Open Accesset al.
McGary2007
Volume8, Issue 12, Article R258

MethodBroad network-based predictability of Saccharomyces cerevisiae
gene loss-of-function phenotypes
Kriston L McGary*, Insuk Lee* and Edward M Marcotte*†

Addresses: *Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, 2500
Speedway, Austin, Texas 78712, USA. †Department of Chemistry & Biochemistry, University of Texas at Austin, 2500 Speedway, Austin, Texas78712, USA.
Correspondence: Edward M Marcotte. Email: marcotte@icmb.utexas.edu

Published: 5 December 2007
Genome Biology 2007, 8:R258 (doi:10.1186/gb-2007-8-12-r258)

Received: 24 July 2007
Revised: 16 October 2007
Accepted: 5 December 2007

The electronic version of this article is the complete one and can befound online at http://genomebiology.com/2007/8/12/R258© 2007 McGary et al.; 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.
gene
Loss-of-function<p>Loss-of-function
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Abstract
We demonstrate that loss-of-function yeast phenotypes are predictable by guilt-by-association infunctional gene networks. Testing 1,102 loss-of-function phenotypes from genome-wide assays ofyeast reveals predictability of diverse phenotypes, spanning cellular morphology, growth,
metabolism, and quantitative cell shape features. We apply the method to extend a genome-wide
screen by predicting, then verifying, genes whose disruption elongates yeast cells, and to predict
human disease genes. To facilitate network-guided screens, a web server is available http://
www.yeastnet.org.

Background

Geneticists have long observed that mutations that lead to thesame organismal phenotype are typically functionally related,and have interpreted epistatic relationships between genes as
genetic pathways and more recently as gene networks. In the
post-genomic period, an abundance of high-throughput data
has encouraged the construction of functional networks [1],which integrate evidence from a wide variety of experiments
to infer functional relationships between genes. Historically,
mutations that lead to the same phenotype were inferred to befunctionally linked; now, with extensive functional networks,
we ask whether the inverse is also true. If gene loss-of-function phenotypes could be successfully inferred on the basis oflinkages in functional gene networks, then this would enable
the directed extension of genetic screens and open the possibility to apply similar approaches in humans for the direct
identification of disease genes.
In particular, important advances over the past decade inboth forward and reverse genetics mean that such predicta-

bility could be exploited in a straightforward manner to associate specific genes with phenotypes. In terms of forward
genetics, genome-wide association studies (for review, see[2]) are showing great power for identifying candidate genes
associated with human traits and diseases, such as recent
studies correlating variants in the ORMDL3 gene with risk forchildhood asthma [3]. In terms of reverse genetics, rapid testing of candidate genes has become more routine because ofavailability of mutant strain collections (for example, yeast
deletion strain collections [4,5]) as well as the relative ease of
RNA interference downregulation of genes (as, for instance,forgenome-wideRNAinterference
screensof
Caenorhabtidis elegans [6,7] or human cell lines; for review[8]). The prediction of loss-of-function phenotypes would
bridge these two aspects of genetics; given an initial set ofgenes associated with a phenotype of interest, such as mightcome from either forward or reverse genetics, computational
predictions of additional genes associated with that phenotype might be rapidly tested using reverse genetics, thereby
extending the original screen. Most importantly, because

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many traits are multifactorial in nature, often based upon
contributions from many genes, such approaches might help
in defining networks of genes that affect a trait of interest. The
potential for discovering such polygenic contributions totraits appears to be particularly strong when one considers
the prediction of phenotypes directly from functional gene
networks.
Functional linkages - statistical associations between pairs ofgenes that are likely to participate in the same cellular pathway or process - have shown great general power for generating hypotheses about gene function, in spite of theirapparently nonmechanistic nature (for examples, see [9-18]).
In a probabilistic functional gene network, each linkage in the
network is scored with the likelihood of the linked genes
belonging to the same pathway [13,16,17]. The accuracy andcoverage of these networks depends on the integration ofmultiple data sources (protein interactions, DNA microarrays, literature mining, and so on) that have each been independently shown to link similarly annotated genes; thecombination of many such datasets means that the networks
often extend well beyond current annotation. Such networks
have therefore been extensively applied to infer gene function, such as by predicting an uncharacterized gene's function
on the basis of its network neighbors (for examples, see[9,13,15,19-22]). Because genes linked in these networks tend
to be in the same pathway, it is reasonable also to expect
linked genes to often share loss-of-function phenotypes.
In this report we show proof-of-principle that genes linked ina functional network are indeed likely to give rise to the sameloss-of-function phenotype, demonstrating efficacy for predicting yeast mutant phenotypes. Diverse yeast gene loss-offunction phenotypes are shown to be predictable, from biochemical to morphologic to fitness effects. The approach we
describe therefore provides a rational and quantitative foundation for targeted reverse genetic studies, as we demonstrateby predicting, then verifying, essential genes whose disruption produces elongated yeast cells. The breadth of applicability suggests that this approach might ultimately be valuable if
it is implemented in humans to identify genes that are likely
to lead to human disease, exploiting extensive functional
genomics data and sets of known disease genes in order to
identify directly new candidate disease genes.

Results
Guilt-by-association in a functional gene network
predicts yeast gene essentiality
In order to predict phenotypes, we took advantage of anestablished principle for inferring gene function from network connections, the principle of guilt-by-association(GBA). In GBA the function of uncharacterized genes isinferred from the functions of characterized neighbors in the
network [9,21,23] (for review, see [19]). We employed GBA toconsider whether the genes linked to a seed set of genes asso-

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McGary et al. R258.2

ciated with a particular loss-of-function phenotype might also
be more likely to result in the same phenotype upon disruption (Figure 1). For these analyses, we employ the most recent
version (v. 2 [24]) of the probabilistic yeast functional gene
network reported by Lee and coworkers [17]. This network
describes 102,803 functional linkages among 5,483 yeast
genes, each linkage scored with a probabilistic score capturing the tendency of the genes to share Gene Ontology (GO)'biological process' annotation [24] versus prior expectation.
Using this network, genes are rank ordered by the strengthsof their linkages to the seed set; the genes linked moststrongly to the seed set would therefore be considered candidates for leading to the same phenotype.
We first investigated whether the network could distinguishviable from nonviable yeast gene deletion strains. Essential
genes of both yeast and humans are known to be more highly
connected in protein physical interaction networks than nonessential genes [25-27], and there is evidence that essential
proteins may also be enriched in the same physical complexes[28,29]. We considered whether essential genes could be predicted on the basis of their connections to other essential
genes in a functional gene network. We employed the GBA
approach, using as the seed set the 1,027 known essential
yeast genes [4,30] and then scoring each gene in yeast for its
likelihood to be essential as a function of connectivity to thisseed set. Each gene in the seed set was withheld in turn fromthe seed set in order to evaluate it (performing leave-one-out
cross-validation). As the prediction score for each gene, we
calculated the sum of the weights of linkages connecting thequery gene to genes in the seed set. Given that each linkage's
weight in this network corresponds to the log likelihood of thelinked genes belonging to the same pathway [24], the sum oflinkage weights therefore represents the naïve Bayesian combination of evidence that the query gene belongs to the same
pathway as the seed set genes. We expect genes in the same
pathway often to exhibit the same loss-of-function phenotypes. Thus, this score should also serve to identify genes thatshare phenotypes with the seed set genes.
To evaluate prediction quality, we calculated the true positive
rate (sensitivity: TP/[TP + FN]) and the false positive rate (1- specificity: FP/[FP + TN]), as a function of the prediction
score, plotting the resulting receiver operating characteristic(ROC) curve. (The terms TP, FN, FP and TN mean true positives, false negatives, false positives and true negatives,
respectively.) As Figure 2 shows, the essential genes arestrongly predictable on the basis of their network neighbors.
Therefore, in addition to the previous observations thatessential genes have larger numbers of physical interaction
partners, we demonstrate that essential yeast genes are alsopreferentially connected to each other in a functional
network.

