Open Access

Fraser and2007
Volume8, Issue
Plotkin11, Article R252

Research

Using protein complexes to predict phenotypic effects of genemutation
Hunter B Fraser* and Joshua B Plotkin†

Addresses: *Broad Institute of Harvard and MIT, 320 Charles St, Cambridge, Massachhusetts 02142, USA. †Department of Biology, Universityof Pennsylvania, 433 S. University Ave, Philadelphia, Pennsylvania 19104, USA.
Correspondence: Hunter B Fraser. Email: hunter@broad.mit.edu

Published: 27 November 2007
Genome Biology 2007, 8:R252 (doi:10.1186/gb-2007-8-11-r252)

Received: 13 June 2007
Revised: 25 September 2007
Accepted: 27 November 2007

The electronic version of this article is the complete one and can befound online at http://genomebiology.com/2007/8/11/R252© 2007 Fraser and Plotkin; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
protein
Predicting<p>Thecomplexbest
a protein’s
predictor
with it.</p>knockoutof a protein's
phenotypeknockout phenotype is shown to be the knockout phenotype of other proteins that are present in a

Abstract
Background: Predicting the phenotypic effects of mutations is a central goal of genetics research;
it has important applications in elucidating how genotype determines phenotype and in identifying
human disease genes.
Results: Using a wide range of functional genomic data from the yeast Saccharomyces cerevisiae, weshow that the best predictor of a protein's knockout phenotype is the knockout phenotype ofother proteins that are present in a protein complex with it. Even the addition of multiple datasets
does not improve upon the predictions made from protein complex membership. Similarly, we findthat a proxy for protein complexes is a powerful predictor of disease phenotypes in humans.
Conclusion: We propose that identifying human protein complexes containing known disease
genes will be an efficient method for large-scale disease gene discovery, and that yeast may prove
to be an informative model system for investigating, and even predicting, the genetic basis of bothMendelian and complex disease phenotypes.

Background

Since the advent of genetic mapping, the approximate
genomic locations of the polymorphisms that cause thousands of human phenotypes have been reported. As compiledby the Online Mendelian Inheritance in Man (OMIM) database, more than 1,500 human genes have been found to be
associated with over 3,000 disorders [1]. This impressive
level of success is tempered by the fact that more than 1,000
disorders have been mapped to a genomic region, but theunderlying 'disease gene' has not yet been identified for these
disorders [1]. Although some fraction of these 1,000 loci aresurely false positives, the statistical significance associatedwith them indicates that most are likely to contain true Mendelian disease genes that have yet to be pinpointed.
This set of mapped disease loci represents an exciting opportunity for rapid advancement in our understanding of humandisease genetics. Any method that can generate high-confidence predictions for which genes within the mapped regions
are responsible for the diseases in question would be an
important step forward. Indeed, some such methods wererecently proposed, for example genomic screens for mitochondria-related genes identified several candidate disease
genes for mitochondrial disorders [2,3].

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Another route to gaining insights into a particular disease is
to study a model of the disease in a nonhuman organism.
Such models, if they are faithful reproductions of a specifichuman disease, can be informative by revealing aspects of thefunction of both wild-type and mutant versions of the diseasegene (or its ortholog in the model organism) and by providing
a testing ground for potential therapies. The mouse has beena particularly useful model in this regard. In general, themore diverged a model organism is from human, the moredifficult it is to create an accurate model of a human disease;more deeply divergent lineages are less likely to have humandisease gene orthologs, and they are also less likely to have aphenotype similar enough to humans to allow detailed studyof a particular disease phenotype.
It is unfortunate that most diseases cannot accurately bemodeled in species such as the bacterium Escherichia coli or
the budding yeast Saccharomyces cerevisise, considering theease of growing, storing, manipulating, and studying theseorganisms. Indeed, largely because of the simplicity of genetic
manipulation in yeast, more functional genomic data havebeen generated for this species than for any other. For mostgenes/proteins, the mRNA expression level is known in thousands of conditions, as are the protein subcellular localization, the mRNA and protein decay rate, the mRNA translation
rate, the protein abundance, the growth rates of systematic
knockout strains across many conditions, a substantial fraction of the physical and genetic interactions, and much more.
Despite the vast amount of published functional genomic
data, yeast and other unicellular organisms generally lack amorphologic phenotype rich enough to allow for detailed phenotypic descriptions based on a single growth condition. For
example, even though different yeast strains may have distinct differences in their size, shape, and growth rate, in general very little information can be gleaned about a gene's
knockout phenotype by observing growing cells in a single
environment. However, if multiple environments are utilized
in defining the phenotype, then even just one characteristic(such as growth rate) can be used to describe the phenotype
with greater specificity, limited only by the diversity of environments tested. The description of a phenotype is simply alist of growth rates (or other measured characteristics) in allconditions tested, and two genes can be said to cause the same
knockout phenotype if strains deleted for each gene exhibit
similar growth rates across all tested environments. It is
worth noting that this definition of phenotype is analogous tohuman disease, because any disease is simply a specific phenotype of lowered fitness in some set of environments.
The concept of identifying genes whose mutation or deletion
leads to similar phenotypes is by no means novel. Indeed,much of classical genetics is based on this idea. Since thedevelopment of nearly comprehensive gene knockout or RNA
interference knockdown resources in yeast, Caenorhabditis
elegans, and Drosophila melanogaster, many researchers

