Group Title: BMC Genomics
Title: Natural genetic variation in transcriptome reflects network structure inferred with major effect mutations: insulin/TOR and associated phenotypes in Drosophila melanogaster
Full Citation
Permanent Link:
 Material Information
Title: Natural genetic variation in transcriptome reflects network structure inferred with major effect mutations: insulin/TOR and associated phenotypes in Drosophila melanogaster
Physical Description: Book
Language: English
Creator: Nuzhdin, Sergey
Brisson, Jennifer
Pickering, Andrew
Wayne, Marta
Harshman, Lawrence
McIntyre, Lauren
Publisher: BMC Genomics
Publication Date: 2009
Abstract: BACKGROUND:A molecular process based genotype-to-phenotype map will ultimately enable us to predict how genetic variation among individuals results in phenotypic alterations. Building such a map is, however, far from straightforward. It requires understanding how molecular variation re-shapes developmental and metabolic networks, and how the functional state of these networks modifies phenotypes in genotype specific way. We focus on the latter problem by describing genetic variation in transcript levels of genes in the InR/TOR pathway among 72 Drosophila melanogaster genotypes.RESULTS:We observe tight co-variance in transcript levels of genes not known to influence each other through direct transcriptional control. We summarize transcriptome variation with factor analyses, and observe strong co-variance of gene expression within the dFOXO-branch and within the TOR-branch of the pathway. Finally, we investigate whether major axes of transcriptome variation shape phenotypes expected to be influenced through the InR/TOR pathway. We find limited evidence that transcript levels of individual upstream genes in the InR/TOR pathway predict fly phenotypes in expected ways. However, there is no evidence that these effects are mediated through the major axes of downstream transcriptome variation.CONCLUSION:In summary, our results question the assertion of the 'sparse' nature of genetic networks, while validating and extending candidate gene approaches in the analyses of complex traits.
General Note: Periodical Abbreviation:BMC Genomics
General Note: Start page 124
General Note: M3: 10.1186/1471-2164-10-124
 Record Information
Bibliographic ID: UF00099927
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: Open Access:
Resource Identifier: issn - 1471-2164


This item has the following downloads:


Full Text

BMC Genomics Bioled Central

Research article

Natural genetic variation in transcriptome reflects network
structure inferred with major effect mutations: insulin/TOR and
associated phenotypes in Drosophila melanogaster
Sergey V Nuzhdin *1, Jennifer A Brisson', Andrew Pickering1,
Marta L Wayne2, Lawrence G Harshman3 and Lauren M Mclntyre2

Address: 'Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA, 2University of Florida Genetics
Institute, University of Florida, Gainesville FL 32610-36103, USA and 3School of Biological Sciences, University of Nebraska at Lincoln, Lincoln,
NE 68588, USA
Email: Sergey V Nuzhdin*; Jennifer A Brisson; Andrew Pickering;
Marta L Wayne; Lawrence G Harshman; Lauren M McIntyre
* Corresponding author

Published: 24 March 2009 Received: 19 December 2008
BMC Genomics 2009, 10:124 doi: 10.1 186/1471-2164-10-124 Accepted: 24 March 2009
This article is available from:
2009 Nuzhdin et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.ore/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: A molecular process based genotype-to-phenotype map will ultimately enable us to
predict how genetic variation among individuals results in phenotypic alterations. Building such a
map is, however, far from straightforward. It requires understanding how molecular variation re-
shapes developmental and metabolic networks, and how the functional state of these networks
modifies phenotypes in genotype specific way. We focus on the latter problem by describing
genetic variation in transcript levels of genes in the InR/TOR pathway among 72 Drosophila
melanogaster genotypes.
Results: We observe tight co-variance in transcript levels of genes not known to influence each
other through direct transcriptional control. We summarize transcriptome variation with factor
analyses, and observe strong co-variance of gene expression within the dFOXO-branch and within
the TOR-branch of the pathway. Finally, we investigate whether major axes of transcriptome
variation shape phenotypes expected to be influenced through the InR/TOR pathway. We find
limited evidence that transcript levels of individual upstream genes in the InR/TOR pathway predict
fly phenotypes in expected ways. However, there is no evidence that these effects are mediated
through the major axes of downstream transcriptome variation.
Conclusion: In summary, our results question the assertion of the 'sparse' nature of genetic
networks, while validating and extending candidate gene approaches in the analyses of complex

Background this question by focusing on one of the best mechanisti-
One of the frontiers of current day genomics is to build cally described processes. The insulin receptor/TOR
predictive models for how molecular variation in known kinase (InR/TOR) pathway underlies many physiological
pathways affects their performance. Here, we approach processes that redirect organismal resources into activity,

Page 1 of 12
(page number not for citation purposes)

Open A

BMC Genomics 2009, 10:124

maintenance, and survival, depending on dietary energy
intake. Experiments performed on rats towards the start of
the twentieth century identified dramatic increases in
lifespan associated with a restricted diet [ 1 ]. Similar exten-
sions in lifespan were subsequently identified in relation
to dietary restriction in a broad range of other organisms,
including the model organisms Caenorhabditis elegans and
Drosophila melanogaster [2]. C. elegans has since served as a
primary model for molecular descriptions of the InR/TOR
pathway. Specifically, the tyrosine kinase receptor DAF-2
induces phosphorylation of the phosphoinositide 3-
kinase (PI3K) catalytic subunit AGE-1 [3]. AGE-1 then
phosphorylates the serine/threonine kinases AKT-1 and
AKT-2, which form a complex with the serine/threonine
protein kinase SGK-1 and subsequently phosphorylate
the transcription factor DAF-16 [4-6]. The phosphorylated
DAF-16 cannot enter the nucleus. Under normal dietary
conditions, DAF-16 is retained in the cytoplasm in a phos-
phorylated state. However, if the receptor DAF-2 is inhib-
ited, DAF-16 is not phosphorylated and so is capable of
diffusing into the nucleus, where it regulates a range of
genes that induce the starvation phenotype [7].

