Evolutionary Pattern of Gene Homogenization between
Primate-Specific Paralogs after Human and MacaqueSpeciation Using the 4-2-4 Method
Kiyoshi Ezawa,1,2 Kazuho Ikeo,2,3 Takashi Gojobori,2,3 and Naruya Saitou*,11Division of Population Genetics, National Institute of Genetics, Mishima, Japan
Human Genome Network Project, National Institute of Genetics, Mishima, Japan
3
DNA Data Analysis Laboratory, National Institute of Genetics, Mishima, Japan*Corresponding author: E-mail: saitounr@lab.nig.ac.jp.
Associate editor: Anne Stone
2

Research article

Abstract
Homogenization of duplicated genes is an important factor for gene family evolution. In the previous study, we developed
a method, named 4-2-4 here, to detect partial homogenization with high sensitivity and high specificity using quartets.
A quartet is a set of four genes generated by a duplication event and the subsequent speciation of two closely related
species. We searched the human and macaque genomes and found 430 nonredundant quartets, which correspond to
primate-specific paralogs. The prevalence of homogenization in these quartets was 10.0% (43/430), which was ca. one-thirdof that (29.8% 5 206/691) in the rodent-specific nonredundant quartets obtained through comparison of mouse and rat
genomes. Part of this difference comes from the fact that primate paralogs tend to be more remotely located to each otherthan rodent paralogs, and the remainder may be explained by the inherent difference in the neutral evolutionary rate
between the primate and rodent lineages. A statistical analysis taking account of the effects of false negatives uncovered
negative correlations between sequence divergence and homogenization prevalence both in primates and rodents. Further
statistical analyses controlling for false-negative rates and sequence divergences revealed two characteristics shared by the
primate and rodent paralogs; 1) significant negative correlations of the homogenization prevalence with physical distances,and 2) no significant correlation between the prevalence and relative transcriptional orientations. Patterns of thehomogenization in the genomic alignments of human–macaque quartets indicate that gene conversion, rather thanunequal crossing-over, is the major cause of the homogenization.
Key words: gene conversion, duplicated genes, human, macaque, 4-2-4 method, genome-wide analysis.

Introduction
When gene duplication occurs, duplicated copies start toaccumulate mutations from the ancestral sequence. Thissequence divergence process may be hampered by homogenization, or retainment of high sequence similarities with
their duplicate partners (Brown et al. 1972; Arnheim 1983;
Nei and Rooney 2005). Two major mechanisms have been
proposed so far to explain the homogenization; one is unequal crossing-over (Smith 1976; Ohta 1976) and the otheris gene conversion (Jeffreys 1979; Slightom et al. 1980; Ohta
1985). Unequal crossing-over takes place between nonequivalent but homologous regions of a pair of sister chromatids or homologous chromosomes, as is well
documented on rRNA genes (Eickbush and Eickbush2007). When occurring in an intergenic region, unequalcrossing-over only changes the copy numbers of tandemly
duplicated genes. When occurring in an intragenic region,
however, it can also create a chimera of two mutually homologous genes, which exhibits complementary homogenization with its parent sequences. Gene conversion, also
known as nonreciprocal recombination, is a process wherea tract of DNA overwrites a homologous one (Petes and Hill
1988; chapter 11 of Li 1997). Gene conversion can be clas-sified into intralocus and interlocus types. Interlocus geneconversion between duplicate genes causes homogenization of relatively short regions, as observed between red
and green opsin genes in catarrhine primates (Nathans
et al. 1986; Ibbotson et al. 1992; Balding et al. 1992).
If the homogenization is relatively sporadic and regionally limited, it causes conflicts between locally inferred phylogenetic relationships (Scott et al. 1984; Kawamura et al.
1992; Shyue et al. 1994; Zhou and Li 1996; Cheung et al.
1999; Kitano and Saitou 1999; Winter and Ponting2005). If it occurs frequently and/or extensively, homogenization can lead to erroneous phylogenetic inferences ormisestimation of duplication dates, thus can confound estimation of evolutionary history of gene families (Slightom
et al. 1985; Gao and Innan 2004; Schienman et al. 2006). It istherefore crucial to grasp the pattern of genome-wide prevalence of homogenization, namely the proportion of duplicate pairs that underwent homogenization, as well as
properties of gene pairs that enhance or reduce the frequency of homogenization.
We are interested in the prevalence of interlocus homogenization in the evolutionary history of the human genome in the present study. Analyses of homogenization for© The Author 2010. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, pleasee-mail: journals.permissions@oxfordjournals.org

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Mol. Biol. Evol. 27(9):2152–2171. 2010 doi:10.1093/molbev/msq109

Advance Access publication April 29, 2010

Homogenization between Primate-Specific Duplicated Genes · doi:10.1093/molbev/msq109

the human genome have been conducted by Jackson et al.
(2005), and recently by Benovoy and Drouin (2009)
and McGrath et al. (2009). Jackson et al. (2005) gathered
24 duplicon families with sequence divergence of at most
4%, and applied a ‘‘quartet method’’ they developed to themultiple alignments of the duplicon families. Their method
is different from our ‘‘4-2-4’’ method in that their quartets
consist only of duplicates of a single species, whereas ourquartets consist of duplicates of two closely related species.
They detected homogenized regions in at least 5% of thesequence alignments whose total length was .8 Mb.
Benovoy and Drouin (2009) examined 55,050 pairs of human duplicate genes showing more than 60% sequence
identity and detected homogenization in only 483 pairs(0.88%) using GENECONV. McGrath et al. (2009) applied
GENECONV to the sets of species-specific duplicate genes
in human, macaque, mouse, and rat genomes. They estimated homogenization rate as 12.5% for human gene pairs
and 14.6% for mouse gene pairs. These three studies showed
an enormous variation (from ,1% to ;13%) in theprevalence of homogenization between human duplicates.
Benovoy and Drouin (2009) and McGrath et al. (2009)
also tried to identify the properties of duplicate pairs thatenhance the occurrence probability of homogenization.
Whereas Benovoy and Drouin (2009) found a negative correlation between susceptibility to homogenization and sequence divergence, McGrath et al. (2009) failed to find sucha correlation. The latter group attributed this failure to thehigh false-negative rate of GENECONV applied to a pair ofhighly similar genes (table S1 of their paper). Both groups
concluded that the homogenization susceptibility does not
significantly depend on the physical distance between duplicates after controlling for the dependence on the sequence identity. This, however, seems inconsistent withthe study on mouse/rat quartets by Ezawa et al. (2006),
who found that homogenization susceptibility negatively
correlated with the physical distance.
What caused such inconsistencies among different studies? Inconsistencies are mainly due to the difference in theconditions to collect duplicate pairs and the difference inthe homogenization detection methods and criteria. First,
the enormous differences in the homogenization rate(,1% vs. 13% in human) is due to the fact that the data
set of Benovoy and Drouin (2009) mainly consists of sequence pairs whose members are so divergent with eachother that they rarely underwent recent gene conversion.
GENECONV cannot efficiently detect ancient homogenization whose tracts subsequently accumulated multiple substitutions. It is misleading to include such divergent pairs
when estimating the impacts of homogenization, althoughsuch pairs may be important when observing how thesequence divergence reduces the homogenization rate.
The presence/absence of a correlation between the‘‘homogenization prevalence’’ and the sequence similarity
must partly be due to this inclusion/exclusion of highly divergent pairs in their studies. The high false-negative rate of
GENECONV on highly similar pairs is another problem asmentioned above.

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Another inconsistency among previous studies is thecorrelation between the homogenization prevalence andthe physical distance. Ezawa et al. (2006) analyzed this correlation, but their study had two problems: 1) it justappealed to visual images and did not conduct statisticaltests to quantify the significance of the correlation; 2) it did
not take account of the dependence on the sequence
divergence. Although Benovoy and Drouin (2009) andMcGrath et al. (2009) did some statistical tests, their studies
also had problems. The most serious one is that both studies included highly similar gene pairs on which GENECONVshow extremely poor true-positive rate (e.g., ,20% of
homogenization detection as shown in table S1 of McGrathet al. 2009). This means that their analyses could have called
gene pairs as ‘‘negative’’ for homogenization when they had
actually undergone homogenization so intensively thatthey have kept high sequence similarities. Assuming thatsuch gene pairs tend to have small physical distances,it would not be surprising if they missed a negative correlation that actually exists between the homogenization rate
and the physical distance.
We would like to uncover the real biological propertiesof human paralog homogenization that might have been
hidden by artifacts in the previous studies. We thus analyzed human and macaque genome data under a settingin which artificial effects are well controlled. For comparison, we also reanalyzed mouse and rat genome data usedby Ezawa et al. (2006). Main features of our well-controlled
setting are the following (for details, see section A of supplementary materials and methods, Supplementary Material online): (i) Use of quartets for the homogenization detection(fig. 1);
 (ii) Use of moderately diverged duplicate pairs;
 (iii) Use of simulated quartets to estimate false-negative
rates;
 (iv) Use of the proportion of positive quartets rather thanthat of ‘‘positive gene pairs’’; (v) Use of logistic regression methods to estimate thestatistical significance of correlations.

Under such a well-controlled setting, we were able toquantify the degree of statistical significance of the dependence on the properties of duplicate pairs. This is the firststudy that took account of the effects of artifacts such as
false-negative rates, and the results shown in this paper are
therefore regarded as reliably reflecting the biologicaltrends of the homogenization susceptibility. This wellcontrolled setting combined with the logistic regression
analysis also enabled the comparison of homogenization
prevalence between primates and rodents after controllingfor various factors. We believe that this study will serve asa sound cornerstone that the future genome-wide analyses
on interlocus homogenization will be based on.
Another important and outstanding issue is the majormechanism that caused the observed homogenization of
duplicated genes in the genome. Ezawa et al. (2006)
assumed that gene conversion should be the dominant2153

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FIG. 1. Evolution of a duplicate pair with and without homogenization (A, B) and resulting informative sites in a quartet (C, D). This figure
illustrates the evolution of a gene in the common ancestor of two species (human and macaque as an example) that underwent duplication
before speciation, generating a quartet of genes, Human1, Human2, Macaque1, and Macaque2. Panel (A) shows the typical evolution ofa nucleotide in a quartet, where an ancestral nucleotide A is substituted with B after duplication and before speciation. This results in a type I
informative site, which clusters orthologous genes together (C). Panel (B) illustrates the evolution of a base involved in homogenization, wherea substitution of the nucleotide A with B occurs after the speciation, then homogenization propagates the base B to the intraspecies paralog(the horizontal dashed arrow). The resulting informative site is type II, which clusters intraspecies paralogs (D). NOTE.—A thick gray horizontal
bar denotes a gene, and a short black rectangle on the bar denotes the nucleotide site in question. In each tree, branches in solid lines give
trajectories of the nucleotide A, and branches in dotted lines give trajectories of the nucleotide B. An open diamond and an open circle
represent a prespeciation duplication event and a speciation event, respectively. An ellipse in a dashed line indicates that the genes are in thesame genome. A horizontal arrow in a dashed line denotes homogenization of a nucleotide site. In panels (C) and (D), the symbols ‘‘H1,’’ ‘‘H2,’’‘‘M1,’’ and ‘‘M2’’ stand for Human1, Human2, Macaque1, and Macaque2, respectively.

mechanism and referred to the homogenization as ‘‘gene
conversion.’’. In some cases, however, homogenization
seems dominated by unequal crossing-over (Eickbush
and Eickbush 2007), which also seems to be one of the driving forces of the ‘‘birth-and-death’’ model of gene family
evolution (Nei and Rooney 2005). Here we addressed thisissue by examining the pattern of regional phylogenetic
signals along the whole-gene alignment of each of ourhuman–macaque quartets. Our analysis indicated thatgene conversion seems to be the major cause of homogenization at least in our set of human–macaque quartets.

