BACKGROUND
in the past years enormous progress has been made in the identification of complex trait
susceptibility loci.
the application of genome-wide association studies  created a
still growing set of genetic markers associated with increased risk for a multitude of
different diseases  <cit> .
in contrast to few single
loci exerting large effects on some phenotypes – mostly immune-related traits
– the majority of traits was only associated with loci displaying small effects of
odds ratios ranging from  <dig>  to  <dig>   <cit> .
meta-analyses of several gwa studies further extended the set of known disease-related
associations with even lower-effect variants.
despite the impressive progress in the
field, for most traits only a small proportion of the total heritability is yet
explained by known risk variants  <cit> .
a notable
exception is type  <dig> diabetes  where validated risk loci explain a large proportion
of the total heritability  <cit> .
in contrast, for
most traits a considerably larger number of variants was reported to be associated, but
typically these explain less than 50% of the total heritability  <cit> .
intriguingly, although published individual gwas are usually carried out for one trait
at a time, a significant overlap in the associations of several complex diseases becomes
apparent  <cit> .
besides effects on a specific
phenotype, loci and single snps thus may also exert pleiotropic effects by contributing
to a variety of traits.
while it is not surprising that susceptibility genes for closely
related traits should be shared, multi-functionality of a gene in phenotype
presentation, i.e.
pleiotropy, sensu stricto refers to seemingly unrelated and
distinct traits  <cit> .
loci or variants affecting
several traits might have small effects on each specific trait, but may be of major
biological interest while indicating shared or branching etiological mechanisms.
in
principle, the influence of such loci can be agonistic or antagonistic, i.e.
involve
concurrent similar or opposite effects of the same variant for different traits.
so far,
few studies attempted to study such loci in a systemic fashion and rather focused on
shared risk variants in closely related traits like autoimmune diseases  <cit> , heart
diseases  <cit>  or cancer  <cit> .
in order to identify shared or branching pathways of related as well as diverse  diseases, we performed a systematic comparative
analysis of genetic commonalities and differences across traditionally defined traits
using the available repository of gwas results.
in the context of network medicine
<cit> , we utilized an approach based on the
diseasome concept  <cit>  and investigated
high-significance associations beyond conventional single-marker analysis in a
hypothesis-free and comprehensive way.
in former studies we found differing approaches
of gene and locus assignment to association markers which partially led to controversial
results .
we therefore developed a
more sophisticated locus assignment method and evaluate its reliability by utilizing the
information contained directly in the reported markers.
for this variant-based approach
we manually curated a high-quality data set to construct a network extending the
knowledge on genetic overlaps between diseases as provided by gwa studies.
RESULTS
considerable discrepancies across gwas through differing genotyping platforms, varying
sample sizes and diverging measures of statistical significance demand accurate data
selection.
therefore, to sustain the genuine variant-linked information provided by
gwas, we combined several steps of data curation and filtering.
to provide a
comprehensive base for the analysis of potentially multi-functional loci and variants,
respectively, we compiled two network representations of the information made available
by gwa studies: the locus-based “shared locus network”  and the variant-based “shared variant network” .
to be able to cluster diseases by their “genetic
relatedness”, we additionally created a disease-centric projection of the svn
.
data.a: the bipartite graph constructed from all association data.
the two disjoint node sets are diseases  and loci
, connected to each
other by an edge if a variant  within the respective locus
is associated with the corresponding trait.
b: the sln  consisting of  <dig> traits and  <dig> loci, retrieved by removing isolated
traits and loci that are associated with a single trait only.
c: the svn
that corresponds to a variant-based representation of the
data.
here, a trait and a locus are linked if the locus contains a variant
comprising associations with this and at least one other trait.
the network
consists of  <dig> snps located in  <dig> loci that are associated with  <dig> diseases .
the colors of the
disease nodes correspond to disease classes according to the mesh ontology,
multi-colored nodes indicate an association with different disease classes; loci
are depicted as transparent, diamond-shaped nodes.
the node size reflects the
number of loci a disease is associated with.
in c, the edge color reflects the
allelic information: gray indicates agonistic variant, red corresponds to
antagonistic variant, and blue mark both agonistic and antagonistic
signals.
we defined the associated loci over the variant-based linkage disequilibrium
measure r <dig> and, accordingly, expected the sln and the svn  to be of similar shape.
however, when visually
comparing the networks, significant differences in size and structure became apparent.
therefore, we performed further analyses to compare established property measures of the
networks in detail to investigate potential reasons for this divergence.
shared locus vs. shared association analysis
despite the size difference, the sln shows greater network heterogeneity
and lower
centralization
and density
values than the svn.
furthermore, the intersection between the svn and the sln lacks
not only 5% of the nodes but also 10% of the edges of the svn.
these numbers imply
that the process of translating ld data into locus information is at least partly
inconsistent.
analysis of the structure of the assigned ld blocks showed two error
sources in shared locus analysis .
first, variants in two independent ld blocks are assigned the same locus but are not
in ld .
thus, shared loci are found that are not reflected in the
variant based data.
second, if two snps are in strong ld but the individual long
range ld patterns of the snps diverge , an assignment error ii occurs.
in this case the two snps are assigned to different loci  and their ld connection is lost.
these observations suggest that ,
the sln contains loci which overlap between traits but the associated markers are not
in strong ld, , there are several traits which are connected to the sln
via a single, potentially misleading link , and, , even a ld-based locus assignment is unable to identify
all shared associations .
due to this limited sensitivity and specificity in
detecting ld-based correlations between the reported markers on locus scale, we used
the smaller but more accurate variant-based svn for further analyses.
moreover, the
risk allele data in the svn allows for the inclusion of the direction of the effects,
i.e.
agonistic and antagonistic, on different traits.
diseases that share
antagonistic or agonistic, respectively, associated variants are listed in tables
<dig> and  <dig>  another advantage is that the
svn can be compared to other variant-based approaches assessing the genetic overlap
between traits.
recently, a statistic to identify snps with effects across phenotypes
was proposed by cotsapas et al.
