Proceedings of the SMBE Tri-National Young Investigators’ Workshop 2005
Genome-Wide Search of Gene Conversions in Duplicated Genes of
Mouse and Rat
Kiyoshi Ezawa, Satoshi OOta,1 and Naruya Saitou
Division of Population Genetics, National Institute of Genetics, Mishima, Japan
Gene conversion is considered to play important roles in the formation of genomic makeup such as homogenization ofmultigene families and diversification of alleles. We devised two statistical tests on quartets for detecting gene conversion
events. Each ‘‘quartet’’ consists of two pairs of orthologous sequences supposed to have been generated by a duplication event and a subsequent speciation of two closely related species. As example data, EnsEMBL mouse and rat cDNA
sequences were used to obtain a genome-wide picture of gene conversion events. We extensively sampled 2,641 quartetsthat appear to have resulted from duplications after the divergence of primates and rodents and before mouse-rat speciation.
Combination of our new tests with Sawyer’s and Takahata’s tests enhanced the detection sensitivity while keeping false
positives as few as possible. About 18% (488 quartets) were shown to be highly positive for gene conversion using thiscombined test. Out of them, 340 (13% of the total) showed signs of gene conversion in mouse sequence pairs. Those geneconversion–positive gene pairs are mostly linked in the same chromosomes, with the proportion of positive pairs in thelinked and unlinked categories being 15% and 1%, respectively. Statistical analyses showed that (1) the susceptibility togene conversion correlates negatively with the physical distance, especially the frequency of 29% was observed for genepairs whose distances are smaller than 55 kb; (2) the occurrence of gene conversions does not depend on the transcriptional
direction; (3) small gene families consisting of between three and six contiguous genes are highly prone to gene conversion;and (4) frequency of gene conversions greatly varies depending on functional categories, and cadherins favor gene conversion, while vomeronasal receptors type 1 and immunoglobulin V–type proteins disfavor it. These findings will be useful
to deepen the understanding of the roles of gene conversion.

Introduction
Gene conversion, also called nonreciprocal recombination, is a process where a tract of DNA overwrites a homologous one (e.g., Petes and Hill 1988; Haber 2000).
According to the positional relationship of the pair of tracts,
gene conversion is classified as (1) intrachromatid, (2) sister
chromatid, (3) classical (allelic), (4) semiclassical (nonallelic between homologous chromosomes), and (5) ectopic(heterochromosomal) (Li 1997, pp. 310–311). The classicalconversion is the interaction between alleles of the same
locus (allelic gene conversion), whereas the others involvetwo different loci (interlocus gene conversion).
There are many studies discussing the evolutionary
implications of gene conversion, and they are broadly classified into two groups. One is the enhancement of allelicdiversity via gene conversion, either allelic or interlocus.
This has been documented for genes involved in immune
response, like major histocompatibility complex genes(e.g., Weiss et al. 1983; Kuhner et al. 1991; Martinsohn
et al. 1999; Richman et al. 2003; Reusch, Schaschi, andWegner 2004), and genes controlling self-incompatibility(Charlesworth et al. 2003). The other is the homogenizationor concerted evolution of multiple-gene families via interlocus gene conversion, possibly in cooperation with unequalcrossing-over. The examples are the rDNA multigene family(Arnheim et al. 1980; Arnheim 1983), red and green opsin
genes in Old World monkeys (e.g., Ibbotson et al. 1992;
1
Present address: Department of Biological Systems, Bioresource
Center, RIKEN Tsukuba Institute, Tsukuba, Japan.

Key words: gene conversion, duplicated genes, mouse, rat, genomewide analysis.
E-mail: nsaitou@genes.nig.ac.jp.
Mol. Biol. Evol. 23(5):927–940. 2006
doi:10.1093/molbev/msj093
Advance Access publication January 11, 2006
Ó The Author 2006. Published by Oxford University Press on behalf of
the Society for Molecular Biology and Evolution. All rights reserved.
For permissions, please e-mail: journals.permissions@oxfordjournals.org

Winderickx et al. 1993; Shyue et al. 1994; Zhou and Li
1996), and genes controlling self-incompatibility (e.g.,
Cabrillac et al. 1999; Prigoda, Nassuth, and Mable 2005).
The homogenization of gene families can be a severe obstacle to the currently dominant paradigm that gene repertoire
expands through neofunctionalization and subfunctionalization of duplicated genes (Ohno 1970; Force et al. 1999;
Lynch and Force 2000). Therefore, knowing the genomic
prevalence and frequency of gene conversion is indispensable for estimating the potential or speed of the genome
evolution.
Detection of gene conversion is also important for themolecular evolutionary study in general. Many instances ofgene conversion were revealed by the regional inconsistencies of gene phylogenies (e.g., Scott et al. 1984; Kawamura,
Saitou, and Ueda 1992; Cheung et al. 1999; Kitano andSaitou 1999). Therefore, we may incorrectly infer the duplication dates and, consequently, the phylogenetic relationships between paralogous genes. This can happen
whenever the effects of gene conversion overpower phylogenetic signals, as exemplified by some instances of theconcerted evolution mentioned above.
In spite of many instances of gene conversion documented so far, their anecdotal nature seems to let someresearchers believe that gene conversion is a rarity, andtherefore, a naive phylogenetic picture still holds true formost of the duplicated genes. So far, genome-wide searches
for gene conversion have been performed only for nematodeworm (Semple and Wolfe 1999) and yeast (Drouin 2002;
Gao and Innan 2004). For vertebrates, the prevalence of geneconversion has been estimated so far only in a small-scale
analysis before the advent of the genome era (Shields 2000).
Now that we have a number of mammalian genome sequences available (Mouse Genome Sequencing Consortium928 Ezawa et al.

FIG. 1.—A quartet and its informative sites (A). Top: A duplication event (an open diamond) and the subsequent divergence of two species (opencircles), mouse and rat in our case, create a quartet of sequences mouse1, rat1, mouse2, and rat2, which are abbreviated as M1, R1, M2, and R2, respectively. The commonest informative sites are mainly produced by a base substitution (a star), from a guanine (G) to an adenine (A) in this example, on
an internal branch. Bottom: This type of informative sites, called the type 1 sites here, indicates the clustering of orthologous sequences. (B) A basesubstitution followed by a gene conversion event (a broken arrow) can create informative sites that indicate the clustering of two sequences in the same
genome. We will call them the type 2 sites here. (C) A third kind of informative sites, the type 3 sites, is generated by parallel substitutions.

2002; International Human Genome Sequencing Consortium 2004; Rat Genome Sequencing Project Consortium
2004), we can use these resources to obtain a genome-wide
view of gene conversion events, especially regarding how
common they are, and what kind of gene pairs are prone
to gene conversion. The results of such analyses should
become precious assets for studies on the evolution of
duplicated genes.
Here, led by the above motivations, we searched thewhole sets of mouse and rat cDNA sequences in theEnsEMBL database (Hubbard et al. 2002, 2005; Birney
et al. 2004; http://www.ensembl.org/index.html) for tracesof gene conversion. Our gene conversion detection is basedon two statistical tests conducted on quartets. A ‘‘quartet’’consists of two pairs of orthologous sequences supposed tohave been generated by a duplication event followed by the
speciation of two closely related species, mouse and rat in
our case (see fig. 1). The genome-wide divergence between
mouse and rat is approximately 17% (Abe et al. 2004; Rat
Genome Sequencing Project Consortium 2004), and this intermediate species divergence is expected to be suitable forthe quartet-based methods. Although the combination ofthese tests gives a fairly strict screening with very low falsepositive rate, some types of gene conversion events turned
out to escape the detection by our method. So we combined
our statistical tests with Sawyer’s (1989) test as well aswith Takahata’s (1993) in a manner that enhances the detection sensitivity and yet keeps the low rate of false positives. The Supplementary File details this theoretical part
(Supplementary Material online).
The frequency of false positives among gene conversion candidates was not considered in the past large-scale
gene conversion searches. However, we think it crucial toalways estimate the false-positive rate. We addressed thisissue by simulating the quartet evolution in two different
ways, one with gene conversion and the other without.
These simulation results also guided us to the optimum
combination of the four statistical tests mentioned
above (see Supplementary File, Supplementary Material
online).

