Genome-Wide Survey of Natural Selection on Functional,
Structural, and Network Properties of Polymorphic Sites in
Saccharomyces paradoxus
Anchal Vishnoi,1 Praveen Sethupathy,2 Daniel Simola,1 Joshua B. Plotkin,*,1 andSridhar Hannenhalli* ,3
1

Department of Biology, University of Pennsylvania
National Human Genome Research Institute, National Institutes of Health3
Department of Genetics, University of Pennsylvania
Present address: Center for Bioinformatics and Computational Biology, Department of Cell and Molecular Biology, University of
Maryland*Corresponding author: E-mail: sridhar@umiacs.umd.edu; jplotkin@sas.upenn.edu.
Associate editor: Barbara Holland
2

Background. To characterize the genetic basis of phenotypic evolution, numerous studies have identified individual genesthat have likely evolved under natural selection. However, phenotypic changes may represent the cumulative effect ofsimilar evolutionary forces acting on functionally related groups of genes. Phylogenetic analyses of divergent yeast species
have identified functional groups of genes that have evolved at significantly different rates, suggestive of differential
selection on the functional properties. However, due to environmental heterogeneity over long evolutionary timescales,
selection operating within a single lineage may be dramatically different, and it is not detectable via interspecificcomparisons alone. Moreover, interspecific studies typically quantify selection on protein-coding regions using the Dn/Ds
ratio, which cannot be extended easily to study selection on noncoding regions or synonymous sites. The population
genetic-based analysis of selection operating within a single lineage ameliorates these limitations. Findings. We investigatedselection on several properties associated with genes, promoters, or polymorphic sites, by analyzing the derived allele
frequency spectrum of single nucleotide polymorphisms (SNPs) in 28 strains of Saccharomyces paradoxus. We foundevidence for significant differential selection between many functionally relevant categories of SNPs, underscoring theutility of function-centric approaches for discovering signatures of natural selection. When comparable, our findings arelargely consistent with previous studies based on interspecific comparisons, with one notable exception: our study findsthat mutations from an ancient amino acid to a relatively new amino acid are selectively disfavored, whereas interspecificcomparisons have found selection against ancient amino acids. Several of our findings have not been addressed throughprior interspecific studies: we find that synonymous mutations from preferred to unpreferred codons are selected against
and that synonymous SNPs in the linker regions of proteins are relatively less constrained than those within protein
domains. Conclusions. We present the first global survey of selection acting on various functional properties in S.
paradoxus. We found that selection pressures previously detected over long evolutionary timescales have also shaped theevolution of S. paradoxus. Importantly, we also make novel discoveries untenable via conventional interspecific analyses.
Key words: evolution, natural selection, yeast, derived allele frequency.

Introduction
Evolution is shaped, in part, by natural selection. Identification of the genomic signatures imparted by natural
selection can provide insight into both the phenotypic
traits and evolutionary forces that have shaped a species’
history. The recent availability of whole genome sequences
for multiple species, as well as for multiple individuals or
strains within the same species, has enabled numerous
studies that aim to identify genes and genomic regions
under selection (Nielsen et al. 2005, 2009; Sabeti et al.
2006; Sawyer and Malik 2006; Kosiol et al. 2008).
Approaches for detecting signatures of selection can
be classified into two broad timescales: 1) those using

interspecific comparison to infer selection over long evolutionary timescales (Nielsen et al. 2005; Kosiol et al. 2008),
where typically the rate of putative functional changes(e.g., amino acid changing mutations in the coding portionof a gene) is compared with the rate of putative neutralchanges (e.g., synonymous mutations) using the Dn/Ds test
(Nielsen et al. 2005); 2) those using intraspecific, population–genetic comparison to infer selection operating in
the recent evolutionary past within a single lineage (Bustamante et al. 2005; Sabetiet al. 2006). Methods that utilize
interspecific divergence data in combination with intraspecific polymorphisms belong to the latter category (McDonald
and Kreitman 1991; Sawyer and Hartl 1992; Andolfatto 2005;
Desai and Plotkin 2008; Torgerson et al. 2009).

© The Author 2011. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, pleasee-mail: journals.permissions@oup.com

Mol. Biol. Evol. 28(9):2615–2627. 2011 doi:10.1093/molbev/msr085

Advance Access publication April 3, 2011

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Research article

Abstract

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Interspecific comparative genomic studies of selection
have largely focused on identifying individual genes thatexhibit evidence of selection (Nielsen et al. 2005; Kosiol
et al. 2008). Selection, however, acts on phenotypes, which
can be a consequence of functional interactions among
multiple genes in a relevant pathway. Thus, investigating
selection on groups of genes that share a functional, structural, or protein interaction network (PIN) property
presents an appealing perspective for understanding the
genomic basis of selection (Zhang and Li 2005; Plotkin
and Fraser 2007; Shapiro et al. 2007; Xia et al. 2009). We
refer to such a group-based approach as the ‘‘function-centric’’ approach to contrast it from the traditional ‘‘genecentric’’ approach. The function-centric approach canidentify the type and relative strength of selection withrespect to various functional categories. This may provide
a more direct insight into the particular traits that haveshaped a species’ evolutionary history because a single testof selection can be applied to a set of related genes. Taking
the gene-centric approach, the biological interpretation ofgenes found to be under selection is typically based on the
genome-wide enrichment of molecular functions and/or
biological processes. For instance, in a recent human–chimpanzee comparative genomic study (Nielsen et al. 2005),
the set of genes undergoing adaptive evolution wasenriched for biological processes related to immunityand defense. The function-centric approach would apply
a single test of selection to the set of genes involved in
immunity and defense.
The function-centric approach that has been taken inprevious interspecific studies (Xia et al. 2009). Xia et al.
(2009) found that for several functional properties, genes
sharing the property have evolved more slowly than genesthat do not, indicating purifying selection on that property.
These groups of genes include essential genes, highly regulated and highly interacting genes, and more abundantly
expressed genes. Curiously, many of these results, whichwere based on phylogenetic analysis of multiple species,
were not recapitulated by paired sequence comparisons.
The authors attributed this inconsistency to evolutionaryrate heterogeneity among yeast species, such that for
certain properties, the selective pressures within a single
lineage can be dramatically different than the overall evolutionary trajectory. This hypothesis motivates the systematic application of a function-centric approach to study
selection within a single lineage, using population–genetic
data from multiple conspecific strains.
Analysis of intraspecific data may reveal trends that areconsistent with interspecific studies. Such a result would
further validate the previous interspecific studies and also
reveal that selection pressures over relatively short timescales are similar to those operating over evolutionary
timescales. Performing an intraspecific study is also usefulto alleviate the confounding influence of demographic
and/or selective heterogeneity among species, which arise
in interspecific studies. Moreover, intraspecific analysis ofa well-studied model organism, with rich functional annotation, may facilitate interpretation of the findings, which is2616

more difficult for nonmodel systems especially when employing interspecific comparison.
Another major advantage of population-based analysis
is that it can utilize polymorphisms to infer selection onvarious noncoding loci of interest (such as promoters)
and on different classes of synonymous codons (Sawyerand Hartl 1992; Andolfatto 2005; Chen and Rajewsky
2006; Sethupathy et al. 2008; Torgerson et al. 2009), insteadof being limited to the Dn/Ds test of selection on nonsynonymous sites. Although evolutionary models have been
proposed to assess selection on specific classes of noncoding regions via interspecific comparison (Tanay et al. 2004;
Doniger and Fay 2007; Babbitt and Kim 2008), these models
are customized to the specific genomic element being investigated and are not generally applicable. Moreover, theDn/Ds statistic may not be a reliable indicator of selection
pressures when applied to con-specific samples (Plotkin
et al. 2006; Kryazhimskiy and Plotkin 2008).
The availability of whole genome sequences for multiple
natural strains of the yeast Saccharomyces paradoxus andother closely related species enables an unbiased determination of polymorphisms and their ancestral and derived
alleles (Liti et al. 2009). By applying inference procedures
based on the derived allele frequency (DAF) distributionof these data (Fay et al. 2001; Chen and Rajewsky 2006; Sethupathy et al. 2008; Torgerson et al. 2009), we provide thefirst genome-scale study of selection acting on a broadcompendium of functional, structural, and PIN properties
of S. paradoxus genes and genomic sites. We demonstratethat groups of yeast genes known to exhibit low substitution rates across a long evolutionary timescale also have an
overrepresentation of single nucleotide polymorphisms(SNPs) with low DAF within S. paradoxus, indicating ongoing purifying selection within the S. paradoxus lineage. We
also present several novel discoveries that provide further
insights into the functional, structural, and PIN attributes
that may have influenced the recent evolution of phenotypic traits in S. paradoxus. Importantly, these findings wereuniquely enabled by our population-based analysis.

