 min by using a module STR55A with a sequencing capillary (product number 401821) cut to a length of 41 cm, sequencing polymer (product number 401673), and a genetic analyzer buffer (product number 401884). A matrix file for the GeneScan analysis was created by using an FdCTP matrix standard (product number 402792). The electrophoresis system used in our study was originally designed for DNA sequencing. This system is also applicable to separate DNA fragments with sizes of fewer than 300 base pairs.

In an initial screening of the markers, we used a small test panel of 20 individual DNA samples. Subsequently, a large panel of DNA samples from 109 deer was tested for polymorphisms within the population.

Data analyses

Genetic polymorphism was estimated as a number of alleles per locus, observed heterozygosity (Ho), and expected heterozygosity from Hardy-Weinberg assumptions (He), using the GENEPOP package, version 3.1b (Raymond and Rousset, 1995). This program was also used to test for deviations from Hardy-Weinberg equilibrium within a population at each locus and over all loci. To obtain unbiased estimates of the exact Fisher test through 1000 iterations, we used the Markov chain method for each locus that has more than four alleles. For each locus that has less than three alleles, we used the complete enumeration method for the test. The polymorphic information content (PIC) value, which indicates the potential usefulness of the markers in pedigree analysis (Bostein et al., 1980), was calculated as

where the corresponding locus has n alleles and the frequency of the i th allele is pi.

Statistical significance (P<0.01) of the difference between heterozygosities was tested by a nonparametric multiple comparison using the computer program NP multi (distributed by Nagata O, 1995).

Computer Simulation

We used Monte Carlo computer simulations to test whether loss of alleles can occur in the the Kinkazan population if the herds are maintained at their current population size. We used a fixed effective population size Ne = 220 for each generation of the Kinkazan deer in the simulation. Ne of the Kinkazan population was estimated as follows:

  1. The total number of the Kinkazan deer (N) was estimated to be around 550 before the 1997 crush (Takatsuki et al., personal communication) by population census.

  2. The number of deer in our study group (N′) is 120.

  3. Based on observations of mating behavior (Minami et al., personal communication), the numbers of reproductive stags (Nm′) and hinds (Nf′) in the study group were estimated to be 20 and 40, respectively.

  4. Only the biased sex ratio was considered as a factor that affects Ne.

Based on the above assumption, the effective population size of the Kinkazan deer was calculated using the following equation:


We began our simulation by creating an initial allele frequency distribution based on the observed data for each loci. We simulated a change in allele frequencies by sampling alleles randomly from the distribution and then generated a new distribution for the next generation. We repeated the entire simulation 1,000 times for each generation, and counted the number of events that ended in loss of alleles from the population. Migration, mutation, and difference in fitness among alleles were not considered in the simulation.


Table 1 shows a list of microsatellite loci tested in the present study and the result of the initial screening. Among the 36 loci used, twelve bovine microsatellites, BL42, BM203, BM888, BM4107, BM4208, BMC1009, BOVIRBP, ETH225, Haut14, IOBT965, TGLA53, and TGLA126, were found to be polymorphic, hence diagnostic, in the Kinkazan sika deer. Two caprine (SPCRSP3 and SPCRSP9), one ovine (OarFCB193), and two white-tailed deer loci (Cervid 2 and Cervid 14) were also found to be diagnostic in the sika deer. The rest of the loci tested in our study were monomorphic otherwise gave no PCR product. The number of alleles per locus and size-ranges of the PCR products for the diagnostic loci are shown in Table 2.

Table 2

Number of alleles, expected heterozygosity (He), observed heterozygosity (Ho), and polymorphic information content (PIC) of microsatellite loci in the Kinkazan population.


Mendelian traits of the diagnostic markers were confirmed by matching genotypes of mothers to that of offspring in 19 maternal pedigrees identified in the Kinkazan deer (Minami, personal communication). We found no discrepancies in matching the genotypes, except in the case of ETH225. When amplifying with ETH225 primers, we observed two different sets of alleles, with size ranges of 151–195 (designated as ETH225-1) and 284–293 (designated as ETH225-2). Statistical analysis showed that ETH225-1 and ETH225-2 were independent loci because there was no association of alleles between the two loci. We did not observe PCR products of ETH225-2 in 14% of 109 samples, while the same PCR gave products of ETH225-1 to all of the samples tested. Because of the possibility that the ETH225-2 locus has null alleles, we did not include this locus in the subsequent analysis of heterozygosities.