Materials and Methods
Statistical Tests to Detect Homogenization
The method to detect homogenization is essentially thesame as that described in Ezawa et al. (2006). The supplementary file of that paper (http://mbe.oxfordjournals.org/cgi/content/full/msj093/DC1) further elaborates on thedetection method. Briefly, our method makes full use ofan alignment of quartet sequences in which a paralogous
gene pair of two closely related species is used (fig. 1). Because of this choice of quartets, type II informative sites
indicating the clustering of intraspecies paralogs (fig. 1D)
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suggest homogenization. We use four statistical tests. 1)
The IScomp test examines the abundance of type II sites
in the quartet alignment. 2) The T2run test examines
whether or not the type II sites segregate from the typeI sites, which indicate the clustering of orthologous sequences (fig. 1C). 3) The SameTrun test, similar to the T2run test,is also used. 4) The CSrun test exploits the GENECONV program version 1.81 (Sawyer 1989; http://www.math.wustl.edu/;sawyer) to enhance the sensitivity of the whole
method. We also use the positive and negative control setsthat are generated by computer simulations of quartet evolutions with and without gene conversion, respectively. We
integrate the results of computer simulations and the four
statistical tests and made a final decision on whether the
subject quartet has undergone homogenization or not in
a sensitive yet specific way. We would like to call thismethod, originally proposed by Ezawa et al. (2006), as
4-2-4, for quartets are units of analysis, and two (positive
and negative) simulations are conducted, as well as fourkinds of statistical tests to detect homogenization.
Both sets of simulations to generate positive and negative control quartets were conducted in almost the same
frameworks as those described in the supplementary file ofEzawa et al. (2006), with such parameters as sequenceHomogenization between Primate-Specific Duplicated Genes · doi:10.1093/molbev/msq109

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divergences, base substitution matrices, average codon
compositions, and dN/dS ratios chosen randomly for eachquartet according to the distributions observed in the actualsets of quartets. We prepared two sets each of positive andnegative control quartets to emulate the actual sets of human–macaque and mouse–rat quartets. For each of thecontrol set, we generated quartet alignments of lengths
450 and 1200 bp. The former and the latter roughly approximate the median lengths of the shorter half and the longer
half of the actual quartet alignments, respectively. In order togenerate positive control quartets, we simulated gene conversion events whose frequency for each quartet in each species was chosen from Poisson distributions of the mean 1/2,
1, and 3/2 for human–macaque quartets, and those of themean 1, 2, or 3 for mouse–rat quartets. The length of eachgene conversion tract was determined according to the geometric distribution of the mean 200 bp. We excluded a quartet from the positive control set if none of its sequences werechanged by gene conversion after speciation.

Dependence of Homogenization Detection Rate on
Background Sequence Divergences andHomogenization Tract Length
In order to examine the dependence of homogenization
detection rate on quartet properties, we applied our
4-2-4 method and GENECONV version 1.81 (Sawyer
1989) to the sets of positive control quartets that simulate
the evolution of human–macaque and mouse–rat quartets
under the definite influence of homogenization (seeabove). We used the SILENT option for GENECONV
because the default NUCLEOTIDES option alone shows
unexpectedly high false-positive rates when the duplicate
sequences undergo different patterns of purifying selection(Ezawa et al. 2006). We took this as a serious problem
because the difference in selection patterns between duplicates is expected to be common in evolution after gene
duplication (Ohno 1970; Force et al. 1999; Lynch and Force
2000; Lynch and Katju 2004; Katju and Lynch 2006).
After applying the detection methods to positive control sets, we examined how true-positive detection rates
depends on quartet properties such as background sequence divergences, which are the divergences that would
have resulted if there had been no homogenization after
speciation, and the lengths of gene conversion tracts.
The divergences were calculated by counting the numberof substitutions that actually occurred in the simulation
and by dividing it by the sequence length, which always
equals the number of nongapped sites because we did
not incorporate insertions/deletions into the simulation.
We then put quartets into bins each defined by a rangeof property values, and estimated the detection rate foreach of the bins.

Peptide and cDNA Sequences as well as Their
Associated Information
We used human and rhesus macaque as the representatives of primates, whereas mouse and rat were used as
the representatives of rodents in this study. Figures 2A

FIG. 2. Evolutionary framework of this study. Shown here is the
species tree giving the background of this study. Vertical bars are
drawn roughly proportional to the time lengths of the corresponding branches (A) or to the corresponding neutral nucleotide
divergences (B). The quartets used in this study were created by
duplication events after the primates–rodents divergence andbefore the speciation of human–macaque or mouse–rat (thick graybranches). In panel A, figures on the right shoulder of each branching
point (an open circle) give a range of estimated dates of thedivergence event. A dashed box encompassing each branching point
displays a time window indicating the range of estimated dates.

and 2B show the phylogenetic relationships of these four
species in terms of divergence time and amount of nucleotide substitutions, respectively. It should be noted that thenucleotide substitutions between mouse and rat are almost
three times more than between human and macaque. The
primates/rodents divergence date and nucleotide divergence values are according to Springer et al. (2003). We referred to Pilbeam (1984), Martin (1993), Takahata and Satta(1997), Glazko and Nei (2003), Steiper et al. (2004), andSteiper and Young (2006) for the time of human–macaque
divergence. Regarding the estimated dates of mouse–rat
divergence, we consulted Jaegar et al. (1986), Jacobs andDowns (1994), Adkins et al. (2003), and Springer et al.
(2003). For the neutral nucleotide divergences, data given
in Rat Genome Sequencing Project Consortium (2004),
Lindblad-Toh et al. (2005), and Rhesus Macaque Genome
Sequencing and Analysis Consortium (2007) were used.
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We downloaded files of the gene transcript (cDNA) sequences and the peptide sequences predicted on mammalian and avian genomes from the FTP site (ftp.ensembl.org/pub) of the Ensembl database (Hubbard et al. 2007; http://
www.ensembl.org) version 43 (updated in February 2007).
We obtained data for the following species: human (Homosapiens, 43,738 peptides), rhesus macaque (Macaca mulatta, 36,546 peptides), mouse (Mus musculus, 32,241 peptides), rat (Rattus norvegicus, 33,745 peptides), dog (Canisfamiliaris, 25,568 peptides), cow (Bos taurus, 28,334 peptides), opossum (Monodelphis domestica, 32,690 peptides),platypus (Ornithorhynchus anatinus, 24,763 peptides),and chicken (Gallus gallus, 22,186 peptides). Sequencesof dog, cow, opossum, platypus, and chicken were used exclusively as outgroups. Mouse and rat sequences were usedas outgroups when collecting human–macaque quartets,and human and macaque sequences were used as outgroups when collecting mouse–rat quartets. As for cDNA
sequences, we only used those with peptide counterparts,
and removed cDNAs of the mitochondrial genes. The
genomic map of exons, exon–transcript relationship,transcript–gene relationship, and translation starts andends of the gene transcripts (cDNAs) were extracted from
the MySQL dumps, which in turn were fetched from theabove FTP site.

Collecting Human–Macaque Quartets and Mouse–
Rat Quartets
Using the cDNA sequences obtained as in the last subsection ‘‘Peptide and cDNA Sequences as well as Their Associated Information,’’ we collected genome-wide sets ofhuman–macaque quartets and mouse–rat quartets following a series of procedures largely similar to those describedin Ezawa et al. (2006). There are, however, a number of modifications to the technical aspects, such as the software andparameters used for the analyses. Briefly, we first retrieved
and screened macaque and rat ortholog candidates of human and mouse cDNAs, respectively, as well as other mammalian or chicken homologs as outgroup sequences. We
then searched for and screened human and mouse intraspecies paralogous pairs that are inferred to have duplicatedafter primates–rodents divergence and before the divergence of human–macaque and mouse–rat, respectively. Finally, we constructed the human–macaque and mouse–rat
quartets by combining these intraspecies paralogous pairs
and ortholog candidates, and retained only those quartets
whose phylogenetic trees indicate the clustering of orthologous pairs. We also removed redundancy due to alternative splicing by only keeping the quartet whose intraspecies
paralogous pair exhibits the highest score among the pairs
transcribed from each gene pair. These procedures are fully
described in sections B to E of the supplementary materials
and methods (Supplementary Material online).
After all these procedures, we finally obtained human–
macaque quartets and mouse–rat quartets that weregenerated by duplication events subsequent to theprimates–rodents divergence and predating the respective
speciation events (fig. 2). The sets of quartets are ‘‘nonre2156

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dundant’’ in the sense that they never contain two or more
cDNA pairs that are produced from the same gene pairs.
Moreover, the sets are devoid of evolutionary correlations
between quartets due to postspeciation duplication events.
Using these two sets of quartets as a basis, we conductedfurther analyses to detect and characterize homogenization
events. For further details, see sections D and E of supplementary materials and methods (Supplementary Material
online).
Here we would like to illustrate how our representative
quartets are constructed from two species X and Y (X-Y areeither human–macaque or mouse–rat for this study).
Because our targets are paralogous genes that duplicatedbefore the speciation, a family does not provide a quartet if
it consists only of genes duplicated after the speciation of
X and Y. Phylogenetic trees in fig. 3A and B are both consisting of three genes from species X and four genes from
species Y. Tree (A) experienced three duplications before
the speciation and yields three quartets. All the three quartets should be used for quartet analysis. Gene Y4 in this tree
is not involved in quartet because its ortholog, X4, is missing due to either gene loss (genuine evolutionary process)or sequencing/annotation error (artifact). Only one duplication event occurred before speciation and the others occurred after speciation in the phylogenetic tree (B). We canconstruct eight quartets out of this family shown in tree(B), and the redundant set contains all of them. These eightquartets as well as the homogenization signals on them are,however, historically correlated in an intricate manner due
to the postspeciation duplications. Our nonredundant set
is devoid of such historical correlation by choosing only one
representative (X1, Y2, X3, and Y3), in this case, out of theeight redundant quartets. The representatives are uniquely
constructed from the reciprocally best ortholog pairs.

Construction and Masking of cDNA Multiple
Alignments
We first constructed peptide alignments via the protein
mode of ClustalW version 1.83 (Thompson et al. 1994) withthe default setting. We transformed them into the cDNA
counterparts replacing amino acids with the correspondingcodons, guided by the translational information downloaded from the Ensembl FTP site. We masked dubiouslyaligned regions to reduce the risk of detecting false signalsof homogenization caused by misalignment. We also
masked CpG dinucleotides because they are often hypermutable (Ehrlich and Wang 1981) and therefore may causemuch more parallel substitutions than expected from naive
substitution models. It should be very rare, if ever, that themultiple alignments masked this way should display spurious signs of homogenization. Detailed information on themasking criteria are described in supplementary materials
and methods (Supplementary Material online).

Inference of Intraspecies Paralogous cDNA Pairs
That Have Undergone Homogenization
Our 4-2-4 method was originally designed to examine
whether either of the two intraspecies duplicate pairs inHomogenization between Primate-Specific Duplicated Genes · doi:10.1093/molbev/msq109

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FIG. 3. Illustration of our method to collect representative quartets. To illustrate our method to construct representative quartets, we gave two
fictitious gene trees, each consisting of three genes from species X and four genes from species Y. For simplicity, all the duplication events are
assumed to have occurred after the primates–rodents divergence (upper dashed horizontal line). Open circles tied with the lower dashedhorizontal line represent the speciation of species X and Y. An open diamond and a solid diamond denote a duplication event before
speciation and that after speciation, respectively. In tree (A), all duplication events occurred before the speciation, and we construct threequartets, all of which are regarded as representatives. We assumed that the ortholog of the gene Y4 in the species X is missing because of geneloss or some other reason. Because of this, the gene Y4 does not contribute to any quartets. Finally in tree (B), one duplication event occurred
before the speciation but the other duplication events occurred after the speciation. We can construct eight quartets from this family, and allof them are contained in the original redundant set. The nonredundant set, however, contains only one representative quartet out of the eight,
in order to avoid the historical correlation of homogenization signals.

a quartet underwent homogenization or not. As it is, our 42-4 method cannot distinguish which duplicate pair was
homogenized. In order to do so, another series of screening
processes are required. The method (Ezawa et al. 2006) basically examines the sequence divergence in the suspected
homogenization tract. Briefly, we judged that the human ormouse cDNA pair in a homogenization-positive quartet
was homogenized if either 1) GENECONV detected a tractof P , 0.05 in that pair, or 2) the best putative gene conversion tract in that pair showed a significantly smaller synonymous distance than the surrounding regions.