Materials and Methods
Overview of Our Approach
Our analysis is based on a genome-wide set of 506,428 SNPs
in S. paradoxus, derived from multiple-sequence alignmentof 28 fully sequenced isolates. We inferred the ancestral andderived allele of each SNP using the recently diverged outgroup species S. mikatae and S. bayanus, allowing us tocompute the DAF for 237,466 SNPs in the sample of 28
genomes (0 , DAF , 1). To investigate selection on sites
associated with any property of interest, we constructed
two disjoint sets of SNPs, U and V, from the total of
237,466 SNPs; U represents all SNPs that satisfy the property and V represents SNPs that do not. As an illustrative
example, suppose the property under consideration is‘‘gene essentiality’’ (defined as genes required for growth
to fertile maturity). In this case, the set U is comprisedof all nonsynonymous SNPs (defined as SNPs with two

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Natural Selection in Yeast · doi:10.1093/molbev/msr085

alleles encoding for distinct amino acids) that fall withina gene tagged as essential, and the set V is comprised of
the nonsynonymous SNPs that fall within a gene nottagged as essential. Let DAFU (or DAFV) represent the collection of DAFs for the SNPs in U (or V).
Under the null model of neutral evolution with assumptions of irreversible mutations, independence among sites,
and unbiased SNP ascertainment, the number of SNP
alleles with derived frequency r is proportional to 1/r(Ewens 1979; Fu 1995). Purifying selection on a derived
SNP allele is expected to drive allele frequency toward zero(i.e., loss), whereas positive selection is expected to drive
allele frequency toward 1 (i.e., fixation). Accordingly, inthe DAF distribution, an excess of SNPs with low (or high)DAF is suggestive of purifying (or positive) selection (Fayet al. 2001; Ganapathy and Uyenoyama 2009). Unfortunately, it is difficult in practice to estimate accurately
the absolute magnitude of selection pressure due to violation of some of the aforementioned assumptions (Bustamante et al. 2001). However, two different DAF
distributions (such as DAFU and DAFV) can be compared
to determine whether the two sets of SNPs are evolving
under different selection regimes, as long as the two sets
do not have differential biases in any of the assumptions(Chen and Rajewsky 2006; Sethupathy et al. 2008; Torgerson et al. 2009). If the proportions of low-frequency derived
alleles are significantly different between the two distributions, it would suggest that the set of SNPs with relative
excess of low-frequency derived alleles is under purifying
selection ‘‘relative’’ to the other set of SNPs—that is, either
more purifying selection or less positive selection. Given
the two DAF distributions, DAFU and DAFV, we use theFisher’s Exact test to determine whether one of these setscontains a significantly greater proportion of SNPs with low
DAF. We refer to this as the ‘‘sample-based Fisher’’ test. To
ensure the robustness of our inferences, we use three different DAF thresholds for what is considered ‘‘low’’ (10,15, and 20%). Unless otherwise mentioned, we will referto the results based on the 15% threshold, with results forother thresholds presented in the supplementary table S1(Supplementary Material online).
The DAFs in our sample of 28 strains may not perfectly
represent the true, underlying population frequencies of each
derived allele. We would like to estimate accurately the true
population frequencies when performing a test of differential
selection between two categories of SNPs. For each SNP, we
use the information in the sample of 28 individuals to estimate the probability that the true population frequency is
below a certain threshold, say 15% (other tests such as
the Poisson Random Field account for sample size explicitly[Sawyer and Hartl 1992]). The sample is assumed to represent a binomial draw from the population. Under thisassumption, the underlying population frequency follows
the conjugate prior of the binomial, that is, a beta distribution. To account for the uncertainty in the estimate of the
population frequency of a derived allele due to small sample
size, we followed two alternative approaches to quantify thedifferences in the proportion of low DAFs in U and in V.

First, for each SNP, we estimated from the corresponding
beta distribution, the tail probability that the population
frequency of the derived allele is below a certain threshold,say, 15%. We then compared the tail probabilities for theforeground and the background SNPs using the Mann–
Whitney test. We refer to this as the ‘‘population-based
MW test.’’Second, for each SNP, we randomly sampled the underlying derived allele population frequency from the corresponding beta distribution 100 times and performed 100
Fisher’s Exact tests using each randomly drawn sample
of frequencies. For each property, we report the median
P value of the 100 tests, as well as the fraction of tests
in which the null hypothesis was rejected (P  0.05).
We refer to this as the ‘‘population-based Fisher’’ test.
The above two procedures are far more conservative than
the sample-based Fisher test, which treats the sampled allele
frequencies as the true underlying population frequencies(Chen and Rajewsky 2006; Sethupathy et al. 2008; Torgerson
et al. 2009). In the main text, we only report the P values for
the Sample-based Fisher test and the population-based MW
test. The results of the population-based Fisher test are provided in the supplementary table S1 (Supplementary Material online). Below, we provide additional details regarding
data acquisition and analysis methods.

Genome Sequences
The complete genome sequences for 28 S. paradoxus isolates
were downloaded from the Sanger Institute (September2007) (http://www.sanger.ac.uk/research/projects/genomein
formatics/sgrp.html) and the Broad Institute (S. paradoxus
NRRL Y-17217; May 2003; http://www.broadinstitute.org/
annotation/fungi/comp_yeasts/). Genome sequences for
S. mikatae and S. bayanus were also downloaded from theBroad Institute (May 2003). The S288c genome (all genomic
open reading frame [ORFs]), along with annotations delimiting exon boundaries for every ORF, was downloaded from
SGD (http://yeastgenome.org; November 2007). Gene ORF
annotations for S. paradoxus NRRL Y-17217 were downloaded from the Broad Institute (May 2003). The collection
and genome sequencing of these S. paradoxus isolates is
described in Kellis et al. (2003) and Liti et al. (2009).

Gene Identification
ORF annotations and multiple alignments for each of the
28 unannotated genomes were obtained from Simola andKim (http://yeastpopgenomics.org/), using a novel gene
identification algorithm. Briefly, this algorithm takes as input a reference genome with corresponding exon, intron,and untranslated regions annotations and an unannotated(target) genome. Following a local alignment of each reference ORF to a target genome (Blast-All), candidate
ORF boundaries were identified by maximal length concatenation of local, overlapping high-scoring sequence pairs(BlastN; E-value 1, default settings otherwise), followed
by Needleman–Wunsch global alignment of each reference
ORF to the recovered maximal length candidate sequence.
Coding sequences (CDSs) were obtained from each ORF
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after removing any introns, which was done by global alignment of an intron from the reference sequence to the homologous candidate ORF in the target genome. A proteincoding locus was retained only if its CDS exhibited proper
start and stop codons and if its sequence length was a multiple of 3. Therefore, any gaps present in a CDS from alignment must occur in multiples of 3.
Target ORFs were identified for each of the unannotatedgenomes using all verified, putative, and transposable element ORFs corresponding to two reference genomes:
S288c for S. cerevisiae and NRRL Y-17217 for S. paradoxus.
Since there are no intron annotations available for S. paradoxus, all intron annotations correspond to the S288c genome. Due to sequence divergence, these intron
annotations do not always match actual exon–intron
boundaries within homologous S. paradoxus ORFs. So,
to identify introns from these ORFs, 322 S. paradoxus intron-containing genes were replaced with homologous
ORFs from the S288c genome, and these S. cerevisiae intronic sequences were used to parse introns from target
S. paradoxus ORFs. To ensure consistent comparative analysis among genomes, all DNA sequences (including thosefrom the S. paradoxus reference genome) were obtained as
output from this gene identification algorithm.

SNP Identification
Multiple alignments of each protein-coding sequence, as
well as the flanking regions up to the closest ORF, of all
the strains of S. paradoxus, were obtained using ClustalW(version 2.0; default settings) (Chenna et al. 2003). The multiple alignments were parsed to identify polymorphic sites.
Only the bi-allelic SNPs were retained. We used the following
phylogenetic tree for the assignment of derived/ancestral
states at each SNP: (S. bayanus, (S. mikatae, (S. paradoxus,
S. cerevisiae))). Specifically, we included those SNPs for which
the two outgroups, S. bayanus and S. mikatae, share an allele
with S. paradoxus; this shared allele was considered ancestralby parsimony. We avoided using S. cerevisiae as an outgroup
because the available strains either contain a mixture of wildand derived/domesticated genomic regions (e.g., woodland
strains could have recombined with domesticated strains,
biasing some of the ancestral state assignments) or appear
to have a predominantly clonal evolutionary history but one
which is closely associated with human domestication (e.g.,vineyard lineages) (Liti et al. 2009, see fig. 2); we wanted to
avoid this confounding factor. We have assessed the effect ofusing 1, 2, 3, and 4 outgroups for ancestral state assignmentand decided to use 2, which provided a reasonable tradeoff
between accuracy and the overall number of SNPs retained.
Based on the inferred ancestral nucleotide, the DAF at eachpolymorphic locus was obtained from the multiple alignment of the S. paradoxus strains.

Estimating Population Frequency of Derived Allele
from the Sample Frequency
As mentioned above, the small sample size makes theobserved sample frequency of a derived allele only a proxyof the true underlying population frequency. This small
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sample effect needs to be corrected prior to applying
any statistical tests to infer differential selection. For each
SNP, starting with the observed sample frequency r, 0 , r, n, where n 5 28 is the number of strains, we reestimate
the sample frequency as follows: 1) we first compute theposterior probability distribution of the population frequency /, 0 , / , 1, as a beta distribution:nrr
Pð/Þ5Betað/; r þ 1; n  r þ 1Þ5 R 1/ rð1/Þnr ; 2) using0

u ð1uÞ

duthe beta distribution, significant biases in DAF distributions
were assessed according to the population-based MW andpopulation-based Fisher tests described above.

