Recombination or Mutational Hot Spots in Human mtDNA?
Hideki Innan and Magnus Nordborg
Molecular and Computational Biology, University of Southern California
Awadalla, Eyre-Walker, and Maynard Smith (1999) recently argued that there might be recombination in human
mitochondrial DNA (mtDNA). Their claim was based on their observation of decaying linkage disequilibrium (LD)
as a function of physical distance. Their study was much criticized, and follow-up studies have failed to find any
evidence for recombination. We argue that the criticisms levied, even if correct, could not possibly explain the
findings of Awadalla, Eyre-Walker, and Maynard Smith (1999). Nonetheless, the test proposed by Awadalla, EyreWalker, and Maynard Smith (1999) is not robust because recombination is not the only explanation for decay of
LD. We show that such a pattern can be caused by mutational hot spots as well. However, a closer look at the data
suggests that the pattern observed was not caused by mutational hot spots but rather by chance. Thus, there appears
to be no evidence for recombination in the mtDNA polymorphism data. In conclusion, we discuss the possibility
of detecting recombination in mtDNA and the implications of its existence.
Introduction
The claim that there may be recombination in human mitochondrial DNA (mtDNA) (Awadalla, EyreWalker, and Maynard Smith 1999; Eyre-Walker, Smith,
and Maynard Smith 1999) has caused a great deal of
controversy. The argument for recombination is based
on the observation that the pattern of polymorphism in
mtDNA is incompatible with a single genealogical tree
and unique mutations. The simplest example is the presence of all four possible haplotypes for a pair of diallelic
loci. It is easy to show that such a pattern cannot exist
in the absence of recombination unless at least one of
the loci experienced multiple mutations. In the language
of phylogenetics, recurrent mutation to the same allele
is an example of ‘‘homoplasy’’ or convergent evolution.
Because incompatibilities can be created by recurrent mutation as well as by recombination, it is necessary to rule out the former explanation in order to conclude that the latter is correct. Eyre-Walker, Smith, and
Maynard Smith (1999) argued that, given what is known
about mutation rates, there were simply too many homoplasies in the data for multiple mutations to be the
explanation. Clearly, such an argument is always dependent on what is assumed about mutation rates. Recognizing this weakness, Awadalla, Eyre-Walker, and Maynard Smith (1999) proposed a much simpler test that
seemingly involves fewer assumptions (we will discuss
the extent to which this is true later). Their test looks at
the spatial behavior of a statistic of association between
the alleles at pairs of polymorphic sites. Such associations, also known as ‘‘linkage disequilibrium’’ (LD),
will decay as a result of recombination or recurrent mutation. The rationale behind the proposed test is that because the frequency of recombination increases with
physical distance, the strength of association should go
down with distance. In contrast, the probability of recurrent mutation on either site should be independent of
the distance between the sites. Thus, if the strength of
Key words: coalescent, linkage disequilibrium, gene conversion.
Address for correspondence and reprints: Magnus Nordborg, Molecular & Computational Biology, University of Southern California,
835 W 37th Street, SHS 172, Los Angeles, California 90089-1340.
E-mail: [email protected].
Mol. Biol. Evol. 19(7):1122–1127. 2002
q 2002 by the Society for Molecular Biology and Evolution. ISSN: 0737-4038
1122
association is negatively correlated with distance, we
may conclude that recombination is responsible. Awadalla, Eyre-Walker, and Maynard Smith (1999, 2000)
found such a correlation and established its significance
by means of a permutation test.
The study of Awadalla, Eyre-Walker, and Maynard
Smith (1999) was immediately challenged on two major
grounds:
• Errors in data. Kivisild and Villems (2000) showed
that several of the polymorphisms analyzed by Awadalla, Eyre-Walker, and Maynard Smith (1999) were
likely to be the result of errors in genotyping or data
handling (or both). The same point had previously
been made by Macaulay, Richards, and Sykes (1999)
in response to Eyre-Walker, Smith, and Maynard
Smith (1999).
• The choice of LD statistic. Awadalla, Eyre-Walker,
and Maynard Smith (2000) used the squared correlation coefficient, r2 (Hill and Robertson 1968); several
researchers argued that they should have used zD9z (Lewontin 1964) instead, because the latter is less dependent on allele frequencies than the former (Jorde and
Bamshad 2000; Kumar et al. 2000).
