The multiscale backbone of the human phenotype network based on biological pathways
© Darabos et al.; licensee BioMed Central Ltd. 2014
Received: 19 December 2013
Accepted: 19 December 2013
Published: 25 January 2014
Networks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over 800 physical attributes, diseases, and behavioral traits; based on about 2,300 genes and 1,200 biological pathways. Using GWAS phenotype-to-genes associations, and pathway data from Reactome, we connect human traits based on the common patterns of human biological pathways, detecting more pleiotropic effects, and expanding previous studies from a gene-centric approach to that of shared cell-processes.
The resulting network has a heavily right-skewed degree distribution, placing it in the scale-free region of the network topologies spectrum. We extract the multi-scale information backbone of the PHPN based on the local densities of the network and discarding weak connection. Using a standard community detection algorithm, we construct phenotype modules of similar traits without applying expert biological knowledge. These modules can be assimilated to the disease classes. However, we are able to classify phenotypes according to shared biology, and not arbitrary disease classes. We present examples of expected clinical connections identified by PHPN as proof of principle.
We unveil a previously uncharacterized connection between phenotype modules and discuss potential mechanistic connections that are obvious only in retrospect. The PHPN shows tremendous potential to become a useful tool both in the unveiling of the diseases’ common biology, and in the elaboration of diagnosis and treatments.
In this age of system-wide biology, in which organisms and their environment are considered as a whole, a new field has emerged; studying diseases in relationship to one another. Pioneering studies, such as Goh et al.’s  have resulted in the definition of the Human Disease Network (HDN). Elucidating relationships between human traits or diseases is becoming increasingly - genetic disorders. These traits may be related through shared genes, proteins, or regulatory elements, and identifying commonalities that may reveal shared biological mechanisms. Ultimately, a thorough understanding of these connections will provide tools necessary to design drug targets. The potential for increased biological understanding, and the future clinical impact, justifies the creation of methods optimized to explore the full phenotype genotype spectrum. Genome-wide association studies (GWAS) have helped identify genetic and environmental variants that affect susceptibility to human disease using an agnostic, or hypothesis-free, approach. Such studies offer the promise of personalized diagnostics, prognostics, and medical treatments . Moreover, they provide us with an unprecedented ability to study the genetic interactions between seemingly unrelated traits. To date, approximately 6,000 single nucleotide polymorphisms (SNPs) have been reported as genetic risk-variants for 800+ diseases and traits. Combining data from hundreds of GWASs, takes advantage of the information gained about genotype-phenotype relationships beyond the scope of any single study. This approach provides a novel perspective to integrate genetic, cellular, physiological and clinical data to elucidate the pathobiology of many traits. To this end, modern computational methods, utilizing integrated modeling, are critical as these methods can tackle unprecedented volumes of data. Computational methods are, however, only as good as their translatability into usable observations. Because of the sheer complexity and the number of phenotype-interactions, the usable models need to be intuitive and scalable and have the ability to filter “irrelevant” information, and highlight commonalities between phenotypes. Modeling complex biological systems using network analysis offers a promising approach to evaluate the macro-relationships between these biological components, particularly between certain phenotypes and diseases. Indeed, networks offer relatively straightforward and intuitive representations of interaction phenomena, and allow sophisticated statistical analysis of their intrinsic properties. In addition, methods derived from information sciences and social sciences have proven to leverage the network topology as a source of knowledge, offering sophisticated filtering and grouping techniques. Moreover, these methods work regardless of the actual underlying data type and are therefore applicable to complex networks of phenotypes.
