Advance Publication

Anthropological Science

The effect of perfection status on mutation ratesof microsatellites in primates
Ming Yin Ngai1,2, Naruya Saitou2,1*
1

Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan
2
Division of Population Genetics, National Institute of Genetics, Mishima 411-8540, Japan
Received 29 June 2015; accepted 24 January 2016

Abstract Microsatellites are highly mutable elements in eukaryotic genomes. Although they are widely used by researchers as genetic markers and tools for evolutionary studies, the full mutation mechanisms and factors affecting the mutation rate of microsatellites are not well understood. Microsatellite
loci shared by human, chimpanzee, gorilla, orangutan and rhesus macaque genomes were sorted into
four different perfection status groups named Perfect, Imperfect, Perfect-Compound, and ImperfectCompound. We found that compound microsatellite loci generally had no significant effect on mutation
rates, while imperfect microsatellite loci significantly lowered mutation rates compared to perfect ones
(P = 1 × 10–4). The significant difference resulted from a small amount of interruption (1–2 bp) within
microsatellites, especially when the loci were shorter than 14–15 repeats. Furthermore, real perfect lociwere used to compare with split up ‘perfect loci’ actually obtained from the imperfect group. We foundthat the mutation rates were significantly different from each other for small numbers of repeats, especially in 7–9 repeats (P < 0.05). This suggests that an imperfect locus should not be considered as two or
more separated perfect loci. This also raises the question that the algorithms currently used to find microsatellite loci based on mismatch penalties may result not only in heterogeneous microsatellite data
sets with heterogeneous numbers of imperfect loci but also in data sets that are biased in mutation rate
because of these imperfect loci.
Key words: microsatellites, mutation rate, primates

Factors affecting microsatellite slippage include repeat number, motif size, motif structure, chromosome type, andgenomic location (Bhargava and Fuentes, 2010). Amongthese factors, the repeat number is the strongest factor positively correlated to mutation rates of microsatellites (Kelkar
et al., 2008).

Introduction
Microsatellites, also called short tandem repeats (STRs),
are highly mutable repetitive short sequences 1–6 bp in size,
and are abundant in the genome of eukaryotic organisms(Ellegren, 2004). Microsatellite instability is directly related
to human cancers (Chung et al., 2010; Lacroix-Triki et al.,2011). Microsatellites are also widely used as genetic markers in forensic science (Song et al., 2010), and are often usedin population genetic studies (e.g. Li et al., 2006; Sun et al.,2009). Many studies of microsatellites at the molecular level
have already been published, focusing especially on theirmutation mechanism (e.g. Kofler et al., 2008; Boyer et al.,2008; Amos, 2010). Slippage is commonly accepted to be
the major mutation mechanism of microsatellites (Ellegren,
2004; Bhargava and Fuentes, 2010), when their repeat number exceeds the mutation threshold, which is nine repeats(rp) for mononucleotide microsatellites, and four repeats forother motif sizes (Lai and Sun, 2003; Kelkar et al., 2010).

Perfection status of microsatellites
In addition to the factors mentioned above, the perfectionof microsatellites also affects their mutation rates. The perfection status of microsatellites was first described by
Oliveira et al. (2006). A perfect microsatellite refers to a repetitive sequence purely composed of one type of motif. An
imperfect microsatellite refers to a locus having at least one
base pair that does not match the repetitive sequence. Interruption refers to a short sequence within the repetitive sequences, while a composite (also called compound) microsatellite refers to two distinctive, consecutive repetitive
sequences that are linked. However, this categorization issomehow idealistic: many microsatellites do not fall into anyof the four categories in real cases. Algorithms used tosearch for microsatellites, such as Sputnik, RepeatMasker,
and Tandem Repeat Finder (TRF), use different parameters,with major differences in the way they deal with mismatches(interruptions), resulting in non-uniform data sets (Leclercq
et al., 2007). Interruptions inside microsatellites are long*  Correspondence to: Naruya Saitou, Division of Population Genetics, National Institute of Genetics, 1111 Yata, Mishima 411-8540,
Japan.
E-mail: saitounr@nig.ac.jp
Published online 31 May 2016
in J-STAGE (www.jstage.jst.go.jp) DOI: 10.1537/ase.160124©  2016 The Anthropological Society of Nippon