Correlations of Homogenization Susceptibility with
Properties of Gene Pairs
We classified the quartets according to their linkages, physical distances, and relative transcriptional orientations
extracted from the Ensembl MySQL dump, and calculated
the prevalence in each category. We defined the physicaldistance of a quartet as the geometric mean of the distances of the two intraspecies paralogous pair. The distance
between a pair of genes is defined as the length of the
genomic region between the coding regions of the genes.
We divided quartets of various physical distances (L) into
seven classes: L , 50 kb for class 1, 50 kb  L , 100 kb forclass 2, 100 kb  L , 200 kb for class 3, 200 kb  L , 400 kb
for class 4, 400 kb  L , 800 kb for class 5, and L  800 kb
for class 6, and unlinked class.

Statistical Tests on the Homogenization
Susceptibilities of Functional Categories
Functional categories were assigned to quartets in therefined nonredundant sets based on the results ofInterProScan (Zdobnov and Apweiler 2001; http://www.ebi.ac.uk/Tools/InterProScan/) version 4.0. We conducted
the following two-step statistical test on each of thehuman–macaque and mouse–rat sets. We first performed
Fisher’s exact test to see whether each functional category
is significantly more or less prone to homogenization than
average in the whole refined set of quartets. For the functional categories with either upper-tailed or lower-tailed
P , 0.05, we conducted the Cochran–Mantel–Haenszel
test (Cochran 1954; Mantel and Haenszel 1959) to control
for the effects of sequence divergences and physical proximities (see supplementary materials and methods, Supplementary Material online). Only those categories that
also had one-tailed P , 0.05 in the Cochran–Mantel–
Haenszel test were judged as prone or insusceptible tohomogenization.

Construction and Masking of the Multiple
Alignment of the Whole-Gene Sequences ina Quartet
We first fetched the chromosomal DNA sequences in thehuman and mouse genomes from the Ensembl FTP site
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Ezawa et al. · doi:10.1093/molbev/msq109(ftp.ensembl.org/pub/). Then, we extracted whole-gene sequences, which include both exons and introns, from thechromosomal sequences with the aid of the transcription
start and end coordinates extracted from the MySQL dumpof Ensembl database. The whole-gene sequences of eachquartet were aligned by the nucleotide mode of ClustalWversion 1.83 with parameters dnamatrix 5 CLUSTALW,
gapopen5 8, and gapext 5 0.01. In our experiences, thisparameter set was known to provide better alignments thandefault when long gaps are involved, which is often the casewith the whole-gene alignment of paralogous sequences.
The resulting whole-gene alignment was scanned for regions that appear dubiously aligned, and such sites weremasked and neglected in the subsequent analyses. We also
masked the CpG dinucleotides in order to reduce false signals of homogenization due to enhanced parallel substitutions. The final product was a masked alignment almost freefrom spurious signals of homogenization. Details of themasking procedure are provided in supplementary materials and methods (Supplementary Material online).

Tallying the Patterns of Phylogenetic Signals Alongthe Whole-Gene Alignments
In the whole-gene alignment of each human–macaquequartet, we tried to detect homogenized regions using aggregated type II sites as signals of homogenization. An‘‘aggregated’’ type II site is defined as a type II site that belongs to a cluster of C or more type II sites: 1) that are notinterrupted by type I sites and 2) whose members are notseparated from the neighboring member(s) by L 5 200 sites
or more. C is an adjustable parameter that we set 2 or 3
(See section K of supplementary materials and methods,
Supplementary Material online, for details). An aggregatedtype I site is defined similarly by swapping the roles of type I
and type II, and it indicates that the region did not undergo
homogenization after speciation. It is much less likely that
substitutions alone generate aggregated informative sites
than isolated informative sites. Aggregated informative
sites can therefore be robust signs of genuine phylogenetic
relationship or homogenization.
We assigned a pattern of phylogenetic signals as an arrangement pattern of type I and type II tracts along thewhole alignment. A type II tract is defined to be a clusterof aggregated type II sites not interrupted by any aggregated type I sites. An aggregated type I tract was defined
conversely to a type II tract.
We also examined the distributions of the upper bounds
and lower bounds of type II tract lengths. Details on theanalyses based on such type I and type II tracts aredescribed in section K of supplementary materials andmethods (Supplementary Material online).

Results
True Positive Detection Rates of our 4-2-4 Method
and GENECONVWe first examined the rates, or probabilities, thatour 4-2-4 method detects homogenization events that
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FIG. 4. Overall results of applying our 4-2-4 method and GENECONV
to positive control quartets. The four horizontal bar graphs show
the results of our 4-2-4 method and GENECONV applied to the39,413 positive control quartets mimicking actual human–macaquequartets (Hum/Mac) and to the 31,243 positive control quartets
mimicking actual mouse-rat quartets (Mus/Rat). In each horizontalbar, black, gray, and white rectangles represent the proportions of
extremely positive quartets satisfying P , 0.01%, moderately
positive quartets satisfying P , 0.5%, and negative, respectively.

actually occurred, and their dependence on the sequence
divergences and on the homogenization tract length. For
this purpose, we used sets of positive control quartets
generated by computer simulations of quartet evolution
under the influences of homogenization. Two sets wereprepared, one mimicking the set of human–macaquequartets and the other mimicking the set of mouse–
rat quartets. For comparison, we also examined thetrue-positive detection rates of GENECONV (Sawyer
1989) in a parallel manner.
Figure 4 shows the overall results. When applied to thehuman-macaque-emulating positive controls, our 4-2-4method showed the true-positive detection rates of
47.3% and 71.1% under the false-positive rates of 0.01%
and 0.5%, respectively. GENECONV, on the other hand,showed the true-positive rates of 9.8% and 29.4% under
the above false-positive rates. This clearly demonstrates
that 4-2-4 substantially outperforms GENECONV regarding
sensitivity. This conclusion holds also for the mouserat-emulating positive controls, with true-positive detection rates 65.8% and 83.3% for 4-2-4, and 19.1% and41.8% for GENECONV, under the false-positive rates of
0.01% and 0.5%, respectively (fig. 4).
Another conclusion is that detection rate is higher
against the mouse-rat-emulating positive controls thanagainst the human-macaque-emulating ones. This isprobably because the former have larger sequence divergences and therefore tend to show stronger signals (suchas type II sites) and backgrounds (like type I sites).
With our 4-2-4 method, the true-positive detection rate
for the mouse–rat data is about 1.2 times that for thehuman–macaque data. Hereafter in this subsection,
the true-positive detection rate is estimated under thefalse-positive rate of 0.5%.

Homogenization between Primate-Specific Duplicated Genes · doi:10.1093/molbev/msq109

We then examined the dependence on the averagebackground nucleotide divergence of orthologous pairs(see supplementary fig. S6, Supplementary Material online). Here the ‘‘background’’ means that the divergenceis estimated by excluding the effects of homogenization
after speciation. As the orthologous divergence increases,
the detection rates of our 4-2-4 method gradually increase, whereas that of GENECONV gradually decreases.
This is probably because the signal tends to be stronger as
the divergence increases, and because the increased rateof recent substitutions tends to disrupt signals (runs ofconcordant sites) for GENECONV more severely than signals (runs of type II sites) for 4-2-4. It should be noted thatour 4-2-4 method maintains fairly high detection rates of.50% across a wide range of orthologous divergence, except the leftmost class of orthologous divergence ,0.01
for human–macaque and ,0.04 for mouse–rat.
We next examined the dependence on the averagebackground nucleotide divergence of intraspecies paralogous pairs (supplementary fig. S7, Supplementary Material online). Here also our 4-2-4 method consistently
performs better than GENECONV throughout the conditions. The detection rate of 4-2-4 gradually increases as
the paralogous divergence increase, and it was greaterthan 50% across a wide range of paralogous divergences,
except the divergence ,0.08 for human–macaque and,0.12 for mouse–rat.
Dependence on the homogenization tract length was
also examined (supplementary fig. S8, Supplementary
Material online). As expected, the detection rate generally
increases as the tract length increases both for our 4-2-4method and GENECONV. Our 4-2-4 outperforms GENECONV through the whole range of tract lengths. Especially,
4-2-4 shows relatively high detection rates of around 50%
or more against homogenization of tract length between
50 and 200 bp, whereas GENECONV shows poor detection
rates against such homogenization.
Generally speaking, our 4-2-4 method outperformed
GENECONV under any conditions examined. It showed
the true-positive detection rate greater than 50% undera wide range of conditions except with small backgroundorthologous and/or paralogous divergences. This implies
that we could conduct relatively reliable correlation analyses after removing the quartets with small sequence
divergences. Another important observation is that thedetection rate of 4-2-4 shows quite similar dependence
patterns on the sequence divergences and on the homogenization tract length when comparing the results against
human–macaque and those against mouse–rat (see supplementary figs. S6–S8, Supplementary Material online).
This means that observed differences in the values orpatterns of homogenization prevalence between primates and rodents detected by 4-2-4, if any, are highly
likely to be true biological differences and not due toartifacts such as differences in false-negative rates. This
opens the possibility to conduct a fair comparison
between primates and rodents regarding susceptibility
to homogenization.

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Frequency of Homogenization: Overall Average and
Dependence on Paralogous Sequence Divergence
We obtained 730 human–macaque quartets and 1,604
mouse–rat quartets that are supposed to have been generated by the duplication after the primates–rodents divergence and before the respective speciation events (thickgray lines in fig. 2). Applying our 4-2-4 method for detecting
homogenization to the cDNA alignments of these quartets,
we found that 103 human–macaque quartets (14.1%) and
458 mouse–rat quartets (28.6%) are positive for homogenization under the false-positive rate of ca. 0.5%. We were
aware, however, that the above set of 730 human–
macaque and 1,604 mouse–rat quartets are redundantin the sense that more than one quartet can containthe same duplicate gene, whereas each duplicate pair iscontained only once in the sets. This could lead to underestimation or overestimation of the homogenization frequency due to overlaps in the histories of the quartets
containing the same genes (evolutionary correlation).
We addressed this issue by constructing a nonredundantset consisting of ‘‘representative’’ quartets that consist of
historically independent gene pairs. In this study, ourrepresentative quartets consist only of reciprocally best orthologous sequence pairs (see fig. 3 and the related text).
This choice has a bonus of retaining only authentic quartets
with type I evolutionary history. The resulting nonredundant set consists of 497 quartets for human–macaqueand 828 quartets for mouse–rat. Of them, 56 (11.3%)
human–macaque quartets and 260 (31.4%) mouse–rat
quartets were positive for homogenization. Supplementarytables S3 and S4 (Supplementary Material online) list information on all the quartets in these nonredundant sets ofhuman–macaque and mouse–rat quartets, respectively.
When a human–macaque quartet is positive for homogenization, either the human gene pair or the macaque genepair or both underwent homogenization. After conducting
additional tests to identify gene pairs that underwent homogenization, we estimated that 30 (6.0%) human genepairs and 179 (21.6%) mouse gene pairs were positivefor homogenization. However, these additional tests provide a source of extra false positives/negatives, which makes
it obscure how accurate the estimates of positive gene pairs
are. Thus, the homogenization prevalence will henceforth
be represented with the proportion of positive quartets,
which can be estimated more accurately than the proportion of positive gene pairs.
To see the influence of P value thresholds, we divided thecategory ‘‘positive’’ into two subcategories; extremely positive quartets with P , 0.01% and moderately positive
quartets with 0.01% , P , 0.5%. The numbers of extremely
positive quartets in the nonredundant sets were 25 (5.0%)
for human–macaque and 137 (16.5%) for mouse–rat.
These results indicate that, on average, mouse–rat quartetsare more than three times as prone to homogenization as
human–macaque quartets, and the ratio is almost uninfluenced by the false-positive rate employed.
2159