We also examined correlations between the prevalence of gene conversion and physical and biological properties of gene pairs, in order to figure out what kind of genepairs are prone to gene conversion.
Materials and Methods
Peptide and cDNA Sequences as well as Their
Associated Information
We retrieved files of the gene transcript (cDNA)
sequences and the peptide sequences predicted on mammalian and avian genomes from the file transfer protocol (FTP)site (ftp://ftp.ensembl.org/pub) of the EnsEMBL database(Hubbard et al. 2002, 2005; Birney et al. 2004; http://www.
ensembl.org/index.html) version 27 (updated in December2004). We obtained data for the following species: mouse(Mus musculus, 32,442 peptides), rat (Rattus norvegicus,
28,545 peptides), human (Homo sapiens, 34,111 peptides),dog (Canis familiaris, 30,308 peptides), and chicken (Gallus gallus, 28,416 peptides). Sequences of the latter three
species are used as outgroup. As for the cDNA sequences,
we only used those with peptide counterparts. We also
fetched mysql dumps of information on the gene transcripts
from the FTP site above, and then we extracted informationon the genomic map of exons, exon-transcript relationship,transcript-gene relationship, and translation starts and endsof the gene transcripts (cDNAs).
Rat Ortholog Candidates and Outgroup Sequences of
Mouse cDNAs
We conducted BlastP searches (Altschul et al. 1990)
using mouse peptides as queries and a target database consisting of peptides of other mammals with the BLOSUM62
scoring matrix and the E value threshold of 1 3 105. Then
we chose subject sequences whose homologous segment
pairs (HSPs) showed more than 35% sequence identity andwere 100 amino acids or longer. We did this first screening
in order to secure the reliability of the subsequent sequence
analyses by restricting our subjects to well above the upper

Gene Conversions in the Mouse and Rat Genomes 929

bound of the ‘‘twilight zone,’’ below which sequence alignments are often unreliable (Doolittle 1986; Rost 1999). The
E value threshold of 1 3 105 is large enough to retrieve mostof the sequences satisfying the above conditions. Out of thisset of homolog candidates, we chose the rat ortholog candidate of each mouse cDNA in the following manner.
We first picked out rat peptide sequences that scoredthe best. However, because our main targets are membersof multigene families, the best-scoring sequence can sometimes be different from the real ortholog. This can occur
when the orthologous sequences have undergone disparate
functional constraints in different species. We therefore kept
those sequences that are ‘‘close to’’ the best sequence and left
the judgment of orthology to the final screening using nucleotide alignments. We regarded the sequences as close
to the best sequence if they satisfy the inequality on theeffective distance, d_eff: d_eff (sb) ,5 d_eff (best) 1 2 *fd_eff (best)g1/2, where ‘‘sb’’ and ‘‘best’’ stand for the subject and the best-scoring sequence, respectively, and we defined d_eff (sb) by –log (score(qu,sb)/score(qu,qu)). Herescore(a,b) is the score of the best HSP between the sequences a and b, and ‘‘qu’’ stands for the query sequence.
The point here is to take account of some fluctuation insequence divergences. And we expect this effective distance should be a good proxy of the sequence divergencebecause it accounts for substitutions, insertions/deletions,and the alignment coverage.
We then constructed pairwise nucleotide alignments ofthe cDNA counterparts of the query and subject sequences
in a manner similar to the construction of multiple alignments described below. We chose the rat cDNA sequence
whose nucleotide alignment with the query sequence scores
the best among the surviving subjects. Finally, the bestscoring subject was kept as the ‘‘ortholog candidate’’ if
the following two criteria are met: (1) the unambiguouslyaligned regions excluding gaps cover 70% or more of the
maximum between the query and the subject lengths and(2) the number of synonymous differences per synonymoussite is 0.3 or less. We counted synonymous sites and synonymous differences by the ‘‘dists’’ program of the ODEN
package (Ina 1994), which implements the method of
Nei and Gojobori (1986). In this way, we found rat ortholog
candidates for 20,910 mouse peptide-coding cDNAs.
We also applied similar procedures to human and dog
sequences with a more lenient threshold of 0.6 for synonymous differences per synonymous site, which yieldedhuman and dog outgroup sequences for 20,278 and 19,405
of mouse cDNAs, respectively.
Intraspecies Paralogous Sequences of Mouse cDNAs
The main purpose here is to retrieve, from the mouse
genome, pairs of paralogous sequences that have duplicatedafter the rodent-primate divergence. To speed up the filtering
process, we introduced an appropriate ‘‘outgroup’’ species
that defines a ‘‘natural cutoff.’’ In contrast to numerical cutoffs, a natural cutoff is given by the best score between thequery sequence and the sequences from the outgroup species. Because the functional constraints on amino acid substitutions vary depending on the proteins, the natural cutoff
should be more suitable to retrieve sequences that diverged

from the query after a specified taxonomical event. We
chose chicken as the outgroup most suitable for the purpose
here. Humans and dogs are too close considering that themouse genome evolved two to three times faster than thehuman genome, in terms of branch lengths (Rat Genome Sequencing Project Consortium 2004). Birds and mammals diverged about 310 MYA (Hedges 2002; Reisz and Muller
2004), about three times older than the primate-rodent divergence (Springer et al. 2003). It should therefore be very rarethat this natural cutoff rejects our target here.
We started with BlastP searches using mouse peptides
as queries and a target database made of mouse and chicken
peptides with the parameters used above. Similar to theabove section, we first prepared a set of homologs using
the thresholds of 35% identity and 100 amino acids. Thenwe screened mouse paralogous sequences for each query
according to the following procedures.
First, we selected only mouse peptides whose alignments cover 70% or more of the maximum between thequery and subject lengths. Then, for each mouse query sequence, we used the best score among the chicken homologs as a natural cutoff and discarded mouse sequencesscoring lower than that. At this stage, we unconditionallyaccepted mouse pairs without any chicken homologs. This
screening yielded 112,711 mouse pairs.
The next step is to screen the surviving mouse pairs byusing the phylogenetic relationship with human and dog outgroup sequences. For each pair of mouse cDNAs, after
adding its best human and dog homologs as outgroupsequences, we constructed a multiple alignment via themethod described below. Then we constructed a phylogenetic tree using the neighbor-joining method (Saitou andNei 1987) and a distance matrix estimated by Kimura’s(1980) two-parameter model that are implemented in ClustalW (Thompson, Higgins, and Gibson 1994) version 1.83.
Finally, we selected only those mouse pairs that satisfy thefollowing criteria: (1) synonymous differences are less than
0.6 per synonymous site, (2) the unrooted tree topology
shows clustering of two mouse genes if they have both human and dog homologs, and (3) the synonymous distance
between the mouse sequences is less than 1.5 times the minimum synonymous distance between the mouse and theoutgroup sequences. Here the synonymous distances aremeasured with dists of the ODEN package.
After filtering out the pairs each consisting of alternative splicing variants of the same gene, we obtained 42,546
pairs of mouse cDNAs that are likely to have duplicatedafter the rodent-primate divergence.
Here let us explain the theory behind the numerical cutoffs of 0.6 and 1.5. The branch length between mouse andthe last common ancestor (LCA) of rodents and primates is
estimated to be two to three times longer than the branch
length between humans and the LCA (Rat Genome Sequencing Project Consortium 2004). Here we take the larger
value of 3 as the ratio and consider the situation where a gene
duplicated at the same time as the rodent-primate divergence. The evolutionary distance in the neutral sites between
such mouse paralogs should be (3 1 3)/(3 1 1) 5 1.5 times
that between a mouse sequence and its human ortholog. This
gives one of the above cutoffs. Now, the average distance
between humans and mice is estimated to be about 0.5 in930 Ezawa et al.