<cit> .
it compares the distribution of
association p-values of a snp across seven gwas on distinct autoimmune diseases to
the exponential-distribution representing the expected decay rate of association
p-values.
as in our approach we use pre-filtered associations, this method cannot
readily be employed on our data.
however, using the data provided by cotsapas and
colleagues on autoimmune loci in the svn, we retrieved cpma p-values on  <dig> snps
corresponding to  <dig> loci  in our data.
the
cpma classified all snps as significantly effective across diseases
.
thus, we were able to validate nearly one third of the loci contained in
the svn by an independent approach, which underlines the suitability of the
network.
chromosome 8q <dig> , ld-based locus assignment is given for  <dig> exemplary snps
.
ld information is given by a color scale displaying the ld-measure
r <dig> with red depicting strong ld, blue low ld and white
no ld.
example snps in ld are connected with black dashed lines.
in the gray
boxes, the two error sources of automated locus assignment are given.
an
assignment error i occurs if two variants not in ld, i.e.
in two independent ld
blocks, are located in the same gene, intergenic region or gene desert and thus
are assigned the same locus.
here, this is the case for the variants rs-a/rs-b
and rs-e/rs-f, respectively.
the consequence of this type of error is a shared
association on the locus level not mirrored on the variant level.
an assignment
error ii is introduced if two variants are in ld but diverge in their assigned
locus.
here, this is the case for rs-c and rs-d. due to such abnormalities in
the ld data the link between both variants is lost if only the locus level is
considered.
t2d ; ra ; sle ; ps
listed are diseases and the traits that share an antagonistic variant with
the respective disorder.
in the first column the considered disease is
given.
the second column specifies the abbreviation of the disorder in the
first column.
the third column contains the abbreviated diseases as defined
in column two which have an antagonistic link to the disorder in column one,
followed by the chromosomal location of the antagonistically associated
variant in parentheses.
the last column lists the count of traits
antagonistically linked to the disorder in column one.
for a more detailed
listing see additional file 2: table s <dig>
ced ; lep ; mg
; t1d ; sc ;
ovarian neoplasms ; as ; ll
; htg ; aa ; ms
; blc ; ss
; fl ; uc ; lbl
; schizophrenia ; vit
; t1d ; blc ;
vit ; hyp ; sle ; mg
ibd ; sc ; ps ; ra
; aaa ; hyp ; htg
; blc ; mg ; uc
; aa ; vit ; uc
; uc ; mg ; as
listed are diseases that share agonistic associations with at least two
traits.
in the first column the considered disease is given.
the second
column specifies the abbreviation of the disorder in the first column.
the
third column contains the disease abbreviations
of traits which have an agonistic link to the disorder in column one,
followed by the chromosomal location of the agonistically associated
variant in parentheses.
here, the full mesh term is given for traits for
which no abbreviation was defined.
the last column lists the count of traits
agonistically linked to the disorder in column one.
for the complete list of
agonistically linked traits and more details see additional file 2: table s <dig>
topology of the svn
its degree distribution attributes the svn a scale-free network, i.e.
it approximates
a power-law
∼ k−γ; γ =  <dig> ; r2 =  <dig> )
.
interestingly, also when
considering the two node types separately, disease nodes
as well as locus nodes
show scale-free degree distributions .
the scale-free property classifies the network  as structured, i.e.
non-random  <cit> .
it has to be considered that the limited size of the svn
leads to inaccuracies in distribution fitting and thus reduces the explanatory value
of this observation.
however, as clinically related diseases  should present a higher genetic overlap than unrelated
disorders, this finding meets expectations.
the variant-based svn also shows no artificial character with regards to its
topology.
both locus and disease node sets comprise hubs, here defined as nodes with
a degree > <dig>  which form the central elements in the network.
as in each gwas multiple
markers are associated with a single disease, one would expect hubs to be constituted
mostly of disease nodes.
in line with that, 74% of the hubs in our network are
disease nodes.
the remaining 26% are loci hubs .
several of these loci have been previously identified as influencing
susceptibility to multiple diseases like the hla region on chromosome 6
<cit> , a cancer locus at chromosome 8q24
<cit> , and a coronary artery disease
locus at chromosome 9p <dig>  <cit> .
further hub
loci are ptpn <dig>  a known player across several autoimmunity disorders
<cit> , and il23r, which has
been shown to direct inflammatory processes  <cit> .
in addition, we observed hubs which have not yet been
described as predisposing to a whole group of diseases, such as tnpo <dig> which
appears to predispose to various autoimmune diseases like systemic lupus
erythematosus, systemic scleroderma, and rheumatoid arthritis  <cit> , or tnfsf <dig>  which shows associations with several
inflammatory diseases  <cit> .
as
expected, in the majority of cases the traits linked to one hub can be assigned to
the same disease group and, further, diseases which are not obviously related to
other disorders linked to the respective hub are mostly associated with antagonistic
signals.
for instance, in a four-gene locus at chromosome 17q12
, four autoimmune diseases are associated
with the same risk allele that in turn has opposite effects on asthma  <cit> .
thus, our results indicate that loci associated
with several diseases have an effect specific to a certain disease group rather than
effects on unrelated diseases, and that, if there is an effect on an unrelated
disease, it can often be distinguished by the direction of the effect.
disease clustering mirrors trait relatedness
to identify shared and branching mechanisms we split the snp association data into
agonistic and antagonistic variants.
since in most cases there is no solid and
comprehensive basis of experimental data that would allow for a more sensitive
classification, we suggest that the best available indication of distinct effects of
a variant on two diseases is the signal itself being different.
therefore, we define
a snp to be agonistic if all disorders are associated with the same risk allele of
the snp.
conversely, we consider a snp antagonistic if the associated risk alleles
differ between diseases.
accordingly, in the analysis of genetic overlaps as a
measure of trait similarity only agonistic variants were included.
as similar diseases are more likely to share associations than diseases in distinct
classes, we expected the svn to be organized in a modular fashion.
this was confirmed
by the decrease of the degree distribution of the topological coefficient with the
number of links per node .
to
retrieve these modules, we applied a hierarchical clustering approach.
the svn
contains two node types .
as we wanted to directly assess
variant-based disease relatedness, we used its disease centric projection for
hierarchical correlation clustering.
for data normalization, we calculated the
pearson correlation coefficient  for all pairs of diseases based on their
genetic agonistic overlap with all other diseases.
the clustering returned  <dig> disease
clusters  and six diseases which show no or only weak
correlation with any other disease.