Ezawa et al. · doi:10.1093/molbev/msq109

We then examined how the homogenization prevalence
depends on the average nucleotide divergence between
intraspecies paralogs. When we estimated the paralogous
divergence of each quartet from the whole coding sequence (CDS) alignment, the prevalence exhibited a strong
negative correlation with the paralogous divergence (supplementary fig. S9, Supplementary Material online). This is
not surprising, because homogenization by definition reduces the divergence between intraspecies paralogs. In order to see whether less divergent paralogous pairs are more
prone to homogenization, we have to examine the dependence on the background paralogous divergence by eliminating the impact of homogenization on the sequence
divergence. We approximated such background sequence
divergence with the sequence divergence estimated only
from the regions whose informative sites are all type I.
The correlation did disappear between the homogenization prevalence and the paralogous divergence (supplementary fig. S10, Supplementary Material online). The P
value of the sequence divergence dependence was 0.99
for human–macaque and 0.29 for mouse–rat under the
likelihood ratio test (LRT) with a linear logistic regression
model.
However, the strange dependence patterns indicate thatsome artifacts may be involved. The sudden reduction of
the homogenization prevalence at small paralogous divergence (,0.075 for human–macaque, and ,0.010 formouse–rat in supplementary fig. S10, Supplementary Material online) may be due to the poor true-positive detection rate at small paralogous divergence (,0.08 forhuman–macaque, and ,0.12 for mouse–rat; see supplementary fig. S7, Supplementary Material online). Anotherpossible cause would be the ‘‘sample exclusion bias,’’ where
we retain quartets with small paralogous divergences only
when they did not undergo detectable homogenization, because such quartets could erroneously exhibit type II phylogenetic relationships if they experienced homogenization.
The anomalous surge of the homogenization prevalence atlarge paralogous divergences (supplementary fig. S10, Supplementary Material online) may also be due to ‘‘sample
inclusion bias,’’ where we erroneously include paralogous
pairs that duplicated before the primates–rodents divergence, when they underwent intense homogenization recently, because such pairs would show smaller sequence
divergences than expected from their actual ages.
In order to mitigate such artificial effects, we further
refined our nonredundant set of quartets by discarding
quartets with: a) low background orthologous divergences (,0.01 for human–macaque and ,0.04 formouse–rat); b) low background intraspecies paralogous
divergences (,0.08 for human–macaque and ,0.12 formouse–rat); or c) high background synonymous distances between intraspecies paralogs (significantly higher
than 0.28 for human–macaque and 0.55 for mouse–
rat). The conditions (a) and (b) were set according to
the analyses on the positive control quartets as described in the previous subsection, and the condition(c) conforms to the phylogenetic screening for duplica2160

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FIG. 5. Dependence of the positive rate on the intraspecies
paralogous divergence estimated from the type I regions (afterrefinements). In each panel, the bar graph shows the numbers ofquartets classified by the nucleotide divergence between intraspecies paralogs, and the line graph gives the dependence of thepositive rate on the paralogous divergence. The nucleotide
divergence was estimated from the regions whose informative sites
are all type I, expected to approximate the ‘‘background’’ divergence
without the effects of postspeciation homogenization. These are theresults after the refinement (see text for details). The panel A is forhuman–macaque quartets, and B is for mouse–rat quartets.

tion events after the primates–rodents divergence(supplementary materials and methods, Supplementary
Material online).
The frequencies of positives under these refined setsof quartets at the false-positive rate 0.5% became 10.0%(43 of 430) and 29.8% (206 of 691) for human–macaqueand mouse–rat, respectively. Under the false-positive rateof 0.01%, the proportion of positive quartets was 4.0% (17of 430) for human–macaque and 14.2% (98 of 691) formouse–rat. These refined quartet sets showed negative
correlations between the homogenization prevalence
and the background paralogous divergence, as shown infig. 5; P 5 0.0014 for human–macaque and 0.017 formouse–rat under the LRT based on a linear logistic regression model. This is consistent with the past studies thatreported that the sequence divergence tends to hamper
paralog homogenization (Liskay et al. 1987; Lukacsovich
and Waldman 1999; Benovoy and Drouin 2009). Figure
5 also suggests that rodent paralogs are more prone tohomogenization than primate paralogs even after controlling for the sequence divergence.

Homogenization between Primate-Specific Duplicated Genes · doi:10.1093/molbev/msq109

Dependence of Homogenization Prevalence on
Physical Proximity between Intraspecies Paralogs
We examined the issue of dependence of homogenization
prevalence on physical proximity by using our refined
nonredundant sets of quartets and a powerful statisticalanalysis tool, namely logistic regression analyses. First, we
divided each of the two refined sets of quartets into sevencategories according to the mean physical proximity between intraspecies paralogs, and estimated the homogenization prevalence in each category, for human–macaqueand mouse–rat quartets. Figure 6 and supplementarytable S5 (Supplementary Material online) indicate thatthe prevalence of homogenization positively correlates withphysical proximity between intraspecies duplicate copies
both for human–macaque and mouse–rat quartets. Thisis consistent with the result of Ezawa et al. (2006), although
their result was devoid of statistical support or consideration of the sequence divergence. In order to take care ofthese problems, we introduced logistic regression analyses(e.g., Sokal and Rohlf 1995; Agresti 2007). Here, we employ
the following regression model for the factor dependenceof the logit, log(p/(1  p)), of the positive rate p:½logðp=ð1  pÞÞ;Const þ logðphdÞ þ Pdiv þ Pdiv2
þ logðphdÞ  Pdiv þ logðphdÞ  Pdiv2 ;where ‘‘Const,’’ ‘‘phd,’’ and ‘‘Pdiv’’ denote constant,physical distance, and paralogous divergence, respectively,and ‘‘’’ denotes an interaction. A more detailed description of the model is in section H of supplementary materials and methods (Supplementary Material online). We will
henceforth omit the symbol ‘‘Const’’ if there are otherterms, because its presence is obvious. In order to examine
the significance of the contributions from each term, we
conducted an LRT that compares an alternative modelwith the term in question and a null model without theterm. A series of hierarchical LRTs is illustrated in supplementary fig. S12 (Supplementary Material online) using therefined set of human–macaque quartets as an example.
The results of LRTs are summarized in table 1, whichshows the statistical significance of the trend represented
by each of the terms. It clearly demonstrates that the negative correlation between the homogenization prevalence
and the mean physical distance is statistically significant
even after controlling for the effects of the paralogous sequence divergence. As for the dependence on the paralogous divergence, the logits of raw nonredundant quartet
sets showed a quadratic dependence and the logits of refined sets showed a linear dependence, both of which are
weakly significant even after controlling for the effects ofphysical distance.
The positive correlation between the homogenization
prevalence and the physical proximity indicated by fig. 6
and supplementary table S5 (Supplementary Material online) also includes the trend that linked duplicate pairs are
more prone to homogenization than unlinked pairs. This
trend was also reexamined by taking account of the effects

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of paralogous nucleotide divergence via the following logistic regression model:
Pdiv þ Pdiv2 þ Lnk þ Lnk  Pdiv þ Lnk  Pdiv2 ;where ‘‘Lnk’’ represents a linkage status indicator, whichequals 0 for a linked pair and 1 for an unlinked pair. The
results of hierarchical LRTs under this model show highly
significant (P , 0.1%) contributions of the Lnk term (supplementary table S6, Supplementary Material online). This
indicates that linked pairs are more prone to homogenization than unlinked pairs even after controlling for the effectof nucleotide divergence, thus confirming the claims madeby three studies on mammalian interlocus homogenization(Ezawa et al. 2006; Benovoy and Drouin 2009; McGrathet al. 2009).

Rodent Paralogs are More Prone to
Homogenization than Primate ones
The overall prevalence of homogenization for the rodent
quartets (29.8%) is about three times higher than thatfor the primate quartets (10.0%). Visual comparison of
the dependence on the paralogous divergence (fig. 5) implies that this is not due to different divergence distributions for these two sets of quartets. Bar graphs in fig. 6 andsupplementary table S5 (Supplementary Material online),
in contrast, indicate that the set of human–macaque quartets is richer in ‘‘physically distant’’ quartets (distance 
800 kb and unlinked) than the mouse–rat set. This may
partly explain why the primate quartets appear less prone
to homogenization.
When comparing the primate quartets with the rodent
ones in the same classes of the physical proximity, however,the latter is still richer in positive quartets, and the difference in the prevalence appears to be significant for fourclasses (fig. 6 and supplementary table S5, Supplementary
Material online). Three classes showing remarkable differences merge together to accommodate linked quartets ofphysical distances greater than 200 kb, with positive rate
less than 10% in human–macaque and more than 20%
in mouse-rat. Differences in these categories are fairly significant (P , 0.5% in Fisher’s exact test) and makes theprimate and the rodent sets of quartets appear quite different concerning the dependence of the homogenization
prevalence on the physical distance. Whereas the mouse–
rat quartets can be homogenized considerably even if the
distances exceed 800 kb, the human–macaque quartets become relatively immune to gene conversion even with the
distance as short as 200 kb (fig. 6). The other significant
difference, observed in the class of distance less than 50
kb (with P 5 0.28% for the raw set and 3.0% for the refinedset), indicates that even short-range homogenization
occurs at a significantly higher frequency in the rodent
genome than in the primate genome.
The above observations suggest that such taxonomic
difference in the homogenization susceptibility should holdeven after controlling for the dependence on the sequence
divergence and physical distance. To confirm this2161

Ezawa et al. · doi:10.1093/molbev/msq109

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FIG. 6. Positive rates and numbers of quartets in seven categories of physical proximity between intraspecies duplicates (refined sets). In eachpanel, the bar graph and the line graph show the number of quartets and the proportion of positives, respectively, of each category of physicalproximity. We used only those quartets in each of which two intraspecies paralogous pairs have the same linkage status. Panel A is for human–
macaque and panel B is for mouse–rat. ‘‘Unlinked’’ means that two genes in each intraspecies paralogous pair are on different chromosomes.
A linked quartet is composed of two intraspecies paralogous pairs each consisting of two genes on the same chromosome. The physicaldistance of such quartet is defined as the geometric mean of the physical distances of two intraspecies paralogs, each of which in turn is thenumber of base pairs between the coding sequences of the duplicate genes. The six classes of linked quartets are labeled as follows according to
the physical distance (L): class 1 for L , 50 kb, class 2 for 50 kb  L , 100 kb, class 3 for 100 kb  L , 200 kb, class 4 for 200 kb  L , 400 kb,
class 5 for 400 kb  L , 800 kb, and class 6 for 800 kb  L. Here we show the results on the refined nonredundant sets of quartets. The results
are almost the same even if we used the raw nonredundant sets of quartets (supplementary fig. S11, Supplementary Material online).

expectation, we performed a logistic regression analysis
using the model:logðphdÞ þ Pdiv þ Pdiv2 þ Spe þ Spe  logðphdÞ
þ Spe  Pdiv þ Spe  Pdiv2 ;where ‘‘Spe’’ denotes the species indicator that equals 1 fora human–macaque quartet and 0 for a mouse–rat quartet.
2162

The results of the hierarchical LRTs for this model are summarized in table 2, and indicate that the ‘‘constant’’ contributions to the homogenization prevalence differ in a highly
significant manner (P , 1  107) between primates androdents, whereas the differences in the dependence on
the physical distance and on the nucleotide divergence
are negligible or marginally significant at best (table 2).
Thus, there is definitely a significant difference in theHomogenization between Primate-Specific Duplicated Genes · doi:10.1093/molbev/msq109
Table 1. Summary of LRTs with the logistic regression models to
examine dependence on the physical distance.
P valuesTermlog (phys-dis)para-div
para-div2**
log (phys-dis) 3 para-divlog (phys-dis) 3 para-div2**

Human–macaque
1.3 3 104***
0.013*
0.40
0.88
0.029

Mouse-rat
1.0 3 1024***0.011*
0.71
0.25
0.15

NOTE.—Results of hierarchical LRTs as shown in supplementary fig. S11(Supplementary Material online) are summarized. In this analysis, we used only
quartets that belong to the refined nonredundant set and both of whose
intraspecies paralogous pairs are linked. phys-dis, physical distance between twoparalogous genes; para-div, paralogous divergence.
***Significant at 0.02%.
*Significant at 2%.

homogenization frequency in the rodent genome and in the
primate genome.