Genotypes of individuals were further tested for association of alleles among loci. We found no genotypic linkage dis-equilibrium between two loci, except for that between the BM203 and ETH225-1 loci (P<0.01).

Among the 17 diagnostic loci, we chose nine loci (BL42, BM203, BM888, BM4107, BMC1009, BOVIRBP, Cervid14, ETH225-1, and OarFCB193) and calculated observed heterozygosity (Ho), expected heterozygosity (He), and PIC values (Table 1). Ho in the Kinkazan population ranged from 0.301 to 0.846, it did not differ significantly from He calculated for each locus. An average PIC value was 0.483 for the nine loci, and three of the loci had a PIC value above 0.5.

Table 3 shows the size and frequency of microsatellite alleles at the nine diagnostic loci. We observed rare alleles in which the allele frequency is less than 0.05, at the BM888, BMC1009, Cervid14, ETH225, and OarFCB193 loci. Individuals with such rare alleles were genotyped again by a separate experiment to confirm that the alleles were correctly scored. None of the rare alleles was found to be an artifact in the repeated experiments.

Table 3

Allele frequencies at nine microsatellite loci in the Kinkazan population.


We also measured allele frequency at the nine diagnostic loci in the reference populations. By using this data set, we calculated an average observed heterozygosity (HO) for each population to determine whether there is a significant difference in heterozygosity between the Kinkazan and the reference populations (Fig. 2). An average expected heterozygosity (HE) was also calculated for each population. HO and HE in the Kinkazan population were 0.525 and 0.516, respectively. A multiple comparison test revealed that HO in the Kinkazan population did not statistically differ from HO in the other populations except for that at Nagasaki; HO in the Nagasaki population was significantly (p<0.01) lower than HO in the Kinkazan, Hyogo, Shimane, Tsushima, and Yamaguchi populations. We also found no significant difference between HO and HE in any of the populations.

Fig. 2

Average observed (HO) and expected (HE) heterozygosities in local populations of the Japanese sika deer. Capital letters denote localities of the sampled populations shown in Fig. 1: HG, Hyogo; YA, Yamaguchi; SM, Shimane; TS, Tsushima; NG, Nagasaki; KZ, Kinkazan. Bars indicate standard deviations. The heterozygosities in the Nagasaki population are significantly lower than the others, as indicated by the asterisks.


Figure 3 shows the result of the simulation of the genetic drift that leads to the loss of rare alleles at BMC1009 locus for 50 generations of the Kinkazan deer, under the condition that Ne is fixed at 220. The result shows that allele 280, which has the lowest frequency (0.008) among the alleles at the loci, is most likely to be lost within 50 generations.

Fig. 3

Computer simulation of the effect of random genetic drift over the three alleles at the BMC1009 locus. Each generation alleles are sampled with replacements, as described in the text. The frequency of the event that a given allele is retained in the population at each generation is shown for each the allele.



Resolution power of microsatellite markers

The usefulness of microsatellite markers in deer population study has been demonstrated by previous studies (Abernethy, 1994; Pemberton et al., 1995; Nagata et al., 1998; Slate et al., 1998), although the number of diagnostic markers was limited. In the present study, we added more microsatellite loci to a list of the diagnostic markers for population genetic analysis of the sika deer. By using those markers, we detected considerable genetic variations in the Kinkazan population in which little polymorphism of mitochondrial DNA and RAPD has been observed (Tamate et al., 1995a, 1995b). The same magnitude of genetic variations that has been observed in the Kinkazan population was also demonstrated in the other populations by using the markers. The microsatellite markers tested in our study were, therefore, proved to be effective tools in dissecting the population genetic structure of the sika deer, not only for the Kinkazan population but for other local populations. Moreover, our result indicated high resolution power of the markers in individual-based analyses. Three of the markers had high PIC values and, therefore, were judged to be particularly useful in identifying individuals. The microsatellite markers tested in the present paper are now utilized successfully in both population-based and individual-based studies of the sika deer (Okada et al., in press).

Genetic variations in the Kinkazan population

It should be noted that the microsatellite alleles were under the Hardy-Weinberg equilibrium (HWE) in the Kinkazan deer, even though the population size is small. The population density is very high (50 deer/km2), and the mating system is promiscuous (Minami, personal communication). These conditions should drive the intermixing of genes within a population, and, therefore, may have contributed to keeping the Kinkazan population at HWE.