Recently, the InR/TOR signaling network was analyzed at
the whole-genome level in D. melanogaster using microar-
ray [8,9] and ChIP-chip analyses [10]. In Figure 1 we sum-
marize these studies, along with other more specific
studies querying regulatory relationships in the pathway.
Mutations in the Drosophila daf-2 homologue InR present
similar extensions in lifespan to daf-2, implying a similar
function [11]. The forkhead transcription factor dFOXO is
likely the functional equivalent of DAF-16 [12]. Insulin
levels regulate InR, which signals to PI3K through chico -
the gene that encodes a likely ortholog to human substrate
protein. PI3K suppresses dFOXO by means of AKT-medi-
ated phosphorylation, causing dFOXO to remain local-
ized in the cytoplasm [13]. As a result, dFOXO cannot
access its direct downstream binding targets, including 4E-
BP and Lk6. In the absence of dFOXO binding, 4E-BP and
Lk6 repress eIF4E, and thus repress translation initiation.
PI3K also activates TOR, which in turn upregulates myc,
one of the dFOXO downstream targets that affects ribos-
ome biogenesis. These highly coordinated responses,
which differ slightly among tissues, result in fine-tuned
regulation of protein biosynthetic capacity. There are sev-
eral feedback loops in this hierarchy as well, including
that InR transcript level is affected by dFOXO itself
through direct binding [14]. Thus, the InR/TOR pathway
is interesting as it combines multiple levels of regulation,
including both transcription and translation. However,
whether the pathway's components are coordinated with
each other at the level of transcript abundance remains

In model species for genetic research, the InR/TOR signal-
ing network structure and its phenotypic effects were stud-


ied via analyses of major effect mutations. It is unknown
if smaller genetic alterations affect gene expression and
phenotypes in an analogous manner. One type of smaller
effect perturbation is genetic variation among natural gen-
otypes, which in turn results in variation among gene
transcript levels [15]. The most comprehensive recent data
set of this type is by Wayne et al. [16] describing whole
genome expression levels in 72 genotypes of males and
females. Here we use this data set to investigate whether
we can detect covariance in transcript variation in InR/
TOR pathway genes. Because the high dimensionality of
genome-wide expression data presents multiple chal-
lenges [17-21], we focused our analyses on three a priori
defined groups of co-regulated genes. Gershman et al. [8]
identified 3519 genes involved in the transcriptional
response to nutrition in Drosophila by assaying RNA of
adults that had been fed yeast following a period of star-
vation. They also identified a 995 gene subset of the nutri-
tion-affected genes using microarray analysis of
Drosophila S2 cells constitutively expressing dFOXO.
Likewise, a list of 1016 genes downstream of TOR has
been inferred via rapamycin treatment of Drosophila S2
cells [9]. Using the gene expression states across 72 geno-
types described in Wayne et al[ 16], we examined how the
patterns of variance/covariance in these gene expression
levels reflect variation in the upstream InR/TOR network.
Specifically, we wanted to determine whether or not gene
expression levels showed tighter covariance among genes
from the individual TOR or dFOXO branches of the path-
ways, relative to the pathway overall.

Given that thousands of genes can be perturbed at once by
variation in only a few upstream genes, the question arises
as to how to summarize variation of their expression lev-
els into fewer gene clusters co-varying for expression
among genotypes. Several dimensionality reduction
approaches are available, including principal compo-
nents, spectral map and correspondence analyses [22,23].
We focused on factor analysis, which uses covariation
among genes to identify factors affecting the transcription
of multiple genes at once [24]. One may interpret the fac-
tor as the mechanism, for example, a transcription factor,
by which genes are co-regulated. Another example would
be that of tissue-specific expression; genotypes with a
larger volume of this particular tissue would have higher
levels of all tissue-specific genes. Whatever the true under-
lying mechanism, the factor model represents sets of coor-
dinately expressed genes. Taking this a step further, these
covarying genes can be viewed as participating jointly in a
network. Each gene has an estimate of its participation in
the common network; this is called the factor load, where
the strength of the load indicates how much that particu-
lar gene contributes to a given factor. The product of the
factor load and the gene expression value for a given gen-
otype can then be summed over the individual genes con-
sidered into a factor value. The factor value then

Page 2 of 12
(page number not for citation purposes)


Insulin-like peptides

.68 |

-3500 geres
,| -
,-- .r, et al

- .84.91

Amino acids


amino acid


Rheb (

99 gq~res \ 5'L transcription ,V
affected in FO .: knock-outs FOXO re946'ua 9 TOR
(Gershman et al 2007) .63
.70 12

Lk6 4EBP .17 myc
.65 51 .25
.28 \ K43 1
eIF4E Ribosol
Translation initiation


-1200 genes
(-400 reported)
r? [= : treatment
(Guertin et al- -..-:-.7

me biogenesis

Protein biosynthesis / growth
Figure I
The core connections in the InR/TOR network. Regulatory connections through phosphorylation are shown in brown
and through direct transcriptional regulation in blue. Arrows indicate activating interactions and bars inhibitory relationships.
Correlation coefficients between genes are shown in blue for males and in red for females, with significant measures italicized;
if there was no genetic variation for a particular gene, then the correlation could not be calculated and this is indicated by a '?'.

represents the functional state of this network, i.e., how
much the individual genes in the network are controlled
by a common regulatory mechanism in each genotype.
Our first goal was therefore to use factor analysis to inves-
tigate the variance/covariance structure of genes in the
InR/TOR pathway.

Our second goal was to investigate the phenotypic conse-
quences of natural genetic variation in the InR/TOR path-
way, since major effect mutations in the pathway are
known to affect lifespan, survival, and other life history

characters [25-27]. Similar phenotypic effects of these
mutations were previously assayed in flies with gross
abnormalities of pathway functioning [e.g., [11,27]]. We
know that knocking out the gene function of dFOXO
results in higher desiccation resistance [28]. Will smaller,
quantitative expression changes of dFOXO result in
accompanying phenotypic modification or it will be buff-
ered? Our goal was to determine whether natural quanti-
tative variation in transcript levels of these genes would
have quantitative effects on the phenotype resembling the
effects of major mutations. We analyzed co-variation

Page 3 of 12
(page number not for citation purposes)


BMC Genomics 2009,10:124

BMC Genomics 2009, 10:124

between transcript levels in the upstream InR/TOR path-
way with candidate life-history associated phenotypes,
and confirmed many patterns expected from previous
analyses of major-effect mutations. Our findings extend
the candidate gene approach as a helpful tool in the anal-
ysis of phenotypic variation.

The summary factor analysis of the downstream InR/TOR-
affected transcriptome can also be examined for associa-
tions with phenotypic traits by correlating the estimated
factors with the phenotypes [29]. This correlation serves
to infer which factors, i. e., major axes of transcriptome
variation, contribute to the modification of which pheno-
types. By studying these correlations, we were unable to
connect major axes of transcriptional variation in the InR/
TOR to phenotypic variation. This observation questions
the utility of this type of'perturbation screen' for identify-
ing de-novo axes of expression variation and simultane-
ously accounting for phenotypic variation.