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Figure 1 of guilt-by-association phenotype prediction
Overview
Overview of guilt-by-association phenotype prediction. Guilt-by-association phenotype prediction employs a functional gene network, represented here as
circles (genes) connected by lines (functional linkages), and a seed set of genes (blue filled circles) whose disruption is known to give rise to the phenotype
of interest. Neighboring genes in a functional gene network (red filled circles) are candidates for also giving rise to the phenotype. Candidates areprioritized by the sum of their network linkage weights to the set of seed genes. A gene strongly linked to multiple seed genes will thus rank more highly
than a gene weakly linked to a single seed gene. Networks in Figures 1, 5, and 7 were drawn with Cytoscape [73].

A yeast gene network predicts varied, specific loss-offunction phenotypes
Although prediction of essential genes is useful (for example,
for prioritizing knockout experiments or drug targets), there
is far more utility in predicting highly specific phenotypes.
Saccharomyces cerevisiae has been richly characterized,with a large number of systematically collected phenotypes,
assayed across all (or, more typically, all nonessential) genes
by taking advantage of yeast deletion strain collections [4,5].
In these collections, a single yeast gene is deleted in each yeast
strain; a phenotypic assay on the complete set of knockout
strains thereby associates that phenotype with those deleted
genes that gave rise to it. These screens are ideal for addressing the general question of whether specific loss-of-function
phenotypes are predictable. Importantly, the yeast gene network was neither trained on such data, and neither were phenotypic data incorporated into the network [24]. These sets
are therefore fully independent test sets, and we could thus
employ these data to evaluate the capacity of a gene network
to predict loss-of-function phenotypes.
We assembled a set of 100 nonredundant phenotypes, either
reported in the Saccharomyces Genome Database (SGD [31])or in one of 32 additional publications in the literature (listed
in full in Table 1). We evaluated each of the phenotypes for
network-based predictability using ROC analysis, as shownfor several examples in Figure 2. Specifically, we used hits
from these screens as seed sets for predicting the associatedphenotypes from the yeast network, performing leave-oneout cross-validation, just as for the prediction of essential
genes. In order to evaluate the overall trends in these data, foreach of the 100 ROC curves we calculated the area under the
curve (AUC) as a measure of prediction strength; an AUC
value of 0.5 indicates random performance, whereas an AUC
value of 1.0 indicates perfect predictions. We find that amajority of phenotypes are reasonably predictable (Figure 3),
with 70% of the phenotypes predictable at AUC above 0.65. In
contrast, none of 100 random gene sets of the same sizes as
the actual phenotypic seed sets exhibited AUC above 0.65.
The AUC of the highest scoring random set was 0.64, whichindicates that phenotypes with AUC above 0.65 were significant to at least P < 0.01.
The most strongly predictable phenotypes vary widely in specificity and character. For example, we observed strong predictability for genes whose disruption leads to shortened

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1
0.9

0.8

0.8

0.6

0.7
0.4

nd
om

0.6

Ra

CPY secretion

0.2

0.5

0.0
0.0

0.2

0.4

0.6

0.8

1.0

Figure 2yeast gene
Diverseby-association
in a functional
loss-of-function
gene network
phenotypes are predictable using guiltDiverse yeast gene loss-of-function phenotypes are predictable using guiltby-association in a functional gene network. Predictability is measured in a
receiver operating characteristic plot of the true positive rate (sensitivity)
versus false positive rate (1 - specificity) for predicting genes giving rise toten specific loss-of-function phenotypes, as well as for essential genes
whose disruption produces nonviable yeast [4]. For each phenotype, eachgene in the yeast genome was prioritized by the sum of the weights of its
network linkages to the seed genes associated with the phenotype. Genes
with higher scores are more tightly linked to the seed set and therefore
more likely to give rise to the phenotype. Each phenotype was evaluatedusing leave-one-out cross-validation, omitting genes from the seed set forthe purposes of evaluation. More predictable phenotypes tend toward the
top-left corner of the graph; random predictability is indicated by the
diagonal. For clarity, the line connecting the final point of each graph to the
top right corner has been omitted. FN, false negative; FP, false positive;
TN, true negative; TP, true positive.

telomeres [32], causes chitin accumulation [33], or increases
secretion of the vacuolar protein carboxypeptidase Y [34].
Even gross cellular morphologies (small cells, round cells,
and so on) are somewhat predictable, as are far more specificphenotypes, such as increased iron uptake [35] and caspofungin sensitivity [36]. Surprisingly, there is little dependence ofpredictability on the size of the seed set (Figure 4), and we
observed strong predictability for both large and small seedsets (for example, bleomycin resistance [37] [four genes, AUC= 0.87] versus nonviability/essential [4,30] [1,027 genes,
AUC = 0.85]).

Integration of functional genomics and proteomics
data is important for phenotype prediction
Because physically interacting proteins often share related
genetic interaction partners (for examples, see [38,39]) andeven human disease associations [25,40,41], it seemed likelythat physical protein interactions might account for a large
fraction of the signal we observe. In particular, Lage and cow-

0.4
Actualphenotypes

Randomphenotypes

Figure 3expectation
Loss-of-function
randomphenotypes are predicted significantly better than
Loss-of-function phenotypes are predicted significantly better thanrandom expectation. Here, predictability is measured as the area under areceiver operating characteristic (ROC) curve (AUC), measuring the AUC
for each of 100 yeast phenotypes observed in genome-wide screens andplotting the resulting AUC distributions. Real phenotypes are significantly
more predictable than size-matched random gene sets. At the left of eachbox-and-whisker plot, the center of the blue diamond indicates the AUC
mean, the top and bottom of the diamond indicate the 95% confidence
interval, and the accompanying solid vertical line indicates ± 2 standard
deviations. The bottom, middle, and top horizontal lines of the box-andwhisker plots represent the first quartile, the median, and the third
quartile of AUCs, respectively; whiskers indicate 1.5 times theinterquartile range. Red plus signs represent individual outliers.

orkers [40] used GBA among protein complexes to predict
disease genes within human genetic linkage groups. Balancing this trend, phenotypes of annotated genes are in part predictable directly from their functional annotations [42]. Thus,
we considered whether the integration of functional genomics
and proteomics data in the functional network yielded additional predictive power over physical interactions alone. We
measured the median AUC across the 100 phenotypes for thefunctional yeast gene network and for each of several published versions of the yeast protein physical interaction network [29,43-45]. We compared these values with the medianfraction of each seed gene set covered by the respective networks. The values of AUC and fraction covered therefore
serve as measures of precision and recall for each network.
As Figure 5 demonstrates, we observe that all networks predict loss-of-function phenotypes to some extent, but find thefunctional network to predict phenotypes at a significantly
higher precision and recall. We attribute this enhanced performance to the increased comprehensiveness of the

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Table 1
Predictability of 100 yeast gene deletion phenotypes

Phenotypea

AUC

Seed genes with phenotype (n)Seed genes in network (n)Ref.