In their response, Awadalla, Eyre-Walker, and Maynard
Smith (1999) argued that both these objections were, in
essence, irrelevant. We agree. Consider first the problem
with errors in data. Awadalla, Eyre-Walker, and Maynard Smith (2000) admitted that there were indeed errors
in their data but pointed out that errors cannot explain
their finding—on the contrary, random sequencing errors would behave like multiple mutations and would
therefore tend to obscure any correlation of LD with
distance, not establish one.
The argument about the choice of LD statistic is
equally unconvincing. Leaving aside the issue of what
it means for one LD statistic to be less frequency dependent than another (Lewontin 1988; Nordborg and Tavaré 2002), it is, as Awadalla, Eyre-Walker, and Maynard Smith (2000) noted, hard to see why frequency
dependence should matter. Under the null hypothesis
that there is no recombination in mtDNA, there is a
single underlying genealogical tree relating all mtDNA
copies, and the distribution of allele frequencies must
Recombination and Mutation in Human mtDNA
1123
FIG. 1.—LD can decay with distance even in the absence of recombination. The figure shows r2 as a function of distance between
sites in a data set simulated without recombination but with highly
hypermutable (hot) sites in one half of the chromosome only. As explained in the text, this can give rise to a negative correlation between
LD and distance. The simulation parameters were chosen to illustrate
the point, not to be realistic: each of the hypermutable sites experienced multiple mutations, whereas none of the others did.
be the same for all sites (modulo differences in the mutation rate). In any case, the argument of Awadalla,
Eyre-Walker, and Maynard Smith (1999) was that r2 is
not expected to decay with distance unless there is recombination; therefore, the observed decay implies recombination. This argument is not contradicted by the
finding that some other statistic which might also be
expected to decay with distance does not appear to do
so.
In this article, we address two questions. First, in
general, is the permutation test proposed by Awadalla,
Eyre-Walker, and Maynard Smith (1999) really a robust
test of recombination or are there alternative explanations for patterns such as those observed? Second, what
explains the pattern they observed; in particular, what
explains the discrepancy between their results and those
of subsequent studies that have found no evidence for
recombination (Ingman et al. 2000; Elson et al. 2001)?
Does a Decay of LD Imply Recombination?
The test proposed by Awadalla, Eyre-Walker, and
Maynard Smith (1999) is attractive because it initially
appears to be nonparametric (Hey 2000). Unfortunately,
as is often the case, there are hidden assumptions behind
the apparent simplicity. The permutation test randomizes
the position of the loci and recalculates the correlation
between LD and distance. The rationale for this is that
under the null hypothesis of no recombination, position
should not matter because every site has the same genealogical history. However, this procedure is not valid
in general unless we also assume that the distribution of
mutations is the same for all positions (Sawyer 1989).
Specifically, the test of Awadalla, Eyre-Walker, and
Maynard Smith (1999) requires that the distribution of
r2 (or whatever LD statistic we choose to use) does not
depend on the distance between the loci under the null
hypothesis that there is no recombination. This may
seem like a reasonable assumption, but it is in fact violated if the mutation rate varies regionally.
An example should make this clearer. Consider a
chromosome that is divided into two regions, one of
which contains multiple mutational hot spots. Because
FIG. 2.—Evidence for recurrent mutation or recombination (or
both) in human mtDNA (data of Ingman et al. 2000). Each point represents the comparison between a pair of polymorphic sites. The point
is black if the pattern of polymorphism for the pair of loci is such that
either recombination must have occurred between the loci or recurrent
mutation affected at least one of the loci. The point is white otherwise.
If recombination has occurred, and (importantly) the probability of
recombination increases with distance between sites, white points are
expected to be clustered along the diagonal (because recombination is
less likely to have effected closely linked sites). Recurrent mutations,
on the other hand, might be expected to give rise to a pattern that does
not depend on the distance from the diagonal, leading to black ‘‘crosses’’ against a white background. The D-loop is visible as a cluster of
such crosses in the upper right corner (position 0 corresponds to the
first position after the D-loop).
multiple mutations erode LD, LD between pairs of loci
in this ‘‘hot’’ region will be much lower than LD between pairs of loci in the ‘‘cold’’ (non–hot spot) region.
Significantly, LD between pairs of loci in different regions (one hot, one cold) will also be low. Because the
distance between loci in different regions is on an average greater than the distance between loci in the same
region, the result is a pattern where high LD is associated with short distance. Thus, as illustrated in figure 1,
mutational hot spots can give rise to a pattern where LD
decays with distance, just like the one observed by Awadalla, Eyre-Walker, and Maynard Smith (1999). If their
test were used in such a case, we would falsely conclude
that recombination was responsible for the pattern.
Should We Expect LD to Decay in mtDNA?