neutral sites (Mouse Genome Sequencing Consortium
2002). So the average neutral distance between the mouseparalogs considered here is expected to be 0.75, which corresponds to 0.47 differences per neutral site under the Jukes
Cantor model (Jukes and Cantor 1969). The dists program of
the ODEN package counts synonymous sites and synonymous differences using the method of Nei and Gojobori.
This method is known to underestimate the number of synonymous sites, resulting in the overestimation of synonymous differences per synonymous site (Nei and Kumar
2000). Let us now use the transition-transversion ratio offour, an approximate average between mouse and rat orthologs. Then the correction factor is approximately 0.83. The
dists program is therefore expected to give the estimation of
0.47/0.83 5 0.57 on average for the synonymous differences per synonymous site between mouse paralogs that
duplicated exactly when rodents and primates diverged.
Allowing for a bit of fluctuation, we set the numerical cutoff
of 0.6.
Construction and Refinement of Quartets
From each of the selected 42,546 pairs of mouse
cDNAs that are likely to have diverged after the rodentprimate divergence, we tried to construct a quartet by adding the rat ortholog candidates of mouse sequences. This
process also filters out mouse cDNA pairs that appear tohave duplicated after the mouse-rat speciation.
When constructing quartets, we imposed the following
conditions:(1) each member of the mouse pair has at least
one rat ortholog candidate and (2) the rat ortholog candidatesof the mouse sequences are transcribed from the genes distinct from each other. By applying these two criteria, we succeeded in constructing quartets for 6,568 mouse pairs.
However, some of these 6,568 quartets contain mouse
cDNA pairs that are transcribed from the same gene pair(and similar situations can also occur for rat gene pairs).
We thus chose 3,657 ‘‘representative quartets,’’ each ofwhose mouse cDNA pair has the highest alignment scoreamong the alternative splicing variants of a gene pair.
We constructed multiple alignments of cDNAs for
those 3,657 quartets in the manner described below. Then
we inferred phylogenetic relationships among the sequencesin each quartet by using the neighbor-joining method witha distance matrix estimated by Kimura’s two-parametermodel. Finally, we selected 2,641 quartets, each of whichcontains two orthologous mouse-rat pairs and whose phylogenetic trees indicate that duplication events occurred
before the mouse-rat divergence. The preceding screening
already narrowed down the duplication events to after
the primate-rodent divergence. These 2,641 quartets are
the basis for the later analyses.
Construction of Multiple Alignments ofcDNA Sequences
Here we explain how we constructed multiple alignments of cDNA sequences used in this study. Given a setof cDNA sequences, we began by constructing a multiplealignment of their peptide counterparts via the protein modeof ClustalW (Thompson, Higgins, and Gibson 1994) ver-sion 1.83 with the default parameters. Then we constructed
its corresponding codon alignment guided by the correspondence between cDNAs and peptides fetched from theEnsEMBL database. The process up to here is a common
practice for constructing a multiple alignment of codon
sequences, which can be seen in papers on codon sequence
analyses. However, we encountered quite a few cases inwhich the alignment constructed this way looks erroneous
due to frameshifts in short intervals or misplacements oflong insertions/deletions. Such alignment errors might befatal when we try to detect gene conversion because it mightcause numerous false positives. We therefore deviseda method to remove potential alignment errors by comparing
three multiple alignments constructed from the same sequence set but under different parameter settings. The
two alignments to be compared to the above codon alignment were produced by applying the nucleotide modeof ClustalW to this codon alignment under two parameter
sets: (dnamatrix, gapopen, gapext) 5 (ClustalW, 4, 0) and(ClustalW, 12, 0). Finally, we masked regions in the codonalignment that showed discrepancies with at least one of thetwo nucleotide alignments constructed as above, thus maintaining only regions that are robust under moderate parameter changes. When actually conducting analyses, we
discarded gene sets that had lost a large fraction of alignments (30% in this study) through this masking process.
Statistical Tests to Detect Gene Conversion
Here let us briefly explain our strategy for detecting
gene conversion. We are considering the situation wherea gene duplication event precedes the speciation of mouseand rat, producing a quartet of genes: mouse1, rat1, mouse2,
and rat2, where mousea and rata are orthologous (a 5 1 or
2), while genes 1 and 2 are paralogous (fig. 1). If base substitutions dominate the sequence evolution, the major informative sites should be of ‘‘type 1,’’ clustering orthologous
sequences (fig. 1A). If gene conversion occurs, however, we
expect a significantly large number of a second type of informative sites, the ‘‘type 2’’ sites, which cluster paralogous
sequences in the same species (fig. 1B). It should be notedthat simple parallel substitutions can also generate type 2
sites besides a third type of informative sites, the ‘‘type
3’’ sites (fig. 1C). Therefore, in order for the observed type
2 sites to indicate gene conversion, they should be significantly more abundant than expected by parallel substitutions
alone. Besides, gene conversion will be further supported if
the type 2 sites are segregated from other types of informative sites along the multiple alignment of a quartet.
In order to examine whether these two conditions are
satisfied or not, we conducted the following four statisticaltests: (1) a test for the count of type 2 informative sites (theIScomp test), which turned out to be similar in spirit to but
technically different from the codouble method of Balding,
Nicholas, and Hunt (1992); (2) a test for the size of the longest run of type 2 sites (the T2run test), which is mathematically similar to Stephens’ (1985) test; (3) a test for thenumber of consecutive runs of informative sites of thesame types (the SameTrun test), which is Takahata’s(1993) two-sample runs test applied to a slightly different
situation; and (4) a test for the regional variation in sequenceGene Conversions in the Mouse and Rat Genomes 931

similarities (the CSrun test), exploiting the GENECONV
software (Sawyer 1989, http://www.math.wustl.edu/;sawyer). Then, with the aid of computer simulations,
we integrated the results of those four tests in order toachieve high sensitivity for gene conversion detection while
keeping low false-positive rate. In order to let the readers
have a feeling on our detection method, we have prepared
Supplementary Figure S1A–C (Supplementary Material
online). The figure exemplifies the results of our statisticaltests conducted on three quartets, one ‘‘extremely positive,’’one ‘‘moderately positive,’’ and one ‘‘gray.’’ Details of thestatistical tests, computer simulations, and integration
procedures are described in Supplementary File (Supplementary Material online). There the readers will also findtests of our method’s performance and comparisons of
the performance between our method and GENECONV.
Inference of Mouse cDNA Pairs That Have Undergone
Gene Conversion
Because the type 2 sites do not tell which of the mousepair and rat pair was affected by gene conversion, we used
GENECONV (in the NUCL mode) and synonymous substitutions in order to infer the pairs struck by gene conversion. We examined each quartet showing signs of geneconversion, and we judged that gene conversion hits themouse pair if either of the following conditions was satisfied: (1) GENECONV detected a tract of P , 0.05 in themouse pair or (2) the best putative gene conversion tract inthe mouse pair had a synonymous distance significantly
smaller than that between orthologs. We used the binomial
test and the threshold of 0.1. The resulting positive mouse
cDNA pairs were used for the correlation analyses.
Correlations of Gene Conversion Susceptibilities with
Properties of Gene Pairs
From the EnsEMBL FTP site (ftp.ensembl.org/pub),
we fetched mapping of exons onto chromosomes as well as
information on exon components, gene origins, and translation start and end points of transcripts. Integration ofthese pieces of information yielded the mapping of peptidecoding regions onto the chromosomes. Using this mapping, we first classified gene pairs according to the linkages,
namely, whether the two genes reside on the same chromosome or not. Linked gene pairs were further classified
according to their relative transcriptional orientations into
three categories: ‘‘head-to-tail’’ (5#-3# 5#-3#), ‘‘head-tohead’’ (3#-5# 5#-3#), and ‘‘tail-to-tail’’ (5#-3# 3#-5#). We
also classified the quartets according to physical distances.
Here the physical distance of a pair was defined as the number of base pairs between the coding regions of the cDNA
sequences mapped on a chromosome. When examining thecorrelations of gene conversion prevalence with relativeorientations and with physical distances, we used only thosequartets whose mouse and rat pairs have the same relativeorientation because such quartets must have rarely, if any,
drastically changed their orientations or physical distances
after the mouse-rat speciation. Because we collected quartets in a mouse-centered manner, we only used the distance
between mouse sequences in each quartet. Then we sorted