Caspofungin sensitive
Increased resistance to calcofluor white
Unipolar budding
CPY secretion (3)Cell cycle arrest defective
UVC sensitive (high)Sensitivity at 15 generations in galactose
CANR mutator (high)Haploinsufficient in rich medium (YPD)
Cellular chitin level increased (3)Bleomycin resistant (3)Morphology: branched (diploid)
Sensitivity at 15 generations in 1.5 M sorbitol
Caspofungin resistantInviable (essential)
Shortened telomeres (3)Sensitivity at 15 generations in minimal +his +leu +ura medium
MMS sensitive (3)Cellular chitin level reduced (2)PetiteSensitivity at 5 generations in minimal +his +leu +ura medium
Long telomeres (3)Decreased calcofluor white resistance
Growth defect on a fermentable carbon source
Transposon cDNA expression changed (high)Morphology: clumpy (3)(diploid)
Gamma radiation sensitive (3)Cell cycle arrest defective and defective shmoo
Sensitivity at 5 generations in galactose
Small (haploid)
Retrotransposition reduced
K1 killer toxin sensitive (40%)
Increased iron uptake
Growth defect on a non-fermentable carbon source
Gentamycin sensitive (high)Proteasome inhibitor sens (high)Reduced fitness in rich medium (YPD)
Mycophenolic acid sensitive
Axial budding
Morphology: elongate (3) (diploid)
Sporulation deficient
Random budding (high)Large (haploid)
Reduced sporulation (3) (normal respiration)
Bleomycin sensitive (4)Sensitivity at 5 generations in synthetic complete - lys medium
Decreased rapamycin resistanceWhi
Sensitivity at 5 generations in 1.5 M sorbitol
Decreased wortmannin resistance

0.996
0.982
0.941
0.937
0.930
0.919
0.908
0.904
0.898
0.873
0.871
0.870
0.867
0.866
0.845
0.843
0.843
0.837
0.835
0.833
0.827
0.824
0.814
0.812
0.810
0.802
0.793
0.782
0.781
0.778
0.772
0.770
0.757
0.755
0.754
0.753
0.748
0.746
0.745
0.739
0.738
0.737
0.728
0.722
0.721
0.715
0.707
0.706
0.704
0.703

20
10
10
46
8
15
17
18
184
22
5
5
6
8
1100
20
77
78
17
179
62
6
65
257
27
18
31
30
11
215
99
72
76
498
11
22
891
38
4
77
261
74
227
136
58
23
272
19
13
89

18
10
10
44
8
14
14
18
184
21
4
5
4
8
1027
18
70
73
17
166
51
6
63
249
26
18
31
29
10
192
89
72
70
448
11
22
872
33
4
73
244
72
205
119
55
22
256
19
11
85

[36]
[33]
[68]
[34]
[74]
[75]
[4]
[76]
[77]
[33]
[37]
[4]
[4]
[36]
[4,30]
[32]
[4]
[78]
[33]
[79]
[4]
[32]
[77,80]
[81]
[82]
[4]
[83]
[74]
[4]
[84]
[82]
[80]
[35]
[81]
[85]
[86]
[77]
[87]
[68]
[4]
[88]
[68]
[84]
[89]
[37]
[4]
[90]
[79]
[4]
[90]

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Table 1 (Continued)
Predictability of 100 yeast gene deletion phenotypes

Sensitivity at 20 generations in 1 M NaCl
K1 killer toxin resistant (40%)
Morphology: round (3) (diploid)Uge
Sensitivity at 5 generations in synthetic complete - trp medium
Sensitivity at 5 generations in 1 M NaCl
Rapamycin resist (2)Reduced iron uptake
Rate of growth loss of growth in 0.85 M NaCl
Sensitivity at 5 generations in medium of pH 8
Sensitivity at 15 generations in medium of pH 8
Morphology: small (3)(diploid)
Sensitivity at 15 generations in 10 uM nystatinMorphology: large (3)(diploid)
Reduced glycogen storage (2)Sensitivity at 5 generations in 10 uM nystatin
Increased rapamycin resistanceMorphology: unusual shmoo (haploid)
Morphology: polarized bud growth (haploid)
Wortmannin resistant (5)Sensitivity at 5 generations in synthetic complete - thr medium
Enhanced glycogen storage (2)Proteasome inhibitor resistant
Reduced spores per ascus
Rate of growth sensitivity in 0.85 M NaCl
Morphology: football (3) (diploid)
Germination deficient
Sporulation promoting
6AU sensitive (3)Increased wortmannin resistanceMorphology: elongated (haploid)
Rapamycin sensitive (4)Efficiency of growth sensitivity in 0.85 M NaCl
Decreased rapamycin resistanceSlow growth in YPD (16× below WT)
MPA sensitive (3)Morphology: round (haploid)
Efficiency of growth resistance in 0.85 M NaCl
Sensitivity at 5 generations in synthetic complete medium
Morphology: large (haploid)
Adaptation time loss of growth in 0.85 M NaCl
Adaptation time sensitivity in 0.85 M NaCl
Decreased sensitivity to the anticancer drug, cisplatin
Morphology: chain (diploid)
Morphology: small (haploid)
Rate of growth resistance in 0.85 M NaCl
Morphology: clumped (haploid)
Adaptation time resistance in 0.85 M NaCl
Efficiency of growth loss of growth in 0.85 M NaCl
Morphology: pointed (haploid)

0.703
0.698
0.696
0.694
0.694
0.693
0.692
0.688
0.682
0.677
0.676
0.672
0.672
0.669
0.666
0.666
0.662
0.661
0.657
0.656
0.647
0.645
0.642
0.641
0.629
0.628
0.627
0.622
0.618
0.617
0.603
0.597
0.597
0.597
0.585
0.563
0.552
0.541
0.531
0.527
0.526
0.521
0.512
0.485
0.480
0.479
0.479
0.465
0.464
0.453

63
19
105
28
48
60
26
5
212
102
128
79
28
88
44
124
114
29
5
25
31
61
7
37
209
59
158
102
28
80
110
20
65
8
23
24
13
44
88
23
103
284
22
5
94
59
32
69
23
99

59
18
99
26
45
56
26
5
189
93
115
69
27
80
41
108
100
25
5
23
29
55
6
34
191
53
147
98
26
75
101
20
58
7
22
22
11
40
78
21
91
259
19
5
89
49
28
60
21
88

aNumbers in parentheses indicate threshold applied to generate seed set; for instance, '(3)' indicates '+++' or '---', as appropriate.

Genome Biology 2007, 8:R258

[4]
[80]
[4]
[79]
[4]
[4]
[91]
[35]
[92]
[4]
[4]
[4]
[4]
[4]
[93]
[4]
[90]
[74]
[74]
[94]
[5]
[93]
[86]
[89]
[92]
[5]
[88]
[88]
[95]
[90]
[74]
[91]
[92]
[90]
[4]
[95]
[74]
[92]
[5]
[74]
[92]
[92]
[96]
[5]
[74]
[92]
[74]
[92]
[92]
[74]

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McGary et al. R258.7

networks for the purposes of predicting phenotypes and suggest that the proteins encoded by genes associated with thesame phenotype often may not physically interact.