We present the Pathway-based Human Phenotype Network (PHPN), a biological pathway-based mathematical model of a network of human phenotypic traits (PT) visually represented as graphs. Previous network-based studies of diseases have proven useful for envisaging large disease datasets grouped by common genes, similar gene expression profiles, or shared protein interactions [1, 3, 4]. However, a gene-centric focus has biased the generation and interpretation of these networks, as coding regions constitute less than 2% of the human genome. In a previous study , we focused our efforts on constructing sets of risk-associated single nucleotide polymorphisms (SNPs) and SNP bins in linkage disequilibrium to find commonalities between PTs; this SNP clustering approach successfully overcame some weaknesses of previous gene-centric models. In the present article, we describe a method of building a PHPN relying on biological pathways rather than genes. We link PTs that have shared pathways, using mapped genes from GWAS data and gene-to-pathway associations from Reactome, a curated pathway database. Furthermore, we extract the information backbone using Serrano et al.’s disparity filter , to capture the most relevant information from the extremely dense network that results from raw data. Finally, we classify each PT in the network and build communities or modules of PTs that are strongly linked, and therefore, show evidence of pleiotropic effects and shared biology. These groups are formed independently of the actual disease classes, based solely on intrinsic network properties.
In this section, we define the fundamental concepts used in the Methods section below to build the PHPN.
The catalog of published GWAS maintained by the National Human Genome Research Institute (NHGRI) at the National Institute of Health aggregates studies that report phenotype-to-SNP(s) and phenotype/SNP-to-gene associations (http://www.genome.gov/gwastudies/). The NHGRI catalog, downloaded in March 2013, was the primary source of PT-to-gene to association data. It reports over 800 PTs associated with approximately 2,300 genes and 6,000 SNPs.
Biological pathways represent elaborate series of cascading biochemical reactions occurring within the cell, and possibly receiving external signals . Pathways govern all major cellular functions, such as cell cycle, cell respiration, or apoptosis (programmed cell death). Biochemical compounds, (e.g. nucleic acids, proteins, complexes and small molecules) participating in reactions form a network of biological processes and are grouped into pathways. Reactome is an open-source, open access, manually curated and peer-reviewed pathway database (http://www.reactome.org). It visually displays structured information about the elements, enzymes, and genes (via their gene products) within many known pathways. The Reactome database was accessed in March 2013.
Networks (or graphs) provide a means of intuitively visualizing and characterizing complex systems, and have proven to be particularly valuable in modeling biological systems. The statistical analysis of the graph properties offers a quantitative and holistic means of revealing underlying connections among vertices, as well as the emergent global properties. Networks are being used with increasing frequency to analyze large-scale systems. A network, such as PHPN, can take an extraordinarily complex system and reduce it to a relatively simple form, revealing underlying connections and important clustering details that would not be evident from studying individual or non-complex relationships among traits .
Formally, a network is a collection of nodes and edges connecting them. The degree, k, of a node is the number of edges incident upon the node, and the degree distribution, P(k) of the network, describes the fraction of nodes in the network with degree k. The degree distribution also characterizes global properties of the graph and how the nodes are connected to one another; for example, if they are connected at random, the nodes’ degrees are expected to be homogeneous, and the degree distribution would a uniform binomial distribution. More often in biology, networks are highly heterogeneous, with a “heavy-tailed” degree distribution, placing them in the scale-free family. This means that the degree distribution follows a power law, or exponential decay. Within the network, this translates into the presence of “hubs” – a minority of highly connected nodes. When the degree distribution of a “scale-free” network is plotted on a logarithmic scale, the resulting curve is approximately linear across the top . In the case of relatively small networks, it is impossible to demonstrate the presence of a scale-free network. We can, at best, show the existence of a power-law type degree distribution, and not dismiss the scale-free hypothesis. The clustering coefficient (CC) of a network measures the degree to which nodes tend to form closely knit communities with a higher than average connectivity . The CC of networks found in nature, in particular social and biological networks show a higher degree of clustering than that observed in randomized networks of identical size. The average path length of a network (APL) represents the average of the minimum number of edges separating any two vertices.
From the bipartite network, one can project the data onto either of the data spaces (Figure 1a,c). In either single dataset space, the nodes are connected to one another through a vertex of the other space. By ignoring the different types of data, all network properties described above remain valid on the bipartite network (as a single data set network) and on either projection. This type of network gives us three degree-distributions, one for each projection, and one for the bipartite network. Each degree distribution shows how many links each node has. Nodes in a projection of a bipartite network are connected if they share at least one node in the other group. This gives us the ability to visualize connections within a group.