the quartets in ascending order of distance and distributed
them into four bins containing almost identical numbers ofquartets and counted ‘‘positive’’ mouse cDNA pairs in eachof the four bins. Then we examined whether there are correlations between these classifications and the proportion of
positive mouse pairs.
In similar manners, we examined correlations of theincidence of gene conversion with other properties of genes,
such as the peptide length, exon count, synonymous differences per synonymous site, degree of bootstrap support, andsize of the gene family the mouse pair belongs to. Here we
defined a ‘‘family’’ as a set of genes that are inferred to havediverged from each other after the rodent-primate divergence. We then categorized the gene pairs by the size ofthe ‘‘subfamily,’’ which is defined as a cluster of family
members contiguous to each other along the chromosome.
We also examined the dependence on the functional
categories. We performed InterProScan (Zdobnov andApweiller 2001; http://www.ebi.ac.uk/interpro) version
4.0 on mouse peptides whose cDNA counterparts belong
to the 2,641 quartets. We then extracted domain candidates
whose exact ID numbers are shared by both sequences in themouse pair. And we kept only those domains each of whose
regions in the two mouse sequences overlap each other in thealignment. We counted the ‘‘positive,’’ ‘‘gray,’’ and ‘‘negative’’ quartets in each of the categories belonging to theInterPro domain or family, as well as in each of the gene
ontology terms (The Gene Ontology Consortium 2000;
http://geneontology.org) whenever defined. Both upper
tailed and lower tailed P values are calculated by using
Fisher’s exact test.
In Silico Verifications of the Robustness of Our Results
Because some of the methods we employed in thisstudy are not yet widely accepted, some anxieties might remain regarding whether the results obtained here are biologically relevant or there are some artifacts that grossly
deform the picture. We therefore conducted three independent additional analyses using (1) the Homologous Vertebrate Genes (HOVERGEN) database (Duret, Mouchiroud,and Gouy 1994), (2) a set of ‘‘syntenic’’ ortholog candidates,and (3) the simpler, ‘‘standard,’’ alignment construction.
(1) Although well motivated and planned, our series ofscreening process to sample quartets is a bit complicatedand different from standard methods of homologous sequence collection. A concern may therefore arise thatsome artifacts might have infiltrated the process. Thus,
we reanalyzed the incidence of gene conversion in a data
set extracted from the HOVERGEN database (Duret,
Mouchiroud, and Gouy 1994; http://pbil.univ-lyon1.
fr/databases/hovergen.html) release 47. The database
contained 49,206 mouse sequences (including redundancy) distributed in 11,776 families. We first examined
the phylogenetic trees and retrieved 1,901 subfamilies
of mouse sequences that were inferred to have been
formed after the mammalian radiation. Because the current version of HOVERGEN contains redundant sequences (see the above Web site), we removed theredundancy by extracting only those mouse entries
that have links to EnsEMBL. This left us with 128

932 Ezawa et al.

subfamilies, containing 436 nonredundant EnsEMBL
mouse gene pairs. A total of 159 pairs successfully
formed quartets. Finally, we chose 134 quartets whose
phylogenetic trees suggested that the duplication events
took place before the mouse-rat speciation. Then we applied our gene conversion search to these quartets.
(2) Although our method to retrieve rat ortholog candidates
are close to standard, sequence similarity alone might
not be enough to infer the orthology when we handle
multiple-gene families. We therefore prepared a more
stringent set of ortholog candidates by imposing ‘‘conserved synteny’’ as an additional condition. Our operational definition of conserved synteny for a pair ofmouse and rat ortholog candidates is the following:(1) the mouse gene has at least a gene that lies within
1 Mb of it and has different ortholog candidates fromthose of the gene in question; (2) when taking the closestof such genes on each side, its ortholog candidate is also
within 1 Mb of the ortholog candidate of the gene inquestion; (3) they have a conserved gene order relative
to the transcriptional direction of the gene in question;and (4) when the gene have such closest genes on bothsides, both must satisfy the conditions (2) and (3). We
finally selected such syntenic ortholog candidates for
12,341 mouse genes. Then we constructed 676 refinedquartets, each consisting of two syntenic mouse-rat ortholog candidates that are inferred to have diverged between the rodent-primate divergence and the mouse-rat
speciation. Using these 676 refined quartets, we reanalyzed the incidence of gene conversion, as well as correlations between the proportion of positive gene pairs
and various properties of genes. We put the results ofstatistical analyses on this refined set of quartets into
Supplementary Tables S1 through S9, S12, and S13(Supplementary Material online).
(3) Although our alignment-masking process was originallydevised to reduce false-positive rate, it would be desirable to check whether the masking process really works
well. We therefore repeated our analysis using simpler,
standard, codon alignments, which were produced byjust replacing amino acids in the protein alignments with
their codon counterparts. We constructed such alignments for 3,657 quartets that we had already prepared,
and 2,582 of them satisfied the condition on the inferredtree topology. Then we compared the result of gene conversion detection on these standard alignments with thatobtained by using our masked alignments.
We also confirmed the credibility of our gene conversion detection methods by conducting performance tests ondata sets generated by computer simulations. The Supplementary File (Supplementary Material online) describes
these analyses, as well as the comparison of our methodwith GENECONV.
Results
Number of Quartets Affected by Gene Conversion
Based on the result of our statistical test, we classified
our 2,641 quartets into four categories: extremely positive,
moderately positive, gray, and negative (see SupplementaryFile for details, Supplementary Material online). Roughly

Table 1
Summary of Statistical Tests Conducted on the Mouse-Rat
Quartets as well as on the Mouse and Rat cDNA Pairs
Test Status

Quartets

Mouse_Pairsa

Rat_Pairsb

Positive1c
Positive2e
Grayf
Negative
Total

244 (9.2%)d
244 (9.2%)
773 (29.3%)
1,380 (52.3%)
2,641 (100%)

151 (5.7%)
189 (7.2%)
770 (29.2%)
1,531 (58.0%)
2,641 (100%)

153 (5.8%)
125 (4.7%)
810 (33.4%)
1,546 (48.2%)
2,641 (100%)aResults of tests on mouse cDNA pairs contained in quartets.
Results of tests on rat cDNA pairs contained in quartets. Because of redundancy, the figures in this column do not necessarily reflect the real situation. In thenonredundant set of rat pairs, the proportions of Positive1 and Positive2 are 10.1%(102/1,009) and 8.3% (84/1,009), respectively.
c
The extremely positive set consisting of quartets that showed almost unequivocal signs of gene conversion (approximately equivalent to the condition:
P , 0.0001).
d
The number on the left of parentheses and that in parentheses are the count andthe proportion, respectively, of quartets or mouse pairs with particular test status.
e
The moderately positive set consisting of quartets whose test results indicatedthe occurrences of gene conversion (approximately equivalent to the condition:
P , 0.005).
f
Sets of quartets that are difficult to judge whether gene conversion affected
them or not (approximately equivalent to the condition: P , 0.125).
b

speaking, an extremely positive quartet is defined as a quartet with the simulated P value under 0.0001, and a moderately positive quartet has the P value under 0.005. The
results of computer simulations suggested that the former
and the latter sets should contain less than two and less than
19 false positives out of the 2,641 quartets, respectively, interms of 95% one-tailed confidence interval (Supplementary File, Supplementary Material online). We also divided
nonpositive categories into gray and negative. A gray status
was assigned to quartets that are difficult to judge whether
gene conversion has occurred or not. The simulated P valueof 0.125 was chosen as the boundary between gray andnegative in this study.
Out of the 2,641 quartets we examined, 244 (ca. 9%)
were classified as extremely positive and 244 more (ca. 9%)
were classified as moderately positive for gene conversion(table 1). In total, we detected significant signs of gene conversion (at the simulated false-positive rate 0.5%) in 488
quartets, which is about 18% of the sample size. We haveto note that the gray and negative categories could also contain quite a few quartets affected by gene conversion. But
the exact number of such quartets can vary enormously
depending on the actual modes of their evolution via substitutions. However, if we naively believe our simulation
results that approximately 20% of true-positive signs escaped our detection method (Supplementary File, Supplementary Material online), about 120 more quartets thatexperienced gene conversion should be hidden in the grayand negative categories, giving the estimate that about 610(ca. 23%) of the 2,641 quartets underwent gene conversion.
We also classified mouse cDNA pairs in a similarmanner and found 151 pairs (ca. 6%) as extremely positive
and 189 (ca. 7%) as moderately positive (table 1). The total
number of positive mouse pairs are therefore 340 (ca. 13%).
Combining these results with the result on rat, 130 quartets
showed highly significant signs of gene conversion in bothmouse and rat gene pairs.