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have systematically measured various phenotypes and identified clusters of genes with similar phenotypic profiles acrossa set of conditions [4-6].
Given such a phenotypic profile, we can ask what other typesof data best predict 'phenotype pairs', that is, pairs of genes
whose loss leads to similar phenotypic profiles. If we could
identify an effective predictor of phenotype pairs, and if thepredictor is sufficiently generic to apply to other species, thenthis predictor may be useful for elucidating human diseasephenotypes as well. If this predictor has been measured for atleast some human gene pairs, then it could then be used to
predict human disease genes simply by searching the genome
for gene pairs that score highly and for which one of the twogenes is a known disease gene. For example, if co-expressed
genes in yeast were found to be the best predictor of phenotype pairs, then it stands to reason that co-expressed humangenes may also lead to the same phenotype when mutated. If
so, then identifying all genes that are co-expressed with aknown disease gene would give a list of candidates for additional genes that cause the same disease. By combining thecandidate list with mapped but unidentified disease genes,
more confidence could be given to candidate genes that fallwithin the mapped susceptibility loci. In this manner, genesthat are likely to be responsible for any type of disease could
be identified, as long as the disease has at least one known
causative gene. Others have previously used physical interactions between proteins or multiple gene-ranking algorithms
to predict new disease genes from within mapped susceptibility loci [7-9]. However, because the functional genomic datafor humans is currently rather sparse (compared with what isavailable for some model organisms), it remains to be seen
whether some type of data not yet explored in humans could
be even more predictive of human disease genes.
To discover what predictor(s) might be the most effective inhuman, we turned to yeast as a model. We reasoned that if
quantitative phenotypes can be studied in yeast, then the vast
amount of functional genomic data available could be used to
predict the phenotypic effects of gene mutations or deletions.
Here, we utilize a general framework for studying phenotypes
to find what types of data are predictive of phenotypes inyeast; we then apply this framework to human diseasephenotypes.

Results
Protein complexes as predictors of phenotype
Several groups have noted that subunits of the same protein
complex tend to have similar knockout/knockdown phenotypes in both yeast and C. elegans [4,5,10]. However, otherpotential predictors of phenotype pairs were not comparedwith protein complexes, so from these studies it is not possible to conclude what type of information is the best available
predictor.