Results
Overview of the Properties Investigated
Our approach (Materials and Methods) is applicable to any
biological property that can be mapped to specific genes orpolymorphic loci. We have tried to be comprehensive inour survey based on the current literature. One recent work
(Xia et al. 2009) that investigated the functional determinants of evolutionary rates based on interspecific comparisons provides a suitable starting point for our investigationof differential selection at the population level. Besides the
genic properties investigated in Xia et al. (2009), we analyzed several additional properties, some of which pertain
to either noncoding regions or synonymous codons; theseare summarized in table 1. Because many of theexperimentally determined properties, such as gene essentiality, have been characterized in S. cerevisiae, our analysis
assumes the conservation of these properties between S.
cerevisiae and S. paradoxus orthologs. As an initial positive
control, we compared the DAF distributions for the 52,113
synonymous and the 133,585 nonsynonymous SNPs. As
expected, we found an almost 2-fold enrichment of low
DAF nonsynonymous SNPs relative to the synonymous
SNPs (P ; 0) suggestive of relative purifying selection acting on amino acid substitutions. We report below our findings with respect to many other biological properties. Table
2 shows the significant results at the 15% DAF threshold.
All results are provided in supplementary table S1 (Supplementary Material online). The fractions of SNPs with low
DAF are provided in supplementary table S2 (Supplementary Material online).
Gene Essentiality
Genes in an organism can be classified according to theirdispensability. Genome-wide single gene deletion experiments in S. cerevisiae have been used to quantify geneessentiality (Giaever et al. 2002). Although it is reasonable
to expect that essential genes evolve under purifying selection (Wilson et al. 1977), previous investigations to test thishypothesis have yielded inconsistent results. Two earlier
genome-wide studies showed a highly significant correlation between gene dispensability and evolutionary ratein bacteria (Hirsh and Fraser 2001) and in yeast (Jordan
et al. 2002). A later study failed to reproduce this observation in yeast (Pal et al. 2003) and suggested that the highgene expression associated with essential genes mightNatural Selection in Yeast · doi:10.1093/molbev/msr085

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Table 1. Genic Properties Investigated for Relative Selection.
Property
Gene dispensability
CAI
Network hubs
Preferred / unpreferred versus
unpreferred / preferred codon
Nonsynonymous SNPs in domains
versus linker regions
Synonymous SNPs in linker versus domains
Buried versus exposed residue

Unordered versus ordered residue

Gene promoter versus synonymous sites
Duplicate genes versus singletons
Old / new amino acid versus
new / old amino acid
Protein length
Old genes versus young genes
GO biological processes

Rationale for the Study
Indispensable or essential genes are expected to be constrained.
Highly expressed genes are known to evolve slowly. CAI is a reasonableproxy for expression level.
Highly interacting genes, or hubs in interaction networks, have been shown to evolve
slower than other genes.
A change from a preferred (frequently used) to an unpreferred (rarely used) codon is
expected to be selected against.
Amino acids within structural and functional domains of a protein may be underconstraint.
Codon usage in the linker (between domain) regions of a protein may affect protein
folding and thus may be under constraint.
Buried residues providing structural stability may be under a different
relative constraint compared with exposed residues directly interacting with theenvironment.
Regions of inherent disorder lower the thermodynamic cost of interactions and thus may beunder a different constraint compared with otherstructurally less dynamic regions.
Transcription is regulated via binding sites in gene promoters; therefore, this regionis expected
to be constrained.
Genes with multiple copies may be under a different constraint than genes present
in a single copy.
We wished to test whether the ancient (primordial) amino acids are under adifferent constraint.
Longer proteins, due to a variety of possible reasons, could be more constrained.
Ancient genes are known to evolve slower compared with recently originated genes.
Various biological processes may be under different constraint.

explain their lower evolutionary rate. However, anotherrelatively recent study (Zhang and He 2005) again demonstrated a positive correlation between the evolutionary rate
and gene dispensability using nine species with varying evolutionary distances from S. cerevisiae, even after controllingfor gene expression level. The authors also noted that genedispensability may vary over evolutionary time, partly
explaining previous inconsistencies. Our study, based ona single species of yeast, is less vulnerable to varying dispensability across different lineages. We divided our setof 5,078 S. paradoxus genes into 1,039 essential and
4,039 nonessential genes based on a previously publishedsingle gene deletion study in S. cerevisiae (Giaever et al.
2002). The essential and nonessential genes harbor
28,056 and 102,844 nonsynonymous SNPs, respectively.
We found that the SNPs in essential genes have a significantly greater frequency of low DAF, suggesting stronger
evolutionary constraint relative to the nonessential genes,
consistent with the findings of Hirsh and Fraser (2001),
Jordan et al. (2002), and Pal et al. (2003).
CAI
Highly expressed genes have previously been shown to
evolve at a slower rate, which is attributed to negative selection against mistranslation-induced misfolding (Drummond and Wilke 2008). Thus, the codon adaptationindex (CAI) has been used as a proxy for gene expression
level (Sharp and Li 1987). We computed the CAI of all genes
using CodonW (http://codonw.sourceforge.net/) and ensured that the genes with high CAI were significantly enriched for ribosomal genes (Bonferroni corrected P value 5
2.3  1037). We then tested whether highly expressed
genes (i.e., those with high CAI) have evolved under greater

constraint in the S. paradoxus lineage. We compared the
DAF spectrum for the synonymous SNPs in 215 genes withthe highest 5% CAI with those in 2,150 genes with lowest
50% CAI. Consistent with previous interspecific studies, wefound that the highly expressed genes are under greater
constraint.
Network Hubs
PINs are generally scale free, meaning that a few genes(hubs) are highly interconnected with the rest (Jeonget al. 2001). Network hubs and their interactions have beenfound to be evolutionarily conserved (Wuchty et al. 2006).
Furthermore, an inverse relationship between the numberof interactions of a protein and its evolutionary rate has
been observed previously (Fraser et al. 2003; Fraser andPlotkin 2007; Kim et al. 2007). We used the previously published S. cerevisiae PIN (Han et al. 2004) and maintainedthat study’s definition of a hub as a protein connected
to at least five other proteins. We compared the hubs withthe set of proteins connected to four or fewer proteins. This
yielded 356 hub proteins and 4,880 non-hub proteins. Consistent with previous findings (Fraser et al. 2003; Kim et al.
2007), we found that nonsynonymous SNPs in the hub proteins have a greater frequency of low DAF, suggesting relatively constrained evolution. Furthermore, our results arerobust to alternative, more stringent definitions of a hub(data not shown).
Preferred Versus Unpreferred Codons
Synonymous codons have differential usage, presumably
due to their differential effect on translational efficiency
and accuracy (Akashi 1994; Cannarozzi et al. 2010; Plotkin
and Kudla 2011). Codons for an amino acid can be classified
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Table 2. Gene Properties with Significant Relative Constraint.
Property
Synonymous sites versus
nonsynonymous sites
Gene dispensability
CAI
Network hubs
Preferred -> unpreferred versus
unpreferred -> preferred codon
Nonsynonymous SNPs in domains
versus linker regions
Ordered versus unordered residue
Gene promoter versus
synonymous sites
Duplicate genes versus singletonsProtein length
GO:Translation
GO:Response to chemical stimuli
GO:Transferase
GO:Transporter
GO:Protein binding
GO:Response to stress
GO:DNA metabolic process
GO:Chromosome org.

Two SNP Categories
U: Synonymous SNPs
V: Nonsynonymous SNPs
U: Nonsynonymous SNPs in essential genes
V: Nonsynonymous SNPs in the rest of the genes
U: Nonsynonymous SNPs in genes with high CAI
V: Nonsynonymous SNPs in genes with low CAI
U: Nonsynonymous SNPs in genes with ‡5 interactions
V: Nonsynonymous SNPs in genes with £4 interactions
U: Synonymous SNPs with Pref / Unpref codon transition
V: Synonymous SNPs with Unpref / Pref codon transition
U: Nonsynonymous SNPs in domain region
V: Nonsynonymous SNPs in the linker region
U: Nonsynonymous SNPs in unordered region
V: Nonsynonymous SNPs in the ordered region
U: Polymorphism in gene promoter region
V: Synonymous SNPs in the coding region
U: Nonsynonymous SNPs in duplicated genes
V: Nonsynonymous SNPs in the singletons
U: Nonsynonymous SNPs in short proteins (£393 aa)
V: Nonsynonymous SNPs in long proteins (>393 aa)
See legend
See legend
See legend
See legend
See legend
See legend
See legend
See legend

