Finding directionality and gene-disease predictions in disease associations

Abstract

Background: Understanding the underlying molecular mechanisms in human diseases is important for diagnosis and treatment of complex conditions and has traditionally been done by establishing associations between disorder-genes and their associated diseases. This kind of network analysis usually includes only the interaction of molecular components and shared genes. The present study offers a network and association analysis under a bioinformatics frame involving the integration of HUGO Gene Nomenclature Committee approved gene symbols, KEGG metabolic pathways and ICD-10-CM codes for the analysis of human diseases based on the level of inclusion and hypergeometric enrichment between genes and metabolic pathways shared by the different human disorders.

Methods: The present study offers the integration of HGNC approved gene symbols, KEGG metabolic pathways andICD-10-CM codes for the analysis of associations based on the level of inclusion and hypergeometricenrichment between genes and metabolic pathways shared by different diseases.

Results: 880 unique ICD-10-CM codes were mapped to the 4315 OMIM phenotypes and 3083 genes with phenotype-causing mutation. From this, a total of 705 ICD-10-CM codes were linked to 1587 genes with phenotype-causing mutations and 801 KEGG pathways creating a tripartite network composed by 15,455 code-gene-pathway interactions. These associations were further used for an inclusion analysis between diseases along with gene-disease predictions based on a hypergeometric enrichment methodology.

Conclusions: The results demonstrate that even though a large number of genes and metabolic pathways are shared between diseases of the same categories, inclusion levels between these genes and pathways are directional and independent of the disease classification. However, the gene-disease-pathway associations can be used for prediction of new gene-disease interactions that will be useful in drug discovery and therapeutic applications.

Fig. 1

Disease-disease interactions. a ICD-10-CM code classification. b Bipartite disease-disease network for 4315 OMIM phenotypes classified into 880 unique ICD-10-CM codes. Nodes represent codes and edges represent shared genes. The size of the node denotes the number of edges involved in each code

Fig. 2

Gene and disease network analysis. a Number of OMIM phenotypes and codes by ICD-10-CM category. b Number of unique disease-genes by ICD-10-CM category and the number of enzyme-producing genes. c 880 unique ICD-10-CM codes are linked to 3083 genes with phenotype-causing mutation creating 4241 disease-gene associations. Of the 880 codes a total of 705 codes and 1587 genes are linked to 801 metabolic pathways creating 15,455 code-gene-pathway interactions. A further analysis revealed a total of 6706 genes being involved in at least one KEGG metabolic pathway, and hereby 5119 genes with no known phenotype-causing mutation could be included in our analysis. These 5119 genes with no known phenotype-causing mutation are linked to 546 different KEGG pathways sharing 479 pathways with genes carrying phenotype-causing mutation and are only linked to 67 additional KEGG metabolic pathways

Fig. 3

Inclusion analysis by disease category. a For two diseases sharing a certain number of elements (genes or pathways), the inclusion index (τ) will be low for a disease with a high number of total elements compared with the number of shared elements. When the number of shared elements increases compared with the total number of elements of the disease, the index level increases. Therefore, different index values can be calculated for two diseases sharing elements depending on the total number of elements of each disease. Consequently, this index allows obtaining not only the degree of interaction between diseases, but also the directionality of the interaction. A value of τ = 1 indicates that one disease is a subset of another. b Boxplot of calculated level of inclusion between disease-pairs belonging to the same ICD-10-CM category based on shared genes. c Boxplot of calculated level of inclusion between disease-pairs belonging to the same ICD-10-CM category based on shared pathways

Fig. 4

Inclusion analysis for Neoplasm category. a Boxplot of calculated level of inclusion (τ) based on shared genes for neoplasms as disease X and Y. b Boxplot of calculated level of inclusion based on shared genes for neoplasms as disease X by ICD-10-CM category. c Boxplot of calculated level of inclusion based on shared genes for neoplasms as disease Y by ICD-10-CM category. d Boxplot of calculated level of inclusion based on shared pathways for neoplasms as disease X and Y. e Boxplot of calculated level of inclusion based on shared pathways for neoplasms as disease X by ICD-10-CM category. f Boxplot of calculated level of inclusion based on shared pathways for neoplasms as disease Y by ICD-10-CM category