1.0
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Extending a genetic screen by network-guided reverse
genetics

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FigureAsignificant
plot of4 seedcorrelation
set size versus predictability of the phenotype shows no
A plot of seed set size versus predictability of the phenotype shows nosignificant correlation. Thus, there does not appear to be an intrinsic
limitation for applying network-guided reverse genetics even when seed
set size is small. Each filled circle indicates the prediction strength (areaunder the receiver operating characteristic [ROC] curve, as calculated in
Figure 3) of one of the 100 loss-of-function phenotypes relative to thenumber of genes in that seed set.

functional gene network, both in terms of additional types ofgene associations and more extensive coverage of the overall
set of yeast genes. The functional network accomplishes thisby incorporating other sources of functional interaction (forexample, mRNA co-expression) in addition to physical interactions from both small-scale (for example, the Database ofInteracting Proteins [DIP] and Munich Information Center
for Protein Sequences [MIPS] databases) and genome scale(for example, mass spectrometry of affinity-purified protein
complexes and yeast two hybrid) experiments. Furthermore,as shown in Figure 6, the sequential addition of progressively
lower confidence functional linkages increases both predictive accuracy and coverage. Low confidence linkages do notundercut the predictive power of high confidence linkages
because they are weighted in proportion to the strength of theevidence that supports them. These observations highlightthe importance of integrating diverse data types into geneFor organisms in which reverse genetics is feasible, the observation that phenotypes can be predicted from network connectivity opens the possibility of extending genetic screens ina directed manner. That is, when in possession of a set ofgenes known to give rise to a phenotype of interest, rather
than randomly screening to identify additional genes, one
could instead exploit the predictability of phenotypes bydirectly screening genes that are most strongly connected to
the known set in the network. In this manner, experiments
could be focused on the genes that are most likely to give riseto the phenotype. We tested this notion for yeast genes whose
disruption gives rise to a simple cell morphology defect, theformation of elongated yeast cells. Across the complete set ofnonessential genes, 145 genes (3.3%) have been identified
that give rise to elongated morphologies in homozygous diploid deletion strains, of which 77 genes (1.7%) show a strong
phenotype [4]. We selected these 77 genes as a seed set andfound the phenotype to be reasonably predictable from the
network using ROC analysis (AUC = 0.74). Because the complete set of nonessential genes was previously screened forcell morphology defects [4,46], we instead considered which
essential genes were most strongly linked to the seed set,
selecting the top-ranked 35 essential genes for further evaluation, and tested 33 of these strains. We examined conditional loss-of-function strains for elongated cellmorphologies, performing light microscopy of yeast strains
carrying tetracycline downregulatable alleles for each candidate gene [47]. Sixteen (about 48%) of the 33 tested wereelongated, as shown for several examples in Figure 7. As
negative controls, we tested 17 strains carrying tetracycline
downregulatable essential genes that were chosen for being
unlinked in the functional network to the seed set. One negative control strain also scored as elongated; this strain had
also been previously identified as such by Mnaimneh andcoworkers [47]. The results represent an eightfold improve-

Figure 5predictive
Relative(see following
power
page)
of functional and physical protein networks
Relative predictive power of functional and physical protein networks. (a) Median values of predictive power (area under the receiver operating
characteristic [ROC] curve [AUC]) across 100 loss-of-function phenotypes are plotted versus the median fraction of each seed gene set covered by a
network (coverage; measured as the fraction of seed genes with at least one linkage in the network). Five networks are compared: the functional yeast
network (YeastNet v. 2 [24]) and four versions of the network of yeast physical protein interactions (Database of Interacting Proteins [DIP] [45],Probabilistic Integrated Co-complex [PICO] [29], Munich Information Center for Protein Sequences [MIPS] physical complexes [44], and Collins andcoworkers [43]). DIP, PICO, and YeastNet are each evaluated at two reported confidence thresholds. The YeastNet functional gene network shows
considerably higher predictive power than for the networks composed only of physical interactions; the full YeastNet shows higher predictive power thana more confident core set of the top 47,000 linkages, indicating that the lower confidence linkages nonetheless add predictive power. Error bars indicate
the first and third quartiles. Panels b and c show example seed gene sets (green circles) and their network connections, indicating functional linkages ingrey lines, physical interactions in thin black lines, and both functional and physical interactions in thick black lines. (b) Genes whose deletion increases
cellular chitin levels [33] (AUC = 0.87), whose prediction relies upon a mix of physical and functional interactions. (c) Genes whose deletion confers
sensitivity at 5 generations in synthetic complete medium lacking threonine [4] (AUC = 0.65), whose prediction derives predominantly from functional
linkages.

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sponding molecular defect. The mechanism is unknown bywhich defects in transcription initiation lead to elongatedcells; nonetheless, the relationship is robust enough thatgenes whose disruption causes cell elongation can be correctly predicted.

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Figureprobability
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linkages continue to improve predictive accuracy
Lower probability linkages continue to improve predictive accuracy. The
continued improvement of predictions, albeit with diminishing returns, isshown in a plot of the predictive accuracy (median area under the receiver
operating characteristic [ROC] curve across the 100 phenotypes,
calculated as in Figure 3) versus median network coverage of the 100
phenotype sets, as calculated for the top-ranked 20,000 (20 K), 40,000 (40K), 60,000 (60 K), 80,000 (80 K), and 100,000 (100 K) linkages in YeastNetv. 2. This trend derives from the fact that all links in this network have atleast a 60% probability of linking genes in the same pathway. The
probabilistic nature of the network means that low confidence linkages areunlikely to undercut high confidence linkages during phenotype prediction
because the links are weighted according to the strength of the evidence
supporting them. Error bars indicate the first and third quartiles.

ment over the negative control set and a more than 15-foldimprovement over genome-wide screening, validating thegeneral strategy of network-guided genetic screening.
To gain further insight into the genes identified, we examined
the network connections among the seed genes and newly
identified genes giving rise to the elongated phenotype (Figure 7b). Strikingly, the genes associated with elongated yeast
cell morphology are strongly enriched for core transcriptional
functions (for example, they are significantly enriched for the
MIPS [48] annotation 'mRNA synthesis';P < 10-12 [49]), withthe set of newly identified genes predominantly belonging to
the RNA polymerase II mediator complex and associatedtranscriptional machinery. In particular, the directed screen
identified the genes MED6, MED7 (confirming an earlier
observation reported by Boone and coworkers [47]), andMED8, all of which are core components of the mediator
complex. It also identified the genes TAF1, TAF5, TAF9, andTAF12, all of which are subunits of the TFIID and SAGA transcriptional complexes, which are required for RNA polymerase II transcriptional initiation. These findings highlight
another advantage of network-guided genetic screening;
because candidate genes are selected directly from the gene
network, functional connections are often already known
among the genes, helping to guide later interpretation of thehits. The findings also highlight the often mysterious
relationship between an observed phenotype and the corre-