Sample-Based Fisher Test P Value;
Population-Based MW Test P Value0; 0
1.4 3 10216; 1.9 3 10208
1.8 3 10246; 8.9 3 10216
9.4 3 10224; 2.2 3 10216
4.7 3 10212; 4.28 3 10203
0; 2.2 3 10216
2.3 3 10285; 2.2 3 10216
0; 2.2 3 10216
9.0 3 10218; 2.2 3 10216
1.7 3 10220; 2.2 3 10216
2.6 3 10205; 2.2 3 10216
2.2 3 10203; 2.2 3 10216
2.0 3 10206; 1.04 3 10204
6.6 3 10225; 1.44 3 10205
1.6 3 10205; 1.46 3 10206
0.01; 2.2 3 10216
2.0 3 10202; 2.2 3 10216
3.8 3 10207; 2.2 3 10216

NOTE.—The first column shows the property investigated. The second column shows precisely how we defined the two classes (U and V) of SNPs to be compared. For all
GO categories investigated, the set U consists of genes annotated with the particular GO terms and set V consists of the remainder of the genes. At a DAF threshold of 15%,
P values are shown for two tests: Sample-based Fisher test and population-based MW test. Excess of low DAF SNPs in U relative to V are indicated by light shading, whereas
significant excess of low DAF SNPs in V relative to U are indicated by dark shading. Here, we only show the properties for which both tests yield significance. All P values forthree different DAF thresholds are shown in supplementary table S1 (Supplementary Material online).

as preferred or unpreferred based on their genome-wide
frequencies. Preferred to unpreferred codon substitutions
have previously been shown to negatively impact gene
expression level, and the opposite has been shown for
unpreferred to preferred codon substitutions (Zhouet al. 2010). Mutations that replace a preferred codonby an unpreferred one are expected to be selected against,
whereas a mutation in the opposite direction should befavored (Akashi 1994; Zhou et al. 2010). To test thishypothesis, we obtained the amino acid-specific sets of preferred and unpreferred codons from Zhou et al. (2010). We
then identified 26,274 synonymous SNPs that change a preferred codon to an unpreferred one and 25,848 synonymous SNPs that show the opposite effect. Consistent
with previous findings, we observe that changes from a preferred codon to an unpreferred codon have a greater
proportion of low DAF, suggesting relative purifying selection against mutations resulting in an unpreferred codon.
Functional Domains
Proteins are composed of modular domains, each of which
typically performs a distinct function. In mammalian cytochrome b protein A, a positive correlation has been observed
between functional constraint and purifying selection invarious domains (McClellan and McCracken 2001). In general, protein domains evolve more slowly than the linker regions of the protein. We tested whether protein domains
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have evolved under greater constraint in S. paradoxus.
We annotated all proteins using the domain profiles compiled in PFam (Finn et al. 2008) and partitioned each protein
into ‘‘domain’’ (regions matching a PFam domain) and‘‘nondomain’’ (the remainder of the protein not matching
any PFam domain) regions. This yielded 45,668 nonsynonymous SNPs within the domains and 57,111 nonsynonymous SNPs in the nondomains. We found a significantly
higher proportion of low DAF for the nonsynonymous
SNPs within the domains, suggesting greater constraint
on the domains relative to the nondomain regions.
Codon Usage and Protein Misfolding
Most of the mutations in the coding region of a gene affect the gene’s function indirectly by disrupting theproper folding of the protein product (Pakula and Sauer
1989). Mistranslation-induced misfolding has been proposed as a dominant force underlying CDS evolution(Drummond and Wilke 2008). Pauses during the translation process can be critical for proper protein folding(Chaney and Morris 1979); therefore, synonymous codon
substitutions can affect protein conformation by changing translation kinetics (Kimchi-Sarfaty et al. 2007). In particular, linker regions that intervene functional domains
and the protein’s C-terminus contain the majority of
the codons that enhance ribosomal pausing (Thanaraj
and Argos 1996). To specifically investigate this effect,Natural Selection in Yeast · doi:10.1093/molbev/msr085

we tested whether synonymous SNPs within linker
regions evolve under greater constraint than those withinfunctional domains. We used the PFam annotations
described above to identify linker regions and functional
domains and obtained 20,705 synonymous SNPs withinlinker regions and 20,391 synonymous SNPs withindomains. Although the population-based MW test did
not yield significance, the sample-based Fisher test as wellas the population-based Fisher test did, at all three low
DAF thresholds; this suggests that synonymous SNPs in
the linker regions may have evolved under greater constraint than those in the domains.
Buried Versus Exposed Residues
While exposed protein residues mediate a protein’s interaction with other macromolecules, residues that are buried
within the protein structure are critical for the stability ofthe protein structure (Pakula and Sauer 1989). Even thoughboth types of residues are important for overall proteinfunction, buried residues have previously been found to
be more conserved (Overington et al. 1992). Also, distinctgroups of evolutionarily compatible amino acids (residues
within a group are frequently substituted for each otherduring evolution) have distinct solvent accessibility (Worthet al. 2009). To estimate the relative constraints on buried
versus exposed residues at a population level, we analyzed
the 758 genes for which buried and exposed residues havebeen determined (Zhou et al. 2009). Although the population-based tests failed to reject the null hypothesis, when
we used the sample-based Fisher test, we found that buried
residues are under greater constraint than the exposed
residues, consistent with the previous findings.
Ordered and Unordered Regions of a Protein
Although a large portion of a typical protein is composed ofrelatively rigid structures such as sheets and helices, the restof the protein lacks any apparent order (referred to as
inherently unordered regions) (Dosztanyi et al. 2005).
These unordered regions either remain unfolded or folddynamically into a variety of unknown transient conformations (Wright and Dyson 1999) and facilitate interactions
with different targets (Bracken et al. 1999). Moreover,unordered regions indirectly facilitate the proper functioning of the protein by providing flexible linker regions(Dunker et al. 2002; Uversky 2002). Prior analysis of theyeast proteome has shown that proteins with unorderedregions are enriched for ‘‘nucleic acid binding’’ and ‘‘kinase
activity’’ functions, both of which involve interactions withdifferent targets (Ward et al. 2004). A previous interspecificstudy found that unordered regions evolve faster thanordered regions (Brown et al. 2002). Using a computationalmodel to predict unordered regions (IUPred [Dosztanyi
et al. 2005]), we partitioned all proteins into ordered
and unordered regions and obtained 67,986 nonsynonymous SNPs in the ordered regions and 65,599
nonsynonymous SNPs in the unordered regions. Consistent
with the previous finding (Brown et al. 2002), we found that
SNPs in unordered regions are under relaxed constraint
relative to the SNPs in the ordered region.

MBE
Gene Promoters
Transcriptional regulation of genes is carried out, in largepart, by binding of transcription factors to specific DNA
cis-elements in upstream promoter regions. Variation inbinding sites can modulate transcription and therefore
the expression and function of the gene (Cowles et al.
2002). Gene promoters have been shown to be more conserved than nonpromoter intergenic regions and intronic
regions distant from splice sites (Blanchette et al. 2006).
Unlike the protein-coding regions of the gene wheresynonymous and nonsynonymous changes provide
a reasonable approximation for whether a mutation isfunctionally relevant, it is difficult to identify functionally
relevant mutations in regulatory regions. We compared
the DAF distribution for the SNPs in the proximal
promoter (which we define as 700-bp upstream of all
genes) to the DAF distribution for synonymous SNPs,
which are expected to evolve nearly neutrally (as our analyses above indicate, synonymous SNPs do experienceevolutionary constraints). Consistent with previous studies
in human (Torgerson et al. 2009), we found that thepromoter regions in S. paradoxus have an excess of low
DAF compared with the synonymous SNPs, suggesting that
promoters undergo highly constrained evolution.
Chromatin structure in the promoter region also plays
an important role in regulating gene transcription (Li et al.
2007). Nucleosome occupancy of transcription factor binding sites is thought to hinder the binding of transcription
factors, thereby affecting transcription (Richmond andDavey 2003). Moreover, genes whose proximal promoters
are densely occupied by nucleosomes exhibit elevated transcriptional plasticity in response to stimuli (Tirosh andBarkai 2008). We obtained yeast promoters previously classified into two groups based on nucleosome occupancy,
yielding 494 nucleosome-enriched promoters and 544
nucleosome-depleted promoters (Tirosh and Barkai2008). Our DAF test did not reveal differential selection
between the two promoter classes, suggesting that the
genomic signature of selection is sufficiently similar between these two promoter classes.
Duplicated Genes Versus Singletons
Gene duplication is a major source of evolution. The possible fates of duplicated genes include pseudogenization,
neofunctionalization, or specialization of some of theancestral gene functions while losing others (Lynch andConery 2000). After a gene duplication event, the duplicated genes tend to undergo accelerated evolution perhaps
due to relaxed selection (Conant and Wagner 2003). Overtime, duplicated genes have been found to be moreconstrained than their single-copy counterparts, possibly
because genes having stronger functional constraints are
more often retained after duplication (Jordan et al.
2004). We assessed the relative constraint on genes withmultiple copies relative to single-copy genes. We definedparalogy based on Blast hits with at least 60% identity
across the protein length and E value  1.0  1010and obtained 134 genes with a paralog and 5,102 genes
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without one. We found that duplicated genes are understronger constraint compared with their singleton counterparts. Our conclusions do not change when we repeat theanalysis based on less stringent criteria for paralog identification (40% identity and E value  1.0 1004 yielding
501 genes with a paralog).
New Amino Acid Versus Old Amino Acid
The 20 amino acids directly encoded by the genetic code
can in principle be ordered according to their time oforigin. Although several theories have been put forth toexplain why certain amino acids are likely to have beencreated before others (Novozhilov and Koonin 2009), we
simply tested whether the estimated age of an amino acidaffects its evolutionary constraint. We used the chronological order of amino acids’ origin proposed in Trifonov(2000), which is consistent with the classical Miller–Urey‘‘primordial soup’’ experiment (Miller 1953). Following
previous work, we partitioned the 20 amino acids into 9
old (G, A, V, D, P, S, E, L, and T) and 11 new amino acids(R, N, K, Q, I, C, H, F, M, Y, and W). We obtained two subsets
of nonsynonymous SNPs from our overall set: one wherea new amino acid changes to an old amino acid (86,229
SNPs) and the other where an old amino acid changes
to a new amino acid (47,361 SNPs). Results from ourpopulation-based MW test, at all three low DAF thresholds,
suggest that a substitution of an older amino acid by
a newer amino acid is unfavorable.
Protein Length
The cost of protein biosynthesis imposes a constraint on
overall protein size and expression level (Seligmann2003). Thus, genes that encode for shorter proteins tend
to be highly expressed (Warringer and Blomberg 2006),
and highly expressed genes tend to be under greater
constraint due to mistranslation-induced misfolding(Drummond and Wilke 2008). We tested whether genesthat encode for shorter proteins are under greater
constraint within S. paradoxus. We partitioned our gene
set into two classes based on protein-coding sequencelength using the median length of 393 amino acids overall proteins as the threshold. This yielded 2,581 short genes
and 2,655 long genes. Consistent with previous findings, wefound that nonsynonymous SNPs in short genes are significantly more constrained.
Old Versus New Genes
Genes can be classified by their time of origin. There are oldgenes that have identifiable orthologs in evolutionary
distant species and young genes with orthologs identifiable
only in closely related species. Young genes can arise via
a number of mechanisms and can exhibit accelerated
amino acid substitution rates, which can impede ortholog
detection even for closely related species (Ohno 1970;
Lynch and Katju 2004). Old genes tend to evolve more
slowly and experience stronger purifying selection than
young genes (Domazet-Loso and Tautz 2003; Wolf et al.
2009). A study in the fungal genus Aspergillus found thata protein’s evolutionary rate depends on whether theprotein has a homolog in a distant species (Alba and2622