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M.Y. NGAI AND N. SAITOU

known to have a stabilizing effect causing the mutation rate
to be greatly lowered. However, direct measurement of mutation rates and a comparison between imperfect microsatellites and perfect microsatellites were only recently analyzed
by Boyer et al. (2008). They proposed that interruptions
could break an imperfect microsatellite into two short perfect microsatellites by an observed mutational behavioral
change.
Different algorithms generate heterogeneous data sets,
and researchers make use of these data sets for their own
studies. This makes it impossible to compare results, especially for studies that depend on loci content and number(Almeida and Penha-Goncalves, 2004; Galindo et al., 2009).
Although a mutational behavioral change of interrupted microsatellites was observed, its interrelationship with the degree of mutations and repeat number, etc., at the genomic
level still remains to be explored.
The main aim of this study is to distinguish microsatellites
according to their perfection characteristics. Microsatellites
located on the non-coding regions of human chromosome 21
were compared with those in other primate species, including chimpanzee, gorilla, orangutan, and rhesus macaque,and were categorized into four perfection status groups,
Perfect, Imperfect, Perfect-Compound, and ImperfectCompound, following the new definitions proposed in thisstudy.
Mutation rates were estimated in order to demonstrate if
there was any significant difference among different perfection status groups. Further investigation was done to investigate the existence of interactions within the loci of imperfectmicrosatellites, which in turn provides evidence for the importance of microsatellite perfection status categorization.

Materials and Methods
Microsatellites and genomic data
Takezaki and Nei (2009) used human and chimpanzee
chromosome 21 genomic sequence data, and microsatellite
coordinates of intronic and intergenic regions data werekindly provided by Professor Takezaki Naoko of Kagawa
University. Coordinates correspond to those of UCSC
Genome Browser (www.genome.ucsc.edu). The human andchimpanzee genome builds used were hg18 and panTro2,
respectively. Perfect microsatellite loci of either human or
chimpanzee with at least six repeats were already selected in
the file.
Chromosomes orthologous to the human chromosome 21
were examined using Ensembl Genome Browser. Therefore,
DNA sequences of chromosome 21 for human (GRCh37.57),
chimpanzee (CHIMP2.1.57), gorilla (gorGor3.57), orangutan (PPYG2.57), and chromosome 3 for rhesus macaque(MMUL_1.57) were downloaded in FASTA format from theEnsembl Genome Browser (http://uswest.ensembl.org/info/
data/ftp/index.html). Chromosome data of human chromosome 21 (version hg18) were also downloaded from the
UCSC database.
Orthologous loci shared among primates
Human–chimpanzee orthologous microsatellite loci together with 100 bp flanking regions were extracted from