We have seen that mutational hot spots can, in principle, give rise to a negative correlation between LD and
distance. Should we expect such a pattern in human
mtDNA? The existence of mutational hot spots in
mtDNA is not in doubt. Figure 2 shows the spatial pattern of pairs of sites that show evidence of either recombination or recurrent mutation. Such plots can be used
to look for traces of recombination as well as mutational
hot spots (Jakobsen and Easteal 1996, although it should
1124
Innan and Nordborg
FIG. 3.—Expected LD (on relative scale) as a function of distance
for a circular chromosome with a hot (high mutation) and cold (low
mutation) region under different assumptions about the relative length
of the hot region (1/16, 4/16, . . . ). The per site mutation rate in the
hot region is assumed to be six times greater than that in the cold
region. The relative values of LD in the cold region, in the hot region,
and between them are assumed to be 1, 0.5, and 0.75, respectively,
reflecting the data of Ingman et al. (2000).
be noted that the significance of patterns is difficult to
evaluate for reasons analogous to those described in the
previous section, see Does a Decay of LD Imply Recombination?). The pattern in figure 2 strongly suggests the
existence of clusters of mutational hot spots. One of
these corresponds to the D-loop, which is known to be
hypermutable (e.g., Meyer, Weiss, and von Haeseler
1999; Ingman et al. 2000; Markovtsova, Marjoram, and
Tavaré 2000). It should also be noted that nonrandom
sequencing errors (perhaps caused by particular regions
being difficult to sequence) would give rise to hypermutable regions.
However, even though hot spots exist, they do not
appear to be as extreme as those assumed in the example
of the previous section (see Does a Decay of LD Imply
Recombination?). In that example, r2 was low for all
pairs of loci that included at least one hot site (see fig.
1) and very high only for pairs of cold sites. The most
complete mtDNA data set is that of Ingman et al.
(2000). We calculated r2 for all pairs of polymorphic
sites in their data. Dividing the comparisons into those
involving sites in the D-loop, those involving sites in
the coding region, and those involving one site in each
region, we found that average r2 was 0.0525, 0.0996,
and 0.0705, respectively. These differences do not appear to be large enough to generate a negative correlation between LD and distance, especially when it is taken into account that there will be more polymorphic
sites in hot regions, generating a large number of pairs
of loci that are close together and have low LD. In fact,
for simple models involving two regions with different
mutation rates on a circular chromosome, we found that
the expected correlation between LD and distance is
weakly positive rather than negative, regardless of the
length of the hot region (fig. 3). This conclusion is in
agreement with the data of Ingman et al. (2000): the
plot of r2 against distance for all 287 informative sites
shows a slight positive correlation (0.015), which dis-
FIG. 4.—r2 as a function of distance in the data of Ingman et al.
(2000). All sites were included in the analysis; eliminating sites where
the minority allele has frequency less than 5% or 10% yields very
similar results. This is expected, given the relative insensitivity of the
rate of decay of r2 to allele frequencies (Nordborg and Tavaré 2002).
appears (20.002) if the 89 sites in the D-loop are excluded (fig. 4).
What Explains the Discrepancy Between Different
Studies?
Although mutational hot spots can, in principle,
generate a negative correlation between LD and distance, they do not appear to do so in human mtDNA, at
least not on a chromosome-wide scale. We are left with
the question of why Awadalla, Eyre-Walker, and Maynard Smith (1999) observed a decay of r2 with distance,
whereas follow-up studies (Ingman et al. 2000; Elson et
al. 2001) did not. Note that this is not a matter of simply
failing to replicate a finding, because there is no true
replication here; there is a single history of mtDNA, and
all samples should reflect it.
To understand the reason for the discrepancy, we
compared the data of Awadalla, Eyre-Walker, and Maynard Smith (1999) with those of Ingman et al. (2000).
Awadalla, Eyre-Walker, and Maynard Smith (1999)
found 49 synonymous informative sites in their data set
of 45 nearly complete mtDNA sequences (table 1). More
than one-third of these sites (18/49) are not polymorphic
in the data of Ingman et al. (2000). Given that both are
samples from the same genealogy, we would expect a
larger overlap (the probability of the observed difference
is less than 3% under a standard coalescent model, but
this is not really an appropriate comparison, given the
star-like genealogy of mtDNA). This supports the notion
that there are errors in the data, and it seems likely that
most of the errors are in the data used by Awadalla,
Eyre-Walker, and Maynard Smith (1999) (Macaulay,
Richards, and Sykes 1999; Kivisild and Villems 2000).