Dependence of Homogenization Prevalence on
Relative Orientation
One of the main results of Ezawa et al. (2006) is that the
proportion of positive cDNA pairs in mouse does not show
a significant correlation with relative transcriptional orientations. Here, we readdressed this issue by taking account of
sequence identities via a logistic regression analysis. We
only used quartets each having the two intraspecies paralogous pairs with the same relative orientation. We first examined the homogenization prevalence for each relativeorientation (table 3). The result indicates that the prevalence does not differ significantly among relative transcriptional orientations, both in the human–macaque and themouse-rat sets (upper-tailed P values: 0.54 for ‘‘head-to-tail’’vs. the rest, 0.80 for head-to-tail vs. ‘‘head-to-head,’’ and 0.31
for head-to-tail vs. ‘‘tail-to-tail,’’ in Fisher’s exact tests applied to the refined sets of human–macaque quartets;
see supplementary table S7, Supplementary Material online
for the other sets of quartets and lower-tailed P values).
As a confirmation, we also controlled for the dependence on the paralogous nucleotide divergence and thephysical distance using the following logistic regression
model:
logðphdÞ þ Pdiv þ Pdiv2 þ Ori
þ Ori  logðphdÞ þ Ori  Pdiv þ Ori  Pdiv2 ;Table 2. Summary of LRTs with the logistic regression models to
examine the difference between primates and rodents.
Term
SpeciesSpecies 3 log (phys-dis)Species 3 para-div
Species 3 para-div2**

P value
4.7 3 10210
0.25
0.33
0.045

NOTE.—Results of hierarchical LRTs to examine the difference between primatesand rodents are summarized. We used only those quartets whose paralogous
pairs are both linked. Tests were conducted on a compound set consisting of therefined sets of nonredundant human-macaque and mouse-rat quartets. Species,
a species indicator, which equals 1 for primates and 0 for rodents; phys-dis,
physical distance between two paralogous genes; para-div, paralogous divergence.

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Table 3. Statistics of Quartets with Different Relative Transcriptional Orientations.
Orientationa
Head-to-tailHead-to-head
Tail-to-tailTotal

Illustrationb Human–Macaque
Mouse–Rat—> —>19/133 (14.3%)c 134/391 (34.3%)<— —>9/50 (18.0%)
18/63 (28.6%)—> <—6/58 (10.3%)
24/63 (38.1%)
34/241 (14.1%) 176/517 (34.0%)

NOTE.—We used only quartets whose intraspecies paralogous pairs show thesame relative orientations.
a
The key for relative transcriptional orientations: head-to-tail: 5#–3# 5#–3#, headto-head: 3#–5# 5#–3#, tail-to-tail: 5#–3# 3#–5#.
b
In this row, a dashed arrow represents the transcriptional orientation of a gene,with the tail and the head of the arrow representing the 5# and 3# ends,
respectively.
c
Ineach cell of the first and second columns, the numbers represent: #{positives}/#{quartets} (proportion of positives) in the category considered.

where ‘‘Ori’’ represents an indicator of the relative orientation, which equals 0 for direct (or head-to-tail) pairs
and 1 for inverted (or head-to-head or tail-to-tail) pairs.
The results of hierarchical LRTs (supplementary table S8,
Supplementary Material online) shows that there is no significant contribution involving the relative orientation for
any of the quartet sets examined. Therefore, this ‘‘lack ofdependence on relative orientation’’ seems to be a genuine
biological property that is common to primates and rodents, and maybe across mammals.

Functional Categories Significantly More or Less
Prone to Homogenization
Ezawa et al. (2006) found quite a few functional categories
that are significantly more or less prone to homogenization
than the whole set of mouse cDNA pairs. In this study, weapplied the same analysis to the refined nonredundant setsof human–macaque and mouse–rat quartets. After controlling for the effects of the physical proximity and family
size (see Materials and Methods), four functional categories, olfactory receptor, Zn-finger B-box, Spla/ryanodine receptor, and guanylate-binding protein, were significantly
prone to homogenization for human–macaque, whereas
no categories were found to be significantly less prone
to homogenization than the whole set (table 4).
The lack of homogenization-insusceptible categories
in human may be due to the relatively small number(43) and small proportion (10.0%) of refined nonredundant
human–macaque quartets positive for homogenization.
Comparing the human–macaque result with that on our
new set of mouse–rat quartets (table 4), we found no functional categories that are significantly more or less susceptible to homogenization both in the human–macaque andmouse–rat sets. This may suggest different patterns of selective pressures between primates and rodents. For example, olfactory receptors are more prone to homogenization
than average in human–macaque, whereas they do notshow significantly deviated homogenization susceptibility
in mouse-rat. This may be due to purifying selections against
homogenization between rodent olfactory receptors because the olfaction is essential for rodents but not for primates. Family-wise analyses might reveal such differences
between primates and rodents in terms of gene functions.
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Table 4. Functional Categories that are Significantly More or Less Prone to Homogenization than Average even after Controlling for Sequence
Divergence and Physical Proximity.
Domain or Family Name
More prone to homogenization in the human–macaque quartets
Olfactory receptor (IPR000725)
Zn-finger, B-box (IPR000315)
Spla/ryanodine receptor SPRY (IPR003877)
Guanylate-binding protein (IPR003191)
Less prone to homogenization in the human–macaque quartets
None
More prone to homogenization in the mouse–rat quartets
Cytochrome P450 (IPR001128)
Cadherin (IPR002126)
Less prone to homogenization in the mouse–rat quartets
Mammalian taste receptor (IPR007960)
Immunoglobulin V-type (IPR003596)
Vomeronasal receptor type 1 (IPR004072)

Total

Positive

P valuea

12
3
6
2

8
3
4
2

0.013
9.5 3 1027
0.0013
0.027

17
5

14
5

7.1 3 1026
0.0030

9
19
62

0
0
9

0.00550.0062
0.017

NOTE.—InterPro IDs assigned to family or domain names are shown in parentheses. Statistics in this table are for the refined nonredundant sets of quartets. Here, we only
show functional categories that are significantly more or less prone to homogenization even after controlling for the effects of their sequence divergences and physicalproximities.
a
P value in the Cochran–Mantel–Haenszel test (Cochran 1954; Mantel and Haenszel 1959) that controls for the effects of sequence divergences and physicalproximities.

Which is Dominant, Unequal Crossing-Over orGene Conversion?
Although we have examined the prevalence and various
properties of homogenization detected in our sets of quartets, so far we have never discussed the mechanisms responsible for it.
Two mechanisms are commonly believed to result in thehomogenization of duplicate genes, one is unequal crossing-over (Smith 1976; Ohta 1976) and the other is geneconversion (Jeffreys 1979; Slightom et al. 1980; Ohta
1985; fig. 7). When occurring at an intergenic site, unequalcrossing-over just changes the number of duplicate copies(Ritossa and Scala 1969; Schalet 1969). When occurring at
an intragenic site, however, it can generate a ‘‘chimeric’’
gene (Donohoue et al. 1989; Lifton et al. 1992; Hampf
et al. 2001; Lee et al. 2002; Ezquieta and Luzuriaga 2004),
and leads to regionally inconsistent phylogenetic signalsthat can be detected by our 4-2-4 method (fig. 7A). The
resulting pattern is expected to be chimeric, consistingof a few relatively long regions containing mostly type I
or mostly type II informative sites that are segregated fromthe other type. A gene conversion event, on the other hand,inherently homogenizes a region or a ‘‘tract’’ of duplicate
genes because a tract of a ‘‘donor’’ gene overwrites a homologous region of an ‘‘acceptor’’ gene. This generally results ina ‘‘patchy’’ pattern of regional phylogenetic signals where
short tracts of type II informative sites are dispersed in the
background of type I informative sites (Kawamura et al.
1992; Kitano and Saitou 1999; Hurles 2001; Winter andPonting 2005; fig. 7B).
Based on this theoretical consideration, we tried to determine the dominant cause of homogenization detectedin our 497 nonredundant human–macaque quartets, by
examining the patterns of phylogenetic signals thewhole-gene alignments display (table 5). In order to reduce
false-positive signs of homogenization, the analysis was
conducted under two conditions, C 5 2 and C 5 3, where2164

C denotes the minimum number of informative sites of thesame type in a run that defines a phylogenetic signal (seeMaterials and Methods for details). Out of the 497 wholegene alignments, 94 and 45 displayed patterns indicatinghomogenization under the conditions C 5 2 and C 5 3,
respectively (table 5). Among the alignments indicating homogenization, only 21 (22%) under C 5 2 and 15 (33%)
under C 5 3 showed the pattern ‘‘12,’’ ‘‘21’’ or ‘‘212,’’ which
can be explained either as unequal crossing-over or as geneconversion with the same minimum number of events. The
remaining 73 (78%) under C 5 2 and 30 (67%) under C 5 3,
however, require more unequal crossing-over events thangene conversion events if we explain the pattern with theformer events alone. It is therefore difficult to explain thestatistics obtained here only by means of unequal crossingover. This indicates that gene conversion should be involved to a considerable degree in the homogenizationof most of the quartets showing regional inconsistencyof phylogenetic signals.
Actually, the frequency of the cases involving only unequal crossing-over events are further reduced if we notethat unequal crossing-over is unlikely to occur between duplicate genes that are unlinked or separated by a long distance. By an analysis detailed in section O of supplementarymaterials and methods (Supplementary Material online),
we estimate that at least 89% (C 5 2) or 84% (C 5 3)of homogenized quartets underwent gene conversion.
In order to pursue this issue further, we also obtained
distributions of the lengths of type II tracts detected inour 497 human–macaque quartets (table 6; see Materials
and Methods). From these distributions, we estimated thatat least 86% (119/138) and 72% (46/64) of the tracts detected under the conditions C 5 2 and C 5 3, respectively,
are estimated to have involved gene conversion in theirgeneration. The detailed arguments are described in section P of supplementary materials and methods (Supplementary Material online).