Human disease networks
In recent years there has been a trend toward studying disease through network based analysis of various systems of connections between diseases. The result was the Human Disease Network (HDN) . The nodes in the HDN represent human genetic disorders and the edges represent various connections between disorders, such as gene-gene or protein-protein interactions, to name a few. The HDN is helpful in visualizing connections among human disorders on a large scale. The underlying connections of the HDN contribute to the understanding of the basis of disorders, which in turn leads to a better comprehension of human diseases.
One study by Goh, et al., explored the HDN built on genes shared by different diseases. Another study, which is similar in some ways to ours, by Li et al. traced the SNPs connecting disease traits. In 2009, Silpa Suthram et al. found that when diseases were compared by an analysis of disease-related mRNA expression data and the human protein interaction network, there were significant similarities between some diseases and between some drug treatments. In 2009, Barrenas et al. further studied the genetic architecture of complex diseases by doing a GWAS, and found that complex disease genes are less central than the essential and monogenic disease genes in the human interactome. In the present work, we expand our study to include not only disease traits, but also behaviors and normal variations in humans, such as hair color, and explore large portions of non-coding variants in the human genome. Links between PTs are based on overlapping biological pathways (Section “Pathway-based human phenotype network”).
Pathway-based human phenotype network
In this paper, we chose to mesh the methods and results sections, as we present multiple different algorithms (i.e. to build, filter, and identify the modules in the PHPN). Each subsection presents and applies a new method, building on the resulting network of the previous one.
Building the PHPN
From the NHGRI catalog, extract all PTs and link them to their mapped genes. PTs with no mapped genes are omitted;
From Reactome, extract all genes in the database and link them to their associated pathways;
Match the genes associated to each phenotype to their associated pathways;
Connect PTs with overlapping pathways with an undirected edge, setting edge weight as the number of overlapping pathways.
We filter out isolate PTs with no connections to the rest of the network. We are only interested in PTs that have been associated with a gene, and their possible shared biology. The original NHGRI database contains over 800 PTs; by removing the isolate nodes, the PHPN contains 401 nodes connected to at least one other node.
This flexible process of building phenotype-gene-pathway associations also allowed us to examine the network from multiple configurations. Specifically, we were also able to construct a pathway network following the same logic as the HDN (Section “Human disease networks”): connecting pathways based on shared phenotypes, as well as a bipartite graph with links between PTs and pathways.
The bipartite network
The unfiltered PHPN
Figure 4 illustrates the sheer density of the unfiltered network and how difficult it becomes to precisely decipher the results. Even when zoomed in (Figure 4b, the network is too dense to provide any easily usable information. The degree distribution in Figure 4c does not give adequate insight into the internal structure of the network. From the results in this section, it was clear that more work had to be done on the “raw” PHPN in order for it to reveal key clinical information, both from a visual and statistical perspective. Below we describe the filtering method used and the new PHPN resulting from this filtering.
Extracting the information backbone
where ω ij is the edge weight and s i is the strength of the node under consideration (i.e. the sum of all weights of edges incident to the node). Edges are then preserved based on an imposed significance level α; in other words, for each edge, if α ij <α, then the edge is preserved. It should be noted that for each edge the algorithm for the DF produces two independent values α ij and α ji based on the two nodes connected by the edge. In order to resolve this, Serrano et al. propose two alternatives: under the OR rule, edges are preserved if (α ij <α OR α ji <α). Under the AND rule, both (α ij <α AND α ji <α) in order for the edge do be preserved. After experimenting with both rule types, we experimentally found the best that conserve the original network properties are obtained using the more restrictive AND rule. This is due to the sheer density of the unfiltered network.
Results in Figure 7 clearly demonstrate the advantages of the disparity filter compared to a global weight for an identical number of edges. The DF conserves over 90% of the phenotypes versus ∼50% for GW. In conclusion, the backbone keeps more phenotypes than the GW filtering, for the same number of connections, making the network less dense. Moreover, the relatively low average degree, the heavy-tailed degree distribution of the PHPN backbone resulting from the DF filtering, and the high clustering coefficient and short average path length indicate an interesting module structure.