Anthropological Science

human chromosome 21 data available at the UCSC database. Extracted loci from the raw file were sorted by theirmotif size into four groups: dinucleotide (2905 loci), trinucleotide (188 loci), tetranucleotide (284 loci), and pentanucleotide (26 loci). There was no hexanucleotide in the data
file. Only dinucleotide loci were used in this study because
they were the most abundant.
The above-mentioned extracted regions were used as queries to search for orthologous regions in the other three primate species using BLASTN version 2.2.22 downloaded
from the NCBI (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/
blast+/2.2.22/) (Altschul et al., 1997). The strand used isdecided according to the synteny results of Ensembl. The
filtering query sequence option was turned off to prevent locibeing filtered out. To account for the large number of gaps in
microsatellites, the open gap penalty (parameter G) and thegap extension cost (parameter E) were each set to the lowest
value allowed (G = 1, E = 1), which corresponds to the default mismatch penalty (parameter q) and match reward(parameter r) values. An E-value of E–10 was used (following Zhang et al., 2006), and the best hits in each primate
species were extracted based on the blast scores.
Full multiple alignments were conducted using ClustalW2 version 2.0.12 (http://www.clustal.org/download/)(Larkin et al., 2007). In addition to usual indel mutations,gap opening is expected for microsatellites because of theirvariable sizes. Therefore the gap open penalty is increased to
4 while the gap extension penalty was decreased to 4, compared to default values. This allows the aligner to open a gap
for at least two nucleotides, which is the minimum motif sizeof a dinucleotide microsatellite. Since a microsatellite can belong and simple, especially when it is in the SINE region, thedelay divergence sequence switch was set to 95% identity so
as to obtain high score sequences by giving priority to alignto the human sequence first.
Only non-overlapping regions are used in this study toprevent double-counting and rate estimation for loci that arevery close to each other, which might be considered as one
imperfect locus. The latter issue will be explained in later
sections.
Microsatellite perfectness and repeat counting
Microsatellites were categorized into four groups in thisstudy: Perfect, Imperfect, Perfect-Compound, and ImperfectCompound. A perfect microsatellite is defined as a locus
with a perfect repetitive run with its own motif type, abbreviated as the locus motif (LM) (Figure 1a). An imperfectmicrosatellite is defined as a locus with a repetitive run that
contains interruptions. Each interruption is 1–9 bp long
(Figure 1b). When the interruption is more than 10 bp, it isconsidered as two perfect loci. Besides perfect and imperfect, a locus could also be either compound or noncompound. A compound microsatellite is defined as a locus
which contains a repetitive sequence composed of a nonlocus motif, abbreviated as nLM, where the repeat number
passes the threshold value, and is within 10 bp flanking region of the locus (Figure 1c, d).
To obtain the repeat number in imperfect microsatellites,
each locus run is first located by using the coordinates provided in the raw data, which is a perfect run of six repeats forPERFECTION STATUS OF MICROSATELLITES IN PRIMATES

3

Figure 2. Divergence times (in million years ago) of primate species used for mutation rate estimation. Divergence times are from
Perelman et al. (2011).
Figure 1. Illustrations of the four perfection status categorizationgroups. LM and nLM refers to locus motif and non-locus motif, respectively. ‘LM run’ region was used for mutation rate estimation. ‘LM/nLM
run’ represents neighboring repetitive sequences. Black bars represent
unique sequences. Each locus should be either compound or noncompound, either perfect or imperfect: (A) Perfect (perfect, noncompound) locus; (B) Imperfect (imperfect, non-compound) locus;(C) Perfect-Compound (perfect, compound) locus; and (D) ImperfectCompound (imperfect, compound) locus.

human and chimpanzee according to the threshold value
applied by Takezaki and Nei (2009), and of two repeats(which infer no threshold value) for the other three primate
species. If a sequence flanking the stated locus contains a
LM run with at least two repeats in the other three species,
and provided the interruption is less than 10 bp, the repeat
number will be increased according to the size of extra LMs
found, and newly considered as an imperfect locus. This
process is repeated until either a non-LM run which passes
the threshold value, or a unique sequence (≥10 bp) is found.
Considering that there will be a chance that the interruption
is due to a mutation occurring on a repeat, one repeat wascompensated for the locus repeat number while the interruption size is ≤1 LM size.
Categorization was done on each locus for all five primate
species. When locus perfection status among species wasdifferent, that locus was discarded in this study, and lociwere binned according to the average repeat number of species considered (i.e. human and chimpanzee in stepwise
mutation model, and all five species in total divergencetime).
Slippage rate calculation
Slippage rates of microsatellite loci were estimated in thisstudy by two methods: the stepwise mutation model (SMM)(Ohta and Kimura, 1973), and the total divergence time(TDT) method. Both methods are based on the counted repeat number (see the previous section). As there will be achance that there is no orthologous microsatellite locus (orlocus with less than two repeats) for a target primate species,that species will not be included in the TDT method for that
locus. This step is introduced to eliminate seriously misaligned loci, and more importantly the sudden appearance ordisappearance of microsatellite loci as they are sometimes
sites for transposition (Kelkar et al., 2008), a case which isunrelated to slippage rate. Human and chimpanzee microsatellites were used in SMM as all the loci are orthologous between these two species.
Slippage rate is estimated by using the SMM, assuming