with the exception of the heterogeneous cluster 5
, the
clusters mostly contain related diseases.
however, many clusters also contain traits
unrelated to the other phenotypes like schizophrenia in the autoimmune cluster  <dig>
this indicates that clinical disease classifications appear to be reflected on the
genetic level in general.
notably, several small clusters contain diseases which are
either linked through common environmental risk factors – like smoking for lung
neoplasms, peripheral vascular diseases, and chronic obstructive pulmonary disease
– or present high frequencies of comorbidity, e.g.
type  <dig> diabetes  and
obesity.
to get an insight into the overall extent of reported comorbidities of the
diseases within the  <dig> clusters, we used publicly available resources  <cit>  and literature mining.
the within-cluster fraction of disease co-occurrence ranged from 75% to 100%
which
provides empirical evidence of the interrelation of diseases clustered together by
genetic information.
such clusters containing diseases that present high ratios of
comorbidity may be potential artifacts due to “contaminated” disease
cohorts including a substantial number of comorbid cases.
the unbiased search for the
relation of a trait marker to a disease phenotype as performed in gwas does not
distinguish between markers for a primary or related secondary  disease.
our results suggest that this aspect may have been underestimated in some studies,
albeit the presence of independently shared etiological mechanisms can naturally not
be ruled out in general.
complete-linkage hierarchical clustering to identify groups of traits which
show homogeneous patterns of genetic overlap to other disorders.
we calculated
for each pair of diseases the pearson correlation of the patterns of overlap to
the other diseases.
the correlation values are ranging from − <dig>
indicating complete negative correlation to + <dig>  reflecting a perfect
positive correlation.
as the minimal value of the correlation coefficient
was > − <dig> , we collapsed the range of negative correlation.
in red numbers, the  <dig> disease clusters are denoted.
the euclidian distance
threshold was chosen as the maximal distance at which the six diseases showing
no or only weak correlation with any other disease
remain non-clustered.
overall, the outcome of the clustering poses the question of the extent of the
influence of phenotype classification and population stratification on gwas results.
frequent comorbidities , diagnostic difficulties in
highly related diseases like crohn’s disease  and ulcerative colitis
<cit> , and structural
differences in population subgroups are known to complicate gwas and impact their
outcomes  <cit> .
with growing sample sizes in
case–control studies, the potential of false positives produced by such
phenomena also increases.
as a response, manifold control procedures to handle these
and other confounding factors have been developed which are widely used and well
appreciated  <cit> .
the heterogeneity of the
clusters we retrieved once more highlights the need for the development and
application of such methods.
odds ratio as potential indicator of primary effects
in the context of agonistic association overlap between related diseases, we used the
odds ratios  reported with the snps to investigate their impact on the
respective traits.
in general, the highest ors are reported for associations of
autoimmune diseases to the hla locus on chromosome  <dig>  associations with traits where
few gene variants with strong effects are reported, e.g.
rs <dig> in the prion
protein prnp associated with creutzfeldt-jakob disease
or rs <dig> in the hemoglobin gene cluster at 11p <dig>
associated with beta-thalassemia , are exceptions from the
majority of associations displaying small ors  <cit> .
based on the effect size of variants associated
with more than one trait and the same risk allele, we identified three patterns.
first, we identified variants which are likely to present general agonistic risk
factors for a group of related diseases or syndromes such as rs <dig> in an
interleukine receptor gene cluster at chromosome 2q <dig> .
this snp is associated with
celiac disease  and crohn’s disease  with equal ors
<cit> .
in cases of frequent comorbidities, though, comparable ors
have limited informative value.
the snp rs <dig> in the fto gene for
instance is associated with t2d and obesity with nearly equal ors
and thus appears to link two
coequal traits of the metabolic syndrome   <cit> .
however, for snps in the fto gene it
has been shown that adjustment for body mass index results in the loss of
significance  of the association with t2d  <cit> .
second, snps appeared in several cases to be primarily associated with one disease,
which in turn represents a risk factor for another associated trait.
for instance,
rs <dig> on chromosome 4q <dig> is linked to atrial fibrillation with a higher or
than to stroke   <cit> .
another example is
rs <dig> which is located proximal to the apolipoprotein gene cluster on chromosome
11q <dig> which is associated with hypertriglyceridemia with a markedly higher or
than to coronary disease
<cit> .
the
lower effects of the markers on the hypothesized “secondary sequels” may
be explained by the fact that these are caused by the primary diseases, but with less
than 100% penetrance.
third, we speculate that the or might allow conclusions with respect to the
evaluation of an association in cases where similar traits are linked to the same snp
with diverging effect sizes.
for instance, cd and ulcerative colitis  share
multiple risk loci.
the two diseases are strongly related in their etiology and
pathology.
thus, a clinical distinction of both diseases is difficult if based only
on few criteria and might lead to inaccurate case ascertainment leading to mixed
associations  <cit> .
however, for several snps
such as rs <dig>  which is located in the il23r gene, we found notably
higher effects on cd  than on uc
<cit> .
conversely, rs <dig> which lies proximal to the il <dig> gene shows a greater
effect on uc  than on cd
<cit> .
interestingly, it has been shown that il <dig> is selectively upregulated in cd
while levels in uc patients are normal and il <dig> expression appears to be
higher in uc as compared to cd  <cit> .
thus, the or might – similar to the above examples
– allow for identifying potentially misleading associations in closely related
diseases which may result from diagnostic errors.
identification of branching etiologies
we searched for evidence that antagonistic signals represent genetic indicators of
branching points in the etiologies of two diseases or disease groups.
for the
assessment of potentially multifunctional variants we therefore focused on markers
with inverse effects.
we identified  <dig> such variants, which represent almost 4% of
the original association data analyzed and about 25% of the snps associated with more
than one disease.
of those  <dig> variants, about one fifth  are
located in the hla region.
snp-markers in that region are known to differ in their
ability to capture the classical hla-alleles  <cit>  and therefore were not considered further for the present
analysis.
for cases where the function of the harboring genes is known, we were able to
identify conclusive models.
for instance, rs <dig> in the telomerase reverse
transcriptase  gene was reported to exert antagonistic effects in
idiopatic pulmonary fibrosis  and testicular germ cell tumor  and two
other cancer traits  <cit> .