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To address this issue, we utilized the most comprehensive
phenotypic profiling dataset published to date: quantitative
growth rates of the yeast haploid deletion collection, including more than 4,200 strains, in 82 diverse conditions [6]. The
conditions include seven crude antifungal extracts, 23 US
Food and Drug Administration approved drugs, and morethan 50 other synthetic compounds. The original authors did
not attempt to test any predictors of phenotypic profile similarity. In this dataset, we defined a 'phenotype pair' as a pairof genes whose knockout strains have growth rate correlation
coefficient r > 0.8 across all 82 conditions. This definition
resulted in approximately one phenotype pair for every 4,000
pairs of genes.
We compiled a list of 20 potential predictors of phenotype
pairs in yeast. The list included genetic interactions compiled
from the literature [11]; pairs of genes sharing a Pfam [12]protein domain or bound by the same transcription factor[13]; mRNA co-expression at several correlation cut-offs
using a large compendium of expression profiles [14]; protein
co-localization measured with a collection of green fluorescent protein tagged strains [15]; co-citation in the literature[16]; similar phylogenetic profiles (that is, pattern of orthologpresence and absence across species) [16]; all known yeast
metabolic pathways [17]; several datasets of physical interactions and protein complexes, either from high-throughput(HTP) screens or from the literature [10,11,18,19], and two
published classifications of gene 'modules' or functional relationships defined using multiple data sources [16,20].
We then devised a test to use each of these data types as a separate predictor of phenotype pairs. We did not wish the test to
penalize data types that cover fewer pairs of genes than others(for example, most gene pairs have not been tested for genetic
interactions, so this data type has low coverage); therefore,
our metric of predictive success was simply the enrichment
for phenotype pairs within the set of gene pairs satisfying thecriterion used. For example, gene pairs in the same metabolic
pathway form one set of predictions; another set consists ofgenes co-expressed with correlation coefficient r > 0.3 in our
expression compendium. The enrichment within that set isthe number of phenotype pairs found within that set, divided
by the number expected by chance. Enrichments greater than
one indicate more predictive power than random.
Testing the enrichment for all 20 predictors, we found a striking pattern (Figure 1a); whereas 19 of the predictors gave atleast a twofold enrichment for phenotype pairs over the random expectation (P < 10-6 for all 19), some yielded muchgreater enrichments than others. The three datasets withgreater than 80-fold enrichment all consisted of protein complexes: two different metrics of stable protein interactions
from a recent HTP screen [10] (see Materials and methods,
below), and the set of high-confidence manually curated protein complexes from the Munich Information Center for Protein Sequences (MIPS) database [19]. In fact, all seven

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predictors with greater than 20-fold enrichment (Figure 1a)were protein complexes, physical interactions compiled fromthe (non-HTP) literature [11], or 'modules' of co-expressed
proteins with many physical interactions among themselves[20]. Considering that both the physical interactions compiled from the literature and the 'modules' [20] are expected
to be highly enriched for protein complexes, it appears thatthe best predictors are united by the theme of stable protein
interactions.
We next sought to test whether combining different datasetsby taking their intersection might improve their predictive
power. For example, we could ask whether gene pairs that areco-expressed and that also have similar phylogenetic profiles
are more enriched for phenotype pairs than are either of thesetwo predictor datasets alone. If the set of pairs matching bothcriteria has a significantly higher frequency of phenotype
pairs than pairs matching either one of the two criteria alone,then we can conclude that the two data sets contain independent information; in other words, each dataset contains
some information that is not present in the other. If, instead,the intersection yields an enrichment that is not greater thanthe enrichment from either criterion alone, then there is noevidence of independent information.
We first measured the predictive power of the intersections
between each of the 20 predictors and co-expression. We
found that intersecting with co-expression significantly (P <0.01 after Bonferroni correction for multiple tests) improved
the enrichment for phenotype pairs in seven datasets, and it
did not significantly diminish the enrichment for any dataset(Figure 1a [inset], compare enrichments before [red] andafter [green] intersection; asterisks indicate significant
improvement). Aside from these seven significant improvements, no dataset scored better than P = 0.37 for improvement in predictive power, indicating a clear distinction
between the datasets improved by intersecting with coexpression and those not improved. Interestingly, six of theseven improved datasets consisted of physical interactions(the seventh was protein co-localization): three lists of HTP
protein complexes from two studies [10,18], one list of all
published HTP physical interactions (excluding the two HTP
protein complex screens treated separately here) [11], andtwo lists (with different confidence levels) of all physicalinteractions from non-HTP publications [11]. Because protein
complex subunits tend to be tightly co-expressed with one
another [21], one possible interpretation of this result is thatintersecting physical interactions with co-expression
improves enrichments by reducing false-positive results (notexpected to be uncommon among some HTP screens, as wellas among non-HTP interactions reported only once in the literature) and/or by decreasing the frequency of transientinteractions (which comprise many of the interactions in thethree noncomplex interaction datasets). One prediction ofthis idea is that for a dataset consisting of protein complexes
with very few false positives, intersecting with co-expression