Castresana 2007; Wolf et al. 2009). Moreover, the differences in evolutionary rates between young and old genes are
significant even when controlling for expression level andfunctional characteristics (Wolf et al. 2009; Vishnoi et al.
2010). We considered a gene to be old if it has identifiablehomology within any bacterial species (Blast E-value  1.0
 1006) and young if it has no detectable homolog outside of yeast. The results of the sample-based test suggestthat the nonsynonymous SNPs in old genes are under
greater constraint than those in young genes. However,the population-based Fisher test supports this conclusion
only at the 20% low DAF threshold.
Relative Selection in Various GO Functional Categories
In addition to the various genic properties discussed above,
we systematically investigated whether genes in a particular
GO biological process or molecular function category have
evolved under selection relative to the other genes that do
not belong to that category. For this analysis, we only considered the 17 GO categories that included at least 5% of all
genes (supplementary table S3, Supplementary Material
online). A majority of the functional categories reveal differential constraint. Categories likely to be under greater
constraint include translation, response to chemical stimulus (involved in mating), transferase activity, transporter
activity, and protein binding, whereas categories likely to beunder relatively relaxed constraint include DNA metabolic
process, transcription, response to stress, cell cycle, chromosome organization, and DNA binding. We were able
to find corroborative evidence for some of these findings.
For example, DNA metabolic process genes have previously
been found to be among the fastest evolving genes inworms (Castillo-Davis et al. 2004), and transcription factors
have been shown to be under relaxed constraint in malarial
parasites (Essien et al. 2008). Specific genes that are highly
expressed during response to stress in yeast have beenshown to be fast evolving (Kellis et al. 2003).

Discussion
Here, we have presented the first comprehensive study oflineage-specific natural selection acting on various functional properties associated either with genes or withgroups of polymorphic sites in yeast S. paradoxus. Previous
function-centric studies based on interspecific comparisons have shown that genes sharing certain functional,
structural, or PIN properties exhibit significantly skewed
evolutionary rates relative to the genomic background, suggesting that those genic properties have been under selection over long evolutionary periods (Xia et al. 2009). The
motivations behind the present study were 2-fold. First,
many of the results previously obtained based on multiple
species comparisons were not recapitulated by pairwise
species comparisons. A proposed reason for this observed
inconsistency is that selection is likely to vary over time
across differing environmental contexts such that only
the most pervasive forces are detectable over long evolutionary timescales. In this case, selection pressures acting
on various functional properties may be lineage specific.

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Natural Selection in Yeast · doi:10.1093/molbev/msr085

A second, equally important motivation is that a population-based approach allows us to assess selection in noncoding regions and across different classes of synonymoussites, which is difficult to do using interspecific data alone.
Even though interspecific analysis of selection in various
classes of noncoding regions have been previously reported(Tanay et al. 2004; Doniger and Fay 2007; Babbitt and Kim
2008), the proposed evolutionary models cannot be readily
generalized.

Choice of Yeast Species
We chose the woodland-associated yeast species
S. paradoxus to investigate lineage-specific selection.
Although its sister species S. cerevisiae is the most studied
yeast species to date, several aspects of its population
history confound simple analysis of selective forces. First,
estimates of both the local and global distribution of
S.cerevisiae populations have indicated extensive population substructure (Aa et al. 2006; Liti et al. 2009), suggestingthat different lineages of S. cerevisiae may have followed
unique evolutionary trajectories. Although dozens ofS. cerevisiae genomes have also become available recently(Liti et al. 2009), no single lineage has been sufficiently
sampled to warrant genome-wide within-subpopulation
evolutionary analysis. Secondly, the occurrence and evolution of most S. cerevisiae lineages is closely associated withhuman activity, suggesting that domestication events havecontributed to the species’ population substructure andevolutionary history (Fay and Benavides 2005). In contrast,
the vast majority of S. paradoxus isolates included in thisstudy form a single recombining lineage (Koufopanou et al.
2006; Liti et al. 2009). Moreover, geographically associatedisolates show low levels of genetic variability in general(Kuehne et al. 2007), despite a predominantly sexual reproductive mode, suggesting that population substructure
does not contribute significantly to genomic differencesamong isolates and that this species may reasonably
approximate a randomly mating population. More importantly, the evolutionary history of S. paradoxus does notappear to have been affected by human activity. Instead,
known isolates of the species are closely associated withnatural woodland habitats (Sniegowski et al. 2002), which
are inferred (based on phylogenetic analysis of S. cerevisiae
isolates) to be the ancestral habitat for yeast, prior tohuman activities (Sniegowski et al. 2002). Thus, ourwithin-species evolutionary analysis of S. paradoxus is likely
to reflect the true evolutionary history of a yeast species.

Identifying the Ancestral Allele
Our DAF-based approach critically relies on the accurate
identification of the ancestral allele at each polymorphic
locus. Of the two alleles at a bi-allelic locus, the one sharedwith an outgroup species is typically inferred to be theancestral allele (Sawyer and Hartl 1992; Chen and Rajewsky
2006; Sethupathy et al. 2008). This assumes the locus to benonsegregating in the outgroup species for which typically
only a single reference sequence is available. Moreover, thisinference also assumes that mutations are rare andexcludes the possibility of a mutation in the lineage leadingfrom most recent common ancestor to the outgroup,
which may not be the case, especially for cytosines at
CG di-nucleotides (Hernandez et al. 2007). To minimize
errors in ancestral allele inference, while still retaining sufficient data, we required that an allele be shared in twooutgroup species—S. mikatae and S. bayanus, separated
from S. paradoxus by 44 and 64 My, respectively (Hahn
et al. 2005). For all properties, on average, 53% of the SNPs
were assigned an ancestral/derived allele, and this fraction
was stable across different properties (42–61%) with a standard deviation of 4.8%. Although the misidentification of
the ancestral allele cannot be entirely eliminated, it is
not likely to be biased between the two classes of SNPs
in our various tests and thus should not affect our results.

Estimating Population Frequency from the Sample
Frequency
Previous analyses based on the DAF spectrum have relied on
the allele frequencies observed in a small sample of sequences (Chen and Rajewsky 2006; Sethupathy et al. 2008;
Torgerson et al. 2009) (although some methods do accountfor sample size explicitly, such as the Poisson random field(Sawyer and Hartl 1992), and the method proposed in Boykoet al. [2008] and implemented in Torgerson et al. [2009]). If
the sample set is small, as in previous reports and in thepresent study, the sample frequency may not accuratelyreflect the true population frequency. Selection cannot be
reliably inferred based on inaccurate representations of
the population frequencies. Here, we account for our sample
size (n 5 28) by basing our inferences on a large number of
estimates of the true, underlying population frequencies that
could have given rise to the observed sample frequencies. Ina vast majority of cases, the population-based tests yielda much less significant P value than the sample-based test.
This suggests that similar tests for selection that do not correct for sample size (Sethupathy et al. 2008; Torgerson et al.
2009) may overestimate the significance in many cases.