(i) Genetic variation-covariation between the upstream
genes in the InRITOR pathway
We have reanalyzed the data of Wayne et al. [16] to deter-
mine whether genetic variation and covariation in tran-
script abundance among genotypes reflect the structure of a
known D. melanogaster network, the InR/TOR pathway.
Wayne et al. [ 16] recorded microarray hybridization signals
of whole body RNA extracted from heterozygous F1 male
and female progeny obtained from all possible crosses
between 9 genotypes from a single natural population,
resulting in 72 replicated measurements of expression in
each sex. Significant co-variation between gene expression
levels of two or more genes in this dataset may be due not
only to biology, but also to experimental artifacts. Linked
alleles within a homozygous parental genome are always
co-inherited in F1 progeny originating from this parent. F,
offspring sharing a parent may then exhibit patterns of
association due only to the co-inheritance of the same
block of alleles rather than true association among the loci
in their underlying mechanism. In such a case, 9 genotypes
out of 72 will have a high combination for their transcript
levels, which is a pattern akin to pseudoreplication. To deal
with this problem, instead of studying the patterns of cov-
ariance among the 72 genotypes, we first estimated breed-
ing values for transcript levels in the 9 parental genotypes
(Additional files 1 and 2) and determined the effect of
breeding values on transcript levels in our subsequent anal-
yses. We focused on the portion of the InR/TOR network
that: i) is well established in flies, ii) genetically varies
among the genotypes, and iii) is represented by high qual-
ity oligonucleotides on the custom microarray designed by
McIntyre et al. [30]. Some genes were tagged by several oli-
gonucleotides and all of them were retained and their sig-
nals averaged in the subsequent analyses.


The InR/TOR signaling network combines regulation at
the level of transcriptional regulation, phosphorylation,
protein relocation and other molecular mechanisms.
Genes that are in the InR/TOR pathway and have been
analyzed by this study are represented on Figure 1. We
determined the covariance of transcript levels of these
genes. Despite the diversity of molecular functions and
mechanisms of signaling, the transcripts of nearly all
genes in the network strongly co-vary with each other (see
numbers on arrows, Figure 1). For instance, the genetic
correlation between the transcript levels of AKT and TOR
(AKT phosphorylates the TOR protein) are 0.88 in females
and 0.92 in males, both highly significant. Clearly, this co-
variation is not necessarily due to causal effects of the
gene's transcript levels on one another, but is most likely
due to the influence of other genetic factors onto both of
these genes. This might indicate a common regulatory
mechanism for the pathway as a whole within a cell. Alter-
natively, as tissue representation might vary among the
genotypes and InR/TOR network function varies between
tissues, the correlations might be due to these organism-
level differences among the genotypes. Note that environ-
mental or developmental fluctuations may be ruled out as
explaining these patterns of co-variation: transcript level
measurements in each heterozygous genotype were bio-
logically replicated to account for dye and environmental
effects. Further, an effect of each genotype (breeding
value, see [31] for definitions) was estimated from a joint
analysis of 72 heterozygous genotypes, further reducing
the opportunity for developmental and environmental
noise to influence the analyses.

While we only present correlation coefficients for the genes
known to signal each other on Figure 1, we also estimated
correlations between all pairs of these genes (Additional file
3). Overall, they represent a highly connected, co-varying
group from which it is difficult to recover any fine structure.
We conclude that, as expected, the genes of the InR/TOR net-
work are strongly co-regulated at the cellular or organismal
level, but the strength of genetic co-variations among them
does not mirror the expectations based on the mechanistic
details known for this signal transduction pathway.

(ii) Genetic variation for the levels of message among
feeding-affected genes
Gershman et al. [8] used changepoint analysis to identify
3519 Affymetrix probe-sets that changed their transcript
level during 7 hours after feeding. Only 3270 of these
probe-sets could be unambiguously identified in Dro-
sophila genome version 5.1. For the remainder, at least one
probe in the probe-set was similar to more than one
genome position using a BLAST algorithm. These 3270
probe-sets correspond to 3171 genes. Oligonucleotides
for 3126 out of 3171 genes were represented on the Agi-
lent microarray. Of these, 3048 genes showed significant

Page 4 of 12
(page number not for citation purposes)

BMC Genomics 2009, 10:124

hybridization and genetic variation in breeding values in
females and 3031 of these were identified in males.

To summarize transcript level variation of these approxi-
mately 3000 genes, we employed factor analysis. Each gene
has 9 biologically independent measurements per sex per
probe. Accordingly, we can infer up to 8 axes of transcrip-
tome variation (factors). An individual gene might or
might not vary in its expression level along a factor, which
is indicated by the magnitude of its loading onto this factor
(Additional file 4). In females, the first factor explained
44.35% of the total transcriptome variation with 2296
genes having bigger than 0.4 loading, but only 10 smaller
than -0.4 loading. The second factor accounted for 22.17%
of the variation contributed by substantial loading of 1306
genes. The third and all subsequent factors explained
12.78% (5.53%, 4.92%, 4.10%, 3.60%, 2.54%) of the tran-
scriptome variation and included 908 (260, 228, 182, 148,
75) genes. In males, likewise, the factors accounted for
49.95, 12.52, 9.95, 7.45, 6.71, 5.47, 4.43, and 3.53 percent
of the variation with 2633 (only four of them with negative
loading), 874, 666, 438, 371, 275, 194, and 116 genes. The
average loading onto the first factor was 0.59 in females
and 0.67 in males. The factor loading was very consistent
between males and females with most of the same genes
loading on the same factors for the two sexes.

We investigated the biological processes and molecular
functions of these genes using gene ontology (GO)
enrichment analysis. For each of the first three factors in
males and females separately, which together accounted
for 72.42% and 79.30% of the transcriptome variation
respectively, we report all of the GO terms that we found
overrepresented in our gene lists using a 0.15 FDR [32]
significance cutoff (Additional file 5). For both sexes, the
first factor reflected terms related to metabolism, and
females were further enriched for terms involved with
translation and transport. The second and third factors for
both sexes were enriched for terms related to signal trans-
duction and electron transport, respectively.

(iii) Covariation of transcript levels in dFOXO and TOR
branches of InRITOR network
Out of the 995 dFOXO-affected genes reported by Gersh-
man et al. [ 8], 911 genes were present on the Agilent array.
887 of these showed evidence of variation in females and
872 showed evidence of variation in males. These genes
are a subset of their larger list of ~3000 feeding-affected
genes described above. We first analyzed whether varia-
tion in these dFOXO genes was distinctly directed along
the major axes of transcriptome variation. Factor 1 influ-
ences the subset of dFOXO-affected genes somewhat
more strongly than the rest of the genes in the InR/TOR
pathway: the average loading of dFOXO affected genes on
the first factor was 0.69 in females and 0.71 in males,


while for the rest of the approximately 2000 genes it was
somewhat smaller at 0.55 in females and 0.65 in males.
dFOXO genes did not consistently co-vary for expression
with the other factors. To further assess variation in these
genes as a subgroup, we conducted a factor analysis for the
dFOXO affected genes separately. The factors from the ini-
tial inclusive analysis and the limited analysis of dFOXO
targets were largely collinear (data not shown).