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*
**

200

*

200

150

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100

100

0

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0

all pairssame TF
co-expr (r >.3)
domains
co-expr (r >.4)
co-expr (r >.5)
co-expr (r >.6)
localization
genetic interactions"finalnet"
phylo profile
cocitation
HTP interactions
metabolic pathway
complex (Krogan)
LTP interactions
modules (Lu06)
LTP interactions 2xcomplex (Gavin1)
complex (Gavin2)
complex (MIPS)

50

700

phenotype pair enrichment

300

300

600
500

200

400
100

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200

0

100
0

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should not increase the predictive power. Consistent with thisidea, the high-confidence MIPS complexes are the only physical interaction data that were not significantly improved byintersecting with co-expression (Figure 1a [inset]; P = 0.66).
As with Figure 1a, all of these results are consistent with protein complexes being the key predictors of phenotype pairs.

all pairssame TF
co-expr (r >.3)
domains
co-expr (r >.4)
co-expr (r >.5)
co-expr (r >.6)
localization
genetic interactions"finalnet"
phylo profile
cocitation
HTP interactions
metabolic pathway
complex (Krogan)
LTP interactions
modules (Lu06)
LTP interactions 2xcomplex (Gavin1)
complex (Gavin2)
complex (MIPS)

phenotype pair enrichment

250

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Figure 1 of phenotype pairs in yeast
Predictors
Predictors of phenotype pairs in yeast. (a) Enrichments for phenotype
pairs among 20 predictors. An enrichment value of 1 reflects random
performance (shown as 'all pairs', the left-most column) and greater than 1
indicates better than random predictive power. Predictors are arranged inorder of increasing predictive power. Error bars indicate thehypergeometric standard deviation, which reflects the range of expectedvariation in the enrichment value. In the inset, red bars are the same as in
the main panel a, and are in the same order. Green bars indicate
enrichments in the intersection of each dataset with co-expression (r >0.3). The seven datasets with significant improvements in predictive power
are indicated by asterisks. Note that the four co-expression datasets are
not counted in the multiple testing correction because they cannot
possibly show any improvement when intersecting with another datasetthat is a superset. (b) Green bars are the same as in Figure 1a inset. Bluebars indicate the level of enrichment that would be expected by chance, if
co-expression was entirely independent of each dataset. Green bars
significantly lower than the paired blue bar indicate a dataset that is notindependent of co-expression. Error bars indicate the hypergeometricstandard deviation, which reflects the range of expected variation in theenrichment value. In the inset, red bars are the same as in panel a and arein the same order as in both panels a and b. Green bars indicate
enrichments in the intersection of each dataset with Munich Information
Center for Protein Sequences (MIPS) complexes. Note that although many
green bars are significantly higher than the paired red bars, no green bars
are significantly higher than the MIPS complexes (rightmost) bars. This
indicates that no dataset adds to the predictive power of complexes
among the set of proteins in MIPS complexes. HTP, high-throughput; LTP,
low-throughput; TF, transcription factor.

It is informative also to compare enrichments for phenotype
pairs seen in Figure 1a (inset) with what would be expected bychance, if each dataset were entirely independent of coexpression (two predictors are independent if the size of theirintersection, both within the set of phenotype pairs andwithin the set of nonphenotype pairs, is no greater than
expected by random chance). The expected enrichment forphenotype pairs within an intersection of two independent
criteria is a simple function of the frequencies of phenotype
pairs satisfying each criterion alone, and the background frequency of all phenotype pairs (see Materials and methods,
below). Comparing these expected enrichments (Figure 1[blue bars]) with the observed intersection enrichments (Figure 1b [green bars]), it is clear that in many cases the observed
enrichment is close to that expected under independence,indicating that co-expression is adding nearly orthogonal
information. In no case, however, is there a significant
increase over the expectation assuming independence. Insummary, for a number of the datasets (in particular, the six
for which intersecting with co-expression significantly
improves the predictive power), the information added byintersecting with co-expression is close to what would be
expected if co-expression contained entirely independent
information about phenotypes.
In stark contrast to co-expression, when intersecting the setof MIPS complexes with all other datasets, there was no
improvement for phenotype pair enrichment above theenrichment found in MIPS complexes alone. This is shown in
Figure 1b (inset), in which the observed phenotype pairenrichments (green bars) can be compared with MIPS complexes alone (the rightmost variable); although four intersections give slightly higher enrichments than MIPS complexes,
the improvement is not significant in any case. In sum, nodataset tested here adds information about phenotypes when
we control for protein complexes, even though nearly every
predictor does have a significant level of predictive power on
its own. This is exactly what would be expected if all datasets
were predictive largely because they are themselves enriched
for members of the same complexes.
We next tested whether the intersection between any two of
our datasets had greater predictive power than MIPS complexes alone. Strikingly, not a single intersection (out of all
190 combinations) gave a significant improvement over MIPS
complexes alone (not shown). The most predictive combination that did not include complexes as one of the predictors
was the intersection of co-expressed pairs with the high-confidence literature-derived physical interaction data (an inter-