The Function-Centric Approach Increases theStatistical Power of Population-Based Analysis
The power of statistical tests to detect selection depends
upon the availability of sequence data. Due to the availability of whole genome sequences for numerous species, interspecific comparisons are reasonably well powered(Kosiol et al. 2008). In contrast, gene-centric population genetic analyses are often hindered by insufficient and biased
sampling, leading to a paucity of known polymorphic sites
and poor resolution of true population frequencies. However, grouping genes (or polymorphic loci) based on sharedproperties increases the number of polymorphic sites toanalyze, thereby improving the statistical power for detecting signatures of selection within a lineage.

Dependence Among Various Properties
Certain properties of a gene are correlated, for example,
essentiality and network connectivity. As such, analyses of
the correlated properties are expected to yield similar results.
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We tested whether similar results for the various properties
we investigated are simply due to statistical dependence (i.e.,due to a large overlap in the foreground SNP sets for a pair ofproperties). We found that the overlap in the positive SNP set
is minimal (4% on average for all pairs of properties). Only
three pairs of properties have over 25% overlap in their positive SNP set—short proteins and GO:Mediated transport(31%), GO:RNA metabolism and GO:Transcription (27%),
and GO:RNA metabolism and old genes (40%). In each ofthese cases, we repeated the hypothesis test after excluding
the SNPs common to the other highly overlapping property,
and in all cases, our conclusions still hold. Therefore, we conclude that the results for each property are (almost entirely)
statistically independent. However, similar results for different
properties might still be attributed to biological dependence.
For instance, robustness against missense translation is a major force behind the low evolutionary rate of highly expressed
genes (Drummond and Wilke 2008). The same force would
also favor shorter proteins. Therefore, our findings should not
be inferred to imply a direct and independent causality
between a property and the DAF spectrum.

Statistical Relevance Versus Biological Relevance
Although for several of the properties tested, there is significant evidence of differential selection, the magnitude of thisdifference, (i.e., the fold difference in the proportion of low
DAF SNPs between sets U and V), is typically small. Such‘‘small but significant’’ results are common to almost all analyses of DAF spectrums (Chen and Rajewsky 2006; Drake et al.
2006; Bird et al. 2007; Sethupathy et al. 2008; Torgerson et al.
2009). For instance, in Torgerson et al. (2009), the differencein the proportion of low DAF between conserved noncoding
SNPs and synonymous SNPs was deemed significant eventhough the fold difference was only 1.06. This parallels resultsfrom successful genome-wide association studies where odds
ratios (i.e., relative risk between two alleles at a polymorphic
locus) at loci that are significantly associated with a disease/
trait are often less than 1.15 and the median odds ratio is only
;1.3 (Hindorff et al. 2009). Nonetheless, this does notundermine the importance of such large-scale studies. Suchstudies can identify groups of genes or groups of sites thatexperience different selective regimes than other groups.
Although not mechanistic at the level of an individual gene,
this kind of information informs our system-level understanding of how an organism evolves. We have ensured that
our results are not due simply to stochastic fluctuations. For
all properties, based on multiple randomized swaps of SNPs
between U and V, we have estimated the nominal false
discovery rate to be less than 2%. In fact, after applying
Bonferroni correction, none of the randomized data yieldedsignificant results. Moreover, we were able to show, based on
random sampling of the equal number of SNPs from U and V,
that our results are not due to the disparate sizes of the U andthe V sets.

GC Content and Genomic Clustering
We also tested whether for some of the gene categories, ourresults may be due to biased nucleotide composition. For
2624

each gene property, we compared the GC content of genes
in the foreground and the background sets using theMann–Whitney U test. We found that for many properties,
the GC content of foreground and background gene sets
were significantly different. For each such property, we
repeated our hypothesis test using a subset of genes in
the foreground and the background ensuring that GC content of the sampled genes were statistically similar in theforeground and the background. Despite a much smaller
data set, and therefore lower statistical power, in all cases,
our original results were still significant. Thus, our findings
are not an artifact of GC biases.
We also tested, for each property, whether the foreground SNPs are clustered in the genome, which may contribute to our observed results due to regional biases inmutation rates. We estimated the linkage disequilibrium(LD) between all SNP pairs (using the R2 metric) and foreach property checked whether there is a greater LD
among SNPs within the foreground compared with thebackground. Specifically, for each property, we computed
the LD value between 100,000 pairs of randomly sampled
SNPs from U and separately for 100,000 random SNP pairs
from V. We then compared the two sets of LD values using
t-tests and performed Bonferroni correction for multiple
testing. Also, because the LD values are bounded by[0,1] and are not normally distributed, we took an arcsin
transform of the LD values, which would make this a more
appropriate test. We found that 25 of 26 properties showedno significant difference between the LD values for U andfor V. We therefore conclude that physical clustering in the
genome is not significantly contributing to our results.

Advantages of a Population-Based Analysis
Several of properties investigated here are suited specifically to an intraspecific analysis and would be difficult
to address via interspecific comparisons based on Dn/Ds
ratios. We highlight these below.
Nonsynonymous SNPs in functional domains have
evolved under greater constraint relative to those in linker
regions. However, we see the opposite effect when considering synonymous SNPs, which seem to have evolved under
greater constraint in linker regions relative to domains(supplementary table S1, Supplementary Material online).
This is consistent with the known enrichment of rarecodons in the intervening linker regions and their association with a slower translation rate (Thanaraj and Argos
1996), and that synonymous codon substitutions canchange the translation kinetics, thus adversely affecting
proper protein folding (Thanaraj and Argos 1996;
Kimchi-Sarfaty et al. 2007). This result presents a clear
counterexample to the assumption of neutrality of synonymous substitutions. Notably, this discovery underscores
an advantage of population-based analysis over interspecific comparative studies.
Relative selection in preferred versus unpreferredcodons represents another analysis uniquely suited tothe population-level approach. We found that the silent
polymorphisms that change a preferred (frequently used)MBE

Natural Selection in Yeast · doi:10.1093/molbev/msr085

to an unpreferred (rarely used) codon are selected against,
relative to the polymorphisms that induce a change in the
opposite direction. This is consistent with the previously
shown negative impact of preferred to unpreferred codon
substitution on gene expression level (Akashi 1994; Zhou
et al. 2010).
We also analyzed the proximal promoter regions for signatures of selection and consistent with a previous intraspecific study in human (Torgerson et al. 2009), we foundthe SNPs in the promoters to be under significantly greater
selection relative to the synonymous sites. However, we did
not find any evidence of different selection between highand low-nucleosome occupancy promoter classes. We also
did not find evidence of differential selection between
putative transcription factor binding sites within the proximal promoter and the rest of the promoter (data notshown), as was found previously in humans (Haygood
et al. 2007; Sethupathy et al. 2008). This discrepancy
may arise because different subclasses of binding sites
evolve under different types of selection depending on
their time of origin, as suggested in Sethupathy et al.
(2008), and our analysis here fails to reveal an overallsignature.

Old Versus New Amino Acids
We specifically focused on coding SNPs for which one of
the alleles corresponds to an old amino acid and the otherallele corresponds to a new amino acid. Our analysis, basedon population-based MW test, suggests that a derived
allele that changes an old amino acid to a new amino acidis typically selected against (supplementary table S1, Supplementary Material online). That is, old amino acids are
preferred in general. Indeed, old amino acids are known
to be 2-fold more abundant than the new amino acids
in extant species and 2.5-fold more abundant in theinferred last universal ancestor (Brooks et al. 2002). We
found this to be true among S. paradoxus proteins as wellwhere old amino acids are 1.4-fold more abundant. Our
analysis suggests that this compositional bias is not simply
a reflection of the ancestral state of protein sequences but
reflects an ongoing selection within the paradoxus lineage
against mutations that replace old amino acids with newamino acids. A previous study based on interspecific comparisons across 15 taxonomic groups identified amino acids
that were lost significantly more frequently than they were
gained by a mutation (losers) as well as amino acids thatwere gained significantly more frequently than they werelost (gainers) (Jordan et al. 2005). It was shown that theloser amino acids were largely old amino acids, whereas
the gainer amino acids were largely recently acquired,
which may be interpreted as selection against old aminoacids. Thus, for reasons that not entirely clear, our finding
based on intraspecific comparison is not consistent withthe previous study based on interspecific comparison.
To conclude, we have shown that many properties
associated either with genes or with specific groups ofpolymorphic sites have evolved under differential selection in the S. paradoxus lineage. Where comparable,

our population-based analysis recapitulates the resultsfrom interspecific comparisons, suggesting that most selection forces acting over long evolutionary timescales
continue to shape the evolution of S. paradoxus. In addition, we also report novel observations, some of which
would be difficult to address by conventional interspecificcomparative studies.

Supplementary Material
Supplementary tables S1–S3 are available at Molecular
Biology and Evolution online (http://www.mbe.
oxfordjournals.org/).

