Download Using gene expression to investigate the genetic basis of complex

Human Molecular Genetics, 2008, Vol. 17, Review Issue 2
doi:10.1093/hmg/ddn285
R129–R134
Using gene expression to investigate the genetic
basis of complex disorders
Alexandra C. Nica and Emmanouil T. Dermitzakis
The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1HH, UK
Received August 8, 2008; Revised and Accepted September 7, 2008
The identification of complex disease susceptibility loci through genome-wide association studies (GWAS)
has recently become possible and is now a method of choice for investigating the genetic basis of complex
traits. The number of results from such studies is constantly increasing but the challenge lying forward is to
identify the biological context in which these statistically significant candidate variants act. Regulatory variation plays an important role in shaping phenotypic differences among individuals and thus is very likely to
also influence disease susceptibility. As such, integrating gene expression data and other disease relevant
intermediate phenotypes with GWAS results could potentially help prioritize fine-mapping efforts and provide
a shortcut to disease biology. Combining these different levels of information in a meaningful way is however
not trivial. In the present review, we outline the several approaches that have been explored so far in
this sense and their achievements. We also discuss the limitations of the methods and how upcoming
technological developments could help circumvent these limitations. Overall, such efforts will be very helpful
in understanding initially regulatory effects on disease and disease etiology in general.
INTRODUCTION
The ability of genome-wide association studies (GWAS) to
help understand the genetic basis of complex disorders
has recently become apparent. Well-documented common
human genetic variation maps (e.g. HapMap project) (1),
large patient samples with accurately recorded phenotypic
information as well as appropriate statistical methods to
assess significance (2) and account for potential biases,
have all contributed to the current outburst of successful
GWAS. Numerous susceptibility variants for a large number
of complex diseases have been reported and effectively
replicated. A present catalog of published GWAS (http://
www.genome.gov/26525384) includes single nucleotide polymorphisms (SNPs) not only associated with major common
disorders [Crohn’s disease (3), type 2 diabetes (4), lung
cancer (5) etc.] but also with disease-relevant or anthropomorphic quantitative traits [e.g. body mass index (6) or
height (7)].
What has not kept the pace however with the capacity
to design and perform successful GWAS is our ability to
understand how variants discovered via this hypothesisfree approach influence complex traits, In fact, few of the
association studies go beyond reporting the most statistically
significant hits and if they do, the suggested functionality is
typically speculative, based on available annotation of genes
in the vicinity of the variants. Since many of the discovered
susceptibility polymorphisms fall in non-coding regions and
with an increasing number of regulatory variants already
implicated in a series of common disorders (8), one conventional approach has been to interrogate disease associated
SNPs for associations with differential gene expression.
Moffatt et al. (9) found that the same most significant SNPs
associated with childhood asthma risk also explain 29.5%
of the variance in ORMDL3 transcript levels, measured in
lymphoblastoid cell lines. While an interesting observation,
this still cannot be regarded as convincing evidence for a
causal relationship between ORMDL3 and asthma onset.
The concurrent progress towards uncovering the genetic
basis of regulatory variation (10) has revealed an abundance
of expression quantitative trait loci (eQTLs) in the human
genome, making an accidental overlap between these and
disease signals very likely. Thus, while gene expression is a
very informative and immediate ‘DNA phenotype’, integrating
expression data and disease studies genetics for an ultimate
understanding of disease etiology is not straightforward.