the populations used are at mutation drift equilibrium. The(δμ)2 between the two populations defined by Goldstein etal. (1995) is:(δμ)2 = (μA – μB)2,(1)

where μA and μB are the mean allele length in species (orpopulation) A and B, respectively. By assuming mutation is
non-directional and considering microsatellites evolve in asingle-step manner, Goldstein et al. (1995) showed that:(δμ)2 = 2vt,

(2)where v and t are mutation rate (slippage rate in this study)
and divergence time, respectively.
Slippage mutation rates can be estimated by counting thetotal slippage events divided by the total divergence time.
The divergence time of primates from human is based on
Perelman et al. (2011), as shown in Figure 2. The minimum
number of slippage changes along the primate lineage evolution is counted, and then divided by the total divergencetime. For instance, the repeat number counted in a locus is 6,
5, 6, 5, 5 repeats in human, chimpanzee, gorilla, orangutan,
and rhesus macaque, respectively; the minimum change will
be 2, i.e. an expansion of 1 in the common ancestor of human, chimpanzee, and gorilla, and a contraction of 1 in the
chimpanzee lineage. An alternative explanation is two independent expansions—one in gorilla and one in human lineages. This new method is called the ‘total divergence time’(TDT) method in this study.

Results
Basic statistics
We extracted 2905 regions containing dinucleotide microsatellite loci in human and chimpanzee from the raw data
file. Among these, 2691 had flanking regions shared among
five primate species and 2173 of them are non-overlapping.
Eleven of the non-overlapping loci were discarded becauseof bad alignment, and 1766 of them contain target microsatellites in all five primate species. The number of loci andtheir relative proportions are presented in Figure 3. For imperfect and imperfect-compound loci, the minimum repeat
number is 4, because of the minimum 2 rp as search starting
point plus 2 rp extra LM run with interruption in between.
For instance, (AC)2i(AC)2, where i denotes interruption.

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M.Y. NGAI AND N. SAITOU

Anthropological Science

results where closely located loci are expected to interact
with each other and affect microsatellite mutation. This phenomenon might be explained by a relatively short non-LMrun (from four repeats) which is considered as a compound
microsatellite data set in this study. These non-LM runs may
not be able to bring about significant rate elevation when the
LM runs becomes relatively long. This explanation is supported by the observation that the difference is statisticallysignificant mainly at shorter repeats (6–11 rp) but not at
longer repeats, i.e. the relative effect of the non-LM run decreases when the LM run becomes relatively longer.

Figure 3. General statistics of dinucleotide loci extracted fromgenome data of five primate species. Ic, Pc, I, and P designate
Imperfect-Compound, Perfect-Compound, Imperfect, and Perfect,
respectively.

Figure 4. Comparison between compound and non-compound microsatellite loci. Rates are estimated by using SMM for human andchimpanzee data. Compound loci used in this graph are with non-LM
run immediately adjacent to LM run.

Compound vs. non-compound microsatellites
The mutation rate profile of compound (immediately next
to the LM run) and non-compound (without non-LM run
within the 10 bp flanking region) dinucleotide microsatellites estimated by SMM, sorted by mean repeat number, areshown in Figure 4. Compound microsatellites have elevatedmutation rates, and some of the data points (6–7 rp, 8–9 rp,
10–11 rp, 16–17 rp, 24–25 rp) even show statistically significant differences. This implies that certain interactions might
exist between the LM run and the non-LM run, lowering thestability of the microsatellite. However, the general trend byanalysis of covariance (ANCOVA) did not show significant