However, these errors cannot explain the different
conclusions reached by these studies. As noted previously, random sequencing errors should erase, rather
than create patterns in the data. Furthermore, Awadalla,
Eyre-Walker, and Maynard Smith (1999) used only 14
highly polymorphic sites for which the minor allele frequency is more than 10%. For these sites, the patterns
Recombination and Mutation in Human mtDNA
1125
Table 1
Comparison of the Data Sets of Awadalla, Eyre-Walker,
and Maynard Smith (1999) and Ingman et al. (2000).
FREQUENCY
POSITION
3594
4104
4117
4883
4985
5147
6455
7028
7256
7274
8697
9540
10810
10873
10915
11251
11299
11467
11812
11914
11944
12372
12612
12705
12771
13368
13590
13617
13650
14783
15043
15301
15535
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
.........
Alleles
Awadalla, EyreWalker, and
Maynard Smith
Ingman et al.
C/T
A/G
C/T
C/T
A/G
A/G
C/T
C/T
C/T
C/T
A/G
C/T
C/T
C/T
C/T
A/G
C/T
A/G
A/G
A/G
C/T
A/G
A/G
C/T
A/G
A/G
A/G
C/T
C/T
C/T
A/G
A/G
C/T
42/3
42/3
2/43
41/4
40/5
3/42
40/5
11/34
42/3
43/2
2/42
14/31
2/43
17/28
2/43
39/6
5/40
38/7
43/2
3/42
2/43
6/39
40/4
27/18
3/42
2/43
2/43
5/39
41/4
9/35
9/36
10/34
42/2
38/15
38/15
1/52
49/4
53/0
3/50
53/0
3/50
38/15
51/2
1/52
34/19
12/41
34/19
8/45
51/2
2/51
52/1
52/1
14/39
3/50
1/52
52/1
16/37
1/52
1/52
6/47
1/52
38/15
13/40
13/40
23/30
52/1
NOTE.—The positions are according to the Cambridge sequence (Anderson
et al. 1981). These numbers correspond to our position numbers in figure 2 plus
about 576. Results for 33 out of the 49 synonymous informative sites that Awadalla, Eyre-Walker, and Maynard Smith (1999, 2000) detected are shown: the
remaining 16 Sites (3480, 4868, 4958, 6023, 6413, 6776, 8071, 9698, 9824,
10397, 10550, 10978, 13827, 14157, 14461, and 14592) are not polymorphic in
the Ingman et al. (2000) data. The 14 emphasized (italic) sites are those used in
figure 1A of Awadalla, Eyre-Walker, and Maynard Smith (1999): Nine of these
are informative in the data of Ingman et al. (2000). Because of missing data, the
frequencies in the third column do not always sum to 45.
of polymorphisms in the two data sets are not as different from each other, with the exception of two sites
(4985 and 6455) that are monomorphic in the data of
Ingman et al. (2000) (table 1). The possibility of sequencing error for these two sites was pointed out by
Kivisild and Villems (2000). Because three of the remaining 12 sites are singleton polymorphisms in the
data of Ingman et al. (2000), we investigated the decay
of LD using the remaining nine informative sites (fig.
5). In the data of Ingman et al. (2000) the correlation
coefficient is r 5 20.295, which is very close to that
obtained using the data of Awadalla, Eyre-Walker, and
Maynard Smith (1999) (r 5 20.248). The negative correlation is almost significant using the permutation test
of Awadalla, Eyre-Walker, and Maynard Smith (1999)
(P 5 0.079). It should be noted that there are no incom-
FIG. 5.—The relationship between r2 and the data of Ingman et
al. (2000) for the sites used by Awadalla, Eyre-Walker, and Maynard
Smith (1999). See table 1 for details.
patible pairs among the sites investigated (in the sense
of fig. 2), so that zD9z 5 1 for all pairs.
Thus, the data of Awadalla, Eyre-Walker, and Maynard Smith (1999) and Ingman et al. (2000) do not, in
fact, disagree. For the small subset of sites used by the
former, the data of the latter also reveal a negative correlation. If we increase the number of sites, the negative
correlation quickly disappears. For example, if we add
the remaining 12 informative sites listed in table 1 (for
a total of 21 sites), the correlation coefficient is nearly
zero (r 5 20.003), and in the entire data set, the correlation is positive (fig. 4). It would appear that Awadalla, Eyre-Walker, and Maynard Smith (1999) were
simply unlucky and happened to pick sites that gave rise
to a negative correlation.
