Pathway networks generated from human disease phenome

Current repositories of disease-associated human genetic variants encompass over 4000 genes and 17,000 disease phenotypes, derived mostly from manual extraction of literature [1]. Our team has compiled these variants from sources including the Online Mendelian Inheritance in Man (OMIM) [2], Clinical Variance database (ClinVar) [3], Universal Protein Resource (UniProt) [4], and Human Gene Mutation Database (HGMD) [1, 5]. Our compilation contains over 80,000 human genetic variants and is the largest collection known to date [1]. Yet despite the large number of genetic variants known to influence disease, only a small percentage of an individual genome is expected to map to known variants. To identify novel disease-variant associations, the functional context of known variants must be expanded.

In prior studies, human disease networks (e.g., the human diseasome) have been generated to link genetic disorders with disease genes, supporting disease-specific functional modules [6]. Genes rarely work alone, often participating in complex pathways and synergic interactions with other genes, proteins, and environmental factors that collectively influence the clinical manifestations of disease [7–9]. Analyzing known disease-associated variants through a network biology approach can provide insight into the relationship between diseases and underlying metabolic pathways, and can expand the functional context of our variants [10]. This may result in novel disease associations not otherwise discovered through single-gene analysis. Existing research has shown that exploring genetic risk through a modular approach allows for more stable and robust results and improves the interpretation of molecular mechanisms underlying disease [8].

The first challenge in identifying disease-pathway associations from known variants is to correctly assign each variant phenotype to an ontology. A standardized terminology provides an accurate mapping of diseases to disease-specific categories and facilitates the identification of enriched pathway associations. Thus, the first step in this work was to map variant phenotypes to the Unified Medical Language System (UMLS) [11] to build a disease phenome. The UMLS [11] leverages the UMLS Metathesaurus to integrate a wide variety of phenotype synonyms from terminologies like OMIM [2] and the Human Phenotype Ontology (HPO) [12]. Once variants were mapped, they were clustered into higher-level categories via Medical Subject Heading (MeSH) identifiers, providing a broader view of the variant relationships.

To identify disease-pathway associations, mapped variants were linked at both the disease and pathway level. Our work builds on the approach taken in Goh et al. [6], expanding gene-based connections to their corresponding networks of human variant-derived Kyoto Encyclopedia of Genes and Genomes (KEGG) [13] pathways. Network representations were used, illustrating the power of our approach by providing a more complete view of human disease-variant associations. Our networks produced over 1300 novel disease associations at the pathway level, not otherwise connected at the gene level. This provides potential new insight into the relationship between metabolic pathways and interacting drugs, and may lead to improved drug efficacy and new potential drug targets for repurposing. [14] These associations will constitute an important resource in the future development of computational tools to optimize patient diagnosis and disease treatment.

Using UMLS string matching algorithms, we mapped the Peterson et al. [1] collection of human variants to the UMLS [11] phenotype ontology and built a collection of almost 70,000 (87%) unique human genetic variants to disorder concepts. UMLS [11] offers wide coverage of variants and higher-level disease categorization through MeSH. This allowed us to cluster phenotypes into broader categories and make inferences about each subgroup. Unique genetic variants associated with phenotypes otherwise unable to be mapped to UMLS [11] were run through a manual curation protocol, through which 56% of variants have currently been manually curated. Mapping to UMLS [11] allowed human variants to be grouped together based on same and/or similar phenotype, alleviating many of the difficulties faced due to the lack of standardization of vocabulary.

Disease-level and pathway-level bipartite networks were constructed using KEGG pathway enrichment, linking human genetic variants by common pathways or CUIs. In both networks, one central cluster of nodes was the most highly connected. The nodes in this cluster generally encompassed pathways involved in essential processes (i.e., reproduction, survival), many of which may be altered in and/or directly linked to cancer. [10] Through network analyzation [15] of the pathway-level network, KEGG Pathways in Cancer was found to have a high connectivity in the central cluster, with the highest node degree of 52 (Table 1).

The disease-level network contained 223 CUIs connected by 2548 unique disease associations through gene- or pathway-level analysis. Of these, 1338 (53%) connections are only observed through disease-pathway associations and not otherwise connected at the gene-level. Additionally, 461 (18%) connections are observed through disease-gene associations only, and 741 (29%) connections are observed through both gene- and pathway-level associations. When CUIs are connected through both common genes and common pathways, the resulting disease associations function as confidence builders with a higher level of evidence to support the connection. Hypoglycaemia, hyperinsulinaemic (C1864903) and Diabetes, type 2 (C0011860) were connected through the genes HNF1A, ABCC8, HNF4A, and GCK, as well as the KEGG pathway Type II Diabetes Mellitus. This association is expected, as hypoglycemia is known to affect type II diabetes patients near insulin-deficiency [16]. CUI connections made through pathways but not through genes extend the functional context of variants and provide new potential disease associations. Noonan Syndrome (C0028326) and Essential Hypertension (C0085580) were connected through the KEGG pathway Vascular Smooth Muscle Contraction, despite associated variants not having any common genes in our repository. A common symptom of Noonan syndrome is hypertrophic cardiomyopathy [17], which in turn is highly related to hypertension and often occurs in conjunction in elderly patients [18], suggesting a logical connection between Noonan Syndrome and Essential Hypertension concepts in our network.

As shown in Fig. 7, comparison of Cardiovascular (C14) and Skin and Connective Tissue (C17) networks shows high overlap in the largest cluster of KEGG pathways, which includes basic cellular functions such as cell signaling, growth, and maintenance. Many of these kinds of pathways are also altered in different types of cancer, as seen by the connections and enrichment of cancerous pathways in the main network cluster. A few examples include Melanoma, MAPK Signaling Pathway, and Pathways in Cancer. The high similarity between C14 and C17 is to be expected, as many cardiac disorders involve the connective tissue within/surrounding the heart, and relationships have been observed between normal development of connective tissue and the cardiovascular system [19]. Comparison of Hemic and Lymphatic (C15) and Immune System (C20) networks shows high overlap in a cluster of immunological KEGG pathways, including Primary Immunodeficiency, Type I Diabetes Mellitus, and Autoimmune Thyroid Disease. This intersection is also expected to be significant, as lymphatic diseases are highly linked to the immune surveillance and adaption [20].

Our next step is to continue analyzing human genetic variants at different levels of clustering, expanding our classifications and extending functional context to find new disease connections. If a pathway is found to link to multiple diseases, a drug being used to treat one disease could potentially be repurposed to treat another disease connected at the same pathway level [7]. In addition, if a disease is found to link to multiple pathways, a patient with this disease may benefit from a pathway-guided combination therapy [7]. With the addition of patient data, variant-based disease-pathway associations can be compared across individuals and provide a platform for incorporating new variant data into our database. In the future, this will allow us to develop computational tools that facilitate the optimization of personalized diagnosis, prognosis, and disease treatment.

Expanding our view of the human diseasome to include human variant-derived KEGG pathway associations allowed for an extended functional view of disease-variant associations. Novel disease connections were made by disease-pathway associations not otherwise detected through single-gene analysis. This shows that seemingly unrelated disease variants can be associated at the pathway level. Additionally, this type of analysis provides new relationships between metabolic pathways and disease-drug networks, potentially enabling novel diagnostic and therapeutic interventions.