Gene Conversions in the Mouse and Rat Genomes 933

Table 2
Counts and Proportions of Positive, Gray, and Negative Mouse Pairs Classified by the Linkage
Linkagea

Both

Mouse Only

Rat Only

Neither

Unknown

Total

Positive
Grayc
Negative
Total

271 (15.1%)b
551 (30.6%)977 (54.3%)
1,799

8 (17.8%)
15 (33.3%)
22 (48.9%)45

0 (0%)
14 (17.3%)
67 (82.7%)
81

2 (1.1%)31 (17.0%)
149 (81.9%)
182

59 (11.0%)
159 (29.8%)316 (59.2%)
534

340 (12.9%)770 (29.2%)
1,531 (58.0%)
2,641a
The key for linkage categories—both: both mouse and rat pairs are linked, neither: neither mouse nor rat pair is linked,
unknown: the linkage of either mouse or rat pair is unknown.
b
The number in parentheses is the proportion of mouse cDNA pairs showing a particular test status in the set of mouse pairs
with a particular linkage category.
cMouse pairs that are difficult to judge whether gene conversion affected them or not.

(table 3, P 5 0.011 in Fisher’s exact test). For the remotegene pairs, however, the frequency hovered between 7%
and 20% and did not fall as the distance increased (table 3).
Because the sample sizes are small, we do not knowwhether the frequency of 20% represents the proper biology
for these remote genes or not, but at least some gene pairs
with their distances over 10 Mb were positive for gene conversion. This might be at least partially attributable to recent
chromosomal remodeling such as translocations and inversions. To support this idea, when we conducted the same
analysis on the refined set of quartets consisting only oforthologous pairs of conserved synteny, we did not findsigns of gene conversion among the 34 pairs over 811 kb(Supplementary Table S3, Supplementary Material online).
Next we classified the pairs into three categories of relative transcriptional orientation: head-to-tail (5#-3# 5#-3#),head-to-head (3#-5# 5#-3#), and tail-to-tail (5#-3# 3#-5#).
The proportion of positive mouse pairs does not depend
so much on the relative orientations (table 4). Although
the head-to-head set may appear more abundant in positive
pairs, the bias is not significant (P . 0.2 by Fisher’s exacttest). We also examined dependence on the relative orientation for each of the four categories of the physical distance(Supplementary table S4B,C, Supplementary Material
online). In most distance categories, gene conversion frequency appeared to vary among relative orientations only
within reasonable sampling fluctuations. Only the third

Correlations with Linkage, Physical Distance, andRelative Orientation
We examined how the proportion of positive mouse
cDNA pairs varies depending on their relative positional
properties.
Most of the collected mouse gene pairs consisted ofgenes that were linked (residing on the same chromosome):
linked pairs accounted for 87.5% (51,844/2,107) of thepairs with known linkage status (table 2). The table also indicates that linked gene pairs (279/1,844 5 15%) are significantly more prone to gene conversion than unlinked ones
(2/263 5 0.8%, P 5 3.0 3 108 by Fisher’s exact test).
To see the dependence on physical distance, we firstsorted the quartets in ascending order of the distance between mouse sequences, and then we distributed the quartets
into four bins of almost equal sizes (table 3). The table showsa clear negative correlation between the proportion of positive pairs and the physical distance (P 5 1.2 3 109 by thechi-square test of df 5 3). While as much as 29% of mousepairs within 55 kb were positive for gene conversion, only
10% of those over 371 kb showed signs of gene conversion(table 3). To see the distance dependence in more detail, we
subdivided the closest and remotest categories into smallerbins. Mouse pairs within 27 kb were even more prone togene conversion, with the frequency of 36% significantly
higher than that of 23% for pairs between 27 and 55 kb

Table 3
Counts of Positive, Gray, and Negative Mouse Pairs Classified by Their Physical Distances
Graya

Negative

Total

0–55 kb
55–167 kb
167–371 kb
371–90 Mb
Totalb

89 (29.3%)
48 (15.8%)
46 (15.1%)
30 (9.9%)213 (17.5%)

99 (32.6%)
97 (32.0%)
94 (30.9%)87 (28.6%)
377 (31.0%)

116 (38.2%)
158 (52.1%)
164 (53.9%)
187 (61.5%)625 (51.4%)

304
303
304
304
1,215

Subdivision of the closest category
0–27 kb
27–55 kb

54 (35.5%)
35 (23.0%)

48 (31.6%)
51 (33.6%)

50 (32.9%)
66 (43.4%)

152
152

Subdivision of the remotest category
371–811 kb
811–1.6 Mb
1.6–9.5 Mb
9.5–25 Mb
25–90 Mb

11 (7.2%)
5 (6.6%)7 (18.4%)
2 (10.5%)5 (26.3%)

46 (30.3%)
26 (34.2%)
6 (15.8%)
4 (21.1%)
5 (26.3%)

95 (62.5%)
45 (59.2%)
25 (65.8%)
13 (68.4%)
9 (47.4%)

152
76
38
19
19

Distance

Positive

NOTE.—We used only quartets whose mouse and rat pairs have the same relative orientation.
aMouse pairs that are difficult to judge whether gene conversion affected them or not.
b
The figure in parentheses is the proportion of mouse pairs showing a particular test status in a particular distance class.

934 Ezawa et al.

Table 4
Counts and Proportions of Positive, Gray, and Negative
Mouse Pairs Classified by Their Relative Orientations
Relative
Orientationa Head-to-Tail Head-to-Head Tail-to-Tailb

Positive
Grayc
Negative
Total

150 (17.4%)
284 (32.9%)
430 (49.8%)864

31 (19.6%)
43 (27.2%)
84 (53.2%)
158

Total

32 (16.6%) 213 (17.5%)
50 (25.9%) 377 (31.0%)111 (57.5%) 625 (51.4%)
193
1,215

NOTE.—We used only quartets in which mouse and rat pairs have the same relative orientation.
a
The key for relative orientations—head-to-tail: 5#-3# 5#-3#, head-to-head: 3#5# 5#-3#, tail-to-tail: 5#-3# 3#-5#.
b
The figure in parentheses is the proportion of mouse pairs showing a particular
test status in the set of mouse pairs with a particular relative orientation.
cMouse pairs that are difficult to judge whether gene conversion affected themor not.

category of distance between 167 and 371 kb showed an interesting behavior, where gene conversion favored pairs of
opposite orientations, with the frequency of 21% (522/105)
for opposite orientations and 12% (524/199) for the sameorientation (P 5 0.031 in Fisher’s exact test). But this bias
lost the statistical significance in the refined set of syntenic
quartets (P 5 0.27), probably due to the small sample size. It
remains to be seen whether this bias is biologically, or evolutionarily, significant or not.
Correlations with Family Sizes, Synonymous Differences,and Other Physical and Evolutionary Parameters
We analyzed how the susceptibility to gene conversion
depends on the sizes of the family and subfamily the genepair belongs to. Here we define a family as a set of genes thathave been generated by gene duplication events after the roTable 5
Counts of Positive, Gray, and Negative Mouse Pairs
Classified by the Sizes of Subfamilies They Belong to
Subfamily
Size (n)bDifferent
n52
n53
n54
n55
n56
n57
n 5 8, 9
10 ,5 n , 15
15 ,5 n , 20
Samed
Total

Positivec

192 (10.4%)
19 (27.5%)
25 (38.5%)
12 (32.4%)
25 (35.7%)
7 (21.9%)
8 (16.3%)
20 (13.0%)
31 (13.2%)
1 (1.2%)148 (18.7%)340 (12.9%)

Graya

Negative

Total

520 (28.1%)
27 (39.1%)
20 (30.8%)
15 (40.5%)
18 (25.7%)
12 (37.5%)
24 (49.0%)
63 (40.9%)
57 (24.4%)
14 (16.9%)
250 (31.5%)770 (29.2%)

1,136 (61.5%)
23 (33.3%)
20 (30.8%)
10 (27.0%)
27 (38.6%)
13 (40.6%)
17 (34.7%)
71 (46.1%)
146 (62.4%)
68 (81.9%)395 (49.8%)
1,531 (58.0%)

1,848
69
65
37
70
32
49
154
234
83
793
2,641

NOTE.—Here we define a subfamily as a cluster of contiguously located mousegenes that are inferred to have diverged from one another after the rodent-primate
divergence.
aMouse pairs that are difficult to judge whether gene conversion affected themor not.
b
The numbers of mouse pairs whose member genes belong to different subfamilies.
c
The figure in parentheses is the proportion of mouse pairs showing a particular
test status in a particular class of family sizes.
d
The subtotal numbers of mouse pairs whose member genes belong to the same
subfamily.