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Genome Biology 2007,section that is itself highly enriched for protein complexes),
which enriched for phenotype pairs 186-fold over random(Figure 1a [inset]) at co-expression r > 0.3. At more extreme
co-expression cut-offs, the enrichment increased even more
(up to 310-fold at r > 0.6), although it was never significantly
better than MIPS complexes alone. These results indicate thateven in the absence of a reliable protein complex membershiplist, phenotype pairs can be effectively predicted by using a
proxy for protein complex membership.

Predicting human disease genes
Having established that protein complexes are the most powerful predictor of phenotype pairs in yeast, we reasoned that
this property might apply to other species as well. Two general lines of evidence support this idea. First, other generalproperties that characterize relationships between genes orproteins (for example, that subunits of the same protein complex are often co-expressed [21]) are usually conserved
between species. Second, there is evidence that subunits
within each of 11 well characterized protein complexes in C.
elegans exhibit similar RNA interference knockdown phenotypes [5], as well as anecdotal evidence that subunits of thesame complex can sometimes cause the same human disease(for instance, Fanconi anemia [22] and limb-girdle muscular
dystrophy [23]).
We therefore sought to test systematically how best to predict
human phenotype pairs. For human, we define a phenotype
pair in a similar although less quantitative manner as foryeast: a pair of genes whose mutation leads to a similar phenotype. Similar disease phenotypes were compiled from the
OMIM database [1] and grouped into clusters, as described
previously [7], resulting in a list containing approximately
one out of every 26,000 gene pairs.
Because the range of human functional genomic data lacks
the breadth of published yeast data, it is not possible to compare a large number of human phenotype pair predictors. In
particular, only a very small number of protein complexes
have been characterized, and so we were unable to test
directly whether complexes enrich for phenotype pairs to thesame extent as in yeast. Furthermore, transferring MIPS
complexes by orthology (assuming that all human orthologsof yeast MIPS complex subunits have conserved interaction
partners) does not result in a large enough list of putativeinteractions to be informative (not shown).
However, there do exist human gene expression data fromthousands of conditions, as well as tens of thousands ofknown physical interactions. Considering how well the coexpressed literature-derived interactions predicted phenotype pairs in yeast (Figure 1a [inset]), we decided to use coexpressed physical interactions as a proxy for protein complexes, with the understanding that this list is likely to containa large number of noncomplex pairs.