HO in the Kinkazan population was greater than HO reported for microsatellite loci in other Cervus species (Engel et al., 1996), indicating that the genetic variability of the Kinkazan deer is not necessarily reduced, as had been anticipated (Tamate et al., 1995b). This was further supported by the data from other local populations: there was no significant difference in either HO or HE, between the Kinkazan and the other populations, except the Nagasaki population. We therefore concluded that heterozygosity of the Kinkazan population is not reduced in comparison with other local populations of the Japanese sika deer. The Nagasaki, Yamaguchi, and Tsushima populations are closely related in mitochondrial DNA phylogeny (Tamate et al., 1998); however, only the Nagasaki population displayed a remarkable reduction in heterozygosity. The lowered heterozygosities could be explained if the Nagasaki population is recovering from a recent bottlenecking. We also found differences in allele frequencies among the populations — the result and its implications for the population differentiation will be discussed in separate papers.

Nagata et al. (1998) reported that HO in the Hokkaido and Chiba populations of sika deer were 0.21 and 0.23, respectively. These values are much smaller than those observed in our study. The difference in the heterozygosity can be attributed to types of microsatellite markers used in their study, which were different from ours. Alternatively, such a difference in HO may indicate that our study populations have greater genetic variability than both the Hokkaido and Chiba populations.

Presence of rare alleles

The Kinkazan deer population has been confined to a small island and has been maintained at a small size (500 animals at the maximum) for at least two hundred years. It is not known whether the Kinkazan deer originated from a natural population or from an introduced population because historical records of the deer are scarce before the 18th century. The population has experienced mass mortalities repeatedly in recent years (Takatsuki et al., 1994). In theory, an isolated small population like the Kinkazan deer will lose rare alleles within a small number of generations due to the effect of genetic drift. In the present study, on the contrary, we observed such rare alleles in the Kinkazan population. If the Kinkazan population has been maintained at the current population size for many generations, and if no mutation or migration had occurred, the rare alleles should have been lost. Our simulation, for example, demonstrated that the rare allele at the BMC1009 locus will be lost within 50 generations in the Kinkazan population.

The presence of rare alleles in the Kinkazan population can be explained by either migration, mutation, or heterosis. Migration from the mainland may introduce rare alleles to the island. This possibility cannot be excluded because local people have occasionally witnessed deer swimming in a channel between Kinkazan Island and the Honshu mainland of which distance is only 500 m. We have no direct evidence of the migration of deer to or from the island, and we have not yet collected genetic data from a mainland population in this area.

Alternatively, mutation can also provide rare alleles in a population. If the mutation rate at microsatellite loci is relatively high, as suggested by Dallas (1992), we may have a chance to observe rare alleles as a result of recent mutation in the population, even if the rare alleles are of short duration. However, some of the rare alleles that were found in the Kinkazan population are not universally rare and exist at a higher frequency in other populations (data not shown). It is, therefore, difficult to adopt the mutation theory to account for the presence of such alleles in multiple populations.

Heterosis may also contribute to the existence of rare alleles in small island populations. Coulson et al. (1998) reported heterosis in the red deer population on Rum, Scotland, and they demonstrated that individuals heterozygous for microsatellite loci had higher fitness than did homozygotes. Heterosis at microsatellite loci was also reported for a harbor seal population in Nova Scotia (Coltman et al., 1998). If the Kinkazan population is under such selective conditions, rare alleles will persist longer than what we expected under non-selective conditions. To clarify this possibility, we are now investigating the correlation between individual fitness and genotypes in the Kinkazan population by recording lifetime histories of individual deer.

The maintenance of genetic polymorphism in a small island population has been a relevant issue in both genetics and conservation biology (Pemberton et al., 1996). Studying microsatellite variations in the Kinkazan deer population will provide further information to clarify the mechanism that controls the level of genetic variability in island animals.


In this study, sampling of sika deer was made possible by the help of K. Tokida at the Japan Wildlife Research Center, E. Hosoi at Yamaguchi University, H. Tado at Yamaguchi Prefecture Forestry Guidance Center, and H. Kanamori at Shimane Prefecture Forestry Guidance Center. This work was supported by the Global Environmental Research Fund (F-1) from the Japan Environment Agency.



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Hidetoshi B. Tamate, Ayumi Okada, Masato Minami, Nobumasa Ohnishi, Hiroshi Higuchi, and Seiki Takatsuki "Genetic Variations Revealed by Microsatellite Markers ina Small Population of the Sika Deer (Cervus nippon)on Kinkazan Island, Northern Japan," Zoological Science 17(1), 47-53, (1 January 2000).
Received: 29 June 1999; Accepted: 1 August 1999; Published: 1 January 2000
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