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Table 5. Distributions of the Patterns of Phylogenetic Signals
Exhibited Along the Whole-Gene Alignments of the 497
Nonredundant Human–Macaque Quartets.
Phylogenetic PatternaNone1
2
Nonhomogenizedc
12 or 21212
121
1212 or 2121
Five or more blocksd
Homogenizede
Total

C 5 2b
16
383
4
403
19
2
52
4
17
94
497

C 5 3b
19
429
4
452
15
0
20
3
7
45
497

a
1, a block of type I sites signaling the normal phylogenetic relationship, namely
the clustering of orthologous genes; 2, a block of type II sites signaling
homogenization of the duplicate genes from the same species; None, noinformative sites providing phylogenetic signals under the condition considered.
Details on how to determine the phylogenetic pattern is described in Materials
and Methods.
b
The number of alignments showing each pattern of phylogenetic signals under
the specified condition. C, the minimum number of ‘‘aggregating’’ informative
sites required for defining a phylogenetic signal. For more details on theconditions, see Materials and Methods.
cSubtotal for the patterns NOT indicating homogenization.
d
The union of quartets showing phylogenetic patterns composed of five or more
blocks of type I or type II informative sites.
e
Subtotal for the patterns indicating homogenization.

Discussion
FIG. 7. Schematic illustration of unequal crossing-over, geneconversion, and resulting homogenization patterns. (A) Unequalcrossing-over at an intragenic site creates a chimeric gene (‘‘Hb-a’’ inthis case). It displays a long-extended homogenization, or a regionalinconsistency of phylogenetic signals. (B) Gene conversion generatesa ‘‘patchy’’ pattern of homogenization. Although for simplicity weonly considered one-way gene conversion here, gene conversion canbe both ways in actual situations.
NOTE.—A black solid rectangle and an open (white) rectangle linked
by a thin line represent a pair of duplicated genes. A chimeric geneis represented by a rectangle with a mixture of white and blackbands. ‘‘Hxxx,’’ and ‘‘Mxxx’’ represent a human gene and a macaquegene, respectively. ‘‘1’’ and ‘‘2’’ in these panels represent a type I site
and a type II site, respectively. In each phylogenetic tree, an open
diamond, an open circle, and a black solid diamond represent
a prespeciation duplication event, a speciation event, anda postspeciation duplication event, respectively. A gray solid
diamond in panel B denotes homogenization caused by geneconversion.

To eliminate the effects of boundaries, we also obtained
distributions of type II tract lengths for different patterns ofphylogenetic signals (supplementary table S11A and B,
Supplementary Material online), and focused on the pattern 121, which accounts for about a half of the quartets
displaying homogenization (table 5). As expected, type II
tracts shorter than 500 bp accounted for a majority inthe pattern 121 (supplementary table S11A and B, Supplementary Material online). Judging from these evidences,a majority of homogenization events in our set ofhuman–macaque quartets are likely to be due to gene conversion rather than unequal crossing-over, at least in termsof the number of events.

Comparison of Homogenization between Human–
Macaque and Mouse–Rat Quartets
In this study, we collected a nonredundant genome-wide
set of 497 human–macaque quartets and another of 828
mouse–rat quartets under comparable conditions and estimated the prevalence of homogenization by applying the4-2-4 method (Ezawa et al. 2006) to the nonredundant setsof quartets. In the statistical analyses, we did our utmost to
mitigate the effects of artifacts by preparing refined sets ofquartets on which the false-negative rate is at most below
50%. With such refined nonredundant sets, we detected
signs of homogenization between paralogous proteincoding regions in 43 (10.0%) of 430 human–macaquequartets and 206 (29.8%) of 691 mouse–rat quartets. Taking account of the average true-positive rate of 71.1%
for human–macaque and 83.3% for mouse–rat, we canestimate the ‘‘per-paralog pair’’ prevalence to be 7.3%
(5 1  (1  0.100/0.711)½) for primates and 19.9%(5 1  (1  0.298/0.833)½) for rodents, if we assume
the independence of homogenization in different species,
uniformity of the prevalence, and the equal prevalence inthe two closely related species. The actual per-paralog-pairprevalence may be slightly higher because the above assumptions do not usually hold. In the refined nonredundant sets of both primates and rodents, the prevalenceof homogenization showed 1) significantly negative correlations with the nucleotide divergence between intraspecies paralogs (fig. 5), 2) significantly positive correlations
with the physical proximity (fig. 6) even after controlling
for the effects of sequence divergence (table 1), and 3)
no significant dependence on the relative transcriptional
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Ezawa et al. · doi:10.1093/molbev/msq109
Table 6. Distributions of the Lengths of Type II Tracts.
Length
(bp)
1–9
10–49
50–99
100–299
300–499
500–999
1000–1999
2000–2999
3000–9999
10000–
Total

C52(Lower)
17
36
19
29
11
12
5
3
4
2
138

C52
(Upper)
0
7
4
24
19
17
17
5
15
30
138

C53
(Lower)
0
10
6
20
8
8
7
1
3
1
64

C53
(Upper)
0
0
0
8
5
10
15
2
7
17
64

NOTE.—The lengths of type II tracts in our 497 nonredundant human–macaquequartets were tallied. A ‘‘tract’’ is a merged set of neighboring clusters of
aggregated informative sites of the same type. All distributions were obtained
under L 5 200, where L is the upper bound of the distance between informative
sites belonging to the same cluster. C is the minimum number of aggregatedinformative sites that defines a cluster. ‘‘Upper’’ and ‘‘Lower’’ are the upper boundand the lower bound, respectively, of the tract length.

orientation (table 3; supplementary table S7, Supplementary Material online), which holds true even after controlling for the effects of sequence divergence and physicaldistance between intraspecies paralogs (supplementarytable S8, Supplementary Material online).
Although similar analyses were conducted previously regarding the frequency of homogenization in mammalianparalogs (Ezawa et al. 2006; Benovoy and Drouin 2009;
McGrath et al. 2009), these analyses did not take accountof either the effect of sequence divergence or the effect offalse negatives, and therefore the conclusions varied inthese previous studies. In this study, we conducted refined
statistical analyses, applying logistic regression analyses tothe refined sets of quartets. The results of these refined statistical analyses supported the result of Ezawa et al. (2006)
and opposed the results of the two recent studies (Benovoy
and Drouin 2009; McGrath et al. 2009) concerning the dependence on the physical distance. Regarding the correlation on the paralogous sequence divergence, the analysis
supported the conclusion of Benovoy and Drouin(2009), which is also consistent with the past experiments
and data analyses (Liskay et al. 1987; Lukacsovich andWaldman 1999). To the best of our knowledge, this study
is the first to take account of both sequence divergence andfalse-negative rate. We are therefore confident that ourconclusions are reliable and robust against artifacts, thus
serving as a sound basis for future genome-wide analysesof homogenization between duplicates.
We also compared primates and rodents in terms of net
homogenization susceptibility. Both lineages shared thepositive correlation with the physical proximity as well
as no significant correlation with relative orientations. Regarding the overall prevalence, the mouse–rat quartets
seem to be about three times more prone to homogenization than the human–macaque quartets (29.8% vs. 10.0%
in prevalence). The bar graphs in fig. 6 imply that this disparity is partly explained by the tendency of the primate
gene pairs to be farther apart than the rodent pairs. We
plan to discuss why the distributions of physical distances2166

between paralogs are different in primates and in rodents(Ezawa K, Ikeo K, Gojobori T, Saitou N, in preparation).
Even within the same category of physical proximity, however, the human–macaque quartets still seem less prone tohomogenization than mouse–rat quartets (fig. 6), which issignificant in four of the seven categories (supplementarytable S5, Supplementary Material online). Especially,
remarkable disparities were observed in the three subsets
containing quartets with paralogous distance .200 kb.
It would be interesting to pursue what caused the huge
difference in these distance classes. By a logistic regression
analysis, we found that the difference between primatesand rodents mainly comes from the constant term andis significant even after controlling for the effects of sequence divergence and physical distance between intraspecies paralogs (table 2). Because the analysis was conductedon the refined sets of nonredundant quartets, the effects ofartifacts such as differences in false-negative rates should be
negligible, if any.
Theoretically, the prevalence disparity of ca. 3-fold on
average and 1.5-fold to 3-fold category-wise between primates and rodents may be reasonable if we consider themas the ratio of the evolutionary distance between mouse andrat to that between human and macaque, as measured by
the frequency of gene conversion. The neutral theory ofmolecular evolution (Kimura 1968, 1983) predicts thatthe evolutionary distance is roughly proportional to thenumber of generations separating the two lineages. If we
use the genome-wide estimate of the neutral sequence distance as a measure of the evolutionary distance, the distance of 0.174 between mouse and rat (Rat Genome
Sequencing Project Consortium 2004) is 2.7 times as large
as the distance of 0.065 between human and macaque(Rhesus Macaque Genome Sequencing and Analysis Consortium 2007). Therefore, if the rate of homogenization follows the prediction of the neutral theory, and if otherconditions are identical between the two lineages, the prevalence of homogenization in mouse–rat should be about 2.7
times higher than in human–macaque. The observed 3-folddifference in the overall prevalence well matches thistheoretical prediction based on the neutral sequence
divergence.
We also examined the dependence of homogenization
prevalence on functional categories. After removing the effects of physical proximity and sequence divergence, nofunctional categories are significantly more or less prone
to homogenization in both primates and rodents sets ofduplicates (table 4). This may indicate different selection
patterns on these two lineages.

Signals of Homogenization in the Whole-GeneAlignments of Quartets
In this study, we examined the pattern of phylogenetic signals exhibited by the whole-gene alignment of each quartet. The observed distributions of patterns (table 5), as wellas the distributions of type II tract lengths (table 6), seem tobe better explained by gene conversion rather than byunequal crossing-over. The proportion of gene conversion

Homogenization between Primate-Specific Duplicated Genes · doi:10.1093/molbev/msq109

further increased after considering that unequal crossingover occurs very rarely, if any, between unlinked or physically distant duplicates. This, along with the fairly highprevalence of homogenization (10.0% for primates and29.8% for rodents), indicates that gene conversion is ubiquitous and can have nonnegligible impacts on the evolution of duplicate genes at least in mammalian genomes.

Results Conflicting with Previous Studies
Some of our results reported in this paper considerably disagree with the conclusions made by Benovoy and Drouin(2009) and McGrath et al. (2009). In the Introduction, we
discussed potential problems in their data sets and methods of homogenization detection when examining the dependence of homogenization prevalence on nucleotide
divergence or on physical distance. In addition, other aspects merit attention. Benovoy and Drouin (2009) foundthat the 401 homogenized intrachormosomal pairs theydetected were rich in pairs with short distance (typicallyaround or less than 10 kb; fig. 3 of their paper). They found,however, that the correlation was no longer significant after normalizing the number of homogenization events ineach class of physical distance by the number of adjacentparalogous gene pairs in the same class. There is an obvious
flaw in this analysis: they compared the physical distance
distribution for ‘‘all’’ (linked) 401 homogenized paralog
pairs with the distribution for only ‘‘adjacent’’ paralog pairs.
From their figure 3, we see that the number of such adjacent paralog pairs is only 925, whereas the number of all thelinked paralog pairs they examined must have been tens of
thousands (but less than the size 55,050 of their whole dataset). To conduct a proper correlation analysis, they should
either have normalized the physical distance distribution
for all linked homogenized paralogs by that for all linked
paralogs examined or have normalized the distribution
for adjacent homogenized paralogs by that for all adjacentparalogs examined.
We can easily imagine that the distance distribution of
all linked paralog pairs would be more broadly spread toward longer distances than the distribution of adjacent paralog pairs, and this is what we actually observed (fig. 6). If
they conducted such a proper normalization, Benovoy andDrouin might have observed a significant negative correlation between the homogenization prevalence and thephysical distance. Other factors could also hide the real correlation inherent in the data set: a low signal-to-noise ratio
and a bad choice of the explanation variable. As discussed
in Introduction, the detection rate of homogenization approaches zero when the sequences are almost identical,
which causes the low signal-to-noise ratio. Regarding thelatter factor, we should note that the logarithmic scaleof physical distance enabled us to detect the significant
correlation between homogenization prevalence and physical distance. The prevalence is nonzero even at the physical distance exceeding 800 kb (fig. 6). With sucha situation, a correlation analysis based on a linear scaleof physical distance can easily miss the correlation inherent
in the data set. These may be why Benovoy and Drouin

MBE(2009) or McGrath et al. (2009) did not detect a significant
correlation.