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We assembled several human datasets for this analysis. To
calculate co-expression, we used a compilation of 2,642
Affymetrix U133a microarrays (see Materials and methods,
below). We also used two physical interaction datasets: literature-derived non-HTP human interactions from the Human
Protein Reference Database (HPRD) database [24] and HTP
interaction data from both human [25,26] and other species
whose interactions were mapped to human by orthology [7].
In agreement with previous results [7], we found that the
HPRD interactions (271-fold above random) were far morepredictive of phenotype pairs than were HTP interactions (17fold above random; Figure 2a). Co-expression was a relatively
weak predictor at a wide range of correlation cutoffs (forexample, 3.7-fold above random at r > 0.3); at high thresholds, however, co-expression equaled or slightly exceeded the
HPRD interactions in predictive power (325-fold enrichment
at r > 0.8). All of these predictors gave highly significant (P <10-8) improvements over random pairs.
As was the case in yeast, taking the intersection of physicalinteractions and co-expression dramatically improved predictive power. For the HTP interactions, the enrichments
improved to 40-fold above random by intersecting with coexpression r > 0.3 and 43-fold with r > 0.5 (at higher coexpression cut-offs no disease pairs were present among the
HTP interactions). Taking the intersection of co-expression
with HPRD interactions resulted in an even better predictor
of disease gene pairs: approximately 500-fold above random
at r > 0.3 and 3,000-fold at r > 0.8 (Figure 2a [inset]). This
impressive approximate 3,000-fold enrichment results in
11% (10/92; listed in the Additional data file 1) of all genepairs satisfying these criteria being pairs known to cause thesame disease. We note that 11% may be an underestimate,because many physical interactions and disease genes are yet
to be discovered; alternatively it may be an overestimate,because of biases in the scientific literature (see Discussion,
below). All of the intersections with HPRD interactions had
significantly (P < 10-4 after Bonferroni correction for seventests) less enrichment than expected by chance under independence (Figure 2b), indicating that neither co-expression
nor HTP interactions are completely orthogonal to HPRD
interactions. In fact, we found that much of the informationin the HTP data is redundant with HPRD, because this intersection was the only one with no significant improvement
over HPRD alone (P = 0.17).
Considering the magnitude of the enrichment among coexpressed literature-based interactions for pairs of genes
involved in the same disease, it is possible to begin to makepredictions about novel disease genes. For example, among
our current predictions are six genes (COL4A1, COL4A2,
SPARC, BGN, DCM, and LUM) whose mutation may lead tophenotypes similar to Ehlers-Danlos syndrome (which ischaracterized by a range of problems related to skin, joints,
eyes, and other areas), based on their co-expression and

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Fraser and Plotkin R252.6

physical interactions with three proteins known to be
involved in this disease (FN1, COL3A1, and COL1A2). Other
predictions include involvement of MCM2 and MCM3 inhypolactasia, S100B in Alexander disease, and CFHL1 inchronic hypocomplementemic nephropathy. These predictions, albeit few in number, serve to illustrate how a largescale protein complex membership list could be used to predict a much greater number of novel human disease genes.

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Figure 2 of disease gene pairs in human
Predictors
Predictors of disease gene pairs in human. (a) Enrichments for diseasegene pairs among eight predictors. An enrichment value of 1 reflects
random performance (shown as 'all pairs', the left-most column).
Predictors are arranged in order of increasing predictive power. Error
bars indicate the hypergeometric standard deviation, which reflects therange of expected variation in the enrichment value. In the inset red bars
are the same as in the main panel a and are in the same order (note thetenfold change in scale). Green bars indicate enrichments in theintersection of each dataset with Human Protein Reference Database(HPRD) interactions. Aside from HPRD intersected with itself or with allpairs, all but one dataset (high-throughput [HTP] interactions) exhibit asignificant improvement in predictive power over HPRD interactions
alone when intersected with HPRD; this indicates that these datasets areat least partially independent of HPRD. (b) Green bars are the same as inpanel a (inset). Blue bars indicate the level of enrichment that would be
expected by chance, if HPRD interactions were entirely independent ofeach dataset. Green bars significantly lower than the paired blue bar
indicate a dataset that is not entirely independent of HPRD interactions.
The three right-most blue bars are truncated for clarity; their enrichment
values are written above each bar. Error bars indicate the hypergeometricstandard deviation, which reflects the range of expected variation in theenrichment value.

We have shown that protein complexes appear to be the mosteffective predictors of similar phenotypic effects for genepairs. Despite the myriad types of functional and evolutionary
genomic data we tested, no dataset was able to increase thepredictive power of complexes alone. Furthermore, all of themost effective predictors of yeast phenotype pairs were either
protein complexes (Figure 1a) or co-expressed physical interactions (Figure 1a [inset]), which are themselves highly
enriched for complexes. Applying this idea to human data, wefound that co-expressed physical interactions are effective
predictors of gene pairs known to cause the same disease(Figure 2a [inset]). This indicates that previous studies thatused only protein interactions to predict disease genes [7,9]might have greatly improved their predictive power by incorporating co-expression information as well.
One possible concern is that the literature-based interactions
are not truly independent of the disease gene pairs. This situation could arise if investigators preferentially look for interactions between proteins that are known to be involved in thesame disease, or if a protein's role in some disease wasdiscovered (at least in part) as a result of its interaction witha known disease-related protein. Unfortunately, it is very
difficult to control for this possibility. For example, if a protein interaction is discovered after both proteins involved
have been found to cause the same disease, then one could in
principle read the publication reporting the interaction to see
if the authors cite the proteins' role in disease as a factor in
their research. However, even if the relation with disease is
not cited as a reason why the interaction was sought out, thisdoes not rule out the possibility that the proteins' role in disease contributed in some way to the discovery of the interaction. In sum, conclusive evidence of either independence ordependence between the discovery of the proteins' interactions and their role in the same disease cannot usually befound.
Fortunately, however, the enrichments for human diseasegene pairs that we observed are strong enough that evenextreme biases would not be sufficient to account for all of theenrichment we observe. For example, if we were to find that
only half of all pairs of genes causing the same Mendelian disease were known, and that among the other half not even asingle pair involved a physical interaction, then our observed
enrichments would be reduced by twofold. Our strongest