To whom correspondence should be addressed at: The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10
1HH, UK. Tel: þ44 1223 4923222; Fax: þ44 1223 494919; Email: [email protected]
# The Author 2008. Published by Oxford University Press. All rights reserved.
For Permissions, please email: [email protected]
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ADVANCES AND CURRENT ISSUES IN
EXPRESSION AND DISEASE STUDIES
Power of current eQTL studies
Natural variation in human gene expression has been recently
quantified on a genome-wide scale using microarray technologies. Linkage and association studies coupling expression
with genetic variability data have started to reveal the genetics
underlying part of this variation, including complex
allele-specific interactions (11) and its relatively high level
of heritability (12– 15). Most of the variants discovered with
these approaches (a field also called Genetical Genomics)
explain variance in transcript levels of nearby genes (so
called cis eQTLs) but a few distal acting regulators have
also been reported (trans associations). The sample sizes of
genome-wide expression association studies have been fairly
small though, meaning that the discoveries made so far
represent generally large genetic effects [Stranger et al. (12)
report an R 2 coefficient of determination ranging from 0.27
to almost 1 for the SNP –gene associations detected in the
270 HapMap individuals]. The magnitude of the discovered
effects drops when pooling populations together with
appropriate corrections, a direct consequence of the increased
statistical power due to the larger sample sizes. The importance of appropriate statistical power has been extensively
demonstrated in complex disease GWAS, where samples of
a few thousand paired cases and controls have become a prerequisite (16). The main reason for this requirement is the fact
that the individual contribution of genetic variants towards
complex trait determination is known to be small. In fact, all susceptibility alleles discovered so far explain only a small fraction
of disease risk, with odds ratios typically in the range of 1.2–1.5
(16,17). Given the marked difference between the magnitudes of
detected genetic effects on expression variation and disease
predisposition, respectively, it is not surprising that only few
instances of overlapping signals have been observed, even
when expression in a disease relevant tissue was considered.
Small genetic effects on expression variation or complex interactions between regulatory variants with moderate or large
effects could become decisive on a permissive environmental
background. Current expression analyses are underpowered
with respect to these kinds of discoveries; hence whole-genome
expression association studies on larger samples would be very
desirable. Such efforts are on the way, including the quantification of expression levels in blood cells of 820 HapMap III
individuals from eight populations (Barbara Stranger, Stephen
Montgomery and Emmanouil Dermitzakis, personal communication). Combined with SNP genotyping data, this resource
will give insight into the level of expression differences
among populations and generate many additional eQTLs with
more subtle effects, some of them potentially related to disease.
Tissue-specific phenotypes
Confined by the availability of human tissue samples,
expression experiments have been initially performed in lymphocytes or immortalized lymphoblastoid cell lines. In
addition to having differential spatial and temporal expression
patterns, some genes are only expressed in specific tissues.
Also, many diseases manifest their phenotype in certain
tissues exclusively. For these reasons, overlaying expression
and disease signals is informative only if expression measurements are carried out in tissue types relevant to disease
(Fig. 1). Particularly because our notion of relevance is still
subjective in this case, identifying regulatory regions in multiple tissues is imperative for both a better understanding of
the regulatory mechanisms in general and also the extent to
which they are shared across tissues. Promising advancement
in this direction can already be found in the literature. For
the first time, genetic variants influencing normal human cortical expression have been reported (18). Myers et al. find little
overlap between their set of eQTLs and results from previous
studies on blood-derived cells. While differences in genotyping
platforms and power can also partially explain this discrepancy,
it is very likely that variants discovered here underlie
brain-specific control of gene expression. These findings in
conjunction with results from GWAS may help uncover the
genetic basis of neurological disorders.
A more comprehensive view on the extent of shared eQTLs
among tissues has been presented in a recent study by Schadt
et al. (19). The authors have analyzed 400 human liver
samples and identified more than 6000 SNP– gene associations.
Many of these genes had already been linked to a variety of
complex diseases, a fact to be expected given that liver is
known to be essential for many human metabolic processes.
In addition to data from other disease GWAS, these eQTLs
were integrated with gene expression and clinical data from
segregating mouse populations in order to build probabilistic
gene networks (20,21). This approach has led to the prioritization of susceptibility genes for coronary artery disease, LDL
cholesterol levels and type I diabetes (T1D). More importantly,
the same expression platform employed for the human liver
cohort here had been also used on a set of human blood and
adipose tissues in another study (22). This allowed a direct
evaluation of the cis eQTL overlap in the three tissues, which
amounted to 30%. While still a rough estimate, this amount
of shared regulatory control makes a good case for interrogating
disease susceptibility variants for expression associations in
any available data set. Nevertheless, the remaining unshared
fraction reflects the expected tissue-specific regulatory activity.