Our Results are Not Likely to be due to Artifacts
Although we found some problems in the previous analyses, this does not necessarily mean that our analyses
are free from artifacts or other problems. We first consider
our method of constructing nonredundant quartet sets. As
explained in fig. 3, the quartet sets we used in our main
analyses are nonredundant in the sense that they are devoid of historical correlation after the speciation of the species X and Y. Although some quartets may show
overlapping histories before speciation, as in fig. 3A, it does
not matter because we only analyzed homogenization
events after speciation. The problem, if any, would becaused by the case in which, for example, a region of geneX1 homogenizes the homologous regions of genes X2 andX3 in fig. 3A. If it happened, the quartet (X2, Y2, X3, Y3) willshow a sign of homogenization even if it never underwent
homogenization. We conducted a follow-up analysis andidentified candidates of such ‘‘spurious’’ homogenizationin 5 of 43 positive human–macaque quartets and in 48
of 206 positive mouse–rat quartets (both in the refined
nonredundant sets). We reconducted our correlation analyses after removing these quartets showing potentially spurious homogenization, but our main conclusions did notchange at all (Supplementary figs. S13 and S14 and supplementary tables S12–S16, Supplementary Material online).
This indicates that the effects of artifacts due to our
method to collect quartets are negligible, if any.
We also conducted the analysis on the sets of gene families each of which experienced only one duplication event
after the primates–rodents divergence. Such families
contain at most one quartet per family, and therefore
are expected to be completely devoid of spurious homogenization. The homogenization prevalence in these subsets
appeared to show no correlation with paralog divergenceor with physical distance (supplementary figs. S15 and S16,
Supplementary Material online). However, the results wereinconclusive because of the small sample sizes (29 forhuman–macaque and 41 for mouse–rat). If such results
hold even when we analyze more such single-quartet
families, we will have to examine the causes.
To some readers, our refinement procedure of nonredundant sets of quartets may sound like data manipulation. We are, however, confident that our preparation ofrefined sets is a scientifically legitimate process. It is common sense in experimental science to use only materials
within the range where the detector performs reliably
and not to use contaminated materials. Failure to do sowould result in erroneous conclusions. Our refinementof the nonredundant quartet sets is just designed to emulate such commonsense experimental practices by weeding out conceivable confounding factors due to thedetector’s performance characteristics and due to the effects of homogenization. Contrasting the results on theraw data sets (supplementary fig. S10, Supplementary Material online) with those on the refined data sets (fig. 5)
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Ezawa et al. · doi:10.1093/molbev/msq109

clearly shows that the suspected artifacts actually affected
the raw data sets (supplementary fig. S10, Supplementary
Material online) but not the refined sets (fig. 5). It should
also be noted that, except the dependence on the sequence
divergence, our main conclusions remain unchanged regardless of whether the analyses are conducted on the refined sets or on the raw data sets (supplementary tables
S17, S18, and S19 and supplementary fig. S11, Supplementary Material online).
In fact, we could have just started from the refined data
sets because they are absolutely the data sets that satisfy
our two criteria: 1) duplication date should be between the
primates–rodents divergence and the speciation (of human–macaque or mouse–rat); 2) the false-negative rateshould be less than 50%. It was our deliberate choice that
we kept the ‘‘strange’’ patterns on the raw data sets (supplementary fig. S10, Supplementary Material online). By
doing so, we intended to show that improperly prepareddata sets could result in misleading conclusions.
It is possible that a considerable number of large-scalebioinformatics analyses are actually more or less erroneous
either because they are based on improper data sets or because they fail to take account of the detectors’ characteristics, such as the false-positive rate and the false-negative
rate. We have to be careful not to be deceived by this kindof misleading results.
Although we restricted our analyses to the subsets where
the false-negative rate of our 4-2-4 method is less than 50%,
there still remains a moderate positive correlation between
the true-positive rate and the sequence divergences (supplementary figs. S6 and S7, Supplementary Material online).
We do not think, however, this moderate correlation causes
serious problems. There are two reasons: 1) the observed
homogenization prevalence displays negative correlation
with the sequence divergence in spite of the positive correlation between the detection rate and the sequence divergence. This means that the observed negative correlation
between the homogenization prevalence and the sequence
divergence would be more remarkable if the detection rate
were uniform; 2) our logistic regression analyses on the dependence of the prevalence on other factors control for the
dependence on the sequence divergence. This should in effect take account of the correlation between the detection
rate and the sequence divergence as well.
Putting all these things together, we can conclude that it
is highly unlikely that our main results are due to artifacts.
This, combined with the problems in the previous analyses(Benovoy and Drouin 2009; McGrath et al. 2009) as discussed in the last subsection, indicates that our results reflect the real biological process rather than artifacts, andtherefore are more reliable.

Performance Tests of Homogenization Detectors
One of the main features of this study is that, when refining
our nonredundant sets of quartets, we took advantage ofthe sequence divergence dependence of the true-positive
rate of our 4-2-4 method (supplementary fig. S7, Supplementary Material online), which was estimated by applying
2168

the method to simulated quartets. It should be noted herethat McGrath et al. (2009) also conducted a performance
test on GENECONV by applying it to their sets of simulated
gene families (table S1 of their paper). Strangely, however,they did not take account of the results of their performance test when conducting the real data analyses. There
are also several other differences between their analyses
and ours. 1) They used the default NUCLEOTIDES option
rather than the SILENT option as we chose. Our choice of
the SILENT option is based on our previous finding that the
NUCLEOTIDES option of GENECONV could suffer unexpectedly inflated false-positive rates when applied tonegative-control quartets whose members underwent different patterns of purifying selection (Ezawa et al. 2006),
which is expected to be common in the evolution of duplicate genes (Ohno 1970; Force et al. 1999; Lynch andForce 2000; Lynch and Katju 2004; Katju and Lynch2006). Our 4-2-4 method, in contrast, displayed stablefalse-positive rates under any conditions examined. Because the SILENT option examines synonymous changes,
which undergoes only weak selection, if any, the use of
the SILENT option is expected to mitigate the risk of inflated false-positive rates. 2) We set the threshold P value
at 0.5%, whereas McGrath–Casole–Hahn set a more lenient
P value threshold of 5%. Our threshold of 0.5% was chosen
in order to reduce ‘‘contamination’’ by false positives in thereal genome-wide data analyses. If we used the threshold of
5%, there would have been around 25 false positives out of
our 497 human–macaque quartets, and around 41 false
positives out of our 828 mouse–rat quartets, which could
have foiled our correlation analyses. Despite these differences between the two studies, the obtained results broadly
agree with each other, especially concerning the poor performance of GENECONV when applied to duplicate pairs
with small sequence divergences and/or short homogenization tracts.

Remaining Issues
The refined nonredundant sets of quartets and wellcontrolled statistical tests employed in this study shed
new light on several questions that were left unresolved
by our previous study (Ezawa et al. 2006). Yet, there still
remain lots of outstanding issues. First, although we
showed that primates and rodents share several trendsof the prevalence of homogenization, we still do not know
how widely these trends are conserved across the phylogenetic tree of life. In order to answer this question, we haveto sample pairs of closely related species with sequenced
genomes from a wide variety of taxa, and examine how
the prevalence of homogenization depends on the properties of gene pairs by the analyses that take account of theeffects of false negatives and sequence divergence. This will
enable us to compare properties of homogenization in different taxa on a comparable footing, unlike the heterogeneous comparison we tried previously (Ezawa et al. 2006).
Because the previous genome-wide studies of gene conversion on a wide variety of species (Semple and Wolfe 1999;
Drouin 2002; Gao and Innan 2004; Wang et al. 2007; Xu

Homogenization between Primate-Specific Duplicated Genes · doi:10.1093/molbev/msq109

et al. 2008) are conducted with different methods of genepair collection and those of gene conversion detection,
mostly without taking good account of the effects of both
false negatives and sequence divergences, it would be difficult to sort out real biological differences from artificial
effects via a naive comparison of the results of these studies, especially when we try to compare the homogenization
prevalence in different species or to conduct sophisticatedcorrelation analyses.
Second, although we defined functional categories basedon InterProScan (Zdobnov and Apweiler 2001), family-wise
analyses would be more appropriate considering the nontrivial dependence of the prevalence of homogenization on
the combination of physical proximity and family size (datanot shown). Especially, a sliding-window analysis along themultiple alignment of each gene family would reveal ‘‘hot
spots’’ and ‘‘cold spots’’ of homogenization.
Third, to avoid the false-positive/negative problems, we
restricted our analyses to the quartets whose phylogenetic
trees clearly indicate duplications before the speciation ofhuman and macaque or of mouse and rat. It will be moreinteresting if we can study quartets whose phylogenetic
trees do not reflect their real duplication history any more
due to intense homogenization. In an attempt to extend
our analyses to such subjects, we applied two-sample runs
test (Takahata 1994; supplementary file of Ezawa et al.
2006) to the quartets whose CDS phylogeny is not significantly type I. Fourteen quartets showed significance in theruns test. The CDSs of these 14 quartets are likely to haveundergone homogenization that is intensive enough tochange the observed phylogeny. They seem to have a wide
range of function, from signal receptor through enzymes totranscriptional factors. It would be worth studying themfurther.
Finally, our ultimate goal is to construct a reasonably
accurate model that can describe the evolution of a wide
variety of gene families under the influence of homogenization. We need to conduct more sophisticated analyses byintegrating evolutionary and comparative genomic approaches as reported here with population genetic approaches (Jeffreys and May 2004; Lindsay et al. 2006;
Teshima and Innan 2008) to achieve this goal. Duplicatedgenes play important roles in evolution of organisms, andtheir homogenization processes should be analyzed morethoroughly in future studies.

Supplementary Material
Supplementary materials and methods, tables S1–S19, andfigures 1–16 are available at Molecular Biology and Evolution online (http://www.mbe.oxfordjournals.org/).

Acknowledgments
We are grateful to Drs K. Sumiyama and K. Kryukov for
discussions on this study. This study was conducted asa part of the Genome Network Project and also as a partof Grant-in-Aid for Scientific Research on Priority Areas

MBE‘‘Comparative Genomics’’ from the Ministry of Education,
Culture, Sports, Science and Technology of Japan.