Perfect vs. imperfect microsatellites
A general picture showing how the mutation rates of imperfect microsatellites are different from those of perfectones is shown in Figure 5. Microsatellite perfection status
for each primate species was determined (see Materials andmethods), and only non-compound microsatellites are considered to illustrate the difference between perfect and imperfect loci. The microsatellites were grouped according to
the number of species that were of the same status. A gradual decrease in mutation rate estimated by the TDT method
could be observed when the number of species that wereimperfect increased. ANCOVA analysis on the difference
between all perfect and imperfect species shows that the
mutation rate is highly significantly different (P = 1 × 10–4).
This agrees with previous studies proposing that interruptions have a stabilizing effect on microsatellites (Kunkel,
1985; Boyer et al., 2008).
Further analysis was done to investigate this phenomenon. To increase sample size for better resolution in repeat
bins, only human and chimpanzee are considered. On theother hand, only non-compound microsatellites are used for
perfect–imperfect separation to eliminate possible effects
where an non-LM run may act on its LM run. A graph similar to Figure 5 is made using human and chimpanzee estimated by SMM, and is shown in Figure 6. ‘Mixed’ refers to
non-compound microsatellites before separation according
to the classification method as shown in Figure 1. The pattern of perfect repeats is highly significantly different fromthat of imperfect repeats (P = 3 × 10–4) by ANCOVA. Thissuggests that the effect of imperfection is much greater thanthat of the adjacently located non-LM run.
Our results show that the categorization algorithm of microsatellites in this study is able to categorize microsatellites
that were claimed to be uninterrupted into significantly different groups, and demonstrate significant differences between perfect and imperfect groups.
Effect of interruption on mutation rate
Our results regarding the effect of imperfection agree withthose of Boyer et al. (2008), whereby a small degree of interruption highly stabilizes microsatellites with a significantly
lowered mutation rate. However, it is uncertain whether
there is any interaction between two sides of a locus runinng
across these interruptions. To investigate this, imperfect microsatellites are sorted by their proportion of interruption, atdifferent bin lengths. The proportion of interruption for eachlocus is calculated, which is the ratio of the number of interrupting bases found within the LM run divided by the totalPERFECTION STATUS OF MICROSATELLITES IN PRIMATES

5

Figure 5. Comparison between perfect and imperfect microsatellites by using the TDT method. ANCOVA analysis on the difference between
all species perfect and all species imperfect shows that the mutation rate is highly significantly different (P = 1 × 10–4).

number of bases of that locus. These proportions are thenaveraged for all loci in human and chimpanzee. Grubbs’(1969) test was used to examine the presence of outliers at
each bin length, and significant outliers are marked with anasterisk in Table 1. The results show that the first data points(0% interruption, i.e. perfect) of 8–9 rp, 10–11 rp, and 12–13 rp are significant outliers (P < 0.04) (see Table 1), while
there were no significant outliers for 16–17 rp, 18–19 rp, and20–21 rp. There is no estimation for 14–15 rp because of thisgroup was too small for Grubbs’ test.
A comparison using perfect loci (0 bp interruption) withimperfect loci interrupted by one motif, i.e. 2 bp interruptions, is shown in Table 2. The ratio of the rate difference
between perfect and imperfect loci is strongly negatively
correlated with repeat number increase (Figure 7), as a smallinterruption could effectively lower the mutation rate of a
microsatellite (Boyer et al., 2008). On the other hand, Student’s t-test clearly shows that there is a sudden increase in
P-value (loss of interruption effect) between the 14–15 rp
and 16–17 rp bins (Figure 8), supported by a significant
slope difference by regression analysis (P = 0.04) whencomparing bin 14–15 rp against bin 16–17 rp (data notshown).
Figure 9 shows the comparison between perfect microsatellites vs. imperfect microsatellites with only one interruption site. Here the same pattern as previous analysis is observed where the difference between the two groups fadedwhen the length increased for repeat numbers >15. All theabove analyses show that interruptions are only one of thefactors affecting mutation rates of microsatellites. This implies that an interaction exists between the two sides of imperfect loci. Careful attention should therefore be paid when
we encounter imperfect microsatellites when searching for
microsatellites.