These biological processes will have to be studied in appropriate tissues before they become useful for understanding tissuespecific complex disorders. It is of course still unclear what the
pattern of diminishing returns is across human tissues and what
set of tissues could serve as highly informative in large cohort
collections.
Finding the causal variants in expression
and disease GWAS
Even once expression data become available for a large
variety of human tissues, the ability to pinpoint causal variants
predisposing to disease via a regulatory mechanism will still
depend on the density at which such variants have been interrogated. Currently, all genotyping platforms survey only a
subset of the human sequence variation (23). Typing all
common SNPs in the genome (currently estimated around 10
million) has been replaced by the inference of tag SNPs, a
subset of approximately 300 000– 600 000 markers, depending
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Figure 1. Expression and disease association studies. Current expression studies are performed on a limited number of tissues derived from a small number of
individuals compared with disease association studies. The availability of large collections of cell types from a large number of individuals will allow the identification of eQTLs that explain disease effects, such as the one at the top of the panel, when the relevant cell type/tissue is analyzed, as indicated for Tissue C at
the bottom of the panel. This may not be successful in less relevant tissues such as in the case of Tissues A and B at the bottom of the panel.
on the population. This reduction exploits the pervasiveness of
linkage disequilibrium (LD) in the human genome (24) and
captures most of the common genetic variation in a region of
interest. Still, this means that reported susceptibility variants
are most probably only tagging the real functional variants
and are not causal themselves. Hence, initial discoveries must
be followed by fine mapping of the regions harboring the
most significant statistical signals (25). So far this has not
been thoroughly attempted, primarily because in the absence
of other prior biological information, such tasks are financially
prohibitive. Next generation sequencing will allow transcriptomic studies that will go deep into the transcript structure
and abundance and will resolve issues of alternative splicing.
In terms of in-depth interrogation of human genetic variation,
a new resource (http://www.1000genomes.org/) promises to
facilitate these efforts by generating a human genetic variation
map at unprecedented resolution. The 1000 Genomes Project
will sequence more than 1000 individuals with the final
goal of cataloging almost all variants found at minor
allele .1% frequency in human populations. Within genes,
sequencing will go even deeper, down to 0.5% frequency. As
a result, much like common variation documented by the
HapMap has been used so far to perform successful GWAS,
this project will further support disease studies aiming at
those relatively rare variants. Importantly, it will allow the
assessment of the individual impact of rare variants on
complex traits, which has not been understood so far.
APPROACHES OF COMBINING EXPRESSION
AND DISEASE DATA
Comparison of expression profiles in cases and controls
Gene expression signatures in relation to complex diseases
have been investigated well before the era of GWAS. The
area of cancer genetics is one such example. Expression profiles of different tumors have been broadly analyzed in order
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to categorize cancer subtypes and even identify the genes that
determine the subtype identification (26). Higher resolution
diagnoses followed, as well as the hope that candidate
cancer genes could be more easily identified. An obvious
approach seemed to be the comparison of gene expression
levels in the same tissue from patients and non-diseased individuals and from the differences observed to infer potentially
causal pathways and biochemical functions. Given the substantial expression profile changes it causes in the cell, the
complexity of genome rearrangements and the somatic
nature of some of the effects, cancer is likely to be an exceptionally difficult case, and requires a slightly different treatment so we do not discuss it in this review. Even in other
diseases, the daunting task in this type of experiment
remains distinguishing causal from reactive effects. Changes
at the cellular level will be produced as a result of the
disease, and discriminating these from causal changes is extremely challenging. In addition, not all individuals have the
same causal pathways so it is not necessarily true that
disease and non-disease individuals have clear and welldefined differences in gene expression when compared.