References
Adkins RM, Walton AH, Honeycutt RL. 2003. Higher-level systems ofrodents and divergence time estimates based on two congruent
nuclear genes. Mol Phylogenet Evol. 26:409–420.
Agresti A. 2007. An introduction to categorical data analysis. 2nd ed.
Hoboken (NJ): John Wiley & Sons, Inc.
Arnheim N. 1983. Concerted evolution of multigene families.. In: M
Nei, and RK Koehn, editors. Evolution of genes and proteins.
Sunderland (MA): Sunauer Associates. p. 38–61.
Balding DJ, Nichols RA, Hunt DM. 1992. Detecting gene conversion:
primate visual pigment genes. Proc R Soc Lond B. 249:275–280.
Benovoy D, Drouin G. 2009. Ectopic gene conversions in the humangenome. Genomics 93:27–32.
Brown DD, Wensink PC, Jordan E. 1972. A comparison of theribosomal DNA’s of Xenopus laevis and Xenopus mulleli: theevolution of tandem genes. J Mol Biol. 63:57–73.
Cheung B, Holmes RS, Easteal S, Beacham IR. 1999. Evolution of class
I alcohol dehydrogenase genes in catarrhine primates: geneconversion, substitution rates, and gene regulation. Mol Biol
Evol. 16:23–36.
Cochran WG. 1954. Some methods for strengthening the common
v2 tests. Biometrics 10:417–451.
Donohoue PA, Jospe N, Migeon CJ, Van Dop C. 1989. Two distinct
areas of unequal crossingover within the steroid 21-hydroxylase
genes produce absence of CYP1B. Genomics 5:397–406.
Drouin G. 2002. Characterization of the gene conversions between
the multigene family members of the yeast genome. J Mol Evol.
55:14–23.
Ehrlich M, Wang RY. 1981. 5-Methylcytosine in eukaryotic DNA.
Science 212:1350–1357.
Eickbush TH, Eickbush DG. 2007. Finely orchestrated movements:
evolution of the ribosomal RNA genes. Genetics 175:477–485.
Ezawa K, OOta S, Saitou N. 2006. Proceedings of the SMBE trinational young investigator’s workshop 2005. Genome-wide
search of gene conversions in duplicated genes of mouse andrat. Mol Biol Evol. 23:927–940.
Ezquieta B, Luzuriaga C. 2004. Neonatal salt-wasting and 11
b-hydroxylase deficiency in a child carrying a homozygous
deletion hybrid CYP11B2 (aldosterone synthase)-CYP11B1(11 b-hydroxylase). Clin Genet. 66:229–235.
Force A, Lynch M, Pickett FB, Amores A, Yan YL, Postlethwait J.
1999. Preservation of duplicate genes by complementary,degenerative mutations. Genetics 151:1531–1545.
Gao LZ, Innan H. 2004. Very low gene duplication rate in the yeast
genome. Science 306:1367–1370.
Glazko GV, Nei M. 2003. Estimation of divergence times for major
lineages of primate species. Mol Biol Evol. 20:424–434.
Hampf M, Dao NTN, Hoan NT, Bernhardt R. 2001. Unequalcrossing-over between aldosterone synthase and 11bhydroxylase genes causes congenital adrenal hyperplasia. J Clin
Endocr Metab. 86:4445–4452.
Hubbard TJP, Aken BL, Beal K, et al (45 co-authors). 2007. Ensembl
2007. Nucl Acids Res. 35:D610–D617.
Hurles ME. 2001. Gene conversion homogenizes the CMT1A
paralogous repeats. BMC Genomics. 2:11.
Ibbotson RE, Hunt DM, Bowmaker JK, Mollon JD. 1992. Sequence
divergence and copy number of the middle- and long-wave
photopigment genes in Old Workd monkeys. Proc R Soc Lond B.
247:145–154.
Jackson MS, Oliver K, Loveland J, Humphray S, Dunham I, Rocchi M,
Viggiano L, Park JP, Hurles ME, Santibanez-Koref M. 2005.

2169

Ezawa et al. · doi:10.1093/molbev/msq109
Evidence for widespread reticulate evolution within human
duplicons. Am J Hum Genet. 77:824–840.
Jacobs LL, Downs WR. 1994. The evolution of murine rodents in
Asia. In: Tomida Y, Li C, Setoguchi T, editors. Rodents andlagomorph families of Asian origin and diversification. Tokyo(Japan): National Science Musium Monograph. p. 149–156.
Jaegar JJ, Tong H, Denys C. 1986. The age of Mus-Rattus divergence:
paleontological data compared with the molecular clock.
C R Acad Sci Paris. 302(ser. II):917–922.
Jeffreys A. 1979. DNA sequence variants in the G gamma-, Agamma-, delta-, and beta- globin genes of man. Cell 18:1–10.
Jeffreys AJ, May CA. 2004. Intense and highly localized geneconversion activity in human meiotic crossover hot spots. Nat
Genet. 36:151–156.
Katju V, Lynch M. 2006. On the formation of novel genes by duplicationin the Caenorhabditis elegans genome. Mol Biol Evol. 23:1056–1067.
Kawamura S, Saitou N, Ueda S. 1992. Concerted evolution of the
primate immunoglobulin a-gene through gene conversion. J Biol
Chem. 267:7359–7367.
Kimura M. 1968. Evolutionary rate at the molecular level. Nature
217:624–626.
Kimura M. 1983. The neutral theory of molecular evolution.
Cambridge: Cambridge University Press.
Kitano T, Saitou N. 1999. Evolution of Rh blood group genes haveexperienced gene conversions and positive selection. J Mol Evol.
16:111–120.
Lee H-H, Niu D-M, Lin R-W, Chan P, Lin C-Y. 2002. Structural
analysis of the chimeric CYP1P/CYP21 gene in steroid 21hydroxylase deficiency. J Hum Genet. 47:517–522.
Li W-H. 1997. Molecular evolution. Sunderland (MA): Sinauer
Associates.
Lifton RP, Dluhy RG, Powers M, Rich GM, Cook S, Ulick S,
Lalouel JM. 1992. A chimaeric 11b-hydroxylase/aldosterone
synthase gene causes glococorticoid-remediable aldosteronism
and human hypertension. Nature 355:262–265.
Lindblad-Toh K, Wade CM, Mikkelsen TS, et al (46 co-authors) 2005.
Genome sequence, comparative analysis and haplotype structure of the domestic dog. Nature 438:803–819.
Lindsay SJ, Khajavi M, Lupski JR, Hurles ME. 2006. A chromosomal
rearrangement hotspot can be identified from population
genetic variation and is coincident with a hotspot for allelicrecombination. Am J Hum Genet. 79:890–902.
Liskay RM, Letsou A, Stachelek JL. 1987. Homology requirement for
efficient gene conversion between duplicated chromosomal
sequences in mammalian cells. Genetics 115:161–167.
Lukacsovich T, Waldman AS. 1999. Suppression of intrachromosomal gene conversion in mammalian cells by small degrees ofsequence divergence. Genetics 151:1559–1568.
Lynch M, Force A. 2000. The probability of duplicated genepreservation by subfunctionalization. Genetics 154:459–473.
Lynch M, Katju V. 2004. The altered evolutionary trajectories of gene
duplicates. Trends Genet. 20:544–549.
Mantel N, Haenszel W. 1959. Statistical aspects of analysis of datafrom retrospective studies of disease. J Natl Cancer Inst.
22:719–748.
Martin RD. 1993. Primate origins: plugging the gaps. Nature
363:223–234.
McGrath CL, Casola C, Hahn MW. 2009. Minimal effect of ectopic
gene conversion among recent duplicates in four mammalian
genomes. Genetics 182:615–622.
Nathans J, Piantanida TP, Eddy RL, Shows TB, Hogness DS. 1986.
Molecular genetics of inherited variation in human color vision.
Science 232:203–210.
Nei M, Rooney AP. 2005. Concerted and birth-and-death evolutionof multigene families. Annu Rev Genet. 39:121–152.

2170

MBE
Ohno S. 1970. Evolution by gene duplication. Berlin (Germany):
Springer-Verlag.
Ohta T. 1976. Simple model for treating evolution of multigene
families. Nature 262:74–76.
Ohta T. 1985. A model of duplicative transposition and geneconversion for repetitive DNA families. Genetics. 110:513–524.
Petes TD, Hill CW. 1988. Recombination between repeated genes in
microorganisms. Annu Rev Genet. 22:147–168.
Pilbeam DR. 1984. The descent of hominids and hominoids. Sci Am.
250:60–69.
Rat Genome Sequencing Project Consortium. 2004. Genome
sequence of the brown Norway rat yields insights into
mammalian evolution. Nature 428:493–521.
Rhesus Macaque Genome Sequencing and Analysis Consortium.
2007. Evolutionary and Biological Insights from the Rhesus
Macaque Genome. Science 316:222–234.
Ritossa FM, Scala G. 1969. Equilibrium variations in the redundancy ofrDNA in Drosophila melanogaster. Genetics 61(Suppl):305–317.
Sawyer S. 1989. Statistical tests for detecting gene conversion. Mol
Biol Evol. 6:526–538.
Schalet A. 1969. Exchanges at the bobbed locus of Drosophilamelanogaster. Genetics. 63:133–153.
Schienman JE, Holt RA, Auerbach MR, Stewart CB. 2006. Duplicationand divergence of 2 distinct pancreatic ribonuclease genes inleaf-eating African and Asian colobine monkeys. Mol Biol Evol.
23:1465–1479.
Scott AF, Heath P, Trusko S, Boyer SH, Prass W, Goodman M,
Czelusniak J, Chang L-Y.E, Slightom JL. 1984. The sequence of thegorilla fetal globin genes: evidence for multiple gene conversions
in human evolution. Mol Biol Evol. 1:371–389.
Semple C, Wolfe KH. 1999. Gene duplication and gene conversion in
the Caenorhabditis elegans genome. J Mol Evol. 48:566–576.
Shyue S-K, Li L, Chang BH-J, Li W-H. 1994. Intronic gene conversion
in the evolution of human X-linked color vision genes. Mol Biol
Evol. 11:548–551.
Slightom JL, Blechl AE, Smithies O. 1980. Human fetal G gama- andA gamma-globin genes: complete nucleotide sequences suggestthat DNA can be exchanged between these duplicated genes.
Cell 21:627–638.
Slightom JL, Chang LY, Koop BF, Goodman M. 1985. Chimpanzee
fetal G gamma- and A gamma-globin gene nucleotide sequences
provide further evidence of gene conversions in hominine
evolution. Mol Biol Evol. 2:370–389.
Smith GP. 1976. Evolution of repeated DNA sequences by unequalcrossover. Science 191:528–535.
Sokal RR, Rohlf FJ. 1995. Biometry, 3rd ed. New York: W.H. Freeman
and Company.
Springer MS, Murphy WJ, Eizirik E, O’Brien SJ. 2003. Placental
mammal diversification and the Crataceous-Tertiary boundary.
Proc Natl Acad Sci U S A. 100:1056–1061.
Steiper ME, Young NM. 2006. Primate molecular divergence dates.
Mol Phylogenet Evol. 41:384–394.
Steiper ME, Young NM, Sukarna TY. 2004. Genomic data support
the hominoid slowdown and an early Oligocene estimate for thehominoid-cercopithecoid divergence. Proc Natl Acad Sci U S A.
101:17021–17026.
Takahata N. 1994. Comments on the detection of reciprocal
recombination or gene conversion. Immunogenetics 39:146–149.
Takahata N, Satta Y. 1997. Evolution of the primate lineage
leading to modern humans: phylogenetic and demographic
inferences from DNA sequences. Proc Natl Acad Sci U S A.
94:4811–4815.
Teshima KM, Innan H. 2008. Neofunctionalization of duplicated genes
under the pressure of gene conversion. Genetics 178:1385–1398.
Thompson JD, Higgins DG, Gibson TJ. 1994. CLUSTAL W: improving
the sensitivity of progressive multiple sequence alignmentHomogenization between Primate-Specific Duplicated Genes · doi:10.1093/molbev/msq109
through sequence weighting, positions-specific gap-penalties
and weight matrix choice. Nucl Acid Res. 22:4673–4680.
Wang X, Tang H, Bowers JE, Feltus FA, Paterson AH. 2007. Extensive
concerted evolution of rice paralogs and the road to regaining
independence. Genetics 177:1753–1763.
Winter EE, Ponting CP. 2005. Mammalian BEX, WEX and GASP
genes: coding and non-coding chimaerism sustained by geneconversion events. BMC Evol Biol. 5:54.

MBE

Xu S, Clark T, Zheng H, Vang S, Li R, Wong GK-S, Wang J, Zheng X. 2008.
Gene conversion in the rice genome. BMC Genomics. 9:93.
Zdobnov EH, Apweiler R. 2001. InterProScan—an integration
platform for the signature-recognition methods in InterPro.
Bioinformatics 17:847–848.
Zhou Y-H, Li W-H. 1996. Gene conversion and natural selection in
the evolution of X-linked color vision genes in higher primates.
Mol Biol Evol. 13:780–783.

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