Given that the phenotypes analyzed thus far are often basedon subjective criteria (judged to be elongated or not), it isimportant to consider whether such predictions can be madefor quantitative phenotypes. We therefore examined quantitative cell shape data that were recently systematically measured for the set of haploid MATa yeast deletion strains [46]. A
total of 281 quantitative features of cell shape, cellular, andsubcellular morphology were measured for each strain,including such parameters as the ratio of long cell axis toshort cell axis, the angle between a mother cell and bud, andthe relative distribution of actin with regards to the bud position. Each feature was measured for many cells from a given
strain, and the mean value reported. For 220 of the features,the coefficient of variance (CV) was also reported, describing
the variability in that feature across single cells in that strain.
Considering the mean value of each feature and the CV as
separate traits (we refer to the former as morphology phenotypes and the latter as CV phenotypes) means that a total of
501 cell shape measurements or CVs were reported for 4,718
strains, and made available through the S. cerevisiae Morphology Database (SCMD) [50]. Because not all measurable
cell shape features are likely to be under selection (for example, they might simply vary stochastically yet neutrally), we
do not expect all such phenotypes to correspond to functional
pathways and therefore be predictable. Nonetheless, wemight expect that a number of these would have functionalcorrelates and therefore be predictable. In order to test thisnotion, we therefore evaluated each of the 501 features forpredictability using the functional gene network.
To generate seed gene sets from these data, for each of the 281
quantitative features we selected as phenotypic seed sets the
40 genes with the highest measured mean value of that feature and the 40 genes with the lowest measured mean valueof that feature, in all generating 562 morphology phenotype
seed gene sets (281 features × 2 seed sets each). We then evaluated each of these seed sets for predictability using ROC
analysis. As for the 100 genome-wide phenotypic screens, weobserved many strongly predictable cell morphology phenotypes, such as those illustrated in Figure 8. For example, oneof the most strongly predictable cell morphology phenotypes
is for the genes whose disruption most increases cell ellipticity during nuclear migration to the bud neck (AUC = 0.87).
Another strongly predictable phenotype is for deletion strains
showing the highest increase in the actin polarization of
unbudded cells (AUC = 0.80). We observe the overall set ofcell morphology phenotypes to be significantly more predictable than random expectation, as shown by comparison of the
distribution of AUC values with those derived from 1,000

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McGary et al. R258.10(a)Negative Control
Tet-O7 -SCM3

Tet-O7 -TAF9

Tet-O7 -RPO21

Tet-O7 -MED7

Tet-O7 -MED6

Tet-O7 -SWI1(b)

Figure 7
Network-guided
extension of a genetic screen
Network-guided extension of a genetic screen. Guilt-by-association (GBA) was applied to predict essential yeast genes whose disruption resulted inelongated yeast cells, based on the genes' network connectivity to a seed set of 77 nonessential genes already known to cause cell elongation when deleted[4]. (a) Five examples of successful predictions, observed in yeast strains carrying tetracycline downregulatable conditional alleles [47] of the essential
genes TAF9, MED6, MED7, SWI1, and RPO21. In contrast, conditional downregulation of an unrelated essential gene, SCM3, caused no such cell elongation.
(b) Sixteen out of 33 tested essential genes (yellow circles) showed elongated cell phenotypes on the basis of their connections to the seed set genes(green circles), with particular enrichment for genes associated with RNA polymerase II transcriptional initiation and the mediator complex. The color of
the edge between two genes indicates the source of evidence supporting the functional link: thick black, multiple types of evidence; blue, affinity
purification/mass spectrometry; green, literature mining by co-citation; cyan, gene neighbors or tertiary structure; pink, literature curated physicalinteraction; and red, genetic interaction.

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McGary et al. R258.11

random seed sets of 40 genes each (Figure 9a). Note that predictability does not depend strongly on the size of the seed
sets; we see similar predictive power with seed sets of 10 to 80
genes (data not shown). These findings confirm that evenspecific quantitative aspects of yeast cell shape often havefunctional correlates, and therefore the sets of genes whose
disruption most affects such features are predictable.

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nd
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Network-based
prediction of quantitative cell morphology phenotypes
Network-based prediction of quantitative cell morphology phenotypes. A
wide variety of phenotypes based upon quantitative yeast cell shape andintracellular features [46] are predictable, as shown for the ten
phenotypes in this receiver operating characteristic (ROC) analysis(selected from S. cerevisiae Morphology Database [SCMD] phenotypes
with area under the ROC curve [AUC] > 0.68). For each of the features,the 40 genes whose deletion mutants show either the 40 highest or 40
lowest values for that quantitative feature (indicated by 'high' or 'low',respectively) were selected as the seed gene set. Predictability wasevaluated using ROC analysis as in Figure 2, plotting the true positive
prediction rate versus false positive rate, using leave-one-out crossvalidation. For clarity, the line connecting the final point of each graph tothe top right corner has been omitted. Labels of features are adapted forclarity from the SCMD [50]; the SCMD labels A, A1B, and C represent
unbudded cells, budded cell with one nucleus in mother cell, and largebudded post-mitotic cells with nuclei in both mother and daughter cell,respectively. Ratio measurements refer to proportions across a
population of cells. FN, false negative; FP, false positive; TN, true negative;
TP, true positive.

Because the SCMD data include both morphology featuresand measurements of their cell-to-cell variability, we considered more specifically whether the CV of a yeast morphology
phenotype across single cells in a population was itself apredictable phenotype. Strikingly, we observed goodpredictability for sets of genes whose disruption mostincreased the CV of a given morphologic feature (for instance,
the 40 genes whose deletion caused the highest increase in
bud neck width CV; AUC = 0.70), but near random prediction
for sets of genes whose disruption most decreased the CV in agiven morphologic feature (for example, the 40 genes whose
deletion most reduced bud neck width CV; AUC = 0.54; Figure 9b). The high CV phenotypes are significantly more predictable than the low CV phenotypes (P < 0.0001, Wilcoxon
signed-ranks test). Across the 220 high CV phenotypes, weobserved 116 to exhibit significantly greater AUC values thansize-matched random sets (at the 95% confidence level, as
judged by Z-score > 1.95), whereas only 26 of the set of 220
low CV phenotypes were better than random at this level.
Because successful prediction of a loss-of-function phenotype
implies functional coherence of the genes - essentially reflecting clustering of the genes in the functional network - thisresult indicates that the genes whose disruption moststrongly reduced the CV in a given morphologic feature do notin general form a functionally coherent set. By contrast, genes
whose disruption most increased morphologic phenotypicvariability were predictable, and thus functionally coherent.
We further observed that the same genes tended to be present
in the phenotypic sets from many different CV phenotypes;
namely, there are particular genes whose deletion increases
the CV of a large number of otherwise unrelated morphologic
properties.

FigureQuantitative
9 (seecell
following
morphologypage) phenotypes are predicted significantly better than random expectation
Quantitative cell morphology phenotypes are predicted significantly better than random expectation. In contrast, genes whose disruption decreases
population co-efficient of variance (CV) were not predictable. (a) A histogram plotting the distribution of the area under the receiver operating
characteristic (ROC) curve (AUC) values for 562 quantitative morphological phenotypes shows a significantly higher proportion of high AUC values thanfor 1,000 size-matched random gene sets. (b) Separate analyses of phenotypes associated with morphologic features and phenotypes associated with cellto-cell variability in the morphologic features reveals asymmetry in predictability. Sets of genes whose disruption causes the 40 largest or smallest meanvalues of a morphological feature (middle plots) are significantly more predictable than random gene sets (left side). By contrast, although the sets of genes
whose disruption most increase the CV tend to be predictable (high AUC), those that most decrease the CV are not (low AUC). Box-and-whisker plots
are drawn as in Figure 3. (c) A comparison of the median phenotypic CVs observed for deletion strains versus replicate analyses of wild-type cells showsthat deletion strains with the most reduced CVs are essentially wild-type-like in character, whereas those with the most increased CVs show significantly
more cell-to-cell variability than wild-type cells. These latter knockout strains carry deletions for genes predominantly involved in maintaining genomic
integrity. This trend is therefore likely to have arisen from nonclonal genetic variation in these strains, recapitulating the classic mutator phenotype.