Discussion
Figure 6. Comparison between perfect and imperfect microsatellites by using SMM for human and chimpanzee data. ‘Mixed’ refers to
the result when perfect and imperfect loci are not separated (i.e.
non-compound in Figure 4). Perfect loci are significantly different
from imperfect loci (P = 3 × 10–4).

Microsatellites were categorized into four distinct groups
and their mutation rates at different repeats were estimated,using intergenic and intronic dinucleotide microsatellites
from primate species; the differences between perfect and6

M.Y. NGAI AND N. SAITOU

Table 1.

Anthropological Science

P-values of Grubbs’ test showing significant outliers for each graph in Figure 7% Interruption

0%

1–5%

6–10%

11–15%

16–20%

21–25%

26–30%

8–9 rp10–11 rp12–13 rp14–15 rp16–17 rp18–19 rp20–21 rp0.033*
0.035*
0.034*

0.335
0.293
0.331

0.308
0.340
0.288

0.338
0.299
0.466

0.364
0.455
0.387

0.345
0.308
0.296

0.060
0.091
0.066

0.460
0.491
0.292

0.415
0.304
0.360

0.484
0.417
0.415
N/A
0.321
0.234
0.333

0.241
0.283
0.454

0.243
0.253
0.205

0.149
0.106
0.133

Asterisk indicates significant values (P < 0.05).
It shows that the 0% interruption (perfect microsatellites) in 8–9 rp, 10–11 rp, and 12–13 rp are significant outliers, indicating that the firstshort interruptions stabilized the loci. 14–15 rp was not analyzed because of insufficient data points (>6 points required for Grubbs’ test).
Table 2. P-values of Student’s t-test comparing loci having nointerruption against up to 2 bp interruption and corresponding folddifference on mutation rate
8–9 rp10–11 rp12–13 rp14–15 rp16–17 rp18–19 rp20–21 rpP-value1

Fold2

4.53E–10*
1.70E–06*
1.61E–06*
2.68E–06*
1.29E–01
1.24E–01
8.24E–02

28.68
25.44
18.90
6.03
1.74
1.97
4.15

1

Asterisk indicates significant values (P < 0.05).
Fold is calculated from the 0 bp group divided by the 2 bp interruption group.
2

Figure 9. Comparison between perfect and imperfect microsatellites with one interruption site. Considering imperfect loci with only
one interruption site, it is observed that two groups of microsatellites
become not significantly different from each other; this is explained by
the countering length factor on the microsatellites, and also shows theexistence of an interaction between the two sides of imperfect loci.

Figure 7. Correlation between ratio of rate difference and repeat
numbers. Ratio of mutation rate differences between 0 rp (perfect) and2 bp interrupted loci decrease when repeat number increases(R2 = 0.84), indicating a negative correlation of stabilization power ofinterruptions against loci size.

Figure 8. Change of P values against repeat numbers. An increasein log P-values refers to a decrease in stabilization power, and the distinct increase of P-value at 16–17 rp suggests some biological differences in addition to physical loci length difference.

imperfect microsatellites were found to be highly significant. This suggests there is a demand for microsatellite selection algorithms that are more advanced in handling interruptions, or pay attention to loci having closely locatedrepetitive sequences, especially with the same motif.
The result of analyzing the effect of interruption on mutation rate suggested that the stabilizing effect of interruptions
is particularly strong in shorter microsatellites (rp < 15), best
illustrated by bin 8–9 rp: the first few base pairs of interruption led to the main decrease in mutation rates of these microsatellites. This stabilizing effect seems to fade out as therepeat number (opposite acting factor) increases. However,
it is observed that the fade out was not gradual but occurredsuddenly, between 14–15 rp and 16–17 rp. This phenomenon could be explained by a function of both physical andbiological properties. Physically, it is found that the slippage
process in one imperfect microsatellite is like two divided
perfect loci when the interruption is introduced in the middle
of a long microsatellite in vivo in a human mismatch repair
defective cell line (Boyer et al., 2008). Although the positionof interruption was not considered in the present study, assuming a random occurrence of point mutations on a locus,
the overall effect of interruption should be like those that