whereas telomerase
activity is generally upregulated in tumors sustaining proliferation and potentiating
mutagenesis and transformation of cancer cells  <cit> , in ipf limited cell division due to decreased telomerase
activity is thought to contribute to the phenomenon of high percentages of apoptotic
cells in fibroblasts  <cit> .
consistent with
that observation, disturbed telomerase activity in tgct is believed to form a
distinct mechanism of cancerogenesis in this tumor type  <cit> .
this distinction from other cancer traits is believed to
be based on the fact that testicular germ cells are the only adult cell type with
high telomerase expression  <cit> .
another
example is the telomerase rna component terc, which is essential for
tert functioning.
opposite alleles of snp rs <dig> are associated with
ced and colorectal cancer   <cit> .
jones et al.
showed that rs <dig>  a variant tagged by
rs <dig>  alone is sufficient to modulate terc expression  <cit> .
while in crc this leads to terc
overexpression and longer telomeres, the opposite might apply to ced, which exhibits
telomere reduction and genomic instability  <cit> .
the observation that both constituents of the
telomerase complex contain independent antagonistic variants is an intriguing
finding.
it suggests parallel, autonomous evolution of two functionally interacting
loci gone to fixation at a trade-off between early cell senescence or increased
apoptosis rates  and oncogenesis.
a further example is rs <dig> in the tyrosinase  gene where the
opposite alleles are linked to vitiligo and melanoma  <cit> , potentially mirroring the
inverse correlation observed for the two traits.
the phenomenon is based on the
presentation of tyr  antigens on the cell surface of melanocytes.
it
is hypothesized that in vitiligo the immune system is hypersensitive towards
tyr antigens, which are overexpressed in melanoma cells  <cit> .
a possible explanation may be that opposite
alleles differentially influence the antigenicity of the tyr protein,
thereby conferring protection from melanoma but susceptibility to vitiligo through
immune surveillance and vice versa.
in cases of functionally less or uncharacterized genes and their involvement in the
associated diseases, our approach can still be used to suggest potential
trait-specific effects.
antagonistic effects of rs <dig>  in cd and psoriasis, for instance, might point towards
different patterns of cytokine signaling in these two diseases  <cit> .
likewise, rs <dig> and rs <dig>  both located in the
park <dig> gene and inversely associated with ced and uc, could indicate
differential effects of oxidative stress on each trait  <cit> .
variant-based analysis of joint and disjoint genetic features
in this study, we identified overlapping genetic associations and their corresponding
loci with analogous or contrasting effects on different diseases.
we addressed the
methodological challenges of the identification of the functional entities affected
by gwas-detected variants.
associations formally implicate genomic regions which are captured via tagging snps
representing haplotype blocks.
by using the population-specific ld-based haplotype
data provided by the hapmap project  <cit>  or, more recently, the  <dig> genomes project  <cit> , snp arrays are constructed aiming at a high
coverage of the total genome variation, but without considering biologically
functional aspects.
the advantage of gwas as a method is its unbiased approach to
identify genomic regions compromised in a disease; a major drawback is that the
association of markers without knowledge of the causal variants and their effects
does not allow for a straightforward biological interpretation.
as we show, the reliability of an automated assignment of ld-based loci to the
trait-associated variants is strongly context-dependent.
especially in cases of high
gene density or, conversely, in intergenic regions/gene deserts, resolving gwas
signals is not possible without further knowledge.
simplifications such as more basic
locus assignment approaches which neglect the ld structure of the genome  may seem more intuitive,
might facilitate analyses and could be useful to identify causal disease-gene
associations.
these correct associations of genes which are detected through
significant enrichment of a harbored tagging variant in a patient cohort may not be
discovered when incorporating ld data in cases where the ld block of the respective
variant spans across several genes.
however, such approaches disregard a basic
principle defining the current gwas paradigm, namely the use of ld information in the
design of genotyping arrays to achieve the genome-wide coverage of common snps.
hence, it can be problematic to project the variant-based gwas data on genes or loci.
accordingly, we decided to use variant-based methods and concentrated on strong gene
candidates identified via the gene function of single-gene loci whenever suggesting
potential biological effects of the considered variants.
in the analysis of genetic overlaps we followed the hypothesis that the effects of
variants shared across several diseases correspond to the reported risk alleles.
if
the risk allele is the same in all associated diseases, we assume the effect to be
the same, i.e.
that there is a common underlying etiology.
for closely related
diseases a positive correlation is not surprising, e.g.
a gwas on psoriatic arthritis
will also detect agonistic variants such as rs <dig> that are also associated
with psoriasis   <cit> .
indeed, the vast majority of agonistic variants in our data
set links groups of related diseases and thus may mark interesting target regions for
therapeutic interventions.
however, we also found a few agonistic signals connecting
apparently unrelated diseases, e.g.
rs <dig> which exerts susceptibility for both
glioma and atopic dermatitis   <cit> .
if our
hypothesis is correct, an endophenotype influencing both diseases may be present
which has yet to be identified.
for antagonistic snps, on the other hand, we describe
plausible mechanisms that may render variants protective against one trait and
predisposing to another, labeling the affected genes/loci as pleiotropic.
if
pleiotropic effects are as frequent as evolutionary modelers postulate  <cit>  and this effects can be
identified by analyses based on gwas, this might have great implications for the
development and use of therapeutics because it would enable avoidance of potential
side effects when targeting such loci.
already, there are more than  <dig> genotype/drug
interactions known for which therapeutic dosing recommendations are available
<cit> .
CONCLUSIONS
our results present new starting points for studying the genetics of complex diseases.
the observation that more than 15% of the snps considered in our study are associated
both agonistically and antagonistically with related as well as unrelated disorders
indicates that the molecular mechanisms influencing causes and progress of human
diseases are in part interrelated.
genetic overlaps between two diseases also suggest
the importance of the affected entities in the specific pathogenic pathways and should
be investigated further.
these may be secondary, such as genes involved in inflammatory
responses related to t2d as well as cancer  <cit> .
the findings presented also demonstrate the need to
clarify the relation of any phenotype linked to an associated marker.
for directly
interrelated diseases such as ps and psa often ps patients without present arthritis or
arthritis in the past are used as additional control group.
associations are then
interpreted as psa-specific if not as strongly associated with ps  <cit> .
comparable procedures may
proof useful in frequently co-occurring diseases genetically linked by agonistic
variants.
nevertheless, the complex genetics of multifactorial diseases asks for a
better understanding of the functions underlying common disorders.
an improved
characterization of the endophenotype, such as metabolite or protein concentrations, may
enhance our understanding of identical pathomechanisms that link agonistic genetic loci
to clinically distinct traits.
pleiotropic effects, on the other hand, that are harbored
in the same locus may trigger different mechanisms interfering with the genetic or
environmental background.
the detailed examination of antagonistically associated loci
may thus lead to first insight into the mechanism of the various types of pleiotropy in
human diseases.