Acknowledgments
J.B.P. acknowledges funding from the Burroughs Wellcome
Fund, the David and Lucile Packard Foundation, the James
S. McDonnell Foundation, the Alfred P. Sloan Foundation,
the Defense Advanced Research Projects Agency (HR001109-1-0055). J.B.P. and S.H. acknowledge support from National Institute of Health grant GM085226.

References
Aa E, Townsend JP, Adams RI, Nielsen KM, Taylor JW. 2006.
Population structure and gene evolution in Saccharomyces
cerevisiae. FEMS Yeast Res. 6(5):702–715.
Akashi H. 1994. Synonymous codon usage in Drosophila melanogaster: natural selection and translational accuracy. Genetics
136(3):927–935.
Alba MM, Castresana J. 2007. On homology searches by protein
Blast and the characterization of the age of genes. BMC Evol Biol.
7:53.
Andolfatto P. 2005. Adaptive evolution of non-coding DNA in
Drosophila. Nature 437(7062):1149–1152.
Babbitt GA, Kim Y. 2008. Inferring natural selection on fine-scalechromatin organization in yeast. Mol Biol Evol. 25(8):1714–1727.
Bird CP, Stranger BE, Liu M, Thomas DJ, Ingle CE, Beazley C,
Miller W, Hurles ME, Dermitzakis ET. 2007. Fast-evolving
noncoding sequences in the human genome. Genome Biol.
8(6):R118.
Blanchette M, Bataille AR, Chen X, et al. (12 co-authors) 2006.
Genome-wide computational prediction of transcriptional
regulatory modules reveals new insights into human gene
expression. Genome Res. 16(5):656–668.
Boyko AR, Williamson SH, Indap AR, et al. (14 co-authors) 2008.
Assessing the evolutionary impact of amino acid mutations in
the human genome. PLoS Genet. 4(5):e1000083.
Bracken C, Carr PA, Cavanagh J, Palmer AG 3rd. 1999. Temperaturedependence of intramolecular dynamics of the basic leucine
zipper of GCN4: implications for the entropy of association with
DNA. J Mol Biol. 285(5):2133–2146.
Brooks DJ, Fresco JR, Lesk AM, Singh M. 2002. Evolution of aminoacid frequencies in proteins over deep time: inferred order of
introduction of amino acids into the genetic code. Mol Biol Evol.
19(10):1645–1655.
Brown CJ, Takayama S, Campen AM, Vise P, Marshall TW,
Oldfield CJ, Williams CJ, Dunker AK. 2002. Evolutionary rate
heterogeneity in proteins with long disordered regions. J Mol
Evol. 55(1):104–110.
Bustamante CD, Fledel-Alon A, Williamson S. 2005. Natural
selection on protein-coding genes in the human genome.
Nature 437(7062):1153–1157.

2625

Vishnoi et al. · doi:10.1093/molbev/msr085
Bustamante CD, Wakeley J, Sawyer S, Hartl DL. 2001. Directional
selection and the site-frequency spectrum. Genetics
159(4):1779–1788.
Cannarozzi G, Schraudolph NN, Faty M, von Rohr P, Friberg MT,
Roth AC, Gonnet P, Gonnet G, Barral Y. 2010. A role for codon
order in translation dynamics. Cell 141(2):355–367.
Castillo-Davis CI, Kondrashov FA, Kondrashov FA, Hartl DL,
Kulathinal RJ. 2004. The functional genomic distribution ofprotein divergence in two animal phyla: coevolution, genomic
conflict, and constraint. Genome Res. 14(5):802–811.
Chaney WG, Morris AJ. 1979. Nonuniform size distribution of
nascent peptides. The effect of messenger RNA structure upon
the rate of translation. Arch Biochem Biophys. 194(1):283–291.
Chen K, Rajewsky N. 2006. Natural selection on human microRNAbinding sites inferred from SNP data. Nat Genet.
38(12):1452–1456.
Chenna R, Sugawara H, Koike T, Lopez R, Gibson TJ, Higgins DG,
Thompson JD. 2003. Multiple sequence alignment with theClustal series of programs. Nucleic Acids Res. 31(13):3497–3500.
Conant GC, Wagner A. 2003. Asymmetric sequence divergence of
duplicate genes. Genome Res. 13(9):2052–2058.
Cowles CR, Hirschhorn JN, Altshuler D, Lander ES. 2002. Detectionof regulatory variation in mouse genes. Nat Genet.
32(3):432–437.
Desai MM, Plotkin JB. 2008. The polymorphism frequency spectrumof finitely many sites under selection. Genetics
180(4):2175–2191.
Domazet-Loso T, Tautz D. 2003. An evolutionary analysis of orphan
genes in Drosophila. Genome Res. 13(10):2213–2219.
Doniger SW, Fay JC. 2007. Frequent gain and loss of functionaltranscription factor binding sites. PLoS Comput Biol. 3(5):e99.
Dosztanyi Z, Csizmok V, Tompa P, Simon I. 2005. The pairwise
energy content estimated from amino acid composition
discriminates between folded and intrinsically unstructured
proteins. J Mol Biol. 347(4):827–839.
Drake JA, Bird C, Nemesh J, et al. (11 co-authors) 2006. Conserved
noncoding sequences are selectively constrained and not
mutation cold spots. Nat Genet. 38(2):223–227.
Drummond DA, Wilke CO. 2008. Mistranslation-induced protein
misfolding as a dominant constraint on coding-sequence
evolution. Cell 134(2):341–352.
Dunker AK, Brown CJ, Lawson JD, Iakoucheva LM, Obradovic Z.
2002. Intrinsic disorder and protein function. Biochemistry41(21):6573–6582.
Essien K, Hannenhalli S, Stoeckert CJ Jr. 2008. Computational
analysis of constraints on noncoding regions, coding regions andgene expression in relation to Plasmodium phenotypic diversity.
PLoS One. 3(9):e3122.
Ewens WJ. 1979. Mathematical population genetics. Berlin (Germany): Springer-Verlag.
Fay JC, Benavides JA. 2005. Evidence for domesticated and wild
populations of Saccharomyces cerevisiae. PLoS Genet. 1(1):66–71.
Fay JC, Wyckoff GJ, Wu CI. 2001. Positive and negative selection on
the human genome. Genetics 158(3):1227–1234.
Finn RD, Tate J, Mistry J, et al. (11 co-authors) 2008. The Pfam
protein families database. Nucleic Acids Res. 36(Databaseissue):D281–D288.
Fraser HB, Plotkin JB. 2007. Using protein complexes to predict
phenotypic effects of gene mutation. Genome Biol. 8(11):R252.
Fraser HB, Wall DP, Hirsh AE. 2003. A simple dependence between
protein evolution rate and the number of protein-protein
interactions. BMC Evol Biol. 3:11.
Fu YX. 1995. Statistical properties of segregating sites. Theor Popul
Biol. 48(2):172–197.
Ganapathy G, Uyenoyama MK. 2009. Site frequency spectra fromgenomic SNP surveys. Theor Popul Biol. 75(4):346–354.