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To explore this observation further, for each of the 4,718 yeast
genes in the SCMD data set, we calculated the median percentile rank across each of the 220 SCMD CV phenotypes. Thus,
the gene whose deletion strain has the highest median percentile rank (the telomere length regulation gene EST1;
median percentile rank of 0.98) exhibits the greatest cell-tocell variability across nearly all of the set of 220 CV phenotypes. By contrast, the gene whose deletion strain has the lowest median percentile rank (YAL004W, a small open reading
frame that overlaps the coding sequence for the heat shockprotein 70 family chaperone SSA1; median percentile rank
0.17) consistently exhibits the lowest cell-to-cell variability
for the tested phenotypes. Thus, these rankings capture thegeneric tendency for a gene to increase or decrease cell-to-cellvariability across many measured morphology parameters.
We tested the top-ranked 40 genes and the bottom-ranked 40
genes for their network-based predictability.
As with our earlier observations, the top-ranked 40 genes(those with highest median percentile rank) exhibit reasonable predictability (AUC = 0.71), whereas the bottom-ranked
40 genes exhibit random predictability (AUC = 0.49). Thus,
either on a phenotype-by-phenotype basis, or across all 220
phenotypes, genes whose disruption most increased morphologic phenotypic variability tended to be more predictableand functionally coherent than those that reduced phenotypicvariability. An examination of the functions of the top-ranked
40 genes suggested a possible explanation. The top-ranked
set show strong enrichment for specific GO terms, with 17 of
the 40 genes encoding nuclear proteins (P < 10-6; measured
using FunSpec [49]); ten of these are DNA-binding proteins(P < 10-4), including genes of DNA recombination and repair(P < 10-6). Among these genes are many that are involved inmaintaining genomic stability, including the repair/recombination proteins RAD27, RAD50, RAD51, RAD52, CTF4,
HEX3, RTT109, and THP1, the histone HTZ1, and the telomere maintenance protein EST1. Thus, although deletionsof these genes may possibly increase phenotypic variation, amore likely possibility is that these particular strains in theyeast deletion collection have accumulated genetic variation
and are no longer clonal, as we discuss below.

McGary et al. R258.13

ble, but also the median predictive accuracy of these phenotypes is even slightly higher than the genome-wide yeast
phenotypes (Figure 3). This is a probable reflection of the factthat genes conserved between yeast and humans generallycompose core cellular machinery, well captured by the gene
network. For example, the most predictable disease weobserved (AUC = 1.0) was leukoencephalopathy with vanishing white matter, arising as the result of mutations in any of
the subunits of the translation initiation factor EIF2B. Likewise, we observed strong predictability for hemolytic anemia(AUC = 0.89), which involves 11 ortholog groups, involved inglycolysis and glutathione metabolism, which are linked primarily by co-expression and co-citation, with only a few physical interaction-based linkages.

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The functional network predicts yeast orthologs ofhuman disease genes
The network's effectiveness at predicting both qualitative andquantitative yeast phenotypes suggests the possibility of
application to other organisms, such as for predicting humandisease genes. We tested the potential of this approach byexamining the power of the yeast network to predict yeast
orthologs of human disease genes, focusing on all human diseases listed in the Online Mendelian Inheritance in Man(OMIM) disease database [51], for which at least four yeast
orthologs existed in YeastNet. We observed strong predictability for the majority of the 28 human diseases that could betested in this manner, as shown in Figure 10. Not only are
many of the yeast orthologs of these disease genes predicta-

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ActualOMIM
diseasesRandomOMIM
diseases

Figuregenes
Yeast
predicted
10betterwith thanhumanrandomorthologs
expectation
linked to the same diseases are
Yeast genes with human orthologs linked to the same diseases are
predicted better than random expectation. Predictability is measured as
the area under a receiver operating characteristic (ROC) curve (AUC), as
in Figure 3, measuring the AUC for each of 28 human diseases reported in
the Online Mendelian Inheritance in Man (OMIM) disease database [51]that have four or more yeast orthologs annotated in the yeast function
network and plotting the resulting AUC distributions. Real disease genesets are significantly more predictable than size-matched random gene sets
drawn from the set of yeast-human orthologs. Box plots are drawn as in
Figure 3.

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Although this test was limited to diseases involving biologic
processes shared between human and yeast, these results
support the notion that an integrated human functional network would guide the discovery of new disease genes. Because
we observe strong disease predictions both from protein
complexes (as in leukoencephalopathy) and pathways (as inhemolytic anemia), it appears likely that a functional humangene network might offer strong predictions for genes associated with diverse human diseases, even in the absence ofgenetic linkage data.

Discussion

Just as functional networks propagate known functionalannotations to un-annotated genes, phenotype prediction via
GBA is limited to propagating known phenotypes. Therefore,
an initial seed set of genes is required, such as might result
from a genetic screen for the phenotype of interest, before
being able to apply the network in order to identify more suchgenes. We might also expect genes in the same pathway often
to exert inverse effects on a phenotype, acting either as activators or repressors. Nonetheless, we demonstrate that GBA
can successfully be applied to identify genes that give rise tosimilar loss-of-function phenotypes. Furthermore, networkguided phenotype prediction can be used to extend a genetic
screen in a targeted manner by providing a ranked list of
potential candidates for evaluation. In principle, the screen
might be expanded by adding the newly identified genes tothe seed set and iterating the prediction and testing.
In particular, large-scale reverse genetic screens using yeast
mutant strain collections have become increasingly common[52]. However, these assays often suffer from high false negative rates, not least by virtue of screening libraries of limited
scope (for example, screening only the nonessential or essential genes). Such partially genome-wide screens can benefit byfollowing up the initial screen with focused screening (or rescreening) of prioritized candidate genes. In order to facilitatesuch efforts, we have created a web server [53] that allows
interactive analysis of a seed gene set, performing ROC analysis to assess the predictability of the phenotype, then returning a ranked list of candidate genes that are most likely toshare the same loss-of-function phenotype.
Note that we have focused here on predicting loss-of-function
phenotypes because of the large number of genome-wide
screens available; it is not clear that gain-of-function phenotypes will be similarly predictable. However, the recent construction of yeast over-expression libraries [54-56] should
soon allow testing of network-based prediction of suchphenotypes.

Why are loss-of-function phenotypes predictable?