 in the past years enormous progress has been made in the identification of complex trait susceptibility loci. the application of genome-wide association studies created a still growing set of genetic markers associated with increased risk for a multitude of different diseases. in contrast to few single loci exerting large effects on some phenotypes – mostly immune-related traits – the majority of traits was only associated with loci displaying small effects of odds ratios ranging from to. meta-analyses of several gwa studies further extended the set of known disease-related associations with even lower-effect variants. despite the impressive progress in the field, for most traits only a small proportion of the total heritability is yet explained by known risk variants. a notable exception is type diabetes where validated risk loci explain a large proportion of the total heritability. in contrast, for most traits a considerably larger number of variants was reported to be associated, but typically these explain less than 50% of the total heritability. intriguingly, although published individual gwas are usually carried out for one trait at a time, a significant overlap in the associations of several complex diseases becomes apparent. besides effects on a specific phenotype, loci and single snps thus may also exert pleiotropic effects by contributing to a variety of traits. while it is not surprising that susceptibility genes for closely related traits should be shared, multi-functionality of a gene in phenotype presentation, i.e. pleiotropy, sensu stricto refers to seemingly unrelated and distinct traits. loci or variants affecting several traits might have small effects on each specific trait, but may be of major biological interest while indicating shared or branching etiological mechanisms. in principle, the influence of such loci can be agonistic or antagonistic, i.e. involve concurrent similar or opposite effects of the same variant for different traits. so far, few studies attempted to study such loci in a systemic fashion and rather focused on shared risk variants in closely related traits like autoimmune diseases, heart diseases or cancer. in order to identify shared or branching pathways of related as well as diverse diseases, we performed a systematic comparative analysis of genetic commonalities and differences across traditionally defined traits using the available repository of gwas results. in the context of network medicine, we utilized an approach based on the diseasome concept and investigated high-significance associations beyond conventional single-marker analysis in a hypothesis-free and comprehensive way. in former studies we found differing approaches of gene and locus assignment to association markers which partially led to controversial results. we therefore developed a more sophisticated locus assignment method and evaluate its reliability by utilizing the information contained directly in the reported markers. for this variant-based approach we manually curated a high-quality data set to construct a network extending the knowledge on genetic overlaps between diseases as provided by gwa studies.  considerable discrepancies across gwas through differing genotyping platforms, varying sample sizes and diverging measures of statistical significance demand accurate data selection. therefore, to sustain the genuine variant-linked information provided by gwas, we combined several steps of data curation and filtering. to provide a comprehensive base for the analysis of potentially multi-functional loci and variants, respectively, we compiled two network representations of the information made available by gwa studies: the locus-based “shared locus network” and the variant-based “shared variant network”. to be able to cluster diseases by their “genetic relatedness”, we additionally created a disease-centric projection of the svn. data.a: the bipartite graph constructed from all association data. the two disjoint node sets are diseases and loci, connected to each other by an edge if a variant within the respective locus is associated with the corresponding trait. b: the sln consisting of traits and loci, retrieved by removing isolated traits and loci that are associated with a single trait only. c: the svn that corresponds to a variant-based representation of the data. here, a trait and a locus are linked if the locus contains a variant comprising associations with this and at least one other trait. the network consists of snps located in loci that are associated with diseases. the colors of the disease nodes correspond to disease classes according to the mesh ontology, multi-colored nodes indicate an association with different disease classes; loci are depicted as transparent, diamond-shaped nodes. the node size reflects the number of loci a disease is associated with. in c, the edge color reflects the allelic information: gray indicates agonistic variant, red corresponds to antagonistic variant, and blue mark both agonistic and antagonistic signals. we defined the associated loci over the variant-based linkage disequilibrium measure r and, accordingly, expected the sln and the svn to be of similar shape. however, when visually comparing the networks, significant differences in size and structure became apparent. therefore, we performed further analyses to compare established property measures of the networks in detail to investigate potential reasons for this divergence. shared locus vs. shared association analysis despite the size difference, the sln shows greater network heterogeneity and lower centralization and density values than the svn. furthermore, the intersection between the svn and the sln lacks not only 5% of the nodes but also 10% of the edges of the svn. these numbers imply that the process of translating ld data into locus information is at least partly inconsistent. analysis of the structure of the assigned ld blocks showed two error sources in shared locus analysis. first, variants in two independent ld blocks are assigned the same locus but are not in ld. thus, shared loci are found that are not reflected in the variant based data. second, if two snps are in strong ld but the individual long range ld patterns of the snps diverge, an assignment error ii occurs. in this case the two snps are assigned to different loci and their ld connection is lost. these observations suggest that, the sln contains loci which overlap between traits but the associated markers are not in strong ld,, there are several traits which are connected to the sln via a single, potentially misleading link, and,, even a ld-based locus assignment is unable to identify all shared associations. due to this limited sensitivity and specificity in detecting ld-based correlations between the reported markers on locus scale, we used the smaller but more accurate variant-based svn for further analyses. moreover, the risk allele data in the svn allows for the inclusion of the direction of the effects, i.e. agonistic and antagonistic, on different traits. diseases that share antagonistic or agonistic, respectively, associated variants are listed in tables and another advantage is that the svn can be compared to other variant-based approaches assessing the genetic overlap between traits. recently, a statistic to identify snps with effects across phenotypes was proposed by cotsapas et al.. it compares the distribution of association p-values of a snp across seven gwas on distinct autoimmune diseases to the exponential-distribution representing the expected decay rate of association p-values. as in our approach we use pre-filtered associations, this method cannot readily be employed on our data. however, using the data provided by cotsapas and colleagues on autoimmune loci in the svn, we retrieved cpma p-values on snps corresponding to loci in our data. the cpma classified all snps as significantly effective across diseases. thus, we were able to validate nearly one third of the loci contained in the svn by an independent approach, which underlines the suitability of the network. chromosome 8q, ld-based locus assignment is given for exemplary snps. ld information