2626

MBE
Giaever G, Chu AM, Ni L, et al. (73 co-authors) 2002. Functional
profiling of the Saccharomyces cerevisiae genome. Nature.
418(6896):387–391.
Hahn MW, De Bie T, Stajich JE, Nguyen C, Cristianini N. 2005.
Estimating the tempo and mode of gene family evolution fromcomparative genomic data. Genome Res. 15(8):1153–1160.
Han JD, Bertin N, Hao T, et al. (11 co-authors) 2004. Evidence fordynamically organized modularity in the yeast protein–protein
interaction network. Nature 430(6995):88–93.
Haygood R, Fedrigo O, Hanson B, Yokoyama KD, Wray GA. 2007.
Promoter regions of many neural- and nutrition-related genes
have experienced positive selection during human evolution.
Nat Genet. 39(9):1140–1144.
Hernandez RD, Williamson SH, Bustamante CD. 2007. Context
dependence, ancestral misidentification, and spurious signaturesof natural selection. Mol Biol Evol. 24(8):1792–1800.
Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP,
Collins FS, Manolio TA. 2009. Potential etiologic and functional
implications of genome-wide association loci for human diseases
and traits. Proc Natl Acad Sci U S A. 106(23):9362–9367.
Hirsh AE, Fraser HB. 2001. Protein dispensability and rate ofevolution. Nature 411(6841):1046–1049.
Jeong H, Mason SP, Barabasi AL, Oltvai ZN. 2001. Lethality andcentrality in protein networks. Nature 411(6833):41–42.
Jordan IK, Kondrashov FA, Adzhubei IA, Wolf YI, Koonin EV,
Kondrashov AS, Sunyaev S. 2005. A universal trend of aminoacid gain and loss in protein evolution. Nature
433(7026):633–638.
Jordan IK, Rogozin IB, Wolf YI, Koonin EV. 2002. Essential genes are
more evolutionarily conserved than are nonessential genes inbacteria. Genome Res. 12(6):962–968.
Jordan IK, Wolf YI, Koonin EV. 2004. Duplicated genes evolve slower
than singletons despite the initial rate increase. BMC Evol Biol. 4:22.
Kellis M, Patterson N, Endrizzi M, Birren B, Lander ES. 2003.
Sequencing and comparison of yeast species to identify genes
and regulatory elements. Nature 423(6937):241–254.
Kim PM, Korbel JO, Gerstein MB. 2007. Positive selection at theprotein network periphery: evaluation in terms of structural
constraints and cellular context. Proc Natl Acad Sci U S A.
104(51):20274–20279.
Kimchi-Sarfaty C, Oh JM, Kim IW, Sauna ZE, Calcagno AM,
Ambudkar SV, Gottesman MM. 2007. A ‘‘silent’’ polymorphismin the MDR1 gene changes substrate specificity. Science
315(5811):525–528.
Kosiol C, Vinar T, da Fonseca RR, Hubisz MJ, Bustamante CD,
Nielsen R, Siepel A. 2008. Patterns of positive selection in six
Mammalian genomes. PLoS Genet. 4(8):e1000144.
Koufopanou V, Hughes J, Bell G, Burt A. 2006. The spatial scale ofgenetic differentiation in a model organism: the wild yeast
Saccharomyces paradoxus. Philos Trans R Soc Lond B Biol Sci.
361(1475):1941–1946.
Kryazhimskiy S, Plotkin JB. 2008. The population genetics of dN/dS.
PLoS Genet. 4(12):e1000304.
Kuehne HA, Murphy HA, Francis CA, Sniegowski PD. 2007.
Allopatric divergence, secondary contact, and genetic isolationin wild yeast populations. Curr Biol. 17(5):407–411.
Li B, Carey M, Workman JL. 2007. The role of chromatin duringtranscription. Cell 128(4):707–719.
Liti G, Carter DM, Moses AM, et al. (26 co-authors) 2009. Population
genomics of domestic and wild yeasts. Nature
458(7236):337–341.
Lynch M, Conery JS. 2000. The evolutionary fate and consequencesof duplicate genes. Science 290(5494):1151–1155.
Lynch M, Katju V. 2004. The altered evolutionary trajectories of gene
duplicates. Trends Genet. 20(11):544–549.

Natural Selection in Yeast · doi:10.1093/molbev/msr085
McClellan DA, McCracken KG. 2001. Estimating the influence of
selection on the variable amino acid sites of the cytochrome B
protein functional domains. Mol Biol Evol. 18(6):917–925.
McDonald JH, Kreitman M. 1991. Adaptive protein evolution at the
Adh locus in Drosophila. Nature 351(6328):652–654.
Miller SL. 1953. A production of amino acids under possible
primitive earth conditions. Science. 117(3046):528–529.
Nielsen R, Bustamante C, Clark AG, et al. (13 co-authors) 2005. A
scan for positively selected genes in the genomes of humans andchimpanzees. PLoS Biol. 3(6):e170.
Nielsen R, Hubisz MJ, Hellmann I, et al. (13 co-authors) 2009.
Darwinian and demographic forces affecting human protein
coding genes. Genome Res. 19(5):838–849.
Novozhilov AS, Koonin EV. 2009. Exceptional error minimization in
putative primordial genetic codes. Biol Direct. 4:44.
Ohno S. 1970. Evolution by gene duplication. London: Allen and
Unwin.
Overington J, Donnelly D, Johnson MS, Sali A, Blundell TL. 1992.
Environment-specific amino acid substitution tables: tertiary
templates and prediction of protein folds. Protein Sci.
1(2):216–226.
Pakula AA, Sauer RT. 1989. Genetic analysis of protein stability andfunction. Annu Rev Genet. 23:289–310.
Pal C, Papp B, Hurst LD. 2003. Genomic function: rate of evolution andgene dispensability. Nature 421(6922):496–497; discussion: 497–
498.
Plotkin JB, Dushoff J, Desai MM, Fraser HB. 2006. Estimating
selection pressures from limited comparative data. Mol Biol Evol.
23(8):1457–1459.
Plotkin JB, Fraser HB. 2007. Assessing the determinants ofevolutionary rates in the presence of noise. Mol Biol Evol.
24(5):1113–1121.
Plotkin JB, Kudla G. 2011. Synonymous but not the same: the causes
and consequences of codon bias. Nat Rev Genet. 12(1):32–42.
Richmond TJ, Davey CA. 2003. The structure of DNA in the
nucleosome core. Nature 423(6936):145–150.
Sabeti PC, Schaffner SF, Fry B, Lohmueller J, Varilly P, Shamovsky O,
Palma A, Mikkelsen TS, Altshuler D, Lander ES. 2006. Positive
natural selection in the human lineage. Science
312(5780):1614–1620.
Sawyer SA, Hartl DL. 1992. Population genetics of polymorphismand divergence. Genetics 132(4):1161–1176.
Sawyer SL, Malik HS. 2006. Positive selection of yeast nonhomologous end-joining genes and a retrotransposon conflict
hypothesis. Proc Natl Acad Sci U S A. 103(47):17614–17619.
Seligmann H. 2003. Cost-minimization of amino acid usage. J Mol
Evol. 56(2):151–161.
Sethupathy P, Giang H, Plotkin JB, Hannenhalli S. 2008. Genomewide analysis of natural selection on human cis-elements. PLoS
One. 3(9):e3137.
Shapiro JA, Huang W, Zhang C, et al. (12 co-authors) 2007. Adaptive
genic evolution in the Drosophila genomes. Proc Natl Acad Sci U
S A. 104(7):2271–2276.
Sharp PM, Li WH. 1987. The codon adaptation index—a measure of
directional synonymous codon usage bias, and its potential
applications. Nucleic Acids Res. 15(3):1281–1295.

MBE
Sniegowski PD, Dombrowski PG, Fingerman E. 2002. Saccharomyces
cerevisiae and Saccharomyces paradoxus coexist in a natural
woodland site in North America and display different levels of
reproductive isolation from European conspecifics. FEMS Yeast
Res. 1(4):299–306.
Tanay A, Gat-Viks I, Shamir R. 2004. A global view of the selection
forces in the evolution of yeast cis-regulation. Genome Res.
14(5):829–834.
Thanaraj TA, Argos P. 1996. Ribosome-mediated translational pause
and protein domain organization. Protein Sci. 5(8):1594–1612.
Tirosh I, Barkai N. 2008. Two strategies for gene regulation bypromoter nucleosomes. Genome Res. 18(7):1084–1091.
Torgerson DG, Boyko AR, Hernandez RD, et al. (11 co-authors) 2009.
Evolutionary processes acting on candidate cis-regulatory
regions in humans inferred from patterns of polymorphismand divergence. PLoS Genet. 5(8):e1000592.
Trifonov EN. 2000. Consensus temporal order of amino acids andevolution of the triplet code. Gene 261(1):139–151.
Uversky VN. 2002. Natively unfolded proteins: a point where biology
waits for physics. Protein Sci. 11(4):739–756.
Vishnoi A, Kryazhimskiy S, Bazykin GA, Hannenhalli S, Plotkin JB.
2010. Young proteins experience more variable selection
pressures than old proteins. Genome Res. 20(11):1574–1581.
Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF, Jones DT. 2004. Prediction
and functional analysis of native disorder in proteins from thethree kingdoms of life. J Mol Biol. 337(3):635–645.
Warringer J, Blomberg A. 2006. Evolutionary constraints on yeast
protein size. BMC Evol Biol. 6:61.
Wilson AC, Carlson SS, White TJ. 1977. Biochemical evolution. Annu
Rev Biochem. 46:573–639.
Wolf YI, Novichkov PS, Karev GP, Koonin EV, Lipman DJ. 2009. The
universal distribution of evolutionary rates of genes and distinct
characteristics of eukaryotic genes of different apparent ages.
Proc Natl Acad Sci U S A. 106(18):7273–7280.
Worth CL, Gong S, Blundell TL. 2009. Structural and functional
constraints in the evolution of protein families. Nat Rev Mol Cell
Biol. 10(10):709–720.
Wright PE, Dyson HJ. 1999. Intrinsically unstructured proteins: reassessing the protein structure-function paradigm. J Mol Biol.
293(2):321–331.
Wuchty S, Barabasi AL, Ferdig MT. 2006. Stable evolutionary signal
in a yeast protein interaction network. BMC Evol Biol. 6:8.
Xia Y, Franzosa EA, Gerstein MB. 2009. Integrated assessment ofgenomic correlates of protein evolutionary rate. PLoS Comput
Biol. 5(6):e1000413.
Zhang J, He X. 2005. Significant impact of protein dispensability on
the instantaneous rate of protein evolution. Mol Biol Evol.
22(4):1147–1155.
Zhang L, Li WH. 2005. Human SNPs reveal no evidence of frequent
positive selection. Mol Biol Evol. 22(12):2504–2507.
Zhou T, Gu W, Wilke CO. 2010. Detecting positive and purifying
selection at synonymous sites in yeast and worm. Mol Biol Evol.
27(8):1912–1922.
Zhou T, Weems M, Wilke CO. 2009. Translationally optimal codons
associate with structurally sensitive sites in proteins. Mol Biol
Evol. 26(7):1571–1580.

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