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McGary et al. R258.14

nisms for how loss of different genes leads to disruption of thesame phenotypically relevant process, primarily participationin the same protein complex or membership in the same biologic pathway. These results are consistent with the partial
predictability of human disease from protein complex
membership [40,41] and of the prediction of knockout phenotypes of annotated yeast genes on the basis of pathway
annotation [42], which we illustrate with the following contrasting examples from among our predictions. In Figure 5b,
the proteins ANP1, MNN9, MNN10, MNN11, VAN1 are members of the same α-1,6-mannosyltransferase protein complex.
Chitin accumulates when the function of the complex is
disrupted by the loss of any one of the five members [33]. In
contrast, in Figure 5c the three genes THR1, THR4, andHOM6 are involved in the biochemical pathway that converts
homoserine to threonine. These genes are linked in the functional network [24] by virtue of the coordinate expression of
their bacterial homologs in operons (for example, as for the
Bacillus subtilis homologs ThrB, ThrC, and ThrA), eventhough there is as yet little evidence that they belong to thesame physical complex. The loss of any of the three genes disrupts the threonine synthesis pathway and leads to reduced
growth after five generations in threonine-depleted media[4]. The functional gene network, which combines both physical and functional interactions, predicts both classes of phenotypes effectively, whether resulting from disruption ofphysical complexes or pathways.
Nevertheless, some phenotypes are not significantly predictable. Three likely causes exist. First, poor predictability may
result from using genome-wide screens with high false positive rates, which would base predictions on incorrectly identified seed sets. We sought to minimize this type of error byadopting stringent thresholds for each phenotype. Second,
incomplete screens (such as by not testing the essential
genes), high false negative rates, and the stringent phenotype
thresholds that we selected could lead to a large number of
positive examples being excluded from the seed sets. Suchomitted positive examples scoring higher than seed genes
would artificially depress prediction accuracies. Third, unpredictable phenotypes could in principle arise from the disruption of functionally unrelated genes. In order to test this, we
compared the GO enrichment for the 25 most predictablephenotypes with the 25 least predictable phenotypes. For
each phenotype, we identified the GO term with the most significant enrichment of genes annotated with the term, measured using the hypergeometric distribution. Using asignificance threshold of P < 10-7, we find that 18 of the 25
highly predictable phenotypes are significantly enriched forat least one GO annotation, as compared with only two of the
25 poorly predictable phenotypes. This suggests that poorly
predictable phenotypes largely result from sets of genes withlittle functional coherence.

Our findings indicate that typical phenotypes represent specific enough defects that they are predictable based upon thegenes' functional associations. We observe multiple mechaGenome Biology 2007, 8:R258

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AUC is a useful measure of gene functional coherence
By definition, the GBA approach we present predicts phenotypes associated with functionally coherent sets of genes, presumably reflecting the clustering of the genes in thefunctional network. Such predictability, which we specificallymeasure as the AUC, can therefore be regarded as a direct
estimate of the functional coherence of the seed gene set.
Thus, beyond simply evaluating phenotype prediction, the
AUC offers an additional measure of functional coherence
that complements other existing measures, such as theenrichment of GO annotations or other biologically meaningful sets of genes (as calculated by FunSpec [49] and Databasefor Annotation, Visualization, and Integrated Discovery [57]).
For example, the five genes giving rise to the branched cell
phenotype are connected by six linkages in the network (AUC
= 0.87), but only a single pair shares any GO annotation (P <0.001, for the GO term 'transcription from RNA polymerase
II promoter'). The network-based AUC measure for functional coherence exploits the massive unbiased data
integration of functional networks, extending well beyond
known annotations, and allows estimates of functional
coherence even among unannotated genes or those spanning
multiple systems.
In principle, the AUC approach can therefore measure thefunctional coherence of genes that annotation-based
methods will miss. Beyond un-annotated genes, the AUCbased estimate of functional coherence might also work effectively when the genes under study span multiple functional
categories; each category may be only partially enriched andtherefore may otherwise be missed for lack of signal. The
functional network, however, considers pair-wise linkages,
not predetermined categories, and so it has the potential toidentify linked genes across multiple annotation categories.

Recapitulation of the classic mutator phenotype in theyeast knockout collection
We observed a strikingly higher predictability for mutations
that increased cell-to-cell phenotypic variation versus thosethat decreased it. The deletion strains exhibiting higher CVs
tended to be consistent across the complete set of CV phenotypes examined, with the deleted genes showing strong
enrichment for functions related to DNA repair,recombination, and genomic stability. Note that strains withthe lowest CV phenotypes exhibited neither predictability nor
functional enrichment; in fact, the CVs exhibited by thesestrains were similar to those observed for replicate analyses ofwild-type cells (Figure 9c). This suggests that the strains thatmost decreased cell-to-cell variation were essentially wildtype-like in this regard.
This outcome is consistent with a recapitulation in the yeast
deletion strain collection of the classic mutator phenotype.
The mutator phenotype was originally observed in DNA
repair mutants; such mutants accumulated mutations so rapidly that they showed high variability in colony sizes when

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McGary et al. R258.15

grown on Petri dishes, high variability in cell morphologies,
high rates of plasmid loss, and increased spontaneous mutagenesis (for example, as previously observed for RAD27 andRAD52 deletion mutants [58,59]). The most likely
explanation is therefore that strains in the deletion collection
harboring deletions in genes related to genomic stability have
simply accumulated mutations at a higher rate. A mixed
population, no longer clonal, would be expected to exhibit
more cell-to-cell variation than other deletion strains, which
would accumulate mutations at a lower rate. Thus, we suspectthat our phenotypic analysis is correctly revealing the functional signature of a legitimate phenotype inadvertently captured in the process of distributing and passaging the yeast
deletion strain collection.

Applying network-based phenotype prediction tohumans and other organisms
In principal, the approach we describe could be applied to any
organism, using functional network data if available or, in theabsence of such data, using physical interaction data, such asavailable protein interaction networks for fly [60], worm [61],or human [25,62-66]. In the absence of an integrated functional gene network or protein interaction network, we expectthat networks of mRNA co-expression associations, such as
can be derived from DNA microarray data, would provide
some utility for phenotype prediction. Such data are a major
contributor to functional gene networks (for examples, see[13,16,17]) and are relatively easily generated from available
data for most model organisms.
In particular, application of this approach in humans may
allow directed identification of disease genes. Indeed, functional linkages derived largely from known GO annotation[67] or protein interactions [40] have shown some utility forprioritizing positional candidate genes from genome-wide
linkage screens. However, our results show that across a wide
range of yeast phenotypes and human diseases the associatedgenes (or their yeast orthologs) can be directly identified evenin the absence of supporting genetic loci data. In order toapply our approach to human diseases, genes that are known
to be associated with a particular disease, such as found fromtwin or genome-wide association studies, would form theseed set. Additional candidate genes that are likely to be associated with that disease could then potentially be identified orprioritized based upon their network connections to the seed
set, using the GBA principle. Potential disease genes could
then be tested in disease model systems or screened genetically in a focused manner. Such a directed approach would
exploit the tremendous existing body of knowledge aboutprotein interactions and functional pathways.

Conclusion

We have demonstrated that yeast gene loss-of-function phenotypes are broadly predictable from connectivity in a functional gene network, with examples presented spanning a

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wide range of cell growth, cell morphology, metabolite transport, chemical sensitivity, and molecular phenotypes. We
demonstrate that this predictability can be used to extend
genetic screens in a directed fashion, and that this approach
might therefore be important in organisms for which genetics
is difficult. We suggest that a similar approach in humans
might enable the directed discovery of disease genes.

Materials and methods
Assembling the set of nonredundant loss-of-function
phenotypes
A literature search was conducted to find genome-scale studies of yeast gene knockout phenotypes. Datasets were compiled from studies that systematically examined a large
fraction of the yeast genome. No effort was made to minimize
redundancy among the gene sets themselves. Nonetheless,only one set is a strict subset of another (genes that havechanged levels of transposon cDNA upon knockout are a subset of the genes that reduce retrotransposition). Most studies
were conducted using one or more of the following strain
collections: haploid, homozygous diploid or heterozygous
diploid [4], or tetracycline titratable [47]. The reported data
were a mix of qualitative, pseudo-quantitative, and quantitative results. Pseudo-quantitative data (often reported as '+','++', '-', '--', and so on) were thresholded at the most stringent
reported value (except for the small set of genes conferring
the phenotype 'branched cells' [4]; all genes with this morphology were included). Quantitative data were arbitrarily
thresholded using cut-offs that appeared consistent with thesensitivity of the assay. Predictability was not used as a criterion for selecting thresholds. In some cases, thresholds lessstringent than those selected result in more predictable phenotype sets (data not shown). In cases in which an uncharacterized open reading frame overlapped a known gene on thechromosome and both shared the same phenotoype (forinstance, Axial budding [68]; the dubious open reading frame
YOR300W overlaps BUD7), the uncharacterized gene wasremoved from the phenotype set. Additional phenotypes werecollected from the SGD stabase [69]; phenotypes extracted
from SGD used the threshold determined by SGD. The complete set of 100 phenotypic seed sets is provided as Additional
data file 1.
For the 281 quantitative phenotypes reported by SCMD [50],
the 40 knockout strains with either the highest or lowest values for each SCMD feature were selected (resulting in 562
seed gene sets). Similarly, 440 CV phenotypes were generated
by considering the 40 knockout strains with either the higher
or lowest CV for each SCMD CV feature (220 total features).