is given by a color scale displaying the ld-measure r with red depicting strong ld, blue low ld and white no ld. example snps in ld are connected with black dashed lines. in the gray boxes, the two error sources of automated locus assignment are given. an assignment error i occurs if two variants not in ld, i.e. in two independent ld blocks, are located in the same gene, intergenic region or gene desert and thus are assigned the same locus. here, this is the case for the variants rs-a/rs-b and rs-e/rs-f, respectively. the consequence of this type of error is a shared association on the locus level not mirrored on the variant level. an assignment error ii is introduced if two variants are in ld but diverge in their assigned locus. here, this is the case for rs-c and rs-d. due to such abnormalities in the ld data the link between both variants is lost if only the locus level is considered. t2d; ra; sle; ps listed are diseases and the traits that share an antagonistic variant with the respective disorder. in the first column the considered disease is given. the second column specifies the abbreviation of the disorder in the first column. the third column contains the abbreviated diseases as defined in column two which have an antagonistic link to the disorder in column one, followed by the chromosomal location of the antagonistically associated variant in parentheses. the last column lists the count of traits antagonistically linked to the disorder in column one. for a more detailed listing see additional file 2: table s ced; lep; mg; t1d; sc; ovarian neoplasms; as; ll; htg; aa; ms; blc; ss; fl; uc; lbl; schizophrenia; vit; t1d; blc; vit; hyp; sle; mg ibd; sc; ps; ra; aaa; hyp; htg; blc; mg; uc; aa; vit; uc; uc; mg; as listed are diseases that share agonistic associations with at least two traits. in the first column the considered disease is given. the second column specifies the abbreviation of the disorder in the first column. the third column contains the disease abbreviations of traits which have an agonistic link to the disorder in column one, followed by the chromosomal location of the agonistically associated variant in parentheses. here, the full mesh term is given for traits for which no abbreviation was defined. the last column lists the count of traits agonistically linked to the disorder in column one. for the complete list of agonistically linked traits and more details see additional file 2: table s topology of the svn its degree distribution attributes the svn a scale-free network, i.e. it approximates a power-law  ∼ k−γ; γ = ; r2 =  ). interestingly, also when considering the two node types separately, disease nodes as well as locus nodes show scale-free degree distributions. the scale-free property classifies the network as structured, i.e. non-random. it has to be considered that the limited size of the svn leads to inaccuracies in distribution fitting and thus reduces the explanatory value of this observation. however, as clinically related diseases should present a higher genetic overlap than unrelated disorders, this finding meets expectations. the variant-based svn also shows no artificial character with regards to its topology. both locus and disease node sets comprise hubs, here defined as nodes with a degree > which form the central elements in the network. as in each gwas multiple markers are associated with a single disease, one would expect hubs to be constituted mostly of disease nodes. in line with that, 74% of the hubs in our network are disease nodes. the remaining 26% are loci hubs. several of these loci have been previously identified as influencing susceptibility to multiple diseases like the hla region on chromosome 6, a cancer locus at chromosome 8q24, and a coronary artery disease locus at chromosome 9p. further hub loci are ptpn a known player across several autoimmunity disorders, and il23r, which has been shown to direct inflammatory processes. in addition, we observed hubs which have not yet been described as predisposing to a whole group of diseases, such as tnpo which appears to predispose to various autoimmune diseases like systemic lupus erythematosus, systemic scleroderma, and rheumatoid arthritis, or tnfsf which shows associations with several inflammatory diseases. as expected, in the majority of cases the traits linked to one hub can be assigned to the same disease group and, further, diseases which are not obviously related to other disorders linked to the respective hub are mostly associated with antagonistic signals. for instance, in a four-gene locus at chromosome 17q12, four autoimmune diseases are associated with the same risk allele that in turn has opposite effects on asthma. thus, our results indicate that loci associated with several diseases have an effect specific to a certain disease group rather than effects on unrelated diseases, and that, if there is an effect on an unrelated disease, it can often be distinguished by the direction of the effect. disease clustering mirrors trait relatedness to identify shared and branching mechanisms we split the snp association data into agonistic and antagonistic variants. since in most cases there is no solid and comprehensive basis of experimental data that would allow for a more sensitive classification, we suggest that the best available indication of distinct effects of a variant on two diseases is the signal itself being different. therefore, we define a snp to be agonistic if all disorders are associated with the same risk allele of the snp. conversely, we consider a snp antagonistic if the associated risk alleles differ between diseases. accordingly, in the analysis of genetic overlaps as a measure of trait similarity only agonistic variants were included. as similar diseases are more likely to share associations than diseases in distinct classes, we expected the svn to be organized in a modular fashion. this was confirmed by the decrease of the degree distribution of the topological coefficient with the number of links per node. to retrieve these modules, we applied a hierarchical clustering approach. the svn contains two node types. as we wanted to directly assess variant-based disease relatedness, we used its disease centric projection for hierarchical correlation clustering. for data normalization, we calculated the pearson correlation coefficient for all pairs of diseases based on their genetic agonistic overlap with all other diseases. the clustering returned disease clusters and six diseases which show no or only weak correlation with any other disease. with the exception of the heterogeneous cluster 5, the clusters mostly contain related diseases. however, many clusters also contain traits unrelated to the other phenotypes like schizophrenia in the autoimmune cluster this indicates that clinical disease classifications appear to be reflected on the genetic level in general. notably, several small clusters contain diseases which are either linked through common environmental risk factors – like smoking for lung neoplasms, peripheral vascular diseases, and chronic obstructive pulmonary disease – or present high frequencies of comorbidity, e.g. type diabetes and obesity. to get an insight into the overall extent of reported comorbidities of the diseases within the clusters, we used publicly available resources and literature mining. the within-cluster fraction of disease co-occurrence ranged from 75% to 100% which provides empirical evidence of the interrelation of diseases clustered together by genetic information. such clusters containing diseases that present high ratios of comorbidity may be potential artifacts due to “contaminated” disease cohorts including a substantial number of comorbid cases. the unbiased search for the relation of a trait marker to a disease phenotype as performed in gwas does not distinguish between markers for a primary or related secondary disease. our results suggest that this aspect may have been underestimated in some studies, albeit the presence of independently shared etiological mechanisms can naturally not be ruled out in general. complete-linkage hierarchical clustering to identify groups of traits