Direct targets of dFOXO have been identified using ChIP-
chip technology and the binding motifs identified by
these assays were found in hundreds of genes [10]. We
asked whether or not genotypes with naturally higher lev-
els of dFOXO were more correlated with their dFOXO
binding targets. This permits us to test whether or not the
dFOXO to downstream target co-variation is explained by
the number or quality of binding sites in the proximity of
the target gene. We determined the correlation between
dFOXO transcript level and the transcript level for the
bound gene among the 9 genotypes (Additional file 6).
We then compared the correlation of transcript levels of
dFOXO and the targets of dFOXO (and their absolute val-
ues) to the intensities of binding of the target gene as
defined by ChIP-chip analyses. The correlation across all
genes was not significant. While the intensity of binding
might not be a good predictor of the quality of the down-
stream gene regulation by dFOXO, we hypothesize that
dFOXO transcript level variation is not mechanistically
responsible for the variation in the transcript level of its
downstream genes among the 9 genotypes. We also made
an analogous analysis with the AKT transcript levels. Our
rationale is that AKT activity, which is potentially depend-
ent on the AKT transcript level, might be responsible for a
varying degree of dFOXO exclusion from the nucleus
among genotypes. This would result in varying degrees of
the dFOXO binding to the downstream genes. Again, we
did not observe this effect (data not shown).

Guertin et al. [9] reported approximately 1200 genes that
changed their expression level in response to rapamycin
treatment and thus are TOR affected genes. However, only
a fraction of these genes were reported in the Supplemen-
tary Tables available for that manuscript. From this paper,
we combined gene lists from Tables S1-S3. Among them,
54 genes had significant transcript level variation in males
and females, and 46 were represented in the gene set
reported by Gershman et al. [8]. Similar to the pattern
detected for the dFOXO affected genes, TOR affected
genes were tightly coregulated, with the average loading
onto the first factor being 0.79 for females and 0.77 for
males. Limiting the factor analysis to only the TOR
affected genes did not change the direction of axes of tran-
scriptome variation (data not shown), again resembling
the pattern detected in the dFOXO branch of InR/TOR

Page 5 of 12
(page number not for citation purposes)

BMC Genomics 2009, 10:124

These factor loadings suggest that the dFOXO affected
genes and the TOR affected genes are more tightly covary-
ing than the overall set of feeding-affected genes. To test
this hypothesis explicitly we examined the distribution of
the pairwise correlation co-efficient in the feeding-
affected genes in females and males (average of p = 0.35,
p = 0.38 for females, males, respectively) to the dFOXO
subset (p = 0.46, p = 0.44) and the TOR subgroup (p =
0.78, p = 0.77). Random samples of the feeding-affected
gene list of the size of the dFOXO and TOR subgroups
were taken 1000 times and the average pairwise correla-
tion calculated. We conclude that at P < 0.001 dFOXO
affected genes [8] and TOR affected genes [9] are more
tightly co-varying sub-groups of a larger group of 3500
feeding-affected genes. However, the major axes of tran-
script level variation in these two subsets of genes do not
seem to differ from those of the larger group of feeding-
affected genes.

(iv) Partial regression analyses of multiply connected genes
For the well-established connections of the InR/TOR
pathway, we have extended our analyses beyond pairwise
comparisons to include multiple connections via partial
correlation analysis following Neto et. al. [33]. We ana-
lyzed all the multiply connected genes in the pathway, but
in many cases partial regression analyses did not add extra
inferences to the simple pairwise correlations. Instances
where it did are reported in Table 1. Here, we briefly sum-
marize them. In the pairwise models, TOR is strongly
associated with TSC1, slimfast (slif) and myc but not Rheb.
In further examination of the pairwise associations, we
find that Rheb is not associated with slif or myc and in con-
trast to the visualization of the pathway (Figure 1) is sig-
nificantly correlated with TSC1, chico and eIF4E.
Considering a larger model and partial correlations, if myc
is the final product of the pathway (the dependent varia-
ble), then we can construct a model and examine the
sequential contribution of TOR, AKT, Rheb, TSC1 and slif.
As expected, the effect of TOR is sufficient to predict myc
and in this larger model the sequential effects of the
remaining genes are not statistically significant. This fully
validated the strong link between TOR and myc. If instead


TOR we examine a model where TOR is the dependent
variable and Rheb, TSC1, and slif are considered sequen-
tially, TSC1 is still significant after considering the effects
of Rheb, and slif is still significant after accounting for the
effects of Rheb and TSC1. This suggests that the proposed
pathway (Figure 1) is not complete and that there is likely
(in non-stress conditions) to be a direct link between slif
and TOR, and between TSC1 and TOR as well. To see if the
link between TSC1 and TOR was mediated by AKT, a
model with AKT upstream of TSC1 and slif was fit. After
accounting for the variation in AKT, TSC1 was still signif-
icant, as was slif, indicating that AKT is unlikely to explain
the feedback loop completely. In contrast, when S6K was
placed downstream of AKT, S6K was no longer signifi-
cantly associated with TOR, indicating that as previously
reported the effect of S6K is mediated by AKT. We con-
sider the above logical conjectures to be somewhat pre-
liminary for two reasons. First, when we see a significant
partial correlation between the genes, it might be caused
not by their direct interactions, but rather by the cumula-
tive effects of variation elsewhere in the network that we
have not accounted for. Second, when we do not detect a
significant partial correlation, it might be due to limited
genetic variation within our 9 breeding values and thus to
insufficient power.

(v) Genotype-to-phenotype map exhibits some effects
expected from the analyses of major effect mutations
Major effect mutations in the InR/TOR pathway affect
lifespan, oxidative stress resistance, body size and other
phenotypes (see Introduction for details). We analyzed
whether the mean trait values of these phenotypes among
the 72 genotypes in males and females genetically co-vary
with transcript levels of individual genes in InR/TOR
pathway. Detailed analyses of the phenotypic data are
reported elsewhere (Wayne et al. [16] and Harshman, per-
sonal communication) and here we only present impor-
tant highlights. We have observed several effects expected
from analyses of large-effect mutations (Table 2). For
instance, knock-outs of the dFOXO gene are known to
have improved oxidative stress resistance [28]. We there-
fore hypothesized that genotypes with weaker dFOXO

Table I: Linear models with dependent effects given to the left of the equals sign and independent effects to the right of the equals


Myc = TOR + AKT + Rheb + TSCI + slif + e

TOR = Rheb + TSCI + slif+ e

TOR = Rheb + AKT I +TSC I + slif +e

TOR = AKT + Pdkl+ S6K+ e

P-values* in the order given by the model

0.0064, 0.0555, 0.0905, 0.7840, 0.3521

0.0002, 0.0001, 0.0099

0.0001, 0.0001, 0.002, 0.0003

0.0017, 0.5166, 0.0724

* P-values for the tests of the sequential sums of squares (type I tests) in females.