Prediction of phenotypes and evaluation of prediction
quality
For each gene in the network, we calculated the sum of its linkweights to genes with the phenotype in question (the seedset), namely assigning each gene i the following score:

Volume 8, Issue 12, Article R258

Si =

∑ LLS

McGary et al. R258.16

ijj∈seedWhere j is a gene in the seed gene set and LLSij is the log likelihood score for the linkage between genes i and j, as reported
by Lee and coworkers [24], except where explicitly analyzingother networks. Genes were then rank-ordered by their Siscores, with the highest scoring genes being the ones most
likely to share the phenotype with the seed set. For networks
reporting only binary linkages (MIPS [44] and DIP [45]), we
considered all linkages to be of weight 1. For calculation of
Figure 5, YeastNet v. 2, DIP and Probabilistic Integrated Cocomplex (PICO) [29] were each evaluated at two different
confidence levels. For analyses of protein interaction networks, the following networks were analyzed: YeastNet v. 2,
which corresponds to all interactions reported by Lee andcoworkers [24]; physical protein interactions (PPIs) from the
DIP [45] (downloaded on 4 February 2007), selecting as thecore set those interactions reported by Deane and coworkers[70]; the network reported by Collins and colleagues [43],using their reported threshold; PICO E-0 and E-2 networks,
which are PPI sets from Hart and coworkers [29]; and MIPS,
including all PPIs in physical complexes reported by Hart andcoworkers [29], derived from the work reported by Guldenerand colleagues [44]. In all cases, self interactions were
removed.
For each phenotype, the predictability was evaluated by generating a ROC curve based upon the gene ranking andcalculating the AUC. The ROC curve indicates the relative rateof true and false positive predictions as a function of the score
Si, plotting the true positive rate (TP/[TP + FN]) versus false
positive rate (FP/[FP + TN]). In calculating Si, self-self links
were not permitted, and each gene in the seed set waswithheld in turn from the seed set for evaluation (leave-oneout cross-validation). TP was defined (for a specific threshold) as the number of genes from the seed set ranked above agiven Si. FP was defined as the number of genes above thethreshold but not in the seed set. FN was defined as thenumber of seed genes ranked below the threshold. Finally, TN
was defined as the number of nonseed genes ranked below
the threshold.
The AUC ranges from 0 to 1, with 0.5 indicating random performance and 1.0 indicating perfect classification. Note that
the AUC is calculated using only seed genes represented in
the network (the network is not penalized for partial coverageof the seed set), allowing the predictive capacity of networks
of differing sizes to be compared. For the purposes of calculating a ROC curve, all genes not linked to the phenotype seed
set were treated as being of the same rank. Note that none ofthe phenotypes have been tested for all genes (most tested
only non-essential genes). Because of ambiguities in thereporting of genes tested, ROC curves for the set of 100 phenotypes were calculated over the entire set of yeast genes inGenome Biology 2007, 8:R258

http://genomebiology.com/2007/8/12/R258

Genome Biology 2007,

the network being tested (5,483 genes for the functional network). Thus, the measures of predictability (AUC) are likely
to be underestimates, because all untested genes are considered false positives.
As an alternative test for functional enrichment, we used
ArrayPlex [71] to calculate the hypergeometric probability ofthe enrichment for each GO annotation within a given geneset.

Prediction of human disease gene sets
For the test of human disease gene prediction, we collected
sets of yeast genes whose human orthologs were linked to thesame OMIM disease [51]. Human disease phenotypes fromOMIM were collapsed into major categories (variants of eachdisease were collapsed into a single category, such as collapsing 'Cataract, polymorphic and lamellar' and 'Cataract, crystalline aculeiform' into a single category of cataract defects).
Each human disease gene was mapped to one of 2,151 humanyeast orthology groups using Inparanoid [72], and seed sets ofyeast genes linked to the same disease were selected such thatat least four of the yeast genes were present in YeastNet.
Calculation of predictability and measurement of AUC was
performed as for yeast phenotypes, considering linkages in
YeastNet between human-yeast orthology groups rather thanbetween individual yeast genes.

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McGary et al. R258.17

USA). Each strain was grown to saturation at 30°C in richmedia (yeast extract/peptone/dextrose (YPD), inoculated
into fresh YPD with 10 ng/ml doxycycline, grown 16 hours,
and imaged [47] to evaluate cell morphology. Two biologists
evaluated the images for each strain (with strain names
hidden) for elongated cell morphologies using a simple qualitative scoring scheme (0 to 2), assigning a final score to eachstrain as the sum of the independent evaluations. Strains
scoring more than 2 were selected as elongated, which minimized false positives, yet recovered NUT2, previously
reported to be elongated [47].

Abbreviations

AUC, area under the curve; CV, coefficient of variance; DIP,
Database of Interacting Proteins; FN, false negative; FP, false
positive; GBA, guilt-by-association; GO, Gene Ontology;
MIPS, Munich Information Center for Protein Sequences;
OMIM, Online Mendelian Inheritance in Man; PICO, Probabilistic Integrated Co-complex; ROC, receiver operating characteristic; SCMD, S. cerevisiae Morphology Database; SGD,
Saccharomyces Genome Database; TN, true negative; TP,true positive.

Authors' contributions

KLM, IL, and EMM conceived of the research. KLM performed the experiments. KLM and EMM wrote the paper.

Generation of random phenotype sets
In order to estimate the random distribution of AUC scoresfor literature phenotypes, sets of genes of the same sizes as
the real phenotype seed sets were drawn from the complete
set of yeast genes and tested for predictability, using as the
background set of genes those designated by SGD as 'verified'or 'uncharacterized' (not dubious or pseudogenes; as of 29
January 2007). For SCMD morphology phenotypes [50],1,000 sets of 40 genes were drawn randomly from the complete set of genes analyzed by SCMD, and then tested for predictability in order to generate the null expectation for the
AUC distribution. For human disease phenotypes, random
gene sets were generated for comparison by randomly drawing from the set of network annotated human-yeast orthologs
such that the set size distribution of the random sets matched
the size distribution of the actual OMIM disease seed sets.

Additional data files

The following additional data are available with the online
version of this paper. Additional data file 1 shows the complete set of 100 phenotypic seed gene sets.

Acknowledgements
The SCMD database was provided freely by the University of Tokyo for use
in this publication only. We thank Andrew Fraser and Tanya Paull for critical discussion and Wei Niu for assistance with microscopy. This work was
supported by grants from the NSF (IIS-0325116, EIA-0219061), NIH(GM06779-01, GM076536-01), Welch (F1515), and a Packard Fellowship(EMM).

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