Table 6Counts of Positive, Gray, and Negative Mouse Pairs
Classified by the Proportional Synonymous Differences
Between the Member Genes
Prop. sdiffa (Ps)
0.1  Ps , 0.2
0.2  Ps , 0.3
0.3  Ps , 0.4
0.4  Ps , 0.5
0.5  Ps , 0.6
Total

Positivec

56 (32.9%)
175 (21.9%)77 (14.2%)
29 (4.6%)
3 (0.6%)340 (12.9%)

Grayb

NegativeTotal

57 (33.5%)292 (36.5%)
174 (32.1%)
143 (22.7%)
104 (20.8%)770 (29.2%)

57 (33.5%)
332 (41.6%)
291 (53.7%)457 (72.7%)
394 (78.6%)
1,531 (58.0%)

170
799
542
629
501
2,641

a
The proportion of synonymous differences (Prop. sdiff), which means synonymous differences per synonymous site of the entire alignment between the twomouse sequences in a pair.
b
Mouse pairs that are difficult to judge whether gene conversion affected themor not.
c
The figure in parentheses is the proportion of mouse pairs showing a particular
test status in a particular class of synonymous differences.

dent-primate divergence. A subfamily is defined as a clusterof physically adjacent genes in a family. Both the dependences showed very similar behaviors to each other. Because
we have seen a clear negative correlation between the geneconversion susceptibility and the physical distance in the last
subsection, a subfamily seems to be the more relevant unit
than a family when discussing gene conversion. We therefore focused on subfamilies.
Gene pairs belonging to smaller subfamilies are more
prone to gene conversion (table 5). For example, pairs belonging to subfamilies of size less than seven show the geneconversion prevalence of 32% (588/273), while the prevalence for subfamilies of size seven or more is 12% (560/520),showinganenormous statisticalsignificance(P 5 3.63
1012 in Fisher’s exact test). Another interesting point isthat ‘‘isolated’’ gene pairs, which are equivalent to subfamilies of size two, look less prone to gene conversion thansubfamilies of size between three and five, although without
statistical significance (P 5 0.12 between sizes two andthree). These observations should help model the evolutionof gene families under the influence of gene conversion.
Table 6 shows the dependence of gene conversion frequency on the number of synonymous differences per synonymous site among mouse paralogous sequences in an
entire quartet alignment. It is obvious from the table thatgene conversion is more prevalent among pairs with higher
sequence similarities. It is unclear, however, whether thehigh similarity accelerated gene conversion or frequentgene conversion maintained the high similarity. Probably,both mechanisms worked together to form a kind of positive feedback loop. To elucidate this relationship, however,a more thorough data analysis would be necessary, maybe
with the aid of some models.
We also examined the dependence of gene conversion
incidence on peptide lengths, exon numbers, and bootstrap
support for the clustering of mouse-rat orthologous pairs.
However, we did not find any trends that can be simply interpreted. So we just presented the results in Supplementary
Tables S7–S9 (Supplementary Material online). It remains
to be seen whether some trend will be revealed or not when
we incorporate correlations among these parameters as wellas with others.

Gene Conversions in the Mouse and Rat Genomes 935

Table 7
Counts of Total and Positive Mouse Pairs in Functional Categories (excerpt)
Interpro ID (name or description)

Total

Positive

Pv_Lowera

Pv_Upperb

Three major families
IPR000276c (rhodopsin-like GPCR receptor)
IPR000725 (olfactory receptor)
IPR004072 (vomeronasal receptor, type 1)

1,465
666
402

200
120
23

0.838*
1.00
1.72 3 107*

0.193
1.52 3 105
1.00

35
21

25
11

1.00
1.00

2.08 3 1015*1.75 3 105

6
21
5
30

5
9
4
10

1.00
1.00
1.00
9.99 3 1012.02 3 104*
7.12 3 1041.30 3 103*
3.48 3 103

17
28

7
9

9.99 3 101
1.00

3.75 3 103
7.22 3 103

2

2

1.00

1.72 3 102*

2

2

1.00

1.72 3 102*

13

3

0.921

0.238*

279

08.06 3 1019

1.00

282
203
160
252
246

1
1
1
10
17

2.47 3 1017
3.97 3 1012
2.13 3 1093.06 3 1078.90E-04

1.00
1.00
1.00
1.00
1.00

Rich in gene conversion
IPR002126 (cadherin)
IPR009072 (histone-fold)
IPR001664 and IPR011000d (intermediatefilament protein and apolipophorin III like)
IPR001254 (peptidase S1, chymotrypsin)
IPR002957 (keratin, type I)
IPR000379 (esterase/lipase/thioesterase)
IPR003597 and IPR001039d (immunoglobulin
C1 type and major histocompatibility complex
protein, class I)
IPR002018e (carboxylesterase, type B)
IPR006862 (Acyl-CoA thioester hydrolase/bile
acid-CoA amino acid N-acetyltransferase)
IPR003439 and IPR003593d (ABC transporter
related and AAA ATPase)
IPR002213 (UDP-glucuronosyl/
UDP-glucosyltransferase)
Poor in gene conversion
IPR004073f (vomeronasal receptor, type 2)
IPR000337 (GPCR family 3, metabotropic
glutamate receptor like)
IPR001500 (nine cysteins of GPCR)
IPR001828 (extracellular ligand binding receptor)
IPR003596 (immunoglobulin V type)
IPR007110 (immunoglobulin like)

NOTE.—ABC, ATP-binding cassette; ATPase, adenosine triphosphatase; GPCR, G-protein-coupled receptor; UDP, uridine
diphosphate. The numbers and P values in this table are estimated using the whole set of quartets, an asterisk indicates the significance when estimated with the refined set of syntenic quartets. You can find complete tables as Supplementary Tables S10A,B
and S11A-C for the analysis using the set of all quartets and S12A,B and S13A-C for that with the set of syntenic quartets (Supplementary Material online).
a
The lower tailed P value, which is the probability that there are positive pairs less than or equal to those observed under thenull hypothesis of the even distribution.
b
The upper tailed P value, which is the probability that there are positive pairs more than or equal to those observed under thenull hypothesis of the even distribution.
cThis family is a composite superfamily consisting of the other two major families, that is, olfactory receptors and vomeronasal receptors, as well as some others.
dThese two domains always occurred in pair.
eThis domain was always accompanied by IPR000379 (esterase/lipase/thioesterase), which is two entries above.
f
This family almost completely overlapped IRP000337 (GPCR family 3), which is just below.