Page 6 of 12
(page number not for citation purposes)

BMC Genomics 2009, 10:124

expression would likewise show stronger oxidative resist-
ance. This expectation holds true in both sexes. Likewise,
other genes exhibit expected genetic co-variations with
multiple phenotypes, though none of these effects is sig-
nificant after multiple testing correction. Furthermore, it
remains unclear which genes in this signal transduction
cascade or in its upstream regulators have causal effects on
these phenotypes because the network appears strongly
co-regulated and lacking finer structure. We conclude that
while the phenotypic effects of these network perturba-
tions reflect a priori expectations, it is difficult, perhaps
impossible, to identify causal genes responsible for the
phenotypic alterations without additional information.

In (ii) and (iii), we summarized overall transcript level
variation using factor analysis. We asked whether or not
these major axes of transcriptome variation are better pre-


doctors of phenotypic variation than the upstream genes
of the InR/TOR pathway by correlating the values for these
factors with the phenotypes among genotypes (Table 3).
While we have detected several suggestive patterns sum-
marized in Additional file 3, none of them stood out after
correction for multiple testing.

A common method of reconstructing and characterizing
gene regulatory networks is to individually analyze the
transcriptional profiles of a collection of single gene
knockouts and infer regulatory relationships based on
gene expression changes [for recent applications and a
review, see [34-3 711. While these approaches have yielded
important information regarding gene regulatory net-
works, the methods are limited given the cost and labor
required to determine the expression profile for a knock-

Table 2: Correlations of individual genes with each of five phenotypes in females.

Gene Desiccation resistance

Oxidative stress

-0.75, 0.02

InR 0.10

Lk6 -0.30

Pi3K -0.31

PDK I -0.41

PTEN -0.29

Rheb -0.18

S6K -0.29

4EBP 0.27

Tor -0.32

Tsc I -0.33

chico -0.18

elF4E -0.43

FOXO -0.42

Tsc2 -0.42

Slif -0.06

-0.81, 0.005

-0.76, 0.01

-0.67, 0.04

-0.72, 0.03


-0.88, 0.0008

-0.69, 0.04


-0.93,6.2x I0-s

-0.89, 0.0005



Longevity Development time














-0.80, 0.008


-0.79, 0.008 -0.41

-0.80, 0.008

Male values are similar and can be found in Additional file 3. If the correlation was significant, then the correlation coefficient is italicized and the P-
value is given. P-values that remain significant at the 0.05 level following Bonferroni correction are bolded.

Page 7 of 12
(page number not for citation purposes)



-0.71, 0.03


BMC Genomics 2009, 10:124

out in every gene. Furthermore, analyzing gene knockouts
does not allow for an assessment of smaller effect pertur-
bations in genes, which may be a more common type of
genetic variation in natural populations. One way around
these limitations is to examine and analyze gene expres-
sion from many individual genotypes that each contains
multiple mutations and to study the covariance in pheno-
types and multiple gene expression levels to hypothesize
which of the gene expression changes might cause pheno-
typic deviations [38-41]. If successful, this would contrib-
ute to elucidating components of the genotype-to-
phenotype map. It might also greatly aid our understand-
ing of the genetic basis of human disease, given that most
human disease is caused by small effect mutations in
many genes [42-44].

If most genetic regulatory networks are sparse, meaning
that there is not a substantial overlap in the genes regu-
lated within different networks [45], it should be possible
to analyze a relatively small number of genotypes and use
mathematical models to infer regulatory relationships
between many genes. Major effect mutations do affect
some downstream genes more strongly than others, thus
establishing main regulatory connections. However, these
strong effects also mask numerous weaker connections.
Indeed, a major effect mutation typically disturbs hun-
dreds or thousands of the genes, albeit to different degrees
[34-37], as revealed by whole genome analyses of tran-
scriptome alterations. With more powerful experimental
and statistical approaches, these effects can likely be
detected on many thousands of genes, or even onto all of
the transcriptome. While with single major effect muta-
tion per genotype, the main regulatory connections are
easy to recognize, would this also hold true for natural


genetic variation? More specifically, are genetic networks
sparse enough for naturally segregating smaller-effect
mutations to reveal individual network connections?
From the analyses of transcriptome data presented here, it
appears that the collective effects of numerous natural
mutations might override a sparse nature of major effect
mutations and dominate the collective properties of
molecular networks.

In unrelated wild type flies, every genotype contains
numerous sequence alterations. With approximately 1/100
bases different between two random unrelated flies, the
total number of whole-genome differences is in the mil-
lions. How many of these differences represent regulatory
mutations is not known, but some estimates may be
offered as follows. Genissel et al. [46] investigated whole
genome expression from an oligonucleotide microarray in
two extensively studied genotypes of Drosophila mela-
nogaster, Ore and 2b3, and six recombinant inbred lines
derived from these parents. Approximately 10% of the tran-
scriptome was differentially regulated among the lines.
Regulatory effects in cis (regulatory mutations in the gene
locus itself or nearby) appeared present in up to 1218
genes. 123 genes were affected by trans mutations, but the
vast majority of trans effects probably remained undetected
because there was not enough consistency among different
analytical procedures. Wang et al. [47] assayed a full set of
chromosome substitution lines between the two behavio-
ral races of D. melanogaster Z and M. Only about 3% of the
genes with an expression difference between races were
purely cis regulated, while transcript levels of 80% of the
genes were controlled by at least two different chromo-
somes. From these two studies, we can conclude that hun-
dreds to thousands of genes in each genotype contain

Table 3: Correlations of factors with each of five phenotypes in females.

Factor Desiccation resistance

Oxidative stress

Starvation Longevity Development time

Factor I -0.32

Factor 2 0.12

Factor 3 0.12

Factor 4 0.02

Factor 5 0.23

Factor 6 0.41




-0.82, 0.005

Factor 7 0.81, 0.005

Factor 8 -0.02

Male values are similar and can be found in Additional file 3. If the correlation was significant, then the correlation coefficient is italicized and the P-
value is given for that correlation. No P-values remained significant after Bonferroni correction.

Page 8 of 12
(page number not for citation purposes)


BMC Genomics 2009, 10:124

regulatory alterations. Therefore, regulatory mutations
likely affect hundreds of networks in each genotype, and
quite a few of them many times. These mutations in turn
affect thousands or tens of thousands of downstream tar-
gets. If we now recall that our data set combines the analysis
of variation over 9 genotypes, it becomes clear that the pat-
terns of covariance we observe represents not individual
gene-to-gene connections, but rather system-wide effects of
massive genetic variation onto transcriptome architecture.