In the medical literature, diseases are grouped in disorder classes according to an ontology of the biomedical domain . In the Goh et al. gene-based HPN, they denote the diseases according to their disorder class . Classes make “bins” in which all diseases are sorted, according to their “natural class”. Therefore, all cancers are grouped together, all cardiovascular diseases together, all gastrointestinal disorders together, and so on. We envisage two major drawbacks to this classification method: the semi-arbitrary nature of the classes, based solely on qualitative clinical observations, and not on the quantitative nature of the disorder and its underlying biology. Additionally, the manual classification is extremely tedious and subjective. We argue that in this case, we can achieve interesting results by applying a community-detecting algorithm on the filtered PHPN. This method sorts the phenotypes into classes of phenotypes with shared biology, rather than shared clinical presentation. Communities, or modules, of nodes within the network can be identified by maximizing the modularity, a measure of strength of division of a network into modules . Communities are identified when a group of nodes are found to have more connections between them than would be expected by random chance, often due to some shared properties (or in our case shared biology) between the nodes in the community. The clustering coefficient (CC) measures the degree to which nodes tend to form closely knit communities with a higher than average connectivity, while a high modularity score indicates the interconnectedness, and thus the strength of the communities. The Louvain method of community detection  uses a greedy optimization method to maximize the modularity and determine the most favorable division of network into communities. It is a widely accepted algorithm to build communities (or modules) within a network with no expert-knowledge, although other methods, such as Infogram are widely used. Refer to Lancichinetti et al.’s comparative analysis  for more details. We run the modules detection algorithm on the backbone of the PHPN, extracting the modules detected (Figure 6).
Modules of the PHPN
Inflammatory bowel disease
Systemic lupus erythematosus
Type 2 diabetes
Body mass index
activity and mass
By applying a community detection algorithm to the filtered network, we are able to classify traits and disease by quantifying their shared genetic mechanisms. This classification allows us to identify non-intuitive relationships between diseases and traits, elucidating the shared etiology for certain phenotypes.
Clinical and biomedical implications
The appropriateness of the PHPN was assessed by examining specific edges within communities (Figure 6). Specially, we interrogated pairwise connections within the community shown in blue and asked (1) whether any constitute links between phenotypes previously known to share biological connections and (2) if they do not contain known relationships, can we understand how they may be indirectly linked based on the primary literature; thereby providing novel insights that are not only reasonable but easily visualized using our method.
HDL cholesterol (HDL) and Alzheimer’s disease (AD)
Basis of inter-node edges between HDL and AD
Amyotrophic lateral sclerosis (ALS)
Cell adhesion molecules (CAMs)
Complement and coagulation cascades
Herpes simplex infection
Iron status biomarkers (IB) and cognitive performance (CP)
Kyoto Encyclopedia of Genes and Genomes (KEGG) defined biological pathways shared between iron status biomarkers and cognitive performance phenotypes
Iron biomarker pathways1
Cognitive performance pathways2
Cytokine-cytokine receptor interaction
Chagas disease (American trypanosomiasis)
Neuroactive ligand-receptor interaction
Endocrine and other factor-regulated calcium reabsorption
Neuroactive ligand-receptor interaction
Ubiquitin mediated proteolysis
Valine leucine and isoleucine degradation
Cytokine-cytokine receptor interaction
Amyotrophic lateral sclerosis (ALS)
Epstein-Barr virus infection
Fatty acid metabolism
Gastric acid secretion
Inositol phosphate metabolism
Neuroactive ligand-receptor interaction
One carbon pool by folate
Phosphatidylinositol signaling system
Retrograde endocannabinoid signaling
Synaptic vesicle cycle
Systemic lupus erythematosus
Transcriptional misregulation in cancer
Vascular smooth muscle contraction
von Willebrand factor and FVIII levels (vWF) and hippocampal atrophy (HA)
Enriched biological pathways assigned to vWF, HA, and AF by PHPN
Von Willebrand factor/
Atrial fibrillation (AF) pathways1
Hippocampal atrophy (HA)1
factor VIII pathways1
Arrhythmogenic right ventricular
B cell receptor signaling pathway
One carbon pool by folate
Cytosolic DNA-sensing pathway
Cell adhesion molecules (CAMs)
Chemokine signaling pathway
Fc epsilon RI signaling pathway
Fc gamma R-mediated phagocytosis
PI3K-Akt signaling pathway
Protein processing in endoplasmic reticulum