which show homogeneous patterns of genetic overlap to other disorders. we calculated for each pair of diseases the pearson correlation of the patterns of overlap to the other diseases. the correlation values are ranging from − indicating complete negative correlation to + reflecting a perfect positive correlation. as the minimal value of the correlation coefficient was > −, we collapsed the range of negative correlation. in red numbers, the disease clusters are denoted. the euclidian distance threshold was chosen as the maximal distance at which the six diseases showing no or only weak correlation with any other disease remain non-clustered. overall, the outcome of the clustering poses the question of the extent of the influence of phenotype classification and population stratification on gwas results. frequent comorbidities, diagnostic difficulties in highly related diseases like crohn’s disease and ulcerative colitis, and structural differences in population subgroups are known to complicate gwas and impact their outcomes. with growing sample sizes in case–control studies, the potential of false positives produced by such phenomena also increases. as a response, manifold control procedures to handle these and other confounding factors have been developed which are widely used and well appreciated. the heterogeneity of the clusters we retrieved once more highlights the need for the development and application of such methods. odds ratio as potential indicator of primary effects in the context of agonistic association overlap between related diseases, we used the odds ratios reported with the snps to investigate their impact on the respective traits. in general, the highest ors are reported for associations of autoimmune diseases to the hla locus on chromosome associations with traits where few gene variants with strong effects are reported, e.g. rs in the prion protein prnp associated with creutzfeldt-jakob disease or rs in the hemoglobin gene cluster at 11p associated with beta-thalassemia, are exceptions from the majority of associations displaying small ors. based on the effect size of variants associated with more than one trait and the same risk allele, we identified three patterns. first, we identified variants which are likely to present general agonistic risk factors for a group of related diseases or syndromes such as rs in an interleukine receptor gene cluster at chromosome 2q. this snp is associated with celiac disease and crohn’s disease with equal ors. in cases of frequent comorbidities, though, comparable ors have limited informative value. the snp rs in the fto gene for instance is associated with t2d and obesity with nearly equal ors and thus appears to link two coequal traits of the metabolic syndrome. however, for snps in the fto gene it has been shown that adjustment for body mass index results in the loss of significance of the association with t2d. second, snps appeared in several cases to be primarily associated with one disease, which in turn represents a risk factor for another associated trait. for instance, rs on chromosome 4q is linked to atrial fibrillation with a higher or than to stroke. another example is rs which is located proximal to the apolipoprotein gene cluster on chromosome 11q which is associated with hypertriglyceridemia with a markedly higher or than to coronary disease. the lower effects of the markers on the hypothesized “secondary sequels” may be explained by the fact that these are caused by the primary diseases, but with less than 100% penetrance. third, we speculate that the or might allow conclusions with respect to the evaluation of an association in cases where similar traits are linked to the same snp with diverging effect sizes. for instance, cd and ulcerative colitis share multiple risk loci. the two diseases are strongly related in their etiology and pathology. thus, a clinical distinction of both diseases is difficult if based only on few criteria and might lead to inaccurate case ascertainment leading to mixed associations. however, for several snps such as rs which is located in the il23r gene, we found notably higher effects on cd than on uc. conversely, rs which lies proximal to the il gene shows a greater effect on uc than on cd. interestingly, it has been shown that il is selectively upregulated in cd while levels in uc patients are normal and il expression appears to be higher in uc as compared to cd. thus, the or might – similar to the above examples – allow for identifying potentially misleading associations in closely related diseases which may result from diagnostic errors. identification of branching etiologies we searched for evidence that antagonistic signals represent genetic indicators of branching points in the etiologies of two diseases or disease groups. for the assessment of potentially multifunctional variants we therefore focused on markers with inverse effects. we identified such variants, which represent almost 4% of the original association data analyzed and about 25% of the snps associated with more than one disease. of those variants, about one fifth are located in the hla region. snp-markers in that region are known to differ in their ability to capture the classical hla-alleles and therefore were not considered further for the present analysis. for cases where the function of the harboring genes is known, we were able to identify conclusive models. for instance, rs in the telomerase reverse transcriptase gene was reported to exert antagonistic effects in idiopatic pulmonary fibrosis and testicular germ cell tumor and two other cancer traits. whereas telomerase activity is generally upregulated in tumors sustaining proliferation and potentiating mutagenesis and transformation of cancer cells, in ipf limited cell division due to decreased telomerase activity is thought to contribute to the phenomenon of high percentages of apoptotic cells in fibroblasts. consistent with that observation, disturbed telomerase activity in tgct is believed to form a distinct mechanism of cancerogenesis in this tumor type. this distinction from other cancer traits is believed to be based on the fact that testicular germ cells are the only adult cell type with high telomerase expression. another example is the telomerase rna component terc, which is essential for tert functioning. opposite alleles of snp rs are associated with ced and colorectal cancer. jones et al. showed that rs a variant tagged by rs alone is sufficient to modulate terc expression. while in crc this leads to terc overexpression and longer telomeres, the opposite might apply to ced, which exhibits telomere reduction and genomic instability. the observation that both constituents of the telomerase complex contain independent antagonistic variants is an intriguing finding. it suggests parallel, autonomous evolution of two functionally interacting loci gone to fixation at a trade-off between early cell senescence or increased apoptosis rates and oncogenesis. a further example is rs in the tyrosinase gene where the opposite alleles are linked to vitiligo and melanoma, potentially mirroring the inverse correlation observed for the two traits. the phenomenon is based on the presentation of tyr antigens on the cell surface of melanocytes. it is hypothesized that in vitiligo the immune system is hypersensitive towards tyr antigens, which are overexpressed in melanoma cells. a possible explanation may be that opposite alleles differentially influence the antigenicity of the tyr protein, thereby conferring protection from melanoma but susceptibility to vitiligo through immune surveillance and vice versa. in cases of functionally less or uncharacterized genes and their involvement in the associated diseases, our approach can still be used to suggest potential trait-specific effects. antagonistic effects of rs in cd and psoriasis, for instance, might point towards different patterns of cytokine signaling in these two diseases. likewise, rs and rs both located in the park gene and inversely associated with ced and uc, could indicate differential effects of oxidative stress on each trait. variant-based analysis of joint and disjoint