Why, then, have some analyses [see [38] for summary,
[48]] been able to reconstruct quite a few network connec-
tions? We believe that there are two main reasons for this,
both stemming from the fact that typical analyses have
been based on Recombinant Inbred Lines (RILs). First,
researchers mapped factors affecting transcription of focal
genes to cis (position of the focal gene) and other regions
of the genome (trans). With approximately a hundred
RILs, only the strongest effects could have been unambig-
uously mapped, especially after corrections for the
number of tested genes. Most regulatory connections
must have been missed, although their composite effects
might be overwhelming. Contrary to this supposition, the
factors mapped typically accounted for an appreciable
portion of the focal gene's transcript variation. We argue,
though, that the Beavis effect, rather than the true Mende-
lian nature of the mapped factors, is a likely reason for
such conclusions, meaning that when the power of QTL
mapping is low, the effects of detected QTLs are strongly
overestimated [49]. The requirement to correct for multi-
ple tested genes in the whole transcriptome eQTL map-
ping makes the power very low even in the best eQTL
mappings. Accordingly, these experiments are likely to i)
detect only the strongest effects and ii) overestimate their
effects. This should result in a substantially oversimplified
picture of genetic network connectedness. Additionally, a
set of RILs is typically built from just two parental geno-
types. We argue that both alleles sampled per gene (one
from the first and another from the second parent) are
likely to be functionally equivalent. Whenever the genetic
network is connected through the gene in which there is
no variation in the mapping RIL population, the network
will appear 'broken' at this gene. This will contribute to
apparent 'resolution' of the network connections. Overall,
we feel that the eQTL based networks are likely to be over-
simplified. We believe that higher power and larger
genetic base network analyses will recover, in the future,
much more connected and complex genetic networks.

We report one of the first attempts to use a population with
a broad genetic basis to decipher the nature of genetic vari-
ation in the transcriptome and to link it with phenotypic
variation. While the genetic architecture of transcriptome
variation appears very complex, the use of prior informa-
tion about the InR/TOR signaling cascade allows several


interesting inferences. First, the transcript levels of the genes
connected within the network appear coordinated. Second,
transcript level variation is reflected in phenotypic variation
in expected ways predicted from mutational analyses.
Third, new connections are proposed that will help to focus
further molecular analyses of InR/TOR network.

Genetic material and expression measurements
The microarray data acquisition and analysis were
described by Wayne et al. [16]. Briefly, nine isogenic lines
of D. melanogaster used as parents were originally captured
in an orchard in Winters, CA, and subjected to > 20 gener-
ations of full sibling inbreeding. Lines were crossed in a
full diallel design with reciprocals, but without
homozygous parents (72 F1 progeny). RNA was extracted
from 20 whole 3-day post eclosion flies, snap-frozen in
liquid nitrogen using Trizol reagent (Invitrogen, Carlsbad,
CA). The chip was synthesized on an Agilent platform
AMADID 012798; 3). Hybridizations were performed
with males and females of the same genotype, labeled in
contrasting dyes, hybridized to the same chip. We ana-
lyzed two independent biological replicates for each gen-
otype and sex combination. Intensity values were
normalized using the natural log transformation. The chip
design contained 503 negative control sequences
designed from human sequences with similar GC content
to Drosophila, but with no known sequence homology.
For each slide and dye combination the 90% value of the
negative controls was used as the detection threshold. For
a particular probe to be used both replicates needed to be
detected. Probes that were not detected in at least one gen-
otype were eliminated from further consideration for that
sex. Additionally, the probe needed to show significant
variation for expression for either genotype or sex to be
considered further. For the probes that were detected,
those corresponding to the list of genes identified as part
of the dFOXO regulatory cascade (n = 1301 probes) were
identified as described above. We found 1281 probes rep-
resenting 887 genes in females and 1262 probes repre-
senting 872 genes in males.

Factor analytical techniques
Initial factor analysis was conducted by determining the
eigenvalues for the matrix of genes, first for the feeding-
affected genes and second for the subgroup of genes in the
dFOXO pathway. Principle components factor analysis
was used to estimate factor loads for 8 factors for both
males and females. A gene was considered to contribute
significantly to the factor if the estimated loading value
had an absolute value of 0.4 or higher.

Gene Ontology Analysis
Genes with load scores greater than 0.4 or less than -0.4
were used to define gene lists for over-representation anal-

Page 9 of 12
(page number not for citation purposes)

ysis of gene ontology (GO) terms implemented in DAVID
(found at http://david.abcc.ncifcrfgov[50]). The P-value
reported is produced from a modified Fisher's Exact Test
called the EASE Score [51] computed in DAVID.

Phenotypic analyses
For the stress assays, mortality was recorded multiple times
each day and for the life span assay mortality was recorded
at three-day intervals. Oxidative stress survival was deter-
mined for flies held on methyl viologen as an oxidant. Star-
vation survival was measured in the absence of food at high
humidity and with access to water. Desiccation survival was
measured in a container with a desiccant. Life span was
determined in small population cages within which flies
had continuous access to food. Development time was the
time between placing eggs on Drosophila food and the
time of adult emergence from pupae (L. Harshman, unpub-
lished data). Ovariole number per ovary was scored from
three females from each of two replicate vials. Body size
was estimated by measuring thorax length on ten males
and ten females from each of two replicate vials [52].

Network analysis
For three expression profiles, G1, G2, and G3, consider the
model where G1 affects G2, and G2 affects G3. In this case,
G2 = a + 1 GI+ e and G3= o + 2 G2 + G, thus, G3 = + f32
G2 + 31G1 + e. In the first two models, we expect the effects
P3 and P2 to be significant, while in the last model the par-
tial regression coefficient of P31should not be significant
since the effect of G2 has been already accounted for in the
model [33,53]. Using this logic, the order of the pathway
in Figure 1 can be tested. For example, to test the hypoth-
esis that Rheb only affects myc via TOR, the model myc = a
+ P TOR + P Rheb + e is fit and the partial regression coef-
ficient for Rheb is examined. If the hypothesis is false, then
P Rheb will be different from zero. In this way, a series of
models examining the relationships proposed by the
known pathway can be tested.

Authors' contributions
SVN co-analyzed the data with AP, JAB and LMM, and
wrote the first draft of the manuscript. JAB also assisted in
preparing the manuscript. LMM contributed to the
remainder of the data analyses and assisted in preparing
the manuscript. MLW and LH provided phenotypic data
and commented on the manuscript.