How unlucky were they? Figure 6 shows the distribution of r when nine sites are randomly sampled
from the 49 highly polymorphic sites in the data of Ingman et al. (2000). The distribution has a positive mode
but is skewed toward negative values. Simulations
(1,000 randomly chosen samples of nine sites, each followed by 1,000 permutations to assess significance)
show that the probability of obtaining a significant negative correlation (at the 5% level) is approximately 4%.
Discussion
We have shown that although the test used by Awadalla, Eyre-Walker, and Maynard Smith (1999) is not
robust to the presence of mutational hot spots, the simplest explanation for their finding is chance: they happened to pick sites that gave a negative correlation between r2 and distance. When more sites are used, there
is no relationship between r2 and distance (Ingman et
al. 2000; Elson et al. 2001). Awadalla, Eyre-Walker, and
Maynard Smith (1999) also found a negative correlation
between r2 and distance in three RFLP data sets. However, these data sets include a very small number of sites
FIG. 6.—The distribution of the correlation between r2 and distance under repeated sampling of nine sites from the data of Ingman
et al. (2000).
1126
Innan and Nordborg
and are furthermore not independent (because they share
some sites). We tried to investigate whether the sites
analyzed in these studies also show a negative correlation between r2 and distance in the data of Ingman et
al. (2000), but we were unfortunately unable to identify
the sites. We conclude that there is no evidence for recombination in the pattern of LD. However, there is no
evidence against recombination either. The test used by
Awadalla, Eyre-Walker, and Maynard Smith (1999) is
based on the assumption that LD should decay with distance in the presence of recombination. This is correct
when recombination occurs as a part of crossing-over in
a linear chromosome, but it is by no means obvious how
LD would be affected by whatever recombinational
mechanism might be envisioned to take place in mitochondria (Wiuf 2001). Recombination in the mitochondria, if it indeed occurs, may well be almost impossible
to detect using polymorphism data. It should also be
noted that there appears to be some evidence for recombination in non-human mtDNA. Using data from the
mitochondrial control region and the ND2 locus, Awadalla, Eyre-Walker, and Maynard Smith (1999) found a
negative correlation between r2 and distance in chimpanzees. If the control region in chimps were hypermutable, such a correlation could easily be caused by
the phenomenon illustrated in figure 1. However, in contrast to the human data, r2 within the chimp control region is actually higher than average, which suggests that
the chimp control region is not particularly hypermutable. The negative correlation is attributed to the fact that
values of r2 between sites in the control region and sites
in the ND2 locus are, on an average, considerably lower
than they are within either region. This suggests recombination (unless it is caused by something like sample
mix-up or contamination during sequencing). At considerably greater phylogenetic distances, there appears to
be evidence for mtDNA recombination in several organisms (Ladoukakis and Zouros 2001).
Finally, we think that the implications of the existence of recombination in mtDNA have been misunderstood. Recombination would clearly have important implications for the evolution of mitochondria, particularly
in the context of the long argument about the evolutionary advantages of sex. Without recombination, mitochondria might be expected to decay because of the
pressure of deleterious mutations (Moran 1996). However, the main reason for the attention given to this question has been the perceived implications for our understanding of human evolution. This seems misguided.
From the point of view of analyzing polymorphism data
from populations, recombination matters because it allows different sites to have different genealogical histories or trees. The extent to which the tree for one site
differs from the tree for another site depends on the rate
of recombination between the sites (e.g., Nordborg and
Tavaré 2002). If recombination does indeed occur in mitochondria, it is surely not very common, and the trees
for different parts of the mtDNA would thus be strongly
correlated. Certainly, they would be much more strongly
correlated to each other than to trees for nuclear sites,
with which they are genetically unlinked. It follows log-
ically that any conclusion about human evolution that is
not robust to a small amount of recombination in
mtDNA cannot be robust to recombination in the rest
of the genome. Putting it in another way, if recombination causes different parts of the mitochondrial genome to tell different stories, then these stories are certainly independent of the stories told by the rest of the
genome. Awadalla, Eyre-Walker, and Maynard Smith
(1999) argued that if recombination in mtDNA existed,
then many inferences about human evolution would
have to be reconsidered. A more correct statement is that
any inference about human evolution that would have
had to be reconsidered had recombination in mtDNA
existed should in fact be reconsidered in any case.
Acknowledgments
We thank Adam Eyre-Walker and Max Ingman for
sending their respective data sets, Maarit Jaarola for discussions about mtDNA, Noah Rosenberg, Simon Tavaré, Carsten Wiuf, Brandon Gaut, and two anonymous
reviewers for comments on the manuscript.
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