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enrichment (Figure 2a [inset]) would thus be reduced toabout 1,500-fold over random, which is still a very useful levelof enrichment for predicting disease gene pairs.
If protein complexes are an even better predictor of diseasegene pairs than co-expressed physical interactions, asappears to be the case in yeast, then a high-quality humanprotein complex membership list could be even more predictive that than the approximate 3,000-fold enrichment weobserved. For this reason, we propose that identifying humanprotein complexes may be the most efficient method for identifying the genes responsible for many mapped disease loci.
Indeed, because of recent technologic advancements, identifying the subunits of human protein complexes is not difficult[27,28]; thousands of human open reading frames, cloned
into Gateway vectors [29], can easily be tagged for affinity
purification, transfected/infected into an appropriate humancell line, purified, and subjected to mass spectrometry to
identify all proteins co-purifying with the tagged protein. The
most promising candidates for this approach would be proteins that are known to cause a disease for which there aremany mapped susceptibility loci with unidentified causal
genes, because these present the best opportunity for discovering the causal genes residing within susceptibility loci. If a
protein encoded by a gene within a mapped susceptibility
locus is found to be in a protein complex with a known diseasegene, then this prediction could be tested by sequencing thegene in the DNA samples used for the original genetic mapping study. Also, in addition to revealing novel disease genes,
identifying the subunits of protein complexes containing disease-associated proteins may greatly improve our understanding of the biology underlying these diseases.
The general framework presented here could also be applied
to more complex, multigenic disease phenotypes. For example, with a large enough set of unbiased genetic interactions
from yeast, the same 20 predictors used here could be applied
to identify the best predictor(s) of genetic interactions inyeast. These predictor(s) could then be used to predict epistatic interactions that are thought to be responsible for many
complex diseases [30-32]. Indeed, such a method could beapplied to any complex phenotype in any species, and could
possibly aid in our general understanding of how genotypes
determine phenotypes.

Materials and methods
Datasets
Yeast data were compiled from a number of sources. Expression data were from a compilation of 1,610 published microarrays [14], and co-expression was calculated as the Pearson
correlation between pairs of genes across all experiments.
Increasing the co-expression cut-off above r > 0.6 did notincrease enrichments, so these cut-offs are not shown in Figure 1. Transcription factor binding sites [13] were required tohave both binding site conservation in at least three out of