Additional material

Additional File 1
Male breeding values.
Male breeding values for transcript levels in the 9 parental genotypes.
Click here for file

Additional File 2
Female breeding values.
Female breeding values for transcript levels in the 9 parental geno-
Click here for file

Additional File 3
Pairwise correlations.
All pairwise correlations between the transcript levels of the genes in
Figure, between transcript levels and phenotypes, or between factors
and phenotypes for females (worksheet 1) and males (worksheet 2).
Click here for file

Additional File 4
The loading of genes onto factors.
The loading values of each gene onto each of the eight possible factors
in males and females and a set of indicator variables to indicate
whether a gene was considered to load onto a factor as well as a set
of indicator variables for genes that load on the same factor in both
Click here for file

Additional File 5
Significantly enriched GO terms associated with factors 1 through 3
for females and males.
Lists all of the significantly enriched (P < 0.01, FDR < 0.15) GO bio-
logical process (BP) and molecular function (MF) terms associated
with factors 1, 2 and 3 for females (F) and males (M).
Click here for file

Additional File 6
Correlation between AKT or dFOXO with the gene bound by
Pairwise correlations between AKT or dFOXO and genes reported by
ChIP-chip [10]to bind todFOXO.
Click here for file

This research was supported by I R24GM065513 and NIH RGM076643 to
SVN, DAAD 19-03-1-0152 to LH, K99ESO 17367 to JAB, and
5RO IGM077618 to LMM and SVN. Thanks to Xiting Yan for analysis help
and thanks to two anonymous reviewers for comments.

1. McCay CM, Crowell MF, Maynard LA: The effects of retarded
growth upon the length of life span and upon the ultimate
body size. J Nutrition 1935, 10:63-79.
2. Klass MR: Aging in the nematode Caenorhabditis elegans:
major biological and environmental factors influencing life
span. Mech Ageing Dev 1977, 6(6):413-429.

Page 10 of 12
(page number not for citation purposes)

BMC Genomics 2009,10:124

BMC Genomics 2009, 10:124

3. Morris JZ, Tissenbaum HA, Ruvkun G: A phosphatidylinositol-3-
OH kinase family member regulating longevity and diapause
in Caenorhabditis elegans. Nature 1996, 382(6591 ):536-539.
4. Paradis S, Ruvkun G: Caenorhabditis elegans Akt/PKB trans-
duces insulin receptor-like signals from AGE-I P13 kinase to
the DAF-16 transcription factor. Genes Dev 1998,
5. Hertweck M, Gobel C, Baumeister R: C. elegans SGK- I is the crit-
ical component in the Akt/PKB kinase complex to control
stress response and life span. Dev Cell 2004, 6(4):577-588.
6. Kenyon C, Chang J, Gensch E, Rudner A, Tabtiang R: A C. elegans
mutant that lives twice as long as wild type. Nature 1993,
7. Lin K, Hsin H, Libina N, Kenyon C: Regulation of the Caenorhab-
ditis elegans longevity protein DAF-16 by insulin/IGF-I and
germline signaling. Nat Genet 2001, 28(2):139-145.
8. Gershman B, Puig 0, Hang L, Peitzsch RM, Tatar M, Garofalo RS:
High-resolution dynamics of the transcriptional response to
nutrition in Drosophila: a key role for dFOXO. Physiol Genom-
ics 2007, 29(l):24-34.
9. Guertin DA, Guntur KV, Bell GW, Thoreen CC, Sabatini DM: Func-
tional genomics identifies TOR-regulated genes that control
growth and division. Curr Biol 2006, 16(10):958-970.
10. Teleman AA, Hietakangas V, Sayadian AC, Cohen SM: Nutritional
control of protein biosynthetic capacity by insulin via Myc in
Drosophila. Cell Metab 2008, 7(1):21-32.
I I. Tatar M, Kopelman A, Epstein D, Tu MP, Yin CM, Garofalo RS: A
mutant Drosophila insulin receptor homolog that extends
life-span and impairs neuroendocrine function. Science 2001,
292(55 14):107- 10.
12. Hwangbo DS, Gershman B, Tu MP, Palmer M, Tatar M: Drosophila
dFOXO controls lifespan and regulates insulin signalling in
brain and fat body. Nature 2004, 429(6991):562-566.
13. Accili D, Arden KC: FoxOs at the crossroads of cellular metab-
olism, differentiation, and transformation. Cell 2004,
I 17(4):421-426.
14. Puig 0, Tjian R: Nutrient availability and growth: regulation of
insulin signaling by dFOXO/FOXOI. Cell Cycle 2006,
15. Nuzhdin SV, Wayne ML, Harmon KL, McIntyre LM: Common pat-
tern of evolution of gene expression level and protein
sequence in Drosophila. Mol Biol Evol 2004, 21:1308-1317.
16. Wayne ML, Telonis-Scott M, Bono LM, Harshman L, Kopp A, Nuzhdin
SV, McIntyre LM: Simpler mode of inheritance of transcrip-
tional variation in male Drosophila melanogaster. Proc Natl
Acad Sci USA 2007, 104(47): 18577-18582.
17. Dudoit S, Fridlyand J: A prediction-based resampling method
for estimating the number of clusters in a dataset. Genome
Biol 2002, 3(7):RESEARCH0036.
18. Tibshirani R, Hastie T, Narasimhan B, Eisen MB, Sherlock G, Brown
PO, Botstein D: Exploratory screening of genes and cluster
from microarray experiments. Stat Sinica 2002, 12:47-59.
19. Parmigiani G, Garrett E, Irizarry RA, Zeger S: The analysis of gene
expression data: an overview of methods and software. In
The Analysis of Gene Expression Data Edited by: Parmigiani G, Garrett
E, Irizarry RA, Zeger S. New York, NY: Springer; 2003:1-36.
20. Fabrigar L, MacCallum R, Wegener D, Stahan E: Evaluating the use
of exploratory factor analysis in psychological research. Psy-
chol Methods 1999, 4:272-299.
21. Preacher KJ, MacCallum RC: Exploratory factor analysis in
behavior genetics research: factor recovery with small sam-
ple sizes. Behav Genet 2002, 32(2):153-161.
22. Fellenberg K, Hauser NC, Brors B, Neutzner A, Hoheisel JD, Vingron
M: Correspondence analysis applied to microarray data. Proc
Natl Acad Sci USA 2001, 98(19): 10781-10786.
23. Wouters L, Gohlmann HW, Bijnens L, Kass SU, Molenberghs G, Lewi
PJ: Graphical exploration of gene expression data: a compar-
ative study of three multivariate methods. Biometrics 2003,
24. Coffman CJ, Wayne ML, Nuzhdin SV, Higgins LA, McIntyre LM: Iden-
tification of co-regulated transcripts affecting male body size
in Drosophila. Genome Biol 2005, 6(6):R53.
25. Vellai T, Takacs-Vellai K, Zhang Y, Kovacs AL, Orosz L, Muller F:
Genetics: influence of TOR kinase on lifespan in C. elegans.
Nature 2003, 426(6967):620.