Finally, it is essential to derive the genetic dimension of
such differences and not simply describe the gene expression
differences. For all these reasons, it becomes evident
that expression information derived from a reference set of
samples rather than patients would be more useful for
determining the disease predisposing genetic factors, while
diagnosis purposes can still be served by quantifying
expression in patient-derived samples. Patient samples and
cellular resources will also be very useful in the context of
cell-based studies that may investigate the impact of challenging these cells with various agents and candidate drugs.
Lessons from gene networks
Individual effect of disease variants on transcription levels
Statistical challenges in combining GWAS
and eQTL studies
Expression variation in the general population is being
actively researched and its genetic determinants uncovered.
Without the burden of chasing a reactive effect, another
common approach has been to use this data for determining
whether disease variants from GWAS are also responsible
for natural variation in human transcript levels. The underlying principle is the following: if one allele is more frequent
in cases than controls and at the same time it is causal for gene
expression effects of a nearby gene, which is itself important
for the disease, then it is likely that causality can be established. Few genes have been prioritized by this method (19),
but several caveats make it a difficult task. For example, the
same best candidate SNP could be significantly associated
with discernible expression differences in several nearby
genes. Deciding which one among those is the best candidate
would not be feasible. Moreover, absence of evidence is not
evidence of absence: not finding expression associations
with some genes flanking the variant of interest (provided
that expression data is available for all of those genes and
there is an effect on expression that eventually induces
disease) does not mean that they are not potential disease contributors. Plenty of reasons from reduced power in the
expression study to interrogation of a cell type irrelevant to
disease could explain that.
The most successful integration of disease and expression data,
circumventing most of the previously listed caveats has been
achieved by Chen et al. (27). The authors exploit for the first
time the complexity of interactions that lead to disease by modeling it as a system. Gene expression networks from liver and
adipose tissues of segregating mouse populations were constructed and subsequently, network disruptions caused by
DNA variations were observed. This allowed the identification
of a particular network (macrophage-enriched metabolic
network) enriched for genes correlated to obesity-related phenotypes. Experimental validation of the top candidate genes
further supports the validity of this method. A similarly constructed network was also identified in a human blood and
adipose tissue cohort (22), overlapping significantly with the
mouse derived one. In addition to this discovery, gene
network theory applied here demonstrated once more the
naivety with which some of the links between expression and
disease could be made. Despite the fact that variants associated
with obesity traits resided in a region with known Apoa2
eQTLs, a gene involved in metabolic activity, it was other
genes in the region that exhibited a significant linkage pattern
with the phenotypes of interest. This argues for an independent
relationship between Apoa2 expression and the metabolic traits
under study, even though the same locus is associated with
both. Clearly, the ability to distinguish between such coincidental and actual causal effects is essential. Studies based on the
use of networks rather than sets of eQTLs show some theoretical promise since they account for additional complexity, but
until we have a good sense of the complexity framework in
which we can embed the impact of genetic variation they will
lag behind.
Finally, the ever-increasing technological advancement supporting both expression and disease GWAS in humans, calls
for the development of proper statistical tools to combine
the two directly. Local genomic regions of interest with
association data available from both types of analyses could
provide gene prioritization clues, if explored properly. Superimposing patterns of association at commonly tested loci in
expression and disease could distinguish between multiple
genes affected in cis by the same variants (Fig. 2). This kind
of discrimination would not be possible by solely examining
the most significant disease variant for effects on expression.
However, the same property that makes such correlations
sound—LD—also forces the need for proper statistical assessment of the results. All variants in a genomic interval that have
been historically co-segregating with the causal variant will be
significantly associated with the two phenotypes by chance.
On one hand, this allows present association studies to be performed on just subsets of SNPs, but on the other hand, regions
of high LD will exhibit tighter relationships between
expression and disease patterns. This is not biologically meaningful in terms of disease and as such, correcting out the LD
effect is necessary. In addition, as already pointed out,
eQTLs are ubiquitously distributed throughout the genome.