Leukocyte trans endothelial migration
Natural killer cell mediated cytotoxicity
Regulation of actin cytoskeleton
T cell receptor signaling pathway
Through this analysis, PHPN exposed atrial fibrillation phenotype as a key connector between VWF and HA even though none of these three PTs share any common genetic risk factors, as reported in the GWAS catalogue, but all three phenotypes shared a common biological pathway (Table 4). Therefore, the PHPN was able to identify a possible, and plausible, indirect relationship between vWF and HA through the unifying, but independent, phenotype of atrial fibrillation Thus, PHPN provides a novel means to identify inter-relationships between hemostatic, cardiovascular, and neurological conditions that may otherwise have gone unnoticed. It is also interesting to note that the single overlapping pathway between vWF and HA, the KEGG aggregate Metabolic Pathway (), is an comprehensive pathway consisting of all the metabolic pathways contained in the KEGG database, comprising approximately 10% of the human genome (≈2000 genes). Of the pathways interrogated by PHPN for this study 142 were linked through this “umbrella” pathway. Further investigation of the overall network revealed that of these 142 unique phenotypes, only three shared only the Metabolic pathway in common; vWF, HA, and AF, although AF was located in a different module which consisted mainly of cardiovascular diseases. The identification of plausible underlying biology between two phenotypes who share only this pathway in common suggest that PHPN displays a certain amount of robustness to ambiguous pathway definition by KEGG.
PHPN provides a means of integrating the accumulating wealth of genomic and phenotypic data and computationally identifies significant links between traits, attributes and diseases. This model has tremendous potential as a clinical tool in identifying risk factors for certain diseases, or common drug targets. By constructing a network based on pathways, we were able to associate phenotypes based on the shared biological processes involving common genetic components and pleiotropic effects. Our network of human traits based on ∼ 2,300 genes, ∼ 1,200 biological pathways and 800+ phenotypes is more comprehensive than that of previous studies. We combine GWAS data, which associates PT to genes, with the data from Reactome, which links genes to pathways. We extract the backbone of the PHPN using the disparity filter, retaining the significant connection. Our statistical analysis of the network properties places the PHPN in the scale-free family, showing once more how ubiquitous network structures with heavy-tailed degree distributions really are in biological, social, and natural networks. The automatic classification of phenotypes into “phenotype classes”, using the network’s topological modularity and a standard community detection algorithm, showed very promising results. Indeed, in contrast to what was achieved in previous studies and manual classification, we are able to highlight modules with phenotypes with potentially interesting shared biology, not by arbitrary disease types. Despite its apparent simplicity, PHPN is an adaptable network algorithm that can elucidate both intuitive and previously undiscovered biological connections between PTs, deftly characterizing the shared genetic mechanisms in the former and identifying unifying genetic traits in the latter. The ability to recognize biological connections, quantified by shared genes and their associated biological pathways, between seemingly disparate phenotypes provides researchers with a unique view of the pleiotropic biological environment that underlies the human condition. Discovering additional, novel, connections between phenotypes known to share certain biological traits provides additional information that could be exploited in future hypothesis based studies. Recognizing the connections between different traits/phenotypes is an integral first step in understanding the dynamic, and highly inter-related, genetic architecture underlying most complex disease; once these connections are illuminated they may provide the necessary framework for the generation of novel and innovative therapeutic interventions. For future work, we are interested in integrating more datasets on gene interactions into our network, such as SNPs and protein-protein interactions. Furthermore, we are currently working on three angles, (1) comparing the HPN to the HDN, and other previous work on phenotype networks, (2) running statistical significance tests, such as data set randomization, and finally (3) on refining our statistical methods, comparing algorithms for pruning our network and identifying communities that may produce optimal results in extracting the significant interactions in the PHPN.
This work was supported by NIH grants R01 EY022300, LM009012, LM010098, AI59694, GM103506, and GM103534.
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