PERFECTION STATUS OF MICROSATELLITES IN PRIMATES

occurred in the middle of the loci, such that imperfect loci atgroup 16–17 rp here could be considered as two separate
perfect 8 rp loci. Biologically, it was proposed in previous
studies that exonucleolytic proofreading efficiency and mismatch repair efficiency sharply decrease when microsatellite
loci length is longer than ~15–20 bp (8–10 rp in a dinucleotide motif) (Kroutil et al., 1996; Sia et al., 1997). Therefore
imperfect loci at 14–15 rp seem to be largely stabilized com­pared to 16–17 rp.
Furthermore, the lost of significance from 15 rp shown in
Figure 9 implies that interaction exists between microsatellites across interruptions. This in turn suggests that a locus
should only be considered as a perfect when flanked byunique sequences. This is particularly important in short lociwhere the interruption effect is relatively strong. However,
the exact value, or definition, of ‘unique sequence’ requires
further investigation.
Inclusion of imperfect loci surely affects the locus numberin the data set claimed to be perfect in this study, as in otherrelated studies. Microsatellite searching programs, such as
TRF, Sputnik, Mrep, etc., have different algorithms for selecting microsatellites, but the output microsatellite profiles
differ considerably among algorithms (Leclercq et al.,2007). Parameters such as mismatch penalty are adjustableby the researcher. Some settings allow a small proportion ofinterruption but some will not. This causes a non-unified
microsatellite data set. Almeida and Penha-Goncalves(2004) observed that there are large proportions of highidentity long loci (in terms of percentage perfection) in vertebrate species. They suggested that the ‘hump’ from 10–
22 rp in their data set could be due to an advanced mismatch
repair system in vertebrates and backward mutation fromlong loci. However, microsatellite profiles given by Kelkar
et al. (2008) showed that the same area (10–22 rp) was aplateau rather than a hump. They only used strictly perfectloci with 10 bp unique flanking sequences. Certainly, themismatch repair system and backward mutation suggested
by Almeida and Penha-Goncalves (2004) are reasonable
explanations, but the results in this study implied that thedata set structure might be one of the additional reasons. It isobserved that the hump rises at ~11 rp and starts to dropfrom 16 rp. We suspect that the increase in the number ofmicrosatellites from 10–15 rp forming the hump is a resultof imperfect loci whose conservation may be more related tothe interruption effect, and the number of loci drops after
16 rp where the interruption effect decreased.
Categorization is more appropriate when up to four levelsof grouping were introduced—in order, these groupings are
by motif size, perfection status, size, and finally by proportion of interruptions. However, sample sizes were sometimes insufficient in this study. Using whole genome data
could hopefully solve this problem. Although the results inthis study suggested that closely located repetitive sequences with same motif structure influence each other significantly, the required length of unique sequence which could
safely separate them remains uncertain and could be the next
step to be explored.

7

Acknowledgments
We would like to give sincere thanks to Professor Naoko
Takezaki who generously provided us the raw data file forthis study. We would also express our appreciation to Dr.
Kirill Kryukov for assistance on computational analysis, andMrs. Masako Mizuguchi and Mrs. Ai Iida for assisting
preparation of figures. We also thank Dr. Timmothy A.
Jinam, Dr. Hideaki Kanzawa-Kiriyama, Mr. Morteza
Mahmoudisaber, Dr. Nilmini Hettiarachchi, Dr. Isaac
Babarinde, and Ms Shayire Shokat, for their comments to themanuscript. We also appreciate comments of two reviewers.

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