26. Jia K, Chen D, Riddle DL: The TOR pathway interacts with the
insulin signaling pathway to regulate C. elegans larval devel-
opment, metabolism and life span. Development 2004,
27. Kapahi P, Zid BM, Harper T, Koslover D, Sapin V, Benzer S: Regula-
tion of lifespan in Drosophila by modulation of genes in the
TOR signaling pathway. Curr Biol 2004, 14:885-890.
28. Curtis C, Landis GN, Folk D, Wehr NB, Hoe N, Waskar M, Abdueva
D, Skvortsov D, Ford D, Luu A, et al.: Transcriptional profiling of
MnSOD-mediated lifespan extension in Drosophila reveals a
species-general network of aging and metabolic genes.
Genome Biol 2007, 8(12):R262.
29. Hatcher L: A Step-by-Step Approach to Using the SAS Sys-
tem for Factor Analysis and Structural Equation Modeling.
Cary, NC: SAS Publishing; 1994.
30. McIntyre LM, Bono LM, Genissel A, Westerman R, Junk D, Telonis-
Scott M, Harshman L, Wayne ML, Kopp A, Nuzhdin SV: Sex-specific
expression of alternative transcripts in Drosophila. Genome
Biol 2006, 7(8):R79.
31. Lynch M, Walsh B: Genetics and Analysis of Quantitative
Traits. Sunderland, MA: Sinauer Associates; 1997.
32. Storey JD: A direct approach to false discovery rates. j Roy Stat
Soc Ser B 2002, 64:479-498.
33. Neto EC, Ferrara CT, Attie AD, Yandell BS: Inferring causal phe-
notype networks from segregating populations. Genetics 2008,
34. Landry CR, Castillo-Davis Cl, Ogura A, Liu JS, Hartl DL: Systems-
level analysis and evolution of the phototransduction net-
work in Drosophila. Proc Natl Acad Sci USA 2007,
35. Hersh BM, Nelson CE, Stoll SJ, Norton JE, Albert TJ, Carroll SB: The
UBX-regulated network in the haltere imaginal disc of D.
melanogaster. Dev Biol 2007, 302(2):717-727.
36. Hansen D, Schedl T: The regulatory network controlling the
proliferation-meiotic entry decision in the Caenorhabditis
elegans germ line. Curr Top Dev Biol 2006, 76:185-215.
37. Friedman A, Perrimon N: Genetic screening for signal transduc-
tion in the era of network biology. Cell 2007, 128(2):225-23 I.
38. Sieberts SK, Schadt EE: Moving toward a system genetics view
of disease. Mamm Genome 2007, 18(6-7):389-40 1.
39. Roberts A, Pardo-Manuel de Villena F, Wang W, McMillan L, Thread-
gill DW: The polymorphism architecture of mouse genetic
resources elucidated using genome-wide resequencing data:
implications for QTL discovery and systems genetics. Mamm
Genome 2007, 18(6-7):473-48 1.
40. Ghazalpour A, Doss S, Zhang B, Wang S, Plaisier C, Castellanos R,
Brozell A, Schadt EE, Drake TA, Lusis AJ, et al.: Integrating genetic
and network analysis to characterize genes related to mouse
weight. PLoS Genet 2006, 2(8):e 130.
41. Schadt EE, Lum PY: Thematic review series: systems biology
approahces to metabolic and cardiovascular disorders.
Reverse engineering gene networks to identify key drivers of
complex disease phenotypes. j Lipid Res 2006, 74:2601-2613.
42. Schadt EE: Exploiting naturally occurring DNA variation and
molecular profiling data to dissect disease and drug response
traits. Curr Opin Biotechnol 2005, 16(6):647-654.
43. Schadt EE, Lamb J, Yang X, Zhu J, Edwards S, Guhathakurta D, Sie-
berts SK, Monks S, Reitman M, Zhang C, et al.: An integrative
genomics approach to infer causal associations between
gene expression and disease. Nat Genet 2005, 37(7):710-717.
44. Zhu J, Lum PY, Lamb J, GuhaThakurta D, Edwards SW, Thieringer R,
Berger JP, Wu MS, Thompson J, Sachs AB, et al.: An integrative
genomics approach to the reconstruction of gene networks
in segregating populations. Cytogenet Genome Res 2004, 105(2-
45. Schadt EE: Novel integrative genomics strategies to identify
genes for complex traits. Anim Genet 2006, 37(Suppl I): 18-23.
46. Genissel A, McIntyre LM, Wayne ML, Nuzhdin SV: Cis and trans
regulatory effects contribute to natural variation in tran-
scriptome of Drosophila melanogaster. Mol Biol Evol 2008,
25(1):101-1 10.
47. Wang HY, Fu YX, McPeek MS, Lu X, Nuzhdin S, Xu A, Lu J, Wu M-L,
Wu C-I: Complex genetic interactions underlying expression
differences between Drosophila races: analysis of chromo-
some substitutions. Proc Nat Acad Sci USA 2008, 105:6362-6367.

Page 11 of 12
(page number not for citation purposes)

BMC Genomics 2009, 10:124


48. Kruglyak L: The road to genome-wide association studies. Nat
Reviews Genet 2008, 9:314-318.
49. Beavis WD: The power and deceit of QTL experiments: les-
sons from comparitive QTL studies. In Proceedings of the Forty-
Ninth Annual Corn & Sorghum Industry Research Conference Washington,
DC: American Seed Trade Association; 1994.
50. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Baseler MW, Lane HC,
Lempicki RA: DAVID: Database for annotation, visualization,
and integrated discovery. Genome Biology 2003, 2003(4):P3.
51. Hosack DA, Dennis G Jr, Sherman BT, Lane HC, Lempicki RA: Iden-
tifying biological themes within lists of genes with EASE.
Genome Biology 2003, 4:R70.
52. Telonis-Scott M: Genetic architecture of two fitness-related
traits in Drosophila melanogaster: ovariole number and tho-
rax length. Genetic 2005, 125:211-222.
53. Zhu J, Zhang B, Smith EN, Drees B, Brem RB, Kruglyak L, Bumgarner
RE, Schadt EE: Integrating large-scale functional genomic data
to dissect the complexity of yeast regulatory networks. Nat
Genet 2008. 40:854-861.

Page 12 of 12
(page number not for citation purposes)

Publish with BioMed Central and every
scientist can read your work free of charge
"BioMed Central will be the most significant development for
disseminating the results of biomedical research in our lifetime."
Sir Paul Nurse, Cancer Research UK
Your research papers will be:
available free of charge to the entire biomedical community
peer reviewed and published immediately upon acceptance
cited in PubMed and archived on PubMed Central
yours you keep the copyright
Submit your manuscript here: BioMedcentral adv.asp

University of Florida Home Page
© 2004 - 2010 University of Florida George A. Smathers Libraries.
All rights reserved.

Acceptable Use, Copyright, and Disclaimer Statement
Last updated October 10, 2010 - - mvs