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Fraser and Plotkin R252.7

four Saccharomyces sensu stricto spp. and 'ChIP-chip' (chromatin immunoprecipitation-chip) binding data at P < 0.005
in order to call a promoter as bound by a particular transcription factor. Pfam domains present in every yeast gene were
downloaded from the Pfam database [12]. Co-localization
data were from Huh and coworkers [15]; two proteins werecalled co-localized if they were present in exactly the same setof subcellular locations. Genetic interactions, HTP interactions, and literature-curated physical interactions were from
Reguly and colleagues [11]. Phylogenetic profile similarity,
co-citation, and 'finalnet' (a composite score calculated frommany datasets) were taken from Lee and coworkers [16]; cutoff scores of 0.5, 2, and 3 were used for each dataset, respectively (altering cut-offs did not greatly affect the results). Metabolic pathways were taken from Forster and colleagues [17].
Functional 'modules' of genes were defined by Lu and colleagues [20] as co-expressed groups of proteins with many
physical interactions among themselves. The four protein
complex datasets were from three sources [10,18,19]. Two different datasets of interactions were provided by Gavin andcoworkers [10]: a list of complexes ('Gavin1') and a socioaffinity score between pairs of proteins ('Gavin2'; cut-off = 5).
For the MIPS complexes, we used all pairs of proteins present
in the same complex, excluding the ribosome (since this single complex has more protein pairs than all others combined,
so would be almost entirely responsible for any results wefound). Raw growth rate data across 82 growth conditions
were taken from [5]; a threshold of Spearman r > 0.8 wasused to define pairs of genes whose knockout causes the same
phenotype (all results were largely robust to changes in thisthreshold; in general, increasing the threshold resulted instronger enrichments but smaller phenotype pair sample
sizes, whereas decreasing the threshold resulted in weakerenrichments but larger sample sizes).
Human datasets were from two sources. For gene expression
data, we chose Affymetrix U133a (Affymetrics Inc., Santa
Clara, CA, USA) as the platform because this microarray has
more raw data (2,642 CEL files) deposited in the Gene
Expression Omnibus database [33] than any other (we did
not attempt to combine data from multiple different microarray platforms, because doing so can be problematic [notshown]). CEL files were downloaded from Gene Expression
Omnibus in August 2006, and Robust Multichip Average normalization [34] was performed (R Lee and B Hayete, personalcommunication). Co-expression values were calculated as thePearson correlation between gene pairs. We obtained theother datasets from Oti and coworkers [7]: disease data, inwhich all diseases from the OMIM database [1] with known
causative genes were grouped by similarity (see Oti and coworkers [7] for details); HTP physical interactions from bothhuman [25,26] and from other species (S. cerevisiae, C. elegans, and D. melanogaster) transferred to human by orthology using the Inparanoid algorithm [7]; and non-HTP
literature-based physical interactions from the HPRD database [24]. All human data were mapped to Ensembl genes

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http://genomebiology.com/2007/8/11/R252

Genome Biology 2007,[35] for analysis; if multiple Affymetrix U133a microarrayprobe sets matched a single gene, then their median value ineach microarray was calculated before calculating co-expression.

Statistics
All P values reported were calculated using the hypergeometric test for enrichment [36]. In all cases, this test was used to
calculate whether a given set of gene pairs had a different frequency of phenotype/disease pairs than would be expected bychance, given the sample sizes involved and the expected frequency of such pairs. The expected random frequency
depended on what was being tested. For example, to compare
single predictors to random pairs, the expected frequency ofphenotype/disease pairs was that of random pairs. To compare intersections of predictors to single predictors, theexpected frequency was the greater of the two predictors
alone. To compare intersections of predictors to the expectation under the assumption of independence, the expected frequency was given by the following equation:e=(1 − f 1 ) f 2 f 3
f 1 (1 − f 2 )(1 − f 3 ) + (1 − f 1 ) f 2 f 3

1.
2.

3.

4.
5.

6.

7.
8.

10.

11.

12.

Abbreviations

HPRD, Human Protein Reference Database; HTP, highthroughput; MIPS, Munich Information Center for Protein
Sequences; OMIM, Online Mendelian Inheritance in Man.

13.
14.

Authors' contributions

15.

HBF and JBP conceived of the analyses and wrote the paper.
HBF performed the analyses. Both authors read andapproved the final manuscript.

16.
17.
18.

Additional data files

The following additional data are available with the online
version of this paper. Additional data file 1 is a table listing the
top 92 predictions of gene pairs most likely to cause the samedisease, as assessed by physical interaction in the HPRD database and co-expression.

19.

AdditionalTop
Presented
diseaselikely
in
Click
the
92
here
to
HPRD
predictions
cause
is
for
data
adatabase
file
table
the
file
same
listingof
1 geneanddisease,theco-expression.
pairs
topas
most
92
assessedpredictions
likelyby
tophysicalcause
of genetheinteraction
pairssamemost

20.

Acknowledgements
We thank EM Woo, DA Drummond, VK Mootha, and ES Lander for advice.
HBF is a Lilly Fellow of the Life Science Research Foundation. JBP acknowledges support from the Burroughs Wellcome Fund.

Fraser and Plotkin R252.8

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Where e is the expected frequency by random chance, f1 is thefrequency of phenotype/disease pairs among all pairs ofgenes, f2 is the frequency among gene pairs satisfying one of
the criteria being used, and f3 is the frequency among genepairs satisfying the other criterion.

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