genetic features in this study, we identified overlapping genetic associations and their corresponding loci with analogous or contrasting effects on different diseases. we addressed the methodological challenges of the identification of the functional entities affected by gwas-detected variants. associations formally implicate genomic regions which are captured via tagging snps representing haplotype blocks. by using the population-specific ld-based haplotype data provided by the hapmap project or, more recently, the genomes project, snp arrays are constructed aiming at a high coverage of the total genome variation, but without considering biologically functional aspects. the advantage of gwas as a method is its unbiased approach to identify genomic regions compromised in a disease; a major drawback is that the association of markers without knowledge of the causal variants and their effects does not allow for a straightforward biological interpretation. as we show, the reliability of an automated assignment of ld-based loci to the trait-associated variants is strongly context-dependent. especially in cases of high gene density or, conversely, in intergenic regions/gene deserts, resolving gwas signals is not possible without further knowledge. simplifications such as more basic locus assignment approaches which neglect the ld structure of the genome may seem more intuitive, might facilitate analyses and could be useful to identify causal disease-gene associations. these correct associations of genes which are detected through significant enrichment of a harbored tagging variant in a patient cohort may not be discovered when incorporating ld data in cases where the ld block of the respective variant spans across several genes. however, such approaches disregard a basic principle defining the current gwas paradigm, namely the use of ld information in the design of genotyping arrays to achieve the genome-wide coverage of common snps. hence, it can be problematic to project the variant-based gwas data on genes or loci. accordingly, we decided to use variant-based methods and concentrated on strong gene candidates identified via the gene function of single-gene loci whenever suggesting potential biological effects of the considered variants. in the analysis of genetic overlaps we followed the hypothesis that the effects of variants shared across several diseases correspond to the reported risk alleles. if the risk allele is the same in all associated diseases, we assume the effect to be the same, i.e. that there is a common underlying etiology. for closely related diseases a positive correlation is not surprising, e.g. a gwas on psoriatic arthritis will also detect agonistic variants such as rs that are also associated with psoriasis. indeed, the vast majority of agonistic variants in our data set links groups of related diseases and thus may mark interesting target regions for therapeutic interventions. however, we also found a few agonistic signals connecting apparently unrelated diseases, e.g. rs which exerts susceptibility for both glioma and atopic dermatitis. if our hypothesis is correct, an endophenotype influencing both diseases may be present which has yet to be identified. for antagonistic snps, on the other hand, we describe plausible mechanisms that may render variants protective against one trait and predisposing to another, labeling the affected genes/loci as pleiotropic. if pleiotropic effects are as frequent as evolutionary modelers postulate and this effects can be identified by analyses based on gwas, this might have great implications for the development and use of therapeutics because it would enable avoidance of potential side effects when targeting such loci. already, there are more than genotype/drug interactions known for which therapeutic dosing recommendations are available.  our results present new starting points for studying the genetics of complex diseases. the observation that more than 15% of the snps considered in our study are associated both agonistically and antagonistically with related as well as unrelated disorders indicates that the molecular mechanisms influencing causes and progress of human diseases are in part interrelated. genetic overlaps between two diseases also suggest the importance of the affected entities in the specific pathogenic pathways and should be investigated further. these may be secondary, such as genes involved in inflammatory responses related to t2d as well as cancer. the findings presented also demonstrate the need to clarify the relation of any phenotype linked to an associated marker. for directly interrelated diseases such as ps and psa often ps patients without present arthritis or arthritis in the past are used as additional control group. associations are then interpreted as psa-specific if not as strongly associated with ps. comparable procedures may proof useful in frequently co-occurring diseases genetically linked by agonistic variants. nevertheless, the complex genetics of multifactorial diseases asks for a better understanding of the functions underlying common disorders. an improved characterization of the endophenotype, such as metabolite or protein concentrations, may enhance our understanding of identical pathomechanisms that link agonistic genetic loci to clinically distinct traits. pleiotropic effects, on the other hand, that are harbored in the same locus may trigger different mechanisms interfering with the genetic or environmental background. the detailed examination of antagonistically associated loci may thus lead to first insight into the mechanism of the various types of pleiotropy in human diseases. 
genome-wide association studygenetic overlapshared variant networkdisease comorbidity antagonistic variants and describe plausible small-scale models of the underlying well as unrelated disorders indicates that the molecular mechanisms influencing causes and progress of human diseases are in part interrelated. specific pathogenic pathways and should be investigated further. with analogous or contrasting effects on different diseases. of hub loci across several diseases. analyze one specific trait in single populations in an isolated fashion without potential use of effect sizes in refining and revising these agonistic signals. genome-wide association studies have provided a large set of genetic loci
 genome-wide association studies have provided a large set of genetic loci influencing the risk for many common diseases. association studies typically analyze one specific trait in single populations in an isolated fashion without taking into account the potential phenotypic and genetic correlation between traits. however, gwa data can be efficiently used to identify overlapping loci with analogous or contrasting effects on different diseases.  here, we describe a new approach to systematically prioritize and interpret available gwa data. we focus on the analysis of joint and disjoint genetic determinants across diseases. using network analysis, we show that variant-based approaches are superior to locus-based analyses. in addition, we provide a prioritization of disease loci based on network properties and discuss the roles of hub loci across several diseases. we demonstrate that, in general, agonistic associations appear to reflect current disease classifications, and present the potential use of effect sizes in refining and revising these agonistic signals. we further identify potential branching points in disease etiologies based on antagonistic variants and describe plausible small-scale models of the underlying molecular switches.  the observation that a surprisingly high fraction of the snps considered in our study are associated both agonistically and antagonistically with related as well as unrelated disorders indicates that the molecular mechanisms influencing causes and progress of human diseases are in part interrelated. genetic overlaps between two diseases also suggest the importance of the affected entities in the specific pathogenic pathways and should be investigated further. genome-wide association studygenetic overlapshared variant networkdisease comorbidity 