Correlation with Functional Categories
In order to see the differences in susceptibilities among
different functional categories, we classified mouse genepairs according to the functional domains shared by the member sequences (table 7; Supplementary Tables S10A,B and
S11A–C, Supplementary Material online). The prevalenceof gene conversion did vary across functional categories.
In terms of the total number of gene pairs, the commonest
category is the rhodopsin-like G-protein-coupled receptor(GPCR) superfamily (1,465 pairs). It consists of many genefamilies including the second and third major ones, namely,
the olfactory receptors (666 pairs) and the vomeronasal receptors type 1 (402 pairs). These two families substantiallyvaried in their susceptibility to gene conversion: the olfactory
receptors were far more susceptible than normal (120 positives, P 5 1.5 3 105) and the vomeronasal receptors were
extremely immunetogeneconversion(23positives, P 5 1.73

107). Many families and domains were significantly rich ingene conversion, and some were significantly poor in geneconversion (table 7). Some of the categories lost the statisticalsignificance when we reanalyzed the functional dependence
using the refined set of syntenic quartets (table 7; Supplementary Tables S12A,B and S13A–C, Supplementary Material
online). This is partly because the refined set is almost freefrom overrepresentation due to recent duplication events.
We further delved into the categories whose statisticalsignificance remained after using the refined set. We reexamined, for each functional categories, correlations of thefrequency of gene conversion with the physical distance,
subfamily size, and synonymous differences per synonymous site. The previous subsections have already shownthat these three quantities have remarkable correlations withthe average prevalence of gene conversion. First, we compared the two major families the olfactory receptors andthe vomeronasal receptors to see what makes these two

936 Ezawa et al.

functionally similar families so differently susceptible togene conversion (Supplementary Table S14A,B, Supplementary Material online). The olfactory receptor familyshows almost average behavior (Supplementary Table
S14A, Supplementary Material online). Its enhanced incidence of gene conversion seems to be attributable to its
properties, such as no unlinked pairs, and a slightly high proportion of pairs each embedded in a subfamily. On the other
hand, the low gene conversion incidence of the vomeronasal
receptor family is hard to explain with the physical properties
alone. It abounds with linked pairs of the remotest category,
and it is poor in pairs belonging to the subfamilies of size fiveor less. However, it showed low frequency even in categories
that are normally prone to gene conversion, such as that ofphysical distance within 55 kb. Although the final conclusion
would need the correlation analysis of the three quantities,some special biological mechanism might have hampered
gene conversion on the vomeronasal receptor family.
We next examined the families of cadherins, intermediate filament proteins, and keratins (Supplementary Table
S14C–E, Supplementary Material online), which are significantly prone to gene conversion (table 7). Intermediate filament proteins and keratins showed ideal combinations ofphysical properties (Supplementary Table S14D,E, Supplementary Material online): small distances within pairs,
contiguous cluster sizes between three and five, and small
synonymous differences per synonymous site. Because
they contain only a small numbers of pairs, their high geneconversion incidence are well accounted for by this ‘‘lucky
combination’’ of favorable conditions. The cadherin family,
however, does not look perfect (Supplementary Tables
S14C, Supplementary Material online). The pair distances
fall within 167 kb, and the pairs belong to contiguous clusters that are either small, with sizes between three and five, orlarge, with sizes between 10 and 14. And their synonymous
differences are not necessarily small enough. These observations indicate that cadherins might have been under someselective pressure that favors the occurrence of gene conversion. We also looked into the functional categories of ‘‘nine
cysteins of GPCR’’ and ‘‘immunoglobulin V type’’ (Supplementary Table S14F,G, Supplementary Material online),
which are significantly immune to gene conversion (whenusing the whole set of quartets, table 7). The former category
turned out to be endowed with the ‘‘worst combination’’ ofconditions that disfavor gene conversion: most of the genepairs are unlinked or in the remotest category, have two
members belonging to different contiguous clusters, andhave large synonymous differences per synonymous site.
The category of immunoglobulin V type is harder to interpret. Although it abounds with physically remotest pairs(and those with ‘‘unknown’’ linkage), it also contains quite
a few pairs in physically closer categories. And it is rich inpairs with small synonymous differences. It seems difficult
for the physical properties alone to explain this paucity ofgene conversion in this family, which is suggestive of somebiology underneath the evolution of this family.
In Silico Verifications of the Robustness of Our Results
In order to alleviate the concern that our data preparation might entail some artifacts, we checked the robustnessof our main results by comparing them with those of independent analyses conducted with three different sets of input
data. The three input data sets were prepared by using (1) the
HOVERGEN database (Duret, Mouchiroud, and Gouy1994), (2) a set of syntenic ortholog candidates, and (3)the simpler, standard, alignment construction.
Supplementary Table S15A–C (Supplementary Material online) summarizes the results of these comparisons. As
we can see, (1) the quartets constructed from HOVERGEN
entries give positive quartets whose proportion of 22%(529/134) is similar to that obtained from our set of quartets (Supplementary Table S15A, Supplementary Material
online; table 1); (2) the refined set of 676 syntenic quartets
contains a larger proportion of positive quartets (175/676 526%) than our whole set does (488/2,641 5 18%, Supplementary Table S15B, Supplementary Material online),
which should be attributable to the enrichment of quartets
with conserved orientation (Supplementary Table S2, Supplementary Material online; tables 2 and 4); and (3) our
masked alignments yield more stringent results of statisticaltests than the standard alignments do (Supplementary Table
S15C, Supplementary Material online), suggesting that ourmasking method did reduce false positives.
Comparison (3) revealed 19 quartets that were positive
when using masked alignments but gray when using standardalignments. Inspection of the alignments showed that eachputative gene conversion tract straddled a masked region
that contains type 1 sites. Because it was difficult to judge
whether these positive calls were false or authentic, we reassigned the gray status to these 19 quartets. This reduced thenumber of positive quartets from 507 to 488. This reassignment of the status has already been, and will always be,reflected in Results and Discussion except this subsection.
We also repeated our analyses using the refined set ofsyntenic quartets. We did not find remarkable differencesfrom the result using the whole set. Because some people
may think it better to use this set of syntenic quartets, we
presented the results with this set in Supplementary Tables
S1 through S9, S12, and S13. Supplementary Tables S1through S6 correspond to tables 1 through 6, respectively.
And Supplementary Tables S12 and S13 correspond to
Supplementary Tables S10 and S11, respectively (Supplementary Material online).
Discussion
Genomic Prevalence of Gene Conversion?
Out of the 2,641 mouse cDNA pairs collected, 340
pairs (ca. 13%) showed significantly positive signs of geneconversion, with at most 20 false positives expected. Thismeans that gene conversion is far from a rarity but rather
is ubiquitous across a mammalian genome. So there is goodreason that we should be cautious when inferring the duplication date and the phylogenetic relationships of duplicatedgenes.
We note that we examined only those mouse pairs eachof which is included in a quartet with the inferred phylogenetic relationship of ((mouse1, rat1), (mouse2, rat2)). Thus,
our subject mouse pairs are expected to have resulted from
duplication events postdating the divergence of rodents andprimates and predating the speciation of mouse and rat. In

Gene Conversions in the Mouse and Rat Genomes 937

this study, in order to reduce the ‘‘contamination’’ by false
positives, we deliberately dismissed about 40,000 mouse
cDNA pairs. These dismissed pairs are divided into two
broad categories: (1) mouse gene pairs that appear to have
duplicated after the mouse-rat speciation and (2) thosethat lack rat ortholog candidates. Many studies report thepositive correlation between gene conversion frequency
and the sequence similarity (Liskay, Letsou, and Stachelek
1987; Elliott et al. 1998; Lukacsovich and Waldman 1999;
Semple and Wolfe 1999), which is consistent with our result (table 6). It would be therefore natural to conjecturethat an enormous number of gene conversion events should
be lurking in those unexamined pairs, especially in category(1). We expect that gene conversion should be more common among those gene pairs, pushing the overall prevalenceof gene conversion much higher than the current estimateof 15%.
In order to see whether this is indeed the case or not, wehave to establish a detection system that suppresses false
positives to a low enough level and yet can efficiently detect
multiple gene conversion tracts. Such multiple tracts tend to
foil any existing detection methods and are therefore almost
indistinguishable from a recent duplication event. Probably,
inclusion of introns and flanking regions should be conducive to the effective detection of multiple gene conversion
events. This is because introns and flanking regions tend
to evolve faster than coding regions, giving enough background information, such as the type 1 sites, that highlights
signs of gene conversion even when coding regions are very
similar to each other. This study, on the other hand, focusedon the mouse gene pairs whose coding regions have diverged
sufficiently far from each other. So they should give enoughbackground information to allow highly sensitive detectionof gene conversion. Incorporating intron sequences into thisstudy would rather have deteriorated the quality of the analyses because of the difficulty in aligning introns, which may
have experienced frequent genomic remodeling.
Comparisons with the Previous Genome-Wide Searches
for Gene Conversion
In the past, genome-wide searches for gene conversion
were performed on nematode worm Caenorhabditis elegans (Semple and Wolfe 1999) and yeast Saccharomyces
cerevisiae (Drouin 2002). We compared our results on themouse genome with those previous genome-wide studies.
In the present study, we detected putative gene conversions in about 13% (340/2,641) of mouse pairs examined.
This figure is about two times larger than in yeast (7.8% 569/879) and about seven times larger than in worm
(2% 5 143/7,829).
Some of these differences are attributable to the different nature of the collections of gene pairs. The set of mousegene pairs we examined abounds with linked pairs (1,844 5
88% out of the 2,107 pairs with known linkage states),
which are a minority in both sets of yeast and worm genepairs (41/879 5 4.7% and 3,347/7,829 5 43%, respectively). We therefore estimated the proportions of positive
pairs separately for linked and unlinked categories. For
linked pairs, the figures are 15% (279/1,844) for mouse,
39% (16/41) for yeast, and 3% (104/3,347) for worm.