Human Molecular Genetics, 2008, Vol. 17, Review Issue 2
Figure 2. Local superimposition of whole-genome disease and expression
associations. For any given genomic interval, same SNPs will have been independently tested for associations with a disease and transcript levels of a set of
genes, respectively. (A) No significant eQTLs detected in the interval for Gene
A. (B) Significant eQTLs for Gene B do not also display significant disease
associations. (C) The association patterns of disease and Gene C expression
fit very well, making it the most likely candidate out of the three for a possible
regulatory-mediated disease effect.
When analyzing genome-wide expression data in lymphoblastoid cell lines of the HapMap II individuals, Stranger et al.
(12) found 4043 genes with at least one eQTL at 0.05 permutation threshold. Considering the 32 996 genome-wide hotspot
intervals estimated by McVean et al. (28) from the HapMap
data, we can approximate that the probability of finding an
eQTL in any hotspot interval in the human genome (and therefore likely in LD with a disease variant in the interval) by
chance is 0.123. Therefore, irrespective of the LD pattern,
the probability of finding a good match between disease and
expression signals is high. These numbers will be even
higher when studies with larger sample sizes and increased
sets of tissues are available where it is possible that every
single SNP may be in LD with an eQTL in some population
and/or some tissue. In this context, a statistical framework
accounting for LD while at the same time evaluating the probability of observing disease and expression correlations by
chance would be ideal.
CONCLUSIONS AND FUTURE DIRECTIONS
Genetics has played a crucial role in the advancement of
medical sciences by facilitating the understanding of human
disease. In addition to the early success in elucidating numerous rare monogenic disorders (29), it has also recently become
possible to discover genetic markers robustly associated with
complex diseases. Being able to overcome the statistical limitation of linkage studies (30), GWAS are now routinely
employed for a variety of human conditions. By themselves
however, statistically significant signals resulting from such
studies still cannot answer important biological questions
such as which are the causal variants and how do they lead
to disease. Thus, the necessity of incorporating additional
information when studying complex disease etiology has
become apparent. Motivated by the simultaneous progress in
understanding the role of regulatory variants in shaping
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human phenotypic variation, gene expression has become an
obvious candidate as informative intermediate ‘DNA phenotype’. How to best integrate the two levels of complexity is
nonetheless far from obvious. Currently, both expression and
disease GWAS suffer from critical limitations. Expression
studies have been performed on relatively small samples and
on a restricted variety of tissue. Also, the transcriptome’s
diversity such studies explore is bound to be incomplete,
with only few probes presently designed per gene. In terms
of the genetic variety underlying phenotypic differences, the
picture is also far from complete. Genotyping platforms
survey only a subset of the known human genetic variability,
so the candidates found are most probably not causal, but just
correlated with the real functional variants because of LD. All
these caveats explain why a clear link between the genetics of
disease and that of gene expression has not been established
yet, despite its existence. Fortunately, the incredibly high
rate at which technology is advancing will soon eliminate
some of these limitations. Next generation sequencing has
already made efforts like the 1000 Genomes Project possible,
which would have seemed unthinkable few years ago. On the
same note, we envisage in the near future a public resource
providing complete expression profiles from a multitude of
healthy human tissues derived from large samples of individuals.
In conjunction with next generation sequencing data which will
also greatly improve the resolution of disease GWAS, this
resource would be the ideal solution to the present-day problems.
In the meantime, alternative methods of integrating genotypic
and phenotypic information towards a better understanding of
human disease will have to be developed.
FUNDING
A.C.N. and E.T.D. are supported by Wellcome Trust funding.
ACKNOWLEDGEMENTS
We would like to thank Barbara Stranger, Stephen Montgomery,
Antigone Dimas, Simon Tavare, Panos Deloukas and Gillean
McVean for helpful discussions.
Conflict of Interest statement. None declared.
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