For unlinked pairs, they became 0.8% (2/263) for mouse,
6.3% (53/838) for yeast, and 0.9% (39/4,482) for worm.
In both categories, yeast stands out in the proportions of positive pairs. One conspicuous feature is that the linked pairs inworm appear poor in gene conversion. Although differencesin the sequence similarity spectrums may explain theseobservations at least partially, we could not find enough dataon yeast and worm to correct for the effect of the sequencesimilarity. So it remains obscure whether the observations,especially the paucity of gene conversion in worm genome,
reflect the actual biology and evolutional history or it is justan artifact. However, the abundance of gene conversion in
the yeast genome looks real, given the literature pointing out
the higher gene conversion rate in yeast (Li 1997, p. 311).
Another remarkable feature is that gene conversion
definitely prefers linked gene pairs to unlinked ones inany of the organisms examined. This bias is consistent withthe experiments on yeast (Petes and Hill 1988; Haber et al.
1991; Goldman and Lichten 1996). We further observed thenegative correlation between the prevalence of gene conversion and the physical distances between duplicated genes inmouse. Similar correlations, with prevalence replaced byfrequency, were also reported for the worm genome (Semple
and Wolfe 1999) as well as in an experiment on the meiotic
recombination in yeast (Goldman and Lichten 1996). Theseobservations are summarized in a statement that a pair ofduplicated genes becomes more prone to gene conversion
as they get physically closer to each other.
As for the prevalence of gene conversion in vertebrate
genomes, Shields (2000) conducted a small-scale analysis
before the genome sequences of vertebrates became available. He sampled 20 sets of homologous genes, each set consists of two pairs of human and rodent orthologous genesthat are likely to have duplicated prior to the rodent-primate
divergence. Using the VTDST3 program (Sawyer 1989) on
nucleotide alignments, he found evidence of gene conversion in 45% (59/20) of his samples. The prevalence appears
very high. It appears interesting to compare his result withours. We, however, became aware that we could not compare them directly, mainly due to the different nature of thedata sets. Shields’ gene pairs were duplicated before therodent-primate divergence, whereas ours were duplicatedafterward. This means different time intervals of gene conversion events detectable by the two analyses. So we will
just mention that our samples are actually as prone to geneconversion as Shields’ if we restrict our attention to physically close categories. For example, 32% (5197/607) of
our quartets whose mouse pairs lie within 167 kb were positive for gene conversion. The result looks consistent with
Shields’ (P 5 0.17 in the binomial test), considering that hecollected only physically adjacent gene pairs.
The present study detected two instances of interchromosomal gene conversion (table 2). The frequency of interchromosomal gene conversion is estimated to be 1% (52/182), which is twice the false-positive ratio of 0.5% that we
expect to suffer. Besides, these two mouse pairs were observed in functional categories that are not particularly prone
to gene conversion: one belonged to the Kruppel associatedbox family (two positive out of 17 pairs including this case)and the other to the fructose-biphosphate aldolase, class I
family (one positive out of two pairs including this case).

938 Ezawa et al.

These observations, along with the fact that both of them aremoderately positive, make us suspect that they may be false
positives. Or, because the refined set of syntenic quartets has
no such instance of interchromosomal gene conversion(Supplementary Table S2, Supplementary Material online),
they may have resulted from chromosomal rearrangements
subsequent to gene conversions. On the other hand, considering the existing evidence for interchromosomal gene conversion in mammals (Arnheim et al. 1980; Murti, Bumbulis,
and Schimenti 1994), they may be authentic. Subtelomericregions may be involved in the phenomenon because theyabound with pseudogenes and gene copies. So we examinedthe dependence on the distance from the chromosomal end(Supplementary Tables S16A,B, Supplementary Material
online). Among the 27 mouse pairs lying within 3 Mb fromthe chromosomal end, eight (30%) were positive in geneconversion. Thus, the subtelomeric region does seem prone
to gene conversion, although the significance is marginal(P 5 0.039 in Fisher’s exact test) due to the small sample
size. Then we found that one of the above two positive pairs
had one mouse gene within 1 Mb of the chromosome end.
But we cannot say anything conclusive due to the lack ofstatistical power.
Our analysis also revealed that a pair of genes with the
opposite transcriptional directions is almost as susceptible togene conversion as a pair with the same direction. We did not
find any analyses addressing this issue in the genome-wide
analysis on either yeast or worm, except a conjecture in thepaper on the worm saying otherwise (Semple and Wolfe
1999). Although our result might have surprised some people, it may not be so aberrant at least in mammalian (or vertebrate) genomes. There are actually several instances where
gene conversion has occurred frequently between duplicated genes or regions with the opposite directions, suchas palindrome arms in the male-specific region of the human
Y chromosome (Rozen et al. 2003) and inverted duplicons
in the human X chromosome (Bagnall et al. 2005). Thesestudies suggest the existence of some mechanisms enabling
gene conversion between duplicated genes with the opposite
directions. It would be interesting to examine the dependence on the transcriptional directions in creatures otherthan mammals.

resulted from the selective pressure that requires the interspecies and/or interlocus divergence of the sequences while
keeping their uniformity within the populations. A recent
study suggests that vomeronasal receptor type 1 genes, pheromone receptors in rodents, have evolved under positive
Darwinian selection to maintain the ability to discriminate
between large and complex pheromonal mixtures (Shi et al.
2005). Thus, our conjecture seems true at least for vomeronasal receptors type I.
In order to see whether these functional categories areindeed under selective pressure or not, however, we have todelve further into each of them, looking for functional evidences or evolutionary hallmarks of such selections. In any
case, the data presented in this report should become a basis
for further data analyses and theoretical as well as experimental studies to elucidate the mechanisms underlying geneconversion.
Supplementary Material
Supplementary Tables S1 through S16 (contained ina file named Ezawa_supplementary_1.xls), SupplementaryFile named Ezawa_supplementary_2.doc, and Supplementary Figure S1 (in a file named Ezawa_supplementary_3.
pdf) are available at Molecular Biology and Evolution online(http://www.mbe.oxfordjournals.org/).
Acknowledgments
This work was mainly conducted by K.E. N.S. proposed the use of quartets, and S.O. helped initial settingof data analyses. The manuscript was mostly written by
K.E., with the help of N.S. and S.O. We are grateful to
K. Sumiyama and T. Kitano for discussions on this study.
We also greatly appreciate three anonymous reviewers,
whose comments definitely helped significantly improve
this study. This study was supported by a grant in aid for scientific studies from Ministry of Education, Science, Sport,
and Culture, Japan, to N.S. K.E. received a fellowship fromthe Genome Network Project presided by the Ministry of Education, Culture, Sports, Science and Technology of Japan.

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Laura Katz, Associate Editor
Accepted January 5, 2006